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Herbert C, Manabe YC, Filippaios A, Lin H, Wang B, Achenbach C, Kheterpal V, Hartin P, Suvarna T, Harman E, Stamegna P, Rao LV, Hafer N, Broach J, Luzuriaga K, Fitzgerald KA, McManus DD, Soni A. Differential Viral Dynamics by Sex and Body Mass Index During Acute SARS-CoV-2 Infection: Results From a Longitudinal Cohort Study. Clin Infect Dis 2024; 78:1185-1193. [PMID: 37972270 PMCID: PMC11093673 DOI: 10.1093/cid/ciad701] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Revised: 10/25/2023] [Accepted: 11/14/2023] [Indexed: 11/19/2023] Open
Abstract
BACKGROUND There is evidence of an association of severe coroanavirus disease (COVID-19) outcomes with increased body mass index (BMI) and male sex. However, few studies have examined the interaction between sex and BMI on severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) viral dynamics. METHODS Participants conducted RT-PCR testing every 24-48 hours over a 15-day period. Sex and BMI were self-reported, and Ct values from E-gene were used to quantify viral load. Three distinct outcomes were examined using mixed-effects generalized linear models, linear models, and logistic models, respectively: all Ct values (model 1), nadir Ct value (model 2), and strongly detectable infection (at least 1 Ct value ≤28 during their infection) (model 3). An interaction term between BMI and sex was included, and inverse logit transformations were applied to quantify the differences by BMI and sex using marginal predictions. RESULTS In total, 7988 participants enrolled in this study and 439 participants (model 1) and 309 (models 2 and 3) were eligible for these analyses. Among males, increasing BMI was associated with lower Ct values in a dose-response fashion. For participants with BMIs greater than 29 kg/m2, males had significantly lower Ct values and nadir Ct values than females. In total, 67.8% of males and 55.3% of females recorded a strongly detectable infection; increasing proportions of men had Ct values <28 with BMIs of 35 and 40 kg/m2. CONCLUSIONS We observed sex-based dimorphism in relation to BMI and COVID-19 viral load. Further investigation is needed to determine the cause, clinical impact, and transmission implications of this sex-differential effect of BMI on viral load.
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Affiliation(s)
- Carly Herbert
- Program in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, Massachusetts, USA
- UMass Center for Clinical and Translational Science, University of Massachusetts Chan Medical School, Worcester, Massachusetts, USA
| | - Yukari C Manabe
- Division of Infectious Disease, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Andreas Filippaios
- Program in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, Massachusetts, USA
| | - Honghuang Lin
- Program in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, Massachusetts, USA
| | - Biqi Wang
- Program in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, Massachusetts, USA
| | - Chad Achenbach
- Division of Infectious Disease, Department of Medicine, Havey Institute for Global Health, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA
| | | | - Paul Hartin
- Program in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, Massachusetts, USA
| | | | | | - Pamela Stamegna
- Program in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, Massachusetts, USA
| | | | - Nathaniel Hafer
- UMass Center for Clinical and Translational Science, University of Massachusetts Chan Medical School, Worcester, Massachusetts, USA
- Program in Molecular Medicine, University of Massachusetts Chan Medical School, Worcester, Massachusetts, USA
| | - John Broach
- Department of Emergency Medicine, University of Massachusetts Chan Medical School, Worcester, Massachusetts, USA
| | - Katherine Luzuriaga
- UMass Center for Clinical and Translational Science, University of Massachusetts Chan Medical School, Worcester, Massachusetts, USA
- Program in Molecular Medicine, University of Massachusetts Chan Medical School, Worcester, Massachusetts, USA
| | - Katherine A Fitzgerald
- Division of Infectious Diseases and Immunology, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, Massachusetts, USA
| | - David D McManus
- Program in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, Massachusetts, USA
- Division of Cardiology, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, Massachusetts, USA
| | - Apurv Soni
- Program in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, Massachusetts, USA
- UMass Center for Clinical and Translational Science, University of Massachusetts Chan Medical School, Worcester, Massachusetts, USA
- Department of Population and Quantitative Health Sciences, University of Massachusetts Chan Medical School, Worcester, Massachusetts, USA
- Division of Health System Science, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, Massachusetts, USA
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Wang Y, Sarnowski C, Lin H, Pitsillides AN, Heard‐Costa NL, Choi SH, Wang D, Bis JC, Blue EE, Boerwinkle E, De Jager PL, Fornage M, Wijsman EM, Seshadri S, Dupuis J, Peloso GM, DeStefano AL. Key variants via the Alzheimer's Disease Sequencing Project whole genome sequence data. Alzheimers Dement 2024; 20:3290-3304. [PMID: 38511601 PMCID: PMC11095439 DOI: 10.1002/alz.13705] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Revised: 12/08/2023] [Accepted: 12/21/2023] [Indexed: 03/22/2024]
Abstract
INTRODUCTION Genome-wide association studies (GWAS) have identified loci associated with Alzheimer's disease (AD) but did not identify specific causal genes or variants within those loci. Analysis of whole genome sequence (WGS) data, which interrogates the entire genome and captures rare variations, may identify causal variants within GWAS loci. METHODS We performed single common variant association analysis and rare variant aggregate analyses in the pooled population (N cases = 2184, N controls = 2383) and targeted analyses in subpopulations using WGS data from the Alzheimer's Disease Sequencing Project (ADSP). The analyses were restricted to variants within 100 kb of 83 previously identified GWAS lead variants. RESULTS Seventeen variants were significantly associated with AD within five genomic regions implicating the genes OARD1/NFYA/TREML1, JAZF1, FERMT2, and SLC24A4. KAT8 was implicated by both single variant and rare variant aggregate analyses. DISCUSSION This study demonstrates the utility of leveraging WGS to gain insights into AD loci identified via GWAS.
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Affiliation(s)
- Yanbing Wang
- Department of BiostatisticsBoston University, School of Public HealthBostonMassachusettsUSA
| | - Chloé Sarnowski
- Department of BiostatisticsBoston University, School of Public HealthBostonMassachusettsUSA
- Human Genetics CenterDepartment of EpidemiologySchool of Public HealthThe University of Texas Health Science Center at HoustonHoustonTexasUSA
| | - Honghuang Lin
- Department of MedicineUniversity of Massachusetts Chan Medical SchoolWorcesterMassachusettsUSA
| | | | - Nancy L. Heard‐Costa
- Department of BiostatisticsBoston University, School of Public HealthBostonMassachusettsUSA
- The Framingham Heart StudyFraminghamMassachusettsUSA
| | - Seung Hoan Choi
- Department of BiostatisticsBoston University, School of Public HealthBostonMassachusettsUSA
| | - Dongyu Wang
- Department of BiostatisticsBoston University, School of Public HealthBostonMassachusettsUSA
| | - Joshua C. Bis
- Cardiovascular Health Research UnitDepartment of MedicineUniversity of WashingtonSeattleWashingtonUSA
| | - Elizabeth E. Blue
- Department of MedicineDivision of Medical GeneticsUniversity of WashingtonSeattleWashingtonUSA
- Brotman Baty InstituteSeattleWashingtonUSA
| | | | - Eric Boerwinkle
- Human Genetics CenterDepartment of EpidemiologySchool of Public HealthThe University of Texas Health Science Center at HoustonHoustonTexasUSA
| | - Philip L. De Jager
- Center for Translational & Computational NeuroimmunologyDepartment of NeurologyColumbia University Irving Medical CenterNew YorkNew YorkUSA
- Taub Institute for Research on Alzheimer's Disease and the Aging BrainColumbia University Irving Medical CenterNew YorkNew YorkUSA
| | - Myriam Fornage
- Human Genetics CenterDepartment of EpidemiologySchool of Public HealthThe University of Texas Health Science Center at HoustonHoustonTexasUSA
- Brown Foundation Institute of Molecular MedicineMcGovern Medical SchoolUniversity of Texas Health Science Center at HoustonHoustonTexasUSA
| | - Ellen M. Wijsman
- Division of Medical Genetics and Department Biostatistics Statistical Genetics LabUniversity of WashingtonHans Rosling Center for Population HealthSeattleWashingtonUSA
| | - Sudha Seshadri
- The Framingham Heart StudyFraminghamMassachusettsUSA
- Glenn Biggs Institute for Alzheimer's & Neurodegenerative DiseasesThe University of Texas Health Science Center at San AntonioSan AntonioTexasUSA
- Department of NeurologyBoston University School of MedicineBostonMassachusettsUSA
| | - Josée Dupuis
- Department of BiostatisticsBoston University, School of Public HealthBostonMassachusettsUSA
- Department of Epidemiology, Biostatistics and Occupational HealthSchool of Population and Global HealthMcGill UniversityMontrealQuebecCanada
| | - Gina M. Peloso
- Department of BiostatisticsBoston University, School of Public HealthBostonMassachusettsUSA
| | - Anita L. DeStefano
- Department of BiostatisticsBoston University, School of Public HealthBostonMassachusettsUSA
- The Framingham Heart StudyFraminghamMassachusettsUSA
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Gao Y, Hu Y, Xu S, Liang H, Lin H, Yin TH, Zhao K. Characterisation of the mitochondrial genome and phylogenetic analysis of Toxocara apodemi (Nematoda: Ascarididae). J Helminthol 2024; 98:e33. [PMID: 38618902 DOI: 10.1017/s0022149x24000221] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/16/2024]
Abstract
We first sequenced and characterised the complete mitochondrial genome of Toxocara apodeme, then studied the evolutionary relationship of the species within Toxocaridae. The complete mitochondrial genome was amplified using PCR with 14 specific primers. The mitogenome length was 14303 bp in size, including 12 PCGs (encoding 3,423 amino acids), 22 tRNAs, 2 rRNAs, and 2 NCRs, with 68.38% A+T contents. The mt genomes of T. apodemi had relatively compact structures with 11 intergenic spacers and 5 overlaps. Comparative analyses of the nucleotide sequences of complete mt genomes showed that T. apodemi had higher identities with T. canis than other congeners. A sliding window analysis of 12 PCGs among 5 Toxocara species indicated that nad4 had the highest sequence divergence, and cox1 was the least variable gene. Relative synonymous codon usage showed that UUG, ACU, CCU, CGU, and UCU most frequently occurred in the complete genomes of T. apodemi. The Ka/Ks ratio showed that all Toxocara mt genes were subject to purification selection. The largest genetic distance between T. apodemi and the other 4 congeneric species was found in nad2, and the smallest was found in cox2. Phylogenetic analyses based on the concatenated amino acid sequences of 12 PCGs demonstrated that T. apodemi formed a distinct branch and was always a sister taxon to other congeneric species. The present study determined the complete mt genome sequences of T. apodemi, which provide novel genetic markers for further studies of the taxonomy, population genetics, and systematics of the Toxocaridae nematodes.
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Affiliation(s)
- Y Gao
- Zhejiang Provincial Key Laboratory of Plant Evolutionary Ecology and Conservation, Taizhou Key Laboratory of Biomedicine and Advanced Dosage Forms, School of Life Sciences, Taizhou University, Zhejiang Taizhou318000, China
- Zhejiang-Malaysia Joint Laboratory for Bioactive Materials and Applied Microbiology, School of Life Sciences, Taizhou University, Zhejiang Taizhou318000, China
| | - Y Hu
- Taizhou City Center for Disease Control and Prevention, Zhejiang Taizhou318000, China
| | - S Xu
- Zhejiang Provincial Key Laboratory of Plant Evolutionary Ecology and Conservation, Taizhou Key Laboratory of Biomedicine and Advanced Dosage Forms, School of Life Sciences, Taizhou University, Zhejiang Taizhou318000, China
- Zhejiang-Malaysia Joint Laboratory for Bioactive Materials and Applied Microbiology, School of Life Sciences, Taizhou University, Zhejiang Taizhou318000, China
| | - H Liang
- Taizhou City Center for Disease Control and Prevention, Zhejiang Taizhou318000, China
| | - H Lin
- Taizhou City Center for Disease Control and Prevention, Zhejiang Taizhou318000, China
| | - T H Yin
- Zhejiang-Malaysia Joint Laboratory for Bioactive Materials and Applied Microbiology, School of Life Sciences, Taizhou University, Zhejiang Taizhou318000, China
- Tunku Abdul Rahman University of Management and Technology, Jalan Genting Kelang, Kuala Lumpur 53300, Malaysia
| | - K Zhao
- Zhejiang Provincial Key Laboratory of Plant Evolutionary Ecology and Conservation, Taizhou Key Laboratory of Biomedicine and Advanced Dosage Forms, School of Life Sciences, Taizhou University, Zhejiang Taizhou318000, China
- Zhejiang-Malaysia Joint Laboratory for Bioactive Materials and Applied Microbiology, School of Life Sciences, Taizhou University, Zhejiang Taizhou318000, China
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Wang D, Scalici A, Wang Y, Lin H, Pitsillides A, Heard-Costa N, Cruchaga C, Ziegemeier E, Bis JC, Fornage M, Boerwinkle E, De Jager PL, Wijsman E, Dupuis J, Renton AE, Seshadri S, Goate AM, DeStefano AL, Peloso GM. Frequency of Variants in Mendelian Alzheimer's Disease Genes within the Alzheimer's Disease Sequencing Project (ADSP). medRxiv 2024:2023.10.24.23297227. [PMID: 37961373 PMCID: PMC10635182 DOI: 10.1101/2023.10.24.23297227] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Background Prior studies using the ADSP data examined variants within presenilin-2 ( PSEN2 ), presenilin-1 ( PSEN1 ), and amyloid precursor protein ( APP ) genes. However, previously-reported clinically-relevant variants and other predicted damaging missense (DM) variants have not been characterized in a newer release of the Alzheimer's Disease Sequencing Project (ADSP). Objective To characterize previously-reported clinically-relevant variants and DM variants in PSEN2, PSEN1, APP within the participants from the ADSP. Methods We identified rare variants (MAF <1%) previously-reported in PSEN2 , PSEN1, and APP in the available ADSP sample of 14,641 individuals with whole genome sequencing and 16,849 individuals with whole exome sequencing available for research-use (N total = 31,490). We additionally curated variants in these three genes from ClinVar, OMIM, and Alzforum and report carriers of variants in clinical databases as well as predicted DM variants in these genes. Results We detected 31 previously-reported clinically-relevant variants with alternate alleles observed within the ADSP: 4 variants in PSEN2 , 25 in PSEN1 , and 2 in APP . The overall variant carrier rate for the 31 clinically-relevant variants in the ADSP was 0.3%. We observed that 79.5% of the variant carriers were cases compared to 3.9% were controls. In those with AD, the mean age of onset of AD among carriers of these clinically-relevant variants was 19.6 ± 1.4 years earlier compared with non-carriers (p-value=7.8×10 -57 ). Conclusion A small proportion of individuals in the ADSP are carriers of a previously-reported clinically-relevant variant allele for AD and these participants have significantly earlier age of AD onset compared to non-carriers.
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Mei Z, Lin YX, Yao PS, Wang F, Huang XF, Lin H, Hu XQ, Lin YQ, Gao L, Kang DZ. [Diagnostic value of high frequency oscillation in localization of type Ⅱ focal cortical dysplasia epilepsy]. Zhonghua Yi Xue Za Zhi 2024; 104:614-617. [PMID: 38389239 DOI: 10.3760/cma.j.cn112137-20231019-00826] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 02/24/2024]
Abstract
Retrospective analysis was conducted on 9 patients with type Ⅱ focal cortical dysplasia (FCD) who underwent stereo-electroencephalography (SEEG) implantation in the Department of Neurosurgery of the First Affiliated Hospital of Fujian Medical University from November 2020 to February 2023. The onset area, onset time, and frequency of high-frequency oscillations (HFO) were analyzed and the correlation of HFOs with interictal, preictal, and ictal periods. SEEG recordings of 80-500 Hz HFOs were observed in both interictal and ictal periods in 9 patients, with 6 patients exhibiting fast ripples (FR) in the range of 250-500 Hz. Surgical resection of the seizure onset area and FR-generating electrodes was performed, and postoperative follow-up for over 2 years indicated Engel I in 5 cases. 6 patients showed continuous discharge during the preictal period, and the distribution index of continuous discharge was positively correlated with seizure frequency. HFOs in the range of 80-500 Hz were present in all four seizure onset patterns during the ictal period. The onset area and FR-emitting electrode were surgically removed in 6 patients with continuous discharge and overlapping HFOs during the preictal period, with 5 cases of Engel I. Type Ⅱ FCD discharges exhibited complexity, high discharge indices, and a close association with HFOs. Compared with the spike wave, the electrode range of HF is more limited, and the incidence of HF before attack is significantly increased, which is closely correlated with the onset area. The simultaneous occurrence of HFO and the spike waves has higher diagnostic value than the individual occurrence, effectively enhancing surgical efficacy.
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Affiliation(s)
- Z Mei
- Department of Neurosurgery, First Affiliated Hospital, Fujian Medical University, Fuzhou 350005, China
| | - Y X Lin
- Department of Neurosurgery, First Affiliated Hospital, Fujian Medical University, Fuzhou 350005, China
| | - P S Yao
- Fujian Institute of Brain Disorders and Brain Science, Fuzhou 350005, China
| | - F Wang
- Fujian Clinical Research Center for Neurological Diseases, Fuzhou 350005, China
| | - X F Huang
- Department of Neurosurgery, First Affiliated Hospital, Fujian Medical University, Fuzhou 350005, China
| | - H Lin
- Department of Neurosurgery, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou 350005, China
| | - X Q Hu
- Department of Neurosurgery, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou 350005, China
| | - Y Q Lin
- Department of Neurosurgery, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou 350005, China
| | - L Gao
- Department of Neurosurgery, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou 350005, China
| | - D Z Kang
- Department of Neurosurgery, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou 350005, China
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Ding H, Kim M, Searls E, Sunderaraman P, De Anda-Duran I, Low S, Popp Z, Hwang PH, Li Z, Goyal K, Hathaway L, Monteverde J, Rahman S, Igwe A, Kolachalama VB, Au R, Lin H. Digital neuropsychological measures by defense automated neurocognitive assessment: reference values and clinical correlates. Front Neurol 2024; 15:1340710. [PMID: 38426173 PMCID: PMC10902432 DOI: 10.3389/fneur.2024.1340710] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2023] [Accepted: 01/29/2024] [Indexed: 03/02/2024] Open
Abstract
Introduction Although the growth of digital tools for cognitive health assessment, there's a lack of known reference values and clinical implications for these digital methods. This study aims to establish reference values for digital neuropsychological measures obtained through the smartphone-based cognitive assessment application, Defense Automated Neurocognitive Assessment (DANA), and to identify clinical risk factors associated with these measures. Methods The sample included 932 cognitively intact participants from the Framingham Heart Study, who completed at least one DANA task. Participants were stratified into subgroups based on sex and three age groups. Reference values were established for digital cognitive assessments within each age group, divided by sex, at the 2.5th, 25th, 50th, 75th, and 97.5th percentile thresholds. To validate these values, 57 cognitively intact participants from Boston University Alzheimer's Disease Research Center were included. Associations between 19 clinical risk factors and these digital neuropsychological measures were examined by a backward elimination strategy. Results Age- and sex-specific reference values were generated for three DANA tasks. Participants below 60 had median response times for the Go-No-Go task of 796 ms (men) and 823 ms (women), with age-related increases in both sexes. Validation cohort results mostly aligned with these references. Different tasks showed unique clinical correlations. For instance, response time in the Code Substitution task correlated positively with total cholesterol and diabetes, but negatively with high-density lipoprotein and low-density lipoprotein cholesterol levels, and triglycerides. Discussion This study established and validated reference values for digital neuropsychological measures of DANA in cognitively intact white participants, potentially improving their use in future clinical studies and practice.
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Affiliation(s)
- Huitong Ding
- Department of Anatomy and Neurobiology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, United States
- The Framingham Heart Study, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, United States
| | - Minzae Kim
- Department of Anatomy and Neurobiology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, United States
| | - Edward Searls
- Department of Anatomy and Neurobiology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, United States
| | - Preeti Sunderaraman
- The Framingham Heart Study, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, United States
- Department of Neurology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, United States
| | - Ileana De Anda-Duran
- School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA, United States
| | - Spencer Low
- Department of Anatomy and Neurobiology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, United States
| | - Zachary Popp
- Department of Anatomy and Neurobiology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, United States
| | - Phillip H. Hwang
- Department of Epidemiology, Boston University School of Public Health, Boston, MA, United States
| | - Zexu Li
- Department of Anatomy and Neurobiology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, United States
| | - Kriti Goyal
- Department of Anatomy and Neurobiology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, United States
| | - Lindsay Hathaway
- Department of Anatomy and Neurobiology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, United States
| | - Jose Monteverde
- Department of Anatomy and Neurobiology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, United States
| | - Salman Rahman
- Department of Anatomy and Neurobiology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, United States
| | - Akwaugo Igwe
- Department of Anatomy and Neurobiology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, United States
| | - Vijaya B. Kolachalama
- Department of Medicine, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, United States
- Department of Computer Science, Faculty of Computing & Data Sciences, Boston University, Boston, MA, United States
| | - Rhoda Au
- Department of Anatomy and Neurobiology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, United States
- The Framingham Heart Study, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, United States
- Department of Neurology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, United States
- Department of Epidemiology, Boston University School of Public Health, Boston, MA, United States
- Department of Medicine, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, United States
- Slone Epidemiology Center, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, United States
| | - Honghuang Lin
- Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, United States
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Leung YY, Naj AC, Chou YF, Valladares O, Schmidt M, Hamilton-Nelson K, Wheeler N, Lin H, Gangadharan P, Qu L, Clark K, Kuzma AB, Lee WP, Cantwell L, Nicaretta H, Haines J, Farrer L, Seshadri S, Brkanac Z, Cruchaga C, Pericak-Vance M, Mayeux RP, Bush WS, Destefano A, Martin E, Schellenberg GD, Wang LS. Human whole-exome genotype data for Alzheimer's disease. Nat Commun 2024; 15:684. [PMID: 38263370 PMCID: PMC10805795 DOI: 10.1038/s41467-024-44781-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Accepted: 01/02/2024] [Indexed: 01/25/2024] Open
Abstract
The heterogeneity of the whole-exome sequencing (WES) data generation methods present a challenge to a joint analysis. Here we present a bioinformatics strategy for joint-calling 20,504 WES samples collected across nine studies and sequenced using ten capture kits in fourteen sequencing centers in the Alzheimer's Disease Sequencing Project. The joint-genotype called variant-called format (VCF) file contains only positions within the union of capture kits. The VCF was then processed specifically to account for the batch effects arising from the use of different capture kits from different studies. We identified 8.2 million autosomal variants. 96.82% of the variants are high-quality, and are located in 28,579 Ensembl transcripts. 41% of the variants are intronic and 1.8% of the variants are with CADD > 30, indicating they are of high predicted pathogenicity. Here we show our new strategy can generate high-quality data from processing these diversely generated WES samples. The improved ability to combine data sequenced in different batches benefits the whole genomics research community.
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Affiliation(s)
- Yuk Yee Leung
- Penn Neurodegeneration Genomics Center, Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
| | - Adam C Naj
- Penn Neurodegeneration Genomics Center, Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Yi-Fan Chou
- Penn Neurodegeneration Genomics Center, Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Otto Valladares
- Penn Neurodegeneration Genomics Center, Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Michael Schmidt
- Dr. John T. Macdonald Foundation Department of Human Genetics, Miller School of Medicine, University of Miami, Miami, FL, USA
- The John P. Hussman Institute for Human Genomics, University of Miami, Miami, FL, USA
| | - Kara Hamilton-Nelson
- Dr. John T. Macdonald Foundation Department of Human Genetics, Miller School of Medicine, University of Miami, Miami, FL, USA
- The John P. Hussman Institute for Human Genomics, University of Miami, Miami, FL, USA
| | - Nicholas Wheeler
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, USA
- Department of Genetics and Genome Sciences, School of Medicine, Case Western Reserve University, Cleveland, OH, USA
| | - Honghuang Lin
- Department of Medicine, UMass Chan Medical School, Boston, MA, USA
| | - Prabhakaran Gangadharan
- Penn Neurodegeneration Genomics Center, Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Liming Qu
- Penn Neurodegeneration Genomics Center, Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Kaylyn Clark
- Penn Neurodegeneration Genomics Center, Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Amanda B Kuzma
- Penn Neurodegeneration Genomics Center, Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Wan-Ping Lee
- Penn Neurodegeneration Genomics Center, Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Laura Cantwell
- Penn Neurodegeneration Genomics Center, Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Heather Nicaretta
- Penn Neurodegeneration Genomics Center, Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Jonathan Haines
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, USA
- Department of Genetics and Genome Sciences, School of Medicine, Case Western Reserve University, Cleveland, OH, USA
| | - Lindsay Farrer
- Department of Medicine (Biomedical Genetics), Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Sudha Seshadri
- Boston University School of Medicine, Boston, MA, USA
- The Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, University of Texas Health Sciences Center, San Antonio, TX, USA
| | - Zoran Brkanac
- Department of Psychiatry and Behavioral Sciences, University of Washington, Seattle, WA, USA
| | - Carlos Cruchaga
- Washington University School of Medicine, St. Louis, MO, USA
| | - Margaret Pericak-Vance
- Dr. John T. Macdonald Foundation Department of Human Genetics, Miller School of Medicine, University of Miami, Miami, FL, USA
- The John P. Hussman Institute for Human Genomics, University of Miami, Miami, FL, USA
| | - Richard P Mayeux
- Department of Neurology, Taub Institute for Research on Alzheimer's Disease and the Aging Brain and the Gertrude H. Sergievsky Center, Columbia University and the New York Presbyterian Hospital, New York, NY, USA
| | - William S Bush
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, USA
- Department of Genetics and Genome Sciences, School of Medicine, Case Western Reserve University, Cleveland, OH, USA
| | - Anita Destefano
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
- Department of Neurology, Boston University School of Medicine, Boston, MA, USA
| | - Eden Martin
- Dr. John T. Macdonald Foundation Department of Human Genetics, Miller School of Medicine, University of Miami, Miami, FL, USA
- The John P. Hussman Institute for Human Genomics, University of Miami, Miami, FL, USA
| | - Gerard D Schellenberg
- Penn Neurodegeneration Genomics Center, Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Li-San Wang
- Penn Neurodegeneration Genomics Center, Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
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8
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De Anda‐Duran I, Sunderaraman P, Searls E, Moukaled S, Jin X, Popp Z, Karjadi C, Hwang PH, Ding H, Devine S, Shih LC, Low S, Lin H, Kolachalama VB, Bazzano L, Libon DJ, Au R. Comparing Cognitive Tests and Smartphone-Based Assessment in 2 US Community-Based Cohorts. J Am Heart Assoc 2024; 13:e032733. [PMID: 38226519 PMCID: PMC10926794 DOI: 10.1161/jaha.123.032733] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/18/2023] [Accepted: 12/04/2023] [Indexed: 01/17/2024]
Abstract
BACKGROUND Smartphone-based cognitive assessments have emerged as promising tools, bridging gaps in accessibility and reducing bias in Alzheimer disease and related dementia research. However, their congruence with traditional neuropsychological tests and usefulness in diverse cohorts remain underexplored. METHODS AND RESULTS A total of 406 FHS (Framingham Heart Study) and 59 BHS (Bogalusa Heart Study) participants with traditional neuropsychological tests and digital assessments using the Defense Automated Neurocognitive Assessment (DANA) smartphone protocol were included. Regression models investigated associations between DANA task digital measures and a neuropsychological global cognitive Z score (Global Cognitive Score [GCS]), and neuropsychological domain-specific Z scores. FHS participants' mean age was 57 (SD, 9.75) years, and 44% (179) were men. BHS participants' mean age was 49 (4.4) years, and 28% (16) were men. Participants in both cohorts with the lowest neuropsychological performance (lowest quartile, GCS1) demonstrated lower DANA digital scores. In the FHS, GCS1 participants had slower average response times and decreased cognitive efficiency scores in all DANA tasks (P<0.05). In BHS, participants in GCS1 had slower average response times and decreased cognitive efficiency scores for DANA Code Substitution and Go/No-Go tasks, although this was not statistically significant. In both cohorts, GCS was significantly associated with DANA tasks, such that higher GCS correlated with faster average response times (P<0.05) and increased cognitive efficiency (all P<0.05) in the DANA Code Substitution task. CONCLUSIONS Our findings demonstrate that smartphone-based cognitive assessments exhibit concurrent validity with a composite measure of traditional neuropsychological tests. This supports the potential of using smartphone-based assessments in cognitive screening across diverse populations and the scalability of digital assessments to community-dwelling individuals.
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Affiliation(s)
- Ileana De Anda‐Duran
- Department of EpidemiologyTulane University School of Public Health and Tropical MedicineNew OrleansLAUSA
| | - Preeti Sunderaraman
- Department of NeurologyBoston University Chobanian & Avedisian School of MedicineBostonMAUSA
- Framingham Heart StudyBoston University Chobanian & Avedisian School of MedicineBostonMAUSA
- Boston University Alzheimer’s Disease Research CenterBoston University Chobanian & Avedisian School of MedicineBostonMAUSA
| | - Edward Searls
- Department of Anatomy and NeurobiologyBoston University Chobanian & Avedisian School of MedicineBostonMAUSA
| | - Shirine Moukaled
- Department of EpidemiologyTulane University School of Public Health and Tropical MedicineNew OrleansLAUSA
| | - Xuanyi Jin
- Department of EpidemiologyTulane University School of Public Health and Tropical MedicineNew OrleansLAUSA
| | - Zachary Popp
- Department of Anatomy and NeurobiologyBoston University Chobanian & Avedisian School of MedicineBostonMAUSA
| | - Cody Karjadi
- Department of Anatomy and NeurobiologyBoston University Chobanian & Avedisian School of MedicineBostonMAUSA
| | - Phillip H. Hwang
- Department of EpidemiologyBoston University School of Public HealthBostonMAUSA
| | - Huitong Ding
- Framingham Heart StudyBoston University Chobanian & Avedisian School of MedicineBostonMAUSA
- Department of Anatomy and NeurobiologyBoston University Chobanian & Avedisian School of MedicineBostonMAUSA
| | - Sherral Devine
- Framingham Heart StudyBoston University Chobanian & Avedisian School of MedicineBostonMAUSA
- Department of Anatomy and NeurobiologyBoston University Chobanian & Avedisian School of MedicineBostonMAUSA
| | - Ludy C. Shih
- Department of NeurologyBoston University Chobanian & Avedisian School of MedicineBostonMAUSA
| | - Spencer Low
- Department of Anatomy and NeurobiologyBoston University Chobanian & Avedisian School of MedicineBostonMAUSA
| | - Honghuang Lin
- University of Massachusetts Chan Medical SchoolWorcesterMAUSA
| | - Vijaya B. Kolachalama
- Department of MedicineBoston University Chobanian & Avedisian School of MedicineBostonMAUSA
- Department of Computer ScienceBoston UniversityBostonMAUSA
| | - Lydia Bazzano
- Department of EpidemiologyTulane University School of Public Health and Tropical MedicineNew OrleansLAUSA
| | - David J. Libon
- Department of PsychologyRowan UniversityMullica HillNJUSA
- New Jersey Institute of Successful AgingRowan University School of Osteopathic MedicineStratfordNJUSA
| | - Rhoda Au
- Framingham Heart StudyBoston University Chobanian & Avedisian School of MedicineBostonMAUSA
- Boston University Alzheimer’s Disease Research CenterBoston University Chobanian & Avedisian School of MedicineBostonMAUSA
- Department of Anatomy and NeurobiologyBoston University Chobanian & Avedisian School of MedicineBostonMAUSA
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9
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Sunderaraman P, De Anda‐Duran I, Karjadi C, Peterson J, Ding H, Devine SA, Shih LC, Popp Z, Low S, Hwang PH, Goyal K, Hathaway L, Monteverde J, Lin H, Kolachalama VB, Au R. Design and Feasibility Analysis of a Smartphone-Based Digital Cognitive Assessment Study in the Framingham Heart Study. J Am Heart Assoc 2024; 13:e031348. [PMID: 38226510 PMCID: PMC10926817 DOI: 10.1161/jaha.123.031348] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/26/2023] [Accepted: 11/09/2023] [Indexed: 01/17/2024]
Abstract
BACKGROUND Smartphone-based digital technology is increasingly being recognized as a cost-effective, scalable, and noninvasive method of collecting longitudinal cognitive and behavioral data. Accordingly, a state-of-the-art 3-year longitudinal project focused on collecting multimodal digital data for early detection of cognitive impairment was developed. METHODS AND RESULTS A smartphone application collected 2 modalities of cognitive data, digital voice and screen-based behaviors, from the FHS (Framingham Heart Study) multigenerational Generation 2 (Gen 2) and Generation 3 (Gen 3) cohorts. To understand the feasibility of conducting a smartphone-based study, participants completed a series of questions about their smartphone and app use, as well as sensory and environmental factors that they encountered while completing the tasks on the app. Baseline data collected to date were from 537 participants (mean age=66.6 years, SD=7.0; 58.47% female). Across the younger participants from the Gen 3 cohort (n=455; mean age=60.8 years, SD=8.2; 59.12% female) and older participants from the Gen 2 cohort (n=82; mean age=74.2 years, SD=5.8; 54.88% female), an average of 76% participants agreed or strongly agreed that they felt confident about using the app, 77% on average agreed or strongly agreed that they were able to use the app on their own, and 81% on average rated the app as easy to use. CONCLUSIONS Based on participant ratings, the study findings are promising. At baseline, the majority of participants are able to complete the app-related tasks, follow the instructions, and encounter minimal barriers to completing the tasks independently. These data provide evidence that designing and collecting smartphone application data in an unsupervised, remote, and naturalistic setting in a large, community-based population is feasible.
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Affiliation(s)
- Preeti Sunderaraman
- Department of NeurologyBoston University Chobanian & Avedisian School of Medicine School of MedicineBostonMAUSA
- Boston University Alzheimer’s Disease Research CenterBoston University Chobanian & Avedisian School of MedicineBostonMAUSA
- Framingham Heart StudyBoston University Chobanian & Avedisian School of Medicine School of MedicineBostonMAUSA
| | - Ileana De Anda‐Duran
- Department of EpidemiologyTulane University School of Public Health & Tropical MedicineNew OrleansLAUSA
| | - Cody Karjadi
- Framingham Heart StudyBoston University Chobanian & Avedisian School of Medicine School of MedicineBostonMAUSA
| | - Julia Peterson
- Framingham Heart StudyBoston University Chobanian & Avedisian School of Medicine School of MedicineBostonMAUSA
| | - Huitong Ding
- Framingham Heart StudyBoston University Chobanian & Avedisian School of Medicine School of MedicineBostonMAUSA
- Department of Anatomy & NeurobiologyBoston University Chobanian & Avedisian School of Medicine School of MedicineBostonMAUSA
| | - Sherral A. Devine
- Framingham Heart StudyBoston University Chobanian & Avedisian School of Medicine School of MedicineBostonMAUSA
- Department of Anatomy & NeurobiologyBoston University Chobanian & Avedisian School of Medicine School of MedicineBostonMAUSA
| | - Ludy C. Shih
- Department of NeurologyBoston University Chobanian & Avedisian School of Medicine School of MedicineBostonMAUSA
- Framingham Heart StudyBoston University Chobanian & Avedisian School of Medicine School of MedicineBostonMAUSA
| | - Zachary Popp
- Boston University Alzheimer’s Disease Research CenterBoston University Chobanian & Avedisian School of MedicineBostonMAUSA
- Department of Anatomy & NeurobiologyBoston University Chobanian & Avedisian School of Medicine School of MedicineBostonMAUSA
| | - Spencer Low
- Boston University Alzheimer’s Disease Research CenterBoston University Chobanian & Avedisian School of MedicineBostonMAUSA
- Department of Anatomy & NeurobiologyBoston University Chobanian & Avedisian School of Medicine School of MedicineBostonMAUSA
- Department of EpidemiologyBoston University School of Public HealthBostonMAUSA
| | - Phillip H. Hwang
- Department of EpidemiologyBoston University School of Public HealthBostonMAUSA
| | - Kriti Goyal
- Department of NeurologyBoston University Chobanian & Avedisian School of Medicine School of MedicineBostonMAUSA
- Framingham Heart StudyBoston University Chobanian & Avedisian School of Medicine School of MedicineBostonMAUSA
| | - Lindsay Hathaway
- Framingham Heart StudyBoston University Chobanian & Avedisian School of Medicine School of MedicineBostonMAUSA
| | - Jose Monteverde
- Framingham Heart StudyBoston University Chobanian & Avedisian School of Medicine School of MedicineBostonMAUSA
| | - Honghuang Lin
- Department of MedicineUniversity of Massachusetts Chan Medical SchoolWorcesterMAUSA
| | - Vijaya B. Kolachalama
- Boston University Alzheimer’s Disease Research CenterBoston University Chobanian & Avedisian School of MedicineBostonMAUSA
- Department of MedicineBoston University Chobanian & Avedisian School of Medicine School of MedicineBostonMAUSA
- Department of Computer Science and Faculty of Computing & Data SciencesBoston UniversityBostonMAUSA
| | - Rhoda Au
- Department of NeurologyBoston University Chobanian & Avedisian School of Medicine School of MedicineBostonMAUSA
- Boston University Alzheimer’s Disease Research CenterBoston University Chobanian & Avedisian School of MedicineBostonMAUSA
- Framingham Heart StudyBoston University Chobanian & Avedisian School of Medicine School of MedicineBostonMAUSA
- Department of Anatomy & NeurobiologyBoston University Chobanian & Avedisian School of Medicine School of MedicineBostonMAUSA
- Department of EpidemiologyBoston University School of Public HealthBostonMAUSA
- Department of MedicineBoston University Chobanian & Avedisian School of Medicine School of MedicineBostonMAUSA
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10
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Popp Z, Low S, Igwe A, Rahman MS, Kim M, Khan R, Oh E, Kumar A, De Anda‐Duran I, Ding H, Hwang PH, Sunderaraman P, Shih LC, Lin H, Kolachalama VB, Au R. Shifting From Active to Passive Monitoring of Alzheimer Disease: The State of the Research. J Am Heart Assoc 2024; 13:e031247. [PMID: 38226518 PMCID: PMC10926806 DOI: 10.1161/jaha.123.031247] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/17/2024]
Abstract
Most research using digital technologies builds on existing methods for staff-administered evaluation, requiring a large investment of time, effort, and resources. Widespread use of personal mobile devices provides opportunities for continuous health monitoring without active participant engagement. Home-based sensors show promise in evaluating behavioral features in near real time. Digital technologies across these methodologies can detect precise measures of cognition, mood, sleep, gait, speech, motor activity, behavior patterns, and additional features relevant to health. As a neurodegenerative condition with insidious onset, Alzheimer disease and other dementias (AD/D) represent a key target for advances in monitoring disease symptoms. Studies to date evaluating the predictive power of digital measures use inconsistent approaches to characterize these measures. Comparison between different digital collection methods supports the use of passive collection methods in settings in which active participant engagement approaches are not feasible. Additional studies that analyze how digital measures across multiple data streams can together improve prediction of cognitive impairment and early-stage AD are needed. Given the long timeline of progression from normal to diagnosis, digital monitoring will more easily make extended longitudinal follow-up possible. Through the American Heart Association-funded Strategically Focused Research Network, the Boston University investigative team deployed a platform involving a wide range of technologies to address these gaps in research practice. Much more research is needed to thoroughly evaluate limitations of passive monitoring. Multidisciplinary collaborations are needed to establish legal and ethical frameworks for ensuring passive monitoring can be conducted at scale while protecting privacy and security, especially in vulnerable populations.
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Affiliation(s)
- Zachary Popp
- Department of Anatomy & NeurobiologyBoston University Chobanian & Avedisian School of MedicineBostonMAUSA
- Boston University Alzheimer’s Disease Research Center, Boston University Chobanian & Avedisian School of MedicineBostonMAUSA
| | - Spencer Low
- Department of Anatomy & NeurobiologyBoston University Chobanian & Avedisian School of MedicineBostonMAUSA
- Boston University Alzheimer’s Disease Research Center, Boston University Chobanian & Avedisian School of MedicineBostonMAUSA
- Department of EpidemiologyBoston University School of Public HealthBostonMAUSA
| | - Akwaugo Igwe
- Department of Anatomy & NeurobiologyBoston University Chobanian & Avedisian School of MedicineBostonMAUSA
- Boston University Alzheimer’s Disease Research Center, Boston University Chobanian & Avedisian School of MedicineBostonMAUSA
| | - Md Salman Rahman
- Department of Anatomy & NeurobiologyBoston University Chobanian & Avedisian School of MedicineBostonMAUSA
- Boston University Alzheimer’s Disease Research Center, Boston University Chobanian & Avedisian School of MedicineBostonMAUSA
| | - Minzae Kim
- Department of Anatomy & NeurobiologyBoston University Chobanian & Avedisian School of MedicineBostonMAUSA
- Boston UniversityBostonMAUSA
| | - Raiyan Khan
- Department of Anatomy & NeurobiologyBoston University Chobanian & Avedisian School of MedicineBostonMAUSA
- Boston UniversityBostonMAUSA
| | - Emily Oh
- Department of Anatomy & NeurobiologyBoston University Chobanian & Avedisian School of MedicineBostonMAUSA
- Boston UniversityBostonMAUSA
| | - Ankita Kumar
- Department of Anatomy & NeurobiologyBoston University Chobanian & Avedisian School of MedicineBostonMAUSA
- Boston UniversityBostonMAUSA
| | - Ileana De Anda‐Duran
- Department of EpidemiologyTulane University School of Public Health & Tropical MedicineNew OrleansLAUSA
| | - Huitong Ding
- Department of Anatomy & NeurobiologyBoston University Chobanian & Avedisian School of MedicineBostonMAUSA
- Framingham Heart StudyBoston University Chobanian & Avedisian School of Medicine School of MedicineBostonMAUSA
| | - Phillip H. Hwang
- Department of EpidemiologyBoston University School of Public HealthBostonMAUSA
| | - Preeti Sunderaraman
- Boston University Alzheimer’s Disease Research Center, Boston University Chobanian & Avedisian School of MedicineBostonMAUSA
- Framingham Heart StudyBoston University Chobanian & Avedisian School of Medicine School of MedicineBostonMAUSA
- Department of NeurologyBoston University Chobanian & Avedisian School of Medicine School of MedicineBostonMAUSA
| | - Ludy C. Shih
- Boston University Alzheimer’s Disease Research Center, Boston University Chobanian & Avedisian School of MedicineBostonMAUSA
- Framingham Heart StudyBoston University Chobanian & Avedisian School of Medicine School of MedicineBostonMAUSA
- Department of NeurologyBoston University Chobanian & Avedisian School of Medicine School of MedicineBostonMAUSA
| | - Honghuang Lin
- Department of MedicineUniversity of Massachusetts Chan Medical SchoolWorcesterMA
| | - Vijaya B. Kolachalama
- Boston University Alzheimer’s Disease Research Center, Boston University Chobanian & Avedisian School of MedicineBostonMAUSA
- Department of MedicineBoston University Chobanian & Avedisian School of Medicine School of MedicineBostonMAUSA
| | - Rhoda Au
- Department of Anatomy & NeurobiologyBoston University Chobanian & Avedisian School of MedicineBostonMAUSA
- Boston University Alzheimer’s Disease Research Center, Boston University Chobanian & Avedisian School of MedicineBostonMAUSA
- Department of EpidemiologyBoston University School of Public HealthBostonMAUSA
- Framingham Heart StudyBoston University Chobanian & Avedisian School of Medicine School of MedicineBostonMAUSA
- Department of NeurologyBoston University Chobanian & Avedisian School of Medicine School of MedicineBostonMAUSA
- Department of MedicineBoston University Chobanian & Avedisian School of Medicine School of MedicineBostonMAUSA
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11
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Xiao K, Xu PS, Lin H. [Research progress on the prevalence and harm of heated tobacco products]. Zhonghua Jie He He Hu Xi Za Zhi 2024; 47:64-69. [PMID: 38062698 DOI: 10.3760/cma.j.cn112147-20230812-00072] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 12/30/2023]
Abstract
Heated tobacco products (HTP) are a new type of tobacco product, also known as heat-not-burn (HnB) tobacco products. They are devices that use an electronic heat source to heat tobacco and produce aerosols containing nicotine for smokers to inhale. Currently, traditional combustible cigarettes and electronic nicotine delivery systems (ENDS) are increasingly being regulated under the Framework Convention on Tobacco Control. Tobacco companies have responded by actively promoting heated tobacco products worldwide, which pose new challenges to global tobacco control efforts and may become a challenge for tobacco control work in China. In reviewing the situation and the potential harm of heated tobacco products, it was noted that HTP are rapidly gaining popularity worldwide, and that their harmfulness may be underestimated. Compared to combustible cigarettes (CC) and ENDS, the long-term health effects of HTP are not fully understood, and they may pose new health risks. Potential health risks include an increase in smoking prevalence, the presence of harmful and potentially harmful compounds not found in CC, and the potential gateway effect on non-smokers. Due to differences in laws, regulations, health policies, institutions, and cultural factors related to the tobacco industry in different countries and regions, attitudes, and regulatory measures towards HTP also vary. It is essential for countries and regions around the world to develop appropriate policies to strengthen control of HTP and prevent their widespread use.
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Affiliation(s)
- K Xiao
- Department of Respiratory and Critical Care Medicine, The Baiyun Hospital of Guangzhou First People's Hospital(the Second People's Hospital of Baiyun District), Guangzhou 510450, China
| | - P S Xu
- Department of Respiratory and Critical Care Medicine, The Second Affiliated Hospital of Guangzhou Medical University, Guangzhou 510260, China
| | - H Lin
- Department of Psychiatry,The Affiliated Brain Hospital of Guangzhou Medical University,Guangzhou 510370, China
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12
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Staplin N, Haynes R, Judge PK, Wanner C, Green JB, Emberson J, Preiss D, Mayne KJ, Ng SYA, Sammons E, Zhu D, Hill M, Stevens W, Wallendszus K, Brenner S, Cheung AK, Liu ZH, Li J, Hooi LS, Liu WJ, Kadowaki T, Nangaku M, Levin A, Cherney D, Maggioni AP, Pontremoli R, Deo R, Goto S, Rossello X, Tuttle KR, Steubl D, Petrini M, Seidi S, Landray MJ, Baigent C, Herrington WG, Abat S, Abd Rahman R, Abdul Cader R, Abdul Hafidz MI, Abdul Wahab MZ, Abdullah NK, Abdul-Samad T, Abe M, Abraham N, Acheampong S, Achiri P, Acosta JA, Adeleke A, Adell V, Adewuyi-Dalton R, Adnan N, Africano A, Agharazii M, Aguilar F, Aguilera A, Ahmad M, Ahmad MK, Ahmad NA, Ahmad NH, Ahmad NI, Ahmad Miswan N, Ahmad Rosdi H, Ahmed I, Ahmed S, Ahmed S, Aiello J, Aitken A, AitSadi R, Aker S, Akimoto S, Akinfolarin A, Akram S, Alberici F, Albert C, Aldrich L, Alegata M, Alexander L, Alfaress S, Alhadj Ali M, Ali A, Ali A, Alicic R, Aliu A, Almaraz R, Almasarwah R, Almeida J, Aloisi A, Al-Rabadi L, Alscher D, Alvarez P, Al-Zeer B, Amat M, Ambrose C, Ammar H, An Y, Andriaccio L, Ansu K, Apostolidi A, Arai N, Araki H, Araki S, Arbi A, Arechiga O, Armstrong S, Arnold T, Aronoff S, Arriaga W, Arroyo J, Arteaga D, Asahara S, Asai A, Asai N, Asano S, Asawa M, Asmee MF, Aucella F, Augustin M, Avery A, Awad A, Awang IY, Awazawa M, Axler A, Ayub W, Azhari Z, Baccaro R, Badin C, Bagwell B, Bahlmann-Kroll E, Bahtar AZ, Baigent C, Bains D, Bajaj H, Baker R, Baldini E, Banas B, Banerjee D, Banno S, Bansal S, Barberi S, Barnes S, Barnini C, Barot C, Barrett K, Barrios R, Bartolomei Mecatti B, Barton I, Barton J, Basily W, Bavanandan S, Baxter A, Becker L, Beddhu S, Beige J, Beigh S, Bell S, Benck U, Beneat A, Bennett A, Bennett D, Benyon S, Berdeprado J, Bergler T, Bergner A, Berry M, Bevilacqua M, Bhairoo J, Bhandari S, Bhandary N, Bhatt A, Bhattarai M, Bhavsar M, Bian W, Bianchini F, Bianco S, Bilous R, Bilton J, Bilucaglia D, Bird C, Birudaraju D, Biscoveanu M, Blake C, Bleakley N, Bocchicchia K, Bodine S, Bodington R, Boedecker S, Bolduc M, Bolton S, Bond C, Boreky F, Boren K, Bouchi R, Bough L, Bovan D, Bowler C, Bowman L, Brar N, Braun C, Breach A, Breitenfeldt M, Brenner S, Brettschneider B, Brewer A, Brewer G, Brindle V, Brioni E, Brown C, Brown H, Brown L, Brown R, Brown S, Browne D, Bruce K, Brueckmann M, Brunskill N, Bryant M, Brzoska M, Bu Y, Buckman C, Budoff M, Bullen M, Burke A, Burnette S, Burston C, Busch M, Bushnell J, Butler S, Büttner C, Byrne C, Caamano A, Cadorna J, Cafiero C, Cagle M, Cai J, Calabrese K, Calvi C, Camilleri B, Camp S, Campbell D, Campbell R, Cao H, Capelli I, Caple M, Caplin B, Cardone A, Carle J, Carnall V, Caroppo M, Carr S, Carraro G, Carson M, Casares P, Castillo C, Castro C, Caudill B, Cejka V, Ceseri M, Cham L, Chamberlain A, Chambers J, Chan CBT, Chan JYM, Chan YC, Chang E, Chang E, Chant T, Chavagnon T, Chellamuthu P, Chen F, Chen J, Chen P, Chen TM, Chen Y, Chen Y, Cheng C, Cheng H, Cheng MC, Cherney D, Cheung AK, Ching CH, Chitalia N, Choksi R, Chukwu C, Chung K, Cianciolo G, Cipressa L, Clark S, Clarke H, Clarke R, Clarke S, Cleveland B, Cole E, Coles H, Condurache L, Connor A, Convery K, Cooper A, Cooper N, Cooper Z, Cooperman L, Cosgrove L, Coutts P, Cowley A, Craik R, Cui G, Cummins T, Dahl N, Dai H, Dajani L, D'Amelio A, Damian E, Damianik K, Danel L, Daniels C, Daniels T, Darbeau S, Darius H, Dasgupta T, Davies J, Davies L, Davis A, Davis J, Davis L, Dayanandan R, Dayi S, Dayrell R, De Nicola L, Debnath S, Deeb W, Degenhardt S, DeGoursey K, Delaney M, Deo R, DeRaad R, Derebail V, Dev D, Devaux M, Dhall P, Dhillon G, Dienes J, Dobre M, Doctolero E, Dodds V, Domingo D, Donaldson D, Donaldson P, Donhauser C, Donley V, Dorestin S, Dorey S, Doulton T, Draganova D, Draxlbauer K, Driver F, Du H, Dube F, Duck T, Dugal T, Dugas J, Dukka H, Dumann H, Durham W, Dursch M, Dykas R, Easow R, Eckrich E, Eden G, Edmerson E, Edwards H, Ee LW, Eguchi J, Ehrl Y, Eichstadt K, Eid W, Eilerman B, Ejima Y, Eldon H, Ellam T, Elliott L, Ellison R, Emberson J, Epp R, Er A, Espino-Obrero M, Estcourt S, Estienne L, Evans G, Evans J, Evans S, Fabbri G, Fajardo-Moser M, Falcone C, Fani F, Faria-Shayler P, Farnia F, Farrugia D, Fechter M, Fellowes D, Feng F, Fernandez J, Ferraro P, Field A, Fikry S, Finch J, Finn H, Fioretto P, Fish R, Fleischer A, Fleming-Brown D, Fletcher L, Flora R, Foellinger C, Foligno N, Forest S, Forghani Z, Forsyth K, Fottrell-Gould D, Fox P, Frankel A, Fraser D, Frazier R, Frederick K, Freking N, French H, Froment A, Fuchs B, Fuessl L, Fujii H, Fujimoto A, Fujita A, Fujita K, Fujita Y, Fukagawa M, Fukao Y, Fukasawa A, Fuller T, Funayama T, Fung E, Furukawa M, Furukawa Y, Furusho M, Gabel S, Gaidu J, Gaiser S, Gallo K, Galloway C, Gambaro G, Gan CC, Gangemi C, Gao M, Garcia K, Garcia M, Garofalo C, Garrity M, Garza A, Gasko S, Gavrila M, Gebeyehu B, Geddes A, Gentile G, George A, George J, Gesualdo L, Ghalli F, Ghanem A, Ghate T, Ghavampour S, Ghazi A, Gherman A, Giebeln-Hudnell U, Gill B, Gillham S, Girakossyan I, Girndt M, Giuffrida A, Glenwright M, Glider T, Gloria R, Glowski D, Goh BL, Goh CB, Gohda T, Goldenberg R, Goldfaden R, Goldsmith C, Golson B, Gonce V, Gong Q, Goodenough B, Goodwin N, Goonasekera M, Gordon A, Gordon J, Gore A, Goto H, Goto S, Goto S, Gowen D, Grace A, Graham J, Grandaliano G, Gray M, Green JB, Greene T, Greenwood G, Grewal B, Grifa R, Griffin D, Griffin S, Grimmer P, Grobovaite E, Grotjahn S, Guerini A, Guest C, Gunda S, Guo B, Guo Q, Haack S, Haase M, Haaser K, Habuki K, Hadley A, Hagan S, Hagge S, Haller H, Ham S, Hamal S, Hamamoto Y, Hamano N, Hamm M, Hanburry A, Haneda M, Hanf C, Hanif W, Hansen J, Hanson L, Hantel S, Haraguchi T, Harding E, Harding T, Hardy C, Hartner C, Harun Z, Harvill L, Hasan A, Hase H, Hasegawa F, Hasegawa T, Hashimoto A, Hashimoto C, Hashimoto M, Hashimoto S, Haskett S, Hauske SJ, Hawfield A, Hayami T, Hayashi M, Hayashi S, Haynes R, Hazara A, Healy C, Hecktman J, Heine G, Henderson H, Henschel R, Hepditch A, Herfurth K, 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Sabarai A, Saccà C, Sachson R, Sadler E, Safiee NS, Sahani M, Saillant A, Saini J, Saito C, Saito S, Sakaguchi K, Sakai M, Salim H, Salviani C, Sammons E, Sampson A, Samson F, Sandercock P, Sanguila S, Santorelli G, Santoro D, Sarabu N, Saram T, Sardell R, Sasajima H, Sasaki T, Satko S, Sato A, Sato D, Sato H, Sato H, Sato J, Sato T, Sato Y, Satoh M, Sawada K, Schanz M, Scheidemantel F, Schemmelmann M, Schettler E, Schettler V, Schlieper GR, Schmidt C, Schmidt G, Schmidt U, Schmidt-Gurtler H, Schmude M, Schneider A, Schneider I, Schneider-Danwitz C, Schomig M, Schramm T, Schreiber A, Schricker S, Schroppel B, Schulte-Kemna L, Schulz E, Schumacher B, Schuster A, Schwab A, Scolari F, Scott A, Seeger W, Seeger W, Segal M, Seifert L, Seifert M, Sekiya M, Sellars R, Seman MR, Shah S, Shah S, Shainberg L, Shanmuganathan M, Shao F, Sharma K, Sharpe C, Sheikh-Ali M, Sheldon J, Shenton C, Shepherd A, Shepperd M, Sheridan R, Sheriff Z, Shibata Y, Shigehara T, Shikata K, Shimamura K, Shimano H, Shimizu Y, Shimoda H, Shin K, Shivashankar G, Shojima N, Silva R, Sim CSB, Simmons K, Sinha S, Sitter T, Sivanandam S, Skipper M, Sloan K, Sloan L, Smith R, Smyth J, Sobande T, Sobata M, Somalanka S, Song X, Sonntag F, Sood B, Sor SY, Soufer J, Sparks H, Spatoliatore G, Spinola T, Squyres S, Srivastava A, Stanfield J, Staplin N, Staylor K, Steele A, Steen O, Steffl D, Stegbauer J, Stellbrink C, Stellbrink E, Stevens W, Stevenson A, Stewart-Ray V, Stickley J, Stoffler D, Stratmann B, Streitenberger S, Strutz F, Stubbs J, Stumpf J, Suazo N, Suchinda P, Suckling R, Sudin A, Sugamori K, Sugawara H, Sugawara K, Sugimoto D, Sugiyama H, Sugiyama H, Sugiyama T, Sullivan M, Sumi M, Suresh N, Sutton D, Suzuki H, Suzuki R, Suzuki Y, Suzuki Y, Suzuki Y, Swanson E, Swift P, Syed S, Szerlip H, Taal M, Taddeo M, Tailor C, Tajima K, Takagi M, Takahashi K, Takahashi K, Takahashi M, Takahashi T, Takahira E, Takai T, Takaoka M, Takeoka J, Takesada A, Takezawa M, Talbot M, Taliercio J, Talsania T, Tamori 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Vinathan J, Visnjic M, Voigt E, von-Eynatten M, Vourvou M, Wada J, Wada J, Wada T, Wada Y, Wakayama K, Wakita Y, Wallendszus K, Walters T, Wan Mohamad WH, Wang L, Wang W, Wang X, Wang X, Wang Y, Wanner C, Wanninayake S, Watada H, Watanabe K, Watanabe K, Watanabe M, Waterfall H, Watkins D, Watson S, Weaving L, Weber B, Webley Y, Webster A, Webster M, Weetman M, Wei W, Weihprecht H, Weiland L, Weinmann-Menke J, Weinreich T, Wendt R, Weng Y, Whalen M, Whalley G, Wheatley R, Wheeler A, Wheeler J, Whelton P, White K, Whitmore B, Whittaker S, Wiebel J, Wiley J, Wilkinson L, Willett M, Williams A, Williams E, Williams K, Williams T, Wilson A, Wilson P, Wincott L, Wines E, Winkelmann B, Winkler M, Winter-Goodwin B, Witczak J, Wittes J, Wittmann M, Wolf G, Wolf L, Wolfling R, Wong C, Wong E, Wong HS, Wong LW, Wong YH, Wonnacott A, Wood A, Wood L, Woodhouse H, Wooding N, Woodman A, Wren K, Wu J, Wu P, Xia S, Xiao H, Xiao X, Xie Y, Xu C, Xu Y, Xue H, Yahaya H, Yalamanchili H, Yamada A, Yamada N, Yamagata K, Yamaguchi M, Yamaji Y, Yamamoto A, Yamamoto S, Yamamoto S, Yamamoto T, Yamanaka A, Yamano T, Yamanouchi Y, Yamasaki N, Yamasaki Y, Yamasaki Y, Yamashita C, Yamauchi T, Yan Q, Yanagisawa E, Yang F, Yang L, Yano S, Yao S, Yao Y, Yarlagadda S, Yasuda Y, Yiu V, Yokoyama T, Yoshida S, Yoshidome E, Yoshikawa H, Young A, Young T, Yousif V, Yu H, Yu Y, Yuasa K, Yusof N, Zalunardo N, Zander B, Zani R, Zappulo F, Zayed M, Zemann B, Zettergren P, Zhang H, Zhang L, Zhang L, Zhang N, Zhang X, Zhao J, Zhao L, Zhao S, Zhao Z, Zhong H, Zhou N, Zhou S, Zhu D, Zhu L, Zhu S, Zietz M, Zippo M, Zirino F, Zulkipli FH. Effects of empagliflozin on progression of chronic kidney disease: a prespecified secondary analysis from the empa-kidney trial. Lancet Diabetes Endocrinol 2024; 12:39-50. [PMID: 38061371 PMCID: PMC7615591 DOI: 10.1016/s2213-8587(23)00321-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Revised: 10/24/2023] [Accepted: 10/25/2023] [Indexed: 12/23/2023]
Abstract
BACKGROUND Sodium-glucose co-transporter-2 (SGLT2) inhibitors reduce progression of chronic kidney disease and the risk of cardiovascular morbidity and mortality in a wide range of patients. However, their effects on kidney disease progression in some patients with chronic kidney disease are unclear because few clinical kidney outcomes occurred among such patients in the completed trials. In particular, some guidelines stratify their level of recommendation about who should be treated with SGLT2 inhibitors based on diabetes status and albuminuria. We aimed to assess the effects of empagliflozin on progression of chronic kidney disease both overall and among specific types of participants in the EMPA-KIDNEY trial. METHODS EMPA-KIDNEY, a randomised, controlled, phase 3 trial, was conducted at 241 centres in eight countries (Canada, China, Germany, Italy, Japan, Malaysia, the UK, and the USA), and included individuals aged 18 years or older with an estimated glomerular filtration rate (eGFR) of 20 to less than 45 mL/min per 1·73 m2, or with an eGFR of 45 to less than 90 mL/min per 1·73 m2 with a urinary albumin-to-creatinine ratio (uACR) of 200 mg/g or higher. We explored the effects of 10 mg oral empagliflozin once daily versus placebo on the annualised rate of change in estimated glomerular filtration rate (eGFR slope), a tertiary outcome. We studied the acute slope (from randomisation to 2 months) and chronic slope (from 2 months onwards) separately, using shared parameter models to estimate the latter. Analyses were done in all randomly assigned participants by intention to treat. EMPA-KIDNEY is registered at ClinicalTrials.gov, NCT03594110. FINDINGS Between May 15, 2019, and April 16, 2021, 6609 participants were randomly assigned and then followed up for a median of 2·0 years (IQR 1·5-2·4). Prespecified subgroups of eGFR included 2282 (34·5%) participants with an eGFR of less than 30 mL/min per 1·73 m2, 2928 (44·3%) with an eGFR of 30 to less than 45 mL/min per 1·73 m2, and 1399 (21·2%) with an eGFR 45 mL/min per 1·73 m2 or higher. Prespecified subgroups of uACR included 1328 (20·1%) with a uACR of less than 30 mg/g, 1864 (28·2%) with a uACR of 30 to 300 mg/g, and 3417 (51·7%) with a uACR of more than 300 mg/g. Overall, allocation to empagliflozin caused an acute 2·12 mL/min per 1·73 m2 (95% CI 1·83-2·41) reduction in eGFR, equivalent to a 6% (5-6) dip in the first 2 months. After this, it halved the chronic slope from -2·75 to -1·37 mL/min per 1·73 m2 per year (relative difference 50%, 95% CI 42-58). The absolute and relative benefits of empagliflozin on the magnitude of the chronic slope varied significantly depending on diabetes status and baseline levels of eGFR and uACR. In particular, the absolute difference in chronic slopes was lower in patients with lower baseline uACR, but because this group progressed more slowly than those with higher uACR, this translated to a larger relative difference in chronic slopes in this group (86% [36-136] reduction in the chronic slope among those with baseline uACR <30 mg/g compared with a 29% [19-38] reduction for those with baseline uACR ≥2000 mg/g; ptrend<0·0001). INTERPRETATION Empagliflozin slowed the rate of progression of chronic kidney disease among all types of participant in the EMPA-KIDNEY trial, including those with little albuminuria. Albuminuria alone should not be used to determine whether to treat with an SGLT2 inhibitor. FUNDING Boehringer Ingelheim and Eli Lilly.
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Ding H, Wang B, Hamel AP, Karjadi C, Ang TFA, Au R, Lin H. Exploring cognitive progression subtypes in the Framingham Heart Study. Alzheimers Dement (Amst) 2024; 16:e12574. [PMID: 38515438 PMCID: PMC10955221 DOI: 10.1002/dad2.12574] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/21/2023] [Revised: 02/23/2024] [Accepted: 02/27/2024] [Indexed: 03/23/2024]
Abstract
INTRODUCTION Alzheimer's disease (AD) is a heterogeneous disorder characterized by complex underlying neuropathology that is not fully understood. This study aimed to identify cognitive progression subtypes and examine their correlation with clinical outcomes. METHODS Participants of this study were recruited from the Framingham Heart Study. The Subtype and Stage Inference (SuStaIn) method was used to identify cognitive progression subtypes based on eight cognitive domains. RESULTS Three cognitive progression subtypes were identified, including verbal learning (Subtype 1), abstract reasoning (Subtype 2), and visual memory (Subtype 3). These subtypes represent different domains of cognitive decline during the progression of AD. Significant differences in age of onset among the different subtypes were also observed. A higher SuStaIn stage was significantly associated with increased mortality risk. DISCUSSION This study provides a characterization of AD heterogeneity in cognitive progression, emphasizing the importance of developing personalized approaches for risk stratification and intervention. Highlights We used the Subtype and Stage Inference (SuStaIn) method to identify three cognitive progression subtypes.Different subtypes have significant variations in age of onset.Higher stages of progression are associated with increased mortality risk.
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Affiliation(s)
- Huitong Ding
- Department of Anatomy and NeurobiologyBoston University Chobanian & Avedisian School of MedicineBostonMassachusettsUSA
- The Framingham Heart StudyBoston University Chobanian & Avedisian School of MedicineBostonMassachusettsUSA
| | - Biqi Wang
- Department of MedicineUniversity of Massachusetts Chan Medical SchoolWorcesterMassachusettsUSA
| | - Alexander P. Hamel
- Department of MedicineUniversity of Massachusetts Chan Medical SchoolWorcesterMassachusettsUSA
| | - Cody Karjadi
- Department of Anatomy and NeurobiologyBoston University Chobanian & Avedisian School of MedicineBostonMassachusettsUSA
- The Framingham Heart StudyBoston University Chobanian & Avedisian School of MedicineBostonMassachusettsUSA
| | - Ting F. A. Ang
- Department of Anatomy and NeurobiologyBoston University Chobanian & Avedisian School of MedicineBostonMassachusettsUSA
- The Framingham Heart StudyBoston University Chobanian & Avedisian School of MedicineBostonMassachusettsUSA
- Slone Epidemiology CenterBoston University Chobanian & Avedisian School of MedicineBostonMassachusettsUSA
| | - Rhoda Au
- Department of Anatomy and NeurobiologyBoston University Chobanian & Avedisian School of MedicineBostonMassachusettsUSA
- The Framingham Heart StudyBoston University Chobanian & Avedisian School of MedicineBostonMassachusettsUSA
- Slone Epidemiology CenterBoston University Chobanian & Avedisian School of MedicineBostonMassachusettsUSA
- Department of EpidemiologyBoston University School of Public HealthBostonMassachusettsUSA
- Departments of Neurology and MedicineBoston University Chobanian & Avedisian School of MedicineBostonMassachusettsUSA
| | - Honghuang Lin
- Department of MedicineUniversity of Massachusetts Chan Medical SchoolWorcesterMassachusettsUSA
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Liu F, Xiang Z, Li Q, Fang X, Zhou J, Yang X, Lin H, Yang Q. 18F-FDG PET/CT-based radiomics model for predicting the degree of pathological differentiation in non-small cell lung cancer: a multicentre study. Clin Radiol 2024; 79:e147-e155. [PMID: 37884401 DOI: 10.1016/j.crad.2023.09.017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Revised: 09/18/2023] [Accepted: 09/20/2023] [Indexed: 10/28/2023]
Abstract
AIM To explore the value of 2-[18F]-fluoro-2-deoxy-d-glucose (FDG) positron-emission tomography (PET)/computed tomography (CT)-based radiomics model for predicting the degree of pathological differentiation in non-small-cell lung cancer (NSCLC). MATERIALS AND METHODS Clinical characteristics of 182 NSCLC patients from four centres were collected, and radiomics features were extracted from 18F-FDG PET/CT images. Three logistic regression prediction models were established: clinical model; radiomics model; and nomogram combining radiomics signatures and clinical features. The predictive ability of the models was assessed using receiver operating characteristics curve analysis. RESULTS Patients from centre 1 were assigned randomly to the training and internal validation cohorts (7:3 ratio); patients from centres 2-4 served as the external validation cohort. The area under the curve (AUC) values for the clinical model in the training, internal validation, and external validation cohort were 0.74 (95% confidence interval [CI] = 0.64-0.84), 0.64 (95% CI = 0.46-0.81), and 0.74 (95% CI = 0.60-0.88), respectively. In the training (AUC: 0.84 [95% CI = 0.77-0.92]), internal validation (AUC: 0.81 [95% CI = 0.67-0.95]), and external validation cohorts (AUC: 0.74 [95% CI = 0.58-0.89]), the radiomics model showed good predictive ability for differentiation. Compared to the clinical and radiomics models, the nomogram has relatively better diagnostic performance, and the AUC values for nomogram in the training, internal validation, and external validation cohort were 0.86 (95% CI = 0.78-0.93), 0.83 (95% CI = 0.70-0.96), and 0.77 (95% CI = 0.62-0.92), respectively. CONCLUSIONS The 18F-FDG PET/CT-based radiomics model showed good ability for predicting the degree of differentiation of NSCLC. The nomogram combining the radiomics signature and clinical features has relatively better diagnostic performance.
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Affiliation(s)
- F Liu
- Department of Radiology, The Second Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
| | - Z Xiang
- Department of Radiology, The Second Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
| | - Q Li
- Department of Radiology, The Second Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China
| | - X Fang
- Department of Radiology, The Second Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China.
| | - J Zhou
- The Affiliated Hospital of Jiangsu University, Zhenjiang, Jiangsu 212001, China
| | - X Yang
- Sichuan Science City Hospital, Mianyang, Sichuan 621000, China
| | - H Lin
- Department of Pharmaceutical Diagnosis, GE Healthcare, Changsha 410005, China
| | - Q Yang
- Center for Molecular Imaging Probe, Hunan Province Key Laboratory of Tumour Cellular and Molecular Pathology, Cancer Research Institute, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China.
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T, Tamori Y, Tamura R, Tamura Y, Tan CHH, Tan EZZ, Tanabe A, Tanabe K, Tanaka A, Tanaka A, Tanaka N, Tang S, Tang Z, Tanigaki K, Tarlac M, Tatsuzawa A, Tay JF, Tay LL, Taylor J, Taylor K, Taylor K, Te A, Tenbusch L, Teng KS, Terakawa A, Terry J, Tham ZD, Tholl S, Thomas G, Thong KM, Tietjen D, Timadjer A, Tindall H, Tipper S, Tobin K, Toda N, Tokuyama A, Tolibas M, Tomita A, Tomita T, Tomlinson J, Tonks L, Topf J, Topping S, Torp A, Torres A, Totaro F, Toth P, Toyonaga Y, Tripodi F, Trivedi K, Tropman E, Tschope D, Tse J, Tsuji K, Tsunekawa S, Tsunoda R, Tucky B, Tufail S, Tuffaha A, Turan E, Turner H, Turner J, Turner M, Tuttle KR, Tye YL, Tyler A, Tyler J, Uchi H, Uchida H, Uchida T, Uchida T, Udagawa T, Ueda S, Ueda Y, Ueki K, Ugni S, Ugwu E, Umeno R, Unekawa C, Uozumi K, Urquia K, Valleteau A, Valletta C, van Erp R, Vanhoy C, Varad V, Varma R, Varughese A, Vasquez P, Vasseur A, Veelken R, Velagapudi C, Verdel K, Vettoretti S, Vezzoli G, Vielhauer V, Viera R, Vilar E, Villaruel S, Vinall L, Vinathan J, Visnjic M, Voigt E, von-Eynatten M, Vourvou M, Wada J, Wada J, Wada T, Wada Y, Wakayama K, Wakita Y, Wallendszus K, Walters T, Wan Mohamad WH, Wang L, Wang W, Wang X, Wang X, Wang Y, Wanner C, Wanninayake S, Watada H, Watanabe K, Watanabe K, Watanabe M, Waterfall H, Watkins D, Watson S, Weaving L, Weber B, Webley Y, Webster A, Webster M, Weetman M, Wei W, Weihprecht H, Weiland L, Weinmann-Menke J, Weinreich T, Wendt R, Weng Y, Whalen M, Whalley G, Wheatley R, Wheeler A, Wheeler J, Whelton P, White K, Whitmore B, Whittaker S, Wiebel J, Wiley J, Wilkinson L, Willett M, Williams A, Williams E, Williams K, Williams T, Wilson A, Wilson P, Wincott L, Wines E, Winkelmann B, Winkler M, Winter-Goodwin B, Witczak J, Wittes J, Wittmann M, Wolf G, Wolf L, Wolfling R, Wong C, Wong E, Wong HS, Wong LW, Wong YH, Wonnacott A, Wood A, Wood L, Woodhouse H, Wooding N, Woodman A, Wren K, Wu J, Wu P, Xia S, Xiao H, Xiao X, Xie Y, Xu C, Xu Y, Xue H, Yahaya H, Yalamanchili H, Yamada A, Yamada N, Yamagata K, Yamaguchi M, Yamaji Y, Yamamoto A, Yamamoto S, Yamamoto S, Yamamoto T, Yamanaka A, Yamano T, Yamanouchi Y, Yamasaki N, Yamasaki Y, Yamasaki Y, Yamashita C, Yamauchi T, Yan Q, Yanagisawa E, Yang F, Yang L, Yano S, Yao S, Yao Y, Yarlagadda S, Yasuda Y, Yiu V, Yokoyama T, Yoshida S, Yoshidome E, Yoshikawa H, Young A, Young T, Yousif V, Yu H, Yu Y, Yuasa K, Yusof N, Zalunardo N, Zander B, Zani R, Zappulo F, Zayed M, Zemann B, Zettergren P, Zhang H, Zhang L, Zhang L, Zhang N, Zhang X, Zhao J, Zhao L, Zhao S, Zhao Z, Zhong H, Zhou N, Zhou S, Zhu D, Zhu L, Zhu S, Zietz M, Zippo M, Zirino F, Zulkipli FH. Impact of primary kidney disease on the effects of empagliflozin in patients with chronic kidney disease: secondary analyses of the EMPA-KIDNEY trial. Lancet Diabetes Endocrinol 2024; 12:51-60. [PMID: 38061372 DOI: 10.1016/s2213-8587(23)00322-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Revised: 10/24/2023] [Accepted: 10/25/2023] [Indexed: 12/23/2023]
Abstract
BACKGROUND The EMPA-KIDNEY trial showed that empagliflozin reduced the risk of the primary composite outcome of kidney disease progression or cardiovascular death in patients with chronic kidney disease mainly through slowing progression. We aimed to assess how effects of empagliflozin might differ by primary kidney disease across its broad population. METHODS EMPA-KIDNEY, a randomised, controlled, phase 3 trial, was conducted at 241 centres in eight countries (Canada, China, Germany, Italy, Japan, Malaysia, the UK, and the USA). Patients were eligible if their estimated glomerular filtration rate (eGFR) was 20 to less than 45 mL/min per 1·73 m2, or 45 to less than 90 mL/min per 1·73 m2 with a urinary albumin-to-creatinine ratio (uACR) of 200 mg/g or higher at screening. They were randomly assigned (1:1) to 10 mg oral empagliflozin once daily or matching placebo. Effects on kidney disease progression (defined as a sustained ≥40% eGFR decline from randomisation, end-stage kidney disease, a sustained eGFR below 10 mL/min per 1·73 m2, or death from kidney failure) were assessed using prespecified Cox models, and eGFR slope analyses used shared parameter models. Subgroup comparisons were performed by including relevant interaction terms in models. EMPA-KIDNEY is registered with ClinicalTrials.gov, NCT03594110. FINDINGS Between May 15, 2019, and April 16, 2021, 6609 participants were randomly assigned and followed up for a median of 2·0 years (IQR 1·5-2·4). Prespecified subgroupings by primary kidney disease included 2057 (31·1%) participants with diabetic kidney disease, 1669 (25·3%) with glomerular disease, 1445 (21·9%) with hypertensive or renovascular disease, and 1438 (21·8%) with other or unknown causes. Kidney disease progression occurred in 384 (11·6%) of 3304 patients in the empagliflozin group and 504 (15·2%) of 3305 patients in the placebo group (hazard ratio 0·71 [95% CI 0·62-0·81]), with no evidence that the relative effect size varied significantly by primary kidney disease (pheterogeneity=0·62). The between-group difference in chronic eGFR slopes (ie, from 2 months to final follow-up) was 1·37 mL/min per 1·73 m2 per year (95% CI 1·16-1·59), representing a 50% (42-58) reduction in the rate of chronic eGFR decline. This relative effect of empagliflozin on chronic eGFR slope was similar in analyses by different primary kidney diseases, including in explorations by type of glomerular disease and diabetes (p values for heterogeneity all >0·1). INTERPRETATION In a broad range of patients with chronic kidney disease at risk of progression, including a wide range of non-diabetic causes of chronic kidney disease, empagliflozin reduced risk of kidney disease progression. Relative effect sizes were broadly similar irrespective of the cause of primary kidney disease, suggesting that SGLT2 inhibitors should be part of a standard of care to minimise risk of kidney failure in chronic kidney disease. FUNDING Boehringer Ingelheim, Eli Lilly, and UK Medical Research Council.
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Møller AL, Vasan RS, Levy D, Andersson C, Lin H. Integrated omics analysis of coronary artery calcifications and myocardial infarction: the Framingham Heart Study. Sci Rep 2023; 13:21581. [PMID: 38062110 PMCID: PMC10703905 DOI: 10.1038/s41598-023-48848-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2023] [Accepted: 11/30/2023] [Indexed: 12/18/2023] Open
Abstract
Gene function can be described using various measures. We integrated association studies of three types of omics data to provide insights into the pathophysiology of subclinical coronary disease and myocardial infarction (MI). Using multivariable regression models, we associated: (1) single nucleotide polymorphism, (2) DNA methylation, and (3) gene expression with coronary artery calcification (CAC) scores and MI. Among 3106 participants of the Framingham Heart Study, 65 (2.1%) had prevalent MI and 60 (1.9%) had incident MI, median CAC value was 67.8 [IQR 10.8, 274.9], and 1403 (45.2%) had CAC scores > 0 (prevalent CAC). Prevalent CAC was associated with AHRR (linked to smoking) and EXOC3 (affecting platelet function and promoting hemostasis). CAC score was associated with VWA1 (extracellular matrix protein associated with cartilage structure in endomysium). For prevalent MI we identified FYTTD1 (down-regulated in familial hypercholesterolemia) and PINK1 (linked to cardiac tissue homeostasis and ischemia-reperfusion injury). Incident MI was associated with IRX3 (enhancing browning of white adipose tissue) and STXBP3 (controlling trafficking of glucose transporter type 4 to plasma). Using an integrative trans-omics approach, we identified both putatively novel and known candidate genes associated with CAC and MI. Replication of findings is warranted.
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Affiliation(s)
- Amalie Lykkemark Møller
- Section of Preventive Medicine and Epidemiology, Department of Medicine, Boston University School of Medicine, Boston, MA, USA.
- Department of Cardiology, Nordsjællands Hospital, Hillerød, Denmark.
| | - Ramachandran S Vasan
- Section of Preventive Medicine and Epidemiology, Department of Medicine, Boston University School of Medicine, Boston, MA, USA
- Boston University's and National Heart, Lung, and Blood Institute's Framingham Heart Study, Framingham, MA, USA
- University of Texas School of Public Health San Antonio, and Departments of Medicine and Population Health Sciences, University of Texas Health Science Center, San Antonio, TX, USA
| | - Daniel Levy
- Boston University's and National Heart, Lung, and Blood Institute's Framingham Heart Study, Framingham, MA, USA
- Population Sciences Branch, Division of Intramural Research, National Institutes of Health, Bethesda, MD, USA
| | - Charlotte Andersson
- Section of Cardiovascular Medicine, Department of Medicine, Boston Medical Center, Boston University School of Medicine, Boston, MA, USA
| | - Honghuang Lin
- Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, USA
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Mensah Otabil E, Dai Q, Anzenberg P, Filippaios A, Ding E, Mehawej J, Mathew JE, Lessard D, Wang Z, Noorishirazi K, Hamel A, Paul T, DiMezza D, Han D, Mohagheghian F, Soni A, Lin H, Barton B, Saczynski J, Chon KH, Tran KV, McManus DD. Technology engagement is associated with higher perceived physical well-being in stroke patients prescribed smartwatches for atrial fibrillation detection. Front Digit Health 2023; 5:1243959. [PMID: 38125757 PMCID: PMC10731012 DOI: 10.3389/fdgth.2023.1243959] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Accepted: 11/20/2023] [Indexed: 12/23/2023] Open
Abstract
Background Increasing ownership of smartphones among Americans provides an opportunity to use these technologies to manage medical conditions. We examine the influence of baseline smartwatch ownership on changes in self-reported anxiety, patient engagement, and health-related quality of life when prescribed smartwatch for AF detection. Method We performed a post-hoc secondary analysis of the Pulsewatch study (NCT03761394), a clinical trial in which 120 participants were randomized to receive a smartwatch-smartphone app dyad and ECG patch monitor compared to an ECG patch monitor alone to establish the accuracy of the smartwatch-smartphone app dyad for detection of AF. At baseline, 14 days, and 44 days, participants completed the Generalized Anxiety Disorder-7 survey, the Health Survey SF-12, and the Consumer Health Activation Index. Mixed-effects linear regression models using repeated measures with anxiety, patient activation, physical and mental health status as outcomes were used to examine their association with smartwatch ownership at baseline. Results Ninety-six participants, primarily White with high income and tertiary education, were randomized to receive a study smartwatch-smartphone dyad. Twenty-four (25%) participants previously owned a smartwatch. Compared to those who did not previously own a smartwatch, smartwatch owners reported significant greater increase in their self-reported physical health (β = 5.07, P < 0.05), no differences in anxiety (β = 0.92, P = 0.33), mental health (β = -2.42, P = 0.16), or patient activation (β = 1.86, P = 0.54). Conclusions Participants who own a smartwatch at baseline reported a greater positive change in self-reported physical health, but not in anxiety, patient activation, or self-reported mental health over the study period.
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Affiliation(s)
- Edith Mensah Otabil
- Division of Cardiovascular Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, United States
| | - Qiying Dai
- Division of Cardiovascular Medicine, Department of Medicine, Saint Vincent Hospital, Worcester, MA, United States
| | - Paula Anzenberg
- Division of Cardiovascular Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, United States
| | - Andreas Filippaios
- Division of Cardiovascular Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, United States
| | - Eric Ding
- Division of Cardiovascular Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, United States
| | - Jordy Mehawej
- Division of Cardiovascular Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, United States
| | - Joanne E. Mathew
- Department of Internal Medicine, Central Michigan University, Mount Pleasant, MI, United States
| | - Darleen Lessard
- Department of Population and Quantitative Health Sciences, University of Massachusetts Chan Medical School, Worcester, MA, United States
| | - Ziyue Wang
- Department of Population and Quantitative Health Sciences, University of Massachusetts Chan Medical School, Worcester, MA, United States
| | - Kamran Noorishirazi
- Division of Cardiovascular Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, United States
| | - Alexander Hamel
- Division of Cardiovascular Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, United States
| | - Tenes Paul
- Division of Cardiovascular Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, United States
| | - Danielle DiMezza
- Division of Cardiovascular Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, United States
| | - Dong Han
- Department of Biomedical Engineering, University of Connecticut, Storrs, CT, United States
| | - Fahimeh Mohagheghian
- Department of Biomedical Engineering, University of Connecticut, Storrs, CT, United States
| | - Apurv Soni
- Division of Cardiovascular Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, United States
| | - Honghuang Lin
- Division of Cardiovascular Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, United States
| | - Bruce Barton
- Department of Population and Quantitative Health Sciences, University of Massachusetts Chan Medical School, Worcester, MA, United States
| | - Jane Saczynski
- Department of Pharmacy and Health Systems Sciences, School of Pharmacy, Northeastern University, Boston, MA, United States
| | - Ki H. Chon
- Department of Biomedical Engineering, University of Connecticut, Storrs, CT, United States
| | - Khanh-Van Tran
- Division of Cardiovascular Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, United States
| | - David D. McManus
- Division of Cardiovascular Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, United States
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Uzunparmak B, Haymaker C, Raso G, Masciari S, Wang L, Lin H, Gorur A, Kirby B, Cimo AM, Kennon A, Ding Q, Urschel G, Yuan Y, Feng G, Rizvi Y, Hussain A, Zhu C, Kim P, Abbadessa G, Subbiah V, Yap TA, Rodon J, Piha-Paul SA, Meric-Bernstam F, Dumbrava EE. HER2-low expression in patients with advanced or metastatic solid tumors. Ann Oncol 2023; 34:1035-1046. [PMID: 37619847 DOI: 10.1016/j.annonc.2023.08.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Revised: 08/02/2023] [Accepted: 08/09/2023] [Indexed: 08/26/2023] Open
Abstract
BACKGROUND Human epidermal growth factor receptor 2 (HER2)-low is a newly defined category with HER2 1+ or 2+ expression by immunohistochemistry (IHC) and lack of HER2 gene amplification measured by in situ hybridization (ISH). Much remains unknown about the HER2-low status across tumor types and changes in HER2 status between primary and metastatic samples. PATIENTS AND METHODS HER2 expression by IHC was evaluated in 4701 patients with solid tumors. We have evaluated the HER2 expression by IHC and amplification by ISH in paired breast and gastric/gastroesophageal (GEJ) primary and metastatic samples. HER2 expression was correlated with ERBB2 genomic alterations evaluated by next-generation sequencing (NGS) in non-breast, non-gastric/GEJ samples. RESULTS HER2 expression (HER2 IHC 1-3+) was found in half (49.8%) of the cancers, with HER2-low (1 or 2+) found in many tumor types: 47.1% in breast, 34.6% in gastric/GEJ, 50.0% in salivary gland, 46.9% in lung, 46.5% in endometrial, 46% in urothelial, and 45.5% of gallbladder cancers. The concordance evaluation of HER2 expression between primary and metastatic breast cancer samples showed that HER2 3+ remained unchanged in 87.1% with a strong agreement between primary and metastatic samples, with a weighted kappa (Κ) of 0.85 (95% confidence interval 0.79-0.91). ERBB2 alterations were identified in 117 (7.5%) patients with non-breast, non-gastric/GEJ solid tumors who had NGS testing. Of 1436 patients without ERBB2 alterations, 512 (35.7%) showed any level HER2 expression by IHC. CONCLUSION Our results show that HER2-low expression is frequently found across tumor types. These findings suggest that many patients with HER2-low solid tumors might benefit from HER2-targeted therapies.
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Affiliation(s)
- B Uzunparmak
- Department of Investigational Cancer Therapeutics, The University of Texas MD Anderson Cancer Center, Houston, USA
| | - C Haymaker
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, The University of Texas MD Anderson Cancer Center, Houston, USA
| | - G Raso
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, The University of Texas MD Anderson Cancer Center, Houston, USA
| | - S Masciari
- Department of Sanofi, The University of Texas MD Anderson Cancer Center, Cambridge, USA
| | - L Wang
- Department of Sanofi, The University of Texas MD Anderson Cancer Center, Cambridge, USA
| | - H Lin
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, USA
| | - A Gorur
- Department of Investigational Cancer Therapeutics, The University of Texas MD Anderson Cancer Center, Houston, USA
| | - B Kirby
- Department of Investigational Cancer Therapeutics, The University of Texas MD Anderson Cancer Center, Houston, USA
| | - A-M Cimo
- Department of Investigational Cancer Therapeutics, The University of Texas MD Anderson Cancer Center, Houston, USA
| | - A Kennon
- Department of Investigational Cancer Therapeutics, The University of Texas MD Anderson Cancer Center, Houston, USA
| | - Q Ding
- Department of Anatomical Pathology, The University of Texas MD Anderson Cancer Center, Houston, USA
| | - G Urschel
- Department of Investigational Cancer Therapeutics, The University of Texas MD Anderson Cancer Center, Houston, USA
| | - Y Yuan
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, USA
| | - G Feng
- Department of Investigational Cancer Therapeutics, The University of Texas MD Anderson Cancer Center, Houston, USA
| | - Y Rizvi
- Department of Investigational Cancer Therapeutics, The University of Texas MD Anderson Cancer Center, Houston, USA
| | - A Hussain
- Department of Investigational Cancer Therapeutics, The University of Texas MD Anderson Cancer Center, Houston, USA
| | - C Zhu
- Department of Sanofi, The University of Texas MD Anderson Cancer Center, Cambridge, USA
| | - P Kim
- Department of Sanofi, The University of Texas MD Anderson Cancer Center, Cambridge, USA
| | - G Abbadessa
- Department of Sanofi, The University of Texas MD Anderson Cancer Center, Cambridge, USA
| | - V Subbiah
- Department of Investigational Cancer Therapeutics, The University of Texas MD Anderson Cancer Center, Houston, USA
| | - T A Yap
- Department of Investigational Cancer Therapeutics, The University of Texas MD Anderson Cancer Center, Houston, USA; Department of The Institute for Applied Cancer Science, The University of Texas MD Anderson Cancer Center, Houston, USA; Department of Khalifa Institute for Personalized Cancer Therapy, The University of Texas MD Anderson Cancer Center, Houston, USA
| | - J Rodon
- Department of Investigational Cancer Therapeutics, The University of Texas MD Anderson Cancer Center, Houston, USA; Department of Khalifa Institute for Personalized Cancer Therapy, The University of Texas MD Anderson Cancer Center, Houston, USA; Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, USA
| | - S A Piha-Paul
- Department of Investigational Cancer Therapeutics, The University of Texas MD Anderson Cancer Center, Houston, USA
| | - F Meric-Bernstam
- Department of Investigational Cancer Therapeutics, The University of Texas MD Anderson Cancer Center, Houston, USA; Department of Khalifa Institute for Personalized Cancer Therapy, The University of Texas MD Anderson Cancer Center, Houston, USA; Department of Surgical Oncology, The University of Texas MD Anderson Cancer Center, Houston, USA
| | - E E Dumbrava
- Department of Investigational Cancer Therapeutics, The University of Texas MD Anderson Cancer Center, Houston, USA.
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Sarnowski C, Huan T, Ma Y, Joehanes R, Beiser A, DeCarli CS, Heard-Costa NL, Levy D, Lin H, Liu CT, Liu C, Meigs JB, Satizabal CL, Florez JC, Hivert MF, Dupuis J, De Jager PL, Bennett DA, Seshadri S, Morrison AC. Multi-tissue epigenetic analysis identifies distinct associations underlying insulin resistance and Alzheimer's disease at CPT1A locus. Clin Epigenetics 2023; 15:173. [PMID: 37891690 PMCID: PMC10612362 DOI: 10.1186/s13148-023-01589-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Accepted: 10/20/2023] [Indexed: 10/29/2023] Open
Abstract
BACKGROUND Insulin resistance (IR) is a major risk factor for Alzheimer's disease (AD) dementia. The mechanisms by which IR predisposes to AD are not well-understood. Epigenetic studies may help identify molecular signatures of IR associated with AD, thus improving our understanding of the biological and regulatory mechanisms linking IR and AD. METHODS We conducted an epigenome-wide association study of IR, quantified using the homeostatic model assessment of IR (HOMA-IR) and adjusted for body mass index, in 3,167 participants from the Framingham Heart Study (FHS) without type 2 diabetes at the time of blood draw used for methylation measurement. We identified DNA methylation markers associated with IR at the genome-wide level accounting for multiple testing (P < 1.1 × 10-7) and evaluated their association with neurological traits in participants from the FHS (N = 3040) and the Religious Orders Study/Memory and Aging Project (ROSMAP, N = 707). DNA methylation profiles were measured in blood (FHS) or dorsolateral prefrontal cortex (ROSMAP) using the Illumina HumanMethylation450 BeadChip. Linear regressions (ROSMAP) or mixed-effects models accounting for familial relatedness (FHS) adjusted for age, sex, cohort, self-reported race, batch, and cell type proportions were used to assess associations between DNA methylation and neurological traits accounting for multiple testing. RESULTS We confirmed the strong association of blood DNA methylation with IR at three loci (cg17901584-DHCR24, cg17058475-CPT1A, cg00574958-CPT1A, and cg06500161-ABCG1). In FHS, higher levels of blood DNA methylation at cg00574958 and cg17058475 were both associated with lower IR (P = 2.4 × 10-11 and P = 9.0 × 10-8), larger total brain volumes (P = 0.03 and P = 9.7 × 10-4), and smaller log lateral ventricular volumes (P = 0.07 and P = 0.03). In ROSMAP, higher levels of brain DNA methylation at the same two CPT1A markers were associated with greater risk of cognitive impairment (P = 0.005 and P = 0.02) and higher AD-related indices (CERAD score: P = 5 × 10-4 and 0.001; Braak stage: P = 0.004 and P = 0.01). CONCLUSIONS Our results suggest potentially distinct epigenetic regulatory mechanisms between peripheral blood and dorsolateral prefrontal cortex tissues underlying IR and AD at CPT1A locus.
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Affiliation(s)
- Chloé Sarnowski
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA.
| | - Tianxiao Huan
- Population Sciences Branch, National Heart, Lung and Blood Institutes of Health, Bethesda, MD, USA
| | - Yiyi Ma
- Center for Translational and Computational Neuroimmunology, Department of Neurology, Columbia University Irving Medical Center, New York, NY, USA
| | - Roby Joehanes
- Population Sciences Branch, National Heart, Lung and Blood Institutes of Health, Bethesda, MD, USA
- The Framingham Heart Study, Framingham, MA, USA
| | - Alexa Beiser
- The Framingham Heart Study, Framingham, MA, USA
- Department of Biostatistics, School of Public Health, Boston University, Boston, MA, USA
- Department of Neurology, Boston University School of Medicine, Boston, MA, USA
| | | | - Nancy L Heard-Costa
- The Framingham Heart Study, Framingham, MA, USA
- Department of Biostatistics, School of Public Health, Boston University, Boston, MA, USA
| | - Daniel Levy
- Center for Translational and Computational Neuroimmunology, Department of Neurology, Columbia University Irving Medical Center, New York, NY, USA
- The Framingham Heart Study, Framingham, MA, USA
| | - Honghuang Lin
- Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, USA
| | - Ching-Ti Liu
- Department of Biostatistics, School of Public Health, Boston University, Boston, MA, USA
| | - Chunyu Liu
- Department of Biostatistics, School of Public Health, Boston University, Boston, MA, USA
| | - James B Meigs
- Division of General Internal Medicine, Massachusetts General Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Programs in Metabolism and Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Claudia L Satizabal
- The Framingham Heart Study, Framingham, MA, USA
- Department of Neurology, Boston University School of Medicine, Boston, MA, USA
- Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, The University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
- Department of Population Health Sciences, The University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
| | - Jose C Florez
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Programs in Metabolism and Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Genomic Medicine and Diabetes Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Marie-France Hivert
- Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Harvard University, Boston, MA, USA
- Diabetes Unit, Massachusetts General Hospital, Boston, MA, USA
- Department of Medicine, Université de Sherbrooke, Sherbrooke, QC, Canada
| | - Josée Dupuis
- Department of Biostatistics, School of Public Health, Boston University, Boston, MA, USA
- Department of Epidemiology, Biostatistics and Occupational Health, School of Population and Global Health, McGill University, Montreal, Canada
| | - Philip L De Jager
- Center for Translational and Computational Neuroimmunology, Department of Neurology, Columbia University Irving Medical Center, New York, NY, USA
- Taub Institute for Research on Alzheimer's Disease and the Aging Brain, Columbia University Irving Medical Center, New York, NY, USA
| | - David A Bennett
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, USA
| | - Sudha Seshadri
- The Framingham Heart Study, Framingham, MA, USA
- Department of Neurology, Boston University School of Medicine, Boston, MA, USA
- Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, The University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
| | - Alanna C Morrison
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA
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Gurnani A, Frank B, Guan C, Andersen S, Devine S, Hurley L, Liu C, Lin H, Au R. A - 3 Quantifying the Richness of the Boston Process Approach (BPA) Using a Novel Analytic Approach: the Framingham Heart Study. Arch Clin Neuropsychol 2023; 38:1148. [PMID: 37807098 DOI: 10.1093/arclin/acad067.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/10/2023] Open
Abstract
OBJECTIVE Neuropsychological (NP) tests are often interpreted with a one test-one domain analytic approach in research, which misrepresents their clinical utility. The Boston Process Approach (BPA) allows for extraction of multi domain features within and across NP tests although its use is hampered by the data complexity and lack of objective quantification methods. In this study, we aim to develop a novel factor analytic approach that quantifies the richness of BPA data in the Framingham Heart Study (FHS). METHOD Data included BPA error scores (n = 172) derived from 10 baseline NP tests from 2238 Offspring participants. The outcome variable was dementia diagnosis. Factor analyses were conducted using Kemeny covariance matrix with maximum likelihood decomposition. Dwyer's (1937) method was used to estimate factor loadings for demographic variables (sex, education, and age) and dementia status. RESULTS Participants' average age was 67.3 ± 9.3 years, 54.5% were female, 41.3% had education ≥ college degree, and 92.5% were without dementia. A bifactor model demonstrated adequate fit (TLI = 0.95, RMSEA = 0.03), similar to the best multi-factor model (TLI = 0.95, RMSEA = 0.03), and improved over a single factor model (TLI = 0.87, RMSEA = 0.05). Omega estimates revealed saturation in general factor versus total factor loadings (ratio: 0.90). Dementia status loaded highly on the general factor (0.82, h2 = 0.81), as did sex (0.44, h2 = 0.27). CONCLUSIONS BPA data fit a bifactor model, with most variation accounted for by the general factor. This study quantifies the richness of BPA measures into a single factor score that can be used as a predictor for several outcomes in clinical research.
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Lattery G, Kaulfers T, Cheng C, Hasan S, Choi IJ, Simone CB, Lin H, Kang M, Chang J. Proton Single-Energy Bragg-Peak FLASH Using Clinical Systems Can Achieve IMPT-Equivalent Plan Quality for Breast and Prostate Cancers. Int J Radiat Oncol Biol Phys 2023; 117:S141. [PMID: 37784361 DOI: 10.1016/j.ijrobp.2023.06.551] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/04/2023]
Abstract
PURPOSE/OBJECTIVE(S) Most current proton FLASH-RT studies focus on transmission proton techniques. In this study, we propose a novel method for achieving FLASH dose rate in hypofractionated proton radiotherapy using the Bragg peak of a single-energy proton beam. The dosimetric characteristics using this novel technique for proton pencil beam scanning (PBS) stereotactic body radiation therapy (SBRT) of prostate and breast cancers were first investigated based on the clinically available cyclotron beam parameters. MATERIALS/METHODS This novel approach uses the distal tracking technique that enables PBS Bragg-peak of the highest proton energy to adapt to the target distally. Positioning of the Bragg peak at different depths is achieved using a universal range shifter and range compensator. To investigate the feasibility of this approach, we developed an in-house treatment planning platform for intensity-modulated proton therapy (IMPT) delivery and performed dosimetric studies on prostate and breast SBRT cases previously treated with conventional proton PBS technique. FLASH plans were generated using a similar clinical beam arrangement to deliver 40 Gy (RBE) in 5 fractions. Dose metrics were compared between the clinical and FLASH plans. Dose-rate volume histograms (DRVH) were also calculated to investigate the 40 Gy/s coverage (V40 Gy/s) of organs-at-risk (OARs) for FLASH plans. RESULTS The distal tracking can precisely stop the Bragg peak at the target distal edge, and Bragg peak plans achieved tumor coverage and dose conformality equivalent to IMPT plans. The clinical IMPT plans yielded slightly superior target dose uniformity -CTV Dmax of FLASH plans was 10% higher for prostate and 2% higher for breast. There was no significant difference between the clinical and FLASH plans in dose metrics for major OARs, including rectum, large bowel, heart, and lung. Higher maximal doses to femoral heads (∼2 Gy) and urethra (∼6 Gy) were observed in prostate FLASH plans than in the clinical plans but were still within clinically accepted dose limits. The V40 Gy/s for OARs were >90% for prostate FLASH plans and >76.5% for breast FLASH plans. CONCLUSION The proposed single-energy Bragg-peak FLASH technique eliminates exit dose associated with transmission proton FLASH and can still yield comparable plan quality and OAR sparing while preserve sufficient FLASH dose rate coverage for prostate and breast proton SBRT. This study demonstrates the potential application of Bragg peaks for highly conformal FLASH-RT using clinical cyclotron systems to treat prostate and breast cancer patients, which moves towards clinical application.
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Affiliation(s)
- G Lattery
- Department of Physics and Astronomy, Hofstra University, HEMPSTEAD, NY
| | - T Kaulfers
- Department of Physics and Astronomy, Hofstra University, HEMPSTEAD, NY
| | - C Cheng
- Department of Radiation Oncology, Rutgers Cancer Institute of New Jersey, New Brunswick, NJ
| | - S Hasan
- Allegheny Health Network, Department of Radiation Oncology, Pittsburgh, PA
| | - I J Choi
- Memorial Sloan Kettering Cancer Center, New York, NY
| | | | - H Lin
- New York Proton Center, New York, NY
| | - M Kang
- New York Proton Center, New York, NY
| | - J Chang
- Center for Advanced Medicine-Northwell Health, Lake Success, NY
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22
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Zhao L, Yang Y, Liu P, Yu F, Hu L, Kang M, Lin H, Ding X. Introducing an Experimental Approach to Predict Spot Scanning Time Parameters for a Superconducting Cyclotron Proton Therapy Machine. Int J Radiat Oncol Biol Phys 2023; 117:e748. [PMID: 37786166 DOI: 10.1016/j.ijrobp.2023.06.2290] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/04/2023]
Abstract
PURPOSE/OBJECTIVE(S) Proton pencil beam scanning (PBS) delivery sequence varies a lot among institutions due to the differences in vendors, machine types, and beamline configurations, which impacts PBS interplay effects and treatment delivery time estimation. This study aims to develop an independent experimental approach to predict the spot scanning time parameters for a clinical superconducting cyclotron proton therapy machine. MATERIALS/METHODS This independent experimental approach employed an open-air parallel-plate detector with a temporal resolution of 0.05ms. A series of spot, energy, and dose rate patterns were designed and delivered, including (1) Spot switching time (SSWT) under different spot spacing for IEC-X, IEC-Y directions and diagonal direction (traveling in both X and Y direction) for three energy layers (110, 170 and 230 MeV); The Wilcoxon test is used to validate the prediction of SSWT along the diagonal direction. (2) Energy layer switching time (ELST) with different descending energy gaps for a fixed initial energy and different initial energies for a fixed descending energy gap. (3) Dose rate (MU/min) are measured for different minimum-MU-per-energy-layer (MMPEL), which are compared with the previous publication. RESULTS A SSWT jump at 10mm (can be customized) spot spacing is observed because of triggering the machine's "raster mode" threshold. Discontinuous two variable piecewise linear functions were used to fit the SSWT in X/Y for spot spacing and energy. SSWT in X/Y is increasing as spot spacing and energy increase. SSWT in the diagonal direction is determined by the time either in the x-direction or y-direction, whichever takes longer (see Table 1 for one example of validations). ELST is linear depending on descending energy gap. The dose rate dependence on MMPEL is confirmed with previous publications of a similar type of machine. CONCLUSION The study provided the first independent quantitative experimental modeling of the beam delivery time parameters without any information from vendors. Such machine-specific delivery sequence models could pave the foundation of precise interplay effect evaluation for clinical decision-making.
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Affiliation(s)
- L Zhao
- Department of Radiation Oncology, Corewell Health William Beaumont University Hospital, Royal Oak, MI
| | - Y Yang
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY
| | - P Liu
- Department of Radiation Oncology, Corewell Health William Beaumont University Hospital, Royal Oak, MI
| | - F Yu
- New York Proton Center, New York, NY
| | - L Hu
- New York Proton Center, New York, NY
| | - M Kang
- New York Proton Center, New York, NY
| | - H Lin
- New York Proton Center, New York, NY
| | - X Ding
- Department of Radiation Oncology, Corewell Health William Beaumont University Hospital, Royal Oak, MI
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Lin H, Yu F, Gorovets D, Kabarriti R, Alektiar KM, Ohri N, Hasan S, Tsai P, Shim A, Kang M, Barker CA, Wolden SL, Hajj C, Mehta KJ, Lee NY, Chhabra AM, Shepherd AF, Choi IJ, Yamada Y, Simone CB. Pencil Beam Scanning Proton Stereotactic Body Radiation Therapy (SBRT): A Robust Single Institution Experience. Int J Radiat Oncol Biol Phys 2023; 117:e686-e687. [PMID: 37786018 DOI: 10.1016/j.ijrobp.2023.06.2155] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/04/2023]
Abstract
PURPOSE/OBJECTIVE(S) To describe the feasibility of treating a complex and diverse group of patients using pencil beam scanning (PBS) proton stereotactic body radiation therapy (SBRT: 5 or fewer fractions, with a fraction size of at least 5 Gy). MATERIALS/METHODS Our center treats on average 105-120 PBS proton treatments daily, of which 9.5% of treatment courses are proton SBRT. Statistics of disease sites, treatment planning parameters (target volume, prescriptions, number of fields, SFO vs. MFO), and treatment efficiencies (scheduled time slots, actual treatment time) are presented for 305 consecutive SBRT patients receiving 1507 fractions in the past three years. Thermoplastic masks or Vacuum-lock bags are used to immobilize SBRT patients and index the patients' treatment position. Imaging guidance of orthogonal kV images and volumetric cone-beam CT is routinely used for patient setup. RESULTS SBRT patients are grouped based on the target locations: pelvis (31%), liver (17%), thoracic (13%), spine (8%), abdominal (8%), brain (7%), non-spine bone (7%), ocular (6%), and head and neck (2%). Only 112 patients (37%) were receiving their 1st RT course, whereas 113 (37%) had one prior in-field RT course, and 80 (26%) had multiple prior in-field RT courses. The median [IQR] target volume was 65.4 [29.3, 168] cc (range: 0.3-2475 cc). 72% of cases were planned with SFO and 28% with MFO. On average, 3.76 fields (range: 2 to 12) were planned for each treatment. 44% of the treatments were planned with three or fewer fields, and 10% received more than five fields, most of which involved repainting for moving targets. Over 97% of treatments were delivered in 5 fractions, with ∼3% delivered in 3 fractions. The median [IQR] prescription per treatment was 8 [7, 10] Gy (range: 5-18 Gy per treatment). 85% (84%) of the SBRT treatments were scheduled (delivered) in a 45-minute or shorter slot, and 6% (7%) of treatments were scheduled (delivered) in over a one-hour slot, most commonly for multiple isocenter treatments. 93% of treatments were delivered within 15 minutes of the planned treatment time or shorter. Deep-inspiration breath-hold (DIBH) was applied to 45% of liver SBRT cases, with the remaining 55% planned on 4D CT with (14%) or without (86%) abdominal compression. DIBH was applied in 13% of lung SBRT cases. The application of other motion mitigation approaches, such as volumetric repainting, was determined by the target motion amplitude and whether the patient could tolerate DIBH. CONCLUSION In the most diverse and largest proton SBRT experience delivered in the world over the past 3 years, over 300 patients were treated, demonstrating the feasibility and efficiency of delivering proton SBRT in a very busy center. The planning and treatment parameter statistics reported serve as a helpful reference for the proton community.
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Affiliation(s)
- H Lin
- New York Proton Center, New York, NY; Memorial Sloan Kettering Cancer Center, New York, NY
| | - F Yu
- New York Proton Center, New York, NY
| | - D Gorovets
- Memorial Sloan Kettering Cancer Center, New York, NY
| | - R Kabarriti
- Department of Radiation Oncology, Albert Einstein College of Medicine and Montefiore Medical Center, Bronx, NY
| | - K M Alektiar
- Memorial Sloan Kettering Cancer Center, New York, NY
| | - N Ohri
- Department of Radiation Oncology, Albert Einstein College of Medicine and Montefiore Medical Center, Bronx, NY
| | - S Hasan
- New York Proton Center, New York, NY; Department of Radiation Oncology, Albert Einstein College of Medicine and Montefiore Medical Center, Bronx, NY
| | - P Tsai
- New York Proton Center, New York, NY
| | - A Shim
- New York Proton Center, New York, NY
| | - M Kang
- New York Proton Center, New York, NY
| | - C A Barker
- Memorial Sloan Kettering Cancer Center, New York, NY
| | - S L Wolden
- Memorial Sloan Kettering Cancer Center, New York, NY
| | - C Hajj
- Memorial Sloan Kettering Cancer Center, New York, NY
| | - K J Mehta
- Department of Radiation Oncology, Albert Einstein College of Medicine and Montefiore Medical Center, Bronx, NY
| | - N Y Lee
- Memorial Sloan Kettering Cancer Center, New York, NY
| | - A M Chhabra
- New York Proton Center, New York, NY; Department of Radiation Oncology, Icahn School of Medicine at Mount Sinai, New York, NY
| | - A F Shepherd
- Memorial Sloan Kettering Cancer Center, New York, NY
| | - I J Choi
- New York Proton Center, New York, NY; Memorial Sloan Kettering Cancer Center, New York, NY
| | - Y Yamada
- Memorial Sloan Kettering Cancer Center, New York, NY
| | - C B Simone
- New York Proton Center, New York, NY; Memorial Sloan Kettering Cancer Center, New York, NY
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Peng SY, Cao JS, Lin H, Chen LH, Luo P, Li JT, Hong DF, Liang X, Zhang B, Liu Y. [Progress in surgical treatment of hepatocellular carcinoma with tumor thrombus in the inferior vena cava]. Zhonghua Wai Ke Za Zhi 2023; 61:821-825. [PMID: 37653982 DOI: 10.3760/cma.j.cn112139-20230412-00160] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 09/02/2023]
Abstract
Hepatocellular carcinoma(HCC) is one of the most common malignancies of the digestive system,which is prone to be associated with microvascular or macrovascular invasion. Among them,HCC with inferior vena cava tumor thrombus(IVCTT) or right atrium tumor thrombus(RATT) is rare and has a poor prognosis. However,surgical treatment of HCC with IVCTT and (or) RATT is rarely reported and summarized. The review described the classification of HCC tumor thrombus with IVCTT and (or) RATT, summarized the progress of surgical approaches and surgical operations,and introduced a case of thrombectomy after pushing from the outer surface of the atrium,rendering the RATT to the inferior vena cava under non-cardiopulmonary bypass. The review also proposed the prospective treatments for HCC with IVCTT or RATT,providing clinical guidance to hepatobiliary surgeons.
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Affiliation(s)
- S Y Peng
- Department of General Surgery,the Second Affiliated Hospital,Zhejiang University School of Medicine,Hangzhou 310009,China
| | - J S Cao
- Department of General Surgery,Sir Run-Run Shaw Hospital,Zhejiang University School of Medicine,Hangzhou 310016,China
| | - H Lin
- Department of General Surgery,Sir Run-Run Shaw Hospital,Zhejiang University School of Medicine,Hangzhou 310016,China
| | - L H Chen
- Department of General Surgery,Sir Run-Run Shaw Hospital,Zhejiang University School of Medicine,Hangzhou 310016,China
| | - P Luo
- Department of General Surgery,Sir Run-Run Shaw Hospital,Zhejiang University School of Medicine,Hangzhou 310016,China
| | - J T Li
- Department of General Surgery,the Second Affiliated Hospital,Zhejiang University School of Medicine,Hangzhou 310009,China
| | - D F Hong
- Department of General Surgery,Sir Run-Run Shaw Hospital,Zhejiang University School of Medicine,Hangzhou 310016,China
| | - X Liang
- Department of General Surgery,Sir Run-Run Shaw Hospital,Zhejiang University School of Medicine,Hangzhou 310016,China
| | - B Zhang
- Department of General Surgery,Sir Run-Run Shaw Hospital,Zhejiang University School of Medicine,Hangzhou 310016,China
| | - Y Liu
- Department of Cardiac Surgery,Sir Run-Run Shaw Hospital,Zhejiang University School of Medicine, Hangzhou 310016,China
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Abeloos CH, Gorovets D, Lewis A, Ji W, Lozano A, Tung CC, Yu F, Hanlon A, Lin H, Kha A, Yamada Y, Kabarriti R, Lazarev S, Hasan S, Chhabra AM, Simone CB, Choi IJ. Prospective Evaluation of Patient-Reported Outcomes of Invisible Ink Tattoos for the Delivery of External Beam Radiation Therapy: The PREFER Trial. Int J Radiat Oncol Biol Phys 2023; 117:e234. [PMID: 37784934 DOI: 10.1016/j.ijrobp.2023.06.1152] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/04/2023]
Abstract
PURPOSE/OBJECTIVE(S) Invisible ink tattoos allow for setup accuracy while avoiding the cosmetic permanence of visible ink tattoos. The goal of this trial was to evaluate patient-reported preference for the use of invisible ink tattoos in a radiation oncology clinic. MATERIALS/METHODS In an IRB-approved, prospective, feasibility trial, patients at a single institution receiving pencil beam scanning proton therapy to the thorax, abdomen, or pelvis underwent invisible ink tattoo-based treatment setup. Patient preference surveys comparing visible and invisible ink tattoos were completed prior to simulation (17 questions), immediately following simulation (5 questions), and at the end of treatment (18 questions), with preference scored on a 5-point Likert scale from strongly disagree to strongly agree, and cosmesis scored on a 4-point Likert scale of excellent-good-fair-poor. Differences in distributions were examined using Wilcoxon rank-sum tests, Fisher's exact tests, or chi-square tests, where statistical significance was considered at p<0.05. RESULTS Of 107 patients screened, 102 were enrolled and 94 completed all surveys. Mean age was 55.0 years, and 58.5% were female. Most patients were white (79.1%) and non-Hispanic (92.6%). Patients most commonly had breast (34.0%), prostate (16.0%), and lung (9.6%) cancer. An average of 5 (range 3-8) invisible ink tattoos were placed per patient. Overall, 75.5% of patients reported that they would prefer to receive invisible tattoos vs. visible tattoos, and 88.3% rated the overall cosmetic outcome of invisible ink tattoo marks as excellent or good. Compared to males, females were more willing to travel farther from their home in order to avoid receiving visible tattoos (45.4% vs. 23.1%, p = 0.035) and would pay additional money to avoid receiving visible tattoos (34.5% vs. 5.1%, p = 0.002). Patients who had previously received any tattoo (cosmetic or visible RT tattoos) were more satisfied with the appearance of their invisible ink tattoos compared to those who had never previously received tattoos (82.9% vs. 61.5%, p = 0.022). Patients receiving definitive intent RT were more satisfied with the appearance of the tattoos compared to those receiving palliative intent RT (67.1% vs. 38.9%, p = 0.011). Patients with at least a college education were less satisfied with the appearance of tattoos compared to those without a college education (67.0% vs. 95.0% p = 0.018). CONCLUSION These findings demonstrate stronger avoidance of visible tattoos and patient preference for invisible tattoos. The standard incorporation of invisible ink tattoos for patient setup should be strongly considered.
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Affiliation(s)
| | - D Gorovets
- Memorial Sloan Kettering Cancer Center, New York, NY
| | - A Lewis
- Rutgers Robert Wood Johnson, Newark, NJ
| | - W Ji
- Virginia Tech, Roanoke, VA
| | | | - C C Tung
- New York Proton Center, New York, NY
| | - F Yu
- New York Proton Center, New York, NY
| | | | - H Lin
- New York Proton Center, New York, NY
| | - A Kha
- New York Proton Center, New York, NY
| | - Y Yamada
- Memorial Sloan Kettering Cancer Center, New York, NY
| | - R Kabarriti
- Department of Radiation Oncology, Albert Einstein College of Medicine and Montefiore Medical Center, Bronx, NY
| | - S Lazarev
- Department of Radiation Oncology, Icahn School of Medicine at Mount Sinai, New York, NY
| | - S Hasan
- New York Proton Center, New York, NY
| | | | - C B Simone
- Memorial Sloan Kettering Cancer Center, New York, NY; New York Proton Center, New York, NY
| | - I J Choi
- Memorial Sloan Kettering Cancer Center, New York, NY; New York Proton Center, New York, NY
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Marshall DC, Shim A, Chen CC, Lin H, Yu F, Argiriadi P, Choi IJ, Chhabra AM, Simone CB. A Dosimetric Assessment of Sexual Organ Sparing Proton Radiotherapy in Female Pelvic Cancer Patients. Int J Radiat Oncol Biol Phys 2023; 117:e695. [PMID: 37786040 DOI: 10.1016/j.ijrobp.2023.06.2174] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/04/2023]
Abstract
PURPOSE/OBJECTIVE(S) Optimizing treatment techniques for female patients undergoing curative treatment for pelvic cancers requires incorporating the goals of maximizing cure while maintaining quality of life. Optimizing treatment to maintain sexual quality of life has received little attention in female patients despite the presence of and toxicity risks to functional anatomic organs and their associated neurovasculature, including the bulboclitoris, vagina, and ovaries. Recent dosimetric data without employing sexual organ sparing suggest that mean VMAT dose to the bulboclitoris in low rectal cancer is around 3300 cGy, and in anal cancer, mean dose is around 2000 cGy to the external genitalia and 4500-5000 cGy to the bulboclitoris, all of which would be expected to result in clinically significant toxicity. Therefore, investigation of the avoidance of these important organs is needed and we hypothesize that proton techniques may achieve greater sparing than photon techniques. MATERIALS/METHODS In this study, we dosimetrically compare proton- vs. photon-based techniques in sparing functional sexual organs. The cohort consisted of four consecutive female pelvic cancer cases that had received 5000 cGy or greater. All cases were re-planned with VMAT and protons while optimizing dose to functional sexual organs and maintaining target coverage. Sexual organ structures assessed include the genitalia, vagina, ovaries, bulboclitoris and internal pudendal arteries. Given the small number of patients included in this demonstration study, statistical tests were not performed. RESULTS MRI was required to appropriately delineate soft tissue. In all cases, dosimetric sparing of sexual organs was improved with proton therapy without compromising target coverage. Mean doses were marginally decreased for structures within the PTV, while structures such as the bulboclitoris were spared substantially. Mean dose to the external genitalia was low with sparing using both VMAT (Median [IQR] (cGy): 852 [811, 1090]) and Proton techniques (Median [IQR] (cGy): 39.4 [11.9, 78.5]). Similarly, mean dose with sparing to the external genitalia was lower than would be expected without sparing, using both VMAT and Proton techniques (Median (IQR) Dmean (cGy) VMAT 3100 [2890, 3580] vs. Proton 1530 [1100, 2090]), with protons demonstrating greater sparing. In one case of a sacral chordoma, ovaries were substantially spared to below ablative thresholds (Dmean (cGy) VMAT 3598.8 and 3548.0 vs Proton 34.1 and 103.3). CONCLUSION Magnetic resonance imaging at simulation combined with proton radiotherapy for female sexual organ sparing may provide a technically feasible route to more equitable sexual outcomes for female patients. These results will guide future studies to optimize proton treatment techniques for female sexual organ sparing for future trials.
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Affiliation(s)
- D C Marshall
- Icahn School of Medicine at Mount Sinai, New York, NY
| | - A Shim
- New York Proton Center, New York, NY
| | - C C Chen
- New York Proton Center, New York, NY
| | - H Lin
- New York Proton Center, New York, NY
| | - F Yu
- New York Proton Center, New York, NY
| | - P Argiriadi
- Icahn School of Medicine at Mount Sinai, Department of Radiology, New York, NY
| | - I J Choi
- New York Proton Center, New York, NY; Memorial Sloan Kettering Cancer Center, New York, NY
| | - A M Chhabra
- New York Proton Center, New York, NY; Department of Radiation Oncology, Icahn School of Medicine at Mount Sinai, New York, NY
| | - C B Simone
- New York Proton Center, New York, NY; Memorial Sloan Kettering Cancer Center, New York, NY
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Christianson K, Prabhu M, Popp ZT, Rahman MS, Drane J, Lee M, Lathan C, Lin H, Au R, Sunderaraman P, Hwang PH. Adherence type impacts completion rates of frequent mobile cognitive assessments among older adults with and without cognitive impairment. Res Sq 2023:rs.3.rs-3350075. [PMID: 37841867 PMCID: PMC10571616 DOI: 10.21203/rs.3.rs-3350075/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/17/2023]
Abstract
Background Prior to a diagnosis of Alzheimer's disease, many individuals experience cognitive and behavioral fluctuations that are not detected during a single session of traditional neuropsychological assessment. Mobile applications now enable high-frequency cognitive data to be collected remotely, introducing new opportunities and challenges. Emerging evidence suggests cognitively impaired older adults are capable of completing mobile assessments frequently, but no study has observed whether completion rates vary by assessment frequency or adherence type. Methods Thirty-three older adults were recruited from the Boston University Alzheimer's Disease Research Center (mean age = 73.5 years; 27.3% cognitively impaired; 57.6% female; 81.8% White, 18.2% Black). Participants remotely downloaded and completed the DANA Brain Vital application on their own mobile devices throughout the study. The study schedule included seventeen assessments to be completed over the course of a year. Specific periods during which assessments were expected to be completed were defined as subsegments, while segments consisted of multiple subsegments. The first segment included three subsegments to be completed within one week, the second segment included weekly subsegments and spanned three weeks, and the third and fourth segments included monthly subsegments spanning five and six months, respectively. Three distinct adherence types - subsegment adherence, segment adherence, and cumulative adherence - were examined to determine how completion rates varied depending on assessment frequency and adherence type. Results Adherence type significantly impacted whether the completion rates declined. When utilizing subsegment adherence, the completion rate significantly declined (p = 0.05) during the fourth segment. However, when considering completion rates from the perspective of segment adherence, a decline in completion rate was not observed. Overall adherence rates increased as adherence parameters were broadened from subsegment adherence (60.6%) to segment adherence (78.8%), to cumulative adherence (90.9%). Conclusions Older adults, including those with cognitive impairment, are able to complete remote cognitive assessments at a high-frequency, but may not necessarily adhere to prescribed schedules.
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Affiliation(s)
| | | | | | | | | | | | | | | | - Rhoda Au
- Boston University School of Medicine
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Herbert C, Shi Q, Baek J, Wang B, Kheterpal V, Nowak C, Suvarna T, Singh A, Hartin P, Durnam B, Schrader S, Harman E, Gerber B, Barton B, Zai A, Cohen-Wolkowiez M, Corbie-Smith G, Kibbe W, Marquez J, Hafer N, Broach J, Lin H, Heetderks W, McManus DD, Soni A. Association of neighborhood-level sociodemographic factors with Direct-to-Consumer (DTC) distribution of COVID-19 rapid antigen tests in 5 US communities. BMC Public Health 2023; 23:1848. [PMID: 37735647 PMCID: PMC10515232 DOI: 10.1186/s12889-023-16642-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Accepted: 08/29/2023] [Indexed: 09/23/2023] Open
Abstract
BACKGROUND Many interventions for widescale distribution of rapid antigen tests for COVID-19 have utilized online, direct-to-consumer (DTC) ordering systems; however, little is known about the sociodemographic characteristics of home-test users. We aimed to characterize the patterns of online orders for rapid antigen tests and determine geospatial and temporal associations with neighborhood characteristics and community incidence of COVID-19, respectively. METHODS This observational study analyzed online, DTC orders for rapid antigen test kits from beneficiaries of the Say Yes! Covid Test program from March to November 2021 in five communities: Louisville, Kentucky; Indianapolis, Indiana; Fulton County, Georgia; O'ahu, Hawaii; and Ann Arbor/Ypsilanti, Michigan. Using spatial autoregressive models, we assessed the geospatial associations of test kit distribution with Census block-level education, income, age, population density, and racial distribution and Census tract-level Social Vulnerability Index. Lag association analyses were used to measure the association between online rapid antigen kit orders and community-level COVID-19 incidence. RESULTS In total, 164,402 DTC test kits were ordered during the intervention. Distribution of tests at all sites were significantly geospatially clustered at the block-group level (Moran's I: p < 0.001); however, education, income, age, population density, race, and social vulnerability index were inconsistently associated with test orders across sites. In Michigan, Georgia, and Kentucky, there were strong associations between same-day COVID-19 incidence and test kit orders (Michigan: r = 0.89, Georgia: r = 0.85, Kentucky: r = 0.75). The incidence of COVID-19 during the current day and the previous 6-days increased current DTC orders by 9.0 (95% CI = 1.7, 16.3), 3.0 (95% CI = 1.3, 4.6), and 6.8 (95% CI = 3.4, 10.2) in Michigan, Georgia, and Kentucky, respectively. There was no same-day or 6-day lagged correlation between test kit orders and COVID-19 incidence in Indiana. CONCLUSIONS Our findings suggest that online ordering is not associated with geospatial clustering based on sociodemographic characteristics. Observed temporal preferences for DTC ordering can guide public health messaging around DTC testing programs.
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Affiliation(s)
- Carly Herbert
- Program in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, 55 Lake Avenue North, WorcesterWorcester, MA, 01655, USA
- Center for Clinical and Translational Science, University of Massachusetts, University of Massachusetts Chan Medical School, Worcester, MA, USA
| | - Qiming Shi
- Program in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, 55 Lake Avenue North, WorcesterWorcester, MA, 01655, USA
- Center for Clinical and Translational Science, University of Massachusetts, University of Massachusetts Chan Medical School, Worcester, MA, USA
| | - Jonggyu Baek
- Department of Population and Quantitative Health Sciences, University of Massachusetts Chan Medical School, Worcester, MA, USA
| | - Biqi Wang
- Program in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, 55 Lake Avenue North, WorcesterWorcester, MA, 01655, USA
| | | | | | | | - Aditi Singh
- Program in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, 55 Lake Avenue North, WorcesterWorcester, MA, 01655, USA
| | - Paul Hartin
- Program in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, 55 Lake Avenue North, WorcesterWorcester, MA, 01655, USA
| | | | | | | | - Ben Gerber
- Department of Population and Quantitative Health Sciences, University of Massachusetts Chan Medical School, Worcester, MA, USA
| | - Bruce Barton
- Department of Population and Quantitative Health Sciences, University of Massachusetts Chan Medical School, Worcester, MA, USA
| | - Adrian Zai
- Department of Population and Quantitative Health Sciences, University of Massachusetts Chan Medical School, Worcester, MA, USA
| | | | - Giselle Corbie-Smith
- Department of Social Medicine, Department of Medicine, Center for Health Equity Research, University of North Carolina School of Medicine, Chapel Hill, NC, USA
| | - Warren Kibbe
- Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, NC, USA
| | - Juan Marquez
- Washtenaw County Health Department, Washtenaw, MI, USA
| | - Nathaniel Hafer
- Center for Clinical and Translational Science, University of Massachusetts, University of Massachusetts Chan Medical School, Worcester, MA, USA
| | - John Broach
- Department of Emergency Medicine, University of Massachusetts Chan Medical School, Worcester, MA, USA
| | - Honghuang Lin
- Program in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, 55 Lake Avenue North, WorcesterWorcester, MA, 01655, USA
| | - William Heetderks
- National Institute of Biomedical Imaging and Bioengineering, NIH, Via Contract With Kelly Services, Bethesda, MD, USA
| | - David D McManus
- Program in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, 55 Lake Avenue North, WorcesterWorcester, MA, 01655, USA
- Division of Cardiology, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, USA
| | - Apurv Soni
- Program in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, 55 Lake Avenue North, WorcesterWorcester, MA, 01655, USA.
- Department of Population and Quantitative Health Sciences, University of Massachusetts Chan Medical School, Worcester, MA, USA.
- Division of Health System Science, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, USA.
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Zhang XS, Liu BC, Du X, Zhang YL, Xu N, Liu XL, Li WM, Lin H, Liang R, Chen CY, Huang J, Yang YF, Zhu HL, Pan L, Wang XD, Li GH, Liu ZG, Zhang YQ, Liu ZF, Hu JD, Liu CS, Li F, Yang W, Meng L, Han YQ, Lin LE, Zhao ZY, Tu CQ, Zheng CF, Bai YL, Zhou ZP, Chen SN, Qiu HY, Yang LJ, Sun XL, Sun H, Zhou L, Liu ZL, Wang DY, Guo JX, Pang LP, Zeng QS, Suo XH, Zhang WH, Zheng YJ, Jiang Q. [To compare the efficacy and incidence of severe hematological adverse events of flumatinib and imatinib in patients newly diagnosed with chronic phase chronic myeloid leukemia]. Zhonghua Xue Ye Xue Za Zhi 2023; 44:728-736. [PMID: 38049316 PMCID: PMC10630575 DOI: 10.3760/cma.j.issn.0253-2727.2023.09.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Indexed: 12/06/2023]
Abstract
Objective: To analyze and compare therapy responses, outcomes, and incidence of severe hematologic adverse events of flumatinib and imatinib in patients newly diagnosed with chronic phase chronic myeloid leukemia (CML) . Methods: Data of patients with chronic phase CML diagnosed between January 2006 and November 2022 from 76 centers, aged ≥18 years, and received initial flumatinib or imatinib therapy within 6 months after diagnosis in China were retrospectively interrogated. Propensity score matching (PSM) analysis was performed to reduce the bias of the initial TKI selection, and the therapy responses and outcomes of patients receiving initial flumatinib or imatinib therapy were compared. Results: A total of 4 833 adult patients with CML receiving initial imatinib (n=4 380) or flumatinib (n=453) therapy were included in the study. In the imatinib cohort, the median follow-up time was 54 [interquartile range (IQR), 31-85] months, and the 7-year cumulative incidences of CCyR, MMR, MR(4), and MR(4.5) were 95.2%, 88.4%, 78.3%, and 63.0%, respectively. The 7-year FFS, PFS, and OS rates were 71.8%, 93.0%, and 96.9%, respectively. With the median follow-up of 18 (IQR, 13-25) months in the flumatinib cohort, the 2-year cumulative incidences of CCyR, MMR, MR(4), and MR(4.5) were 95.4%, 86.5%, 58.4%, and 46.6%, respectively. The 2-year FFS, PFS, and OS rates were 80.1%, 95.0%, and 99.5%, respectively. The PSM analysis indicated that patients receiving initial flumatinib therapy had significantly higher cumulative incidences of CCyR, MMR, MR(4), and MR(4.5) and higher probabilities of FFS than those receiving the initial imatinib therapy (all P<0.001), whereas the PFS (P=0.230) and OS (P=0.268) were comparable between the two cohorts. The incidence of severe hematologic adverse events (grade≥Ⅲ) was comparable in the two cohorts. Conclusion: Patients receiving initial flumatinib therapy had higher cumulative incidences of therapy responses and higher probability of FFS than those receiving initial imatinib therapy, whereas the incidence of severe hematologic adverse events was comparable between the two cohorts.
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Affiliation(s)
- X S Zhang
- Peking University People's Hospital, Peking University Institute of Hematology, National Clinical Research Center for Hematologic Disease, Beijing Key Laboratory of Hematopoietic Stem Cell Transplantation, Beijing 100044, China
| | - B C Liu
- National Clinical Research Center for Blood Diseases, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin 300020, China
| | - X Du
- The Second People's Hospital of Shenzhen, Shenzhen 518035, China
| | - Y L Zhang
- Henan Cancer Hospital, The Affiliated Cancer Hospital of Zhengzhou University, Zhengzhou 450008, China
| | - N Xu
- Nanfang Hospital, Southern Medical University, Guangzhou 510515, China
| | - X L Liu
- Nanfang Hospital, Southern Medical University, Guangzhou 510515, China
| | - W M Li
- Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China
| | - H Lin
- First Hospital of Jilin University, Changchun 130021, China
| | - R Liang
- Xijing Hospital, Airforce Military Medical University, Xi'an 710032, China
| | - C Y Chen
- Qilu Hospital of Shandong University, Jinan 250012, China
| | - J Huang
- The Fourth Affiliated Hospital of Zhejiang University, Hangzhou 322000, China
| | - Y F Yang
- Institute of Hematology, West China Hospital, Sichuan University, Chengdu 610041, China
| | - H L Zhu
- Institute of Hematology, West China Hospital, Sichuan University, Chengdu 610041, China
| | - L Pan
- Institute of Hematology, West China Hospital, Sichuan University, Chengdu 610041, China
| | - X D Wang
- Sichuan Academy of Medical Sciences Sichuan Provincial People's Hospital, Chengdu 610072, China
| | - G H Li
- Xi'an International Medical Center Hospital, Xi'an 710038, China
| | - Z G Liu
- Shengjing Hospital of China Medical University, Shenyang 110020, China
| | - Y Q Zhang
- The Second Affiliated Hospital of Harbin Medical University, Harbin 150086, China
| | - Z F Liu
- The First Affiliated Hospital of Guangxi Medical University, Nanning 530021, China
| | - J D Hu
- Fujian Medical University Union Hospital, Fuzhou 350001, China
| | - C S Liu
- First Hospital of Jilin University, Changchun 130021, China
| | - F Li
- The First Affiliated Hospital of Nanchang University, Nanchang 330006, China
| | - W Yang
- Shengjing Hospital of China Medical University, Shenyang 110020, China
| | - L Meng
- Tongji Hospital of Tongji Medical College, Tongji Medical College of Huazhong University of Science and Technology, Wuhan 430030, China
| | - Y Q Han
- The Affiliated Hospital of Inner Mongolia Medical University, Hohhot 010050, China
| | - L E Lin
- Hainan General Hospital, Haikou 570311, China
| | - Z Y Zhao
- Hainan General Hospital, Haikou 570311, China
| | - C Q Tu
- Shenzhen Baoan Hospital, Shenzhen University Second Affiliated Hospital, Shenzhen 518101, China
| | - C F Zheng
- Shenzhen Baoan Hospital, Shenzhen University Second Affiliated Hospital, Shenzhen 518101, China
| | - Y L Bai
- Henan Provincial People's Hospital, Zhengzhou University People's Hospital, Zhengzhou 450003, China
| | - Z P Zhou
- The Second Hospital Affiliated to Kunming Medical University, Kunming 650106, China
| | - S N Chen
- National Clinical Research Center for Hematologic Diseases, Jiangsu Institute of Hematology, The First Affiliated Hospital of Soochow University, Institute of Blood and Marrow Transplantation of Soochow University, Suzhou 215006, China
| | - H Y Qiu
- National Clinical Research Center for Hematologic Diseases, Jiangsu Institute of Hematology, The First Affiliated Hospital of Soochow University, Institute of Blood and Marrow Transplantation of Soochow University, Suzhou 215006, China
| | - L J Yang
- Xi'an International Medical Center Hospital, Xi'an 710117, China
| | - X L Sun
- The First Affiliated Hospital of Dalian Medical University, Dalian 116011, China
| | - H Sun
- The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450000, China
| | - L Zhou
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Z L Liu
- Huazhong University of Science and Technology Union Shenzhen Hospital, Nanshan Hospital, Shenzhen 518000, China
| | - D Y Wang
- Huazhong University of Science and Technology Union Shenzhen Hospital, Nanshan Hospital, Shenzhen 518000, China
| | - J X Guo
- The Second Affiliated Hospital of Fujian Medical University, Quanzhou 362000, China
| | - L P Pang
- Peking University Shenzhen Hospital, Shenzhen 516473, China
| | - Q S Zeng
- The First Affiliated Hospital of Anhui Medical University, Hefei 230022, China
| | - X H Suo
- Handan Central Hospital, Handan 057150, China
| | - W H Zhang
- First Hospital of Shangxi Medical University, Taiyuan 300012, China
| | - Y J Zheng
- First Hospital of Shangxi Medical University, Taiyuan 300012, China
| | - Q Jiang
- Peking University People's Hospital, Peking University Institute of Hematology, National Clinical Research Center for Hematologic Disease, Beijing Key Laboratory of Hematopoietic Stem Cell Transplantation, Beijing 100044, China
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30
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Jiang MZ, Gaynor SM, Li X, Van Buren E, Stilp A, Buth E, Wang FF, Manansala R, Gogarten SM, Li Z, Polfus LM, Salimi S, Bis JC, Pankratz N, Yanek LR, Durda P, Tracy RP, Rich SS, Rotter JI, Mitchell BD, Lewis JP, Psaty BM, Pratte KA, Silverman EK, Kaplan RC, Avery C, North K, Mathias RA, Faraday N, Lin H, Wang B, Carson AP, Norwood AF, Gibbs RA, Kooperberg C, Lundin J, Peters U, Dupuis J, Hou L, Fornage M, Benjamin EJ, Reiner AP, Bowler RP, Lin X, Auer PL, Raffield LM. Whole Genome Sequencing Based Analysis of Inflammation Biomarkers in the Trans-Omics for Precision Medicine (TOPMed) Consortium. bioRxiv 2023:2023.09.10.555215. [PMID: 37745480 PMCID: PMC10515765 DOI: 10.1101/2023.09.10.555215] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/26/2023]
Abstract
Inflammation biomarkers can provide valuable insight into the role of inflammatory processes in many diseases and conditions. Sequencing based analyses of such biomarkers can also serve as an exemplar of the genetic architecture of quantitative traits. To evaluate the biological insight, which can be provided by a multi-ancestry, whole-genome based association study, we performed a comprehensive analysis of 21 inflammation biomarkers from up to 38,465 individuals with whole-genome sequencing from the Trans-Omics for Precision Medicine (TOPMed) program. We identified 22 distinct single-variant associations across 6 traits - E-selectin, intercellular adhesion molecule 1, interleukin-6, lipoprotein-associated phospholipase A2 activity and mass, and P-selectin - that remained significant after conditioning on previously identified associations for these inflammatory biomarkers. We further expanded upon known biomarker associations by pairing the single-variant analysis with a rare variant set-based analysis that further identified 19 significant rare variant set-based associations with 5 traits. These signals were distinct from both significant single variant association signals within TOPMed and genetic signals observed in prior studies, demonstrating the complementary value of performing both single and rare variant analyses when analyzing quantitative traits. We also confirm several previously reported signals from semi-quantitative proteomics platforms. Many of these signals demonstrate the extensive allelic heterogeneity and ancestry-differentiated variant-trait associations common for inflammation biomarkers, a characteristic we hypothesize will be increasingly observed with well-powered, large-scale analyses of complex traits.
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Affiliation(s)
- Min-Zhi Jiang
- Department of Genetics, University of North Carolina at Chapel Hill, 120 Mason Farm Road, Chapel Hill, NC, 27599, USA
| | - Sheila M. Gaynor
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, 655 Huntington Avenue, Boston, MA, 02115, USA
- Regeneron Genetics Center, Tarrytown, NY, 10591, USA
| | - Xihao Li
- Department of Genetics, University of North Carolina at Chapel Hill, 120 Mason Farm Road, Chapel Hill, NC, 27599, USA
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Eric Van Buren
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, 655 Huntington Avenue, Boston, MA, 02115, USA
| | - Adrienne Stilp
- Department of Biostatistics, University of Washington, Seattle, WA, 98105, USA
| | - Erin Buth
- Department of Biostatistics, University of Washington, Seattle, WA, 98105, USA
| | - Fei Fei Wang
- Department of Biostatistics, University of Washington, Seattle, WA, 98105, USA
| | - Regina Manansala
- Centre for Health Economics Research & Modelling Infectious Diseases (CHERMID), Vaccine & Infectious Disease Institute (VAXINFECTIO) WHO Collaborating Centre, University of Antwerp, Antwerp, BE
| | | | - Zilin Li
- School of Mathematics and Statistics, Northeast Normal University, Changchun, Jilin, 130024, China
| | - Linda M. Polfus
- Department of Preventive Medicine, Center for Genetic Epidemiology, University of Southern California, Los Angeles, CA, 90033, USA
| | - Shabnam Salimi
- Department of Epidemiology and Public Health, Division of Gerontology, University of Maryland School of Medicine, Baltimore, MD, 21201, USA
| | - Joshua C. Bis
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, 4333 Brooklyn Ave NE, Box 359458, Seattle, WA, 98195, USA
| | - Nathan Pankratz
- Department of Laboratory Medicine and Pathology, University of Minnesota Medical School, Minneapolis, MN, 55455, USA
| | - Lisa R. Yanek
- Department of Medicine, General Internal Medicine, Johns Hopkins University School of Medicine, 1830 E Monument St Rm 8024, Baltimore, MD, 21287, USA
| | - Peter Durda
- Department of Pathology & Laboratory Medicine, University of Vermont Larner College of Medicine, 360 South Park Drive, Colchester, VT, 05446, USA
| | - Russell P. Tracy
- Department of Pathology & Laboratory Medicine, University of Vermont Larner College of Medicine, 360 South Park Drive, Colchester, VT, 05446, USA
| | - Stephen S. Rich
- Center for Public Health Genomics, University of Virginia School of Medicine, 200 Jeanette Lancaster Way, Charlottesville, VA, 22903, USA
| | - Jerome I. Rotter
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, 1124 W. Carson Street, Torrance, CA, 90502, USA
| | - Braxton D. Mitchell
- Department of Medicine, Division of Endocrinology, Diabetes, and Nutrition, University of Maryland School of Medicine, 670 W. Baltimore St., Baltimore, MD, 21201, USA
| | - Joshua P. Lewis
- Department of Medicine, Division of Endocrinology, Diabetes, and Nutrition, University of Maryland School of Medicine, 670 W. Baltimore St., Baltimore, MD, 21201, USA
| | - Bruce M. Psaty
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, 4333 Brooklyn Ave NE, Box 359458, Seattle, WA, 98195, USA
- Departments of Epidemiology and Health Systems and Population Health, University of Washington, 4333 Brooklyn Ave NE, Seattle, WA, 98101, USA
| | - Katherine A. Pratte
- Department of Medicine, Division of Pulmonary, Critical Care, and Sleep Medicine, National Jewish Health, Denver, CO, 80206, USA
| | - Edwin K. Silverman
- Department of Medicine, Channing Division of Network Medicine, Brigham and Women’s Hospital, 181 Longwood Avenue, Boston, MA, 02115, USA
| | - Robert C. Kaplan
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY, 10461, USA
| | - Christy Avery
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Kari North
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Rasika A. Mathias
- Department of Medicine, Allergy and Clinical Immunology, Johns Hopkins University School of Medicine, 5501 Hopkins Bayview Cir JHAAC Room 3B53, Baltimore, MD, 21287, USA
| | - Nauder Faraday
- Department of Anesthesiology and Critical Care Medicine, Johns Hopkins University School of Medicine, 600 N Wolfe St, Baltimore, MD, 21287, USA
| | - Honghuang Lin
- Department of Medicine, University of Massachusetts Chan Medical School, 55 Lake Ave North, Worcester, MA, 01655, USA
| | - Biqi Wang
- Department of Medicine, University of Massachusetts Chan Medical School, 55 Lake Ave North, Worcester, MA, 01655, USA
| | - April P. Carson
- Department of Medicine, University of Mississippi Medical Center, 350 W. Woodrow Wilson Avenue, Suite 701, Jackson, MS, 39213, USA
| | - Arnita F. Norwood
- Department of Medicine, University of Mississippi Medical Center, 350 W. Woodrow Wilson Avenue, Suite 701, Jackson, MS, 39213, USA
| | - Richard A. Gibbs
- Department of Molecular and Human Genetics, Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Charles Kooperberg
- Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA, 98109, USA
| | - Jessica Lundin
- Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA, 98109, USA
| | - Ulrike Peters
- Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA, 98109, USA
| | - Josée Dupuis
- Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, Québec, H3A 1G1, Canada
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, 02118, USA
| | - Lifang Hou
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, 60611, USA
| | - Myriam Fornage
- Brown Foundation Institute of Molecular Medicine, McGovern Medical School, University of Texas Health Science Center at Houston, Houston, TX, 77030, USA
| | - Emelia J. Benjamin
- Department of Medicine, Cardiovascular Medicine, Boston Medical Center, Boston University Chobanian and Avedisian School of Medicine, Boston, MA, 02118, USA
- Department of Epidemiology, Boston University School of Public Health, Boston, MA, 02118, USA
- Boston University and National Heart, Lung, and Blood Institute’s Framingham Heart Study, Framingham, MA, 01702, USA
| | - Alexander P. Reiner
- Department of Epidemiology, University of Washington, Seattle, WA, 98105, USA
| | - Russell P. Bowler
- Department of Medicine, Division of Pulmonary, Critical Care, and Sleep Medicine, National Jewish Health, Denver, CO, 80206, USA
| | - Xihong Lin
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, 655 Huntington Avenue, Boston, MA, 02115, USA
| | - Paul L. Auer
- Division of Biostatistics, Institute for Health and Equity, and Cancer Center, Medical College of Wisconsin, Milwaukee, WI, 53226, USA
| | - Laura M. Raffield
- Department of Genetics, University of North Carolina at Chapel Hill, 120 Mason Farm Road, Chapel Hill, NC, 27599, USA
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31
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Lee WP, Choi SH, Shea MG, Cheng PL, Dombroski BA, Pitsillides AN, Heard-Costa NL, Wang H, Bulekova K, Kuzma AB, Leung YY, Farrell JJ, Lin H, Naj A, Blue EE, Nusetor F, Wang D, Boerwinkle E, Bush WS, Zhang X, De Jager PL, Dupuis J, Farrer LA, Fornage M, Martin E, Pericak-Vance M, Seshadri S, Wijsman EM, Wang LS, Schellenberg GD, Destefano AL, Haines JL, Peloso GM. Association of Common and Rare Variants with Alzheimer's Disease in over 13,000 Diverse Individuals with Whole-Genome Sequencing from the Alzheimer's Disease Sequencing Project. medRxiv 2023:2023.09.01.23294953. [PMID: 37693521 PMCID: PMC10491367 DOI: 10.1101/2023.09.01.23294953] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/12/2023]
Abstract
Alzheimer's Disease (AD) is a common disorder of the elderly that is both highly heritable and genetically heterogeneous. Here, we investigated the association between AD and both common variants and aggregates of rare coding and noncoding variants in 13,371 individuals of diverse ancestry with whole genome sequence (WGS) data. Pooled-population analyses identified genetic variants in or near APOE, BIN1, and LINC00320 significantly associated with AD (p < 5×10-8). Population-specific analyses identified a haplotype on chromosome 14 including PSEN1 associated with AD in Hispanics, further supported by aggregate testing of rare coding and noncoding variants in this region. Finally, we observed suggestive associations (p < 5×10-5) of aggregates of rare coding rare variants in ABCA7 among non-Hispanic Whites (p=5.4×10-6), and rare noncoding variants in the promoter of TOMM40 distinct of APOE in pooled-population analyses (p=7.2×10-8). Complementary pooled-population and population-specific analyses offered unique insights into the genetic architecture of AD.
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Affiliation(s)
- Wan-Ping Lee
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Penn Neurodegeneration Genomics Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Seung Hoan Choi
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Margaret G Shea
- Biostatistics and Epidemiology Data Analytics Center, Boston University School of Public Health, Boston, MA, USA
| | - Po-Liang Cheng
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Penn Neurodegeneration Genomics Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Beth A Dombroski
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | | | - Nancy L Heard-Costa
- Department of Neurology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
- Framingham Heart Study, Framingham, MA, USA
| | - Hui Wang
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Penn Neurodegeneration Genomics Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Katia Bulekova
- Research Computing Services, Information Services & Technology, Boston University, Boston, MA, USA
| | - Amanda B Kuzma
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Penn Neurodegeneration Genomics Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Yuk Yee Leung
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Penn Neurodegeneration Genomics Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - John J Farrell
- Biomedical Genetics, Department of Medicine, Boston University Medical School, Boston, MA, USA
| | - Honghuang Lin
- Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, USA
| | - Adam Naj
- Department of Biostatistics, Epidemiology, and Informatics, Department of Pathology and Laboratory Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Elizabeth E Blue
- Department of Medicine, Division of Medical Genetics, University of Washington, Seattle, WA, USA
- Brotman Baty Institute for Precision Medicine, Seattle, WA, USA
| | - Frederick Nusetor
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Dongyu Wang
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Eric Boerwinkle
- Department of Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, University of Texas Health Science Center at Houston; Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA
| | - William S Bush
- Cleveland Institute for Computational Biology, Cleveland, OH, USA
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, USA
| | - Xiaoling Zhang
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
- Biomedical Genetics, Department of Medicine, Boston University Medical School, Boston, MA, USA
| | - Philip L De Jager
- Center for Translational and Computational Neuroimmunology, Columbia University Medical Center, New York, NY, USA
| | - Josée Dupuis
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
- Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, Canada
| | - Lindsay A Farrer
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
- Department of Neurology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
- Framingham Heart Study, Framingham, MA, USA
- Biomedical Genetics, Department of Medicine, Boston University Medical School, Boston, MA, USA
- Department of Ophthalmology, Department of Medicine, Boston University Medical School, Boston, MA, USA
- Department of Epidemiology, Boston University School of Public Health, Boston, MA, USA
| | - Myriam Fornage
- Brown Foundation Institute of Molecular Medicine, McGovern Medical School, University of Texas Health Science Center at Houston, Houston, TX, USA
- Human Genetics Center, School of Public Health, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Eden Martin
- John P Hussman Institute for Human Genomics, Miami, FL, USA
- John T Macdonald Department of Human Genetics, Miami, FL, USA
- University of Miami Miller School of Medicine, Miami, FL, USA
| | - Margaret Pericak-Vance
- John P Hussman Institute for Human Genomics, Miami, FL, USA
- John T Macdonald Department of Human Genetics, Miami, FL, USA
- University of Miami Miller School of Medicine, Miami, FL, USA
| | - Sudha Seshadri
- Glenn Biggs Institute for Alzheimer's & Neurodegenerative Diseases, University of Texas Health Science Center, San Antonio, TX, USA
| | - Ellen M Wijsman
- Department of Medicine, Division of Medical Genetics, University of Washington, Seattle, WA, USA
- Department of Biostatistics, University of Washington, Seattle, WA, USA
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | - Li-San Wang
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Penn Neurodegeneration Genomics Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Gerard D Schellenberg
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Penn Neurodegeneration Genomics Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Anita L Destefano
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
- Department of Neurology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
| | - Jonathan L Haines
- Cleveland Institute for Computational Biology, Cleveland, OH, USA
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, USA
| | - Gina M Peloso
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
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Xu D, Zhu XX, Zou HJ, Lin H, Zhao Y. [Recommendations for the diagnosis and treatment of gout in China]. Zhonghua Nei Ke Za Zhi 2023; 62:1068-1076. [PMID: 37650180 DOI: 10.3760/cma.j.cn112138-20221027-00796] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 09/01/2023]
Abstract
Gout is a metabolic disease resulting from the accumulation of monosodium urate (MSU) in joints, leading to crystal-induced arthritis. In China, gout is common, but there is insufficient knowledge regarding standardized criteria for the diagnosis and treatment of this condition. Based on evidence and guidelines from China and other countries, the Chinese Rheumatology Association developed standardized criteria for the diagnosis and treatment of gout in China. The purpose was to standardize gout diagnosis methods as well as treatment opportunities and strategies in order to reduce misdiagnosis, missed diagnosis, and irreversible damage.
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Affiliation(s)
- D Xu
- Department of Rheumatology and Clinical Immunology, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, National Clinical Research Center for Dermatologic and Immunologic Diseases, Ministry of Science & Technology,State Key Laboratory of Complex Severe and Rare Diseases, Key Laboratory of Rheumatology and Clinical Immunology, Ministry of Education,Beijing 100730, China
| | - X X Zhu
- Division of Rheumatology, Huashan Hospital, Fudan University, Shanghai 200040, China
| | - H J Zou
- Division of Rheumatology, Huashan Hospital, Fudan University, Shanghai 200040, China
| | - H Lin
- Department of Rheumatology,Fujian Provincial Hospital, Fuzhou 350013, China
| | - Y Zhao
- Department of Rheumatology and Clinical Immunology, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, National Clinical Research Center for Dermatologic and Immunologic Diseases, Ministry of Science & Technology,State Key Laboratory of Complex Severe and Rare Diseases, Key Laboratory of Rheumatology and Clinical Immunology, Ministry of Education,Beijing 100730, China
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Frederiksen TC, Dahm CC, Preis SR, Lin H, Trinquart L, Benjamin EJ, Kornej J. The bidirectional association between atrial fibrillation and myocardial infarction. Nat Rev Cardiol 2023; 20:631-644. [PMID: 37069297 DOI: 10.1038/s41569-023-00857-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 03/09/2023] [Indexed: 04/19/2023]
Abstract
Atrial fibrillation (AF) is associated with an increased risk of myocardial infarction (MI) and vice versa. This bidirectional association relies on shared risk factors as well as on several direct and indirect mechanisms, including inflammation, atrial ischaemia, left ventricular remodelling, myocardial oxygen supply-demand mismatch and coronary artery embolism, through which one condition can predispose to the other. Patients with both AF and MI are at greater risk of stroke, heart failure and death than patients with only one of the conditions. In this Review, we describe the bidirectional association between AF and MI. We discuss the pathogenic basis of this bidirectional relationship, describe the risk of adverse outcomes when the two conditions coexist, and review current data and guidelines on the prevention and management of both conditions. We also identify important gaps in the literature and propose directions for future research on the bidirectional association between AF and MI. The Review also features a summary of methodological approaches for the study of bidirectional associations in population-based studies.
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Affiliation(s)
- Tanja Charlotte Frederiksen
- Department of Cardiology, Aarhus University Hospital, Aarhus, Denmark
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | | | - Sarah R Preis
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Honghuang Lin
- Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, USA
| | - Ludovic Trinquart
- Institute for Clinical Research and Health Policy Studies, Tufts Medical Center, Boston, MA, USA
- Tufts Clinical and Translational Science Institute, Tufts University, Boston, MA, USA
| | - Emelia J Benjamin
- Department of Epidemiology, Boston University School of Public Health, Boston, MA, USA
- Section of Cardiovascular Medicine, Department of Medicine, Boston Medical Center and Boston University Chobanian and Avedisian School of Medicine, Boston, MA, USA
- Framingham Heart Study, Framingham, MA, USA
| | - Jelena Kornej
- Section of Cardiovascular Medicine, Department of Medicine, Boston Medical Center and Boston University Chobanian and Avedisian School of Medicine, Boston, MA, USA.
- Framingham Heart Study, Framingham, MA, USA.
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Wang Y, Sarnowski C, Lin H, Pitsillides AN, Heard-Costa NL, Choi SH, Wang D, Bis JC, Blue EE, Boerwinkle E, De Jager PL, Fornage M, Wijsman EM, Seshadri S, Dupuis J, Peloso GM, DeStefano AL. Key variants via Alzheimer's Disease Sequencing Project whole genome sequence data. medRxiv 2023:2023.08.28.23294631. [PMID: 37693453 PMCID: PMC10491364 DOI: 10.1101/2023.08.28.23294631] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/12/2023]
Abstract
INTRODUCTION Genome-wide association studies (GWAS) have identified loci associated with Alzheimer's disease (AD) but did not identify specific causal genes or variants within those loci. Analysis of whole genome sequence (WGS) data, which interrogates the entire genome and captures rare variations, may identify causal variants within GWAS loci. METHODS We performed single common variant association analysis and rare variant aggregate analyses in the pooled population (N cases=2,184, N controls=2,383) and targeted analyses in sub-populations using WGS data from the Alzheimer's Disease Sequencing Project (ADSP). The analyses were restricted to variants within 100 kb of 83 previously identified GWAS lead variants. RESULTS Seventeen variants were significantly associated with AD within five genomic regions implicating the genes OARD1/NFYA/TREML1, JAZF1, FERMT2, and SLC24A4. KAT8 was implicated by both single variant and rare variant aggregate analyses. DISCUSSION This study demonstrates the utility of leveraging WGS to gain insights into AD loci identified via GWAS.
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Affiliation(s)
- Yanbing Wang
- Department of Biostatistics, Boston University, School of Public Health, Boston, MA, USA
| | - Chloé Sarnowski
- Department of Biostatistics, Boston University, School of Public Health, Boston, MA, USA
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Honghuang Lin
- Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, USA
| | | | - Nancy L Heard-Costa
- Department of Biostatistics, Boston University, School of Public Health, Boston, MA, USA
- The Framingham Heart Study, Framingham, MA, USA
| | - Seung Hoan Choi
- Department of Biostatistics, Boston University, School of Public Health, Boston, MA, USA
| | - Dongyu Wang
- Department of Biostatistics, Boston University, School of Public Health, Boston, MA, USA
| | - Joshua C Bis
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, USA
| | - Elizabeth E Blue
- Department of Medicine, Division of Medical Genetics, University of Washington, Seattle, WA, USA
- Brotman Baty Institute, Seattle, WA, USA
| | | | - Eric Boerwinkle
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Philip L De Jager
- Center for Translational & Computational Neuroimmunology, Department of Neurology, Columbia University Irving Medical Center, New York, NY, USA
- Taub Institute for Research on Alzheimer’s Disease and the Aging Brain, Columbia University Irving Medical Center, New York, NY, USA
| | - Myriam Fornage
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA
- Brown Foundation Institute of Molecular Medicine, McGovern Medical School, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Ellen M Wijsman
- Div. of Medical Genetics and Dept. Biostatistics Statistical Genetics Lab, University of Washington, Seattle, WA, USA
| | - Sudha Seshadri
- The Framingham Heart Study, Framingham, MA, USA
- Glenn Biggs Institute for Alzheimer’s & Neurodegenerative Diseases, The University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
- Boston University School of Medicine, Department of Neurology, Boston, MA, USA
| | - Josée Dupuis
- Department of Biostatistics, Boston University, School of Public Health, Boston, MA, USA
- Department of Epidemiology, Biostatistics and Occupational Health, School of Population and Global Health, McGill University, Montreal, Canada
| | - Gina M Peloso
- Department of Biostatistics, Boston University, School of Public Health, Boston, MA, USA
| | - Anita L DeStefano
- Department of Biostatistics, Boston University, School of Public Health, Boston, MA, USA
- The Framingham Heart Study, Framingham, MA, USA
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Pirruccello JP, Khurshid S, Lin H, Lu-Chen W, Zamirpour S, Kany S, Raghavan A, Koyama S, Vasan RS, Benjamin EJ, Lindsay ME, Ellinor PT. AORTA Gene: Polygenic prediction improves detection of thoracic aortic aneurysm. medRxiv 2023:2023.08.23.23294513. [PMID: 37662232 PMCID: PMC10473783 DOI: 10.1101/2023.08.23.23294513] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/05/2023]
Abstract
Background Thoracic aortic disease is an important cause of morbidity and mortality in the US, and aortic diameter is a heritable contributor to risk. Could a polygenic prediction of ascending aortic diameter improve detection of aortic aneurysm? Methods Deep learning was used to measure ascending thoracic aortic diameter in 49,939 UK Biobank participants. A genome-wide association study (GWAS) was conducted in 39,524 participants and leveraged to build a 1.1 million-variant polygenic score with PRScs-auto. Aortic diameter prediction models were built with the polygenic score ("AORTA Gene") and without it. The models were tested in a held-out set of 4,962 UK Biobank participants and externally validated in 5,469 participants from Mass General Brigham Biobank (MGB), 1,298 from the Framingham Heart Study (FHS), and 610 participants from All of Us. Results In each test set, the AORTA Gene model explained more of the variance in thoracic aortic diameter compared to clinical factors alone: 39.9% (95% CI 37.8-42.0%) vs 29.2% (95% CI 27.1-31.4%) in UK Biobank, 36.5% (95% CI 34.4-38.5%) vs 32.5% (95% CI 30.4-34.5%) in MGB, 41.8% (95% CI 37.7-45.9%) vs 33.0% (95% CI 28.9-37.2%) in FHS, and 34.9% (95% CI 28.8-41.0%) vs 28.9% (95% CI 22.9-35.0%) in All of Us. AORTA Gene had a greater AUROC for identifying diameter ≥4cm in each test set: 0.834 vs 0.765 (P=7.3E-10) in UK Biobank, 0.808 vs 0.767 in MGB (P=4.5E-12), 0.856 vs 0.818 in FHS (P=8.5E-05), and 0.827 vs 0.791 (P=7.8E-03) in All of Us. Conclusions Genetic information improved estimation of thoracic aortic diameter when added to clinical risk factors. Larger and more diverse cohorts will be needed to develop more powerful and equitable scores.
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Affiliation(s)
- James P. Pirruccello
- Division of Cardiology, University of California San Francisco, San Francisco, California, USA
- Institute for Human Genetics, University of California San Francisco, San Francisco, California, USA
- Bakar Computational Health Sciences Institute, University of California San Francisco, San Francisco, California, USA
| | - Shaan Khurshid
- Cardiology Division, Massachusetts General Hospital, Boston, Massachusetts, USA
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, Massachusetts, USA
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
- Harvard Medical School, Boston, Massachusetts, USA
| | - Honghuang Lin
- Framingham Heart Study, Boston University and National Heart, Lung, and Blood Institute, Framingham, Massachusetts, USA
- Department of Medicine, University of Massachusetts Chan Medical School, Worcester, Massachusetts, USA
| | - Weng Lu-Chen
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, Massachusetts, USA
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
| | - Siavash Zamirpour
- School of Medicine, University of California San Francisco, San Francisco, California, USA
| | - Shinwan Kany
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
- Department of Cardiology, University Heart and Vascular Center Hamburg-Eppendorf, Hamburg, Germany
| | - Avanthi Raghavan
- Cardiology Division, Massachusetts General Hospital, Boston, Massachusetts, USA
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
| | - Satoshi Koyama
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, Massachusetts, USA
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
- Laboratory for Cardiovascular Genomics and Informatics, RIKEN Center for Integrative Medical Sciences, Kanagawa, Japan
| | - Ramachandran S. Vasan
- Framingham Heart Study, Boston University and National Heart, Lung, and Blood Institute, Framingham, Massachusetts, USA
- Department of Medicine, Cardiology and Preventive Medicine Sections, Boston Medical Center, Boston University Chobanian and Avedisian School of Medicine, Boston, Massachusetts, USA
- Epidemiology Department, Boston University School of Public Health, Boston, Massachusetts, USA
| | - Emelia J. Benjamin
- Framingham Heart Study, Boston University and National Heart, Lung, and Blood Institute, Framingham, Massachusetts, USA
- Department of Medicine, Cardiology and Preventive Medicine Sections, Boston Medical Center, Boston University Chobanian and Avedisian School of Medicine, Boston, Massachusetts, USA
- Epidemiology Department, Boston University School of Public Health, Boston, Massachusetts, USA
| | - Mark E. Lindsay
- Cardiology Division, Massachusetts General Hospital, Boston, Massachusetts, USA
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, Massachusetts, USA
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
- Harvard Medical School, Boston, Massachusetts, USA
- Thoracic Aortic Center, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Patrick T. Ellinor
- Cardiology Division, Massachusetts General Hospital, Boston, Massachusetts, USA
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, Massachusetts, USA
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
- Harvard Medical School, Boston, Massachusetts, USA
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Keshawarz A, Bui H, Joehanes R, Ma J, Liu C, Huan T, Hwang SJ, Tejada B, Sooda M, Courchesne P, Munson PJ, Demirkale CY, Yao C, Heard-Costa NL, Pitsillides AN, Lin H, Liu CT, Wang Y, Peloso GM, Lundin J, Haessler J, Du Z, Cho M, Hersh CP, Castaldi P, Raffield LM, Wen J, Li Y, Reiner AP, Feolo M, Sharopova N, Vasan RS, DeMeo DL, Carson AP, Kooperberg C, Levy D. Expression quantitative trait methylation analysis elucidates gene regulatory effects of DNA methylation: the Framingham Heart Study. Sci Rep 2023; 13:12952. [PMID: 37563237 PMCID: PMC10415314 DOI: 10.1038/s41598-023-39936-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2022] [Accepted: 08/02/2023] [Indexed: 08/12/2023] Open
Abstract
Expression quantitative trait methylation (eQTM) analysis identifies DNA CpG sites at which methylation is associated with gene expression. The present study describes an eQTM resource of CpG-transcript pairs derived from whole blood DNA methylation and RNA sequencing gene expression data in 2115 Framingham Heart Study participants. We identified 70,047 significant cis CpG-transcript pairs at p < 1E-7 where the top most significant eGenes (i.e., gene transcripts associated with a CpG) were enriched in biological pathways related to cell signaling, and for 1208 clinical traits (enrichment false discovery rate [FDR] ≤ 0.05). We also identified 246,667 significant trans CpG-transcript pairs at p < 1E-14 where the top most significant eGenes were enriched in biological pathways related to activation of the immune response, and for 1191 clinical traits (enrichment FDR ≤ 0.05). Independent and external replication of the top 1000 significant cis and trans CpG-transcript pairs was completed in the Women's Health Initiative and Jackson Heart Study cohorts. Using significant cis CpG-transcript pairs, we identified significant mediation of the association between CpG sites and cardiometabolic traits through gene expression and identified shared genetic regulation between CpGs and transcripts associated with cardiometabolic traits. In conclusion, we developed a robust and powerful resource of whole blood eQTM CpG-transcript pairs that can help inform future functional studies that seek to understand the molecular basis of disease.
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Affiliation(s)
- Amena Keshawarz
- Framingham Heart Study, Framingham, MA, USA
- Population Sciences Branch, Division of Intramural Research, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA
| | - Helena Bui
- Framingham Heart Study, Framingham, MA, USA
- Population Sciences Branch, Division of Intramural Research, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA
| | - Roby Joehanes
- Framingham Heart Study, Framingham, MA, USA
- Population Sciences Branch, Division of Intramural Research, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA
| | - Jiantao Ma
- Framingham Heart Study, Framingham, MA, USA
- Friedman School of Nutrition Science and Policy, Tufts University, Boston, MA, USA
| | - Chunyu Liu
- Framingham Heart Study, Framingham, MA, USA
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Tianxiao Huan
- Framingham Heart Study, Framingham, MA, USA
- Department of Ophthalmology and Visual Sciences, University of Massachusetts Chan Medical School, Worcester, MA, USA
| | - Shih-Jen Hwang
- Framingham Heart Study, Framingham, MA, USA
- Population Sciences Branch, Division of Intramural Research, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA
| | - Brandon Tejada
- Framingham Heart Study, Framingham, MA, USA
- Population Sciences Branch, Division of Intramural Research, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA
| | - Meera Sooda
- Framingham Heart Study, Framingham, MA, USA
- Population Sciences Branch, Division of Intramural Research, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA
| | - Paul Courchesne
- Framingham Heart Study, Framingham, MA, USA
- Population Sciences Branch, Division of Intramural Research, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA
| | - Peter J Munson
- Framingham Heart Study, Framingham, MA, USA
- Population Sciences Branch, Division of Intramural Research, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA
| | - Cumhur Y Demirkale
- Mathematical and Statistical Computing Laboratory, Office of Intramural Research, Center for Information Technology, National Institutes of Health, Bethesda, MD, USA
| | - Chen Yao
- Framingham Heart Study, Framingham, MA, USA
| | - Nancy L Heard-Costa
- Framingham Heart Study, Framingham, MA, USA
- Department of Neurology, Boston University School of Medicine, Boston, MA, USA
| | - Achilleas N Pitsillides
- Framingham Heart Study, Framingham, MA, USA
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Honghuang Lin
- Framingham Heart Study, Framingham, MA, USA
- Division of Clinical Informatics, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, USA
| | - Ching-Ti Liu
- Framingham Heart Study, Framingham, MA, USA
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Yuxuan Wang
- Framingham Heart Study, Framingham, MA, USA
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Gina M Peloso
- Framingham Heart Study, Framingham, MA, USA
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | | | | | - Zhaohui Du
- Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - Michael Cho
- Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Craig P Hersh
- Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Peter Castaldi
- Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- General Medicine and Primary Care, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Laura M Raffield
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Jia Wen
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Yun Li
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Alexander P Reiner
- Fred Hutchinson Cancer Center, Seattle, WA, USA
- Department of Epidemiology, University of Washington, Seattle, WA, USA
| | - Mike Feolo
- National Center for Biotechnology Information, National Institutes of Health, Bethesda, MD, USA
| | - Nataliya Sharopova
- National Center for Biotechnology Information, National Institutes of Health, Bethesda, MD, USA
| | - Ramachandran S Vasan
- Framingham Heart Study, Framingham, MA, USA
- Department of Medicine, Preventive Medicine and Epidemiology, Boston University School of Medicine, Boston, MA, USA
| | - Dawn L DeMeo
- Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - April P Carson
- Department of Medicine, University of Mississippi Medical Center, Jackson, MS, USA
| | | | - Daniel Levy
- Framingham Heart Study, Framingham, MA, USA.
- Population Sciences Branch, Division of Intramural Research, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA.
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Xiao H, Zhang L, Lin H, Xiao YL, Zhang HT, Jia QR, Xu F, Meng J. [The value of aspirin challenge tests in the diagnosis of non-steroidal anti-inflammatory drugs-exacerbated respiratory disease]. Zhonghua Er Bi Yan Hou Tou Jing Wai Ke Za Zhi 2023; 58:741-746. [PMID: 37550033 DOI: 10.3760/cma.j.cn115330-20230120-00035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 08/09/2023]
Abstract
Objective: To investigate the value of aspirin challenge tests in the diagnosis of non-steroidal anti-inflammatory drugs-exacerbated respiratory disease (NERD). Methods: Fifty patients (22 males and 28 females; aged 16-61 years) who were diagnosed with chronic rhinosinusitis with nasal polyps (CRSwNP) with/without asthma, and underwent NERD standardized diagnosis in the Allergy Centre of West China Hospital, Sichuan University from December 2021 to November 2022 were included in the study. The first step was asking about the history of exacerbation respiratory symptoms after intake of any non-steroidal anti-inflammatory drug, including aspirin; the second step was performing intranasal aspirin challenge (IAC); and the third step was performing oral aspirin challenge (OAC). The diagnosis of NERD was made if any of the above steps was positive, and the subsequent steps were not performed, otherwise the diagnosis was made to OAC. If OAC was negative, the diagnosis was non-NERD. All patients completed the sino-nasal outcome test 22 (SNOT 22) score, Lund-Kennedy score by nasal endoscopic, allergen skin prick test, blood routine and serum total IgE test. SPSS version 20.0 was used for statistical analysis. Results: The diagnosis of NRED was confirmed in 27 patients (27/50, 54%). Seven (7/50, 14%) of them were diagnosed by clinical history and 20 (20/50, 40%) were diagnosed by aspirin challenge tests, of which 17 (17/20, 85%) were positive to IAC and 3 (3/20, 15%) to OAC. Of the 43 patients who underwent IAC testing, only 2 (2/43, 5%) developed asthma attacks during challenge. Comparing the clinical characteristics of patients in NERD and non-NERD group, there were significant differences between the two groups in gender (P=0.001), hyposmia (P=0.003), history of repeated CRSwNP surgeries (P=0.028), comorbid asthma (P=0.013), SNOT-22 score (P=0.004) and the percentage of peripheral blood eosinophil (P=0.043). Conclusions: Patients may be underdiagnosed if the diagnosis of NERD is made only by medical history, and it is necessary to carry out aspirin challenge tests. IAC is an important means to diagnose NERD with high accuracy and good safety. However, If IAC is negative, further OAC is required.
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Affiliation(s)
- H Xiao
- Department of Otorhinolaryngology, West China Hospital, Sichuan University, Chengdu 610041, China Allergy Center of West China Hospital, Sichuan University, Chengdu 610041, China
| | - L Zhang
- Department of Otorhinolaryngology, West China Hospital, Sichuan University, Chengdu 610041, China Allergy Center of West China Hospital, Sichuan University, Chengdu 610041, China
| | - H Lin
- Department of Otorhinolaryngology, West China Hospital, Sichuan University, Chengdu 610041, China Allergy Center of West China Hospital, Sichuan University, Chengdu 610041, China
| | - Y L Xiao
- Department of Otorhinolaryngology, West China Hospital, Sichuan University, Chengdu 610041, China
| | - H T Zhang
- Department of Otorhinolaryngology, West China Hospital, Sichuan University, Chengdu 610041, China Allergy Center of West China Hospital, Sichuan University, Chengdu 610041, China
| | - Q R Jia
- Department of Otorhinolaryngology, West China Hospital, Sichuan University, Chengdu 610041, China Allergy Center of West China Hospital, Sichuan University, Chengdu 610041, China
| | - F Xu
- Department of Otorhinolaryngology, West China Hospital, Sichuan University, Chengdu 610041, China Allergy Center of West China Hospital, Sichuan University, Chengdu 610041, China
| | - J Meng
- Department of Otorhinolaryngology, West China Hospital, Sichuan University, Chengdu 610041, China Allergy Center of West China Hospital, Sichuan University, Chengdu 610041, China
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Charisis S, Lin H, Ray R, Joehanes R, Beiser AS, Levy D, Seshadri S, Sargurupremraj M, Satizabal CL. Obesity impacts the expression of Alzheimer's disease-related genes: The Framingham Heart Study. Alzheimers Dement 2023; 19:3496-3505. [PMID: 36811231 PMCID: PMC10435662 DOI: 10.1002/alz.12954] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2022] [Revised: 12/01/2022] [Accepted: 12/12/2022] [Indexed: 02/24/2023]
Abstract
INTRODUCTION We investigated associations of obesity with the expression of Alzheimer's disease (AD)-related genes in a large community-based cohort. METHODS The sample consisted of 5619 participants from the Framingham Heart Study. Obesity metrics included body mass index (BMI) and waist-to-hip ratio (WHR). Gene expression was measured for a set of 74 AD-related genes, derived by integrating genome-wide association study results with functional genomics data. RESULTS Obesity metrics were associated with the expression of 21 AD-related genes. The strongest associations were observed with CLU, CD2AP, KLC3, and FCER1G. Unique associations were noted with TSPAN14, SLC24A4 for BMI, and ZSCAN21, BCKDK for WHR. After adjustment for cardiovascular risk factors, 13 associations remained significant for BMI and 8 for WHR. Dichotomous obesity metrics exhibited unique associations with EPHX2 for BMI, and with TSPAN14 for WHR. DISCUSSION Obesity was associated with AD-related gene expression; these findings shed light on the molecular pathways linking obesity to AD.
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Affiliation(s)
- Sokratis Charisis
- Glenn Biggs Institute for Alzheimer’s and Neurodegenerative Diseases, UT Health San Antonio, San Antonio, TX, USA
| | - Honghuang Lin
- Boston University School of Medicine, Department of Neurology, Boston, MA, USA
- The Framingham Heart Study, Framingham, MA, USA
- University of Massachusetts Chan Medical School, Worcester, MA, USA
| | - Roshni Ray
- Long School of Medicine, UT Health San Antonio, San Antonio, TX, USA
| | - Roby Joehanes
- The Framingham Heart Study, Framingham, MA, USA
- Population Sciences Branch, National Heart, Lung and Blood Institutes of Health, Bethesda, MD, USA
| | - Alexa S Beiser
- Boston University School of Medicine, Department of Neurology, Boston, MA, USA
- The Framingham Heart Study, Framingham, MA, USA
- Boston University School of Public Health, Department of Biostatistics, Boston, MA, USA
| | - Daniel Levy
- The Framingham Heart Study, Framingham, MA, USA
- Population Sciences Branch, National Heart, Lung and Blood Institutes of Health, Bethesda, MD, USA
| | - Sudha Seshadri
- Glenn Biggs Institute for Alzheimer’s and Neurodegenerative Diseases, UT Health San Antonio, San Antonio, TX, USA
- Boston University School of Medicine, Department of Neurology, Boston, MA, USA
- The Framingham Heart Study, Framingham, MA, USA
| | - Muralidharan Sargurupremraj
- Glenn Biggs Institute for Alzheimer’s and Neurodegenerative Diseases, UT Health San Antonio, San Antonio, TX, USA
| | - Claudia L Satizabal
- Glenn Biggs Institute for Alzheimer’s and Neurodegenerative Diseases, UT Health San Antonio, San Antonio, TX, USA
- Boston University School of Medicine, Department of Neurology, Boston, MA, USA
- The Framingham Heart Study, Framingham, MA, USA
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Wang X, Pathiravasan CH, Zhang Y, Trinquart L, Borrelli B, Spartano NL, Lin H, Nowak C, Kheterpal V, Benjamin EJ, McManus DD, Murabito JM, Liu C. Association of Depressive Symptom Trajectory With Physical Activity Collected by mHealth Devices in the Electronic Framingham Heart Study: Cohort Study. JMIR Ment Health 2023; 10:e44529. [PMID: 37450333 PMCID: PMC10382951 DOI: 10.2196/44529] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Revised: 06/02/2023] [Accepted: 06/05/2023] [Indexed: 07/18/2023] Open
Abstract
BACKGROUND Few studies have examined the association between depressive symptom trajectories and physical activity collected by mobile health (mHealth) devices. OBJECTIVE We aimed to investigate if antecedent depressive symptom trajectories predict subsequent physical activity among participants in the electronic Framingham Heart Study (eFHS). METHODS We performed group-based multi-trajectory modeling to construct depressive symptom trajectory groups using both depressive symptoms (Center for Epidemiological Studies-Depression [CES-D] scores) and antidepressant medication use in eFHS participants who attended 3 Framingham Heart Study research exams over 14 years. At the third exam, eFHS participants were instructed to use a smartphone app for submitting physical activity index (PAI) surveys. In addition, they were provided with a study smartwatch to track their daily step counts. We performed linear mixed models to examine the association between depressive symptom trajectories and physical activity including app-based PAI and smartwatch-collected step counts over a 1-year follow-up adjusting for age, sex, wear hour, BMI, smoking status, and other health variables. RESULTS We identified 3 depressive symptom trajectory groups from 722 eFHS participants (mean age 53, SD 8.5 years; n=432, 60% women). The low symptom group (n=570; mean follow-up 287, SD 109 days) consisted of participants with consistently low CES-D scores, and a small proportion reported antidepressant use. The moderate symptom group (n=71; mean follow-up 280, SD 118 days) included participants with intermediate CES-D scores, who showed the highest and increasing likelihood of reporting antidepressant use across 3 exams. The high symptom group (n=81; mean follow-up 252, SD 116 days) comprised participants with the highest CES-D scores, and the proportion of antidepressant use fell between the other 2 groups. Compared to the low symptom group, the high symptom group had decreased PAI (mean difference -1.09, 95% CI -2.16 to -0.01) and the moderate symptom group walked fewer daily steps (823 fewer, 95% CI -1421 to -226) during the 1-year follow-up. CONCLUSIONS Antecedent depressive symptoms or antidepressant medication use was associated with lower subsequent physical activity collected by mHealth devices in eFHS. Future investigation of interventions to improve mood including via mHealth technologies to help promote people's daily physical activity is needed.
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Affiliation(s)
- Xuzhi Wang
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, United States
| | | | - Yuankai Zhang
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, United States
| | - Ludovic Trinquart
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, United States
- Tufts Clinical and Translational Science Institute, Tufts University, Boston, MA, United States
- Institute for Clinical Research and Health Policy Studies, Tufts Medical Center, Boston, MA, United States
| | - Belinda Borrelli
- Center for Behavioral Science Research, Boston University Henry M Goldman School of Dental Medicine, Boston, MA, United States
| | - Nicole L Spartano
- Section of Endocrinology, Diabetes, Nutrition, and Weight Management, Boston University Chobanian and Avedisian School of Medicine, Boston, MA, United States
| | - Honghuang Lin
- Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, United States
| | | | | | - Emelia J Benjamin
- Section of Preventive Medicine and Epidemiology and Cardiovascular Medicine, Department of Medicine, Boston University Chobanian and Avedisian School of Medicine, Boston, MA, United States
- Framingham Heart Study, Boston University and National Heart, Lung, and Blood Institute, Framingham, MA, United States
- Department of Epidemiology, Boston University School of Public Health, Boston, MA, United States
| | - David D McManus
- Cardiology Division, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, United States
- Department of Quantitative Health Sciences, University of Massachusetts Chan Medical School, Worcester, MA, United States
| | - Joanne M Murabito
- Framingham Heart Study, Boston University and National Heart, Lung, and Blood Institute, Framingham, MA, United States
- Section of General Internal Medicine, Department of Medicine, Boston University Chobanian and Avedisian School of Medicine, Boston, MA, United States
| | - Chunyu Liu
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, United States
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Brant LCC, Ribeiro AH, Pinto-Filho MM, Kornej J, Preis SR, Fetterman JL, Eromosele OB, Magnani JW, Murabito JM, Larson MG, Benjamin EJ, Ribeiro ALP, Lin H. Association Between Electrocardiographic Age and Cardiovascular Events in Community Settings: The Framingham Heart Study. Circ Cardiovasc Qual Outcomes 2023; 16:e009821. [PMID: 37381910 PMCID: PMC10524985 DOI: 10.1161/circoutcomes.122.009821] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Accepted: 05/17/2023] [Indexed: 06/30/2023]
Abstract
BACKGROUND Deep neural networks have been used to estimate age from ECGs, the electrocardiographic age (ECG-age), which predicts adverse outcomes. However, this prediction ability has been restricted to clinical settings or relatively short periods. We hypothesized that ECG-age is associated with death and cardiovascular outcomes in the long-standing community-based FHS (Framingham Heart Study). METHODS We tested the association of ECG-age with chronological age in the FHS cohorts in ECGs from 1986 to 2021. We calculated the gap between chronological and ECG-age (Δage) and classified individuals as having normal, accelerated, or decelerated aging, if Δage was within, higher, or lower than the mean absolute error of the model, respectively. We assessed the associations of Δage, accelerated and decelerated aging with death or cardiovascular outcomes (atrial fibrillation, myocardial infarction, and heart failure) using Cox proportional hazards models adjusted for age, sex, and clinical factors. RESULTS The study population included 9877 FHS participants (mean age, 55±13 years; 54.9% women) with 34 948 ECGs. ECG-age was correlated to chronological age (r=0.81; mean absolute error, 9±7 years). After 17±8 years of follow-up, every 10-year increase of Δage was associated with 18% increase in all-cause mortality (hazard ratio [HR], 1.18 [95% CI, 1.12-1.23]), 23% increase in atrial fibrillation risk (HR, 1.23 [95% CI, 1.17-1.29]), 14% increase in myocardial infarction risk (HR, 1.14 [95% CI, 1.05-1.23]), and 40% increase in heart failure risk (HR, 1.40 [95% CI, 1.30-1.52]), in multivariable models. In addition, accelerated aging was associated with a 28% increase in all-cause mortality (HR, 1.28 [95% CI, 1.14-1.45]), whereas decelerated aging was associated with a 16% decrease (HR, 0.84 [95% CI, 0.74-0.95]). CONCLUSIONS ECG-age was highly correlated with chronological age in FHS. The difference between ECG-age and chronological age was associated with death, myocardial infarction, atrial fibrillation, and heart failure. Given the wide availability and low cost of ECG, ECG-age could be a scalable biomarker of cardiovascular risk.
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Affiliation(s)
- Luisa C C Brant
- Faculty of Medicine and Telehealth Center, Hospital das Clínicas; from Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | - Antônio H Ribeiro
- Department of Information Technology, Uppsala University, Uppsala, Sweden
| | - Marcelo M Pinto-Filho
- Faculty of Medicine and Telehealth Center, Hospital das Clínicas; from Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | - Jelena Kornej
- Section of Cardiovascular Medicine, Boston Medical Center, Boston University Chobanian and Avedisian School of Medicine, Boston, MA, USA
| | - Sarah R. Preis
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Jessica L Fetterman
- Evans Department of Medicine and The Whitaker Cardiovascular Institute, Boston University Chobanian and Avedisian School of Medicine, Boston, MA, USA
| | | | - Jared W. Magnani
- Center for Research on Health Care, Department of Medicine, University of Pittsburgh, PA, USA
| | - Joanne M. Murabito
- National Heart, Lung, and Blood Institute and Boston University’s Framingham Heart Study, Framingham, MA, USA
- Section of General Internal Medicine, Boston Medical Center, Boston University Chobanian and Avedisian School of Medicine, Boston, MA, USA
| | - Martin G Larson
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
- National Heart, Lung, and Blood Institute and Boston University’s Framingham Heart Study, Framingham, MA, USA
| | - Emelia J Benjamin
- National Heart, Lung, and Blood Institute and Boston University’s Framingham Heart Study, Framingham, MA, USA
- Section of Cardiovascular Medicine, Boston Medical Center, Boston University Chobanian and Avedisian School of Medicine, Boston, MA, USA
- Department of Epidemiology, Boston University School of Public Health, Boston, MA, USA
| | - Antonio L P Ribeiro
- Faculty of Medicine and Telehealth Center, Hospital das Clínicas; from Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | - Honghuang Lin
- Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, USA
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Soni A, Herbert C, Lin H, Yan Y, Pretz C, Stamegna P, Wang B, Orwig T, Wright C, Tarrant S, Behar S, Suvarna T, Schrader S, Harman E, Nowak C, Kheterpal V, Rao LV, Cashman L, Orvek E, Ayturk D, Gibson L, Zai A, Wong S, Lazar P, Wang Z, Filippaios A, Barton B, Achenbach CJ, Murphy RL, Robinson ML, Manabe YC, Pandey S, Colubri A, O'Connor L, Lemon SC, Fahey N, Luzuriaga KL, Hafer N, Roth K, Lowe T, Stenzel T, Heetderks W, Broach J, McManus DD. Performance of Rapid Antigen Tests to Detect Symptomatic and Asymptomatic SARS-CoV-2 Infection : A Prospective Cohort Study. Ann Intern Med 2023; 176:975-982. [PMID: 37399548 PMCID: PMC10321467 DOI: 10.7326/m23-0385] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 07/05/2023] Open
Abstract
BACKGROUND The performance of rapid antigen tests (Ag-RDTs) for screening asymptomatic and symptomatic persons for SARS-CoV-2 is not well established. OBJECTIVE To evaluate the performance of Ag-RDTs for detection of SARS-CoV-2 among symptomatic and asymptomatic participants. DESIGN This prospective cohort study enrolled participants between October 2021 and January 2022. Participants completed Ag-RDTs and reverse transcriptase polymerase chain reaction (RT-PCR) testing for SARS-CoV-2 every 48 hours for 15 days. SETTING Participants were enrolled digitally throughout the mainland United States. They self-collected anterior nasal swabs for Ag-RDTs and RT-PCR testing. Nasal swabs for RT-PCR were shipped to a central laboratory, whereas Ag-RDTs were done at home. PARTICIPANTS Of 7361 participants in the study, 5353 who were asymptomatic and negative for SARS-CoV-2 on study day 1 were eligible. In total, 154 participants had at least 1 positive RT-PCR result. MEASUREMENTS The sensitivity of Ag-RDTs was measured on the basis of testing once (same-day), twice (after 48 hours), and thrice (after a total of 96 hours). The analysis was repeated for different days past index PCR positivity (DPIPPs) to approximate real-world scenarios where testing initiation may not always coincide with DPIPP 0. Results were stratified by symptom status. RESULTS Among 154 participants who tested positive for SARS-CoV-2, 97 were asymptomatic and 57 had symptoms at infection onset. Serial testing with Ag-RDTs twice 48 hours apart resulted in an aggregated sensitivity of 93.4% (95% CI, 90.4% to 95.9%) among symptomatic participants on DPIPPs 0 to 6. When singleton positive results were excluded, the aggregated sensitivity on DPIPPs 0 to 6 for 2-time serial testing among asymptomatic participants was lower at 62.7% (CI, 57.0% to 70.5%), but it improved to 79.0% (CI, 70.1% to 87.4%) with testing 3 times at 48-hour intervals. LIMITATION Participants tested every 48 hours; therefore, these data cannot support conclusions about serial testing intervals shorter than 48 hours. CONCLUSION The performance of Ag-RDTs was optimized when asymptomatic participants tested 3 times at 48-hour intervals and when symptomatic participants tested 2 times separated by 48 hours. PRIMARY FUNDING SOURCE National Institutes of Health RADx Tech program.
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Affiliation(s)
- Apurv Soni
- Program in Digital Medicine, Department of Medicine; Division of Health Systems Science, Department of Medicine; and Department of Population and Quantitative Health Sciences, University of Massachusetts Chan Medical School, Worcester, Massachusetts (A.S.)
| | - Carly Herbert
- Program in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, Massachusetts (C.H., C.P., P.S., T.O., C.W., S.T., S.B., A.F., S.P.)
| | - Honghuang Lin
- Program in Digital Medicine and Division of Health Systems Science, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, Massachusetts (H.L., B.W.)
| | - Yi Yan
- Office of In Vitro Diagnostics, Office of Product Evaluation and Quality, Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, Maryland (Y.Y., K.R., T.L.)
| | - Caitlin Pretz
- Program in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, Massachusetts (C.H., C.P., P.S., T.O., C.W., S.T., S.B., A.F., S.P.)
| | - Pamela Stamegna
- Program in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, Massachusetts (C.H., C.P., P.S., T.O., C.W., S.T., S.B., A.F., S.P.)
| | - Biqi Wang
- Program in Digital Medicine and Division of Health Systems Science, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, Massachusetts (H.L., B.W.)
| | - Taylor Orwig
- Program in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, Massachusetts (C.H., C.P., P.S., T.O., C.W., S.T., S.B., A.F., S.P.)
| | - Colton Wright
- Program in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, Massachusetts (C.H., C.P., P.S., T.O., C.W., S.T., S.B., A.F., S.P.)
| | - Seanan Tarrant
- Program in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, Massachusetts (C.H., C.P., P.S., T.O., C.W., S.T., S.B., A.F., S.P.)
| | - Stephanie Behar
- Program in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, Massachusetts (C.H., C.P., P.S., T.O., C.W., S.T., S.B., A.F., S.P.)
| | - Thejas Suvarna
- CareEvolution, Ann Arbor, Michigan (T.S., S.S., E.H., C.N., V.K.)
| | - Summer Schrader
- CareEvolution, Ann Arbor, Michigan (T.S., S.S., E.H., C.N., V.K.)
| | - Emma Harman
- CareEvolution, Ann Arbor, Michigan (T.S., S.S., E.H., C.N., V.K.)
| | - Chris Nowak
- CareEvolution, Ann Arbor, Michigan (T.S., S.S., E.H., C.N., V.K.)
| | - Vik Kheterpal
- CareEvolution, Ann Arbor, Michigan (T.S., S.S., E.H., C.N., V.K.)
| | - Lokinendi V Rao
- Quest Diagnostics, Marlborough, Massachusetts (L.V.R., L.C.)
| | - Lisa Cashman
- Quest Diagnostics, Marlborough, Massachusetts (L.V.R., L.C.)
| | - Elizabeth Orvek
- Department of Population and Quantitative Health Sciences, University of Massachusetts Chan Medical School, Worcester, Massachusetts (E.O., D.A., A.Z., S.W., P.L., B.B., S.C.L.)
| | - Didem Ayturk
- Department of Population and Quantitative Health Sciences, University of Massachusetts Chan Medical School, Worcester, Massachusetts (E.O., D.A., A.Z., S.W., P.L., B.B., S.C.L.)
| | - Laura Gibson
- Division of Infectious Diseases and Immunology, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, Massachusetts (L.G.)
| | - Adrian Zai
- Department of Population and Quantitative Health Sciences, University of Massachusetts Chan Medical School, Worcester, Massachusetts (E.O., D.A., A.Z., S.W., P.L., B.B., S.C.L.)
| | - Steven Wong
- Department of Population and Quantitative Health Sciences, University of Massachusetts Chan Medical School, Worcester, Massachusetts (E.O., D.A., A.Z., S.W., P.L., B.B., S.C.L.)
| | - Peter Lazar
- Department of Population and Quantitative Health Sciences, University of Massachusetts Chan Medical School, Worcester, Massachusetts (E.O., D.A., A.Z., S.W., P.L., B.B., S.C.L.)
| | - Ziyue Wang
- Division of Cardiovascular Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, Massachusetts (Z.W.)
| | - Andreas Filippaios
- Program in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, Massachusetts (C.H., C.P., P.S., T.O., C.W., S.T., S.B., A.F., S.P.)
| | - Bruce Barton
- Department of Population and Quantitative Health Sciences, University of Massachusetts Chan Medical School, Worcester, Massachusetts (E.O., D.A., A.Z., S.W., P.L., B.B., S.C.L.)
| | - Chad J Achenbach
- Division of Infectious Diseases, Department of Medicine, Havey Institute for Global Health, Feinberg School of Medicine, Northwestern University, Chicago, Illinois (C.J.A., R.L.M.)
| | - Robert L Murphy
- Division of Infectious Diseases, Department of Medicine, Havey Institute for Global Health, Feinberg School of Medicine, Northwestern University, Chicago, Illinois (C.J.A., R.L.M.)
| | - Matthew L Robinson
- Division of Infectious Diseases, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland (M.L.R., Y.C.M.)
| | - Yukari C Manabe
- Division of Infectious Diseases, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland (M.L.R., Y.C.M.)
| | - Shishir Pandey
- Program in Digital Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, Massachusetts (C.H., C.P., P.S., T.O., C.W., S.T., S.B., A.F., S.P.)
| | - Andres Colubri
- Department of Microbiology and Physiological Systems, University of Massachusetts Chan Medical School, Worcester, Massachusetts (A.C.)
| | - Laurel O'Connor
- Department of Emergency Medicine, University of Massachusetts Chan Medical School, Worcester, Massachusetts (L.O., J.B.)
| | - Stephenie C Lemon
- Department of Population and Quantitative Health Sciences, University of Massachusetts Chan Medical School, Worcester, Massachusetts (E.O., D.A., A.Z., S.W., P.L., B.B., S.C.L.)
| | - Nisha Fahey
- Program in Digital Medicine, Department of Medicine; Department of Population and Quantitative Health Sciences; and Department of Pediatrics, University of Massachusetts Chan Medical School, Worcester, Massachusetts (N.F.)
| | - Katherine L Luzuriaga
- University of Massachusetts Center for Clinical and Translational Science and Program in Molecular Medicine, University of Massachusetts Chan Medical School, Worcester, Massachusetts (K.L.L., N.H.)
| | - Nathaniel Hafer
- University of Massachusetts Center for Clinical and Translational Science and Program in Molecular Medicine, University of Massachusetts Chan Medical School, Worcester, Massachusetts (K.L.L., N.H.)
| | - Kristian Roth
- Office of In Vitro Diagnostics, Office of Product Evaluation and Quality, Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, Maryland (Y.Y., K.R., T.L.)
| | - Toby Lowe
- Office of In Vitro Diagnostics, Office of Product Evaluation and Quality, Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, Maryland (Y.Y., K.R., T.L.)
| | - Timothy Stenzel
- Division of Microbiology, Office of In Vitro Diagnostics, Office of Product Evaluation and Quality, Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, Maryland (T.S.)
| | - William Heetderks
- National Institute of Biomedical Imaging and Bioengineering, National Institutes of Health, Bethesda, Maryland (W.H.)
| | - John Broach
- Department of Emergency Medicine, University of Massachusetts Chan Medical School, Worcester, Massachusetts (L.O., J.B.)
| | - David D McManus
- Program in Digital Medicine, Division of Health Systems Science, and Division of Cardiovascular Medicine, Department of Medicine, University of Massachusetts Chan Medical School, Worcester, Massachusetts (D.D.M.)
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Anda-Duran ID, Hwang PH, Popp ZT, Low S, Ding H, Rahman S, Igwe A, Kolachalama VB, Lin H, Au R. Matching science to reality: how to deploy a participant-driven digital brain health platform. Front Dement 2023; 2:1135451. [PMID: 38706716 PMCID: PMC11067045 DOI: 10.3389/frdem.2023.1135451] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 05/07/2024]
Abstract
Introduction Advances in digital technologies for health research enable opportunities for digital phenotyping of individuals in research and clinical settings. Beyond providing opportunities for advanced data analytics with data science and machine learning approaches, digital technologies offer solutions to several of the existing barriers in research practice that have resulted in biased samples. Methods A participant-driven, precision brain health monitoring digital platform has been introduced to two longitudinal cohort studies, the Boston University Alzheimer's Disease Research Center (BU ADRC) and the Bogalusa Heart Study (BHS). The platform was developed with prioritization of digital data in native format, multiple OS, validity of derived metrics, feasibility and usability. A platform including nine remote technologies and three staff-guided digital assessments has been introduced in the BU ADRC population, including a multimodal smartphone application also introduced to the BHS population. Participants select which technologies they would like to use and can manipulate their personal platform and schedule over time. Results Participants from the BU ADRC are using an average of 5.9 technologies to date, providing strong evidence for the usability of numerous digital technologies in older adult populations. Broad phenotyping of both cohorts is ongoing, with the collection of data spanning cognitive testing, sleep, physical activity, speech, motor activity, cardiovascular health, mood, gait, balance, and more. Several challenges in digital phenotyping implementation in the BU ADRC and the BHS have arisen, and the protocol has been revised and optimized to minimize participant burden while sustaining participant contact and support. Discussion The importance of digital data in its native format, near real-time data access, passive participant engagement, and availability of technologies across OS has been supported by the pattern of participant technology use and adherence across cohorts. The precision brain health monitoring platform will be iteratively adjusted and improved over time. The pragmatic study design enables multimodal digital phenotyping of distinct clinically characterized cohorts in both rural and urban U.S. settings.
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Affiliation(s)
- Ileana De Anda-Duran
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA, United States
| | - Phillip H. Hwang
- Department of Epidemiology, Boston University School of Public Health, Boston, MA, United States
| | - Zachary Thomas Popp
- Department of Anatomy and Neurobiology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, United States
| | - Spencer Low
- Department of Anatomy and Neurobiology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, United States
| | - Huitong Ding
- Department of Anatomy and Neurobiology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, United States
- Framingham Heart Study, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, United States
| | - Salman Rahman
- Department of Anatomy and Neurobiology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, United States
| | - Akwaugo Igwe
- Department of Anatomy and Neurobiology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, United States
| | - Vijaya B. Kolachalama
- Boston University Alzheimer’s Disease Research Center, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, United States
- Department of Medicine, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, United States
- Department of Computer Science and Faculty of Computing & Data Sciences, Boston University, Boston, MA, United States
| | - Honghuang Lin
- Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, United States
| | - Rhoda Au
- Department of Epidemiology, Boston University School of Public Health, Boston, MA, United States
- Department of Anatomy and Neurobiology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, United States
- Framingham Heart Study, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, United States
- Boston University Alzheimer’s Disease Research Center, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, United States
- Department of Neurology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, United States
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Ma Y, Zou L, Liang Y, Liu Q, Sun Q, Pang Y, Lin H, Deng X, Tang S. [Rapid detection and genotyping of SARS-CoV-2 Omicron BA.4/5 variants using a RT-PCR and CRISPR-Cas12a-based assay]. Nan Fang Yi Ke Da Xue Xue Bao 2023; 43:516-526. [PMID: 37202186 DOI: 10.12122/j.issn.1673-4254.2023.04.03] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 05/20/2023]
Abstract
OBJECTIVE To establish a rapid detection and genotyping method for SARS-CoV-2 Omicron BA.4/5 variants using CRISPPR-Cas12a gene editing technology. METHODS We combined reverse transcription-polymerase chain reaction (RT-PCR) and CRISPR gene editing technology and designed a specific CRISPPR RNA (crRNA) with suboptimal protospacer adjacent motifs (PAM) for rapid detection and genotyping of SARS- CoV-2 Omicron BA.4/5 variants. The performance of this RT- PCR/ CRISPPR-Cas12a assay was evaluated using 43 clinical samples of patients infected by wild-type SARS-CoV-2 and the Alpha, Beta, Delta, Omicron BA. 1 and BA. 4/5 variants and 20 SARS- CoV- 2-negative clinical samples infected with 11 respiratory pathogens. With Sanger sequencing method as the gold standard, the specificity, sensitivity, concordance (Kappa) and area under the ROC curve (AUC) of RT-PCR/CRISPPR-Cas12a assay were calculated. RESULTS This assay was capable of rapid and specific detection of SARS- CoV-2 Omicron BA.4/5 variant within 30 min with the lowest detection limit of 10 copies/μL, and no cross-reaction was observed in SARS-CoV-2-negative clinical samples infected with 11 common respiratory pathogens. The two Omicron BA.4/5 specific crRNAs (crRNA-1 and crRNA-2) allowed the assay to accurately distinguish Omicron BA.4/5 from BA.1 sublineage and other major SARS-CoV-2 variants of concern. For detection of SARS-CoV-2 Omicron BA.4/5 variants, the sensitivity of the established assay using crRNA-1 and crRNA-2 was 97.83% and 100% with specificity of 100% and AUC of 0.998 and 1.000, respectively, and their concordance rate with Sanger sequencing method was 92.83% and 96.41%, respectively. CONCLUSION By combining RT-PCR and CRISPPR-Cas12a gene editing technology, we successfully developed a new method for rapid detection and identification of SARS-CoV-2 Omicron BA.4/5 variants with a high sensitivity, specificity and reproducibility, which allows rapid detection and genotyping of SARS- CoV-2 variants and monitoring of the emerging variants and their dissemination.
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Affiliation(s)
- Y Ma
- Department of Epidemiology, School of Public Health, Southern Medical University, Guangzhou 510515, China
| | - L Zou
- Institute of Pathogenic Microbiology, Guangdong Provincial Center for Disease Control and Prevention, Guangdong Workstation for Emerging Infectious Disease Control and Prevention, Chinese Academy of Medical Sciences, Guangzhou 511430, China
| | - Y Liang
- Department of Epidemiology, School of Public Health, Southern Medical University, Guangzhou 510515, China
| | - Q Liu
- Department of Epidemiology, School of Public Health, Southern Medical University, Guangzhou 510515, China
| | - Q Sun
- Department of Epidemiology, School of Public Health, Southern Medical University, Guangzhou 510515, China
| | - Y Pang
- Department of Epidemiology, School of Public Health, Southern Medical University, Guangzhou 510515, China
| | - H Lin
- Department of Epidemiology, School of Public Health, Southern Medical University, Guangzhou 510515, China
| | - X Deng
- Institute of Pathogenic Microbiology, Guangdong Provincial Center for Disease Control and Prevention, Guangdong Workstation for Emerging Infectious Disease Control and Prevention, Chinese Academy of Medical Sciences, Guangzhou 511430, China
| | - S Tang
- Department of Epidemiology, School of Public Health, Southern Medical University, Guangzhou 510515, China
- Department of Infectious Diseases, Nanfang Hospital, Southern Medical University, Guangzhou 510515, China
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Lin H, Kwan AC, Castro-Diehl C, Short MI, Xanthakis V, Yola IM, Salto G, Mitchell GF, Larson MG, Vasan RS, Cheng S. Sex-specific differences in the genetic and environmental effects on cardiac phenotypic variation assessed by echocardiography. Sci Rep 2023; 13:5786. [PMID: 37031215 PMCID: PMC10082757 DOI: 10.1038/s41598-023-32577-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2022] [Accepted: 03/29/2023] [Indexed: 04/10/2023] Open
Abstract
The drivers of sexual dimorphism in heart failure phenotypes are currently poorly understood. Divergent phenotypes may result from differences in heritability and genetic versus environmental influences on the interplay of cardiac structure and function. To assess sex-specific heritability and genetic versus environmental contributions to variation and inter-relations between echocardiography traits in a large community-based cohort. We studied Framingham Heart Study participants of Offspring Cohort examination 8 (2005-2008) and Third Generation Cohort examination 1 (2002-2005). Five cardiac traits and six functional traits were measured using standardized echocardiography. Sequential Oligogenic Linkage Analysis Routines (SOLAR) software was used to perform singular and bivariate quantitative trait linkage analysis. In our study of 5674 participants (age 49 ± 15 years; 54% women), heritability for all traits was significant for both men and women. There were no significant differences in traits between men and women. Within inter-trait correlations, there were two genetic, and four environmental trait pairs with sex-based differences. Within both significant genetic trait pairs, men had a positive relation, and women had no significant relation. We observed significant sex-based differences in inter-trait genetic and environmental correlations between cardiac structure and function. These findings highlight potential pathways of sex-based divergent heart failure phenotypes.
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Affiliation(s)
- Honghuang Lin
- Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, USA
- Framingham Heart Study, Framingham, MA, USA
| | - Alan C Kwan
- Department of Cardiology, Cedars-Sinai Medical Center, 127 S. San Vicente Blvd, Suite A3100, Los Angeles, CA, 90048, USA
| | - Cecilia Castro-Diehl
- Section of Preventive Medicine and Epidemiology, Department of Medicine, Boston University School of Medicine, Boston, MA, USA
| | - Meghan I Short
- Glenn Biggs Institute for Alzheimer's & Neurodegenerative Diseases, University of Texas Health San Antonio, San Antonio, TX, USA
- Department of Biostatistics, Boston University School of Public Heath, Boston, MA, USA
| | - Vanessa Xanthakis
- Framingham Heart Study, Framingham, MA, USA
- Section of Preventive Medicine and Epidemiology, Department of Medicine, Boston University School of Medicine, Boston, MA, USA
- Department of Biostatistics, Boston University School of Public Heath, Boston, MA, USA
| | - Ibrahim M Yola
- Section of Preventive Medicine and Epidemiology, Department of Medicine, Boston University School of Medicine, Boston, MA, USA
| | - Gerran Salto
- Department of Cardiology, Cedars-Sinai Medical Center, 127 S. San Vicente Blvd, Suite A3100, Los Angeles, CA, 90048, USA
| | | | - Martin G Larson
- Framingham Heart Study, Framingham, MA, USA
- Department of Biostatistics, Boston University School of Public Heath, Boston, MA, USA
| | - Ramachandran S Vasan
- Framingham Heart Study, Framingham, MA, USA
- Section of Preventive Medicine and Epidemiology, Department of Medicine, Boston University School of Medicine, Boston, MA, USA
- Section of Cardiovascular Medicine, Department of Medicine, Boston University School of Medicine, Boston, MA, USA
- Department of Epidemiology, Boston University School of Public Heath, Boston, MA, USA
- Center for Computing and Data Sciences, Boston University, Boston, MA, USA
| | - Susan Cheng
- Framingham Heart Study, Framingham, MA, USA.
- Department of Cardiology, Cedars-Sinai Medical Center, 127 S. San Vicente Blvd, Suite A3100, Los Angeles, CA, 90048, USA.
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Han J, Dela Cruz M, Lin H, Adler E, Khalid M, Cantoral J, Moran A, Sundararajan A, Sidebottom A, Alegre M, Pamer E, Nguyen A. Pre-Transplant Sensitization is Associated with Lower Levels of Immunomodulatory Metabolite Concentrations after Heart Transplantation. J Heart Lung Transplant 2023. [DOI: 10.1016/j.healun.2023.02.1657] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/05/2023] Open
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Lu L, Zhong J, Wu X, Chen Q, Lin H, Chen L, Luo Y. [Resting heart rate correlates with major adverse cardiovascular and cerebrovascular events in patients with post-myocardial infarction ventricular aneurysms: a retrospective cohort study]. Nan Fang Yi Ke Da Xue Xue Bao 2023; 43:400-404. [PMID: 37087584 PMCID: PMC10122741 DOI: 10.12122/j.issn.1673-4254.2023.03.09] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 04/24/2023]
Abstract
OBJECTIVE To analyze the association of resting heart rate (RHR) with the prognosis of patients with post-infarction ventricular aneurysms. METHODS We retrospectively analyzed the clinical data of 227 patients with post-infarction ventricular aneurysms admitted to our hospital during 2017-2019. The endpoint event was the occurrence of any major adverse cardiovascular and cerebrovascular events (MACCEs) during the follow-up for 24 months. According to RHR measurements, the patients were divided into 3 groups with baseline RHR < 10%, 10%-90%, and >90%. The Cox proportional risk model and restricted cubic spline (RCS) model were used to analyze the effect of RHR on MACCEs. RESULTS During the 24-month followup, 90 patients (39.6%) experienced MACCEs. The fully adjusted RCS curves showed a nonlinear "U" shaped correlation between RHR and the occurrence of MACCEs. In the fully adjusted model, the risk of MACCEs increased by 3.01-fold (Hazard ratio [HR]=4.01, 95% CI: 2.07-7.76, P < 0.001) in patients with RHR>90%, as compared with patients with RHR of 10%-90%. In patients with RHR in 1-9th percentile, 10th-90th percentile and 91st-100th percentile, the incidences of MACCEs were 39.1%, 36.6% and 66.7% (P=0.027), the incidences of ventricular tachycardia/ventricular fibrillation (VT/VF) were 17.4%, 2.7% and 4.8% (P=0.005), and the incidences of readmission for heart failure were 8.7%, 26.8% and 42.9% (P=0.036), respectively. CONCLUSION Continuous monitoring and management of heart rate range may provide guidance for prognosis prediction in patients with post-infarction ventricular aneurysms.
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Affiliation(s)
- L Lu
- Department of Cardiology, Fujian Medical University Union Hospital, Fuzhou 350001, China
- Fujian Heart Medical Center, Fuzhou 350001, China
- Fujian Institute of Coronary Artery Disease, Fuzhou 350001, China
| | - J Zhong
- Department of Cardiology, Fujian Medical University Union Hospital, Fuzhou 350001, China
- Fujian Heart Medical Center, Fuzhou 350001, China
- Fujian Institute of Coronary Artery Disease, Fuzhou 350001, China
| | - X Wu
- Department of Neurology, Fujian Medical University Union Hospital, Fuzhou 350001, China
| | - Q Chen
- Department of Cardiology, Fujian Medical University Union Hospital, Fuzhou 350001, China
- Fujian Heart Medical Center, Fuzhou 350001, China
- Fujian Institute of Coronary Artery Disease, Fuzhou 350001, China
| | - H Lin
- Department of Cardiology, Fujian Medical University Union Hospital, Fuzhou 350001, China
- Fujian Heart Medical Center, Fuzhou 350001, China
- Fujian Institute of Coronary Artery Disease, Fuzhou 350001, China
| | - L Chen
- Department of Cardiology, Fujian Medical University Union Hospital, Fuzhou 350001, China
- Fujian Heart Medical Center, Fuzhou 350001, China
- Fujian Institute of Coronary Artery Disease, Fuzhou 350001, China
| | - Y Luo
- Department of Cardiology, Fujian Medical University Union Hospital, Fuzhou 350001, China
- Fujian Heart Medical Center, Fuzhou 350001, China
- Fujian Institute of Coronary Artery Disease, Fuzhou 350001, China
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Shapira-Daniels A, Kornej J, Spartano NL, Wang X, Zhang Y, Pathiravasan CH, Liu C, Trinquart L, Borrelli B, McManus DD, Murabito JM, Benjamin EJ, Lin H. Step Count, Self-reported Physical Activity, and Predicted 5-Year Risk of Atrial Fibrillation: Cross-sectional Analysis. J Med Internet Res 2023; 25:e43123. [PMID: 36877540 PMCID: PMC10028513 DOI: 10.2196/43123] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Revised: 11/24/2022] [Accepted: 11/30/2022] [Indexed: 03/07/2023] Open
Abstract
BACKGROUND Physical inactivity is a known risk factor for atrial fibrillation (AF). Wearable devices, such as smartwatches, present an opportunity to investigate the relation between daily step count and AF risk. OBJECTIVE The objective of this study was to investigate the association between daily step count and the predicted 5-year risk of AF. METHODS Participants from the electronic Framingham Heart Study used an Apple smartwatch. Individuals with diagnosed AF were excluded. Daily step count, watch wear time (hours and days), and self-reported physical activity data were collected. Individuals' 5-year risk of AF was estimated, using the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE)-AF score. The relation between daily step count and predicted 5-year AF risk was examined via linear regression, adjusting for age, sex, and wear time. Secondary analyses examined effect modification by sex and obesity (BMI≥30 kg/m2), as well as the relation between self-reported physical activity and predicted 5-year AF risk. RESULTS We examined 923 electronic Framingham Heart Study participants (age: mean 53, SD 9 years; female: n=563, 61%) who had a median daily step count of 7227 (IQR 5699-8970). Most participants (n=823, 89.2%) had a <2.5% CHARGE-AF risk. Every 1000 steps were associated with a 0.08% lower CHARGE-AF risk (P<.001). A stronger association was observed in men and individuals with obesity. In contrast, self-reported physical activity was not associated with CHARGE-AF risk. CONCLUSIONS Higher daily step counts were associated with a lower predicted 5-year risk of AF, and this relation was stronger in men and participants with obesity. The utility of a wearable daily step counter for AF risk reduction merits further investigation.
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Affiliation(s)
- Ayelet Shapira-Daniels
- Section of General Internal Medicine, Department of Medicine, Boston Medical Center, Boston University Chobanian and Avedisian School of Medicine, Boston, MA, United States
| | - Jelena Kornej
- Boston University's Framingham Heart Study, National Heart, Lung, and Blood Institute, Framingham, MA, United States
| | - Nicole L Spartano
- Section of Endocrinology, Diabetes, Nutrition and Weight Management, Department of Medicine, Boston Medical Center, Boston University Chobanian and Avedisian School of Medicine, Boston, MA, United States
| | - Xuzhi Wang
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, United States
| | - Yuankai Zhang
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, United States
| | | | - Chunyu Liu
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, United States
| | - Ludovic Trinquart
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, United States
| | - Belinda Borrelli
- Center for Behavioral Science Research, Henry M Goldman School of Dental Medicine, Boston University, Boston, MA, United States
| | - David D McManus
- Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, United States
- Department of Quantitative Health Sciences, University of Massachusetts Chan Medical School, Worcester, MA, United States
| | - Joanne M Murabito
- Section of General Internal Medicine, Department of Medicine, Boston Medical Center, Boston University Chobanian and Avedisian School of Medicine, Boston, MA, United States
- Boston University's Framingham Heart Study, National Heart, Lung, and Blood Institute, Framingham, MA, United States
| | - Emelia J Benjamin
- Boston University's Framingham Heart Study, National Heart, Lung, and Blood Institute, Framingham, MA, United States
- Department of Epidemiology, Boston University School of Public Health, Boston, MA, United States
- Section of Cardiovascular Medicine, Department of Medicine, Boston Medical Center, Boston University Chobanian and Avedisian School of Medicine, Boston, MA, United States
| | - Honghuang Lin
- Boston University's Framingham Heart Study, National Heart, Lung, and Blood Institute, Framingham, MA, United States
- Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, United States
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48
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Herbert C, Wang B, Lin H, Hafer N, Pretz C, Stamegna P, Tarrant S, Hartin P, Ferranto J, Behar S, Wright C, Orwig T, Suvarna T, Harman E, Schrader S, Nowak C, Kheterpal V, Orvek E, Wong S, Zai A, Barton B, Gerber B, Lemon SC, Filippaios A, D'Amore K, Gibson L, Greene S, Howard-Wilson S, Colubri A, Achenbach C, Murphy R, Heetderks W, Manabe YC, O'Connor L, Fahey N, Luzuriaga K, Broach J, McManus DD, Soni A. Performance of Rapid Antigen Tests Based on Symptom Onset and Close Contact Exposure: A secondary analysis from the Test Us At Home prospective cohort study. medRxiv 2023:2023.02.21.23286239. [PMID: 36865199 PMCID: PMC9980261 DOI: 10.1101/2023.02.21.23286239] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/02/2023]
Abstract
Background The performance of rapid antigen tests for SARS-CoV-2 (Ag-RDT) in temporal relation to symptom onset or exposure is unknown, as is the impact of vaccination on this relationship. Objective To evaluate the performance of Ag-RDT compared with RT-PCR based on day after symptom onset or exposure in order to decide on 'when to test'. Design Setting and Participants The Test Us at Home study was a longitudinal cohort study that enrolled participants over 2 years old across the United States between October 18, 2021 and February 4, 2022. All participants were asked to conduct Ag-RDT and RT-PCR testing every 48 hours over a 15-day period. Participants with one or more symptoms during the study period were included in the Day Post Symptom Onset (DPSO) analyses, while those who reported a COVID-19 exposure were included in the Day Post Exposure (DPE) analysis. Exposure Participants were asked to self-report any symptoms or known exposures to SARS-CoV-2 every 48-hours, immediately prior to conducting Ag-RDT and RT-PCR testing. The first day a participant reported one or more symptoms was termed DPSO 0, and the day of exposure was DPE 0. Vaccination status was self-reported. Main Outcome and Measures Results of Ag-RDT were self-reported (positive, negative, or invalid) and RT-PCR results were analyzed by a central laboratory. Percent positivity of SARS-CoV-2 and sensitivity of Ag-RDT and RT-PCR by DPSO and DPE were stratified by vaccination status and calculated with 95% confidence intervals. Results A total of 7,361 participants enrolled in the study. Among them, 2,086 (28.3%) and 546 (7.4%) participants were eligible for the DPSO and DPE analyses, respectively. Unvaccinated participants were nearly twice as likely to test positive for SARS-CoV-2 than vaccinated participants in event of symptoms (PCR+: 27.6% vs 10.1%) or exposure (PCR+: 43.8% vs. 22.2%). The highest proportion of vaccinated and unvaccinated individuals tested positive on DPSO 2 and DPE 5-8. Performance of RT-PCR and Ag-RDT did not differ by vaccination status. Ag-RDT detected 78.0% (95% Confidence Interval: 72.56-82.61) of PCR-confirmed infections by DPSO 4. For exposed participants, Ag-RDT detected 84.9% (95% CI: 75.0-91.4) of PCR-confirmed infections by day five post-exposure (DPE 5). Conclusions and Relevance Performance of Ag-RDT and RT-PCR was highest on DPSO 0-2 and DPE 5 and did not differ by vaccination status. These data suggests that serial testing remains integral to enhancing the performance of Ag-RDT.
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Wang J, Man QW, Fu QY, Zhong NN, Wang HQ, Li SR, Gao X, Lin H, Su FC, Bu LL, Chen G, Liu B. Preliminary Extracellular Vesicle Profiling in Drainage Fluid After Neck Dissection in OSCC. J Dent Res 2023; 102:178-186. [PMID: 36331313 DOI: 10.1177/00220345221130013] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022] Open
Abstract
Lymph node metastasis is related to poor prognosis in oral squamous cell carcinoma (OSCC), and few studies have explored the relevance of postoperative drainage fluid (PDF) in metastasis. Extracellular vesicles (EVs) are nanosized vesicles that can transfer oncogenic molecules to regulate tumorigenesis. However, the proteomic profile of postoperative drainage fluid-derived EVs (PDF-EVs) in OSCC has not been elucidated. Herein, we collected drainage fluid from OSCC patients after neck dissection to investigate the difference in PDF-EVs between patients with metastatic lymph nodes (the LN+ group) and nonmetastatic lymph nodes (the LN- group). The proteomic profile of PDF-EVs from the LN+ and LN- groups was compared using label-free liquid chromatography tandem-mass spectrometry-based protein quantification. The results revealed that PDF-EVs were mainly derived from epithelial cells and immune cells. A total of 2,134 proteins in the PDF-EVs were identified, and 313 were differentially expressed between the LN+ and LN- groups. Metabolic proteins, such as EHD2 and CAVIN1, were expressed at higher levels in the LN+ group than in the LN- group, and the levels of EHD2 and CAVIN1 in the postoperative drainage fluid were positively correlated with lymph node metastasis. Our study revealed previously undocumented postoperative drainage fluid-associated proteins in patients with metastatic OSCC, providing a starting point for understanding their role in metastatic and nonmetastatic OSCC.
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Affiliation(s)
- J Wang
- The State Key Laboratory Breeding Base of Basic Science of Stomatology (Hubei-MOST) & Key Laboratory of Oral Biomedicine Ministry of Education, School & Hospital of Stomatology, Wuhan University, Wuhan, China
| | - Q-W Man
- The State Key Laboratory Breeding Base of Basic Science of Stomatology (Hubei-MOST) & Key Laboratory of Oral Biomedicine Ministry of Education, School & Hospital of Stomatology, Wuhan University, Wuhan, China.,Department of Oral and Maxillofacial Head Neck Oncology, School & Hospital of Stomatology, Wuhan University, Wuhan, China
| | - Q-Y Fu
- The State Key Laboratory Breeding Base of Basic Science of Stomatology (Hubei-MOST) & Key Laboratory of Oral Biomedicine Ministry of Education, School & Hospital of Stomatology, Wuhan University, Wuhan, China
| | - N-N Zhong
- The State Key Laboratory Breeding Base of Basic Science of Stomatology (Hubei-MOST) & Key Laboratory of Oral Biomedicine Ministry of Education, School & Hospital of Stomatology, Wuhan University, Wuhan, China
| | - H-Q Wang
- The State Key Laboratory Breeding Base of Basic Science of Stomatology (Hubei-MOST) & Key Laboratory of Oral Biomedicine Ministry of Education, School & Hospital of Stomatology, Wuhan University, Wuhan, China
| | - S-R Li
- The State Key Laboratory Breeding Base of Basic Science of Stomatology (Hubei-MOST) & Key Laboratory of Oral Biomedicine Ministry of Education, School & Hospital of Stomatology, Wuhan University, Wuhan, China
| | - X Gao
- The State Key Laboratory Breeding Base of Basic Science of Stomatology (Hubei-MOST) & Key Laboratory of Oral Biomedicine Ministry of Education, School & Hospital of Stomatology, Wuhan University, Wuhan, China
| | - H Lin
- The State Key Laboratory Breeding Base of Basic Science of Stomatology (Hubei-MOST) & Key Laboratory of Oral Biomedicine Ministry of Education, School & Hospital of Stomatology, Wuhan University, Wuhan, China
| | - F-C Su
- The State Key Laboratory Breeding Base of Basic Science of Stomatology (Hubei-MOST) & Key Laboratory of Oral Biomedicine Ministry of Education, School & Hospital of Stomatology, Wuhan University, Wuhan, China
| | - L-L Bu
- The State Key Laboratory Breeding Base of Basic Science of Stomatology (Hubei-MOST) & Key Laboratory of Oral Biomedicine Ministry of Education, School & Hospital of Stomatology, Wuhan University, Wuhan, China.,Department of Oral and Maxillofacial Head Neck Oncology, School & Hospital of Stomatology, Wuhan University, Wuhan, China
| | - G Chen
- The State Key Laboratory Breeding Base of Basic Science of Stomatology (Hubei-MOST) & Key Laboratory of Oral Biomedicine Ministry of Education, School & Hospital of Stomatology, Wuhan University, Wuhan, China.,Department of Oral and Maxillofacial Head Neck Oncology, School & Hospital of Stomatology, Wuhan University, Wuhan, China
| | - B Liu
- The State Key Laboratory Breeding Base of Basic Science of Stomatology (Hubei-MOST) & Key Laboratory of Oral Biomedicine Ministry of Education, School & Hospital of Stomatology, Wuhan University, Wuhan, China.,Department of Oral and Maxillofacial Head Neck Oncology, School & Hospital of Stomatology, Wuhan University, Wuhan, China
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Lin R, Lin H, Elder E, Cerullo A, Carrington A, Stuart G. Nurse-led dexmedetomidine sedation for magnetic resonance imaging in children: a 6-year quality improvement project. Anaesthesia 2023; 78:598-606. [PMID: 36708590 DOI: 10.1111/anae.15973] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/23/2022] [Indexed: 01/29/2023]
Abstract
We aimed to safely introduce dexmedetomidine into a nurse-led sedation service for magnetic resonance imaging in children. Secondary aims were to increase the number of children eligible for sedation and to increase the actual number of children having sedation performed by our nurse sedation team. We analysed 1768 consecutive intravenous and 219 intranasal dexmedetomidine sedation episodes in infants, children and adolescents having magnetic resonance imaging scans between March 2016 and March 2022. The overall sedation success rate was 98.4%, with a 98.9% success rate for intravenous dexmedetomidine and a 95.0% success rate for intranasal dexmedetomidine. The incidence of scan interruption during intravenous and intranasal dexmedetomidine sedation was 8.8% and 21.9%, respectively. We conclude that paediatric sedation with dexmedetomidine for magnetic resonance scanning is safe and successful.
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Affiliation(s)
- R Lin
- Department of Anaesthesia, Great Ormond Street Hospital for Children, London, UK
| | - H Lin
- University of Cambridge, UK
| | - E Elder
- University College London, UK
| | - A Cerullo
- Department of Radiology, Great Ormond Street Hospital for Children, London, UK
| | - A Carrington
- Department of Radiology, Great Ormond Street Hospital for Children, London, UK
| | - G Stuart
- Department of Anaesthesia, Great Ormond Street Hospital for Children, London, UK
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