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Yun JS, Jung SH, Jung SM, Won HH, Kim D, Choi JA. Corneal hysteresis as a biomarker in glaucoma development among patients with myopia: a prospective study based on the UK Biobank. Br J Ophthalmol 2025:bjo-2024-326817. [PMID: 40368440 DOI: 10.1136/bjo-2024-326817] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2024] [Accepted: 04/28/2025] [Indexed: 05/16/2025]
Abstract
BACKGROUND/AIMS We investigated the impact of potential risk factors on the development of incident glaucoma in individuals with myopia. METHODS This prospective cohort study investigated participants aged 40-69 years from the UK Biobank. Participants were classified based on the degree of myopia: non-myopic (spherical equivalent (SE)≤-0.5D), mild myopia (-3.0D RESULTS Of 105 548 participants who did not have prior glaucoma, 35 870 were classified as myopic and 69 678 as non-myopic. A total of 1877 incident glaucoma cases (1.78%) were documented during a median 11.1 years of follow-up. Those who developed glaucoma had lower SE, lower corneal hysteresis (CH) and higher intraocular pressure. Systemic factors such as type 2 diabetes, dyslipidaemia, baseline fasting blood sugar and haemoglobin A1c were significantly higher in those who developed glaucoma, but only in the non-myopia and mild myopia subgroups. Degree of myopia and low CH (≤10.1 mm Hg) were significantly associated with increased glaucoma incidence. When we explored the impact of CH on the risk of incident glaucoma according to myopia grade, consistent elevation of risk with low CH was observed, with the highest risk being exhibited in the high myopia population (HR 1.67 (95% CI 1.10 to 2.57)). CONCLUSIONS Our results demonstrated a significant joint association between CH and myopia regarding glaucoma development, suggesting that altered ocular biomechanical properties could play a pronounced role in glaucoma development, particularly within the highly myopic population.
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Affiliation(s)
- Jae-Seung Yun
- Department of Internal Medicine, The Catholic University of Korea St Vincent's Hospital, Suwon, Korea (the Republic of)
| | - Sang-Hyuk Jung
- Department of Medical Informatics, Kangwon National University College of Medicine, Chuncheon, Korea (the Republic of)
| | - Seung Min Jung
- The Catholic University of Korea College of Medicine, Seocho-gu, Seoul, Korea (the Republic of)
| | - Hong-Hee Won
- Samsung Biomedical Research Institute, Samsung Medical Center, Seoul, Korea (the Republic of)
| | - Dokyoon Kim
- University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Jin A Choi
- Ophthalmology, The Catholic University of Korea St Vincent's Hospital, Suwon, Korea (the Republic of)
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Zhang S, Liang Z, Zhong Y, Luo Q, Wang D, Xia B, Wang X, Kang Y, Zhou Z, Sheng P, Yuan J, Zhang Z, Wei F. Sleep characteristics and intervertebral disc degeneration risk: an observational and Mendelian randomization study. EUROPEAN SPINE JOURNAL : OFFICIAL PUBLICATION OF THE EUROPEAN SPINE SOCIETY, THE EUROPEAN SPINAL DEFORMITY SOCIETY, AND THE EUROPEAN SECTION OF THE CERVICAL SPINE RESEARCH SOCIETY 2025; 34:1685-1696. [PMID: 39865174 DOI: 10.1007/s00586-025-08669-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/05/2024] [Revised: 11/22/2024] [Accepted: 01/10/2025] [Indexed: 01/28/2025]
Abstract
OBJECTIVES Sleep disorders are considered a risk factor for aging and skeletal degeneration, but their impact on intervertebral disc degeneration (IDD) remains unclear. The aim of this study was to assess associations between sleep characteristics and IDD, and to identify potential causal relationships. METHODS Exposure factors included six unhealthy sleep characteristics: insomnia, short sleep duration (< 7 h), long sleep duration (≥ 9 h), evening chronotype, daytime sleepiness, and snoring. The primary outcomes included cervical disc degeneration (CDD) and lumbar disc degeneration (LDD). Firstly, we examined the associations between sleep characteristics and IDD risk in 368,348 participants from the UK Biobank using Cox proportional hazards model. Two-sample Mendelian randomization (MR) analyses were conducted to validate associations found in observational analyses, using genome-wide association data from the UK Biobank and FinnGen consortia. RESULTS During a median follow-up time of 13.8 years, a total of 1,637 cases of CDD and 7,654 cases of LDD were identified. Observational analyses found that almost all unhealthy sleep characteristics were associated with an elevated risk of IDD, except snoring. Conversely, the risk of IDD decreased linearly with an increasing number of healthy sleep characteristics. MR analyses supported a causal association between genetically determined insomnia and increased risk of LDD (OR 1.25 [1.07-1.47]), and between short sleep duration and increased risk of both IDD phenotypes (OR 5.41 [1.95-15.01] for CDD; OR 3.48 [1.76-6.89] for LDD). However, long sleep duration was causally associated with a reduced risk of LDD (OR 0.13 [0.03-0.53]), which contrasts with the observational findings. CONCLUSION We found associations between multiple sleep characteristics and IDD risk and confirmed that insomnia and short sleep duration increased IDD risk. Although more research is needed to confirm the underlying mechanisms, prioritizing interventions to improve sleep quality and ensure adequate sleep could help mitigate IDD.
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Affiliation(s)
- Shiyong Zhang
- Department of Orthopedics, the Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen, 518000, Guangdong, China
- Department of Epidemiology and Biostatistics, Clinical Big Data Research Center, The Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen, 518000, Guangdong, China
- Department of Joint Surgery, the First Affiliated Hospital, Sun Yat-sen University, Guangzhou, 510080, Guangdong, China
| | - Zixin Liang
- Department of Epidemiology and Biostatistics, Clinical Big Data Research Center, The Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen, 518000, Guangdong, China
| | - Yanlin Zhong
- Department of Joint Surgery, the First Affiliated Hospital, Sun Yat-sen University, Guangzhou, 510080, Guangdong, China
| | - Qingfeng Luo
- Chongqing Municipality Clinical Research Center for Geriatrics, the Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Danni Wang
- Institute of Inflammation and Ageing, University of Birmingham, Birmingham, UK
| | - Bin Xia
- Department of Epidemiology and Biostatistics, Clinical Big Data Research Center, The Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen, 518000, Guangdong, China
| | - Xudong Wang
- Department of Joint Surgery, the First Affiliated Hospital, Sun Yat-sen University, Guangzhou, 510080, Guangdong, China
| | - Yunze Kang
- Department of Joint Surgery, the First Affiliated Hospital, Sun Yat-sen University, Guangzhou, 510080, Guangdong, China
| | - Zijian Zhou
- Department of Orthopedics, the Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen, 518000, Guangdong, China
| | - Puyi Sheng
- Department of Joint Surgery, the First Affiliated Hospital, Sun Yat-sen University, Guangzhou, 510080, Guangdong, China
| | - Jinqiu Yuan
- Department of Epidemiology and Biostatistics, Clinical Big Data Research Center, The Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen, 518000, Guangdong, China.
- Chinese Health RIsk MAnagement Collaboration (CHRIMAC), Shenzhen, 518000, Guangdong, China.
- Guangdong Provincial Key Laboratory of Gastroenterology, Center for Digestive Disease, The Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen, 518000, Guangdong, China.
| | - Ziji Zhang
- Department of Joint Surgery, the First Affiliated Hospital, Sun Yat-sen University, Guangzhou, 510080, Guangdong, China.
| | - Fuxin Wei
- Department of Orthopedics, the Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen, 518000, Guangdong, China.
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Samms GL, Ponting CP. Defining a High-Quality Myalgic Encephalomyelitis/Chronic Fatigue Syndrome cohort in UK Biobank. NIHR OPEN RESEARCH 2025; 5:39. [PMID: 40443420 PMCID: PMC12120426 DOI: 10.3310/nihropenres.13956.1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Accepted: 04/21/2025] [Indexed: 06/02/2025]
Abstract
Background Progress in Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) research is being slowed by the relatively small-scale studies being performed whose results are often not replicated. Progress could be accelerated by analyses of large population-scale projects, such as UK Biobank (UKB), which provide extensive phenotype and genotype data linked to both ME/CFS cases and controls. Methods Here, we analysed the overlap and discordance among four UKB-defined ME/CFS cohorts, and additional questionnaire data when available. Results A total of 5,354 UKB individuals were linked to at least one piece of evidence of MECFS, a higher proportion (1.1%) than most prevalence estimates. Only a third (36%; n=1,922) had 2 or more pieces of evidence for MECFS, in part due to data missingness. For the same UKB participant, ME/CFS status defined by ICD-10 (International Classification of Diseases, Tenth Revision) code G93.3 (Post-viral fatigue syndrome) was most likely to be supported by another data type (72%); ME/CFS status defined by Pain Questionnaire responses is least likely to be supported (43%), in part due to data missingness. Conclusions We conclude that ME/CFS status in UKB, and potentially other biobanks, is best supported by multiple, and not single, lines of evidence. Finally, we raise the estimated ME/CFS prevalence in the UK to 410,000 using the most consistent evidence for ME/CFS status, and accounting for those who had no opportunity to participate in UKB due to being bed- or house-bound.
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Affiliation(s)
- Gemma L. Samms
- Institute of Genetics and Cancer, The University of Edinburgh MRC Human Genetics Unit, Edinburgh, Scotland, UK
| | - Chris P. Ponting
- Institute of Genetics and Cancer, The University of Edinburgh MRC Human Genetics Unit, Edinburgh, Scotland, UK
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Hamy V, Creagh A, Garcia-Gancedo L. Assessment of physical activity patterns in patients with rheumatoid arthritis using the UK Biobank. PLoS One 2025; 20:e0319908. [PMID: 40138300 PMCID: PMC11940758 DOI: 10.1371/journal.pone.0319908] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2024] [Accepted: 02/11/2025] [Indexed: 03/29/2025] Open
Abstract
Measures of physical activity patterns that may characterize rheumatoid arthritis status were investigated, using actigraphy data from a large, prospective database study (UK Biobank). Population characterization identified 1080 individuals with rheumatoid arthritis who participated in accelerometer-measured physical activity data collection and met the eligibility criteria; these individuals were subsequently matched with 2160 non-rheumatoid arthritis controls. Raw actigraphy data were pre-processed to interpretable acceleration magnitude and general signal-based features were used to derive activity labels from a human activity recognition model. Qualitative assessment of average activity profiles indicated small differences between groups for activity in the first 5 hours of the day, engagement in moderate-to-vigorous activity, and evening sleep patterns. Of 145 metrics capturing different aspects of physical activity, 57 showed an ability to differentiate between participants with rheumatoid arthritis and non-rheumatoid arthritis controls, most notably activities related to moderate-to-vigorous activity, sleep and the ability to perform sustained activity, which remained different when adjusting for baseline imbalances. Objective measures derived from wrist-worn accelerometer data may be used to assess and quantify the impact of rheumatoid arthritis on daily activity and may reflect rheumatoid arthritis symptoms. This work represents an initial step towards the characterization of such impact. Importantly, this study offers a glimpse of the potential use of large-scale datasets to support the analysis of smaller clinical study datasets.
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Affiliation(s)
| | - Andrew Creagh
- University of Oxford, Oxford, United Kingdom
- GSK, Stevenage, Hertfordshire, United Kingdom
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Marquina C, Lloyd M, Ng W, Hess J, Evans S, Ademi Z. Evaluating Health and Well-Being Returns on Investment in a Cancer Biobank. Biopreserv Biobank 2025; 23:3-10. [PMID: 38828511 DOI: 10.1089/bio.2024.0014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/05/2024] Open
Abstract
Objectives: To evaluate the population health returns from investment in the Victorian Cancer Biobank (VCB), a research consortium including five hospital-integrated sample repositories located in Melbourne, Australia. Methods: This economic evaluation assigned monetary values to the health gains attributable to VCB-supported research. These were then compared with the total investment in VCB infrastructure since inception (2006-2022) to determine the return on investment (ROI). A time lag of 40 years was incorporated, recognizing the delay from investment to impact in scientific research. Health gains were therefore measured for the years 2046-2066, with a 3% discount rate applied. Health gains were measured in terms of disability-adjusted life years (DALYs) attributable to VCB-associated research, with monetary cost assigned via the standardized value of a statistical life year (AU$227,000). The age-standardized DALY rate attributable to cancer was modeled for two standpoints (1) extrapolating the current decreasing trajectory and (2) assuming nil future improvement from current rates, with 33% of the difference attributed to scientific innovation. The proportion of the aggregate health gain attributable to VCB-supported research was estimated from the number of VCB-credited scientific publications as a proportion of total oncology publications over the same period. Results: The AU$32,628,016 of public funding invested in VCB activities over the years 2006-2022 is projected to generate AU$84,561,373 in total (discounted) savings. ROI was AU$1.59 for each AU$1 invested. Conclusions: The VCB offers a strong ROI in terms of impacts on health, justifying the expenditure of public funds and supporting the use of biobanks to advance scientific research.
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Affiliation(s)
- Clara Marquina
- Health Economics and Policy Evaluation Research (HEPER) Group, Centre for Medicine Use and Safety, Monash University, Melbourne, Australia
| | - Melanie Lloyd
- Health Economics and Policy Evaluation Research (HEPER) Group, Centre for Medicine Use and Safety, Monash University, Melbourne, Australia
| | - Wayne Ng
- Victorian Cancer Biobank, Melbourne, Australia
- Cancer Council Victoria, Melbourne, Australia
| | - Jonas Hess
- Victorian Cancer Biobank, Melbourne, Australia
- Cancer Council Victoria, Melbourne, Australia
| | - Sue Evans
- Victorian Cancer Registry, Melbourne, Australia
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia
| | - Zanfina Ademi
- Health Economics and Policy Evaluation Research (HEPER) Group, Centre for Medicine Use and Safety, Monash University, Melbourne, Australia
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia
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Liu J, Sun H, Zhang K, Hussain I, Wang Y, Sun H, Tang Z. Having two children might be best for women's mental health: Evidence from UK Biobank. J Affect Disord 2025; 369:615-624. [PMID: 39413883 DOI: 10.1016/j.jad.2024.10.037] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/25/2024] [Revised: 10/07/2024] [Accepted: 10/09/2024] [Indexed: 10/18/2024]
Abstract
BACKGROUND Limited research has examined the association between the number of live births and bipolar disorder and major depression (BDMD). This study aims to investigate the relationship between the number of live births and BDMD among women in the UK, providing a theoretical basis for reproductive decision-making. METHODS This cross-sectional study utilized data from >55,000 women in the UK Biobank. Inverse probability treatment weighting (IPTW) logistic regression models were employed to reduce bias and confounding. Multivariate logistic regression was applied to examine the relationship between live births and BDMD, while restricted cubic spline regression was utilized to analyze the nonlinear association between the number of live births and BDMD. Subgroup analyses were conducted, stratifying by age, BMI, spontaneous abortion, induced abortion, and spontaneous abortion. RESULTS The IPTW-multivariate logistic regression analysis indicated that live births serve as an independent protective factor against BDMD. In IPTW-multivariate Model 4, the odds ratio (OR) for the live births group was 0.71 (95 % CI 0.69-0.73, P < 0.001). A significant nonlinear relationship was identified between the number of live births and the risk of BDMD. Additionally, it was found that women with two live births exhibited the lowest risk of BDMD. CONCLUSION Our study demonstrated that live births are an independent protective factor against BDMD. This research holds particular significance in the context of globally declining fertility rates and the increasing prevalence of mental disorders. It provides guidance for women on fertility decisions amid declining global fertility rates and increasing mental health issues.
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Affiliation(s)
- Jingfang Liu
- Department of Gynecology and Obstetrics, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu 215123, PR China..
| | - Hao Sun
- Department of Biostatistics, School of Public Health, Jiangsu Key Laboratory of Preventive and Translational Medicine for Major Chronic Non-communicable Diseases, MOE Key Laboratory of Geriatric Diseases and Immunology, Suzhou Medical College of Soochow University, Suzhou, Jiangsu 215123, PR China
| | - Ke Zhang
- Department of Biostatistics, School of Public Health, Jiangsu Key Laboratory of Preventive and Translational Medicine for Major Chronic Non-communicable Diseases, MOE Key Laboratory of Geriatric Diseases and Immunology, Suzhou Medical College of Soochow University, Suzhou, Jiangsu 215123, PR China
| | - Ibrar Hussain
- Department of Biostatistics, School of Public Health, Jiangsu Key Laboratory of Preventive and Translational Medicine for Major Chronic Non-communicable Diseases, MOE Key Laboratory of Geriatric Diseases and Immunology, Suzhou Medical College of Soochow University, Suzhou, Jiangsu 215123, PR China
| | - Yuying Wang
- Department of Biostatistics, School of Public Health, Jiangsu Key Laboratory of Preventive and Translational Medicine for Major Chronic Non-communicable Diseases, MOE Key Laboratory of Geriatric Diseases and Immunology, Suzhou Medical College of Soochow University, Suzhou, Jiangsu 215123, PR China
| | - Hongpeng Sun
- Department of Biostatistics, School of Public Health, Jiangsu Key Laboratory of Preventive and Translational Medicine for Major Chronic Non-communicable Diseases, MOE Key Laboratory of Geriatric Diseases and Immunology, Suzhou Medical College of Soochow University, Suzhou, Jiangsu 215123, PR China
| | - Zaixiang Tang
- Department of Biostatistics, School of Public Health, Jiangsu Key Laboratory of Preventive and Translational Medicine for Major Chronic Non-communicable Diseases, MOE Key Laboratory of Geriatric Diseases and Immunology, Suzhou Medical College of Soochow University, Suzhou, Jiangsu 215123, PR China
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Tamuhla T, Coussens AK, Abrahams M, Blumenthal MJ, Lakay F, Wilkinson RJ, Riou C, Raubenheimer P, Dave JA, Tiffin N. Implementation of a genotyped African population cohort, with virtual follow-up: A feasibility study in the Western Cape Province, South Africa. Wellcome Open Res 2025; 9:620. [PMID: 39925651 PMCID: PMC11806245 DOI: 10.12688/wellcomeopenres.23009.2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/10/2025] [Indexed: 02/11/2025] Open
Abstract
Background There is limited knowledge regarding African genetic drivers of disease due to prohibitive costs of large-scale genomic research in Africa. Methods We piloted a scalable virtual genotyped cohort in South Africa that was affordable in this resource-limited context, cost-effective, scalable virtual genotyped cohort in South Africa, with participant recruitment using a tiered informed consent model and DNA collection by buccal swab. Genotype data was generated using the H3Africa Illumina micro-array, and phenotype data was derived from routine health data of participants. We demonstrated feasibility of nested case control genome wide association studies using these data for phenotypes type 2 diabetes mellitus (T2DM) and severe COVID-19. Results 2267346 variants were analysed in 459 participant samples, of which 229 (66.8%) are female. 78.6% of SNPs and 74% of samples passed quality control (QC). Principal component analysis showed extensive ancestry admixture in study participants. Of the 343 samples that passed QC, 93 participants had T2DM and 63 had severe COVID-19. For 1780 previously published COVID-19-associated variants, 3 SNPs in the pre-imputation data and 23 SNPS in the imputed data were significantly associated with severe COVID-19 cases compared to controls (p<0.05). For 2755 published T2DM associated variants, 69 SNPs in the pre-imputation data and 419 SNPs in the imputed data were significantly associated with T2DM cases when compared to controls (p<0.05). Conclusions The results shown here are illustrative of what will be possible as the cohort expands in the future. Here we demonstrate the feasibility of this approach, recognising that the findings presented here are preliminary and require further validation once we have a sufficient sample size to improve statistical significance of findings.We implemented a genotyped population cohort with virtual follow up data in a resource-constrained African environment, demonstrating feasibility for scale up and novel health discoveries through nested case-control studies.
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Affiliation(s)
- Tsaone Tamuhla
- South African Medical Research Council Bioinformatics Unit, South African National Bioinformatics Institute, University of the Western Cape, Cape Town, South Africa
| | - Anna K Coussens
- Wellcome CIDRI-Africa, Faculty of Health Sciences, University of Cape Town, Rondebosch, Western Cape, South Africa
- Infectious Diseases and Immune Defence Division, The Walter and Eliza Hall Institute of Medical Research, Parkville Victoria, Australia
- Department of Medical Biology, The University of Melbourne, Melbourne, Victoria, Australia
| | - Maleeka Abrahams
- Division of Endocrinology, Department of Medicine, University of Cape Town, Rondebosch, Cape Town, South Africa
| | - Melissa J Blumenthal
- International Centre for Genetic Engineering and Biotechnology, Cape Town, South Africa
- Institute of Infectious Disease and Molecular Medicine, University of Cape Town, Rondebosch, Western Cape, South Africa
| | - Francisco Lakay
- Wellcome CIDRI-Africa, Faculty of Health Sciences, University of Cape Town, Rondebosch, Western Cape, South Africa
| | - Robert J Wilkinson
- Wellcome CIDRI-Africa, Faculty of Health Sciences, University of Cape Town, Rondebosch, Western Cape, South Africa
- The Francis Crick Institute, London, England, UK
- Department of Infectious Diseases, Imperial College London, London, England, UK
| | - Catherine Riou
- Wellcome CIDRI-Africa, Faculty of Health Sciences, University of Cape Town, Rondebosch, Western Cape, South Africa
- Institute of Infectious Disease and Molecular Medicine, University of Cape Town, Rondebosch, Western Cape, South Africa
- Department of Pathology, University of Cape Town, Rondebosch, Western Cape, South Africa
| | - Peter Raubenheimer
- Division of Endocrinology, Department of Medicine, University of Cape Town, Rondebosch, Cape Town, South Africa
| | - Joel A Dave
- Division of Endocrinology, Department of Medicine, University of Cape Town, Rondebosch, Cape Town, South Africa
| | - Nicki Tiffin
- South African Medical Research Council Bioinformatics Unit, South African National Bioinformatics Institute, University of the Western Cape, Cape Town, South Africa
- Wellcome CIDRI-Africa, Faculty of Health Sciences, University of Cape Town, Rondebosch, Western Cape, South Africa
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Vanoli J, de la Cruz Libardi A, Sera F, Stafoggia M, Masselot P, Mistry MN, Rajagopalan S, Quint JK, Ng CFS, Madaniyazi L, Gasparrini A. Long-term Associations Between Time-varying Exposure to Ambient PM 2.5 and Mortality: An Analysis of the UK Biobank. Epidemiology 2025; 36:1-10. [PMID: 39435892 DOI: 10.1097/ede.0000000000001796] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2024]
Abstract
BACKGROUND Evidence for long-term mortality risks of PM 2.5 comes mostly from large administrative studies with incomplete individual information and limited exposure definitions. Here we assess PM 2.5 -mortality associations in the UK Biobank cohort using detailed information on confounders and exposure. METHODS We reconstructed detailed exposure histories for 498,090 subjects by linking residential data with high-resolution PM 2.5 concentrations from spatiotemporal machine-learning models. We split the time-to-event data and assigned yearly exposures over a lag window of 8 years. We fitted Cox proportional hazard models with time-varying exposure controlling for contextual- and individual-level factors, as well as trends. In secondary analyses, we inspected the lag structure using distributed lag models and compared results with alternative exposure sources and definitions. RESULTS In fully adjusted models, an increase of 10 μg/m³ in PM 2.5 was associated with hazard ratios of 1.27 (95% confidence interval: 1.06, 1.53) for all-cause, 1.24 (1.03, 1.50) for nonaccidental, 2.07 (1.04, 4.10) for respiratory, and 1.66 (0.86, 3.19) for lung cancer mortality. We found no evidence of association with cardiovascular deaths (hazard ratio = 0.88, 95% confidence interval: 0.59, 1.31). We identified strong confounding by both contextual- and individual-level lifestyle factors. The distributed lag analysis suggested differences in relevant exposure windows across mortality causes. Using more informative exposure summaries and sources resulted in higher risk estimates. CONCLUSIONS We found associations of long-term PM 2.5 exposure with all-cause, nonaccidental, respiratory, and lung cancer mortality, but not with cardiovascular mortality. This study benefits from finely reconstructed time-varying exposures and extensive control for confounding, further supporting a plausible causal link between long-term PM 2.5 and mortality.
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Affiliation(s)
- Jacopo Vanoli
- From the Environment & Health Modelling (EHM) Lab, Department of Public Health Environments and Society, London School of Hygiene & Tropical Medicine, London, United Kingdom
| | - Arturo de la Cruz Libardi
- From the Environment & Health Modelling (EHM) Lab, Department of Public Health Environments and Society, London School of Hygiene & Tropical Medicine, London, United Kingdom
| | - Francesco Sera
- From the Environment & Health Modelling (EHM) Lab, Department of Public Health Environments and Society, London School of Hygiene & Tropical Medicine, London, United Kingdom
- Department of Statistics, Computer Science and Applications "G. Parenti," University of Florence, Florence, Italy
| | - Massimo Stafoggia
- Department of Epidemiology, Lazio Region Health Service, ASL Roma 1, Rome, Italy
| | - Pierre Masselot
- From the Environment & Health Modelling (EHM) Lab, Department of Public Health Environments and Society, London School of Hygiene & Tropical Medicine, London, United Kingdom
| | - Malcolm N Mistry
- From the Environment & Health Modelling (EHM) Lab, Department of Public Health Environments and Society, London School of Hygiene & Tropical Medicine, London, United Kingdom
- Department of Economics, Ca' Foscari University of Venice, Venice, Italy
| | - Sanjay Rajagopalan
- Harrington Heart and Vascular Institute, University Hospitals Cleveland Medical Center, Cleveland, OH
- School of Medicine, Case Western Reserve University, Cleveland, OH
| | - Jennifer K Quint
- School of Public Health, Imperial College London, London, United Kingdom
| | - Chris Fook Sheng Ng
- Department of Global Health Policy, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
- School of Tropical Medicine and Global Health, Nagasaki University, Nagasaki, Japan
| | - Lina Madaniyazi
- School of Tropical Medicine and Global Health, Nagasaki University, Nagasaki, Japan
| | - Antonio Gasparrini
- From the Environment & Health Modelling (EHM) Lab, Department of Public Health Environments and Society, London School of Hygiene & Tropical Medicine, London, United Kingdom
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Chen Z, Adegboro AA, Gu L, Li X. Constructing and exploring neuroimaging projects: a survey from clinical practice to scientific research. Insights Imaging 2024; 15:272. [PMID: 39546176 PMCID: PMC11568082 DOI: 10.1186/s13244-024-01848-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2024] [Accepted: 10/13/2024] [Indexed: 11/17/2024] Open
Abstract
Over the past decades, numerous large-scale neuroimaging projects that involved the collection and release of multimodal data have been conducted globally. Distinguished initiatives such as the Human Connectome Project, UK Biobank, and Alzheimer's Disease Neuroimaging Initiative, among others, stand as remarkable international collaborations that have significantly advanced our understanding of the brain. With the advancement of big data technology, changes in healthcare models, and continuous development in biomedical research, various types of large-scale projects are being established and promoted worldwide. For project leaders, there is a need to refer to common principles in project construction and management. Users must also adhere strictly to rules and guidelines, ensuring data safety and privacy protection. Organizations must maintain data integrity, protect individual privacy, and foster stakeholders' trust. Regular updates to legislation and policies are necessary to keep pace with evolving technologies and emerging data-related challenges. CRITICAL RELEVANCE STATEMENT: By reviewing global large-scale neuroimaging projects, we have summarized the standards and norms for establishing and utilizing their data, and provided suggestions and opinions on some ethical issues, aiming to promote higher-quality neuroimaging data development. KEY POINTS: Global neuroimaging projects are increasingly advancing but still face challenges. Constructing and utilizing neuroimaging projects should follow set rules and guidelines. Effective data management and governance should be developed to support neuroimaging projects.
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Affiliation(s)
- Ziyan Chen
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, China
- Hunan International Scientific and Technological Cooperation Base of Brain Tumor Research, Xiangya Hospital, Central South University, Changsha, China
| | - Abraham Ayodeji Adegboro
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, China
- Hunan International Scientific and Technological Cooperation Base of Brain Tumor Research, Xiangya Hospital, Central South University, Changsha, China
| | - Lan Gu
- School of Foreign Languages, Central South University, Changsha, China.
| | - Xuejun Li
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, China.
- Hunan International Scientific and Technological Cooperation Base of Brain Tumor Research, Xiangya Hospital, Central South University, Changsha, China.
- Xiangya School of Medicine, Central South University, Changsha, China.
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Lu L, Gu X, Yang D, Wang B, Long G. Circulating fatty acids, genetic susceptibility and hypertension: a prospective cohort study. Front Nutr 2024; 11:1454364. [PMID: 39545052 PMCID: PMC11562856 DOI: 10.3389/fnut.2024.1454364] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2024] [Accepted: 10/07/2024] [Indexed: 11/17/2024] Open
Abstract
Background Combining genetic risk factors and plasma fatty acids (FAs) can be used as an effective method of precision medicine to prevent hypertension risk. Methods A total of 195,250 participants in the UK Biobank cohort were included in this study from 2006-2010. Polygenic risk scores (PRSs) were calculated for hypertension using single-nucleotide polymorphisms (SNPs). Concentrations of plasma FAs, including polyunsaturated fatty acids (PUFAs), monounsaturated fatty acids (MUFAs) and saturated fatty acids (SFAs), were tested by nuclear magnetic resonance. The Cox model was used to test for the main effects of PRS, different plasma FAs and their joint effects on hypertension. Relative excess risk due to interaction (RERI) and the attributable proportion due to interaction (AP) were used to test the additive interaction. Results Plasma PUFAs, n-3 PUFAs, MUFAs and SFAs were related to the risk of hypertension (PUFAs: HR, 0.878; 95% CI, 0.868-0.888; MUFAs: HR, 1.13; 95% CI, 1.123-1.150; SFAs: HR, 1.086; 95% CI, 1.074-1.098; n-3 PUFAs: HR, 0.984; 95% CI, 0.973-0.995). Moreover, an additive interaction was found between PRS and plasma FAs, which could contribute to an approximately 10-18% risk of hypertension, and the associations between high plasma MUFAs and a high PRS of hypertension were the strongest positive [RERI: 0.178 (95% CI: 0.062, 0.294), AP: 0.079 (95% CI: 0.027, 0.130)]. Conclusion Increased plasma MUFAs or SFAs and decreased plasma PUFAs or n-3 PUFAs were associated with hypertension risk, especially among people at high genetic risk.
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Affiliation(s)
- Lingling Lu
- Department of Infectious Disease, Children’s Hospital of Nanjing Medical University, Nanjing, China
| | - Xiaoli Gu
- Department of Party and Government Office, Children’s Hospital of Nanjing Medical University, Nanjing, China
| | - Daheng Yang
- Department of Clinical Laboratory, Children’s Hospital of Nanjing Medical University, Nanjing, China
| | - Bingjian Wang
- Department of Cardiology, Huai’an First People’s Hospital Affiliated with Nanjing Medical University, Huai’an, China
| | - Guangfeng Long
- Department of Clinical Laboratory, Children’s Hospital of Nanjing Medical University, Nanjing, China
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11
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Zhang J, Cao W, Xie J, Pang C, Gao L, Zhu L, Li Y, Yu H, Du L, Fan D, Deng B. Metabolic Syndrome and Risk of Amyotrophic Lateral Sclerosis: Insights from a Large-Scale Prospective Study. Ann Neurol 2024; 96:788-801. [PMID: 38934512 DOI: 10.1002/ana.27019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2024] [Revised: 06/08/2024] [Accepted: 06/12/2024] [Indexed: 06/28/2024]
Abstract
OBJECTIVE Although metabolic abnormalities are implicated in the etiology of neurodegenerative diseases, their role in the development of amyotrophic lateral sclerosis (ALS) remains a subject of controversy. We aimed to identify the association between metabolic syndrome (MetS) and the risk of ALS. METHODS This study included 395,987 participants from the UK Biobank to investigate the relationship between MetS and ALS. Cox regression model was used to estimate hazard ratios (HR). Stratified analyses were performed based on gender, body mass index (BMI), smoking status, and education level. Mediation analysis was conducted to explore potential mechanisms. RESULTS In this study, a total of 539 cases of ALS were recorded after a median follow-up of 13.7 years. Patients with MetS (defined harmonized) had a higher risk of developing ALS after adjusting for confounding factors (HR: 1.50, 95% CI: 1.19-1.89). Specifically, hypertension and high triglycerides were linked to a higher risk of ALS (HR: 1.53, 95% CI: 1.19-1.95; HR: 1.31, 95% CI: 1.06-1.61, respectively). Moreover, the quantity of metabolic abnormalities showed significant results. Stratified analysis revealed that these associations are particularly significant in individuals with a BMI <25. These findings remained stable after sensitivity analysis. Notably, mediation analysis identified potential metabolites and metabolomic mediators, including alkaline phosphatase, cystatin C, γ-glutamyl transferase, saturated fatty acids to total fatty acids percentage, and omega-6 fatty acids to omega-3 fatty acids ratio. INTERPRETATION MetS exhibits a robust association with an increased susceptibility to ALS, particularly in individuals with a lower BMI. Furthermore, metabolites and metabolomics, as potential mediators, provide invaluable insights into the intricate biological mechanisms. ANN NEUROL 2024;96:788-801.
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Affiliation(s)
- Junwei Zhang
- Department of Neurology, First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
- Zhejiang Key Laboratory of Intelligent Cancer Biomarker Discovery and Translation, First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Wen Cao
- Department of Neurology, Peking University Third Hospital, Beijing, China
- Beijing Key Laboratory of Biomarker and Translational Research in Neurodegenerative Disorders, Beijing, China
- Key Laboratory for Neuroscience, National Health Commission/Ministry of Education, Peking University, Beijing, China
| | - Jiali Xie
- Department of Neurology, First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
- Department of Neurology, Shanghai East Hospital, Tongji University School of Medicine, Shanghai, China
| | - Chunyang Pang
- Department of Neurology, First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Lingfei Gao
- Department of Neurology, First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Luyi Zhu
- Department of Neurology, First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Yaojia Li
- Department of Neurology, First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Huan Yu
- Department of Pediatrics, Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, China
| | - Lihuai Du
- College of Mathematics and Physics, Wenzhou University, Wenzhou, China
| | - Dongsheng Fan
- Department of Neurology, Peking University Third Hospital, Beijing, China
- Beijing Key Laboratory of Biomarker and Translational Research in Neurodegenerative Disorders, Beijing, China
- Key Laboratory for Neuroscience, National Health Commission/Ministry of Education, Peking University, Beijing, China
| | - Binbin Deng
- Department of Neurology, First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
- Zhejiang Key Laboratory of Intelligent Cancer Biomarker Discovery and Translation, First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
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12
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Herzog CMS, Goeminne LJE, Poganik JR, Barzilai N, Belsky DW, Betts-LaCroix J, Chen BH, Chen M, Cohen AA, Cummings SR, Fedichev PO, Ferrucci L, Fleming A, Fortney K, Furman D, Gorbunova V, Higgins-Chen A, Hood L, Horvath S, Justice JN, Kiel DP, Kuchel GA, Lasky-Su J, LeBrasseur NK, Maier AB, Schilling B, Sebastiano V, Slagboom PE, Snyder MP, Verdin E, Widschwendter M, Zhavoronkov A, Moqri M, Gladyshev VN. Challenges and recommendations for the translation of biomarkers of aging. NATURE AGING 2024; 4:1372-1383. [PMID: 39285015 DOI: 10.1038/s43587-024-00683-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/10/2024] [Accepted: 07/12/2024] [Indexed: 10/01/2024]
Abstract
Biomarkers of aging (BOA) are quantitative parameters that predict biological age and ideally its changes in response to interventions. In recent years, many promising molecular and omic BOA have emerged with an enormous potential for translational geroscience and improving healthspan. However, clinical translation remains limited, in part due to the gap between preclinical research and the application of BOA in clinical research and other translational settings. We surveyed experts in these areas to better understand current challenges for the translation of aging biomarkers. We identified six key barriers to clinical translation and developed guidance for the field to overcome them. Core recommendations include linking BOA to clinically actionable insights, improving affordability and availability to broad populations and validation of biomarkers that are robust and responsive at the level of individuals. Our work provides key insights and practical recommendations to overcome barriers impeding clinical translation of BOA.
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Affiliation(s)
- Chiara M S Herzog
- European Translational Oncology Prevention and Screening Institute, Universität Innsbruck, Innsbruck, Austria
| | - Ludger J E Goeminne
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Jesse R Poganik
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Nir Barzilai
- Institute for Aging Research, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Daniel W Belsky
- Department of Epidemiology, Butler Columbia Aging Center, Mailman School of Public Health, Columbia University, New York, NY, USA
| | | | - Brian H Chen
- San Francisco Coordinating Center, California Pacific Medical Center Research Institute, San Francisco, CA, USA
- Department of Epidemiology and Biostatistics, University of California, San Francisco, CA, USA
| | | | - Alan A Cohen
- Department of Environmental Health Sciences, Butler Columbia Aging Center, Mailman School of Public Health, Columbia University, New York, NY, USA
| | - Steven R Cummings
- San Francisco Coordinating Center, California Pacific Medical Center Research Institute, San Francisco, CA, USA
- Department of Epidemiology and Biostatistics, University of California, San Francisco, CA, USA
| | | | | | | | | | - David Furman
- Buck Institute for Research on Aging, Novato, CA, USA
- Stanford 1000 Immunomes Project, Stanford School of Medicine, Stanford, CA, USA
- The National Scientific and Research Council, Austral University, Buenos Aires, Argentina
| | - Vera Gorbunova
- Departments of Biology and Medicine, University of Rochester, Rochester, NY, USA
| | | | - Lee Hood
- Buck Institute for Research on Aging, Novato, CA, USA
- Phenome Health, Seattle, WA, USA
| | | | - Jamie N Justice
- XPRIZE Foundation, Culver City, CA, USA
- Department of Internal Medicine, Section on Gerontology and Geriatric Medicine, Wake Forest University School of Medicine, Winston-Salem, NC, USA
| | - Douglas P Kiel
- Hinda and Arthur Marcus Institute for Aging Research, Hebrew SeniorLife, Roslindale, MA, USA
- Department of Medicine, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, USA
| | - George A Kuchel
- University of Connecticut School of Medicine, @UConnAging, Farmington, CT, USA
| | - Jessica Lasky-Su
- Department of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Nathan K LeBrasseur
- Department of Physical Medicine and Rehabilitation, Robert and Arlene Kogod Center on Aging, Mayo Clinic, Rochester, MN, USA
| | - Andrea B Maier
- Healthy Longevity Translational Research Program, Yong Loo Lin School of Medicine, National University of Singapore, Centre for Healthy Longevity, @AgeSingapore, National University Health System, Singapore, Singapore
- Department of Human Movement Sciences, @AgeAmsterdam, Amsterdam Movement Sciences, Faculty of Behavioural and Movement Sciences, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | | | - Vittorio Sebastiano
- Department of Obstetrics and Gynecology, School of Medicine, Stanford University, Stanford, CA, USA
| | - P Eline Slagboom
- Section of Molecular Epidemiology, Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, the Netherlands
| | - Michael P Snyder
- Department of Genetics, School of Medicine, Stanford University, Stanford, CA, USA
| | - Eric Verdin
- Buck Institute for Research on Aging, Novato, CA, USA
| | - Martin Widschwendter
- European Translational Oncology Prevention and Screening Institute, Universität Innsbruck, Innsbruck, Austria
- Department of Women's Cancer, EGA Institute for Women's Health, University College London, London, UK
- Department of Women's and Children's Health, Division of Obstetrics and Gynaecology, Karolinska Institutet, Stockholm, Sweden
| | | | - Mahdi Moqri
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
- Department of Genetics, School of Medicine, Stanford University, Stanford, CA, USA.
| | - Vadim N Gladyshev
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
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13
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Zhang S, Zhong Y, Wang X, Jiang W, Chen X, Kang Y, Li Z, Liao W, Zheng L, Sheng P, Zhang Z. Association of peripheral inflammatory indicators with osteoarthritis risk. OSTEOARTHRITIS AND CARTILAGE OPEN 2024; 6:100496. [PMID: 39021876 PMCID: PMC11254169 DOI: 10.1016/j.ocarto.2024.100496] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2024] [Accepted: 06/13/2024] [Indexed: 07/20/2024] Open
Abstract
Objectives Numerous studies have established the role of inflammation in osteoarthritis (OA) progression, yet limited research explores the association between systemic inflammatory indicators and pre-diagnosis OA risk. This study aimed to investigate the association between peripheral inflammatory indicators and the risk of OA using data from the UK Biobank. Methods The study analyzed data from 417,507 participants in the UK Biobank, including neutrophil count, lymphocyte count, monocyte count, platelet count, and C-reactive protein meter. Additionally, derived ratios such as NLR(neutrophils-lymphocytes ratio), PLR(Platelets-lymphocytes ratio), SII(systemic immune-inflammation index), and LMR (lymphocytes-monocytes ratio) were examined. Cox proportional hazards models and restricted cubic spline models were used to assess both linear and nonlinear associations. Results Over a mean follow-up period of 12.7 years, a total of 49,509 OA events were identified. The findings revealed that CRP (HR:1.06, 95%CI:1.05-1.07), NLR (HR:1.02, 95%CI:1.01-1.03), PLR (HR:1.02, 95%CI:1.01-1.03), and SII (HR:1.03, 95%CI:1.01-1.04) were associated with an increased risk of OA, while LMR (HR:0.97, 95%CI:0.96-0.99) showed a significant negative correlation with OA risk. Subgroup analyses further emphasized that these associations were significant across most of the population. Although neutrophils, lymphocytes, monocytes, and platelets showed a nominal association with the risk of OA, the results were unreliable, especially for specific joint OA. Conclusion The study provides evidence of a significant association between elevated peripheral inflammatory indicators and OA risk. These findings underscore the importance of low-grade chronic inflammation in OA development. The potential clinical utility of these indicators as early predictors of OA is suggested, warranting further exploration.
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Affiliation(s)
- Shiyong Zhang
- Department of Joint Surgery, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong 510080, China
| | - Yanlin Zhong
- Department of Joint Surgery, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong 510080, China
| | - Xudong Wang
- Department of Joint Surgery, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong 510080, China
| | - Wei Jiang
- Department of Bone and Joint, Shenzhen People's Hospital (The Second Clinical Medical College, Jinan University, The First Affiliated Hospital, Southern University of Science and Technology), Shenzhen 518020, Guangdong, China
| | - Xicong Chen
- The Tenth Department of Orthopedics, Foshan Hospital of Traditional Chinese Medicine, China
| | - Yunze Kang
- Department of Joint Surgery, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong 510080, China
| | - Zhiwen Li
- Department of Joint Surgery, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong 510080, China
| | - Weiming Liao
- Department of Joint Surgery, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong 510080, China
| | - Linli Zheng
- Department of Joint Surgery, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong 510080, China
| | - Puyi Sheng
- Department of Joint Surgery, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong 510080, China
| | - Ziji Zhang
- Department of Joint Surgery, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong 510080, China
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14
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Al-Aly Z, Davis H, McCorkell L, Soares L, Wulf-Hanson S, Iwasaki A, Topol EJ. Long COVID science, research and policy. Nat Med 2024; 30:2148-2164. [PMID: 39122965 DOI: 10.1038/s41591-024-03173-6] [Citation(s) in RCA: 88] [Impact Index Per Article: 88.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2024] [Accepted: 07/02/2024] [Indexed: 08/12/2024]
Abstract
Long COVID represents the constellation of post-acute and long-term health effects caused by SARS-CoV-2 infection; it is a complex, multisystem disorder that can affect nearly every organ system and can be severely disabling. The cumulative global incidence of long COVID is around 400 million individuals, which is estimated to have an annual economic impact of approximately $1 trillion-equivalent to about 1% of the global economy. Several mechanistic pathways are implicated in long COVID, including viral persistence, immune dysregulation, mitochondrial dysfunction, complement dysregulation, endothelial inflammation and microbiome dysbiosis. Long COVID can have devastating impacts on individual lives and, due to its complexity and prevalence, it also has major ramifications for health systems and economies, even threatening progress toward achieving the Sustainable Development Goals. Addressing the challenge of long COVID requires an ambitious and coordinated-but so far absent-global research and policy response strategy. In this interdisciplinary review, we provide a synthesis of the state of scientific evidence on long COVID, assess the impacts of long COVID on human health, health systems, the economy and global health metrics, and provide a forward-looking research and policy roadmap.
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Affiliation(s)
- Ziyad Al-Aly
- VA St. Louis Health Care System, Saint Louis, MO, USA.
- Washington University in St. Louis, Saint Louis, MO, USA.
| | - Hannah Davis
- Patient-led Research Collaborative, Calabasas, CA, USA
| | | | | | | | - Akiko Iwasaki
- Yale University, New Haven, CT, USA
- Howard Hughes Medical Institute, Chevy Chase, MD, USA
| | - Eric J Topol
- Scripps Institute, San Diego, California, CA, USA
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15
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Venkatesh SS, Ganjgahi H, Palmer DS, Coley K, Linchangco GV, Hui Q, Wilson P, Ho YL, Cho K, Arumäe K, Wittemans LBL, Nellåker C, Vainik U, Sun YV, Holmes C, Lindgren CM, Nicholson G. Characterising the genetic architecture of changes in adiposity during adulthood using electronic health records. Nat Commun 2024; 15:5801. [PMID: 38987242 PMCID: PMC11237142 DOI: 10.1038/s41467-024-49998-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2023] [Accepted: 06/25/2024] [Indexed: 07/12/2024] Open
Abstract
Obesity is a heritable disease, characterised by excess adiposity that is measured by body mass index (BMI). While over 1,000 genetic loci are associated with BMI, less is known about the genetic contribution to adiposity trajectories over adulthood. We derive adiposity-change phenotypes from 24.5 million primary-care health records in over 740,000 individuals in the UK Biobank, Million Veteran Program USA, and Estonian Biobank, to discover and validate the genetic architecture of adiposity trajectories. Using multiple BMI measurements over time increases power to identify genetic factors affecting baseline BMI by 14%. In the largest reported genome-wide study of adiposity-change in adulthood, we identify novel associations with BMI-change at six independent loci, including rs429358 (APOE missense variant). The SNP-based heritability of BMI-change (1.98%) is 9-fold lower than that of BMI. The modest genetic correlation between BMI-change and BMI (45.2%) indicates that genetic studies of longitudinal trajectories could uncover novel biology of quantitative traits in adulthood.
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Affiliation(s)
- Samvida S Venkatesh
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK.
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK.
| | - Habib Ganjgahi
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
- Department of Statistics, University of Oxford, Oxford, UK
| | - Duncan S Palmer
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
- Nuffield Department of Population Health, Medical Sciences Division, University of Oxford, Oxford, UK
| | - Kayesha Coley
- Department of Population Health Sciences, University of Leicester, Leicester, UK
| | - Gregorio V Linchangco
- Department of Epidemiology, Emory University Rollins School of Public Health, Atlanta, GA, USA
- Atlanta VA Health Care System, Decatur, GA, USA
| | - Qin Hui
- Department of Epidemiology, Emory University Rollins School of Public Health, Atlanta, GA, USA
- Atlanta VA Health Care System, Decatur, GA, USA
| | - Peter Wilson
- Atlanta VA Health Care System, Decatur, GA, USA
- Department of Medicine, Emory University School of Medicine, Atlanta, GA, USA
| | - Yuk-Lam Ho
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), Veterans Affairs Boston Healthcare System, Boston, MA, USA
| | - Kelly Cho
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), Veterans Affairs Boston Healthcare System, Boston, MA, USA
- Division of Aging, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Kadri Arumäe
- Institute of Psychology, Faculty of Social Sciences, University of Tartu, Tartu, Estonia
| | - Laura B L Wittemans
- Novo Nordisk Research Centre Oxford, Oxford, UK
- Nuffield Department of Women's and Reproductive Health, Medical Sciences Division, University of Oxford, Oxford, UK
| | - Christoffer Nellåker
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
- Nuffield Department of Women's and Reproductive Health, Medical Sciences Division, University of Oxford, Oxford, UK
| | - Uku Vainik
- Institute of Psychology, Faculty of Social Sciences, University of Tartu, Tartu, Estonia
- Estonian Genome Centre, Institute of Genomics, Faculty of Science and Technology, University of Tartu, Tartu, Estonia
- Department of Neurology and Neurosurgery, Faculty of Medicine and Health Sciences, University of McGill, Montreal, Canada
| | - Yan V Sun
- Department of Epidemiology, Emory University Rollins School of Public Health, Atlanta, GA, USA
- Atlanta VA Health Care System, Decatur, GA, USA
| | - Chris Holmes
- Department of Statistics, University of Oxford, Oxford, UK
- Nuffield Department of Medicine, Medical Sciences Division, University of Oxford, Oxford, UK
- The Alan Turing Institute, London, UK
| | - Cecilia M Lindgren
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK.
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK.
- Nuffield Department of Women's and Reproductive Health, Medical Sciences Division, University of Oxford, Oxford, UK.
- Broad Institute of Harvard and MIT, Cambridge, MA, USA.
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16
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Park HK, Marston L, Mukadam N. The Effects of Estrogen on the Risk of Developing Dementia: A Cohort Study Using the UK Biobank Data. Am J Geriatr Psychiatry 2024; 32:792-805. [PMID: 38310026 DOI: 10.1016/j.jagp.2024.01.025] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Revised: 01/12/2024] [Accepted: 01/17/2024] [Indexed: 02/05/2024]
Abstract
OBJECTIVES The protective role of estrogen in the development of dementia remains uncertain. We investigated the role of lifetime cumulative exposure to estrogen in dementia in the UK Biobank. METHODS Reproductive characteristics, including estrogen length and history of surgery (hysterectomy/oophorectomy), were used as exposure variables. Cox Proportional Hazard models were used to estimate hazard ratios (HR) for the development of dementia. RESULTS A total of 273,260 female participants were included in this study. Compared to women with the shortest estrogen length, women with the longer estrogen length (38-42) had a 28% decreased risk of dementia (HR = 0.718, 95% confidence interval [CI] = 0.651-0.793). Women with later last age at estrogen exposure (50-52) had a 24% decreased risk for dementia (HR = 0.763, 95% CI = 0.695-0.839) compared to women with younger age at last estrogen exposure (≤45). Later age at menarche (≥15) was associated with a 12% increased risk for dementia (HR = 1.121, 95% CI = 1.018-1.234) compared to women with earlier age at menarche (≤12). Women with a history of surgery had an 8% increased risk of dementia (HR = 1.079, 95% CI = 1.002-1.164) compared to women without a history of surgery. CONCLUSION This study found that more prolonged exposure to estrogen (longer estrogen length and later age at last estrogen exposure) had a decreased risk for dementia, and shorter exposure to estrogen (later age at menarche and history of reproductive surgery) had an increased risk for dementia. Based on the results of this study, estrogen might have a protective role in women in the development of dementia.
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Affiliation(s)
- Hee Kyung Park
- Division of Psychiatry (HKP, NM), University College London, London, UK.
| | - Louise Marston
- Department of Primary Care and Population Health (LM), University College London, London, UK
| | - Naaheed Mukadam
- Division of Psychiatry (HKP, NM), University College London, London, UK
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17
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Fabbri C, Lewis CM, Serretti A. Polygenic risk scores for mood and related disorders and environmental factors: Interaction effects on wellbeing in the UK biobank. Prog Neuropsychopharmacol Biol Psychiatry 2024; 132:110972. [PMID: 38367896 DOI: 10.1016/j.pnpbp.2024.110972] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Revised: 12/15/2023] [Accepted: 02/14/2024] [Indexed: 02/19/2024]
Abstract
Mood disorders have a genetic and environmental component and interactions (GxE) on the risk of psychiatric diseases have been investigated. The same GxE interactions may affect wellbeing measures, which go beyond categorical diagnoses and reflect the health-disease continuum. We evaluated GxE effects in the UK Biobank, considering as outcomes subjective wellbeing (feeling good and functioning well) and objective measures (education and income). We estimated the polygenic risk scores (PRSs) of major depressive disorder, bipolar disorder, schizophrenia, and attention deficit hyperactivity disorder. Stressful/traumatic events during adulthood or childhood were considered as E variables, as well as social support. The addition of the PRSxE interaction to PRS and E variables was tested in linear or multinomial regression models, adjusting for confounders. We included 33 k-380 k participants, depending on the variables considered. Most PRSs and E factors showed additive effects on outcomes, with effect sizes generally 3-5 times larger for E variables than PRSs. We found some interaction effects, particularly when considering recent stress, history of a long illness/disability/infirmity, and social support. Higher PRSs increased the negative effects of stress on wellbeing, but they also increased the positive effects of social support, with interaction effects particularly for the outcomes health satisfaction, loneliness, and income (p < Bonferroni corrected threshold of 1.92e-4). PRSxE terms usually added ∼0.01-0.02% variance explained to the corresponding additive model. PRSxE effects on wellbeing involve both positive and negative E factors. Despite small variance explained at the population level, preventive/therapeutic interventions that modify E factors could be beneficial at the individual level.
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Affiliation(s)
- Chiara Fabbri
- Department of Biomedical and NeuroMotor Sciences, University of Bologna, Bologna, Italy.
| | - Cathryn M Lewis
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Alessandro Serretti
- Department of Biomedical and NeuroMotor Sciences, University of Bologna, Bologna, Italy; Department of Medicine and Surgery, Kore University of Enna, Enna, Italy
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18
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Jennum P, Kjellberg J, Carls G, Ibsen R, Mettam S. Real-world impact of continuous positive airway pressure on sleepiness in patients with obstructive sleep apnea in a national registry. Sleep Med 2024; 118:93-100. [PMID: 38657350 DOI: 10.1016/j.sleep.2024.03.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/28/2023] [Revised: 02/29/2024] [Accepted: 03/09/2024] [Indexed: 04/26/2024]
Abstract
OBJECTIVE Excessive daytime sleepiness (EDS) persists in some patients with obstructive sleep apnea (OSA) despite continuous positive airway pressure (CPAP) treatment. This study characterized response to CPAP and factors associated with residual EDS. METHODS Danish National Patient Registry data were analyzed. Patients with OSA diagnosis (1994-2016), Epworth Sleepiness Scale (ESS) scores and apnea-hypopnea index recorded before beginning CPAP (baseline) and after 1-13 months of CPAP use, and CPAP adherence were included. Odds ratios (OR) for residual EDS after CPAP treatment were estimated using multivariate logistic regression. RESULTS Of 1174 patients (mean age, 57 years; 75.5% male), 41.1% had baseline EDS (mild, 13.2%; moderate, 14.0%; severe, 13.9%); 58.9% did not. After CPAP treatment, follow-up mean ESS scores were normal (≤10) for all baseline EDS subgroups; however, 15.6% (n = 183) of patients had residual EDS (mild, 6.7%; moderate, 5.5%; severe, 3.4%). Odds of residual EDS were higher for patients with mild (OR, 5.2; 95% confidence interval [CI], 3.2-8.6), moderate (OR, 4.5; 95% CI, 2.7-7.4), and severe (OR, 13.0; 95% CI, 8.0-21.2) EDS at baseline compared with those with normal daytime sleepiness at baseline. Patients adherent with CPAP use were 38.2% less likely to have residual EDS compared with nonadherent patients (OR, 0.62; 95% CI, 0.43-0.88). CONCLUSIONS EDS was common in this cohort of Danish patients with OSA. Baseline EDS severity predicted higher odds of residual EDS. After CPAP treatment, adherence was associated with reduced odds of residual EDS, but EDS persisted in a subgroup of patients.
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Affiliation(s)
- Poul Jennum
- Danish Center for Sleep Medicine, Department of Clinical Neurophysiology, Faculty of Health Sciences, University of Copenhagen, Glostrup, Denmark.
| | - Jakob Kjellberg
- Danish National Institute for Local and Regional Government Research, Copenhagen, Denmark.
| | | | | | - Sam Mettam
- Jazz Pharmaceuticals, Oxford, England, UK.
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张 宁, 张 圆, 魏 君, 向 毅, 胡 逸, 肖 雄. [Hypothetical Alcohol Consumption Interventions and Hepatic Steatosis: A Longitudinal Study in a Large Cohort]. SICHUAN DA XUE XUE BAO. YI XUE BAN = JOURNAL OF SICHUAN UNIVERSITY. MEDICAL SCIENCE EDITION 2024; 55:653-661. [PMID: 38948274 PMCID: PMC11211800 DOI: 10.12182/20240560503] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/05/2023] [Indexed: 07/02/2024]
Abstract
Objective Non-alcoholic fatty liver disease (NAFLD) and alcohol-associated fatty liver disease (ALD) are the most common chronic liver diseases. Hepatic steatosis is an early histological subtype of both NAFLD and ALD. Excessive alcohol consumption is widely known to lead to hepatic steatosis and subsequent liver damage. However, reported findings concerning the association between moderate alcohol consumption and hepatic steatosis remain inconsistent. Notably, alcohol consumption as a modifiable lifestyle behavior is likely to change over time, but most previous studies covered alcohol intake only once at baseline. These inconsistent findings from existing studies do not inform decision-making concerning policies and clinical guidelines, which are of greater interest to health policymakers and clinician-scientists. Additionally, recommendations on the types of alcoholic beverages are not available. Usually, assessing the effects of two or more hypothetical alcohol consumption interventions on hepatic steatosis provides answers to questions concerning the population risk of hepatic steatosis if everyone changes from heavy drinking to abstinence, or if everyone keeps on drinking moderately, or if everyone of the drinking population switches from red wine to beer? Thus, we simulated a target trial to estimate the effects of several hypothetical interventions, including changes in the amount of alcohol consumption or the types of alcoholic beverages consumed, on hepatic steatosis using longitudinal data, to inform decisions about alcohol-related policymaking and clinical care. Methods This longitudinal study included 12687 participants from the UK Biobank (UKB), all of whom participated in both baseline and repeat surveys. We excluded participants with missing data related to components of alcohol consumption and fatty liver index (FLI) in the baseline and the repeat surveys, as well as those who had reported liver diseases or cancer at the baseline survey. We used FLI as an outcome indicator and divided the participants into non-, moderate, and heavy drinkers. The surrogate marker FLI has been endorsed by many international organizations' guidelines, such as the European Association for the Study of the Liver. The calculation of FLI was based on laboratory and anthropometric data, including triglyceride, gamma-glutamyl transferase, body mass index, and waist circumference. Participants responded to questions about the types of alcoholic beverages, which were defined in 5 categories, including red wine, white wine/fortified wine/champagne, beer or cider, spirits, and mixed liqueurs, along with the average weekly or monthly amounts of alcohol consumed. Alcohol consumption was defined as pure alcohol consumed per week and was calculated according to the amount of alcoholic beverages consumed per week and the average ethanol content by volume in each alcoholic beverage. Participants were categorized as non-drinkers, moderate drinkers, and heavy drinkers according to the amount of their alcohol consumption. Moderate drinking was defined as consuming no more than 210 g of alcohol per week for men and 140 g of alcohol per week for women. We defined the following hypothetical interventions for the amount of alcohol consumed: sustaining a certain level of alcohol consumption from baseline to the repeat survey (e.g., none to none, moderate to moderate, heavy to heavy) and changing from one alcohol consumption level to another (e.g., none to moderate, moderate to heavy). The hypothetical interventions for the types of alcoholic beverages were defined in a similar way to those for the amount of alcohol consumed (e.g., red wine to red wine, red wine to beer/cider). We applied the parametric g-formula to estimate the effect of each hypothetical alcohol consumption intervention on the FLI. To implement the parametric g-formula, we first modeled the probability of time-varying confounders and FLI conditional on covariates. We then used these conditional probabilities to estimate the FLI value if the alcohol consumption level of each participant was under a specific hypothetical intervention. The confidence interval was obtained by 200 bootstrap samples. Results For the alcohol consumption from baseline to the repeat surveys, 6.65% of the participants were sustained non-drinkers, 63.68% were sustained moderate drinkers, and 14.74% were sustained heavy drinkers, while 8.39% changed from heavy drinking to moderate drinking. Regarding the types of alcoholic beverages from baseline to the repeat surveys, 27.06% of the drinkers sustained their intake of red wine. Whatever the baseline alcohol consumption level, the hypothetical interventions for increasing alcohol consumption from the baseline alcohol consumption were associated with a higher FLI than that of the sustained baseline alcohol consumption level. When comparing sustained non-drinking with the hypothetical intervention of changing from non-drinking to moderate drinking, the mean ratio of FLI was 1.027 (95% confidence interval [CI]: 0.997-1.057). When comparing sustained non-drinking with the hypothetical intervention of changing from non-drinking to heavy drinking, the mean ratio of FLI was 1.075 (95% CI: 1.042-1.108). When comparing sustained heavy drinking with the hypothetical intervention of changing from heavy drinking to moderate drinking, the mean ratio of FLI was 0.953 (95% CI: 0.938-0.968). The hypothetical intervention of changing to red wine in the UKB was associated with lower FLI levels, compared with sustained consumption of other types of alcoholic beverages. For example, when comparing sustaining spirits with the hypothetical intervention of changing from spirits to red wine, the mean ratio of FLI was 0.981 (95% CI: 0.948-1.014). Conclusions Regardless of the current level of alcohol consumption, interventions that increase alcohol consumption could raise the risk of hepatic steatosis in Western populations. The findings of this study could inform the formulation of future practice guidelines and health policies. If quitting drinking is challenging, red wine may be a better option than other types of alcoholic beverages in Western populations.
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Affiliation(s)
- 宁 张
- 四川大学华西公共卫生学院/四川大学华西第四医院 流行病与卫生统计学系 (成都 610041)Department of Epidemiology and Biostatistics, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu 610041, China
| | - 圆 张
- 四川大学华西公共卫生学院/四川大学华西第四医院 流行病与卫生统计学系 (成都 610041)Department of Epidemiology and Biostatistics, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu 610041, China
| | - 君 魏
- 四川大学华西公共卫生学院/四川大学华西第四医院 流行病与卫生统计学系 (成都 610041)Department of Epidemiology and Biostatistics, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu 610041, China
| | - 毅 向
- 四川大学华西公共卫生学院/四川大学华西第四医院 流行病与卫生统计学系 (成都 610041)Department of Epidemiology and Biostatistics, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu 610041, China
| | - 逸凡 胡
- 四川大学华西公共卫生学院/四川大学华西第四医院 流行病与卫生统计学系 (成都 610041)Department of Epidemiology and Biostatistics, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu 610041, China
| | - 雄 肖
- 四川大学华西公共卫生学院/四川大学华西第四医院 流行病与卫生统计学系 (成都 610041)Department of Epidemiology and Biostatistics, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu 610041, China
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Yun JS, Jung SH, Lee SN, Jung SM, Won HH, Kim D, Choi JA. Polygenic risk score-based phenome-wide association for glaucoma and its impact on disease susceptibility in two large biobanks. J Transl Med 2024; 22:355. [PMID: 38622600 PMCID: PMC11020996 DOI: 10.1186/s12967-024-05152-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2024] [Accepted: 04/01/2024] [Indexed: 04/17/2024] Open
Abstract
BACKGROUND Glaucoma is a leading cause of worldwide irreversible blindness. Considerable uncertainty remains regarding the association between a variety of phenotypes and the genetic risk of glaucoma, as well as the impact they exert on the glaucoma development. METHODS We investigated the associations of genetic liability for primary open angle glaucoma (POAG) with a wide range of potential risk factors and to assess its impact on the risk of incident glaucoma. The phenome-wide association study (PheWAS) approach was applied to determine the association of POAG polygenic risk score (PRS) with a wide range of phenotypes in 377, 852 participants from the UK Biobank study and 43,623 participants from the Penn Medicine Biobank study, all of European ancestry. Participants were stratified into four risk tiers: low, intermediate, high, and very high-risk. Cox proportional hazard models assessed the relationship of POAG PRS and ocular factors with new glaucoma events. RESULTS In both discovery and replication set in the PheWAS, a higher genetic predisposition to POAG was specifically correlated with ocular disease phenotypes. The POAG PRS exhibited correlations with low corneal hysteresis, refractive error, and ocular hypertension, demonstrating a strong association with the onset of glaucoma. Individuals carrying a high genetic burden exhibited a 9.20-fold, 11.88-fold, and 28.85-fold increase in glaucoma incidence when associated with low corneal hysteresis, high myopia, and elevated intraocular pressure, respectively. CONCLUSION Genetic susceptibility to POAG primarily influences ocular conditions, with limited systemic associations. Notably, the baseline polygenic risk for POAG robustly associates with new glaucoma events, revealing a large combined effect of genetic and ocular risk factors on glaucoma incidents.
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Affiliation(s)
- Jae-Seung Yun
- Department of Internal Medicine, St. Vincent's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - Sang-Hyuk Jung
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Su-Nam Lee
- Department of Internal Medicine, St. Vincent's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - Seung Min Jung
- Department of Internal Medicine, St. Vincent's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - Hong-Hee Won
- Samsung Advanced Institute for Health Sciences and Technology (SAIHST), Samsung Medical Center, Sungkyunkwan University, Seoul, Republic of Korea.
- Samsung Genome Institute, Samsung Medical Center, Seoul, Republic of Korea.
| | - Dokyoon Kim
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
- Institute for Biomedical Informatics, University of Pennsylvania, Philadelphia, PA, USA.
| | - Jin A Choi
- Department of Ophthalmology, College of Medicine, St. Vincent's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea.
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Yun HR, Koh HB, Park JT, Han SH, Kang SW, Yoo TH, Ahn SS. Presence of periodontal disease and the incidence of inflammatory arthritides in the general population: data from the UK Biobank. Rheumatology (Oxford) 2024; 63:1084-1092. [PMID: 37436715 DOI: 10.1093/rheumatology/kead345] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2023] [Revised: 06/11/2023] [Accepted: 06/28/2023] [Indexed: 07/13/2023] Open
Abstract
OBJECTIVES To investigate the association between periodontal disease and the development of inflammatory arthritides in the general population. METHODS In total, 489 125 participants from the UK Biobank without a previous history of RA, AS and PsA were enrolled. The primary outcome was the incidence of inflammatory arthritides, which was a composite of RA, AS and PsA according to the presence of periodontal disease based on self-reported oral health indicators. Multivariate Cox proportional hazard regression analyses using four different models were performed to assess the association between periodontal disease and inflammatory arthritides development. RESULTS In all, 86 905 and 402 220 individuals were categorized as with and without periodontal disease, respectively. Cox hazard analysis indicated that the presence of periodontal disease was an independent predictor of the occurrence of composite outcomes of inflammatory arthritides, which was also consistent for RA and AS. Significant associations were found to be consistent in the four Cox models and were replicated even when different criteria were used to define periodontal disease. Subgroup analyses indicated that periodontal disease was associated with an increased RA risk in those aged <60 years, and this risk was persistent for both male and female patients and for patients with seropositive/seronegative RA. CONCLUSION Self-reported periodontal disease is associated with inflammatory arthritides incidence in participants included in the UK Biobank, particularly for RA and AS. Higher clinical attention and optimal dental care in patients with signs of periodontal disease may be recommended for early disease detection and for reducing this risk.
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Affiliation(s)
- Hae-Ryong Yun
- Department of Internal Medicine, Yongin Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Hee Byung Koh
- Department of Internal Medicine, College of Medicine, Institute of Kidney Disease Research, Yonsei University, Seoul, Republic of Korea
| | - Jung Tak Park
- Department of Internal Medicine, College of Medicine, Institute of Kidney Disease Research, Yonsei University, Seoul, Republic of Korea
| | - Seung Hyeok Han
- Department of Internal Medicine, College of Medicine, Institute of Kidney Disease Research, Yonsei University, Seoul, Republic of Korea
| | - Shin-Wook Kang
- Department of Internal Medicine, College of Medicine, Institute of Kidney Disease Research, Yonsei University, Seoul, Republic of Korea
| | - Tae-Hyun Yoo
- Department of Internal Medicine, College of Medicine, Institute of Kidney Disease Research, Yonsei University, Seoul, Republic of Korea
| | - Sung Soo Ahn
- Division of Rheumatology, Department of Internal Medicine, Yongin Severance Hospital, Yonsei University College of Medicine, Yongin, Republic of Korea
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Sha F, Li H, Zhang L, Liang F. Evidence for Genetic Causal Relationships Between Multiple Immune-Mediated Inflammatory Diseases and Age-Related Macular Degeneration: A Univariable and Multivariable Mendelian Randomization Study. Ophthalmol Ther 2024; 13:955-967. [PMID: 38315350 PMCID: PMC10912070 DOI: 10.1007/s40123-024-00895-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Accepted: 01/19/2024] [Indexed: 02/07/2024] Open
Abstract
INTRODUCTION With the global aging population on the rise, age-related macular degeneration (AMD) poses a growing healthcare burden. Prior research hints at immune-mediated inflammatory diseases (IMIDs) potentially elevating AMD risk via diverse mechanisms. However, causality remains disputed as a result of confounding factors. Hence, our Mendelian randomization (MR) study aims to untangle this link, mitigating confounding effects to explore the IMID-AMD causal relationship. This study aims to investigate the causal relationship between IMIDs and AMD, providing new strategies for the prevention and treatment of AMD in clinical practice. METHODS This study was registered with PROSPERO, CRD42023469815. We obtained data on IMIDs and AMD from Genome-Wide Association Studies (GWAS) summary statistics and the FinnGen consortium. Rigorous selection steps were applied to screen for eligible instrumental single nucleotide polymorphisms (SNPs). We conducted univariate Mendelian randomization, inverse variance-weighted (IVW), weighted median, Mendelian randomization-Egger (MR-Egger), and multivariate Mendelian randomization (MVMR) analyses. Various sensitivity analysis methods were employed to assess pleiotropy and heterogeneity. The aim was to explore the causal relationships between IMIDs and AMD. RESULTS The MR analysis revealed that Crohn's disease (CD) (IVW: odd ratios (OR) 1.05, 95% CI (confidence interval) 1.01-1.10, p = 0.007), rheumatoid arthritis (RA) (IVW: OR 1.09, 95% CI 1.04-1.15, p = 0.0001), and type 1 diabetes (T1D) (IVW: OR 1.05, 95% CI 1.02-1.09, p = 0.001) were correlated with an elevated risk of AMD, while multiple sclerosis (MS) (IVW: OR 2.78E-18, 95% CI 2.23E-31 to 3.48E-05, p = 0.008) appeared to be protective against AMD. These findings were supported by an array of MR analysis methodologies and the MVMR approach. CONCLUSION Our study results, based on MR, provide genetic evidence indicating a causal relationship between specific IMIDs and AMD. CD, RA, and T1D are factors increasing the risk of AMD, while MS may have a protective effect.
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Affiliation(s)
- Fuhui Sha
- The First Teaching Hospital of Tianjin University of Traditional Chinese Medicine, Tianjin, China
- National Clinical Research Center for Chinese Medicine Acupuncture and Moxibustion, Tianjin, China
| | - Hongmei Li
- The First Teaching Hospital of Tianjin University of Traditional Chinese Medicine, Tianjin, China
- National Clinical Research Center for Chinese Medicine Acupuncture and Moxibustion, Tianjin, China
| | - Longyao Zhang
- The First Teaching Hospital of Tianjin University of Traditional Chinese Medicine, Tianjin, China
- National Clinical Research Center for Chinese Medicine Acupuncture and Moxibustion, Tianjin, China
| | - Fengming Liang
- Eye School of Chengdu, University of Traditional Chinese Medicine, Chengdu, Sichuan Province, China.
- Key Laboratory of Sichuan Province Ophthalmopathy Prevention and Cure and Visual Function Protection with Traditional Chinese Medicine Laboratory, Chengdu, Sichuan Province, China.
- Retinal Image Technology and Chronic Vascular Disease Prevention and Control and Collaborative Innovation Center, Chengdu, Sichuan Province, China.
- Ineye Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan Province, China.
- Sichuan Integrated Traditional Chinese and Western Medicine Myopia Prevention and Treatment Center, Sichuan Vision Protection Science Popularization Base, Chengdu, Sichuan Province, China.
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Kueper JK, Emu M, Banbury M, Bjerre LM, Choudhury S, Green M, Pimlott N, Slade S, Tsuei SH, Sisler J. Artificial intelligence for family medicine research in Canada: current state and future directions: Report of the CFPC AI Working Group. CANADIAN FAMILY PHYSICIAN MEDECIN DE FAMILLE CANADIEN 2024; 70:161-168. [PMID: 38499374 PMCID: PMC11280631 DOI: 10.46747/cfp.7003161] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/20/2024]
Abstract
OBJECTIVE To understand the current landscape of artificial intelligence (AI) for family medicine (FM) research in Canada, identify how the College of Family Physicians of Canada (CFPC) could support near-term positive progress in this field, and strengthen the community working in this field. COMPOSITION OF THE COMMITTEE Members of a scientific planning committee provided guidance alongside members of a CFPC staff advisory committee, led by the CFPC-AMS TechForward Fellow and including CFPC, FM, and AI leaders. METHODS This initiative included 2 projects. First, an environmental scan of published and gray literature on AI for FM produced between 2018 and 2022 was completed. Second, an invitational round table held in April 2022 brought together AI and FM experts and leaders to discuss priorities and to create a strategy for the future. REPORT The environmental scan identified research related to 5 major domains of application in FM (preventive care and risk profiling, physician decision support, operational efficiencies, patient self-management, and population health). Although there had been little testing or evaluation of AI-based tools in practice settings, progress since previous reviews has been made in engaging stakeholders to identify key considerations about AI for FM and opportunities in the field. The round-table discussions further emphasized barriers to and facilitators of high-quality research; they also indicated that while there is immense potential for AI to benefit FM practice, the current research trajectory needs to change, and greater support is needed to achieve these expected benefits and to avoid harm. CONCLUSION Ten candidate action items that the CFPC could adopt to support near-term positive progress in the field were identified, some of which an AI working group has begun pursuing. Candidate action items are roughly divided into avenues where the CFPC is well-suited to take a leadership role in tackling priority issues in AI for FM research and specific activities or initiatives the CFPC could complete. Strong FM leadership is needed to advance AI research that will contribute to positive transformation in FM.
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Affiliation(s)
- Jacqueline K Kueper
- Adjunct Research Professor in the Department of Epidemiology and Biostatistics in the Schulich School of Medicine and Dentistry at Western University in London, Ont
| | - Mahzabeen Emu
- Doctoral candidate in the School of Computing at Queen's University in Kingston, Ont
| | - Mark Banbury
- Executive Director, Information and Technology Services, at the College of Family Physicians of Canada
| | - Lise M Bjerre
- Associate Professor at the University of Ottawa in Ontario and Chair in Family Medicine at the Institut du Savoir Montfort in Ottawa
| | - Salimur Choudhury
- Associate Professor in the School of Computing at Queen's University
| | - Michael Green
- Professor in the Department of Family Medicine at Queen's University and President of the College of Family Physicians of Canada
| | - Nicholas Pimlott
- Professor in the Department of Family and Community Medicine in the Temerty Faculty of Medicine at the University of Toronto in Ontario and Editor of Canadian Family Physician
| | - Steve Slade
- Research Director at the College of Family Physicians of Canada
| | - Sian H Tsuei
- Clinical Assistant Professor in the Department of Family Practice at the University of British Columbia in Vancouver and Adjunct Professor in the Faculty of Health Sciences at Simon Fraser University in Victoria, BC
| | - Jeff Sisler
- Former Executive Director of Professional Development and Practice Support at the College of Family Physicians of Canada
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Liu J, Dong Y, Wang X, Sun H, Huang J, Tang Z, Sun H. Association of spontaneous abortion with bipolar disorder and major depression based on inverse probability treatment weighting of multigroup propensity scores: Evidence from the UK Biobank. J Affect Disord 2024; 347:453-462. [PMID: 38065472 DOI: 10.1016/j.jad.2023.12.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Revised: 11/20/2023] [Accepted: 12/02/2023] [Indexed: 01/08/2024]
Abstract
BACKGROUND Few studies have explored the association between the number of SAs and bipolar disorder and major depression (BDMD). This study aims to investigate the association between SA and BDMD, and the possible dose-response relationship between them. METHODS We conducted a cross-sectional study of 13,200 female UK Biobank participants. Participants were classified into BDMD and no-BDMD groups based on their BDMD status. The number of SAs was grouped into non-SA, occasional SA (OSA), and recurrent SA (RSA). Baseline characteristics of the three groups were balanced using inverse probability treatment weighting (IPTW) based on propensity scores. The three-knots restricted cubic spline regression model was utilized to assess the dose-response relationship between the number of SAs and BDMD. RESULTS The IPTW-adjusted multivariate logistic regression revealed that SA was an independent risk factor for BDMD, with adjusted OR of 1.12 (95 % CI: 1.07-1.19) and 1.32 (95 % CI: 1.25-1.40) in the OSA and RSA groups, respectively. The strength of this association amplified as the number of SAs (P for trend <0.001). There was a nonlinear relationship between the number of SAs and the risk of BDMD, with an approximately inverted L-shaped curve. LIMITATIONS The information of the SA and BDMD status relied on self-reported by volunteers, and the study sample was mostly of European descent. CONCLUSIONS Women who reported experiencing multiple SAs are more likely to have BDMD. Therefore, it is imperative to provide psychological care and interventions for women in the postpartum period.
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Affiliation(s)
- Jingfang Liu
- Department of Gynecology and Obstetrics, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu 215123, PR China
| | - Yongfei Dong
- Department of Biostatistics, School of Public Health, Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, MOE Key Laboratory of Geriatric Diseases and Immunology, Suzhou Medical College of Soochow University, Suzhou, Jiangsu 215123, PR China
| | - Xichao Wang
- Department of Biostatistics, School of Public Health, Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, MOE Key Laboratory of Geriatric Diseases and Immunology, Suzhou Medical College of Soochow University, Suzhou, Jiangsu 215123, PR China
| | - Hao Sun
- Department of Biostatistics, School of Public Health, Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, MOE Key Laboratory of Geriatric Diseases and Immunology, Suzhou Medical College of Soochow University, Suzhou, Jiangsu 215123, PR China
| | - Jie Huang
- Department of Biostatistics, School of Public Health, Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, MOE Key Laboratory of Geriatric Diseases and Immunology, Suzhou Medical College of Soochow University, Suzhou, Jiangsu 215123, PR China
| | - Zaixiang Tang
- Department of Biostatistics, School of Public Health, Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, MOE Key Laboratory of Geriatric Diseases and Immunology, Suzhou Medical College of Soochow University, Suzhou, Jiangsu 215123, PR China.
| | - Hongpeng Sun
- Department of Biostatistics, School of Public Health, Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, MOE Key Laboratory of Geriatric Diseases and Immunology, Suzhou Medical College of Soochow University, Suzhou, Jiangsu 215123, PR China.
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Jiang W, Tang Y, Yang R, Long Y, Sun C, Han T, Wei W. Maternal smoking, nutritional factors at different life stage, and the risk of incident type 2 diabetes: a prospective study of the UK Biobank. BMC Med 2024; 22:50. [PMID: 38302923 PMCID: PMC10835913 DOI: 10.1186/s12916-024-03256-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Accepted: 01/15/2024] [Indexed: 02/03/2024] Open
Abstract
BACKGROUND This study aims to investigate potential interactions between maternal smoking around birth (MSAB) and type 2 diabetes (T2D) pathway-specific genetic risks in relation to the development of T2D in offspring. Additionally, it seeks to determine whether and how nutritional factors during different life stages may modify the association between MSAB and risk of T2D. METHODS This study included 460,234 participants aged 40 to 69 years, who were initially free of T2D from the UK Biobank. MSAB and breastfeeding were collected by questionnaire. The Alternative health eating index(AHEI) and dietary inflammation index(DII) were calculated. The polygenic risk scores(PRS) of T2D and pathway-specific were established, including β-cell function, proinsulin, obesity, lipodystrophy, liver function and glycated haemoglobin(HbA1c). Cox proportion hazards models were performed to evaluate the gene/diet-MSAB interaction on T2D. The relative excess risk due to additive interaction (RERI) were calculated. RESULTS During a median follow-up period of 12.7 years, we identified 27,342 cases of incident T2D. After adjustment for potential confounders, participants exposed to MSAB had an increased risk of T2D (HR=1.11, 95%CI:1.08-1.14), and this association remained significant among the participants with breastfeeding (HR= HR=1.10, 95%CI: 1.06-1.14). Moreover, among the participants in the highest quartile of AHEI or in the lowest quartile of DII, the association between MSAB and the increased risk of T2D become non-significant (HR=0.94, 95%CI: 0.79-1.13 for AHEI; HR=1.09, 95%CI:0.99-1.20 for DII). Additionally, the association between MSAB and risk of T2D became non-significant among the participants with lower genetic risk of lipodystrophy (HR=1.06, 95%CI:0.99-1.14), and exposed to MSAB with a higher genetic risk for β-cell dysfunction or lipodystrophy additively elevated the risk of T2D(RERI=0.18, 95%CI:0.06-0.30 for β-cell function; RERI=0.16, 95%CI:0.04-0.28 for lipodystrophy). CONCLUSIONS This study indicates that maintaining a high dietary quality or lower dietary inflammation in diet may reduce the risk of T2D associated with MSAB, and the combination of higher genetic risk of β-cell dysfunction or lipodystrophy and MSAB significantly elevate the risk of T2D in offspring.
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Affiliation(s)
- Wenbo Jiang
- Key Laboratory of Precision Nutrition and Health, Ministry of Education, Department of Nutrition and Food Hygiene, the National Key Discipline, School of Public Health, Harbin Medical University, Harbin, P. R. China
- Department of Cardiology, the First Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Yiwei Tang
- Key Laboratory of Precision Nutrition and Health, Ministry of Education, Department of Nutrition and Food Hygiene, the National Key Discipline, School of Public Health, Harbin Medical University, Harbin, P. R. China
| | - Ruiming Yang
- Key Laboratory of Precision Nutrition and Health, Ministry of Education, Department of Nutrition and Food Hygiene, the National Key Discipline, School of Public Health, Harbin Medical University, Harbin, P. R. China
| | - Yujia Long
- Key Laboratory of Precision Nutrition and Health, Ministry of Education, Department of Nutrition and Food Hygiene, the National Key Discipline, School of Public Health, Harbin Medical University, Harbin, P. R. China
| | - Changhao Sun
- Key Laboratory of Precision Nutrition and Health, Ministry of Education, Department of Nutrition and Food Hygiene, the National Key Discipline, School of Public Health, Harbin Medical University, Harbin, P. R. China
| | - Tianshu Han
- Key Laboratory of Precision Nutrition and Health, Ministry of Education, Department of Nutrition and Food Hygiene, the National Key Discipline, School of Public Health, Harbin Medical University, Harbin, P. R. China.
| | - Wei Wei
- Key Laboratory of Precision Nutrition and Health, Ministry of Education, Department of Nutrition and Food Hygiene, the National Key Discipline, School of Public Health, Harbin Medical University, Harbin, P. R. China.
- Department of Pharmacology, College of Pharmacy Key Laboratory of Cardiovascular Research, Ministry of Education, Harbin Medical University, Harbin, P. R. China.
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Ma Z, Geng H, Yang H, Meng G, Gu Y, Wu H, Zhang S, Zhang J, Wang X, Huang T, Niu K. Adherence to a healthy sleep pattern and risk of urologic cancers: A large prospective cohort study. Prev Med 2024; 179:107844. [PMID: 38176446 DOI: 10.1016/j.ypmed.2023.107844] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/29/2023] [Revised: 11/28/2023] [Accepted: 12/28/2023] [Indexed: 01/06/2024]
Abstract
OBJECTIVE A variety of unhealthy sleep behaviors have been shown to be associated with an increased risk of urologic cancers. However, little is known about the association between the overall sleep patterns and urologic cancers. To prospectively investigate the associations between a healthy sleep pattern and the risks of urologic cancers, including bladder cancer (BCa) and renal cell carcinoma (RCC). METHODS In this prospective cohort study, 377,144 participants free of cancer at baseline were recruited from the UK Biobank. Data on sleep behaviors were collected through questionnaires at recruitment. The incident urologic cancer cases were determined through linkage to national cancer and death registries. We established a healthy sleep score according to five sleep traits (sleep duration, chronotype, insomnia, snoring, and daytime sleepiness). Cox proportional hazard regression models were used to calculate the adjusted hazard ratios and 95% confidence intervals to assess the relationship between the healthy sleep score and the risk of urologic cancers. RESULTS During a median of ≥9 years of follow-up, we identified 1986 incident urologic cancer cases, including 1272 BCa cases and 706 RCC cases. Compared with the participants with a poor sleep pattern (score of 0-2), the multivariable-adjusted hazard ratio and 95% confidence interval were 0.85 (0.75 to 0.96) for urologic cancers, 0.80 (0.68 to 0.93) for BCa, and 0.91 (0.74, 1.12) for RCC, respectively, for those with the healthier sleep pattern (score of 4-5). CONCLUSION Our results indicate that a healthy sleep pattern is associated with lower risks of urologic cancers.
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Affiliation(s)
- Zheng Ma
- Nutritional Epidemiology Institute and School of Public Health, Tianjin Medical University, Tianjin, China; School of Public Health of Tianjin University of Traditional Chinese Medicine, Tianjin, China; School of Integrative Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, China
| | - Hao Geng
- Nutritional Epidemiology Institute and School of Public Health, Tianjin Medical University, Tianjin, China; School of Public Health of Tianjin University of Traditional Chinese Medicine, Tianjin, China; School of Integrative Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, China
| | - Honghao Yang
- Nutritional Epidemiology Institute and School of Public Health, Tianjin Medical University, Tianjin, China; School of Public Health of Tianjin University of Traditional Chinese Medicine, Tianjin, China; School of Integrative Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, China
| | - Ge Meng
- Nutritional Epidemiology Institute and School of Public Health, Tianjin Medical University, Tianjin, China; Department of Toxicology and Health Inspection and Quarantine, School of Public Health, Tianjin Medical University, Tianjin, China.
| | - Yeqing Gu
- Institute of Radiation Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, China
| | - Hongmei Wu
- Nutritional Epidemiology Institute and School of Public Health, Tianjin Medical University, Tianjin, China; School of Public Health of Tianjin University of Traditional Chinese Medicine, Tianjin, China
| | - Shunming Zhang
- Nutritional Epidemiology Institute and School of Public Health, Tianjin Medical University, Tianjin, China; School of Public Health of Tianjin University of Traditional Chinese Medicine, Tianjin, China
| | - Juanjuan Zhang
- Nutritional Epidemiology Institute and School of Public Health, Tianjin Medical University, Tianjin, China; School of Public Health of Tianjin University of Traditional Chinese Medicine, Tianjin, China
| | - Xuena Wang
- Nutritional Epidemiology Institute and School of Public Health, Tianjin Medical University, Tianjin, China; School of Public Health of Tianjin University of Traditional Chinese Medicine, Tianjin, China
| | - Tao Huang
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing, China.
| | - Kaijun Niu
- Nutritional Epidemiology Institute and School of Public Health, Tianjin Medical University, Tianjin, China; School of Public Health of Tianjin University of Traditional Chinese Medicine, Tianjin, China; School of Integrative Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, China; Institute of Radiation Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, China.
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27
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Tian C, Ye Z, McCoy RG, Pan Y, Bi C, Gao S, Ma Y, Chen M, Yu J, Lu T, Hong LE, Kochunov P, Ma T, Chen S, Liu S. The causal effect of HbA1c on white matter brain aging by two-sample Mendelian randomization analysis. Front Neurosci 2024; 17:1335500. [PMID: 38274506 PMCID: PMC10808780 DOI: 10.3389/fnins.2023.1335500] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2023] [Accepted: 12/28/2023] [Indexed: 01/27/2024] Open
Abstract
Background Poor glycemic control with elevated levels of hemoglobin A1c (HbA1c) is associated with increased risk of cognitive impairment, with potentially varying effects between sexes. However, the causal impact of poor glycemic control on white matter brain aging in men and women is uncertain. Methods We used two nonoverlapping data sets from UK Biobank cohort: gene-outcome group (with neuroimaging data, (N = 15,193; males/females: 7,101/8,092)) and gene-exposure group (without neuroimaging data, (N = 279,011; males/females: 122,638/156,373)). HbA1c was considered the exposure and adjusted "brain age gap" (BAG) was calculated on fractional anisotropy (FA) obtained from brain imaging as the outcome, thereby representing the difference between predicted and chronological age. The causal effects of HbA1c on adjusted BAG were studied using the generalized inverse variance weighted (gen-IVW) and other sensitivity analysis methods, including Mendelian randomization (MR)-weighted median, MR-pleiotropy residual sum and outlier, MR-using mixture models, and leave-one-out analysis. Results We found that for every 6.75 mmol/mol increase in HbA1c, there was an increase of 0.49 (95% CI = 0.24, 0.74; p-value = 1.30 × 10-4) years in adjusted BAG. Subgroup analyses by sex and age revealed significant causal effects of HbA1c on adjusted BAG, specifically among men aged 60-73 (p-value = 2.37 × 10-8). Conclusion Poor glycemic control has a significant causal effect on brain aging, and is most pronounced among older men aged 60-73 years, which provides insights between glycemic control and the susceptibility to age-related neurodegenerative diseases.
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Affiliation(s)
- Cheng Tian
- Key Laboratory of Computing Power Network and Information Security, Ministry of Education, Shandong Computer Science Center (National Supercomputer Center in Jinan), Qilu University of Technology (Shandong Academy of Sciences), Jinan, China
- Shandong Engineering Research Center of Big Data Applied Technology, Faculty of Computer Science and Technology, Qilu University of Technology (Shandong Academy of Sciences), Jinan, China
- Shandong Provincial Key Laboratory of Computer Networks, Shandong Fundamental Research Center for Computer Science, Jinan, China
| | - Zhenyao Ye
- Maryland Psychiatric Research Center, Department of Psychiatry, School of Medicine, University of Maryland, Baltimore, MD, United States
- Division of Biostatistics and Bioinformatics, Department of Epidemiology and Public Health, School of Medicine, University of Maryland, Baltimore, MD, United States
| | - Rozalina G. McCoy
- Division of Endocrinology, Diabetes, & Nutrition, Department of Medicine, School of Medicine, University of Maryland, Baltimore, MD, United States
- University of Maryland Institute for Health Computing, Bethesda, MD, United States
| | - Yezhi Pan
- Division of Biostatistics and Bioinformatics, Department of Epidemiology and Public Health, School of Medicine, University of Maryland, Baltimore, MD, United States
| | - Chuan Bi
- Division of Biostatistics and Bioinformatics, Department of Epidemiology and Public Health, School of Medicine, University of Maryland, Baltimore, MD, United States
| | - Si Gao
- Division of Biostatistics and Bioinformatics, Department of Epidemiology and Public Health, School of Medicine, University of Maryland, Baltimore, MD, United States
| | - Yizhou Ma
- Division of Biostatistics and Bioinformatics, Department of Epidemiology and Public Health, School of Medicine, University of Maryland, Baltimore, MD, United States
| | - Mo Chen
- Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, United States
| | - Jiaao Yu
- Department of Mathematics, University of Maryland, College Park, MD, United States
| | - Tong Lu
- Department of Mathematics, University of Maryland, College Park, MD, United States
| | - L. Elliot Hong
- Maryland Psychiatric Research Center, Department of Psychiatry, School of Medicine, University of Maryland, Baltimore, MD, United States
| | - Peter Kochunov
- Maryland Psychiatric Research Center, Department of Psychiatry, School of Medicine, University of Maryland, Baltimore, MD, United States
| | - Tianzhou Ma
- Department of Epidemiology and Biostatistics, School of Public Health, University of Maryland, College Park, MD, United States
| | - Shuo Chen
- Maryland Psychiatric Research Center, Department of Psychiatry, School of Medicine, University of Maryland, Baltimore, MD, United States
- Division of Biostatistics and Bioinformatics, Department of Epidemiology and Public Health, School of Medicine, University of Maryland, Baltimore, MD, United States
| | - Song Liu
- Key Laboratory of Computing Power Network and Information Security, Ministry of Education, Shandong Computer Science Center (National Supercomputer Center in Jinan), Qilu University of Technology (Shandong Academy of Sciences), Jinan, China
- Shandong Engineering Research Center of Big Data Applied Technology, Faculty of Computer Science and Technology, Qilu University of Technology (Shandong Academy of Sciences), Jinan, China
- Shandong Provincial Key Laboratory of Computer Networks, Shandong Fundamental Research Center for Computer Science, Jinan, China
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28
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Allen NE, Lacey B, Lawlor DA, Pell JP, Gallacher J, Smeeth L, Elliott P, Matthews PM, Lyons RA, Whetton AD, Lucassen A, Hurles ME, Chapman M, Roddam AW, Fitzpatrick NK, Hansell AL, Hardy R, Marioni RE, O’Donnell VB, Williams J, Lindgren CM, Effingham M, Sellors J, Danesh J, Collins R. Prospective study design and data analysis in UK Biobank. Sci Transl Med 2024; 16:eadf4428. [PMID: 38198570 PMCID: PMC11127744 DOI: 10.1126/scitranslmed.adf4428] [Citation(s) in RCA: 41] [Impact Index Per Article: 41.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Accepted: 12/13/2023] [Indexed: 01/12/2024]
Abstract
Population-based prospective studies, such as UK Biobank, are valuable for generating and testing hypotheses about the potential causes of human disease. We describe how UK Biobank's study design, data access policies, and approaches to statistical analysis can help to minimize error and improve the interpretability of research findings, with implications for other population-based prospective studies being established worldwide.
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Affiliation(s)
- Naomi E Allen
- UK Biobank Ltd, Stockport, UK
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Ben Lacey
- UK Biobank Ltd, Stockport, UK
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Deborah A Lawlor
- Population Health Science, Bristol Medical School University of Bristol, Bristol, UK
- Medical Research Council Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
| | - Jill P Pell
- School of Health and Wellbeing, University of Glasgow, Scotland
| | - John Gallacher
- Department of Psychiatry, University of Oxford, Oxford, UK
- Dementias Platform UK, Department of Psychiatry, University of Oxford, Oxford, UK
| | - Liam Smeeth
- London School of Hygiene and Tropical Medicine, London, UK
| | - Paul Elliott
- MRC Centre for Environment and Health, School of Public Health, Imperial College London, London, UK
- NIHR Health Protection Research Unit in Chemical Radiation Threats and Hazards, Imperial College London, UK
| | - Paul M Matthews
- UK Dementia Research Centre Institute and Department of Brain Sciences, Imperial College London, London, UK
| | - Ronan A Lyons
- Population Data Science, Swansea University Medical School, Swansea, Wales
| | - Anthony D Whetton
- Veterinary Health Innovation Engine, University of Surrey, Guildford, UK
| | - Anneke Lucassen
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Faculty of Medicine, Southampton University, Southampton, UK
| | - Matthew E Hurles
- Wellcome Trust Sanger Institute, Wellcome Genome Campus, Cambridge, UK
| | | | | | | | - Anna L Hansell
- Centre for Environmental Health and Sustainability, University of Leicester, Leicester, UK
| | - Rebecca Hardy
- School of Sport, Exercise and Health Sciences, Loughborough University, Loughborough, UK
| | - Riccardo E Marioni
- Centre for Genomic and Experimental Medicine, University of Edinburgh, Edinburgh, Scotland
| | | | - Julie Williams
- UK Dementia Research Institute, Cardiff University, Cardiff, Wales
| | - Cecilia M Lindgren
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
| | | | | | - John Danesh
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK
- National Institute for Health Research Cambridge Biomedical Research Centre, University of Cambridge and Cambridge University Hospitals, Cambridge, UK
| | - Rory Collins
- UK Biobank Ltd, Stockport, UK
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
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29
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Kumar L, Kumar H, Samiullah F. Comment on: Association Between the AHA Life's Essential 8 Score and Incident All-Cause Dementia: A Prospective Cohort Study From UK Biobank. Curr Probl Cardiol 2024; 49:102077. [PMID: 37716541 DOI: 10.1016/j.cpcardiol.2023.102077] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Accepted: 09/11/2023] [Indexed: 09/18/2023]
Affiliation(s)
- Laksh Kumar
- Shaheed Mohtarma Benazir Bhutto Medical College Lyari, Karachi, Pakistan.
| | - Haresh Kumar
- Liaquat National Medical College, Karachi, Pakistan
| | - Fnu Samiullah
- Shaheed Mohtarma Benazir Bhutto Medical College Lyari, Karachi, Pakistan
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30
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Fan H, Han X, Shang X, Zhu Z, He M, Xu G, Chen Z, Deng R. Fruit and vegetable intake and the risk of cataract: insights from the UK Biobank study. Eye (Lond) 2023; 37:3234-3242. [PMID: 36973404 PMCID: PMC10564725 DOI: 10.1038/s41433-023-02498-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2022] [Revised: 02/15/2023] [Accepted: 03/07/2023] [Indexed: 03/29/2023] Open
Abstract
PURPOSE A prospective cohort study to investigate the association between fruit and vegetable (F&V) intake and the risk of cataract. METHODS We included 72,160 participants who were free of cataract at baseline from the UK Biobank. Frequency and type of F&V intake were assessed using a web-based 24 h dietary questionnaire from 2009 to 2012. Development of cataract during the follow-up was defined by self-report or hospital inpatient records up to 2021. Cox proportional regression models were used to estimate the association between F&V intake and incident cataract. RESULTS During a mean follow-up of 9.1 years, 5753 participants developed cataract with a corresponding incidence of 8.0%. After adjusting for multiple demographic, medical and lifestyle covariates, higher intake of F&V were associated with a lower risk of cataract (≥6.5 vs. <2 servings/week: hazards ratio [HR]: 0.82, 95% CI: 0.76 to 0.89; P < 0.0001). Regarding specific types, significant reduced risk of cataract was found for higher intake of legumes (P = 0.0016), tomatoes (≥5.2 vs. <1.8 servings/week: HR: 0.94, 95% CI: 0.88 to 1.00), and apple and pear (>7 vs. <3.5 servings/week: 0.89, 95% CI: 0.83 to 0.94; P < 0.0001), but not for cruciferous vegetables, green leafy vegetables, berry, citrus fruit or melon. Smokers were found to benefit more from F&V intake than former and never smokers. Men also could benefit more from higher vegetable intake than women. CONCLUSIONS More F&V intake, especially legumes, tomatoes, apple, and pear, was associated with a lower risk of cataract in this UK Biobank cohort.
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Affiliation(s)
- Huiya Fan
- Department of Ophthalmology, Huizhou Central People's Hospital, Huizhou, China
| | - Xiaotong Han
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, China
| | - Xianwen Shang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, China
| | - Zhuoting Zhu
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, China
| | - Mingguang He
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, China
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, Melbourne, VIC, Australia
- Ophthalmology, Department of Surgery, University of Melbourne, Melbourne, VIC, Australia
| | - Guihua Xu
- Department of Ophthalmology, Huizhou Central People's Hospital, Huizhou, China
| | - Zilin Chen
- Department of Ophthalmology, Huizhou Central People's Hospital, Huizhou, China
| | - Ruidong Deng
- Department of Ophthalmology, Huizhou Central People's Hospital, Huizhou, China.
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31
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Sun BB, Chiou J, Traylor M, Benner C, Hsu YH, Richardson TG, Surendran P, Mahajan A, Robins C, Vasquez-Grinnell SG, Hou L, Kvikstad EM, Burren OS, Davitte J, Ferber KL, Gillies CE, Hedman ÅK, Hu S, Lin T, Mikkilineni R, Pendergrass RK, Pickering C, Prins B, Baird D, Chen CY, Ward LD, Deaton AM, Welsh S, Willis CM, Lehner N, Arnold M, Wörheide MA, Suhre K, Kastenmüller G, Sethi A, Cule M, Raj A, Burkitt-Gray L, Melamud E, Black MH, Fauman EB, Howson JMM, Kang HM, McCarthy MI, Nioi P, Petrovski S, Scott RA, Smith EN, Szalma S, Waterworth DM, Mitnaul LJ, Szustakowski JD, Gibson BW, Miller MR, Whelan CD. Plasma proteomic associations with genetics and health in the UK Biobank. Nature 2023; 622:329-338. [PMID: 37794186 PMCID: PMC10567551 DOI: 10.1038/s41586-023-06592-6] [Citation(s) in RCA: 472] [Impact Index Per Article: 236.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2022] [Accepted: 08/31/2023] [Indexed: 10/06/2023]
Abstract
The Pharma Proteomics Project is a precompetitive biopharmaceutical consortium characterizing the plasma proteomic profiles of 54,219 UK Biobank participants. Here we provide a detailed summary of this initiative, including technical and biological validations, insights into proteomic disease signatures, and prediction modelling for various demographic and health indicators. We present comprehensive protein quantitative trait locus (pQTL) mapping of 2,923 proteins that identifies 14,287 primary genetic associations, of which 81% are previously undescribed, alongside ancestry-specific pQTL mapping in non-European individuals. The study provides an updated characterization of the genetic architecture of the plasma proteome, contextualized with projected pQTL discovery rates as sample sizes and proteomic assay coverages increase over time. We offer extensive insights into trans pQTLs across multiple biological domains, highlight genetic influences on ligand-receptor interactions and pathway perturbations across a diverse collection of cytokines and complement networks, and illustrate long-range epistatic effects of ABO blood group and FUT2 secretor status on proteins with gastrointestinal tissue-enriched expression. We demonstrate the utility of these data for drug discovery by extending the genetic proxied effects of protein targets, such as PCSK9, on additional endpoints, and disentangle specific genes and proteins perturbed at loci associated with COVID-19 susceptibility. This public-private partnership provides the scientific community with an open-access proteomics resource of considerable breadth and depth to help to elucidate the biological mechanisms underlying proteo-genomic discoveries and accelerate the development of biomarkers, predictive models and therapeutics1.
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Affiliation(s)
- Benjamin B Sun
- Translational Sciences, Research & Development, Biogen, Cambridge, MA, USA.
| | - Joshua Chiou
- Internal Medicine Research Unit, Worldwide Research, Development and Medical, Pfizer, Cambridge, MA, USA
| | - Matthew Traylor
- Human Genetics Centre of Excellence, Novo Nordisk Research Centre Oxford, Oxford, UK
| | | | | | - Tom G Richardson
- Human Genetics Centre of Excellence, Novo Nordisk Research Centre Oxford, Oxford, UK
- Genomic Sciences, GlaxoSmithKline, Stevenage, UK
| | | | | | - Chloe Robins
- Genomic Sciences, GlaxoSmithKline, Collegeville, PA, USA
| | | | - Liping Hou
- Population Analytics, Janssen Research & Development, Spring House, PA, USA
| | | | - Oliver S Burren
- Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK
| | | | - Kyle L Ferber
- Biostatistics, Research and Development, Biogen, Cambridge, MA, USA
| | | | - Åsa K Hedman
- External Science and Innovation Target Sciences, Worldwide Research, Development and Medical, Pfizer, Stockholm, Sweden
| | - Sile Hu
- Human Genetics Centre of Excellence, Novo Nordisk Research Centre Oxford, Oxford, UK
| | - Tinchi Lin
- Analytics and Data Sciences, Biogen, Cambridge, MA, USA
| | - Rajesh Mikkilineni
- Data Science Institute, Takeda Development Center Americas, Cambridge, MA, USA
| | | | | | - Bram Prins
- Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK
| | - Denis Baird
- Translational Sciences, Research & Development, Biogen, Cambridge, MA, USA
| | - Chia-Yen Chen
- Translational Sciences, Research & Development, Biogen, Cambridge, MA, USA
| | - Lucas D Ward
- Alnylam Human Genetics, Discovery & Translational Research, Alnylam Pharmaceuticals, Cambridge, MA, USA
| | - Aimee M Deaton
- Alnylam Human Genetics, Discovery & Translational Research, Alnylam Pharmaceuticals, Cambridge, MA, USA
| | | | - Carissa M Willis
- Alnylam Human Genetics, Discovery & Translational Research, Alnylam Pharmaceuticals, Cambridge, MA, USA
| | - Nick Lehner
- Institute of Computational Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Matthias Arnold
- Institute of Computational Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, USA
| | - Maria A Wörheide
- Institute of Computational Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Karsten Suhre
- Bioinformatics Core, Weill Cornell Medicine-Qatar, Doha, Qatar
| | - Gabi Kastenmüller
- Institute of Computational Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | | | | | - Anil Raj
- Calico Life Sciences, San Francisco, CA, USA
| | | | | | - Mary Helen Black
- Population Analytics, Janssen Research & Development, Spring House, PA, USA
| | - Eric B Fauman
- Internal Medicine Research Unit, Worldwide Research, Development and Medical, Pfizer, Cambridge, MA, USA
| | - Joanna M M Howson
- Human Genetics Centre of Excellence, Novo Nordisk Research Centre Oxford, Oxford, UK
| | | | | | - Paul Nioi
- Alnylam Human Genetics, Discovery & Translational Research, Alnylam Pharmaceuticals, Cambridge, MA, USA
| | - Slavé Petrovski
- Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK
- Department of Medicine, University of Melbourne, Austin Health, Melbourne, Victoria, Australia
| | | | - Erin N Smith
- Takeda Development Center Americas, San Diego, CA, USA
| | - Sándor Szalma
- Takeda Development Center Americas, San Diego, CA, USA
| | | | | | | | | | - Melissa R Miller
- Internal Medicine Research Unit, Worldwide Research, Development and Medical, Pfizer, Cambridge, MA, USA
| | - Christopher D Whelan
- Translational Sciences, Research & Development, Biogen, Cambridge, MA, USA.
- Neuroscience Data Science, Janssen Research & Development, Cambridge, MA, USA.
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Zhang L, Zhang C, Zhang J, Liu A, Wang P, Xu J. A Bidirectional Mendelian Randomization Study of Sarcopenia-Related Traits and Knee Osteoarthritis. Clin Interv Aging 2023; 18:1577-1586. [PMID: 37731961 PMCID: PMC10508245 DOI: 10.2147/cia.s424633] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2023] [Accepted: 09/03/2023] [Indexed: 09/22/2023] Open
Abstract
Background With the development of population aging worldwide, sarcopenia and knee osteoarthritis (KOA), two age-related diseases, will continue to impose increasing medical and economic burdens on the society. Previous studies have discovered an association between the two, but the causality remains controversial, and it is difficult to eliminate confounding factors. Therefore, a Mendelian randomization (MR) study was conducted to overcome these confounding factors and investigate the causal relationship between sarcopenia and KOA. Objective The present work focused on assessing the causality between KOA and sarcopenia, so as to provide new strategies to prevent and treat these two conditions in clinic. Methods We registered the title with PROSPERO (ID: CRD42023421096). The two-sample bidirectional MR analysis was conducted in two steps, with sarcopenia being the exposure whereas KOA being the outcome in the first step, and vice versa in the second step. Genome-wide association studies (GWAS) data on low hand-grip strength (n=256,523), walking pace (n=459,915), appendicular lean mass (ALM, n=450,243), and KOA (n=403,124) were obtained from the UK Biobank. Methods such as the inverse variance weighted (IVW) and weighted median were utilized for assessing the causality of KOA with sarcopenia, and sensitivity analyses were also conducted. Results In the main MR analysis using the IVW method, evidence suggested that low hand-grip strength, walking pace, and ALM had adverse effects on KOA (p-value 0.0001, odds ratio (OR) 1.4569, 95% confidence interval (CI) 1.2007-1.7677 for low hand-grip strength; p-value 0.0003, OR 1.1500, 95% CI 1.050-1.183 for ALM; p-value 5.29E-19, OR 0.0932, 95% CI 0.0553-0.1572 for walking pace). However, there was no causality of KOA with sarcopenia in the opposite direction. Conclusion Our study suggests an obvious unidirectional causality of KOA with sarcopenia, and supports the notion that patients with sarcopenia are more susceptible to the development of KOA.
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Affiliation(s)
- Longyao Zhang
- Orthopedics Department, the First Teaching Hospital of Tianjin University of Traditional Chinese Medicine, Tianjin, People’s Republic of China
- National Clinical Research Center for Chinese Medicine Acupuncture and Moxibustion, Tianjin, People’s Republic of China
| | - Chao Zhang
- Orthopedics Department, the First Teaching Hospital of Tianjin University of Traditional Chinese Medicine, Tianjin, People’s Republic of China
- National Clinical Research Center for Chinese Medicine Acupuncture and Moxibustion, Tianjin, People’s Republic of China
| | - Juntao Zhang
- Orthopedics Department, the First Teaching Hospital of Tianjin University of Traditional Chinese Medicine, Tianjin, People’s Republic of China
- National Clinical Research Center for Chinese Medicine Acupuncture and Moxibustion, Tianjin, People’s Republic of China
| | - Aifeng Liu
- Orthopedics Department, the First Teaching Hospital of Tianjin University of Traditional Chinese Medicine, Tianjin, People’s Republic of China
- National Clinical Research Center for Chinese Medicine Acupuncture and Moxibustion, Tianjin, People’s Republic of China
| | - Ping Wang
- Orthopedics Department, the First Teaching Hospital of Tianjin University of Traditional Chinese Medicine, Tianjin, People’s Republic of China
- National Clinical Research Center for Chinese Medicine Acupuncture and Moxibustion, Tianjin, People’s Republic of China
| | - Jiankang Xu
- Orthopedics Department, the First Teaching Hospital of Tianjin University of Traditional Chinese Medicine, Tianjin, People’s Republic of China
- National Clinical Research Center for Chinese Medicine Acupuncture and Moxibustion, Tianjin, People’s Republic of China
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Rodriguez Llorian E, Kopac N, Waliji LA, Borle K, Dragojlovic N, Elliott AM, Lynd LD. A Rapid Review on the Value of Biobanks Containing Genetic Information. VALUE IN HEALTH : THE JOURNAL OF THE INTERNATIONAL SOCIETY FOR PHARMACOECONOMICS AND OUTCOMES RESEARCH 2023; 26:1286-1295. [PMID: 36921900 DOI: 10.1016/j.jval.2023.02.017] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/10/2022] [Revised: 01/20/2023] [Accepted: 02/24/2023] [Indexed: 06/18/2023]
Abstract
OBJECTIVES Increasing access to health data through biobanks containing genetic information has the potential to expand the knowledge base and thereby improve screening, diagnosis, and treatment options for many diseases. Nevertheless, although privacy concerns and risks surrounding genetic data sharing are well documented, direct evidence in favor of the hypothesized benefits of data integration is scarce, which complicates decision making in this area. Therefore, the objective of this study is to summarize the available evidence on the research and clinical impacts of biobanks containing genetic information, so as to better understand how to quantify the value of expanding genomic data access. METHODS Using a rapid review methodology, we performed a search of MEDLINE/PubMed and Embase databases; and websites of biobanks and genomic initiatives published from 2010 to 2022. We classified findings into 11 indicators including outputs (a direct product of the biobank activities) and outcomes (changes in scientific and clinical capacity). RESULTS Of 8479 abstracts and 101 gray literature sources were reviewed, 96 records were included. Although most records did not report key indicators systematically, the available evidence concentrated on research indicators such as publications and gene-disorder association discoveries (63% of studies), followed by research infrastructure (26%), and clinical indicators (11%) such as supporting the diagnosis of individual patients. CONCLUSIONS Existing evidence on the benefits of biobanks is skewed toward easily quantifiable research outputs. Measuring a comprehensive set of outputs and outcomes inspired by value frameworks is necessary to generate better evidence on the benefits of genomic data sharing.
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Affiliation(s)
- Elisabet Rodriguez Llorian
- Collaboration for Outcomes Research and Evaluation (CORE), Faculty of Pharmaceutical Sciences, The University of British Columbia, Vancouver, British Columbia, Canada.
| | - Nicola Kopac
- Collaboration for Outcomes Research and Evaluation (CORE), Faculty of Pharmaceutical Sciences, The University of British Columbia, Vancouver, British Columbia, Canada
| | - Louloua Ashikhusein Waliji
- Collaboration for Outcomes Research and Evaluation (CORE), Faculty of Pharmaceutical Sciences, The University of British Columbia, Vancouver, British Columbia, Canada
| | - Kennedy Borle
- Collaboration for Outcomes Research and Evaluation (CORE), Faculty of Pharmaceutical Sciences, The University of British Columbia, Vancouver, British Columbia, Canada
| | - Nick Dragojlovic
- Collaboration for Outcomes Research and Evaluation (CORE), Faculty of Pharmaceutical Sciences, The University of British Columbia, Vancouver, British Columbia, Canada
| | - Alison M Elliott
- Department of Medical Genetics, The University of British Columbia, Vancouver, British Columbia, Canada
| | - Larry D Lynd
- Collaboration for Outcomes Research and Evaluation (CORE), Faculty of Pharmaceutical Sciences, The University of British Columbia, Vancouver, British Columbia, Canada; Centre for Health Evaluation and Outcome Sciences (CHÉOS), St. Paul's Hospital, Vancouver, BC, Canada
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Ye Z, Mo C, Liu S, Gao S, Feng L, Zhao B, Canida T, Wu YC, Hatch KS, Ma Y, Mitchell BD, Hong L, Kochunov P, Chen C, Zhao B, Chen S, Ma T. Deciphering the causal relationship between blood pressure and regional white matter integrity: A two-sample Mendelian randomization study. J Neurosci Res 2023; 101:1471-1483. [PMID: 37330925 PMCID: PMC10444533 DOI: 10.1002/jnr.25205] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2022] [Revised: 04/30/2023] [Accepted: 05/10/2023] [Indexed: 06/20/2023]
Abstract
Elevated arterial blood pressure (BP) is a common risk factor for cerebrovascular and cardiovascular diseases, but no causal relationship has been established between BP and cerebral white matter (WM) integrity. In this study, we performed a two-sample Mendelian randomization (MR) analysis with individual-level data by defining two nonoverlapping sets of European ancestry individuals (genetics-exposure set: N = 203,111; mean age = 56.71 years, genetics-outcome set: N = 16,156; mean age = 54.61 years) from UK Biobank to evaluate the causal effects of BP on regional WM integrity, measured by fractional anisotropy of diffusion tensor imaging. Two BP traits: systolic and diastolic blood pressure were used as exposures. Genetic variant was carefully selected as instrumental variable (IV) under the MR analysis assumptions. We existing large-scale genome-wide association study summary data for validation. The main method used was a generalized version of inverse-variance weight method while other MR methods were also applied for consistent findings. Two additional MR analyses were performed to exclude the possibility of reverse causality. We found significantly negative causal effects (FDR-adjusted p < .05; every 10 mmHg increase in BP leads to a decrease in FA value by .4% ~ 2%) of BP traits on a union set of 17 WM tracts, including brain regions related to cognitive function and memory. Our study extended the previous findings of association to causation for regional WM integrity, providing insights into the pathological processes of elevated BP that might chronically alter the brain microstructure in different regions.
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Affiliation(s)
- Zhenyao Ye
- Maryland Psychiatric Research Center, Department of Psychiatry, School of Medicine, University of Maryland, Baltimore, Maryland, United States of America
- Division of Biostatistics and Bioinformatics, Department of Epidemiology and Public Health, School of Medicine, University of Maryland, Baltimore, Maryland, United States of America
| | - Chen Mo
- Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Song Liu
- School of Computer Science and Technology, Qilu University of Technology (Shandong Academy of Sciences), Jinan, Shandong, China
| | - Si Gao
- Maryland Psychiatric Research Center, Department of Psychiatry, School of Medicine, University of Maryland, Baltimore, Maryland, United States of America
| | - Li Feng
- Department of Nutrition and Food Science, College of Agriculture & Natural Resources, University of Maryland, College Park, Maryland, United States of America
| | - Boao Zhao
- Department of Epidemiology and Biostatistics, School of Public Health, University of Maryland, College Park, Maryland, United States of America
| | - Travis Canida
- Department of Mathematics, The college of Computer, Mathematical, and Natural Sciences, University of Maryland, College Park, Maryland, United States of America
| | - Yu-Chia Wu
- Department of Epidemiology and Biostatistics, School of Public Health, University of Maryland, College Park, Maryland, United States of America
| | - Kathryn S Hatch
- Maryland Psychiatric Research Center, Department of Psychiatry, School of Medicine, University of Maryland, Baltimore, Maryland, United States of America
| | - Yizhou Ma
- Maryland Psychiatric Research Center, Department of Psychiatry, School of Medicine, University of Maryland, Baltimore, Maryland, United States of America
| | - Braxton D. Mitchell
- Department of Medicine, University of Maryland School of Medicine, Baltimore, Maryland, United States of America
| | - L.Elliot Hong
- Maryland Psychiatric Research Center, Department of Psychiatry, School of Medicine, University of Maryland, Baltimore, Maryland, United States of America
| | - Peter Kochunov
- Maryland Psychiatric Research Center, Department of Psychiatry, School of Medicine, University of Maryland, Baltimore, Maryland, United States of America
| | - Chixiang Chen
- Division of Biostatistics and Bioinformatics, Department of Epidemiology and Public Health, School of Medicine, University of Maryland, Baltimore, Maryland, United States of America
| | - Bingxin Zhao
- Department of Statistics, Purdue University, West Lafayette, Indiana, United States of America
| | - Shuo Chen
- Maryland Psychiatric Research Center, Department of Psychiatry, School of Medicine, University of Maryland, Baltimore, Maryland, United States of America
- Division of Biostatistics and Bioinformatics, Department of Epidemiology and Public Health, School of Medicine, University of Maryland, Baltimore, Maryland, United States of America
| | - Tianzhou Ma
- Department of Epidemiology and Biostatistics, School of Public Health, University of Maryland, College Park, Maryland, United States of America
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Cruz LA, Cooke Bailey JN, Crawford DC. Importance of Diversity in Precision Medicine: Generalizability of Genetic Associations Across Ancestry Groups Toward Better Identification of Disease Susceptibility Variants. Annu Rev Biomed Data Sci 2023; 6:339-356. [PMID: 37196357 PMCID: PMC10720270 DOI: 10.1146/annurev-biodatasci-122220-113250] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/19/2023]
Abstract
Genome-wide association studies (GWAS) revolutionized our understanding of common genetic variation and its impact on common human disease and traits. Developed and adopted in the mid-2000s, GWAS led to searchable genotype-phenotype catalogs and genome-wide datasets available for further data mining and analysis for the eventual development of translational applications. The GWAS revolution was swift and specific, including almost exclusively populations of European descent, to the neglect of the majority of the world's genetic diversity. In this narrative review, we recount the GWAS landscape of the early years that established a genotype-phenotype catalog that is now universally understood to be inadequate for a complete understanding of complex human genetics. We then describe approaches taken to augment the genotype-phenotype catalog, including the study populations, collaborative consortia, and study design approaches aimed to generalize and then ultimately discover genome-wide associations in non-European descent populations. The collaborations and data resources established in the efforts to diversify genomic findings undoubtedly provide the foundations of the next chapters of genetic association studies with the advent of budget-friendly whole-genome sequencing.
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Affiliation(s)
- Lauren A Cruz
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, Ohio, USA;
- Cleveland Institute for Computational Biology, Case Western Reserve University, Cleveland, Ohio, USA
| | - Jessica N Cooke Bailey
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, Ohio, USA;
- Department of Genetics and Genome Sciences, Case Western Reserve University, Cleveland, Ohio, USA
- Cleveland Institute for Computational Biology, Case Western Reserve University, Cleveland, Ohio, USA
| | - Dana C Crawford
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, Ohio, USA;
- Department of Genetics and Genome Sciences, Case Western Reserve University, Cleveland, Ohio, USA
- Cleveland Institute for Computational Biology, Case Western Reserve University, Cleveland, Ohio, USA
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Lyall LM, Sangha N, Zhu X, Lyall DM, Ward J, Strawbridge RJ, Cullen B, Smith DJ. Subjective and objective sleep and circadian parameters as predictors of depression-related outcomes: A machine learning approach in UK Biobank. J Affect Disord 2023; 335:83-94. [PMID: 37156273 DOI: 10.1016/j.jad.2023.04.138] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Revised: 04/25/2023] [Accepted: 04/29/2023] [Indexed: 05/10/2023]
Abstract
BACKGROUND Sleep and circadian disruption are associated with depression onset and severity, but it is unclear which features (e.g., sleep duration, chronotype) are important and whether they can identify individuals showing poorer outcomes. METHODS Within a subset of the UK Biobank with actigraphy and mental health data (n = 64,353), penalised regression identified the most useful of 51 sleep/rest-activity predictors of depression-related outcomes; including case-control (Major Depression (MD) vs. controls; postnatal depression vs. controls) and within-case comparisons (severe vs. moderate MD; early vs. later onset, atypical vs. typical symptoms; comorbid anxiety; suicidality). Best models (of lasso, ridge, and elastic net) were selected based on Area Under the Curve (AUC). RESULTS For MD vs. controls (n(MD) = 24,229; n(control) = 40,124), lasso AUC was 0.68, 95 % confidence interval (CI) 0.67-0.69. Discrimination was reasonable for atypical vs. typical symptoms (n(atypical) = 958; n(typical) = 18,722; ridge: AUC 0.74, 95 % CI 0.71-0.77) but poor for remaining models (AUCs 0.59-0.67). Key predictors across most models included: difficulty getting up, insomnia symptoms, snoring, actigraphy-measured daytime inactivity and lower morning activity (~8 am). In a distinct subset (n = 310,718), the number of these factors shown was associated with all depression outcomes. LIMITATIONS Analyses were cross-sectional and in middle-/older aged adults: comparison with longitudinal investigations and younger cohorts is necessary. DISCUSSION Sleep and circadian measures alone provided poor to moderate discrimination of depression outcomes, but several characteristics were identified that may be clinically useful. Future work should assess these features alongside broader sociodemographic, lifestyle and genetic features.
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Affiliation(s)
- Laura M Lyall
- School of Health and Wellbeing, University of Glasgow, Glasgow, UK; Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK.
| | - Natasha Sangha
- School of Health and Wellbeing, University of Glasgow, Glasgow, UK; Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - Xingxing Zhu
- School of Health and Wellbeing, University of Glasgow, Glasgow, UK
| | - Donald M Lyall
- School of Health and Wellbeing, University of Glasgow, Glasgow, UK
| | - Joey Ward
- School of Health and Wellbeing, University of Glasgow, Glasgow, UK
| | - Rona J Strawbridge
- School of Health and Wellbeing, University of Glasgow, Glasgow, UK; Health Data Research, UK; Cardiovascular Medicine Unit, Department of Medicine Solna, Karolinska Institute, Stockholm, Sweden
| | - Breda Cullen
- School of Health and Wellbeing, University of Glasgow, Glasgow, UK
| | - Daniel J Smith
- School of Health and Wellbeing, University of Glasgow, Glasgow, UK; Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
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Xia W, Basford M, Carroll R, Clayton EW, Harris P, Kantacioglu M, Liu Y, Nyemba S, Vorobeychik Y, Wan Z, Malin BA. Managing re-identification risks while providing access to the All of Us research program. J Am Med Inform Assoc 2023; 30:907-914. [PMID: 36809550 PMCID: PMC10114067 DOI: 10.1093/jamia/ocad021] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2022] [Revised: 01/23/2023] [Accepted: 02/09/2023] [Indexed: 02/23/2023] Open
Abstract
OBJECTIVE The All of Us Research Program makes individual-level data available to researchers while protecting the participants' privacy. This article describes the protections embedded in the multistep access process, with a particular focus on how the data was transformed to meet generally accepted re-identification risk levels. METHODS At the time of the study, the resource consisted of 329 084 participants. Systematic amendments were applied to the data to mitigate re-identification risk (eg, generalization of geographic regions, suppression of public events, and randomization of dates). We computed the re-identification risk for each participant using a state-of-the-art adversarial model specifically assuming that it is known that someone is a participant in the program. We confirmed the expected risk is no greater than 0.09, a threshold that is consistent with guidelines from various US state and federal agencies. We further investigated how risk varied as a function of participant demographics. RESULTS The results indicated that 95th percentile of the re-identification risk of all the participants is below current thresholds. At the same time, we observed that risk levels were higher for certain race, ethnic, and genders. CONCLUSIONS While the re-identification risk was sufficiently low, this does not imply that the system is devoid of risk. Rather, All of Us uses a multipronged data protection strategy that includes strong authentication practices, active monitoring of data misuse, and penalization mechanisms for users who violate terms of service.
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Affiliation(s)
- Weiyi Xia
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Melissa Basford
- Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Robert Carroll
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Ellen Wright Clayton
- Law School, Vanderbilt University, Nashville, Tennessee, USA
- Department of Pediatrics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- Department of Health Policy, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Paul Harris
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee, USA
| | - Murat Kantacioglu
- Department of Computer Science, University of Texas at Dallas, Dallas, Texas, USA
| | - Yongtai Liu
- Department of Computer Science, Vanderbilt University, Nashville, Tennessee, USA
| | - Steve Nyemba
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Yevgeniy Vorobeychik
- Department of Computer Science and Engineering, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Zhiyu Wan
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Bradley A Malin
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- Department of Computer Science, Vanderbilt University, Nashville, Tennessee, USA
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
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Mo C, Wang J, Ye Z, Ke H, Liu S, Hatch K, Gao S, Magidson J, Chen C, Mitchell BD, Kochunov P, Hong LE, Ma T, Chen S. Evaluating the causal effect of tobacco smoking on white matter brain aging: a two-sample Mendelian randomization analysis in UK Biobank. Addiction 2023; 118:739-749. [PMID: 36401354 PMCID: PMC10443605 DOI: 10.1111/add.16088] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Accepted: 11/07/2022] [Indexed: 11/21/2022]
Abstract
BACKGROUND AND AIMS Tobacco smoking is a risk factor for impaired brain function, but its causal effect on white matter brain aging remains unclear. This study aimed to measure the causal effect of tobacco smoking on white matter brain aging. DESIGN Mendelian randomization (MR) analysis using two non-overlapping data sets (with and without neuroimaging data) from UK Biobank (UKB). The group exposed to smoking and control group consisted of current smokers and never smokers, respectively. Our main method was generalized weighted linear regression with other methods also included as sensitivity analysis. SETTING United Kingdom. PARTICIPANTS The study cohort included 23 624 subjects [10 665 males and 12 959 females with a mean age of 54.18 years, 95% confidence interval (CI) = 54.08, 54.28]. MEASUREMENTS Genetic variants were selected as instrumental variables under the MR analysis assumptions: (1) associated with the exposure; (2) influenced outcome only via exposure; and (3) not associated with confounders. The exposure smoking status (current versus never smokers) was measured by questionnaires at the initial visit (2006-10). The other exposure, cigarettes per day (CPD), measured the average number of cigarettes smoked per day for current tobacco users over the life-time. The outcome was the 'brain age gap' (BAG), the difference between predicted brain age and chronological age, computed by training machine learning model on a non-overlapping set of never smokers. FINDINGS The estimated BAG had a mean of 0.10 (95% CI = 0.06, 0.14) years. The MR analysis showed evidence of positive causal effect of smoking behaviors on BAG: the effect of smoking is 0.21 (in years, 95% CI = 6.5 × 10-3 , 0.41; P-value = 0.04), and the effect of CPD is 0.16 year/cigarette (UKB: 95% CI = 0.06, 0.26; P-value = 1.3 × 10-3 ; GSCAN: 95% CI = 0.02, 0.31; P-value = 0.03). The sensitivity analyses showed consistent results. CONCLUSIONS There appears to be a significant causal effect of smoking on the brain age gap, which suggests that smoking prevention can be an effective intervention for accelerated brain aging and the age-related decline in cognitive function.
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Affiliation(s)
- Chen Mo
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Jingtao Wang
- Department of Hematology, Qilu Hospital of Shandong University, Jinan, Shandong, China
| | - Zhenyao Ye
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Hongjie Ke
- Department of Mathematics, University of Maryland, College Park, MD, USA
| | - Song Liu
- School of Computer Science and Technology, Qilu University of Technology (Shandong Academy of Sciences), Jinan, Shandong, China
| | - Kathryn Hatch
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Si Gao
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Jessica Magidson
- Department of Psychology, University of Maryland, College Park, MD, USA
| | - Chixiang Chen
- Division of Biostatistics and Bioinformatics, Department of Epidemiology and Public Health, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Braxton D. Mitchell
- Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Peter Kochunov
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA
| | - L. Elliot Hong
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Tianzhou Ma
- Department of Epidemiology and Biostatistics, School of Public Health, University of Maryland, College Park, MD, USA
| | - Shuo Chen
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA
- Division of Biostatistics and Bioinformatics, Department of Epidemiology and Public Health, University of Maryland School of Medicine, Baltimore, MD, USA
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Huang BH, Del Pozo Cruz B, Teixeira-Pinto A, Cistulli PA, Stamatakis E. Influence of poor sleep on cardiovascular disease-free life expectancy: a multi-resource-based population cohort study. BMC Med 2023; 21:75. [PMID: 36859313 PMCID: PMC9979412 DOI: 10.1186/s12916-023-02732-x] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/25/2022] [Accepted: 01/10/2023] [Indexed: 03/03/2023] Open
Abstract
BACKGROUND The complexity of sleep hinders the formulation of sleep guidelines. Recent studies suggest that different unhealthy sleep characteristics jointly increase the risks for cardiovascular disease (CVD). This study aimed to estimate the differences in CVD-free life expectancy between people with different sleep profiles. METHODS We included 308,683 middle-aged adults from the UK Biobank among whom 140,181 had primary care data linkage. We used an established composite sleep score comprising self-reported chronotype, duration, insomnia complaints, snoring, and daytime sleepiness to derive three sleep categories: poor, intermediate, and healthy. We also identified three clinical sleep disorders captured by primary care and inpatient records within 2 years before enrollment in the cohort: insomnia, sleep-related breathing disorders, and other sleep disorders. We estimated sex-specific CVD-free life expectancy with three-state Markov models conditioning on survival at age 40 across different sleep profiles and clinical disorders. RESULTS We observed a gradual loss in CVD-free life expectancy toward poor sleep such as, compared with healthy sleepers, poor sleepers lost 1.80 [95% CI 0.96-2.75] and 2.31 [1.46-3.29] CVD-free years in females and males, respectively, while intermediate sleepers lost 0.48 [0.41-0.55] and 0.55 [0.49-0.61] years. Among men, those with clinical insomnia or sleep-related breathing disorders lost CVD-free life by 3.84 [0.61-8.59] or 6.73 [5.31-8.48] years, respectively. Among women, sleep-related breathing disorders or other sleep disorders were associated with 7.32 [5.33-10.34] or 1.43 [0.20-3.29] years lost, respectively. CONCLUSIONS Both self-reported and doctor-diagnosed poor sleep are negatively associated with CVD-free life, especially pronounced in participants with sleep-related breathing disorders.
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Affiliation(s)
- Bo-Huei Huang
- Charles Perkins Centre, School of Health Sciences, Faculty of Medicine and Health, the University of Sydney, Camperdown, Australia
| | - Borja Del Pozo Cruz
- Centre for Active and Healthy Ageing, Department of Sports Science and Clinical Biomechanics, University of Southern Denmark, Odense, Denmark
| | - Armando Teixeira-Pinto
- Sydney School of Public Health, Faculty of Medicine and Health, the University of Sydney, Camperdown, Australia
| | - Peter A Cistulli
- Charles Perkins Centre, School of Health Sciences, Faculty of Medicine and Health, the University of Sydney, Camperdown, Australia.,Charles Perkins Centre, Northern Clinical School, Faculty of Medicine and Health, University of Sydney, Sydney, Australia
| | - Emmanuel Stamatakis
- Charles Perkins Centre, School of Health Sciences, Faculty of Medicine and Health, the University of Sydney, Camperdown, Australia.
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Association of glaucoma and lifestyle with incident cardiovascular disease: a longitudinal prospective study from UK Biobank. Sci Rep 2023; 13:2712. [PMID: 36792671 PMCID: PMC9931750 DOI: 10.1038/s41598-023-29613-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Accepted: 02/07/2023] [Indexed: 02/17/2023] Open
Abstract
The shared pathophysiological features of the cerebrovascular disease (CVD) and glaucoma suggest an association between the two diseases. Using the prospective UK Biobank cohort, we examined the associations between glaucoma and incident CVD and assessed the extent to which a healthy lifestyle reduced the CVD risk in subjects with glaucoma, using a scoring system consisting of four factors: current smoking, obesity, regular physical activity, and a healthy diet. During a mean follow-up time of 8.9 years, 22,649 (4.9%) incident CVD cases were documented. Multivariable Cox regression analyses revealed that subjects with glaucoma were significantly more likely to exhibit incident CVD (hazard ratio [HR]:1.19, 95% confidence interval [CI] 1.03-1.37; p = 0.016) than controls. In the further subgroup analyses, glaucoma increased incident CVD risk both in the young (40-55 years) and the old (56-70 years) and in both sexes, with higher risk in the young (HR: 1.33, CI 1.02-1.74) and female subjects (HR: 1.32, CI 1.14-1.52). When we analyze the associations between glaucoma and incident CVD by lifestyle factors, the highest absolute risks were observed in individuals with both glaucoma and an unhealthy lifestyle (HR: 2.66, CI 2.22-3.19). In conclusion, glaucoma was an independent risk factor for incident CVD. A healthy lifestyle was associated with a substantially lower risk for CVD incidence among adults with glaucoma.
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Conroy MC, Lacey B, Bešević J, Omiyale W, Feng Q, Effingham M, Sellers J, Sheard S, Pancholi M, Gregory G, Busby J, Collins R, Allen NE. UK Biobank: a globally important resource for cancer research. Br J Cancer 2023; 128:519-527. [PMID: 36402876 PMCID: PMC9938115 DOI: 10.1038/s41416-022-02053-5] [Citation(s) in RCA: 64] [Impact Index Per Article: 32.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Revised: 10/26/2022] [Accepted: 10/27/2022] [Indexed: 11/21/2022] Open
Abstract
UK Biobank is a large-scale prospective study with deep phenotyping and genomic data. Its open-access policy allows researchers worldwide, from academia or industry, to perform health research in the public interest. Between 2006 and 2010, the study recruited 502,000 adults aged 40-69 years from the general population of the United Kingdom. At enrolment, participants provided information on a wide range of factors, physical measurements were taken, and biological samples (blood, urine and saliva) were collected for long-term storage. Participants have now been followed up for over a decade with more than 52,000 incident cancer cases recorded. The study continues to be enhanced with repeat assessments, web-based questionnaires, multi-modal imaging, and conversion of the stored biological samples to genomic and other '-omic' data. The study has already demonstrated its value in enabling research into the determinants of cancer, and future planned enhancements will make the resource even more valuable to cancer researchers. Over 26,000 researchers worldwide are currently using the data, performing a wide range of cancer research. UK Biobank is uniquely placed to transform our understanding of the causes of cancer development and progression, and drive improvements in cancer treatment and prevention over the coming decades.
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Affiliation(s)
- Megan C Conroy
- Nuffield Department of Population Health (NDPH), University of Oxford, Oxford, UK.
| | - Ben Lacey
- Nuffield Department of Population Health (NDPH), University of Oxford, Oxford, UK
| | - Jelena Bešević
- Nuffield Department of Population Health (NDPH), University of Oxford, Oxford, UK
| | - Wemimo Omiyale
- Nuffield Department of Population Health (NDPH), University of Oxford, Oxford, UK
| | - Qi Feng
- Nuffield Department of Population Health (NDPH), University of Oxford, Oxford, UK
| | | | | | | | | | | | - John Busby
- UK Biobank, Stockport, Greater Manchester, UK
| | - Rory Collins
- Nuffield Department of Population Health (NDPH), University of Oxford, Oxford, UK
- UK Biobank, Stockport, Greater Manchester, UK
| | - Naomi E Allen
- Nuffield Department of Population Health (NDPH), University of Oxford, Oxford, UK
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Sathyanarayanan A, Mueller TT, Ali Moni M, Schueler K, Baune BT, Lio P, Mehta D, Baune BT, Dierssen M, Ebert B, Fabbri C, Fusar-Poli P, Gennarelli M, Harmer C, Howes OD, Janzing JGE, Lio P, Maron E, Mehta D, Minelli A, Nonell L, Pisanu C, Potier MC, Rybakowski F, Serretti A, Squassina A, Stacey D, van Westrhenen R, Xicota L. Multi-omics data integration methods and their applications in psychiatric disorders. Eur Neuropsychopharmacol 2023; 69:26-46. [PMID: 36706689 DOI: 10.1016/j.euroneuro.2023.01.001] [Citation(s) in RCA: 28] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/03/2022] [Revised: 11/22/2022] [Accepted: 01/02/2023] [Indexed: 01/27/2023]
Abstract
To study mental illness and health, in the past researchers have often broken down their complexity into individual subsystems (e.g., genomics, transcriptomics, proteomics, clinical data) and explored the components independently. Technological advancements and decreasing costs of high throughput sequencing has led to an unprecedented increase in data generation. Furthermore, over the years it has become increasingly clear that these subsystems do not act in isolation but instead interact with each other to drive mental illness and health. Consequently, individual subsystems are now analysed jointly to promote a holistic understanding of the underlying biological complexity of health and disease. Complementing the increasing data availability, current research is geared towards developing novel methods that can efficiently combine the information rich multi-omics data to discover biologically meaningful biomarkers for diagnosis, treatment, and prognosis. However, clinical translation of the research is still challenging. In this review, we summarise conventional and state-of-the-art statistical and machine learning approaches for discovery of biomarker, diagnosis, as well as outcome and treatment response prediction through integrating multi-omics and clinical data. In addition, we describe the role of biological model systems and in silico multi-omics model designs in clinical translation of psychiatric research from bench to bedside. Finally, we discuss the current challenges and explore the application of multi-omics integration in future psychiatric research. The review provides a structured overview and latest updates in the field of multi-omics in psychiatry.
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Affiliation(s)
- Anita Sathyanarayanan
- Queensland University of Technology, Centre for Genomics and Personalised Health, School of Biomedical Sciences, Faculty of Health, Kelvin Grove, Queensland 4059, Australia
| | - Tamara T Mueller
- Institute for Artificial Intelligence and Informatics in Medicine, TU Munich, 80333 Munich, Germany
| | - Mohammad Ali Moni
- Artificial Intelligence and Digital Health Data Science, School of Health and Rehabilitation Sciences, Faculty of Health and Behavioural Sciences, The University of Queensland, St Lucia, QLD, 4072, Australia
| | - Katja Schueler
- Clinic for Psychosomatics, Hospital zum Heiligen Geist, Frankfurt am Main, Germany; Frankfurt Psychoanalytic Institute, Frankfurt am Main, Germany
| | - Bernhard T Baune
- Department of Psychiatry and Psychotherapy, University of Münster, Germany; Department of Psychiatry, Melbourne Medical School, University of Melbourne, Australia; The Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Australia
| | - Pietro Lio
- Department of Computer Science and Technology, University of Cambridge, Cambridge, United Kingdom
| | - Divya Mehta
- Queensland University of Technology, Centre for Genomics and Personalised Health, School of Biomedical Sciences, Faculty of Health, Kelvin Grove, Queensland 4059, Australia.
| | | | - Bernhard T Baune
- Department of Psychiatry and Psychotherapy, University of Münster, Germany; Department of Psychiatry, Melbourne Medical School, University of Melbourne, Australia; The Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Australia
| | - Mara Dierssen
- Center for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology; Universitat Pompeu Fabra (UPF), Barcelona, Spain
| | - Bjarke Ebert
- Medical Strategy & Communication, H. Lundbeck A/S, Valby, Denmark
| | - Chiara Fabbri
- Department of Biomedical and NeuroMotor Sciences, University of Bologna, Bologna, Italy; Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Paolo Fusar-Poli
- Early Psychosis: Intervention and Clinical-detection (EPIC) Lab, Department of Psychosis Studies, King's College London, United Kingdom; Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
| | - Massimo Gennarelli
- Department of Molecular and Translational Medicine, University of Brescia; Genetics Unit, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | | | - Oliver D Howes
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom; Psychiatric Imaging, Medical Research Council Clinical Sciences Centre, Imperial College London, Hammersmith Hospital Campus, London, United Kingdom
| | | | - Pietro Lio
- Department of Computer Science and Technology, University of Cambridge, Cambridge, United Kingdom
| | - Eduard Maron
- Department of Psychiatry, University of Tartu, Tartu, Estonia; Centre for Neuropsychopharmacology, Division of Brain Sciences, Imperial College London, London, United Kingdom; Documental Ltd, Tallin, Estonia; West Tallinn Central Hospital, Tallinn, Estonia
| | - Divya Mehta
- Queensland University of Technology, Centre for Genomics and Personalised Health, School of Biomedical Sciences, Faculty of Health, Kelvin Grove, Queensland 4059, Australia
| | - Alessandra Minelli
- Department of Molecular and Translational Medicine, University of Brescia; Genetics Unit, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Lara Nonell
- MARGenomics, IMIM (Hospital del Mar Research Institute), Barcelona, Spain
| | - Claudia Pisanu
- Department of Biomedical Sciences, Section of Neuroscience and Clinical Pharmacology, University of Cagliari, Cagliari, Italy
| | | | - Filip Rybakowski
- Department of Psychiatry, Poznan University of Medical Sciences, Poznan, Poland
| | - Alessandro Serretti
- Department of Biomedical and NeuroMotor Sciences, University of Bologna, Bologna, Italy
| | - Alessio Squassina
- Department of Biomedical Sciences, Section of Neuroscience and Clinical Pharmacology, University of Cagliari, Cagliari, Italy
| | - David Stacey
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom
| | - Roos van Westrhenen
- Parnassia Psychiatric Institute, Amsterdam, the Netherlands; Department of Psychiatry and Neuropsychology, Faculty of Health and Sciences, Maastricht University, Maastricht, the Netherlands; Institute of Psychiatry, Psychology & Neuroscience (IoPPN) King's College London, United Kingdom
| | - Laura Xicota
- Paris Brain Institute ICM, Salpetriere Hospital, Paris, France
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Venkatesh SS, Ganjgahi H, Palmer DS, Coley K, Wittemans LBL, Nellaker C, Holmes C, Lindgren CM, Nicholson G. The genetic architecture of changes in adiposity during adulthood. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.01.09.23284364. [PMID: 36711652 PMCID: PMC9882550 DOI: 10.1101/2023.01.09.23284364] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
Abstract
Obesity is a heritable disease, characterised by excess adiposity that is measured by body mass index (BMI). While over 1,000 genetic loci are associated with BMI, less is known about the genetic contribution to adiposity trajectories over adulthood. We derive adiposity-change phenotypes from 1.5 million primary-care health records in over 177,000 individuals in UK Biobank to study the genetic architecture of weight-change. Using multiple BMI measurements over time increases power to identify genetic factors affecting baseline BMI. In the largest reported genome-wide study of adiposity-change in adulthood, we identify novel associations with BMI-change at six independent loci, including rs429358 (a missense variant in APOE). The SNP-based heritability of BMI-change (1.98%) is 9-fold lower than that of BMI, and higher in women than in men. The modest genetic correlation between BMI-change and BMI (45.2%) indicates that genetic studies of longitudinal trajectories could uncover novel biology driving quantitative trait values in adulthood.
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Affiliation(s)
- Samvida S. Venkatesh
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, UK
- Big Data Institute at the Li Ka Shing Centre for Health Information and Discovery, University of Oxford, UK
| | | | - Duncan S. Palmer
- Big Data Institute at the Li Ka Shing Centre for Health Information and Discovery, University of Oxford, UK
- Nuffield Department of Women’s and Reproductive Health, Medical Sciences Division, University of Oxford, UK
| | - Kayesha Coley
- Department of Population Health Sciences, University of Leicester, UK
| | - Laura B. L. Wittemans
- Big Data Institute at the Li Ka Shing Centre for Health Information and Discovery, University of Oxford, UK
- Nuffield Department of Women’s and Reproductive Health, Medical Sciences Division, University of Oxford, UK
| | - Christoffer Nellaker
- Big Data Institute at the Li Ka Shing Centre for Health Information and Discovery, University of Oxford, UK
- Nuffield Department of Women’s and Reproductive Health, Medical Sciences Division, University of Oxford, UK
| | - Chris Holmes
- Department of Statistics, University of Oxford, UK
- Nuffield Department of Medicine, Medical Sciences Division, University of Oxford, UK
- The Alan Turing Institute, London, UK
| | - Cecilia M. Lindgren
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, UK
- Big Data Institute at the Li Ka Shing Centre for Health Information and Discovery, University of Oxford, UK
- Nuffield Department of Women’s and Reproductive Health, Medical Sciences Division, University of Oxford, UK
- Broad Institute of Harvard and MIT, Cambridge, Massachusetts, USA
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Manera KE, Stamatakis E, Huang BH, Owen K, Phongsavan P, Smith BJ. Joint associations of social health and movement behaviours with mortality and cardiovascular disease: an analysis of 497,544 UK biobank participants. Int J Behav Nutr Phys Act 2022; 19:137. [PMID: 36384558 PMCID: PMC9670497 DOI: 10.1186/s12966-022-01372-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2021] [Revised: 10/07/2022] [Accepted: 10/11/2022] [Indexed: 11/17/2022] Open
Abstract
BACKGROUND Poor physical activity and excessive sedentary behaviour are well-established risk factors for morbidity and mortality. In the presence of emerging social problems, including loneliness and social isolation, these risks may be even greater. We aimed to investigate the joint effects of social health and movement behaviours on mortality and cardiovascular disease (CVD). METHODS 497,544 UK Biobank participants were followed for an average of 11 years. Loneliness and social isolation were measured via self-report. Physical activity was categorised around current World Health Organisation (WHO) guidelines as low (< 600 metabolic equivalent of task [MET]-mins/week), moderate (600 < 1200) and high (≥ 1200). Sedentary behaviour was classified as low (≤ 3.5 h/day), moderate (3.5 ≤ 5) and high (> 5.5). We derived 24 social health-movement behaviour combinations, accordingly. Mortality and hospitalisations were ascertained to May 2020 for all-cause and CVD mortality, and non-fatal cardiovascular events. RESULTS Social isolation amplified the risk of both all-cause and CVD death across all physical activity and sedentary levels (hazard ratio, 95% confidence interval [HR, 95% CIs] for all-cause mortality; 1.58 [1.49 to 1.68] for low active-isolated vs. 1.26 [1.22 to 1.30] for low active-not isolated). Loneliness was only found to amplify the risk of death from cardiovascular disease among the high active and low sedentary participants. Loneliness and social isolation did not add to the risk of non-fatal cardiovascular events across most activity levels. CONCLUSION The detrimental associations of poor physical activity and sedentary behaviour with mortality were consistently amplified by social isolation. Our study supports the need to target the socially isolated as a priority group in preventive public health strategies.
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Affiliation(s)
- Karine Estelle Manera
- Sydney School of Public Health, Faculty of Medicine and Health, The Charles Perkins Centre, The University of Sydney, Sydney, Australia.
| | - Emmanuel Stamatakis
- Sydney School of Health Sciences, Faculty of Medicine and Health , The Charles Perkins Centre, The University of Sydney, Sydney, Australia
| | - Bo-Huei Huang
- Sydney School of Health Sciences, Faculty of Medicine and Health , The Charles Perkins Centre, The University of Sydney, Sydney, Australia
| | - Katherine Owen
- Sydney School of Public Health, Faculty of Medicine and Health, The Charles Perkins Centre, The University of Sydney, Sydney, Australia
| | - Philayrath Phongsavan
- Sydney School of Public Health, Faculty of Medicine and Health, The Charles Perkins Centre, The University of Sydney, Sydney, Australia
| | - Ben J Smith
- Sydney School of Public Health, Faculty of Medicine and Health, The Charles Perkins Centre, The University of Sydney, Sydney, Australia
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Fabbri C, Mutz J, Lewis CM, Serretti A. Stratification of individuals with lifetime depression and low wellbeing in the UK Biobank. J Affect Disord 2022; 314:281-292. [PMID: 35878836 DOI: 10.1016/j.jad.2022.07.023] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/08/2022] [Revised: 04/30/2022] [Accepted: 07/17/2022] [Indexed: 11/28/2022]
Abstract
BACKGROUND Previous studies stratified patients with major depressive disorder (MDD) based on their clinical characteristics. This study used this approach in individuals with lifetime MDD who reported low wellbeing, a group of high clinical relevance. METHODS We selected participants in the UK Biobank (UKB) with lifetime MDD and a wellbeing score in the lowest 25 %. A wellbeing score was previously created considering happiness, belief that own life is meaningful, health satisfaction and functioning in relevant areas. In the selected group, we applied latent class analysis using mood-spectrum symptoms and personality traits as input variables, then we compared the clinical-demographic and genetic (polygenic risk scores, PRSs) characteristics of the identified classes. RESULTS A total of 13,896 individuals were included and a model with five classes showed the best performance. The most common class (31.25 %) was characterised by periods of irritable mood and trait irritability with high neuroticism. A rarer class (16.49 %) showed depressive-manic mood fluctuations and risk-taking personality, higher percentage of males, atypical depressive symptoms, lower socio-economic status, higher PRS for attention-deficit hyperactivity disorder and lower PRS for education. The second most common class (29.79 %) showed worry as main personality trait with low risk of manic/irritable manifestations. The remaining classes showed an anxious-irritable personality profile and a purely depressive profile (4.92 % and 17.55 %, respectively). LIMITATIONS Our results may reflect the characteristics of UKB participants. CONCLUSIONS Subthreshold manic/irritable mood fluctuations and personality traits irritability and neuroticism may distinguish the most common groups with poor wellbeing in lifetime MDD.
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Affiliation(s)
- Chiara Fabbri
- Department of Biomedical and NeuroMotor Sciences, University of Bologna, Bologna, Italy; Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom.
| | - Julian Mutz
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Cathryn M Lewis
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Alessandro Serretti
- Department of Biomedical and NeuroMotor Sciences, University of Bologna, Bologna, Italy
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Aggarwal B, Jelic S. Harnessing the Potential of Genetics to Understand the Impact of Sleep Apnea on Cardiovascular Risk. CIRCULATION. GENOMIC AND PRECISION MEDICINE 2022; 15:e003922. [PMID: 36173702 PMCID: PMC9588754 DOI: 10.1161/circgen.122.003922] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Affiliation(s)
- Brooke Aggarwal
- Division of Cardiology, Department of Medicine, Columbia University Irving Medical Center, New York, NY
- Sleep Center of Excellence, Department of Medicine, Columbia University Irving Medical Center, New York, NY
| | - Sanja Jelic
- Sleep Center of Excellence, Department of Medicine, Columbia University Irving Medical Center, New York, NY
- Division of Pulmonary, Allergy, and Critical Care Medicine, Columbia University Irving Medical Center, New York, NY
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Tan VY, Timpson NJ. The UK Biobank: A Shining Example of Genome-Wide Association Study Science with the Power to Detect the Murky Complications of Real-World Epidemiology. Annu Rev Genomics Hum Genet 2022; 23:569-589. [PMID: 35508184 DOI: 10.1146/annurev-genom-121321-093606] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Genome-wide association studies (GWASs) have successfully identified thousands of genetic variants that are reliably associated with human traits. Although GWASs are restricted to certain variant frequencies, they have improved our understanding of the genetic architecture of complex traits and diseases. The UK Biobank (UKBB) has brought substantial analytical opportunity and performance to association studies. The dramatic expansion of many GWAS sample sizes afforded by the inclusion of UKBB data has improved the power of estimation of effect sizes but, critically, has done so in a context where phenotypic depth and precision enable outcome dissection and the application of epidemiological approaches. However, at the same time, the availability of such a large, well-curated, and deeply measured population-based collection has the capacity to increase our exposure to the many complications and inferential complexities associated with GWASs and other analyses. In this review, we discuss the impact that UKBB has had in the GWAS era, some of the opportunities that it brings, and exemplar challenges that illustrate the reality of using data from this world-leading resource.
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Affiliation(s)
- Vanessa Y Tan
- Medical Research Council (MRC) Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom;
- Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Nicholas J Timpson
- Medical Research Council (MRC) Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom;
- Bristol Medical School, University of Bristol, Bristol, United Kingdom
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Bešević J, Lacey B, Conroy M, Omiyale W, Feng Q, Collins R, Allen N. New Horizons: the value of UK Biobank to research on endocrine and metabolic disorders. J Clin Endocrinol Metab 2022; 107:2403-2410. [PMID: 35793237 PMCID: PMC9387695 DOI: 10.1210/clinem/dgac407] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Indexed: 11/24/2022]
Abstract
UK Biobank is an intensively characterized prospective study of 500 000 men and women, aged 40 to 69 years when recruited, between 2006 and 2010, from the general population of the United Kingdom. Established as an open-access resource for researchers worldwide to perform health research that is in the public interest, UK Biobank has collected (and continues to collect) a vast amount of data on genetic, physiological, lifestyle, and environmental factors, with prolonged follow-up of heath conditions through linkage to administrative electronic health records. The study has already demonstrated its unique value in enabling research into the determinants of common endocrine and metabolic diseases. The importance of UK Biobank, heralded as a flagship project for UK health research, will only increase over time as the number of incident disease events accrue, and the study is enhanced with additional data from blood assays (such as whole-genome sequencing, metabolomics, and proteomics), wearable technologies (including physical activity and cardiac monitors), and body imaging (magnetic resonance imaging and dual-energy X-ray absorptiometry). This unique research resource is likely to transform our understanding of the causes, diagnosis, and treatment of many endocrine and metabolic disorders.
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Affiliation(s)
- Jelena Bešević
- Oxford Population Health (Nuffield Department of Population Health), University of Oxford
| | - Ben Lacey
- Oxford Population Health (Nuffield Department of Population Health), University of Oxford
| | - Megan Conroy
- Oxford Population Health (Nuffield Department of Population Health), University of Oxford
| | - Wemimo Omiyale
- Oxford Population Health (Nuffield Department of Population Health), University of Oxford
| | - Qi Feng
- Oxford Population Health (Nuffield Department of Population Health), University of Oxford
| | - Rory Collins
- Oxford Population Health (Nuffield Department of Population Health), University of Oxford
- UK Biobank, Stockport, Greater Manchester, UK
| | - Naomi Allen
- Oxford Population Health (Nuffield Department of Population Health), University of Oxford
- UK Biobank, Stockport, Greater Manchester, UK
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Choi J, Oh TG, Jung HW, Park KY, Shin H, Jo T, Kang DS, Chanda D, Hong S, Kim J, Hwang H, Ji M, Jung M, Shoji T, Matsushima A, Kim P, Mun JY, Paik MJ, Cho SJ, Lee IK, Whitcomb DC, Greer P, Blobner B, Goodarzi MO, Pandol SJ, Rotter JI, Fan W, Bapat SP, Zheng Y, Liddle C, Yu RT, Atkins AR, Downes M, Yoshihara E, Evans RM, Suh JM. Estrogen-Related Receptor γ Maintains Pancreatic Acinar Cell Function and Identity by Regulating Cellular Metabolism. Gastroenterology 2022; 163:239-256. [PMID: 35461826 PMCID: PMC9233018 DOI: 10.1053/j.gastro.2022.04.013] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/02/2021] [Revised: 02/22/2022] [Accepted: 04/03/2022] [Indexed: 12/02/2022]
Abstract
BACKGROUND & AIMS Mitochondrial dysfunction disrupts the synthesis and secretion of digestive enzymes in pancreatic acinar cells and plays a primary role in the etiology of exocrine pancreas disorders. However, the transcriptional mechanisms that regulate mitochondrial function to support acinar cell physiology are poorly understood. Here, we aim to elucidate the function of estrogen-related receptor γ (ERRγ) in pancreatic acinar cell mitochondrial homeostasis and energy production. METHODS Two models of ERRγ inhibition, GSK5182-treated wild-type mice and ERRγ conditional knock-out (cKO) mice, were established to investigate ERRγ function in the exocrine pancreas. To identify the functional role of ERRγ in pancreatic acinar cells, we performed histologic and transcriptome analysis with the pancreas isolated from ERRγ cKO mice. To determine the relevance of these findings for human disease, we analyzed transcriptome data from multiple independent human cohorts and conducted genetic association studies for ESRRG variants in 2 distinct human pancreatitis cohorts. RESULTS Blocking ERRγ function in mice by genetic deletion or inverse agonist treatment results in striking pancreatitis-like phenotypes accompanied by inflammation, fibrosis, and cell death. Mechanistically, loss of ERRγ in primary acini abrogates messenger RNA expression and protein levels of mitochondrial oxidative phosphorylation complex genes, resulting in defective acinar cell energetics. Mitochondrial dysfunction due to ERRγ deletion further triggers autophagy dysfunction, endoplasmic reticulum stress, and production of reactive oxygen species, ultimately leading to cell death. Interestingly, ERRγ-deficient acinar cells that escape cell death acquire ductal cell characteristics, indicating a role for ERRγ in acinar-to-ductal metaplasia. Consistent with our findings in ERRγ cKO mice, ERRγ expression was significantly reduced in patients with chronic pancreatitis compared with normal subjects. Furthermore, candidate locus region genetic association studies revealed multiple single nucleotide variants for ERRγ that are associated with chronic pancreatitis. CONCLUSIONS Collectively, our findings highlight an essential role for ERRγ in maintaining the transcriptional program that supports acinar cell mitochondrial function and organellar homeostasis and provide a novel molecular link between ERRγ and exocrine pancreas disorders.
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Affiliation(s)
- Jinhyuk Choi
- Graduate School of Medical Science and Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea
| | - Tae Gyu Oh
- Gene Expression Laboratory, Salk Institute for Biological Studies, La Jolla, California
| | - Hee-Won Jung
- Graduate School of Medical Science and Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea
| | - Kun-Young Park
- Graduate School of Medical Science and Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea
| | - Hyemi Shin
- Graduate School of Medical Science and Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea
| | - Taehee Jo
- Graduate School of Medical Science and Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea
| | - Du-Seock Kang
- Graduate School of Medical Science and Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea
| | - Dipanjan Chanda
- Leading-Edge Research Center for Drug Discovery and Development for Diabetes and Metabolic Disease, Kyungpook National University Hospital, Daegu, Republic of Korea; Bio-Medical Research Institute, Kyungpook National University Hospital, Daegu, Republic of Korea
| | - Sujung Hong
- Graduate School of Medical Science and Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea
| | - Jina Kim
- New Drug Development Center, Daegu-Gyeongbuk Medical Innovation Foundation, Daegu, Republic of Korea
| | - Hayoung Hwang
- New Drug Development Center, Daegu-Gyeongbuk Medical Innovation Foundation, Daegu, Republic of Korea
| | - Moongi Ji
- College of Pharmacy, Sunchon National University, Suncheon, Republic of Korea
| | - Minkyo Jung
- Neural Circuit Research Group, Korea Brain Research Institute, Daegu, Republic of Korea
| | - Takashi Shoji
- Department of Medicine, Kyoto University, Kyoto, Japan
| | - Ayami Matsushima
- Laboratory of Structure-Function Biochemistry, Department of Chemistry, Faculty of Science, Kyushu University, Fukuoka, Japan
| | - Pilhan Kim
- Graduate School of Medical Science and Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea
| | - Ji Young Mun
- Neural Circuit Research Group, Korea Brain Research Institute, Daegu, Republic of Korea
| | - Man-Jeong Paik
- College of Pharmacy, Sunchon National University, Suncheon, Republic of Korea
| | - Sung Jin Cho
- New Drug Development Center, Daegu-Gyeongbuk Medical Innovation Foundation, Daegu, Republic of Korea
| | - In-Kyu Lee
- Leading-Edge Research Center for Drug Discovery and Development for Diabetes and Metabolic Disease, Kyungpook National University Hospital, Daegu, Republic of Korea; Bio-Medical Research Institute, Kyungpook National University Hospital, Daegu, Republic of Korea; Research Institute of Aging and Metabolism, Kyungpook National University, Daegu, Republic of Korea; Department of Internal Medicine, Kyungpook National University Hospital, School of Medicine, Kyungpook National University, Daegu, Republic of Korea
| | - David C Whitcomb
- Ariel Precision Medicine, Pittsburgh, Pennsylvania; Department of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania; Department of Cell Biology and Molecular Physiology and the Department of Human Genetics, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Phil Greer
- Ariel Precision Medicine, Pittsburgh, Pennsylvania; Department of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Brandon Blobner
- Department of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Mark O Goodarzi
- Division of Endocrinology, Diabetes and Metabolism, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, California
| | - Stephen J Pandol
- Cedars-Sinai Cancer, Cedars-Sinai Medical Center, Los Angeles, California; Karsh Division of Gastroenterology and Hepatology, Cedars-Sinai Medical Center, Los Angeles, California
| | - 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, Torrance, California; Departments of Pediatrics and Human Genetics, David Geffen School of Medicine at University of California-Los Angeles, Los Angeles, California
| | - Weiwei Fan
- Gene Expression Laboratory, Salk Institute for Biological Studies, La Jolla, California
| | - Sagar P Bapat
- Gene Expression Laboratory, Salk Institute for Biological Studies, La Jolla, California; Department of Laboratory Medicine, University of California-San Francisco, San Francisco, California; Diabetes Center, University of California-San Francisco, San Francisco, California; Nomis Laboratories for Immunobiology and Microbial Pathogenesis, Salk Institute for Biological Studies, La Jolla, California
| | - Ye Zheng
- Nomis Laboratories for Immunobiology and Microbial Pathogenesis, Salk Institute for Biological Studies, La Jolla, California
| | - Chris Liddle
- Storr Liver Centre, Westmead Institute for Medical Research and Sydney School of Medicine, University of Sydney, Westmead, New South Wales, Australia
| | - Ruth T Yu
- Gene Expression Laboratory, Salk Institute for Biological Studies, La Jolla, California
| | - Annette R Atkins
- Gene Expression Laboratory, Salk Institute for Biological Studies, La Jolla, California
| | - Michael Downes
- Gene Expression Laboratory, Salk Institute for Biological Studies, La Jolla, California
| | - Eiji Yoshihara
- Gene Expression Laboratory, Salk Institute for Biological Studies, La Jolla, California; The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, California; David Geffen School of Medicine at University of California-Los Angeles, Los Angeles, California.
| | - Ronald M Evans
- Gene Expression Laboratory, Salk Institute for Biological Studies, La Jolla, California.
| | - Jae Myoung Suh
- Graduate School of Medical Science and Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea.
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Lyall DM, Quinn T, Lyall LM, Ward J, Anderson JJ, Smith DJ, Stewart W, Strawbridge RJ, Bailey MES, Cullen B. Quantifying bias in psychological and physical health in the UK Biobank imaging sub-sample. Brain Commun 2022; 4:fcac119. [PMID: 35651593 PMCID: PMC9150072 DOI: 10.1093/braincomms/fcac119] [Citation(s) in RCA: 39] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2021] [Revised: 03/01/2022] [Accepted: 05/06/2022] [Indexed: 11/12/2022] Open
Abstract
UK Biobank is a prospective cohort study of around half-a-million general population participants, recruited between 2006 and 2010, with baseline studies at recruitment and multiple assessments since. From 2014 to date, magnetic resonance imaging (MRI) has been pursued in a participant sub-sample, with the aim to scan around n = 100k. This sub-sample is studied widely and therefore understanding its relative characteristics is important for future reports. We aimed to quantify psychological and physical health in the UK Biobank imaging sub-sample, compared with the rest of the cohort. We used t-tests and χ2 for continuous/categorical variables, respectively, to estimate average differences on a range of cognitive, mental and physical health phenotypes. We contrasted baseline values of participants who attended imaging (versus had not), and compared their values at the imaging visit versus baseline values of participants who were not scanned. We also tested the hypothesis that the associations of established risk factors with worse cognition would be underestimated in the (hypothesized) healthier imaging group compared with the full cohort. We tested these interactions using linear regression models. On a range of cognitive, mental health, cardiometabolic, inflammatory and neurological phenotypes, we found that 47 920 participants who were scanned by January 2021 showed consistent statistically significant 'healthy' bias compared with the ∼450 000 who were not scanned. These effect sizes were small to moderate based on Cohen's d/Cramer's V metrics (range = 0.02 to -0.21 for Townsend, the largest effect size). We found evidence of interaction, where stratified analysis demonstrated that associations of established cognitive risk factors were smaller in the imaging sub-sample compared with the full cohort. Of the ∼100 000 participants who ultimately will undergo MRI assessment within UK Biobank, the first ∼50 000 showed some 'healthy' bias on a range of metrics at baseline. Those differences largely remained at the subsequent (first) imaging visit, and we provide evidence that testing associations in the imaging sub-sample alone could lead to potential underestimation of exposure/outcome estimates.
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Affiliation(s)
- Donald M. Lyall
- Institute of Health and Wellbeing, University of
Glasgow, 1 Lilybank Gardens, Scotland G12 8RZ, UK
| | - Terry Quinn
- Institute of Cardiovascular and Medical Sciences,
University of Glasgow, Scotland, UK
| | - Laura M. Lyall
- Institute of Health and Wellbeing, University of
Glasgow, 1 Lilybank Gardens, Scotland G12 8RZ, UK
| | - Joey Ward
- Institute of Health and Wellbeing, University of
Glasgow, 1 Lilybank Gardens, Scotland G12 8RZ, UK
| | - Jana J. Anderson
- Institute of Health and Wellbeing, University of
Glasgow, 1 Lilybank Gardens, Scotland G12 8RZ, UK
| | - Daniel J. Smith
- Division of Psychiatry, University of
Edinburgh, Edinburgh, Scotland, UK
| | - William Stewart
- Department of Neuropathology, Queen Elizabeth
University Hospital, Scotland, UK
| | - Rona J. Strawbridge
- Institute of Health and Wellbeing, University of
Glasgow, 1 Lilybank Gardens, Scotland G12 8RZ, UK
- Cardiovascular Medicine Unit, Department of Medicine
Solna, Karolinska Institutet, Stockholm, Sweden
| | - Mark E. S. Bailey
- School of Life Sciences, College of Medical,
Veterinary and Life Sciences, University of Glasgow, Glasgow,
Scotland, UK
| | - Breda Cullen
- Institute of Health and Wellbeing, University of
Glasgow, 1 Lilybank Gardens, Scotland G12 8RZ, UK
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