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Twum F, Tome J, Ledel E, Roy V, Mallhi AK, Aguirre D, Wei Y, Zhang J. The Diverging Trend in Exposure to Environmental Tobacco Smoke Among US Children. J Racial Ethn Health Disparities 2024; 11:1718-1729. [PMID: 37326795 DOI: 10.1007/s40615-023-01645-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2022] [Revised: 05/14/2023] [Accepted: 05/15/2023] [Indexed: 06/17/2023]
Abstract
OBJECTIVE Environmental tobacco smoke exposure (ETSE) was race/ethnicity-specific, but how the race/ethnicity-specific ETSE has changed over time, diverging or converging, remains unclear. We examined ETSE trends by race/ethnicity in US children aged 3-11 years. METHODS We analyzed the data of 9678 children who participated in the biennial National Health and Nutrition Examination Surveys, 1999-2018. ETSE was defined as serum cotinine ≥ 0.05 ng/ml, with ≥ 1 ng/ml as heavy exposure. For trend description, adjusted biennial prevalence ratios (abiPR: the ratio associated with a 2-year increase in time) were estimated by race/ethnicity. The prevalence ratios between races/ethnicities were used to quantify ethnoracial differences in different survey periods. Analyses were performed in 2021. RESULTS The overall ETSE prevalence was cut by almost half, from 61.59% (95% confidence interval = 56.55%, 66.62%) in the 1999-2004 survey to 37.61% (33.90%, 41.31%) in 2013-2018, exceeding the national 2020 health target (47.0%). However, the decrease occurred unequally between races/ethnicities. Heavy ETSE declined significantly in white [abiPR = 0.80 (0.74, 0.86)] and Hispanic children [0.83 (0.74, 0.93)], but insignificantly in black children [0.97 (0.92, 1.03)]. Consequently, the adjusted prevalence ratio between black children and white children increased from 0.82 (0.47, 1.44) in 1999-2004 to 2.73 (1.51, 4.92) in 2013-2018 for heavy ETSE. Hispanic children remained at the lowest risk throughout the study period. CONCLUSION Overall ETSE prevalence was cut by half between 1999 and 2018. However, due to uneven declines, the gaps between black children and others have expanded in heavy ETSE. Special vigilance is needed in preventive medicine practice with black children.
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Affiliation(s)
- Felix Twum
- Department of Biostatistics, Epidemiology and Environmental Health Sciences, Jiann-Ping Hsu College of Public Health, Georgia Southern University, Statesboro, GA, 30460, USA.
- The Dr. Lynn Cook Hartwig Public Health Program, School of Health Professions, The University of Southern Mississippi, Hattiesburg, MS, 39406, USA.
| | - Joana Tome
- Department of Biostatistics, Epidemiology and Environmental Health Sciences, Jiann-Ping Hsu College of Public Health, Georgia Southern University, Statesboro, GA, 30460, USA
| | - Erica Ledel
- Department of Biostatistics, Epidemiology and Environmental Health Sciences, Jiann-Ping Hsu College of Public Health, Georgia Southern University, Statesboro, GA, 30460, USA
| | - Victoria Roy
- Department of Biostatistics, Epidemiology and Environmental Health Sciences, Jiann-Ping Hsu College of Public Health, Georgia Southern University, Statesboro, GA, 30460, USA
| | - Arshpreet Kaur Mallhi
- Department of Biostatistics, Epidemiology and Environmental Health Sciences, Jiann-Ping Hsu College of Public Health, Georgia Southern University, Statesboro, GA, 30460, USA
| | - Diana Aguirre
- Department of Biostatistics, Epidemiology and Environmental Health Sciences, Jiann-Ping Hsu College of Public Health, Georgia Southern University, Statesboro, GA, 30460, USA
| | - Yudan Wei
- Department of Community Medicine, Mercer University School of Medicine, Macon, GA, 31207, USA
| | - Jian Zhang
- Department of Biostatistics, Epidemiology and Environmental Health Sciences, Jiann-Ping Hsu College of Public Health, Georgia Southern University, Statesboro, GA, 30460, USA
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Carrasquilla GD, García-Ureña M, Romero-Lado MJ, Kilpeläinen TO. Estimating causality between smoking and abdominal obesity by Mendelian randomization. Addiction 2024; 119:1024-1034. [PMID: 38509034 DOI: 10.1111/add.16454] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/09/2023] [Accepted: 01/12/2024] [Indexed: 03/22/2024]
Abstract
BACKGROUND AND AIMS Smokers tend to have a lower body weight than non-smokers, but also more abdominal fat. It remains unclear whether or not the relationship between smoking and abdominal obesity is causal. Previous Mendelian randomization (MR) studies have investigated this relationship by relying upon a single genetic variant for smoking heaviness. This approach is sensitive to pleiotropic effects and may produce imprecise causal estimates. We aimed to estimate causality between smoking and abdominal obesity using multiple genetic instruments. DESIGN MR study using causal analysis using summary effect estimates (CAUSE) and latent heritable confounder MR (LHC-MR) methods that instrument smoking using genome-wide data, and also two-sample MR (2SMR) methods. SETTING Genome-wide association studies (GWAS) summary statistics from participants of European ancestry, obtained from the GWAS and Sequencing Consortium of Alcohol and Nicotine use (GSCAN), Genetic Investigation of Anthropometric Traits (GIANT) Consortium and the UK Biobank. PARTICIPANTS We used GWAS results for smoking initiation (n = 1 232 091), life-time smoking (n = 462 690) and smoking heaviness (n = 337 334) as exposure traits, and waist-hip ratio (WHR) and waist and hip circumferences (WC and HC) (n up to 697 734), with and without adjustment for body mass index (adjBMI), as outcome traits. MEASUREMENTS Smoking initiation, life-time smoking, smoking heaviness, WHR, WC, HC, WHRadjBMI, WCadjBMI and HCadjBMI. FINDINGS Both CAUSE and LHC-MR indicated a positive causal effect of smoking initiation on WHR (0.13 [95% confidence interval (CI) = 0.10, 0.16 and 0.49 (0.41, 0.57), respectively] and WHRadjBMI (0.07 (0.03, 0.10) and 0.31 (0.26, 0.37). Similarly, they indicated a positive causal effect of life-time smoking on WHR [0.35 (0.29, 0.41) and 0.44 (0.38, 0.51)] and WHRadjBMI [0.18 (0.13, 0.24) and 0.26 (0.20, 0.31)]. In follow-up analyses, smoking particularly increased visceral fat. There was no evidence of a mediating role by cortisol or sex hormones. CONCLUSIONS Smoking initiation and higher life-time smoking may lead to increased abdominal fat distribution. The increase in abdominal fat due to smoking is characterized by an increase in visceral fat. Thus, efforts to prevent and cease smoking can have the added benefit of reducing abdominal fat.
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Affiliation(s)
- Germán D Carrasquilla
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- Novo Nordisk Foundation Center for Genomic Mechanisms of Disease, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Mario García-Ureña
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - María J Romero-Lado
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Tuomas O Kilpeläinen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- Novo Nordisk Foundation Center for Genomic Mechanisms of Disease, Broad Institute of MIT and Harvard, Cambridge, MA, USA
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Uotila I, Siiskonen H, Haimakainen S, Harvima I. Tobacco smoking is associated with cutaneous squamous cell carcinoma but not with basal cell carcinoma or melanoma in adult subjects at risk of skin cancer: A cross-sectional study. Tob Induc Dis 2024; 22:TID-22-76. [PMID: 38745594 PMCID: PMC11091865 DOI: 10.18332/tid/185299] [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: 08/26/2023] [Revised: 01/02/2024] [Accepted: 02/23/2024] [Indexed: 05/16/2024] Open
Abstract
INTRODUCTION The relationship between tobacco smoking and cutaneous photodamage or malignancies is still unclear. In addition to smoking, both ultraviolet radiation and immunosuppression have an impact on carcinogenesis. The purpose was to study the association of smoking with cutaneous photoaging, actinic keratosis (AK), skin cancers, and pigment cell nevi in adult subjects at risk of any type of skin cancer. METHODS In this cross-sectional study at Kuopio University Hospital, Finland, between May 2017 and October 2020, 488 subjects (aged 21-79 years, 246 males and 242 females, 94 with immunosuppression) were examined for a variety of skin lesions, photoaging severity, nevi, tobacco pack-years (TPY), as well as for possible confounding factors. RESULTS In logistic regression analyses, no marked association was found between TPY and total skin photoaging, facial photoaging, AK, or nevi, especially when other confounding factors, such as age, were considered. In addition, TPY was not associated with melanoma, basal cell carcinoma, or any type of skin cancer. However, ever smokers produced an elevated crude odds ratio (OR=1.99; 95% CI: 1.02-3.88, p=0.043) for squamous cell carcinoma (SCC) compared to non-smokers. In further analysis, TPY of ≤10 produced an elevated multivariable adjusted odds ratio (AOR=4.90; 95% CI: 1.31-18.26, p=0.018) for SCC, but TPY >10 did not (AOR=1.14; 95% CI: 0.22-6.05, p=0.876). CONCLUSIONS Smoking was associated, though not dose-dependently, with an increased likelihood of SCC, but it was not associated with basal cell carcinoma or melanoma. However, the impact of smoking on cutaneous photoaging severity, AK, and nevi, appears to be weak.
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Affiliation(s)
- Ilmari Uotila
- Department of Dermatology, Kuopio University Hospital, University of Eastern Finland, Kuopio, Finland
| | - Hanna Siiskonen
- Department of Dermatology, Kuopio University Hospital, University of Eastern Finland, Kuopio, Finland
| | - Salla Haimakainen
- Department of Dermatology, Kuopio University Hospital, University of Eastern Finland, Kuopio, Finland
| | - Ilkka Harvima
- Department of Dermatology, Kuopio University Hospital, University of Eastern Finland, Kuopio, Finland
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Bouras E, Gill D, Zuber V, Murphy N, Dimou N, Aleksandrova K, Lewis SJ, Martin RM, Yarmolinsky J, Albanes D, Brenner H, Castellví-Bel S, Chan AT, Cheng I, Gruber S, Van Guelpen B, Li CI, Le Marchand L, Newcomb PA, Ogino S, Pellatt A, Schmit SL, Wolk A, Wu AH, Peters U, Gunter MJ, Tsilidis KK. Identification of potential mediators of the relationship between body mass index and colorectal cancer: a Mendelian randomization analysis. Int J Epidemiol 2024; 53:dyae067. [PMID: 38725300 PMCID: PMC11082423 DOI: 10.1093/ije/dyae067] [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] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Accepted: 04/15/2024] [Indexed: 05/13/2024] Open
Abstract
BACKGROUND Colorectal cancer (CRC) is the third-most-common cancer worldwide and its rates are increasing. Elevated body mass index (BMI) is an established risk factor for CRC, although the molecular mechanisms behind this association remain unclear. Using the Mendelian randomization (MR) framework, we aimed to investigate the mediating effects of putative biomarkers and other CRC risk factors in the association between BMI and CRC. METHODS We selected as mediators biomarkers of established cancer-related mechanisms and other CRC risk factors for which a plausible association with obesity exists, such as inflammatory biomarkers, glucose homeostasis traits, lipids, adipokines, insulin-like growth factor 1 (IGF1), sex hormones, 25-hydroxy-vitamin D, smoking, physical activity (PA) and alcohol consumption. We used inverse-variance weighted MR in the main univariable analyses and performed sensitivity analyses (weighted-median, MR-Egger, Contamination Mixture). We used multivariable MR for the mediation analyses. RESULTS Genetically predicted BMI was positively associated with CRC risk [odds ratio per SD (5 kg/m2) = 1.17, 95% CI: 1.08-1.24, P-value = 1.4 × 10-5] and robustly associated with nearly all potential mediators. Genetically predicted IGF1, fasting insulin, low-density lipoprotein cholesterol, smoking, PA and alcohol were associated with CRC risk. Evidence for attenuation was found for IGF1 [explained 7% (95% CI: 2-13%) of the association], smoking (31%, 4-57%) and PA (7%, 2-11%). There was little evidence for pleiotropy, although smoking was bidirectionally associated with BMI and instruments were weak for PA. CONCLUSIONS The effect of BMI on CRC risk is possibly partly mediated through plasma IGF1, whereas the attenuation of the BMI-CRC association by smoking and PA may reflect confounding and shared underlying mechanisms rather than mediation.
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Affiliation(s)
- Emmanouil Bouras
- Department of Hygiene and Epidemiology, University of Ioannina School of Medicine, Ioannina, Greece
| | - Dipender Gill
- Chief Scientific Advisor Office, Research and Early Development, Novo Nordisk, Copenhagen, Denmark
- Department of Epidemiology and Biostatistics, Imperial College London, School of Public Health, London, UK
| | - Verena Zuber
- Department of Epidemiology and Biostatistics, Imperial College London, School of Public Health, London, UK
| | - Neil Murphy
- Nutrition and Metabolism Branch, International Agency for Research on Cancer, Lyon, France
| | - Niki Dimou
- Nutrition and Metabolism Branch, International Agency for Research on Cancer, Lyon, France
| | - Krasimira Aleksandrova
- Faculty of Human and Health Sciences, University of Bremen, Bremen, Germany
- Department Epidemiological Methods and Etiological Research, Leibniz Institute for Prevention Research and Epidemiology, Bremen, Germany
| | - Sarah J Lewis
- Department of Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
| | - Richard M Martin
- Department of Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- NIHR Bristol Biomedical Research Centre, University Hospitals Bristol and Weston NHS Foundation Trust and the University of Bristol
| | - James Yarmolinsky
- Department of Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
| | - Demetrius Albanes
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Hermann Brenner
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Division of Preventive Oncology, German Cancer Research Center (DKFZ) and National Center for Tumor Diseases (NCT), Heidelberg, Germany
- German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Sergi Castellví-Bel
- Department of Gastroenterology, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBERehd), Hospital Clínic, Barcelona, Spain
| | - Andrew T Chan
- Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Department of Epidemiology, Harvard TH Chan School of Public Health, Harvard University, Boston, MA, USA
- Department of Immunology and Infectious Diseases, Harvard TH Chan School of Public Health, Harvard University, Boston, MA, USA
| | - Iona Cheng
- Department of Epidemiology and Biostatistics, University of California-San Francisco, San Francisco, CA, USA
| | - Stephen Gruber
- Department of Medical Oncology & Therapeutics Research and Center for Precision Medicine, City of Hope National Medical Center, Duarte, CA, USA
| | - Bethany Van Guelpen
- Department of Radiation Sciences, Oncology Unit, Umeå University, Umeå, Sweden
- Wallenberg Centre for Molecular Medicine, Umeå University, Umeå, Sweden
| | - Christopher I Li
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, Washington, USA
| | | | - Polly A Newcomb
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, Washington, USA
- Department of Epidemiology, University of Washington, Seattle, WA, USA
| | - Shuji Ogino
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Department of Epidemiology, Harvard TH Chan School of Public Health, Harvard University, Boston, MA, USA
- Program in MPE Molecular Pathological Epidemiology, Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Oncologic Pathology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Andrew Pellatt
- Department of Medicine, University of Utah, Salt Lake City, UT, USA
| | - Stephanie L Schmit
- Genomic Medicine Institute, Cleveland Clinic, Cleveland, OH, USA
- Population and Cancer Prevention Program, Case Comprehensive Cancer Center, Cleveland, OH, USA
| | - Alicja Wolk
- Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Anna H Wu
- University of Southern California, Preventative Medicine, Los Angeles, CA, USA
| | - Ulrike Peters
- Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, Washington, USA
- Department of Epidemiology, University of Washington, Seattle, WA, USA
| | - Marc J Gunter
- Department of Epidemiology and Biostatistics, Imperial College London, School of Public Health, London, UK
- Nutrition and Metabolism Branch, International Agency for Research on Cancer, Lyon, France
| | - Konstantinos K Tsilidis
- Department of Hygiene and Epidemiology, University of Ioannina School of Medicine, Ioannina, Greece
- Department of Epidemiology and Biostatistics, Imperial College London, School of Public Health, London, UK
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Easwaran M, Maria CS, Martinez JD, Hung B, Yu X, Soo J, Kimura A, Gross ER, Erickson-DiRenzo E. Effects of Short-term Electronic(e)-Cigarette Aerosol Exposure in the Mouse Larynx. Laryngoscope 2024; 134:1316-1326. [PMID: 37698394 PMCID: PMC10922082 DOI: 10.1002/lary.31043] [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: 03/24/2023] [Revised: 08/17/2023] [Accepted: 08/29/2023] [Indexed: 09/13/2023]
Abstract
OBJECTIVES The effects of electronic cigarettes (e-cigarettes) on the larynx are relatively unknown. This study examined the short-term effects of e-cigarette inhalation on cellular and inflammatory responses within the mouse laryngeal glottic and subglottic regions after exposure to pod-based devices (JUUL). METHODS Male C57BL6/J mice (8-9 weeks) were assigned to control (n = 9), JUUL flavors Mint (JMi; n = 10) or Mango (JMa; n = 10). JUUL mice were exposed to 2 h/day for 1, 5, and 10 days using the inExpose inhalation system. Control mice were in room air. Vocal fold (VF) epithelial thickness, cell proliferation, subglandular area and composition, inflammatory cell infiltration, and surface topography were evaluated in the harvested larynges. Mouse body weight and urinary nicotine biomarkers were also measured. Chemical analysis of JUUL aerosols was conducted using selective ion flow tube mass spectrometry. RESULTS JUUL-exposed mice had reduced body weight after day 5. Urinary nicotine biomarker levels indicated successful JUUL exposure and metabolism. Quantitative analysis of JUUL aerosol indicated that chemical constituents differ between JMi and JMa flavors. VF epithelial thickness, cellular proliferation, glandular area, and surface topography remained unchanged after JUUL exposures. Acidic mucus content increased after 1 day of JMi exposure. VF macrophage and T-cell levels slightly increased after 10 days of JMi exposures. CONCLUSIONS Short-term e-cigarette exposures cause minimal flavor- and region-specific cellular and inflammatory changes in the mouse larynx. This work provides a foundation for long-term studies to determine if these responses are altered with multiple e-cigarette components and concentrations. LEVEL OF EVIDENCE N/A Laryngoscope, 134:1316-1326, 2024.
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Affiliation(s)
- Meena Easwaran
- Department of Otolaryngology - Head & Neck Surgery, School of Medicine, Stanford University, Stanford, CA
| | - Chloe Santa Maria
- Department of Otolaryngology - Head & Neck Surgery, School of Medicine, Stanford University, Stanford, CA
| | - Joshua D. Martinez
- Department of Otolaryngology - Head & Neck Surgery, School of Medicine, Stanford University, Stanford, CA
| | - Barbara Hung
- Department of Anesthesiology, Perioperative and Pain Medicine, School of Medicine, Stanford University, Stanford, CA
| | - Xuan Yu
- Department of Anesthesiology, Perioperative and Pain Medicine, School of Medicine, Stanford University, Stanford, CA
| | - Joanne Soo
- Department of Otolaryngology - Head & Neck Surgery, School of Medicine, Stanford University, Stanford, CA
| | - Akari Kimura
- Department of Otolaryngology - Head & Neck Surgery, School of Medicine, Stanford University, Stanford, CA
| | - Eric R. Gross
- Department of Anesthesiology, Perioperative and Pain Medicine, School of Medicine, Stanford University, Stanford, CA
| | - Elizabeth Erickson-DiRenzo
- Department of Otolaryngology - Head & Neck Surgery, School of Medicine, Stanford University, Stanford, CA
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Greco F, Piccolo CL, D’Andrea V, Scardapane A, Beomonte Zobel B, Mallio CA. Fat Matters: Exploring Cancer Risk through the Lens of Computed Tomography and Visceral Adiposity. J Clin Med 2024; 13:453. [PMID: 38256587 PMCID: PMC10817009 DOI: 10.3390/jcm13020453] [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: 12/16/2023] [Revised: 01/08/2024] [Accepted: 01/12/2024] [Indexed: 01/24/2024] Open
Abstract
Obesity is an established risk factor for cancer. However, conventional measures like body mass index lack precision in assessing specific tissue quantities, particularly of the two primary abdominal fat compartments, visceral adipose tissue (VAT) and subcutaneous adipose tissue (SAT). Computed tomography (CT) stands as the gold standard for precisely quantifying diverse tissue types. VAT, distinguished by heightened hormonal and metabolic activity, plays a pivotal role in obesity-related tumor development. Excessive VAT is linked to aberrant secretion of adipokines, proinflammatory cytokines, and growth factors, fostering the carcinogenesis of obesity-related tumors. Accurate quantification of abdominal fat compartments is crucial for understanding VAT as an oncological risk factor. The purpose of the present research is to elucidate the role of CT, performed for staging purposes, in assessing VAT (quantity and distribution) as a critical factor in the oncogenesis of obesity-related tumors. In the field of precision medicine, this work takes on considerable importance, as quantifying VAT in oncological patients becomes fundamental in understanding the influence of VAT on cancer development-the potential "phenotypic expression" of excessive VAT accumulation. Previous studies analyzed in this research showed that VAT is a risk factor for clear cell renal cell carcinoma, non-clear cell renal cell carcinoma, prostate cancer, and hepatocarcinoma recurrence. Further studies will need to quantify VAT in other oncological diseases with specific mutations or gene expressions, in order to investigate the relationship of VAT with tumor genomics.
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Affiliation(s)
- Federico Greco
- Department of Radiology, Cittadella della Salute Azienda Sanitaria Locale di Lecce, Piazza Filippo Bottazzi 2, 73100 Lecce, Italy
- Research Unit of Radiology, Department of Medicine and Surgery, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo 21, 00128 Roma, Italy; (C.L.P.); (B.B.Z.); (C.A.M.)
| | - Claudia Lucia Piccolo
- Research Unit of Radiology, Department of Medicine and Surgery, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo 21, 00128 Roma, Italy; (C.L.P.); (B.B.Z.); (C.A.M.)
- Fondazione Policlinico Universitario Campus Bio-Medico, Via Alvaro del Portillo 200, 00128 Roma, Italy
| | - Valerio D’Andrea
- Research Unit of Radiology, Department of Medicine and Surgery, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo 21, 00128 Roma, Italy; (C.L.P.); (B.B.Z.); (C.A.M.)
- Fondazione Policlinico Universitario Campus Bio-Medico, Via Alvaro del Portillo 200, 00128 Roma, Italy
| | - Arnaldo Scardapane
- Dipartimento Interdisciplinare di Medicina, Sezione di Diagnostica per Immagini, Università degli Studi di Bari “Aldo Moro”, Piazza Giulio Cesare 11, 70124 Bari, Italy;
| | - Bruno Beomonte Zobel
- Research Unit of Radiology, Department of Medicine and Surgery, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo 21, 00128 Roma, Italy; (C.L.P.); (B.B.Z.); (C.A.M.)
- Fondazione Policlinico Universitario Campus Bio-Medico, Via Alvaro del Portillo 200, 00128 Roma, Italy
| | - Carlo Augusto Mallio
- Research Unit of Radiology, Department of Medicine and Surgery, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo 21, 00128 Roma, Italy; (C.L.P.); (B.B.Z.); (C.A.M.)
- Fondazione Policlinico Universitario Campus Bio-Medico, Via Alvaro del Portillo 200, 00128 Roma, Italy
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Chang CS, Chang LY, Wu CC, Chang HY. Associations between social jetlag trajectories and body mass index among young adults. Sleep 2024; 47:zsad270. [PMID: 37855456 DOI: 10.1093/sleep/zsad270] [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: 06/06/2023] [Revised: 10/11/2023] [Indexed: 10/20/2023] Open
Abstract
STUDY OBJECTIVES This study employed longitudinal data collected repeatedly from individuals over the course of several years to examine the trajectories of social jetlag from ages 11 to 22 years and their associations with subsequent body mass index (BMI). Potential sex differences were also investigated. METHODS Data were obtained from two longitudinal studies conducted in Taiwan (N = 4287). Social jetlag was defined as ≥ 2 hours of absolute difference in sleep midpoint between weekdays and weekends. BMI was calculated using weight (kg)/height(m)2 and categorized as underweight (<18 kg/m2), normal weight (18 kg/m2 ≤ BMI < 24 kg/m2), overweight (24 kg/m2 ≤ BMI < 27 kg/m2), and obese (≥27 kg/m2). Group-based trajectory modeling and multinomial logistic regression were applied to investigate study objectives. RESULTS Four distinct trajectories of social jetlag throughout the adolescent years were identified, with corresponding proportions as follows: low-stable (42%), moderate-decreasing (19%), low-increasing (22%), and chronic (17%) trajectories. Among males, the risk of being underweight (aOR, 1.96; 95% CI: 1.35 to 2.84) or obese (aOR, 1.40; 95% CI: 1.02 to 1.92) was higher in individuals with a low-increasing trajectory than in those with a low-stable trajectory. Among females, those with a low-increasing (aOR, 1.61; 95% CI: 1.02 to 2.54) or chronic (aOR, 2.04; 95% CI: 1.27 to 3.25) trajectory were at a higher risk of being obese relative to those with a low-stable trajectory. CONCLUSIONS Addressing the development of increasing or chronic social jetlag during adolescence can help prevent abnormal BMI in young adulthood. Practitioners should consider sex differences in treatment or consultation.
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Affiliation(s)
- Chia-Shuan Chang
- Institute of Health Behaviors and Community Sciences, College of Public Health, National Taiwan University, Taipei, Taiwan
| | - Ling-Yin Chang
- Institute of Health Behaviors and Community Sciences, College of Public Health, National Taiwan University, Taipei, Taiwan
| | - Chi-Chen Wu
- Institute of Population Health Sciences, National Health Research Institutes, Miaoli County, Taiwan
| | - Hsing-Yi Chang
- Institute of Population Health Sciences, National Health Research Institutes, Miaoli County, Taiwan
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Lee SS, Chang Y, Rigotti NA, Singer DE, Levy DE, Tyndale RF, Davis EM, Freiberg MS, King S, Wells QS, Tindle HA. Can Treatment Support Mitigate Nicotine Metabolism-Based Disparities in Smoking Abstinence? Secondary Analysis of the Helping HAND 4 Trial. Nicotine Tob Res 2023; 25:1575-1584. [PMID: 37209421 PMCID: PMC10439488 DOI: 10.1093/ntr/ntad079] [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: 11/02/2022] [Revised: 04/23/2023] [Accepted: 05/17/2023] [Indexed: 05/22/2023]
Abstract
INTRODUCTION The nicotine metabolite ratio (NMR), a biomarker of CYP2A6-mediated nicotine metabolism, predicts the efficacy of nicotine replacement therapy (NRT), with fast metabolizers benefiting less than slow metabolizers. Whether treatment support to optimize NRT use (henceforth "treatment support") modifies this pharmacogenetic relationship is unknown. METHODS Hospitalized adult daily smokers were assigned to one of two post-discharge smoking cessation interventions offering NRT and counseling: (1) Transitional Tobacco Care Management, which delivered enhanced treatment support via free combination NRT at discharge and automated counseling, and (2) a quitline-based approach representing usual care (UC). The primary outcome was biochemically verified 7-day point prevalence abstinence 6 months after discharge. Secondary outcomes were the use of NRT and counseling during the 3-month intervention period. Logistic regression models tested for interactions between NMR and intervention, controlling for sex, race, alcohol use, and BMI. RESULTS Participants (N = 321) were classified as slow (n = 80) or fast (n = 241) metabolizers relative to the first quartile of NMR (0.012-0.219 vs. 0.221-3.455, respectively). Under UC, fast (vs. slow) metabolizers had lower odds of abstinence at 6 months (aOR 0.35, 95% CI 0.13-0.95) and similar odds of NRT and counseling use. Compared to UC, enhanced treatment support increased abstinence (aOR 2.13, 95% CI 0.98-4.64) and use of combination NRT (aOR 4.62, 95% CI 2.57-8.31) in fast metabolizers, while reducing abstinence in slow metabolizers (aOR 0.21, 95% CI 0.05-0.87; NMR-by-intervention interaction p = .004). CONCLUSIONS Treatment support increased abstinence and optimal use of NRT among fast nicotine metabolizers, thereby mitigating the gap in abstinence between fast and slow metabolizers. IMPLICATIONS In this secondary analysis of two smoking cessation interventions for recently hospitalized smokers, fast nicotine metabolizers quit at lower rates than slow metabolizers, but providing fast metabolizers with enhanced treatment support doubled the odds of quitting in this group and mitigated the disparity in abstinence between fast and slow metabolizers. If validated, these findings could lead to personalized approaches to smoking cessation treatment that improve outcomes by targeting treatment support to those who need it most.
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Affiliation(s)
- Scott S Lee
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Yuchiao Chang
- Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Nancy A Rigotti
- Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Daniel E Singer
- Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Douglas E Levy
- Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Rachel F Tyndale
- Departments of Psychiatry, and Pharmacology and Toxicology, University of Toronto and Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - Esa M Davis
- Department of Medicine, University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Matthew S Freiberg
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Stephen King
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Quinn S Wells
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Hilary A Tindle
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
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Burgess S, Davey Smith G, Davies NM, Dudbridge F, Gill D, Glymour MM, Hartwig FP, Kutalik Z, Holmes MV, Minelli C, Morrison JV, Pan W, Relton CL, Theodoratou E. Guidelines for performing Mendelian randomization investigations: update for summer 2023. Wellcome Open Res 2023; 4:186. [PMID: 32760811 PMCID: PMC7384151 DOI: 10.12688/wellcomeopenres.15555.3] [Citation(s) in RCA: 81] [Impact Index Per Article: 81.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/19/2023] [Indexed: 08/08/2023] Open
Abstract
This paper provides guidelines for performing Mendelian randomization investigations. It is aimed at practitioners seeking to undertake analyses and write up their findings, and at journal editors and reviewers seeking to assess Mendelian randomization manuscripts. The guidelines are divided into ten sections: motivation and scope, data sources, choice of genetic variants, variant harmonization, primary analysis, supplementary and sensitivity analyses (one section on robust statistical methods and one on other approaches), extensions and additional analyses, data presentation, and interpretation. These guidelines will be updated based on feedback from the community and advances in the field. Updates will be made periodically as needed, and at least every 24 months.
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Affiliation(s)
- Stephen Burgess
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
- BHF Cardiovascular Epidemiology Unit, University of Cambridge, Cambridge, UK
| | - George Davey Smith
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Neil M. Davies
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Division of Psychiatry, University College London, London, UK
- Department of Statistical Sciences, University College London, London, WC1E 6BT, UK
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
| | - Frank Dudbridge
- Department of Health Sciences, University of Leicester, Leicester, UK
| | - Dipender Gill
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK
| | - M. Maria Glymour
- Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, USA
| | - Fernando P. Hartwig
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Postgraduate Program in Epidemiology, Federal University of Pelotas, Pelotas, Brazil
| | - Zoltán Kutalik
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
- University Center for Primary Care and Public Health (Unisanté), Lausanne, Switzerland
| | - Michael V. Holmes
- MRC Population Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
- Clinical Trial Service Unit and Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Cosetta Minelli
- National Heart and Lung Institute, Imperial College London, London, UK
| | - Jean V. Morrison
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA
| | - Wei Pan
- Division of Biostatistics, University of Minnesota, Minneapolis, MN, USA
| | - Caroline L. Relton
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- London School of Hygiene & Tropical Medicine, Keppel Street, London, WC1E 7HT, UK
| | - Evropi Theodoratou
- Centre for Global Health, Usher Institute, University of Edinburgh, Edinburgh, UK
- Edinburgh Cancer Research Centre, Institute of Genetics and Cancer, The University of Edinburgh, Edinburgh, UK
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Le TTT, Issabakhsh M, Li Y, María Sánchez-Romero L, Tan J, Meza R, Levy D, Mendez D. Are the Relevant Risk Factors Being Adequately Captured in Empirical Studies of Smoking Initiation? A Machine Learning Analysis Based on the Population Assessment of Tobacco and Health Study. Nicotine Tob Res 2023; 25:1481-1488. [PMID: 37099744 PMCID: PMC10347975 DOI: 10.1093/ntr/ntad066] [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: 09/20/2022] [Revised: 02/23/2023] [Accepted: 04/21/2023] [Indexed: 04/28/2023]
Abstract
INTRODUCTION Cigarette smoking continues to pose a threat to public health. Identifying individual risk factors for smoking initiation is essential to further mitigate this epidemic. To the best of our knowledge, no study today has used machine learning (ML) techniques to automatically uncover informative predictors of smoking onset among adults using the Population Assessment of Tobacco and Health (PATH) study. AIMS AND METHODS In this work, we employed random forest paired with Recursive Feature Elimination to identify relevant PATH variables that predict smoking initiation among adults who have never smoked at baseline between two consecutive PATH waves. We included all potentially informative baseline variables in wave 1 (wave 4) to predict past 30-day smoking status in wave 2 (wave 5). Using the first and most recent pairs of PATH waves was found sufficient to identify the key risk factors of smoking initiation and test their robustness over time. The eXtreme Gradient Boosting method was employed to test the quality of these selected variables. RESULTS As a result, classification models suggested about 60 informative PATH variables among many candidate variables in each baseline wave. With these selected predictors, the resulting models have a high discriminatory power with the area under the specificity-sensitivity curves of around 80%. We examined the chosen variables and discovered important features. Across the considered waves, two factors, (1) BMI, and (2) dental and oral health status, robustly appeared as important predictors of smoking initiation, besides other well-established predictors. CONCLUSIONS Our work demonstrates that ML methods are useful to predict smoking initiation with high accuracy, identifying novel smoking initiation predictors, and to enhance our understanding of tobacco use behaviors. IMPLICATIONS Understanding individual risk factors for smoking initiation is essential to prevent smoking initiation. With this methodology, a set of the most informative predictors of smoking onset in the PATH data were identified. Besides reconfirming well-known risk factors, the findings suggested additional predictors of smoking initiation that have been overlooked in previous work. More studies that focus on the newly discovered factors (BMI and dental and oral health status,) are needed to confirm their predictive power against the onset of smoking as well as determine the underlying mechanisms.
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Affiliation(s)
- Thuy T T Le
- Department of Health Management and Policy, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Mona Issabakhsh
- Department of Oncology, School of Medicine, Georgetown University, Washington, DC, USA
| | - Yameng Li
- Department of Oncology, School of Medicine, Georgetown University, Washington, DC, USA
| | | | - Jiale Tan
- Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Rafael Meza
- Integrative Oncology, BC Cancer Research Institute, Vancouver BC, USA
| | - David Levy
- Department of Oncology, School of Medicine, Georgetown University, Washington, DC, USA
| | - David Mendez
- Department of Health Management and Policy, School of Public Health, University of Michigan, Ann Arbor, MI, USA
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11
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Richardson TG, Urquijo H, Holmes MV, Davey Smith G. Leveraging family history data to disentangle time-varying effects on disease risk using lifecourse mendelian randomization. Eur J Epidemiol 2023; 38:765-769. [PMID: 37156976 PMCID: PMC10276123 DOI: 10.1007/s10654-023-01001-8] [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: 07/16/2022] [Accepted: 03/31/2023] [Indexed: 05/10/2023]
Abstract
Lifecourse Mendelian randomization is a causal inference technique which harnesses genetic variants with time-varying effects to develop insight into the influence of age-dependent lifestyle factors on disease risk. Here, we apply this approach to evaluate whether childhood body size has a direct consequence on 8 major disease endpoints by analysing parental history data from the UK Biobank study.Our findings suggest that, whilst childhood body size increases later risk of outcomes such as heart disease (odds ratio (OR) = 1.15, 95% CI = 1.07 to 1.23, P = 7.8 × 10- 5) and diabetes (OR = 1.43, 95% CI = 1.31 to 1.56, P = 9.4 × 10- 15) based on parental history data, these findings are likely attributed to a sustained influence of being overweight for many years over the lifecourse. Likewise, we found evidence that remaining overweight throughout the lifecourse increases risk of lung cancer, which was partially mediated by lifetime smoking index. In contrast, using parental history data provided evidence that being overweight in childhood may have a protective effect on risk of breast cancer (OR = 0.87, 95% CI = 0.78 to 0.97, P = 0.01), corroborating findings from observational studies and large-scale genetic consortia.Large-scale family disease history data can provide a complementary source of evidence for epidemiological studies to exploit, particularly given that they are likely more robust to sources of selection bias (e.g. survival bias) compared to conventional case control studies. Leveraging these data using approaches such as lifecourse Mendelian randomization can help elucidate additional layers of evidence to dissect age-dependent effects on disease risk.
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Affiliation(s)
- Tom G Richardson
- MRC Integrative Epidemiology Unit (IEU), Population Health Sciences, Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK.
| | - Helena Urquijo
- MRC Integrative Epidemiology Unit (IEU), Population Health Sciences, Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
| | - Michael V Holmes
- MRC Integrative Epidemiology Unit (IEU), Population Health Sciences, Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
| | - George Davey Smith
- MRC Integrative Epidemiology Unit (IEU), Population Health Sciences, Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
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12
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Gormley M, Dudding T, Thomas SJ, Tyrrell J, Ness AR, Pring M, Legge D, Davey Smith G, Richmond RC, Vincent EE, Bull C. Evaluating the effect of metabolic traits on oral and oropharyngeal cancer risk using Mendelian randomization. eLife 2023; 12:e82674. [PMID: 37042641 PMCID: PMC10147379 DOI: 10.7554/elife.82674] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2022] [Accepted: 04/11/2023] [Indexed: 04/13/2023] Open
Abstract
A recent World Health Organization report states that at least 40% of all cancer cases may be preventable, with smoking, alcohol consumption, and obesity identified as three of the most important modifiable lifestyle factors. Given the significant decline in smoking rates, particularly within developed countries, other potentially modifiable risk factors for head and neck cancer warrant investigation. Obesity and related metabolic disorders such as type 2 diabetes (T2D) and hypertension have been associated with head and neck cancer risk in multiple observational studies. However, adiposity has also been correlated with smoking, with bias, confounding or reverse causality possibly explaining these findings. To overcome the challenges of observational studies, we conducted two-sample Mendelian randomization (inverse variance weighted [IVW] method) using genetic variants which were robustly associated with adiposity, glycaemic and blood pressure traits in genome-wide association studies (GWAS). Outcome data were taken from the largest available GWAS of 6034 oral and oropharyngeal cases, with 6585 controls. We found limited evidence of a causal effect of genetically proxied body mass index (BMI; OR IVW = 0.89, 95% CI 0.72-1.09, p = 0.26 per 1 standard deviation in BMI [4.81kg/m2]) on oral and oropharyngeal cancer risk. Similarly, there was limited evidence for related traits including T2D and hypertension. Small effects cannot be excluded given the lack of power to detect them in currently available GWAS.
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Affiliation(s)
- Mark Gormley
- MRC Integrative Epidemiology Unit, Population Health Sciences, Bristol Medical School, University of BristolBristolUnited Kingdom
- Bristol Dental Hospital and School, University of BristolBristolUnited Kingdom
| | - Tom Dudding
- Bristol Dental Hospital and School, University of BristolBristolUnited Kingdom
| | - Steven J Thomas
- Bristol Dental Hospital and School, University of BristolBristolUnited Kingdom
| | - Jessica Tyrrell
- University of Exeter Medical School, RILD Building, RD&E HospitalExeterUnited Kingdom
| | - Andrew R Ness
- University Hospitals Bristol and Weston NHS Foundation Trust National Institute for Health Research Bristol Biomedical Research Centre, University of BristolBristolUnited Kingdom
| | - Miranda Pring
- Bristol Dental Hospital and School, University of BristolBristolUnited Kingdom
| | - Danny Legge
- Translational Health Sciences, Bristol Medical School, University of BristolBristolUnited Kingdom
| | - George Davey Smith
- MRC Integrative Epidemiology Unit, Population Health Sciences, Bristol Medical School, University of BristolBristolUnited Kingdom
| | - Rebecca C Richmond
- MRC Integrative Epidemiology Unit, Population Health Sciences, Bristol Medical School, University of BristolBristolUnited Kingdom
| | - Emma E Vincent
- MRC Integrative Epidemiology Unit, Population Health Sciences, Bristol Medical School, University of BristolBristolUnited Kingdom
- Translational Health Sciences, Bristol Medical School, University of BristolBristolUnited Kingdom
| | - Caroline Bull
- MRC Integrative Epidemiology Unit, Population Health Sciences, Bristol Medical School, University of BristolBristolUnited Kingdom
- Translational Health Sciences, Bristol Medical School, University of BristolBristolUnited Kingdom
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13
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Taylor K, Wootton RE, Yang Q, Oddie S, Wright J, Yang TC, Magnus M, Andreassen OA, Borges MC, Caputo M, Lawlor DA. The effect of maternal BMI, smoking and alcohol on congenital heart diseases: a Mendelian randomisation study. BMC Med 2023; 21:35. [PMID: 36721200 PMCID: PMC9890815 DOI: 10.1186/s12916-023-02731-y] [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: 03/28/2022] [Accepted: 01/10/2023] [Indexed: 02/02/2023] Open
Abstract
BACKGROUND Congenital heart diseases (CHDs) remain a significant cause of infant morbidity and mortality. Epidemiological studies have explored maternal risk factors for offspring CHDs, but few have used genetic epidemiology methods to improve causal inference. METHODS Three birth cohorts, including 65,510 mother/offspring pairs (N = 562 CHD cases) were included. We used Mendelian randomisation (MR) analyses to explore the effects of genetically predicted maternal body mass index (BMI), smoking and alcohol on offspring CHDs. We generated genetic risk scores (GRS) using summary data from large-scale genome-wide association studies (GWAS) and validated the strength and relevance of the genetic instrument for exposure levels during pregnancy. Logistic regression was used to estimate the odds ratio (OR) of CHD per 1 standard deviation (SD) higher GRS. Results for the three cohorts were combined using random-effects meta-analyses. We performed several sensitivity analyses including multivariable MR to check the robustness of our findings. RESULTS The GRSs associated with the exposures during pregnancy in all three cohorts. The associations of the GRS for maternal BMI with offspring CHD (pooled OR (95% confidence interval) per 1SD higher GRS: 0.95 (0.88, 1.03)), lifetime smoking (pooled OR: 1.01 (0.93, 1.09)) and alcoholic drinks per week (pooled OR: 1.06 (0.98, 1.15)) were close to the null. Sensitivity analyses yielded similar results. CONCLUSIONS Our results do not provide robust evidence of an effect of maternal BMI, smoking or alcohol on offspring CHDs. However, results were imprecise. Our findings need to be replicated, and highlight the need for more and larger studies with maternal and offspring genotype and offspring CHD data.
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Affiliation(s)
- Kurt Taylor
- Bristol Medical School, Population Health Science, Bristol, BS8 2BN, UK.
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, BS8 2BN, UK.
| | - Robyn E Wootton
- Bristol Medical School, Population Health Science, Bristol, BS8 2BN, UK
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, BS8 2BN, UK
- Nic Waals Institute, Lovisenberg Diaconal Hospital, Oslo, Norway
| | - Qian Yang
- Bristol Medical School, Population Health Science, Bristol, BS8 2BN, UK
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, BS8 2BN, UK
| | - Sam Oddie
- University of York, Heslington, York, UK
| | - John Wright
- Bradford Institute for Health Research, Bradford Teaching Hospitals NHS Foundation Trust, Bradford, UK
| | - Tiffany C Yang
- Bradford Institute for Health Research, Bradford Teaching Hospitals NHS Foundation Trust, Bradford, UK
| | - Maria Magnus
- Bristol Medical School, Population Health Science, Bristol, BS8 2BN, UK
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, BS8 2BN, UK
- Centre for Fertility and Health, Norwegian Institute of Public Health, Oslo, Norway
| | - Ole A Andreassen
- Division of Mental Health and Addiction, NORMENT Centre, Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- KG Jebsen Centre for Neurodevelopmental Disorders, Oslo University Hospital and Institute of Clinical Medicine, Oslo, Norway
| | - Maria Carolina Borges
- Bristol Medical School, Population Health Science, Bristol, BS8 2BN, UK
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, BS8 2BN, UK
| | - Massimo Caputo
- Bristol Medical School, Translational Science, Bristol, UK
| | - Deborah A Lawlor
- Bristol Medical School, Population Health Science, Bristol, BS8 2BN, UK
- MRC Integrative Epidemiology Unit, University of Bristol, Bristol, BS8 2BN, UK
- Bristol Medical School, Translational Science, Bristol, UK
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14
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Leyden GM, Greenwood MP, Gaborieau V, Han Y, Amos CI, Brennan P, Murphy D, Davey Smith G, Richardson TG. Disentangling the aetiological pathways between body mass index and site-specific cancer risk using tissue-partitioned Mendelian randomisation. Br J Cancer 2023; 128:618-625. [PMID: 36434155 PMCID: PMC9938133 DOI: 10.1038/s41416-022-02060-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Revised: 10/30/2022] [Accepted: 11/02/2022] [Indexed: 11/27/2022] Open
Abstract
BACKGROUND Body mass index (BMI) is known to influence the risk of various site-specific cancers, however, dissecting which subcomponents of this heterogenous risk factor are predominantly responsible for driving disease effects has proven difficult to establish. We have leveraged tissue-specific gene expression to separate the effects of distinct phenotypes underlying BMI on the risk of seven site-specific cancers. METHODS SNP-exposure estimates were weighted in a multivariable Mendelian randomisation analysis by their evidence for colocalization with subcutaneous adipose- and brain-tissue-derived gene expression using a recently developed methodology. RESULTS Our results provide evidence that brain-tissue-derived BMI variants are predominantly responsible for driving the genetically predicted effect of BMI on lung cancer (OR: 1.17; 95% CI: 1.01-1.36; P = 0.03). Similar findings were identified when analysing cigarettes per day as an outcome (Beta = 0.44; 95% CI: 0.26-0.61; P = 1.62 × 10-6), highlighting a possible shared aetiology or mediator effect between brain-tissue BMI, smoking and lung cancer. Our results additionally suggest that adipose-tissue-derived BMI variants may predominantly drive the effect of BMI and increased risk for endometrial cancer (OR: 1.71; 95% CI: 1.07-2.74; P = 0.02), highlighting a putatively important role in the aetiology of endometrial cancer. CONCLUSIONS The study provides valuable insight into the divergent underlying pathways between BMI and the risk of site-specific cancers.
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Affiliation(s)
- Genevieve M Leyden
- MRC Integrative Epidemiology Unit, Bristol Population Health Science Institute, University of Bristol, Bristol, BS8 2BN, UK.
- Bristol Medical School: Translational Health Sciences, Dorothy Hodgkin Building, University of Bristol, Bristol, BS1 3NY, UK.
| | - Michael P Greenwood
- Bristol Medical School: Translational Health Sciences, Dorothy Hodgkin Building, University of Bristol, Bristol, BS1 3NY, UK
| | - Valérie Gaborieau
- Genomic Epidemiology Branch, International Agency for Research on Cancer (IARC/WHO), Lyon, France
| | - Younghun Han
- Institute for Clinical and Translational Research, Baylor College of Medicine, Houston, TX, USA
- Section of Epidemiology and Population Sciences, Department of Medicine, Baylor College of Medicine, Houston, TX, USA
| | - Christopher I Amos
- Institute for Clinical and Translational Research, Baylor College of Medicine, Houston, TX, USA
- Section of Epidemiology and Population Sciences, Department of Medicine, Baylor College of Medicine, Houston, TX, USA
- Dan L Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, TX, USA
| | - Paul Brennan
- Bristol Medical School: Translational Health Sciences, Dorothy Hodgkin Building, University of Bristol, Bristol, BS1 3NY, UK
| | - David Murphy
- Bristol Medical School: Translational Health Sciences, Dorothy Hodgkin Building, University of Bristol, Bristol, BS1 3NY, UK
| | - George Davey Smith
- MRC Integrative Epidemiology Unit, Bristol Population Health Science Institute, University of Bristol, Bristol, BS8 2BN, UK
| | - Tom G Richardson
- MRC Integrative Epidemiology Unit, Bristol Population Health Science Institute, University of Bristol, Bristol, BS8 2BN, UK.
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15
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Pérez-Martín H, Lidón-Moyano C, González-Marrón A, Fu M, Pérez-Ortuño R, Ballbè M, Martín-Sánchez JC, Pascual JA, Fernández E, Martínez-Sánchez JM. Variation in Nicotine Metabolization According to Biological Factors and Type of Nicotine Consumer. Healthcare (Basel) 2023; 11:healthcare11020179. [PMID: 36673548 PMCID: PMC9859072 DOI: 10.3390/healthcare11020179] [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: 11/29/2022] [Revised: 01/03/2023] [Accepted: 01/04/2023] [Indexed: 01/10/2023] Open
Abstract
This study aims to describe the nicotine metabolite ratio among tobacco smokers and electronic cigarette (e-cigarette) users and nonusers. We analyzed pooled data from a longitudinal and a cross-sectional study of the adult population from the city of Barcelona. The final sample included information on 166 smokers, 164 e-cigarettes users with nicotine, 41 e-cigarette users without nicotine, 95 dual users (users of both products), and 508 nonusers. We used log-linear models to control for the potential confounding effect of the daily number of cigarettes smoked. Salivary nicotine metabolic rate assessment included the rate of nicotine metabolism (cotinine/nicotine) and the nicotine metabolite ratio (trans-3′-hydroxycotinine/cotinine). Exclusive users of e-cigarette without nicotine have the lowest rate of nicotine metabolism (Geometric mean: 0.08, p-values < 0.001) while cigarette smokers have the highest (Geometric mean: 2.08, p-values < 0.001). Nonusers have lower nicotine metabolic rate than cigarette smokers (Geometric means: 0.23 vs. 0.18, p-value < 0.05). Younger individuals (18−44 years) have a higher rate of nicotine metabolism than older individuals (45−64 years and 65−89) (Geometric means: 0.53 vs. 0.42 and 0.31, respectively, p-values < 0.01) and individuals with lower body mass index (21−25 kg/m2) have a higher rate of nicotine metabolism than the rest (26−30 kg/m2 and 31−60 kg/m2) (Geometric means: 0.52 vs. 0.35 and 0.36, respectively-values < 0.01). Nicotine metabolic rates are useful biomarkers when reporting smoking status and biological differences between individuals.
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Affiliation(s)
- Hipólito Pérez-Martín
- Group of Evaluation of Health Determinants and Health Policies, Department of Basic Sciences, Universitat Internacional de Catalunya, Carrer de Josep Trueta s/n, 08195 Barcelona, Spain
| | - Cristina Lidón-Moyano
- Group of Evaluation of Health Determinants and Health Policies, Department of Basic Sciences, Universitat Internacional de Catalunya, Carrer de Josep Trueta s/n, 08195 Barcelona, Spain
- Correspondence: (C.L.-M.); (A.G.-M.)
| | - Adrián González-Marrón
- Group of Evaluation of Health Determinants and Health Policies, Department of Basic Sciences, Universitat Internacional de Catalunya, Carrer de Josep Trueta s/n, 08195 Barcelona, Spain
- Correspondence: (C.L.-M.); (A.G.-M.)
| | - Marcela Fu
- Tobacco Control Unit, Cancer Prevention and Control Program, Institut Català d’Oncologia, 08908 Barcelona, Spain
- Tobacco Control Research Group, Epidemiology and Public Health Program, Institut d’Investigació Biomèdica de Bellvitge—IDIBELL, 08908 Barcelona, Spain
- School of Medicine and Health Sciences, Universitat de Barcelona, 08007 Barcelona, Spain
- Center for Biomedical Research in Respiratory Diseases (CIBERES), Instituto de Salud Carlos III, 28220 Madrid, Spain
| | - Raúl Pérez-Ortuño
- Group of Integrative Pharmacology and Systems Neuroscience, Neurosciences Programme, IMIM (Hospital del Mar Medical Research Institute), Parc de Recerca Biomèdica de Barcelona, 08003 Barcelona, Spain
| | - Montse Ballbè
- Tobacco Control Unit, Cancer Prevention and Control Program, Institut Català d’Oncologia, 08908 Barcelona, Spain
- Tobacco Control Research Group, Epidemiology and Public Health Program, Institut d’Investigació Biomèdica de Bellvitge—IDIBELL, 08908 Barcelona, Spain
- Center for Biomedical Research in Respiratory Diseases (CIBERES), Instituto de Salud Carlos III, 28220 Madrid, Spain
- Addictions Unit, Institute of Neurosciences, Hospital Clínic de Barcelona, 08036 Barcelona, Spain
| | - Juan Carlos Martín-Sánchez
- Group of Evaluation of Health Determinants and Health Policies, Department of Basic Sciences, Universitat Internacional de Catalunya, Carrer de Josep Trueta s/n, 08195 Barcelona, Spain
| | - José A. Pascual
- Group of Integrative Pharmacology and Systems Neuroscience, Neurosciences Programme, IMIM (Hospital del Mar Medical Research Institute), Parc de Recerca Biomèdica de Barcelona, 08003 Barcelona, Spain
- Department of Medicine and Life Sciences, Universitat Pompeu Fabra, Barcelona Biomedical Research Park (PRBB), 08003 Barcelona, Spain
| | - Esteve Fernández
- Tobacco Control Unit, Cancer Prevention and Control Program, Institut Català d’Oncologia, 08908 Barcelona, Spain
- Tobacco Control Research Group, Epidemiology and Public Health Program, Institut d’Investigació Biomèdica de Bellvitge—IDIBELL, 08908 Barcelona, Spain
- School of Medicine and Health Sciences, Universitat de Barcelona, 08007 Barcelona, Spain
- Center for Biomedical Research in Respiratory Diseases (CIBERES), Instituto de Salud Carlos III, 28220 Madrid, Spain
| | - Jose M. Martínez-Sánchez
- Group of Evaluation of Health Determinants and Health Policies, Department of Basic Sciences, Universitat Internacional de Catalunya, Carrer de Josep Trueta s/n, 08195 Barcelona, Spain
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16
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Body mass index and incidence of lung cancer in the HUNT study: using observational and Mendelian randomization approaches. BMC Cancer 2022; 22:1152. [DOI: 10.1186/s12885-022-10215-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2021] [Accepted: 10/23/2022] [Indexed: 11/11/2022] Open
Abstract
Abstract
Background
Traditional observational studies have shown an inverse association between body mass index (BMI) and lung cancer risk. Mendelian randomization (MR) analysis using genetic variants as instruments for BMI may clarify the nature of the association.
Aims
We studied the causal association between BMI and lung cancer incidence using observational and MR approaches.
Methods
We followed up 62,453 cancer-free Norwegian adults from 1995–97 (HUNT2) until 2017. BMI at baseline in HUNT2 was classified as < 25.0, 25.0–29.9 and ≥ 30.0 kg/m2. BMI change over ten years between HUNT1 (1984–86) and HUNT2 was calculated and classified into quartiles. Seventy-five genetic variants were included as instruments for BMI (among which 14 also associated with smoking behavior). Incident lung cancer cases were ascertained from the Cancer Registry of Norway. Cox regression models were used to estimate hazard ratios (HRs) with 95% confidence intervals (CIs). Multivariable MR was used to examine the effect of BMI after genetically controlling for smoking.
Results
During a median follow-up of 21.1 years, 1009 participants developed lung cancer including 327 with lung adenocarcinoma. The HRs and 95% CIs for incidence of adenocarcinoma were 0.73 (0.58–0.92) for BMI 25.0–29.9 kg/m2 and 0.53 (0.37–0.76) for BMI ≥ 30 kg/m2 compared with BMI < 25.0 kg/m2 in HUNT2 (P for trend < 0.001). However, there was little evidence of a dose–response relationship between the BMI change from HUNT1 to HUNT2 in quartiles and the incidence of adenocarcinoma (P for trend = 0.08). Furthermore, multivariable MR approach suggested a positive association between genetically determined 1 kg/m2 increase in BMI and the incidence of adenocarcinoma (HR 1.25, 95% CI 1.02–1.53). No associations were found with other lung cancer histologic types.
Conclusions
Our study suggests that the inverse association between baseline BMI and lung adenocarcinoma in observational analysis may not be causal. More MR studies are needed to confirm our finding of a positive association between BMI and lung adenocarcinoma.
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17
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Gao X, Zhang M, Yang Z, Niu X, Chen J, Zhou B, Wang W, Wei Y, Cheng J, Han S, Zhang Y. Explore the effects of overweight and smoking on spontaneous brain activity: Independent and reverse. Front Neurosci 2022; 16:944768. [PMCID: PMC9597461 DOI: 10.3389/fnins.2022.944768] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2022] [Accepted: 09/20/2022] [Indexed: 11/13/2022] Open
Abstract
Accumulating evidence suggested that overweight and smoking often co-exist. However, current neuroimaging researches have almost always studied smoking or overweight status separately. Here we sought to investigate the neurobiological mechanisms of this comorbid association, by detecting spontaneous brain activity changes associated with smoking and weight status separately and collectively. We used 2 × 2 factorial design and included the following four groups: overweight/normal-weight smokers (n = 34/n = 30) and overweight/normal-weight non-smokers (n = 22/n = 24). The spontaneous brain activity among the four groups was comparable using an amplitude of low-frequency fluctuation (ALFF) method based on resting-state fMRI (rs-fMRI). Furthermore, correlation analyses between brain activity changes, smoking severity and BMI values were performed. A main effect of smoking was discovered in the default mode network (DMN) and visual network related brain regions. Moreover, overweight people had high ALFF value in the brain regions associated with reward and executive control. More importantly, smoking and overweight both affected brain activity of the middle temporal gyrus (MTG), but the effect was opposite. And the brain activity of MTG was negatively correlated with smoking years, pack year and BMI value. These results suggest that smoking and overweight not only affect spontaneous brain activity alone, but also paradoxically affect spontaneous brain activity in the MTG. This suggests that we need to control for weight as a variable when studying spontaneous brain activity in smokers. Besides, this interaction may provide a neurological explanation for the comorbidity of overweight and smoking and a target for the treatment of comorbid populations.
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Affiliation(s)
- Xinyu Gao
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China
- Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China
- Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, China
- Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, China
- Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China
- Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, Zhengzhou, China
| | - Mengzhe Zhang
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China
- Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China
- Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, China
- Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, China
- Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China
- Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, Zhengzhou, China
| | - Zhengui Yang
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China
- Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China
- Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, China
- Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, China
- Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China
- Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, Zhengzhou, China
| | - Xiaoyu Niu
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China
- Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China
- Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, China
- Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, China
- Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China
- Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, Zhengzhou, China
| | - Jingli Chen
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China
- Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China
- Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, China
- Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, China
- Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China
- Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, Zhengzhou, China
| | - Bingqian Zhou
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China
- Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China
- Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, China
- Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, China
- Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China
- Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, Zhengzhou, China
| | - Weijian Wang
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China
- Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China
- Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, China
- Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, China
- Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China
- Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, Zhengzhou, China
| | - Yarui Wei
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China
- Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China
- Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, China
- Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, China
- Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China
- Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, Zhengzhou, China
| | - Jingliang Cheng
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China
- Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China
- Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, China
- Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, China
- Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China
- Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, Zhengzhou, China
- Jingliang Cheng,
| | - Shaoqiang Han
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China
- Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China
- Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, China
- Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, China
- Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China
- Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, Zhengzhou, China
- Shaoqiang Han,
| | - Yong Zhang
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Key Laboratory for Functional Magnetic Resonance Imaging and Molecular Imaging of Henan Province, Zhengzhou, China
- Engineering Technology Research Center for Detection and Application of Brain Function of Henan Province, Zhengzhou, China
- Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment of Henan Province, Zhengzhou, China
- Key Laboratory of Magnetic Resonance and Brain Function of Henan Province, Zhengzhou, China
- Key Laboratory of Brain Function and Cognitive Magnetic Resonance Imaging of Zhengzhou, Zhengzhou, China
- Key Laboratory of Imaging Intelligence Research Medicine of Henan Province, Zhengzhou, China
- *Correspondence: Yong Zhang,
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18
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A quantitative synthesis study on body mass index and associated factors among adult men and women in Switzerland. J Nutr Sci 2022; 11:e65. [PMID: 35992574 PMCID: PMC9379928 DOI: 10.1017/jns.2022.66] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2022] [Revised: 07/18/2022] [Accepted: 07/21/2022] [Indexed: 11/27/2022] Open
Abstract
Excess weight is caused by multiple factors and has increased sharply in Switzerland since the 1990s. Its consequences represent a major challenge for Switzerland, both in terms of health and the economy. Until now, there has been no cross-dataset overview study on excess weight in adults in Switzerland. Therefore, our aim was to conduct the first synthesis on excess weight in Switzerland. We included all existing nationwide Swiss studies (eight total), which included information on body mass index (BMI). Mixed multinomial logistic regression analyses were performed to assess the associations between different socio-demographic, lifestyle cofactors and the World Health Organization (WHO) categories for BMI. Along with lifestyle factors, socio-demographic factors were among the strongest determinants of BMI. In addition, self-rated health status was significantly lower for underweight, pre-obese and obese men and women than for normal weight persons. The present study is the first to synthesise all nationwide evidence on the importance of several socio-demographic and lifestyle factors as risk factors for excess weight. In particular, the highlighted importance of lifestyle factors for excess weight opens up the opportunity for further public health interventions.
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19
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Mendelian randomization for causal inference accounting for pleiotropy and sample structure using genome-wide summary statistics. Proc Natl Acad Sci U S A 2022; 119:e2106858119. [PMID: 35787050 PMCID: PMC9282238 DOI: 10.1073/pnas.2106858119] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023] Open
Abstract
Mendelian randomization (MR) is a valuable tool for inferring causal relationships among a wide range of traits using summary statistics from genome-wide association studies (GWASs). Existing summary-level MR methods often rely on strong assumptions, resulting in many false-positive findings. To relax MR assumptions, ongoing research has been primarily focused on accounting for confounding due to pleiotropy. Here, we show that sample structure is another major confounding factor, including population stratification, cryptic relatedness, and sample overlap. We propose a unified MR approach, MR-APSS, which 1) accounts for pleiotropy and sample structure simultaneously by leveraging genome-wide information; and 2) allows the inclusion of more genetic variants with moderate effects as instrument variables (IVs) to improve statistical power without inflating type I errors. We first evaluated MR-APSS using comprehensive simulations and negative controls and then applied MR-APSS to study the causal relationships among a collection of diverse complex traits. The results suggest that MR-APSS can better identify plausible causal relationships with high reliability. In particular, MR-APSS can perform well for highly polygenic traits, where the IV strengths tend to be relatively weak and existing summary-level MR methods for causal inference are vulnerable to confounding effects.
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20
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Coscia C, Gill D, Benítez R, Pérez T, Malats N, Burgess S. Avoiding collider bias in Mendelian randomization when performing stratified analyses. Eur J Epidemiol 2022; 37:671-682. [PMID: 35639294 PMCID: PMC9329404 DOI: 10.1007/s10654-022-00879-0] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2021] [Accepted: 04/28/2022] [Indexed: 11/03/2022]
Abstract
Mendelian randomization (MR) uses genetic variants as instrumental variables to investigate the causal effect of a risk factor on an outcome. A collider is a variable influenced by two or more other variables. Naive calculation of MR estimates in strata of the population defined by a collider, such as a variable affected by the risk factor, can result in collider bias. We propose an approach that allows MR estimation in strata of the population while avoiding collider bias. This approach constructs a new variable, the residual collider, as the residual from regression of the collider on the genetic instrument, and then calculates causal estimates in strata defined by quantiles of the residual collider. Estimates stratified on the residual collider will typically have an equivalent interpretation to estimates stratified on the collider, but they are not subject to collider bias. We apply the approach in several simulation scenarios considering different characteristics of the collider variable and strengths of the instrument. We then apply the proposed approach to investigate the causal effect of smoking on bladder cancer in strata of the population defined by bodyweight. The new approach generated unbiased estimates in all the simulation settings. In the applied example, we observed a trend in the stratum-specific MR estimates at different bodyweight levels that suggested stronger effects of smoking on bladder cancer among individuals with lower bodyweight. The proposed approach can be used to perform MR studying heterogeneity among subgroups of the population while avoiding collider bias.
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Affiliation(s)
- Claudia Coscia
- Genetic and Molecular Epidemiology Group, Spanish National Cancer Research Centre (CNIO), and CIBERONC, Madrid, Spain
- Department of Statistics and Data Science, Complutense University of Madrid, Madrid, Spain
| | - Dipender Gill
- Department of Epidemiology and Biostatistics, Imperial College London, London, UK
- Novo Nordisk Research Centre Oxford, Old Road Campus, Oxford, UK
- Clinical Pharmacology Group, Pharmacy and Medicines Directorate, St George's University Hospitals NHS Foundation Trust, London, UK
- Clinical Pharmacology and Therapeutics Section, Institute for Infection and Immunity, St George's, University of London, London, UK
| | - Raquel Benítez
- Genetic and Molecular Epidemiology Group, Spanish National Cancer Research Centre (CNIO), and CIBERONC, Madrid, Spain
| | - Teresa Pérez
- Department of Statistics and Data Science, Complutense University of Madrid, Madrid, Spain
| | - Núria Malats
- Genetic and Molecular Epidemiology Group, Spanish National Cancer Research Centre (CNIO), and CIBERONC, Madrid, Spain
| | - Stephen Burgess
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK.
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21
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Soares AG, Howe LD, Heron J, Hammerton G, Rich-Edwards J, Magnus MC, Halligan SL, Fraser A. How does childhood maltreatment influence cardiovascular disease? A sequential causal mediation analysis. Int J Epidemiol 2022; 51:555-566. [PMID: 34041541 PMCID: PMC9082818 DOI: 10.1093/ije/dyab085] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/07/2021] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND Childhood maltreatment has been consistently associated with cardiovascular disease (CVD). However, the mechanisms of this relationship are not yet fully understood. We explored the relative contribution of anxiety/depression, smoking, body mass index (BMI) and inflammation (C-reactive protein, CRP) to the association between childhood maltreatment and CVD in men and women aged 40-69 years in the UK. METHODS We used data from 40 596 men and 59 511 women from UK Biobank. To estimate the indirect effects of childhood maltreatment (physical, sexual and emotional abuse, and emotional and physical neglect) on incident CVD via each of the mediators, we applied a sequential mediation approach. RESULTS All forms of maltreatment were associated with increased CVD risk [hazard ratios (HRs) ranging from 1.09 to 1.27]. Together, anxiety/depression, smoking, BMI and inflammation (indexed by CRP) mediated 26-90% of the association between childhood maltreatment and CVD, and the contribution of these mediators differed by type of maltreatment and sex. Anxiety/depression mediated the largest proportion of the association of sexual abuse, emotional abuse and emotional neglect with CVD (accounting for 16-43% of the total effect), especially in women. In men, BMI contributed the most to the indirect effect of associations of physical abuse and physical neglect with CVD; in women, anxiety/depression and BMI had similar contributions. CONCLUSIONS These findings add to the understanding of how childhood maltreatment affects CVD risk and identify modifiable mediating factors that could potentially reduce the burden of CVD in people exposed to maltreatment in early life.
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Affiliation(s)
- Ana G Soares
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
| | - Laura D Howe
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
| | - Jon Heron
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Gemma Hammerton
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
| | - Janet Rich-Edwards
- Department of Medicine, Harvard Medical School and Brigham and Women’s Hospital and Department of Epidemiology, Harvard Chan School of Public Health, Boston, MA, USA
| | - Maria C Magnus
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
- Centre for Fertility and Health, Norwegian Institute of Public Health, Oslo, Norway
| | - Sarah L Halligan
- Department of Psychology, University of Bath, Bath, UK
- Department of Psychiatry and Mental Health, University of Cape Town, Cape Town, South Africa
| | - Abigail Fraser
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK
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22
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Novel insights into the consequences of obesity: a phenotype-wide Mendelian randomization study. Eur J Hum Genet 2022; 30:540-546. [PMID: 34974530 PMCID: PMC9091238 DOI: 10.1038/s41431-021-00978-8] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2020] [Revised: 06/02/2021] [Accepted: 09/28/2021] [Indexed: 12/12/2022] Open
Abstract
Obesity is thought to significantly impact the quality of life. In this study, we sought to evaluate the health consequences of obesity on the risk of a broad spectrum of human diseases. The causal effects of exposing to obesity on health outcomes were inferred using Mendelian randomization (MR) analyses using a fixed effects inverse-variance weighted model. The instrumental variables were SNPs associated with obesity as measured by body mass index (BMI) reported by GIANT consortium. The spectrum of outcome consisted of the phenotypes from published GWAS and the UK Biobank. The MR-Egger intercept test was applied to estimate horizontal pleiotropic effects, along with Cochran's Q test to assess heterogeneity among the causal effects of instrumental variables. Our MR results confirmed many putative disease risks due to obesity, such as diabetes, dyslipidemia, sleep disorder, gout, smoking behaviors, arthritis, myocardial infarction, and diabetes-related eye disease. The novel findings indicated that elevated red blood cell count was inferred as a mediator of BMI-induced type 2 diabetes in our bidirectional MR analysis. Intriguingly, the effects that higher BMI could decrease the risk of both skin and prostate cancers, reduce calorie intake, and increase the portion size warrant further studies. Our results shed light on a novel mechanism of the disease-causing roles of obesity.
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23
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Hou L, Yu Y, Sun X, Liu X, Yu Y, Li H, Xue F. Causal mediation analysis with multiple causally non-ordered and ordered mediators based on summarized genetic data. Stat Methods Med Res 2022; 31:1263-1279. [PMID: 35345945 DOI: 10.1177/09622802221084599] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Causal mediation analysis investigates the mechanism linking exposure and outcome. Dealing with the impact of unobserved confounders among exposure, mediator and outcome is an issue of great concern. Moreover, when multiple mediators exist, this causal pathway intertwines with other causal pathways, rendering it difficult to estimate the path-specific effects. In this study, we propose a method (PSE-MR) to identify and estimate path-specific effects of an exposure (e.g. education) on an outcome (e.g. osteoarthritis risk) through multiple causally ordered and non-ordered mediators (e.g. body mass index and pack-years of smoking) using summarized genetic data, when the sequential ignorability assumption is violated. Specifically, PSE-MR requires a specific rank condition in which the number of instrumental variables is larger than the number of mediators. Furthermore, we illustrate the utility of PSE-MR by providing guidance for practitioners and exploring the mediation effects of body mass index and pack-years of smoking in the causal pathways from education to osteoarthritis risk. Additionally, the results of simulation reveal that the causal estimates of path-specific effects are almost unbiased with good coverage and Type I error properties. Also, we summarize the least number of instrumental variables for the specific number of mediators to achieve 80% power.
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Affiliation(s)
- Lei Hou
- Department of Epidemiology and Health Statistics, School of Public Health, Cheeloo College of Medicine, 12589Shandong University, Jinan, People's Republic of China.,Institute for Medical Dataology, Cheeloo College of Medicine, 12589Shandong University, Jinan, People's Republic of China
| | - Yuanyuan Yu
- Department of Epidemiology and Health Statistics, School of Public Health, Cheeloo College of Medicine, 12589Shandong University, Jinan, People's Republic of China.,Institute for Medical Dataology, Cheeloo College of Medicine, 12589Shandong University, Jinan, People's Republic of China
| | - Xiaoru Sun
- Department of Epidemiology and Health Statistics, School of Public Health, Cheeloo College of Medicine, 12589Shandong University, Jinan, People's Republic of China.,Institute for Medical Dataology, Cheeloo College of Medicine, 12589Shandong University, Jinan, People's Republic of China
| | - Xinhui Liu
- Department of Epidemiology and Health Statistics, School of Public Health, Cheeloo College of Medicine, 12589Shandong University, Jinan, People's Republic of China.,Institute for Medical Dataology, Cheeloo College of Medicine, 12589Shandong University, Jinan, People's Republic of China
| | - Yifan Yu
- Department of Epidemiology and Health Statistics, School of Public Health, Cheeloo College of Medicine, 12589Shandong University, Jinan, People's Republic of China.,Institute for Medical Dataology, Cheeloo College of Medicine, 12589Shandong University, Jinan, People's Republic of China
| | - Hongkai Li
- Department of Epidemiology and Health Statistics, School of Public Health, Cheeloo College of Medicine, 12589Shandong University, Jinan, People's Republic of China.,Institute for Medical Dataology, Cheeloo College of Medicine, 12589Shandong University, Jinan, People's Republic of China
| | - Fuzhong Xue
- Department of Epidemiology and Health Statistics, School of Public Health, Cheeloo College of Medicine, 12589Shandong University, Jinan, People's Republic of China.,Institute for Medical Dataology, Cheeloo College of Medicine, 12589Shandong University, Jinan, People's Republic of China
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24
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Dietary Choices Impact on Greenhouse Gas Emissions: Determinants and Correlates in a Sample of Adults from Eastern Germany. SUSTAINABILITY 2022. [DOI: 10.3390/su14073854] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/04/2022]
Abstract
The present study estimated diet-related greenhouse gas emissions (GHGE) and land use (LU) in a sample of adults, examined main dietary contributors of GHGE, and evaluated socio demographic, lifestyle, and wellbeing factors as potential determinants of high environmental impact. A cross-sectional design based on data collected from the European Prospective Investigation into Cancer and Nutrition (EPIC)—Potsdam cohort (2010–2012) was used. Usual diet was assessed using food frequency questionnaires. Diet-related GHGE and LU were calculated using a European-average lifecycle analyses-food-item database (SHARP-ID). Information on potential determinants were collected using self-administered questionnaires. Men (n = 404) and women (n = 401) at an average age of 66.0 ± 8.4 years were included. Dietary-related energy-adjusted GHGE in men was 6.6 ± 0.9 and in women was 7.0 ± 1.1 kg CO2 eq per 2000 kcal. LU in men was 7.8 ± 1.2 and in women was 7.7 ± 1.2 m2/year per 2000 kcal. Food groups contributing to most GHGE included dairy, meat and non-alcoholic beverages. Among women, being single, having a job, being a smoker and having higher BMI were characteristics associated with higher GHGE, whereas for men these included being married, longer sleeping duration and higher BMI. Further studies are warranted to provide insights into population-specific determinants of sustainable dietary choices.
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25
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Lu Y, Tang H, Huang P, Wang J, Deng P, Li Y, Zheng J, Weng L. Assessment of causal effects of visceral adipose tissue on risk of cancers: a Mendelian randomization study. Int J Epidemiol 2022; 51:1204-1218. [PMID: 35218345 PMCID: PMC9380424 DOI: 10.1093/ije/dyac025] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2021] [Accepted: 02/05/2022] [Indexed: 12/20/2022] Open
Abstract
BACKGROUND Despite the established association between obesity and cancer risk, it remains unclear whether visceral obesity is causally related to cancer risk and whether it is more pro-oncogenic than total body fat. METHODS We conducted two-sample Mendelian randomization (MR) analysis to assess the causal effects of visceral adipose tissue (VAT) on six common cancers. For exposure data, 221 genetic variants associated with the predicted volume of VAT in 325 153 Europeans from UK Biobank were used as instrumental variables. Genetic association data of six common cancers (breast, lung, colorectal, ovarian, pancreatic and prostate cancers) were obtained from large-scale consortia with an average of 19 576 cases and 43 272 controls. We performed univariable MR with five MR methods [inverse-variance weighted (IVW), MR-Egger regression, weighted median, MR-Pleiotropy Residual Sum and Outlier (MR-PRESSO) and Radial MR] and multivariable MR to estimate the effect of VAT independent of body mass index (BMI). Finally, we performed a series of sensitivity analyses as validation of primary MR results. RESULTS Two associations survived the false discovery rate correction for multiple testing (q-value < 0.05): in IVW, the odds ratios (95% CIs) per unit increase in genetically determined VAT were 1.65 (1.03 to 2.62) for pancreatic cancer and 1.47 (1.20 to 1.82) for lung squamous-cell carcinoma, respectively, which showed the same directions and overlapped confidence intervals with MR-Egger regression and weighted median results. There were no outlier variants identified by MR-PRESSO and no evidence supporting the presence of heterogeneity and pleiotropy in sensitivity analyses, although with wider confidence intervals that included the null, multivariable MR results for these two cancers showed the same directions and similar effect sizes as in IVW, which were independent of the effect from BMI. There was no evidence for a causal effect of VAT on the risk of other types of cancer. CONCLUSION Our findings suggest that lifelong exposure to elevated volumes of VAT might increase the risk of pancreatic cancer and lung squamous-cell carcinoma, highlighting the importance of revealing the underlying mechanisms for intervention targets.
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Affiliation(s)
| | | | | | - Jie Wang
- Center of Clinical Pharmacology, The Third Xiangya Hospital, Central South University, Changsha, China
| | - Peizhi Deng
- Center of Clinical Pharmacology, The Third Xiangya Hospital, Central South University, Changsha, China
| | - Yalan Li
- Center of Clinical Pharmacology, The Third Xiangya Hospital, Central South University, Changsha, China
| | | | - Liang Weng
- Corresponding author. Department of Pathology, Xiangya Hospital, Central South University, Changsha 410008, China. E-mail:
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Mbutiwi FIN, Dessy T, Sylvestre MP. Mendelian Randomization: A Review of Methods for the Prevention, Assessment, and Discussion of Pleiotropy in Studies Using the Fat Mass and Obesity-Associated Gene as an Instrument for Adiposity. Front Genet 2022; 13:803238. [PMID: 35186031 PMCID: PMC8855149 DOI: 10.3389/fgene.2022.803238] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2021] [Accepted: 01/14/2022] [Indexed: 11/25/2022] Open
Abstract
Pleiotropy assessment is critical for the validity of Mendelian randomization (MR) analyses, and its management remains a challenging task for researchers. This review examines how the authors of MR studies address bias due to pleiotropy in practice. We reviewed Pubmed, Medline, Embase and Web of Science for MR studies published before 21 May 2020 that used at least one single-nucleotide polymorphism (SNP) in the fat mass and obesity-associated (FTO) gene as instrumental variable (IV) for body mass index, irrespective of the outcome. We reviewed: 1) the approaches used to prevent pleiotropy, 2) the methods cited to detect or control the independence or the exclusion restriction assumption highlighting whether pleiotropy assessment was explicitly stated to justify the use of these methods, and 3) the discussion of findings related to pleiotropy. We included 128 studies, of which thirty-three reported one approach to prevent pleiotropy, such as the use of multiple (independent) SNPs combined in a genetic risk score as IVs. One hundred and twenty studies cited at least one method to detect or account for pleiotropy, including robust and other IV estimation methods (n = 70), methods for detection of heterogeneity between estimated causal effects across IVs (n = 72), methods to detect or account associations between IV and outcome outside thought the exposure (n = 85), and other methods (n = 5). Twenty-one studies suspected IV invalidity, of which 16 explicitly referred to pleiotropy, and six incriminating FTO SNPs. Most reviewed MR studies have cited methods to prevent or to detect or control bias due to pleiotropy. These methods are heterogeneous, their triangulation should increase the reliability of causal inference.
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Affiliation(s)
- Fiston Ikwa Ndol Mbutiwi
- University of Montreal Hospital Research Centre (CRCHUM), Montreal, QC, Canada
- Faculty of Medicine, University of Kikwit, Kikwit, Democratic Republic of the Congo
| | - Tatiana Dessy
- University of Montreal Hospital Research Centre (CRCHUM), Montreal, QC, Canada
| | - Marie-Pierre Sylvestre
- University of Montreal Hospital Research Centre (CRCHUM), Montreal, QC, Canada
- Department of Social and Preventive Medicine, University of Montreal Public Health School (ESPUM), Montreal, QC, Canada
- *Correspondence: Marie-Pierre Sylvestre,
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Ely AV, Keyser H, Spilka N, Franklin TR, Wetherill RR, Audrain-McGovern J. An exploration of associations between smoking motives and behavior as a function of body mass index. DRUG AND ALCOHOL DEPENDENCE REPORTS 2021; 1:100008. [PMID: 36843906 PMCID: PMC9948816 DOI: 10.1016/j.dadr.2021.100008] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/07/2021] [Revised: 11/05/2021] [Accepted: 11/18/2021] [Indexed: 11/19/2022]
Abstract
Objective Cigarette smoking and obesity are the leading causes of premature morbidity and mortality and increase the risk of all-cause mortality four-fold when comorbid. Although research suggests that smoking motives may differ based on body mass index (BMI), it is unclear how these differences translate to smoking behavior. Method Three groups of adults who smoke cigarettes (N = 79; obese n = 25, overweight n = 30, and lean n = 24) completed measures of smoking and the Smoking Motivations Questionnaire. Groups did not differ on age, education, cigarettes per day (CPD), pack-years, or nicotine dependence, as measured by the Fagerström Test for Cigarette Dependence (FTCD). Results Analyses revealed different associations between reasons for smoking and smoking behavior depending on lean, overweight, or obesity status. Participants (N = 37 female, average age 39.8 years) self-reported smoking was positively associated with Addictive, and Automatic subscale scores among lean participants, with only the Addictive subscale score among those with overweight, and only the Automatic subscale score among those with obesity. Post hoc MANCOVA analysis revealed a significant interaction effect of Group x Automatic Smoking on Pack-years (F(2, 79)=3.34, p = 0.04). Conclusion Findings suggest smoking motives are differentially associated with smoking behavior in adults who smoke depending on weight status. The daily smoking rate of participants with obesity may be less related to the addictive quality of smoking, and automaticity may be less associated with smoking history in those with overweight. Additional research on the influence of BMI on cigarette smoking is necessary to fully elucidate how obesity may impact treatment outcomes to optimize smoking cessation treatment among those with excess body weight.
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Affiliation(s)
- Alice V. Ely
- University of Pennsylvania, Department of Psychiatry, 3535 Market St Suite 500, Philadelphia, PA 19104, United States
| | - Heather Keyser
- University of Pennsylvania, Department of Psychiatry, 3535 Market St Suite 500, Philadelphia, PA 19104, United States
| | - Nathaniel Spilka
- University of Pennsylvania, Department of Psychiatry, 3535 Market St Suite 500, Philadelphia, PA 19104, United States
| | - Teresa R. Franklin
- University of Pennsylvania, Department of Psychiatry, 3535 Market St Suite 500, Philadelphia, PA 19104, United States
| | - Reagan R. Wetherill
- University of Pennsylvania, Department of Psychiatry, 3535 Market St Suite 500, Philadelphia, PA 19104, United States
| | - Janet Audrain-McGovern
- University of Pennsylvania, Department of Psychiatry, 3535 Market St Suite 500, Philadelphia, PA 19104, United States
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Ali AM, Hori H, Kim Y, Kunugi H. Predictors of Nutritional Status, Depression, Internet Addiction, Facebook Addiction, and Tobacco Smoking Among Women With Eating Disorders in Spain. Front Psychiatry 2021; 12:735109. [PMID: 34899416 PMCID: PMC8663168 DOI: 10.3389/fpsyt.2021.735109] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/02/2021] [Accepted: 10/19/2021] [Indexed: 01/04/2023] Open
Abstract
Eating disorders (EDs) are a complex group of psychiatric conditions that involve dysfunctional eating patterns, nutritional alterations, and other comorbid psychopathologies. Some women with EDs may develop problematic internet use while they attempt to get information on dieting/weight control or get online support from people with similar problems. They may also drift toward tobacco smoking as a method to regulate their weight or to cope with their weight-related dysphoria. The occurrence of these conditions in EDs may prolong disease course and impede recovery. This study used structural equation modeling to investigate nutritional status (noted by body mass index, BMI), depression psychopathology, internet addiction (depicted by the Internet Addiction Test), Facebook addiction (depicted by the Bergen Facebook Addiction Scale), and smoking among 123 Spanish women diagnosed with EDs (mean age = 27.3 ± 10.6 years). History of hospitalization, marital status, age, and the level of education predicted BMI in certain ED groups. BMI did not predict depression, but it predicted internet addiction, Facebook addiction, and smoking in certain ED groups. Depression did not predict BMI, internet/Facebook addition, or smoking in any ED group. Some sociodemographic and clinical variables had indirect effects on depression, internet addiction, and Facebook addiction while age was the only variable expressing a direct effect on all outcome measures. Age, education, and history of prolonged treatment predicted smoking in certain ED patients. The findings signify that a considerable target for interventional strategies addressing nutritional and addictive problems in EDs would be women with high BMI, history of hospitalization, history of prolonged treatment, who are particularly young, single, and less educated. Replication studies in larger samples, which comprise various subtypes of EDs from both genders, are warranted to define the exact interaction among the addressed variables.
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Affiliation(s)
- Amira Mohammed Ali
- Department of Behavioral Medicine, National Institute of Mental Health, National Center of Neurology and Psychiatry, Tokyo, Japan
- Department of Psychiatric Nursing and Mental Health, Faculty of Nursing, Alexandria University, Alexandria, Egypt
| | - Hiroaki Hori
- Department of Behavioral Medicine, National Institute of Mental Health, National Center of Neurology and Psychiatry, Tokyo, Japan
| | - Yoshiharu Kim
- Department of Behavioral Medicine, National Institute of Mental Health, National Center of Neurology and Psychiatry, Tokyo, Japan
| | - Hiroshi Kunugi
- Department of Psychiatry, Teikyo University School of Medicine, Tokyo, Japan
- Department of Mental Disorder Research, National Institute of Neuroscience, National Center of Neurology and Psychiatry, Tokyo, Japan
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Ding J, Tu Z, Chen H, Liu Z. Identifying modifiable risk factors of lung cancer: Indications from Mendelian randomization. PLoS One 2021; 16:e0258498. [PMID: 34662362 PMCID: PMC8523078 DOI: 10.1371/journal.pone.0258498] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2021] [Accepted: 09/28/2021] [Indexed: 11/18/2022] Open
Abstract
Background Lung cancer is the major cause of mortality in tumor patients. While its incidence rate has recently declined, it is still far from satisfactory and its potential modifiable risk factors should be explored. Methods We performed a two-sample Mendelian randomization (MR) study to investigate the causal relationship between potentially modifiable risk factors (namely smoking behavior, alcohol intake, anthropometric traits, blood pressure, lipidemic traits, glycemic traits, and fasting insulin) and lung cancer. Besides, a bi-directional MR analysis was carried out to disentangle the complex relationship between different risk factors. Inverse-variance weighted (IVW) was utilized to combine the estimation for each SNP. Cochrane’s Q value was used to evaluate heterogeneity and two methods, including MR-Egger intercept and MR-PRESSO, were adopted to detect horizontal pleiotropy. Results Three kinds of smoking behavior were all causally associated with lung cancer. Overall, smokers were more likely to suffer from lung cancer compared with non-smokers (OR = 2.58 [1.95, 3.40], p-value = 2.07 x 10−11), and quitting smoking could reduce the risk (OR = 4.29[2.60, 7.07], p-value = 1.23 x 10−8). Furthermore, we found a dose-response relationship between the number of cigarettes and lung cancer (OR = 6.10 [5.35, 6.96], p-value = 4.43x10-161). Lower HDL cholesterol could marginally increase the risk of lung cancer, but become insignificant after Bonferroni correction (OR = 0.82 [0.68, 1.00], p-value = 0.045). In addition, we noted no direct causal relationship between other risk factors and lung cancer. Neither heterogeneity nor pleiotropy was observed in this study. However, when treating the smoking behavior as the outcome, we found the increased BMI could elevate the number of cigarettes per day (beta = 0.139[0.104, 0.175], p-value = 1.99x10-14) and a similar effect was observed for the waist circumference and hip circumference. Additionally, the elevation of SBP could also marginally increase the number of cigarettes per day (beta = 0.001 [0.0002, 0.002], p-value = 0.018). Conclusion Smoking behavior might be the most direct and effective modifiable way to reduce the risk of lung cancer. Meanwhile, smoking behavior can be affected by other risk factors, especially obesity.
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Affiliation(s)
- Jie Ding
- Cancer Center, The Affiliated Changzhou No.2 People’s Hospital of Nanjing Medical University, Changzhou, Jiangsu Province, China
| | - Zhenxing Tu
- Department of Hand Surgery, The Second Hospital of Tangshan, Tangshan, Hebei Province, China
| | - Hongquan Chen
- Department of Bone Surgery, Affiliated Hospital of North China University of Science and Technology, Tangshan, Hebei Province, China
| | - Zhiguang Liu
- Department of Pulmonary and Critical Care Medicine, Affiliated Changzhou Second People’s Hospital affiliated to Nanjing Medical University, Changzhou, Jiangsu Province, China
- * E-mail:
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30
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Fell M, Dack K, Chummun S, Sandy J, Wren Y, Lewis S. Maternal Cigarette Smoking and Cleft Lip and Palate: A Systematic Review and Meta-Analysis. Cleft Palate Craniofac J 2021; 59:1185-1200. [PMID: 34569861 PMCID: PMC9411693 DOI: 10.1177/10556656211040015] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022] Open
Abstract
Objectives
A systematic review and meta-analysis to determine the association between active maternal smoking and cleft lip and palate etiology.
Data sources
Medline, Embase, Web of Science and the Cochrane Library from inception to November, 2020.
Study selection
Observational studies of cigarette smoking habits in pregnant women. Outcomes included cleft lip and/or palate, cleft lip ± palate and cleft palate only.
Data analysis
Publication bias analyses were performed and the Newcastle Ottawa scales were used to assess study quality. Fixed or random effect models were used in the meta-analysis, dependent on risk of statistical heterogeneity.
Results
Forty-five studies were eligible for inclusion of which 11 were cohort and 34 were case–control studies. Sixteen studies were of sufficient standard for inclusion in the meta-analysis. The summary odds ratio for the association between smoking and cleft lip and/or palate was 1.42 (95%CI 1.27-1.59) with a population attributable fraction of 4% (95%CI 3%-5%). There was limited evidence to show a dose–response effect of smoking.
Conclusions
This review reports a moderate association between maternal smoking and orofacial cleft but the overall quality of the conventional observational studies included was poor. There is a need for high quality and novel research strategies to further define the role of smoking in the etiology of cleft lip and palate.
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Affiliation(s)
| | - Kyle Dack
- 1980University of Bristol, Bristol, UK
| | - Shaheel Chummun
- 2394University Hospitals Bristol and Weston NHS Trust, Bristol, UK
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Fouda S, Khan A, Chan S, Mahzari A, Zhou X, Qin C, Vlahos R, Ye JM. Exposure to cigarette smoke precipitates simple hepatosteatosis to NASH in high-fat diet fed mice by inducing oxidative stress. Clin Sci (Lond) 2021; 135:2103-2119. [PMID: 34427662 PMCID: PMC8436265 DOI: 10.1042/cs20210628] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2021] [Revised: 08/23/2021] [Accepted: 08/24/2021] [Indexed: 12/11/2022]
Abstract
Consumption of diet rich in fat and cigarette smoking (CS) are independent risk factors of non-alcoholic steatohepatitis (NASH), and they often occur together in some populations. The present study investigated the mechanisms of high-fat diet (HFD) and CS, individually and in combination, on the pathogenesis of NASH in mice. C57BL/6 male mice were subjected to either a low-fat chow (CH) or HFD with or without mainstream CS-exposure (4 cigarettes/day, 5 days/ week for 14 weeks). HFD alone caused hepatosteatosis (2.5-fold increase in TG content) and a significant increase in 3-nitrotyrisine (by ∼40-fold) but without an indication of liver injury, inflammation or fibrosis. CS alone in CH-fed mice increased in Tnfα expression and macrophage infiltration by 2-fold and relatively less increase in 3-nitrotyrosine (18-fold). Combination of HFD and CS precipitated hepatosteatosis to NASH reflected by exacerbated makers of liver inflammation and fibrosis which were associated with much severe liver oxidative stress (90-fold increase in 3-nitrotyrisine along with 6-fold increase in carbonylated proteins and 56% increase in lipid oxidations). Further studies were performed to administer the antioxidant tempol to CS exposed HFD mice and the results showed that the inhibition of liver oxidative stress prevented inflammatory and fibrotic changes in liver despite persisting hepatosteatosis. Our findings suggest that oxidative stress is a key mechanism underlying CS-promoted progression of simple hepatosteatosis to NASH. Targeting hepatic oxidative stress may be a viable strategy in halting the progression of metabolic associated fatty liver disease.
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Affiliation(s)
- Sherouk Fouda
- School of Health and Biomedical Sciences, RMIT University, Melbourne, VIC, Australia
| | - Anwar Khan
- School of Health and Biomedical Sciences, RMIT University, Melbourne, VIC, Australia
| | - Stanley M.H. Chan
- School of Health and Biomedical Sciences, RMIT University, Melbourne, VIC, Australia
| | - Ali Mahzari
- School of Health and Biomedical Sciences, RMIT University, Melbourne, VIC, Australia
- Department of Laboratory Medicine, Faculty of Applied Medical Sciences, Albaha University, Albaha 65527, Saudi Arabia
| | - Xiu Zhou
- School of Health and Biomedical Sciences, RMIT University, Melbourne, VIC, Australia
| | - Cheng Xue Qin
- Faculty of Pharmacy and Pharmaceutical Sciences, Monash University, VIC, Australia
| | - Ross Vlahos
- School of Health and Biomedical Sciences, RMIT University, Melbourne, VIC, Australia
| | - Ji-Ming Ye
- School of Health and Biomedical Sciences, RMIT University, Melbourne, VIC, Australia
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32
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Chen MM, Guo SE, Yuan CP, Okoli C, Liao YC. Association between Self-Reported Survey Measures and Biomarkers of Second-Hand Tobacco Smoke Exposure in Non-Smoking Pregnant Women. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:ijerph18179197. [PMID: 34501784 PMCID: PMC8431449 DOI: 10.3390/ijerph18179197] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/13/2021] [Revised: 08/24/2021] [Accepted: 08/28/2021] [Indexed: 11/16/2022]
Abstract
Second-hand tobacco smoke (SHS) causes adverse health outcomes in adults. Further studies are needed to evaluate psychosocial SHS exposure measures in comparison to SHS exposure biomarkers, particularly in pregnant women. This study aimed to compare self-reported SHS exposure to urinary cotinine levels in pregnant women. A cross-sectional correlation design was conducted using a convenience sample of 70 non-smoking pregnant women. Measures included self-reported questionnaires and laboratory confirmation of cotinine levels in the urinary samples. Multiple regression analysis was used to assess the correlation after controlling for potential confounding variables. The average level of urinary cotinine among non-smoking pregnant women was 6.77 ng/mL. Medium-strength correlations were found among psychosocial SHS exposure measures and urine cotinine levels. Questions regarding ‘instances of smoking in front of the individual’ and ‘subjective perceived frequency of SHS exposure in past 7 days’ are feasible items for pregnant women in clinics (particularly the first question). Hence, we suggest that these simple questions should be used to assist pregnant women in reducing the harm associated with SHS exposure.
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Affiliation(s)
- Meiman Maggie Chen
- Department of Nursing and Graduate Institute of Nursing, College of Nursing, Chang Gung University of Science and Technology (CGUST), Puzi City 613016, Taiwan; (M.M.C.); (S.-E.G.)
- Chronic Diseases and Health Promotion Research Center, Chang Gung University of Science and Technology (CGUST), Puzi City 613016, Taiwan
| | - Su-Er Guo
- Department of Nursing and Graduate Institute of Nursing, College of Nursing, Chang Gung University of Science and Technology (CGUST), Puzi City 613016, Taiwan; (M.M.C.); (S.-E.G.)
- Chronic Diseases and Health Promotion Research Center, Chang Gung University of Science and Technology (CGUST), Puzi City 613016, Taiwan
- Division of Pulmonary and Critical Care Medicine, Chang Gung Memorial Hospital, Puzi City 613016, Taiwan
- Department of Safety Health and Environmental Engineering, Ming Chi University of Technology, New Taipei City 243303, Taiwan
| | - Chi-Pin Yuan
- Department of Nursing, Ditmanson Medical Foundation Chiayi Christian Hospital, Chiayi City 600566, Taiwan;
| | - Chizimuzo Okoli
- Behavioral Health Wellness Environments for Living and Learning (BH WELL), College of Nursing, University of Kentucky, 315 College of Nursing Building, Lexington, KY 40536, USA;
| | - Yen-Chi Liao
- Department of Nursing, Ditmanson Medical Foundation Chiayi Christian Hospital, Chiayi City 600566, Taiwan;
- Correspondence: ; Tel.: +886-5-2765041-1248
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Thom CS, Ding Z, Levin MG, Damrauer SM, Lee KM, Lynch J, Chang KM, Tsao PS, Cho K, Wilson PWF, Assimes TL, Sun YV, O'Donnell CJ, Vujkovic M, Voight BF. Genetic determinants of increased body mass index mediate the effect of smoking on increased risk for type 2 diabetes but not coronary artery disease. Hum Mol Genet 2021; 29:3327-3337. [PMID: 32833022 PMCID: PMC7689293 DOI: 10.1093/hmg/ddaa193] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2020] [Revised: 07/15/2020] [Accepted: 08/16/2020] [Indexed: 12/20/2022] Open
Abstract
Clinical observations have linked tobacco smoking with increased type 2 diabetes risk. Mendelian randomization analysis has recently suggested smoking may be a causal risk factor for type 2 diabetes. However, this association could be mediated by additional risk factors correlated with smoking behavior, which have not been investigated. We hypothesized that body mass index (BMI) could help to explain the association between smoking and diabetes risk. First, we confirmed that genetic determinants of smoking initiation increased risk for type 2 diabetes (OR 1.21, 95% CI: 1.15–1.27, P = 1 × 10−12) and coronary artery disease (CAD; OR 1.21, 95% CI: 1.16–1.26, P = 2 × 10−20). Additionally, 2-fold increased smoking risk was positively associated with increased BMI (~0.8 kg/m2, 95% CI: 0.54–0.98 kg/m2, P = 1.8 × 10−11). Multivariable Mendelian randomization analyses showed that BMI accounted for nearly all the risk smoking exerted on type 2 diabetes (OR 1.06, 95% CI: 1.01–1.11, P = 0.03). In contrast, the independent effect of smoking on increased CAD risk persisted (OR 1.12, 95% CI: 1.08–1.17, P = 3 × 10−8). Causal mediation analyses agreed with these estimates. Furthermore, analysis using individual-level data from the Million Veteran Program independently replicated the association of smoking behavior with CAD (OR 1.24, 95% CI: 1.12–1.37, P = 2 × 10−5), but not type 2 diabetes (OR 0.98, 95% CI: 0.89–1.08, P = 0.69), after controlling for BMI. Our findings support a model whereby genetic determinants of smoking increase type 2 diabetes risk indirectly through their relationship with obesity. Smokers should be advised to stop smoking to limit type 2 diabetes and CAD risk. Therapeutic efforts should consider pathophysiology relating smoking and obesity.
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Affiliation(s)
- Christopher S Thom
- Division of Neonatology, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA.,Department of Systems Pharmacology and Translational Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.,Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.,Institute of Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Zhuoran Ding
- Department of Systems Pharmacology and Translational Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.,Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.,Institute of Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.,Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Michael G Levin
- Corporal Michael J. Crescenz VA Medical Center, Philadelphia, PA 19104, USA.,Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.,Division of Cardiovascular Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Scott M Damrauer
- Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.,Division of Cardiovascular Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.,Department of Surgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Kyung Min Lee
- VA Informatics and Computing Infrastructure, VA Salt Lake City Health Care System, Salt Lake City, UT 84148, USA
| | - Julie Lynch
- VA Informatics and Computing Infrastructure, VA Salt Lake City Health Care System, Salt Lake City, UT 84148, USA.,University of Massachusetts College of Nursing & Health Sciences, Boston, MA 02125, USA
| | - Kyong-Mi Chang
- Corporal Michael J. Crescenz VA Medical Center, Philadelphia, PA 19104, USA.,Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Philip S Tsao
- VA Palo Alto Health Care System, Palo Alto, CA 94304, USA.,Department of Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Kelly Cho
- VA Boston Healthcare System, Boston, MA 02130, USA.,Department of Medicine, Brigham and Women's Hospital, Boston, MA 02115, USA
| | - Peter W F Wilson
- Atlanta VA Health Care System, Decatur, GA 30033, USA.,Division of Cardiology, Emory University School of Medicine, Atlanta, GA 30322, USA
| | - Themistocles L Assimes
- VA Palo Alto Health Care System, Palo Alto, CA 94304, USA.,Department of Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Yan V Sun
- Atlanta VA Health Care System, Decatur, GA 30033, USA.,Department of Epidemiology, Emory University Rollins School of Public Health, Atlanta, GA 30322, USA
| | - Christopher J O'Donnell
- VA Boston Healthcare System, Boston, MA 02130, USA.,Department of Medicine, Brigham and Women's Hospital, Boston, MA 02115, USA.,Department of Medicine, Harvard Medical School, Boston, MA 02115, USA
| | | | - Marijana Vujkovic
- Corporal Michael J. Crescenz VA Medical Center, Philadelphia, PA 19104, USA.,Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Benjamin F Voight
- Department of Systems Pharmacology and Translational Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.,Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.,Institute of Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.,Corporal Michael J. Crescenz VA Medical Center, Philadelphia, PA 19104, USA
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Gill D, Zuber V, Dawson J, Pearson-Stuttard J, Carter AR, Sanderson E, Karhunen V, Levin MG, Wootton RE, Klarin D, Tsao PS, Tsilidis KK, Damrauer SM, Burgess S, Elliott P. Risk factors mediating the effect of body mass index and waist-to-hip ratio on cardiovascular outcomes: Mendelian randomization analysis. Int J Obes (Lond) 2021; 45:1428-1438. [PMID: 34002035 PMCID: PMC8236409 DOI: 10.1038/s41366-021-00807-4] [Citation(s) in RCA: 35] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/03/2020] [Revised: 02/23/2021] [Accepted: 03/22/2021] [Indexed: 12/29/2022]
Abstract
BACKGROUND Higher body mass index (BMI) and waist-to-hip ratio (WHR) increase the risk of cardiovascular disease, but the extent to which this is mediated by blood pressure, diabetes, lipid traits, and smoking is not fully understood. METHODS Using consortia and UK Biobank genetic association summary data from 140,595 to 898,130 participants predominantly of European ancestry, Mendelian randomization mediation analysis was performed to investigate the degree to which systolic blood pressure (SBP), diabetes, lipid traits, and smoking mediated an effect of BMI and WHR on the risk of coronary artery disease (CAD), peripheral artery disease (PAD) and stroke. RESULTS The odds ratio of CAD per 1-standard deviation increase in genetically predicted BMI was 1.49 (95% CI 1.39 to 1.60). This attenuated to 1.34 (95% CI 1.24 to 1.45) after adjusting for genetically predicted SBP (proportion mediated 27%, 95% CI 3% to 50%), to 1.27 (95% CI 1.17 to 1.37) after adjusting for genetically predicted diabetes (41% mediated, 95% CI 18% to 63%), to 1.47 (95% CI 1.36 to 1.59) after adjusting for genetically predicted lipids (3% mediated, 95% -23% to 29%), and to 1.46 (95% CI 1.34 to 1.58) after adjusting for genetically predicted smoking (6% mediated, 95% CI -20% to 32%). Adjusting for all the mediators together, the estimate attenuated to 1.14 (95% CI 1.04 to 1.26; 66% mediated, 95% CI 42% to 91%). A similar pattern was observed when considering genetically predicted WHR as the exposure, and PAD or stroke as the outcome. CONCLUSIONS Measures to reduce obesity will lower the risk of cardiovascular disease primarily by impacting downstream metabolic risk factors, particularly diabetes and hypertension. Reduction of obesity prevalence alongside control and management of its mediators is likely to be most effective for minimizing the burden of obesity.
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Affiliation(s)
- Dipender Gill
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK.
- Clinical Pharmacology and Therapeutics Section, Institute of Medical and Biomedical Education and Institute for Infection and Immunity, St George's, University of London, London, UK.
- Clinical Pharmacology Group, Pharmacy and Medicines Directorate, St George's University Hospitals NHS Foundation Trust, London, UK.
- Novo Nordisk Research Centre Oxford, Oxford, UK.
| | - Verena Zuber
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK
- Medical Research Council Biostatistics Unit, University of Cambridge, Cambridge, UK
- Medical Research Council Centre for Environment and Health, School of Public Health, Imperial College London, London, UK
| | - Jesse Dawson
- University of Glasgow, Institute of Cardiovascular and Medical Sciences, Glasgow, UK
| | - Jonathan Pearson-Stuttard
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK
- Medical Research Council Centre for Environment and Health, School of Public Health, Imperial College London, London, UK
| | - Alice R Carter
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Eleanor Sanderson
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Ville Karhunen
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK
| | - Michael G Levin
- Division of Cardiovascular Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
- Department of Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
- Corporal Michael J. Crescenz VA Medical Center, Philadelphia, PA, USA
| | - Robyn E Wootton
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, UK
- School of Psychological Science, University of Bristol, Bristol, UK
- National Institute for Health Research Biomedical Research Centre, University Hospitals Bristol NHS Foundation Trust and the University of Bristol, Bristol, UK
| | - Derek Klarin
- Malcom Randall VA Medical Center, Gainesville, FL, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Boston, MA, USA
- Division of Vascular Surgery and Endovascular Therapy, University of Florida School of Medicine, Gainesville, Fl, USA
| | - Philip S Tsao
- VA Palo Alto Health Care System, Livermore, CA, USA
- Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Konstantinos K Tsilidis
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK
- Department of Hygiene and Epidemiology, University of Ioannina Medical School, Ioannina, Greece
| | - Scott M Damrauer
- Corporal Michael J. Crescenz VA Medical Center, Philadelphia, PA, USA
- Department of Surgery, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Stephen Burgess
- Medical Research Council Biostatistics Unit, University of Cambridge, Cambridge, UK
- Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Paul Elliott
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK
- Medical Research Council Centre for Environment and Health, School of Public Health, Imperial College London, London, UK
- UK Dementia Research Institute at Imperial College London, London, UK
- Imperial Biomedical Research Centre, Imperial College London and Imperial College NHS Healthcare Trust, London, UK
- Health Data Research UK-London, London, UK
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35
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Easwaran M, Martinez JD, Ramirez DJ, Gall PA, Erickson-DiRenzo E. Short-term whole body cigarette smoke exposure induces regional differences in cellular response in the mouse larynx. Toxicol Rep 2021; 8:920-937. [PMID: 33996505 PMCID: PMC8099918 DOI: 10.1016/j.toxrep.2021.04.007] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2020] [Revised: 04/11/2021] [Accepted: 04/16/2021] [Indexed: 11/29/2022] Open
Abstract
Short-term CSE induced regional differences in murine laryngeal cellular responses. Basal cell hyperplasia accompanies adaptive cell proliferation in the vocal folds. Increased subglottic cell proliferation persists even after CS cessation. SEM revealed vocal fold microprojection damage with possible necrotic features. Subglandular acidic mucins decreased and neutral mucins increased post-CSE.
The larynx is an essential organ in the respiratory tract and necessary for airway protection, respiration, and phonation. Cigarette smoking is a significant risk factor associated with benign and malignant laryngeal diseases. Despite this association, the underlying mechanisms by which cigarette smoke (CS) drives disease development are not well elucidated. In the current study, we developed a short-term murine whole body inhalation model to evaluate the first CS-induced cellular responses in the glottic [i.e. vocal fold (VF)] and subglottic regions of the larynx. Specifically, we investigated epithelial cell proliferation, cell death, surface topography, and mucus production, at various time points (1 day, 5 days, 10 days) after ∼ 2 h exposure to 3R4F cigarettes (Delivered dose: 5.6968 mg/kg per cigarette) and following cessation for 5 days after a 5 day CS exposure (CSE). CSE elevated levels of BrdU labeled proliferative cells and p63 labeled epithelial basal cells on day 1 in the VF. CSE increased proliferative cells in the subglottis at days 5, 10 and following cessation in the subglottis. Cleaved caspase-3 apoptotic activity was absent in VF at all time points and increased at day 1 in the subglottis. Evaluation of the VF surface by scanning electron microscopy (SEM) revealed significant epithelial microprojection damage at day 10 and early signs of necrosis at days 5 and 10 post-CSE. SEM visualizations additionally indicated the presence of deformed cilia at days 5 and 10 after CSE and post-cessation in the respiratory epithelium lined subglottis. In terms of mucin content, the impact of short-term CSE was observed only at day 10, with decreasing acidic mucin levels and increasing neutral mucin levels. Overall, these findings reveal regional differences in murine laryngeal cellular responses following short-term CSE and provide insight into potential mechanisms underlying CS-induced laryngeal disease development.
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Key Words
- AB/PAS, Alcian blue/Periodic acid Schiff
- BLOQ, below limits of quantitation
- BSA, bovine serum albumin
- BrdU, 5-bromo-2′-deoxyuridine
- CBF, ciliary beat frequency
- CC3, cleaved caspase-3
- CO, Carbon monoxide
- CS, cigarette smoke
- CSE, cigarette smoke exposure
- Cell death
- Cell proliferation
- Cigarette smoke
- DAB, 3,3′-diaminobenzidine
- FTC/ISO, Federal Trade Commission/International Standard Organization
- GSD, geometric standard deviation
- H&E, Hematoxylin and Eosin
- HIER, heat-induced antigen retrieval
- HPF, high power field
- MCC, mucociliary clearance
- MMAD, Mass median aerodynamic diameter
- Mucus production
- Murine larynx
- NMR, nicotine metabolite ratio
- OECD, organization for economic co-operation and development
- PAHs, polycyclic aromatic hydrocarbons
- RE, respiratory epithelium
- REV, reversibility
- ROS, reactive oxygen species
- SCIREQ, Scientific Respiratory Equipment Inc
- SEM, scanning electron microscopy
- SSE, stratified squamous epithelium
- SWGTOX, Scientific Working Group for Forensic Toxicology
- Surface topography
- TBST, tris-buffered saline-tween 20
- TPM, total particulate matter
- TSNA, tobacco-specific nitrosamines
- UPLC-MS/MS, ultra-performance liquid chromatography-tandem mass spectrometer
- VF, vocal fold
- VSC, veterinary service center
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Affiliation(s)
- Meena Easwaran
- Department of Otolaryngology-Head and Neck Surgery, Stanford University School of Medicine, Stanford, CA, USA
| | - Joshua D Martinez
- Department of Otolaryngology-Head and Neck Surgery, Stanford University School of Medicine, Stanford, CA, USA
| | - Daniel J Ramirez
- Department of Otolaryngology-Head and Neck Surgery, Stanford University School of Medicine, Stanford, CA, USA
| | - Phillip A Gall
- Department of Otolaryngology-Head and Neck Surgery, Stanford University School of Medicine, Stanford, CA, USA
| | - Elizabeth Erickson-DiRenzo
- Department of Otolaryngology-Head and Neck Surgery, Stanford University School of Medicine, Stanford, CA, USA
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36
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Are Mendelian randomization investigations immune from bias due to reverse causation? Eur J Epidemiol 2021; 36:253-257. [PMID: 33611685 PMCID: PMC8032609 DOI: 10.1007/s10654-021-00726-8] [Citation(s) in RCA: 44] [Impact Index Per Article: 14.7] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2020] [Accepted: 02/01/2021] [Indexed: 01/27/2023]
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37
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Berro J, Akel M, Hallit S, Obeid S. Relationships between inappropriate eating habits and problematic alcohol use, cigarette and waterpipe dependence among male adolescents in Lebanon. BMC Public Health 2021; 21:140. [PMID: 33446162 PMCID: PMC7809860 DOI: 10.1186/s12889-021-10184-2] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2020] [Accepted: 01/06/2021] [Indexed: 12/11/2022] Open
Abstract
BACKGROUND it is important to investigate the relationship between disordered eating in male adolescents and smoking and alcohol consumption as they are risk factors to other diseases as well. For example, high levels of alcohol accompanied by the acidic damage and nutritional deficit exhibited in people with disordered eating habits - due to induced vomiting - has been shown to increase risk of esophageal cancer. Considering the very few studies done on disordered eating prevailing in males and the prevalence of smoking and drinking habits, our study aims to investigate the correlation between inappropriate eating habits and one's dependence on cigarettes, waterpipes, and alcohol all the while focusing on male adolescents. METHODS This was a cross-sectional observational study that enrolled 389 male students (13-17 years of age) drawn from five Lebanese schools between October and December 2019. RESULTS The MANCOVA analysis was performed taking the addiction scales as the dependent variables and the EAT-26 score as an independent variable, adjusting for the covariates (age, BMI and household crowding index). Higher EAT-26 scores (more inappropriate eating attitudes) were significantly associated with higher waterpipe dependence (B = 0.11; CI 0.06-0.17) and more problematic alcohol use (B = 0.08; CI 0.04-0.12), but not cigarette dependence. CONCLUSIONS This study revealed an association between inappropriate eating and increased drinking and smoking the waterpipe, but not cigarettes. The manifestation of inappropriate eating habits was found to be significant among male adolescents; in the literature, this is also true for female adolescents. In Lebanon, the engagement of adolescents in alcohol drinking and waterpipe smoking is frequent as they are accessible due to the lack of law enforcement and supervision in the country. In addition, while this study found an association between inappropriate eating habits and dependency on smoking and drinking, further investigation should be done on the role of one's psychology in their exhibition of disordered eating as means to prevent the manifestation of these habits.
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Affiliation(s)
- Jana Berro
- Faculty of Arts & Sciences, Lebanese American University, Byblos, Lebanon
| | - Marwan Akel
- School of Pharmacy, Lebanese International University, Beirut, Lebanon
- INSPECT-LB: Institut National de Santé Publique, Epidemiologie Clinique et Toxicologie-Liban, Beirut, Lebanon
| | - Souheil Hallit
- INSPECT-LB: Institut National de Santé Publique, Epidemiologie Clinique et Toxicologie-Liban, Beirut, Lebanon.
- Faculty of Medicine and Medical Sciences, Holy Spirit University of Kaslik (USEK), Jounieh, Lebanon.
| | - Sahar Obeid
- INSPECT-LB: Institut National de Santé Publique, Epidemiologie Clinique et Toxicologie-Liban, Beirut, Lebanon.
- Faculty of Arts and Sciences, Holy Spirit University of Kaslik (USEK), Jounieh, Lebanon.
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38
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Carter AR, Santos Ferreira DL, Taylor AE, Lawlor DA, Davey Smith G, Sattar N, Chaturvedi N, Hughes AD, Howe LD. Role of the Metabolic Profile in Mediating the Relationship Between Body Mass Index and Left Ventricular Mass in Adolescents: Analysis of a Prospective Cohort Study. J Am Heart Assoc 2020; 9:e016564. [PMID: 33030065 PMCID: PMC7763376 DOI: 10.1161/jaha.120.016564] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Background We aimed to quantify the role of the plasma metabolic profile in explaining the effect of adiposity on cardiac structure. Methods and Results Body mass index (BMI) was measured at age 11 in the Avon Longitudinal Study of Parents and Children. Left ventricular mass indexed to height2.7 (LVMI) was assessed by echocardiography at age 17. The metabolic profile was quantified via 1H-nuclear magnetic resonance spectroscopy at age 15. Multivariable confounder (maternal age, parity, highest qualification, maternal smoking, prepregnancy BMI, prepregnancy height, household social class, adolescent birthweight, adolescent smoking, fruit and vegetable consumption, and physical activity)-adjusted linear regression estimated the association of BMI with LVMI and mediation by metabolic traits. We considered 156 metabolomic traits individually and jointly as principal components explaining 95% of the variance in the nuclear magnetic resonance platform and assessed whether the principal components for the metabolic traits added to the proportion of the association explained by putative cardiovascular risk factors (systolic and diastolic blood pressures, insulin, triglycerides, low-density lipoprotein cholesterol, and glucose). A 1 kg/m2 higher BMI was associated with a 0.70 g/m2.7 (95% CI, 0.53-0.88 g/m2.7) and 0.66 g/m2.7 (95% CI, 0.53-0.79 g/m2.7) higher LVMI in males (n=437) and females (n=536), respectively. Putative risk factors explained 3% (95% CI, 2%-5%) of this association in males, increasing to 10% (95% CI, 8%-13%) when including metabolic principal components. In females, the standard risk factors explained 3% (95% CI, 2%-5%) of the association and did not increase when including the metabolic principal components. Conclusions The addition of the nuclear magnetic resonance-measured metabolic traits appears to mediate more of the association of BMI on LVMI than the putative risk factors alone in adolescent males, but not females.
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Affiliation(s)
- Alice R Carter
- MRC Integrative Epidemiology Unit Population Health Sciences University of Bristol United Kingdom
| | - Diana L Santos Ferreira
- MRC Integrative Epidemiology Unit Population Health Sciences University of Bristol United Kingdom
| | - Amy E Taylor
- MRC Integrative Epidemiology Unit Population Health Sciences University of Bristol United Kingdom.,National Institute for Health Research Biomedical Research Centre at the University Hospitals Bristol NHS Foundation Trust and the University of Bristol Bristol United Kingdom
| | - Deborah A Lawlor
- MRC Integrative Epidemiology Unit Population Health Sciences University of Bristol United Kingdom.,National Institute for Health Research Biomedical Research Centre at the University Hospitals Bristol NHS Foundation Trust and the University of Bristol Bristol United Kingdom
| | - George Davey Smith
- MRC Integrative Epidemiology Unit Population Health Sciences University of Bristol United Kingdom.,National Institute for Health Research Biomedical Research Centre at the University Hospitals Bristol NHS Foundation Trust and the University of Bristol Bristol United Kingdom
| | - Naveed Sattar
- Institute of Cardiovascular and Medical Science University of Glasgow United Kingdom
| | - Nishi Chaturvedi
- Institute of Cardiovascular Science University College London London United Kingdom
| | - Alun D Hughes
- Institute of Cardiovascular Science University College London London United Kingdom
| | - Laura D Howe
- MRC Integrative Epidemiology Unit Population Health Sciences University of Bristol United Kingdom
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39
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Seoane-Collazo P, Diéguez C, Nogueiras R, Rahmouni K, Fernández-Real JM, López M. Nicotine' actions on energy balance: Friend or foe? Pharmacol Ther 2020; 219:107693. [PMID: 32987056 DOI: 10.1016/j.pharmthera.2020.107693] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2020] [Accepted: 09/21/2020] [Indexed: 12/12/2022]
Abstract
Obesity has reached pandemic proportions and is associated with severe comorbidities, such as type 2 diabetes mellitus, hepatic and cardiovascular diseases, and certain cancer types. However, the therapeutic options to treat obesity are limited. Extensive epidemiological studies have shown a strong relationship between smoking and body weight, with non-smokers weighing more than smokers at any age. Increased body weight after smoking cessation is a major factor that interferes with their attempts to quit smoking. Numerous controlled studies in both humans and rodents have reported that nicotine, the main bioactive component of tobacco, exerts a marked anorectic action. Furthermore, nicotine is also known to modulate energy expenditure, by regulating the thermogenic activity of brown adipose tissue (BAT) and the browning of white adipose tissue (WAT), as well as glucose homeostasis. Many of these actions occur at central level, by controlling the activity of hypothalamic neuropeptide systems such as proopiomelanocortin (POMC), or energy sensors such as AMP-activated protein kinase (AMPK). However, direct impact of nicotine on metabolic tissues, such as BAT, WAT, liver and pancreas has also been described. Here, we review the actions of nicotine on energy balance. The relevance of this interaction is interesting, because considering the restricted efficiency of obesity treatments, a possible complementary approach may focus on compounds with known pharmacokinetic profile and pharmacological actions, such as nicotine or nicotinic acetylcholine receptors signaling.
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Affiliation(s)
- Patricia Seoane-Collazo
- Department of Physiology, CIMUS, University of Santiago de Compostela-Instituto de Investigación Sanitaria, Santiago de Compostela 15782, Spain; CIBER Fisiopatología de la Obesidad y Nutrición (CIBERobn), 15706, Spain; International Institute for Integrative Sleep Medicine (WPI-IIIS), University of Tsukuba, Tsukuba, Ibaraki 305-8575, Japan.
| | - Carlos Diéguez
- Department of Physiology, CIMUS, University of Santiago de Compostela-Instituto de Investigación Sanitaria, Santiago de Compostela 15782, Spain; CIBER Fisiopatología de la Obesidad y Nutrición (CIBERobn), 15706, Spain
| | - Rubén Nogueiras
- Department of Physiology, CIMUS, University of Santiago de Compostela-Instituto de Investigación Sanitaria, Santiago de Compostela 15782, Spain; CIBER Fisiopatología de la Obesidad y Nutrición (CIBERobn), 15706, Spain
| | - Kamal Rahmouni
- Department of Neuroscience and Pharmacology, University of Iowa Carver College of Medicine and Veterans Affairs Health Care System, Iowa City, IA 52242, USA
| | - José Manuel Fernández-Real
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBERobn), 15706, Spain; Institut d'Investigació Biomèdica de Girona (IDIBGI), Girona, Spain; Department of Diabetes, Endocrinology and Nutrition (UDEN), Hospital of Girona "Dr Josep Trueta" and Department of Medical Sciences, Faculty of Medicine, University of Girona, Girona, Spain
| | - Miguel López
- Department of Physiology, CIMUS, University of Santiago de Compostela-Instituto de Investigación Sanitaria, Santiago de Compostela 15782, Spain; CIBER Fisiopatología de la Obesidad y Nutrición (CIBERobn), 15706, Spain.
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40
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Howe LD, Kanayalal R, Harrison S, Beaumont RN, Davies AR, Frayling TM, Davies NM, Hughes A, Jones SE, Sassi F, Wood AR, Tyrrell J. Effects of body mass index on relationship status, social contact and socio-economic position: Mendelian randomization and within-sibling study in UK Biobank. Int J Epidemiol 2020; 49:1173-1184. [PMID: 31800047 PMCID: PMC7750981 DOI: 10.1093/ije/dyz240] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/01/2019] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND We assessed whether body mass index (BMI) affects social and socio-economic outcomes. METHODS We used Mendelian randomization (MR), non-linear MR and non-genetic and MR within-sibling analyses, to estimate relationships of BMI with six socio-economic and four social outcomes in 378 244 people of European ancestry in UK Biobank. RESULTS In MR of minimally related individuals, higher BMI was related to higher deprivation, lower income, fewer years of education, lower odds of degree-level education and skilled employment. Non-linear MR suggested both low (bottom decile, <22 kg/m2) and high (top seven deciles, >24.6 kg/m2) BMI, increased deprivation and reduced income. Non-genetic within-sibling analysis supported an effect of BMI on socio-economic position (SEP); precision in within-sibling MR was too low to draw inference about effects of BMI on SEP. There was some evidence of pleiotropy, with MR Egger suggesting limited effects of BMI on deprivation, although precision of these estimates is also low. Non-linear MR suggested that low BMI (bottom three deciles, <23.5 kg/m2) reduces the odds of cohabiting with a partner or spouse in men, whereas high BMI (top two deciles, >30.7 kg/m2) reduces the odds of cohabitation in women. Both non-genetic and MR within-sibling analyses supported this sex-specific effect of BMI on cohabitation. In men only, higher BMI was related to lower participation in leisure and social activities. There was little evidence that BMI affects visits from friends and family or having someone to confide in. CONCLUSIONS BMI may affect social and socio-economic outcomes, with both high and low BMI being detrimental for SEP, although larger within-family MR studies may help to test the robustness of MR results in unrelated individuals. Triangulation of evidence across MR and within-family analyses supports evidence of a sex-specific effect of BMI on cohabitation.
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Affiliation(s)
- Laura D Howe
- MRC Integrative Epidemiology Unit at the University of Bristol, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Roshni Kanayalal
- Genetics of Complex Traits, University of Exeter Medical School, RILD Level 3, Royal Devon & Exeter Hospital, Exeter, UK
| | - Sean Harrison
- MRC Integrative Epidemiology Unit at the University of Bristol, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Robin N Beaumont
- Genetics of Complex Traits, University of Exeter Medical School, RILD Level 3, Royal Devon & Exeter Hospital, Exeter, UK
| | - Alisha R Davies
- Research and Evaluation Division, Public Health Wales, 2 Capital Quarter, Cardiff, UK
| | - Timothy M Frayling
- Genetics of Complex Traits, University of Exeter Medical School, RILD Level 3, Royal Devon & Exeter Hospital, Exeter, UK
| | - Neil M Davies
- MRC Integrative Epidemiology Unit at the University of Bristol, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Amanda Hughes
- MRC Integrative Epidemiology Unit at the University of Bristol, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Samuel E Jones
- Genetics of Complex Traits, University of Exeter Medical School, RILD Level 3, Royal Devon & Exeter Hospital, Exeter, UK
| | - Franco Sassi
- Centre for Health Economics and Policy Innovation, Imperial College Business School, London, UK
| | - Andrew R Wood
- Genetics of Complex Traits, University of Exeter Medical School, RILD Level 3, Royal Devon & Exeter Hospital, Exeter, UK
| | - Jessica Tyrrell
- Genetics of Complex Traits, University of Exeter Medical School, RILD Level 3, Royal Devon & Exeter Hospital, Exeter, UK
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41
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Vandebergh M, Goris A. Smoking and multiple sclerosis risk: a Mendelian randomization study. J Neurol 2020; 267:3083-3091. [PMID: 32529581 PMCID: PMC7501136 DOI: 10.1007/s00415-020-09980-4] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2020] [Revised: 06/03/2020] [Accepted: 06/05/2020] [Indexed: 11/02/2022]
Abstract
BACKGROUND Striking changes in the demographic pattern of multiple sclerosis (MS) strongly indicate an influence of modifiable exposures, which lend themselves well to intervention. It is important to pinpoint which of the many environmental, lifestyle, and sociodemographic changes that have occurred over the past decades, such as higher smoking and obesity rates, are responsible. Mendelian randomization (MR) is an elegant tool to overcome limitations inherent to observational studies and leverage human genetics to inform prevention strategies in MS. METHODS We use genetic variants from the largest genome-wide association study for smoking phenotypes (initiation: N = 378, heaviness: N = 55, lifetime smoking: N = 126) and body mass index (BMI, N = 656) and apply these as instrumental variables in a two-sample MR analysis to the most recent meta-analysis for MS. We adjust for the genetic correlation between smoking and BMI in a multivariable MR. RESULTS In univariable and multivariable MR, smoking does not have an effect on MS risk nor explains part of the association between BMI and MS risk. In contrast, in both analyses each standard deviation increase in BMI, corresponding to roughly 5 kg/m2 units, confers a 30% increase in MS risk. CONCLUSION Despite observational studies repeatedly reporting an association between smoking and increased risk for MS, MR analyses on smoking phenotypes and MS risk could not confirm a causal relationship. This is in contrast with BMI, where observational studies and MR agree on a causal contribution. The reasons for the discrepancy between observational studies and our MR study concerning smoking and MS require further investigation.
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Affiliation(s)
- Marijne Vandebergh
- Department of Neurosciences, Laboratory for Neuroimmunology, KU Leuven, Herestraat 49 bus 1022, 3000, Leuven, Belgium.,Leuven Brain Institute, KU Leuven, Leuven, Belgium
| | - An Goris
- Department of Neurosciences, Laboratory for Neuroimmunology, KU Leuven, Herestraat 49 bus 1022, 3000, Leuven, Belgium. .,Leuven Brain Institute, KU Leuven, Leuven, Belgium.
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42
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Vie GÅ, Wootton RE, Bjørngaard JH, Åsvold BO, Taylor AE, Gabrielsen ME, Davey Smith G, Romundstad PR, Munafò MR. The effect of smoking intensity on all-cause and cause-specific mortality-a Mendelian randomization analysis. Int J Epidemiol 2020; 48:1438-1446. [PMID: 31074779 DOI: 10.1093/ije/dyz081] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/05/2019] [Indexed: 11/14/2022] Open
Abstract
BACKGROUND Smoking is an important cause of mortality and recent studies have suggested that even low-intensity smoking might be associated with increased mortality. Still, smoking is associated with lower socio-economic status as well as other potential risk factors, and disease onset might motivate smoking cessation, thus residual confounding and reverse causality might bias results. We aimed to assess the evidence of a causal relationship between smoking intensity and cause-specific as well as all-cause-mortality using Mendelian randomization analyses. METHODS We included 56 019 participants from the Norwegian HUNT2 Study and 337 103 participants from UK Biobank, linked to national registry data on causes of death. We estimated associations of self-reported smoking as well as the genetic variant rs1051730 as an instrument for smoking intensity with all-cause and cause-specific mortality. We subsequently meta-analysed the results from the two cohorts. RESULTS Each effect allele of the rs1051730 was associated with a 9% increased hazard of all-cause mortality [95% confidence interval (CI) 6-11] among ever smokers. Effect alleles were also associated with death by neoplasms [hazard ratio (HR) 1.11, 95% CI 1.06-1.15], circulatory diseases (HR 1.06, 95% CI 1.01-1.11) and respiratory diseases (HR 1.15, 95% CI 1.05-1.26) among ever smokers. The association was stronger among ever than never smokers for all-cause mortality (p < 0.001), neoplasms (p = 0.001) and respiratory diseases (p = 0.038). CONCLUSIONS Our results indicate a causal effect of smoking intensity on all-cause mortality and death by neoplasms and respiratory diseases. There was weaker evidence of a causal effect of smoking intensity on death by circulatory diseases.
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Affiliation(s)
- Gunnhild Åberge Vie
- Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
| | - Robyn E Wootton
- MRC Integrative Epidemiology Unit (IEU), University of Bristol, Bristol, UK.,UK Centre for Tobacco and Alcohol Studies, University of Bristol, Bristol, UK.,Tobacco and Alcohol Research Group, School of Psychological Science, University of Bristol, Bristol, UK.,NIHR Bristol Biomedical Research Centre, University Hospitals Bristol NHS Foundation Trust and University of Bristol, Bristol, UK
| | - Johan Håkon Bjørngaard
- Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
| | - Bjørn Olav Åsvold
- Department of Endocrinology, St Olavs Hospital, Trondheim University Hospital, Trondheim, Norway.,KG Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
| | - Amy E Taylor
- MRC Integrative Epidemiology Unit (IEU), University of Bristol, Bristol, UK.,Department of Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Maiken Elvestad Gabrielsen
- KG Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
| | - George Davey Smith
- MRC Integrative Epidemiology Unit (IEU), University of Bristol, Bristol, UK.,NIHR Bristol Biomedical Research Centre, University Hospitals Bristol NHS Foundation Trust and University of Bristol, Bristol, UK.,Department of Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Pål Richard Romundstad
- Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim, Norway
| | - Marcus R Munafò
- MRC Integrative Epidemiology Unit (IEU), University of Bristol, Bristol, UK.,UK Centre for Tobacco and Alcohol Studies, University of Bristol, Bristol, UK.,Tobacco and Alcohol Research Group, School of Psychological Science, University of Bristol, Bristol, UK.,NIHR Bristol Biomedical Research Centre, University Hospitals Bristol NHS Foundation Trust and University of Bristol, Bristol, UK
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Dixon P, Hollingworth W, Harrison S, Davies NM, Davey Smith G. Mendelian Randomization analysis of the causal effect of adiposity on hospital costs. JOURNAL OF HEALTH ECONOMICS 2020; 70:102300. [PMID: 32014825 PMCID: PMC7188219 DOI: 10.1016/j.jhealeco.2020.102300] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/25/2019] [Revised: 01/06/2020] [Accepted: 01/14/2020] [Indexed: 05/12/2023]
Abstract
Estimates of the marginal effect of measures of adiposity such as body mass index (BMI) on healthcare costs are important for the formulation and evaluation of policies targeting adverse weight profiles. Most estimates of this association are affected by endogeneity bias. We use a novel identification strategy exploiting Mendelian Randomization - random germline genetic variation modelled using instrumental variables - to identify the causal effect of BMI on inpatient hospital costs. Using data on over 300,000 individuals, the effect size per person per marginal unit of BMI per year varied according to specification, including £21.22 (95% confidence interval (CI): £14.35-£28.07) for conventional inverse variance weighted models to £18.85 (95% CI: £9.05-£28.65) for penalized weighted median models. Effect sizes from Mendelian Randomization models were larger in most cases than non-instrumental variable multivariable adjusted estimates (£13.47, 95% CI: £12.51-£14.43). There was little evidence of non-linearity. Within-family estimates, intended to address dynastic biases, were imprecise.
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Affiliation(s)
- Padraig Dixon
- Population Health Sciences, University of Bristol, United Kingdom; MRC Integrative Epidemiology Unit, University of Bristol, United Kingdom.
| | | | - Sean Harrison
- Population Health Sciences, University of Bristol, United Kingdom; MRC Integrative Epidemiology Unit, University of Bristol, United Kingdom
| | - Neil M Davies
- Population Health Sciences, University of Bristol, United Kingdom; MRC Integrative Epidemiology Unit, University of Bristol, United Kingdom
| | - George Davey Smith
- Population Health Sciences, University of Bristol, United Kingdom; MRC Integrative Epidemiology Unit, University of Bristol, United Kingdom; NIHR Biomedical Research Centre, University of Bristol, United Kingdom
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44
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Profound alteration in reward processing due to a human polymorphism in CHRNA5: a role in alcohol dependence and feeding behavior. Neuropsychopharmacology 2019; 44:1906-1916. [PMID: 31288250 PMCID: PMC6785024 DOI: 10.1038/s41386-019-0462-0] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/01/2019] [Revised: 06/12/2019] [Accepted: 07/01/2019] [Indexed: 01/08/2023]
Abstract
Human genetic variation in the nicotinic receptor gene cluster CHRNA5/A3/B4, in particular the non-synonymous and frequent CHRNA5 variant rs16969968 (α5SNP), has an important consequence on smoking behavior in humans. A number of genetic association studies have additionally implicated the CHRNA5 gene in addictions to other drugs, and also body mass index (BMI). Here, we model the α5SNP, in a transgenic rat line, and establish its role in alcohol dependence, and feeding behavior. Rats expressing the α5SNP consume more alcohol, and exhibit increased relapse to alcohol seeking after abstinence. This high-relapsing phenotype is reflected in altered activity in the insula, linked to interoception, as established using c-Fos immunostaining. Similarly, relapse to food seeking is increased in the transgenic group, while a nicotine treatment reduces relapse in both transgenic and control rats. These findings point to a general role of this human polymorphism in reward processing, and multiple addictions other than smoking. This could pave the way for the use of medication targeting the nicotinic receptor in the treatment of alcohol use and eating disorders, and comorbid conditions in smokers.
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45
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Dixon P, Davey Smith G, Hollingworth W. The Association Between Adiposity and Inpatient Hospital Costs in the UK Biobank Cohort. APPLIED HEALTH ECONOMICS AND HEALTH POLICY 2019; 17:359-370. [PMID: 30599049 PMCID: PMC6535149 DOI: 10.1007/s40258-018-0450-2] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
BACKGROUND High adiposity is associated with higher risks for a variety of adverse health outcomes, including higher rates of age-adjusted mortality and increased morbidity. This has important implications for the management of healthcare systems, since the endocrinal, cardiometabolic and other changes associated with increased adiposity may be associated with substantial healthcare costs. METHODS We studied the association between various measures of adiposity and inpatient hospital costs through record linkage between UK Biobank and records of inpatient care in England and Wales. UK Biobank is a large prospective cohort study that aimed to recruit men and women aged between 40 and 69 from 2006 to 2010. We applied generalised linear models to cost per person year to estimate the marginal effect of adiposity, and average adjusted predicted costs of adiposity. RESULTS Valid cost and body mass index (BMI) data from 457,689 participants were available for inferential analysis. Some 54.4% of individuals included in the analysis sample had positive inpatient healthcare costs over the period of follow-up. Median hospital costs per person-year of follow-up were £89, compared to mean costs of £481. Mean BMI overall was 27.4 kg/m2 (standard deviation 4.8). The marginal effect of a unit increase in BMI was £13.61 (99% confidence interval £12.60-£14.63) per person-year of follow up. The marginal effect of a standard deviation increase in BMI was £69.20 (99% confidence interval £64.98-£73.42). The marginal effect of becoming obese was £136.35 (99% confidence interval £124.62-£148.08). Average adjusted predicted inpatient hospital costs increased almost linearly when modelled using continuous measure of adiposity. Sensitivity analysis of different scenarios did not substantially change these conclusions, although there was some evidence of attenuation of the effects of adiposity when controlling for waist-hip ratios, and when individuals who self-reported any pre-existing conditions were excluded from analysis. CONCLUSIONS Higher adiposity is associated with higher inpatient hospital costs. Further scrutiny using causal inferential methods is warranted to establish if further public health investments are required to manage the large healthcare costs observationally associated with overweight and obesity.
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Affiliation(s)
- Padraig Dixon
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, BS8 2PS, UK.
- MRC Integrative Epidemiology Unit, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK.
| | - George Davey Smith
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, BS8 2PS, UK
- MRC Integrative Epidemiology Unit, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
- NIHR Biomedical Research Centre, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
| | - William Hollingworth
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, BS8 2PS, UK
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