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Marjani A, Poursharifi N, Sajedi A, Tatari M. Age and Sex-related Chromogranin A Gene Polymorphisms and its Association with Metabolic Syndrome Components. J ASEAN Fed Endocr Soc 2024; 39:45-52. [PMID: 38863909 PMCID: PMC11163322 DOI: 10.15605/jafes.039.01.09] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2023] [Accepted: 07/22/2023] [Indexed: 06/13/2024] Open
Abstract
Introduction The purpose of this study was to determine the possible differences in genetic polymorphisms and serum levels of chromogranin A (CgA), according to age and sex, in subjects with and without metabolic syndrome (MetS). Methodology The genotyping and serum level of CgA and biochemical parameters were measured by the T-ARMS-PCR and PCR-RFLP and ELISA and spectrophotometer methods, respectively. Results A comparison of males with and without MetS showed significantly lower high-density lipoprotein-cholesterol (HDL-C) levels than those of females.At ages 30-70 years, both sexes showed significant differences in triglycerides (TG), fasting blood sugar (FBS), CgA levels and waist circumference (WC) when compared to the two groups. Both sexes with MetS indicated significant differences in systolic blood pressure (SBP) at ages 40-70 years, while at ages 40-59 years, there was a significant difference in HDL-C level in males.There was a significant correlation between serum levels of FBS, TG, SBP and WC (in both sexes), and CgA in subjects with MetS. Significant correlation was found between HDL-C level and diastolic blood pressure (DBP), and CgA level in males and females, respectively. CgA genotype frequency (T-415C and C+87T polymorphisms) showed no significant differences between males and females with and without MetS, while there was only a significant difference in frequency of the genotypes T-415C when compared to males with and without MetS. Conclusion The CgA appears to be strongly associated with MetS components in both sexes. Variation in CgA gene expression may affect the T-415C polymorphism in males. This may mean that the structure of CgA genetics differs in different ethnic groups. Differences in the serum level and expression of CgA gene may show valuable study results that it may be expected a relationship between these variables and the MetS.
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Affiliation(s)
- Abdoljalal Marjani
- Metabolic Disorders Research Center, Department of Biochemistry and Biophysics, Gorgan Faculty of Medicine, Golestan University of Medical Sciences, Golestan Province, Gorgan, Iran
| | - Nahid Poursharifi
- Metabolic Disorders Research Center, Department of Biochemistry and Biophysics, Gorgan Faculty of Medicine, Golestan University of Medical Sciences, Golestan Province, Gorgan, Iran
| | - Atefe Sajedi
- Metabolic Disorders Research Center, Department of Biochemistry and Biophysics, Gorgan Faculty of Medicine, Golestan University of Medical Sciences, Golestan Province, Gorgan, Iran
| | - Mahin Tatari
- Biostatistics Counseling and Reproductive Health Research Center, Golestan University of Medical Sciences, Golestan Province, Gorgan, Iran
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Yu H, Armstrong N, Pavela G, Kaiser K. Sex and Race Differences in Obesity-Related Genetic Susceptibility and Risk of Cardiometabolic Disease in Older US Adults. JAMA Netw Open 2023; 6:e2347171. [PMID: 38064210 PMCID: PMC10709778 DOI: 10.1001/jamanetworkopen.2023.47171] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/04/2023] [Accepted: 10/29/2023] [Indexed: 12/18/2023] Open
Abstract
Importance The fat mass and obesity-associated gene (FTO) is associated with obesity phenotypes, but the association is inconsistent across populations. Within-population differences may explain some of the variability observed. Objective To investigate sex differences in the association between FTO single-nucleotide variants (SNVs) and obesity traits among self-identified non-Hispanic Black and non-Hispanic White US adults, to examine whether the SNVs were associated with cardiometabolic diseases, and to evaluate whether obesity mediated the association between FTO SNVs and cardiometabolic diseases. Design, Setting, and Participants This cross-sectional study used data from the Reasons for Geographic and Racial Differences in Stroke (REGARDS) study, a US population-based cohort study with available genetic data (assayed in 2018) and phenotypic data at baseline (enrolled 2003-2007). Participants were aged 45 to 98 years at baseline. Data were analyzed from October 2021 to October 2022. Exposures Eleven SNVs in the FTO gene present among both Black and White participants. Main Outcomes and Measures Objectively measured obesity indicators (body mass index and waist-to-height ratio), objectively measured and/or self-reported cardiometabolic diseases (hypertension, stroke history, heart disease, and diabetes), and self-reported social-economic and psychosocial status. Results A total of 10 447 participants (mean [SD] age, 64.4 [9.7] years; 5276 [55.8%] women; 8743 [83.7%] Black and 1704 [16.3%] White) were included. In the White group, 11 FTO SNVs were significantly associated with obesity, hypertension, and diabetes using linear models (eg, body mass index: β = 0.536; 95% CI, 0.197-0.875), but none of the FTO SNVs were associated with obesity traits in the Black group. White males had a higher risk of obesity while White females had a higher risk of hypertension and diabetes. However, 1 FTO SNV (rs1121980) was associated with a direct increase in the risk of heart disease in Black participants not mediated by obesity (c' = 0.145 [SE, 0.0517]; P = .01). Conclusions and Relevance In this cross-sectional study of obesity phenotypes and their association with cardiometabolic diseases, the tested FTO SNVs reflected sex differences in White participants. Different patterns of associations were observed among self-identified Black participants. Therefore, these results could inform future work discovering risk alleles or risk scores unique to Black individuals or further investigating genetic risk in all US residents.
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Affiliation(s)
- Hairui Yu
- Department of Health Behavior, School of Public Health, University of Alabama at Birmingham
- Department of Family and Community Medicine, School of Medicine, University of Alabama at Birmingham
| | - Nicole Armstrong
- Department of Epidemiology, School of Public Health, University of Alabama at Birmingham
| | - Greg Pavela
- Department of Health Behavior, School of Public Health, University of Alabama at Birmingham
| | - Kathryn Kaiser
- Department of Health Behavior, School of Public Health, University of Alabama at Birmingham
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Fernández-Rhodes L, McArdle CE, Rao H, Wang Y, Martinez-Miller EE, Ward JB, Cai J, Sofer T, Isasi CR, North KE. A Gene-Acculturation Study of Obesity Among US Hispanic/Latinos: The Hispanic Community Health Study/Study of Latinos. Psychosom Med 2023; 85:358-365. [PMID: 36917487 PMCID: PMC10159946 DOI: 10.1097/psy.0000000000001193] [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] [Indexed: 03/16/2023]
Abstract
OBJECTIVE In the United States, Hispanic/Latino adults face a high burden of obesity; yet, not all individuals are equally affected, partly due in part to this ethnic group's marked sociocultural diversity. We sought to analyze the modification of body mass index (BMI) genetic effects in Hispanic/Latino adults by their level of acculturation, a complex biosocial phenomenon that remains understudied. METHODS Among 11,747 Hispanic/Latinos adults in the Hispanic Community Health Study/Study of Latinos aged 18 to 76 years from four urban communities (2008-2011), we a) tested our hypothesis that the effect of a genetic risk score (GRS) for increased BMI may be exacerbated by higher levels of acculturation and b) examined if GRS acculturation interactions varied by gender or Hispanic/Latino background group. All genetic modeling controlled for relatedness, age, gender, principal components of ancestry, center, and complex study design within a generalized estimated equation framework. RESULTS We observed a GRS increase of 0.34 kg/m 2 per risk allele in weighted mean BMI. The estimated main effect of GRS on BMI varied both across acculturation level and across gender. The difference between high and low acculturation ranged from 0.03 to 0.23 kg/m 2 per risk allele, but varied across acculturation measure and gender. CONCLUSIONS These results suggest the presence of effect modification by acculturation, with stronger effects on BMI among highly acculturated individuals and female immigrants. Future studies of obesity in the Hispanic/Latino community should account for sociocultural environments and consider their intersection with gender to better target obesity interventions.
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Affiliation(s)
- Lindsay Fernández-Rhodes
- Department of Biobehavioral Health, Pennsylvania State University, University Park, PA
- Carolina Population Center, University of North Carolina at Chapel Hill, Chapel Hill, NC
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC
| | - Cristin E. McArdle
- Department of Biobehavioral Health, Pennsylvania State University, University Park, PA
| | - Hridya Rao
- Department of Biobehavioral Health, Pennsylvania State University, University Park, PA
| | - Yujie Wang
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC
| | - Erline E. Martinez-Miller
- Carolina Population Center, University of North Carolina at Chapel Hill, Chapel Hill, NC
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC
- Department of Clinical Sciences, University of Texas Southwestern Medical Center, Dallas, TX
| | - Julia B. Ward
- Carolina Population Center, University of North Carolina at Chapel Hill, Chapel Hill, NC
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC
- Social & Scientific Systems, a DLH Holdings Company, Durham, NC
| | - Jianwen Cai
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC
| | - Tamar Sofer
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA
| | - Carmen R. Isasi
- Departments of Epidemiology & Population Health and Pediatrics, Albert Einstein College of Medicine, Bronx, NY
| | - Kari E North
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC
- Carolina Center for Genome Sciences, University of North Carolina at Chapel Hill, Chapel Hill, NC
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4
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Chew HSJ, Loong SSE, Lim SL, Tam WSW, Chew NWS, Chin YH, Chao AM, Dimitriadis GK, Gao Y, So BYJ, Shabbir A. Socio-Demographic, Behavioral and Psychological Factors Associated with High BMI among Adults in a Southeast Asian Multi-Ethnic Society: A Structural Equation Model. Nutrients 2023; 15:nu15081826. [PMID: 37111045 PMCID: PMC10144460 DOI: 10.3390/nu15081826] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2023] [Revised: 03/31/2023] [Accepted: 04/08/2023] [Indexed: 04/29/2023] Open
Abstract
While various influencing factors of overweight and obesity have been identified, the underlying mechanism remains unclear. We examined the relationships among sociodemographic, behavioral, and psychological factors on anthropometry in a multi-ethnic population with overweight and obesity. Participants (N = 251) were recruited from January to October 2022. Mean age and self-reported BMI were 31.7 ± 10.1 years and 29.2 ± 7.2 kg/m2. Participants were mostly female (52.4%) and overweight (58.2%). Multivariate multiple regression was performed using maximum likelihood estimation. Body mass index was associated with waist circumference, age, sex, race, marital status, education level, residential region, overeating habit, immediate thinking, self-regulation, and physical activity, but not anxiety, depression, or the intention to change eating habits. Final model indicated good fit: χ2 (30, N = 250) = 33.5, p = 0.32, CFI = 0.993, TLI = 0.988, RMSEA = 0.022, and SRMR = 0.041. Direct effects were found between BMI and overeating (β = 0.10, p = 0.004), race (β = -0.82, p < 0.001), marital status (β = -0.42, p = 0.001), and education level (β = -0.28, p = 0.019). Crisps (68.8%), cake (66.8%) and chocolate (65.6%) were identified as the most tempting foods. Immediate thinking indirectly increased overeating habits through poor self-regulation, although sociodemographic characteristics better predicted anthropometry than psycho-behavioral constructs.
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Affiliation(s)
- Han Shi Jocelyn Chew
- Alice Lee Centre for Nursing Studies, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117597, Singapore
| | - Shaun Seh Ern Loong
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117597, Singapore
| | - Su Lin Lim
- Dietetics Department, National University Hospital, Singapore 119074, Singapore
| | - Wai San Wilson Tam
- Alice Lee Centre for Nursing Studies, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117597, Singapore
| | - Nicholas W S Chew
- Department of Cardiology, National University Heart Centre, Singapore 119074, Singapore
| | - Yip Han Chin
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117597, Singapore
| | - Ariana M Chao
- Department of Biobehavioral Health Sciences, School of Nursing, University of Pennsylvania, Philadelphia, PA 19104-4217, USA
| | - Georgios K Dimitriadis
- Department of Endocrinology ASO/EASO COM, King's College Hospital NHS Foundation Trust, Denmark Hill, London SE5 9RS, UK
- Obesity, Type 2 Diabetes and Immunometabolism Research Group, Department of Diabetes, Faculty of Cardiovascular Medicine & Sciences, School of Life Course Sciences, King's College London, London WC2R 2LS, UK
| | - Yujia Gao
- Division of Hepatobiliary & Pancreatic Surgery, Department of Surgery, National University Hospital, Singapore 119074, Singapore
| | - Bok Yan Jimmy So
- Division of General Surgery (Upper Gastrointestinal Surgery), Department of Surgery, National University Hospital, Singapore 119074, Singapore
| | - Asim Shabbir
- Division of General Surgery (Upper Gastrointestinal Surgery), Department of Surgery, National University Hospital, Singapore 119074, Singapore
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Westbury S, Oyebode O, van Rens T, Barber TM. Obesity Stigma: Causes, Consequences, and Potential Solutions. Curr Obes Rep 2023; 12:10-23. [PMID: 36781624 PMCID: PMC9985585 DOI: 10.1007/s13679-023-00495-3] [Citation(s) in RCA: 16] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 12/20/2022] [Indexed: 02/15/2023]
Abstract
PURPOSE OF REVIEW This review aims to examine (i) the aetiology of obesity; (ii) how and why a perception of personal responsibility for obesity so dominantly frames this condition and how this mindset leads to stigma; (iii) the consequences of obesity stigma for people living with obesity, and for the public support for interventions to prevent and manage this condition; and (iv) potential strategies to diminish our focus on personal responsibility for the development of obesity, to enable a reduction of obesity stigma, and to move towards effective interventions to prevent and manage obesity within the population. RECENT FINDINGS We summarise literature which shows that obesity stems from a complex interplay of genetic and environment factors most of which are outside an individual's control. Despite this, evidence of obesity stigmatisation remains abundant throughout areas of media, entertainment, social media and the internet, advertising, news outlets, and the political and public health landscape. This has damaging consequences including psychological, physical, and socioeconomic harm. Obesity stigma does not prevent obesity. A combined, concerted, and sustained effort from multiple stakeholders and key decision-makers within society is required to dispel myths around personal responsibility for body weight, and to foster more empathy for people living in larger bodies. This also sets the scene for more effective policies and interventions, targeting the social and environmental drivers of health, to ultimately improve population health.
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Affiliation(s)
- Susannah Westbury
- School of Public Health and Preventative Medicine, Monash University, Melbourne, VIC, Australia.
| | - Oyinlola Oyebode
- Wolfson Institute of Population Health, Queen Mary University of London, London, UK
| | - Thijs van Rens
- Department of Economics, University of Warwick, Coventry, UK
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6
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Duchen D, Vergara C, Thio CL, Kundu P, Chatterjee N, Thomas DL, Wojcik GL, Duggal P. Pathogen exposure misclassification can bias association signals in GWAS of infectious diseases when using population-based common control subjects. Am J Hum Genet 2023; 110:336-348. [PMID: 36649706 PMCID: PMC9943744 DOI: 10.1016/j.ajhg.2022.12.013] [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: 06/13/2022] [Accepted: 12/20/2022] [Indexed: 01/18/2023] Open
Abstract
Genome-wide association studies (GWASs) have been performed to identify host genetic factors for a range of phenotypes, including for infectious diseases. The use of population-based common control subjects from biobanks and extensive consortia is a valuable resource to increase sample sizes in the identification of associated loci with minimal additional expense. Non-differential misclassification of the outcome has been reported when the control subjects are not well characterized, which often attenuates the true effect size. However, for infectious diseases the comparison of affected subjects to population-based common control subjects regardless of pathogen exposure can also result in selection bias. Through simulated comparisons of pathogen-exposed cases and population-based common control subjects, we demonstrate that not accounting for pathogen exposure can result in biased effect estimates and spurious genome-wide significant signals. Further, the observed association can be distorted depending upon strength of the association between a locus and pathogen exposure and the prevalence of pathogen exposure. We also used a real data example from the hepatitis C virus (HCV) genetic consortium comparing HCV spontaneous clearance to persistent infection with both well-characterized control subjects and population-based common control subjects from the UK Biobank. We find biased effect estimates for known HCV clearance-associated loci and potentially spurious HCV clearance associations. These findings suggest that the choice of control subjects is especially important for infectious diseases or outcomes that are conditional upon environmental exposures.
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Affiliation(s)
- Dylan Duchen
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA
| | - Candelaria Vergara
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA
| | - Chloe L Thio
- Division of Infectious Diseases, Johns Hopkins School of Medicine, Baltimore, MD 21205, USA
| | - Prosenjit Kundu
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA
| | - Nilanjan Chatterjee
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA
| | - David L Thomas
- Division of Infectious Diseases, Johns Hopkins School of Medicine, Baltimore, MD 21205, USA
| | - Genevieve L Wojcik
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA
| | - Priya Duggal
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA.
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7
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McArdle CE, Bokhari H, Rodell CC, Buchanan V, Preudhomme LK, Isasi CR, Graff M, North K, Gallo LC, Pirzada A, Daviglus ML, Wojcik G, Cai J, Perreira K, Fernandez-Rhodes L. Findings from the Hispanic Community Health Study/Study of Latinos on the Importance of Sociocultural Environmental Interactors: Polygenic Risk Score-by-Immigration and Dietary Interactions. Front Genet 2021; 12:720750. [PMID: 34938310 PMCID: PMC8685455 DOI: 10.3389/fgene.2021.720750] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2021] [Accepted: 09/08/2021] [Indexed: 01/05/2023] Open
Abstract
Introduction: Hispanic/Latinos experience a disproportionate burden of obesity. Acculturation to US obesogenic diet and practices may lead to an exacerbation of innate genetic susceptibility. We examined the role of gene-environment interactions to better characterize the sociocultural environmental determinants and their genome-scale interactions, which may contribute to missing heritability of obesity. We utilized polygenic risk scores (PRSs) for body mass index (BMI) to perform analyses of PRS-by-acculturation and other environmental interactors among self-identified Hispanic/Latino adults from the Hispanic Community Health Study/Study of Latinos (HCHS/SOL). Methods: PRSs were derived using genome-wide association study (GWAS) weights from a publicly available, large meta-analysis of European ancestry samples. Generalized linear models were run using a set of a priori acculturation-related and environmental factors measured at visit 1 (2008-2011) and visit 2 (2014-2016) in an analytic subsample of 8,109 unrelated individuals with genotypic, phenotypic, and complete case data at both visits. We evaluated continuous measures of BMI and waist-to-hip ratio. All models were weighted for complex sampling design, combined, and sex-stratified. Results: Overall, we observed a consistent increase of BMI with greater PRS across both visits. We found the best-fitting model adjusted for top five principal components of ancestry, sex, age, study site, Hispanic/Latino background genetic ancestry group, sociocultural factors and PRS interactions with age at immigration, years since first arrival to the United States (p < 0.0104), and healthy diet (p < 0.0036) and explained 16% of the variation in BMI. For every 1-SD increase in PRS, there was a corresponding 1.10 kg/m2 increase in BMI (p < 0.001). When these results were stratified by sex, we observed that this 1-SD effect of PRS on BMI was greater for women than men (1.45 vs. 0.79 kg/m2, p < 0.001). Discussion: We observe that age at immigration and the adoption of certain dietary patterns may play a significant role in modifying the effect of genetic risk on obesity. Careful consideration of sociocultural and immigration-related factors should be evaluated. The role of nongenetic factors, including the social environment, should not be overlooked when describing the performance of PRS or for promoting population health in understudied populations in genomics.
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Affiliation(s)
- Cristin E. McArdle
- Department of Biobehavioral Health, College of Health and Human Development, The Pennsylvania State University, University Park, PA, United States,*Correspondence: Cristin E. McArdle,
| | - Hassan Bokhari
- Department of Biobehavioral Health, College of Health and Human Development, The Pennsylvania State University, University Park, PA, United States
| | - Clinton C. Rodell
- Carey Business School, Johns Hopkins University, Baltimore, MD, United States
| | - Victoria Buchanan
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Liana K. Preudhomme
- Department of Psychology, University of Miami, Coral Gables, FL, United States
| | - Carmen R. Isasi
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY, United States
| | - Mariaelisa Graff
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Kari North
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States,Carolina Center for Genome Sciences, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Linda C. Gallo
- Department of Psychology, San Diego State University, San Diego, CA, United States
| | - Amber Pirzada
- Institute for Minority Health Research, Carle Illinois College of Medicine, University of Illinois at Urbana–Champaign, Champaign, IL, United States
| | - Martha L. Daviglus
- Institute for Minority Health Research, University of Illinois at Chicago, Chicago, IL, United States
| | - Genevieve Wojcik
- Department of Epidemiology, Johns Hopkins University, Baltimore, MD, United States
| | - Jianwen Cai
- Department of Biostatistics, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Krista Perreira
- Department of Social Medicine, University of North Carolina School of Medicine, Chapel Hill, NC, United States
| | - Lindsay Fernandez-Rhodes
- Department of Biobehavioral Health, College of Health and Human Development, The Pennsylvania State University, University Park, PA, United States,Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
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8
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Тимашева ЯР, Балхиярова ЖР, Кочетова ОВ. [Current state of the obesity research: genetic aspects, the role of microbiome, and susceptibility to COVID-19]. PROBLEMY ENDOKRINOLOGII 2021; 67:20-35. [PMID: 34533011 PMCID: PMC9753850 DOI: 10.14341/probl12775] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Revised: 07/21/2021] [Accepted: 08/02/2021] [Indexed: 11/06/2022]
Abstract
Obesity affects over 700 million people worldwide and its prevalence keeps growing steadily. The problem is particularly relevant due to the increased risk of COVID-19 complications and mortality in obese patients. Obesity prevalence increase is often associated with the influence of environmental and behavioural factors, leading to stigmatization of people with obesity due to beliefs that their problems are caused by poor lifestyle choices. However, hereditary predisposition to obesity has been established, likely polygenic in nature. Morbid obesity can result from rare mutations having a significant effect on energy metabolism and fat deposition, but the majority of patients does not present with monogenic forms. Microbiome low diversity significantly correlates with metabolic disorders (inflammation, insulin resistance), and the success of weight loss (bariatric) surgery. However, data on the long-term consequences of bariatric surgery and changes in the microbiome composition and genetic diversity before and after surgery are currently lacking. In this review, we summarize the results of studies of the genetic characteristics of obesity patients, molecular mechanisms of obesity, contributing to the unfavourable course of coronavirus infection, and the evolution of their microbiome during bariatric surgery, elucidating the mechanisms of disease development and creating opportunities to identify potential new treatment targets and design effective personalized approaches for the diagnosis, management, and prevention of obesity.
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Affiliation(s)
- Я. Р. Тимашева
- Институт биохимии и генетики Уфимского федерального исследовательского центра Российской академии наук;
Башкирский государственный медицинский университет
| | - Ж. Р. Балхиярова
- Институт биохимии и генетики Уфимского федерального исследовательского центра Российской академии наук;
Башкирский государственный медицинский университет;
Университет Суррея
| | - О. В. Кочетова
- Институт биохимии и генетики Уфимского федерального исследовательского центра Российской академии наук
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Abstract
The pathophysiology of obesity is complex and includes changes in eating behavior, genetic, epigenetic, environmental factors, and much more. To date, ~40 genetic polymorphisms are associated with obesity and fat distribution. However, since these options do not fully explain the inheritance of obesity, other options, such as epigenetic changes, need to be considered. Epigenetic modifications affect gene expression without changing the deoxyribonucleic acid sequence. In addition, environmental exposure during critical periods of development can affect the epigenetic tags and lead to obesity. A deeper understanding of the epigenetic mechanisms underlying obesity can aid in prevention based on lifestyle changes. This review focuses on the role of epigenetic modifications in the development of obesity and related conditions.
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Affiliation(s)
- O. M. Drapkina
- National Research Center for Therapy and Preventive Medicine
| | - O. T. Kim
- National Research Center for Therapy and Preventive Medicine
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10
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Sun C, Kovacs P, Guiu-Jurado E. Genetics of Obesity in East Asians. Front Genet 2020; 11:575049. [PMID: 33193685 PMCID: PMC7606890 DOI: 10.3389/fgene.2020.575049] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2020] [Accepted: 09/17/2020] [Indexed: 12/31/2022] Open
Abstract
Obesity has become a public health problem worldwide. Compared with Europe, people in Asia tend to suffer from type 2 diabetes with a lower body mass index (BMI). Genome-wide association studies (GWASs) have identified over 750 loci associated with obesity. Although the majority of GWAS results were conducted in individuals of European ancestry, a recent GWAS in individuals of Asian ancestry has made a significant contribution to the identification of obesity susceptibility loci. Indeed, owing to the multifactorial character of obesity with a strong environmental component, the revealed loci may have distinct contributions in different ancestral genetic backgrounds and in different environments as presented through diet and exercise among other factors. Uncovering novel, yet unrevealed genes in non-European ancestries may further contribute to explaining the missing heritability for BMI. In this review, we aimed to summarize recent advances in obesity genetics in individuals of Asian ancestry. We therefore compared proposed mechanisms underlying susceptibility loci for obesity associated with individuals of European and Asian ancestries and discussed whether known genetic variants might explain ethnic differences in obesity risk. We further acknowledged that GWAS implemented in individuals of Asian ancestries have not only validated the potential role of previously specified obesity susceptibility loci but also exposed novel ones, which have been missed in the initial genetic studies in individuals of European ancestries. Thus, multi-ethnic studies have a great potential not only to contribute to a better understanding of the complex etiology of human obesity but also potentially of ethnic differences in the prevalence of obesity, which may ultimately pave new avenues in more targeted and personalized obesity treatments.
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Affiliation(s)
| | - Peter Kovacs
- Medical Department III – Endocrinology, Nephrology, Rheumatology, University of Leipzig Medical Center, Leipzig, Germany
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Interaction between Metabolic Genetic Risk Score and Dietary Fatty Acid Intake on Central Obesity in a Ghanaian Population. Nutrients 2020; 12:nu12071906. [PMID: 32605047 PMCID: PMC7400498 DOI: 10.3390/nu12071906] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2020] [Revised: 06/04/2020] [Accepted: 06/24/2020] [Indexed: 02/07/2023] Open
Abstract
Obesity is a multifactorial condition arising from the interaction between genetic and lifestyle factors. We aimed to assess the impact of lifestyle and genetic factors on obesity-related traits in 302 healthy Ghanaian adults. Dietary intake and physical activity were assessed using a 3 day repeated 24 h dietary recall and global physical activity questionnaire, respectively. Twelve single nucleotide polymorphisms (SNPs) were used to construct 4-SNP, 8-SNP and 12-SNP genetic risk scores (GRSs). The 4-SNP GRS showed significant interactions with dietary fat intakes on waist circumference (WC) (Total fat, Pinteraction = 0.01; saturated fatty acids (SFA), Pinteraction = 0.02; polyunsaturated fatty acids (PUFA), Pinteraction = 0.01 and monounsaturated fatty acids (MUFA), Pinteraction = 0.01). Among individuals with higher intakes of total fat (>47 g/d), SFA (>14 g/d), PUFA (>16 g/d) and MUFA (>16 g/d), individuals with ≥3 risk alleles had a significantly higher WC compared to those with <3 risk alleles. This is the first study of its kind in this population, suggesting that a higher consumption of dietary fatty acid may have the potential to increase the genetic susceptibility of becoming centrally obese. These results support the general dietary recommendations to decrease the intakes of total fat and SFA, to reduce the risk of obesity, particularly in individuals with a higher genetic predisposition to central obesity.
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12
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El Hajj Chehadeh S, Osman W, Nazar S, Jerman L, Alghafri A, Sajwani A, Alawlaqi M, AlObeidli M, Jelinek HF, AlAnouti F, Khalaf K, Alsafar H. Implication of genetic variants in overweight and obesity susceptibility among the young Arab population of the United Arab Emirates. Gene 2020; 739:144509. [PMID: 32109558 DOI: 10.1016/j.gene.2020.144509] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2019] [Revised: 02/22/2020] [Accepted: 02/24/2020] [Indexed: 12/20/2022]
Abstract
OBJECTIVE Overweight and obesity are major risk factors for Type 2 Diabetes Mellitus (T2DM), cardiovascular disease (CVD) and cancer. Genetic predisposition has been shown to play a key role in obesity, and genome-wide association studies (GWAS) have identified multiple loci linked with obesity in various ethnic groups. The aim of this study was to validate the reported genetic variants associated with obesity and overweight in a young UAE Arab population. METHODS Twenty-two associated single nucleotide polymorphisms (SNPs) at 11 loci (FTO, MC4R, TMEM18, KCTD15, MTCH2, SH2B1, TFAP2B, GNPDA2, NEGR1, PCSK1 and BDNF) were studied in 392 controls and 318 overweight/obese young Emiratis (aged 18-35 years). RESULTS After adjusting for age and smoking, rs3751812 of the FTO gene was associated with overweight/obesity in male participants (p-value < 0.016), while SNPs rs17782313, rs571312 of the MC4R gene and rs12463617 of the TMEM18 gene were significantly associated with overweight/obesity in female participants (p-value = 0.001, 0.028, 0.044, respectively). Follow-up association tests and logistic regression revealed the contribution of the FTO rs3751812 and MC4R rs571213 SNPs to the risk of overweight/obesity after adjusting for age, sex and smoking (p-value = 0.044, 0.049, respectively). In addition, the FTO rs3751812 was associated with the risk of overweight/obesity after adjusting for the effect of other markers (rs17782313, rs571312, rs2867125, rs6548238 and rs12463617) (p-value = 0.035). A significant gene-gene interaction was seen between FTO, MCR4 and TMEM18 (p-value = 0.013). CONCLUSIONS Our data demonstrates that rs3751812 of the FTO gene is the key SNP associated with risk of overweight/obesity among the young UAE Arab population, in alignment with previous findings. Our results also indicate that the identified genes stratify with sex and risk of overweight/obesity. In addition to their direct association with overweight/obesity, rs17782313 and rs571312, as well as rs2867125 and rs6548238, may have a modifying effect on the risk of overweight/obesity caused by the rs3751812. Population-specific, sex-specific genetic profiling is important in understanding the heritability of obesity.
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Affiliation(s)
| | - Wael Osman
- Center for Biotechnology, Khalifa University, Abu Dhabi, United Arab Emirates; College of Arts and Sciences, Khalifa University, Abu Dhabi, United Arab Emirates
| | - Suna Nazar
- Center for Biotechnology, Khalifa University, Abu Dhabi, United Arab Emirates
| | - Laila Jerman
- Center for Biotechnology, Khalifa University, Abu Dhabi, United Arab Emirates
| | - Ameera Alghafri
- College of Medicine, Mohammad Bin Rashid University of Medicine and Health Sciences, Dubai, United Arab Emirates
| | - Ali Sajwani
- College of Medicine, Mohammad Bin Rashid University of Medicine and Health Sciences, Dubai, United Arab Emirates
| | - Mohamed Alawlaqi
- School of Medicine, The Royal College of Surgeons, Dublin, Ireland
| | - Mohamed AlObeidli
- College of Medicine and Health Sciences, United Arab Emirates University, AlAin, United Arab Emirates
| | - Herbert F Jelinek
- School of Community Health, Charles Sturt University, Albury, Australia; Department of Biomedical Engineering, Khalifa University, Abu Dhabi, United Arab Emirates
| | - Fatme AlAnouti
- College of Natural and Health Sciences, Zayed University, Abu Dhabi, United Arab Emirates
| | - Kinda Khalaf
- Department of Biomedical Engineering, Khalifa University, Abu Dhabi, United Arab Emirates
| | - Habiba Alsafar
- Center for Biotechnology, Khalifa University, Abu Dhabi, United Arab Emirates; Department of Genetics and Molecular Biology, College of Medicine and Health Sciences, Khalifa University, Abu Dhabi, United Arab Emirates.
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13
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Hammad MM, Abu-Farha M, Hebbar P, Cherian P, Al Khairi I, Melhem M, Alkayal F, Alsmadi O, Thanaraj TA, Al-Mulla F, Abubaker J. MC4R Variant rs17782313 Associates With Increased Levels of DNAJC27, Ghrelin, and Visfatin and Correlates With Obesity and Hypertension in a Kuwaiti Cohort. Front Endocrinol (Lausanne) 2020; 11:437. [PMID: 32733386 PMCID: PMC7358550 DOI: 10.3389/fendo.2020.00437] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/16/2020] [Accepted: 06/03/2020] [Indexed: 12/26/2022] Open
Abstract
Melanocortin 4 receptor (MC4R), a notable component of the melanocortin system, regulates appetite, body weight, and energy homeostasis. Genome-wide association studies have identified several MC4R variants associated with adiposity; of these, rs17782313, which is associated with increased body mass index (BMI) and overeating behavior, is of particular interest. Another gene associated with increased adiposity in global genome-wide association studies is DNAJC27, a heat shock protein known to be elevated in obesity. The detailed mechanisms underlying the role of MC4R variants in the biological pathways underlying metabolic disorders are not well-understood. To address this, we assessed variations of rs17782313 in a cohort of 282 Arab individuals from Kuwait, who are deeply phenotyped for anthropometric and metabolic traits and various biomarkers, including DNAJC27. Association tests showed that the rs17782313_C allele was associated with BMI and DNAJC27 levels. Increased levels of DNAJC27 reduced the MC4R-mediated formation of cAMP in MC4R ACTOne stable cells. In conclusion, this study demonstrated an association between the rs17782313 variant near MC4R and increased BMI and DNAJC27 levels and established a link between increased DNAJC27 levels and lower cAMP levels. We propose that regulation of MC4R activity by DNAJC27 enhances appetite through its effect on cAMP, thereby regulating obesity.
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Affiliation(s)
- Maha M. Hammad
- Research Division, Department of Biochemistry and Molecular Biology, Dasman Diabetes Institute, Kuwait City, Kuwait
| | - Mohamed Abu-Farha
- Research Division, Department of Biochemistry and Molecular Biology, Dasman Diabetes Institute, Kuwait City, Kuwait
| | - Prashantha Hebbar
- Research Division, Department of Genetics and Bioinformatics, Dasman Diabetes Institute, Kuwait City, Kuwait
| | - Preethi Cherian
- Research Division, Department of Biochemistry and Molecular Biology, Dasman Diabetes Institute, Kuwait City, Kuwait
| | - Irina Al Khairi
- Research Division, Department of Biochemistry and Molecular Biology, Dasman Diabetes Institute, Kuwait City, Kuwait
| | - Motasem Melhem
- Research Division, Department of Genetics and Bioinformatics, Dasman Diabetes Institute, Kuwait City, Kuwait
| | - Fadi Alkayal
- Research Division, Department of Genetics and Bioinformatics, Dasman Diabetes Institute, Kuwait City, Kuwait
- Department of Human Genetics, McGill University, Montreal, QC, Canada
| | | | - Thangavel Alphonse Thanaraj
- Research Division, Department of Genetics and Bioinformatics, Dasman Diabetes Institute, Kuwait City, Kuwait
- *Correspondence: Thangavel Alphonse Thanaraj
| | - Fahd Al-Mulla
- Research Division, Department of Genetics and Bioinformatics, Dasman Diabetes Institute, Kuwait City, Kuwait
- Fahd Al-Mulla
| | - Jehad Abubaker
- Research Division, Department of Biochemistry and Molecular Biology, Dasman Diabetes Institute, Kuwait City, Kuwait
- Jehad Abubaker
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Counting Oceanians of Non-European, Non-Asian Descent (ONENA) in the South Pacific to Make Them Count in Global Health. Trop Med Infect Dis 2019; 4:tropicalmed4030114. [PMID: 31405081 PMCID: PMC6789437 DOI: 10.3390/tropicalmed4030114] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2019] [Revised: 07/30/2019] [Accepted: 08/06/2019] [Indexed: 01/07/2023] Open
Abstract
Several diseases and vulnerabilities associated with genetic or microbial factors are more frequent among populations of Oceanian, Non-European, Non-Asian descent (ONENA). ONENA are specific and have long been isolated geographically. To our knowledge, there are no published official, quantitative, aggregated data on the populations impacted by these excess vulnerabilities in Oceania. We searched official census reports for updated estimates of the total population for each of the Pacific Island Countries and Territories (including Australia) and the US State of Hawaii, privileging local official statistical or censual sources. We multiplied the most recent total population estimate by the cumulative percentage of the ONENA population as determined in official reports. Including Australia and the US State of Hawaii, Oceania counts 27 countries and territories, populated in 2016 by approximately 41 M inhabitants (17 M not counting Australia) among which approximately 12.5 M (11.6 M not counting Australia) consider themselves of entire or partial ONENA ancestry. Specific genetic and microbiome traits of ONENA may be unique and need further investigation to adjust risk estimates, risk prevention, diagnostic and therapeutic strategies, to the benefit of populations in the Pacific and beyond.
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15
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Graydon JS, Claudio K, Baker S, Kocherla M, Ferreira M, Roche-Lima A, Rodríguez-Maldonado J, Duconge J, Ruaño G. Ethnogeographic prevalence and implications of the 677C>T and 1298A>C MTHFR polymorphisms in US primary care populations. Biomark Med 2019; 13:649-661. [PMID: 31157538 PMCID: PMC6630484 DOI: 10.2217/bmm-2018-0392] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2018] [Accepted: 03/25/2019] [Indexed: 02/04/2023] Open
Abstract
Aim: Variants of the MTHFR gene have been associated with a wide range of diseases. Materials & methods: The present study analyzed data from clinical genotyping of MTHFR 677C>T and 1298A>C in 1405 patients in urban primary care settings. Results: Striking differences in ethnogeographic frequencies of MTHFR polymorphisms were observed. African-Americans appear to be protected from MTHFR deficiency. Hispanics and Caucasians may be at elevated risk due to increased frequencies of 677C>T and 1298A>C, respectively. Conclusion: Individuals carrying mutations for both genes were rare and doubly homozygous mutants were absent, suggesting the TTcc is extremely rare in the greater population. The results suggest multilocus MTHFR genotyping may yield deeper insight into the ethnogeographic association between MTHFR variants and disease.
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Affiliation(s)
- James S Graydon
- Laboratory of Personalized Health, Genomas, Inc., Hartford, CT 06102, USA
| | - Karla Claudio
- Pharmaceutical Sciences department, University of Puerto Rico School of Pharmacy, San Juan, PR 00936, USA
| | - Seth Baker
- Clinical Laboratory Partners, Hartford Healthcare, Hartford, CT 06102, USA
| | - Mohan Kocherla
- Laboratory of Personalized Health, Genomas, Inc., Hartford, CT 06102, USA
| | - Mark Ferreira
- Laboratory of Personalized Health, Genomas, Inc., Hartford, CT 06102, USA
| | - Abiel Roche-Lima
- Center for Collaborative Research in Health Disparities, University of Puerto Rico School of Medicine, San Juan, PR 00936, USA
| | - Jovaniel Rodríguez-Maldonado
- Center for Collaborative Research in Health Disparities, University of Puerto Rico School of Medicine, San Juan, PR 00936, USA
| | - Jorge Duconge
- Pharmaceutical Sciences department, University of Puerto Rico School of Pharmacy, San Juan, PR 00936, USA
| | - Gualberto Ruaño
- Laboratory of Personalized Health, Genomas, Inc., Hartford, CT 06102, USA
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16
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Bien SA, Wojcik GL, Hodonsky CJ, Gignoux CR, Cheng I, Matise TC, Peters U, Kenny EE, North KE. The Future of Genomic Studies Must Be Globally Representative: Perspectives from PAGE. Annu Rev Genomics Hum Genet 2019; 20:181-200. [PMID: 30978304 DOI: 10.1146/annurev-genom-091416-035517] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
The past decade has seen a technological revolution in human genetics that has empowered population-level investigations into genetic associations with phenotypes. Although these discoveries rely on genetic variation across individuals, association studies have overwhelmingly been performed in populations of European descent. In this review, we describe limitations faced by single-population studies and provide an overview of strategies to improve global representation in existing data sets and future human genomics research via diversity-focused, multiethnic studies. We highlight the successes of individual studies and meta-analysis consortia that have provided unique knowledge. Additionally, we outline the approach taken by the Population Architecture Using Genomics and Epidemiology (PAGE) study to develop best practices for performing genetic epidemiology in multiethnic contexts. Finally, we discuss how limiting investigations to single populations impairs findings in the clinical domain for both rare-variant identification and genetic risk prediction.
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Affiliation(s)
- Stephanie A Bien
- Department of Public Health Science, Fred Hutchinson Cancer Research Center, Seattle, Washington 98109, USA; ,
| | - Genevieve L Wojcik
- Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, California 94305, USA;
| | - Chani J Hodonsky
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, USA; ,
| | - Christopher R Gignoux
- Colorado Center for Personalized Medicine, Anschutz Medical Campus, University of Colorado, Aurora, Colorado 80045, USA;
| | - Iona Cheng
- Department of Epidemiology and Biostatistics, University of California, San Francisco, California 94158, USA;
| | - Tara C Matise
- Department of Genetics, Rutgers University, New Brunswick, New Jersey 08554, USA;
| | - Ulrike Peters
- Department of Public Health Science, Fred Hutchinson Cancer Research Center, Seattle, Washington 98109, USA; ,
| | - Eimear E Kenny
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA;
| | - Kari E North
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, USA; ,
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17
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Goh KK, Chen CH, Lu ML. Topiramate mitigates weight gain in antipsychotic-treated patients with schizophrenia: meta-analysis of randomised controlled trials. Int J Psychiatry Clin Pract 2019; 23:14-32. [PMID: 29557263 DOI: 10.1080/13651501.2018.1449864] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
OBJECTIVE Weight gain is one of the most challenging issues in patients with schizophrenia treated with antipsychotics. Several meta-analyses have been conducted to review the efficacy of topiramate in reducing weight, however, several issues regarding the methodology had arisen of which make the results remain ambiguous. METHODS We conducted a meta-analysis of randomised controlled trials about the use of topiramate in patients with schizophrenia for weight reduction. Ten double-blinded randomised placebo-controlled trials and seven open-label randomised controlled trials included 905 patients. RESULTS Patients treated with topiramate experienced a greater reduction in body weight and BMI. Patients in countries of the lower overweight population showed more significant BMI reduction. Besides, studies from the Middle East and South Asia showed the greatest effect in body weight change, followed by East Asia, then Europe/America. Topiramate group was outperformed control group with significant psychopathology improvement. No difference between two groups regarding the overall side effects. CONCLUSIONS Topiramate was significantly superior to control group in mitigating weight gain and psychopathology in antipsychotic-treated patients with schizophrenia. The effects of topiramate augmentation need further investigations in larger definitive studies using methodological rigor and thorough assessments.
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Affiliation(s)
- Kah Kheng Goh
- a Department of Psychiatry , Wan-Fang Hospital, Taipei Medical University , Taipei , Taiwan
| | - Chun-Hsin Chen
- a Department of Psychiatry , Wan-Fang Hospital, Taipei Medical University , Taipei , Taiwan.,b Department of Psychiatry , School of Medicine, College of Medicine, Taipei Medical University , Taipei , Taiwan
| | - Mong-Liang Lu
- a Department of Psychiatry , Wan-Fang Hospital, Taipei Medical University , Taipei , Taiwan.,b Department of Psychiatry , School of Medicine, College of Medicine, Taipei Medical University , Taipei , Taiwan
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18
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Rohde K, Keller M, la Cour Poulsen L, Blüher M, Kovacs P, Böttcher Y. Genetics and epigenetics in obesity. Metabolism 2019; 92:37-50. [PMID: 30399374 DOI: 10.1016/j.metabol.2018.10.007] [Citation(s) in RCA: 195] [Impact Index Per Article: 39.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/06/2018] [Revised: 10/15/2018] [Accepted: 10/21/2018] [Indexed: 12/20/2022]
Abstract
Obesity is among the most threatening health burdens worldwide and its prevalence has markedly increased over the last decades. Obesity maybe considered a heritable trait. Identifications of rare cases of monogenic obesity unveiled that hypothalamic circuits and the brain-adipose axis play an important role in the regulation of energy homeostasis, appetite, hunger and satiety. For example, mutations in the leptin gene cause obesity through almost unsuppressed overeating. Common (multifactorial) obesity, most likely resulting from a concerted interplay of genetic, epigenetic and environmental factors, is clearly linked to genetic predisposition by multiple risk variants, which, however only account for a minor part of the general BMI variability. Although GWAS opened new avenues in elucidating the complex genetics behind common obesity, understanding the biological mechanisms relative to the specific risk contributing to obesity remains poorly understood. Non-genetic factors such as eating behavior or physical activity strongly modulate the individual risk for developing obesity. These factors may interact with genetic predisposition for obesity through epigenetic mechanisms. Thus, here, we review the current knowledge about monogenic and common (multifactorial) obesity highlighting the important recent advances in our knowledge on how epigenetic regulation is involved in the etiology of obesity.
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Affiliation(s)
- Kerstin Rohde
- Leipzig University Medical Center, IFB Adiposity Diseases, Leipzig 04103, Germany; University of Oslo, Institute of Clinical Medicine, Oslo 0316, Norway.
| | - Maria Keller
- Leipzig University Medical Center, IFB Adiposity Diseases, Leipzig 04103, Germany.
| | - Lars la Cour Poulsen
- Akershus University Hospital, Department of Clinical Molecular Biology, Medical Division, Lørenskog 1478, Norway.
| | - Matthias Blüher
- Department of Medicine, University of Leipzig, Leipzig 04103, Germany.
| | - Peter Kovacs
- Leipzig University Medical Center, IFB Adiposity Diseases, Leipzig 04103, Germany.
| | - Yvonne Böttcher
- Leipzig University Medical Center, IFB Adiposity Diseases, Leipzig 04103, Germany; University of Oslo, Institute of Clinical Medicine, Oslo 0316, Norway; Akershus University Hospital, Department of Clinical Molecular Biology, Medical Division, Lørenskog 1478, Norway.
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19
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Tam V, Turcotte M, Meyre D. Established and emerging strategies to crack the genetic code of obesity. Obes Rev 2019; 20:212-240. [PMID: 30353704 DOI: 10.1111/obr.12770] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/05/2018] [Revised: 08/27/2018] [Accepted: 08/28/2018] [Indexed: 12/11/2022]
Abstract
Tremendous progress has been made in the genetic elucidation of obesity over the past two decades, driven largely by technological, methodological and organizational innovations. Current strategies for identifying obesity-predisposing loci/genes, including cytogenetics, linkage analysis, homozygosity mapping, admixture mapping, candidate gene studies, genome-wide association studies, custom genotyping arrays, whole-exome sequencing and targeted exome sequencing, have achieved differing levels of success, and the identified loci in aggregate explain only a modest fraction of the estimated heritability of obesity. This review outlines the successes and limitations of these approaches and proposes novel strategies, including the use of exceptionally large sample sizes, the study of diverse ethnic groups and deep phenotypes and the application of innovative methods and study designs, to identify the remaining obesity-predisposing genes. The use of both established and emerging strategies has the potential to crack the genetic code of obesity in the not-too-distant future. The resulting knowledge is likely to yield improvements in obesity prediction, prevention and care.
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Affiliation(s)
- V Tam
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, ON, Canada
| | - M Turcotte
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, ON, Canada
| | - D Meyre
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, ON, Canada.,Department of Pathology and Molecular Medicine, McMaster University, Hamilton, ON, Canada
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20
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Standley RA, Vega RB. Furthering Precision Medicine Genomics With Healthy Living Medicine. Prog Cardiovasc Dis 2019; 62:60-67. [DOI: 10.1016/j.pcad.2018.12.009] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/28/2018] [Accepted: 12/28/2018] [Indexed: 12/23/2022]
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21
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A linear mixed-model approach to study multivariate gene-environment interactions. Nat Genet 2018; 51:180-186. [PMID: 30478441 DOI: 10.1038/s41588-018-0271-0] [Citation(s) in RCA: 85] [Impact Index Per Article: 14.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2018] [Accepted: 10/04/2018] [Indexed: 12/27/2022]
Abstract
Different exposures, including diet, physical activity, or external conditions can contribute to genotype-environment interactions (G×E). Although high-dimensional environmental data are increasingly available and multiple exposures have been implicated with G×E at the same loci, multi-environment tests for G×E are not established. Here, we propose the structured linear mixed model (StructLMM), a computationally efficient method to identify and characterize loci that interact with one or more environments. After validating our model using simulations, we applied StructLMM to body mass index in the UK Biobank, where our model yields previously known and novel G×E signals. Finally, in an application to a large blood eQTL dataset, we demonstrate that StructLMM can be used to study interactions with hundreds of environmental variables.
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22
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Ozdemir Erdoğan M, Avci K, Yildiz SH, Arikan Terzi ES, Söylemez Z, Varol N, Solak M. Effect of gene polymorphisms in transmembrane protein 18 ( TMEM18) and neuronal growth regulator 1 ( NEGR1) on body mass index in obese subjects. BIOTECHNOL BIOTEC EQ 2018. [DOI: 10.1080/13102818.2018.1533430] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022] Open
Affiliation(s)
- Müjgan Ozdemir Erdoğan
- Faculty of Medicine, Department of Medical Biology, Afyon Kocatepe University, Afyonkarahisar, Turkey
| | - Kamuran Avci
- Faculty of Medicine, Department of Medical Genetics, Afyon Kocatepe University, Afyonkarahisar, Turkey
| | - Saliha Handan Yildiz
- Faculty of Medicine, Department of Medical Genetics, Afyon Kocatepe University, Afyonkarahisar, Turkey
| | - Evrim Suna Arikan Terzi
- Faculty of Medicine, Department of Medical Biology, Afyon Kocatepe University, Afyonkarahisar, Turkey
| | - Zafer Söylemez
- Faculty of Medicine, Department of Medical Genetics, Afyon Kocatepe University, Afyonkarahisar, Turkey
| | - Nuray Varol
- Faculty of Medicine, Department of Medical Genetics, Afyon Kocatepe University, Afyonkarahisar, Turkey
| | - Mustafa Solak
- Faculty of Medicine, Department of Medical Genetics, Afyon Kocatepe University, Afyonkarahisar, Turkey
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23
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A positive association between dietary sodium intake and obesity and central obesity: results from the National Health and Nutrition Examination Survey 1999-2006. Nutr Res 2018; 55:33-44. [DOI: 10.1016/j.nutres.2018.04.008] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2017] [Revised: 04/09/2018] [Accepted: 04/10/2018] [Indexed: 12/24/2022]
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24
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Dong SS, Zhang YJ, Chen YX, Yao S, Hao RH, Rong Y, Niu HM, Chen JB, Guo Y, Yang TL. Comprehensive review and annotation of susceptibility SNPs associated with obesity-related traits. Obes Rev 2018. [PMID: 29527783 DOI: 10.1111/obr.12677] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
We aimed to summarize the results of genetic association studies for obesity and provide a comprehensive annotation of all susceptibility single nucleotide polymorphisms (SNPs). A total of 72 studies were summarized, resulting in 90,361 susceptibility SNPs (738 index SNPs and 89,623 linkage disequilibrium SNPs). Over 90% of the susceptibility SNPs are located in non-coding regions, and it is challenging to understand their functional significance. Therefore, we annotated these SNPs by using various functional databases. We identified 24,623 functional SNPs, including 4 nonsense SNPs, 479 missense SNPs, 399 untranslated region SNPs which might affect microRNA binding, 262 promoter and 5,492 enhancer SNPs which might affect transcription factor binding, 7 splicing sites, 76 SNPs which might affect gene methylation levels, 1,839 SNPs under natural selection and 17,351 SNPs which might modify histone binding. Expression quantitative trait loci analyses for functional SNPs identified 98 target genes, including 69 protein coding genes, 27 long non-coding RNAs and 3 processed transcripts. The percentage of protein coding genes that could be correlated with obesity-related pathways directly or through gene-gene interaction is 75.36 (52/69). Our results may serve as an encyclopaedia of obesity susceptibility SNPs and offer guide for functional experiments.
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Affiliation(s)
- S-S Dong
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, and Institute of Molecular Genetics, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China
| | - Y-J Zhang
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, and Institute of Molecular Genetics, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China
| | - Y-X Chen
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, and Institute of Molecular Genetics, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China
| | - S Yao
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, and Institute of Molecular Genetics, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China
| | - R-H Hao
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, and Institute of Molecular Genetics, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China
| | - Y Rong
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, and Institute of Molecular Genetics, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China
| | - H-M Niu
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, and Institute of Molecular Genetics, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China
| | - J-B Chen
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, and Institute of Molecular Genetics, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China
| | - Y Guo
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, and Institute of Molecular Genetics, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China
| | - T-L Yang
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, and Institute of Molecular Genetics, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China
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25
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Gong J, Nishimura KK, Fernandez-Rhodes L, Haessler J, Bien S, Graff M, Lim U, Lu Y, Gross M, Fornage M, Yoneyama S, Isasi CR, Buzkova P, Daviglus M, Lin DY, Tao R, Goodloe R, Bush WS, Farber-Eger E, Boston J, Dilks HH, Ehret G, Gu CC, Lewis CE, Nguyen KDH, Cooper R, Leppert M, Irvin MR, Bottinger EP, Wilkens LR, Haiman CA, Park L, Monroe KR, Cheng I, Stram DO, Carlson CS, Jackson R, Kuller L, Houston D, Kooperberg C, Buyske S, Hindorff LA, Crawford DC, Loos RJ, Le Marchand L, Matise TC, North KE, Peters U. Trans-ethnic analysis of metabochip data identifies two new loci associated with BMI. Int J Obes (Lond) 2018; 42:384-390. [PMID: 29381148 PMCID: PMC5876082 DOI: 10.1038/ijo.2017.304] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/30/2016] [Revised: 11/03/2017] [Accepted: 11/21/2017] [Indexed: 01/05/2023]
Abstract
OBJECTIVE Body mass index (BMI) is commonly used to assess obesity, which is associated with numerous diseases and negative health outcomes. BMI has been shown to be a heritable, polygenic trait, with close to 100 loci previously identified and replicated in multiple populations. We aim to replicate known BMI loci and identify novel associations in a trans-ethnic study population. SUBJECTS Using eligible participants from the Population Architecture using Genomics and Epidemiology consortium, we conducted a trans-ethnic meta-analysis of 102 514 African Americans, Hispanics, Asian/Native Hawaiian, Native Americans and European Americans. Participants were genotyped on over 200 000 SNPs on the Illumina Metabochip custom array, or imputed into the 1000 Genomes Project (Phase I). Linear regression of the natural log of BMI, adjusting for age, sex, study site (if applicable), and ancestry principal components, was conducted for each race/ethnicity within each study cohort. Race/ethnicity-specific, and combined meta-analyses used fixed-effects models. RESULTS We replicated 15 of 21 BMI loci included on the Metabochip, and identified two novel BMI loci at 1q41 (rs2820436) and 2q31.1 (rs10930502) at the Metabochip-wide significance threshold (P<2.5 × 10-7). Bioinformatic functional investigation of SNPs at these loci suggests a possible impact on pathways that regulate metabolism and adipose tissue. CONCLUSION Conducting studies in genetically diverse populations continues to be a valuable strategy for replicating known loci and uncovering novel BMI associations.
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Affiliation(s)
- Jian Gong
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America
| | - Katherine K. Nishimura
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America
| | - Lindsay Fernandez-Rhodes
- Department of Epidemiology, School of Public Health, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
| | - Jeffery Haessler
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America
| | - Stephanie Bien
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America
| | - Misa Graff
- Department of Epidemiology, School of Public Health, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
| | - Unhee Lim
- Cancer Research Center, University of Hawaii, Honolulu, Hawaii, United States of America
| | - Yingchang Lu
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America
- The Genetics of Obesity and Related Metabolic Traits Program, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America
- Division of Epidemiology, Department of Medicine, Vanderbilt-Ingram Cancer Center, Vanderbilt Epidemiology Center, Vanderbilt University School of Medicine, Nashville, Tennessee, United States of America
| | - Myron Gross
- Masonic Cancer Center, University of Minnesota, Minneapolis, Minnesota, United States of America
| | - Myriam Fornage
- Health Science Center, University of Texas, Austin, Texas, United States of America
| | - Sachiko Yoneyama
- Department of Epidemiology, School of Public Health, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
| | - Carmen R. Isasi
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, New York, United States of America
| | - Petra Buzkova
- Department of Biostatistics, University of Washington, Seattle, Washington, United States of America
| | - Martha Daviglus
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois, U United States of America SA
| | - Dan-Yu Lin
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
| | - Ran Tao
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
| | - Robert Goodloe
- Center for Human Genetics Research, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America
| | - William S. Bush
- Department of Epidemiology and Biostatistics, Institute for Computational Biology, Case Western Reserve University, Cleveland, Ohio, United States of America
| | - Eric Farber-Eger
- Center for Human Genetics Research, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America
| | - Jonathan Boston
- Center for Human Genetics Research, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America
| | - Holli H. Dilks
- Sarah Cannon Research Institute, Nashville, Tennessee, United States of America
| | - Georg Ehret
- Center for Complex Disease Genomics, McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America
- Division of Cardiology, Geneva University Hospital, Geneva, Switzerland
| | - C. Charles Gu
- Department of Biostatistics, Washington University, St. Louis, Missouri, United States of America
| | - Cora E. Lewis
- Department of Medicine, University of Alabama, Birmingham, Alabama, United States of America
| | - Khanh-Dung H. Nguyen
- Center for Complex Disease Genomics, McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America
| | - Richard Cooper
- Preventive Medicine and Epidemiology, Loyola University, Chicago, Illinois, United States of America
| | - Mark Leppert
- Department of Human Genetics, University of Utah, Salt Lake City, Utah, United States of America
| | - Marguerite R. Irvin
- Department of Epidemiology, University of Alabama, Birmingham, Alabama, United States of America
| | - Erwin P. Bottinger
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America
| | - Lynne R. Wilkens
- Cancer Research Center, University of Hawaii, Honolulu, Hawaii, United States of America
| | - Christopher A. Haiman
- Keck School of Medicine, University of Southern California, Los Angeles, California, United States of America
| | - Lani Park
- Cancer Research Center, University of Hawaii, Honolulu, Hawaii, United States of America
| | - Kristine R. Monroe
- Keck School of Medicine, University of Southern California, Los Angeles, California, United States of America
| | - Iona Cheng
- Cancer Prevention Institute of California, Fremont, California, United States of America
| | - Daniel O. Stram
- Keck School of Medicine, University of Southern California, Los Angeles, California, United States of America
| | - Christopher S. Carlson
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America
| | - Rebecca Jackson
- Department of Internal Medicine, Ohio State Medical Center, Columbus, Ohio, United States of America
| | - Lew Kuller
- Department of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
| | - Denise Houston
- Wake Forest University School of Medicine, Winston-Salem, North Carolina, United States of America
| | - Charles Kooperberg
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America
| | - Steven Buyske
- Department of Genetics, Rutgers University, Piscataway, New Jersey, United States of America
- Department of Statistics and Biostatistics, Rutgers University, Piscataway, New Jersey, United States of America
| | - Lucia A. Hindorff
- Division of Genomic Medicine, National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland, United States of America
| | - Dana C. Crawford
- Center for Human Genetics Research, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America
| | - Ruth J.F. Loos
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America
- The Genetics of Obesity and Related Metabolic Traits Program, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America
- The Mindich Child Health and Development Institute, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America
| | - Loic Le Marchand
- Cancer Research Center, University of Hawaii, Honolulu, Hawaii, United States of America
| | - Tara C. Matise
- Department of Genetics, Rutgers University, Piscataway, New Jersey, United States of America
| | - Kari E. North
- Department of Epidemiology, School of Public Health, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
| | - Ulrike Peters
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America
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26
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Romero-Ibarguengoitia ME, Vadillo-Ortega F, Caballero AE, Ibarra-González I, Herrera-Rosas A, Serratos-Canales MF, León-Hernández M, González-Chávez A, Mummidi S, Duggirala R, López-Alvarenga JC. Family history and obesity in youth, their effect on acylcarnitine/aminoacids metabolomics and non-alcoholic fatty liver disease (NAFLD). Structural equation modeling approach. PLoS One 2018; 13:e0193138. [PMID: 29466466 PMCID: PMC5821462 DOI: 10.1371/journal.pone.0193138] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2017] [Accepted: 02/05/2018] [Indexed: 01/21/2023] Open
Abstract
BACKGROUND Structural equation modeling (SEM) can help understanding complex functional relationships among obesity, non-alcoholic fatty liver disease (NAFLD), family history of obesity, targeted metabolomics and pro-inflammatory markers. We tested two hypotheses: 1) If obesity precedes an excess of free fatty acids that increase oxidative stress and mitochondrial dysfunction, there would be an increase of serum acylcarnitines, amino acids and cytokines in obese subjects. Acylcarnitines would be related to non-alcoholic fatty disease that will induce insulin resistance. 2) If a positive family history of obesity and type 2 diabetes are the major determinants of the metabolomic profile, there would be higher concentration of amino acids and acylcarnitines in patients with this background that will induce obesity and NAFLD which in turn will induce insulin resistance. METHODS/RESULTS 137 normoglycemic subjects, mean age (SD) of 30.61 (8.6) years divided in three groups: BMI<25 with absence of NAFLD (G1), n = 82; BMI>30 with absence of NAFLD (G2), n = 24; and BMI>30 with NAFLD (G3), n = 31. Family history of obesity (any) was present in 53%. Both models were adjusted in SEM. Family history of obesity predicted obesity but could not predict acylcarnitines and amino acid concentrations (effect size <0.2), but did predict obesity phenotype. CONCLUSION Family history of obesity is the major predictor of obesity, and the metabolic abnormalities on amino acids, acylcarnitines, inflammation, insulin resistance, and NAFLD.
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Affiliation(s)
| | - Felipe Vadillo-Ortega
- Vinculation Unit Faculty of Medicine UNAM, Instituto Nacional de Medicina Genomica (INMEGEN), Mexico City, Mexico
| | | | | | | | | | | | | | - Srinivas Mummidi
- South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley, Edinburg, TX, United States of America
| | - Ravindranath Duggirala
- South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley, Edinburg, TX, United States of America
| | - Juan Carlos López-Alvarenga
- South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley, Edinburg, TX, United States of America
- Research department, Universidad Mexico Americana del Norte, Reynosa, Tamaulipas, Mexico
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27
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Stryjecki C, Alyass A, Meyre D. Ethnic and population differences in the genetic predisposition to human obesity. Obes Rev 2018; 19:62-80. [PMID: 29024387 DOI: 10.1111/obr.12604] [Citation(s) in RCA: 87] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/10/2017] [Revised: 06/17/2017] [Accepted: 08/02/2017] [Indexed: 12/22/2022]
Abstract
Obesity rates have escalated to the point of a global pandemic with varying prevalence across ethnic groups. These differences are partially explained by lifestyle factors in addition to genetic predisposition to obesity. This review provides a comprehensive examination of the ethnic differences in the genetic architecture of obesity. Using examples from evolution, heritability, admixture, monogenic and polygenic studies of obesity, we provide explanations for ethnic differences in the prevalence of obesity. The debate over definitions of race and ethnicity, the advantages and limitations of multi-ethnic studies and future directions of research are also discussed. Multi-ethnic studies have great potential to provide a better understanding of ethnic differences in the prevalence of obesity that may result in more targeted and personalized obesity treatments.
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Affiliation(s)
- C Stryjecki
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, ON, Canada
| | - A Alyass
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, ON, Canada
| | - D Meyre
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, ON, Canada.,Department of Pathology and Molecular Medicine, McMaster University, Hamilton, ON, Canada
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28
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Subramanian L, Khan AA, Allu PKR, Kiranmayi M, Sahu BS, Sharma S, Khullar M, Mullasari AS, Mahapatra NR. A haplotype variant of the human chromogranin A gene ( CHGA) promoter increases CHGA expression and the risk for cardiometabolic disorders. J Biol Chem 2017; 292:13970-13985. [PMID: 28667172 PMCID: PMC5572921 DOI: 10.1074/jbc.m117.778134] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2017] [Revised: 06/27/2017] [Indexed: 12/13/2022] Open
Abstract
The acidic glycoprotein chromogranin A (CHGA) is co-stored/co-secreted with catecholamines and crucial for secretory vesicle biogenesis in neuronal/neuroendocrine cells. CHGA is dysregulated in several cardiovascular diseases, but the underlying mechanisms are not well established. Here, we sought to identify common polymorphisms in the CHGA promoter and to explore the mechanistic basis of their plausible contribution to regulating CHGA protein levels in circulation. Resequencing of the CHGA promoter in an Indian population (n = 769) yielded nine single-nucleotide polymorphisms (SNPs): G-1106A, A-1018T, T-1014C, T-988G, G-513A, G-462A, T-415C, C-89A, and C-57T. Linkage disequilibrium (LD) analysis indicated strong LD among SNPs at the -1014, -988, -462, and -89 bp positions and between the -1018 and -57 bp positions. Haplotype analysis predicted five major promoter haplotypes that displayed differential promoter activities in neuronal cells; specifically, haplotype 2 (containing variant T alleles at -1018 and -57 bp) exhibited the highest promoter activity. Systematic computational and experimental analyses revealed that transcription factor c-Rel has a role in activating the CHGA promoter haplotype 2 under basal and pathophysiological conditions (viz. inflammation and hypoxia). Consistent with the higher in vitro CHGA promoter activity of haplotype 2, individuals carrying this haplotype had higher plasma CHGA levels, plasma glucose levels, diastolic blood pressure, and body mass index. In conclusion, these results suggest a functional role of the CHGA promoter haplotype 2 (occurring in a large proportion of the world population) in enhancing CHGA expression in haplotype 2 carriers who may be at higher risk for cardiovascular/metabolic disorders.
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Affiliation(s)
- Lakshmi Subramanian
- From the Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai 600036
| | - Abrar A Khan
- From the Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai 600036
| | - Prasanna K R Allu
- From the Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai 600036
| | - Malapaka Kiranmayi
- From the Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai 600036
| | - Bhavani S Sahu
- From the Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai 600036
| | - Saurabh Sharma
- Department of Experimental Medicine and Biotechnology, Postgraduate Institute of Medical Education and Research, Chandigarh 160012, India
| | - Madhu Khullar
- Department of Experimental Medicine and Biotechnology, Postgraduate Institute of Medical Education and Research, Chandigarh 160012, India
| | - Ajit S Mullasari
- Institute of Cardiovascular Diseases, Madras Medical Mission, Chennai 600037
| | - Nitish R Mahapatra
- From the Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai 600036.
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29
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Salgado-Montilla JL, Rodríguez-Cabán JL, Sánchez-García J, Sánchez-Ortiz R, Irizarry-Ramírez M. Impact of FTO SNPs rs9930506 and rs9939609 in Prostate Cancer Severity in a Cohort of Puerto Rican Men. ACTA ACUST UNITED AC 2017; 5. [PMID: 29333375 DOI: 10.21767/2254-6081.1000148] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Background Obesity is prevalent in PR and has been associated with prostate cancer (PCa) mortality and aggressiveness. Polymorphisms (SNPs) rs9930506 and rs9939609 in the FTO gene have been associated with both obesity and PCa. The aim of this work was to ascertain whether the presence of these SNPs is associated with PCa risk and severity in a cohort of Puerto Rican men. Methods and findings The study population consisted of 513 Puerto Rican men age ranging from 40-79 years old who underwent radical prostatectomy (RP) as the first treatment for PCa and 128 healthy Puerto Rican men age ranging from 40-79 years old. Genomic DNA (gDNA) was extracted and SNPs were determined by Real-Time PCR. PCa severity was defined based on RP stage and Gleason Score. The relationship of FTO SNPs with demographic, clinical characteristics, PCa status and PCa severity were assessed. Logistic regression models with a 95% confidence interval (CI) determined SNPs interaction with PCa risk and severity odds ratio (ORs). Results and discussion BMI, age and PSA were considered as confounders. Hardy-Weinberg equilibrium was present for both SNPs. The heterozygous forms (A/G; T/A) were the most prevalent genotypes and the frequency of alleles and genotypes for both SNPs agreed with those published in 1000 genomes. Results suggest an inverse association between the mutated rs9939609 and the risk of having PCa (OR: 0.53, 95% CI: 0.31-0.92) and a positive association with overweight (OR: 1.05, 95% CI: 0.68-1.62). Importantly, among the cases that were overweight, those with mutated rs9939609 had a greater chance of high severity PCa (OR: 1.39, 95% CI: 0.84-2.32) although these results were not statistical significant upon adjustment. Limitations of the study were the relatively small cohort and lack of access to the weight history of all our subjects. Conclusion Results offer a research line to be followed with an expanded number of subjects that may provide a better statistical significance, to unravel the high mortality rate in this population.
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Affiliation(s)
- Jeannette L Salgado-Montilla
- University of Puerto Rico/MD Anderson Cancer Center Partnership for Excellence in Cancer Research, University of Puerto Rico, Medical Sciences Campus, San Juan, Puerto Rico, USA
| | - Jorge L Rodríguez-Cabán
- School of Health Professions, University of Puerto Rico, Medical Sciences Campus, San Juan, Puerto Rico, USA
| | - Jonathan Sánchez-García
- School of Public Health, Department of Biostatistics and Epidemiology, University of Puerto Rico, Medical Sciences Campus, San Juan, Rico, USA
| | - Ricardo Sánchez-Ortiz
- School of Medicine, Urology Section, University of Puerto Rico, Medical Sciences Campus, San Juan, Puerto Rico, USA
| | - Margarita Irizarry-Ramírez
- School of Health Professions, University of Puerto Rico, Medical Sciences Campus, San Juan, Puerto Rico, USA
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30
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Fernández-Rhodes L, Gong J, Haessler J, Franceschini N, Graff M, Nishimura KK, Wang Y, Highland HM, Yoneyama S, Bush WS, Goodloe R, Ritchie MD, Crawford D, Gross M, Fornage M, Buzkova P, Tao R, Isasi C, Avilés-Santa L, Daviglus M, Mackey RH, Houston D, Gu CC, Ehret G, Nguyen KDH, Lewis CE, Leppert M, Irvin MR, Lim U, Haiman CA, Le Marchand L, Schumacher F, Wilkens L, Lu Y, Bottinger EP, Loos RJL, Sheu WHH, Guo X, Lee WJ, Hai Y, Hung YJ, Absher D, Wu IC, Taylor KD, Lee IT, Liu Y, Wang TD, Quertermous T, Juang JMJ, Rotter JI, Assimes T, Hsiung CA, Chen YDI, Prentice R, Kuller LH, Manson JE, Kooperberg C, Smokowski P, Robinson WR, Gordon-Larsen P, Li R, Hindorff L, Buyske S, Matise TC, Peters U, North KE. Trans-ethnic fine-mapping of genetic loci for body mass index in the diverse ancestral populations of the Population Architecture using Genomics and Epidemiology (PAGE) Study reveals evidence for multiple signals at established loci. Hum Genet 2017; 136:771-800. [PMID: 28391526 PMCID: PMC5485655 DOI: 10.1007/s00439-017-1787-6] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2016] [Accepted: 03/23/2017] [Indexed: 11/26/2022]
Abstract
Most body mass index (BMI) genetic loci have been identified in studies of primarily European ancestries. The effect of these loci in other racial/ethnic groups is less clear. Thus, we aimed to characterize the generalizability of 170 established BMI variants, or their proxies, to diverse US populations and trans-ethnically fine-map 36 BMI loci using a sample of >102,000 adults of African, Hispanic/Latino, Asian, European and American Indian/Alaskan Native descent from the Population Architecture using Genomics and Epidemiology Study. We performed linear regression of the natural log of BMI (18.5-70 kg/m2) on the additive single nucleotide polymorphisms (SNPs) at BMI loci on the MetaboChip (Illumina, Inc.), adjusting for age, sex, population stratification, study site, or relatedness. We then performed fixed-effect meta-analyses and a Bayesian trans-ethnic meta-analysis to empirically cluster by allele frequency differences. Finally, we approximated conditional and joint associations to test for the presence of secondary signals. We noted directional consistency with the previously reported risk alleles beyond what would have been expected by chance (binomial p < 0.05). Nearly, a quarter of the previously described BMI index SNPs and 29 of 36 densely-genotyped BMI loci on the MetaboChip replicated/generalized in trans-ethnic analyses. We observed multiple signals at nine loci, including the description of seven loci with novel multiple signals. This study supports the generalization of most common genetic loci to diverse ancestral populations and emphasizes the importance of dense multiethnic genomic data in refining the functional variation at genetic loci of interest and describing several loci with multiple underlying genetic variants.
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Affiliation(s)
- Lindsay Fernández-Rhodes
- Department of Epidemiology, UNC Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
| | - Jian Gong
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Jeffrey Haessler
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Nora Franceschini
- Department of Epidemiology, UNC Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Mariaelisa Graff
- Department of Epidemiology, UNC Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Katherine K Nishimura
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Yujie Wang
- Department of Epidemiology, UNC Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Heather M Highland
- Department of Epidemiology, UNC Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Sachiko Yoneyama
- Department of Epidemiology, UNC Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - William S Bush
- Department of Epidemiology and Biostatistics, Institute for Computational Biology, Case Western Reserve University, Cleveland, OH, USA
| | - Robert Goodloe
- Center for Human Genetics Research, Vanderbilt University, Nashville, TN, USA
| | - Marylyn D Ritchie
- Biochemistry and Molecular Biology, The Pennsylvania State University, University Park, PA, USA
| | - Dana Crawford
- Department of Epidemiology and Biostatistics, Institute for Computational Biology, Case Western Reserve University, Cleveland, OH, USA
| | - Myron Gross
- Department of Laboratory Medicine and Pathology, University of Minnesota, Minneapolis, MN, USA
| | - Myriam Fornage
- Center for Human Genetics, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Petra Buzkova
- Department of Biostatistics, School of Public Health, University of Washington, Seattle, WA, USA
| | - Ran Tao
- Department of Biostatistics, UNC Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Carmen Isasi
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY, USA
| | | | - Martha Daviglus
- Insitute of Minority Health Research, University of Illinois at Chicago, Chicago, IL, USA
| | - Rachel H Mackey
- Department of Epidemiology, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA
| | - Denise Houston
- Geriatrics and Gerontology, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - C Charles Gu
- Division of Biostatistics, School of Medicine, Washington University in St. Louis, St. Louis, MO, USA
| | - Georg Ehret
- Center for Complex Disease Genomics, McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University, Baltimore, MD, USA
- Division of Cardiology, Geneva University Hospital, Geneva, OH, Switzerland
| | - Khanh-Dung H Nguyen
- Center for Complex Disease Genomics, McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - Cora E Lewis
- Department of Medicine, University of Alabama, Birmingham, AL, USA
| | - Mark Leppert
- Department of Human Genetics, University of Utah, Salt Lake City, UT, USA
| | | | - Unhee Lim
- Epidemiology Program, University of Hawaii Cancer Center, Honolulu, HI, USA
| | - Christopher A Haiman
- Department of Preventive Medicine, Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Loic Le Marchand
- Epidemiology Program, University of Hawaii Cancer Center, Honolulu, HI, USA
| | - Fredrick Schumacher
- Department of Preventive Medicine, Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Lynne Wilkens
- Epidemiology Program, University of Hawaii Cancer Center, Honolulu, HI, USA
| | - Yingchang Lu
- Charles R. Bronfman Instituted for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Erwin P Bottinger
- Charles R. Bronfman Instituted for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Ruth J L Loos
- Charles R. Bronfman Instituted for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Wayne H-H Sheu
- Division of Endocrine and Metabolism, Department of Internal Medicine, Taichung Veterans General Hospital, Taichung, Taiwan
- School of Medicine, National Defense Medical Center, National Yang-Ming University, Taipei, Taiwan
| | - Xiuqing Guo
- Department of Pediatrics, Institute for Translational Genomics and Population Sciences, LABioMed at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Wen-Jane Lee
- Department of Medical Research, Taichung Veterans General Hospital, Taichung, Taiwan
| | - Yang Hai
- Department of Pediatrics, Institute for Translational Genomics and Population Sciences, LABioMed at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Yi-Jen Hung
- Division of Endocrinology and Metabolism, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan
| | - Devin Absher
- HudsonAlpha Institute for Biotechnology, Huntsville, AL, USA
| | - I-Chien Wu
- Division of Biostatistics and Bioinformatics, Institute of Population Health Sciences, National Health Research Institutes, Zhunan Town, Taiwan
| | - Kent D Taylor
- Department of Pediatrics, Institute for Translational Genomics and Population Sciences, LABioMed at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - I-Te Lee
- Division of Endocrine and Metabolism, Department of Internal Medicine, Taichung Veterans General Hospital, Taichung, Taiwan
- School of Medicine, National Defense Medical Center, National Yang-Ming University, Taipei, Taiwan
| | - Yeheng Liu
- Department of Pediatrics, Institute for Translational Genomics and Population Sciences, LABioMed at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Tzung-Dau Wang
- Division of Cardiology, Department of Internal Medicine, Cardiovascular Center, National Taiwan University Hospital, Taipei, Taiwan
| | - Thomas Quertermous
- Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Jyh-Ming J Juang
- Division of Cardiology, Department of Internal Medicine, Cardiovascular Center, National Taiwan University Hospital, Taipei, Taiwan
| | - Jerome I Rotter
- Department of Pediatrics, Institute for Translational Genomics and Population Sciences, LABioMed at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Themistocles Assimes
- Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Chao A Hsiung
- Division of Biostatistics and Bioinformatics, Institute of Population Health Sciences, National Health Research Institutes, Zhunan Town, Taiwan
| | - Yii-Der Ida Chen
- Department of Pediatrics, Institute for Translational Genomics and Population Sciences, LABioMed at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Ross Prentice
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Lewis H Kuller
- Department of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - JoAnn E Manson
- Division of Preventive Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Charles Kooperberg
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Paul Smokowski
- School of Social Welfare, The University of Kansas, Lawrence, KS, USA
| | - Whitney R Robinson
- Department of Epidemiology, UNC Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Penny Gordon-Larsen
- Department of Nutrition, UNC Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Rongling Li
- Division of Genomic Medicine, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Lucia Hindorff
- Division of Genomic Medicine, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Steven Buyske
- Department of Statistics and Biostatistics, Rutgers University, Piscataway, NJ, USA
| | - Tara C Matise
- Department of Genetics, Rutgers University, Piscataway, NJ, USA
| | - Ulrike Peters
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Kari E North
- Department of Epidemiology, UNC Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
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Goutzelas Y, Kotsa K, Vasilopoulos Y, Tsekmekidou X, Stamatis C, Yovos JG, Sarafidou T, Mamuris Z. Association analysis of FTO gene polymorphisms with obesity in Greek adults. Gene 2017; 613:10-13. [DOI: 10.1016/j.gene.2017.02.033] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2016] [Revised: 11/18/2016] [Accepted: 02/27/2017] [Indexed: 02/08/2023]
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Li Q, Du X, Zhang Y, Yin G, Zhang G, Walss-Bass C, Quevedo J, Soares JC, Xia H, Li X, Zheng Y, Ning Y, Zhang XY. The prevalence, risk factors and clinical correlates of obesity in Chinese patients with schizophrenia. Psychiatry Res 2017; 251:131-136. [PMID: 28199911 DOI: 10.1016/j.psychres.2016.12.041] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/26/2016] [Revised: 10/17/2016] [Accepted: 12/26/2016] [Indexed: 01/04/2023]
Abstract
Obesity is a common comorbidity in schizophrenia. Few studies have addressed obesity in Chinese schizophrenia patients. The aims of this current study were to evaluate the prevalence, risk factors and clinical correlates of obesity in Chinese patients with schizophrenia. A total of 206 patients were recruited from a hospital in Beijing. Their clinical and anthropometric data together with plasma glucose and lipid parameters were collected. Positive and Negative Syndrome Scale (PANSS) was rated for all patients. Overall, 43 (20.9%) patients were obese and 67 (32.5%) were overweight. The obese patients had significantly higher glucose levels, triglyceride levels than non-obese patients. Females and patients with type 2 diabetes mellitus had increased risk for obesity. Correlation analysis showed that BMI was associated with sex, education levels, negative symptoms, total PANSS score, triglyceride levels and type 2 diabetes mellitus. Further stepwise regression analysis showed that sex, type 2 diabetes, education level, triglyceride and amount of smoking/day were significant predictors for obesity. Our study showed that the prevalence of obesity in Chinese patients with schizophrenia is higher than that in the general population. Some demographic and clinical variables are risk factors for obesity in schizophrenia.
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Affiliation(s)
- Qiongzhen Li
- Suzhou Psychiatric Hospital, The Affiliated Guangji Hospital of Soochow University, Jiangsu, China; Department of Psychiatry and Behavioral Sciences, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Xiangdong Du
- Suzhou Psychiatric Hospital, The Affiliated Guangji Hospital of Soochow University, Jiangsu, China
| | - Yingyang Zhang
- Suzhou Psychiatric Hospital, The Affiliated Guangji Hospital of Soochow University, Jiangsu, China
| | - Guangzhong Yin
- Suzhou Psychiatric Hospital, The Affiliated Guangji Hospital of Soochow University, Jiangsu, China
| | - Guangya Zhang
- Suzhou Psychiatric Hospital, The Affiliated Guangji Hospital of Soochow University, Jiangsu, China
| | - Consuelo Walss-Bass
- Department of Psychiatry and Behavioral Sciences, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - João Quevedo
- Department of Psychiatry and Behavioral Sciences, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Jair C Soares
- Department of Psychiatry and Behavioral Sciences, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Haishen Xia
- Hefei Fourth People's Hospital, Anhui Mental Health Center, Hefei, China
| | - Xiaosi Li
- Hefei Fourth People's Hospital, Anhui Mental Health Center, Hefei, China
| | - Yingjun Zheng
- The Affiliated Brain Hospital of Guangzhou Medical University (Guangzhou Huiai Hospital), Guangzhou, China
| | - Yuping Ning
- The Affiliated Brain Hospital of Guangzhou Medical University (Guangzhou Huiai Hospital), Guangzhou, China
| | - Xiang Yang Zhang
- Department of Psychiatry and Behavioral Sciences, The University of Texas Health Science Center at Houston, Houston, TX, USA; Psychiatry Research Center, Beijing HuiLongGuan Hospital, Beijing, China.
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Recent progress in genetics, epigenetics and metagenomics unveils the pathophysiology of human obesity. Clin Sci (Lond) 2017; 130:943-86. [PMID: 27154742 DOI: 10.1042/cs20160136] [Citation(s) in RCA: 227] [Impact Index Per Article: 32.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2015] [Accepted: 02/24/2016] [Indexed: 12/19/2022]
Abstract
In high-, middle- and low-income countries, the rising prevalence of obesity is the underlying cause of numerous health complications and increased mortality. Being a complex and heritable disorder, obesity results from the interplay between genetic susceptibility, epigenetics, metagenomics and the environment. Attempts at understanding the genetic basis of obesity have identified numerous genes associated with syndromic monogenic, non-syndromic monogenic, oligogenic and polygenic obesity. The genetics of leanness are also considered relevant as it mirrors some of obesity's aetiologies. In this report, we summarize ten genetically elucidated obesity syndromes, some of which are involved in ciliary functioning. We comprehensively review 11 monogenic obesity genes identified to date and their role in energy maintenance as part of the leptin-melanocortin pathway. With the emergence of genome-wide association studies over the last decade, 227 genetic variants involved in different biological pathways (central nervous system, food sensing and digestion, adipocyte differentiation, insulin signalling, lipid metabolism, muscle and liver biology, gut microbiota) have been associated with polygenic obesity. Advances in obligatory and facilitated epigenetic variation, and gene-environment interaction studies have partly accounted for the missing heritability of obesity and provided additional insight into its aetiology. The role of gut microbiota in obesity pathophysiology, as well as the 12 genes associated with lipodystrophies is discussed. Furthermore, in an attempt to improve future studies and merge the gap between research and clinical practice, we provide suggestions on how high-throughput '-omic' data can be integrated in order to get closer to the new age of personalized medicine.
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Transethnic Genetic-Correlation Estimates from Summary Statistics. Am J Hum Genet 2016; 99:76-88. [PMID: 27321947 DOI: 10.1016/j.ajhg.2016.05.001] [Citation(s) in RCA: 180] [Impact Index Per Article: 22.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2015] [Accepted: 05/03/2016] [Indexed: 11/22/2022] Open
Abstract
The increasing number of genetic association studies conducted in multiple populations provides an unprecedented opportunity to study how the genetic architecture of complex phenotypes varies between populations, a problem important for both medical and population genetics. Here, we have developed a method for estimating the transethnic genetic correlation: the correlation of causal-variant effect sizes at SNPs common in populations. This methods takes advantage of the entire spectrum of SNP associations and uses only summary-level data from genome-wide association studies. This avoids the computational costs and privacy concerns associated with genotype-level information while remaining scalable to hundreds of thousands of individuals and millions of SNPs. We applied our method to data on gene expression, rheumatoid arthritis, and type 2 diabetes and overwhelmingly found that the genetic correlation was significantly less than 1. Our method is implemented in a Python package called Popcorn.
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Salinas YD, Wang L, DeWan AT. Multiethnic genome-wide association study identifies ethnic-specific associations with body mass index in Hispanics and African Americans. BMC Genet 2016; 17:78. [PMID: 27296613 PMCID: PMC4907283 DOI: 10.1186/s12863-016-0387-0] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2015] [Accepted: 06/01/2016] [Indexed: 01/03/2023] Open
Abstract
Background Genome-wide association studies of obesity have typically assumed fixed genetic effects across ethnicities, rarely attempting to thoroughly compare and contrast findings across various ethnic groups. Therefore, our study aimed to identify novel genetic associations with body mass index (BMI), a common measure of obesity, and explore their cross-ethnic generalizability in a multiethnic population. To that end, we conducted ethnic-specific genome-wide association analyses among 1235 Hispanic, 706 Asian, 1549 African American, and 2395 European American subjects from the Multi-ethnic Study of Atherosclerosis (MESA). We compared findings across ethnicities and investigated single-nucleotide polymorphisms (SNPs) with suggestive BMI-association p-values among 3379 Hispanic and 6871 African American subjects from the Women’s Health Initiative (WHI). Results We identified a genome-wide significant association in MESA Hispanics—rs12253976 in KLF6 (beta = 5.792 kg/m2 per-allele, 95 % confidence interval (CI): 3.885, 7.698; p = 3.43 × 10−9)—and suggestive SNPs with p < 5 × 10−6 in MESA Hispanics, European Americans and African Americans that display ethnic-specific effects on BMI. Of these suggestive SNPs, Hispanic SNP rs12255372 and African American SNP rs6435678 had the most evidence of replication in WHI. rs12255372 (in TCF7L2) was associated with lower BMI in both MESA (beta = −1.111 kg/m2, 95 % CI: −1.578, −0.645; p = 3.33 × 10−6) and WHI Hispanics (beta = −0.304 kg/m2, 95 % CI: −0.613, 0.006; p = 0.054). This TCF7L2 intronic region contains several SNPs (rs7901695, rs4506565, rs4132670, and rs12243326) with low p-values (p < 10−3) in MESA and betas of similar magnitude and direction in MESA and WHI, but only rs12243326 is in strong linkage disequilibrium with rs12255372 in our Hispanic populations, suggesting independent signals in this region. rs6435678 (in ERBB4) was associated with greater BMI in both MESA (beta = 1.104 kg/m2, 95 % CI: 0.643, 1.564; p = 2.85 × 10−6) and WHI African Americans (beta = 0.219 kg/m2, 95 % CI: −0.021, 0.460; p = 0.074). Conclusions Two BMI-association signals are present in the TCF7L2 intronic region of Hispanics, one of which is tagged by rs12255372. ERBB4 rs6435678 is a novel BMI-association signal in African Americans. Overall, our data suggest that ethnic-specific associations are involved in the genetic determination of BMI. Ethnic-specificity has potential implications for the development of gene-based therapies for obesity. Electronic supplementary material The online version of this article (doi:10.1186/s12863-016-0387-0) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Yasmmyn D Salinas
- Department of Chronic Disease Epidemiology, Yale School of Public Health, 60 College Street, New Haven, CT, 06520, USA
| | - Leyao Wang
- Department of Chronic Disease Epidemiology, Yale School of Public Health, 60 College Street, New Haven, CT, 06520, USA
| | - Andrew T DeWan
- Department of Chronic Disease Epidemiology, Yale School of Public Health, 60 College Street, New Haven, CT, 06520, USA.
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Arnold M, Leitzmann M, Freisling H, Bray F, Romieu I, Renehan A, Soerjomataram I. Obesity and cancer: An update of the global impact. Cancer Epidemiol 2016; 41:8-15. [PMID: 26775081 DOI: 10.1016/j.canep.2016.01.003] [Citation(s) in RCA: 177] [Impact Index Per Article: 22.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2015] [Revised: 12/28/2015] [Accepted: 01/03/2016] [Indexed: 02/06/2023]
Abstract
In view of the growing global obesity epidemic, this paper reviews the relation between recent trends in body mass index (BMI) and the changing profile of cancer worldwide. By examining seven selected countries, each representing a world region, a pattern of increasing BMI with region and gender-specific diversity is noted: increasing levels of BMI were most pronounced in the Middle East (Saudi Arabia), rather modest in Eastern Asia (India) and generally more rapid in females than in males. This observation translates into a disproportionate distribution of cancer attributable to high levels of BMI, ranging by sex from 4-9% in Saudi Arabia and from 0.2-1.2% in India. Overweight and obesity may also influence cancer outcomes, and hence have a varying impact on cancer survival and death in different world regions. Future challenges in cancer studies exploring the association with overweight and obesity concern the measurement of adiposity and its potentially cumulative effect over the life course. Given the limitations of BMI as an imperfect measure of body fatness, routine anthropometric data collection needs to be extended to develop more informative measures, such as waist circumference in settings where the gold standard tools remain unaffordable. Furthermore, questions surrounding the dose-response and timing of obesity and their associations with cancer remain to be answered. Improved surveillance of health risk factors including obesity as well as the scale and profile of cancer in every country of the world is urgently needed. This will enable the design of cost-effective actions to curb the growing burden of cancer related to excess body weight.
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Affiliation(s)
- Melina Arnold
- Section of Cancer Surveillance, International Agency for Research on Cancer (IARC), Lyon, France
| | - Michael Leitzmann
- Department of Epidemiology and Preventive Medicine, University of Regensburg, Germany
| | - Heinz Freisling
- Section of Nutrition and Metabolism, Dietary Exposure Assessment Group, International Agency for Research on Cancer (IARC), Lyon, France
| | - Freddie Bray
- Section of Cancer Surveillance, International Agency for Research on Cancer (IARC), Lyon, France
| | - Isabelle Romieu
- Section of Nutrition and Metabolism, Nutritional Epidemiology Group, International Agency for Research on Cancer (IARC), Lyon, France
| | - Andrew Renehan
- Institute of Cancer Sciences, University of Manchester, Manchester, United Kingdom
| | - Isabelle Soerjomataram
- Section of Cancer Surveillance, International Agency for Research on Cancer (IARC), Lyon, France.
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Song Z, Qiu L, Hu Z, Liu J, Liu D, Hou D. Evaluation of the Obesity Genes FTO and MC4R for Contribution to the Risk of Large Artery Atherosclerotic Stroke in a Chinese Population. Obes Facts 2016; 9:353-362. [PMID: 27701175 PMCID: PMC5644882 DOI: 10.1159/000448588] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/10/2016] [Accepted: 07/13/2016] [Indexed: 11/30/2022] Open
Abstract
BACKGROUND Obesity is a well-established risk factor for large artery atherosclerotic (LAA) stroke. The aim of the study was to explore whether obesity genes, such as MC4R and FTO, contribute to LAA stroke risk in the Chinese Han population. METHODS 322 LAA stroke patients and 473 controls were recruited. Gene polymorphism of MC4R (rs17782313) and FTO (rs8050136 and rs9939609) were genotyped. RESULTS No differences were observed in genotype frequencies of variants of FTO (rs8050136 and rs9939609) or MC4R (rs17782313) between LAA stroke patients and control subjects. However, rs17782313 of the MC4R gene was associated with LAA stroke susceptibility in smokers (rs17782313: p = 0.020, OR (95% CI) = 1.55 (1.07-2.23)) in the stratified analysis. Furthermore, multifactor dimensionality reduction analysis revealed that the combination of MC4R variant (rs17782313), hypertension and smoking habit was significantly associated with increased risk of LAA stroke (p < 0.0001, OR (95% CI) = 6.57 (4.79-9.01)). CONCLUSION Our study indicated that the synergistic effects of MC4R variants, hypertension, and smoking habit contribute significantly to the risk of LAA stroke in the Chinese Han population. The finding revealed that obesity gene MC4R contribute to the risk of LAA stroke via a synergistic mechanism, which will provide new insight into the genetic architecture of LAA stroke.
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Affiliation(s)
| | | | - Zhongyang Hu
- *Dr. Zhongyang Hu, Department of Neurology, Third Xiangya Hospital, Central South University, 410013 Changsha, China,
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Srivastava A, Srivastava N, Mittal B. Genetics of Obesity. Indian J Clin Biochem 2015; 31:361-71. [PMID: 27605733 DOI: 10.1007/s12291-015-0541-x] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2015] [Accepted: 12/08/2015] [Indexed: 12/29/2022]
Abstract
Numerous classical genetic studies have proved that genes are contributory factors for obesity. Genes are directly responsible for obesity associated disorders such as Bardet-Biedl and Prader-Willi syndromes. However, both genes as well as environment are associated with obesity in the general population. Genetic epidemiological approaches, particularly genome-wide association studies, have unraveled many genes which play important roles in human obesity. Elucidation of their biological functions can be very useful for understanding pathobiology of obesity. In the near future, further exploration of obesity genetics may help to develop useful diagnostic and predictive tests for obesity treatment.
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Affiliation(s)
- Apurva Srivastava
- Department of Medical Genetics, Sanjay Gandhi Post Graduate Institute of Medical Sciences, Rae Bareli Road, Lucknow, Uttar Pradesh 226014 India ; Department of Physiology, King George's Medical University, Chowk, Lucknow, Uttar Pradesh 226003 India
| | - Neena Srivastava
- Department of Physiology, King George's Medical University, Chowk, Lucknow, Uttar Pradesh 226003 India
| | - Balraj Mittal
- Department of Medical Genetics, Sanjay Gandhi Post Graduate Institute of Medical Sciences, Rae Bareli Road, Lucknow, Uttar Pradesh 226014 India
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Malinowski J, Goodloe R, Brown-Gentry K, Crawford DC. Cryptic relatedness in epidemiologic collections accessed for genetic association studies: experiences from the Epidemiologic Architecture for Genes Linked to Environment (EAGLE) study and the National Health and Nutrition Examination Surveys (NHANES). Front Genet 2015; 6:317. [PMID: 26579192 PMCID: PMC4620157 DOI: 10.3389/fgene.2015.00317] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2015] [Accepted: 10/08/2015] [Indexed: 11/13/2022] Open
Abstract
Epidemiologic collections have been a major resource for genotype-phenotype studies of complex disease given their large sample size, racial/ethnic diversity, and breadth and depth of phenotypes, traits, and exposures. A major disadvantage of these collections is they often survey households and communities without collecting extensive pedigree data. Failure to account for substantial relatedness can lead to inflated estimates and spurious associations. To examine the extent of cryptic relatedness in an epidemiologic collection, we as the Epidemiologic Architecture for Genes Linked to Environment (EAGLE) study accessed the National Health and Nutrition Examination Surveys (NHANES) linked to DNA samples ("Genetic NHANES") from NHANES III and NHANES 1999-2002. NHANES are population-based cross-sectional surveys conducted by the National Center for Health Statistics at the Centers for Disease Control and Prevention. Genome-wide genetic data is not yet available in NHANES, and current data use agreements prohibit the generation of GWAS-level data in NHANES samples due issues in maintaining confidentiality among other ethical concerns. To date, only hundreds of single nucleotide polymorphisms (SNPs) genotyped in a variety of candidate genes are available for analysis in NHANES. We performed identity-by-descent (IBD) estimates in three self-identified subpopulations of Genetic NHANES (non-Hispanic white, non- Hispanic black, and Mexican American) using PLINK software to identify potential familial relationships from presumed unrelated subjects. We then compared the PLINKidentified relationships to those identified by an alternative method implemented in Kinship-based INference for Genome-wide association studies (KING). Overall, both methods identified familial relationships in NHANES III and NHANES 1999-2002 for all three subpopulations, but little concordance was observed between the two methods due in major part to the limited SNP data available in Genetic NHANES. Despite the lack of genome-wide data, our results suggest the presence of cryptic relatedness in this epidemiologic collection and highlight the limitations of restricted datasets such as NHANES in the context of modern day genetic epidemiology studies.
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Affiliation(s)
| | - Robert Goodloe
- Center for Human Genetics Research, Vanderbilt University, NashvilleTN, USA
| | | | - Dana C. Crawford
- Department of Epidemiology and Biostatistics, Institute for Computational Biology, Case Western Reserve University, ClevelandOH, USA
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Xie L, Man E, Cheung PT, Cheung YF. Myocardial Integrated Backscatter in Obese Adolescents: Associations with Measures of Adiposity and Left Ventricular Deformation. PLoS One 2015; 10:e0141149. [PMID: 26492195 PMCID: PMC4619589 DOI: 10.1371/journal.pone.0141149] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2015] [Accepted: 10/04/2015] [Indexed: 12/30/2022] Open
Abstract
Background Myocardial fibrosis has been proposed to play an important pathogenetic role in left ventricular (LV) dysfunction in obesity. This study tested the hypothesis that calibrated integrated backscatter (cIB) as a marker of myocardial fibrosis is altered in obese adolescents and explored its associations with adiposity, LV myocardial deformation, and metabolic parameters. Methods/Principal Findings Fifty-two obese adolescents and 38 non-obese controls were studied with conventional and speckle tracking echocardiography. The average cIB of ventricular septum and LV posterior wall was measured. In obese subjects, insulin resistance as estimated by homeostasis model assessment (HOMA-IR) and glucose tolerance were determined. Compared with controls, obese subjects had significantly greater cIB of ventricular septum (-16.8±7.8 dB vs -23.2±7.8 dB, p<0.001), LV posterior wall (-20.5±5.6 dBvs -25.0±5.1 dB, p<0.001) and their average (-18.7±5.7 dB vs -24.1±5.0 dB, p<0.001). For myocardial deformation, obese subjects had significantly reduced LV longitudinal systolic strain rate (SR) (p = 0.045) and early diastolic SR (p = 0.015), and LV circumferential systolic strain (p = 0.008), but greater LV longitudinal late diastolic SR (p<0.001), and radial early (p = 0.037) and late (p = 0.002) diastolic SR than controls. For the entire cohort, myocardial cIB correlated positively with body mass index (r = 0.45, p<0.001) and waist circumference (r = 0.45, p<0.001), but negatively with LV circumferential systolic strain (r = -0.23, p = 0.03) and systolic SR (r = -0.25, p = 0.016). Among obese subjects, cIB tended to correlate with HOMA-IR (r = 0.26, p = 0.07). Conclusion Obese adolescents already exhibit evidence of increased myocardial fibrosis, which is associated with measures of adiposity and impaired LV circumferential myocardial deformation.
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Affiliation(s)
- Lijian Xie
- Shanghai Children’s Hospital, Shanghai Jiaotong University, Shanghai, China
- Department of Paediatrics and Adolescent Medicine, Queen Mary Hospital, The University of Hong Kong, Hong Kong, China
| | - Elim Man
- Department of Paediatrics and Adolescent Medicine, Queen Mary Hospital, The University of Hong Kong, Hong Kong, China
| | - Pik-to Cheung
- Department of Paediatrics and Adolescent Medicine, Queen Mary Hospital, The University of Hong Kong, Hong Kong, China
| | - Yiu-fai Cheung
- Department of Paediatrics and Adolescent Medicine, Queen Mary Hospital, The University of Hong Kong, Hong Kong, China
- * E-mail:
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Adult BMI change and risk of Breast Cancer: National Health and Nutrition Examination Survey (NHANES) 2005-2010. Breast Cancer 2015; 22:648-56. [PMID: 26350490 DOI: 10.1007/s12282-015-0638-3] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2015] [Accepted: 08/28/2015] [Indexed: 12/24/2022]
Abstract
OBJECTIVE Breast cancer is the second leading cause of cancer mortality among women in the developed world. This study assessed the association between occurrence of breast cancer and body mass index (BMI) change from age 25 to age closest to breast cancer diagnosis while exploring the modifying effects of demographic variables. METHODS The National Health and Nutrition Examination Survey data were used. Women included were ≥50 years, not pregnant and without a diagnosis of any cancer but breast. The total sample included 2895 women (172 with breast cancer and 2723 controls with no breast cancer diagnosis). Multivariate logistic regression was used to estimate the OR and 95 % CIs and interaction evaluated by including an interaction term in the model. RESULTS Women whose BMI increased from normal or overweight to obese compared to those who remained at a normal BMI were found to have a 2 times higher odds (OR = 2.1; 95 % CI 1.11-3.79) of developing breast cancer. No significant association was observed for women who increased to overweight. However, a more pronounced association was observed in non-Hispanic black women (OR = 6.6; 95 % CI 1.68-25.86) and a significant association observed when they increased from normal to overweight (OR = 4.2; 95 % CI 1.02-17.75). CONCLUSIONS Becoming obese after age 25 is associated with increased risk of breast cancer in women over 50 years old, with non-Hispanic black women being at greatest risk.
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Fulford AJ, Ong KK, Elks CE, Prentice AM, Hennig BJ. Progressive influence of body mass index-associated genetic markers in rural Gambians. J Med Genet 2015; 52:375-80. [PMID: 25921383 PMCID: PMC4453496 DOI: 10.1136/jmedgenet-2014-102784] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2014] [Accepted: 12/13/2014] [Indexed: 01/06/2023]
Abstract
Background In populations of European ancestry, the genetic contribution to body mass index (BMI) increases with age during childhood but then declines during adulthood, possibly due to the cumulative effects of environmental factors. How the effects of genetic factors on BMI change with age in other populations is unknown. Subjects and methods In a rural Gambian population (N=2535), we used a combined allele risk score, comprising genotypes at 28 ‘Caucasian adult BMI-associated’ single nucleotide polymorphisms (SNPs), as a marker of the genetic influence on body composition, and related this to internally-standardised z-scores for birthweight (zBW), weight-for-height (zWT-HT), weight-for-age (zWT), height-for-age (zHT), and zBMI cross-sectionally and longitudinally. Results Cross-sectionally, the genetic score was positively associated with adult zWT (0.018±0.009 per allele, p=0.034, N=1426) and zWT-HT (0.025±0.009, p=0.006), but not with size at birth or childhood zWT-HT (0.008±0.005, p=0.11, N=2211). The effect of the genetic score on zWT-HT strengthened linearly with age from birth through to late adulthood (age interaction term: 0.0083 z-scores/allele/year; 95% CI 0.0048 to 0.0118, p=0.0000032). Conclusions Genetic variants for obesity in populations of European ancestry have direct relevance to bodyweight in nutritionally deprived African settings. In such settings, genetic obesity susceptibility appears to regulate change in weight status throughout the life course, which provides insight into its potential physiological role.
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Affiliation(s)
- Anthony J Fulford
- MRC International Nutrition Group at LSHTM, UK & MRC Unit, The Gambia; Department of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, Keppel Street, London, UK
| | - Ken K Ong
- MRC Epidemiology Unit, University of Cambridge, School of Clinical Medicine, Box 285 Institute of Metabolic Science, Cambridge Biomedical Campus, Cambridge, UK
| | - Cathy E Elks
- MRC Epidemiology Unit, University of Cambridge, School of Clinical Medicine, Box 285 Institute of Metabolic Science, Cambridge Biomedical Campus, Cambridge, UK
| | - Andrew M Prentice
- MRC International Nutrition Group at LSHTM, UK & MRC Unit, The Gambia; Department of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, Keppel Street, London, UK
| | - Branwen J Hennig
- MRC International Nutrition Group at LSHTM, UK & MRC Unit, The Gambia; Department of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, Keppel Street, London, UK
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Apalasamy YD, Mohamed Z. Obesity and genomics: role of technology in unraveling the complex genetic architecture of obesity. Hum Genet 2015; 134:361-74. [PMID: 25687726 DOI: 10.1007/s00439-015-1533-x] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2014] [Accepted: 02/02/2015] [Indexed: 01/15/2023]
Abstract
Obesity is a complex and multifactorial disease that occurs as a result of the interaction between "obesogenic" environmental factors and genetic components. Although the genetic component of obesity is clear from the heritability studies, the genetic basis remains largely elusive. Successes have been achieved in identifying the causal genes for monogenic obesity using animal models and linkage studies, but these approaches are not fruitful for polygenic obesity. The developments of genome-wide association approach have brought breakthrough discovery of genetic variants for polygenic obesity where tens of new susceptibility loci were identified. However, the common SNPs only accounted for a proportion of heritability. The arrival of NGS technologies and completion of 1000 Genomes Project have brought other new methods to dissect the genetic architecture of obesity, for example, the use of exome genotyping arrays and deep sequencing of candidate loci identified from GWAS to study rare variants. In this review, we summarize and discuss the developments of these genetic approaches in human obesity.
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Affiliation(s)
- Yamunah Devi Apalasamy
- Department of Pharmacology, Pharmacogenomics Laboratory, Faculty of Medicine, University of Malaya, 50603, Kuala Lumpur, Malaysia,
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Mansur RB, Brietzke E, McIntyre RS. Is there a "metabolic-mood syndrome"? A review of the relationship between obesity and mood disorders. Neurosci Biobehav Rev 2015; 52:89-104. [PMID: 25579847 DOI: 10.1016/j.neubiorev.2014.12.017] [Citation(s) in RCA: 193] [Impact Index Per Article: 21.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2014] [Revised: 12/19/2014] [Accepted: 12/31/2014] [Indexed: 12/12/2022]
Abstract
Obesity and mood disorders are highly prevalent and co-morbid. Epidemiological studies have highlighted the public health relevance of this association, insofar as both conditions and its co-occurrence are associated with a staggering illness-associated burden. Accumulating evidence indicates that obesity and mood disorders are intrinsically linked and share a series of clinical, neurobiological, genetic and environmental factors. The relationship of these conditions has been described as convergent and bidirectional; and some authors have attempted to describe a specific subtype of mood disorders characterized by a higher incidence of obesity and metabolic problems. However, the nature of this association remains poorly understood. There are significant inconsistencies in the studies evaluating metabolic and mood disorders; and, as a result, several questions persist about the validity and the generalizability of the findings. An important limitation in this area of research is the noteworthy phenotypic and pathophysiological heterogeneity of metabolic and mood disorders. Although clinically useful, categorical classifications in both conditions have limited heuristic value and its use hinders a more comprehensive understanding of the association between metabolic and mood disorders. A recent trend in psychiatry is to move toward a domain specific approach, wherein psychopathology constructs are agnostic to DSM-defined diagnostic categories and, instead, there is an effort to categorize domains based on pathogenic substrates, as proposed by the National Institute of Mental Health (NIMH) Research Domain Criteria Project (RDoC). Moreover, the substrates subserving psychopathology seems to be unspecific and extend into other medical illnesses that share in common brain consequences, which includes metabolic disorders. Overall, accumulating evidence indicates that there is a consistent association of multiple abnormalities in neuropsychological constructs, as well as correspondent brain abnormalities, with broad-based metabolic dysfunction, suggesting, therefore, that the existence of a "metabolic-mood syndrome" is possible. Nonetheless, empirical evidence is necessary to support and develop this concept. Future research should focus on dimensional constructs and employ integrative, multidisciplinary and multimodal approaches.
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Affiliation(s)
- Rodrigo B Mansur
- Mood Disorders Psychopharmacology Unit (MDPU), University Health Network, University of Toronto, Toronto, Canada; Interdisciplinary Laboratory of Clinical Neuroscience (LINC), Department of Psychiatry, Federal University of São Paulo, São Paulo, Brazil.
| | - Elisa Brietzke
- Interdisciplinary Laboratory of Clinical Neuroscience (LINC), Department of Psychiatry, Federal University of São Paulo, São Paulo, Brazil
| | - Roger S McIntyre
- Mood Disorders Psychopharmacology Unit (MDPU), University Health Network, University of Toronto, Toronto, Canada
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Seyerle AA, Young AM, Jeff JM, Melton PE, Jorgensen NW, Lin Y, Carty CL, Deelman E, Heckbert SR, Hindorff LA, Jackson RD, Martin LW, Okin PM, Perez MV, Psaty BM, Soliman EZ, Whitsel EA, North KE, Laston S, Kooperberg C, Avery CL. Evidence of heterogeneity by race/ethnicity in genetic determinants of QT interval. Epidemiology 2014; 25:790-8. [PMID: 25166880 PMCID: PMC4380285 DOI: 10.1097/ede.0000000000000168] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
Abstract
BACKGROUND QT interval (QT) prolongation is an established risk factor for ventricular tachyarrhythmia and sudden cardiac death. Previous genome-wide association studies in populations of the European descent have identified multiple genetic loci that influence QT, but few have examined these loci in ethnically diverse populations. METHODS Here, we examine the direction, magnitude, and precision of effect sizes for 21 previously reported SNPs from 12 QT loci, in populations of European (n = 16,398), African (n = 5,437), American Indian (n = 5,032), Hispanic (n = 1,143), and Asian (n = 932) descent as part of the Population Architecture using Genomics and Epidemiology (PAGE) study. Estimates obtained from linear regression models stratified by race/ethnicity were combined using inverse-variance weighted meta-analysis. Heterogeneity was evaluated using Cochran's Q test. RESULTS Of 21 SNPs, 7 showed consistent direction of effect across all 5 populations, and an additional 9 had estimated effects that were consistent across 4 populations. Despite consistent direction of effect, 9 of 16 SNPs had evidence (P < 0.05) of heterogeneity by race/ethnicity. For these 9 SNPs, linkage disequilibrium plots often indicated substantial variation in linkage disequilibrium patterns among the various racial/ethnic groups, as well as possible allelic heterogeneity. CONCLUSIONS These results emphasize the importance of analyzing racial/ethnic groups separately in genetic studies. Furthermore, they underscore the possible utility of trans-ethnic studies to pinpoint underlying casual variants influencing heritable traits such as QT.
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Affiliation(s)
- Amanda A Seyerle
- From the aDepartment of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC; bDivision of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA; cCharles Bronfman Institute of Personalized Medicine, Mount Sinai School of Medicine, New York, NY; dCentre for Genetic Origins of Health and Disease, University of Western Australia, Crawley, Australia; eDepartment of Biostatistics, University of Washington, Seattle, WA; fInformation Sciences Institute and Computer Science Department, University of Southern California, Marina Del Rey, CA; gDepartment of Epidemiology, University of Washington, Seattle, WA; hCardiovascular Health Research Unit, University of Washington, Seattle, WA; iGroup Health Research Institute, Group Health Cooperative, Seattle, WA; jOffice of Population Genomics, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD; kDepartment of Internal Medicine, Ohio State Medical Center, Columbus, OH; lDivision of Cardiology, George Washington University, Washington, DC; mDepartment of Medicine, Weill Cornell Medical College, New York, NY; nDivision of Cardiovascular Medicine, Stanford University, Stanford, CA; oDivision of Medicine, University of Washington, Seattle, WA; pDivision of Health Services, University of Washington, Seattle, WA; qEpidemiological Cardiology Research Center (EPICARE), Wake Forest School of Medicine, Winston-Salem, NC; rDepartment of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC; and sDepartment of Genetics, Texas Biomedical Research Institute, San Antonio, TX
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Xie W, Kantarcioglu M, Bush WS, Crawford D, Denny JC, Heatherly R, Malin BA. SecureMA: protecting participant privacy in genetic association meta-analysis. ACTA ACUST UNITED AC 2014; 30:3334-41. [PMID: 25147357 DOI: 10.1093/bioinformatics/btu561] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
MOTIVATION Sharing genomic data is crucial to support scientific investigation such as genome-wide association studies. However, recent investigations suggest the privacy of the individual participants in these studies can be compromised, leading to serious concerns and consequences, such as overly restricted access to data. RESULTS We introduce a novel cryptographic strategy to securely perform meta-analysis for genetic association studies in large consortia. Our methodology is useful for supporting joint studies among disparate data sites, where privacy or confidentiality is of concern. We validate our method using three multisite association studies. Our research shows that genetic associations can be analyzed efficiently and accurately across substudy sites, without leaking information on individual participants and site-level association summaries. AVAILABILITY AND IMPLEMENTATION Our software for secure meta-analysis of genetic association studies, SecureMA, is publicly available at http://github.com/XieConnect/SecureMA. Our customized secure computation framework is also publicly available at http://github.com/XieConnect/CircuitService.
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Affiliation(s)
- Wei Xie
- Department of Electrical Engineering & Computer Science, Vanderbilt University, Nashville, TN 37232, USA, Department of Computer Science, University of Texas at Dallas, Richardson, TX 75080, USA, Department of Biomedical Informatics, Center for Human Genetics Research, Department of Molecular Physiology and Biophysics and Department of Medicine, Vanderbilt University, Nashville, TN 37232, USA
| | - Murat Kantarcioglu
- Department of Electrical Engineering & Computer Science, Vanderbilt University, Nashville, TN 37232, USA, Department of Computer Science, University of Texas at Dallas, Richardson, TX 75080, USA, Department of Biomedical Informatics, Center for Human Genetics Research, Department of Molecular Physiology and Biophysics and Department of Medicine, Vanderbilt University, Nashville, TN 37232, USA
| | - William S Bush
- Department of Electrical Engineering & Computer Science, Vanderbilt University, Nashville, TN 37232, USA, Department of Computer Science, University of Texas at Dallas, Richardson, TX 75080, USA, Department of Biomedical Informatics, Center for Human Genetics Research, Department of Molecular Physiology and Biophysics and Department of Medicine, Vanderbilt University, Nashville, TN 37232, USA Department of Electrical Engineering & Computer Science, Vanderbilt University, Nashville, TN 37232, USA, Department of Computer Science, University of Texas at Dallas, Richardson, TX 75080, USA, Department of Biomedical Informatics, Center for Human Genetics Research, Department of Molecular Physiology and Biophysics and Department of Medicine, Vanderbilt University, Nashville, TN 37232, USA
| | - Dana Crawford
- Department of Electrical Engineering & Computer Science, Vanderbilt University, Nashville, TN 37232, USA, Department of Computer Science, University of Texas at Dallas, Richardson, TX 75080, USA, Department of Biomedical Informatics, Center for Human Genetics Research, Department of Molecular Physiology and Biophysics and Department of Medicine, Vanderbilt University, Nashville, TN 37232, USA Department of Electrical Engineering & Computer Science, Vanderbilt University, Nashville, TN 37232, USA, Department of Computer Science, University of Texas at Dallas, Richardson, TX 75080, USA, Department of Biomedical Informatics, Center for Human Genetics Research, Department of Molecular Physiology and Biophysics and Department of Medicine, Vanderbilt University, Nashville, TN 37232, USA
| | - Joshua C Denny
- Department of Electrical Engineering & Computer Science, Vanderbilt University, Nashville, TN 37232, USA, Department of Computer Science, University of Texas at Dallas, Richardson, TX 75080, USA, Department of Biomedical Informatics, Center for Human Genetics Research, Department of Molecular Physiology and Biophysics and Department of Medicine, Vanderbilt University, Nashville, TN 37232, USA Department of Electrical Engineering & Computer Science, Vanderbilt University, Nashville, TN 37232, USA, Department of Computer Science, University of Texas at Dallas, Richardson, TX 75080, USA, Department of Biomedical Informatics, Center for Human Genetics Research, Department of Molecular Physiology and Biophysics and Department of Medicine, Vanderbilt University, Nashville, TN 37232, USA
| | - Raymond Heatherly
- Department of Electrical Engineering & Computer Science, Vanderbilt University, Nashville, TN 37232, USA, Department of Computer Science, University of Texas at Dallas, Richardson, TX 75080, USA, Department of Biomedical Informatics, Center for Human Genetics Research, Department of Molecular Physiology and Biophysics and Department of Medicine, Vanderbilt University, Nashville, TN 37232, USA
| | - Bradley A Malin
- Department of Electrical Engineering & Computer Science, Vanderbilt University, Nashville, TN 37232, USA, Department of Computer Science, University of Texas at Dallas, Richardson, TX 75080, USA, Department of Biomedical Informatics, Center for Human Genetics Research, Department of Molecular Physiology and Biophysics and Department of Medicine, Vanderbilt University, Nashville, TN 37232, USA Department of Electrical Engineering & Computer Science, Vanderbilt University, Nashville, TN 37232, USA, Department of Computer Science, University of Texas at Dallas, Richardson, TX 75080, USA, Department of Biomedical Informatics, Center for Human Genetics Research, Department of Molecular Physiology and Biophysics and Department of Medicine, Vanderbilt University, Nashville, TN 37232, USA
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Butrick MN, Vanhusen L, Leventhal KG, Hooker GW, Nusbaum R, Peshkin BN, Salehizadeh Y, Pavlick J, Schwartz MD, Graves KD. Discussing race-related limitations of genomic testing for colon cancer risk: implications for education and counseling. Soc Sci Med 2014; 114:26-37. [PMID: 24908172 DOI: 10.1016/j.socscimed.2014.05.014] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2013] [Revised: 05/05/2014] [Accepted: 05/09/2014] [Indexed: 12/23/2022]
Abstract
This study examines communication about limitations of genomic results interpretation for colon cancer risk during education and counseling of minority participants. As part of a larger study conducted from 2010 to 2012, participants recruited from a large primary care clinic were offered testing for a research panel of 3 genomic markers (single nucleotide polymorphisms or SNPs) for colorectal cancer risk. Genetic counselors conducted pre- and post-test sessions which included discussion of limitations of result interpretation due to the lack of racial/ethnic diversity in research populations from which risk data are derived. Sessions were audio-recorded, transcribed and thematically analyzed. Many participants did not respond directly to this limitation. Among the participants that responded directly to this race-related limitation, many responses were negative. However, a few participants connected the limited minority information about SNPs with the importance of their current research participation. Genetic counselor discussions of this limitation were biomedically focused with limited explanations for the lacking data. The communication process themes identified included: low immediacy (infrequent use of language directly involving a participant), verbal dominance (greater speaking ratio of the counselor to the patient) and wide variation in the degree of interactivity (or the amount of turn-taking during the discussion). Placed within the larger literature on patient-provider communication, these present results provide insight into the dynamics surrounding race-related educational content for genomic testing and other emerging technologies. Clinicians may be better able to engage patients in the use of new genomic technology by increasing their awareness of specific communication processes and patterns during education or counseling sessions.
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Affiliation(s)
- Morgan N Butrick
- Fisher Center for Familial Cancer Research, Lombardi Comprehensive Cancer Center, Georgetown University, 3300 Whitehaven Street, NW, Suite 4100, Washington, DC 20007, USA
| | - Lauren Vanhusen
- Fisher Center for Familial Cancer Research, Lombardi Comprehensive Cancer Center, Georgetown University, 3300 Whitehaven Street, NW, Suite 4100, Washington, DC 20007, USA
| | - Kara-Grace Leventhal
- Fisher Center for Familial Cancer Research, Lombardi Comprehensive Cancer Center, Georgetown University, 3300 Whitehaven Street, NW, Suite 4100, Washington, DC 20007, USA
| | - Gillian W Hooker
- Fisher Center for Familial Cancer Research, Lombardi Comprehensive Cancer Center, Georgetown University, 3300 Whitehaven Street, NW, Suite 4100, Washington, DC 20007, USA
| | - Rachel Nusbaum
- Fisher Center for Familial Cancer Research, Lombardi Comprehensive Cancer Center, Georgetown University, 3300 Whitehaven Street, NW, Suite 4100, Washington, DC 20007, USA
| | - Beth N Peshkin
- Fisher Center for Familial Cancer Research, Lombardi Comprehensive Cancer Center, Georgetown University, 3300 Whitehaven Street, NW, Suite 4100, Washington, DC 20007, USA
| | - Yasmin Salehizadeh
- Fisher Center for Familial Cancer Research, Lombardi Comprehensive Cancer Center, Georgetown University, 3300 Whitehaven Street, NW, Suite 4100, Washington, DC 20007, USA
| | - Jessica Pavlick
- Fisher Center for Familial Cancer Research, Lombardi Comprehensive Cancer Center, Georgetown University, 3300 Whitehaven Street, NW, Suite 4100, Washington, DC 20007, USA
| | - Marc D Schwartz
- Fisher Center for Familial Cancer Research, Lombardi Comprehensive Cancer Center, Georgetown University, 3300 Whitehaven Street, NW, Suite 4100, Washington, DC 20007, USA
| | - Kristi D Graves
- Fisher Center for Familial Cancer Research, Lombardi Comprehensive Cancer Center, Georgetown University, 3300 Whitehaven Street, NW, Suite 4100, Washington, DC 20007, USA.
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Abstract
Obesity is an escalating threat of pandemic proportions, currently affecting billions of people worldwide and exerting a devastating socioeconomic influence in industrialized countries. Despite intensive efforts to curtail obesity, results have proved disappointing. Although it is well recognized that obesity is a result of gene-environment interactions and that predisposition to obesity lies predominantly in our evolutionary past, there is much debate as to the precise nature of how our evolutionary past contributed to obesity. The "thrifty genotype" hypothesis suggests that obesity in industrialized countries is a throwback to our ancestors having undergone positive selection for genes that favored energy storage as a consequence of the cyclical episodes of famine and surplus after the advent of farming 10 000 years ago. Conversely, the "drifty genotype" hypothesis contends that the prevalence of thrifty genes is not a result of positive selection for energy-storage genes but attributable to genetic drift resulting from the removal of predative selection pressures. Both theories, however, assume that selection pressures the ancestors of modern humans living in western societies faced were the same. Moreover, neither theory adequately explains the impact of globalization and changing population demographics on the genetic basis for obesity in developed countries, despite clear evidence for ethnic variation in obesity susceptibility and related metabolic disorders. In this article, we propose that the modern obesity pandemic in industrialized countries is a result of the differential exposure of the ancestors of modern humans to environmental factors that began when modern humans left Africa around 70 000 years ago and migrated through the globe, reaching the Americas around 20 000 years ago. This article serves to elucidate how an understanding of ethnic differences in genetic susceptibility to obesity and the metabolic syndrome, in the context of historic human population redistribution, could be used in the treatment of obesity in industrialized countries.
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Affiliation(s)
- Dyan Sellayah
- MRC Harwell (D.S., R.D.C.), Genetics of Type 2 Diabetes, Harwell Science and Innovation Campus, Harwell OX11 ORD, United Kingdom; Department of Physiology, Anatomy and Genetics (D.S.), University of Oxford, Oxford OX1 3PT, United Kingdom; and Institute of Developmental Sciences (F.R.C.), University of Southampton, Southampton SO16 6YD, United Kingdom
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Gribble MO, Around Him DM. Ethics and Community Involvement in Syntheses Concerning American Indian, Alaska Native, or Native Hawaiian Health: A Systematic Review. AJOB Empir Bioeth 2014; 5:1-24. [PMID: 25089283 DOI: 10.1080/21507716.2013.848956] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
BACKGROUND The objective of the research was to review reporting of ethical concerns and community involvement in peer-reviewed systematic reviews or meta-analyses concerning American Indian, Alaska Native, or Native Hawaiian (AI/AN/NH) health. METHODS Text words and indexed vocabulary terms were used to query PubMed, Embase, Cochrane Library, and the Native Health Database for systematic reviews or meta-analyses concerning AI/AN/NH health published in peer-reviewed journals, followed by a search through reference lists. Each article was abstracted by two independent reviewers; results were discussed until consensus was reached. RESULTS We identified 107 papers published from 1986-2012 that were primarily about AI/AN/NH health or presented findings separately for AI/AN/NH communities. Two reported seeking indigenous reviewer feedback; none reported seeking input from tribes and communities. Approximately 7% reported on institutional review board (IRB) approval of included studies, 5% reported on tribal approval, and 4% referenced the sovereignty of AI/AN tribes. Approximately 63% used evidence from more than one AI/AN/NH population study, and 28% discussed potential benefits to communities from the synthesis research. CONCLUSIONS Reporting of ethics and community involvement are not prominent. Systematic reviews and meta-analyses making community-level inferences may pose risks to communities. Future systematic reviews and meta-analyses should consider ethical and participatory dimensions of research.
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Affiliation(s)
- Matthew O Gribble
- Department of Preventive Medicine, University of Southern California Keck School of Medicine
| | - Deana M Around Him
- Department of Population, Family, and Reproductive Health, Johns Hopkins Bloomberg School of Public Health
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Graves KD, Hay JL, O'Neill SC. The promise of using personalized genomic information to promote behavior change: is the debate over, or just beginning? Per Med 2014; 11:173-185. [PMID: 29751381 DOI: 10.2217/pme.13.110] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Abstract
Over recent years, significant debate has centered on whether and how communication of personalized genomic risk information can positively influence health behavior change. Several thoughtful commentaries have cautioned that efforts to incorporate genomic risk feedback to motivate health behavior change have had little success. As a field, we should consider the reasons for this limited success and be strategic in the next steps for this line of research. In this article, we consider several reasons that prior research that integrates personalized genomic information has had relative degrees of success in changing or maintaining health behaviors. We suggest ways forward and outline the possibilities presented by emerging technologies and novel approaches in translational genomic research.
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Affiliation(s)
- Kristi D Graves
- Department of Oncology, Jess & Mildred Fisher Center for Familial Cancer Research, Lombardi Comprehensive Cancer Center, Georgetown University, Washington, DC, USA
| | - Jennifer L Hay
- Department of Psychiatry & Behavioral Sciences, Memorial Sloan-Kettering Cancer Center, New York, NY, USA
| | - Suzanne C O'Neill
- Department of Oncology, Jess & Mildred Fisher Center for Familial Cancer Research, Lombardi Comprehensive Cancer Center, Georgetown University, Washington, DC, USA
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