Associations between body mass index-related genetic variants and adult body composition: The Fenland cohort study

Dairy consumption and risk of prediabetes and type 2 diabetes in the Fenland study

BACKGROUND & AIMS

Limited observational evidence suggests that a higher intake of high-fat dairy may be associated with lower prediabetes risk, while opposite associations have been observed for low-fat milk intake. This study aimed to examine associations between baseline and changes in dairy consumption, risk of prediabetes, and glycaemic status.

METHODS

7521 participants from the prospective UK Fenland study were included (mean age 48.7 ± 2.0 years, 51.9 % female). Dairy intake was measured using self-reported food frequency questionnaires. Associations with prediabetes risk and glycaemic status were analysed using Poisson regression models adjusted for social demographics, health behaviours, family history of diabetes and food group intake.

RESULTS

At a mean follow-up of 6.7 ± 2.0 years, 290 participants developed prediabetes (4.3 %). Most dairy products were not significantly associated with prediabetes risk. A higher baseline intake of high-fat dairy (RRservings/day 1.20, 95%CI 1.03-1.39) and high-fat milk (RRservings/day 1.22, 1.01-1.47) were associated with higher prediabetes risk. Conversely, low-fat milk was associated with lower prediabetes risk (RRservings/day 0.86, 0.75-0.98). In the analyses evaluating dietary changes over time, increases in high-fat milk were inversely associated with risk of progressing from normoglycaemia to prediabetes or type 2 diabetes (RRservings/day 0.86, 95%CI 0.75-0.99).

CONCLUSIONS

This population-based study showed that most dairy products are not associated with prediabetes risk or progression in glycaemic status. Positive associations of high-fat dairy, high-fat milk, and the inverse association of low-fat milk with prediabetes risk found were inconsistent with prior literature and suggestive of the need for future research on environmental, behavioural, and biological factors that explain the available evidence.

Anthropometric and DXA-derived measures of body composition in relation to pre-diabetes among adults

INTRODUCTION

Abdominal obesity is the most common risk factor of pre-diabetes and diabetes. Currently, several types of indices are used for the determination of visceral fat-related abdominal obesity. To better understand the effect of the different adiposity indices, we sought to evaluate the association of different adiposity measurements, assessed using dual-energy X-ray absorptiometry (DXA), and pre-diabetes.

RESEARCH DESIGN AND METHODS

This cross-sectional study included 1184 adults between 18 and 65 years who participated in the Kuwait Wellbeing Study. Anthropometry measurements included body mass index (BMI) and waist-to-hip ratio. Total body fat (TBF) mass, android fat mass, gynoid fat, and visceral adipose tissue (VAT) mass were measured using the Lunar iDXA. Pre-diabetes was defined as 5.7≤HbA1c%≤6.4. Adjusted prevalence ratios (aPRs) and 95% CIs were estimated. Area under the curve (AUC) was estimated for each adiposity measurement as predictor of pre-diabetes.

RESULTS

A total of 585 (49.4%) males and 599 (50.6%) females were enrolled in the study. Increased BMI (aPR obese vs normal=1.59, 95% CI: 1.19 to 2.12), waist-to-hip ratio (aPR Q4 vs Q1=1.25, 0.96 to 1.61), TBF (aPR Q4 vs Q1=1.58, 1.20 to 2.07), android fat (aPR Q4 vs Q1=1.67, 1.27 to 2.20), gynoid fat (aPR Q4 vs Q1=1.48, 1.16 to 1.89), android-to-gynoid fat ratio (aPR Q4 vs Q1=1.70, 1.27 to 2.28), and VAT mass (aPR Q4 vs Q1=2.05, 1.49 to 2.82) were associated with elevated pre-diabetes prevalence. Gynoid fat was associated with pre-diabetes among males (aPR Q4 vs Q1=1.71, 1.22 to 2.41), but not among females (aPR Q4 vs Q1=1.27, 0.90 to 1.78). Moreover, in terms of AUC, VAT had the highest estimated AUC of 0.680, followed by android-to-gynoid fat ratio (AUC: 0.647) and android fat (AUC: 0.646).

CONCLUSIONS

Pre-diabetes prevalence increased as adiposity measurements increased, with VAT mass demonstrating the highest AUC for pre-diabetes.

Opportunities and challenges for the use of common controls in sequencing studies

Genome-wide association studies using large-scale genome and exome sequencing data have become increasingly valuable in identifying associations between genetic variants and disease, transforming basic research and translational medicine. However, this progress has not been equally shared across all people and conditions, in part due to limited resources. Leveraging publicly available sequencing data as external common controls, rather than sequencing new controls for every study, can better allocate resources by augmenting control sample sizes or providing controls where none existed. However, common control studies must be carefully planned and executed as even small differences in sample ascertainment and processing can result in substantial bias. Here, we discuss challenges and opportunities for the robust use of common controls in high-throughput sequencing studies, including study design, quality control and statistical approaches. Thoughtful generation and use of large and valuable genetic sequencing data sets will enable investigation of a broader and more representative set of conditions, environments and genetic ancestries than otherwise possible.

Associations between three diet quality indices, genetic risk and body composition: A prospective cohort study

BACKGROUND & AIMS

Diet and genetic predisposition to adiposity are independent predictors of body composition, yet few cohort studies have examined the association between overall diet quality indices, genetic risk and body composition. This study examined the prospective association of three diet quality indices and a polygenic risk score (PRS) with trunk fat mass, total fat mass, lean mass and bone mineral content.

METHODS

Adults from UK Biobank cohort were included. Dietary intake was assessed using the Oxford WebQ and three diet quality indices calculated: Recommended Food Score (RFS); Mediterranean Diet Score (MDS); Healthy Diet Indicator (HDI). Bioimpedance data were available for trunk fat, total fat and lean mass (kg). Trunk fat mass (kg), total fat mass (kg) and lean mass (kg) were assessed using bioelectrical impedance (BIA) in 17,478 adults. Bone mineral content (g) was available from dual energy x-ray absorptiometry (DXA) scans in 11,887 participants. Linear regression analyses, adjusted for demographic and lifestyle confounders, were used to estimate prospective associations between each diet quality index and body composition outcomes. A PRS created from 97 adiposity-related single nucleotide polymorphisms was used to examine interaction effects.

RESULTS

A total of 17,478 adults (M = 55.9, SD 7.5 years) were followed up for up to 10 years. RFS, HDI and MDS were inversely associated with trunk fat (RFS: B -0.29; 95% CI: -0.33, -0.25; HDI: -0.23; -0.27, -0.19; MDS: -0.22; -0.26, -0.18), total fat (RFS: B -0.49; 95% CI: -0.56, -0.42; HDI: -0.38; -0.45, -0.32; MDS: -0.38; -0.44, -0.32) and lean (RFS: B -0.10; 95% CI: -0.14, -0.06; HDI: -0.07; -0.11, -0.03; MDS: -0.07; -0.11, -0.04) mass. Diet quality was positively associated with bone mineral content (RFS: B 8.23; 95% CI: 2.14, 14.3; HDI: 6.77; 1.00, 12.5). There was evidence of non-linear associations between diet quality (RFS and HDI only) and trunk fat (p < 0.01) and total fat mass (p < 0.05). There was limited evidence PRS was associated with body composition, with interaction effects of PRS and HDI (p-interaction = 0.039) and MDS (p-interaction = 0.031) on total fat mass.

CONCLUSION

Higher diet quality was associated with lower trunk fat, total fat and lean mass, regardless of the diet quality index examined (RFS, HDI or MDS), while higher diet quality (RFS and HDI only) was associated with higher bone mineral content. The benefit of higher diet quality on reducing total fat mass was most evident in individuals with higher generic risk of adiposity. These findings underscore the importance of a high-quality diet for maintaining optimal body composition, particularly in individuals with genetic pre-disposition to adiposity.

Do adverse childhood experiences and genetic obesity risk interact in relation to body mass index in young adulthood? Findings from the National Longitudinal Study of Adolescent to Adult Health

BACKGROUND

Few studies have focused on the role of adverse childhood experiences (ACEs) in relation to genetic susceptibility to obesity.

OBJECTIVE

We aimed to examine the interaction between the presence of ACEs (i.e., physical, psychological and sexual abuse) before the age of 18 and BMI polygenic score.

METHODS

Data came from the National Longitudinal Study of Adolescent to Adult Health (Add Health) Wave IV (2007/2008) where saliva samples were collected for DNA genotyping and information on BMI and ACEs were obtained from 5854 European American (EA), 2073 African American (AA) and 1448 Hispanic American (HA) participants aged 24 to 32 years old. Polygenic scores were calculated as the sum of the number of risk alleles of BMI-related SNPs which were weighted by effect size. A race/ethnicity-stratified mixed-effects linear regression model was used to test for differential association between BMI polygenic score and BMI by the presence of ACEs.

RESULTS

We did not find any evidence of significant interaction between ACEs and polygenic score in relation to BMI among EA (p = 0.289), AA (p = 0.618) or HA (p = 0.870). In main effects models, polygenic score was positively associated with BMI in all race/ethnic groups, yet the presence of ACEs was associated with increased BMI only among EA.

CONCLUSION

We did not find any evidence that ACEs exacerbate genetic predisposition to increased BMI in early adulthood.

Genetic underpinnings of regional adiposity distribution in African Americans: Assessments from the Jackson Heart Study

BACKGROUND

African ancestry individuals with comparable overall anthropometric measures to Europeans have lower abdominal adiposity. To explore the genetic underpinning of different adiposity patterns, we investigated whether genetic risk scores for well-studied adiposity phenotypes like body mass index (BMI) and waist circumference (WC) also predict other, less commonly measured adiposity measures in 2420 African American individuals from the Jackson Heart Study.

METHODS

Polygenic risk scores (PRS) were calculated using GWAS-significant variants extracted from published studies mostly representing European ancestry populations for BMI, waist-hip ratio (WHR) adjusted for BMI (WHRBMIadj), waist circumference adjusted for BMI (WCBMIadj), and body fat percentage (BF%). Associations between each PRS and adiposity measures including BF%, subcutaneous adiposity tissue (SAT), visceral adiposity tissue (VAT) and VAT:SAT ratio (VSR) were examined using multivariable linear regression, with or without BMI adjustment.

RESULTS

In non-BMI adjusted models, all phenotype-PRS were found to be positive predictors of BF%, SAT and VAT. WHR-PRS was a positive predictor of VSR, but BF% and BMI-PRS were negative predictors of VSR. After adjusting for BMI, WHR-PRS remained a positive predictor of BF%, VAT and VSR but not SAT. WC-PRS was a positive predictor of SAT and VAT; BF%-PRS was a positive predictor of BF% and SAT only.

CONCLUSION

These analyses suggest that genetically driven increases in BF% strongly associate with subcutaneous rather than visceral adiposity and BF% is strongly associated with BMI but not central adiposity-associated genetic variants. How common genetic variants may contribute to observed differences in adiposity patterns between African and European ancestry individuals requires further study.

Independent and combined associations between fast-food outlet exposure and genetic risk for obesity: a population-based, cross-sectional study in the UK

BACKGROUND

Characteristics of the built environment, such as neighbourhood fast-food outlet exposure, are increasingly recognised as risk factors for unhealthy diet and obesity. Obesity also has a genetic component, with common genetic variants explaining a substantial proportion of population-level obesity susceptibility. However, it is not known whether and to what extent associations between fast-food outlet exposure and body weight are modified by genetic predisposition to obesity.

METHODS

We used data from the Fenland Study, a population-based sample of 12,435 UK adults (mean age 48.6 years). We derived a genetic risk score associated with BMI (BMI-GRS) from 96 BMI-associated single nucleotide polymorphisms. Neighbourhood fast-food exposure was defined as quartiles of counts of outlets around the home address. We used multivariable regression models to estimate the associations of each exposure, independently and in combination, with measured BMI, overweight and obesity, and investigated interactions.

RESULTS

We found independent associations between BMI-GRS and risk of overweight (RR = 1.34, 95% CI 1.23-1.47) and obesity (RR = 1.73, 95% CI 1.55-1.93), and between fast-food outlet exposure and risk of obesity (highest vs lowest quartile RR = 1.58, 95% CI 1.21-2.05). There was no evidence of an interaction of fast-food outlet exposure and genetic risk on BMI (P = 0.09), risk of overweight (P = 0.51), or risk of obesity (P = 0.27). The combination of higher BMI-GRS and highest fast-food outlet exposure was associated with 2.70 (95% CI 1.99-3.66) times greater risk of obesity.

CONCLUSIONS

Our study demonstrated independent associations of both genetic obesity risk and neighbourhood fast-food outlet exposure with adiposity. These important drivers of the obesity epidemic have to date been studied in isolation. Neighbourhood fast-food outlet exposure remains a potential target of policy intervention to prevent obesity and promote the public's health.

Associations of types of dairy consumption with adiposity: cross-sectional findings from over 12 000 adults in the Fenland Study, UK

Evidence from randomised controlled trials supports beneficial effects of total dairy products on body weight, fat and lean mass, but evidence on associations of dairy types with distributions of body fat and lean mass is limited. We aimed to investigate associations of total and different types of dairy products with markers of adiposity, and body fat and lean mass distribution. We evaluated cross-sectional data from 12 065 adults aged 30-65 years recruited to the Fenland Study between 2005 and 2015 in Cambridgeshire, UK. Diet was assessed with an FFQ. We estimated regression coefficients (or percentage differences) and their 95 % CI using multiple linear regression models. The medians of milk, yogurt and cheese consumption were 293 (interquartile range (IQR) 146-439), 35·3 (IQR 8·8-71·8) and 14·6 (IQR 4·8-26·9) g/d, respectively. Low-fat dairy consumption was inversely associated with visceral:subcutaneous fat ratio estimated with dual-energy X-ray absorptiometry (-2·58 % (95 % CI -3·91, -1·23 %) per serving/d). Habitual consumption per serving/d (200 g) of milk was associated with 0·33 (95 % CI 0·19, 0·46) kg higher lean mass. Other associations were not significant after false discovery correction. Our findings suggest that the influence of milk consumption on lean mass and of low-fat dairy consumption on fat mass distribution may be potential pathways for the link between dairy consumption and metabolic risk. Our cross-sectional findings warrant further research in prospective and experimental studies in diverse populations.

Exome Sequencing Identifies Genes and Gene Sets Contributing to Severe Childhood Obesity, Linking PHIP Variants to Repressed POMC Transcription

Obesity is genetically heterogeneous with monogenic and complex polygenic forms. Using exome and targeted sequencing in 2,737 severely obese cases and 6,704 controls, we identified three genes (PHIP, DGKI, and ZMYM4) with an excess burden of very rare predicted deleterious variants in cases. In cells, we found that nuclear PHIP (pleckstrin homology domain interacting protein) directly enhances transcription of pro-opiomelanocortin (POMC), a neuropeptide that suppresses appetite. Obesity-associated PHIP variants repressed POMC transcription. Our demonstration that PHIP is involved in human energy homeostasis through transcriptional regulation of central melanocortin signaling has potential diagnostic and therapeutic implications for patients with obesity and developmental delay. Additionally, we found an excess burden of predicted deleterious variants involving genes nearest to loci from obesity genome-wide association studies. Genes and gene sets influencing obesity with variable penetrance provide compelling evidence for a continuum of causality in the genetic architecture of obesity, and explain some of its missing heritability.

The Consortium of Metabolomics Studies (COMETS): Metabolomics in 47 Prospective Cohort Studies

The Consortium of Metabolomics Studies (COMETS) was established in 2014 to facilitate large-scale collaborative research on the human metabolome and its relationship with disease etiology, diagnosis, and prognosis. COMETS comprises 47 cohorts from Asia, Europe, North America, and South America that together include more than 136,000 participants with blood metabolomics data on samples collected from 1985 to 2017. Metabolomics data were provided by 17 different platforms, with the most frequently used labs being Metabolon, Inc. (14 cohorts), the Broad Institute (15 cohorts), and Nightingale Health (11 cohorts). Participants have been followed for a median of 23 years for health outcomes including death, cancer, cardiovascular disease, diabetes, and others; many of the studies are ongoing. Available exposure-related data include common clinical measurements and behavioral factors, as well as genome-wide genotype data. Two feasibility studies were conducted to evaluate the comparability of metabolomics platforms used by COMETS cohorts. The first study showed that the overlap between any 2 different laboratories ranged from 6 to 121 metabolites at 5 leading laboratories. The second study showed that the median Spearman correlation comparing 111 overlapping metabolites captured by Metabolon and the Broad Institute was 0.79 (interquartile range, 0.56-0.89).

Kernel machine SNP set analysis provides new insight into the association between obesity and polymorphisms located on the chromosomal 16q.12.2 region: Tehran Lipid and Glucose Study

INTRODUCTION

Obesity is a serious health problem that leads to low quality of life and early mortality. To the purpose of prevention and gene therapy for such a worldwide disease, genome wide association study is a powerful tool for finding SNPs associated with increased risk of obesity. To conduct an association analysis, kernel machine regression is a generalized regression method, has an advantage of considering the epistasis effects as well as the correlation between individuals due to unknown factors.

MATERIALS AND METHODS

In this study, information of the people who participated in Tehran cardio-metabolic genetic study was used. They were genotyped for the chromosomal region, evaluation 986 variations located at 16q12.2; build 38hg. Kernel machine regression and single SNP analysis were used to assess the association between obesity and SNPs genotyped data.

RESULTS

We found that associated SNP sets with obesity, were almost in the FTO (P = 0.01), AIKTIP (P = 0.02) and MMP2 (P = 0.02) genes. Moreover, two SNPs, i.e., rs10521296 and rs11647470, showed significant association with obesity using kernel regression (P = 0.02).

CONCLUSION

In conclusion, significant sets were randomly distributed throughout the region with more density around the FTO, AIKTIP and MMP2 genes. Furthermore, two intergenic SNPs showed significant association after using kernel machine regression. Therefore, more studies have to be conducted to assess their functionality or precise mechanism.

Convergence between biological, behavioural and genetic determinants of obesity

Multiple biological, behavioural and genetic determinants or correlates of obesity have been identified to date. Genome-wide association studies (GWAS) have contributed to the identification of more than 100 obesity-associated genetic variants, but their roles in causal processes leading to obesity remain largely unknown. Most variants are likely to have tissue-specific regulatory roles through joint contributions to biological pathways and networks, through changes in gene expression that influence quantitative traits, or through the regulation of the epigenome. The recent availability of large-scale functional genomics resources provides an opportunity to re-examine obesity GWAS data to begin elucidating the function of genetic variants. Interrogation of knockout mouse phenotype resources provides a further avenue to test for evidence of convergence between genetic variation and biological or behavioural determinants of obesity.