Body Mass Index Genetic Risk Score Research Articles

Fast-food environments and BMI changes in the Dutch adult general population: the Lifelines cohort.

This study investigated cross-sectional and longitudinal associations of fast-food outlet exposure with BMI and BMI change, as well as moderation by age and genetic predisposition. This study used Lifelines' baseline (n = 141,973) and 4-year follow-up (n = 103,050) data. Participant residential addresses were linked to a register with fast-food outlet locations (Nationwide Information System of Workplaces [Dutch: Landelijk Informatiesysteem van Arbeidsplaatsen,LISA]) using geocoding, and the number of fast-food outlets within 1 km was computed. BMI was measured objectively. A weighted BMI genetic risk score was computed, representing overall genetic predisposition toward elevated BMI, based on 941 single-nucleotide polymorphisms genome-wide significantly associated with BMI for a subsample with genetic data (BMI: n = 44,996; BMI change: n = 36,684). Multivariable multilevel linear regression analyses and exposure-moderator interactions were tested. Participants with ≥1 fast-food outlet within 1 km had a higher BMI (B [95% CI]: 0.17 [0.09 to 0.25]), and those with ≥2 fast-food outlets within 1 km increased more in BMI (B [95% CI]: 0.06 [0.02 to 0.09]) than participants with no fast-food outlets within 1 km. Effect sizes on baseline BMI were largest among young adults (age 18-29 years; B [95% CI]: 0.35 [0.10 to 0.59]) and especially young adults with a medium (B [95% CI]: 0.57 [-0.02 to 1.16]) or high genetic risk score (B [95% CI]: 0.46 [-0.24 to 1.16]). Fast-food outlet exposure was identified as a potentially important determinant of BMI and BMI change. Young adults, especially those with a medium or high genetic predisposition, had a higher BMI when exposed to fast-food outlets.

Mediation and moderation of genetic risk of obesity through eating behaviours in two UK cohorts.

The mechanisms underlying genetic predisposition to higher body mass index (BMI) remain unclear. We hypothesized that the relationship between BMI-genetic risk score (BMI-GRS) and BMI was mediated via disinhibition, emotional eating and hunger, and moderated by flexible (but not rigid) restraint within two UK cohorts: the Genetics of Appetite Study (GATE) (n = 2101, 2010-16) and the Avon Longitudinal Study of Parents and Children (ALSPAC) (n = 1679, 2014-18). Eating behaviour was measured by the Adult Eating Behaviour Questionnaire and Three-Factor Eating Questionaire-51. The association between BMI-GRS and BMI were partially mediated by habitual, emotional and situational disinhibition in the GATE/ALSPAC meta-mediation [standardized betaindirect 0.04, 95% confidence interval (CI) 0.02-0.06; 0.03, 0.01-0.04; 0.03, 0.01-0.04, respectively] external hunger and internal hunger in the GATE study (0.02, 0.01-0.03; 0.01, 0.001-0.02, respectively). There was evidence of mediation by emotional over/undereating and hunger in the ALSPAC study (0.02, 0.01-0.03; 0.01, 0.001-0.02; 0.01, 0.002-0.01, respectively). Rigid or flexible restraint did not moderate the direct association between BMI-GRS and BMI, but high flexible restraint moderated the effect of disinhibition subscales on BMI (reduction of the indirect mediation by -5% to -11% in GATE/ALSPAC) and external hunger (-5%) in GATE. High rigid restraint reduced the mediation via disinhibition subscales in GATE/ALSPAC (-4% to -11%) and external hunger (-3%) in GATE. Genetic predisposition to a higher BMI was partly explained by disinhibition and hunger in two large cohorts. Flexible/rigid restraint may play an important role in moderating the impact of predisposition to higher BMI.

1385-P: Maternal Gestational Weight Gain and Children’s Genetic Susceptibility on Childhood Adiposity—A Longitudinal Cohort Study

Few studies have assessed the association of early-life environmental and genetic factors with the risk of childhood adiposity. The aim of the present study was to investigate the interaction between maternal gestational weight gain (GWG) and children’s genetic susceptibility on childhood adiposity. The present study included 1038 mother-child pairs. Body mass index (BMI) for age Z-score (BMI-Z) and childhood overweight were evaluated according to the World Health Organization (WHO) growth reference. Genetic risk score (GRS) was established by aggregating alleles of 10 BMI susceptibility single-nucleotide polymorphisms identified from a recent study. Children born to mothers with excessive GWG had higher BMI-Z (P<0.001), percentage of body fat (P<0.001), and prevalence of overweight (28.4% vs 15.9%, P<0.001) than those born to mothers with adequate GWG. Children’s BMI GRS was significantly and positively associated with BMI-Z, percentage of body fat and overweight risk (all P<0.05). Stratified analyses showed the positive association of children’s BMI GRS with adiposity risk was especially concentrated among mothers with pre-pregnancy normal weight but excessive GWG. The present study identified an interactive effect between maternal GWG and children’s genetic susceptibility on childhood adiposity risk. The findings provided new insight into the etiology of childhood obesity and highlighted the importance of GWG management. Disclosure H.Liu: None. W.Li: None. L.Wang: None. S.Zhang: None. W.Li: None. X.Yang: None. J.Leng: None. G.Hu: None. S.Wang: None. Funding European Foundation for the Study of Diabetes/Chinese Diabetes Society/Lilly Programme for Collaborative Research between China and Europe; National Institute of Diabetes and Digestive and Kidney Diseases (R01DK100790, R01DK132011); National Institute of General Medical Sciences (U54GM104940)

Genetic Effect on Body Mass Index and Cardiovascular Disease Across Generations.

Whether genetics contribute to the rising prevalence of obesity or its cardiovascular consequences in today's obesogenic environment remains unclear. We sought to determine whether the effects of a higher aggregate genetic burden of obesity risk on body mass index (BMI) or cardiovascular disease (CVD) differed by birth year. We split the FHS (Framingham Heart Study) into 4 equally sized birth cohorts (birth year before 1932, 1932 to 1946, 1947 to 1959, and after 1960). We modeled a genetic predisposition to obesity using an additive genetic risk score (GRS) of 941 BMI-associated variants and tested for GRS-birth year interaction on log-BMI (outcome) when participants were around 50 years old (N=7693). We repeated the analysis using a GRS of 109 BMI-associated variants that increased CVD risk factors (type 2 diabetes, blood pressure, total cholesterol, and high-density lipoprotein) in addition to BMI. We then evaluated whether the effects of the BMI GRSs on CVD risk differed by birth cohort when participants were around 60 years old (N=5493). Compared with participants born before 1932 (mean age, 50.8 yrs [2.4]), those born after 1960 (mean age, 43.3 years [4.5]) had higher BMI (median, 25.4 [23.3-28.0] kg/m2 versus 26.9 [interquartile range, 23.7-30.6] kg/m2). The effect of the 941-variant BMI GRS on BMI and CVD risk was stronger in people who were born in later years (GRS-birth year interaction: P=0.0007 and P=0.04 respectively). The significant GRS-birth year interactions indicate that common genetic variants have larger effects on middle-age BMI and CVD risk in people born more recently. These findings suggest that the increasingly obesogenic environment may amplify the impact of genetics on the risk of obesity and possibly its cardiovascular consequences.

Genomics and phenomics of body mass index reveals a complex disease network

Elevated body mass index (BMI) is heritable and associated with many health conditions that impact morbidity and mortality. The study of the genetic association of BMI across a broad range of common disease conditions offers the opportunity to extend current knowledge regarding the breadth and depth of adiposity-related diseases. We identify 906 (364 novel) and 41 (6 novel) genome-wide significant loci for BMI among participants of European (N~1.1 million) and African (N~100,000) ancestry, respectively. Using a BMI genetic risk score including 2446 variants, 316 diagnoses are associated in the Million Veteran Program, with 96.5% showing increased risk. A co-morbidity network analysis reveals seven disease communities containing multiple interconnected diseases associated with BMI as well as extensive connections across communities. Mendelian randomization analysis confirms numerous phenotypes across a breadth of organ systems, including conditions of the circulatory (heart failure, ischemic heart disease, atrial fibrillation), genitourinary (chronic renal failure), respiratory (respiratory failure, asthma), musculoskeletal and dermatologic systems that are deeply interconnected within and across the disease communities. This work shows that the complex genetic architecture of BMI associates with a broad range of major health conditions, supporting the need for comprehensive approaches to prevent and treat obesity.

A multivariant recall-by-genotype study of the metabolomic signature of BMI.

ObjectiveThis study estimated the effect of BMI on circulating metabolites in young adults using a recall‐by‐genotype study design.MethodsA recall‐by‐genotype study was implemented in the Avon Longitudinal Study of Parents and Children. Samples from 756 participants were selected for untargeted metabolomics analysis based on low versus high genetic liability for higher BMI defined by a genetic risk score (GRS). Regression analyses were performed to investigate associations between BMI GRS group and relative abundance of 973 metabolites.ResultsAfter correction for multiple testing, 29 metabolites were associated with BMI GRS group. Bilirubin was among the most strongly associated metabolites, with reduced levels measured in individuals in the high‐BMI GRS group (β = −0.32, 95% CI: −0.46 to −0.18, Benjamini‐Hochberg adjusted p = 0.005). This study observed associations between BMI GRS group and the levels of several potentially diet‐related metabolites, including hippurate, which had lower mean abundance in individuals in the high‐BMI GRS group (β = −0.29, 95% CI: −0.44 to −0.15, Benjamini‐Hochberg adjusted p = 0.008).ConclusionsTogether with existing literature, these results suggest that a genetic predisposition to higher BMI captures differences in metabolism leading to adiposity gain. In the absence of prospective data, separating these effects from the downstream consequences of weight gain is challenging.

Association between genetic predisposition to childhood obesity and the risk of adult ischemic heart disease in China

Objective: To examine the associations of childhood obesity, assessed by genetic variations of childhood body mass index (BMI), with the risk of adult ischemic heart disease (IHD) and major coronary event (MCE). Methods: More than 69 000 participants from the China Kadoorie Biobank were genotyped. After excluding those with coronary heart disease, stroke, or cancer at baseline, a total of 64 454 participants were included in this study. Based on genome-wide significant single nucleotide polymorphisms (SNPs), childhood BMI genetic risk score were constructed for every participant and divided into quintiles, with the lowest quintile as the low genetic risk group and the highest quintile as the high genetic risk group. Cox proportional hazards regression models were used to estimate the association between genetic predisposition to childhood obesity and the risk of ischemic heart disease. Results: During a median of 10.7 years of follow-up, 7 073 incident cases of IHD and 1 845 cases of MCE were documented. After adjusting for sex, age, region, and the first ten genetic principal components, the HRs (95%CIs) for IHD and MCE in the high genetic risk group were 1.10 (1.02-1.18) and 1.10 (0.95-1.27), compared with the low genetic risk group. IHD risk increased by 4% (2%-6%) for each one standard deviation increase in genetic risk score (trend P=0.001). After further adjustment for baseline BMI, the differences between genetic risk groups were not statistically significant, but there was still a linear trend between genetic risk score and IHD risk (trend P=0.019). Conclusions: IHD risk increased with genetic predisposition to childhood obesity, suggesting that childhood obesity is an important risk factor for the development of IHD in China. As an easily identifiable feature, changes of childhood BMI should be monitored regularly to realize early intervention of IHD in adults.

Non-linear interaction between physical activity and polygenic risk score of body mass index in Danish and Russian populations

Body mass index (BMI) is a highly heritable polygenic trait. It is also affected by various environmental and behavioral risk factors. We used a BMI polygenic risk score (PRS) to study the interplay between the genetic and environmental factors defining BMI. First, we generated a BMI PRS that explained more variance than a BMI genetic risk score (GRS), which was using only genome-wide significant BMI-associated variants (R2 = 13.1% compared to 6.1%). Second, we analyzed interactions between BMI PRS and seven environmental factors. We found a significant interaction between physical activity and BMI PRS, even when the well-known effect of the FTO region was excluded from the PRS, using a small dataset of 6,179 samples. Third, we stratified the study population into two risk groups using BMI PRS. The top 22% of the studied populations were included in a high PRS risk group. Engagement in self-reported physical activity was associated with a 1.66 kg/m2 decrease in BMI in this group, compared to a 0.84 kg/m2 decrease in BMI in the rest of the population. Our results (i) confirm that genetic background strongly affects adult BMI in the general population, (ii) show a non-linear interaction between BMI genetics and physical activity, and (iii) provide a standardized framework for future gene-environment interaction analyses.

204Effects of adiposity on the human proteome: Mendelian randomization study using individual-level data

Abstract Background Variation in body mass index (BMI) is associated with cardiometabolic health outcomes such as diabetes, but the mechanism(s) leading from BMI to disease remain unclear. This study used proteomic data measured by SomaLogic from healthy adults from the INTERVAL study to explore the effect of BMI on 3,622 unique plasma proteins using observational and genetically informed methods. Methods Linear regression models were used, complemented by one-sample Mendelian randomization (MR) analyses. A BMI genetic risk score (GRS) comprised of 654 SNPs from a recent genome-wide association study (GWAS) of adult BMI was used in both observational and MR analysis. Results Observationally, BMI was associated with 1,576 proteins at p <1.4x10-5 including leptin and sex hormone binding globulin (SHBG). The BMI-GRS was positively associated with BMI (R2=0.028) but not with reported confounders. MR analysis indicated a causal association between each standard deviation increase in BMI and eight unique proteins at p <1.4x10-5, including leptin (0.63 SD, 95% CI 0.48-0.79, p = 1.6x10-15) and SHBG (-0.45 SD, 95% CI -0.65 to -0.25, p = 1.4x10-5). There was strong agreement in the direction and magnitude of observational and MR estimates (R2 = 0.33). Finally, there was evidence that proteins which showed associations with BMI were enriched in cardiovascular disease. Conclusions This study provides evidence for a profound impact of higher adiposity on the human proteome. Such protein alterations could be important mechanistic drivers of obesity-related diseases. Key messages Changes in plasma proteins could be important intermediates between obesity and the onset of disease.

Metabolic risk factors, genetic predisposition, and risk of severe liver disease in Chinese: a prospective study of 0.5 million people

Metabolic risk factors have been shown to be associated with severe liver disease (SLD) in Chinese populations. However, there is limited evidence on the combined impact of these factors, or the genetic variants associated with SLD. We examined the associations of combined metabolic risk factors with risks of SLD, both overall and by genetic predisposition to SLD. The study population involved 486,828 participants of the prospective China Kadoorie Biobank aged 30-79 years from 10 diverse areas in China without a history of cancer or liver disease at baseline. Cox regression was used to estimate adjusted HRs for SLD associated with combined metabolic risk factors (central adiposity, physical inactivity, and diabetes) by stratum of genetic risk [assessed separately by a PNPLA3 variant (rs738409) and a BMI genetic risk score]. During ∼10 years of follow-up, 3279 incident cases of SLD were recorded. The overall mean BMI was 23.8 kg/m2 (SD, 3.4 kg/m2), and 5.9% participants had diabetes. Compared with those with 3 metabolic factors, participants with 2, 1, and 0 metabolic factors had 31% (HR, 0.69; 95% CI: 0.65-0.73), 43% (HR, 0.57; 95% CI: 0.53-0.60), and 52% (HR, 0.48; 95% CI: 0.42-0.56) lower risks of SLD, respectively. For both BMI and nonalcoholic fatty liver disease variants, participants with fewer metabolic factors had a lower risk of SLD, lower levels of gamma-glutamyl transferase, and lower fatty liver index scores, in participants with low and high genetic risks (P value for interaction > 0.05). In relatively lean Chinese adults, individuals with fewer metabolic risk factors had a lower relative risk of SLD and a more favorable profile of liver biomarkers across all strata of genetic risk.

Shared genetic architecture underlying sleep and weight in children

Meta-analyses suggest shorter sleep as a risk factor for obesity in children. The prevailing hypothesis is that shorter sleep causes obesity by impacting homeostatic processes. Sleep duration and adiposity are both heritable, and the association may reflect shared genetic aetiology. We examined the association between a body mass index (BMI) genetic risk score (GRS) and objectively-measured total sleep time (TST) in a cohort of Norwegian children (enrolled at age four in 2007–2008) using cross-sectional data at age six. The analytical sample included 452 six-year old children with complete genotype and phenotype data. The outcome was actigraphic total sleep time (TST) measured at age six years. Genetic risk of obesity was inferred using a 32-single nucleotide polymorphism (SNP) weighted GRS of BMI. Covariates were BMI-Standard deviation scores (SDS) (which takes into account age and sex) and, in a sensitivity analysis socioeconomic status. Analyses consisted of Pearson's correlations and linear regressions. In our sample, 54% of participants were male; mean (SD) TST, age and BMI were 9.6 (0.8) hours, 6.0 (0.2) years and 15.3 (1.2) kg/m2, respectively. BMI and TST were not correlated, r = −0.003, p = 0.946. However, the BMI GRS was associated with TST after adjusting for BMI-SDS, standardised β = −0.11; 95% confidence interval (CI) = -0.22, −0.01. To our knowledge, this is the first study to establish a relationship between genetic risk of obesity and objective sleep duration in children. Findings suggest some shared genetic aetiology underlying these traits. Future research could identify the common biological pathways through which common genes predispose to both shorter sleep and increased risk of obesity.

Validation of BMI genetic risk score and DNA methylation in a Korean population.

When DNA profiles obtained from biological evidence at a crime scene fail to match suspects or anyone in the database, forensic DNA phenotyping, which is the prediction of externally visible characteristics, can facilitate a traced search for an unknown suspect by limiting the search range. Therefore, age, trait, or lifestyle predictors, as well as the predictor for colorations, have been researched in the forensic field. In the present study, for the development of a prediction model for BMI or obesity, we investigated several previously reported BMI- or obesity-associated genetic and epigenetic markers that included four CpGs (cg06500161, cg00574958, cg12593793, and cg10505902 of the ABCG1, CPT1A, LMNA, and PDE4DIP genes, respectively), and eight SNPs (rs12463617, rs1558902, rs591166, rs11030104, rs11671664, rs6545814, rs16858082, and rs574367 near the TMEM18, FTO, MC4R, BDNF, GIPR/QPCTL, ADCY3/RBJ, GNPDA2, and SEC16B genes, respectively) in 700 Koreans within the BMI ranging from 16.1 to 40.6 (27.6 ± 4.5) kg/m2. Linear regression analysis showed that DNA methylation of the four CpG sites explained 10.9% total variance in BMI, and the model constructed using age information, genetic score from eight SNPs, and DNA methylation at four CpG sites could account for 17.4% of BMI variance. Using data mining techniques, i.e., decision tree (Entropy and Gini), random forest, and bagging, a total of eight models with BMI 31 or 32 as a cutoff value were also constructed based on the data obtained from 490 training samples with age and sex as a covariate. Among them, a random forest model with a cutoff value of 31 showed the best performance with 63.3% accuracy and the AUC value of 0.682 in 210 test set samples. In the present study, we could replicate the previous finding that DNA methylation contributes more to BMI than do genetic factors. In addition, although the accuracy for the prediction of BMI was not high, our study is meaningful in respect of the ability to use a small number of markers to achieve similar prediction accuracy to that obtained from a model composed of more than a thousand markers, which adds support to continued research to identify a small set of predictive markers for practical application in the forensic field.

Mendelian randomization provides evidence for a causal effect of higher serum IGF-1 concentration on risk of hip and knee osteoarthritis.

ObjectivesHow insulin-like growth factor-1 (IGF-1) is related to OA is not well understood. We determined relationships between IGF-1 and hospital-diagnosed hand, hip and knee OA in UK Biobank, using Mendelian randomization (MR) to determine causality.MethodsSerum IGF-1 was assessed by chemiluminescent immunoassay. OA was determined using Hospital Episode Statistics. One-sample MR (1SMR) was performed using two-stage least-squares regression, with an unweighted IGF-1 genetic risk score as an instrument. Multivariable MR included BMI as an additional exposure (instrumented by BMI genetic risk score). MR analyses were adjusted for sex, genotyping chip and principal components. We then performed two-sample MR (2SMR) using summary statistics from Cohorts for Heart and Aging Research in Genetic Epidemiology (CHARGE) (IGF-1, N = 30 884) and the recent genome-wide association study meta-analysis (N = 455 221) of UK Biobank and Arthritis Research UK OA Genetics (arcOGEN).ResultsA total of 332 092 adults in UK Biobank had complete data. Their mean (s.d.) age was 56.5 (8.0) years and 54% were female. IGF-1 was observationally related to a reduced odds of hand OA [odds ratio per doubling = 0.87 (95% CI 0.82, 0.93)], and an increased odds of hip OA [1.04 (1.01, 1.07)], but was unrelated to knee OA [0.99 (0.96, 1.01)]. Using 1SMR, we found strong evidence for an increased risk of hip [odds ratio per s.d. increase = 1.57 (1.21, 2.01)] and knee [1.30 (1.07, 1.58)] OA with increasing IGF-1 concentration. By contrast, we found no evidence for a causal effect of IGF-1 concentration on hand OA [0.98 (0.57, 1.70)]. Results were consistent when estimated using 2SMR and in multivariable MR analyses accounting for BMI.ConclusionWe have found evidence that increased serum IGF-1 is causally related to higher risk of hip and knee OA.

Assessment of a causal relationship between body mass index and atopic dermatitis

Assessment of a causal relationship between body mass index and atopic dermatitis

A data-driven approach for studying the role of body mass in multiple diseases: a phenome-wide registry-based case-control study in the UK Biobank

Mendelian randomisation allows for the testing of causal effects in situations where clinical trials are challenging to do. In this hypothesis-free, data-driven phenome-wide association study (PheWAS), we sought to assess possible associations of high body-mass index (BMI) with multiple disease outcomes. For this registry-based case-control PheWAS, we used genome-wide data available from the UK Biobank to construct a genetic risk score of 76 variants related to BMI. Eligible UK Biobank participants were aged 37-73 years during recruitment, were white British, were unrelated to each other, and had available genetic information. Disease outcomes from these participants were mapped to a phenotype code (phecode). Participants with a phecode of interest were recoded as cases, whereas participants without a phecode of interest or any codes under a parent phecode were classified as controls. We did a PheWAS to analyse possible associations between the BMI genetic risk score and a range of disease outcomes. Disease associations passing stringent correction for multiple testing (Bonferroni corrected threshold p<5·4 × 10-5, false discovery rate corrected p<0·0074) were assessed for causal association with use of inverse-variance weighted mendelian randomisation. We did sensitivity analyses to assess pleiotropy and stability of estimation with use of weighted median, weighted mode, Egger regression, and mendelian randomisation pleiotropy residual sum and outlier methods. Our study population comprised 337 536 UK Biobank participants, and analyses were done for 925 unique phecodes from 17 different disease categories. After Bonferroni correction, PheWAS identified that BMI genetic risk score was associated with hospital-diagnosed obesity and 58 other outcomes; 30 distinct disease associations were supported by the mendelian randomisation analyses. 30 distinct disease associations were supported by the mendelian randomisation analyses. In inverse-variance weighted mendelian randomisation, genetically determined BMI was associated with endocrine disorders (odds ratio per one SD or 4·1 kg/m2 higher BMI 2·72, 95% CI 2·33-3·29 for type 2 diabetes; 2·11, 1·62-2·76 for type 1 diabetes; and 1·46, 1·25-1·70 for hypothyroidism), circulatory diseases (1·96, 1·53-2·51 for phlebitis and thrombophlebitis; 1·89, 1·39-2·57 for cardiomegaly; 1·68, 1·35-2·09 for congestive heart failure; 1·55, 1·37-1·76 for hypertension; 1·31, 1·13-1·52 for ischaemic heart disease; and 1·25, 1·14-1·37 for cardiac dysrhythmias), and inflammatory or dermatological conditions (2·00, 1·72-2·23 for superficial cellulitis and abscess; 3·37, 2·17-5·25 for chronic ulcers of leg and foot; 4·99, 2·54-9·82 for gangrene; and 2·24, 1·53-3·28 for atopy). Mendelian randomisation analyses provided further support for a causal effect of BMI on renal failure, osteoarthrosis, neurological (insomnia and peripheral nerve disorders) and respiratory diseases (asthma and chronic bronchitis), structural problems (hernias and knee derangement), and chemotherapy treatment. Mendelian randomisation with Egger regression produced consistently wider CIs compared with those of other methods. 26 of 72 distinct diseases detected under false discovery rate correction produced consistent estimates across at least four mendelian randomisation methods, and consistent evidence across all five approaches was obtained for 14 diseases. Our data-driven approach identified a range of diseases as possibly affected by high BMI. This population-level screening approximated the accumulated consequences of high BMI, whereas the true effects might be more complex and vary by life stage. Our results highlight the importance of obesity prevention and effective management of obesity-related comorbidities. National Health and Medical Research Council of Australia.

Phenome-Wide Analysis to Establish Causal Evidence for an Association Between Body Mass Index and Multiple Disease Outcomes in the UK Biobank

Background: Mendelian randomization (MR) allows the testing of causal effects in situations where clinical trials are challenging to conduct. In this phenome-wide association MR study (MR-PheWAS), we sought to establish the full extent of comorbidity risk associated with high body mass index (BMI). Methods: Analyses of BMI-genetic risk score (GRS) were conducted against 925 registry linked disease outcomes in 337,536 participants of the UK Biobank. Disease associations passing stringent correction for multiple testing were explored to establish causal relationship with BMI using several complementary MR approaches. Findings: After correction for multiple testing (P<10-5), PheWAS suggested associations between BMI-GRS and 58 outcomes, with 30 distinct disease associations supported by the MR analyses. In inverse-variance weighted MR, genetically instrumented BMI was associated with endocrine disorders (type 2 diabetes odds ratio 2·72, 95%CI 2·33-3·291; type 1 diabetes 2·11, 1·62-2·76; hypothyroidism 1·46, 1·25-1·70), circulatory diseases (phlebitis/thrombophlebitis 1·96, 1·53-2·51; cardiomegaly 1·89, 1·39-2·57; congestive heart failure 1·68, 1·35-2·09, hypertension 1·55, 1·37-1·76; ischemic heart disease 1·31,1·13-1·52; cardiac dysrhythmias 1·25, 1·14-1·37), and inflammatory or dermatologic conditions (superficial cellulitis and abscess 2·00, 1·72-2·23; chronic ulcers of leg and foot 3·37 ,2·17-5·25; gangrene 4·99,2·54-9·82; atopy 2·24, 1·53- 3·28). BMI-GRS was associated with renal failure, osteoarthrosis, neurological (insomnia, peripheral nerve disorders) and respiratory diseases (asthma, chronic bronchitis), as well as structural problems (hernias, knee derangement), and the need for chemotherapy treatment. Interpretation: The burden of comorbidity associated with high BMI is enormous, with our study guiding comorbidity prevention, and highlighting the wide-spread economic and health implications by the ongoing obesity epidemic. Funding Statement: National Health and Medical Research Council. Declaration of Interests: The authors state: none. Ethics Approval Statement: The UK Biobank study was approved by the National Information Governance Board for Health and Social Care and North West Multicentre Research Ethics Committee (11/NW/0382).

Association between genetic obesity susceptibility and mother-reported eating behaviour in children up to 5years.

Many genetic polymorphisms identified by genome-wide association studies for adult body mass index (BMI) have been suggested to regulate food intake. The objective was to study the associations between a genetic obesity risk score, appetitive traits, and growth of children up to age 5years, with a longitudinal design. In 1142 children from the Etude des Déterminants pre et post natals de la santé de l'ENfant (EDEN) birth cohort, a combined obesity risk-allele score (BMI genetic risk score [GRS]) was related to appetitive traits (energy intake up to 12mo, a single item on appetite from 4mo to 3y, a validated appetite score at 5y) using Poisson regressions with robust standard errors. The potential mediation of appetitive traits on the association between BMI-GRS and growth was assessed by the Sobel test. Children with a high BMI-GRS were more likely to have high energy intake at 1year and high appetite at 2 and 5years. High energy intake in infancy and high appetite from 1year were related to higher subsequent BMI. High 2-year appetite seemed to partially mediate the associations between BMI-GRS and BMI from 2 to 5years (all P≤0.05). Genetic susceptibility to childhood obesity seems to be partially explained by appetitive traits in infancy, followed by an early childhood rise in BMI.

Association of Maternal Prepregnancy Weight with Offspring Adiposity Throughout Adulthood over 37 Years of Follow-up.

This study aimed to determine the relation of maternal prepregnancy weight with offspring BMI across adulthood from almost 40 years of follow-up. BMI was measured in Framingham Heart Study Offspring cohort participants between 1971 and 2008. The association of maternal prepregnancy weight category (ascertained via direct measure and questionnaire) with serial offspring BMI, overweight, obesity, and change in BMI over time was tested, adjusted for age, sex, and a BMI genetic risk score; secondary models additionally adjusted for physical activity, dietary factors, smoking, education, and familial relatedness. Among 863 participants at initial assessment (83 exposed and 780 controls), mean (SD) age was 33 (10) years, 53% were female, and mean BMI was 24.5 (4.1) kg/m2 . Exposed offspring BMI was higher at every examination cycle, ranging from 1.5 (0.5) to 3.0 (0.5) kg/m2 (P < 0.001), with larger differences at later assessments. The rate of increase in offspring BMI over time was higher in exposed offspring before the age of 50 years (β [SE] = 0.07 [0.02] kg/m2 per year; P = 0.004) but not after the age of 50 years (-0.05 [0.04] kg/m2 per year; P = 0.2). Maternal prepregnancy weight is associated with greater offspring BMI throughout adulthood, with more rapid weight acceleration in early and midadulthood.

Using genetics to understand the causal influence of higher BMI on depression.

BackgroundDepression is more common in obese than non-obese individuals, especially in women, but the causal relationship between obesity and depression is complex and uncertain. Previous studies have used genetic variants associated with BMI to provide evidence that higher body mass index (BMI) causes depression, but have not tested whether this relationship is driven by the metabolic consequences of BMI nor for differences between men and women.MethodsWe performed a Mendelian randomization study using 48 791 individuals with depression and 291 995 controls in the UK Biobank, to test for causal effects of higher BMI on depression (defined using self-report and Hospital Episode data). We used two genetic instruments, both representing higher BMI, but one with and one without its adverse metabolic consequences, in an attempt to ‘uncouple’ the psychological component of obesity from the metabolic consequences. We further tested causal relationships in men and women separately, and using subsets of BMI variants from known physiological pathways.ResultsHigher BMI was strongly associated with higher odds of depression, especially in women. Mendelian randomization provided evidence that higher BMI partly causes depression. Using a 73-variant BMI genetic risk score, a genetically determined one standard deviation (1 SD) higher BMI (4.9 kg/m2) was associated with higher odds of depression in all individuals [odds ratio (OR): 1.18, 95% confidence interval (CI): 1.09, 1.28, P = 0.00007) and women only (OR: 1.24, 95% CI: 1.11, 1.39, P = 0.0001). Meta-analysis with 45 591 depression cases and 97 647 controls from the Psychiatric Genomics Consortium (PGC) strengthened the statistical confidence of the findings in all individuals. Similar effect size estimates were obtained using different Mendelian randomization methods, although not all reached P < 0.05. Using a metabolically favourable adiposity genetic risk score, and meta-analysing data from the UK biobank and PGC, a genetically determined 1 SD higher BMI (4.9 kg/m2) was associated with higher odds of depression in all individuals (OR: 1.26, 95% CI: 1.06, 1.50], P = 0.010), but with weaker statistical confidence.ConclusionsHigher BMI, with and without its adverse metabolic consequences, is likely to have a causal role in determining the likelihood of an individual developing depression.

Physical activity modifies genetic susceptibility to obesity in postmenopausal women.

We conducted a gene-environment interaction study to evaluate whether the association of body mass index (BMI) associated meta genome-wide association study single-nucleotide polymorphisms (SNPs) (as a genetic risk score) and BMI is modified by physical activity and age. In 8,206 women of European ancestry from the Women's Health Initiative (WHI), we used linear regression to examine main effects of the 95 SNP BMI genetic risk score (GRS) and physical activity on BMI, and evaluated whether genetic associations are modified by physical activity (two-way interaction) and age (three-way interaction). We found evidence for modification of the BMI GRS-BMI association according to both physical activity and age. We observed a significant two-way interaction of BMI GRS × physical activity in the crude model (P interaction = 0.05), where a smaller effect of the BMI GRS on BMI with increasing physical activity. The beta coefficient was 0.05 (standard error [SE] = 0.02, P = 0.01) for the high-activity group compared with beta = 0.13 (SE = 0.02, P = 4.8 × 10) for the sedentary group. The three-way interaction was statistically significant (adjusted P interaction = 0.01). Notably, in the 70+ age group, the BMI GRS-BMI association was attenuated and no longer significant in the high-activity group; the beta coefficient for the 70+ high-activity group was relatively small and nonsignificant (beta = 0.02, SE = 0.03, P = 0.58) compared with 70+ sedentary group (beta = 0.17, SE = 0.03, P = 2.5 × 10). Our study suggests that physical activity attenuates the influence of genetic predisposition to obesity, and this effect is more profound in the oldest age group.