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Background: It is often assumed that selection (including participation and dropout) does not represent an important source of bias in genetic studies. However, there is little evidence to date on the effect of genetic factors on participation., Methods: Using data on mothers (N = 7486) and children (N = 7508) from the Avon Longitudinal Study of Parents and Children, we: (i) examined the association of polygenic risk scores for a range of sociodemographic and lifestyle characteristics and health conditions related to continued participation; (ii) investigated whether associations of polygenic scores with body mass index (BMI; derived from self-reported weight and height) and self-reported smoking differed in the largest sample with genetic data and a subsample who participated in a recent follow-up; and (iii) determined the proportion of variation in participation explained by common genetic variants, using genome-wide data., Results: We found evidence that polygenic scores for higher education, agreeableness and openness were associated with higher participation; and polygenic scores for smoking initiation, higher BMI, neuroticism, schizophrenia, attention-deficit hyperactivity disorder (ADHD) and depression were associated with lower participation. Associations between the polygenic score for education and self-reported smoking differed between the largest sample with genetic data [odds ratio (OR) for ever smoking per standard deviation (SD) increase in polygenic score: 0.85, 95% confidence interval (CI): 0.81, 0.89} and subsample (OR: 0.96, 95% CI: 0.89, 1.03). In genome-wide analysis, single nucleotide polymorphism based heritability explained 18-32% of variability in participation., Conclusions: Genetic association studies, including Mendelian randomization, can be biased by selection, including loss to follow-up. Genetic risk for dropout should be considered in all analyses of studies with selective participation.

Background: Smoking influences body weight, but there is little evidence as to whether body mass index (BMI) and body dissatisfaction increase smoking initiation in adolescents., Methods: We evaluated the association between measured BMI, body dissatisfaction and latent classes of smoking initiation (never smokers, experimenters, late onset regular smokers, early onset regular smokers) in the Avon Longitudinal Study of Parents and Children. In observational analyses we used BMI (N=3754) and body dissatisfaction at age 10.5 years (N=3349). In Mendelian randomisation (MR) analysis, we used a BMI genetic risk score of 76 single nucleotide polymorphisms (N=4017)., Results: In females, higher BMI was associated with increased odds of early onset regular smoking (OR: 1.11, 95% CI: 1.04, 1.18) compared to being a never smoker, but not clearly associated with experimenting with smoking (OR: 1.04, 95% CI: 0.99, 1.10) or late onset regular smoking (OR: 1.01, 95% CI: 0.94, 1.09). No clear evidence was found for associations between BMI and smoking initiation classes in males (p-value for sex interaction≤0.001). Body dissatisfaction was associated with increased odds of late-onset regular smoking (OR: 1.71, 95% CI: 1.32, 1.99) in males and females combined (P-value for sex interaction=0.32). There was no clear evidence for an association between the BMI genetic risk score and smoking latent classes in males or females but estimates were imprecise., Conclusions: BMI in females and body dissatisfaction in males and females are associated with increased odds of smoking initiation, highlighting these as potentially important factors for consideration in smoking prevention strategies., (Copyright © 2017 The Authors. Published by Elsevier B.V. All rights reserved.)

We previously used a single nucleotide polymorphism (SNP) in the CHRNA5-A3-B4 gene cluster associated with heaviness of smoking within smokers to confirm the causal effect of smoking in reducing body mass index (BMI) in a Mendelian randomisation analysis. While seeking to extend these findings in a larger sample we found that this SNP is associated with 0.74% lower body mass index (BMI) per minor allele in current smokers (95% CI -0.97 to -0.51, P = 2.00 × 10(-10)), but also unexpectedly found that it was associated with 0.35% higher BMI in never smokers (95% CI +0.18 to +0.52, P = 6.38 × 10(-5)). An interaction test confirmed that these estimates differed from each other (P = 4.95 × 10(-13)). This difference in effects suggests the variant influences BMI both via pathways unrelated to smoking, and via the weight-reducing effects of smoking. It would therefore be essentially undetectable in an unstratified genome-wide association study of BMI, given the opposite association with BMI in never and current smokers. This demonstrates that novel associations may be obscured by hidden population sub-structure. Stratification on well-characterized environmental factors known to impact on health outcomes may therefore reveal novel genetic associations.

Background: Measures of regional adiposity have been proposed as alternatives to the measurement of body mass index (BMI) for identifying persons at risk of future disease., Objective: The objective was to compare the magnitudes of association of BMI and alternative measurements of adiposity with coronary heart disease, diabetes, and cardiovascular disease risk factors and all-cause mortality., Design: Data from 4 cohorts of adults [3937 women from the British Women's Heart and Health Study (BWHHS); 2367 and 1950 men from phases 1 and 3, respectively, of the Caerphilly Prospective Study (CaPS); 403 men and women from the Boyd Orr Study; and 789 men and women from the Maidstone-Dewsbury Study] were analyzed., Results: The magnitudes of associations of BMI with incident coronary heart disease and cardiovascular disease risk factors were similar to those with measurements of central adiposity [waist circumference (WC), waist-hip ratio (WHR), or waist-height ratio (WHtR)] and more direct measurements of fat mass (bioimpedance/skinfold thickness). In CaPS (men only), there was no strong evidence of differences in the strengths of association with incident diabetes between BMI, WC, WHR, and WHtR (P for heterogeneity > 0.49 for all). In the BWHHS (women only), there was statistical evidence that WC [hazard ratio (HR): 2.35; 95% CI: 2.03, 2.73] and WHtR (HR: 2.29; 95% CI: 1.98, 2.66) were more strongly associated with diabetes than with BMI (HR: 1.80; 95% CI: 1.59, 2.04) (P for heterogeneity < 0.02 for both). Central adiposity measurements were positively associated with all-cause mortality, as was BMI, but only when those with a BMI (in kg/m(2)) <22.5 were removed from the analyses., Conclusion: No strong evidence supports replacing BMI in clinical or public health practice with other adiposity measures.

Biomarkers can be used to assess smoking behaviour more accurately and objectively than self-report. This study assessed the association between cotinine (a biomarker of smoke exposure) and later e-cigarette use among a population who were unexposed to e-cigarettes in youth. Young people in the Avon Longitudinal Study of Parents and Children took part in the study. We observed associations between cotinine at 15 years (measured between 2006 and 2008 before the wide availability of e-cigarettes) and self-reported ever use of e-cigarettes at 22 (measured between 2014 and 2015 when e-cigarettes were widely available) using logistic regression. A range of potential confounders were adjusted for (age, sex, body mass index, alcohol use and passive smoke exposure). Additionally, we adjusted for the young people's self-reported smoking status/history to explore potential misreporting and measurement error. In a sample of N = 1,194 young people, cotinine levels consistent with active smoking at 15 years were associated with increased odds of e-cigarette ever use at 22 years (Odds Ratio [OR] = 7.24, 95% CI 3.29 to 15.93) even when self-reported active smoking status at age 16 (OR = 3.14, 95% CI 1.32 to 7.48) and latent classes of smoking behaviour from 14 to 16 (OR = 2.70, 95% CI 0.98 to 7.44) were included in the model. Cotinine levels consistent with smoking in adolescence were strongly associated with increased odds of later e-cigarette use, even after adjusting for reported smoking behaviour at age 16 and smoking transitions from 14 to 16. [ABSTRACT FROM AUTHOR]

Background: Undernutrition and physical inactivity are both associated with lower bone mass. Objective: This study aimed to investigate the combined effects of early-life undernutrition and urbanized lifestyles in later life on bone mass accrual in young adults from a rural community in India that is undergoing rapid socioeconomic development. Design: This was a prospective cohort study of participants of the Hyderabad Nutrition Trial (1987-1990), which offered balanced protein-calorie supplementation to pregnant women and preschool children younger than 6 y in the intervention villages. The 2009-2010 follow-up study collected data on current anthropometric measures, bone mineral density (BMD) measured by dual-energy X-ray absorptiometry, blood samples, diet, physical activity, and living standards of the trial participants (n = 1446, aged 18-23 y). Results: Participants were generally lean and had low BMD [mean hip BMD: 0.83 (women), 0.95 (men) g/cm²; lumbar spine: 0.86 (women), 0.93 (men) g/cm²]. In models adjusted for current risk factors, no strong evidence of a positive association was found between BMD and early-life supplementation. On the other hand, current lean mass and weight-bearing physical activity were positively associated with BMD. No strong evidence of an association was found between BMD and current serum 25-hydroxyvitamin D or dietary intake of calcium, protein, or calories. Conclusions: Current lean mass and weight-bearing physical activity were more important determinants of bone mass than was early-life undernutrition in this population. In transitional rural communities from low-income countries, promotion of physical activity may help to mitigate any potential adverse effects of early nutritional disadvantage. [ABSTRACT FROM AUTHOR]

Lean body mass (LBM) and muscle mass remain difficult to quantify in large epidemiological studies due to the unavailability of inexpensive methods. We therefore developed anthropometric prediction equations to estimate the LBM and appendicular lean soft tissue (ALST) using dual-energy X-ray absorptiometry (DXA) as a reference method. Healthy volunteers (n = 2,220; 36% women; age 18-79 yr), representing a wide range of body mass index (14-44 kg/m2), participated in this study. Their LBM, including ALST, was assessed by DXA along with anthropometric measurements. The sample was divided into prediction (60%) and validation (40%) sets. In the prediction set, a number of prediction models were constructed using DXA-measured LBM and ALST estimates as dependent variables and a combination of anthropometric indices as independent variables. These equations were cross-validated in the validation set. Simple equations using age, height, and weight explained >90% variation in the LBM and ALST in both men and women. Additional variables (hip and limb circumferences and sum of skinfold thicknesses) increased the explained variation by 5-8% in the fully adjusted models predicting LBM and ALST. More complex equations using all of the above anthropometric variables could predict the DXA-measured LBM and ALST accurately, as indicated by low standard error of the estimate (LBM: 1.47 kg and 1.63 kg for men and women, respectively), as well as good agreement by Bland-Altman analyses (Bland JM, Altman D. Lancet 1: 307-310, 1986). These equations could be a valuable tool in large epidemiological studies assessing these body compartments in Indians and other population groups with similar body composition. [ABSTRACT FROM AUTHOR]

Obesity is an established risk factor for type 2 diabetes (T2D) and they are metabolically related through the mechanism of insulin resistance. In order to explore how common genetic variants associated with T2D correlate with body mass index (BMI), we examined the influence of 25 T2D associated loci on obesity risk. We used 5056 individuals (2528 sib-pairs) recruited in Indian Migration Study and conducted within sib-pair analysis for six obesity phenotypes. We found associations of variants in CXCR4 (rs932206) and HHEX (rs5015480) with higher body mass index (BMI) (β = 0.13, p = 0.001) and (β = 0.09, p = 0.002), respectively and weight (β = 0.13, p = 0.001) and (β = 0.09, p = 0.001), respectively. CXCR4 variant was also strongly associated with body fat (β = 0.10, p = 0.0004). In addition, we demonstrated associations of CXCR4 and HHEX with overweight/obesity (OR = 1.6, p = 0.003) and (OR = 1.4, p = 0.002), respectively, in 1333 sib-pairs (2666 individuals). We observed marginal evidence of associations between variants at six loci (TCF7L2, NGN3, FOXA2, LOC646279, FLJ3970 and THADA) and waist hip ratio (WHR), BMI and/or overweight which needs to be validated in larger set of samples. All the above findings were independent of daily energy consumption and physical activity level. The risk score estimates based on eight significant loci (including nominal associations) showed associations with WHR and body fat which were independent of BMI. In summary, we establish the role of T2D associated loci in influencing the measures of obesity in Indian population, suggesting common underlying pathophysiology across populations. [ABSTRACT FROM AUTHOR]

Background Mendelian randomisation uses genetic variants randomly allocated at conception as instrumental variables for an exposure. Methodological advances allow for mediation analysis to be carried out using Mendelian randomisation using either multivariable Mendelian randomisation or two-step Mendelian randomisation. Methods We use simulations and an applied example to demonstrate when multivariable Mendelian randomisation and two-step Mendelian randomisation methods are valid and how they relate to traditional phenotypic regression-based approaches to mediation. We demonstrate how Mendelian randomisation methods can relax assumptions required for causal inference in phenotypic mediation, as well as which Mendelian randomisation specific assumptions are required. We illustrate our methods in data from UK Biobank, estimating the role of body mass index mediating the association between education and cardiovascular outcomes. Results Both multivariable Mendelian randomization and two-step Mendelian randomization are unbiased when estimating the total effect, direct effect, indirect effect and proportion mediated when both confounding, and measurement error are present. Multivariable Mendelian Randomization can be used when multiple mediators are to be investigated in a single model. Conclusions Mendelian randomisation provides an opportunity to improve causal inference in mediation analysis. Although Mendelian randomisation specific assumptions apply, such as no weak instrument bias and no pleiotropic pathways, strong phenotypic assumptions of no confounding and no measurement error can be relaxed. Key messages Mendelian randomisation offers an opportunity to address bias by unmeasured confounding, measurement error and reverse causality in mediation analysis. [ABSTRACT FROM AUTHOR]