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Background: Adiposity shows opposing associations with mortality within COVID-19 versus non-COVID-19 respiratory conditions. We assessed the likely causality of adiposity for mortality among intensive care patients with COVID-19 versus non-COVID-19 by examining the consistency of associations across temporal and geographical contexts where biases vary., Methods: We used data from 297 intensive care units (ICUs) in England, Wales, and Northern Ireland (Intensive Care National Audit and Research Centre Case Mix Programme). We examined associations of body mass index (BMI) with 30-day mortality, overall and by date and region of ICU admission, among patients admitted with COVID-19 (N = 34,701; February 2020-August 2021) and non-COVID-19 respiratory conditions (N = 25,205; February 2018-August 2019)., Results: Compared with non-COVID-19 patients, COVID-19 patients were younger, less often of a white ethnic group, and more often with extreme obesity. COVID-19 patients had fewer comorbidities but higher mortality. Socio-demographic and comorbidity factors and their associations with BMI and mortality varied more by date than region of ICU admission. Among COVID-19 patients, higher BMI was associated with excess mortality (hazard ratio (HR) per standard deviation (SD) = 1.05; 95% CI = 1.03-1.07). This was evident only for extreme obesity and only during February-April 2020 (HR = 1.52, 95% CI = 1.30-1.77 vs. recommended weight); this weakened thereafter. Among non-COVID-19 patients, higher BMI was associated with lower mortality (HR per SD = 0.83; 95% CI = 0.81-0.86), seen across all overweight/obesity groups and across dates and regions, albeit with a magnitude that varied over time., Conclusions: Obesity is associated with higher mortality among COVID-19 patients, but lower mortality among non-COVID-19 respiratory patients. These associations appear vulnerable to confounding/selection bias in both patient groups, questioning the existence or stability of causal effects., (© 2024. The Author(s).)

Background: Higher mean body mass index (BMI) among lower socioeconomic position (SEP) groups is well established in Western societies, but the influence of genetic factors on these differences is not well characterized., Methods: We analyzed these associations using Finnish health surveys conducted between 1992 and 2017 (N = 33 523; 53% women) with information on measured weight and height, polygenic risk scores of BMI (PGS-BMI) and linked data from administrative registers to measure educational attainment, occupation-based social class and personal income., Results: In linear regressions, largest adjusted BMI differences were found between basic and tertiary educated men (1.4 kg/m 2 , 95% confidence interval [CI] 1.2; 1.6) and women (2.5 kg/m 2 , 95% CI 2.3; 2.8), and inverse BMI gradients were also found for social class and income. These SEP differences arose partly because mean PGS-BMI was higher and partly because PGS-BMI predicted BMI more strongly in lower SEP groups. The inverse SEP gradients of BMI were steeper in women than in men, but sex differences were not found in the genetic contributions to these differences., Conclusions: Better understanding of the interplay between genes and environment provides insight into the mechanisms explaining SEP differences in BMI., (© 2024. The Author(s).)

Mendelian randomisation (MR) is an established technique in epidemiological investigation, using the principle of random allocation of genetic variants at conception to estimate the causal linear effect of an exposure on an outcome. Extensions to this technique include non-linear approaches that allow for differential effects of the exposure on the outcome depending on the level of the exposure. A widely used non-linear method is the residual approach, which estimates the causal effect within different strata of the non-genetically predicted exposure (i.e. the "residual" exposure). These "local" causal estimates are then used to make inferences about non-linear effects. Recent work has identified that this method can lead to estimates that are seriously biased, and a new method-the doubly-ranked method-has been introduced as a possibly more robust approach. In this paper, we perform negative control outcome analyses in the MR context. These are analyses with outcomes onto which the exposure should have no predicted causal effect. Using both methods we find clearly biased estimates in certain situations. We additionally examined a situation for which there are robust randomised controlled trial estimates of effects-that of low-density lipoprotein cholesterol (LDL-C) reduction onto myocardial infarction, where randomised trials have provided strong evidence of the shape of the relationship. The doubly-ranked method did not identify the same shape as the trial data, and for LDL-C and other lipids they generated some highly implausible findings. Therefore, we suggest there should be extensive simulation and empirical methodological examination of performance of both methods for NLMR under different conditions before further use of these methods. In the interim, use of NLMR methods needs justification, and a number of sanity checks (such as analysis of negative and positive control outcomes, sensitivity analyses excluding removal of strata at the extremes of the distribution, examination of biological plausibility and triangulation of results) should be performed., (© 2024. The Author(s).)

Body mass index (BMI) is a complex disease risk factor known to be influenced by genes acting via both metabolic pathways and appetite regulation. In this study, we aimed to gain insight into the phenotypic consequences of BMI-associated genetic variants, which may be mediated by their expression in different tissues. First, we harnessed meta-analyzed gene expression datasets derived from subcutaneous adipose (n = 1257) and brain (n = 1194) tissue to identify 86 and 140 loci, respectively, which provided evidence of genetic colocalization with BMI. These two sets of tissue-partitioned loci had differential effects with respect to waist-to-hip ratio, suggesting that the way they influence fat distribution might vary despite their having very similar average magnitudes of effect on BMI itself (adipose = 0.0148 and brain = 0.0149 standard deviation change in BMI per effect allele). For instance, BMI-associated variants colocalized with TBX15 expression in adipose tissue (posterior probability [PPA] = 0.97), but not when we used TBX15 expression data derived from brain tissue (PPA = 0.04) This gene putatively influences BMI via its role in skeletal development. Conversely, there were loci where BMI-associated variants provided evidence of colocalization with gene expression in brain tissue (e.g., NEGR1, PPA = 0.93), but not when we used data derived from adipose tissue, suggesting that these genes might be more likely to influence BMI via energy balance. Leveraging these tissue-partitioned variant sets through a multivariable Mendelian randomization framework provided strong evidence that the brain-tissue-derived variants are predominantly responsible for driving the genetically predicted effects of BMI on cardiovascular-disease endpoints (e.g., coronary artery disease: odds ratio = 1.05, 95% confidence interval = 1.04-1.07, p = 4.67 × 10 -14 ). In contrast, our analyses suggested that the adipose tissue variants might predominantly be responsible for the underlying relationship between BMI and measures of cardiac function, such as left ventricular stroke volume (beta = 0.21, 95% confidence interval = 0.09-0.32, p = 6.43 × 10 -4 )., Competing Interests: Declaration of interests T.G.R. is a part-time employee of Novo Nordisk outside of the work presented in this manuscript. All other co-authors declare no competing interests., (Copyright © 2022 American Society of Human Genetics. Published by Elsevier Inc. All rights reserved.)

Objective: High body mass index (BMI) is an important predictor of mortality but estimating underlying causality is hampered by confounding and pre-existing disease. Here, we use information from the offspring to approximate parental BMIs, with an aim to avoid biased estimation of mortality risk caused by reverse causality., Methods: The analyses were based on information on 9674 offspring-mother and 9096 offspring-father pairs obtained from the 1958 British birth cohort. Parental BMI-mortality associations were analysed using conventional methods and using offspring BMI as a proxy, or instrument, for their parents' BMI., Results: In the conventional analysis, associations between parental BMI and all-cause mortality were U-shaped (P curvature  < 0.001), while offspring BMI had linear associations with parental mortality (P trend  < 0.001, P curvature  > 0.46). Curvature was particularly pronounced for mortality from respiratory diseases and from lung cancer. Instrumental variable analyses suggested a positive association between BMI and mortality from all causes [mothers: HR per SD of BMI 1.43 (95% CI 1.21-1.69), fathers: HR 1.17 (1.00-1.36)] and from coronary heart disease [mothers: HR 1.65 (1.15-2.36), fathers: HR 1.51 (1.17-1.97)]. These were larger than HR from the equivalent conventional analyses, despite some attenuation by adjustment for social indicators and smoking., Conclusions: Analyses using offspring BMI as a proxy for parental BMI suggest that the apparent adverse consequences of low BMI are considerably overestimated and adverse consequences of overweight are underestimated in conventional epidemiological studies., (© 2021. The Author(s), under exclusive licence to Springer Nature Limited.)

Background: High body mass index (BMI) is associated with mortality, but the pervasive problem of confounding and reverse causality in observational studies limits inference about the direction and magnitude of causal effects. We aimed to obtain estimates of the causal association of BMI with all-cause and cause-specific mortality., Methods and Findings: In a record-linked, intergenerational prospective study from the general population of Sweden, we used two-sample instrumental variable (IV) analysis with data from 996,898 fathers (282,407 deaths) and 1,013,083 mothers (153,043 deaths) and their sons followed up from January 1, 1961, until December 31, 2004. Sons' BMI was used as the instrument for parents' BMI to compute hazard ratios (HRs) for risk of mortality per standard deviation (SD) higher parents' BMI. Using offspring exposure as an instrument for parents' exposure is unlikely to be affected by reverse causality (an important source of bias in this context) and reduces confounding. IV analyses supported causal associations between higher BMI and greater risk of all-cause mortality (HR [95% confidence interval (CI)] per SD higher fathers' BMI: 1.29 [1.26-1.31] and mothers' BMI: 1.39 [1.35-1.42]) and overall cancer mortality (HR per SD higher fathers' BMI: 1.20 [1.16-1.24] and mothers' BMI: 1.29 [1.24-1.34]), including 9 site-specific cancers in men (bladder, colorectum, gallbladder, kidney, liver, lung, lymphatic system, pancreas, and stomach) and 11 site-specific cancers in women (gallbladder, kidney, liver, lung, lymphatic system, ovaries, pancreas, stomach, uterus, cervix, and endometrium). There was evidence supporting causal associations between higher BMI in mothers and greater risk of mortality from kidney disease (HR: 2.17 [1.68-2.81]) and lower risk of mortality from suicide (HR: 0.77 [0.65-0.90]). In both sexes, there was evidence supporting causal associations between higher BMI and mortality from cardiovascular diseases (CVDs), stroke, diabetes, and respiratory diseases. We were unable to test the association between sons' and mothers' BMIs (as mothers' data were unavailable) or whether the instrument was independent of unmeasured or residual confounding; however, the associations between parents' mortality and sons' BMI were negligibly influenced by adjustment for available confounders., Conclusions: Consistent with previous large-scale meta-analyses and reviews, results supported the causal role of higher BMI in increasing the risk of several common causes of death, including cancers with increasing global incidence. We also found positive effects of BMI on mortality from respiratory disease, prostate cancer, and lung cancer, which has been inconsistently reported in the literature, suggesting that the causal role of higher BMI in mortality from these diseases may be underestimated. Furthermore, we expect different patterns of bias in the current observational and IV analyses; therefore, the similarities between our findings from both methods increases confidence in the results. These findings support efforts to understand the mechanisms underpinning these effects to inform targeted interventions and develop population-based strategies to reduce rising obesity levels for disease prevention., Competing Interests: I have read the journal's policy and the authors of this manuscript have the following competing interests: GDS is a member of the Editorial Board of PLOS Medicine. All other authors have declared that no competing interests exist.

Background: Mendelian randomization (MR) is a powerful tool in epidemiology that can be used to estimate the causal effect of an exposure on an outcome in the presence of unobserved confounding, by utilizing genetic variants that are instrumental variables (IVs) for the exposure. This has been extended to multivariable MR (MVMR) to estimate the effect of two or more exposures on an outcome., Methods and Results: We use simulations and theory to clarify the interpretation of estimated effects in a MVMR analysis under a range of underlying scenarios, where a secondary exposure acts variously as a confounder, a mediator, a pleiotropic pathway and a collider. We then describe how instrument strength and validity can be assessed for an MVMR analysis in the single-sample setting, and develop tests to assess these assumptions in the popular two-sample summary data setting. We illustrate our methods using data from UK Biobank to estimate the effect of education and cognitive ability on body mass index., Conclusion: MVMR analysis consistently estimates the direct causal effect of an exposure, or exposures, of interest and provides a powerful tool for determining causal effects in a wide range of scenarios with either individual- or summary-level data., (© The Author(s) 2018. Published by Oxford University Press on behalf of the International Epidemiological Association.)

Importance: Individually, higher body mass index (BMI) and alcohol consumption increase the risk of liver disease. Evidence of a joint association is mixed; however, previous studies have not used causal inference methods robust to confounding and reverse causation. Understanding any true effect is key to developing effective interventions to reduce liver disease., Objective: To investigate the joint association of BMI and alcohol consumption with liver injury biomarkers and incident liver disease using factorial mendelian randomization (MR)., Design, Setting, and Participants: A population-based cohort study (Copenhagen General Population Study) recruited a random sample of Copenhagen, Denmark, residents aged 20 years or older of white, Danish descent (N = 98 643) between November 25, 2003, and July 1, 2014. Data were also obtained from ongoing links to national registers, and then analyzed from September 30, 2016, to April 23, 2018., Exposures: High and low BMI and alcohol consumption categories from baseline-measured or self-reported observational data and genetic variants predicting BMI and alcohol consumption., Main Outcomes and Measures: Plasma biomarkers of liver injury (alanine aminotransferase [ALT] and γ-glutamyltransferase [GGT]) and incident cases of liver disease from hospital records were the outcomes., Results: Of the 98 643 individuals recruited, 91 552 (54 299 [45.2%] women; mean [SD] age, 58 [13.05] years) with no baseline liver disease were included in main analyses. Individuals had a mean (SD) BMI of 26.2 (4.3) and consumed a mean (SD) of 10.6 (10.2) U/wk of alcohol. In factorial MR analyses, considering the high BMI/high alcohol group as the reference, mean circulating ALT and GGT levels were lowest in the low BMI/low alcohol group (ALT: -2.32%; 95% CI, -4.29% to -0.35%, and GGT: -3.56%; 95% CI, -5.88% to -1.24%). Individuals with low BMI/high alcohol use and high BMI/low alcohol use also had lower mean circulating ALT levels (low BMI/high alcohol use: -1.31%; 95% CI, -1.88% to -0.73%, and high BMI/low alcohol use: -0.81%; 95% CI, -2.86% to 1.22%) and GGT levels (low BMI/high alcohol use: -0.91%; 95% CI, -1.60% to -0.22%, and high BMI/low alcohol use: -1.13%; 95% CI, -3.55% to 1.30%) compared with the high BMI/high alcohol use reference group. These patterns were similar in multivariable factorial analyses. For incident liver disease (N = 580), factorial MR results were less conclusive (odds ratio of liver disease vs high BMI/high alcohol group: 1.01; 95% CI, 0.84 to 1.18, for the low BMI/high alcohol group, 1.22; 95% CI, 0.56 to 1.88 for the high BMI/low alcohol group, and 0.99 (95% CI, 0.41 to 1.56 for the low BMI/low alcohol group)., Conclusions and Relevance: Interventions to reduce both BMI and alcohol consumption might reduce population levels of biomarkers of liver injury more than interventions aimed at either BMI or alcohol use alone. However, it is not clear whether this intervention will directly translate to a reduced risk of liver disease.

Mendelian randomization (MR) has been used to estimate the causal effect of body mass index (BMI) on particular traits thought to be affected by BMI. However, BMI may also be a modifiable, causal risk factor for outcomes where there is no prior reason to suggest that a causal effect exists. We performed a MR phenome-wide association study (MR-pheWAS) to search for the causal effects of BMI in UK Biobank (n = 334 968), using the PHESANT open-source phenome scan tool. A subset of identified associations were followed up with a formal two-stage instrumental variable analysis in UK Biobank, to estimate the causal effect of BMI on these phenotypes. Of the 22 922 tests performed, our MR-pheWAS identified 587 associations below a stringent P value threshold corresponding to a 5% estimated false discovery rate. These included many previously identified causal effects, for instance, an adverse effect of higher BMI on risk of diabetes and hypertension. We also identified several novel effects, including protective effects of higher BMI on a set of psychosocial traits, identified initially in our preliminary MR-pheWAS in circa 115,000 UK Biobank participants and replicated in a different subset of circa 223,000 UK Biobank participants. Our comprehensive MR-pheWAS identified potential causal effects of BMI on a large and diverse set of phenotypes. This included both previously identified causal effects, and novel effects such as a protective effect of higher BMI on feelings of nervousness., Competing Interests: Dr Gaunt is currently conducting unrelated precompetitive research sponsored by GlaxoSmithKline and Biogen. All other authors have declared that no competing interests exist.

Background: Psoriasis is a common inflammatory skin disease that has been reported to be associated with obesity. We aimed to investigate a possible causal relationship between body mass index (BMI) and psoriasis., Methods and Findings: Following a review of published epidemiological evidence of the association between obesity and psoriasis, mendelian randomization (MR) was used to test for a causal relationship with BMI. We used a genetic instrument comprising 97 single-nucleotide polymorphisms (SNPs) associated with BMI as a proxy for BMI (expected to be much less confounded than measured BMI). One-sample MR was conducted using individual-level data (396,495 individuals) from the UK Biobank and the Nord-Trøndelag Health Study (HUNT), Norway. Two-sample MR was performed with summary-level data (356,926 individuals) from published BMI and psoriasis genome-wide association studies (GWASs). The one-sample and two-sample MR estimates were meta-analysed using a fixed-effect model. To test for a potential reverse causal effect, MR analysis with genetic instruments comprising variants from recent genome-wide analyses for psoriasis were used to test whether genetic risk for this skin disease has a causal effect on BMI. Published observational data showed an association of higher BMI with psoriasis. A mean difference in BMI of 1.26 kg/m2 (95% CI 1.02-1.51) between psoriasis cases and controls was observed in adults, while a 1.55 kg/m2 mean difference (95% CI 1.13-1.98) was observed in children. The observational association was confirmed in UK Biobank and HUNT data sets. Overall, a 1 kg/m2 increase in BMI was associated with 4% higher odds of psoriasis (meta-analysis odds ratio [OR] = 1.04; 95% CI 1.03-1.04; P = 1.73 × 10(-60)). MR analyses provided evidence that higher BMI causally increases the odds of psoriasis (by 9% per 1 unit increase in BMI; OR = 1.09 (1.06-1.12) per 1 kg/m2; P = 4.67 × 10(-9)). In contrast, MR estimates gave little support to a possible causal effect of psoriasis genetic risk on BMI (0.004 kg/m2 change in BMI per doubling odds of psoriasis (-0.003 to 0.011). Limitations of our study include possible misreporting of psoriasis by patients, as well as potential misdiagnosis by clinicians. In addition, there is also limited ethnic variation in the cohorts studied., Conclusions: Our study, using genetic variants as instrumental variables for BMI, provides evidence that higher BMI leads to a higher risk of psoriasis. This supports the prioritization of therapies and lifestyle interventions aimed at controlling weight for the prevention or treatment of this common skin disease. Mechanistic studies are required to improve understanding of this relationship., Competing Interests: I have read the journal’s policy and the authors of this manuscript have the following competing interests: IBM has received grants from Arthritis Research UK during the conduct of the study; grants and personal fees from Novartis, grants and personal fees from Janssen, grants and personal fees from Celgene, grants and personal fees from Abbvie, grants from BI, grants and personal fees from UCB, personal fees from Lilly, grants and personal fees from BMS. LP has received personal fees from Merck for Scientific Input Engagement related to MR methodology. SS has received grants from Arthritis Research UK during the conduct of the study; research grants from Janssen, BMS, Celgene, UCB, Boehringer Ingelheim and consultancy/speaker’s fees from AbbVie, UCB, Celgene, Pfizer, Janssen, Boehringer Ingelheim, and Novartis. SJB has received honoraria for invited lectures at the British Association of Dermatologists’ annual meetings and the AAAAI annual meetings. GDS is a member of the Editorial Board of PLOS Medicine.

Background: Body mass index (BMI) is criticized for not distinguishing fat from lean mass and ignoring fat distribution, leaving its ability to detect health effects unclear., Objectives: The aim of this study was to compare BMI with total and regional fat indexes from dual-energy x-ray absorptiometry in their associations with cardiometabolic traits. Duration of exposure to and change in each index across adolescence were examined in relation to detailed traits in young adulthood., Methods: BMI was examined alongside total, trunk, arm, and leg fat indexes (each in kilograms per square meter) from dual-energy x-ray absorptiometry at ages 10 and 18 years in relation to 230 traits from targeted metabolomics at age 18 years in 2,840 offspring from the Avon Longitudinal Study of Parents and Children., Results: Higher total fat mass index and BMI at age 10 years were similarly associated with cardiometabolic traits at age 18 years, including higher systolic and diastolic blood pressure, higher very low-density lipoprotein and low-density lipoprotein cholesterol, lower high-density lipoprotein cholesterol, higher triglycerides, and higher insulin and glycoprotein acetyls. Associations were stronger for both indexes measured at age 18 years and for gains in each index from age 10 to 18 years (e.g., 0.45 SDs [95% confidence interval: 0.38 to 0.53] in glycoprotein acetyls per SD unit gain in fat mass index vs. 0.38 SDs [95% confidence interval: 0.27 to 0.48] per SD unit gain in BMI). Associations resembled those for trunk fat index. Higher lean mass index was weakly associated with traits and was not protective against higher fat mass index., Conclusions: The results of this study support abdominal fatness as a primary driver of cardiometabolic dysfunction and BMI as a useful tool for detecting its effects., (Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.)

Background: Body mass index (BMI) has been suggested to be causally related to cardiovascular health in mid-to-late life, but this has not been explored systematically at younger ages - nor with detailed cardiovascular phenotyping. Recall-by-Genotype (RbG) is an approach that enables the collection of precise phenotypic measures in smaller studies, whilst maintaining statistical power and ability for causal inference., Methods: In this study, we used a combination of conventional multivariable regression analysis, Mendelian randomization (MR) and sub-sample RbG methodologies to estimate the causal effect of BMI on gross-level and detailed cardiovascular health in healthy participants from the Avon Longitudinal Study of Parents and Children at age 17 (N=1420-3108 for different outcomes) and an independent sample from the same cohort (for RbG) study at age 21 (N=386-418)., Results: In both MR and RbG analyses, results suggested that higher BMI causes higher blood pressure (BP) and left ventricular mass index (LVMI) in young adults (e.g., difference in LVMI per kg/m 2 using MR: 1.07g/m 2.7 ; 95% CI: 0.62, 1.52; P =3.87x10 -06 and per 3.58kg/m 2 using RbG: 1.65g/m 2.7 95% CI: 0.83, 2.47; P =0.0001). Additionally, RbG results suggested a causal role of higher BMI on higher stroke volume (SV: difference per 3.58kg/m 2 : 1.49ml/m 2.04 ; 95% CI: 0.62, 2.35; P =0.001) and cardiac output (CO: difference per 3.58kg/m 2 : 0.11l/min/m 1.83 ; 95% CI: 0.03, 0.19; P =0.01) but no strong evidence for a causal role on systemic vascular resistance or total arterial compliance. Neither analysis supported a causal role of higher BMI on heart rate., Conclusions: Complementary MR and RbG causal methodologies, together with a range of sensitivity analyses, suggest that higher BMI is likely to cause worse cardiovascular health, specifically higher BP and LVMI, even in youth. Higher BMI also resulted in increased CO in the RbG study, which appeared to be solely driven by SV, as neither MR nor RbG analyses suggested a causal effect of BMI on heart rate. These consistent results support efforts to reduce BMI from a young age to prevent later adverse cardiovascular health and illustrate the potential for phenotypic resolution with maintained analytical power using RbG.

Objective: The aim of this study was to obtain estimates of the causal relationship between BMI and mortality., Methods: Mendelian randomization (MR) with BMI-associated genotypic variation was used to test the causal effect of BMI on all-cause and cause-specific mortality in UK Biobank participants of White British ancestry., Results: MR analyses supported a causal association between higher BMI and greater risk of all-cause mortality (hazard ratio [HR] per 1 kg/m 2 : 1.03; 95% CI: 0.99-1.07) and mortality from cardiovascular diseases (HR: 1.10; 95% CI: 1.01-1.19), specifically coronary heart disease (HR: 1.12; 95% CI: 1.00-1.25) and those excluding coronary heart disease/stroke/aortic aneurysm (HR: 1.24; 95% CI: 1.03-1.48), stomach cancer (HR: 1.18; 95% CI: 0.87-1.62), and esophageal cancer (HR: 1.22; 95% CI: 0.98-1.53), and a decreased risk of lung cancer mortality (HR: 0.96; 95% CI: 0.85-1.08). Sex stratification supported the causal role of higher BMI increasing bladder cancer mortality risk (males) but decreasing respiratory disease mortality risk (males). The J-shaped observational association between BMI and mortality was visible with MR analyses, but the BMI at which mortality was minimized was lower and the association was flatter over a larger BMI range., Conclusions: Results support a causal role of higher BMI in increasing the risk of all-cause mortality and mortality from several specific causes., (© 2018 The Obesity Society.)

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: Observational studies have shown that higher body mass index (BMI) is associated with increased risk of developing disordered eating patterns. However, the causal direction of this relation remains ambiguous. Objective: We used Mendelian randomization (MR) to infer the direction of causality between BMI and disordered eating in childhood, adolescence, and adulthood. Design: MR analyses were conducted with a genetic score as an instrumental variable for BMI to assess the causal effect of BMI at age 7 y on disordered eating patterns at age 13 y with the use of data from the Avon Longitudinal Study of Parents and Children (ALSPAC) ( n = 4473). To examine causality in the reverse direction, MR analyses were used to estimate the effect of the same disordered eating patterns at age 13 y on BMI at age 17 y via a split-sample approach in the ALSPAC. We also investigated the causal direction of the association between BMI and eating disorders (EDs) in adults via a two-sample MR approach and publically available genome-wide association study data. Results: MR results indicated that higher BMI at age 7 y likely causes higher levels of binge eating and overeating, weight and shape concerns, and weight-control behavior patterns in both males and females and food restriction in males at age 13 y. Furthermore, results suggested that higher levels of binge eating and overeating in males at age 13 y likely cause higher BMI at age 17 y. We showed no evidence of causality between BMI and EDs in adulthood in either direction. Conclusions: This study provides evidence to suggest a causal effect of higher BMI in childhood and increased risk of disordered eating at age 13 y. Furthermore, higher levels of binge eating and overeating may cause higher BMI in later life. These results encourage an exploration of the ways to break the causal chain between these complex phenotypes, which could inform and prevent disordered eating problems in adolescence.

Objectives: High Bone Mass (HBM) is associated with (a) radiographic knee osteoarthritis (OA), partly mediated by increased BMI, and (b) pelvic enthesophytes and hip osteophytes, suggestive of a bone-forming phenotype. We aimed to establish whether HBM is associated with radiographic features of OA in non-weight-bearing (hand) joints, and whether such OA demonstrates a bone-forming phenotype., Methods: HBM cases (BMD Z-scores≥+3.2) were compared with family controls. A blinded assessor graded all PA hand radiographs for: osteophytes (0-3), joint space narrowing (JSN) (0-3), subchondral sclerosis (0-1), at the index Distal Interphalangeal Joint (DIPJ) and 1st Carpometacarpal Joint (CMCJ), using an established atlas. Analyses used a random effects logistic regression model, adjusting a priori for age and gender. Mediating roles of BMI and bone turnover markers (BTMs) were explored by further adjustment., Results: 314 HBM cases (mean age 61.1years, 74% female) and 183 controls (54.3years, 46% female) were included. Osteophytes (grade≥1) were more common in HBM (DIPJ: 67% vs. 45%, CMCJ: 69% vs. 50%), with adjusted OR [95% CI] 1.82 [1.11, 2.97], p=0.017 and 1.89 [1.19, 3.01], p=0.007 respectively; no differences were seen in JSN. Further adjustment for BMI failed to attenuate ORs for osteophytes in HBM cases vs. controls; DIPJ 1.72 [1.05, 2.83], p=0.032, CMCJ 1.76 [1.00, 3.06], p=0.049. Adjustment for BTMs (concentrations lower amongst HBM cases) did not attenuate ORs., Conclusions: HBM is positively associated with OA in non-weight-bearing joints, independent of BMI. HBM-associated OA is characterised by osteophytes, consistent with a bone-forming phenotype, rather than JSN reflecting cartilage loss. Systemic factors (e.g. genetic architecture) which govern HBM may also increase bone-forming OA risk., (Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.)

Background: Previous studies have suggested that modern obesogenic environments accentuate the genetic risk of obesity. However, these studies have proven controversial as to which, if any, measures of the environment accentuate genetic susceptibility to high body mass index (BMI)., Methods: We used up to 120 000 adults from the UK Biobank study to test the hypothesis that high-risk obesogenic environments and behaviours accentuate genetic susceptibility to obesity. We used BMI as the outcome and a 69-variant genetic risk score (GRS) for obesity and 12 measures of the obesogenic environment as exposures. These measures included Townsend deprivation index (TDI) as a measure of socio-economic position, TV watching, a 'Westernized' diet and physical activity. We performed several negative control tests, including randomly selecting groups of different average BMIs, using a simulated environment and including sun-protection use as an environment., Results: We found gene-environment interactions with TDI (Pinteraction = 3 × 10 -10 ), self-reported TV watching (Pinteraction = 7 × 10 -5 ) and self-reported physical activity (Pinteraction = 5 × 10 -6 ). Within the group of 50% living in the most relatively deprived situations, carrying 10 additional BMI-raising alleles was associated with approximately 3.8 kg extra weight in someone 1.73 m tall. In contrast, within the group of 50% living in the least deprivation, carrying 10 additional BMI-raising alleles was associated with approximately 2.9 kg extra weight. The interactions were weaker, but present, with the negative controls, including sun-protection use, indicating that residual confounding is likely., Conclusions: Our findings suggest that the obesogenic environment accentuates the risk of obesity in genetically susceptible adults. Of the factors we tested, relative social deprivation best captures the aspects of the obesogenic environment responsible., (© The Author 2017. Published by Oxford University Press on behalf of the International Epidemiological Association)

Background: It has been suggested that greater maternal adiposity during pregnancy affects lifelong risk of offspring fatness via intrauterine mechanisms. Our aim was to use Mendelian randomisation (MR) to investigate the causal effect of intrauterine exposure to greater maternal body mass index (BMI) on offspring BMI and fat mass from childhood to early adulthood., Methods and Findings: We used maternal genetic variants as instrumental variables (IVs) to test the causal effect of maternal BMI in pregnancy on offspring fatness (BMI and dual-energy X-ray absorptiometry [DXA] determined fat mass index [FMI]) in a MR approach. This was investigated, with repeat measurements, from ages 7 to 18 in the Avon Longitudinal Study of Parents and Children (ALSPAC; n = 2,521 to 3,720 for different ages). We then sought to replicate findings with results for BMI at age 6 in Generation R (n = 2,337 for replication sample; n = 6,057 for total pooled sample). In confounder-adjusted multivariable regression in ALSPAC, a 1 standard deviation (SD, equivalent of 3.7 kg/m2) increase in maternal BMI was associated with a 0.25 SD (95% CI 0.21-0.29) increase in offspring BMI at age 7, with similar results at later ages and when FMI was used as the outcome. A weighted genetic risk score was generated from 32 genetic variants robustly associated with BMI (minimum F-statistic = 45 in ALSPAC). The MR results using this genetic risk score as an IV in ALSPAC were close to the null at all ages (e.g., 0.04 SD (95% CI -0.21-0.30) at age 7 and 0.03 SD (95% CI -0.26-0.32) at age 18 per SD increase in maternal BMI), which was similar when a 97 variant generic risk score was used in ALSPAC. When findings from age 7 in ALSPAC were meta-analysed with those from age 6 in Generation R, the pooled confounder-adjusted multivariable regression association was 0.22 SD (95% CI 0.19-0.25) per SD increase in maternal BMI and the pooled MR effect (pooling the 97 variant score results from ALSPAC with the 32 variant score results from Generation R) was 0.05 SD (95%CI -0.11-0.21) per SD increase in maternal BMI (p-value for difference between the two results = 0.05). A number of sensitivity analyses exploring violation of the MR results supported our main findings. However, power was limited for some of the sensitivity tests and further studies with relevant data on maternal, offspring, and paternal genotype are required to obtain more precise (and unbiased) causal estimates., Conclusions: Our findings provide little evidence to support a strong causal intrauterine effect of incrementally greater maternal BMI resulting in greater offspring adiposity., Competing Interests: I have read the journal's policy and the authors of this manuscript have the following competing interests: GDS is a member of the Editorial Board of PLOS Medicine. In addition to grant funding that is acknowledged in the paper and that was relevant to the conduct of the study reported in the paper, the University of Bristol has received funds from public, charity, and industry funders from grants on which DAL is the Principal applicant (UK Medical Research Council, UK Economic and Social Research Council, UK National Institute of Health Research, Wellcome Trust, British Heart Foundation, Roche Diagnostics, Ferring Pharmaceuticals, and Medtronic PLC.) These funders had no impact on any aspect of the work presented in this paper.

Recently it has been suggested that rearing conditions during preadolescence in one generation may affect health outcomes in subsequent generations. Such parental effects, potentially induced by epigenetic modifications in the germ line, have attracted considerable attention because of their implications for public health and social policies. Yet, to date, evidence in humans has been rare due to data limitations and much further investigation in large studies is required. The aim of this paper is to reproduce and extend a recent study which found that paternal smoking before age 11 was associated with elevated body mass index (BMI) among male offspring in the Avon Longitudinal Study of Parents and Children (ALSPAC). Using the Nord-Trøndelag Health (HUNT) Study, we find that paternal smoking during pre-adolescence (45,000 offspring), we cannot rule out a weaker association, perhaps common to sons and daughters, which would be consistent with the ALSPAC study. Alternatively, we discuss whether confounding, chance in parallel tests, or sample selection effects might explain the observed associations of early paternal smoking with offspring BMI., Competing Interests: The authors have declared that no competing interests exist.

A mean-centric view of populations, whereby a change in the mean of a health variable at the population level is assumed to result in uniform change across the distribution, is a core component of Geoffrey Rose's concept of the "population strategy" to disease prevention. This idea also has a critical role in Rose's observation that individuals who are considered abnormal or sick (the rightward tail of the distribution) and those who are considered normal (the center) are very closely related, and that true preventive medicine must focus on shifting the normal or average. In this Perspective, we revisit these core tenets of Rose's concept of preventive medicine after providing an overview of the key concepts that he developed. We examine whether these assumptions apply to population changes in body mass index (BMI) and show that there is considerable evidence of a widening of the BMI distribution in populations over time. We argue that, with respect to BMI, the idea of using statistical measures of a population solely on the basis of means and the assumption that populations are coherent entities that change uniformly over time may not fully capture the true nature of changes in the population. These issues have important implications for how we assess and interpret the health of populations over time with implications for the balance between universal and targeted strategies aimed at improving health., (© 2016 American Society for Nutrition.)

Observational analyses of the association between body mass index (BMI) and all-cause mortality often suggest that overweight is neutral or beneficial, but such analyses are potentially confounded by smoking or by reverse causation. The use of BMI measured in early adulthood offers one means of reducing the latter problem. We used a cohort who were first measured while 16-24 year old students at Glasgow University in 1948-1968 and subsequently re-measured in 2000-2003, offering a rare opportunity to compare BMI measured at different ages as a predictor of mortality. Analysis of the later BMI measurements suggested that overweight was beneficial to survival, while analysis of BMI measured in early adulthood suggested that overweight was harmful and that the optimum BMI lay towards the lower end of the recommended range of 18.5-25 kg m -2 . We interpret the association with later BMI as being probably distorted by reverse causality, although it remains possible instead that the optimum BMI increases with age. Differences when analyses were restricted to healthy non-smokers also suggested some residual confounding by smoking. These results suggest that analyses of BMI recorded in middle or old age probably over-estimate the optimum BMI for survival and should be treated with caution.

Studies have suggested both adverse and protective associations of obesity with depressive symptoms. We examined the contribution of environmental and heritable factors in this association. Participants were same-sex twin pairs from two population-based twin cohort studies, the Older Finnish Twin Cohort (n = 8,215; mean age = 44.1) and the US Midlife Development in the United States (MIDUS; n = 1,105; mean age = 45.1). Body mass index (BMI) was calculated from self-reported height and weight. Depressive symptoms were assessed using Beck's Depression Inventory (BDI; Finnish Twin Cohort), and by negative and positive affect scales (MIDUS). In the Finnish Twin Cohort, higher BMI was associated with higher depressive symptoms in monozygotic (MZ) twins (B = 2.01, 95% CI = 1.0, 3.0) and dizygotic (DZ) twins (B = 1.17, 0.5, 1.9) with BMI >22. This association was observed in within-pair analysis in DZ twins (B = 1.47, CI = 0.4, 2.6) but not in within-pair analysis of MZ twins (B = 0.03, CI = -1.9, 2.0). Consistent with the latter result, a bivariate genetic model indicated that the association between higher BMI and higher depressive symptoms was largely mediated by genetic factors. The results of twin-pair analysis and bivariate genetic model were replicated in the MIDUS sample. These findings suggest an association between obesity and higher depressive symptoms, which is largely explained by shared heritable biological mechanisms.

Background: Maternal prepregnancy adiposity may influence child adiposity beyond the transmitted genetic effects, which, if true, may accelerate the obesity epidemic, but the evidence for this mechanism is inconsistent., Objective: The aim was to assess whether the associations of maternal body mass index (BMI) with child anthropometric measurements from birth through infancy and at 7 y of age exceed those of paternal associations., Design: In the Danish National Birth Cohort, information on parental and child anthropometric measures is available for 30,655 trio families from maternal interviews during pregnancy and the postpartum period and from a 7-y follow-up. By using multiple linear and logistic regression models of child SD (z) scores of weight and BMI at birth, 5 mo, 12 mo, and 7 y of age, and of child overweight at age 7 y, we compared associations with maternal prepregnancy and postpartum BMI z scores and with paternal BMI z scores., Results: When comparing maternal-child and paternal-child BMI z score associations, the strongest associations were observed with mothers' BMI at birth [maternal and paternal BMI z scores: 0.143 (95% CI: 0.130, 0.155) and 0.017 (95% CI: 0.005, 0.029), respectively] and throughout infancy, but the relative difference in the associations declined by child age [for BMI z score at child age 7 y per maternal and paternal BMI z scores: 0.208 (95% CI: 0.196, 0.220) and 0.154 (95% CI: 0.143, 0.166), respectively]. At 7 y of age, ORs of child overweight were 2.30 (95% CI: 1.99, 2.67) by maternal overweight and 1.96 (95% CI: 1.74, 2.21) by paternal overweight. There were no differences between the results based on maternal BMI before and after pregnancy or on child's weight adjusted for length or height., Conclusions: The associations of child weight and BMI with maternal BMI were stronger than with paternal BMI. The differences between the associations were strong at birth but declined with child aging., (© 2016 American Society for Nutrition.)

Multiple differentially methylated sites and regions associated with adiposity have now been identified in large-scale cross-sectional studies. We tested for replication of associations between previously identified CpG sites at HIF3A and adiposity in ∼1,000 mother-offspring pairs from the Avon Longitudinal Study of Parents and Children (ALSPAC). Availability of methylation and adiposity measures at multiple time points, as well as genetic data, allowed us to assess the temporal associations between adiposity and methylation and to make inferences regarding causality and directionality. Overall, our results were discordant with those expected if HIF3A methylation has a causal effect on BMI and provided more evidence for causality in the reverse direction (i.e., an effect of BMI on HIF3A methylation). These results are based on robust evidence from longitudinal analyses and were also partially supported by Mendelian randomization analysis, although this latter analysis was underpowered to detect a causal effect of BMI on HIF3A methylation. Our results also highlight an apparent long-lasting intergenerational influence of maternal BMI on offspring methylation at this locus, which may confound associations between own adiposity and HIF3A methylation. Further work is required to replicate and uncover the mechanisms underlying the direct and intergenerational effect of adiposity on DNA methylation., (© 2016 by the American Diabetes Association. Readers may use this article as long as the work is properly cited, the use is educational and not for profit, and the work is not altered.)

Importance: Neonates born to overweight or obese women are larger and at higher risk of birth complications. Many maternal obesity-related traits are observationally associated with birth weight, but the causal nature of these associations is uncertain., Objective: To test for genetic evidence of causal associations of maternal body mass index (BMI) and related traits with birth weight., Design, Setting, and Participants: Mendelian randomization to test whether maternal BMI and obesity-related traits are potentially causally related to offspring birth weight. Data from 30,487 women in 18 studies were analyzed. Participants were of European ancestry from population- or community-based studies in Europe, North America, or Australia and were part of the Early Growth Genetics Consortium. Live, term, singleton offspring born between 1929 and 2013 were included., Exposures: Genetic scores for BMI, fasting glucose level, type 2 diabetes, systolic blood pressure (SBP), triglyceride level, high-density lipoprotein cholesterol (HDL-C) level, vitamin D status, and adiponectin level., Main Outcome and Measure: Offspring birth weight from 18 studies., Results: Among the 30,487 newborns the mean birth weight in the various cohorts ranged from 3325 g to 3679 g. The maternal genetic score for BMI was associated with a 2-g (95% CI, 0 to 3 g) higher offspring birth weight per maternal BMI-raising allele (P = .008). The maternal genetic scores for fasting glucose and SBP were also associated with birth weight with effect sizes of 8 g (95% CI, 6 to 10 g) per glucose-raising allele (P = 7 × 10(-14)) and -4 g (95% CI, -6 to -2 g) per SBP-raising allele (P = 1×10(-5)), respectively. A 1-SD ( ≈ 4 points) genetically higher maternal BMI was associated with a 55-g higher offspring birth weight (95% CI, 17 to 93 g). A 1-SD ( ≈ 7.2 mg/dL) genetically higher maternal fasting glucose concentration was associated with 114-g higher offspring birth weight (95% CI, 80 to 147 g). However, a 1-SD ( ≈ 10 mm Hg) genetically higher maternal SBP was associated with a 208-g lower offspring birth weight (95% CI, -394 to -21 g). For BMI and fasting glucose, genetic associations were consistent with the observational associations, but for systolic blood pressure, the genetic and observational associations were in opposite directions., Conclusions and Relevance: In this mendelian randomization study, genetically elevated maternal BMI and blood glucose levels were potentially causally associated with higher offspring birth weight, whereas genetically elevated maternal SBP was potentially causally related to lower birth weight. If replicated, these findings may have implications for counseling and managing pregnancies to avoid adverse weight-related birth outcomes.

Observational cohort studies can provide rich datasets with a diverse range of phenotypic variables. However, hypothesis-driven epidemiological analyses by definition only test particular hypotheses chosen by researchers. Furthermore, observational analyses may not provide robust evidence of causality, as they are susceptible to confounding, reverse causation and measurement error. Using body mass index (BMI) as an exemplar, we demonstrate a novel extension to the phenome-wide association study (pheWAS) approach, using automated screening with genotypic instruments to screen for causal associations amongst any number of phenotypic outcomes. We used a sample of 8,121 children from the ALSPAC dataset, and tested the linear association of a BMI-associated allele score with 172 phenotypic outcomes (with variable sample sizes). We also performed an instrumental variable analysis to estimate the causal effect of BMI on each phenotype. We found 21 of the 172 outcomes were associated with the allele score at an unadjusted p < 0.05 threshold, and use Bonferroni corrections, permutation testing and estimates of the false discovery rate to consider the strength of results given the number of tests performed. The most strongly associated outcomes included leptin, lipid profile, and blood pressure. We also found novel evidence of effects of BMI on a global self-worth score.

Previous research has provided conflicting evidence regarding fetal roots of adiposity in India. To compare the strength of association between maternal and paternal body mass indexes (BMIs) corrected for height with offspring BMI in India to examine the potential for intrauterine mechanisms to influence offspring adiposity in India, we analysed a sample of 16,528 mother-father-offspring trios from the 2005 to 2006 Indian National Family Health Survey. Children were aged 0-59 months with parents aged 15-49 years (mothers) and 15-54 years (fathers). Linear and logistic regression models, specified in multiple ways, were used to estimate associations between parental BMI* (BMI redefined by power term x (kg/m(x)) to be independent from height), and child BMI/top decile of child BMI. Higher values of maternal BMI and paternal BMI were associated with higher values of offspring BMI. In comparing the effects of maternal BMI and paternal BMI, however, no consistent differences were found in the strength of these parental influences on offspring BMI. In the fully adjusted linear model, the standardised coefficient was 0.131 (95% CI 0.110 to 0.154) for maternal BMI* and 0.079 (95% CI 0.056 to 0.103) for paternal BMI*; with evidence of heterogeneity between maternal-offspring and paternal-offspring associations (p=0.005). This was not robust in the unstandardised regression (β=0.056, 95% CI 0.044 to 0.067 for maternal BMI and β=0.039, 95% CI 0.025 to 0.053 for paternal BMI, p=0.093). Mixed results indicate that compared with paternal BMI, maternal BMI did not have a consistently stronger influence on offspring BMI in India., (Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.)

Background: Evidence suggests that in utero exposure to undernutrition and overnutrition might affect adiposity in later life. Epigenetic modification is suggested as a plausible mediating mechanism., Methods: We used multivariable linear regression and a negative control design to examine offspring epigenome-wide DNA methylation in relation to maternal and offspring adiposity in 1018 participants., Results: Compared with neonatal offspring of normal weight mothers, 28 and 1621 CpG sites were differentially methylated in offspring of obese and underweight mothers, respectively [false discovert rate (FDR)-corrected P-value < 0.05), with no overlap in the sites that maternal obesity and underweight relate to. A positive association, where higher methylation is associated with a body mass index (BMI) outside the normal range, was seen at 78.6% of the sites associated with obesity and 87.9% of the sites associated with underweight. Associations of maternal obesity with offspring methylation were stronger than associations of paternal obesity, supporting an intrauterine mechanism. There were no consistent associations of gestational weight gain with offspring DNA methylation. In general, sites that were hypermethylated in association with maternal obesity or hypomethylated in association with maternal underweight tended to be positively associated with offspring adiposity, and sites hypomethylated in association with maternal obesity or hypermethylated in association with maternal underweight tended to be inversely associated with offspring adiposity., Conclusions: Our data suggest that both maternal obesity and, to a larger degree, underweight affect the neonatal epigenome via an intrauterine mechanism, but weight gain during pregnancy has little effect. We found some evidence that associations of maternal underweight with lower offspring adiposity and maternal obesity with greater offspring adiposity may be mediated via increased DNA methylation., (© The Author 2015. Published by Oxford University Press on behalf of the International Epidemiological Association.)

Objective: While high body mass index is associated with an increased risk of depression and anxiety, cumulative evidence indicates that it is a protective factor for suicide. The associations from conventional observational studies of body mass index with mental health outcomes are likely to be influenced by reverse causality or confounding by ill-health. In the present study, we investigated the associations between offspring body mass index and parental anxiety, depression and suicide in order to avoid problems with reverse causality and confounding by ill-health., Methods: We used data from 32,457 mother-offspring and 27,753 father-offspring pairs from the Norwegian HUNT-study. Anxiety and depression were assessed using the Hospital Anxiety and Depression Scale and suicide death from national registers. Associations between offspring and own body mass index and symptoms of anxiety and depression and suicide mortality were estimated using logistic and Cox regression. Causal effect estimates were estimated with a two sample instrument variable approach using offspring body mass index as an instrument for parental body mass index., Results: Both own and offspring body mass index were positively associated with depression, while the results did not indicate any substantial association between body mass index and anxiety. Although precision was low, suicide mortality was inversely associated with own body mass index and the results from the analysis using offspring body mass index supported these results. Adjusted odds ratios per standard deviation body mass index from the instrumental variable analysis were 1.22 (95% CI: 1.05, 1.43) for depression, 1.10 (95% CI: 0.95, 1.27) for anxiety, and the instrumental variable estimated hazard ratios for suicide was 0.69 (95% CI: 0.30, 1.63)., Conclusion: The present study's results indicate that suicide mortality is inversely associated with body mass index. We also found support for a positive association between body mass index and depression, but not for anxiety.

Background: Several studies have investigated the effect of known adult body mass index (BMI) associated single nucleotide polymorphisms (SNPs) on BMI in childhood. There has been no genome-wide association study (GWAS) of BMI trajectories over childhood., Methods: We conducted a GWAS meta-analysis of BMI trajectories from 1 to 17 years of age in 9377 children (77,967 measurements) from the Avon Longitudinal Study of Parents and Children (ALSPAC) and the Western Australian Pregnancy Cohort (Raine) Study. Genome-wide significant loci were examined in a further 3918 individuals (48,530 measurements) from Northern Finland. Linear mixed effects models with smoothing splines were used in each cohort for longitudinal modelling of BMI., Results: A novel SNP, downstream from the FAM120AOS gene on chromosome 9, was detected in the meta-analysis of ALSPAC and Raine. This association was driven by a difference in BMI at 8 years (T allele of rs944990 increased BMI; PSNP = 1.52 × 10(-8)), with a modest association with change in BMI over time (PWald(Change) = 0.006). Three known adult BMI-associated loci (FTO, MC4R and ADCY3) and one childhood obesity locus (OLFM4) reached genome-wide significance (PWald < 1.13 × 10(-8)) with BMI at 8 years and/or change over time., Conclusions: This GWAS of BMI trajectories over childhood identified a novel locus that warrants further investigation. We also observed genome-wide significance with previously established obesity loci, making the novel observation that these loci affected both the level and the rate of change in BMI. We have demonstrated that the use of repeated measures data can increase power to allow detection of genetic loci with smaller sample sizes., (© The Author 2015; all rights reserved. Published by Oxford University Press on behalf of the International Epidemiological Association.)

The long-term effects on bone health of nutritional status in adolescence are unclear. The impact of adolescent and current body mass on bone mass in young adulthood in rural India was assessed. Current lean mass was a more important determinant of bone mass than thinness during adolescence in this population., Purpose/introduction: Adolescence is a crucial period for skeletal growth. However, the long-term effects on bone health of nutritional status in adolescence, particularly in the context of nutritional transition, are unclear. The current manuscript assessed the impact of adolescent and current body size on bone mass in young adulthood in an Indian rural community that is undergoing rapid socioeconomic changes., Methods: The Andhra Pradesh Children and Parents Study is a prospective cohort study in Hyderabad, India. In 2003-2005, the study collected anthropometric and cardiovascular data on adolescents (mean age = 16 years old). The second and third waves of the study in 2009-2012 collected data on current anthropometric measures, areal bone mineral density (aBMD) in hip and lumbar spine (L1-L4) measured by dual-energy X-ray absorptiometry, and living standards of the trial participants who were now young adults (mean age = 22 years old)., Results: The median body mass index (BMI) of the 722 participants included in this analysis was 16.8 kg/m(2) during adolescence, while the median BMI as young adults was 19.3 kg/m(2). Lower aBMD during adulthood was associated with lower adolescent BMI (β (95 % confidence interval) for hip aBMD 0.017 (0.013 to 0.022) and LS aBMD 0.012 (0.008 to 0.016)). This association was attenuated upon adjustment for current fat and lean mass (β (95 % CI) for hip aBMD 0.00 (-0.005 to 0.005) and LS aBMD 0.005 (0.000 to 0.01)). There was clear evidence for positive associations between aBMDs and current lean mass., Conclusions: Current lean mass was a more important determinant of bone mass than thinness during adolescence in this population. Weight gain during late adolescence and young adulthood coupled with improvement in lean mass may help to mitigate any adverse effects that pre-adulthood undernutrition may have on bone mass accrual.

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.

Objective: Genome-wide association studies (GWAS) of BMI are mostly undertaken under the assumption that "kg/m(2) " is an index of weight fully adjusted for height, but in general this is not true. The aim here was to assess the contribution of common genetic variation to a adjusted version of that phenotype which appropriately accounts for covariation in height in children., Methods: A GWAS of height-adjusted BMI (BMI[x] = weight/height(x) ), calculated to be uncorrelated with height, in 5809 participants (mean age 9.9 years) from the Avon Longitudinal Study of Parents and Children (ALSPAC) was performed., Results: GWAS based on BMI[x] yielded marked differences in genomewide results profile. SNPs in ADCY3 (adenylate cyclase 3) were associated at genome-wide significance level (rs11676272 (0.28 kg/m(3.1) change per allele G (0.19, 0.38), P = 6 × 10(-9) ). In contrast, they showed marginal evidence of association with conventional BMI [rs11676272 (0.25 kg/m(2) (0.15, 0.35), P = 6 × 10(-7) )]. Results were replicated in an independent sample, the Generation R study., Conclusions: Analysis of BMI[x] showed differences to that of conventional BMI. The association signal at ADCY3 appeared to be driven by a missense variant and it was strongly correlated with expression of this gene. Our work highlights the importance of well understood phenotype use (and the danger of convention) in characterising genetic contributions to complex traits., (Copyright © 2014 The Obesity Society.)

Background: Small size at birth and greater BMI in childhood are associated with greater brachial blood pressure (BP) in later life. Aortic (central) BP differs from brachial BP and is more predictive of organ damage and cardiovascular events; the relationship between BMI in childhood and central BP is not known., Methods: Using data from 3154 people from the Avon Longitudinal Study of Parents and Children, we assessed associations between repeated measures of BMI from birth to age 10 with central and brachial BP at age 17., Results: Lower BMI at birth (thinness) was associated with greater central and brachial BP. No associations were seen between BMI in early childhood (<7 years) and later BP, but greater BMI from 7 to 10 years was associated with higher BP. Associations were similar for central and brachial SBP and for DBP, and were stronger in males compared with females. The highest BP was seen in participants who were low-birth-weight and overweight or obese at both the end of infancy (age 2) and at the time of BP assessment (age 17); mean central SBP was 104.2  mmHg (SD = 11.0) compared with 100.7 (SD = 10.5) in participants who were normal-birth-weight and overweight or obese at 2 and 17 years., Conclusion: Small size at birth followed by rapid adiposity gain in infancy and continued overweight/obesity are associated with greater BP in young adulthood. These findings emphasize the importance of maintenance of normal weight in childhood for the prevention of high BP.

Little is known about genes regulating male puberty. Further, while many identified pubertal timing variants associate with age at menarche, a late manifestation of puberty, and body mass, little is known about these variants' relationship to pubertal initiation or tempo. To address these questions, we performed genome-wide association meta-analysis in over 11 000 European samples with data on early pubertal traits, male genital and female breast development, measured by the Tanner scale. We report the first genome-wide significant locus for male sexual development upstream of myocardin-like 2 (MKL2) (P = 8.9 × 10(-9)), a menarche locus tagging a developmental pathway linking earlier puberty with reduced pubertal growth (P = 4.6 × 10(-5)) and short adult stature (p = 7.5 × 10(-6)) in both males and females. Furthermore, our results indicate that a proportion of menarche loci are important for pubertal initiation in both sexes. Consistent with epidemiological correlations between increased prepubertal body mass and earlier pubertal timing in girls, body mass index (BMI)-increasing alleles correlated with earlier breast development. In boys, some BMI-increasing alleles associated with earlier, and others with delayed, sexual development; these genetic results mimic the controversy in epidemiological studies, some of which show opposing correlations between prepubertal BMI and male puberty. Our results contribute to our understanding of the pubertal initiation program in both sexes and indicate that although mechanisms regulating pubertal onset in males and females may largely be shared, the relationship between body mass and pubertal timing in boys may be complex and requires further genetic studies., (© The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.)

Background: Cross-sectional studies have shown that objectively measured physical activity is associated with childhood adiposity, and a strong inverse dose-response association with body mass index (BMI) has been found. However, few studies have explored the extent to which this association reflects reverse causation. We aimed to determine whether childhood adiposity causally influences levels of physical activity using genetic variants reliably associated with adiposity to estimate causal effects., Methods and Findings: The Avon Longitudinal Study of Parents and Children collected data on objectively assessed activity levels of 4,296 children at age 11 y with recorded BMI and genotypic data. We used 32 established genetic correlates of BMI combined in a weighted allelic score as an instrumental variable for adiposity to estimate the causal effect of adiposity on activity. In observational analysis, a 3.3 kg/m² (one standard deviation) higher BMI was associated with 22.3 (95% CI, 17.0, 27.6) movement counts/min less total physical activity (p = 1.6×10⁻¹⁶), 2.6 (2.1, 3.1) min/d less moderate-to-vigorous-intensity activity (p = 3.7×10⁻²⁹), and 3.5 (1.5, 5.5) min/d more sedentary time (p = 5.0×10⁻⁴). In Mendelian randomization analyses, the same difference in BMI was associated with 32.4 (0.9, 63.9) movement counts/min less total physical activity (p = 0.04) (∼5.3% of the mean counts/minute), 2.8 (0.1, 5.5) min/d less moderate-to-vigorous-intensity activity (p = 0.04), and 13.2 (1.3, 25.2) min/d more sedentary time (p = 0.03). There was no strong evidence for a difference between variable estimates from observational estimates. Similar results were obtained using fat mass index. Low power and poor instrumentation of activity limited causal analysis of the influence of physical activity on BMI., Conclusions: Our results suggest that increased adiposity causes a reduction in physical activity in children and support research into the targeting of BMI in efforts to increase childhood activity levels. Importantly, this does not exclude lower physical activity also leading to increased adiposity, i.e., bidirectional causation.

Background: While the number of established genetic variants associated with adult body mass index (BMI) is growing, the relationships between these variants and growth during childhood are yet to be fully characterised. We examined the association between validated adult BMI associated single nucleotide polymorphisms (SNPs) and growth trajectories across childhood. We investigated the timing of onset of the genetic effect and whether it was sex specific., Methods: Children from the ALSPAC and Raine birth cohorts were used for analysis (n = 9,328). Genotype data from 32 adult BMI associated SNPs were investigated individually and as an allelic score. Linear mixed effects models with smoothing splines were used for longitudinal modelling of the growth parameters and measures of adiposity peak and rebound were derived., Results: The allelic score was associated with BMI growth throughout childhood, explaining 0.58% of the total variance in BMI in females and 0.44% in males. The allelic score was associated with higher BMI at the adiposity peak (females  =  0.0163 kg/m(2) per allele, males  =  0.0123 kg/m(2) per allele) and earlier age (-0.0362 years per allele in males and females) and higher BMI (0.0332 kg/m(2) per allele in females and 0.0364 kg/m(2) per allele in males) at the adiposity rebound. No gene:sex interactions were detected for BMI growth., Conclusions: This study suggests that known adult genetic determinants of BMI have observable effects on growth from early childhood, and is consistent with the hypothesis that genetic determinants of adult susceptibility to obesity act from early childhood and develop over the life course.

Objectives: To assess the associations between both uric acid levels and hyperuricaemia, with ischaemic heart disease and blood pressure, and to explore the potentially confounding role of body mass index., Design: Mendelian randomisation analysis, using variation at specific genes (SLC2A9 (rs7442295) as an instrument for uric acid; and FTO (rs9939609), MC4R (rs17782313), and TMEM18 (rs6548238) for body mass index)., Setting: Two large, prospective cohort studies in Denmark., Participants: We measured levels of uric acid and related covariables in 58,072 participants from the Copenhagen General Population Study and 10,602 from the Copenhagen City Heart Study, comprising 4890 and 2282 cases of ischaemic heart disease, respectively., Main Outcome: Blood pressure and prospectively assessed ischaemic heart disease., Results: Estimates confirmed known observational associations between plasma uric acid and hyperuricaemia with risk of ischaemic heart disease and diastolic and systolic blood pressure. However, when using genotypic instruments for uric acid and hyperuricaemia, we saw no evidence for causal associations between uric acid, ischaemic heart disease, and blood pressure. We used genetic instruments to investigate body mass index as a potentially confounding factor in observational associations, and saw a causal effect on uric acid levels. Every four unit increase of body mass index saw a rise in uric acid of 0.03 mmol/L (95% confidence interval 0.02 to 0.04), and an increase in risk of hyperuricaemia of 7.5% (3.9% to 11.1%)., Conclusion: By contrast with observational findings, there is no strong evidence for causal associations between uric acid and ischaemic heart disease or blood pressure. However, evidence supports a causal effect between body mass index and uric acid level and hyperuricaemia. This finding strongly suggests body mass index as a confounder in observational associations, and suggests a role for elevated body mass index or obesity in the development of uric acid related conditions.

The literature that examines the relationship between child or adolescent Body Mass Index (BMI) and academic attainment generally finds mixed results. This may be due to the use of different data sets, conditioning variables, or methodologies: studies either use an individual fixed effects (FE) approach and/or an instrumental variable (IV) specification. Using one common dataset, the Avon Longitudinal Study of Parents and Children, and a common set of controls, this paper compares the different approaches (including using different types of IV's), discusses their appropriateness, and contrasts their findings. We show that, although the results differ depending on the approach, most estimates cannot be statistically distinguished from OLS, nor from each other. Examining the potential violations of key assumptions of the different approaches and comparing their point estimates, we conclude that fat mass is unlikely to be causally related to academic achievement in adolescence., (Copyright © 2012 Elsevier B.V. All rights reserved.)

In the present study, the authors investigated the role of the intrauterine environment in childhood adiposity by comparing the maternal-offspring body mass index (BMI) association with the paternal-offspring BMI association when the offspring were 3 years of age, using parental prepregnancy BMI (measured as weight in kilograms divided by height in meters squared). The parent-offspring trios (n = 29,216) were recruited during pregnancy from 2001 to 2008 into the Norwegian Mother and Child Cohort Study conducted by The Norwegian Institute of Public Health. Data from self-administered questionnaires were used in linear regression analyses. Crude analyses showed similar parental-offspring BMI associations; the mean difference in offspring BMI was 0.15 (95% confidence interval: 0.13, 0.16) per each 1-standard-deviation increase in maternal BMI and 0.15 (95% confidence interval: 0.13, 0.17) per each 1-standard-deviation increase in paternal BMI. After all adjustments, the mean difference in offspring BMI per each 1-standard-deviation increment of maternal BMI was 0.12, and the mean difference in offspring BMI per each 1-standard-deviation increment of paternal BMI was 0.13. There was no strong support for heterogeneity between the associations (P > 0.6). In conclusion, results from the present large population-based study showed similar parental-offspring BMI associations when the offspring were 3 years of age, which indicates that the maternal-offspring association may be explained by shared familial (environmental and genetic) risk factors rather than by the intrauterine environment.

Background: Adiposity, assessed as elevated body mass index (BMI), is associated with increased risk of ischemic heart disease (IHD); however, whether this is causal is unknown. We tested the hypothesis that positive observational associations between BMI and IHD are causal., Methods and Findings: In 75,627 individuals taken from two population-based and one case-control study in Copenhagen, we measured BMI, ascertained 11,056 IHD events, and genotyped FTO(rs9939609), MC4R(rs17782313), and TMEM18(rs6548238). Using genotypes as a combined allele score in instrumental variable analyses, the causal odds ratio (OR) between BMI and IHD was estimated and compared with observational estimates. The allele score-BMI and the allele score-IHD associations used to estimate the causal OR were also calculated individually. In observational analyses the OR for IHD was 1.26 (95% CI 1.19-1.34) for every 4 kg/m(2) increase in BMI. A one-unit allele score increase associated with a 0.28 kg/m(2) (95 CI% 0.20-0.36) increase in BMI and an OR for IHD of 1.03 (95% CI 1.01-1.05) (corresponding to an average 1.68 kg/m(2) BMI increase and 18% increase in the odds of IHD for those carrying all six BMI increasing alleles). In instrumental variable analysis using the same allele score the causal IHD OR for a 4 kg/m(2) increase in BMI was 1.52 (95% CI 1.12-2.05)., Conclusions: For every 4 kg/m(2) increase in BMI, observational estimates suggested a 26% increase in odds for IHD while causal estimates suggested a 52% increase. These data add evidence to support a causal link between increased BMI and IHD risk, though the mechanism may ultimately be through intermediate factors like hypertension, dyslipidemia, and type 2 diabetes. This work has important policy implications for public health, given the continuous nature of the BMI-IHD association and the modifiable nature of BMI. This analysis demonstrates the value of observational studies and their ability to provide unbiased results through inclusion of genetic data avoiding confounding, reverse causation, and bias.

Background: Cigarette smoking is associated with lower body mass index (BMI), and a commonly cited reason for unwillingness to quit smoking is a concern about weight gain. Common variation in the CHRNA5-CHRNA3-CHRNB4 gene region (chromosome 15q25) is robustly associated with smoking quantity in smokers, but its association with BMI is unknown. We hypothesized that genotype would accurately reflect smoking exposure and that, if smoking were causally related to weight, it would be associated with BMI in smokers, but not in never smokers., Methods: We stratified nine European study samples by smoking status and, in each stratum, analysed the association between genotype of the 15q25 SNP, rs1051730, and BMI. We meta-analysed the results (n = 24,198) and then tested for a genotype × smoking status interaction., Results: There was no evidence of association between BMI and genotype in the never smokers {difference per T-allele: 0.05 kg/m(2) [95% confidence interval (95% CI): -0.05 to 0.18]; P = 0.25}. However, in ever smokers, each additional smoking-related T-allele was associated with a 0.23 kg/m(2) (95% CI: 0.13-0.31) lower BMI (P = 8 × 10(-6)). The effect size was larger in current [0.33 kg/m(2) lower BMI per T-allele (95% CI: 0.18-0.48); P = 6 × 10(-5)], than in former smokers [0.16 kg/m(2) (95% CI: 0.03-0.29); P = 0.01]. There was strong evidence of genotype × smoking interaction (P = 0.0001)., Conclusions: Smoking status modifies the association between the 15q25 variant and BMI, which strengthens evidence that smoking exposure is causally associated with reduced BMI. Smoking cessation initiatives might be more successful if they include support to maintain a healthy BMI.

An age-dependent association between variation at the FTO locus and BMI in children has been suggested. We meta-analyzed associations between the FTO locus (rs9939609) and BMI in samples, aged from early infancy to 13 years, from 8 cohorts of European ancestry. We found a positive association between additional minor (A) alleles and BMI from 5.5 years onwards, but an inverse association below age 2.5 years. Modelling median BMI curves for each genotype using the LMS method, we found that carriers of minor alleles showed lower BMI in infancy, earlier adiposity rebound (AR), and higher BMI later in childhood. Differences by allele were consistent with two independent processes: earlier AR equivalent to accelerating developmental age by 2.37% (95% CI 1.87, 2.87, p = 10(-20)) per A allele and a positive age by genotype interaction such that BMI increased faster with age (p = 10(-23)). We also fitted a linear mixed effects model to relate genotype to the BMI curve inflection points adiposity peak (AP) in infancy and AR. Carriage of two minor alleles at rs9939609 was associated with lower BMI at AP (-0.40% (95% CI: -0.74, -0.06), p = 0.02), higher BMI at AR (0.93% (95% CI: 0.22, 1.64), p = 0.01), and earlier AR (-4.72% (-5.81, -3.63), p = 10(-17)), supporting cross-sectional results. Overall, we confirm the expected association between variation at rs9939609 and BMI in childhood, but only after an inverse association between the same variant and BMI in infancy. Patterns are consistent with a shift on the developmental scale, which is reflected in association with the timing of AR rather than just a global increase in BMI. Results provide important information about longitudinal gene effects and about the role of FTO in adiposity. The associated shifts in developmental timing have clinical importance with respect to known relationships between AR and both later-life BMI and metabolic disease risk., Competing Interests: The authors have declared that no competing interests exist.

Background: The increasing trend in body mass index (BMI) and overweight in rapidly developing economies is well recognized., Objective: We assessed the association between socioeconomic status and BMI and overweight in low- to middle-income countries., Design: We conducted a cross-sectional analysis of nationally representative samples of 538,140 women aged 15-49 y drawn from 54 Demographic and Health Surveys conducted between 1994 and 2008. BMI, calculated as weight in kilograms divided by height squared in meters, was specified as the outcome, and a BMI (in kg/m(2)) of ≥25 was additionally specified to model the likelihood of being overweight. Household wealth and education were included as markers of individual socioeconomic status, and per capita Gross Domestic Product (pcGDP) was included as a marker of country-level economic development., Results: Globally, a one-quartile increase in wealth was associated with a 0.54 increase in BMI (95% CI: 0.50, 0.64) and a 33% increase in overweight (95% CI: 26%, 41%) in adjusted models. Although the strength of this association varied across countries, the association between wealth and BMI and overweight was positive in 96% (52 of 54) of the countries. Similar patterns were observed in urban and rural areas, although SES gradients tended to be greater in urban areas. There was a positive association between pcGDP and BMI or overweight, with only weak evidence of an interaction between pcGDP and wealth., Conclusion: Higher BMI and overweight remain concentrated in higher socioeconomic groups, even though increasing BMI and overweight prevalence are important global public concerns.

Objective: To investigate the relationship between body mass index (BMI) in young adulthood and cancer incidence and mortality., Methods: A total of 9,549 men and 2,657 women had weight and height measured while at university (1948-1968). Participants were traced through the National Health Service Central Register, and cancer registration and death certificates were obtained. Cox proportional hazard models were used to estimate hazard ratios (HR) of associations between BMI and cancer incidence and mortality., Results: The mean age at university was 20 years. During a median follow-up of 49 years, 2,349 deaths occurred, including 55 breast, 111 prostate, and 131 colorectal cancer. There were 1,305 cancer registrations, including 102 breast, 211 prostate, and 125 colorectal cancers. There was weak evidence of an association between BMI and colorectal cancer incidence (adjusted HR per 1 kg/m(2): 1.06, 95% CI: 0.98-1.15) and mortality (HR 1.05, 95% CI: 0.98-1.14). There was no evidence of an association between BMI and either breast or prostate cancer., Conclusions: Targeting a reduction in obesity in young adulthood may prevent a significant number of colorectal cancer deaths, although whether this relationship is direct or through tracking to later adult BMI has yet to be established.

It has recently been suggested that there are substantial differences in mother-daughter and father-son associations of body mass index and obesity among contemporary UK children, but much larger studies of older cohorts have failed to find evidence of substantial sex-specific effects. We have tested this hypothesis using the Avon Longitudinal Study of Parents and Children, a large contemporary cohort. Our analyses are based on 4654 complete parent-offspring trios (2323 with male offspring and 2331 with female offspring, all aged approximately 7.5 years). We found maternal body mass index to be a little more strongly associated with female than with male offspring body mass index (beta=0.18 (95% confidence interval 0.16-0.20) for females vs 0.13 (0.12, 0.15) for males). However, associations between paternal body mass index and male compared with female offspring were very similar (beta=0.16 (0.14, 0.19) for females vs 0.15 (0.12, 0.17) for males). Hence, our study suggests that there is no compelling reason to integrate the belief that there are large differences in parent-offspring body mass index associations with obesity prevention strategies.

Objective: To investigate whether alcohol consumption and raised body mass index (BMI) act together to increase risk of liver disease., Design: Analysis of data from prospective cohort studies., Setting: Scotland., Participants: Data were from two of the Midspan prospective cohort studies (9559 men): "Main" study 1965-8, participants from workplaces across central belt of Scotland, population of island of Tiree, and mainland relatives, and "Collaborative" study, 1970-3, participants from 27 workplaces in Glasgow, Clydebank, and Grangemouth. Follow-up was to 31 December 2007 (median 29 years, range 0.13-42). We divided participants into nine groups based on measures of body mass index (BMI) (underweight/normal weight <25, overweight 25 to <30, and obese >or=30) and alcohol consumption (none, 1-14, and >or=15 units per week)., Main Outcome Measures: Liver disease morbidity and mortality., Results: 80 (0.8%) men died with liver disease as the main cause and 146 (1.5%) with liver disease as any cause. In the Collaborative study, 196 men (3.3%) had liver disease defined by a death, admission, or cancer registration. BMI and alcohol consumption were strongly associated with liver disease mortality in analyses adjusted for other confounders (P=0.001 and P<0.0001 respectively). Drinkers of 15 or more units per week in any BMI category and obese drinkers had raised relative rates for all definitions of liver disease, compared with underweight/normal weight non-drinkers. Drinkers of 15 or more units per week had adjusted relative rates for liver disease mortality of 3.16 (95% confidence interval 1.28 to 7.8) for underweight/normal weight men, 7.01 (3.02 to 16.3) for overweight, and 18.9 (6.84 to 52.4) for obese men. The relative rate for obese men who consumed 1-14 units per week was 5.3 (1.36 to 20.7). The relative excess risk due to interaction between BMI and alcohol consumption was 5.58 (1.09 to 10.1); synergy index=2.89 (1.29 to 6.47)., Conclusions: Raised BMI and alcohol consumption are both related to liver disease, with evidence of a supra-additive interaction between the two. The occurrence of both factors in the same populations should inform health promotion and public health policies.