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Lung-function impairment underlies chronic obstructive pulmonary disease (COPD) and predicts mortality. In the largest multi-ancestry genome-wide association meta-analysis of lung function to date, comprising 580,869 participants, we identified 1,020 independent association signals implicating 559 genes supported by ≥2 criteria from a systematic variant-to-gene mapping framework. These genes were enriched in 29 pathways. Individual variants showed heterogeneity across ancestries, age and smoking groups, and collectively as a genetic risk score showed strong association with COPD across ancestry groups. We undertook phenome-wide association studies for selected associated variants as well as trait and pathway-specific genetic risk scores to infer possible consequences of intervening in pathways underlying lung function. We highlight new putative causal variants, genes, proteins and pathways, including those targeted by existing drugs. These findings bring us closer to understanding the mechanisms underlying lung function and COPD, and should inform functional genomics experiments and potentially future COPD therapies.

Estimates from genome-wide association studies of unrelated individuals (GWAS) capture effects of inherited genetic variation (direct effects), demography (population stratification, assortative mating) and genetic nurture from relatives (indirect genetic effects). GWAS using family-based designs can control for demographic and indirect genetic effects, but large-scale family datasets have been lacking. We combined data from 178,086 siblings from 19 cohorts to generate population (between-family) and within-sibship (within-family) estimates of genome-wide genetic associations for 25 phenotypes. Consistent with previous studies, within-sibship GWAS estimates were smaller than corresponding population estimates for height, educational attainment, age at first birth, number of children, cognitive ability, depressive symptoms and smoking. We also observed some differences between population and within-sibship model estimates in downstream SNP-heritability, genetic correlations and Mendelian randomization analyses. For example, the genetic correlation between educational attainment and BMI attenuated towards zero. In contrast, analyses of most molecular phenotypes (e.g., LDL-cholesterol) were generally consistent between population and within-sibship models. We also report compelling evidence of polygenic adaptation on taller human height using within-sibship data. Large-scale family datasets provide new opportunities to understand the relationships between genetic variation and phenotypes influenced by demographic and indirect genetic effects.

Importance: Most previous genome-wide association studies (GWAS) of depression have used data from individuals of European (EUR) descent. This limits our understanding of the underlying biology of depression and raises questions about the transferability of findings between populations.\ud Objectives: to investigate the genetics of depression across East Asian and European cultural contexts and outcome definitions.\ud Design: Genome-wide association studies, followed by meta-analysis. \ud Setting: Nine cohort and case-control studies from China, Taiwan, USA and UK. \ud Participants: 15,771 depression cases and 178,777 controls of East Asian descent (EAS). \ud Exposures: Associations of genetic variants with depression risk were assessed using generalised linear mixed models and logistic regression. The results were combined across studies using fixed effects meta-analyses. These were subsequently also meta-analysed with the largest published GWAS for depression in EUR samples. Additional meta-analyses were carried out separately by outcome definition (clinical vs symptom-based) and region (East Asian countries vs Western countries). \ud Main outcomes and measures: Depression status was defined based on health records and self-report questionnaires.\ud Results: In total we identified five novel associations, including one in the EAS meta-analysis for broad depression: rs4656484 (beta=-0.018, SE=0.003, P=4.43x10-8) at 1q24.1. Another locus at 7p21.2 was associated in a meta-analysis restricted to geographically East Asian studies (P=5.03x10-9). Both associations were specific to EAS samples (P=0.53 and P=0.28 in EUR, respectively). Only 11% of depression loci previously identified in EUR reached nominal significance levels in the EAS samples. The trans-ancestry genetic correlation estimates with depression in EUR ranged from 0.223 to 0.558, depending on the outcome definition. Clinical depression risk was negatively genetically correlated with BMI in EAS (rg=-0.212), contrary to findings from EUR samples.\ud Conclusions and relevance: Our results suggest that cultural differences further add to the heterogeneity of depression and thereby impact on the genetic architecture. This cautions against generalising findings about depression risk factors across populations and highlights the need to increase the ancestral and geographic diversity of samples with consistent phenotyping.

Background Pancreatic cancer has a very poor prognosis. Biomarkers that may help predict or diagnose pancreatic cancer may lead to earlier diagnosis and improved survival. Methods The prospective China Kadoorie Biobank (CKB) recruited 512 891 adults aged 30–79 years during 2004–08, recording 702 incident cases of pancreatic cancer during 9 years of follow-up. We conducted a case-subcohort study measuring 92 proteins in 610 cases and a subcohort of 623 individuals, using the OLINK immuno-oncology panel in stored baseline plasma samples. Cox regression with the Prentice pseudo-partial likelihood was used to estimate adjusted hazard ratios (HRs) for risk of pancreatic cancer by protein levels. Results Among 1233 individuals (including 610 cases), several chemokines, interleukins, growth factors and membrane proteins were associated with risk of pancreatic cancer, with adjusted HRs per 1 standard deviation (SD) of 0.86 to 1.86, including monocyte chemotactic protein 3 (MCP3/CCL7) {1.29 [95% CI (confidence interval) (1.10, 1.51)]}, angiopoietin-2 (ANGPT2) [1.27 (1.10, 1.48)], interleukin-18 (IL18) [1.24 (1.07, 1.43)] and interleukin-6 (IL6) [1.21 (1.06, 1.38)]. Associations between some proteins [e.g. matrix metalloproteinase-7 (MMP7), hepatocyte growth factor (HGF) and tumour necrosis factor receptor superfamily member 9 [TNFRSF9)] and risk of pancreatic cancer were time-varying, with higher levels associated with higher short-term risk. Within the first year, the discriminatory ability of a model with known risk factors (age, age squared, sex, region, smoking, alcohol, education, diabetes and family history of cancer) was increased when several proteins were incorporated (weighted C-statistic changed from 0.85 to 0.99; P for difference = 4.5 × 10–5), although only a small increase in discrimination (0.77 to 0.79, P = 0.04) was achieved for long-term risk. Conclusions Several plasma proteins were associated with subsequent diagnosis of pancreatic cancer. The potential clinical utility of these biomarkers warrants further investigation.

Rupture of an intracranial aneurysm leads to subarachnoid hemorrhage, a severe type of stroke. To discover new risk loci and the genetic architecture of intracranial aneurysms, we performed a cross-ancestry, genome-wide association study in 10,754 cases and 306,882 controls of European and East Asian ancestry. We discovered 17 risk loci, 11 of which are new. We reveal a polygenic architecture and explain over half of the disease heritability. We show a high genetic correlation between ruptured and unruptured intracranial aneurysms. We also find a suggestive role for endothelial cells by using gene mapping and heritability enrichment. Drug-target enrichment shows pleiotropy between intracranial aneurysms and antiepileptic and sex hormone drugs, providing insights into intracranial aneurysm pathophysiology. Finally, genetic risks for smoking and high blood pressure, the two main clinical risk factors, play important roles in intracranial aneurysm risk, and drive most of the genetic correlation between intracranial aneurysms and other cerebrovascular traits.

Objective: To describe the genetic structure of populations in different areas of China, and explore the effects of different strategies to control the confounding factors of the genetic structure in cohort studies. Methods: By using the genome-wide association study (GWAS) on data of 4 500 samples from 10 areas of the China Kadoorie Biobank (CKB), we performed principal components analysis to extract the first and second principal components of the samples for the component two-dimensional diagram generation, and then compared them with the source of sample area to analyze the characteristics of genetic structure of the samples from different areas of China. Based on the CKB cohort data, a simulation data set with cluster sample characteristics such as genetic structure differences and extensive kinship was generated; and the effects of different analysis strategies including traditional analysis scheme and mixed linear model on the inflation factor (λ) were evaluated. Results: There were significant genetic structure differences in different areas of China. Distribution of the principal components of the population genetic structure was basically consistent with the geographical distribution of the project area. The first principal component corresponds to the latitude of different areas, and the second principal component corresponds to the longitude of different areas. The generated simulation data showed high false positive rate (λ=1.16), even if the principal components of the genetic structure was adjusted or the area specific subgroup analysis was performed, λ could not be effectively controlled (λ>1.05); while, by using a mixed linear model adjusting for the kinship matrix, λ was effectively controlled regardless of whether the genetic structure principal component was further adjusted (λ=0.99). Conclusions: There were large differences in genetic structure among populations in different areas of China. In molecular epidemiology studies, bias caused by population genetic structure needs to be carefully treated. For large cohort data with complex genetic structure and extensive kinship, it is necessary to use a mixed linear model for association analysis.

BackgroundIn China, alcohol consumption has increased significantly in recent decades. Little evidence exists, however, about temporal trends in levels and patterns of alcohol consumption and associated factors in adult populations. Methods In 2004–08, the China Kadoorie Biobank recruited ~ 512,000 adults (41% men, mean age 52 years [SD 10.7]) from 10 (5 urban, 5 rural) geographically diverse regions across China, with ~ 25,000 randomly selected participants resurveyed in 2013–14. The self-reported prevalence and patterns (e.g., amount, beverage type, heavy drinking episodes) of alcohol drinking at baseline and resurvey were compared and related to socio-demographic, health and other factors. Results At baseline, 33% of men drank alcohol at least weekly (i.e., current regular), compared to only 2% of women. In men, current regular drinking was more common in urban (38%) than in rural (29%) areas at baseline. Among men, the proportion of current regular drinkers slightly decreased at resurvey (33% baseline vs. 29% resurvey), while the proportion of ex-regular drinkers slightly increased (4% vs. 6%), particularly among older men, with more than half of ex-regular drinkers stopping for health reasons. Among current regular drinkers, the proportion engaging in heavy episodic drinking (i.e., > 60 g/session) increased (30% baseline vs. 35% resurvey) in both rural (29% vs. 33%) and urban (31% vs. 36%) areas, particularly among younger men born in the 1970s (41% vs. 47%). Alcohol intake involved primarily spirits, at both baseline and resurvey. Those engaging in heavy drinking episodes tended to have multiple other health-related risk factors (e.g., regular smoking, low fruit intake, low physical activity and hypertension). Conclusions Among Chinese men, the proportion of drinkers engaging in harmful drinking behaviours increased in the past decade, particularly among younger men. Harmful drinking patterns tended to cluster with other unhealthy lifestyles and health-related risk factors.

Central adiposity is associated with liver cancer risk beyond general adiposity in Western populations. However, there is little prospective evidence in East Asian populations who are more likely to have central adiposity at given BMI levels. The prospective China Kadoorie Biobank recruited 512,713 adults aged 30–79 years from 10 diverse areas. During 10 years follow‐up, 2,847 incident cases of liver cancer were identified. Cox regression was used to estimate adjusted hazard ratios (HR) for liver cancer associated with central adiposity, excluding individuals with cancers and liver diseases at baseline and the first 5 years of follow‐up (1,049 incident liver cancer cases). Overall, mean waist circumference (WC) was 82.2 (SD 9.8) cm in men and 79.1 (9.5) cm in women. Central adiposity showed positive associations with liver cancer risk. Associations were strongest for WC and waist‐to‐hip ratio (WHR), with adjusted HRs per 1‐SD of 1.09 (95%CI 1.01–1.18) and 1.12 (1.02–1.23), respectively. The positive associations became stronger when additionally adjusting for BMI (1.26 [1.09–1.46] and 1.14 [1.02–1.28]). The positive association of central obesity (WC ≥90 cm in men and ≥ 80 cm in women) with liver cancer increased progressively with the number of other presenting metabolic risk factors (physical inactivity, diabetes, and hypertension), with HRs of 1.07 (0.90–1.28), 1.17 (1.00–1.38), and 1.91 (1.40–2.59) in those with one, two, and three factors (p for trend 0.006). In this relatively lean Chinese population, there were positive associations of central adiposity with risk of liver cancer, with WHR and WC showing the strongest associations., What's new? In this relatively lean Chinese population, central adiposity was positively associated with risk of liver cancer, and the positive association was stronger at given BMI levels. Among central adiposity traits examined, waist circumference and waist‐to‐hip ratio were the best indicators of liver cancer risk. The association of central adiposity with liver cancer risk increased progressively with the number of other presenting metabolic risk factors, including physical inactivity, diabetes, and hypertension.

The maintenance of normal body weight is disrupted in patients with anorexia nervosa (AN) for prolonged periods of time. Prior to the onset of AN, premorbid body mass index (BMI) spans the entire range from underweight to obese. After recovery, patients have reduced rates of overweight and obesity. As such, loci involved in body weight regulation may also be relevant for AN and vice versa. Our primary analysis comprised a cross-trait analysis of the 1000 single-nucleotide polymorphisms (SNPs) with the lowest P-values in a genome-wide association meta-analysis (GWAMA) of AN (GCAN) for evidence of association in the largest published GWAMA for BMI (GIANT). Subsequently we performed sex-stratified analyses for these 1000 SNPs. Functional ex vivo studies on four genes ensued. Lastly, a look-up of GWAMA-derived BMI-related loci was performed in the AN GWAMA. We detected significant associations (P-values

Background Uncertainty remains about the associations of adiposity with intestinal cancer in China and by its anatomical subtype. Methods The prospective China Kadoorie Biobank recorded 3024 incident cases of colorectal (CRC) and 143 cases of small intestine (SIC) cancer during a 10-year follow-up among 509 568 participants without prior cancer at baseline. Cox regression was used to estimate adjusted hazard ratios (HRs) for specific cancers associated with adiposity. Results Overall mean body mass index (BMI) was 23.7 kg/m2. BMI was positively associated with CRC (HR per SD 1.10 [95% CI 1.06–1.14]), colon (1.13 [1.07–1.18]), and rectal (1.07 [1.02–1.13]) cancer. For waist circumference, the corresponding HRs per SD were 1.14 (1.10–1.18), 1.18 (1.13–1.24), and 1.11 (1.05–1.16), respectively. The adjusted HRs were somewhat greater in men than women. Adiposity was positively, but non-significantly, associated with SIC risk. Conclusions Among relatively lean Chinese adults, adiposity was associated with risks of colon and rectal cancer, with the associations somewhat stronger in men than women.

Background: Observational studies have reported that higher plasma 25-hydroxy vitamin D (25[OH]D) concentrations are associated with lower risks of diabetes, but it is unclear if these associations are causal. The aim of this study was to test the relevance of 25(OH)D for type 2 diabetes using genetically-instrumented differences in plasma 25(OH)D concentrations. Methods and findings: Data were available on four 25(OH)D single nucleotide polymorphisms (SNPs: n=82,464), plasma 25(OH)D concentrations (n=13,565) and cases with diabetes (n=5565) in China Kadoorie Biobank (CKB). The effects on risk of diabetes were assessed by a genetic score using two 25(OH)D synthesis SNPs (DHCR7-rs12785878 and CYP2R1-rs10741657), or the addition of SNPs affecting transport (GC/DBP-rs2282679) and catabolism (CYP24A1-rs6013897) of 25(OH)D. The CKB results were combined in a meta-analysis of 10 studies for the two synthesis SNPs (n=58,312 cases) and 7 studies for four SNPs (n=32,796 cases). Mean (SD) 25(OH)D concentration was 62 (20) nmol/L in CKB and the per allele effects of genetic scores on 25(OH)D were 2.87 (SE 0.39) for the synthesis SNPs and 3.54 (SE 0.32) for all SNPs. A 25 nmol/L higher biochemically measured 25(OH)D was associated with 9% (95%CI: 0-18%) lower risk of diabetes in CKB. In a meta-analysis of all studies, a 25 nmol/L higher genetically-instrumented 25(OH)D concentration was associated with a 14% (95%CI: 3-23%) lower risk of diabetes (p=0.01) using the two synthesis SNPs. An equivalent difference in 25(OH)D using a genetic score with four SNPs was not significantly associated with diabetes (OR 8%, 95% CI: -1-16% lower risk, p=0.07), but had some evidence of pleiotropy. A limitation of the meta-analysis was the access only to study level rather than individual level data. Conclusions: The concordant risks of diabetes for biochemically-measured and genetically-instrumented differences in 25(OH)D using synthesis SNPs provide evidence for a causal effect of higher 25(OH)D for prevention of diabetes.

Adiposity is an increasing public health problem in China. We aimed to examine the associations of adiposity with non-alcoholic fatty liver disease (NAFLD) and other chronic liver diseases in Chinese adults. The prospective China Kadoorie Biobank recruited 512,891 adults aged 30–79 years from 10 areas. During 10 years of follow-up, 7,386 incident liver disease cases were recorded among 503,991 participants without prior cancer or chronic liver disease at baseline. The mean body mass index (BMI) (SD) was 23.7 (3.3) kg/m2 and mean waist circumference (WC) 80.3 (9.8) cm, with 33% having BMI ≥25 kg/m2. Throughout the range examined (BMI 15–50) BMI showed a log-linear positive association with NAFLD (n = 1,298), with adjusted HR per 5 kg/m2 of 2.81 (95% CI 2.63–3.01), adjusting for regression dilution. There were also positive associations of percent body fat, WC, and waist-to-hip ratio with NAFLD, with HRs per 1-SD of 2.27 (2.14–2.41), 2.60 (2.44–2.76), and 1.84 (1.76–1.92). BMI was unrelated to viral hepatitis (n = 1,477), and had a U-shaped association with cirrhosis (n = 2,082) and an inverse association with liver cancer (n = 2,568), which disappeared after excluding the first 5 years of follow-up. Among Chinese adults, adiposity was a major risk factor for NAFLD but not other chronic liver diseases.

Background: Blood lipids are established risk factors for myocardial infarction (MI), but uncertainty persists about the relevance of lipids, lipoprotein particles, and circulating metabolites for MI and stroke subtypes. Objectives: This study sought to investigate the associations of plasma metabolic markers with risks of incident MI, ischemic stroke (IS), and intracerebral hemorrhage (ICH). Methods: In a nested case-control study (912 MI, 1,146 IS, and 1,138 ICH cases, and 1,466 common control subjects) 30 to 79 years of age in China Kadoorie Biobank, nuclear magnetic resonance spectroscopy measured 225 metabolic markers in baseline plasma samples. Logistic regression was used to estimate adjusted odds ratios (ORs) for a 1-SD higher metabolic marker. Results: Very low-, intermediate-, and low-density lipoprotein particles were positively associated with MI and IS. High-density lipoprotein (HDL) particles were inversely associated with MI apart from small HDL. In contrast, no lipoprotein particles were associated with ICH. Cholesterol in large HDL was inversely associated with MI and IS (OR: 0.79 and 0.88, respectively), whereas cholesterol in small HDL was not (OR: 0.99 and 1.06, respectively). Triglycerides within all lipoproteins, including most HDL particles, were positively associated with MI, with a similar pattern for IS. Glycoprotein acetyls, ketone bodies, glucose, and docosahexaenoic acid were associated with all 3 diseases. The 225 metabolic markers showed concordant associations between MI and IS, but not with ICH. Conclusions: Lipoproteins and lipids showed similar associations with MI and IS, but not with ICH. Within HDL particles, cholesterol concentrations were inversely associated, whereas triglyceride concentrations were positively associated with MI. Glycoprotein acetyls and several non–lipid-related metabolites associated with all 3 diseases.

Blood pressure (BP) is a major risk factor for cardiovascular disease and more than 200 genetic loci associated with BP are known. Here, we perform a multi-stage genome-wide association study for BP (max N = 289,038) principally in East Asians and meta-analysis in East Asians and Europeans. We report 19 new genetic loci and ancestry-specific BP variants, conforming to a common ancestry-specific variant association model. At 10 unique loci, distinct non-rare ancestry-specific variants colocalize within the same linkage disequilibrium block despite the significantly discordant effects for the proxy shared variants between the ethnic groups. The genome-wide transethnic correlation of causal-variant effect-sizes is 0.898 and 0.851 for systolic and diastolic BP, respectively. Some of the ancestry-specific association signals are also influenced by a selective sweep. Our results provide new evidence for the role of common ancestry-specific variants and natural selection in ethnic differences in complex traits such as BP. Acknowledgements Marie Loh71,72 (Institute of Health Sciences, University of Oulu, P.O.Box 5000FI-90014 Oulu, Finland and Department of Epidemiology and Biostatistics, Imperial College London, London W2 1PG, UK), Niek Verweij73 (Department of Cardiology, University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9713 GZ Groningen, Netherlands), Weihua Zhang72,74 (Department of Epidemiology and Biostatistics, Imperial College London, London W2 1PG, UK and Ealing Hospital NHS Trust, Middlesex UB1 3HW, UK), Benjamin Lehne72 (Department of Epidemiology and Biostatistics, Imperial College London, London W2 1PG, UK), Irene Mateo Leach73 (Department of Cardiology, University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9713 GZ Groningen, Netherlands), Alexander Drong75 (Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK), James Abbott76 (Bioinformatics Support Service, Imperial College London, South Kensington, London SW7 2AZ, UK), Sian-Tsung Tan74,77 (Ealing Hospital NHS Trust, Middlesex UB1 3HW, UK and National Heart and Lung Institute, Imperial College London, London W12 0NN, UK), William R. Scott72,77 (Department of Epidemiology and Biostatistics, Imperial College London, London W2 1PG, UK and Lung Institute, Imperial College London, London W12 0NN, UK), Gianluca Campanella72 (Department of Epidemiology and Biostatistics, Imperial College London, London W2 1PG, UK), Marc Chadeau-Hyam72 (Department of Epidemiology and Biostatistics, Imperial College London, London W2 1PG, UK), Uzma Afzal72,74 (Department of Epidemiology and Biostatistics, Imperial College London, London W2 1PG, UK and Ealing Hospital NHS Trust, Middlesex UB1 3HW, UK), Tõnu Esko78,79,80,81 (Estonian Genome Center, University of Tartu, Riia 23c, 51010 Tartu, Estonia and Division of Endocrinology, Children’s Hospital Boston, Longwood 300, Boston, MA 02115, USA and Department of Genetics, Harvard Medical School, 77 Avenue Louis Pasteur, Boston, MA 02115, USA and Program in Medical and Population Genetics, Broad Institute, 7 Cambridge Center, Cambridge, MA 02142, USA), Sarah E. Harris82,83 (Medical Genetics Section, University of Edinburgh Molecular Medicine Centre and MRC Institute of Genetics and Molecular Medicine, Western General Hospital, Crewe Road, Edinburgh EH4 2XU, UK and Centre for Cognitive Aging and Cognitive Epidemiology, University of Edinburgh, Edinburgh EH8 9JZ, UK), Jaana Hartiala84,85 (Department of Preventive Medicine, USC Keck School of Medicine, Los Angeles, CA 90033, USA and Institute for Genetic Medicine, USC Keck School of Medicine, Los Angeles, CA 90033, USA), Marcus E. Kleber86 (Medical Clinic V, Mannheim Medical Faculty, University of Heidelberg, Theodor-Kutzer-Ufer 1-3, 68167 Mannheim, Germany), Richa Saxena87 (Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA), Alexandre F.R. Stewart88,89 (University of Ottawa Heart Institute, Cardiovascular Research Methods Centre, Ontario K1Y 4W7, Canada and Ruddy Canadian Cardiovascular Genetics Centre, Ontario K1Y 4W7, Canada), Tarunveer S. Ahluwalia90 (Novo Nordisk Foundation Centre for Basic Metabolic Research, Section of Metabolic Genetics, Faculty of Health and Medical Sciences, University of Copenhagen, 2100 Copenhagen, Denmark), Imke Aits91 (Institute of Epidemiology and Biobank Popgen, Christian-Albrechts-University of Kiel, 24105 Kiel, Germany), Alexessander Da Silva Couto Alves92 (Department of Epidemiology and Biostatistics, MRC Health Protection Agency (HPE) Centre for Environment and Health, School of Public Health, Imperial College London, London SW7 2AZ, UK), Shikta Das92 (Department of Epidemiology and Biostatistics, MRC Health Protection Agency (HPE) Centre for Environment and Health, School of Public Health, Imperial College London, London SW7 2AZ, UK), Jemma C. Hopewell93 (Clinical Trial Service Unit & Epidemiological Studies Unit, University of Oxford, Richard Doll Building, Old Road Campus, Roosevelt Drive, Oxford OX3 7LF, UK), Robert W. Koivula94 (Department of Clinical Sciences, Genetic and Molecular Epidemiology Unit, Skåne University Hospital Malmö, SE-205 02 Malmö, Sweden), Leo-Pekka Lyytikäinen95,96 (Department of Clinical Chemistry, Fimlab Laboratories, FI-33520 Tampere, Finland and Department of Clinical Chemistry, University of Tampere School of Medicine, FI-33014 Tampere, Finland), Iris Postmus97,98 (Department of Gerontology and Geriatrics, Leiden University Medical Center, 2300 RC Leiden, Netherlands and Netherlands Consortium for Healthy Ageing, Leiden 2333 ZC, Netherlands), Olli T. Raitakari99,100 (Department of Clinical Physiology and Nuclear Medicine, Turku University Hospital, FI-20521 Turku, Finland and Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, FI-20520 Turku, Finland), Robert A. Scott101 (MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge CB2 0QQ, UK), Rossella Sorice102 (Institute of Genetics and Biophysics A. Buzzati-Traverso, CNR, 80131 Naples, Italy), Vinicius Tragante103 (Department of Cardiology, Division Heart and Lungs, University Medical Center Utrecht, 3508 GA Utrecht, Netherlands), Michela Traglia104,105 (Division of Genetics and Cell Biology, San Raffaele Scientific Institute, 20132 Milano, Italy and Institute for Maternal and Child Health—IRCCS ‘‘Burlo Garofolo’’—Trieste, 34137 Trieste, Italy), Jon White106 (UCL Genetics Institute, Department of Genetics, Environment and Evolution, UCL, London WC1E 6BT, UK), Inês Barroso107,108,109 (Metabolic Disease Group, The Wellcome Trust Sanger Institute, Cambridge CB10 1SA, UK and NIHR Cambridge Biomedical Research Centre, Institute of Metabolic Science, Addenbrooke’s Hospital, Cambridge CB2 0QQ, UK and University of Cambridge Metabolic Research Laboratories, Institute of Metabolic Science, Addenbrooke’s Hospital, Cambridge CB2 0QQ, UK), Andrew Bjonnes87 (Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA), Rory Collins103 (Department of Cardiology, Division Heart and Lungs, University Medical Center Utrecht, 3508 GA Utrecht, Netherlands), Gail Davies110 (Department of Psychology, University of Edinburgh, 7 George Square, Edinburgh EH8 9JZ, UK), Graciela Delgado86 (Medical Clinic V, Mannheim Medical Faculty, University of Heidelberg, Theodor-Kutzer-Ufer 1-3, 68167 Mannheim, Germany), Pieter A. Doevendans103 (Department of Cardiology, Division Heart and Lungs, University Medical Center Utrecht, 3508 GA Utrecht, Netherlands), Lude Franke111 (Department of Genetics, University Medical Center, University of Groningen, Hanzeplein 1, 9713 GZ Groningen, Netherlands), Ron T. Gansevoort112 (Department of Internal Medicine, University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9713 GZ Groningen, Netherlands), Tanja B. Grammer86 (Medical Clinic V, Mannheim Medical Faculty, University of Heidelberg, Theodor-Kutzer-Ufer 1-3, 68167 Mannheim, Germany), Niels Grarup86 (Medical Clinic V, Mannheim Medical Faculty, University of Heidelberg, Theodor-Kutzer-Ufer 1-3, 68167 Mannheim, Germany), Jagvir Grewal72,74 (Department of Epidemiology and Biostatistics, Imperial College London, London W2 1PG, UK and Ealing Hospital NHS Trust, Middlesex UB1 3HW, UK), Anna-Liisa Hartikainen113,114 (Department of Obstetrics and Gynecology, University Hospital of Oulu, University of Oulu, Oulu FI-90014, Finland and Department of Clinical Sciences/Obsterics and Gynecology, University of Oulu, Oulu FI-90014, Finland), Stanley L. Hazen115,116 (Center for Cardiovascular Diagnostics and Prevention, Cleveland Clinic, Cleveland, OH 44195, USA and Department of Cellular and Molecular Medicine, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA), Chris Hsu117 (Keck School of Medicine, University of Southern California, Los Angeles, CA 90089, USA), Lise L.N. Husemoen118 (Research Centre for Prevention and Health, Glostrup University Hospital, 2600 Glostrup, Denmark), Johanne M. Justesen90 (Novo Nordisk Foundation Centre for Basic Metabolic Research, Section of Metabolic Genetics, Faculty of Health and Medical Sciences, University of Copenhagen, 2100 Copenhagen, Denmark), Meena Kumari119 (Department of Epidemiology and Public Health, UCL, London WC1E 6BT, UK), Wolfgang Lieb91 (Institute of Epidemiology and Biobank Popgen, Christian-Albrechts-University of Kiel, 24105 Kiel, Germany), David C.M. Liewald110 (Department of Psychology, University of Edinburgh, 7 George Square, Edinburgh EH8 9JZ, UK), Evelin Mihailov78 (Estonian Genome Center, University of Tartu, Riia 23c, 51010 Tartu, Estonia), Lili Milani78 (Estonian Genome Center, University of Tartu, Riia 23c, 51010 Tartu, Estonia), Rebecca Mills74 (Ealing Hospital NHS Trust, Middlesex UB1 3HW, UK), Nina Mononen95,96 (Department of Clinical Chemistry, Fimlab Laboratories, FI-33520 Tampere, Finland and Department of Clinical Chemistry, University of Tampere School of Medicine, FI-33014 Tampere, Finland), Kjell Nikus120 (Heart Centre, Department of Cardiology, Tampere University Hospital, and University of Tampere School of Medicine, FI-33521 Tampere, Finland), Teresa Nutile102 (Institute of Genetics and Biophysics A. Buzzati-Traverso, CNR, 80131 Naples, Italy), Sarah Parish93 (Clinical Trial Service Unit & Epidemiological Studies Unit, University of Oxford, Richard Doll Building, Old Road Campus, Roosevelt Drive, Oxford OX3 7LF, UK), Olov Rolandsson121 (Department of Public Health & Clinical Medicine, Section for Family Medicine, Umeå universitet, SE-901 85 Umeå, Sweden), Daniela Ruggiero102 (Institute of Genetics and Biophysics A. Buzzati-Traverso, CNR, 80131 Naples, Italy), Cinzia F. Sala104 (Division of Genetics and Cell Biology, San Raffaele Scientific Institute, 20132 Milano, Italy), Harold Snieder122 (Department of Epidemiology, University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9713 GZ Groningen, Netherlands), Thomas H.W. Spasø90 (Novo Nordisk Foundation Centre for Basic Metabolic Research, Section of Metabolic Genetics, Faculty of Health and Medical Sciences, University of Copenhagen, 2100 Copenhagen, Denmark), Wilko Spiering123 (Department of Vascular Medicine, University Medical Center Utrecht, 3508 GA Utrecht, Netherlands), John M. Starr83,124 (Centre for Cognitive Aging and Cognitive Epidemiology, University of Edinburgh, Edinburgh EH8 9JZ, UK and Alzheimer Scotland Dementia Research Centre, University of Edinburgh, 7 George Square, Edinburgh EH8 9JZ, UK), David J. Stott125 (Academic Section of Geriatric Medicine, Institute of Cardiovascular and Medical Sciences, Faculty of Medicine, University of Glasgow, Glasgow G4 0SF, UK), Daniel O. Stram117 (Keck School of Medicine, University of Southern California, Los Angeles, CA 90089, USA), Silke Szymczak126 (Institute of Clinical Molecular Biology, Christian-Albrechts-University of Kiel, Kiel 24105, Germany), W.H.Wilson Tang115,116 (Center for Cardiovascular Diagnostics and Prevention, Cleveland Clinic, Cleveland, OH 44195, USA and Department of Cellular and Molecular Medicine, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA), Stella Trompet127 (Department of Cardiology, Leiden University Medical Center, 2300 RC Leiden, Netherlands), Väinö Turjanmaa128,129 (Department of Clinical Physiology, Tampere University Hospital, FI-33521 Tampere, Finland and Department of Clinical Physiology, University of Tampere School of Medicine, FI-33014 Tampere, Finland), Marja Vaarasmaki130 (Department of Obstetrics and Gynecology, Oulu University Hospital, PO Box 23FI-90029 Oulu, Finland), Wiek H. van Gilst73 (Department of Cardiology, University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9713 GZ Groningen, Netherlands), Dirk J. van Veldhuisen73 (Department of Cardiology, University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9713 GZ Groningen, Netherlands), Jorma S. Viikari131,132 (Department of Medicine, Turku University Hospital, FI-20521 Turku, Finland and Department of Medicine, University of Turku, FI-20014 Turku, Finland), Folkert W. Asselbergs103,133,134 (Department of Cardiology, Division Heart and Lungs, University Medical Center Utrecht, 3508 GA Utrecht, Netherlands and Durrer Center for Cardiogenetic Research, ICIN-Netherlands Heart Institute, 3511 GC Utrecht, Netherlands and Institute of Cardiovascular Science, Faculty of Population Health Sciences, University College London, London WC1E 6BT, UK), Marina Ciullo102 (Institute of Genetics and Biophysics A. Buzzati-Traverso, CNR, 80131 Naples, Italy), Andre Franke126 (Institute of Clinical Molecular Biology, Christian-Albrechts-University of Kiel, Kiel 24105, Germany), Paul W. Franks94,121,135 (Department of Clinical Sciences, Genetic and Molecular Epidemiology Unit, Skåne University Hospital Malmö, SE-205 02 Malmö, Sweden and Department of Public Health & Clinical Medicine, Section for Family Medicine, Umeå universitet, SE-901 85 Umeå, Sweden and Department of Nutrition, Harvard School of Public Health, Boston, MA 02115, USA), Steve Franks136 (Institute of Reproductive and Developmental Biology, Imperial College London, Hammersmith Hospital, London W120HS, UK), Myron D. Gross137 (School of Medicine, University of Minnesota, Minneapolis, MN 55455, USA), Torben Hansen90 (Novo Nordisk Foundation Centre for Basic Metabolic Research, Section of Metabolic Genetics, Faculty of Health and Medical Sciences, University of Copenhagen, 2100 Copenhagen, Denmark), Marjo-Riitta Jarvelin72,92,138,139,140 (Department of Epidemiology and Biostatistics, Imperial College London, London W2 1PG, UK and Department of Epidemiology and Biostatistics, MRC Health Protection Agency (HPE) Centre for Environment and Health, School of Public Health, Imperial College London, London SW7 2AZ, UK and Biocenter Oulu, University of Oulu, P.O. Box 5000 Aapistie 5A, FI-90014 Oulu, Finland and Unit of Primary Care, Oulu University Hospital, Kajaanintie 50 P.O.Box 20FI-90220 Oulu, Finland and Department of Children and Young People and Families, National Institute for Health and Welfare, Aapistie 1, Box 310, FI-90101 Oulu, Finland), Torben Jørgensen118 (Research Centre for Prevention and Health, Glostrup University Hospital, 2600 Glostrup, Denmark), Wouter J. Jukema127,133,141 (Department of Cardiology, Leiden University Medical Center, 2300 RC Leiden, Netherlands and Durrer Center for Cardiogenetic Research, ICIN-Netherlands Heart Institute, 3511 GC Utrecht, Netherlands and Interuniversity Cardiology Institute of the Netherlands, Utrecht 3511 EP, Netherlands), Mika Kähönen128,129 (Department of Clinical Physiology, Tampere University Hospital, FI-33521 Tampere, Finland and Department of Clinical Physiology, University of Tampere School of Medicine, FI-33014 Tampere, Finland), Mika Kivimaki119 (Department of Epidemiology and Public Health, UCL, London WC1E 6BT, UK), Terho Lehtimäki95,96 (Department of Clinical Chemistry, Fimlab Laboratories, FI-33520 Tampere, Finland and Department of Clinical Chemistry, University of Tampere School of Medicine, FI-33014 Tampere, Finland), Allan Linneberg118 (Research Centre for Prevention and Health, Glostrup University Hospital, 2600 Glostrup, Denmark), Oluf Pedersen90 (Novo Nordisk Foundation Centre for Basic Metabolic Research, Section of Metabolic Genetics, Faculty of Health and Medical Sciences, University of Copenhagen, 2100 Copenhagen, Denmark), Nilesh J. Samani142,143 (Department of Cardiovascular Sciences, University of Leicester, Glenfield Hospital, Leicester LE3 9QP, UK and National Institute for Health Research Leicester Cardiovascular Biomedical Research Unit, Glenfield Hospital, Leicester LE3 9QP, UK), Daniela Toniolo104,144 (Division of Genetics and Cell Biology, San Raffaele Scientific Institute, 20132 Milano, Italy and Institute of Molecular GeneticsCNR, 27100 Pavia, Italy), Hooman Allayee84,85 (Department of Preventive Medicine, USC Keck School of Medicine, Los Angeles, CA 90033, USA and Institute for Genetic Medicine, USC Keck School of Medicine, Los Angeles, CA 90033, USA), Ian J. Deary83,110 (Centre for Cognitive Aging and Cognitive Epidemiology, University of Edinburgh, Edinburgh EH8 9JZ, UK and Department of Psychology, University of Edinburgh, 7 George Square, Edinburgh EH8 9JZ, UK), Winfried März86,145,146 (Medical Clinic V, Mannheim Medical Faculty, University of Heidelberg, Theodor-Kutzer-Ufer 1-3, 68167 Mannheim, Germany and Clinical Institute of Medical and Chemical Laboratory Diagnostics, Medical University of Graz, Auenbruggerplatz 15, 8036 Graz, Austria and Synlab Academy, Synlab Services GmbH, Gottlieb-Daimler-Straße 25, 68165 Mannheim, Germany), Andres Metspalu78 (Estonian Genome Center, University of Tartu, Riia 23c, 51010 Tartu, Estonia), Cisca Wijmenga111 (Department of Genetics, University Medical Center, University of Groningen, Hanzeplein 1, 9713 GZ Groningen, Netherlands), Bruce H.W. Wolffenbuttel147 (Department of Endocrinology, University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9713 GZ Groningen, Netherlands), Paolo Vineis72 (Department of Epidemiology and Biostatistics, Imperial College London, London W2 1PG, UK), Soterios A. KyrtopoulosNational Hellenic Research Foundation, Institute of Biological Research and Biotechnology, Athens 116 35, Greece), Jos C.S. Kleinjans149 (Department of Toxicogenomics, Maastricht University, Universiteitssingel 50, 6229ER Maastricht, Netherlands), Mark I. McCarthy75,150 (Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK and Oxford Centre for Diabetes Endocrinology and Metabolism, University of Oxford, Oxford OX3 7LE, UK), James Scott77 (National Heart and Lung Institute, Imperial College London, London W12 0NN, UK)

Importance: Raising HDL-cholesterol through pharmacological inhibition of cholesteryl ester transfer protein is a potentially important strategy for prevention and treatment of cardiovascular diseases. Objective: To use genetic variants in the CETP gene to assess potential risks and benefits of lifelong lower cholesteryl ester transfer protein activity on cardiovascular diseases and other outcomes.Design: A prospective biobank study, with 9-year follow-up through linkage to health insurance records and death and disease registries.Setting: Five urban and five rural areas of China. Participants: 151,217 individuals aged 30-79 years at baseline with genotype data, 17,854 of whom also had lipid measurements and 4,657 had lipoprotein particle measurements.Exposures: Five CETP variants including an East Asian loss-of-function variant (rs2303790), combined in a genetic score weighted to effects on HDL-cholesterol.Main outcomes and measures: Baseline lipids and lipoprotein particles, cardiovascular risk factors, and incidence of carotid plaque and pre-defined major vascular and non-vascular diseases and a phenome-wide range of diseases.Results: Overall the mean LDL-cholesterol was 91 and HDL-cholesterol was 48 mg/dL. CETP variants were strongly associated with higher concentrations of HDL-cholesterol (e.g. 6.1 mg/dL per rs2303790 allele; P=9.4x10-4766 ), but were not associated with lower LDL-cholesterol. Within HDL particles, cholesterol esters were increased and triglycerides reduced, while within VLDL particles, cholesterol esters were reduced and triglycerides increased. Scaled to 10mg/dL higher HDL-cholesterol, the CETP genetic score was not associated with occlusive cardiovascular disease (18,550 events; OR=0.98 [95%CI 0.91-1.06]), major coronary events (5,767 events; 1.08 [0.95- 1.22]), myocardial infarction (3,118 events; 1.14 [0.97-1.34]) ischemic stroke (13,759 events; 0.94 [0.83-1.06]), intracerebral haemorrhage (6,532 events; 0.94 [0.86-1.02]) or other vascular diseases or carotid plaque. Similarly, rs2303790 was not associated with any vascular diseases or plaque. There were no associations with non-vascular diseases other than an increased risk of eye diseases with rs2303790 (4,090 events; 1.43 [1.13-1.80], P=0.0028).Conclusions and relevance: CETP variants were associated with altered HDL metabolism but did not lower LDL-cholesterol and had no significant effects on cardiovascular disease risk. These results suggest that in the absence of reduced LDL-cholesterol, increasing HDL-cholesterol by inhibition of cholesteryl ester transfer protein may not confer significant benefits for cardiovascular diseases.

Background Higher fruit consumption is associated with lower risk of cardiovascular disease (CVD). Substantial uncertainties remain, however, about its associations with all-cause mortality and mortality from subtypes of CVD and major non-vascular diseases, especially in Chinese population. Methods In 2004-08, the nationwide China Kadoorie Biobank Study recruited >0.5 million adults aged 30-79 years from 10 diverse localities in China. Fresh fruit consumption was estimated using an interviewer-administered electronic questionnaire and mortality data were collected from death registries. Among the 462,342 participants who were free of major chronic diseases at baseline, 17,894 deaths were recorded during ~ 7 years of follow-up. Cox regression yielded adjusted rate ratios (RRs) for all-cause and cause-specific mortality associated with fruit consumption. Results At baseline, 28% of participants reported consuming fruit ≥4 days/week (regular consumers) and 6% reported never/rarely consuming fruit (non-consumers). Compared with non-consumers, regular consumers had 27% (RR=0.73, 95% CI 0.70-0.76) lower all-cause mortality, 34% lower CVD mortality (n=6166; RR=0.66, 0.61-0.71), 17% lower cancer mortality (n=6796; RR=0.83, 0.78-0.89), and 42% lower mortality from chronic obstructive pulmonary disease (COPD, n=1119; RR=0.58, 0.47-0.71). For each, there was an approximately log-linear dose-response relationship with amount consumed. For mortality from site-specific cancers, fruit consumption was inversely associated with digestive tract cancer (n=2265; RR=0.72, 0.64-0.81), particularly oesophageal cancer (n=801; RR=0.65, 0.50-0.83), but not with cancers of lung and liver. Conclusions Among Chinese adults, higher fresh fruit consumption was associated with significantly lower mortality from several major vascular and non-vascular diseases. Given the current low population level of fruit consumption, substantial health benefits could be gained from increased fruit consumption in China.

Background Adiposity is increasing rapidly in China but little is known about the relevance to it of women’s reproductive factors, which differ intergenerationally and from that in the West. We assess associations of adiposity with life-course reproductive factors in Chinese women. Methods In 2004-8 the nationwide China Kadoorie Biobank recruited 303,000 women aged 30-79 (mean 50) years from 10 diverse regions. Multivariable linear regression was used to examine associations of reproductive factors (e.g. age at menarche/first birth/menopause, parity, breastfeeding, and reproductive years) with measures of general (e.g. body mass index [BMI]) and central (e.g. waist circumference [WC]) adiposity in adulthood. Results Overall, the mean BMI was 23.7 (SD 3.3) kg/m2, mean age at menarche was 15 (2) years, and nearly all had given birth (99%) and breastfed children (98%). Adiposity was associated inversely with age at menarche and at first birth, with 0.19 and 0.05 kg/m2 lower BMI, 0.38 and 0.12 cm lower WC per 1 year delay respectively (p Conclusion Among Chinese women, earlier age at menarche and at first birth, later age at and longer reproductive years were independently associated with increased adiposity late in life.

BACKGROUND: Despite the great burden of chronic respiratory diseases in China, few large multicentre, spirometry-based studies have examined its prevalence, rate of underdiagnosis regionally or the relevance of socioeconomic and lifestyle factors. METHODS: We analysed data from 512 891 adults in the China Kadoorie Biobank, recruited from 10 diverse regions of China during 2004-2008. Air flow obstruction (AFO) was defined by the lower limit of normal criteria based on spirometry-measured lung function. The prevalence of AFO was analysed by region, age, socioeconomic status, body mass index (BMI) and smoking history and compared with the prevalence of self-reported physician-diagnosed chronic bronchitis or emphysema (CB/E) and its symptoms. FINDINGS: The prevalence of AFO was 7.3% in men (range 2.5-18.2%) and 6.4% in women (1.5-18.5%). Higher prevalence of AFO was associated with older age (p

The pubertal height growth spurt is a distinctive feature of childhood growth reflecting both the central onset of puberty and local growth factors. Although little is known about the underlying genetics, growth variability during puberty correlates with adult risks for hormone-dependent cancer and adverse cardiometabolic health. The only gene so far associated with pubertal height growth, LIN28B, pleiotropically influences childhood growth, puberty and cancer progression, pointing to shared underlying mechanisms. To discover genetic loci influencing pubertal height and growth and to place them in context of overall growth and maturation, we performed genome-wide association meta-analyses in 18 737 European samples utilizing longitudinally collected height measurements. We found significant associations (P

In adult Chinese men, smoking prevalence is high, but little is known about its association with chronic respiratory disease, which is still poorly diagnosed and managed.A nationwide study recruited 0.5 million men and women aged 30-79 years during 2004-2008 from ten geographically diverse areas across the Mainland China. Information was collected from each participant regarding smoking and self-reported physician diagnosis of chronic bronchitis/emphysema (CB/E), along with measurement of lung function indices. Logistic regression was used to yield sex-specific odds ratios (ORs) relating smoking to airflow obstruction (AFO), defined as forced expiratory volume in 1 second (FEV1)/forced vital capacity (FVC)

Recent genome-wide association (GWA) studies have identified dozens of common variants associated with adult height. However, it is unknown how these variants influence height growth during childhood. We derived peak height velocity in infancy (PHV1) and puberty (PHV2) and timing of pubertal height growth spurt from parametric growth curves fitted to longitudinal height growth data to test their association with known height variants. The study consisted of N = 3,538 singletons from the prospective Northern Finland Birth Cohort 1966 with genotype data and frequent height measurements (on average 20 measurements per person) from 0-20 years. Twenty-six of the 48 variants tested associated with adult height (p

Background It has been suggested that there is a link between fetal growth and chronic diseases later in life. Several studies have shown a negative association between birthweight and cardiovascular diseases, as well as cardiovascular disease risk factors, such as blood pressure and type 2 diabetes. Far fewer studies have focused on the association between size at birth and blood lipid concentrations. We have conducted a qualitative assessment of the direction and consistency of the relationship between size at birth and blood lipid concentrations to see whether the suggested relationship between intrauterine growth and cardiovascular diseases is mediated by lipid metabolism. Methods A literature search covering the period January 1966 to January 2003 was performed using Medline, Embase, and Web of Science. All papers written in English and reporting the relationship between size at birth and lipid levels in humans were assessed. Bibliographies were searched for further publications. Results From an initial screen of 1198 references, 39 papers were included involving 28 578 individuals. There was no consistent relationship between size at birth and blood lipid levels; the one exception being triglyceride concentration, which showed statistically significant negative or U-shaped, but not positive, relationships with birthweight. Conclusion This review does not strongly support a link between birthweight and blood lipid levels in later life. However, the research in this area is limited and in order to make any definitive conclusions, longitudinal studies with sufficient power, data, and prospective follow-up are needed.

Background: Oesophageal, stomach and colorectal cancers are three major gastrointestinal cancers that together account for approximately 1 million deaths in China annually. Certain dietary factors and adiposity traits have been associated with the risks of these cancers in Western populations, but prospective evidence is still limited in China, where dietary patterns, adiposity levels, and the burden and subtypes of these cancers differ from the West. Methods: During 2004-2008, the prospective China Kadoorie Biobank (CKB) study recruited 512,715 adults (41% men) aged 30-79 (mean 52) years from 10 diverse areas in China, including one high-risk area for oesophageal cancer (Huixian in Henan province). Self-reported dietary intake, objective measurements of body size and composition, and other socio-demographic and lifestyle factors were collected at baseline and two subsequent resurveys. During ~10 years of follow-up, 26,594 incident cancer cases were recorded (2350 oesophageal [1198 from Huixian]; 3350 stomach; 3061 colorectal) in participants without a prior history of cancer. Cox regression was used to estimate adjusted hazard ratios (HRs) for each cancer associated with the consumption of major food groups (including spicy food) and with different adiposity traits. Results: At baseline, 28%, 47%, 23% and 10% of participants reported regular intake (≥4 days/week) of fruits, red and processed meat, preserved vegetables and soy products, respectively, while 30% reported daily intake of spicy food. The mean (SD) baseline BMI and body fat percentage were 23.7 (3.4) kg/m² and 27.9 (8.4)%. The consumption of fruits, soy products and spicy food were inversely associated with oesophageal cancer risk (HR=0.85; 95%CI 0.78-0.93 per 50g/day higher usual fruit intake, 0.82; 0.73-0.92 per 25g/day higher usual soy intake, and 0.81; 0.71-0.93 for daily vs. no spicy food intake), while daily consumption of preserved vegetables was positively associated with risk (1.25; 1.08-1.46). For stomach cancer, only fruit intake showed a statistically-significant association (0.92; 0.86-0.97 per 50g/day), while for colorectal cancer none of the food groups examined showed clear significant associations. All general (BMI, fat-percentage, fat and fat-free mass) and central (waist circumference and waist-to-hip ratio) adiposity measures were inversely associated with oesophageal cancer risk, in an approximately log-linear manner (e.g. 0.73; 0.68-0.79 per 5kg/m² higher usual BMI). General and central adiposity (except fat-free mass) were also inversely associated with the risk of stomach cancer. The above inverse associations persisted after excluding participants with self-reported prior chronic diseases and the first three years of follow-up. Conversely, BMI at age 25 years was positively associated with these cancers. For colorectal cancer, all adiposity measures, except BMI at age 25, were positively associated with risk (e.g. 1.15; 1.09-1.22 per 5kg/m² higher usual BMI). Conclusion: The findings in this Chinese population were generally consistent with those reported in previous large cohorts, with several exceptions (e.g. meat and colorectal cancer). The same dietary factor and adiposity trait appeared to affect the risks of the three cancers differently.

Background Observational epidemiological studies have consistently demonstrated a lower risk of ischaemic heart disease (IHD) and ischaemic stroke (IS) among moderate compared with non-drinkers, and heterogeneous associations of heavy alcohol intake with these cardiovascular disease (CVD) outcomes. However, the causal nature of these associations remains controversial, and potential mechanisms are not well understood. Methods The UK Biobank is a prospective study of 502,536 adults aged 40-69 years enrolled between 2006 and 2010. For observational analyses, 180,565 White male participants free of baseline CVD were included in Cox regression analyses to estimate risk of incident IHD or IS associated with self-reported baseline alcohol intake, to explore the impact of measurement error, confounding, and reverse causality bias, accounting for these where possible, and to estimate mediating pathways. A Mendelian randomisation (MR) analysis was conducted among 148,488 unrelated male participants with White British ancestry using an externally-weighted genetic instrument based on 93 SNPs associated with alcohol intake. Logistic regression was used to estimate the associations of genetically-predicted log alcohol intake with incident and prevalent IHD and IS. Localised average causal effect (LACE) estimation was used to assess linearity of the MR associations. Results The mean (SD) age of participants was 57 (8) years, and 80% consumed alcohol at least weekly. The age-adjusted hazard ratio (HR) for the association of lifetime abstention versus moderate alcohol intake (0.1-14 units/week) with IHD (1918 events) was attenuated from 1.27 (95%CI 1.06-1.51) to 1.11 (0.85-1.45) after adjustment for confounding by SES, smoking, diet, physical activity, and region, and exclusion of sources of reverse causality bias including reduced-intake drinkers and those with long-standing illness or disability; for IS (499 events), the corresponding HR increased from 1.31 (0.94-1.83) to 1.74 (1.10-2.73), but there were too few events for reliable conclusions. The association of heaviest (>42 units/week) versus moderate alcohol intake with IHD was null (0.92; 0.80-1.07), and with IS was positive (1.38; 1.05-1.82); these associations were mediated mainly by an inverse mediating effect of HDL-c (stronger for IHD than IS) and positive mediating effects of systolic blood pressure (SBP), body mass index (BMI) and, for IS, atrial fibrillation (AF). The MR analyses provided weak evidence that higher alcohol intake causally increases risk of IHD (OR per doubling of genetically-predicted alcohol intake 1.09, 95% CI 1.01-1.19, p=0.035), with borderline evidence of non-linearity (p=0.027) (14,253 events). There was no evidence of a casual association between alcohol intake and risk of IS (OR per doubling of genetically-predicted alcohol intake 1.08, 95% CI 0.90-1.30, p=0.383) but this analysis was not well-powered (2,651 events). Conclusion Classical observational and MR analyses support the hypothesis that the lower observed risk of CVD among moderate drinkers compared with non-drinkers is not causal, and that the association is largely attributable to residual observational biases, particularly reverse causality bias related to longstanding illness or disability among lifetime abstainers. Heterogeneous associations of heavy alcohol intake with IHD and IS may be attributable to differential mediating effects, including those of HDL-c and AF (although the possibility of a causal role for HDL-c is uncertain).

Increased blood lipid levels are heritable risk factors of cardiovascular disease with varied prevalence worldwide owing to different dietary patterns and medication use 1 . Despite advances in prevention and treatment, in particular through reducing low-density lipoprotein cholesterol levels 2 , heart disease remains the leading cause of death worldwide 3 . Genome-wideassociation studies (GWAS) of blood lipid levels have led to important biological and clinical insights, as well as new drug targets, for cardiovascular disease. However, most previous GWAS 4-23 have been conducted in European ancestry populations and may have missed genetic variants that contribute to lipid-level variation in other ancestry groups. These include differences in allele frequencies, effect sizes and linkage-disequilibrium patterns 24 . Here we conduct a multi-ancestry, genome-wide genetic discovery meta-analysis of lipid levels in approximately 1.65 million individuals, including 350,000 of non-European ancestries. We quantify the gain in studying non-European ancestries and provide evidence to support the expansion of recruitment of additional ancestries, even with relatively small sample sizes. We find that increasing diversity rather than studying additional individuals of European ancestry results in substantial improvements in fine-mapping functional variants and portability of polygenic prediction (evaluated in approximately 295,000 individuals from 7 ancestry groupings). Modest gains in the number of discovered loci and ancestry-specific variants were also achieved. As GWAS expand emphasis beyond the identification of genes and fundamental biology towards the use of genetic variants for preventive and precision medicine 25 , we anticipate that increased diversity of participants will lead to more accurate and equitable 26 application of polygenic scores in clinical practice.