Low-risk limits recommended for alcohol consumption vary substantially across different national guidelines. To define thresholds associated with lowest risk for all-cause mortality and cardiovascular disease, we studied individual-participant data from 599 912 current drinkers without previous cardiovascular disease. We did a combined analysis of individual-participant data from three large-scale data sources in 19 high-income countries (the Emerging Risk Factors Collaboration, EPIC-CVD, and the UK Biobank). We characterised dose-response associations and calculated hazard ratios (HRs) per 100 g per week of alcohol (12·5 units per week) across 83 prospective studies, adjusting at least for study or centre, age, sex, smoking, and diabetes. To be eligible for the analysis, participants had to have information recorded about their alcohol consumption amount and status (ie, non-drinker vs current drinker), plus age, sex, history of diabetes and smoking status, at least 1 year of follow-up after baseline, and no baseline history of cardiovascular disease. The main analyses focused on current drinkers, whose baseline alcohol consumption was categorised into eight predefined groups according to the amount in grams consumed per week. We assessed alcohol consumption in relation to all-cause mortality, total cardiovascular disease, and several cardiovascular disease subtypes. We corrected HRs for estimated long-term variability in alcohol consumption using 152 640 serial alcohol assessments obtained some years apart (median interval 5·6 years [5th-95th percentile 1·04-13·5]) from 71 011 participants from 37 studies. In the 599 912 current drinkers included in the analysis, we recorded 40 310 deaths and 39 018 incident cardiovascular disease events during 5·4 million person-years of follow-up. For all-cause mortality, we recorded a positive and curvilinear association with the level of alcohol consumption, with the minimum mortality risk around or below 100 g per week. Alcohol consumption was roughly linearly associated with a higher risk of stroke (HR per 100 g per week higher consumption 1·14, 95% CI, 1·10-1·17), coronary disease excluding myocardial infarction (1·06, 1·00-1·11), heart failure (1·09, 1·03-1·15), fatal hypertensive disease (1·24, 1·15-1·33); and fatal aortic aneurysm (1·15, 1·03-1·28). By contrast, increased alcohol consumption was log-linearly associated with a lower risk of myocardial infarction (HR 0·94, 0·91-0·97). In comparison to those who reported drinking >0-≤100 g per week, those who reported drinking >100-≤200 g per week, >200-≤350 g per week, or >350 g per week had lower life expectancy at age 40 years of approximately 6 months, 1-2 years, or 4-5 years, respectively. In current drinkers of alcohol in high-income countries, the threshold for lowest risk of all-cause mortality was about 100 g/week. For cardiovascular disease subtypes other than myocardial infarction, there were no clear risk thresholds below which lower alcohol consumption stopped being associated with lower disease risk. These data support limits for alcohol consumption that are lower than those recommended in most current guidelines. UK Medical Research Council, British Heart Foundation, National Institute for Health Research, European Union Framework 7, and European Research Council.

The Rotterdam Study is an ongoing prospective cohort study that started in 1990 in the city of Rotterdam, The Netherlands. The study aims to unravel etiology, preclinical course, natural history and potential targets for intervention for chronic diseases in mid-life and late-life. The study focuses on cardiovascular, endocrine, hepatic, neurological, ophthalmic, psychiatric, dermatological, otolaryngological, locomotor, and respiratory diseases. As of 2008, 14,926 subjects aged 45 years or over comprise the Rotterdam Study cohort. Since 2016, the cohort is being expanded by persons aged 40 years and over. The findings of the Rotterdam Study have been presented in over 1700 research articles and reports. This article provides an update on the rationale and design of the study. It also presents a summary of the major findings from the preceding 3 years and outlines developments for the coming period.

Type 2 diabetes is a common complex metabolic disorder. Currently, more than 380 million people live with type 2 diabetes globally, and this number is expected to increase to more than 550 million by 2030.¹ Recently, studies^2-5 have indicated a role of gut microbiome in type 2 diabetes. Differences in gut microbiome composition with type 2 diabetes status may comprise pathways on how dietary and other environmental factors affect development of insulin resistance and type 2 diabetes. Several studies^2-4 have indicated that, compared with healthy participants, patients with type 2 diabetes have a lower overall α diversity of gut microbiome composition. More specifically, lower abundance of certain butyrate-producing bacteria, such as class Clostridia and genus Faecalibacterium, have been observed in patients with type 2 diabetes.^2-5 For example, Larsen et al³ reported a lower abundance of Clostridia in patients with type 2 diabetes, and Qin et al⁵ observed that patients with type 2 diabetes had lower abundance of Faecalibacterium. In addition, some nonbutyrate bacteria, such as Haemophilus parainfluenzae, have also been reported to be associated with type 2 diabetes.⁵ However, these associations were not very strong, and most of these specific bacteria have not been reproduced in other cohorts.⁶ Furthermore, these previous studies^2-6 had several limitations. They were limited in sample sizes, ranging from 20³ to 784² participants. Larger samples with more statistical power may be required to detect true associations. Moreover, in most of these previous observational studies,^2-5 key confounders, such as lifestyle and socioeconomic status, that are known to be determinants of gut microbiome and type 2 diabetes⁷ were not controlled for. Finally, because almost all these previous studies^2-5 were conducted in patient populations or case-control settings, it is unclear whether associations are also relevant on a population level and whether type 2 diabetes–associated bacteria are associated to key subclinical parameters, such as insulin resistance and fasting glucose, among groups without diabetes. Examining associations for gut microbiome and such subclinical parameters will help investigators infer potential pathways behind gut microbiome and type 2 diabetes risk. Acquiring and updating this knowledge are particularly relevant, considering the increasing recognition that gut microbiome may play an important role in the development of type 2 diabetes and could be a promising target for prevention and treatment of type 2 diabetes. Therefore, we aimed to investigate the associations between gut microbiome composition with insulin resistance and type 2 diabetes in 2 large population-based cohorts, using a microbiome-wide approach and taking into account various sociodemographic and lifestyle factors.

High blood cholesterol is typically considered a feature of wealthy western countries ^1,2 . However, dietary and behavioural determinants of blood cholesterol are changing rapidly throughout the world ³ and countries are using lipid-lowering medications at varying rates. These changes can have distinct effects on the levels of high-density lipoprotein (HDL) cholesterol and non-HDL cholesterol, which have different effects on human health ^4,5 . However, the trends of HDL and non-HDL cholesterol levels over time have not been previously reported in a global analysis. Here we pooled 1,127 population-based studies that measured blood lipids in 102.6 million individuals aged 18 years and older to estimate trends from 1980 to 2018 in mean total, non-HDL and HDL cholesterol levels for 200 countries. Globally, there was little change in total or non-HDL cholesterol from 1980 to 2018. This was a net effect of increases in low- and middle-income countries, especially in east and southeast Asia, and decreases in high-income western countries, especially those in northwestern Europe, and in central and eastern Europe. As a result, countries with the highest level of non-HDL cholesterol—which is a marker of cardiovascular risk—changed from those in western Europe such as Belgium, Finland, Greenland, Iceland, Norway, Sweden, Switzerland and Malta in 1980 to those in Asia and the Pacific, such as Tokelau, Malaysia, The Philippines and Thailand. In 2017, high non-HDL cholesterol was responsible for an estimated 3.9 million (95% credible interval 3.7 million–4.2 million) worldwide deaths, half of which occurred in east, southeast and south Asia. The global repositioning of lipid-related risk, with non-optimal cholesterol shifting from a distinct feature of high-income countries in northwestern Europe, north America and Australasia to one that affects countries in east and southeast Asia and Oceania should motivate the use of population-based policies and personal interventions to improve nutrition and enhance access to treatment throughout the world.

Age-related body composition (BC) changes are characterized by an increase in fat mass and a steady decrease in both muscle function and mass. These changes lead to different clinical and functional phenotypes, such as sarcopenia and sarcopenic obesity (SO), which contribute to increased morbidity and mortality.^1-5 As life expectancy has increased, the proportion of older people with high body fat and/or low muscle function and mass has progressively risen.^6-8 Nevertheless, sarcopenia and SO are closely related conditions that are still rarely detected and untreated in clinical practice.^9-11

Novel prognostic inflammatory markers of cancer survival and cardiovascular disease are; the neutrophil-to-lymphocyte ratio (NLR), the platelet-to-lymphocyte ratio (PLR) and the systemic immune-inflammation index (SII). As normal values for these markers are unknown, our objective was to obtain reference values in the general population. We obtained data from a population-based prospective cohort study of individuals aged 45 years and over between 2002 and 2014. Absolute blood counts were used to calculate the NLR, PLR and SII. All inflammatory indices followed a log-normal distribution. We calculated the mean and 95% reference intervals in an unselected population. Furthermore we studied whether the inflammatory markers differed between age categories and gender. In total 8,711 participants (57.1% female; mean age 65.9 years, standard deviation 10.5 years) were included. Mean values and corresponding 95% reference intervals for the NLR were: 1.76 (0.83–3.92), for PLR: 120 (61–239) and for SII: 459 (189–1168). The inflammatory markers increased with age. The PLR and SII were higher in females, whilst the NLR was higher in males. In conclusion, we provided reference values for new inflammatory markers. All increase with age and vary with gender. This provides context that allows for proper interpretation of their potential value in future clinical practice and research.