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Type 2 diabetes mellitus (T2DM) is a global epidemic associated with increased health expenditure, and low quality of life. Many non-genetic risk factors have been suggested, but their overall epidemiological credibility has not been assessed. We searched PubMed to capture all meta-analyses and Mendelian randomization studies for risk factors of T2DM. For each association, we estimated the summary effect size, its 95% confidence and prediction interval, and the I2 metric. We examined the presence of small-study effects and excess significance bias. We assessed the epidemiological credibility through a set of predefined criteria. We captured 86 eligible papers (142 associations) covering a wide range of biomarkers, medical conditions, and dietary, lifestyle, environmental and psychosocial factors. Adiposity, low hip circumference, serum biomarkers (increased level of alanine aminotransferase, gamma-glutamyl transferase, uric acid and C-reactive protein, and decreased level of adiponectin and vitamin D), an unhealthy dietary pattern (increased consumption of processed meat and sugar-sweetened beverages, decreased intake of whole grains, coffee and heme iron, and low adherence to a healthy dietary pattern), low level of education and conscientiousness, decreased physical activity, high sedentary time and duration of television watching, low alcohol drinking, smoking, air pollution, and some medical conditions (high systolic blood pressure, late menarche age, gestational diabetes, metabolic syndrome, preterm birth) presented robust evidence for increased risk of T2DM. A healthy lifestyle pattern could lead to decreased risk for T2DM. Future randomized clinical trials should focus on identifying efficient strategies to modify harmful daily habits and predisposing dietary patterns.

Background Although neutrophils have been linked to the progression of cancer, uncertainty exists around their association with cancer outcomes, depending on the site, outcome and treatments considered. We aimed to evaluate the strength and validity of evidence on the association between either the neutrophil to lymphocyte ratio (NLR) or tumour-associated neutrophils (TAN) and cancer prognosis. Methods We searched MEDLINE, Embase and Cochrane Database of Systematic Reviews from inception to 29 May 2020 for systematic reviews and meta-analyses of observational studies on neutrophil counts (here NLR or TAN) and specific cancer outcomes related to disease progression or survival. The available evidence was graded as strong, highly suggestive, suggestive, weak or uncertain through the application of pre-set GRADE criteria. Results A total of 204 meta-analyses from 86 studies investigating the association between either NLR or TAN and cancer outcomes met the criteria for inclusion. All but one meta-analyses found a hazard ratio (HR) which increased risk (HR > 1). We did not find sufficient meta-analyses to evaluate TAN and cancer outcomes ( N  = 9). When assessed for magnitude of effect, significance and bias related to heterogeneity and small study effects, 18 (9%) associations between NLR and outcomes in composite cancer endpoints (combined analysis), cancers treated with immunotherapy and some site specific cancers (urinary, nasopharyngeal, gastric, breast, endometrial, soft tissue sarcoma and hepatocellular cancers) were supported by strong evidence. Conclusion In total, 60 (29%) meta-analyses presented strong or highly suggestive evidence. Although the NLR and TAN hold clinical promise in their association with poor cancer prognosis, further research is required to provide robust evidence, assess causality and test clinical utility. Trial registration PROSPERO CRD42017069131 .

Most studies of severe/fatal COVID-19 risk have used routine/hospitalisation data without detailed pre-morbid characterisation. Using the community-based UK Biobank cohort, we investigate risk factors for COVID-19 mortality in comparison with non-COVID-19 mortality. We investigated demographic, social (education, income, housing, employment), lifestyle (smoking, drinking, body mass index), biological (lipids, cystatin C, vitamin D), medical (comorbidities, medications) and environmental (air pollution) data from UK Biobank (N = 473,550) in relation to 459 COVID-19 and 2626 non-COVID-19 deaths to 21 September 2020. We used univariate, multivariable and penalised regression models. Age (OR = 2.76 [2.18–3.49] per S.D. [8.1 years], p = 2.6 × 10–17), male sex (OR = 1.47 [1.26–1.73], p = 1.3 × 10–6) and Black versus White ethnicity (OR = 1.21 [1.12–1.29], p = 3.0 × 10–7) were independently associated with and jointly explanatory of (area under receiver operating characteristic curve, AUC = 0.79) increased risk of COVID-19 mortality. In multivariable regression, alongside demographic covariates, being a healthcare worker, current smoker, having cardiovascular disease, hypertension, diabetes, autoimmune disease, and oral steroid use at enrolment were independently associated with COVID-19 mortality. Penalised regression models selected income, cardiovascular disease, hypertension, diabetes, cystatin C, and oral steroid use as jointly contributing to COVID-19 mortality risk; Black ethnicity, hypertension and oral steroid use contributed to COVID-19 but not non-COVID-19 mortality. Age, male sex and Black ethnicity, as well as comorbidities and oral steroid use at enrolment were associated with increased risk of COVID-19 death. Our results suggest that previously reported associations of COVID-19 mortality with body mass index, low vitamin D, air pollutants, renin–angiotensin–aldosterone system inhibitors may be explained by the aforementioned factors.

The cause of multiple sclerosis is believed to involve environmental exposure and genetic susceptibility. We aimed to summarise the environmental risk factors that have been studied in relation to onset of multiple sclerosis, assess whether there is evidence for diverse biases in this literature, and identify risk factors without evidence of biases. We searched PubMed from inception to Nov 22, 2014, to identify systematic reviews and meta-analyses of observational studies that examined associations between environmental factors and multiple sclerosis. For each meta-analysis we estimated the summary effect size by use of random-effects and fixed-effects models, the 95% CI, and the 95% prediction interval. We estimated the between-study heterogeneity expressed by I2 (defined as large for I2≥50%), evidence of small-study effects (ie, large studies had significantly more conservative results than smaller studies), and evidence of excess significance bias (ie, more studies than expected with significant results). Overall, 44 unique meta-analyses including 416 primary studies of different risk factors and multiple sclerosis were examined, covering a wide range of risk factors: vaccinations, comorbid diseases, surgeries, traumatic events and accidents, exposure to environmental agents, and biochemical, infectious, and musculoskeletal biomarkers. 23 of 44 meta-analyses had results that were significant at p values less than 0·05 and 11 at p values less than 0·001 under the random-effects model. Only three of the 11 significant meta-analyses (p<0·001) included more than 1000 cases, had 95% prediction intervals excluding the null value, and were not suggestive of large heterogeneity (I2<50%), small-study effects (p for Egger's test >0·10), or excess significance (p>0·05). These were IgG seropositivity to Epstein-Barr virus nuclear antigen (EBNA) (random effects odds ratio [OR] 4·46, 95% CI 3·26–6·09; p for effect size=1·5 × 10−19; I2=43%), infectious mononucleosis (2·17, 1·97–2·39; p=3·1 × 10−50; I2=0%), and smoking (1·52, 1·39–1·66; p=1·7 × 10−18;I2=0%). Many studies on environmental factors associated with multiple sclerosis have caveats casting doubts on their validity. Data from more and better-designed studies are needed to establish robust evidence. A biomarker of Epstein-Barr virus (anti-EBNA IgG seropositivity), infectious mononucleosis, and smoking showed the strongest consistent evidence of an association. None.

To evaluate how often newly developed risk prediction models undergo external validation and how well they perform in such validations. We reviewed derivation studies of newly proposed risk models and their subsequent external validations. Study characteristics, outcome(s), and models' discriminatory performance [area under the curve, (AUC)] in derivation and validation studies were extracted. We estimated the probability of having a validation, change in discriminatory performance with more stringent external validation by overlapping or different authors compared to the derivation estimates. We evaluated 127 new prediction models. Of those, for 32 models (25%), at least an external validation study was identified; in 22 models (17%), the validation had been done by entirely different authors. The probability of having an external validation by different authors within 5 years was 16%. AUC estimates significantly decreased during external validation vs. the derivation study [median AUC change: −0.05 (P < 0.001) overall; −0.04 (P = 0.009) for validation by overlapping authors; −0.05 (P < 0.001) for validation by different authors]. On external validation, AUC decreased by at least 0.03 in 19 models and never increased by at least 0.03 (P < 0.001). External independent validation of predictive models in different studies is uncommon. Predictive performance may worsen substantially on external validation.

Brain structure in later life reflects both influences of intrinsic aging and those of lifestyle, environment and disease. We developed a deep neural network model trained on brain MRI scans of healthy people to predict “healthy” brain age. Brain regions most informative for the prediction included the cerebellum, hippocampus, amygdala and insular cortex. We then applied this model to data from an independent group of people not stratified for health. A phenome-wide association analysis of over 1,410 traits in the UK Biobank with differences between the predicted and chronological ages for the second group identified significant associations with over 40 traits including diseases (e.g., type I and type II diabetes), disease risk factors (e.g., increased diastolic blood pressure and body mass index), and poorer cognitive function. These observations highlight relationships between brain and systemic health and have implications for understanding contributions of the latter to late life dementia risk.

Key Points Death rates from ischaemic heart disease (IHD), stroke, and other cardiovascular diseases (CVDs) are decreasing in high-income and many Latin American countries, and this trend shows no signs of slowing Declines in some behavioural risk factors, including smoking, and physiological risk factors, such as blood pressure and serum cholesterol, are likely to have helped to reduce CVDs By contrast, the nearly universal increase in adiposity seems not to have modified the long-term declining trend in CVD mortality, although it might have had some slowing effect Improved medical care, including effective treatment of physiological risk factors, diagnosis, treatment of acute CVDs, and post-hospital care, has also contributed to declining CVD events and mortality Measured risk factor and treatment variables, while important, explain neither why the decline began when it did nor many of the similarities and differences between countries or between men and women Substantial fluctuations in CVDs, and in alcohol intake, in former communist countries of Europe have followed times of massive political and social changes since the early 1990s Cardiovascular disease (CVD) remains a leading cause of death worldwide, but age-standardized CVD death rates are decreasing steadily. In this Review, Ezzati and colleagues use the available epidemiological data to examine regional and global changes in CVD mortality, as well as trends in smoking, alcohol consumption, diet, physiological risk factors, and improvements in medical care that might underlie these changes. Ischaemic heart disease, stroke, and other cardiovascular diseases (CVDs) lead to 17.5 million deaths worldwide per year. Taking into account population ageing, CVD death rates are decreasing steadily both in regions with reliable trend data and globally. The declines in high-income countries and some Latin American countries have been ongoing for decades without slowing. These positive trends have broadly coincided with, and benefited from, declines in smoking and physiological risk factors, such as blood pressure and serum cholesterol levels. These declines have also coincided with, and benefited from, improvements in medical care, including primary prevention, diagnosis, and treatment of acute CVDs, as well as post-hospital care, especially in the past 40 years. These variables, however, explain neither why the decline began when it did, nor the similarities and differences in the start time and rate of the decline between countries and sexes. In Russia and some other former Soviet countries, changes in volume and patterns of alcohol consumption have caused sharp rises in CVD mortality since the early 1990s. An important challenge in reaching firm conclusions about the drivers of these remarkable international trends is the paucity of time-trend data on CVD incidence, risk factors throughout the life-course, and clinical care.

C-reactive protein is involved in a plethora of pathophysiological conditions. Many genetic loci associated with C-reactive protein are annotated to lipid and glucose metabolism genes supporting common biological pathways between inflammation and metabolic traits. To identify novel pleiotropic loci, we perform multi-trait analysis of genome-wide association studies on C-reactive protein levels along with cardiometabolic traits, followed by a series of in silico analyses including colocalization, phenome-wide association studies and Mendelian randomization. We find 41 novel loci and 19 gene sets associated with C-reactive protein with various pleiotropic effects. Additionally, 41 variants colocalize between C-reactive protein and cardiometabolic risk factors and 12 of them display unexpected discordant effects between the shared traits which are translated into discordant associations with clinical outcomes in subsequent phenome-wide association studies. Our findings provide insights into shared mechanisms underlying inflammation and lipid metabolism, representing potential preventive and therapeutic targets. Chronic inflammation and lipometabolism share many causal genes and possibly pathways. Here, the authors use a multi-trait GWAS approach to study shared genetic determinants of low-grade inflammation, measured by C-reactive protein (CRP), and closely linked lipid and metabolic pathways.

Chronic low-grade inflammation is linked to a multitude of chronic diseases. We report the largest genome-wide association study (GWAS) on C-reactive protein (CRP), a marker of systemic inflammation, in UK Biobank participants (N = 427,367, European descent) and the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium (total N = 575,531 European descent). We identify 266 independent loci, of which 211 are not previously reported. Gene-set analysis highlighted 42 gene sets associated with CRP levels ( p  ≤ 3.2 ×10 −6 ) and tissue expression analysis indicated a strong association of CRP related genes with liver and whole blood gene expression. Phenome-wide association study identified 27 clinical outcomes associated with genetically determined CRP and subsequent Mendelian randomisation analyses supported a causal association with schizophrenia, chronic airway obstruction and prostate cancer. Our findings identified genetic loci and functional properties of chronic low-grade inflammation and provided evidence for causal associations with a range of diseases. Inflammation is associated with a variety of diseases. Here, the authors identify 266 genetic loci associated with C-reactive protein levels, a marker of inflammation, in >500,000 Europeans, along with associated pathways, clinical outcomes and potential causal associations with disease.

Plasma proteomics provides a unique opportunity to enhance disease prediction by capturing protein expression patterns linked to diverse pathological processes. Leveraging data from 2,923 proteins measured in 53,030 UK Biobank participants, we developed proteomic risk scores for 27 common outcomes over 5- and 15-year follow-up periods using two approaches: a linear ElasticNet regression model and a deep learning neural network (NN) model. Using Cox regression, we assessed the discrimination of proteomic risk scores either in isolation or as incremental improvements over clinical risk factors. We also studied the shared and unique protein predictors across conditions. Proteomic risk scores demonstrated strong discrimination for most outcomes, with a C-index > 0.80 for 12 diseases. NN models outperformed linear models for 11 outcomes, particularly for diseases such as Parkinson’s disease (C-index 0.84) and pulmonary embolism (C-index 0.83), where nonlinear relationships contributed significantly to prediction. Across all outcomes, the addition of proteomic scores to clinical models improved predictive accuracy (ΔC-index 0.03), with the greatest gains observed in 9 diseases (ΔC-index > 0.1), including end-stage renal disease, pulmonary embolism, and Parkinson’s disease. Analysis of protein contributions revealed shared predictors across multiple diseases, such as growth differentiation factor 15 (GDF15), as well as unique predictors like PAEP for endometriosis. While NN models may capture complex relationships, linear models provided value through simplicity and interpretability. These findings underscore the importance of tailoring predictive approaches to specific diseases and demonstrate the pivotal potential of proteomics in advancing risk stratification and early detection.