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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.

Genetic studies have examined body-shape measures adjusted for body mass index (BMI), while allometric indices are additionally adjusted for height. We performed the first genome-wide association study of A Body Shape Index (ABSI), Hip Index (HI) and the new Waist-to-Hip Index and compared these with traditional indices, using data from the UK Biobank Resource for 219,872 women and 186,825 men with white British ancestry and Bayesian linear mixed-models (BOLT-LMM). One to two thirds of the loci identified for allometric body-shape indices were novel. Most prominent was rs72959041 variant in RSPO3 gene, expressed in visceral adipose tissue and regulating adrenal cell renewal. Highly ranked were genes related to morphogenesis and organogenesis, previously additionally linked to cancer development and progression. Genetic associations were fewer in men compared to women. Prominent region-specific associations showed variants in loci VEGFA and HMGA1 for ABSI and KLF14 for HI in women, and C5orf67 and HOXC4/5 for ABSI and RSPO3, VEGFA and SLC30A10 for HI in men. Although more variants were associated with waist and hip circumference adjusted for BMI compared to ABSI and HI, associations with height had previously been reported for many of the additional variants, illustrating the importance of adjusting correctly for height.

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 .

Key Points Meta-analysis of genome-wide association studies has contributed to the discovery of most of the recently identified genetic risk factors for complex diseases. Common meta-analytical approaches have been successfully applied; however, novel methods have been proposed that may have some advantages and disadvantages. Heterogeneity in meta-analysis can be introduced from various sources and should not be disregarded. Several methods have been proposed that may optimize power in the presence of heterogeneity from known or unknown sources. Next-generation sequence data will boost the study of rare variants; however, larger sample sizes are required. Several techniques have been developed for the meta-analysis of rare variants. Tools other than P values may be useful for inference. Scientists will benefit from publicly available data sets and collaboration between consortia that will facilitate a wide range of methodological and applied research. The authors review statistical methods for meta-analysis of genome-wide association studies (GWASs) and extensions of these methods to complex data. They discuss how low-frequency variants can be incorporated into meta-analyses as next-generation sequencing data become more commonly used in GWASs. Meta-analysis of genome-wide association studies (GWASs) has become a popular method for discovering genetic risk variants. Here, we overview both widely applied and newer statistical methods for GWAS meta-analysis, including issues of interpretation and assessment of sources of heterogeneity. We also discuss extensions of these meta-analysis methods to complex data. Where possible, we provide guidelines for researchers who are planning to use these methods. Furthermore, we address special issues that may arise for meta-analysis of sequencing data and rare variants. Finally, we discuss challenges and solutions surrounding the goals of making meta-analysis data publicly available and building powerful consortia.

Associations of liver, metabolic, and inflammatory biomarkers in blood with body shape are unclear, because waist circumference (WC) and hip circumference (HC) are dependent on overall body size, resulting in bias. We have used the allometric “a body shape index” (ABSI = WC (mm) ∗ Weight (kg) -2/3 ∗ Height (m) 5/6 ) and hip index (HI women  = HC (cm) ∗ Weight (kg) -0.482 ∗ Height (cm) 0.310 , HI men  = HC (cm) ∗ Weight (kg) -2/5 ∗ Height (cm) 1/5 ), which are independent of body mass index (BMI) by design, in multivariable linear regression models for 121,879 UK Biobank men and 135,559 women. Glucose, glycated haemoglobin (HbA1c), triglycerides, low-density-lipoprotein cholesterol, apolipoprotein-B, alanine aminotransferase (ALT), gamma-glutamyltransferase, and lymphocytes were associated positively with BMI and ABSI but inversely with HI. High-density-lipoprotein cholesterol and apolipoprotein-A1 were associated inversely with BMI and ABSI but positively with HI. Lipid-related biomarkers and ALT were associated only with HI in obese men. C-reactive protein, neutrophils, monocytes, and alkaline phosphatase were associated positively, while bilirubin was associated inversely, with BMI and ABSI but not with HI. Associations were consistent within the clinical reference ranges but were lost or changed direction for low or high biomarker levels. Our study confirms associations with waist and hip size, independent of BMI, for metabolic biomarkers but only with waist size for inflammatory biomarkers, suggesting different contribution of the mechanistic pathways related to body shape.

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.

Background Preterm birth defined as delivery before 37 gestational weeks is a leading cause of neonatal and infant morbidity and mortality. The aim of this study is to summarize the evidence from meta-analyses of observational studies on risk factors associated with PTB, evaluate whether there are indications of biases in this literature, and identify which of the previously reported associations are supported by robust evidence. Methods We searched PubMed and Scopus until February 2021, in order to identify meta-analyses examining associations between risk factors and PTB. For each meta-analysis, we estimated the summary effect size, the 95% confidence interval, the 95% prediction interval, the between-study heterogeneity, evidence of small-study effects, and evidence of excess-significance bias. Evidence was graded as robust, highly suggestive, suggestive, and weak. Results Eighty-five eligible meta-analyses were identified, which included 1480 primary studies providing data on 166 associations, covering a wide range of comorbid diseases, obstetric and medical history, drugs, exposure to environmental agents, infections, and vaccines. Ninety-nine (59.3%) associations were significant at P  < 0.05, while 41 (24.7%) were significant at P  < 10 −6 . Ninety-one (54.8%) associations had large or very large heterogeneity. Evidence for small-study effects and excess significance bias was found in 37 (22.3%) and 12 (7.2%) associations, respectively. We evaluated all associations according to prespecified criteria. Seven risk factors provided robust evidence: amphetamine exposure, isolated single umbilical artery, maternal personality disorder, sleep-disordered breathing (SDB), prior induced termination of pregnancy with vacuum aspiration (I-TOP with VA), low gestational weight gain (GWG), and interpregnancy interval (IPI) following miscarriage < 6 months. Conclusions The results from the synthesis of observational studies suggest that seven risk factors for PTB are supported by robust evidence. Routine screening for sleep quality and mental health is currently lacking from prenatal visits and should be introduced. This assessment can promote the development and training of prediction models using robust risk factors that could improve risk stratification and guide cost-effective preventive strategies. Trial registration PROSPERO 2021 CRD42021227296.

Differences in cardiac and aortic structure and function are associated with cardiovascular diseases and a wide range of other types of disease. Here we analyzed cardiovascular magnetic resonance images from a population-based study, the UK Biobank, using an automated machine-learning-based analysis pipeline. We report a comprehensive range of structural and functional phenotypes for the heart and aorta across 26,893 participants, and explore how these phenotypes vary according to sex, age and major cardiovascular risk factors. We extended this analysis with a phenome-wide association study, in which we tested for correlations of a wide range of non-imaging phenotypes of the participants with imaging phenotypes. We further explored the associations of imaging phenotypes with early-life factors, mental health and cognitive function using both observational analysis and Mendelian randomization. Our study illustrates how population-based cardiac and aortic imaging phenotypes can be used to better define cardiovascular disease risks as well as heart–brain health interactions, highlighting new opportunities for studying disease mechanisms and developing image-based biomarkers. Using magnetic resonance images of the heart and aorta from 26,893 individuals in the UK Biobank, a phenome-wide association study associates cardiovascular imaging phenotypes with a wide range of demographic, lifestyle and clinical features.

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.