Search

Polygenic scores for body mass index and obesity constructed using data from 5 million people with different ancestries were associated with distinct weight gain trajectories from early childhood to adulthood.

A large genome-wide association study of more than 5 million individuals reveals that 12,111 single-nucleotide polymorphisms account for nearly all the heritability of height attributable to common genetic variants.

A trans-ancestry meta-analysis of GWAS of glycemic traits in up to 281,416 individuals identifies 99 novel loci, of which one quarter was found due to the multi-ancestry approach, which also improves fine-mapping of credible variant sets.

Patricia Munroe, Joanna Howson and colleagues genotype ∼350,000 individuals and identify 30 new blood pressure– or hypertension-associated risk loci. Their analyses provide insights into the pathophysiology of hypertension and highlight new potential targets for clinical intervention.

A genome-wide association meta-analysis study of blood lipid levels in roughly 1.6 million individuals demonstrates the gain of power attained when diverse ancestries are included to improve fine-mapping and polygenic score generation, with gains in locus discovery related to sample size.

GWAS have identified more than 500 genetic loci associated with blood lipid levels. Here, the authors report a genome-wide analysis of interactions between genetic markers and physical activity, and find that physical activity modifies the effects of four genetic loci on HDL or LDL cholesterol.

A multi-ancestry genome-wide gene–smoking interaction study identifies 13 new loci associated with serum lipids.

A model of how local relatedness changes with age fits empirical patterns from seven group-living mammal species and reveals that patterns differ between the sexes and the potential behavioural consequences of these changes.

Sex differences in fasting glucose and insulin have been identified, but the genetic loci underlying these differences have not. Here, the authors perform a meta-analysis of genome-wide association studies to detect sex-specific and sex-dimorphic loci associated with fasting glucose and insulin.

A systematic review of evidence, across the key pillars of prevention, diagnosis, treatment and prognosis, outlines milestones that need to be met to enable the broad clinical implementation of precision medicine in diabetes care.

Trans-ethnic analyses of exome array data identify new risk loci for type 2 diabetes. Fine-mapping analyses using genome-wide association data show that the index coding variants represent the likely causal variants at only a subset of these loci.

This meta-analysis of genome-wide association studies identifies four genetic loci associated with circulating leptin levels independent of adiposity. Examination in mouse adipose tissue explants provides functional support for the leptin-associated loci.

Jose Florez, Claudia Langenberg, Erik Ingelsson, Inga Prokopenko, Inês Barroso and colleagues perform large-scale association analyses using the Metabochip to gain further insights into the genetic architecture of glucose regulation. They identify 38 new loci influencing 1 or more glycemic traits and show that many of these loci also modify risk of type 2 diabetes.

This Review summarizes the epidemiology literature linking data-driven and investigator-defined dietary patterns to cancer risk, providing expert appraisal of new developments in the field and highlighting both emerging mechanistic insights and key areas for future research.

Grieger, Takele, Vesco, et al. perform a systematic review and meta-analysis of gestational weight gain interventions. Findings indicate lifestyle interventions that reduce excessive gestational weight gain provide greater benefits for women with a normal BMI and higher HDL cholesterol levels at the initiation of interventions.

Cristen Willer and colleagues report genome-wide association analyses for blood lipid levels in 188,578 individuals. They identify 62 loci newly associated with blood lipid levels, refine the association signals at 12 loci and examine associations with cardiovascular and metabolic traits.

Sekar Kathiresan and colleagues examine 185 common variants using a modified mendelian randomization approach and provide evidence supporting a causal role of triglyceride-rich lipoproteins in the development of coronary artery disease.

Benham, Gingras, McLennan, Most, Yamamoto, Aiken et al. conduct two systematic reviews and meta-analyses to evaluate whether a precision-based medicine approach can be adopted to improve the clinical management of gestational diabetes (GDM). They find some precision markers that may improve the treatment course of GDM but further research is needed.

Bodhini et al. systematically review the evidence on sociodemographic, clinical, behavioral, and molecular factors that modify the effect of interventions for type 2 diabetes prevention. The certainty of evidence that such factors modify the effectiveness of lifestyle and behavioral interventions is low to very low.

Prostate Cancer Diagnosis and Treatment Enhancement Through the Power of Big Data in Europe (PIONEER) is a European network of excellence for big data in prostate cancer, consisting of 32 private and public stakeholders from 9 countries across Europe. In this Perspectives article, the authors introduce the PIONEER project and describe its aims and plans for ultimately improving prostate cancer care through the use of big data.

Lim et al. perform a systematic review and meta-analysis to identify participant characteristics associated with response to gestational diabetes prevention. Characteristics such as BMI, polycystic ovary syndrome and being in the preconception phase could determine responses to certain preventive interventions.

Naylor et al. systematically review the efficacy of treatments for beta-cell monogenic diabetes. Limited evidence from the mostly non-randomized, small studies supports no treatment in glucokinase-related hyperglycemia and sulfonylureas for HNF1A-diabetes; further evidence is needed on the optimum treatments in these and other monogenic subtypes.

Ahmad, Lim, Morieri, Tam et al perform a systematic review and meta-analysis of biomarkers, genetic markers, and risk scores for prediction of cardiovascular outcomes in Type 2 diabetes. A few prognostic markers are identified that provide incremental predictive utility beyond established cardiovascular risk factors.

Semnani-Azad et al. review the evidence on prognostic factors that predict cardiovascular disease and type 2 diabetes for women, and cardiometabolic profile in offspring subsequent to gestational diabetes. The evidence was of low quality, but some maternal characteristics were predictive of unfavourable outcomes in women and their offspring.

Takele et al. conduct a systematic review and meta-analysis of the effects of intervention characteristics on preventing gestational diabetes. Group or healthcare facility-based physical activity interventions are found to be more effective in preventing gestational diabetes than community-based interventions.

Felton et al. conduct a systematic review to determine the utility of islet autoantibodies as biomarkers of type 1 diabetes heterogeneity. They find that islet autoantibodies are most likely to be useful for patient stratification prior to clinical diagnosis.

Jacobsen, Sherr et al. evaluate the utility of novel technologies in the treatment of type 1 diabetes. Their systematic review finds technologies such as continuous glucose monitoring, insulin pumps, and decision support tools improve important measures (e.g., HbA1c, time in range, quality of life) allowing precision-directed uptake of technology.

Misra, Wagner et al. systematically review if strategies to subclassify type 2 diabetes (T2D) are associated with high quality evidence and patient outcomes. Cluster-based stratification yields T2D subtypes that associate with outcomes, suggesting subclassification could have future clinical use.

Francis et al. perform a systematic review and meta-analysis to evaluate studies comparing perinatal outcomes among individuals with gestational diabetes mellitus (GDM). Their review and post hoc analysis find that maternal preconception weight and non-glucose-dependent biochemical markers could be a precision diagnostic approach to reducing variability in clinical outcomes following treatment.

Semple et al. review the literature to assess the effects of pharmacologic or surgical interventions in monogenic insulin resistance when stratified by genotype. The evidence guiding genotype-specific treatment of monogenic insulin resistance is of low to very low quality, but suggestive of benefits of metreleptin, thiazolidinediones, and rhIGF-1.

Young, McInnes, Massey et al. systematically review published studies evaluating features associated with heterogenous treatment effects for SGLT2-inhibitor and GLP1-receptor agonist therapies in people with type 2 diabetes. They find limited current evidence on treatment effect heterogeneity, for glycaemic, cardiovascular and renal outcomes.

Felton et al. conducted a systematic review to evaluate studies testing disease-modifying therapies and features linked to treatment response for type 1 diabetes prevention. While the quality of prevention and intervention trials is found to be high, precision analyses on factors associated with treatment response are of poorer quality.

Murphy, Kevin, Pollin et al. perform a systematic review of the evidence on the criteria used to select individuals with diabetes for genetic testing and of the evidence for the optimal methods for variant detection in genes involved in monogenic diabetes. Based on the findings the authors make recommendations and highlight challenges for the field.