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An analysis of 24,202 critical cases of COVID-19 identifies potentially druggable targets in inflammatory signalling (JAK1), monocyte–macrophage activation and endothelial permeability (PDE4A), immunometabolism (SLC2A5 and AK5), and host factors required for viral entry and replication (TMPRSS2 and RAB2A).

Known genetic loci account for only a fraction of the genetic contribution to Alzheimer’s disease. Here, the authors have performed a large genome-wide meta-analysis comprising 409,435 individuals to discover 6 new loci and demonstrate the efficacy of an Alzheimer’s disease polygenic risk score.

Here the authors apply machine learning approaches to Alzheimer’s genetics, confirm known associations and suggest novel risk loci. These methods demonstrate predictive power comparable to traditional approaches, while also offering potential new insights beyond standard genetic analyses.

Polygenic risk scores for Alzheimer’s disease derived from individuals of European ancestry can show improved performance in multiancestry settings after incorporating genome-wide association summary statistics from diverse populations.

Meta-analysis of genome-wide association studies on Alzheimer’s disease and related dementias identifies new loci and enables generation of a new genetic risk score associated with the risk of future Alzheimer’s disease and dementia.

Experimental and geochemical observations show that silicate mineral inclusions in diamonds from Juina, Brazil, crystallized from primary and evolved carbonatite melts in the mantle transition zone and deep upper mantle. Such small-degree melts of subducted crust can be viewed as agents of chemical mass-transfer in the upper mantle and transition zone, leaving a chemical imprint of ocean crust that can possibly endure for billions of years.

Sven van der Lee, Julie Williams, Gerard Schellenberg and colleagues identify rare coding variants in PLCG2, ABI3 and TREM2 associated with Alzheimer's disease. These genes are highly expressed in microglia and provide additional evidence that the microglia-mediated immune response contributes to the development of Alzheimer's disease.

The Vertebrate Genome Project has used an optimized pipeline to generate high-quality genome assemblies for sixteen species (representing all major vertebrate classes), which have led to new biological insights.

The notothenioid radiation is a remarkable group of fish adapted to life in the icy waters of the Southern Ocean. This study investigates the evolutionary history of this group and the basis of their adaption to cold environments through genomic analysis of 24 new genome assemblies.

A high-quality sequence assembly of the zebrafish genome reveals the largest gene set of any vertebrate and provides information on key genomic features, and comparison to the human reference genome shows that approximately 70% of human protein-coding genes have at least one clear zebrafish orthologue.

Eye contact is a key social behavior and its measurement could facilitate the diagnosis and treatment of autism. Here the authors show that a deep neural network model can detect eye contact as accurately has human experts.

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.

MDM2 and BET inhibitors have shown efficacy in AML treatment. Here, the authors show that the two compounds can synergize through both p53 protein stabilization and inhibition of BRD4-mediated repression of p53 target genes.

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.

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.

Maternal exercise, before and during pregnancy, has a beneficial effect on offspring. Harris et al. report that exercise-induced release of oligosaccharide 3′-sialyllactose in mouse breast milk mediates the metabolic health benefits of maternal exercise on offspring by improving glucose and insulin tolerance and cardiac function.

Methods that are based on next-generation sequencing technology are used for a range of applications from genome sequencing to transcriptomic and epigenomic studies. Here, the authors discuss the important issue of sequencing depth in the design of such experiments.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.