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In this issue of Nature Metabolism, Li et al. reveal that disruptions in mitochondrial protein folding are an early trigger of β-cell dysfunction in type 2 diabetes and highlight that LONP1 has a key role in the maintenance of mitochondrial proteostasis. Boosting mitochondrial protein folding capacity may offer a promising strategy to protect β-cells and prevent or delay diabetes.

Advances in islet cell biology over the past few years highlight a fundamental role for intra-islet endocrine cell interactions and δ-cells in the regulation of glycaemia. New insights into how cellular heterogeneity and individual-level heterogeneity effect hormone secretion have also emerged.

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.

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.

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.

Mass-spectrometry-based proteomics is a powerful approach for discovering disease biomarkers. This tutorial provides advice on the study design, including cohort selection, evaluating statistical power, blinding and randomization, and quality control.

Decades of scientific accomplishment have transformed diabetes treatment; this Review describes key milestones in our understanding of the insulin molecule and looks to the future of insulin therapy.

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.

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.

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.