Diabetes in China: epidemiology, pathophysiology and multi-omics
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A machine learning approach to leveraging electronic health records for enhanced omics analysis
Omics studies produce a large number of measurements, enabling the development, validation and interpretation of systems-level biological models. Large cohorts are required to power these complex models; yet, the cohort size remains limited due to clinical and budgetary constraints. We introduce clinical and omics multimodal analysis enhanced with transfer learning (COMET), a machine learning framework that incorporates large, observational electronic health record databases and transfer learning to improve the analysis of small datasets from omics studies. By pretraining on electronic health record data and adaptively blending both early and late fusion strategies, COMET overcomes the limitations of existing multimodal machine learning methods. Using two independent datasets, we showed that COMET improved the predictive modelling performance and biological discovery compared with the analysis of omics data with traditional methods. By incorporating electronic health record data into omics analyses, COMET enables more precise patient classifications, beyond the simplistic binary reduction to cases and controls. This framework can be broadly applied to the analysis of multimodal omics studies and reveals more powerful biological insights from limited cohort sizes.
Multiple long-term conditions as the next transition in the global diabetes epidemic
Several transitions, or new patterns and dynamics in the contributors and health outcomes, have altered the character and burden of the multi-decade, worldwide growth in prevalence of type 2 diabetes (T2DM). These changes have led to different needs for prevention and care. These dynamics have been driven by diverse demographic, socio-economic, behavioural, and health system response factors. In this Perspective, we describe these transitions and how their attributes have set the stage for multimorbidity, or multiple long-term conditions (MLTCs), to be the next major challenge in the diabetes epidemic. We also describe how the timing and character of these stages differ in high-, middle-, and low-income countries. These challenges call for innovation and a stronger focus on MLTCs across the spectrum of cause, effectiveness, and implementation studies to guide prevention and treatment priorities.
Effect of broccoli sprout extract and baseline gut microbiota on fasting blood glucose in prediabetes: a randomized, placebo-controlled trial
More effective treatments are needed for impaired fasting glucose or glucose intolerance, known as prediabetes. Sulforaphane is an isothiocyanate that reduces hepatic gluconeogenesis in individuals with type 2 diabetes and is well tolerated when provided as a broccoli sprout extract (BSE). Here we report a randomized, double-blind, placebo-controlled trial in which drug-naive individuals with prediabetes were treated with BSE (n = 35) or placebo (n = 39) once daily for 12 weeks. The primary outcome was a 0.3 mmol l−1 reduction in fasting blood glucose compared with placebo from baseline to week 12. Gastro-intestinal side effects but no severe adverse events were observed in response to treatment. BSE did not meet the prespecified primary outcome, and the overall effect in individuals with prediabetes was a 0.2 mmol l−1 reduction in fasting blood glucose (95% confidence interval −0.44 to −0.01; P = 0.04). Exploratory analyses to identify subgroups revealed that individuals with mild obesity, low insulin resistance and reduced insulin secretion had a pronounced response (0.4 mmol l−1 reduction) and were consequently referred to as responders. Gut microbiota analysis further revealed an association between baseline gut microbiota and pathophysiology and that responders had a different gut microbiota composition. Genomic analyses confirmed that responders had a higher abundance of a Bacteroides-encoded transcriptional regulator required for the conversion of the inactive precursor to bioactive sulforaphane. The abundance of this gene operon correlated with sulforaphane serum concentration. These findings suggest a combined influence of host pathophysiology and gut microbiota on metabolic treatment response, and exploratory analyses need to be confirmed in future trials. ClinicalTrials.gov registration: NCT03763240.
Co-benefit of forestation on ozone air quality and carbon storage in South China
Substantial forestation-induced greening has occurred over South China, affecting the terrestrial carbon storage and atmospheric chemistry. However, these effects have not been systematically quantified due to complex biosphere-atmosphere interactions. Here we integrate satellite observations, forestry statistics, and an improved atmospheric chemistry model to investigate the impacts of forestation on both carbon storage and ozone air quality. We find that forestation alleviates surface ozone via enhanced dry deposition and suppressed turbulence mixing, outweighing the effect of enhanced biogenic emissions. The 2005-2019 greening mitigated the growing season mean surface ozone by 1.4 ± 2.3 ppbv, alleviated vegetation exposure by 15%-41% (depending on ozone metrics) in forests over South China, and increased Chinese forest carbon storage by 1.8 (1.6-2.1) Pg C. Future forestation may enhance carbon storage by 4.3 (3.8-4.8) Pg C and mitigate surface ozone over South China by 1.4 ± 1.2 ppbv in 2050. Air quality management should consider such co-benefits as forestation becomes necessary for carbon neutrality.
Diurnal timing of physical activity in relation to obesity and diabetes in the German National Cohort (NAKO)
Physical activity supports weight regulation and metabolic health, but its timing in relation to obesity and diabetes remains unclear. We aimed to assess the diurnal timing of physical activity and its association with obesity and diabetes.
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