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Extreme Temperature and Incident Diabetes Risk Among Middle-Aged and Older Adults in China: A National Longitudinal Cohort Study.

BACKGROUND AND AIMS: The metabolic consequences of extreme temperature exposure in nondiabetic populations remain poorly understood. This study aimed to examine associations between heatwave and coldwave exposure and incident diabetes mellitus (DM) and impaired glucose tolerance (IGT) in middle-aged and older Chinese adults. METHODS: A total of 1803 China Health and Retirement Longitudinal Study participants aged &#x2265;45 years with normoglycemia at baseline were followed from Wave 1 (2011) to Wave 3 (2015). Eighteen extreme-temperature indicators were derived from city-level fifth-generation European Centre for Medium-Range Weather Forecasts atmospheric reanalysis data. Outcomes were classified according to American Diabetes Association criteria. Generalized linear mixed-effects models (GLMMs) were pooled across five imputed datasets, with Bonferroni correction for multiple comparisons (&#x3b1; = 0.0028) and sensitivity analysis adjusting for individual follow-up duration. RESULTS: All nine heatwave indicators showed odds ratios (ORs) < 1.0 for DM and IGT. HT9 (&#x2265;97.5th percentile, &#x2265;4 consecutive days) was the sole Bonferroni-significant result: OR = 0.845 (95% confidence interval [CI]: 0.802-0.890). Coldwave indicators showed no consistent associations. Age significantly modified the HT9 effect (P-interaction = 0.004): adults aged 65-84 showed a stronger inverse association (OR = 0.616) than those aged < 65 (OR = 0.921). CONCLUSION: Prolonged heatwave exposure was consistently associated with reduced diabetes risk, with pronounced age heterogeneity. Replication in larger prospective studies is warranted.

Humans

From population to individual: advocating personalised digital tools for heat-health early warning in a changing climate.

Escalating heat extremes under climate change are imposing substantial health burdens, with 2023 and 2024 consecutively breaking global temperature records. Mounting evidence suggests that heatwaves elevate the risks of hospitalisation and mortality across multiple disease categories, including ischaemic heart disease, stroke, chronic obstructive pulmonary disease, and acute kidney injury. Nonetheless, most existing heat-health warning systems remain primarily reliant on population-level predictions, and considering individual differences and disease-specific considerations when defining warning levels would benefit the effectiveness of early prevention for high-risk groups. In this Viewpoint, which is based on the framework of precision public health-delivering the right intervention to the right population at the right time-we propose a framework for personalised digital heat-health early warning tools comprising three dimensions: individualised, risk-stratified prediction models that generate tiered early warnings; personalised health prompts coupled with theory-informed behavioural interventions; and adaptive, equity-oriented alert delivery mechanisms tailored to diverse populations. Such tools have the potential to bridge precision disease prevention and climate adaptation, thereby helping to mitigate heat exposure risks and disease burdens, particularly among high-risk populations. Future implementation research will be essential to address substantial challenges related to feasibility, validation, and equity.

Journal Article