ArticleFrontiers in endocrinology2026
Calendar month seasonality of HbA1c and its phase relationship with climatological temperature in 633,838 clinical measurements.
Article in Frontiers in endocrinology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Objective: HbA1c reflects glucose exposure over the preceding 8-12 weeks. We characterised the lagged seasonal association between monthly climatological temperature normals and HbA1c in a 12-year hospital cohort and examined whether a similar pattern was present in patient-level records. Research design and methods: We analysed 633,838 HbA1c measurements (387,678 patients; 633,737 with complete covariates) from a Chinese tertiary hospital (2014-2025). Cross-correlation was assessed for lags of 0-6 months using monthly HbA1c means and fixed monthly climatological temperature normals. Patient-level distributed-lag regression with cluster-robust standard errors and generalised estimating equations was used as a complementary analysis. Results: HbA1c was inversely correlated with the climatological temperature series most strongly at a 2-month lag (r = -0.297, P < 0.001) rather than concurrently (r = -0.127, P = 0.14). Pooled by calendar month, the lag-2 seasonal association was strong (r = -0.977, R² = 0.955), with a seasonal HbA1c amplitude of 0.235% (2.6 mmol/mol; peak March, trough September), strongest under age 60 and attenuated during COVID-19. Patient-level regression showed a similar lag pattern, with the largest coefficient at lag 2 (β = -0.0088% per °C; 95% CI -0.0093 to -0.0082; P < 0.001), and the individual-level partial R² was 0.15%. Conclusions: The study identified a reproducible seasonal HbA1c pattern that aligned most strongly with the 2-month-shifted climatological temperature series, with significant correspondence also at 1 and 3 months. Because the exposure was a fixed calendar-month normal, the findings should be interpreted as a lagged seasonal association rather than evidence of a causal temperature effect or a specific 2-month biological delay. The small individual-level partial R² (0.15%) also argues against applying the population amplitude directly to individual treatment decisions. Future research should evaluate whether seasonal contextualisation can optimise laboratory monitoring and re-testing strategies near diagnostic thresholds.
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