Calendar month seasonality of HbA1c and its phase relationship with climatological temperature in 633,838 clinical measurements.
- 1Laboratory Department of Ruian People's Hospital, Zhejiang, Wenzhou, China.
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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BibTeX
@article{li2026calendar,
title = {{Calendar month seasonality of HbA1c and its phase relationship with climatological temperature in 633,838 clinical measurements.}},
author = {Kecheng Li and Fei Xia},
journal = {Frontiers in endocrinology},
year = {2026},
month = oct,
doi = {10.3389/fendo.2026.1990386},
url = {https://europepmc.org/article/MED/42908711},
note = {CC BY},
}
RIS
TY - JOUR
TI - Calendar month seasonality of HbA1c and its phase relationship with climatological temperature in 633,838 clinical measurements.
AU - Kecheng Li
AU - Fei Xia
PY - 2026
DA - 2026/10/07
JO - Frontiers in endocrinology
DO - 10.3389/fendo.2026.1990386
UR - https://europepmc.org/article/MED/42908711
AB - 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.
SN - 1664-2392
ER -