Effects of seasonal variation on glucose levels among patients with acute ischemic stroke in Tianjin, China

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Patients were grouped by season of onset, with collection of demographic characteristics, laboratory results, admission NIHSS scores and meteorological data. In patients with diabetes, fasting plasma glucose (FPG) levels in winter increased by 1.22 mmol/l (95% CI :1.14,1.29 mmol/l, P < 0.01) versus summer. General linear model analysis revealed reduced FPG risk in summer ( β = -0.083, 95% CI : -0.062, -0.726; P < 0.05) and autumn ( β = -0.125, 95% CI : -0.073, -0.590; P < 0.05) versus winter. HbA1c demonstrated similar seasonal variation, particularly when values exceeding 7.0%, with spring-autumn differences reaching 0.81 units (95% CI : 0.61, 1.02; P < 0.05). HbA1c variations were most pronounced in spring ( β = 0.201, 95% CI : 0.099, 0.557; P < 0.001) and summer ( β = 0.107, 95% CI : 0.096, 0.449; P < 0.05) versus autumn. Winter admissions correlated with greater stroke severity, supported by 1 ◦C temperature decrease was associated with 2.5 (95% CI : -0.5, -4.5, P < 0.01) NIHSS scores increase. Of note, each 0.1 mmol/L increment in FPG corresponding to a 0.8 rise in NIHSS scores (95% CI : 0.5, 1.1, P < 0.01), while HbA1c showed no association. Our findings demonstrated AIS patients showed seasonal glucose fluctuations, with the highest hyperglycemia and severity in winter. Acute hyperglycemia rather than chronic glycemic control was associated with early neurological impairment, highlighting the need for seasonally-adjusted glucose management in high-risk populations. Acute ischemic stroke Blood glucose parameters Seasonal variation Temperature effect Clinical severity Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction As one of the major worldwide causes of death and disability, ischemic stroke is proven to be of seasonal variation across diverse populations and geographical regions (Kurtz et al. 2021 ; Fujii et al. 2021 ; Xue et al. 2023 ). Several trigger factors have been proposed to explain the seasonality in vascular diseases, such as acute infections, hypercoagulable state and increases in blood pressure due to low ambient temperature, as well as seasonal fluctuations in serum lipids and glucose level (Narita and Kario 2023 ; Huta-Osiecka et al. 2021 ). Seasonal variables have been evaluated for their impact on cerebrovascular risk factors, with winter months consistently associated with adverse changes in physiological parameters and risk profiles. Substantial evidence supports the seasonal variation of fasting plasma glucose (FPG), with peak levels typically observed during colder months (Honda et al. 2021 ; Vallianou et al. 2021 ). This phenomenon appears physiologically linked to the acute effects of ambient temperature, as previous studies have demonstrated a negative relationship between temperature and FPG levels (Luo et al. 2021 ; He et al. 2022 ). Given that elevated FPG levels are associated with an increased risk of stroke (Bian et al. 2025 ), these periodic glucose metabolism shifts may contribute to the observed seasonal patterns in stroke incidence and severity. However, the underlying mechanisms and the independent contributions of seasonal factors remain incompletely characterized. Hemoglobin A1c (HbA1c) provides a more comprehensive reflection of glycemic control compared to point-in-time FPG measurements. Seasonal variations in HbA1c levels have been documented among patients with diabetes (Cheng et al. 2023 ; Raphael et al. 2021 ), with important clinical implications since each 1% absolute increase corresponds to an 18% elevation in cardiovascular event risk (Lattanzi et al. 2016 ). Notably, even transient seasonal HbA1c elevations may have cumulative vascular consequences. Evidence from diverse regions, including Japan(Sakamoto et al. 2019 ), Korea (Kim et al. 2014 ), China(Liang 2007 ), Portugal (Pereira et al. 2015 ), the United Kingdom(Carney et al. 2000 ), and the United States(Tseng et al. 2005 ), reveals consistent winter peaks in HbA1c levels exhibit a seasonal decline of 0.13–0.6%, particularly in climates with subzero winter temperatures. Despite these advances, few studies have systematically examined glucose parameter variations in acute ischemic stroke (AIS) populations. Furthermore, comprehensive analyses integrating meteorological data with glucose parameter and stroke severity are lacking. Therefore, in the current study, we investigated seasonal and monthly variations in blood glucose levels among AIS patients across selected areas of the Tianjin district of China. Additionally, we evaluated the influence of meteorological parameters on these seasonal patterns and explored the potential associations with clinical outcomes. Our findings provide novel insights into environmental determinants of AIS risk and opportunities for seasonally-tailored prevention strategies. Materials and methods Research ethics approval All data used in the present study area anonymous without identifiable personal information. Ethical approval was obtained from the the Ethics Committee of the Second Hospital of Tianjin Medical University (KY2020K142). Study Area and Regional Climate Tianjin Municipality is located in the northeast North China Plain between 38°34′-40°15 N and 116°43′།118°194′ E. It is an important component of the Beijing-Tianjin-Hebei (BTH) city cluster with a total area of 11947 km 2 and a resident population of 15568700. The area belongs to the continental monsoon climate. The average annual temperature is between 10℃ and 12℃, with the mean temperature of − 1.9℃ in January and 26.4℃ in July. The weather in Tianjin follows 4 distinct seasons, winter, spring, summer, and autumn. The Second Hospital of Tianjin Medical University is the regional tertiary hospital of the western district in the city of Tianjin, which is one of the comprehensive stroke centers qualified to treat stroke. Meteorological Data The meteorological variables studied included daily mean ambient temperature (maximum and minimum) of the 24-hr calendar day period (0:00AM-11:59PM), monthly measures of mean temperature and diurnal temperature range (the difference between the monthly average maximum and minimum temperatures) for the 10-year study period, which were obtained from the Meteorological Administration of Tianjin. The division of seasons was made according to the local criteria, which was basically based on the temperature patterns. Spring was counted from 1 March to 31 May, summer from 1 June to 31 August, autumn from 1 September to 30 November and winter from 1 December to 28 February. Study population To ascertain the variation of blood glucose and HbA1c levels in different seasons, we retrospectively searched the records based on the Second Hospital of Tianjin Medical University from January 2013 to December 2022 and identified 9694 consecutive patients who were admitted with AIS. No exceptional events concerning weather or environment were noted in this period. We then excluded 964 patients who were not eligible for this study for the reasons of no blood samples were collected to detect HbA1c or FPG. A total of 8730 patients diagnosed as AIS were finally enrolled, of whom 5864 were non-diabetic patients and another 2866 were diabetic patients with cerebral infarction. Stroke was defined according to the definition of the World Health Organization (WHO) Statistical analysis. Diagnosis was confirmed by CT or MRI in all cases. Data collection Demographic and clinical data of age, sex, body mass index (BMI), medical history, blood pressure, results of laboratory tests and admission National Institute of Health Stroke Scale (NIHSS) score were collected. Diabetes mellitus was defined as glycated hemoglobin levels of above 6.5%, fasting plasma glucose levels of above 7mmol/L, or the use of anti-diabetic medication. The first systolic and diastolic blood pressure (SBP, DBP) measured on admission was used for the analyses. Blood samples were collected from the antecubital vein in the morning after an overnight fasting period (> 8 h) and transfused into vacuum tubes containing Ethylene Diamine Tetraacetic Acid (EDTA). Physical activity was assessed as participation in regular sporting activities at least once a week for a minimum of 30 min. BMI was defined as body weight (kg) divided by the square of height (meters). Admission severity was measured using NIHSS scores. The NIHSS scores were classified into three categories and proportion of patients: A score from 0–3 was defined as the mild level, 4–7 as the moderate level and > 7 as the severe level. After enrollment, patients were divided into four groups based on the onset seasons: spring, summer, autumn and winter. Statistics analysis We performed all statistical analyses using IBM SPSS Statistics for Macintosh, Version 26.0 (IBM Corp., Armonk, New York, USA) and R software (version 4.4.2). Continuous data were expressed as mean ± standard deviation (SD) or median and interquartile range (IQR). Categorical data were presented as absolute values and percentages. Chi-test was used to analyze distribution of frequencies, while one-way ANOVA was used for normally distributed continuous variables comparisons, followed by the Tukey–Kramer post hoc test. The Kruskal-Wallis H test was used for comparisons of non-normally distributed continuous variables between groups. The general linear model was applied for calculating the seasonal effects on glycemic parameters. The associations between ambient temperature, glycemic parameters and NIHSS scores were assessed using generalized additive mixed model (GAMM). A natural cubic spline with 5 degrees of freedom (df) was used to model potential nonlinear effects of ambient temperature and glycemic parameters on NIHSS scores. All statistical tests were two-tailed, and P < 0.05 was considered statistically significant. Results Characteristics of study subjects and seasonal patterns Table 1 provided baseline characteristics of the participants. Over the 10-year study period, 8730 AIS patients occurred in 5527 male patients, wherein 62.20% were ≥ 65 years old. Significant seasonal variations were observed in several clinical parameters, including body mass index (BMI, P = 0.013), systolic blood pressure (SBP, P = 0.027), diastolic blood pressure (DBP, P = 0.045), and FPG levels in diabetic patients ( P = 0.033). Analysis of clinical outcomes revealed distinct seasonal patterns, with poorer outcomes predominantly observed during spring and winter compared to summer and autumn. Among diabetic patients, the winter group exhibited the highest median NIHSS score of 7, while the summer group showed the lowest median score of 5. The proportion of patients admitted with NIHSS scores > 7 varied seasonally: 31.8% in winter, 18.8% in spring, 14.7% in summer, and 20.7% in autumn. Table 1 Baseline characteristics of study participants distributed by season Total(n = 8730) Spring(n = 2226) Summer(n = 1641) Autumn(n = 2169) Winter(n = 2694) P value Days per year 365 92 92 91 90 - First-ever stroke cases (%) 6277(71.90) 1606(72.15) 1128(68.74) 1560(71.91) 1983(73.61) 0.813 Male (%) 5527(63.31) 1414(63.51) 1069(65.17) 1370(63.16) 1674(62.15) 0.626 Age, years 70.61 ± 12.07 71.75 ± 11.42 69.04 ± 12.24 69.44 ± 12.76 71.04 ± 12.28 0.875 Age, years(%) <65 3298(37.78) 873(39.23) 630(38.42) 832(38.38) 963(35.76) 0.208 65–80 3658(41.90) 865(38.85) 725(44.21) 920(42.42) 1148(42.61) ≥ 80 1772(20.30) 488(21.92) 285(17.37) 416(19.20) 583(21.63) Body mass index (kg/m2) 25.58 ± 3.49 25.8 ± 3.21 24.3 ± 3.30 a 25.1 ± 3.92 26.6 ± 3.57 b 0.013 Blood pressure (mmHg) Systolic 151.98 ± 21.69 153.69 ± 22.39 146.14 ± 20.92 a 151.26 ± 22.48 b 154.73 ± 23.06 b 0.027 Diastolic 85.10 ± 12.91 86.79 ± 13.67 81.92 ± 12.84 a 85.48 ± 13.25 b 85.38 ± 12.91 b 0.045 FPG (mmol/L) Diabetes 9.75(9.39, 10.1) 9.46(8.60, 10.32) 9.07(8.45, 9.70) a 9.50(8.56, 10.44) b 10.29(9.74, 10.84) abc 0.033 Non-diabetes 5.20(5.14, 5.25) 5.14(5.00, 5.28) 5.27(5.16, 5.38) 5.19(5.06, 5.32) 5.17(5.05, 5.28) 0.339 HbA1c Diabetes 8.04(7.89, 8.18) 8.07(7.75, 8.39) 8.02(7.68, 8.35) 8.01(7.53, 8.48) 8.05(7.83, 8.27) 0.764 Non-diabetes 5.70(5.67, 5.72) 5.69(5.63, 5.75) 5.75(5.69, 5.80) 5.66(5.59, 5.71) 5.68(5.63, 5.74) 0.239 Diabetes (%) 5615(64.32) 1438(64.62) 1071(65.26) 1358(62.63) 1748(64.88) 0.877 Hyperlipemia(%) 3463(39.67) 869(39.05) 660(40.20) 861(39.68) 1073(39.84) 0.488 Chronic kidney disease (%) 1527(17.49) 391(17.57) 276(16.83) 374(17.26) 486(18.04) 0.579 Physical activity(%) 1113(12.75) 327(14.69) 203(12.39) 293(13.52) 290(10.78) 0.179 Current smoking (%) 2510(28.75) 582(26.13) 462(28.15) 633(29.18) 833(30.93) 0.432 Current alcohol drinking (%) 1329(15.22) 316(14.21) 226(13.78) 340(15.69) 447(16.59) 0.544 Anti-diabetic drugs (%) Insulin 1238(14.18) 327(14.71) 236(14.36) 304(14.02) 371(13.78) 0.713 Oral drugs only 2755(31.56) 708(31.82) 508(30.95) 674(31.07) 865(32.12) 0.625 Anti-hypertensive drugs (%) 4936(56.54) 1266(56.89) 939(57.20) 1198(55.24) 1533(56.90) 0.335 Lipid-lowering agents (%) 1396(15.99) 358(16.07) 253(15.43) 346(15.96) 439(16.28) 0.417 NIHSS score Diabetes 6(2, 10) 6(1, 10) 5(2, 9) 5(2, 10) 7(2, 10) abc 0.021 Non-diabetes 4(1, 9) 5(2, 9) 3(1, 8) 4(2, 8) 6(2, 10) 0.065 Severity levels (%) NIHSS 0–3 3953(45.28) 1139(51.16) 829(50.51) 1014(46.75) 944(35.05%) 7 1574(18.03) 419 (18.84) 241(14.70) 49(20.68) 865(31.78) Clinical outcomes Death (%) 502(5.75) 122(5.46) 75(4.56) 112(5.18) 193(7.17) 0.062 Data is mean ± standard deviation, number (percentage) or median (interquartile range). a P < 0.05 vs spring; b P < 0.05 vs summer; c P < 0.05 vs autumn. FPG, fasting plasma glucose; NIHSS, National Institute of Health Stroke Scale. Bold values indicate statistical significance. Table 2 summarizes the meteorological and glucose parameters during the study period, including FPG, HbA1c, daily mean temperature, air pressure, relative humidity, and wind speed. Diabetic patients demonstrated significantly higher serum levels of FPG and HbA1c compared to non-diabetic individuals. The mean environmental conditions were as follows: temperature 15.05°C, air pressure 1016.17 hPa, relative humidity 51.73%, and wind speed 1.73 m/s. Table 2 Summary statistics of glucose levels and weather conditions during 2013–2022 Variables Minimum 5% 25% 50% 75% 95% Maximum Mean SD P values for difference of means FPG(mmol/L) All 1.15 4.18 4.96 5.84 7.72 14.46 34.33 7.02 3.55 - Diabetes 1.93 4.58 5.95 7.51 9.64 15.17 29.80 8.35 3.43 <0.001 Non-diabetes 1.15 4.13 4.65 5.12 5.67 6.60 6.99 5.18 0.78 HbA1c(%) All 4.30 5.20 5.60 6.10 7.20 10.40 14.30 6.67 1.63 - Diabetes 4.60 5.40 6.30 7.10 8.40 11.30 14.30 7.55 1.79 <0.001 Non-diabetes 4.30 5.10 5.40 5.70 5.90 6.30 6.60 5.68 0.36 Temeparature(℃) -13.85 -2.17 5.23 15.32 25.24 29.49 33.53 15.05 10.83 - Air pressure(hPa) 994.05 1000.98 1006.96 1016.61 1024.23 1033.59 1043.35 1016.17 10.38 - Relative humidity(%) 12.00 21.85 37.30 51.63 66.32 81.56 94.21 51.73 18.45 - Wind speed(m/s) 0.56 0.87 1.24 1.56 2.03 3.10 7.65 1.73 0.76 - Seasonal variations in the level of FPG Figure 2 illustrated the monthly variations in FPG levels in relation to ambient temperature. Additionally, we analyzed FPG levels using box-plot diagrams with descriptive statistics, stratified by quartile groups of meteorological factors. The analysis revealed significant seasonal fluctuations in FPG levels across the study population. Specifically, FPG levels exhibited an inverse relationship with temperature, demonstrating lower values during warmer months and higher values during colder months. Among diabetic patients, the peak FPG level was observed in January (11.17 mmol/L), while the lowest level was recorded in June (8.69 mmol/L). FPG levels were significantly higher in winter compared to summer ( P = 0.032) and autumn ( P = 0.045). A statistically significant increase in FPG levels was observed during winter compared to summer, with a mean difference of 1.22 mmol/L (95% CI : 1.10–1.29 mmol/L; P = 0.005). Seasonal variations in the level of HbA1c and its consistent cyclic variation with FPG Furthermore, we applied a 3-month lag to the monthly average HbA1c values. These lagged variables were incorporated into a linear trend model to assess the correlation between HbA1c and FPG. As illustrated in Fig. 3 A and B, HbA1c values exhibited significant monthly fluctuations that were closely associated with FPG levels. After adjusting for linear trends, the current average HbA1c values were found to be significantly influenced by FPG levels from 3 months prior. Notably, early spring exhibited a significant increase in HbA1c levels, while late summer showed a marked decrease (Fig. 3 C and D). The analysis revealed significantly higher HbA1c levels during spring compared to autumn and winter in diabetes patients with suboptimal glycemic control (HbA1c > 7.0%), as shown in Fig. 3 E. Seasonal fluctuation quantitative assessment demonstrated that diabetic patients had significantly elevated HbA1c levels during spring, showing 0.77 units (95% CI : 0.59–0.92; P = 0.027) and 0.81 units (95% CI : 0.61–1.02; P = 0.025) increases compared to winter and autumn measurements, respectively. However, no significant seasonal fluctuations were observed among patients who achieved the goal of HbA1c < 7%. Given that HbA1c reflects average blood glucose levels over approximately 3 months, these findings suggested that both extreme cold temperatures and large diurnal temperature variations may contribute to increased blood glucose levels, potentially explaining the observed seasonal patterns in glucose control. The effects of seasons on FPG and HbA1c After adjusted for age, sex, BMI and other covariates, summer and autumn group had 0.083 times (95% CI : -0.062, -0.726, P = 0.032) and 0.125 times (95% CI : -0.590, -0.073, P = 0.012) the risk of suffering lower FPG levels than winter group, respectively (Table 3 ). Meanwhile, general linear model revealed that risk of higher HbA1c levels increased to 0.201 times in spring (95% CI : 0.099, 0.557, P < 0.001) and 0.107 times in summer (95% CI : 0.096, 0.449, P = 0.032) compared with autumn group, respectively (Table 4 ). Table 3 Estimates of season effects on FPG levels in the general linear model Dependent variable Model 1 Model 2 β coefficien (95% CI ) P value β coefficient (95% CI ) P value Spring -0.071(-1.451,0.358) 0.236 -0.023(-0.944, 0.583) 0.583 Summer -0.124(-0.033, -0.971) 0.036 -0.083(-0.062, -0.726) 0.042 Autumu -0.176(-0.160, -0.778) 0.003 -0.125(-0.073, -0.590) 0.012 Winter(reference) 0 0 Model 1 was unadjusted; Model 2 was adjusted for age, sex, BMI, physical activity, current smoking, blood pressure, combined with diabetes, using anti-diabetic drugs, serum creatinine, low density lipoprotein and NIHSS scores. CI, confidential interval. Table 4 Estimates of season effects on HbA1c levels ≥ 7% in the general linear model Dependent variable Model 1 Model 2 β coefficien (95% CI ) P value β coefficient (95% CI ) P value Spring 0.232(0.012, 0.447) <0.001 0.201(0.099, 0.557) <0.001 Summer 0.182(0.101, 0.492) 0.003 0.107(0.096, 0.449) 0.032 Autumu(reference) 0 0 Winter 0.054(-0.077, 0.082) 0.112 0.017(-0.051, 0.049) 0.153 Model 1 was unadjusted; Model 2 was adjusted for age, sex, BMI, physical activity, current smoking, blood pressure, combined with diabetes, using anti-diabetic drugs, serum creatinine, low density lipoprotein and NIHSS scores. CI, confidential interval. Association between glycemic parameters and seasonal variations in admission stroke severity Figure 4 A demonstrated the exposure response curves between mean temperatures and admission NIHSS scores, which seems to be non-linear negative correlation. Gaussian generalized additive mixture model is adjusted according to age, gender, season, relative humidity, atmospheric pressure and past history. It means that as the temperature decreased, the admission NIHSS scores increased. Of note, 1 ◦C decrease of mean temperature was associated with 2.5 (95% CI : -0.5, -4.5, P < 0.01) increase of admission NIHSS scores. After adjusting for age, sex, season, past history, SBP, DBP, TC, TG, LDL-C, HDL-C, Creatinine, UA, HbA1c, BMI, lipid lowering drugs, antidiabetic drugs, and antiplatelet drugs, the smooth curve fitting plot demonstrated a non-linear positive relationship between FPG and admission NIHSS scores (Fig. 4 B). Specifically, higher FPG levels were positively correlated with increased stroke severity at admission, with each 0.1 mmol/L increment in FPG corresponding to a 0.8 rise in NIHSS scores (95% CI : 0.5, 1.1, P < 0.01). However, HbA1c values has no effect on admission NIHSS scores. Discussion In this population-based stroke registry study, we identified significant seasonal variations in both FPG and HbA1c levels among AIS patients, particularly those with diabetes. The results demonstrated that mean FPG concentrations exhibited a distinct seasonal pattern, peaking in January (11.07 mmol/L) and reaching their lowest levels in June (8.69 mmol/L). Notably, AIS patients with diabetic displayed more pronounced fluctuations, with winter FPG levels being approximately 1.22 mmol/L higher than those in summer. The risk of elevated FPG levels was markedly reduced in both summer ( β = -0.083, 95% CI : -0.062, -0.726, P = 0.042) and autumn ( β = -0.125, 95% CI : -0.073, -0.590, P = 0.012) when compared to winter. Our findings were consistent with previous studies demonstrating seasonal variations in glycemic control among diabetic patients, characterized by higher glucose levels in winter and lower levels in summer (Belsare et al. 2023 ; Takai et al. 2020 ). This observation aligned with a study of 49,417 participants, which revealed a U-shaped relationship between ambient temperature and FPG levels, with particularly elevated glucose concentrations during winter months (Li et al. 2016 ). A multi-center Chinese study analyzing 1.4 million physical examination population confirmed both the seasonal discrepancy in FPG levels and a distinct north-south gradient, with northern regions exhibiting higher winter glucose values than southern areas (Zhang et al. 2021 ). Meanwhile, a comparable seasonal trend was observed for HbA1c levels, especially in patients with suboptimal glycemic control (HbA1c > 7.0%). The difference between the highest HbA1c value (observed in spring) and the lowest (observed in autumn) was approximately 0.81 units. HbA1c exhibited a significant upward trend in early spring and a downward trend in late summer, underscoring the dynamic nature of glycemic control across seasons. General linear model analysis confirmed the risk of elevated HbA1c being significantly higher in spring ( β = 0.201, 95% CI : 0.099, 0.557, P < 0.001) and summer ( β = 0.107, 95% CI : 0.096, 0.449, P = 0.032) compared to autumn. The spring HbA1c peak likely reflected the delayed integration of winter hyperglycemia (December–February), when cold temperatures exacerbated glycemic dysregulation. Conversely, improved glucose control during warmer months manifests as lower autumn HbA1c. This temporal shift aligned with HbA1c’s role as a 2–3 month glycemic marker (Ahuja et al. 2024 ) . The above results indicated that although the extreme temperature difference might affect the fluctuation of blood glucose, the overall mean blood glucose levels remained highest in winter, which may further influence the incidence and severity of stroke. Higgin et al. (Higgins et al. 2009 ) found significant seasonal fluctuations in both northern and southern hemispheres (higher HbA1c in cold months and lower in warm months), while Singapore's stable climate showed no fluctuations, confirming temperature's influence on glycemic control. The biological mechanisms that underlay the seasonality in blood glucose levels might include seasonal variation in HbA1c and physiological changes. Studies have found that a complex of potential factors including the stressful to carbohydrate tolerance in winter, alteration in diet, decreased physical activity, exposure to sunlight, weight gain and increase in counter-insulin hormones (Iwata et al. 2024 ; Nicolo and Boullata 2019 ; Banihani et al. 2020 ), could partially explain the seasonal variation of blood glucose levels. These findings highlighted the importance of considering seasonal and environmental factors in the management of diabetes and stroke prevention strategies. Furthermore, our findings corroborated a significant nonlinear negative correlation between ambient temperature and NIHSS scores, while an association between FBG levels and NIHSS scores. A growing body of evidence had identified an association between elevated glucose at admission and increased mortality or poor outcome following AIS (Kim et al. 2023 ; Shi et al. 2025 ; Zhang et al. 2024 ). Post-stroke hyperglycemia was known to correlate with stroke severity, potentially mediated by a stress-induced cortisol response (Mosenzon et al. 2023 ). Hyperglycemia may exacerbate ischemic brain injury by amplifying inflammatory responses, thereby promoting neuronal damage(Climent et al. 2024 ; Bains et al. 2023 ). At the vascular level, dysregulation of glucose metabolism induced endothelial dysfunction through oxidative stress-mediated pathways, accelerating atherogenesis and increasing vulnerability to acute cerebrovascular incidents(Karakasis et al. 2025 ). The present study showed notable seasonal variations in admission severity and clinical outcomes, with more severe neurological deficits observed in winter. This temporal pattern aligned with the hypothesis that acute hyperglycemia, as reflected by elevated FPG levels, may exacerbate ischemic injury and contribute to adverse functional recovery. In contrast, no statistically significant relationship was observed between HbA1c levels and admission NIHSS scores, suggesting that acute hyperglycemia (FPG) rather than chronic glycemic control (HbA1c) may drive early neurological impairment. These observations underscored the potential clinical relevance of acute glucose management in AIS. The robust association between FPG and outcomes highlighted the need for further investigation into targeted glycemic control strategies during the acute phase of ischemic stroke. Despite its significant insights, our single hospital-based study was not without limitations. Firstly, as a retrospective analysis, potential biases in data collection and residual confounding factors might exist. Secondly, selection bias might have occurred since cases who died before hospitalization or sought treatment elsewhere were excluded. Thirdly, unmeasured lifestyle factors (e.g., dietary patterns and physical activity) could potentially confound the observed seasonal glucose variations. Most importantly, the establishment of causality required prospective cohort studies with larger sample sizes due to the cross-sectional nature of our research. While acknowledging these limitations, the rigorous exclusion of potential confounding factors and clinical validation strengthened the reliability of our findings. Future investigations should incorporate longitudinal designs to establish causal relationships between glycemic parameters and AIS progression. Furthermore, molecular-level studies were warranted to elucidate the precise mechanisms, particularly through characterization of involved inflammatory pathways. Such mechanistic insights might reveal novel therapeutic targets for glycemic control in stroke management. Conclusions In conclusion, this 10-year longitudinal study demonstrated a consistent cyclic pattern in FPG and HbA1c levels, with significant seasonal and monthly variations. Notably, extreme cold temperatures and large diurnal temperature differences were associated with elevated blood glucose, which in turn correlated with increased stroke incidence and severity. These findings highlight the potential influence of climatic factors on metabolic dysregulation and cerebrovascular risk. Further research is warranted to elucidate the underlying pathophysiological mechanisms and explore their implications for the management of AIS, particularly in high-risk populations exposed to extreme weather conditions. Declarations Competing interests The authors declare no competing interests. Funding This work was supported by Youth Foundation of the Second Hospital of Tianjin Medical University (2018ydey12), Key Projects of Tianjin Municipal Health Commission (TJWJ2024XK008), Tianjin Key Medical Discipline (Specialty) Construction Project (TYXZDXK065B), Tianjin Center for Health and Meteorology Multidisciplinary Innovation and the National Natural Science Foundation of China (42275197). Author contributions M.M.W. conducted the statistical analyses and drafted the initial manuscript. F.Y.W. was responsible for data acquisition and curation. X.S.X. and L.W. provided critical intellectual input during manuscript revision. X.L. supervised the study and had primary responsibility for the final content. All authors reviewed and approved the final version of the manuscript. Acknowledgements The authors thank all the study participants, staff of the participating hospitals. 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Int J Mol Sci 26(5). 10.3390/ijms26052196 Kim JT, Lee JS, Kim BJ, Kang J, Lee KJ, Park JM, Kang K, Lee SJ, Kim JG, Cha JK, Kim DH, Park TH, Lee KB, Lee J, Hong KS, Cho YJ, Park HK, Lee BC, Yu KH, Oh MS, Kim DE, Choi JC, Kwon JH, Kim WJ, Shin DI, Yum KS, Sohn SI, Hong JH, Lee SH, Park MS, Choi KH, Ryu WS, Lee J, Saver JL, Bae HJ (2023) Admission hyperglycemia, stroke subtypes, outcomes in acute ischemic stroke. Diabetes Res Clin Pract 196:110257. 10.1016/j.diabres.2023.110257 Kim YJ, Park S, Yi W, Yu KS, Kim TH, Oh TJ, Choi J, Cho YM (2014) Seasonal variation in hemoglobin a1c in korean patients with type 2 diabetes mellitus. J Korean Med Sci 29(4):550–555. 10.3346/jkms.2014.29.4.550 Kurtz P, Bastos LS, Aguilar S, Hamacher S, Bozza FA (2021) Effect of seasonal and temperature variation on hospitalizations for stroke over a 10-year period in Brazil. Int J stroke: official J Int Stroke Soc 16(4):406–410. 10.1177/1747493020947333 Lattanzi S, Bartolini M, Provinciali L, Silvestrini M (2016) Glycosylated Hemoglobin and Functional Outcome after Acute Ischemic Stroke. J Stroke Cerebrovasc Dis 25(7):1786–1791. 10.1016/j.jstrokecerebrovasdis.2016.03.018 Li S, Zhou Y, Williams G, Jaakkola JJ, Ou C, Chen S, Yao T, Qin T, Wu S, Guo Y (2016) Seasonality and temperature effects on fasting plasma glucose: A population-based longitudinal study in China. Diabetes Metab 42(4):267–275. 10.1016/j.diabet.2016.01.002 Liang WW (2007) Seasonal changes in preprandial glucose, A1C, and blood pressure in diabetic patients. Diabetes Care 30(10):2501–2502. 10.2337/dc07-0597 Luo J, He G, Xu Y, Chen Z, Xu X, Peng J, Chen S, Hu J, Ji G, Liu T, Zeng W, Li X, Xiao J, Guo L, He Q, Ma W (2021) The relationship between ambient temperature and fasting plasma glucose, temperature-adjusted type 2 diabetes prevalence and control rate: a series of cross-sectional studies in Guangdong Province, China. BMC Public Health 21(1):1534. 10.1186/s12889-021-11563-5 Mosenzon O, Cheng AY, Rabinstein AA, Sacco S (2023) Diabetes and Stroke: What Are the Connections? J stroke 25(1):26–38. 10.5853/jos.2022.02306 Narita K, Kario K (2023) Seasonal variation in blood pressure and its impact on target organ damage and cardiovascular disease incidence. Hypertens research: official J Japanese Soc Hypertens 46(7):1710–1711. 10.1038/s41440-023-01289-9 Nicolo M, Boullata JI (2019) Serum 25OHD concentration as a predictor of haemoglobin A1c among adults living in the USA: NHANES 2003 to 2010. BMJ nutrition, prevention & health 2. 135–38. 10.1136/bmjnph-2019-000029 Pereira MT, Lira D, Bacelar C, Oliveira JC, de Carvalho AC (2015) Seasonal variation of haemoglobin A1c in a Portuguese adult population. Arch Endocrinol Metab 59(3):231–235. 10.1590/2359-3997000000043 Raphael A, Friger M, Biderman A (2021) Seasonal variations in HbA1c among type 2 diabetes patients on a semi-arid climate between the years 2005–2015. Prim Care Diabetes 15(3):502–506. 10.1016/j.pcd.2020.11.013 Sakamoto M, Matsutani D, Minato S, Tsujimoto Y, Kayama Y, Takeda N, Ichikawa S, Horiuchi R, Utsunomiya K, Nishikawa M (2019) Seasonal Variations in the Achievement of Guideline Targets for HbA1c, Blood Pressure, and Cholesterol Among Patients With Type 2 Diabetes: A Nationwide Population-Based Study (ABC Study: JDDM49). Diabetes Care 42(5):816–823. 10.2337/dc18-1953 Shi X, Yang S, Guo C, Sun W, Song J, Fan S, Yang J, Yue C, Huang J, Li L, Tian Y, Ma J, Xu X, Wang Z, Kong W, Ye D, Peng Z, Li F, Zi W (2025) Impact of stress hyperglycemia on outcomes in patients with large ischemic stroke. J neurointerventional Surg. 10.1136/jnis-2024-021899 Takai M, Ishikawa M, Maeda H, Kubota A, Iemitsu K, Umezawa S, Kawata T, Takuma T, Takeda H, Tanaka K, Machimura H, Minagawa F, Mokubo A, Motomiya T, Kanamori A, Matsuba I (2020) A Study of Seasonal Variation in the Effect of Add-On Sitagliptin on Blood Glucose Control in Insulin-Treated Patients With Type 2 Diabetes. J Clin Med Res 12(3):200–208. 10.14740/jocmr4103 Tseng CL, Brimacombe M, Xie M, Rajan M, Wang H, Kolassa J, Crystal S, Chen TC, Pogach L, Safford M (2005) Seasonal patterns in monthly hemoglobin A1c values. Am J Epidemiol 161(6):565–574. 10.1093/aje/kwi071 Vallianou NG, Geladari EV, Kounatidis D, Geladari CV, Stratigou T, Dourakis SP, Andreadis EA, Dalamaga M (2021) Diabetes mellitus in the era of climate change. Diabetes Metab 47(4):101205. 10.1016/j.diabet.2020.10.003 Xue J, Liu P, Xia X, Qi X, Han S, Wang L, Li X (2023) Seasonal Variation in Neurological Severity and Clinical Outcomes in Ischemic Stroke Patients - A 9-Year Study of 5,238 Patients. Circulation journal: official J Japanese Circulation Soc 87(9):1187–1195. 10.1253/circj.CJ-22-0801 Zhang Y, Tong M, Wang B, Shi Z, Wang P, Li L, Ning Y, Lu T (2021) Geographic, Gender, and Seasonal Variation of Diabetes: A Nationwide Study With 1.4 Million Participants. J Clin Endocrinol Metab 106(12):e4981–e4992. 10.1210/clinem/dgab543 Zhang Y, Yin X, Liu T, Ji W, Wang G (2024) Association between the stress hyperglycemia ratio and mortality in patients with acute ischemic stroke. Sci Rep 14(1):20962. 10.1038/s41598-024-71778-5 Supplementary Files SUPPLEMENTALMATERIAL.pdf Cite Share Download PDF Status: Published Journal Publication published 05 Jan, 2026 Read the published version in International Journal of Biometeorology → Version 1 posted Reviewers agreed at journal 08 May, 2025 Reviewers invited by journal 08 May, 2025 Editor assigned by journal 16 Apr, 2025 First submitted to journal 15 Apr, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6460523","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":453701834,"identity":"8aa7f884-9065-498c-949c-355c353c1a4d","order_by":0,"name":"Wei Miaomiao","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Wei","middleName":"","lastName":"Miaomiao","suffix":""},{"id":453701835,"identity":"3dc12f01-68da-4e59-b184-bd7d89b29ee5","order_by":1,"name":"Wang Fuyin","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Wang","middleName":"","lastName":"Fuyin","suffix":""},{"id":453701836,"identity":"4ecb7113-75ab-44ab-869a-204e1b42762c","order_by":2,"name":"Xia Xiaoshuang","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Xia","middleName":"","lastName":"Xiaoshuang","suffix":""},{"id":453701837,"identity":"b4a995f2-639b-49fe-84a9-b4685eba2e54","order_by":3,"name":"Wang Lin","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Wang","middleName":"","lastName":"Lin","suffix":""},{"id":453701838,"identity":"c7eedc56-5a93-49c9-bbf1-9ccc2cdb06a7","order_by":4,"name":"Xin Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAtklEQVRIiWNgGAWjYBACPmYGhgNAmoefmfngA6K0sMG0SLazJRsQpwXGMDjPYyZAnBZ2HsPDBX/sZIwPM5gxMNTYRBPhMLaEwzPbknnMDjOkPWA4lpbbQFgL84HDvA0HQFqOGzA2HCZGC1AZz58DPMbNjG0SRGoB2sLDdoDHgJmZjVgtQL/wAv0icZiN2SCBGL/w858x/szzx86ev//8xwcfamwIa0EFCaQpHwWjYBSMglGACwAA6sY0Tv8aHB8AAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0002-2977-5075","institution":"Tianjin Medical University Second Hospital: The Second Hospital of Tianjin Medical University","correspondingAuthor":true,"prefix":"","firstName":"Xin","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2025-04-16 07:17:52","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6460523/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6460523/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s00484-025-03103-2","type":"published","date":"2026-01-05T15:58:34+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":82621095,"identity":"eb869983-a5fe-48b2-a171-50a8c3a64efb","added_by":"auto","created_at":"2025-05-13 12:19:06","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":43410,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of study enrollment\u003c/p\u003e","description":"","filename":"Fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-6460523/v1/b39ae7474c4c6ef021bb9d78.png"},{"id":82622304,"identity":"11bde199-33c3-48db-a719-54a06eb8f9e8","added_by":"auto","created_at":"2025-05-13 12:27:06","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":552917,"visible":true,"origin":"","legend":"\u003cp\u003eMonthly mean FPG levels and seasonal changes (a) Monthly mean FPGlevels in patients with diabetes; (b) Seasonal variation in FPG levels in patients with diabetes; (c) Monthly mean FPG levels in patients without diabetes; (d) Seasonal variation in FPG levels in patients without diabetes.\u003c/p\u003e","description":"","filename":"Fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-6460523/v1/9b6171ae52da396c726210b6.png"},{"id":82622300,"identity":"5867bfbb-b673-4a3e-a2f7-ae27aacf4b9f","added_by":"auto","created_at":"2025-05-13 12:27:06","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":748550,"visible":true,"origin":"","legend":"\u003cp\u003eMonthly mean HbA1c values and seasonal changes (a) Linear trend between FPG levels and HbA1c values 3 months lagged in patients with diabetes; (b) Linear trend between FPG levels and HbA1c values 3 months lagged in patients without diabetes; (c) Monthly mean HbA1c values 3 months lagged in patients with diabetes; (d) Monthly mean HbA1c values 3 months lagged in patients without diabetes; (e) Seasonal variation in HbA1c values greater than 7.0% in patients with diabetes; (f) Seasonal variation in HbA1c values in patients without diabetes.\u003c/p\u003e","description":"","filename":"Fig3.png","url":"https://assets-eu.researchsquare.com/files/rs-6460523/v1/2a134ab6a07a9ce1d25c8e9e.png"},{"id":82622301,"identity":"3728a509-e8dd-4ca3-8a73-bac1db252160","added_by":"auto","created_at":"2025-05-13 12:27:06","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":249374,"visible":true,"origin":"","legend":"\u003cp\u003eAssociation between glycemic parameters and seasonal variations in admission stroke severity (a) Curve fitting plot between daily average temperature and NIHSS scores. The Gaussian generalized additive mixture model is adjusted according to age, gender, season, relative humidity, atmospheric pressure and past history; (b) Curve fitting plot between FBG and NIHSS scores. The Gaussian generalized additive mixture model is adjusted according to age, gender, season, past history, SBP, DBP, TC, TG, LDL-C, HDL-C, Creatinine, UA, HbA1c, BMI, lipid lowering drugs, antidiabetic drugs, and antiplatelet drugs; (c) Curve fitting plot between HbA1c and NIHSS scores. The Gaussian generalized additive mixture model is adjusted according to age, gender, season, past history, SBP, DBP, TC, TG, LDL-C, HDL-C, Creatinine, UA, FPG, BMI, lipid lowering drugs, antidiabetic drugs, and antiplatelet drugs. The shadow represents the 95 % CI confidence interval.\u003c/p\u003e","description":"","filename":"Fig4.png","url":"https://assets-eu.researchsquare.com/files/rs-6460523/v1/69bde3faa7f2012237e36179.png"},{"id":100069993,"identity":"c5e75810-100f-4573-8f4d-85705a5a79e8","added_by":"auto","created_at":"2026-01-12 16:15:43","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2390059,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6460523/v1/cbff419b-468b-4a23-9313-bd2fcf0c105a.pdf"},{"id":82621109,"identity":"a0db5faf-f53e-4b0d-88e3-36e6bf42bad7","added_by":"auto","created_at":"2025-05-13 12:19:06","extension":"pdf","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":1734503,"visible":true,"origin":"","legend":"","description":"","filename":"SUPPLEMENTALMATERIAL.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6460523/v1/b477fd093f9254f107e0b11e.pdf"}],"financialInterests":"","formattedTitle":"Effects of seasonal variation on glucose levels among patients with acute ischemic stroke in Tianjin, China","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAs one of the major worldwide causes of death and disability, ischemic stroke is proven to be of seasonal variation across diverse populations and geographical regions (Kurtz et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Fujii et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Xue et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Several trigger factors have been proposed to explain the seasonality in vascular diseases, such as acute infections, hypercoagulable state and increases in blood pressure due to low ambient temperature, as well as seasonal fluctuations in serum lipids and glucose level (Narita and Kario \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Huta-Osiecka et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Seasonal variables have been evaluated for their impact on cerebrovascular risk factors, with winter months consistently associated with adverse changes in physiological parameters and risk profiles. Substantial evidence supports the seasonal variation of fasting plasma glucose (FPG), with peak levels typically observed during colder months (Honda et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Vallianou et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This phenomenon appears physiologically linked to the acute effects of ambient temperature, as previous studies have demonstrated a negative relationship between temperature and FPG levels (Luo et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; He et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Given that elevated FPG levels are associated with an increased risk of stroke (Bian et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), these periodic glucose metabolism shifts may contribute to the observed seasonal patterns in stroke incidence and severity. However, the underlying mechanisms and the independent contributions of seasonal factors remain incompletely characterized.\u003c/p\u003e \u003cp\u003eHemoglobin A1c (HbA1c) provides a more comprehensive reflection of glycemic control compared to point-in-time FPG measurements. Seasonal variations in HbA1c levels have been documented among patients with diabetes (Cheng et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Raphael et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), with important clinical implications since each 1% absolute increase corresponds to an 18% elevation in cardiovascular event risk (Lattanzi et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Notably, even transient seasonal HbA1c elevations may have cumulative vascular consequences. Evidence from diverse regions, including Japan(Sakamoto et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), Korea (Kim et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), China(Liang \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2007\u003c/span\u003e), Portugal (Pereira et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), the United Kingdom(Carney et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2000\u003c/span\u003e), and the United States(Tseng et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2005\u003c/span\u003e), reveals consistent winter peaks in HbA1c levels exhibit a seasonal decline of 0.13\u0026ndash;0.6%, particularly in climates with subzero winter temperatures. Despite these advances, few studies have systematically examined glucose parameter variations in acute ischemic stroke (AIS) populations. Furthermore, comprehensive analyses integrating meteorological data with glucose parameter and stroke severity are lacking.\u003c/p\u003e \u003cp\u003eTherefore, in the current study, we investigated seasonal and monthly variations in blood glucose levels among AIS patients across selected areas of the Tianjin district of China. Additionally, we evaluated the influence of meteorological parameters on these seasonal patterns and explored the potential associations with clinical outcomes. Our findings provide novel insights into environmental determinants of AIS risk and opportunities for seasonally-tailored prevention strategies.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eResearch ethics approval\u003c/h2\u003e \u003cp\u003eAll data used in the present study area anonymous without identifiable personal information. Ethical approval was obtained from the the Ethics Committee of the Second Hospital of Tianjin Medical University (KY2020K142).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStudy Area and Regional Climate\u003c/h3\u003e\n\u003cp\u003eTianjin Municipality is located in the northeast North China Plain between 38\u0026deg;34\u0026prime;-40\u0026deg;15 N and 116\u0026deg;43\u0026prime;།118\u0026deg;194\u0026prime; E. It is an important component of the Beijing-Tianjin-Hebei (BTH) city cluster with a total area of 11947 km\u003csup\u003e2\u003c/sup\u003e and a resident population of 15568700. The area belongs to the continental monsoon climate. The average annual temperature is between 10℃ and 12℃, with the mean temperature of \u0026minus;\u0026thinsp;1.9℃ in January and 26.4℃ in July. The weather in Tianjin follows 4 distinct seasons, winter, spring, summer, and autumn. The Second Hospital of Tianjin Medical University is the regional tertiary hospital of the western district in the city of Tianjin, which is one of the comprehensive stroke centers qualified to treat stroke.\u003c/p\u003e\n\u003ch3\u003eMeteorological Data\u003c/h3\u003e\n\u003cp\u003eThe meteorological variables studied included daily mean ambient temperature (maximum and minimum) of the 24-hr calendar day period (0:00AM-11:59PM), monthly measures of mean temperature and diurnal temperature range (the difference between the monthly average maximum and minimum temperatures) for the 10-year study period, which were obtained from the Meteorological Administration of Tianjin. The division of seasons was made according to the local criteria, which was basically based on the temperature patterns. Spring was counted from 1 March to 31 May, summer from 1 June to 31 August, autumn from 1 September to 30 November and winter from 1 December to 28 February.\u003c/p\u003e\n\u003ch3\u003eStudy population\u003c/h3\u003e\n\u003cp\u003eTo ascertain the variation of blood glucose and HbA1c levels in different seasons, we retrospectively searched the records based on the Second Hospital of Tianjin Medical University from January 2013 to December 2022 and identified 9694 consecutive patients who were admitted with AIS. No exceptional events concerning weather or environment were noted in this period. We then excluded 964 patients who were not eligible for this study for the reasons of no blood samples were collected to detect HbA1c or FPG. A total of 8730 patients diagnosed as AIS were finally enrolled, of whom 5864 were non-diabetic patients and another 2866 were diabetic patients with cerebral infarction. Stroke was defined according to the definition of the World Health Organization (WHO) Statistical analysis. Diagnosis was confirmed by CT or MRI in all cases.\u003c/p\u003e\n\u003ch3\u003eData collection\u003c/h3\u003e\n\u003cp\u003eDemographic and clinical data of age, sex, body mass index (BMI), medical history, blood pressure, results of laboratory tests and admission National Institute of Health Stroke Scale (NIHSS) score were collected. Diabetes mellitus was defined as glycated hemoglobin levels of above 6.5%, fasting plasma glucose levels of above 7mmol/L, or the use of anti-diabetic medication. The first systolic and diastolic blood pressure (SBP, DBP) measured on admission was used for the analyses. Blood samples were collected from the antecubital vein in the morning after an overnight fasting period (\u0026gt;\u0026thinsp;8 h) and transfused into vacuum tubes containing Ethylene Diamine Tetraacetic Acid (EDTA). Physical activity was assessed as participation in regular sporting activities at least once a week for a minimum of 30 min. BMI was defined as body weight (kg) divided by the square of height (meters). Admission severity was measured using NIHSS scores. The NIHSS scores were classified into three categories and proportion of patients: A score from 0\u0026ndash;3 was defined as the mild level, 4\u0026ndash;7 as the moderate level and \u0026gt;\u0026thinsp;7 as the severe level. After enrollment, patients were divided into four groups based on the onset seasons: spring, summer, autumn and winter.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistics analysis\u003c/h2\u003e \u003cp\u003eWe performed all statistical analyses using IBM SPSS Statistics for Macintosh, Version 26.0 (IBM Corp., Armonk, New York, USA) and R software (version 4.4.2). Continuous data were expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD) or median and interquartile range (IQR). Categorical data were presented as absolute values and percentages. Chi-test was used to analyze distribution of frequencies, while one-way ANOVA was used for normally distributed continuous variables comparisons, followed by the Tukey\u0026ndash;Kramer post hoc test. The Kruskal-Wallis H test was used for comparisons of non-normally distributed continuous variables between groups. The general linear model was applied for calculating the seasonal effects on glycemic parameters. The associations between ambient temperature, glycemic parameters and NIHSS scores were assessed using generalized additive mixed model (GAMM). A natural cubic spline with 5 degrees of freedom (df) was used to model potential nonlinear effects of ambient temperature and glycemic parameters on NIHSS scores. All statistical tests were two-tailed, and \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eCharacteristics of study subjects and seasonal patterns\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e provided baseline characteristics of the participants. Over the 10-year study period, 8730 AIS patients occurred in 5527 male patients, wherein 62.20% were \u0026ge;\u0026thinsp;65 years old. Significant seasonal variations were observed in several clinical parameters, including body mass index (BMI, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.013), systolic blood pressure (SBP, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.027), diastolic blood pressure (DBP, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.045), and FPG levels in diabetic patients (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.033). Analysis of clinical outcomes revealed distinct seasonal patterns, with poorer outcomes predominantly observed during spring and winter compared to summer and autumn. Among diabetic patients, the winter group exhibited the highest median NIHSS score of 7, while the summer group showed the lowest median score of 5. The proportion of patients admitted with NIHSS scores\u0026thinsp;\u0026gt;\u0026thinsp;7 varied seasonally: 31.8% in winter, 18.8% in spring, 14.7% in summer, and 20.7% in autumn.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline characteristics of study participants distributed by season\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal(n\u0026thinsp;=\u0026thinsp;8730)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSpring(n\u0026thinsp;=\u0026thinsp;2226)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSummer(n\u0026thinsp;=\u0026thinsp;1641)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAutumn(n\u0026thinsp;=\u0026thinsp;2169)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eWinter(n\u0026thinsp;=\u0026thinsp;2694)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDays per year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e365\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFirst-ever stroke cases (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6277(71.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1606(72.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1128(68.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1560(71.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1983(73.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.813\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5527(63.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1414(63.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1069(65.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1370(63.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1674(62.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.626\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e70.61\u0026thinsp;\u0026plusmn;\u0026thinsp;12.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e71.75\u0026thinsp;\u0026plusmn;\u0026thinsp;11.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e69.04\u0026thinsp;\u0026plusmn;\u0026thinsp;12.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e69.44\u0026thinsp;\u0026plusmn;\u0026thinsp;12.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e71.04\u0026thinsp;\u0026plusmn;\u0026thinsp;12.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.875\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, years(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3298(37.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e873(39.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e630(38.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e832(38.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e963(35.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.208\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e65\u0026ndash;80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3658(41.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e865(38.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e725(44.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e920(42.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1148(42.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1772(20.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e488(21.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e285(17.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e416(19.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e583(21.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBody mass index (kg/m2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25.58\u0026thinsp;\u0026plusmn;\u0026thinsp;3.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25.8\u0026thinsp;\u0026plusmn;\u0026thinsp;3.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24.3\u0026thinsp;\u0026plusmn;\u0026thinsp;3.30\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25.1\u0026thinsp;\u0026plusmn;\u0026thinsp;3.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e26.6\u0026thinsp;\u0026plusmn;\u0026thinsp;3.57\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.013\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlood pressure (mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSystolic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e151.98\u0026thinsp;\u0026plusmn;\u0026thinsp;21.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e153.69\u0026thinsp;\u0026plusmn;\u0026thinsp;22.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e146.14\u0026thinsp;\u0026plusmn;\u0026thinsp;20.92\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e151.26\u0026thinsp;\u0026plusmn;\u0026thinsp;22.48 \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e154.73\u0026thinsp;\u0026plusmn;\u0026thinsp;23.06\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.027\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiastolic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e85.10\u0026thinsp;\u0026plusmn;\u0026thinsp;12.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e86.79\u0026thinsp;\u0026plusmn;\u0026thinsp;13.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e81.92\u0026thinsp;\u0026plusmn;\u0026thinsp;12.84\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e85.48\u0026thinsp;\u0026plusmn;\u0026thinsp;13.25\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e85.38\u0026thinsp;\u0026plusmn;\u0026thinsp;12.91\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.045\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFPG (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.75(9.39, 10.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.46(8.60, 10.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.07(8.45, 9.70)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.50(8.56, 10.44)\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10.29(9.74, 10.84)\u003csup\u003eabc\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.033\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-diabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.20(5.14, 5.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.14(5.00, 5.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.27(5.16, 5.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.19(5.06, 5.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.17(5.05, 5.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.339\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHbA1c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.04(7.89, 8.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.07(7.75, 8.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.02(7.68, 8.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.01(7.53, 8.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.05(7.83, 8.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.764\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-diabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.70(5.67, 5.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.69(5.63, 5.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.75(5.69, 5.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.66(5.59, 5.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.68(5.63, 5.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.239\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5615(64.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1438(64.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1071(65.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1358(62.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1748(64.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.877\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHyperlipemia(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3463(39.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e869(39.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e660(40.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e861(39.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1073(39.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.488\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChronic kidney disease (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1527(17.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e391(17.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e276(16.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e374(17.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e486(18.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.579\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhysical\u0026ensp;activity(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1113(12.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e327(14.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e203(12.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e293(13.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e290(10.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.179\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent smoking (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2510(28.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e582(26.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e462(28.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e633(29.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e833(30.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.432\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent alcohol drinking (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1329(15.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e316(14.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e226(13.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e340(15.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e447(16.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.544\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnti-diabetic drugs (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInsulin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1238(14.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e327(14.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e236(14.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e304(14.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e371(13.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.713\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOral drugs only\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2755(31.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e708(31.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e508(30.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e674(31.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e865(32.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.625\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnti-hypertensive drugs (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4936(56.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1266(56.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e939(57.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1198(55.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1533(56.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.335\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLipid-lowering agents (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1396(15.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e358(16.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e253(15.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e346(15.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e439(16.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.417\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNIHSS score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6(2, 10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6(1, 10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5(2, 9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5(2, 10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7(2, 10)\u003csup\u003eabc\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.021\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-diabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4(1, 9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5(2, 9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3(1, 8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4(2, 8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6(2, 10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.065\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeverity levels (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNIHSS 0\u0026ndash;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3953(45.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1139(51.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e829(50.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1014(46.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e944(35.05%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNIHSS 4\u0026ndash;7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2892(33.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e673(30.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e580(35.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e745(34.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e894(33.17%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNIHSS \u0026gt;7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1574(18.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e419\u0026nbsp;(18.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e241(14.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e49(20.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e865(31.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClinical outcomes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDeath (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e502(5.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e122(5.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e75(4.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e112(5.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e193(7.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.062\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eData is mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation, number (percentage) or median (interquartile range).\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003csup\u003ea\u003c/sup\u003e \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 vs spring; \u003csup\u003eb\u003c/sup\u003e \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 vs summer; \u003csup\u003ec\u003c/sup\u003e \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 vs autumn. FPG, fasting plasma glucose; NIHSS, National Institute of Health Stroke Scale. Bold values indicate statistical significance.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e summarizes the meteorological and glucose parameters during the study period, including FPG, HbA1c, daily mean temperature, air pressure, relative humidity, and wind speed. Diabetic patients demonstrated significantly higher serum levels of FPG and HbA1c compared to non-diabetic individuals. The mean environmental conditions were as follows: temperature 15.05\u0026deg;C, air pressure 1016.17 hPa, relative humidity 51.73%, and wind speed 1.73 m/s.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary statistics of glucose levels and weather conditions during 2013\u0026ndash;2022\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMinimum\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e50%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e75%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e95%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMaximum\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e values for difference of means\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFPG(mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAll\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e7.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e14.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e34.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e7.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e3.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e9.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e15.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e29.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e8.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e3.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-diabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e6.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e6.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e5.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHbA1c(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAll\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e7.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e10.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e14.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e6.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e11.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e14.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e7.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-diabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e6.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e6.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e5.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTemeparature(℃)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-13.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-2.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e15.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e25.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e29.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e33.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e15.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e10.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAir pressure(hPa)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e994.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1000.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1006.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1016.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1024.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1033.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1043.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1016.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e10.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRelative humidity(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e37.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e51.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e66.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e81.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e94.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e51.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e18.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWind speed(m/s)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e7.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eSeasonal variations in the level of FPG\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e illustrated the monthly variations in FPG levels in relation to ambient temperature. Additionally, we analyzed FPG levels using box-plot diagrams with descriptive statistics, stratified by quartile groups of meteorological factors. The analysis revealed significant seasonal fluctuations in FPG levels across the study population. Specifically, FPG levels exhibited an inverse relationship with temperature, demonstrating lower values during warmer months and higher values during colder months. Among diabetic patients, the peak FPG level was observed in January (11.17 mmol/L), while the lowest level was recorded in June (8.69 mmol/L). FPG levels were significantly higher in winter compared to summer (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.032) and autumn (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.045). A statistically significant increase in FPG levels was observed during winter compared to summer, with a mean difference of 1.22 mmol/L (95% \u003cem\u003eCI\u003c/em\u003e: 1.10\u0026ndash;1.29 mmol/L; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.005).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eSeasonal variations in the level of HbA1c and its consistent cyclic variation with FPG\u003c/h2\u003e \u003cp\u003eFurthermore, we applied a 3-month lag to the monthly average HbA1c values. These lagged variables were incorporated into a linear trend model to assess the correlation between HbA1c and FPG. As illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA and B, HbA1c values exhibited significant monthly fluctuations that were closely associated with FPG levels. After adjusting for linear trends, the current average HbA1c values were found to be significantly influenced by FPG levels from 3 months prior. Notably, early spring exhibited a significant increase in HbA1c levels, while late summer showed a marked decrease (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC and D). The analysis revealed significantly higher HbA1c levels during spring compared to autumn and winter in diabetes patients with suboptimal glycemic control (HbA1c\u0026thinsp;\u0026gt;\u0026thinsp;7.0%), as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE. Seasonal fluctuation quantitative assessment demonstrated that diabetic patients had significantly elevated HbA1c levels during spring, showing 0.77 units (95% \u003cem\u003eCI\u003c/em\u003e: 0.59\u0026ndash;0.92; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.027) and 0.81 units (95% \u003cem\u003eCI\u003c/em\u003e: 0.61\u0026ndash;1.02; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.025) increases compared to winter and autumn measurements, respectively. However, no significant seasonal fluctuations were observed among patients who achieved the goal of HbA1c\u0026thinsp;\u0026lt;\u0026thinsp;7%. Given that HbA1c reflects average blood glucose levels over approximately 3 months, these findings suggested that both extreme cold temperatures and large diurnal temperature variations may contribute to increased blood glucose levels, potentially explaining the observed seasonal patterns in glucose control.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eThe effects of seasons on FPG and HbA1c\u003c/h2\u003e \u003cp\u003eAfter adjusted for age, sex, BMI and other covariates, summer and autumn group had 0.083 times (95%\u003cem\u003eCI\u003c/em\u003e: -0.062, -0.726, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.032) and 0.125 times (95%\u003cem\u003eCI\u003c/em\u003e: -0.590, -0.073, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.012) the risk of suffering lower FPG levels than winter group, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Meanwhile, general linear model revealed that risk of higher HbA1c levels increased to 0.201 times in spring (95%\u003cem\u003eCI\u003c/em\u003e: 0.099, 0.557, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and 0.107 times in summer (95%\u003cem\u003eCI\u003c/em\u003e: 0.096, 0.449, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.032) compared with autumn group, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEstimates of season effects on FPG levels in the general linear model\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDependent variable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eβ\u003c/em\u003e coefficien (95% \u003cem\u003eCI\u003c/em\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eβ\u003c/em\u003e coefficient (95% \u003cem\u003eCI\u003c/em\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpring\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.071(-1.451,0.358)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.236\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.023(-0.944, 0.583)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.583\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSummer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.124(-0.033, -0.971)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.083(-0.062, -0.726)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.042\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAutumu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.176(-0.160, -0.778)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.125(-0.073, -0.590)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWinter(reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eModel 1 was unadjusted; Model 2 was adjusted for age, sex, BMI, physical\u0026ensp;activity, current smoking, blood pressure, combined with diabetes, using anti-diabetic drugs, serum creatinine, low density lipoprotein and NIHSS scores. CI, confidential interval.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEstimates of season effects on HbA1c levels\u0026thinsp;\u0026ge;\u0026thinsp;7% in the general linear model\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDependent variable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eβ\u003c/em\u003e coefficien (95% \u003cem\u003eCI\u003c/em\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eβ\u003c/em\u003e coefficient (95% \u003cem\u003eCI\u003c/em\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpring\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.232(0.012, 0.447)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.201(0.099, 0.557)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSummer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.182(0.101, 0.492)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.107(0.096, 0.449)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.032\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAutumu(reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWinter\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.054(-0.077, 0.082)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.017(-0.051, 0.049)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.153\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eModel 1 was unadjusted; Model 2 was adjusted for age, sex, BMI, physical\u0026ensp;activity, current smoking, blood pressure, combined with diabetes, using anti-diabetic drugs, serum creatinine, low density lipoprotein and NIHSS scores. CI, confidential interval.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eAssociation between glycemic parameters and seasonal variations in admission stroke severity\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA demonstrated the exposure response curves between mean temperatures and admission NIHSS scores, which seems to be non-linear negative correlation. Gaussian generalized additive mixture model is adjusted according to age, gender, season, relative humidity, atmospheric pressure and past history. It means that as the temperature decreased, the admission NIHSS scores increased. Of note, 1 ◦C decrease of mean temperature was associated with 2.5 (95% \u003cem\u003eCI\u003c/em\u003e: -0.5, -4.5, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) increase of admission NIHSS scores. After adjusting for age, sex, season, past history, SBP, DBP, TC, TG, LDL-C, HDL-C, Creatinine, UA, HbA1c, BMI, lipid lowering drugs, antidiabetic drugs, and antiplatelet drugs, the smooth curve fitting plot demonstrated a non-linear positive relationship between FPG and admission NIHSS scores (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). Specifically, higher FPG levels were positively correlated with increased stroke severity at admission, with each 0.1 mmol/L increment in FPG corresponding to a 0.8 rise in NIHSS scores (95% \u003cem\u003eCI\u003c/em\u003e: 0.5, 1.1, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01). However, HbA1c values has no effect on admission NIHSS scores.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this population-based stroke registry study, we identified significant seasonal variations in both FPG and HbA1c levels among AIS patients, particularly those with diabetes. The results demonstrated that mean FPG concentrations exhibited a distinct seasonal pattern, peaking in January (11.07 mmol/L) and reaching their lowest levels in June (8.69 mmol/L). Notably, AIS patients with diabetic displayed more pronounced fluctuations, with winter FPG levels being approximately 1.22 mmol/L higher than those in summer. The risk of elevated FPG levels was markedly reduced in both summer (\u003cem\u003eβ\u003c/em\u003e = -0.083, 95% \u003cem\u003eCI\u003c/em\u003e: -0.062, -0.726, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.042) and autumn (\u003cem\u003eβ\u003c/em\u003e = -0.125, 95% \u003cem\u003eCI\u003c/em\u003e: -0.073, -0.590, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.012) when compared to winter. Our findings were consistent with previous studies demonstrating seasonal variations in glycemic control among diabetic patients, characterized by higher glucose levels in winter and lower levels in summer (Belsare et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Takai et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). This observation aligned with a study of 49,417 participants, which revealed a U-shaped relationship between ambient temperature and FPG levels, with particularly elevated glucose concentrations during winter months (Li et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). A multi-center Chinese study analyzing 1.4\u0026nbsp;million physical examination population confirmed both the seasonal discrepancy in FPG levels and a distinct north-south gradient, with northern regions exhibiting higher winter glucose values than southern areas (Zhang et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMeanwhile, a comparable seasonal trend was observed for HbA1c levels, especially in patients with suboptimal glycemic control (HbA1c\u0026thinsp;\u0026gt;\u0026thinsp;7.0%). The difference between the highest HbA1c value (observed in spring) and the lowest (observed in autumn) was approximately 0.81 units. HbA1c exhibited a significant upward trend in early spring and a downward trend in late summer, underscoring the dynamic nature of glycemic control across seasons. General linear model analysis confirmed the risk of elevated HbA1c being significantly higher in spring (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.201, 95% \u003cem\u003eCI\u003c/em\u003e: 0.099, 0.557, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and summer (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.107, 95% \u003cem\u003eCI\u003c/em\u003e: 0.096, 0.449, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.032) compared to autumn. The spring HbA1c peak likely reflected the delayed integration of winter hyperglycemia (December\u0026ndash;February), when cold temperatures exacerbated glycemic dysregulation. Conversely, improved glucose control during warmer months manifests as lower autumn HbA1c. This temporal shift aligned with HbA1c\u0026rsquo;s role as a 2\u0026ndash;3 month glycemic marker (Ahuja et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) .\u003c/p\u003e \u003cp\u003eThe above results indicated that although the extreme temperature difference might affect the fluctuation of blood glucose, the overall mean blood glucose levels remained highest in winter, which may further influence the incidence and severity of stroke. Higgin et al. (Higgins et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) found significant seasonal fluctuations in both northern and southern hemispheres (higher HbA1c in cold months and lower in warm months), while Singapore's stable climate showed no fluctuations, confirming temperature's influence on glycemic control. The biological mechanisms that underlay the seasonality in blood glucose levels might include seasonal variation in HbA1c and physiological changes. Studies have found that a complex of potential factors including the stressful to carbohydrate tolerance in winter, alteration in diet, decreased physical activity, exposure to sunlight, weight gain and increase in counter-insulin hormones (Iwata et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Nicolo and Boullata \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Banihani et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), could partially explain the seasonal variation of blood glucose levels. These findings highlighted the importance of considering seasonal and environmental factors in the management of diabetes and stroke prevention strategies.\u003c/p\u003e \u003cp\u003eFurthermore, our findings corroborated a significant nonlinear negative correlation between ambient temperature and NIHSS scores, while an association between FBG levels and NIHSS scores. A growing body of evidence had identified an association between elevated glucose at admission and increased mortality or poor outcome following AIS (Kim et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Shi et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Zhang et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Post-stroke hyperglycemia was known to correlate with stroke severity, potentially mediated by a stress-induced cortisol response (Mosenzon et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Hyperglycemia may exacerbate ischemic brain injury by amplifying inflammatory responses, thereby promoting neuronal damage(Climent et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Bains et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). At the vascular level, dysregulation of glucose metabolism induced endothelial dysfunction through oxidative stress-mediated pathways, accelerating atherogenesis and increasing vulnerability to acute cerebrovascular incidents(Karakasis et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). The present study showed notable seasonal variations in admission severity and clinical outcomes, with more severe neurological deficits observed in winter. This temporal pattern aligned with the hypothesis that acute hyperglycemia, as reflected by elevated FPG levels, may exacerbate ischemic injury and contribute to adverse functional recovery. In contrast, no statistically significant relationship was observed between HbA1c levels and admission NIHSS scores, suggesting that acute hyperglycemia (FPG) rather than chronic glycemic control (HbA1c) may drive early neurological impairment. These observations underscored the potential clinical relevance of acute glucose management in AIS. The robust association between FPG and outcomes highlighted the need for further investigation into targeted glycemic control strategies during the acute phase of ischemic stroke.\u003c/p\u003e \u003cp\u003eDespite its significant insights, our single hospital-based study was not without limitations. Firstly, as a retrospective analysis, potential biases in data collection and residual confounding factors might exist. Secondly, selection bias might have occurred since cases who died before hospitalization or sought treatment elsewhere were excluded. Thirdly, unmeasured lifestyle factors (e.g., dietary patterns and physical activity) could potentially confound the observed seasonal glucose variations. Most importantly, the establishment of causality required prospective cohort studies with larger sample sizes due to the cross-sectional nature of our research. While acknowledging these limitations, the rigorous exclusion of potential confounding factors and clinical validation strengthened the reliability of our findings. Future investigations should incorporate longitudinal designs to establish causal relationships between glycemic parameters and AIS progression. Furthermore, molecular-level studies were warranted to elucidate the precise mechanisms, particularly through characterization of involved inflammatory pathways. Such mechanistic insights might reveal novel therapeutic targets for glycemic control in stroke management.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn conclusion, this 10-year longitudinal study demonstrated a consistent cyclic pattern in FPG and HbA1c levels, with significant seasonal and monthly variations. Notably, extreme cold temperatures and large diurnal temperature differences were associated with elevated blood glucose, which in turn correlated with increased stroke incidence and severity. These findings highlight the potential influence of climatic factors on metabolic dysregulation and cerebrovascular risk. Further research is warranted to elucidate the underlying pathophysiological mechanisms and explore their implications for the management of AIS, particularly in high-risk populations exposed to extreme weather conditions.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis work was supported by Youth Foundation of the Second Hospital of Tianjin Medical University (2018ydey12), Key Projects of Tianjin Municipal Health Commission (TJWJ2024XK008), Tianjin Key Medical Discipline (Specialty) Construction Project (TYXZDXK065B), Tianjin Center for Health and Meteorology Multidisciplinary Innovation and the National Natural Science Foundation of China (42275197).\u003c/p\u003e\u003ch2\u003eAuthor contributions\u003c/h2\u003e \u003cp\u003eM.M.W. conducted the statistical analyses and drafted the initial manuscript. F.Y.W. was responsible for data acquisition and curation. X.S.X. and L.W. provided critical intellectual input during manuscript revision. X.L. supervised the study and had primary responsibility for the final content. All authors reviewed and approved the final version of the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eThe authors thank all the study participants, staff of the participating hospitals.\u003c/p\u003e\u003ch2\u003eData availability\u003c/h2\u003e \u003cp\u003eThe data that support the findings of this study are available from the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAhuja S, Sugandha S, Kumar R, Zaheer S, Singh M (2024) Seasonal variation of HbA1c levels in diabetic and non-diabetic patients. 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Sci Rep 14(1):20962. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41598-024-71778-5\u003c/span\u003e\u003cspan address=\"10.1038/s41598-024-71778-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"international-journal-of-biometeorology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ijbm","sideBox":"Learn more about [International Journal of Biometeorology](http://link.springer.com/journal/484)","snPcode":"484","submissionUrl":"https://www.editorialmanager.com/ijbm/default2.aspx","title":"International Journal of Biometeorology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Acute ischemic stroke, Blood glucose parameters, Seasonal variation, Temperature effect, Clinical severity","lastPublishedDoi":"10.21203/rs.3.rs-6460523/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6460523/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study investigated seasonal variations in glycemic parameters and their clinical correlates in 8730 acute ischemic stroke (AIS) patients admitted from 2013 to 2022. Patients were grouped by season of onset, with collection of demographic characteristics, laboratory results, admission NIHSS scores and meteorological data. In patients with diabetes, fasting plasma glucose (FPG) levels in winter increased by 1.22 mmol/l (95% \u003cem\u003eCI\u003c/em\u003e:1.14,1.29 mmol/l, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01) versus summer. General linear model analysis revealed reduced FPG risk in summer (\u003cem\u003eβ\u003c/em\u003e = -0.083, 95% \u003cem\u003eCI\u003c/em\u003e: -0.062, -0.726; \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05) and autumn (\u003cem\u003eβ\u003c/em\u003e = -0.125, 95% \u003cem\u003eCI\u003c/em\u003e: -0.073, -0.590; \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05) versus winter. HbA1c demonstrated similar seasonal variation, particularly when values exceeding 7.0%, with spring-autumn differences reaching 0.81 units (95% \u003cem\u003eCI\u003c/em\u003e: 0.61, 1.02; \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05). HbA1c variations were most pronounced in spring (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.201, 95% \u003cem\u003eCI\u003c/em\u003e: 0.099, 0.557; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and summer (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.107, 95% \u003cem\u003eCI\u003c/em\u003e: 0.096, 0.449; \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05) versus autumn. Winter admissions correlated with greater stroke severity, supported by 1 ◦C temperature decrease was associated with 2.5 (95% \u003cem\u003eCI\u003c/em\u003e: -0.5, -4.5, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) NIHSS scores increase. Of note, each 0.1 mmol/L increment in FPG corresponding to a 0.8 rise in NIHSS scores (95% \u003cem\u003eCI\u003c/em\u003e: 0.5, 1.1, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01), while HbA1c showed no association. Our findings demonstrated AIS patients showed seasonal glucose fluctuations, with the highest hyperglycemia and severity in winter. Acute hyperglycemia rather than chronic glycemic control was associated with early neurological impairment, highlighting the need for seasonally-adjusted glucose management in high-risk populations.\u003c/p\u003e","manuscriptTitle":"Effects of seasonal variation on glucose levels among patients with acute ischemic stroke in Tianjin, China","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-13 12:19:01","doi":"10.21203/rs.3.rs-6460523/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2025-05-08T11:07:58+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-05-08T09:31:02+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-04-17T03:17:22+00:00","index":"","fulltext":""},{"type":"submitted","content":"International Journal of Biometeorology","date":"2025-04-16T03:07:06+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"international-journal-of-biometeorology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ijbm","sideBox":"Learn more about [International Journal of Biometeorology](http://link.springer.com/journal/484)","snPcode":"484","submissionUrl":"https://www.editorialmanager.com/ijbm/default2.aspx","title":"International Journal of Biometeorology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"6943e9c2-08c1-429f-bd29-6a9536196f3a","owner":[],"postedDate":"May 13th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-01-12T16:09:04+00:00","versionOfRecord":{"articleIdentity":"rs-6460523","link":"https://doi.org/10.1007/s00484-025-03103-2","journal":{"identity":"international-journal-of-biometeorology","isVorOnly":false,"title":"International Journal of Biometeorology"},"publishedOn":"2026-01-05 15:58:34","publishedOnDateReadable":"January 5th, 2026"},"versionCreatedAt":"2025-05-13 12:19:01","video":"","vorDoi":"10.1007/s00484-025-03103-2","vorDoiUrl":"https://doi.org/10.1007/s00484-025-03103-2","workflowStages":[]},"version":"v1","identity":"rs-6460523","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6460523","identity":"rs-6460523","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}