Association Between Stress Hyperglycemia Ratio and Early Neurological Deterioration After Acute Ischemic Stroke: A Retrospective Cohort Study

Association Between Stress Hyperglycemia Ratio and Early Neurological Deterioration After Acute Ischemic Stroke: A Retrospective Cohort Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Association Between Stress Hyperglycemia Ratio and Early Neurological Deterioration After Acute Ischemic Stroke: A Retrospective Cohort Study Wei Yang, Yiji Shen, Yuehua Fei, Tongcai Tan, Yong Liu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8037569/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 21 Jan, 2026 Read the published version in BMC Neurology → Version 1 posted 13 You are reading this latest preprint version Abstract Background Stress hyperglycemia ratio (SHR) has emerged as a more accurate indicator of stress-related hyperglycemia than absolute glucose levels. However, its relationship with early neurological deterioration (END) after acute ischemic stroke (AIS) remains unclear. Methods We retrospectively analyzed 1,479 AIS patients admitted within 24 hours of symptom onset. END was defined as an increase of ≥ 2 points in the NIHSS total or motor score within 72 hours. SHR was calculated as the ratio of fasting plasma glucose to estimated average glucose derived from HbA1c and categorized into quartiles. Logistic regression, generalized additive models (GAM), two-piecewise logistic regression, and causal mediation analyses were performed. Results Among 1,479 patients, 270 (18.3%) developed END. Higher SHR was independently associated with increased END risk (fully adjusted OR = 6.19, 95% CI: 2.68–14.28, P < 0.0001), showing a clear dose-response relationship across quartiles (P for trend = 0.0015). GAM revealed a non-linear relationship, and two-piecewise regression identified a potential inflection point at SHR ≈ 1.06. Subgroup analysis showed a stronger association in non-diabetic patients (interaction P = 0.0033), with no significant interactions for other variables. Sensitivity analysis adjusting for C-reactive protein (CRP) and white blood cell (WBC) count remained robust after adjustment. Mediation analysis indicated that CRP and WBC partially mediated the SHR-END association, with mediation proportions of 12.89% and 8.03%, respectively. Conclusions Elevated SHR is significantly associated with an increased risk of END in AIS patients, in a non-linear and threshold-dependent manner. This association is partly mediated by systemic inflammatory markers and appears stronger in non-diabetic populations. These findings highlight the potential utility of SHR for early risk stratification and warrant further prospective validation. Stress Hyperglycemia Ratio Early Neurological Deterioration Acute Ischemic Stroke Inflammation Causal Mediation Analysis Figures Figure 1 Figure 2 Introduction Acute ischemic stroke (AIS) remains a major cause of death and long-term disability worldwide, and up to one-fifth of patients experience early neurological deterioration (END) within the first days of admission[ 1 , 2 ]. END, typically defined as a measurable worsening of neurological status shortly after onset, is strongly associated with poor functional outcomes and increased mortality[ 3 , 4 ]. Timely identification of patients at high risk for END is therefore essential for guiding early treatment and preventing secondary brain injury[ 5 , 6 ]. Acute hyperglycemia is a frequent metabolic response to cerebral ischemia, even in patients without pre-existing diabetes[ 7 – 9 ]. This stress-related hyperglycemia results from neuroendocrine activation, systemic inflammation, and impaired glucose utilization[ 10 , 11 ]. Traditional markers such as absolute blood glucose are strongly influenced by chronic glycemic status and may underestimate the acute stress response[ 12 , 13 ]. To overcome this limitation, the stress hyperglycemia ratio (SHR)—calculated as the ratio of fasting plasma glucose to estimated average glucose derived from HbA1c—has been proposed as a more robust index by accounting for background glycemia[ 14 , 15 ]. Previous studies have linked elevated SHR to adverse outcomes in critical illness and AIS, but its specific role in predicting END remains insufficiently explored[ 16 , 17 ]. The biological link between SHR and END may involve inflammatory activation. Hyperglycemia can amplify ischemia-induced neuroinflammation, promote leukocyte infiltration, and disrupt the blood–brain barrier, accelerating neuronal injury[ 18 – 20 ]. Circulating inflammatory markers such as C-reactive protein (CRP) and white blood cell (WBC) count are easily measurable, and both have been independently associated with hyperglycemia and poor stroke outcomes[ 21 – 23 ]. Whether CRP and WBC mediate the relationship between SHR and END has not been clearly established. In this study, we investigated the association between SHR and END in a well-characterized cohort of AIS patients admitted within 24 hours of onset. We further examined potential non-linear and threshold effects, evaluated subgroup differences, and conducted mediation analyses to clarify the contribution of CRP and WBC. Our findings aim to establish the prognostic value of SHR in the acute phase of stroke and provide new insight into metabolic–inflammatory interactions in risk stratification. Materials and Methods Study Design and Participants This retrospective cohort study included consecutive patients with AIS admitted to Zhejiang Provincial People's Hospital (Affiliated People's Hospital, Hangzhou Medical College) between October 2018 and September 2021. The study was approved by the Ethics Committee of Zhejiang Provincial People's Hospita (No. 2021QT391) and conducted in accordance with the Declaration of Helsinki. The requirement for informed consent was waived due to the use of anonymized data. Eligible patients met all of the following criteria: (1) AIS diagnosed and admitted within 24 hours of symptom onset; (2) brain MRI within 48 hours confirming acute infarction on standard sequences; (3) fasting plasma glucose and HbA1c measured within 48 hours; and (4) sufficient data to calculate the SHR. Exclusion criteria were: (1) severe hepatic dysfunction (ALT > 10× ULN or AST > 3× ULN), severe renal dysfunction (creatinine > 443 µmol/L), active malignancy, or hematologic disease; (2) significant cardiopulmonary insufficiency, including NYHA class III–IV heart failure, LVEF < 40%, chronic obstructive pulmonary disease (COPD), or respiratory infection on admission; (3) active infection at admission; (4) pregnancy or lactation; (5) inability to assess stroke severity using the NIHSS due to coma, profound aphasia, or other neurological limitations (exclusion based on feasibility, not severity); (6) multiple AIS admissions during the study period (only the first included); or (7) missing key laboratory data. Baseline Data Collection Baseline demographic and clinical data were obtained from electronic medical records, including age, sex, body temperature, systolic blood pressure (SBP), diastolic blood pressure (DBP), smoking status, hypertension, type 2 diabetes mellitus, and atrial fibrillation (AF). Stroke severity was evaluated using the NIHSS, swallowing function by the Kubota Water Drinking Test (KWDT), and consciousness level by the Glasgow Coma Scale (GCS). NIHSS assessments were performed at admission, and repeated at 24 and 72 hours, by the same experienced neurologist to ensure consistency and reduce inter-observer variability. Fasting venous blood samples were drawn on the second morning after admission (06:00) by trained nurses, kept at 4°C, and analyzed within 2 hours by certified laboratory staff. Laboratory tests included white blood cell count (WBC), C-reactive protein (CRP), fasting blood glucose (FBG), aspartate aminotransferase (AST), alanine aminotransferase (ALT), glycated hemoglobin (HbA1c), homocysteine (HCY), triglycerides (TG), total cholesterol (TC), high-density lipoprotein cholesterol (HDL-c), and low-density lipoprotein cholesterol (LDL-c), as well as free thyroxine (FT4), free triiodothyronine (FT3), and thyroid-stimulating hormone (TSH). Estimated glomerular filtration rate (eGFR) was calculated using the CKD-EPI equation. Normal reference ranges were: TSH 0.27–4.2 mIU/L, FT4 12–22 pmol/L, and FT3 3.1–6.8 pmol/L. Definitions END was defined as an increase of at least two points in either the NIHSS motor score or the total NIHSS score within the first 72 hours after admission, following definitions widely applied in previous AIS studies[ 2 ]. Classification as END-positive was based solely on the presence of initial neurological worsening, irrespective of subsequent improvement during the observation period. The SHR was defined as the fasting plasma glucose (FPG) level divided by the estimated average glucose (both expressed in mg/dL)[ 24 ]. The estimated average glucose was calculated from HbA1c (%) using the validated formula: 28.7 × HbA1c – 46.7. Statistical Analysis All statistical analyses were performed using R software (version 4.2.2; R Foundation for Statistical Computing, Vienna, Austria) and EmpowerStats (version 2.0; X&Y Solutions, Inc., Boston, MA, USA). A two-tailed P-value < 0.05 was considered statistically significant. The Shapiro–Wilk test was used to assess normality. Continuous variables are presented as mean ± standard deviation (SD) or median (interquartile range, IQR) and compared using the independent-samples t-test or Mann–Whitney U test, as appropriate. Categorical variables are expressed as counts (percentages) and compared using the chi-square test. Multivariable logistic regression was used to examine the association between SHR and END, with SHR analyzed both as a continuous variable and by quartiles (P for trend calculated). Non-linear associations were explored using generalized additive models (GAM) with a binomial distribution and logit link, and threshold effects were evaluated using two-piecewise logistic regression, with inflection points determined by likelihood-based search. Sensitivity analyses additionally adjusted for C-reactive protein and white blood cell count. Subgroup analyses were conducted by age, sex, smoking status, hypertension, diabetes, atrial fibrillation, chronic obstructive pulmonary disease, and TOAST subtype, with interactions tested by adding multiplicative terms. Causal mediation analyses were performed to assess whether C-reactive protein and white blood cell count mediated the association between SHR and END, using generalized linear models with probit link and nonparametric bootstrapping (1,000 resamples) to estimate indirect, direct, and total effects, as well as the proportion mediated. Covariates for all models are listed in the corresponding table footnotes. Results Baseline Characteristics A total of 1,479 patients with acute ischemic stroke were included, of whom 270 (18.3%) developed END occurring within 72 hours of admission. Patients in the END group were older, had lower body temperature, and had a lower proportion of males and current smokers (all P < 0.05) (Table 1 ). Atrial fibrillation was more frequent, whereas diabetes was less prevalent. Laboratory tests showed higher fasting plasma glucose, LDL-C, and homocysteine, but lower triglycerides and FT3 in the END group. Inflammatory markers (CRP and WBC) were elevated, and SHR was significantly higher. Clinically, patients with END had lower GCS and higher mRS, with slightly lower NIHSS scores at admission (all P < 0.05). Table 1 Baseline characteristics of acute ischemic stroke patients stratified by early neurological deterioration (END) status Demographic characteristic ALL(N = 1479) non-END (N = 1209) END (N = 270) P -value* Age (years) 69.01 ± 12.57 68.29 ± 12.57 72.21 ± 12.08 < 0.001 SBP (mmHg) 153.03 ± 21.62 152.65 ± 21.56 154.70 ± 21.85 0.205 DBP (mmHg) 83.03 ± 12.98 83.11 ± 12.96 82.65 ± 13.09 0.559 Body temperature (℃) 36.47 ± 0.47 36.49 ± 0.48 36.41 ± 0.39 < 0.001 Gender, n (%) 0.031 Female 588 (39.76%) 465 (38.46%) 123 (45.56%) Male 891 (60.24%) 744 (61.54%) 147 (54.44%) Current smoking, n (%) 0.045 No 912 (61.66%) 731 (60.46%) 181 (67.04%) Yes 567 (38.34%) 478 (39.54%) 89 (32.96%) Hypertension, n (%) 0.899 No 333 (22.52%) 273 (22.58%) 60 (22.22%) Yes 1146 (77.48%) 936 (77.42%) 210 (77.78%) Diabetes, n (%) 0.324 No 937 (63.35%) 773 (63.94%) 164 (60.74%) Yes 542 (36.65%) 436 (36.06%) 106 (39.26%) Atrial fibrillation, n (%) < 0.001 No 1224 (82.76%) 1025 (84.78%) 199 (73.70%) Yes 255 (17.24%) 184 (15.22%) 71 (26.30%) COPD, n (%) 0.436 No 1387 (93.78%) 1131 (93.55%) 256 (94.81%) Yes 92 (6.22%) 78 (6.45%) 14 (5.19%) TOAST, n (%) 0.052 LAA 724 (57.37%) 605 (56.97%) 119 (59.50%) SAO 304 (24.09%) 255 (24.01%) 49 (24.50%) CE 183 (14.50%) 152 (14.31%) 31 (15.50%) Other 51 (4.04%) 50 (4.71%) 1 (0.50%) Laboratory parameters HbA1c, (%) 6.00 (5.50–7.20) 6.00 (5.60–7.20) 6.05 (5.50–7.20) 0.786 FPG (mg/dL) 102.06 (90.00-130.41) 100.98 (89.82-128.52) 106.20 (93.02-140.53) 0.008 TG (mg/dL) 113.37 (80.60-157.65) 114.26 (81.48-160.31) 104.96 (73.73-146.14) 0.029 TC (mmol/L) 4.29 (3.65–5.02) 4.28 (3.62–5.01) 4.34 (3.77–5.16) 0.162 HDL-C (mmol/L) 1.15 ± 0.31 1.15 ± 0.31 1.18 ± 0.33 0.128 LDL-C (mmol/L) 2.88 ± 1.03 2.85 ± 1.01 3.01 ± 1.10 0.033 Homocysteine (mmol/L) 15.30 (12.07–19.60) 14.92 (11.70–19.40) 17.00 (13.60–20.00) < 0.001 FT3 (pmol/L) 3.85 (3.32–4.38) 3.88 (3.35–4.39) 3.66 (3.16–4.28) 0.012 FT4 (pmol/L) 13.06 (11.34–14.68) 13.06 (11.37–14.78) 13.07 (11.02–14.43) 0.195 FT3/FT4 0.29 (0.24–0.34) 0.29 (0.24–0.34) 0.28 (0.23–0.33) 0.139 TSH (µIU/mL) 1.62 (0.97–2.63) 1.62 (0.98–2.59) 1.55 (0.89–2.79) 0.716 AST (U/L) 19.00 (15.40–24.00) 19.00 (15.30–24.00) 20.00 (16.00–25.00) 0.161 ALT (U/L) 17.00 (12.00–25.00) 17.00 (12.00–25.00) 16.00 (12.00–26.00) 0.522 CRP (mg/L) 3.00 (1.30-7.00) 2.82 (1.23–6.26) 4.33 (2.00–15.00) < 0.001 WBC (×10 9 /L) 7.07 (5.72–8.80) 6.96 (5.60–8.57) 7.81 (6.16–9.96) < 0.001 EGFR (ml/min/1.73m 2 ) 99.29 (79.50-118.94) 99.21 (80.93-118.72) 100.10 (74.66-120.68) 0.474 UA (umol/L) 314.00 (255.80-383.10) 313.00 (256.80-379.10) 318.80 (250.65-397.85) 0.430 SHR 0.81 (0.73–0.91) 0.81 (0.72–0.90) 0.84 (0.77–0.96) < 0.001 Clinical characteristics KWDT 1.00 (1.00–1.00) 1.00 (1.00–1.00) 1.00 (1.00–1.00) 0.603 A2DS2 3.00 (2.00–4.00) 2.00 (2.00–4.00) 3.00 (2.00–4.00) 0.224 GCS 15.00 (14.00–15.00) 15.00 (14.00–15.00) 15.00 (13.00–15.00) < 0.001 mRS 1.00 (0.00–3.00) 1.00 (0.00–2.00) 4.00 (2.00–5.00) < 0.001 NHISS 2.00 (1.00–5.00) 3.00 (1.00–6.00) 2.00 (1.00–4.00) 0.001 Data are presented as mean ± standard deviation (SD), median (interquartile range, IQR), or number (percentage), as appropriate. P-values were calculated using independent t-test, Mann–Whitney U test, or chi-square test, depending on variable distribution. Bold P -values indicate statistical significance ( P < 0.05). Abbreviations: END, early neurological deterioration; COPD, chronic obstructive pulmonary disease; TOAST, Trial of Org 10172 in Acute Stroke Treatment; LAA, large-artery atherosclerosis; SAO, small-artery occlusion; CE, cardioembolism; Other, other determined or undetermined etiology; FPG, fasting plasma glucose; SHR, stress hyperglycemia ratio; TG, triglyceride; FT3, free triiodothyronine; FT4, free thyroxine; TSH, thyroid-stimulating hormone; AST, aspartate aminotransferase; CRP, C-reactive protein; WBC, white blood cell count; UA, uric acid; KWDT, Kubota water drinking test; A2DS2, age, atrial fibrillation, dysphagia, sex, and stroke severity score; GCS, Glasgow Coma Scale; mRS, modified Rankin Scale; NIHSS, National Institutes of Health Stroke Scale. Across SHR quartiles, significant differences were observed in metabolic, inflammatory, and clinical characteristics (Table 2 ). Higher SHR was associated with increased atrial fibrillation, HbA1c, and fasting glucose. LDL-C and total cholesterol showed modest upward trends. FT3 decreased, FT4 increased, and the FT3/FT4 ratio declined. Inflammatory markers (CRP, WBC) and uric acid were elevated in the highest quartile. Clinical severity scores, including KWDT, A2DS2, mRS, and NIHSS, also increased with higher SHR. Table 2 Baseline characteristics of acute ischemic stroke patients across quartiles of stress hyperglycemia ratio (SHR) SHR quartile Q1 (n = 370) Q2 (n = 369) Q3 (n = 370) Q4 (n = 370) P -value* Demographic characteristic Age (years) 69.71 ± 11.42 68.30 ± 13.18 68.19 ± 12.89 69.83 ± 12.67 0.201 SBP (mmHg) 150.95 ± 21.64 153.98 ± 22.08 152.76 ± 21.50 154.43 ± 21.18 0.127 DBP (mmHg) 81.66 ± 12.42 83.28 ± 13.86 83.01 ± 12.63 84.15 ± 12.88 0.059 Body temperature (℃) 36.51 ± 0.41 36.46 ± 0.37 36.48 ± 0.35 36.45 ± 0.67 0.338 Gender, n (%) 0.059 Female 144 (38.92%) 127 (34.42%) 159 (42.97%) 158 (42.70%) Male 226 (61.08%) 242 (65.58%) 211 (57.03%) 212 (57.30%) Current smoking, n (%) 0.059 No 215 (58.11%) 216 (58.54%) 245 (66.22%) 236 (63.78%) Yes 155 (41.89%) 153 (41.46%) 125 (33.78%) 134 (36.22%) Hypertension, n (%) 0.362 No 81 (21.89%) 95 (25.75%) 81 (21.89%) 76 (20.54%) Yes 289 (78.11%) 274 (74.25%) 289 (78.11%) 294 (79.46%) Diabetes, n (%) < 0.001 No 185 (50.00%) 268 (72.63%) 263 (71.08%) 221 (59.73%) Yes 185 (50.00%) 101 (27.37%) 107 (28.92%) 149 (40.27%) Atrial fibrillation, n (%) < 0.001 No 304 (82.16%) 319 (86.45%) 318 (85.95%) 283 (76.49%) Yes 66 (17.84%) 50 (13.55%) 52 (14.05%) 87 (23.51%) COPD, n (%) 0.247 No 345 (93.24%) 353 (95.66%) 348 (94.05%) 341 (92.16%) Yes 25 (6.76%) 16 (4.34%) 22 (5.95%) 29 (7.84%) TOAST, n (%) 0.180 LAA 171 (53.44%) 184 (56.27%) 203 (60.96%) 166 (58.87%) SAO 80 (25.00%) 91 (27.83%) 67 (20.12%) 66 (23.40%) CE 55 (17.19%) 43 (13.15%) 44 (13.21%) 41 (14.54%) Other 14 (4.38%) 9 (2.75%) 19 (5.71%) 9 (3.19%) Laboratory parameters HbA1c, (%) 6.60 (6.00-8.40) 6.00 (5.60–6.70) 5.80 (5.40–6.60) 5.90 (5.20–7.18) < 0.001 FPG (mg/dL) 94.05 (82.98-118.71) 96.84 (88.38–113.40) 101.70 (93.60-121.81) 124.02 (102.60-168.21) < 0.001 TG (mg/dL) 116.03 (78.83-150.57) 117.80 (82.37-163.85) 109.38 (81.71-156.99) 110.27 (77.06–159.20) 0.452 TC (mmol/L) 4.16 (3.49–4.84) 4.27 (3.68–5.09) 4.41 (3.71–5.02) 4.31 (3.74–5.15) 0.045 LDL-C (mmol/L) 2.76 ± 1.09 2.91 ± 0.97 2.94 ± 1.03 2.91 ± 1.03 0.018 HDL-C (mmol/L) 1.14 ± 0.28 1.15 ± 0.31 1.14 ± 0.28 1.18 ± 0.37 0.382 Homocysteine (mmol/L) 14.70 (11.71–18.80) 15.80 (12.08–20.10) 14.90 (12.10–19.40) 15.98 (12.40–19.70) 0.075 FT3 (pmol/L) 3.88 (3.31–4.39) 3.88 (3.40–4.33) 3.88 (3.37–4.49) 3.71 (3.18–4.32) 0.021 FT4 (pmol/L) 12.82 (10.95–14.63) 12.89 (11.34–14.46) 12.69 (11.10-14.34) 13.54 (11.95–15.33) < 0.001 FT3/FT4 0.29 (0.25–0.34) 0.29 (0.25–0.34) 0.29 (0.26–0.34) 0.26 (0.22–0.32) < 0.001 TSH (mIU/L) 1.69 (1.10–2.65) 1.63 (0.93–2.57) 1.68 (0.98–2.69) 1.41 (0.81–2.61) 0.083 AST (U/L) 19.00 (15.33–24.62) 19.00 (16.00–24.00) 19.00 (15.00–23.00) 20.00 (15.20–25.30) 0.223 ALT (U/L) 17.45 (12.00–24.00) 17.50 (12.00-26.10) 16.00 (12.00–24.00) 16.00 (12.00–25.00) 0.543 CRP (mg/L) 2.58 (1.16–5.91) 2.45 (1.20–6.21) 3.00 (1.29–5.97) 4.13 (2.00-12.79) < 0.001 WBC (×10 9 /L) 6.97 (5.59–8.72) 6.90 (5.59–8.54) 6.79 (5.54–8.34) 7.71 (6.20–9.75) < 0.001 EGFR (ml/min/1.73m 2 ) 97.42 (77.52-118.14) 99.48 (81.05-119.18) 102.62 (83.16-121.68) 97.70 (76.46-116.05) 0.069 UA (umol/L) 318.85 (252.53-387.77) 323.30 (270.20-392.60) 302.60 (250.70-368.97) 310.80 (245.53–385.40) 0.035 SHR 0.67 (0.61–0.70) 0.78 (0.76–0.79) 0.86 (0.84–0.88) 1.00 (0.95–1.10) < 0.001 Clinical characteristics KWDT 1.00 (1.00–1.00) 1.00 (1.00–1.00) 1.00 (1.00–1.00) 1.00 (1.00-2.75) < 0.001 A2DS2 2.00 (2.00–4.00) 2.00 (2.00–4.00) 2.00 (2.00–4.00) 3.00 (2.00–5.00) < 0.001 GCS 15.00 (15.00–15.00) 15.00 (14.00–15.00) 15.00 (13.00–15.00) 15.00 (13.00–15.00) < 0.001 mRS 1.00 (1.00–2.00) 1.00 (1.00–3.00) 1.00 (1.00–3.00) 2.00 (1.00–4.00) < 0.001 NHISS 2.00 (1.00–5.00) 2.00 (1.00–5.00) 2.00 (1.00–5.00) 3.00 (1.00–8.00) < 0.001 Data are presented as mean ± standard deviation (SD), median (interquartile range, IQR), or number (percentage), as appropriate. P-values were calculated using one-way ANOVA, Kruskal–Wallis test, or chi-square test, depending on variable distribution. Bold P -values indicate statistical significance ( P < 0.05). Abbreviations: SBP, systolic blood pressure; DBP, diastolic blood pressure; AF, atrial fibrillation; COPD, chronic obstructive pulmonary disease; TOAST, Trial of Org 10172 in Acute Stroke Treatment; LAA, large-artery atherosclerosis; SAO, small-artery occlusion; CE, cardioembolism; Other, other determined or undetermined etiology; HbA1c, glycated hemoglobin; FPG, fasting plasma glucose; TG, triglyceride; TC, total cholesterol; LDL-C, low-density lipoprotein cholesterol; HDL-C, high-density lipoprotein cholesterol; UA, uric acid; WBC, white blood cell count; CRP, C-reactive protein; eGFR, estimated glomerular filtration rate; FT3, free triiodothyronine; FT4, free thyroxine; HCY, homocysteine; KWDT, Kubota water drinking test; A2DS2, age, atrial fibrillation, dysphagia, sex, and stroke severity score; GCS, Glasgow Coma Scale; mRS, modified Rankin Scale; NIHSS, National Institutes of Health Stroke Scale; SHR, stress hyperglycemia ratio. Multivariable Logistic Regression Analysis When modeled as a continuous variable, SHR was associated with higher odds of END: unadjusted OR = 3.78 (95% CI: 2.01–7.09, P < 0.0001); partially adjusted OR = 3.52 (95% CI: 1.92–6.46, P < 0.0001); and fully adjusted OR = 6.19 (95% CI: 2.68–14.28, P < 0.0001). When categorized by quartiles, compared with Q1, the fully adjusted ORs were 1.78 (95% CI: 1.09–2.89, P = 0.0211) for Q2, 2.23 (95% CI: 1.38–3.62, P = 0.0011) for Q3, and 2.14 (95% CI: 1.32–3.46, P = 0.0021), with a P for trend of 0.0015 (Table 3 ). Table 3 Association Between Stress Hyperglycemia Ratio and Risk of Early Neurological Deterioration: Multivariable Logistic Regression Analysis Exposure Crude Model (Model 1) Partially Adjusted Model (Model 2) Fully Adjusted Model (Model 3) OR (95% CI) P -value OR (95% CI) P -value OR (95% CI) P -value SHR 3.78 (2.01, 7.09) < 0.0001 3.52 (1.92, 6.46) < 0.0001 6.19 (2.68, 14.28) < 0.0001 SHR quartile Q1 1.0 1.0 1.0 Q2 1.55 (1.04, 2.32) 0.0327 1.62 (1.08, 2.43) 0.0208 1.78 (1.09, 2.89) 0.0211 Q3 1.75 (1.17, 2.60) 0.0059 1.81 (1.21, 2.69) 0.0038 2.23 (1.38, 3.62) 0.0011 Q4 1.90 (1.28, 2.81) 0.0014 1.88 (1.27, 2.80) 0.0018 2.14 (1.32, 3.46) 0.0021 P for trend 0.0015 0.0020 0.0015 Values are expressed as odds ratios (ORs) with 95% confidence intervals (CIs). P -values < 0.05 were considered statistically significant. Bold values indicate statistical significance ( P < 0.05). Model 1: unadjusted. Model 2: adjusted for age and sex. Model 3: adjusted for age, sex, smoking status, hypertension, diabetes mellitus, atrial fibrillation (AF), chronic obstructive pulmonary disease (COPD), Trial of Org 10172 in Acute Stroke Treatment (TOAST) classification, systolic blood pressure (SBP), diastolic blood pressure (DBP), and the following laboratory variables: triglycerides (TG, Box-Cox transformed), estimated glomerular filtration rate (eGFR), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), aspartate aminotransferase (AST, Box-Cox transformed), alanine aminotransferase (ALT, Box-Cox transformed), uric acid (UA, Box-Cox transformed), and homocysteine (HCY, Box-Cox transformed). Abbreviations: OR, odds ratio; CI, confidence interval; SHR, stress hyperglycemia ratio; END, early neurological deterioration. Non-linear Relationship Between SHR and END Risk Modeled by GAM In the primary generalized additive model (GAM) without adjustment for CRP and WBC, SHR showed a significant non-linear association with the risk of END (edf = 3.86, χ² = 15.56, P for non-linearity = 0.0084; deviance explained = 7.32%; adjusted R² = 0.0525) (Fig. 1 ; Supplementary Table 1, Model A). Other significant covariates included atrial fibrillation, homocysteine, and NIHSS score (all P < 0.05). In a sensitivity analysis additionally adjusting for CRP and WBC, the non-linear relationship remained significant with a modest increase in deviance explained (edf = 4.07, χ² = 12.97, P = 0.0243; deviance explained = 9.04%; adjusted R² = 0.0664) (Supplementary Fig. 1; Supplementary Table 1, Model B). Threshold analysis using two-piecewise logistic regression identified a potential inflection point at SHR = 1.06 (Supplementary Table 2). Below this threshold, the association between SHR and END was not statistically significant (OR = 3.56, 95% CI: 0.95–13.35, P = 0.0599). Above the threshold, the effect estimate was imprecise and not statistically significant (OR = 4816.92, 95% CI: 0.55–∞, P = 0.0673). The likelihood ratio test showed no significant improvement of the two-piecewise model over the linear model ( P = 0.151). Subgroup Analyses of SHR and Risk of END Subgroup analyses using multivariable logistic regression demonstrated that the association between SHR and END remained significant across most clinical strata, including patients aged ≥ 70 years, both sexes, non-smokers, those with hypertension, atrial fibrillation, and large-artery atherosclerosis (Table 4 ). A significant interaction was found for diabetes status ( P for interaction = 0.0033). The association was stronger in non-diabetic patients (OR = 11.92, 95% CI: 2.98–47.72, P = 0.0005), whereas in diabetic patients it was attenuated but remained significant (OR = 2.41, 95% CI: 1.11–5.25, P = 0.0259). No significant interactions were detected for age, sex, smoking, hypertension, atrial fibrillation, COPD, or TOAST subtype (all P for interaction > 0.05) Table 4 Subgroup Analyses of the Association Between Stress Hyperglycemia Ratio and Early Neurological Deterioration END SHR N OR (95% CI) P -value P for interaction AGE 0.9630 =70 756 7.09 (2.44, 20.60) 0.0003 Gender 0.8932 Female 588 8.81 (2.85, 27.26) 0.0002 Male 891 6.30 (1.53, 25.97) 0.0108 Smoking status 0.3969 No 912 7.43 (2.76, 20.03) < 0.0001 Yes 567 4.19 (0.74, 23.78) 0.1060 Hypertension 0.6387 No 333 6.08 (0.90, 41.29) 0.0648 Yes 1146 7.18 (2.75, 18.76) < 0.0001 Diabetes 0.0308 No 937 18.23 (4.43, 75.13) < 0.0001 Yes 542 3.39 (1.16, 9.93) 0.0259 Atrial fibrillation 0.4700 No 1224 4.67 (1.68, 13.03) 0.0032 Yes 255 13.70 (2.38, 78.78) 0.0034 COPD 0.7866 No 1387 6.64 (2.72, 16.20) < 0.0001 Yes 92 0.00 (0.00, Inf) 0.9998 TOAST 0.9695 (0.9146 #) LAA 724 6.60 (1.78, 24.49) 0.0048 SAO 304 9.65 (1.98, 46.97) 0.0050 CE 183 3.11 (0.25, 38.72) 0.3787 Other 51 14647.19 (0.00, Inf) 1.0000 Odds ratios (ORs) and 95% confidence intervals (CIs) were estimated using multivariable logistic regression models adjusted for age, sex, smoking status, hypertension, diabetes mellitus, atrial fibrillation (AF), chronic obstructive pulmonary disease (COPD), Trial of Org 10172 in Acute Stroke Treatment (TOAST) classification, systolic blood pressure (SBP), diastolic blood pressure (DBP), and laboratory covariates. Interaction effects were tested by including multiplicative terms between SHR and each stratifying variable in the fully adjusted model. For TOAST subtype, the trend test (treated as ordinal variable) is shown in parentheses (#). Extremely large ORs or infinite CIs in certain subgroups (e.g., COPD = Yes, TOAST = CE/Other) are due to sparse data and should be interpreted with caution. Abbreviations: END, early neurological deterioration; SHR, stress hyperglycemia ratio; AF, atrial fibrillation; COPD, chronic obstructive pulmonary disease; TOAST, Trial of Org 10172 in Acute Stroke Treatment; LAA, large-artery atherosclerosis; SAO, small-artery occlusion; CE, cardioembolism. Bold P values indicate statistical significance (P < 0.05). Mediation Analysis Causal mediation analysis indicated that CRP partially mediated the association between SHR and END, with an indirect effect of 0.004687 (95% CI: 0.001851–0.008594, P < 0.0001) and a mediation proportion of 12.89% (Fig. 2 ). WBC also showed a smaller but significant mediation effect, with an indirect effect of 0.002874 (95% CI: 0.000653–0.005980, P = 0.008) and a mediation proportion of 8.03%. Direct effects remained significant after adjustment for potential confounders. Discussion In this retrospective cohort study of patients with AIS, we found that elevated SHR was independently associated with an increased risk of END[ 25 , 26 ]. This association showed a nonlinear pattern, but no clear threshold effect was observed[ 27 , 28 ]. Subgroup analysis further revealed that the relationship was more pronounced in non-diabetic patients, suggesting that premorbid glycemic status may modify the prognostic value of SHR[ 29 ]. Moreover, mediation analysis indicated that inflammatory markers, particularly CRP, partially mediated this association. These findings highlight the interplay between metabolic and inflammatory stress responses in acute stroke[ 30 – 32 ]. Our findings are consistent with a growing body of evidence linking SHR to adverse outcomes after AIS[ 25 , 33 ]. For instance, Xiao et al. demonstrated that higher SHR was significantly associated with 90-day poor functional outcomes in a large cohort of AIS patients[ 25 ]. Similarly, Huang et al. conducted a meta-analysis including more than 180,000 patients and confirmed a nonlinear dose-response relationship between SHR and adverse outcomes[ 33 ]. Moreover, Zhang et al. reported that higher SHR levels were robustly associated with increased 30-day and 90-day mortality, regardless of diabetes status[ 34 , 35 ]. Most prior studies have primarily focused on long-term outcomes, such as mortality and functional disability[ 36 – 38 ]. In contrast, our study identified SHR as a predictor of early neurological deterioration. This finding expands the clinical relevance of SHR from long-term prognosis to early in-hospital risk stratification[ 39 ]. The biological mechanisms underlying the association between SHR and END remain incompletely understood. Hyperglycemia during acute stress may exacerbate ischemic injury through multiple pathways, including promotion of oxidative stress and blood–brain barrier disruption[ 40 ]. Inflammatory activation also appears to play an important role. Our mediation analysis suggests that CRP partly mediates the association between SHR and END[ 41 ]. This finding indicates that systemic inflammation may serve as a downstream effector linking stress hyperglycemia to neurological deterioration. Beyond oxidative stress and systemic inflammation, several additional mechanisms may contribute to this association[ 42 , 43 ]. First, impaired energy metabolism plays a central role: acute hyperglycemia leads to excessive anaerobic glycolysis, lactate accumulation, and tissue acidosis, which aggravates neuronal injury and reduces ischemic tolerance[ 44 ]. Second, hyperglycemia amplifies immune–inflammatory responses. Elevated SHR promotes excessive neutrophil activation, macrophage infiltration, and release of pro-inflammatory cytokines, thereby accelerating secondary neuroinflammation[ 45 , 46 ]. Third, vascular and coagulation abnormalities represent another key pathway. Stress hyperglycemia can induce endothelial dysfunction, increase vascular permeability, and promote microthrombosis, which together impair microcirculatory perfusion and exacerbate ischemic damage[ 30 , 47 ]. In addition, central nervous system–peripheral immune crosstalk may further explain this association: acute stroke triggers sympathetic nervous system activation, with abrupt catecholamine release driving hepatic glycogenolysis and hyperglycemia[ 48 , 49 ]; simultaneously, activation of the hypothalamic–pituitary–adrenal (HPA) axis increases cortisol secretion, leading to insulin resistance, immune suppression, and inflammatory imbalance[ 50 – 52 ]. Finally, differences between diabetic and non-diabetic patients deserve attention. In diabetics, long-term adaptation to chronic hyperglycemia may blunt the effects of acute metabolic fluctuations. By contrast, non-diabetics experience sudden glycemic surges that trigger stronger inflammatory and vascular responses[ 53 , 54 ]. These differences together may explain why the prognostic impact of SHR is more evident in non-diabetic patients. Prior studies have similarly shown that elevated SHR is associated with a higher risk of hemorrhagic transformation[ 30 ] and worse outcomes after intravenous thrombolysis[ 55 ] or endovascular therapy[ 56 ]. Interestingly, the effect of SHR differed according to diabetes status. Several studies including Duan et al.[ 57 ] and Wang et al.[ 56 ], demonstrated that the prognostic impact of SHR is more evident in non-diabetic patients. This observation aligns with our findings and underscores the importance of considering premorbid glycemic status when interpreting the prognostic role of SHR[ 58 ]. From a clinical perspective, our findings support incorporating SHR into risk stratification models for AIS. Unlike absolute glucose levels, SHR adjusts for background glycemic status and therefore provides a more individualized assessment[ 59 ]. Several studies have also shown that SHR outperforms conventional glycemic indices in predicting outcomes after thrombolysis[ 47 , 55 ] and mechanical thrombectomy[ 60 ]. Although large randomized controlled trials such as SHINE did not show benefits from intensive glucose lowering in AIS[ 61 ], our findings suggest that SHR may help identify subgroups of patients—particularly non-diabetics—who face a higher risk of early deterioration. Such patients could benefit from closer monitoring, early intervention, and more targeted management strategies. Importantly, current stroke management guidelines mainly emphasize controlling absolute hyperglycemia, but do not consider the relative degree of stress-induced hyperglycemia. Our results indicate that SHR could complement existing approaches, offering a more dynamic and individualized risk assessment tool. In practice, SHR can be rapidly calculated from routinely available FPG and HbA1c, suggesting that it has potential for bedside application in emergency and inpatient stroke care to guide risk stratification and personalized management. The strengths of our study include a relatively large sample size, systematic adjustment for potential confounders, and the application of mediation analysis to explore underlying mechanisms[ 62 ]. Nevertheless, several limitations should be acknowledged. First, this was a single-center retrospective study, which may restrict the generalizability of our findings despite strict inclusion and exclusion criteria. In particular, the homogeneity of the study population in terms of ethnicity, lifestyle, and access to medical resources may limit the applicability of the results to other settings. Second, blood glucose and HbA1c were measured only once at admission, precluding evaluation of their temporal dynamics, which might provide additional prognostic insights as suggested by previous studies. Third, although our multivariable models adjusted for important confounders such as stroke severity (NIHSS) and dysphagia, residual confounding cannot be fully excluded. Unmeasured factors, such as level of consciousness, aspiration risk, use of nasogastric or jejunal tubes, prolonged bed rest, and infarct location, may still influence the results. Fourth, while we employed advanced methods including GAM, subgroup analyses, and mediation models, the possibility of multiple testing bias or model overfitting cannot be completely ruled out. Fifth, we focused on SHR alone and did not incorporate other glycemic markers (e.g., glycemic variability, glucose clearance rate, or insulin resistance indices), nor did we assess inflammatory or immune biomarkers (e.g., cytokines or immune cell function), which may provide complementary prognostic information. Finally, we mainly assessed in-hospital and short-term outcomes, without evaluating long-term endpoints such as stroke recurrence, post-discharge mortality, or quality of life. Future prospective multicenter studies with larger and more diverse populations are needed to validate our findings and enhance external validity. In particular, dynamic monitoring of SHR and inflammatory biomarkers may help clarify the temporal relationship between metabolic stress and neurological deterioration. Moreover, integrating SHR into established prognostic models could enhance predictive accuracy, and interventional studies should explore whether targeting metabolic–inflammatory coupling can mitigate the risk of END in high-risk AIS patients. Conclusion In this retrospective study of patients with acute ischemic stroke, we demonstrated that elevated SHR was independently associated with an increased risk of END, with a nonlinear association, though no definitive threshold was established. Subgroup analysis revealed that this association was more pronounced among non-diabetic patients, suggesting that baseline metabolic status may modulate the adverse impact of stress hyperglycemia on stroke outcomes. Although inflammatory markers such as CRP showed strong associations with outcomes, SHR provides complementary prognostic value and is easily obtainable from routine tests. Incorporating SHR into clinical risk stratification models may facilitate the early identification of high-risk patients and guide preventive and therapeutic strategies, particularly in non-diabetic populations. Future prospective, multicenter studies are warranted to validate these findings and explore the potential utility of combining SHR with other metabolic and inflammatory biomarkers. Declarations Availability of data and materials The datasets generated and/or analyzed during the current study are available in the supplementary materials of this article. Acknowledgements Not applicable Author information Authors and Affiliations Center for Rehabilitation Medicine, Rehabilitation & Sports Medicine Research Institute of Zhejiang Province, Department of Rehabilitation Medicine, Zhejiang Provincial People's Hospital (Affiliated People's Hospital, Hangzhou Medical College), Hangzhou, Zhejiang, China. Wei Yang: [email protected] ; Yiji Shen: [email protected] ; Yuehua Fei: [email protected] ; Tongcai Tan: [email protected] ; Yong Liu: [email protected] , ORCID: 0000-0001-8964-6112. Contributions Yong Liu, Yuehua Fei, and Tongcai Tan: Writing – original draft, Writing – review & editing, Conceptualization, Software, Formal Analysis, Project administration, Validation, Visualization. Wei Yang and Yiji Shen: Writing – review & editing, Conceptualization, Investigation, Date curation, Methodology, Supervision, Project administration, Validation, Resources, Visualization. Corresponding author: Yong Liu Ethics approval and consent to participate This study was approved by the Ethics Committee of Zhejiang Provincial People's Hospital (Affiliated People's Hospital, Hangzhou Medical College) (Approval Number: 2021QT391). Informed consent was waived due to the retrospective nature of the study and the use of de-identified patient data. All data were anonymized prior to analysis. Declaration of interests None Competing interests The authors declare there are no conflicts of interest. Funding This study is supported by the Medical and Health Science and Technology Project of Zhejiang Province, China (Grant No. 2023KY032 and 2024KY706); and the Zhejiang Provincial Program of Traditional Chinese Medicine Science and Technology (Grant No.2023ZL238). References Thanvi B, Treadwell S, Robinson T. Early neurological deterioration in acute ischaemic stroke: predictors, mechanisms and management. Postgrad Med J. 2008;84:412–7. 10.1136/pgmj.2007.066118 . Liu H, Liu K, Zhang K, Zong C, Yang H, Li Y, et al. Early neurological deterioration in patients with acute ischemic stroke: a prospective multicenter cohort study. Ther Adv Neurol Disord. 2023;16:17562864221147743. 10.1177/17562864221147743 . Heitkamp C, Winkelmeier L, Flottmann F, Schell M, Kniep H, Broocks G, et al. Thrombectomy patients with minor stroke: factors of early neurological deterioration. 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Supplementary Files SupplementaryMaterial.zip Cite Share Download PDF Status: Published Journal Publication published 21 Jan, 2026 Read the published version in BMC Neurology → Version 1 posted Editorial decision: Revision requested 22 Dec, 2025 Reviews received at journal 16 Dec, 2025 Reviews received at journal 06 Dec, 2025 Reviews received at journal 26 Nov, 2025 Reviewers agreed at journal 25 Nov, 2025 Reviewers agreed at journal 25 Nov, 2025 Reviewers agreed at journal 25 Nov, 2025 Reviewers agreed at journal 25 Nov, 2025 Reviewers invited by journal 24 Nov, 2025 Editor invited by journal 19 Nov, 2025 Editor assigned by journal 06 Nov, 2025 Submission checks completed at journal 06 Nov, 2025 First submitted to journal 05 Nov, 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. 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08:26:49","extension":"html","order_by":19,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":295757,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8037569/v1/d707343d888df6e719eaf899.html"},{"id":97127132,"identity":"82f2891d-7247-43b4-b192-888daed42b8f","added_by":"auto","created_at":"2025-12-01 08:26:49","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":69565,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eNon-linear association between stress hyperglycemia ratio (SHR) and early neurological deterioration (END) in acute ischemic stroke\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 1 legends:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe figure shows the generalized additive model (GAM) evaluating the non-linear relationship between SHR and the risk of END. In the primary model (Model A), fully adjusted for age, sex, smoking status, hypertension, diabetes mellitus, atrial fibrillation (AF), chronic obstructive pulmonary disease (COPD), Trial of Org 10172 in Acute Stroke Treatment (TOAST) classification, systolic blood pressure (SBP), diastolic blood pressure (DBP), triglycerides (TG, Box-Cox transformed), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), aspartate aminotransferase (AST, Box-Cox transformed), alanine aminotransferase (ALT, Box-Cox transformed), uric acid (UA, Box-Cox transformed), homocysteine (HCY, Box-Cox transformed), estimated glomerular filtration rate (eGFR), and NIHSS score, SHR exhibited a significant non-linear association with END risk (edf = 3.86, χ² = 15.56, P = 0.0084). In sensitivity analysis additionally adjusting for C-reactive protein (CRP) and white blood cell count (WBC) (Model B), the association remained significant (edf = 4.07, χ² = 12.97, P = 0.0243). Shaded areas represent 95% confidence intervals of the smoothed fit. END: early neurological deterioration; SHR: stress hyperglycemia ratio; GAM: generalized additive model.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8037569/v1/61afe149cdf277399e90afe3.png"},{"id":97141351,"identity":"191c23e0-e552-4781-aa2a-2584c44a4ab2","added_by":"auto","created_at":"2025-12-01 10:06:37","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":132609,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMediation analysis showing partial indirect effects of C-reactive protein (a) and white blood cell count (b) on the association between stress hyperglycemia ratio and early neurological deterioration in acute ischemic stroke\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 2 legends:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePanels (a) and (b) display the results of causal mediation analysis evaluating the indirect effects of C-reactive protein (CRP) and white blood cell count (WBC), respectively, on the relationship between the stress hyperglycemia ratio (SHR) and early neurological deterioration (END).\u003c/p\u003e\n\u003cp\u003eFor CRP (a), the indirect effect was 0.004687 (95% CI: 0.001851–0.008594, P \u0026lt; 0.0001), accounting for 12.89% of the total effect.\u003c/p\u003e\n\u003cp\u003eFor WBC (b), the indirect effect was 0.002874 (95% CI: 0.000653–0.005980, P = 0.008), accounting for 8.03% of the total effect.\u003c/p\u003e\n\u003cp\u003eDirect effects of SHR on END remained statistically significant after adjustment for potential confounders, indicating that inflammation only partially mediated the observed association.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8037569/v1/ae2f2a8be657ce71b7857050.png"},{"id":101153270,"identity":"91740990-a549-4ed3-94ac-eb5b76478269","added_by":"auto","created_at":"2026-01-26 16:14:29","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1949894,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8037569/v1/2be9df67-e292-442d-aa55-b4c5c5513b2c.pdf"},{"id":97141395,"identity":"d4a39ffe-1f24-449d-b496-b22b23760e2b","added_by":"auto","created_at":"2025-12-01 10:06:39","extension":"zip","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1357217,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterial.zip","url":"https://assets-eu.researchsquare.com/files/rs-8037569/v1/7f84c1ff353b102165e47a05.zip"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association Between Stress Hyperglycemia Ratio and Early Neurological Deterioration After Acute Ischemic Stroke: A Retrospective Cohort Study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAcute ischemic stroke (AIS) remains a major cause of death and long-term disability worldwide, and up to one-fifth of patients experience early neurological deterioration (END) within the first days of admission[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. END, typically defined as a measurable worsening of neurological status shortly after onset, is strongly associated with poor functional outcomes and increased mortality[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Timely identification of patients at high risk for END is therefore essential for guiding early treatment and preventing secondary brain injury[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAcute hyperglycemia is a frequent metabolic response to cerebral ischemia, even in patients without pre-existing diabetes[\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. This stress-related hyperglycemia results from neuroendocrine activation, systemic inflammation, and impaired glucose utilization[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Traditional markers such as absolute blood glucose are strongly influenced by chronic glycemic status and may underestimate the acute stress response[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. To overcome this limitation, the stress hyperglycemia ratio (SHR)\u0026mdash;calculated as the ratio of fasting plasma glucose to estimated average glucose derived from HbA1c\u0026mdash;has been proposed as a more robust index by accounting for background glycemia[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Previous studies have linked elevated SHR to adverse outcomes in critical illness and AIS, but its specific role in predicting END remains insufficiently explored[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe biological link between SHR and END may involve inflammatory activation. Hyperglycemia can amplify ischemia-induced neuroinflammation, promote leukocyte infiltration, and disrupt the blood\u0026ndash;brain barrier, accelerating neuronal injury[\u003cspan additionalcitationids=\"CR19\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Circulating inflammatory markers such as C-reactive protein (CRP) and white blood cell (WBC) count are easily measurable, and both have been independently associated with hyperglycemia and poor stroke outcomes[\u003cspan additionalcitationids=\"CR22\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Whether CRP and WBC mediate the relationship between SHR and END has not been clearly established.\u003c/p\u003e\u003cp\u003eIn this study, we investigated the association between SHR and END in a well-characterized cohort of AIS patients admitted within 24 hours of onset. We further examined potential non-linear and threshold effects, evaluated subgroup differences, and conducted mediation analyses to clarify the contribution of CRP and WBC. Our findings aim to establish the prognostic value of SHR in the acute phase of stroke and provide new insight into metabolic\u0026ndash;inflammatory interactions in risk stratification.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy Design and Participants\u003c/h2\u003e\u003cp\u003eThis retrospective cohort study included consecutive patients with AIS admitted to Zhejiang Provincial People's Hospital (Affiliated People's Hospital, Hangzhou Medical College) between October 2018 and September 2021. The study was approved by the Ethics Committee of Zhejiang Provincial People's Hospita (No. 2021QT391) and conducted in accordance with the Declaration of Helsinki. The requirement for informed consent was waived due to the use of anonymized data.\u003c/p\u003e\u003cp\u003eEligible patients met all of the following criteria: (1) AIS diagnosed and admitted within 24 hours of symptom onset; (2) brain MRI within 48 hours confirming acute infarction on standard sequences; (3) fasting plasma glucose and HbA1c measured within 48 hours; and (4) sufficient data to calculate the SHR.\u003c/p\u003e\u003cp\u003eExclusion criteria were: (1) severe hepatic dysfunction (ALT\u0026thinsp;\u0026gt;\u0026thinsp;10\u0026times; ULN or AST\u0026thinsp;\u0026gt;\u0026thinsp;3\u0026times; ULN), severe renal dysfunction (creatinine\u0026thinsp;\u0026gt;\u0026thinsp;443 \u0026micro;mol/L), active malignancy, or hematologic disease; (2) significant cardiopulmonary insufficiency, including NYHA class III\u0026ndash;IV heart failure, LVEF\u0026thinsp;\u0026lt;\u0026thinsp;40%, chronic obstructive pulmonary disease (COPD), or respiratory infection on admission; (3) active infection at admission; (4) pregnancy or lactation; (5) inability to assess stroke severity using the NIHSS due to coma, profound aphasia, or other neurological limitations (exclusion based on feasibility, not severity); (6) multiple AIS admissions during the study period (only the first included); or (7) missing key laboratory data.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eBaseline Data Collection\u003c/h3\u003e\n\u003cp\u003eBaseline demographic and clinical data were obtained from electronic medical records, including age, sex, body temperature, systolic blood pressure (SBP), diastolic blood pressure (DBP), smoking status, hypertension, type 2 diabetes mellitus, and atrial fibrillation (AF). Stroke severity was evaluated using the NIHSS, swallowing function by the Kubota Water Drinking Test (KWDT), and consciousness level by the Glasgow Coma Scale (GCS). NIHSS assessments were performed at admission, and repeated at 24 and 72 hours, by the same experienced neurologist to ensure consistency and reduce inter-observer variability.\u003c/p\u003e\u003cp\u003eFasting venous blood samples were drawn on the second morning after admission (06:00) by trained nurses, kept at 4\u0026deg;C, and analyzed within 2 hours by certified laboratory staff. Laboratory tests included white blood cell count (WBC), C-reactive protein (CRP), fasting blood glucose (FBG), aspartate aminotransferase (AST), alanine aminotransferase (ALT), glycated hemoglobin (HbA1c), homocysteine (HCY), triglycerides (TG), total cholesterol (TC), high-density lipoprotein cholesterol (HDL-c), and low-density lipoprotein cholesterol (LDL-c), as well as free thyroxine (FT4), free triiodothyronine (FT3), and thyroid-stimulating hormone (TSH). Estimated glomerular filtration rate (eGFR) was calculated using the CKD-EPI equation.\u003c/p\u003e\u003cp\u003eNormal reference ranges were: TSH 0.27\u0026ndash;4.2 mIU/L, FT4 12\u0026ndash;22 pmol/L, and FT3 3.1\u0026ndash;6.8 pmol/L.\u003c/p\u003e\n\u003ch3\u003eDefinitions\u003c/h3\u003e\n\u003cp\u003eEND was defined as an increase of at least two points in either the NIHSS motor score or the total NIHSS score within the first 72 hours after admission, following definitions widely applied in previous AIS studies[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Classification as END-positive was based solely on the presence of initial neurological worsening, irrespective of subsequent improvement during the observation period.\u003c/p\u003e\u003cp\u003eThe SHR was defined as the fasting plasma glucose (FPG) level divided by the estimated average glucose (both expressed in mg/dL)[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe estimated average glucose was calculated from HbA1c (%) using the validated formula: 28.7 \u0026times; HbA1c \u0026ndash; 46.7.\u003c/p\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eAll statistical analyses were performed using R software (version 4.2.2; R Foundation for Statistical Computing, Vienna, Austria) and EmpowerStats (version 2.0; X\u0026amp;Y Solutions, Inc., Boston, MA, USA). A two-tailed P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant. The Shapiro\u0026ndash;Wilk test was used to assess normality. Continuous variables are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD) or median (interquartile range, IQR) and compared using the independent-samples t-test or Mann\u0026ndash;Whitney U test, as appropriate. Categorical variables are expressed as counts (percentages) and compared using the chi-square test.\u003c/p\u003e\u003cp\u003eMultivariable logistic regression was used to examine the association between SHR and END, with SHR analyzed both as a continuous variable and by quartiles (P for trend calculated). Non-linear associations were explored using generalized additive models (GAM) with a binomial distribution and logit link, and threshold effects were evaluated using two-piecewise logistic regression, with inflection points determined by likelihood-based search. Sensitivity analyses additionally adjusted for C-reactive protein and white blood cell count. Subgroup analyses were conducted by age, sex, smoking status, hypertension, diabetes, atrial fibrillation, chronic obstructive pulmonary disease, and TOAST subtype, with interactions tested by adding multiplicative terms.\u003c/p\u003e\u003cp\u003eCausal mediation analyses were performed to assess whether C-reactive protein and white blood cell count mediated the association between SHR and END, using generalized linear models with probit link and nonparametric bootstrapping (1,000 resamples) to estimate indirect, direct, and total effects, as well as the proportion mediated.\u003c/p\u003e\u003cp\u003eCovariates for all models are listed in the corresponding table footnotes.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eBaseline Characteristics\u003c/h2\u003e\u003cp\u003eA total of 1,479 patients with acute ischemic stroke were included, of whom 270 (18.3%) developed END occurring within 72 hours of admission. Patients in the END group were older, had lower body temperature, and had a lower proportion of males and current smokers (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Atrial fibrillation was more frequent, whereas diabetes was less prevalent. Laboratory tests showed higher fasting plasma glucose, LDL-C, and homocysteine, but lower triglycerides and FT3 in the END group. Inflammatory markers (CRP and WBC) were elevated, and SHR was significantly higher. Clinically, patients with END had lower GCS and higher mRS, with slightly lower NIHSS scores at admission (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\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 acute ischemic stroke patients stratified by early neurological deterioration (END) status\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=\"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=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDemographic characteristic\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eALL(N\u0026thinsp;=\u0026thinsp;1479)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003enon-END (N\u0026thinsp;=\u0026thinsp;1209)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eEND\u003c/p\u003e\u003cp\u003e(N\u0026thinsp;=\u0026thinsp;270)\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\u003eAge (years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e69.01\u0026thinsp;\u0026plusmn;\u0026thinsp;12.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e68.29\u0026thinsp;\u0026plusmn;\u0026thinsp;12.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e72.21\u0026thinsp;\u0026plusmn;\u0026thinsp;12.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSBP (mmHg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e153.03\u0026thinsp;\u0026plusmn;\u0026thinsp;21.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e152.65\u0026thinsp;\u0026plusmn;\u0026thinsp;21.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e154.70\u0026thinsp;\u0026plusmn;\u0026thinsp;21.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.205\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDBP (mmHg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e83.03\u0026thinsp;\u0026plusmn;\u0026thinsp;12.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e83.11\u0026thinsp;\u0026plusmn;\u0026thinsp;12.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e82.65\u0026thinsp;\u0026plusmn;\u0026thinsp;13.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.559\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBody temperature (℃)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e36.47\u0026thinsp;\u0026plusmn;\u0026thinsp;0.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e36.49\u0026thinsp;\u0026plusmn;\u0026thinsp;0.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e36.41\u0026thinsp;\u0026plusmn;\u0026thinsp;0.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender, n (%)\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=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.031\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e588 (39.76%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e465 (38.46%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e123 (45.56%)\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\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e891 (60.24%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e744 (61.54%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e147 (54.44%)\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\u003eCurrent smoking, n (%)\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=\"char\" char=\".\" colname=\"c5\"\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\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e912 (61.66%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e731 (60.46%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e181 (67.04%)\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\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e567 (38.34%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e478 (39.54%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e89 (32.96%)\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\u003eHypertension, n (%)\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=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.899\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e333 (22.52%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e273 (22.58%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e60 (22.22%)\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\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1146 (77.48%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e936 (77.42%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e210 (77.78%)\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\u003eDiabetes, n (%)\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=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.324\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e937 (63.35%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e773 (63.94%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e164 (60.74%)\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\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e542 (36.65%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e436 (36.06%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e106 (39.26%)\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\u003eAtrial fibrillation, n (%)\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=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1224 (82.76%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1025 (84.78%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e199 (73.70%)\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\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e255 (17.24%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e184 (15.22%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e71 (26.30%)\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\u003eCOPD, n (%)\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=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.436\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1387 (93.78%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1131 (93.55%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e256 (94.81%)\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\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e92 (6.22%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e78 (6.45%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e14 (5.19%)\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\u003eTOAST, n (%)\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=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.052\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLAA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e724 (57.37%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e605 (56.97%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e119 (59.50%)\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\u003eSAO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e304 (24.09%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e255 (24.01%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e49 (24.50%)\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\u003eCE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e183 (14.50%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e152 (14.31%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e31 (15.50%)\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\u003eOther\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e51 (4.04%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e50 (4.71%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1 (0.50%)\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\u003e\u003cb\u003eLaboratory parameters\u003c/b\u003e\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\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\u003cp\u003e6.00 (5.50\u0026ndash;7.20)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.00 (5.60\u0026ndash;7.20)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6.05 (5.50\u0026ndash;7.20)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.786\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFPG (mg/dL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e102.06 (90.00-130.41)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e100.98 (89.82-128.52)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e106.20 (93.02-140.53)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.008\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTG (mg/dL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e113.37 (80.60-157.65)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e114.26 (81.48-160.31)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e104.96 (73.73-146.14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.029\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTC (mmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.29 (3.65\u0026ndash;5.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.28 (3.62\u0026ndash;5.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.34 (3.77\u0026ndash;5.16)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.162\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHDL-C (mmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.15\u0026thinsp;\u0026plusmn;\u0026thinsp;0.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.15\u0026thinsp;\u0026plusmn;\u0026thinsp;0.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.18\u0026thinsp;\u0026plusmn;\u0026thinsp;0.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.128\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLDL-C (mmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.88\u0026thinsp;\u0026plusmn;\u0026thinsp;1.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.85\u0026thinsp;\u0026plusmn;\u0026thinsp;1.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.01\u0026thinsp;\u0026plusmn;\u0026thinsp;1.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\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\u003eHomocysteine (mmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e15.30 (12.07\u0026ndash;19.60)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e14.92 (11.70\u0026ndash;19.40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e17.00 (13.60\u0026ndash;20.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFT3 (pmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.85 (3.32\u0026ndash;4.38)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.88 (3.35\u0026ndash;4.39)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.66 (3.16\u0026ndash;4.28)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.012\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFT4 (pmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e13.06 (11.34\u0026ndash;14.68)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13.06 (11.37\u0026ndash;14.78)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e13.07 (11.02\u0026ndash;14.43)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.195\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFT3/FT4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.29 (0.24\u0026ndash;0.34)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.29 (0.24\u0026ndash;0.34)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.28 (0.23\u0026ndash;0.33)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.139\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTSH (\u0026micro;IU/mL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.62 (0.97\u0026ndash;2.63)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.62 (0.98\u0026ndash;2.59)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.55 (0.89\u0026ndash;2.79)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.716\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAST (U/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e19.00 (15.40\u0026ndash;24.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e19.00 (15.30\u0026ndash;24.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e20.00 (16.00\u0026ndash;25.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.161\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eALT (U/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e17.00 (12.00\u0026ndash;25.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e17.00 (12.00\u0026ndash;25.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e16.00 (12.00\u0026ndash;26.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.522\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCRP (mg/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.00 (1.30-7.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.82 (1.23\u0026ndash;6.26)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.33 (2.00\u0026ndash;15.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWBC (\u0026times;10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7.07 (5.72\u0026ndash;8.80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.96 (5.60\u0026ndash;8.57)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7.81 (6.16\u0026ndash;9.96)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEGFR (ml/min/1.73m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e99.29 (79.50-118.94)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e99.21 (80.93-118.72)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e100.10 (74.66-120.68)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.474\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUA (umol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e314.00 (255.80-383.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e313.00 (256.80-379.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e318.80 (250.65-397.85)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.430\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSHR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.81 (0.73\u0026ndash;0.91)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.81 (0.72\u0026ndash;0.90)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.84 (0.77\u0026ndash;0.96)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eClinical characteristics\u003c/b\u003e\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKWDT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.00 (1.00\u0026ndash;1.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.00 (1.00\u0026ndash;1.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.00 (1.00\u0026ndash;1.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.603\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eA2DS2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.00 (2.00\u0026ndash;4.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.00 (2.00\u0026ndash;4.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.00 (2.00\u0026ndash;4.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.224\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGCS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e15.00 (14.00\u0026ndash;15.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e15.00 (14.00\u0026ndash;15.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e15.00 (13.00\u0026ndash;15.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003emRS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.00 (0.00\u0026ndash;3.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.00 (0.00\u0026ndash;2.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.00 (2.00\u0026ndash;5.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNHISS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.00 (1.00\u0026ndash;5.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.00 (1.00\u0026ndash;6.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.00 (1.00\u0026ndash;4.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eData are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD), median (interquartile range, IQR), or number (percentage), as appropriate.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eP-values were calculated using independent t-test, Mann\u0026ndash;Whitney U test, or chi-square test, depending on variable distribution. Bold \u003cem\u003eP\u003c/em\u003e-values indicate statistical significance (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eAbbreviations: END, early neurological deterioration; COPD, chronic obstructive pulmonary disease; TOAST, Trial of Org 10172 in Acute Stroke Treatment; LAA, large-artery atherosclerosis; SAO, small-artery occlusion; CE, cardioembolism; Other, other determined or undetermined etiology; FPG, fasting plasma glucose; SHR, stress hyperglycemia ratio; TG, triglyceride; FT3, free triiodothyronine; FT4, free thyroxine; TSH, thyroid-stimulating hormone; AST, aspartate aminotransferase; CRP, C-reactive protein; WBC, white blood cell count; UA, uric acid; KWDT, Kubota water drinking test; A2DS2, age, atrial fibrillation, dysphagia, sex, and stroke severity score; GCS, Glasgow Coma Scale; mRS, modified Rankin Scale; NIHSS, National Institutes of Health Stroke Scale.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eAcross SHR quartiles, significant differences were observed in metabolic, inflammatory, and clinical characteristics (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Higher SHR was associated with increased atrial fibrillation, HbA1c, and fasting glucose. LDL-C and total cholesterol showed modest upward trends. FT3 decreased, FT4 increased, and the FT3/FT4 ratio declined. Inflammatory markers (CRP, WBC) and uric acid were elevated in the highest quartile. Clinical severity scores, including KWDT, A2DS2, mRS, and NIHSS, also increased with higher SHR.\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\u003eBaseline characteristics of acute ischemic stroke patients across quartiles of stress hyperglycemia ratio (SHR)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSHR quartile\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQ1 (n\u0026thinsp;=\u0026thinsp;370)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eQ2 (n\u0026thinsp;=\u0026thinsp;369)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eQ3 (n\u0026thinsp;=\u0026thinsp;370)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eQ4 (n\u0026thinsp;=\u0026thinsp;370)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value*\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDemographic characteristic\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\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\u003e69.71\u0026thinsp;\u0026plusmn;\u0026thinsp;11.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e68.30\u0026thinsp;\u0026plusmn;\u0026thinsp;13.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e68.19\u0026thinsp;\u0026plusmn;\u0026thinsp;12.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e69.83\u0026thinsp;\u0026plusmn;\u0026thinsp;12.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.201\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSBP (mmHg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e150.95\u0026thinsp;\u0026plusmn;\u0026thinsp;21.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e153.98\u0026thinsp;\u0026plusmn;\u0026thinsp;22.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e152.76\u0026thinsp;\u0026plusmn;\u0026thinsp;21.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e154.43\u0026thinsp;\u0026plusmn;\u0026thinsp;21.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.127\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDBP (mmHg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e81.66\u0026thinsp;\u0026plusmn;\u0026thinsp;12.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e83.28\u0026thinsp;\u0026plusmn;\u0026thinsp;13.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e83.01\u0026thinsp;\u0026plusmn;\u0026thinsp;12.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e84.15\u0026thinsp;\u0026plusmn;\u0026thinsp;12.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.059\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBody temperature (℃)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e36.51\u0026thinsp;\u0026plusmn;\u0026thinsp;0.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e36.46\u0026thinsp;\u0026plusmn;\u0026thinsp;0.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e36.48\u0026thinsp;\u0026plusmn;\u0026thinsp;0.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e36.45\u0026thinsp;\u0026plusmn;\u0026thinsp;0.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.338\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender, n (%)\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=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.059\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e144 (38.92%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e127 (34.42%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e159 (42.97%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e158 (42.70%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\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\u003e226 (61.08%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e242 (65.58%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e211 (57.03%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e212 (57.30%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCurrent smoking, n (%)\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=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.059\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e215 (58.11%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e216 (58.54%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e245 (66.22%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e236 (63.78%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e155 (41.89%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e153 (41.46%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e125 (33.78%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e134 (36.22%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHypertension, n (%)\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=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.362\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e81 (21.89%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e95 (25.75%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e81 (21.89%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e76 (20.54%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e289 (78.11%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e274 (74.25%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e289 (78.11%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e294 (79.46%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiabetes, n (%)\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=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e185 (50.00%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e268 (72.63%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e263 (71.08%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e221 (59.73%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e185 (50.00%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e101 (27.37%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e107 (28.92%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e149 (40.27%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAtrial fibrillation, n (%)\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=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e304 (82.16%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e319 (86.45%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e318 (85.95%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e283 (76.49%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e66 (17.84%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e50 (13.55%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e52 (14.05%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e87 (23.51%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCOPD, n (%)\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=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.247\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e345 (93.24%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e353 (95.66%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e348 (94.05%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e341 (92.16%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e25 (6.76%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e16 (4.34%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e22 (5.95%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e29 (7.84%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTOAST, n (%)\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=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.180\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLAA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e171 (53.44%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e184 (56.27%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e203 (60.96%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e166 (58.87%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSAO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e80 (25.00%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e91 (27.83%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e67 (20.12%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e66 (23.40%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e55 (17.19%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e43 (13.15%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e44 (13.21%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e41 (14.54%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOther\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e14 (4.38%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9 (2.75%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e19 (5.71%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e9 (3.19%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLaboratory parameters\u003c/b\u003e\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\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\u003cp\u003e6.60 (6.00-8.40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.00 (5.60\u0026ndash;6.70)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.80 (5.40\u0026ndash;6.60)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5.90 (5.20\u0026ndash;7.18)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFPG (mg/dL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e94.05 (82.98-118.71)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e96.84 (88.38\u0026ndash;113.40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e101.70 (93.60-121.81)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e124.02 (102.60-168.21)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTG (mg/dL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e116.03 (78.83-150.57)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e117.80 (82.37-163.85)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e109.38 (81.71-156.99)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e110.27 (77.06\u0026ndash;159.20)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.452\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTC (mmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.16 (3.49\u0026ndash;4.84)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.27 (3.68\u0026ndash;5.09)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.41 (3.71\u0026ndash;5.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.31 (3.74\u0026ndash;5.15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\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\u003eLDL-C (mmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.76\u0026thinsp;\u0026plusmn;\u0026thinsp;1.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.91\u0026thinsp;\u0026plusmn;\u0026thinsp;0.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.94\u0026thinsp;\u0026plusmn;\u0026thinsp;1.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.91\u0026thinsp;\u0026plusmn;\u0026thinsp;1.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.018\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHDL-C (mmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.14\u0026thinsp;\u0026plusmn;\u0026thinsp;0.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.15\u0026thinsp;\u0026plusmn;\u0026thinsp;0.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.14\u0026thinsp;\u0026plusmn;\u0026thinsp;0.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.18\u0026thinsp;\u0026plusmn;\u0026thinsp;0.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.382\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHomocysteine (mmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e14.70 (11.71\u0026ndash;18.80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e15.80 (12.08\u0026ndash;20.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e14.90 (12.10\u0026ndash;19.40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e15.98 (12.40\u0026ndash;19.70)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.075\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFT3 (pmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.88 (3.31\u0026ndash;4.39)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.88 (3.40\u0026ndash;4.33)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.88 (3.37\u0026ndash;4.49)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.71 (3.18\u0026ndash;4.32)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\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\u003eFT4 (pmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12.82 (10.95\u0026ndash;14.63)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12.89 (11.34\u0026ndash;14.46)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12.69 (11.10-14.34)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e13.54 (11.95\u0026ndash;15.33)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFT3/FT4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.29 (0.25\u0026ndash;0.34)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.29 (0.25\u0026ndash;0.34)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.29 (0.26\u0026ndash;0.34)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.26 (0.22\u0026ndash;0.32)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTSH (mIU/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.69 (1.10\u0026ndash;2.65)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.63 (0.93\u0026ndash;2.57)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.68 (0.98\u0026ndash;2.69)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.41 (0.81\u0026ndash;2.61)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.083\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAST (U/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e19.00 (15.33\u0026ndash;24.62)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e19.00 (16.00\u0026ndash;24.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e19.00 (15.00\u0026ndash;23.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e20.00 (15.20\u0026ndash;25.30)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.223\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eALT (U/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e17.45 (12.00\u0026ndash;24.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e17.50 (12.00-26.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e16.00 (12.00\u0026ndash;24.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e16.00 (12.00\u0026ndash;25.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.543\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCRP (mg/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.58 (1.16\u0026ndash;5.91)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.45 (1.20\u0026ndash;6.21)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.00 (1.29\u0026ndash;5.97)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.13 (2.00-12.79)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWBC (\u0026times;10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6.97 (5.59\u0026ndash;8.72)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.90 (5.59\u0026ndash;8.54)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6.79 (5.54\u0026ndash;8.34)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7.71 (6.20\u0026ndash;9.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEGFR (ml/min/1.73m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e97.42 (77.52-118.14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e99.48 (81.05-119.18)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e102.62 (83.16-121.68)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e97.70 (76.46-116.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.069\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUA (umol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e318.85 (252.53-387.77)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e323.30 (270.20-392.60)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e302.60 (250.70-368.97)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e310.80 (245.53\u0026ndash;385.40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.035\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSHR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.67 (0.61\u0026ndash;0.70)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.78 (0.76\u0026ndash;0.79)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.86 (0.84\u0026ndash;0.88)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.00 (0.95\u0026ndash;1.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eClinical characteristics\u003c/b\u003e\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKWDT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.00 (1.00\u0026ndash;1.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.00 (1.00\u0026ndash;1.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.00 (1.00\u0026ndash;1.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.00 (1.00-2.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eA2DS2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.00 (2.00\u0026ndash;4.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.00 (2.00\u0026ndash;4.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.00 (2.00\u0026ndash;4.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.00 (2.00\u0026ndash;5.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGCS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e15.00 (15.00\u0026ndash;15.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e15.00 (14.00\u0026ndash;15.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e15.00 (13.00\u0026ndash;15.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e15.00 (13.00\u0026ndash;15.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003emRS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.00 (1.00\u0026ndash;2.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.00 (1.00\u0026ndash;3.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.00 (1.00\u0026ndash;3.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.00 (1.00\u0026ndash;4.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNHISS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.00 (1.00\u0026ndash;5.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.00 (1.00\u0026ndash;5.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.00 (1.00\u0026ndash;5.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.00 (1.00\u0026ndash;8.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003eData are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD), median (interquartile range, IQR), or number (percentage), as appropriate.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003eP-values were calculated using one-way ANOVA, Kruskal\u0026ndash;Wallis test, or chi-square test, depending on variable distribution. Bold \u003cem\u003eP\u003c/em\u003e-values indicate statistical significance (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003eAbbreviations: SBP, systolic blood pressure; DBP, diastolic blood pressure; AF, atrial fibrillation; COPD, chronic obstructive pulmonary disease; TOAST, Trial of Org 10172 in Acute Stroke Treatment; LAA, large-artery atherosclerosis; SAO, small-artery occlusion; CE, cardioembolism; Other, other determined or undetermined etiology; HbA1c, glycated hemoglobin; FPG, fasting plasma glucose; TG, triglyceride; TC, total cholesterol; LDL-C, low-density lipoprotein cholesterol; HDL-C, high-density lipoprotein cholesterol; UA, uric acid; WBC, white blood cell count; CRP, C-reactive protein; eGFR, estimated glomerular filtration rate; FT3, free triiodothyronine; FT4, free thyroxine; HCY, homocysteine; KWDT, Kubota water drinking test; A2DS2, age, atrial fibrillation, dysphagia, sex, and stroke severity score; GCS, Glasgow Coma Scale; mRS, modified Rankin Scale; NIHSS, National Institutes of Health Stroke Scale; SHR, stress hyperglycemia ratio.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eMultivariable Logistic Regression Analysis\u003c/h3\u003e\n\u003cp\u003eWhen modeled as a continuous variable, SHR was associated with higher odds of END: unadjusted OR\u0026thinsp;=\u0026thinsp;3.78 (95% CI: 2.01\u0026ndash;7.09, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001); partially adjusted OR\u0026thinsp;=\u0026thinsp;3.52 (95% CI: 1.92\u0026ndash;6.46, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001); and fully adjusted OR\u0026thinsp;=\u0026thinsp;6.19 (95% CI: 2.68\u0026ndash;14.28, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). When categorized by quartiles, compared with Q1, the fully adjusted ORs were 1.78 (95% CI: 1.09\u0026ndash;2.89, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0211) for Q2, 2.23 (95% CI: 1.38\u0026ndash;3.62, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0011) for Q3, and 2.14 (95% CI: 1.32\u0026ndash;3.46, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0021), with a P for trend of 0.0015 (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\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\u003eAssociation Between Stress Hyperglycemia Ratio and Risk of Early Neurological Deterioration: Multivariable Logistic Regression Analysis\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\u003cp\u003eExposure\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eCrude Model (Model 1)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003ePartially Adjusted Model\u003c/p\u003e\u003cp\u003e(Model 2)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003eFully Adjusted Model\u003c/p\u003e\u003cp\u003e(Model 3)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOR (95% CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eOR (95% CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eOR (95% CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSHR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.78 (2.01, 7.09)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.52 (1.92, 6.46)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e6.19 (2.68, 14.28)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSHR quartile\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\u003eQ1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.0\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\u003eQ2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.55 (1.04, 2.32)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.0327\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.62 (1.08, 2.43)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.0208\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.78 (1.09, 2.89)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e0.0211\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.75 (1.17, 2.60)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.0059\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.81 (1.21, 2.69)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.0038\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.23 (1.38, 3.62)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e0.0011\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.90 (1.28, 2.81)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.0014\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.88 (1.27, 2.80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.0018\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.14 (1.32, 3.46)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e0.0021\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP for trend\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003e\u003cb\u003e0.0015\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.0020\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.0015\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003eValues are expressed as odds ratios (ORs) with 95% confidence intervals (CIs). \u003cem\u003eP\u003c/em\u003e-values\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were considered statistically significant. Bold values indicate statistical significance (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003eModel 1: unadjusted.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003eModel 2: adjusted for age and sex.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003eModel 3: adjusted for age, sex, smoking status, hypertension, diabetes mellitus, atrial fibrillation (AF), chronic obstructive pulmonary disease (COPD), Trial of Org 10172 in Acute Stroke Treatment (TOAST) classification, systolic blood pressure (SBP), diastolic blood pressure (DBP), and the following laboratory variables: triglycerides (TG, Box-Cox transformed), estimated glomerular filtration rate (eGFR), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), aspartate aminotransferase (AST, Box-Cox transformed), alanine aminotransferase (ALT, Box-Cox transformed), uric acid (UA, Box-Cox transformed), and homocysteine (HCY, Box-Cox transformed).\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003eAbbreviations: OR, odds ratio; CI, confidence interval; SHR, stress hyperglycemia ratio; END, early neurological deterioration.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\n\u003ch3\u003eNon-linear Relationship Between SHR and END Risk Modeled by GAM\u003c/h3\u003e\n\u003cp\u003eIn the primary generalized additive model (GAM) without adjustment for CRP and WBC, SHR showed a significant non-linear association with the risk of END (edf\u0026thinsp;=\u0026thinsp;3.86, χ\u0026sup2; = 15.56, \u003cem\u003eP\u003c/em\u003e for non-linearity\u0026thinsp;=\u0026thinsp;0.0084; deviance explained\u0026thinsp;=\u0026thinsp;7.32%; adjusted R\u0026sup2; = 0.0525) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003e; Supplementary Table\u0026nbsp;1, Model A). Other significant covariates included atrial fibrillation, homocysteine, and NIHSS score (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\u003cp\u003eIn a sensitivity analysis additionally adjusting for CRP and WBC, the non-linear relationship remained significant with a modest increase in deviance explained (edf\u0026thinsp;=\u0026thinsp;4.07, χ\u0026sup2; = 12.97, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0243; deviance explained\u0026thinsp;=\u0026thinsp;9.04%; adjusted R\u0026sup2; = 0.0664) (Supplementary Fig.\u0026nbsp;1; Supplementary Table\u0026nbsp;1, Model B).\u003c/p\u003e\u003cp\u003eThreshold analysis using two-piecewise logistic regression identified a potential inflection point at SHR\u0026thinsp;=\u0026thinsp;1.06 (Supplementary Table\u0026nbsp;2). Below this threshold, the association between SHR and END was not statistically significant (OR\u0026thinsp;=\u0026thinsp;3.56, 95% CI: 0.95\u0026ndash;13.35, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0599). Above the threshold, the effect estimate was imprecise and not statistically significant (OR\u0026thinsp;=\u0026thinsp;4816.92, 95% CI: 0.55\u0026ndash;\u0026infin;, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0673). The likelihood ratio test showed no significant improvement of the two-piecewise model over the linear model (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.151).\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eSubgroup Analyses of SHR and Risk of END\u003c/h2\u003e\u003cp\u003eSubgroup analyses using multivariable logistic regression demonstrated that the association between SHR and END remained significant across most clinical strata, including patients aged\u0026thinsp;\u0026ge;\u0026thinsp;70 years, both sexes, non-smokers, those with hypertension, atrial fibrillation, and large-artery atherosclerosis (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). A significant interaction was found for diabetes status (\u003cem\u003eP\u003c/em\u003e for interaction\u0026thinsp;=\u0026thinsp;0.0033). The association was stronger in non-diabetic patients (OR\u0026thinsp;=\u0026thinsp;11.92, 95% CI: 2.98\u0026ndash;47.72, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0005), whereas in diabetic patients it was attenuated but remained significant (OR\u0026thinsp;=\u0026thinsp;2.41, 95% CI: 1.11\u0026ndash;5.25, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0259). No significant interactions were detected for age, sex, smoking, hypertension, atrial fibrillation, COPD, or TOAST subtype (all \u003cem\u003eP\u003c/em\u003e for interaction\u0026thinsp;\u0026gt;\u0026thinsp;0.05)\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\u003e\u003cb\u003eSubgroup Analyses of the Association Between Stress Hyperglycemia Ratio and Early Neurological Deterioration\u003c/b\u003e\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=\"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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEND\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSHR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eOR (95% CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e for interaction\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAGE\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\u003cp\u003e0.9630\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026lt;70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e723\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.88 (1.57, 30.12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0104\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\u003e\u0026gt;=70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e756\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.09 (2.44, 20.60)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.0003\u003c/b\u003e\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\u003eGender\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\u003cp\u003e0.8932\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e588\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8.81 (2.85, 27.26)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0002\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\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e891\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.30 (1.53, 25.97)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.0108\u003c/b\u003e\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\u003eSmoking status\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\u003cp\u003e0.3969\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e912\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.43 (2.76, 20.03)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\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\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e567\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.19 (0.74, 23.78)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.1060\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\u003eHypertension\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\u003cp\u003e0.6387\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e333\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.08 (0.90, 41.29)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0648\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\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1146\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.18 (2.75, 18.76)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\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\u003eDiabetes\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\u003cp\u003e\u003cb\u003e0.0308\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e937\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e18.23 (4.43, 75.13)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\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\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e542\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.39 (1.16, 9.93)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0259\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\u003eAtrial fibrillation\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\u003cp\u003e0.4700\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1224\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.67 (1.68, 13.03)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0032\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\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e255\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13.70 (2.38, 78.78)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0034\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\u003eCOPD\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\u003cp\u003e0.7866\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1387\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.64 (2.72, 16.20)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.0001\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\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.00 (0.00, Inf)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.9998\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\u003eTOAST\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\u003cp\u003e0.9695 (0.9146 #)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLAA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e724\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.60 (1.78, 24.49)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0048\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\u003eSAO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e304\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9.65 (1.98, 46.97)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0050\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\u003eCE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e183\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.11 (0.25, 38.72)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.3787\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\u003eOther\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e14647.19 (0.00, Inf)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.0000\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\"\u003eOdds ratios (ORs) and 95% confidence intervals (CIs) were estimated using multivariable logistic regression models adjusted for age, sex, smoking status, hypertension, diabetes mellitus, atrial fibrillation (AF), chronic obstructive pulmonary disease (COPD), Trial of Org 10172 in Acute Stroke Treatment (TOAST) classification, systolic blood pressure (SBP), diastolic blood pressure (DBP), and laboratory covariates.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eInteraction effects were tested by including multiplicative terms between SHR and each stratifying variable in the fully adjusted model. For TOAST subtype, the trend test (treated as ordinal variable) is shown in parentheses (#).\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eExtremely large ORs or infinite CIs in certain subgroups (e.g., COPD\u0026thinsp;=\u0026thinsp;Yes, TOAST\u0026thinsp;=\u0026thinsp;CE/Other) are due to sparse data and should be interpreted with caution.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eAbbreviations: END, early neurological deterioration; SHR, stress hyperglycemia ratio; AF, atrial fibrillation; COPD, chronic obstructive pulmonary disease; TOAST, Trial of Org 10172 in Acute Stroke Treatment; LAA, large-artery atherosclerosis; SAO, small-artery occlusion; CE, cardioembolism. Bold P values indicate statistical significance (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eMediation Analysis\u003c/h2\u003e\u003cp\u003eCausal mediation analysis indicated that CRP partially mediated the association between SHR and END, with an indirect effect of 0.004687 (95% CI: 0.001851\u0026ndash;0.008594, P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) and a mediation proportion of 12.89% (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e2\u003c/span\u003e). WBC also showed a smaller but significant mediation effect, with an indirect effect of 0.002874 (95% CI: 0.000653\u0026ndash;0.005980, P\u0026thinsp;=\u0026thinsp;0.008) and a mediation proportion of 8.03%. Direct effects remained significant after adjustment for potential confounders.\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this retrospective cohort study of patients with AIS, we found that elevated SHR was independently associated with an increased risk of END[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. This association showed a nonlinear pattern, but no clear threshold effect was observed[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Subgroup analysis further revealed that the relationship was more pronounced in non-diabetic patients, suggesting that premorbid glycemic status may modify the prognostic value of SHR[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Moreover, mediation analysis indicated that inflammatory markers, particularly CRP, partially mediated this association. These findings highlight the interplay between metabolic and inflammatory stress responses in acute stroke[\u003cspan additionalcitationids=\"CR31\" citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eOur findings are consistent with a growing body of evidence linking SHR to adverse outcomes after AIS[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. For instance, Xiao et al. demonstrated that higher SHR was significantly associated with 90-day poor functional outcomes in a large cohort of AIS patients[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Similarly, Huang et al. conducted a meta-analysis including more than 180,000 patients and confirmed a nonlinear dose-response relationship between SHR and adverse outcomes[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Moreover, Zhang et al. reported that higher SHR levels were robustly associated with increased 30-day and 90-day mortality, regardless of diabetes status[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Most prior studies have primarily focused on long-term outcomes, such as mortality and functional disability[\u003cspan additionalcitationids=\"CR37\" citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. In contrast, our study identified SHR as a predictor of early neurological deterioration. This finding expands the clinical relevance of SHR from long-term prognosis to early in-hospital risk stratification[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe biological mechanisms underlying the association between SHR and END remain incompletely understood. Hyperglycemia during acute stress may exacerbate ischemic injury through multiple pathways, including promotion of oxidative stress and blood\u0026ndash;brain barrier disruption[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Inflammatory activation also appears to play an important role. Our mediation analysis suggests that CRP partly mediates the association between SHR and END[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. This finding indicates that systemic inflammation may serve as a downstream effector linking stress hyperglycemia to neurological deterioration.\u003c/p\u003e\u003cp\u003eBeyond oxidative stress and systemic inflammation, several additional mechanisms may contribute to this association[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. First, impaired energy metabolism plays a central role: acute hyperglycemia leads to excessive anaerobic glycolysis, lactate accumulation, and tissue acidosis, which aggravates neuronal injury and reduces ischemic tolerance[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Second, hyperglycemia amplifies immune\u0026ndash;inflammatory responses. Elevated SHR promotes excessive neutrophil activation, macrophage infiltration, and release of pro-inflammatory cytokines, thereby accelerating secondary neuroinflammation[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Third, vascular and coagulation abnormalities represent another key pathway. Stress hyperglycemia can induce endothelial dysfunction, increase vascular permeability, and promote microthrombosis, which together impair microcirculatory perfusion and exacerbate ischemic damage[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. In addition, central nervous system\u0026ndash;peripheral immune crosstalk may further explain this association: acute stroke triggers sympathetic nervous system activation, with abrupt catecholamine release driving hepatic glycogenolysis and hyperglycemia[\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]; simultaneously, activation of the hypothalamic\u0026ndash;pituitary\u0026ndash;adrenal (HPA) axis increases cortisol secretion, leading to insulin resistance, immune suppression, and inflammatory imbalance[\u003cspan additionalcitationids=\"CR51\" citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. Finally, differences between diabetic and non-diabetic patients deserve attention. In diabetics, long-term adaptation to chronic hyperglycemia may blunt the effects of acute metabolic fluctuations. By contrast, non-diabetics experience sudden glycemic surges that trigger stronger inflammatory and vascular responses[\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. These differences together may explain why the prognostic impact of SHR is more evident in non-diabetic patients.\u003c/p\u003e\u003cp\u003ePrior studies have similarly shown that elevated SHR is associated with a higher risk of hemorrhagic transformation[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] and worse outcomes after intravenous thrombolysis[\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e] or endovascular therapy[\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. Interestingly, the effect of SHR differed according to diabetes status. Several studies including Duan et al.[\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e] and Wang et al.[\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e], demonstrated that the prognostic impact of SHR is more evident in non-diabetic patients. This observation aligns with our findings and underscores the importance of considering premorbid glycemic status when interpreting the prognostic role of SHR[\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eFrom a clinical perspective, our findings support incorporating SHR into risk stratification models for AIS. Unlike absolute glucose levels, SHR adjusts for background glycemic status and therefore provides a more individualized assessment[\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. Several studies have also shown that SHR outperforms conventional glycemic indices in predicting outcomes after thrombolysis[\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e] and mechanical thrombectomy[\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. Although large randomized controlled trials such as SHINE did not show benefits from intensive glucose lowering in AIS[\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e], our findings suggest that SHR may help identify subgroups of patients\u0026mdash;particularly non-diabetics\u0026mdash;who face a higher risk of early deterioration. Such patients could benefit from closer monitoring, early intervention, and more targeted management strategies. Importantly, current stroke management guidelines mainly emphasize controlling absolute hyperglycemia, but do not consider the relative degree of stress-induced hyperglycemia. Our results indicate that SHR could complement existing approaches, offering a more dynamic and individualized risk assessment tool. In practice, SHR can be rapidly calculated from routinely available FPG and HbA1c, suggesting that it has potential for bedside application in emergency and inpatient stroke care to guide risk stratification and personalized management.\u003c/p\u003e\u003cp\u003eThe strengths of our study include a relatively large sample size, systematic adjustment for potential confounders, and the application of mediation analysis to explore underlying mechanisms[\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]. Nevertheless, several limitations should be acknowledged. First, this was a single-center retrospective study, which may restrict the generalizability of our findings despite strict inclusion and exclusion criteria. In particular, the homogeneity of the study population in terms of ethnicity, lifestyle, and access to medical resources may limit the applicability of the results to other settings. Second, blood glucose and HbA1c were measured only once at admission, precluding evaluation of their temporal dynamics, which might provide additional prognostic insights as suggested by previous studies. Third, although our multivariable models adjusted for important confounders such as stroke severity (NIHSS) and dysphagia, residual confounding cannot be fully excluded. Unmeasured factors, such as level of consciousness, aspiration risk, use of nasogastric or jejunal tubes, prolonged bed rest, and infarct location, may still influence the results. Fourth, while we employed advanced methods including GAM, subgroup analyses, and mediation models, the possibility of multiple testing bias or model overfitting cannot be completely ruled out. Fifth, we focused on SHR alone and did not incorporate other glycemic markers (e.g., glycemic variability, glucose clearance rate, or insulin resistance indices), nor did we assess inflammatory or immune biomarkers (e.g., cytokines or immune cell function), which may provide complementary prognostic information. Finally, we mainly assessed in-hospital and short-term outcomes, without evaluating long-term endpoints such as stroke recurrence, post-discharge mortality, or quality of life.\u003c/p\u003e\u003cp\u003eFuture prospective multicenter studies with larger and more diverse populations are needed to validate our findings and enhance external validity. In particular, dynamic monitoring of SHR and inflammatory biomarkers may help clarify the temporal relationship between metabolic stress and neurological deterioration. Moreover, integrating SHR into established prognostic models could enhance predictive accuracy, and interventional studies should explore whether targeting metabolic\u0026ndash;inflammatory coupling can mitigate the risk of END in high-risk AIS patients.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn this retrospective study of patients with acute ischemic stroke, we demonstrated that elevated SHR was independently associated with an increased risk of END, with a nonlinear association, though no definitive threshold was established. Subgroup analysis revealed that this association was more pronounced among non-diabetic patients, suggesting that baseline metabolic status may modulate the adverse impact of stress hyperglycemia on stroke outcomes. Although inflammatory markers such as CRP showed strong associations with outcomes, SHR provides complementary prognostic value and is easily obtainable from routine tests. Incorporating SHR into clinical risk stratification models may facilitate the early identification of high-risk patients and guide preventive and therapeutic strategies, particularly in non-diabetic populations. Future prospective, multicenter studies are warranted to validate these findings and explore the potential utility of combining SHR with other metabolic and inflammatory biomarkers.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and/or analyzed during the current study are available in the supplementary materials of this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor information\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors and Affiliations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCenter for Rehabilitation Medicine, Rehabilitation \u0026amp; Sports Medicine Research Institute of Zhejiang Province, Department of Rehabilitation Medicine, Zhejiang Provincial People\u0026apos;s Hospital (Affiliated People\u0026apos;s Hospital, Hangzhou Medical College), Hangzhou, Zhejiang, China.\u003c/p\u003e\n\u003cp\u003eWei Yang: [email protected];\u003c/p\u003e\n\u003cp\u003eYiji Shen: [email protected];\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eYuehua Fei: [email protected];\u003c/p\u003e\n\u003cp\u003eTongcai Tan: [email protected];\u003c/p\u003e\n\u003cp\u003eYong Liu: [email protected], ORCID: 0000-0001-8964-6112.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eContributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYong Liu, Yuehua Fei, and Tongcai Tan: Writing \u0026ndash; original draft, Writing \u0026ndash; review \u0026amp; editing, Conceptualization, Software, Formal Analysis, Project administration, Validation, Visualization.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWei Yang and Yiji Shen: Writing \u0026ndash; review \u0026amp; editing, Conceptualization, Investigation, Date curation, Methodology, Supervision, Project administration, Validation, Resources, Visualization.\u003c/p\u003e\n\u003cp\u003eCorresponding author: Yong Liu\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Ethics Committee of Zhejiang Provincial People\u0026apos;s Hospital (Affiliated People\u0026apos;s Hospital, Hangzhou Medical College) (Approval Number: 2021QT391). Informed consent was waived due to the retrospective nature of the study and the use of de-identified patient data. All data were anonymized prior to analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare there are no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study is supported by the Medical and Health Science and Technology Project of Zhejiang Province, China (Grant No. 2023KY032 and 2024KY706); and the Zhejiang Provincial Program of Traditional Chinese Medicine Science and Technology (Grant No.2023ZL238).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eThanvi B, Treadwell S, Robinson T. 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Cardiovasc Diabetol. 2025;24(1:58). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12933-025-02613-y\u003c/span\u003e\u003cspan address=\"10.1186/s12933-025-02613-y\" 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":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-neurology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nurl","sideBox":"Learn more about [BMC Neurology](http://bmcneurol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/nurl","title":"BMC Neurology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Stress Hyperglycemia Ratio, Early Neurological Deterioration, Acute Ischemic Stroke, Inflammation, Causal Mediation Analysis","lastPublishedDoi":"10.21203/rs.3.rs-8037569/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8037569/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003eBackground\u003c/b\u003e\u003c/p\u003e\u003cp\u003eStress hyperglycemia ratio (SHR) has emerged as a more accurate indicator of stress-related hyperglycemia than absolute glucose levels. However, its relationship with early neurological deterioration (END) after acute ischemic stroke (AIS) remains unclear.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethods\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWe retrospectively analyzed 1,479 AIS patients admitted within 24 hours of symptom onset. END was defined as an increase of \u0026ge;\u0026thinsp;2 points in the NIHSS total or motor score within 72 hours. SHR was calculated as the ratio of fasting plasma glucose to estimated average glucose derived from HbA1c and categorized into quartiles. Logistic regression, generalized additive models (GAM), two-piecewise logistic regression, and causal mediation analyses were performed.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults\u003c/b\u003e\u003c/p\u003e\u003cp\u003eAmong 1,479 patients, 270 (18.3%) developed END. Higher SHR was independently associated with increased END risk (fully adjusted OR\u0026thinsp;=\u0026thinsp;6.19, 95% CI: 2.68\u0026ndash;14.28, P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), showing a clear dose-response relationship across quartiles (P for trend\u0026thinsp;=\u0026thinsp;0.0015). GAM revealed a non-linear relationship, and two-piecewise regression identified a potential inflection point at SHR\u0026thinsp;\u0026asymp;\u0026thinsp;1.06. Subgroup analysis showed a stronger association in non-diabetic patients (interaction P\u0026thinsp;=\u0026thinsp;0.0033), with no significant interactions for other variables. Sensitivity analysis adjusting for C-reactive protein (CRP) and white blood cell (WBC) count remained robust after adjustment. Mediation analysis indicated that CRP and WBC partially mediated the SHR-END association, with mediation proportions of 12.89% and 8.03%, respectively.\u003c/p\u003e\u003cp\u003e\u003cb\u003eConclusions\u003c/b\u003e\u003c/p\u003e\u003cp\u003eElevated SHR is significantly associated with an increased risk of END in AIS patients, in a non-linear and threshold-dependent manner. This association is partly mediated by systemic inflammatory markers and appears stronger in non-diabetic populations. These findings highlight the potential utility of SHR for early risk stratification and warrant further prospective validation.\u003c/p\u003e","manuscriptTitle":"Association Between Stress Hyperglycemia Ratio and Early Neurological Deterioration After Acute Ischemic Stroke: A Retrospective Cohort Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-01 08:26:44","doi":"10.21203/rs.3.rs-8037569/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-12-22T06:06:21+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-16T21:49:11+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-06T10:14:53+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-26T12:40:03+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"224797488920020087297168103332946364715","date":"2025-11-25T11:30:46+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"237670020311233881022021795088049344269","date":"2025-11-25T09:25:29+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"110633619239272990131357832756598447285","date":"2025-11-25T07:39:21+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"216634977969370257271653556541007178444","date":"2025-11-25T06:06:08+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-11-25T01:28:30+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-11-19T09:49:39+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-06T13:52:21+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-11-06T13:51:29+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Neurology","date":"2025-11-05T10:51:35+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-neurology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nurl","sideBox":"Learn more about [BMC Neurology](http://bmcneurol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/nurl","title":"BMC Neurology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ba497d4c-abe5-4f00-b292-c7d4de784f54","owner":[],"postedDate":"December 1st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-01-26T16:11:53+00:00","versionOfRecord":{"articleIdentity":"rs-8037569","link":"https://doi.org/10.1186/s12883-026-04654-6","journal":{"identity":"bmc-neurology","isVorOnly":false,"title":"BMC Neurology"},"publishedOn":"2026-01-21 15:58:12","publishedOnDateReadable":"January 21st, 2026"},"versionCreatedAt":"2025-12-01 08:26:44","video":"","vorDoi":"10.1186/s12883-026-04654-6","vorDoiUrl":"https://doi.org/10.1186/s12883-026-04654-6","workflowStages":[]},"version":"v1","identity":"rs-8037569","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8037569","identity":"rs-8037569","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}