Association of HbA1c with functional outcome by ischemic stroke subtypes and age

Objectives: To determine whether high HbA1c levels are related to short-and long-term functional outcomes in patients with ischemic stroke (IS) and whether this association differs according to the IS subtype and the patient’s age.

Methods: The data of 7,380 IS patients admitted to 16 hospitals or regional stroke centers in South-Korea, between May 2017 and December 2019, were obtained from the Clinical Research Collaboration for Stroke-Korea-National Institute of Health database and retrospectively analyzed. Among these patients, 4,598 were followed-up for one-year. The HbA1c levels were classified into three groups (<5.7, 5.7 to <6.5%, ≥6.5%). Short-and long-term poor functional outcomes were defined using the modified Rankin Scale score of 2 to 6 at three-months and one-year, respectively. IS subtypes were categorized according to the Trial of ORG 10172 in Acute Stroke Treatment (TOAST) classification.

Results: There was an association between higher HbA1c (≥6.5%) and poor functional outcomes at three-months in all patients (three-months; OR, 1.299, 95% CI 1.098, 1.535, one-year; OR, 1.181, 95% CI 0.952, 1.465). When grouped by age, the associations after both 3 months and 1 year observed in younger adult group (<65 years), but not in group aged 65 years and older (three-months; <65 years OR, 1.467, 95% CI 1.112, 1.936, ≥65 years OR, 1.220, 95% CI 0.987, 1.507, p for interaction = 0.038, one-year; <65 years OR, 1.622, 95% CI 1.101, 2.388, ≥65 years OR, 1.010, 95% CI 0.778, 1.312, p for interaction = 0.018). Among younger adult group, the higher HbA1c level was related to short-and long-term functional loss in patients with the small vessel occlusion subtype (three-months; OR, 2.337, 95%CI 1.334, 4.095, one-year; OR, 3.004, 95% CI 1.301, 6.938). However, in patients with other TOAST subtypes, a high HbA1c level did not increase the risk of poor outcomes, regardless of the age of onset.

Conclusion: High HbA1c levels increase the risk of short-and long-term poor functional outcomes after IS onset. However, this association differs according to stroke subtype and age. Thus, pre-stroke hyperglycemia, reflected by HbA1c, may be a significant predictor for a poor prognosis after ischemic stroke, particular in young- and middle-aged adults.

Introduction

Stroke remains a serious global health problem (1–3) and is estimated to affect more than 101 million people worldwide (4). Although stroke mortality has decreased over the past two decades (5), the number of patients with stroke has been steadily increasing, resulting in an increased economic burden due to the rising need for stroke after-care (6). Diabetes mellitus (DM) is considered a prognostic factor as well as the risk factor for stroke. Stroke outcomes are exacerbated by DM, which increases mortality and disability (7, 8). HbA1c, a glucose indicators, is used as a reliable marker to diagnose DM and assess the effects of chronic hyperglycemia in patients with stroke (9). Although several studies (10–17) have used HbA1c markers to assess the association between high glucose levels and functional loss, few have investigated the effects of HbA1c levels on poor long-term functional outcomes.

According to stroke subtypes, denoted by the Trial of ORG10172 in Acute Stroke Treatment (TOAST) classification, the profiles for stroke risk (18), prognosis (19), and functional loss (20) differ. DM and hyperglycemia are independent risk factors for stroke (21), but not for all ischemic stroke (IS) subtypes. Previous studies have suggested that DM is a risk factor in Large Artery Atherosclerosis (LAA) and Small Vessel Occlusion (SVO), but not cardioembolism (CE) (22, 23). Moreover, since IS is a heterogeneous disease (12, 24), DM (25) and hyperglycemia (26–28) may have different associations with poor outcomes by stroke subtype. Aging is not only a risk factor for stroke (29) but also a prognostic factor for functional outcomes (30). However, it is difficult to distinguish between the direct effects of age and age-associated effects on functional loss such as ischemic heart disease, hypertension, DM, and altered cognitive capacity (30–33). There is a need to assess the association between hyperglycemia-poor outcomes and age because DM is an age-related disease (34).

In the present study, we investigated the association between HbA1c levels and short-term (three-months) and long-term (one-year) functional outcomes in IS patients. We also determined whether this association differs by IS subtype and age (<65 vs. ≥65 years).

Methods

Study design and data collection

The study population was derived from the Clinical Research Collaboration for Stroke in Korea (CRCS-K)-National Institute of Health (NIH) database. The CRCS-K-NIH is a prospective, multicenter, web-based cohort study for ischemia stroke patients supported by a grant of the Korea Disease Control and prevention Agency. The data from patients diagnosed with acute stroke and transient ischemic attack (TIA) who were admitted to 16 hospitals or regional stroke centers in South-Korea, between May 2017 and December 2019, were obtained and retrospectively analyzed. The institutional review boards approved this study. Written, informed consent was obtained from patients and caregivers. The participants also recorded mRS score and CVD events at three-months and one-year after the stroke index (Supplementary Figure S1). The CRCS-K-NIH is a follow-up study from the previous CRCS-K study that was a prospective, nationwide, multicenter, web-based acute stroke registry of consecutive patients with acute IS admitted to 16 hospitals in South-Korea since April 2008 (35, 36). The CRCS-K study forms part of the multinational Assessment of Real-World Evidence in Stroke/TIA program and complements the SOCRATES (Acute Stroke or Transient Ischemic Attack Treated with Aspirin or Ticagrelor and Patient Outcomes) trial (37). Information on baseline demographics, medical history, risk factors, laboratory findings, stroke characteristics, prescribed medications, and post-stroke cardiovascular events was obtained from the CRCS-K database (38). CVD events during hospitalization and up to one-year after the index stroke were captured by medical record review and telephone interviews by experienced stroke coordinators in each center (38, 39).

Study participants

Among the 15,118 patients registered in the CRCS-K-NIH study, between May 2017 and December 2019, we identified the final IS patients following the criteria. Patients with transient symptoms with positive MRI were classified as stroke patients. Patients with posterior circulation presented symptoms of headache, dizziness, nausea, or trunk ataxia was included. We excluded the following patients: aged <19 years (n = 8), patients with hemorrhagic stroke or TIA (n = 1,753), patients with history of stroke (n = 2,709) or TIA (n = 143) at admission, and recurrence of stroke (n = 107) or TIA (n = 13) within followed-up period, hospitalization for more than 24 h from onset (n = 2,381), missing data on modified Rankin Scale (mRS) score at three-months (n = 94), and follow-up loss at three-months (n = 102). We also excluded missing data on other covariates (n = 428). Finally, we used data from 7,380 patients to assess an association between HbA1c levels and poor outcomes at three-months (short-term). When we assessed HbA1c effect for long-term (one-year) outcomes, we additionally excluded follow-up loss at one-year (n = 47) and no data on the mRS score at one-year (n = 2,735). Finally, the data of 4,598 patients were used to assess the association between hyperglycemia and long-term functional outcomes among IS patients, not include TIA and hemorrhagic stroke (Figure 1). With an approval from the Ethics Committee, clinical data were obtained from the selected participants from the CRCS-K-NIH study.

FIGURE 1

Figure 1. A flow chart representing the study population.

Clinical assessment and study outcome

Hypertension, dyslipidemia, and atrial fibrillation (AF) were diagnosed on the basis of the history of each disease. DM was defined as; (1) a history of DM, (2) take antidiabetic drugs. Smoking was defined as “Yes” or “No” and those who answered, “Smoking status = Yes” also responded about “current smoker,” “Smoking cessation more than 5 years” or “Smoking cessation within 5 years.” The HbA1c levels were categorized by the criteria of the American Diabetes Association (40) and the Korean Diabetes Association (41) as follows: low (HbA1c < 5.7%), middle (5.7% ≤ HbA1c < 6.5%) and high (6.5% ≤ HbA1c). We classified IS into LAA, SVO, CE, other determined etiologies, and undetermined etiologies using the Trial of Org 10,172 in Acute Stroke Treatment (TOAST) criteria (42) in the present study. The mRS, modified Rankin disability scale is a clinician reported measure of global disability (43). The mRS score was measured face-to-face and telephone surveys by trained operator (44, 45). The mean data collection windows were 3 months±6 days, 1 year ±15 days. The mRS was used to evaluate short-and long-term functional outcomes at three-months and one-year, respectively. Considering the ability to perform outdoor activities, a poor outcome was defined as an mRS score of 2–6, and a favorable outcome as an mRS score of 0 or 1 (46, 47).

Statistical analysis

The clinical and demographic differences at baseline were showed by HbA1c group using the t-test and ${\chi }^2$ test. Multivariate-adjusted ORs and 95% CIs for poor outcomes (mRS score = 2–6) were analyzed by logistic regression analysis. We adjusted for the following variables for poor outcome at three-months: age, sex, National Institutes of Health Stroke Scale (NIHSS) score, IS subtype (TOAST classification), systolic blood pressure (SBP), dyslipidemia, AF, body mass index (BMI), glucose level, hypertension, low-density lipoprotein (LDL) -cholesterol, and triglycerides.

We added stroke recurrence at three-months to assess HbA1c effect for one-year poor outcomes. Subgroup analysis for age (<65 or ≥ 65 years) and TOAST classification (LAA, SVO, CE, other determined and undetermined) was conducted. Statistical significance was set at p-value <0.05 SAS software (version 9.4; SAS Institute Inc., Cary, NC, United States) was used for all statistical analyses.

Results

Baseline characteristics of participants

The baseline characteristics of the total patients are presented in Supplementary Table S1. The mean age was 68.02 years, and the proportion of male patients (59.02%) was higher than the proportion of female patients (40.98%). Among patients with IS stroke, LAA was the most common subtype (31.73%), followed by undetermined (22.76%), CE (21.91%) and SVO (19.86%).

Table 1 shows the clinical and demographic characteristics of the patients according to their HbA1c levels. Among the 7,380 patients, 1,840 (24.93%) had high HbA1c at baseline. The mean age was highest in the middle HbA1c level group (low; 66.57, mid; 69.85, high; 67.75 years, value of p <0.0001). SBP, triglyceride levels and BMI were higher in patients with high HbA1c levels. The history of hypertension and dyslipidemia was high in the patients with high HbA1c levels. The proportion of patients with poor functional outcomes (mRS score = 2–6) was higher in the high HbA1c group at three-months and one-year, respectively (Supplementary Figure S2).

TABLE 1

Table 1. Clinical characteristics according to HbA1c levels at baseline.

Elevated HbA1c associated with poor functional outcomes

In Table 2, a high HbA1c level related to short-term poor outcomes among entire IS patients, but not to long-term poor outcomes (three-months: OR 1.299, 95% CI 1.098, 1.535, one-year: OR 1.181, 95% CI 0.952, 1.465). We investigated the association between HbA1c-poor outcomes by age groups to assess to age associated effect. In Table 3, the risk of poor outcome was significantly increase in <65 aged group (three-months: OR 1.467, 95% CI 1.112, 1.936, one-year: OR 1.622, 95% CI 1.101, 2.388). However there was no significant association in the ≥65 aged group. There was an interaction effect between age and HbA1c level for the short and long term poor functional outcomes (three-months: p = 0.038, one-year: p = 0.018, Table 3).

TABLE 2

Table 2. Odds Ratios (95% CIs) for poor functional outcomes according to HbA1c levels.

TABLE 3

Table 3. Odds Ratios (95% CIs) for poor functional outcomes according to HbA1c levels, stratified by age group.

Elevated HbA1c associated with poor functional outcomes by stroke subtypes and age

We also assessed the association between HbA1c-poor outcomes by TOAST classification and age. In all age patients, the association was noted for short-and long-term in SVO subtype (SVO, three-months: OR 1.652, 95% CI 1.138, 2.397, one-year: OR 1.814, 95% CI 1.091, 3.015; Supplementary Table S2). In patients with other TOAST subtypes, a high HbA1c level did not increase the risk of poor outcomes. In patients aged <65 years, HbA1c had an increasing risk of short-and long-term poor outcomes in SVO (SVO, three-months: OR 2.337, 95% CI 1.334, 4.095, one-year: OR 3.004, 95%CI 1.301, 6.938; Table 4). However, there was no significant effect in the older adult patients with SVO (Supplementary Table S3).

TABLE 4

Table 4. Odds Ratios (95% CIs) for functional outcomes according to HbA1c levels by TOAST classification among young patients.

Discussion

To the best of our knowledge this study is the first to investigate the association between high HbA1c levels with short-and long-term functional outcomes according to the TOAST classification and age. There was the association between higher HbA1c (≥6.5%) and poor functional outcome at three-months and one-year among younger adult patients (<65 years). In the subgroup analysis, higher HbA1c was associated with short-and long-term functional loss in the SVO subtype among the <65 age group.

We showed that high admission HbA1c level (≥6.5%) is associated with poor functional outcomes after three-months in all patients with IS. When grouped by age, the associations after both 3 months and 1 year were particularly evident in patients with IS who were younger than 65 years but not in patients who were older than 65 years. Interactions between hyperglycemia and age were observed for short-and long functional outcomes. The association between hyperglycemia and functional outcomes may be affected by patient age, which is explained by the potential ceiling effect on the functional outcomes of chronic hyperglycemia in older adults (48). Because older age is a critical predictor of poor outcome factors in ischemic and hemorrhagic stroke (49, 50), the association between HbA1c levels and poor outcomes is not sufficiently challenging for older age. Therefore, a worsening prognosis due to elevated HbA1c levels after stroke may not be observed in older adults with high HbA1c levels. Our findings are consistent with previous results on the interaction between age and DM and functional outcomes (48, 51).

Stratified by the TOAST classification, the association between elevated HbA1c levels and short-and long-term functional outcomes was observed only in patients with SVO. Although some studies reposted no association between stress hyperglycemia, defined as fasting glucose levels at admission, and poor functional outcomes in the SVO subtype (52, 53), other studies analyzing chronic hyperglycemia, define as HbA1c level at admission, were consistent with our results (12, 54). A Chinese hospital-based study (12) showed an HbA1c-functional outcome association among patients with small artery occlusion and those with a mean age of 61.7 years. This study reported that higher HbA1c levels at admission increased poor functional loss at three-months after stroke onset, which is consistent with our data. Moreover, in the present study, when grouped by age, an association was observed in those aged <65 years but not in those aged ≥65 years.

The mechanism of an association between hyperglycemia at admission and poor functional outcome after stroke onset is still unclear (10, 12). Hyperglycemia can aggravate ischemic damage by disrupting recanalization and increasing reperfusion injury, which may be associated with poor functional outcome (55). In particular, diabetes and elevated HbA1c levels are associated with the number of lacunar infarcts, also known as SVO, less than 7 mm in diameter (56). Elevated HbA1c levels can increase blood viscosity (57, 58), and elevated blood viscosity could impair microvascular tissue perfusion (59), which may be associated with poor functional outcomes in patients with SVO.

Our study has the following strengths. First, we used data from a large sized multicenter cohort study. The large-sample size provided sufficient statistical power to examine the association between hyperglycemia and functional outcomes according to stroke subtype and age. Second, the participants were followed-up for a one-year period and we used the mRS score at one-year to assess long-term poor outcomes. To the best of our knowledge this is the first study to assess the association between high HbA1c levels and the risk of poor long-term functional outcomes in Korea.

However, despite our strengths our study has several limitations. First, we did not consider management of glucose levels using antidiabetic drugs or other treatments during hospitalization. However, we adjusted for the glucose levels at admission when an HbA1c-poor outcome association was found. Second, there may have been selection bias because we excluded data on missing HbA1c levels and other covariate variables at admission. In addition, a high number of one-year follow-up losses occurred due to patients simply not reaching one-year of follow-up owing to study closing. Third, we adjusted for confounders in our study but several confounding factors such as alcohol consumption and physical activity were not considered because this information was not included in our data. Fourth, our study includes only Korean ethnic population and results may not be generalizable to other ethnic groups. Fifth, choosing a different reasonable mRS cut-off point of favorable and poor functional outcome could have produced a different result.

Conclusion

In this study, the high HbA1c level, especially HbA1c ≥6.5%, increased the risk of short-and long- term poor functional outcomes after stroke onset. This association was observed in patients with the SVO subtype in aged <65 years. Thus, elevated HbA1c may be a significant predictor for a poor prognosis after ischemic stroke, particular in young- and middle-aged adults.

Data availability statement

The data analyzed in this study is subject to the following licenses/restrictions: The clinical data are available upon request following the submission of a legitimate academic research proposal to be assessed by the Clinical Research Collaboration for Stroke in Korea (CRCS-K) steering committee. Requests to access these datasets should be directed to J-MP, jmpark@eulji.ac.kr.

Ethics statement

The studies involving humans were approved by the institutional review boards in Korea Disease Control and Prevention Agency (KDCA). The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study.

Author contributions

JJ and S-MY contributed to the conception and design of the study. J-MP and H-JB collected clinical data. JJ carried out data curation, formal analysis, and drafting of the manuscript. JP, YK, and S-MY reviewed and edited the manuscript. All authors contributed to the article and approved the submitted version.

Funding

This research was supported by the “National Institute of Health” research project (project No. 2020-NI-025-00 and 2022-NG-007-00).

Acknowledgments

We thank participants of the current study and contributors to clinical data collection and management. We would like to thank Editage (www.editage.co.kr) for English language editing.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Supplementary material

The Supplementary material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fneur.2023.1247693/full#supplementary-material

References

1. Australian Institute of Health and Welfare. Leading cause of premature mortality in Australia fact sheet: cerebrovascular disease. Cat. no. PHE 195. Canberra: AIHW (2015).

Google Scholar

2. Feigin, VL, Forouzanfar, MH, Krishnamurthi, R, Mensah, GA, Connor, M, Bennett, DA, et al. Global and regional burden of stroke during 1990–2010: findings from the global burden of disease study 2010. Lancet. (2014) 383:245–55. doi: 10.1016/S0140-6736(13)61953-4

PubMed Abstract | CrossRef Full Text | Google Scholar

3. Krishnamurthi, RV, Feigin, VL, Forouzanfar, MH, Mensah, GA, Connor, M, Bennett, DA, et al. Global and regional burden of fi rst-ever ischaemic and haemorrhagic stroke during 1990–2010: fi ndings from the global burden of disease study 2010. Lancet Glob Health. (2013) 1:e259–81. doi: 10.1016/S2214-109X(13)70089-5

PubMed Abstract | CrossRef Full Text | Google Scholar

4. Feigin, VL, Brainin, M, Norrving, B, Martins, S, Sacco, RL, Hacke, W, et al. World stroke organization (WSO): global stroke fact sheet 2022. Int J Stroke. (2022) 17:18–29. doi: 10.1177/17474930211065917

PubMed Abstract | CrossRef Full Text | Google Scholar

5. Feigin, VL, Krishnamurthi, RV, Parmar, P, Norrving, B, Mensah, GA, Bennett, DA, et al. Update on the global burden of ischemic and hemorrhagic stroke in 1990–2013: the GBD 2013 study. Neuroepidemiology. (2015) 45:161–76. doi: 10.1159/000441085

PubMed Abstract | CrossRef Full Text | Google Scholar

6. Rajsic, S, Gothe, H, Borba, HH, Sroczynski, G, Vujicic, J, Toell, T, et al. Economic burden of stroke; a systematic review on post-stroke care. Eur J Health Econ. (2019) 20:107–34. doi: 10.1007/s10198-018-0984-0

CrossRef Full Text | Google Scholar

7. National Center for Chronic Disease Prevention and Health Promotion (U.S.), Division of Diabetes Translation. National diabetes fact sheet. (2011). Available at: https://stacks.cdc.gov/view/cdc/13329

Google Scholar

8. Kaarisalo, MM, Raiha, I, Sivenius, J, Immonen-Raiha, P, Lehtonen, A, Sarti, C, et al. Diabetes worsens the outcome of acute ischemic stroke. Diabetes Res Clin Pract. (2005) 69:293–8. doi: 10.1016/j.diabres.2005.02.001

CrossRef Full Text | Google Scholar

9. Shariq, I, Sherwani, HAK, Ekhzaimy, A, Masood, A, and Sakharkar, MK. Significance of HbA1c test in diagnosis and prognosis of diabetic patients. Biomark Insights. (2016) 11:BMI.S38440–104. doi: 10.4137/BMI.S38440

CrossRef Full Text | Google Scholar

10. Masahiro Kamouchi, MTM, Hata, J, Kuwashiro, T, Ago, T, Sambongi, Y, Fukushima, Y, et al. Prestroke glycemic control is associated with the functional outcome in acute ischemic stroke the Fukuoka stroke registry. Stroke. (2011) 42:2788–94. doi: 10.1161/STROKEAHA.111.617415

CrossRef Full Text | Google Scholar

11. Kang-Ho Choi, M, Kim, J-H, Kang, K-W, Joon-Tae Kim, M, Choi, S-M, Lee, S-H, et al. HbA1c (glycated hemoglobin) levels and clinical outcome post-mechanical Thrombectomy in patients with large vessel occlusion. Stroke. (2019) 50:119–26. doi: 10.1161/STROKEAHA.118.021598

PubMed Abstract | CrossRef Full Text | Google Scholar

12. Yuan Gao, LJ, Wang, H, Changshen, Y, Wang, W, Liu, S, Gao, C, et al. Association between elevated hemoglobin A1c levels and the outcomes of patients with small-artery occlusion: a hospital-based study. PLoS One. (2016) 11:e0160223. doi: 10.1371/journal.pone.0160223

CrossRef Full Text | Google Scholar

13. Stead, LG, Gilmore, RM, Bellolio, MF, Mishra, S, Bhagra, A, Vaidyanathan, L, et al. Hyperglycemia as an independent predictor of worse outcome in non-diabetic patients presenting with acute ischemic stroke. Neurocrit Care. (2009) 10:181–6. doi: 10.1007/s12028-008-9080-0

CrossRef Full Text | Google Scholar

14. Diprose, WK, McFetridge, A, Sutcliffe, J, and Barber, PA. Glycated hemoglobin (HbA1c) and outcome following endovascular thrombectomy for ischemic stroke. J Neurointerv Surg. (2020) 12:30–2. doi: 10.1136/neurintsurg-2019-015023

CrossRef Full Text | Google Scholar

15. Guo Shuangxi, TS, Bo, S, Avinash, C, Anna, M, Hui, F, Si, C, et al. Study of the relationship of glycated hemoglobin levels and neurological impairment and three months prognosis in patients with acute ischemic stroke. Life Sci J. (2012) 9:119–23.

Google Scholar

16. Huihui Liu, XM, Liu, C-F, Wang, D, Zheng, H, Li, H, Liu, L, et al. Higher hemoglobin A1c level is associated with poor outcome of intracerebral hemorrhage. Front Neurol. (2019) 10:1073. doi: 10.3389/fneur.2019.01073

PubMed Abstract | CrossRef Full Text | Google Scholar

17. Clara Hjalmarsson, K, Bokemark, L, and Andersson, B. The role of Prestroke glycemic control on severity and outcome of acute ischemic stroke. Stroke Res Treat. (2014) 2014:694569. doi: 10.1155/2014/694569

CrossRef Full Text | Google Scholar

18. Ihle-Hansen, H, Thommessen, B, Wyller, TB, Engedal, K, and Fure, B. Risk factors for and incidence of subtypes of ischemic stroke. Funct Neurol. (2012) 27:35–40.

PubMed Abstract | Google Scholar

19. Peter, L, Kolominsky-Rabas, MMW, Gefeller, O, Bernhard Neundoerfer, M, and Heuschmann, PU. Epidemiology of ischemic stroke subtypes according to TOAST criteria incidence, recurrence, and long-term survival in ischemic stroke subtypes: a population-based study. Stroke. (2001) 32:2735–40. doi: 10.1161/hs1201.100209

CrossRef Full Text | Google Scholar

20. Petty, GW Jr, Whisnant, JP, Sicks, JRD, O’Fallon, WM, and Wiebers, DO. Ischemic stroke subtypes. a population-based study of functional outcome, survival, and recurrence. Stroke. (2000) 31:1062–8. doi: 10.1161/01.STR.31.5.1062

CrossRef Full Text | Google Scholar

21. Amelia, K, Boehme, CE, and Elkind, MSV. Stroke risk factors, genetics, and prevention. Circ Res. (2017) 120:472–95. doi: 10.1161/CIRCRESAHA.116.308398

CrossRef Full Text | Google Scholar

22. Tsai, CFAN, Anderson, N, Thomas, B, and Sudlow, CLM. Risk factors for ischemic stroke and its subtypes in Chinese vs. Caucasians: systematic review and meta-analysis. Int J Stroke. (2015) 10:485–93. doi: 10.1111/ijs.12508

PubMed Abstract | CrossRef Full Text | Google Scholar

23. Aquil, NBI, Ahmed, A, Vohra, EA, and Soomro, BA. Risk factors in various subtypes of ischemic stroke according to TOAST criteria. J Coll Physicians Surg Pak. (2011) 21:280–3.

PubMed Abstract | Google Scholar

24. Saeed, M. Unraveling the pagodian architecture of stroke as a complex disorder. Stroke. (2004) 35:824–5. doi: 10.1161/01.STROKE.0000121646.23955.0f

CrossRef Full Text | Google Scholar

25. Jia, Q, Zhao, X, Wang, Y, Wang, Y, Yan, Y, Li, H, et al. Diabetes and poor outcomes within 6 months after acute ischemic stroke. The China National Stroke Registry. Stroke. (2011) 42:2758–62. doi: 10.1161/STROKEAHA.111.621649

PubMed Abstract | CrossRef Full Text | Google Scholar

26. Kruyt, NDBG, Devries, JH, and Roos, YB. Hyperglycemia in acute ischemic stroke: pathophysiology and clinical management. Nat Rev Neurol. (2010) 6:145–55. doi: 10.1038/nrneurol.2009.231

PubMed Abstract | CrossRef Full Text | Google Scholar

27. Yong, M, and Kaste, M. Dynamic of hyperglycemia as a predictor of stroke outcome in the ECASS-II trial. Stroke. (2008) 39:2749–55. doi: 10.1161/STROKEAHA.108.514307

PubMed Abstract | CrossRef Full Text | Google Scholar

28. Sarah, E, Capes, MDH, Malmberg, K, Parbeen Pathak, B, and Gerstein, HC. Stress hyperglycemia and prognosis of stroke in nondiabetic and diabetic patients. A systematic overview. Stroke. (2001) 32:2426–32. doi: 10.1161/hs1001.096194

CrossRef Full Text | Google Scholar

29. Yousufuddin, M, and Young, N. Aging and ischemic stroke. Aging (Albany NY). (2019) 11:2542–4. doi: 10.18632/aging.101931

PubMed Abstract | CrossRef Full Text | Google Scholar

30. Jongbloed, LYN. Prediction of function after stroke: a critical review. Stroke. (1986) 17:765–76. doi: 10.1161/01.STR.17.4.765

CrossRef Full Text | Google Scholar

31. Feigenson, JSMM, Greenberg, SD, and Feigenson, WD. Factors influencing outcome and length of stay in a stroke rehabilitation unit, II: comparison of 318 screened and 248 unscreened patients. Stroke. (1977) 8:657–62. doi: 10.1161/01.STR.8.6.657

CrossRef Full Text | Google Scholar

32. Falconer, JANB, Strasser, DC, and Sinacore, JM. Stroke inpatient rehabilitation: a comparison across age groups. J Am Geriatr Soc. (1994) 42:39–44. doi: 10.1111/j.1532-5415.1994.tb06071.x

PubMed Abstract | CrossRef Full Text | Google Scholar

33. Diamond, PTFG, Macchiocchi, SN, Butler, DH, and Lally-Cassidy, D. Effect of cognitive impairment on rehabilitation outcome. Am J Phys Med Rehabil. (1996) 75:40–3. doi: 10.1097/00002060-199601000-00011

PubMed Abstract | CrossRef Full Text | Google Scholar

34. Jaul, E, and Barron, J. Age-related diseases and clinical and public health implications for the 85 years old and over population. Front Public Health. (2017) 5:335. doi: 10.3389/fpubh.2017.00335

CrossRef Full Text | Google Scholar

35. Kim, BJ, Park, JM, Kang, K, Lee, SJ, Ko, Y, Kim, JG, et al. Case characteristics, Hyperacute treatment, and outcome information from the clinical research Center for Stroke-Fifth Division Registry in South Korea. J Stroke. (2015) 17:38–53. doi: 10.5853/jos.2015.17.1.38

PubMed Abstract | CrossRef Full Text | Google Scholar

36. Kim, JY, Kang, K, Kang, J, Koo, J, Kim, DH, Kim, BJ, et al. Executive summary of stroke statistics in Korea 2018: a report from the epidemiology research Council of the Korean Stroke Society. J Stroke. (2019) 21:42–59. doi: 10.5853/jos.2018.03125

CrossRef Full Text | Google Scholar

37. Johnston, SC, Amarenco, P, Albers, GW, Denison, H, Easton, JD, Evans, SR, et al. Ticagrelor versus aspirin in acute stroke or transient ischemic attack. N Engl J Med. (2016) 375:35–43. doi: 10.1056/NEJMoa1603060

CrossRef Full Text | Google Scholar

38. Park, HK, Kim, BJ, Han, MK, Park, JM, Kang, K, Lee, SJ, et al. One-year outcomes after minor stroke or high-risk transient ischemic attack. Korean multicenter stroke registry analysis. Stroke. (2017) 48:2991–8. doi: 10.1161/STROKEAHA.117.018045

CrossRef Full Text | Google Scholar

39. Kim, BJ, Han, MK, Park, TH, Park, SS, Lee, KB, Lee, BC, et al. Current status of acute stroke management in Korea: a report on a multicenter, comprehensive acute stroke registry. Int J Stroke. (2014) 9:514–8. doi: 10.1111/ijs.12199

PubMed Abstract | CrossRef Full Text | Google Scholar

40. Care, Diabetes. Standards of medical Care in DIABETES—2010. Diabetes Care. (2010) 33:S11–61. doi: 10.2337/dc10-S011

CrossRef Full Text | Google Scholar

41. Ko, SH, Kim, SR, Kim, DJ, Oh, SJ, Lee, HJ, Shim, KH, et al. 2011 clinical practice guidelines for type 2 diabetes in Korea. Diabetes Metab J. (2011) 35:431–6. doi: 10.4093/dmj.2011.35.5.431

PubMed Abstract | CrossRef Full Text | Google Scholar

42. Adams, HP Jr, Bendixen, BH, Kappelle, LJ, Biller, J, Love, BB, Gordon, DL, et al. Classification of subtype of acute ischemic stroke definitions for use in a multicenter clinical trial. Stroke. (1993) 24:35–41. doi: 10.1161/01.STR.24.1.35

PubMed Abstract | CrossRef Full Text | Google Scholar

43. Saver, JL, Campbell, BCV, Grotta, JC, Khatri, P, Landen, J, Lansberg, MG, et al. On behalf of the XIth stroke treatment academic industry roundtable. Standardized nomenclature for modified Rankin scale global disability outcomes. Stroke. (2021) 52:3054–62. doi: 10.1161/STROKEAHA.121.034480

PubMed Abstract | CrossRef Full Text | Google Scholar

44. Kim, JY, Kang, K, Kang, J, Koo, JS, Kim, DH, and Kim, BJ. Stroke Statistics in Korea 2018: A Report from the Epidemiology Research Council of the Korean Stroke Society. The Epidemiology Research Council of the Korean Stroke Society. (2018).

Google Scholar

45. Choi, JC, Jang, MU, Kang, K, Park, JM, Ko, Y, Lee, SJ, et al. Comparative effectiveness of standard care with IV thrombolysis versus without IV thrombolysis for mild ischemic stroke. J Am Heart Assoc. (2015) 4:e001306. doi: 10.1161/JAHA.114.001306

CrossRef Full Text | Google Scholar

46. Pan, Y, Wang, Y, Li, H, Gaisano, HY, Wang, Y, and He, Y. Association of Diabetes and Prognosis of minor stroke and its subtypes: a prospective observational study. PLoS One. (2016) 11:e0153178. doi: 10.1371/journal.pone.0153178

PubMed Abstract | CrossRef Full Text | Google Scholar

47. Weisscher, N, Vermeulen, M, Roos, YB, and De Haan, RJ. What should be defined as good outcome in stroke trials; a modified Rankin score of 0–1 or 0–2? J Neurol. (2008) 255:867–74. doi: 10.1007/s00415-008-0796-8

CrossRef Full Text | Google Scholar

48. De Silva, DA, Narasimhalu, K, Huang, IW, Woon, FP, Allen, JC, and Wong, MC. Long-term post-stroke functional outcomes: a comparison of diabetics and nondiabetics. Cerebrovasc Dis Extra. (2022) 12:7–13. doi: 10.1159/000521442

CrossRef Full Text | Google Scholar

49. Radholm, K, Arima, H, Lindley, RI, Wang, J, Tzourio, C, Robinson, T, et al. Older age is a strong predictor for poor outcome in intracerebral haemorrhage: the INTERACT2 study. Age Ageing. (2015) 44:422–7. doi: 10.1093/ageing/afu198

CrossRef Full Text | Google Scholar

50. Denti, L, Scoditti, U, Tonelli, C, Saccavini, M, Caminiti, C, Valcavi, R, et al. The poor outcome of ischemic stroke in very old people: a cohort study of its determinants. J Am Geriatr Soc. (2010) 58:12–7. doi: 10.1111/j.1532-5415.2009.02616.x

CrossRef Full Text | Google Scholar

51. Chiu, CJ, and Wray, LA. Physical disability trajectories in older Americans with and without diabetes: the role of age, gender, race or ethnicity, and education. Gerontologist. (2011) 51:51–63. doi: 10.1093/geront/gnq069

PubMed Abstract | CrossRef Full Text | Google Scholar

52. Miedema, I, Luijckx, GJ, Brouns, R, De Keyser, J, and Uyttenboogaart, M. Admission hyperglycemia and outcome after intravenous thrombolysis: is there a difference among the stroke-subtypes? BMC Neurol. (2016) 16:104. doi: 10.1186/s12883-016-0617-0

PubMed Abstract | CrossRef Full Text | Google Scholar

53. Fang, Y, Zhang, S, Wu, B, and Liu, M. Hyperglycaemia in acute lacunar stroke: a Chinese hospital-based study. Diab Vasc Dis Res. (2013) 10:216–21. doi: 10.1177/1479164112459663

PubMed Abstract | CrossRef Full Text | Google Scholar

54. Lei, C, Wu, B, Liu, M, and Chen, Y. Association between hemoglobin a (1) C levels and clinical outcome in ischemic stroke patients with or without diabetes. J Clin Neurosci. (2015) 22:498–503. doi: 10.1016/j.jocn.2014.08.030

CrossRef Full Text | Google Scholar

55. Luitse, MJ, Biessels, GJ, Rutten, GE, and Kappelle, LJ. Diabetes, hyperglycaemia, and acute ischaemic stroke. Lancet Neurol. (2012) 11:261–71. doi: 10.1016/S1474-4422(12)70005-4

CrossRef Full Text | Google Scholar

56. Bezerra, DC, Sharrett, AR, Matsushita, K, Gottesman, RF, Shibata, D, Mosley, TH Jr, et al. Risk factors for lacune subtypes in the atherosclerosis risk in communities (ARIC) study. Neurology. (2012) 78:102–8. doi: 10.1212/WNL.0b013e31823efc42

CrossRef Full Text | Google Scholar

57. Marini, MA, Fiorentino, TV, Andreozzi, F, Mannino, GC, Succurro, E, Sciacqua, A, et al. Hemorheological alterations in adults with prediabetes identified by hemoglobin A1c levels. Nutr Metab Cardiovasc Dis. (2017) 27:601–8. doi: 10.1016/j.numecd.2017.04.001

PubMed Abstract | CrossRef Full Text | Google Scholar

58. Li, Q, and Yang, LZ. Hemoglobin A1c level higher than 9.05% Causes a significant impairment of erythrocyte deformability in diabetes mellitus. Acta Endocrinol (Buchar). (2018) 14:66–75. doi: 10.4183/aeb.2018.66

CrossRef Full Text | Google Scholar

59. Song, SH, Kim, JH, Lee, JH, Yun, YM, Choi, DH, and Kim, HY. Elevated blood viscosity is associated with cerebral small vessel disease in patients with acute ischemic stroke. BMC Neurol. (2017) 17:20. doi: 10.1186/s12883-017-0808-3

PubMed Abstract | CrossRef Full Text | Google Scholar