Personalized Consideration of Admission-Glucose Gap between Estimated Average and Initial Glucose Levels on Short-Term Stroke Outcome
by
, , ,
Yerim Kim
1
,
Sang-Hwa Lee
2,
Chulho Kim
2,
Min Kyoung Kang
3,
Byung-Woo Yoon
3,
Tae Jung Kim
4,
Jong Seok Bae
1 and
Ju-Hun Lee
1,* 1
Department of Neurology, Kangdong Sacred Heart Hospital, Hallym University College of Medicine, Seoul 05355, Korea
2
Department of Neurology, Chuncheon Sacred Heart Hospital, Hallym University College of Medicine, Chuncheon 24253, Korea
3
Department of Neurology, Uijeongbu Eulji Medical Center, Eulji University College of Medicine, Uijeonbu-si 11759, Korea
4
Department of Neurology, Seoul National University College of Medicine, Seoul 03080, Korea
*
Author to whom correspondence should be addressed.
J. Pers. Med. 2021, 11(2), 139; https://doi.org/10.3390/jpm11020139
Submission received: 23 January 2021
/
Revised: 13 February 2021
/
Accepted: 14 February 2021
/
Published: 18 February 2021
(This article belongs to the Special Issue Cardiovascular Disease Prevention in the Era of Personalized Medicine)
Abstract
:Background: Poststroke hyperglycemia is associated with poor outcomes. Most prior studies used initial glucose as an indicator of poststroke hyperglycemia without considering glycemic control status at the time of stroke occurrence. We aimed to investigate the effect of an admission-glucose gap on short-term functional outcomes in acute ischemic stroke (AIS). Methods: We enrolled patients with AIS or transient ischemic attack who had been admitted within 7 days of symptom onset to three stroke centers from May 2016 to December 2019. The admission-glucose gap between estimated average glucose levels (eAG) and initial glucose level (eAG–initial glucose) was categorized into four groups. The short-term functional outcome was evaluated using the modified Rankin Scale (mRS) score at 3 months after stroke onset and was dichotomized. Results: Among 1332 included subjects, 548 (41.1%) had poor short-term functional outcomes. After adjusting for multiple variables, a severe negative glucose gap (eAG–initial glucose ≤ −50 mg/dL) was significantly associated with poor short-term functional outcome (OR, 1.573; 95% CI, 1.101–2.248). After dichotomizing glycemic control status, its significance was only maintained in the good glycemic control group (HbA1c < 6.5%) (OR, 1.914; 95% CI, 1.155–3.169). Conclusions: An elevated admission-glucose gap, in which the initial glucose level was much higher than the estimated glucose level was based on HbA1c, was associated with poor stroke prognosis. In addition to admission-glucose levels, glycemic control status at the time of stroke onset should be considered when predicting short-term stroke outcomes.
1. Introduction
The effect of variable glucose parameters on stroke outcomes is not yet clear. Previous studies reported that glycated hemoglobin (HbA1c), prestroke glycemic variability, and glucose dynamics after stroke were associated with stroke morbidity and mortality [1,2,3,4,5]. Hyperglycemia after stroke is common and occurs in 20% to 60% of patients with acute ischemic stroke (AIS) [6,7]. Although the association between poststroke hyperglycemia and outcomes remains controversial, poststroke hyperglycemia was shown to be associated with poor outcomes in several studies [3,8,9]. Although we cannot confirm the exact pathomechanism, according to the previous literature, hyperglycemia may affect stroke occurrence and outcome through modulating the pathway, including acidosis, free radical generation, inflammation, mitochondrial dysfunction, and other factors such as hypoxia inducible factor-1 (HIF-1) [10].
Poststroke hyperglycemia may provoke increased infarct volume [6], risk of hemorrhagic transformation [9,11], and recanalization failure after thrombolysis, thus leading to poor stroke outcomes [7,12]. Until now, most studies used random glucose, admission glucose, and fasting glucose levels as variables to assess poststroke hyperglycemia without consideration of glycemic control status at the time of stroke occurrence [3,8,13,14]. However, although there are various mechanisms of poststroke hyperglycemia development, the effect of hyperglycemia on stroke prognosis differs depending on glycemic control status at the time of stroke onset. Furthermore, it is difficult to clearly determine whether it is the effect of poststroke stress hyperglycemia or higher-than-usual glucose level at the time of stroke.
In the present study, we investigated the effect of the glucose gap, calculated on the basis of the difference between estimated average glucose level (eAG), calculated using HbA1c, and initial glucose level (eAG–initial glucose) on short-term functional outcome in AIS.
2. Materials and Methods
2.1. Study Population
We enrolled into our prospective stroke registry system patients with AIS or transient ischemic attack who had been admitted within 7 days of symptom onset to three stroke centers (Kangdong Sacred Heart Hospital, Hallym University College of Medicine; Chuncheon Sacred Heart Hospital, Hallym University College of Medicine; and Seoul National University Hospital) from May 2016 to December 2019. Among the 1562 patients, 30 subjects in whom the HbA1c (n = 21) or initial glucose (n = 9) levels were not evaluated were excluded. Additionally, 89 patients without a 3 month functional outcome capture and 111 patients with premodified Rankin Scale (pre-mRS) score ≥ 3 were excluded. As a result, we included a total of 1332 patients (Figure 1).
All patients received standard and optimal medical therapy during hospitalization. The institutional review boards of three centers (Kangdong Sacred Heart Hospital IRB no. 2020-02-006-001, Seoul National University Hospital IRB no. H-2002-023-1098, and Chuncheon Sacred Heart Hospital IRB no. 2017-89) approved the study protocol, and written informed consent was obtained from all participants or from their next of kin when the patient was unable to provide consent.
2.2. Clinical Information
Baseline and demographic characteristics, including age and sex, risk factors (hypertension, diabetes, dyslipidemia, smoking history, and atrial fibrillation), stroke subtype according to Trial of Org 10,172 in Acute Stroke Treatment (TOAST) classification, imaging findings (computed tomography and/or magnetic resonance imaging), and laboratory data, were collected. Body weight and height were measured at admission. Body-mass index (BMI) was calculated as weight (kg) divided by height squared (m).
Initial neurological severity (INS) was measured using the National Institute of Health Stroke Scale (NIHSS) score at admission. Short-term functional outcome was evaluated using the modified Rankin Scale (mRS) score at 3 months after stroke onset. Short-term functional outcomes were dichotomized (good outcome: 3 month mRS, 0–1; poor outcome: 3 month mRS, 2–6).
Initial glucose level was obtained upon admission. Glycated albumin (GA) and HbA1c levels were obtained after an overnight fast, and eAG was calculated using a validated formula [15]:
eAG (mg/dL) = 28.7 × A1c (mg/dL) − 46.7 or
eAG (mmol/L) = 1.59 × A1c (mmol/L) − 2.59
The glucose gap between eAG and initial glucose level (eAG–initial glucose) was calculated, and patients were divided into four groups: mild-positive (0 ≤ eAG–initial glucose < 50 mg/dL), severe-positive (eAG–initial glucose ≥ 50 mg/dL), mild-negative (−50 < eAG–initial glucose < 0 mg/dL), and severe-negative (eAG–initial glucose ≤ −50 mg/dL; Figure 1) groups.
Glucose control status was divided into good (HbA1c < 6.5%) and poor (HbA1c ≥ 6.5%) glycemic control status.
2.3. Statistical Analysis
The distribution of demographic, clinical, laboratory, and stroke subtype data according to short-term functional outcomes was analyzed using a χ2 test, Student’s t-test, or Kruskal–Wallis test, as appropriate. The trend in the baseline data was also calculated using the χ2 test for trends in proportions. Associations between glucose gap and short-term functional outcomes were investigated using binary logistic-regression analysis. The mild-positive glucose gap group (0 ≤ eAG–initial glucose < 50 mg/dL) was used as a reference.
Values for continuous variables are expressed as mean ± standard deviation (SD). Odds ratios (ORs) and 95% confidence intervals (CIs) are expressed for the results and probability values. A p value ≤ 0.05 was considered statistically significant. Analyses were performed using SPSS version 19.0 (SPSS Inc., Chicago, IL, USA).
2.4. Data-Availability Statement
Data cannot be shared because there are embargoes on datasets. Anonymized data will be shared by request from any qualified investigator.
3. Results
Between May 2016 and September 2019, 1332 patients were included in our study. Of these patients, 548 patients (41.1%) had poor short-term functional outcomes (Table 1). Those subjects were older, more likely to have a history of stroke, hypertension, diabetes, smoking history, or atrial fibrillation, and had cardioembolic or undetermined etiology. The categorized glucose gap group was distributed as follows: severe negative, 12.6% in good outcome versus 17.9% in poor outcome; mild negative, 43.4% versus 41.6%; mild positive, 40.7% versus 36.7%; and severe positive, 3.3% versus 3.8% (p = 0.044; Figure 2). Baseline and demographic characteristics (dichotomized according to short-term functional outcome) are shown in Table 1. Initial glucose level was 246.3 ± 80.2 mg/dL (severe-negative group), 140.4 ± 34.4 mg/dL (mild-negative group), 113.4 ± 30.4 mg/dL (mild-positive group), and 115.8 ± 36.0 mg/dL (severe-positive group). HbA1c was 7.02 ± 1.78% (severe-negative group), 5.89 ± 1.04% (mild-negative group), 6.17 ± 1.08% (mild-positive group), and 8.49 ± 1.58% (severe-positive group). There were no statistically significant differences in stroke mechanism or severity between groups (Table 2).
In binary logistic-regression analysis, we adjusted for multiple variables: age, sex, BMI, prior ischemic stroke, hypertension, diabetes, smoking, atrial fibrillation, hematocrit, blood urea nitrogen, high-sensitivity C-reactive protein, glucose gap between eAG and initial glucose, and initial stroke severity. Age, prior ischemic stroke, atrial fibrillation, initial stroke severity, and glucose gap were associated with poor functional outcome (3 month mRS, 0–1). Among glucose gap groups, the severe-negative group (eAG–initial glucose ≤ −50, mg/dL) was significantly associated with poor short-term functional outcome (OR, 1.573; 95% CI, 1.101–2.248; Table 3 and Figure 3). Additionally, we conducted binary logistic-regression analysis only in the female group and had similar results (supplementary Table S1).
After categorization by glycemic control status (HbA1c < 6.5% versus HbA1c ≥ 6.5%), the severe-negative group (eAG–initial glucose ≤ −50 mg/dL) was significantly associated with poor functional outcome (OR, 1.914; 95% CI, 1.155–3.169) in the good glycemic control status group (HbA1c < 6.5%), whereas the association was not significant in the poor glycemic control status group (Table 4 and Figure 3).
4. Discussion
As far as we are aware, this study is the first to investigate the admission glucose gap between eAG and initial glucose level in association with stroke outcome considering glycemic control status at the time of stroke onset. We identified that patients with a severe negative glucose gap (eAG–initial glucose ≤ −50 mg/dL) were significantly associated with poor short-term functional outcome. Its significance was only maintained in the good glycemic control group (HbA1c < 6.5%). This finding suggests that initial glucose level might have limited prognostic importance as a predictor of short-term stroke outcome without reflecting glycemic control status.
Several clinical studies demonstrated an association between admission hyperglycemia and poor stroke outcome [1,2,3,6,8]. It was proposed that elevated glucose causes infarct evolution through anaerobic glycolysis, leading to lactic acidosis, reperfusion-induced superoxide production [16], increased matrix-metalloproteinase-9 expression, and provoking blood–brain barrier damage [17,18]. Most previous studies concentrated on initial glucose level without considering the estimated glucose level at the time of stroke occurrence [13,14]. However, the initial glucose level should be considered in light of glycemic control status at the time of stroke onset. The effect of hyperglycemia at admission between patients with good and poor glycemic control status on stroke outcome might be different. Supporting our suggestion, in the second European Cooperative Acute Stroke Study (ECASS-II), hyperglycemia at admission was not related to any stroke outcomes, including 7 day neurological improvement, 30 day Barthel Index, 90 day mRS, and 90 day death [3]. In this regard, we demonstrated that a severe negative glucose gap between eAG and initial glucose was a significant predictor for poor short-term functional outcome.
Although initial glucose level has an effect on stroke prognosis, it is difficult to clearly determine whether it is the effect of poststroke stress hyperglycemia or the effect of higher-than-usual glucose level at the time of stroke. Interestingly, in this study, the initial glucose level of the severe-negative group was the highest, but there was no statistically significant difference in stroke severity or mechanism between groups (Table 2). Therefore, considering that stroke severity is related to stress hyperglycemia, initial glucose status at the time of stroke rather than poststroke stress hyperglycemia may be an important predictor of short-term stroke prognosis.
Furthermore, the group with poor glycemic control (HbA1c ≥ 6.5%) had poor prognosis, as expected, whereas the glucose gap affected short-term outcomes, particularly in the group without diabetes or with good glycemic control (HbA1c < 6.5%). Similar to our result, although the association between admission glucose level and stroke outcome was inconsistent, a systematic review of 31 articles demonstrated that the relationship between admission hyperglycemia and mortality was stronger in nondiabetic stroke patients than it was in diabetic patients (RR, 3.07 versus 1.3) [7]. There are some explanations for this finding. First, because patients with uncontrolled glucose have a higher baseline glucose level, threshold values affecting short-term functional outcomes might be different between the two groups. In our study, the mean value of the glucose gap between eAG and initial glucose was −10.03 ± 32.75 mg/dL in the good glycemic control group (HbA1c < 6.5%) and −17.58 ± 68.76 mg/dL in the poor glycemic control group (HbA1c ≥ 6.5%) (p = 0.034). Second, chronic hyperglycemic exposure may provoke cerebrovascular remodeling [19]. Glycemic control status seems to have different effects on microvascular and macrovascular complications [20]. Third, glucose-lowering drugs may reduce the toxic metabolic effect of hyperglycemia upon admission.
It remains undetermined why HbA1c is not associated with short-term functional outcomes in our study. Reported associations between HbA1c levels and short-term stroke outcomes were inconsistent. In a retrospective study of 317 diabetic patients with coronary syndrome, HbA1c before admission was not associated with inhospital mortality or 6 month major adverse cardiovascular events [21]. In 608 patients with myocardial infarction, HbA1c was not an independent predictor of short-term outcomes, including 7 and 30 day mortality [1]. However, in a total of 534 patients with AIS undergoing mechanical thrombectomy, HbA1c was an independent predictor of poor short-term functional outcome [2]. Although the exact pathomechanism is unclear because HbA1c is a good indicator of glycemic control over 2 or 3 months, HbA1c seems to be a better marker of long-term outcomes and mortality than of short-term outcomes.
The strength of this study is that it was the first to investigate the effect of a glucose gap on stroke prognosis by considering glycemic control status at the time of stroke onset. Another advantage was the large population of the multicenter studies. Despite these strengths, there were a few limitations to this study. First, it was an observational retrospective study. Second, missing data may influence the interpretation of the results. However, we included a relatively large population from three tertiary hospitals to reduce selection bias. Third, poststroke hyperglycemia may be a secondary stress response after stroke. We cannot distinguish stress hyperglycemia from hyperglycemic status at the time of stroke onset. However, accumulating evidence suggests that poststroke hyperglycemia is not merely an epiphenomenon; rather, it has a critical pathologic role in stroke evolution. One study based on magnetic resonance imaging reported on the correlation of acute hyperglycemia with perfusion–diffusion mismatch, greater infarct size, and poor functional outcomes [22]. Another study reported that increased serum glucose level (>8.9 mmol/L) triggers antifibrinolytic cascades, contributing to hypercoagulable status [23]. Accordingly, in our study, there were no statistically significant differences in stroke mechanism or severity between groups that could cause stress hyperglycemia. Fourth, since this was a clinical retrospective study, we did not measure some biomarkers to better understand how the glucose gap affected stroke outcome. Recent studies demonstrated that the multifaceted role of mitochondria, free radical generation, and inflammatory cytokines can affect ischemic brain injury [10,24,25]. In order to advance our research, it is better to consider such experimental parameters in future research. Lastly, although we obtained all initial glucose levels on the day of admission, day of admission and stroke onset were different for each patient. However, since we enrolled patients with AIS who had been admitted within 7 days of symptom onset, duration differences were limited.
5. Conclusions
In conclusion, an elevated initial glucose gap, of which the initial glucose level is much higher than the estimated glucose level is based on HbA1c, was associated with poor stroke prognosis. In addition to glucose level at admission, glycemic control status at the time of stroke onset should be considered in the prediction of short-term stroke outcomes.
Supplementary Materials
The following are available online at https://www.mdpi.com/2075-4426/11/2/139/s1, Supplementary Table S1: Binary logistic regression analysis for poor short-term functional outcome, mRS 2–6 three months after stroke (compared to mRS 0–1 three months after stroke) in the female group.
Author Contributions
Conceptualization, Y.K., S.-H.L., and J.-H.L.; methodology, formal analysis, investigation, data curation, and writing—original-draft preparation, Y.K.; writing—review and editing, S.-H.L., M.K.K., T.J.K., C.K., B.-W.Y., J.S.B., and J.-H.L.; visualization, Y.K.; supervision, J.-H.L.; project administration and funding acquisition, Y.K. All authors have read and agreed to the published version of the manuscript.
Funding
This research was supported by Basic Science Research Program through the National Research Foundation (NRF) of Korea funded by the Ministry of Science and ICT (NRF-2018R1C1B5086320); supported by a grant no. 2020-07 from the Kangdong Sacred Heart Hospital Fund and supported by Hallym University Research Fund 2020 (HURF-2020-37).
Institutional Review Board Statement
The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the institutional review boards of the three centers (Kangdong Sacred Heart Hospital, IRB no. 2020-02-006-001; Seoul National University Hospital, IRB no. H-2002-023-1098; and Chuncheon Sacred Heart Hospital, IRB no. 2017-89).
Informed Consent Statement
Written informed consent was obtained from the patients to publish this paper.
Data Availability Statement
All data generated or analyzed during this study are included in this published article. Anonymized data will be shared by reasonable request from any qualified investigator.
Acknowledgments
Biospecimens and data used in this study were provided by the Biobank of Seoul National University Hospital, a member of the Korea Biobank Network.
Conflicts of Interest
The authors declare no conflict of interest.
Abbreviations
| AIS | acute ischemic stroke |
| eAG | estimated average glucose levels |
| mRS | modified Rankin Scale |
| OR | odds ratio |
| HbA1c | glycated hemoglobin |
| TOAST | Trial of Org 10172 in Acute Stroke Treatment |
| BMI | body-mass index |
| INS | initial neurological severity |
| NIHSS | National Institute of Health Stroke Scale (NIHSS) score |
| GA | glycated albumin |
| SD | standard deviation |
| CIs | confidence intervals |
| ECASS-II | second European Cooperative Acute Stroke Study |
References
- Tian, L.; Zhu, J.; Liu, L.; Liang, Y.; Li, J.; Yang, Y. Hemoglobin A1c and short-term outcomes in patients with acute myocardial infarction undergoing primary angioplasty: An observational multicenter study. Coron. Artery Dis. 2013, 24, 16–22. [Google Scholar] [CrossRef]
- Choi, K.H.; Kim, J.H.; Kang, K.W.; Kim, J.T.; Choi, S.M.; Lee, S.H.; Park, M.S.; Kim, B.C.; Kim, M.K.; Cho, K.H. HbA1c (Glycated Hemoglobin) Levels and Clinical Outcome Post-Mechanical Thrombectomy in Patients With Large Vessel Occlusion. Stroke 2018. [Google Scholar] [CrossRef] [PubMed]
- Yong, M.; Kaste, M. Dynamic of hyperglycemia as a predictor of stroke outcome in the ECASS-II trial. Stroke 2008, 39, 2749–2755. [Google Scholar] [CrossRef] [Green Version]
- Song, T.-J.; Chang, Y.; Chun, M.-Y.; Lee, C.-Y.; Kim, A.R.; Kim, Y.; Kim, Y.-J. High Dietary Glycemic Load is Associated with Poor Functional Outcome in Patients with Acute Cerebral Infarction. J. Clin. Neurol. 2018, 14, 165–173. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Broocks, G.; Kemmling, A.; Aberle, J.; Kniep, H.; Bechstein, M.; Flottmann, F.; Leischner, H.; Faizy, T.D.; Nawabi, J.; Schön, G.; et al. Ischemic Lesion Water Uptake in Acute Stroke: Is Blood Glucose Related to Cause and Effect? J. Stroke 2019, 21, 347–349. [Google Scholar] [CrossRef]
- Baird, T.A.; Parsons, M.W.; Phan, T.; Butcher, K.S.; Desmond, P.M.; Tress, B.M.; Colman, P.G.; Chambers, B.R.; Davis, S.M. Persistent poststroke hyperglycemia is independently associated with infarct expansion and worse clinical outcome. Stroke 2003, 34, 2208–2214. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Capes, S.E.; Hunt, D.; Malmberg, K.; Pathak, P.; Gerstein, H.C. Stress hyperglycemia and prognosis of stroke in nondiabetic and diabetic patients: A systematic overview. Stroke 2001, 32, 2426–2432. [Google Scholar] [CrossRef] [Green Version]
- Zsuga, J.; Gesztelyi, R.; Kemeny-Beke, A.; Fekete, K.; Mihalka, L.; Adrienn, S.M.; Kardos, L.; Csiba, L.; Bereczki, D. Different effect of hyperglycemia on stroke outcome in non-diabetic and diabetic patients--a cohort study. Neurol. Res. 2012, 34, 72–79. [Google Scholar] [CrossRef] [PubMed]
- Klingbeil, K.D.; Koch, S.; Dave, K.R. Potential link between post-acute ischemic stroke exposure to hypoglycemia and hemorrhagic transformation. Int. J. Stroke Off. J. Int. Stroke Soc. 2020, 15, 477–483. [Google Scholar] [CrossRef]
- Zhang, Z.; Yan, J.; Shi, H. Hyperglycemia as a Risk Factor of Ischemic Stroke. J. Drug Metab. Toxicol. 2013, 4. [Google Scholar] [CrossRef]
- Bruno, A.; Levine, S.R.; Frankel, M.R.; Brott, T.G.; Lin, Y.; Tilley, B.C.; Lyden, P.D.; Broderick, J.P.; Kwiatkowski, T.G.; Fineberg, S.E. Admission glucose level and clinical outcomes in the NINDS rt-PA Stroke Trial. Neurology 2002, 59, 669–674. [Google Scholar] [CrossRef]
- Cheng, Y.; Luo, J.; Lin, Y.; Zeng, Y.; Yu, J.; Lin, Y. Impact of hyperglycaemia on complications in patients who had a stroke after thrombolysis. Postgrad. Med. J. 2020. [Google Scholar] [CrossRef]
- Ahmed, N.; Dávalos, A.; Eriksson, N.; Ford, G.A.; Glahn, J.; Hennerici, M.; Mikulik, R.; Kaste, M.; Lees, K.R.; Lindsberg, P.J.; et al. Association of admission blood glucose and outcome in patients treated with intravenous thrombolysis: Results from the Safe Implementation of Treatments in Stroke International Stroke Thrombolysis Register (SITS-ISTR). Arch. Neurol. 2010, 67, 1123–1130. [Google Scholar] [CrossRef] [Green Version]
- Hu, G.C.; Hsieh, S.F.; Chen, Y.M.; Hu, Y.N.; Kang, C.L.; Chien, K.L. The prognostic roles of initial glucose level and functional outcomes in patients with ischemic stroke: Difference between diabetic and nondiabetic patients. Disabil. Rehabil. 2012, 34, 34–39. [Google Scholar] [CrossRef]
- Nathan, D.M.; Kuenen, J.; Borg, R.; Zheng, H.; Schoenfeld, D.; Heine, R.J. Translating the A1C assay into estimated average glucose values. Diabetes Care 2008, 31, 1473–1478. [Google Scholar] [CrossRef] [Green Version]
- Suh, S.W.; Shin, B.S.; Ma, H.; Van Hoecke, M.; Brennan, A.M.; Yenari, M.A.; Swanson, R.A. Glucose and NADPH oxidase drive neuronal superoxide formation in stroke. Ann. Neurol. 2008, 64, 654–663. [Google Scholar] [CrossRef] [PubMed]
- Kamada, H.; Yu, F.; Nito, C.; Chan, P.H. Influence of hyperglycemia on oxidative stress and matrix metalloproteinase-9 activation after focal cerebral ischemia/reperfusion in rats: Relation to blood-brain barrier dysfunction. Stroke 2007, 38, 1044–1049. [Google Scholar] [CrossRef]
- Ennis, S.R.; Keep, R.F. Effect of sustained-mild and transient-severe hyperglycemia on ischemia-induced blood-brain barrier opening. J. Cereb. Blood Flow Metab. Off. J. Int. Soc. Cereb. Blood Flow Metab. 2007, 27, 1573–1582. [Google Scholar] [CrossRef] [Green Version]
- Ergul, A.; Li, W.; Elgebaly, M.M.; Bruno, A.; Fagan, S.C. Hyperglycemia, diabetes and stroke: Focus on the cerebrovasculature. Vasc. Pharmacol. 2009, 51, 44–49. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Zoungas, S.; Chalmers, J.; Ninomiya, T.; Li, Q.; Cooper, M.E.; Colagiuri, S.; Fulcher, G.; de Galan, B.E.; Harrap, S.; Hamet, P.; et al. Association of HbA1c levels with vascular complications and death in patients with type 2 diabetes: Evidence of glycaemic thresholds. Diabetologia 2012, 55, 636–643. [Google Scholar] [CrossRef] [PubMed]
- Chan, C.Y.; Li, R.; Chan, J.Y.; Zhang, Q.; Chan, C.P.; Dong, M.; Yan, B.P.; Lam, Y.Y.; Yu, C.M. The value of admission HbA(1c) level in diabetic patients with acute coronary syndrome. Clin. Cardiol. 2011, 34, 507–512. [Google Scholar] [CrossRef]
- Parsons, M.W.; Barber, P.A.; Desmond, P.M.; Baird, T.A.; Darby, D.G.; Byrnes, G.; Tress, B.M.; Davis, S.M. Acute hyperglycemia adversely affects stroke outcome: A magnetic resonance imaging and spectroscopy study. Ann. Neurol. 2002, 52, 20–28. [Google Scholar] [CrossRef] [PubMed]
- Ribo, M.; Molina, C.; Montaner, J.; Rubiera, M.; Delgado-Mederos, R.; Arenillas, J.F.; Quintana, M.; Alvarez-Sabín, J. Acute hyperglycemia state is associated with lower tPA-induced recanalization rates in stroke patients. Stroke 2005, 36, 1705–1709. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Zhong, C.; Yang, J.; Xu, T.; Xu, T.; Peng, Y.; Wang, A.; Wang, J.; Peng, H.; Li, Q.; Ju, Z.; et al. Serum matrix metalloproteinase-9 levels and prognosis of acute ischemic stroke. Neurology 2017, 89, 805–812. [Google Scholar] [CrossRef]
- Yang, J.L.; Mukda, S.; Chen, S.D. Diverse roles of mitochondria in ischemic stroke. Redox Biol. 2018, 16, 263–275. [Google Scholar] [CrossRef] [PubMed]
Figure 1.
Flow diagram of study population.
Figure 2.
Distribution of levels of glucose gap groups after dichotomizing modified Rankin scale (mRS) at three months after stroke onset (mRS 0–1 versus mRS 2–6).
Figure 2.
Distribution of levels of glucose gap groups after dichotomizing modified Rankin scale (mRS) at three months after stroke onset (mRS 0–1 versus mRS 2–6).
Figure 3.
Logistic-regression plot of odds ratios and 95% confidence intervals. Effects of glucose gap on short-term stroke outcomes stratified by glycemic control status.
Figure 3.
Logistic-regression plot of odds ratios and 95% confidence intervals. Effects of glucose gap on short-term stroke outcomes stratified by glycemic control status.
Table 1.
Baseline characteristics according to initial stroke severity.
| Good Outcome (3 Month mRS 0–1) | Poor Outcome (3 Month mRS 2–6) | p Value | |
|---|---|---|---|
| No. (%) | 784 (58.9) | 548 (41.1) | |
| Age, years | 66 ± 13 | 71 ± 12 | <0.001 |
| Male sex, % | 518 (66.1) | 314 (57.3) | 0.001 |
| Body-mass index, kg/m2 | 24.2 ± 3.3 | 23.8 ± 3.6 | 0.038 |
| Cardiovascular risk factor | |||
| Prior ischemic stroke | 131 (16.7) | 121 (22.1) | 0.014 |
| Hypertension | 459 (58.5) | 352 (64.2) | 0.036 |
| Diabetes | 245 (31.3) | 210 (38.3) | 0.007 |
| Dyslipidemia | 155 (19.8) | 100 (18.2) | 0.487 |
| Smoking | 228 (29.1) | 103 (18.8) | <0.001 |
| Atrial fibrillation | 96 (12.2) | 124 (22.6) | <0.001 |
| Laboratory | |||
| White-blood-cell count | 7778 ± 3482 | 7900 ± 2979 | 0.507 |
| Hematocrit, g/dL | 41.1 ± 5.5 | 40.0 ± 6.4 | 0.001 |
| Fasting blood sugar, mg/dL | 128.5 ± 61.8 | 144.1 ± 88.2 | <0.001 |
| HbA1c, % | 6.19 ± 1.23 | 6.36 ± 1.48 | 0.027 |
| Estimated average glucose (eAG), mg/dL | 130.8 ± 35.2 | 135.7 ± 42.4 | 0.027 |
| eAG–initial glucose, mg/dL | −10.26 ± 43.40 | −14.11 ± 48.89 | 0.139 |
| eAG–initial glucose–four groups | 0.044 * | ||
| eAG–initial glucose ≤ −50, mg/dL | 99 (12.6) | 98 (17.9) | |
| −50 < eAG–initial glucose < 0, mg/dL | 340 (43.4) | 228 (41.6) | |
| 0 ≤ eAG–initial glucose < 50, mg/dL | 319 (40.7) | 201 (36.7) | |
| eAG–initial glucose ≥ 50, mg/dL | 26 (3.3) | 21 (3.8) | |
| Initial glucose, mg/dL | 141.1 ± 58.5 | 149.8 ± 65.7 | 0.013 |
| Glycated albumin, % | 16.0 ± 4.4 | 17.2 ± 4.8 | <0.001 |
| High-sensitivity C-reactive protein, mg/L | 6.49 ± 23.03 | 10.33 ± 26.60 | 0.008 |
| Total cholesterol, mg/dL | 171.1 ± 43.3 | 166.8 ± 42.7 | 0.026 |
| Triglyceride, mg/dL | 144.2 ± 106.2 | 127.0 ± 97.1 | 0.002 |
| Low-density lipoprotein, mg/dL | 104.4 ± 35.4 | 102.3 ± 51.6 | 0.395 |
| Blood urea nitrogen, mg/dL | 16.8 ± 8.2 | 18.6 ± 11.2 | 0.001 |
| Creatinine, mg/dL | 0.99 ± 0.80 | 1.04 ± 0.84 | 0.298 |
| Systolic blood pressure, mmHg | 152 ± 29 | 152 ± 29 | 0.597 |
| Diastolic blood pressure, mmHg | 86 ± 15 | 85 ± 16 | 0.91 |
| Mechanism | <0.001 * | ||
| Large artery atherosclerosis | 266 (34.5) | 195 (35.9) | |
| Small vessel occlusion | 240 (31.0) | 109 (20.1) | |
| Cardioembolic | 114 (14.8) | 115 (21.2) | |
| Other determined | 33 (4.3) | 22 (4.1) | |
| Undetermined | 119 (15.4) | 102 (18.8) | |
| Previous antiplatelet user, n (%) | 208 (26.5) | 158 (28.8) | 0.312 |
| Previous anticoagulant user, n (%) | 25 (3.2) | 33 (6.0) | 0.043 |
| Initial stroke severity, median (IQR) | 2 (1,5) | 4 (2,7) | <0.001 |
Abbreviations: IQR, interquartile range. No. (%) or mean ± standard deviation (SD). p values calculated using χ2 test for trends in proportions. * Linear by linear association was used for analysis.
Table 2.
Baseline characteristics grouped by weight change.
| eAG–Initial Glucose ≤ −50, mg/dL | −50 < eAG–Initial Glucose < 0, mg/dL | 0 ≤ eAG–Initial Glucose < 50, mg/dL | eAG–Initial Glucose ≥ 50, mg/dL | p Value | |
|---|---|---|---|---|---|
| No. (%) | 197 (14.8) | 568 (42.6) | 520 (39.0) | 47 (3.5) | |
| Age, years | 69.5 ± 11.6 | 68.1 ± 13.7 | 68.0 ± 12.5 | 68.7 ± 12.8 | 0.712 |
| Male sex, % | 122 (61.9) | 364 (64.1) | 318 (61.2) | 28 (59.6) | 0.751 |
| Body mass index, kg/m2 | 24.40 ± 3.60 | 23.83±3.43 | 23.96 ± 3.37 | 24.62 ± 3.88 | 0.248 |
| Cardiovascular risk factor | |||||
| Prior ischemic stroke | 37 (18.8) | 99 (17.4) | 106 (20.4) | 10 (21.3) | 0.632 |
| Hypertension | 121 (61.4) | 346 (60.9) | 308 (59.2) | 36 (76.6) | 0.139 |
| Diabetes | 116 (58.9) | 146 (25.7) | 159 (30.6) | 34 (72.3) | <0.001 |
| Dyslipidemia | 39 (19.8) | 95 (16.7) | 109 (21.0) | 12 (25.5) | 0.208 |
| Smoking | 47 (23.9) | 140 (24.6) | 132 (25.4) | 12 (25.5) | 0.976 |
| Atrial fibrillation | 36 (18.3) | 81 (14.3) | 93 (17.9) | 10 (21.3) | 0.260 |
| Laboratory | |||||
| White blood cell count | 8052 ± 2981 | 7812 ± 2554 | 7736 ± 4008 | 8099 ± 3484 | 0.258 |
| Hematocrit, g/dL | 39.8 ± 6.1 | 40.8 ± 5.9 | 40.7 ± 5.1 | 42.1 ± 10.3 | 0.321 |
| Fasting blood sugar, mg/dL | 193.1 ± 108.6 | 129.9 ± 56.6 | 119.2 ± 66.7 | 129.6 ± 49.0 | <0.001 |
| HbA1c, % | 7.02 ± 1.78 | 5.89 ± 1.04 | 6.17 ± 1.08 | 8.49 ± 1.58 | <0.001 |
| HbA1c ≥ 6.5% | 101 (51.3) | 109 (19.2) | 130 (25.0) | 43 (91.5) | <0.001 |
| Initial glucose, mg/dL | 246.3 ± 80.2 | 140.4 ± 34.4 | 113.4 ± 30.4 | 115.8 ± 36.0 | <0.001 |
| Glycated albumin, % | 19.3 ± 6.1 | 15.6 ± 3.6 | 15.9 ± 3.8 | 22.6 ± 6.8 | <0.001 |
| Glycated albumin > 15.5% | 114 (65.9) | 201 (39.7) | 189 (43.9) | 36 (85.7) | <0.001 |
| High-sensitivity C-reactive protein, mg/L | 11.69 ± 33.57 | 8.52 ± 26.83 | 6.21 ± 17.36 | 8.67 ± 22.49 | 0.105 |
| Total cholesterol, mg/dL | 168.8 ± 44.3 | 171.9 ± 44.3 | 166.3 ± 41.3 | 161.3 ± 41.8 | 0.153 |
| Triglyceride, mg/dL | 155.7 ± 111.6 | 137.5 ± 106.1 | 129.2 ± 96.1 | 141.8 ± 86.2 | 0.010 |
| Low-density lipoprotein, mg/dL | 100.7 ± 34.7 | 105.2 ± 35.5 | 103.0 ± 52.2 | 101.7 ± 39.2 | 0.357 |
| Blood urea nitrate, mg/dL | 19.2 ± 11.1 | 17.2±9.2 | 17.0 ± 8.7 | 21.3 ± 14.4 | 0.006 |
| Creatinine, mg/dL | 1.07 ± 0.87 | 0.99 ± 0.82 | 0.99 ± 0.71 | 1.28 ± 1.45 | 0.100 |
| Systolic blood pressure, mmHg | 151 ± 31 | 152 ± 30 | 152 ± 26 | 146 ± 29 | 0.230 |
| Diastolic blood pressure, mmHg | 83 ± 15 | 86 ± 16 | 86 ± 14 | 80 ± 15 | 0.006 |
| Mechanism | 0.635 * | ||||
| Large artery atherosclerosis | 71 (36.0) | 211 (37.5) | 159 (31.2) | 20 (42.6) | |
| Small vessel occlusion | 42 (21.3) | 149 (26.5) | 149 (29.2) | 9 (19.1) | |
| Cardioembolic | 34 (17.3) | 87 (15.5) | 98 (19.3) | 10 (21.3) | |
| Other determined | 11 (5.6) | 25 (4.4) | 18 (3.5) | 1 (2.1) | |
| Undetermined | 39 (19.8) | 90 (16.0) | 85 (16.7) | 7 (14.9) | |
| Previous antiplatelet user, n (%) | 53 (26.9) | 133 (23.4) | 165 (31.7) | 15 (31.9) | 0.071 |
| Previous anticoagulant user, n (%) | 11 (5.6) | 20 (3.5) | 25 (4.8) | 2 (4.3) | 0.355 |
| Initial stroke severity, median (IQR) | 3 (1,6) | 3 (1,6) | 3 (1,6) | 3 (2,5) | 0.883 |
| 3 month mRS 2–6 | 98 (49.7) | 228 (40.1) | 201 (38.7) | 21 (44.7) | 0.050 |
Abbreviations: IQR, interquartile range. No. (%) or mean ± SD. p values calculated using χ2 for trends in proportions. * Linear by linear association was used for analysis.
Table 3.
Binary logistic-regression analysis for poor short-term functional outcomes, mRS 2–6, 3 months after stroke (compared to mRS 0–1, 3 months after stroke).
Table 3.
Binary logistic-regression analysis for poor short-term functional outcomes, mRS 2–6, 3 months after stroke (compared to mRS 0–1, 3 months after stroke).
| Odds Ratio (95% Cl) | p Value | |
|---|---|---|
| Male sex | 0.854 (0.653–1.117) | 0.248 |
| Age, years | 1.023 (1.012–1.034) | <0.001 |
| Body mass index, kg/m2 | 0.985 (0.951–1.020) | 0.392 |
| Conventional risk factors | ||
| Prior ischemic stroke | 1.384 (1.030–1.859) | 0.031 |
| Hypertension | 0.974 (0.753–1.259) | 0.840 |
| Dyslipidemia | 0.910 (0.672–1.231) | 0.541 |
| Smoking | 0.846 (0.621–1.153) | 0.289 |
| Atrial fibrillation | 1.534 (1.120–2.101) | 0.008 |
| Hematocrit, g/dL | 1.003 (0.981–1.026) | 0.794 |
| Blood urea nitrogen, mg/dL | 1.010 (0.997–1.023) | 0.116 |
| High-sensitivity C-reactive protein, mg/L | 1.004 (0.999–1.009) | 0.136 |
| eAG–initial glucose, four groups | ||
| eAG–initial glucose ≤ −50 mg/dL | 1.573 (1.101–2.248) | 0.013 |
| −50 < eAG–initial glucose < 0 mg/dL | 1.082 (0.836–1.400) | 0.551 |
| 0 ≤ eAG–initial glucose < 50 mg/dL | reference | |
| eAG–initial glucose ≥ 50 mg/dL | 1.344 (0.711–2.540) | 0.363 |
| Initial stroke severity | 1.039 (1.016–1.062) | 0.001 |
Abbreviation: eAG, estimated average glucose. Adjusted for age, sex, body mass index, previous stroke history, hypertension, dyslipidemia, smoking, atrial fibrillation, hematocrit, blood urea nitrogen, high-sensitivity C-reactive protein, glucose gap, and initial stroke severity.
Table 4.
Binary logistic-regression analysis for poor short-term functional outcomes after categorization by glycemic control status (HbA1c < 6.5% versus HbA1c ≥ 6.5%).
Table 4.
Binary logistic-regression analysis for poor short-term functional outcomes after categorization by glycemic control status (HbA1c < 6.5% versus HbA1c ≥ 6.5%).
| Odds Ratio (95% Cl) | p Value | |
|---|---|---|
| HbA1c < 6.5% | ||
| eAG–initial glucose, four groups | ||
| eAG–initial glucose ≤ −50, mg/dL | 1.914 (1.155–3.169) | 0.012 |
| −50 < eAG–initial glucose < 0, mg/dL | 1.161 (0.864–1.562) | 0.322 |
| 0 ≤ eAG–initial glucose < 50, mg/dL | Reference | |
| eAG–initial glucose ≥ 50, mg/dL | 4.738 (0.464–43.384) | 0.189 |
| HbA1c ≥ 6.5% | ||
| eAG–initial glucose, four groups | ||
| eAG–initial glucose ≤ −50, mg/dL | 1.152 (0.650–2.042) | 0.628 |
| −50 < eAG–initial glucose < 0, mg/dL | 1.013 (0.574–1.788) | 0.965 |
| 0 ≤ eAG–initial glucose < 50, mg/dL | Reference | |
| eAG–initial glucose ≥ 50, mg/dL | 0.883 (0.415–1.881) | 0.747 |
Abbreviations: CI, confidence interval; eAG, estimated average glucose. Adjusted for age, sex, body-mass index, stroke history, hypertension, dyslipidemia, smoking, atrial fibrillation, hematocrit, blood urea nitrogen, high sensitivity C-reactive protein, glucose gap, and initial stroke severity.
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |
© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Share and Cite
MDPI and ACS Style
Kim, Y.; Lee, S.-H.; Kim, C.; Kang, M.K.; Yoon, B.-W.; Kim, T.J.; Bae, J.S.; Lee, J.-H. Personalized Consideration of Admission-Glucose Gap between Estimated Average and Initial Glucose Levels on Short-Term Stroke Outcome. J. Pers. Med. 2021, 11, 139. https://doi.org/10.3390/jpm11020139
AMA Style
Kim Y, Lee S-H, Kim C, Kang MK, Yoon B-W, Kim TJ, Bae JS, Lee J-H. Personalized Consideration of Admission-Glucose Gap between Estimated Average and Initial Glucose Levels on Short-Term Stroke Outcome. Journal of Personalized Medicine. 2021; 11(2):139. https://doi.org/10.3390/jpm11020139
Chicago/Turabian StyleKim, Yerim, Sang-Hwa Lee, Chulho Kim, Min Kyoung Kang, Byung-Woo Yoon, Tae Jung Kim, Jong Seok Bae, and Ju-Hun Lee. 2021. "Personalized Consideration of Admission-Glucose Gap between Estimated Average and Initial Glucose Levels on Short-Term Stroke Outcome" Journal of Personalized Medicine 11, no. 2: 139. https://doi.org/10.3390/jpm11020139
Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.
