Abstract
Objectives
The sarcopenia (SI) index, defined as the serum creatinine to cystatin C ratio, is considered a predictor of poor muscle health and malnutrition, which is related to major adverse cardiovascular events. However, the effect of the SI index on cognitive function in stroke patients remains unknown. In this study, we aimed to examine the association between the SI and longitudinal cognitive impairment in patients with acute ischemic stroke or transient ischemic attack.
Research design and methods
Participants who met the inclusion criteria in this national, multicenter, prospective cohort study were enrolled from the Impairment of Cognition and Sleep (ICONS) study of the China National Stroke Registry-3 (CNSR-3). They were categorized into four groups according to the quartile of the SI index. Cognitive function was assessed using the Montreal Cognitive Assessment (MoCA) scale. Multivariable-adjusted logistic regression models were performed to evaluate the association between the SI index and post-stroke cognitive impairment (PSCI) at the 3-month follow-up. Moreover, discrimination tests were used to evaluate the incremental predictive value of the SI index beyond the potential risk factors. Furthermore, we performed subgroup analyses to test interactions.
Results
Among the enrolled participants, the lower the SI index was, the worse the cognitive performance. At the 3-month follow-up, participants in the lowest SI quartile group exhibited a 42% increase in the risk of cognitive impairment relative to the highest quartile group [OR 0.58 (95% CI 0.37–0.90)]. Moreover, after applying the discrimination test, adding the SI index into the potential risk factors resulted in a slight improvement in predicting the risk of cognitive impairment [NRI 14% (P = 0.01)].
Conclusion
This study demonstrated that a lower sarcopenia index was positively associated with a higher prevalence of PSCI. Monitoring the SI index in stroke patients and early identification and treatment of individuals with low SI level may be helpful to reduce the risk of cognitive impairment.
Keywords: Post-stroke cognitive impairment, The sarcopenia index, Risk factor, Association
1. Introduction
Stroke is the leading cause of disabilities and mortalities worldwide [1,2]. As a common complication of stroke, post-stroke cognitive impairment (PSCI) has been reported to be closely related to the recurrence of ischemic stroke and has gradually become the major source of morbidity and mortality after stroke [3,4]. According to an epidemiological survey in China, approximately 53.1% of stroke survivors manifested cognitive dysfunction and were more likely to develop dementia in the next 3 years, which seriously decreased their quality of life and daily life ability, affected their mental health status, and increased the burden of family and society care [5,6]. However, the diagnosis and prognosis of PSCI remain a challenge [7]. Therefore, the high morbidity of PSCI demands effective management strategies, especially prevention strategies targeting modifiable risk factors that may influence cognitive function in stroke patients.
Recently, the sarcopenia (SI) index, defined as the ratio of serum creatinine (Cr) to cystatin C (CysC) eliminates the effect of potential differences in renal function. It was found to be an inexpensive, accessible, and reliable tool to predict muscle health (mass, strength, and/or function) and nutritional risk in various conditions [[8], [9], [10], [11], [12], [13], [14]]. Moreover, previous studies have shown that a lower SI index is not only an independent risk factor for the severity of sarcopenia and malnutrition in patients but also a predictor of an increased prevalence of major adverse cardiovascular events (MACE), hospitalization, and 90-day mortality, but population-based longitudinal studies on the correlation between the SI index and cognitive function are lacking [15,16]. One study found that a lower SI index was associated with an increased risk of cognitive impairment among middle-aged and older populations [17]. However, to date, the relationship between the SI index and cognitive impairment in stroke patients has not been clear. Therefore, in the present study, we aimed to determine the association of the SI index with cognitive impairment in stroke patients during a 3-month follow-up.
2. Methods
2.1. Study population
Participants were recruited into the Impairment of Cognition and Sleep (ICONS) study of the China National Stroke Registry-3 (CNSR-3) from 2015 to 2018 [18]. ICONS is a large national, multicenter, prospective cohort study of approximately 40 hospitals in China that continuously recruits patients with acute ischemic stroke (AIS) and transient ischemic attack (TIA) without cognitive impairment before stroke and aims to investigate factors associated with cognitive impairment after AIS or TIA and their impact on clinical outcomes [19]. Generally, stroke is diagnosed by symptoms, physical signs, scale evaluations, and neuroimages (magnetic resonance or brain computed tomography), according to the World Health Organization criteria [20].
The inclusion criteria for participants in this study were (i) diagnosed with AIS or TIA and hospitalized from symptom onset within 7 days; (ii) had their baseline completed standard cognitive function evaluation; (iii) had no prior diagnosis of cognitive impairment at baseline; (iv) no related diseases that may affect the cognitive function assessments, for instance, severe aphasia, hearing loss, visual impairment, psychosis or schizophrenia (documented in the questionnaire); and (v) had a Glasgow Coma Scale 15 points and muscle strength of handedness ≥ level 4 after Manual Muscle Testing. Among these participants, the following were further excluded: 836 patients without serum creatinine and cystatin C data at baseline, 821 patients with a history of kidney disease or an eGFR of less than 30 mL/(min × 1.73 m2), and 251 patients without cognitive examinations at the 3-month follow-up. Eventually, a total of 1,515 participants were enrolled in this study.
The present study was performed in accordance with the guidelines described by the Helsinki Declaration and was approved by the Ethics Committees of Beijing Tiantan Hospital (No. KY2015-001-01). All participants signed a written informed consent form prior to their inclusion in the study.
2.2. Data collection
The baseline data of enrolled participants were collected following the standard data collection protocol developed by the steering committee. The protocol and the statistical analysis plan have been published in previous studies [18,19]. The information from all participants was collected by a series of comprehensive and precise assessments on admission and detailed medical records, including a collection of their demographic information (age, sex, body mass index (BMI), smoking, alcohol consumption, educational level, among others), medical histories (stroke or TIA, hypertension, diabetes mellitus, dyslipidemia, atrial fibrillation, coronary artery disease, heart failure, liver disease, and cancer), and physical examination (blood pressure, 10-Meter Walk Test (10MWT), modified Rankin Scale (mRS) score, National Institutes of Health Stroke Scale (NIHSS) score, ABCD2 score, Glasgow Coma Scale, and Manual Muscle Testing). All imaging data were collected in DICOM format on disks and analyzed by two professional neurologists. Moreover, we also determined exposure to medications during hospitalization (antiplatelet aggregation therapy, anticoagulation treatment, antihypertensive treatment, lipid-lowering drugs, and hypoglycemic therapy). In addition, serum creatinine (Cr), cystatin C (CysC), effective glomerular filtration rate (eGFR), alanine aminotransferase (ALT), aspartate aminotransferase (AST), total cholesterol (TC), total cholesterol (TG), low-density lipoprotein (LDL), high-density lipoprotein (HDL), and serum uric acid (UA) levels from fasting blood samples were collected carefully and with high quality within 24 hours of admission. All blood samples were stored in EDTA anticoagulant collection tubes and serum-separation tubes in a −80 °C refrigerator to address potential sources of bias.
2.3. Assessment of the SI index
The SI index was calculated as the serum creatinine (mg/dL) to cystatin C (mg/L) ratio * 100, as previously described [13,14,21].
2.4. Outcome evaluation
The clinical outcome included the incidence of PSCI at 3 months after stroke onset during the follow-up. All participants in this study completed the Chinese version of the Montreal Cognitive Assessment (MoCA) to evaluate cognitive function, including eight cognitive domains: visuospatial ability and executive function (5 points), naming (3 points), attention and calculation (6 points), verbal fluency and repetition (3 points), abstraction (2 points), short-term memory recall (5 points), and orientation (6 points). Cognitive impairment was defined as a MoCA cutoff point of <23/30, which has previously been shown to have the best sensitivity and specificity for detecting PSCI in Chinese patients [22]. Baseline MoCA evaluations were performed by a certified neuropsychologist, while follow-up MoCA evaluations were performed by a neurologist who was blinded to the baseline assessment. Generally, higher scores represent better cognitive functioning.
2.5. Statistical analysis
The data were tested for normal distribution using the Kolmogorov‒Smirnov test. Continuous variables are presented as the means ± standard deviations for normally distributed data and were compared using ANOVA. Continuous variables that did not exhibit a normal distribution are presented as medians with interquartile ranges and were compared using the Kruskal‒Wallis U test. Categorical variables were expressed as frequencies (proportions) and were compared using the χ2 test or Fisher’s exact test.
Patients in this study were divided into four categories according to the quartile of the SI index, which was assessed at baseline. The association of the SI index with PSCI was estimated by using multivariable logistic regression models. Data reported as odds ratios (ORs) with 95% confidence intervals (CIs) were calculated after adjusting for potential confounding factors. Model 1 was adjusted for age, sex and educational levels. Model 2 was additionally adjusted for BMI, smoking, alcohol consumption, medical histories, and medications during hospitalization. Model 3 further added laboratory tests, NIHSS at admission, Pre-stroke mRS, and the 2-week cognitive status (MoCA score). To avoid collinearity, hypertension rather than systolic and diastolic blood pressure and BMI but not waist circumference were used in the model fitting. Then, restricted cubic spline analyses were used to address the association. Furthermore, discrimination tests (C statistic, net reclassification improvement (NRI) and integrated discrimination improvement (IDI)) were used to evaluate the incremental predictive value of the SI index beyond potential risk factors. Additionally, subgroup analyses were performed after stratification by age (<60 y or ≥60 y), sex, BMI (<25 or ≥25 kg/m2), smoking, alcohol consumption, and eGFR to test interactions and assess whether the effect of the SI index on cognitive performance differed between different subgroups, adjusted by model 3. Moreover, Considering that the SI index is an indicator of muscle health and physical performance, we further tested the association of the SI index with 10-meter walking speed by using 10MWT, which has been recognized as a reliable and sensitive indicator for the assessment of physiologic performance [[23], [24], [25]]. In addition, we further performed sensitivity analysis using CysC alone to see if the SI index was better associated to the outcome. In this study, all analyses were two-sided, with data associated with a P value of <0.05 considered statistically significant.
All statistical analyses were conducted using SAS version 9.4 (SAS Institute Inc., Cary, North Carolina, USA).
3. Results
3.1. Baseline characteristics
As shown in Fig. 1, among 2,625 patients in the ICONS study, 1,515 patients who met the inclusion criteria were enrolled, and their baseline characteristics are presented in Tables 1 and S1. The participants were categorized into four groups according to the quartile of the SI index at baseline (Q1, <70.70; Q2, 70.70–81.07; Q3, 81.07–91.92; Q4, ≥91.92). We found that patients with a lower SI index were predominantly female and older overall, were less likely to be current smokers and drinkers, had a higher prevalence of diabetes mellitus and cardiovascular diseases, and had lower UA levels. Moreover, patients in the lowest SI-level group had worst levels in 10 m walking speed and cognitive performance than those in the highest SI-level group, whatever at 2-week or after during 3-month follow-up. Besides, there was no statistically significant correlation between the SI index and declines in 10 m walking speed during the follow-up (Table S2).
Fig. 1.
Flowchart of this study. Abbreviations: SI = the sarcopenia index; MoCA = The Montreal Cognitive Assessment; eGFR = estimated glomerular filtration rate; ICONS = The Impairment of Cognition and Sleep study of the China National Stroke Registry-3.
Table 1.
Baseline characteristics of participants according to quartiles of the SI index.
| Characteristic | Total | SI index |
P value | |||
|---|---|---|---|---|---|---|
| Q1 | Q2 | Q3 | Q4 | |||
| N, (%) | 1515 | 380 | 377 | 379 | 379 | |
| Age, year, median (IQR) | 62.00(54.00−70.00) | 66.00(60.00−73.00) | 62.00(55.00−71.00) | 61.00(53.00−67.00) | 57.00(50.00−65.00) | <0.0001 |
| Male, n (%) | 1087(71.75) | 146(38.42) | 263(69.76) | 326(86.02) | 352(92.88) | <0.0001 |
| Education level, n (%) | 0.8299 | |||||
| College or above | 167(11.02) | 23(6.05) | 37(9.81) | 58(15.30) | 49(12.93) | |
| High school | 362(23.89) | 75(19.74) | 80(21.22) | 92(24.27) | 115(30.34) | |
| Middle school | 538(35.51) | 136(35.79) | 131(34.75) | 132(34.83) | 139(36.68) | |
| Elementary or below | 376(24.82) | 124(32.63) | 116(30.77) | 82(21.81) | 54(14.25) | |
| Not known | 72(4.75) | 22(5.79) | 13(3.45) | 15(3.96) | 22(5.80) | |
| BMI, kg/m2, median (IQR) | 24.73(22.84−26.89) | 24.73(22.54−27.34) | 24.39(22.43−26.29) | 24.77(22.91−26.81) | 24.91(23.18−26.95) | 0.0219 |
| Current smoking, n (%) | 541(35.71) | 70(18.42) | 132(35.01) | 173(45.65) | 166(43.80) | <0.0001 |
| Current drinking, n (%) | 243(16.04) | 41(10.79) | 52(13.79) | 74(19.53) | 76(20.05) | <0.0001 |
| Medical histories, n (%) | ||||||
| Stroke or TIA | 364(24.03) | 84(22.11) | 99(26.26) | 88(23.22) | 93(24.54) | 0.6627 |
| Hypertension | 958(63.23) | 267(70.26) | 215(57.03) | 232(61.21) | 244(64.38) | 0.2214 |
| Diabetes mellitus | 363(23.96) | 102(26.84) | 100(26.53) | 81(21.37) | 80(21.11) | 0.0227 |
| Dyslipidemia | 159(10.50) | 41(10.79) | 27(7.16) | 39(10.29) | 52(13.72) | 0.0912 |
| Cardiovascular diseases | 224(14.79) | 81(21.32) | 63(16.71) | 38(10.03) | 42(11.08) | <0.0001 |
| Liver disease | 81(5.35) | 16(4.21) | 21(5.57) | 19(5.01) | 25(6.60) | 0.2012 |
| Cancer | 10(0.66) | 4(1.05) | 1(0.27) | 4(1.06) | 1(0.26) | 0.3956 |
| Medications during hospitalization, n (%) | ||||||
| Antiplatelet therapy | 1478(98.01) | 370(97.63) | 369(98.14) | 367(97.61) | 372(98.67) | 0.4158 |
| Anticoagulation therapy | 81(5.37) | 31(8.18) | 9(2.39) | 15(3.99) | 26(6.90) | 0.6580 |
| Antihypertensive therapy | 729(48.34) | 212(55.94) | 175(46.54) | 171(45.48) | 171(45.36) | 0.0043 |
| Lipid-lowering drugs | 1466(97.21) | 368(97.10) | 365(97.07) | 368(97.87) | 365(96.82) | 0.9907 |
| Hypoglycemic therapy | 411(27.25) | 126(33.25) | 109(28.99) | 86(22.87) | 90(23.87) | 0.0008 |
| NIHSS at admission, median (IQR) | 3.00(1.00−5.00) | 3.00(1.00−5.00) | 2.00(1.00−4.00) | 3.00(1.00−5.00) | 3.00(1.00−5.00) | 0.2872 |
| Pre-stroke mRS≤1, n (%) | 873(57.62) | 201(52.89) | 233(61.80) | 214(56.46) | 225(59.37) | 0.2134 |
| Laboratory tests, median (IQR) | ||||||
| SI | 81.07(70.70−91.92) | 63.73(58.22−68.43) | 76.05(73.42−78.92) | 86.46(83.66−89.14) | 99.27(94.75−108.71) | <0.0001 |
| Creatinine, mg/dL | 0.70(0.59−0.80) | 0.58(0.48−0.68) | 0.66(0.56−0.75) | 0.73(0.65−0.82) | 0.78(0.70−0.88) | <0.0001 |
| Cystatin C, mg/L | 0.95(0.83−1.09) | 1.04(0.91−1.22) | 0.97(0.84−1.12) | 0.95(0.86−1.07) | 0.86(0.77−0.97) | <0.0001 |
| eGFR, ml/min/1.73 m2 | 94.55(84.14−103.11) | 95.42(86.87−103.69) | 95.53(86.87−103.83) | 93.60(82.49−101.83) | 93.06(79.62−102.37) | 0.0014 |
| ALT, U/L | 18.00(13.00−25.00) | 18.00(13.00−25.00) | 17.00(13.00−22.00) | 18.00(13.00−25.00) | 19.00(14.00−26.00) | 0.0176 |
| AST, U/L | 19.00(16.00−24.00) | 19.35(15.90−25.00) | 19.00(15.00−23.60) | 19.00(16.00−23.80) | 19.00(16.00−23.70) | 0.4655 |
| TC, mmol/L | 3.87(3.24−4.59) | 4.02(3.31−4.71) | 3.75(3.19−4.62) | 3.84(3.23−4.49) | 3.86(3.24−4.56) | 0.1893 |
| TG, mmol/L | 1.35(1.00−1.83) | 1.37(1.01−1.79) | 1.26(0.97−1.72) | 1.34(1.00−1.76) | 1.44(1.04−2.25) | 0.0003 |
| LDL, mmol/L | 2.22(1.63−2.94) | 2.31(1.71−3.03) | 2.18(1.57−2.93) | 2.25(1.68−2.92) | 2.16(1.59−2.85) | 0.1304 |
| HDL, mmol/L | 0.94(0.79−1.12) | 0.99(0.82−1.17) | 0.95(0.79−1.15) | 0.95(0.81−1.09) | 0.86(0.74−1.06) | <0.0001 |
| UA, μmol/L | 293.00(242.00−353.00) | 271.00(225.00−333.00) | 283.00(229.50−336.50) | 306.00(250.00−360.00) | 319.00(256.00−369.50) | <0.0001 |
| 10 m walking speed, median (IQR) | ||||||
| At 2 weeks | 0.98(0.67−1.16) | 0.90(0.65−1.10) | 0.95(0.67−1.12) | 0.99(0.70−1.24) | 1.01(0.71−1.25) | <0.0001 |
| At 3 months | 1.00(0.77−1.25) | 0.94(0.67−1.11) | 1.00(0.77−1.25) | 1.03(0.83−1.25) | 1.11(0.83−1.25) | <0.0001 |
| MoCA score, median (IQR) | ||||||
| At 2 weeks | 21.00(18.00−25.00) | 22.00(16.50−25.00) | 23.00(18.00−26.00) | 23.00(19.00−26.00) | 24.00(19.00−27.00) | <0.0001 |
| At 3 months | 24.00(21.00−27.00) | 24.00(19.00−27.00) | 25.00(21.00−27.00) | 25.00(22.00−27.00) | 26.00(23.00−28.00) | <0.0001 |
Variables were expressed as median (s) or percentages.
Patients were divided into four categories according to the quartile of the SI index: Q1, <70.70; Q2, 70.70–81.07; Q3, 81.07–91.92; Q4, ≥91.92.
Abbreviation: BMI = Body mass index; TIA = transient ischemic attack; NIHSS = the National Institutes of Health Stroke Scale; mRS = the modified Rankin Scale; SI = the sarcopenia index; eGFR = effective glomerular filtration rate; ALT = alanine aminotransferase; AST = aspartate aminotransferase; TC = total cholesterol; TG = total cholesterol; LDL = low-density lipoprotein; HDL = high-density lipoprotein; UA = uric acid; MoCA = the Montreal Cognitive Assessment; IQR = interquartile range.
Cardiovascular disease included atrial fibrillation, coronary heart disease, heart failure.
Medication use indicated treatment during hospitalization.
The P value < 0.05 was considered to be statistically significant.
3.2. Clinical outcomes
During the 3-month follow-up period, among the eligible participants, 495 (32.67%) patients had cognitive impairment. Associations of the SI index with PSCI are presented in Table 2. Compared with those in the lowest group, patients in the highest SI group recorded a 42% decrease in the risk of cognitive impairment. Even after adjusting for the potential confounding factors mentioned above, the association persisted, and the odds ratio for PSCI decreased across SI quartiles: OR 0.97 (95% CI: 0.66−1.43), OR 0.79 (95% CI: 0.53−1.18), and OR 0.58 (95% CI 0.37−0.90) for the 2nd, 3rd, 4th quartiles, respectively, using the 1st quartile as the reference (p value for trend < 0.0001). Notably, the restricted cubic spline analysis also demonstrated a decrease in the risk of PSCI with increasing SI levels (Fig. 2).
Table 2.
Association between the SI index and PSCI incidence at 3 months follow-up.
| Outcomes | The SI index |
P for trend | |||
|---|---|---|---|---|---|
| Q1 | Q2 | Q3 | Q4 | ||
| 3 months PSCI | |||||
| Events, N (%) | 163(32.93) | 135(27.27) | 112(22.63) | 85(17.17) | |
| Unadjusted | 1 | 0.74(0.55−0.99) | 0.56(0.41−0.75) | 0.39(0.28−0.53) | 0.0058 |
| Model 1 | 1 | 0.90(0.65−1.24) | 0.81(0.57−1.14) | 0.64(0.44−0.92) | <0.0001 |
| Model 2 | 1 | 0.95(0.68−1.31) | 0.81(0.57−1.15) | 0.66 (0.46−0.96) | <0.0001 |
| Model 3 | 1 | 0.97(0.66−1.43) | 0.79(0.53−1.18) | 0.58(0.37−0.90) | <0.0001 |
Odds ratios (ORs) with 95% confidence intervals (CIs) were expressed by using multivariate logistic regression models. The OR of 1st quartile was set as the reference.
Model 1 was adjusted for age, sex, educational levels.
Model 2 was adjusted for the factors in model 1 plus BMI, smoking, alcohol consumption, medical histories, and medications during hospitalization.
Model 3 was adjusted for the factors in model 2 plus laboratory tests; NIHSS at admission, Pre-stroke mRS, and the 2-week cognitive status (MoCA score).
Abbreviation: Q = quartile; SI = the sarcopenia index; PSCI = post stroke cognitive impairment; NIHSS = the National Institutes of Health Stroke Scale; mRS = the modified Rankin Scale; MoCA = the Montreal Cognitive Assessment.
Fig. 2.
Spline models of the association between the SI and the incidence of PSCI at the 3-month follow-up. The ORs from the multivariate logistic regression model were adjusted for the variables of model 3 in Table 2. The red lines indicate the adjusted odds ratio, and the blue lines indicate the 95% confidence interval. Abbreviations: SI = sarcopenia index; PSCI = post-stroke cognitive impairment; OR = odds ratio; CI = confidence interval.
After applying discrimination tests, there was a slight improvement in predicting the risk of PSCI when adding the SI index into the potential risk factors (including age, sex, educational level, BMI, smoking status, alcohol consumption, medical history of stroke, hypertension, diabetes mellitus, dyslipidemia, cardiovascular diseases, liver disease, cancer, medications during hospitalization, and laboratory tests) [3,6,[26], [27], [28], [29]] [NRI 14% (P = 0.01), IDI 0.01% (P = 0.004)] (Table 3).
Table 3.
Reclassification and disclination statistics for the clinical outcome when adding to the SI index.
| Clinical outcome | Model | C-statistic |
NRI |
IDI |
|||
|---|---|---|---|---|---|---|---|
| Estimate (95% CI) | P value | Estimate (95% CI) | P value | Estimate (95% CI) | P value | ||
| 3m-PSCI | Potential risk factors | 0.66(0.63−0.69) | 0.101 | Ref. | 0.0147 | Ref. | 0.004 |
| Potential risk factors + SI | 0.68(0.65−0.70) | 0.14(0.03−0.25) | 0.01(0.002−0.01) | ||||
Potential risk factors: added to factor-adjusted models, including age, sex, educational level, BMI, smoking, alcohol consumption, medical history of stroke, hypertension, diabetes mellitus, dyslipidemia, cardiovascular diseases, liver disease, and cancer, medications during hospitalization, and laboratory test of eGFR, ALT, AST, TC, TG, LDL, HDL, UA.
Abbreviation: SI = the sarcopenia index; PSCI = post stroke cognitive impairment; BMI = Body mass index; NIHSS = the National Institutes of Health Stroke Scale; mRS = the modified Rankin Scale; TIA = transient ischemic attack; eGFR = effective glomerular filtration rate; ALT = alanine aminotransferase; AST = aspartate aminotransferase; TC = total cholesterol; TG = total cholesterol; LDL = low-density lipoprotein; HDL = high-density lipoprotein; UA = uric acid; OR = odds ratio; CI = confidence interval; NRI = net reclassification improvement; IDI = integrated discrimination improvement.
According to previous studies, some demographic and physiological factors might result in different effects of the SI index on cognitive dysfunction [17,30]. Thus, we further conducted an interaction analysis in this study. Regardless of the stratification of age, sex, BMI, smoking, alcohol consumption, and eGFR, the analysis showed no significant interactions between the variables and the SI index and PSCI. Patients in the lowest quartile of the SI index had a higher risk of PSCI than those in the top quartile (P value for interaction >0.05) (Table 4).
Table 4.
Subgroup analysis of the association between the SI index and the incidence of 3-m PSCI.
| Characteristics | 3m-PSCI |
P for interaction | |||
|---|---|---|---|---|---|
| Q1 | Q2 | Q3 | Q4 | ||
| Age, years | 0.5716 | ||||
| <60 | Ref. | 0.80(0.39−1.63) | 0.57(0.27−1.22) | 0.53(0.25−1.14) | |
| ≥60 | Ref. | 0.96(0.60−1.54) | 0.87(0.53−1.42) | 0.53(0.29−0.94) | |
| Sex | 0.5809 | ||||
| Male | Ref. | 0.99(0.59−1.68) | 0.83(0.50−1.39) | 0.66(0.39−1.13) | |
| Female | Ref. | 0.93(0.50−1.73) | 0.76(0.35−1.65) | 0.21(0.06−0.81) | |
| Body mass index | 0.3899 | ||||
| <25 | Ref. | 1.15(0.68−1.94) | 0.66(0.37−1.17) | 0.64(0.34−1.20) | |
| ≥25 | Ref. | 0.81(0.45−1.45) | 0.96(0.53−1.75) | 0.59(0.31−1.14) | |
| Current smoking | 0.7292 | ||||
| Yes | Ref. | 1.07(0.68−1.69) | 0.73(0.44−1.20) | 0.51(0.29−0.90) | |
| None | Ref. | 0.88(0.41−1.91) | 0.86(0.40−1.87) | 0.69(0.31−1.57) | |
| Alcohol consumption | 0.2442 | ||||
| Yes | Ref. | 0.93(0.61−1.40) | 0.76(0.49−1.20) | 0.65(0.40−1.06) | |
| None | Ref. | 1.35(0.47−3.84) | 0.97(0.35−2.70) | 0.42(0.13−1.37) | |
| eGFR, mL/(min × 1.73 m2) | 0.6879 | ||||
| ＜90 | Ref. | 0.69(0.39−1.24) | 0.60(0.34−1.08) | 0.45(0.23−0.86) | |
| ≥90 | Ref. | 1.11(0.72−1.71) | 0.91(0.56−1.49) | 0.77(0.46−1.31) | |
The ORs for incidence of PSCI were adjusted for variables of the model 3 on Table 2.
Odds ratios for SI and PSCI are stratified by age, sex, BMI, smoking, alcohol consumption, and eGFR.
Abbreviation: SI = the sarcopenia index; PSCI = post stroke cognitive impairment; BMI = Body mass index; eGFR = effective glomerular filtration rate; OR = odds ratio; CI = confidence interval.
In addition, the sensitivity analysis showed that after adjusting for potential confounding factors, there was no statistically significant correlation between Cys C and PSCI during the follow-up (Table S3).
4. Discussion
This cohort study revealed that patients with a lower SI index are more likely to have a higher risk of cognitive impairment after stroke. Compared with the top of the fourth quartile, patients in the bottom of the fourth quartile showed a 42% increased risk of PSCI. This significant relationship persisted even after adjusting for confounding factors, such as age, sex, educational levels, BMI, smoking, alcohol consumption, medical histories, medications during hospitalization, and laboratory tests. Interestingly, adding the SI index into the potential risk factors resulted in a 14% increase in predicting PSCI. Moreover, we found that the effect of the SI index on the risk of PSCI remained consistent across the correlation analysis in different subgroups.
The results of this study could be explained by the following possible mechanisms. Stroke patients experience brain injury, resulting in motor unit denervation, reduced hypoexcitability of local neurons, and dopamine dysfunction, leading to decreased muscle function and an increased nutritional risk [31,32]. In stroke patients, poor muscle health and malnutrition are often associated with frequent falls, reduced life independence and low quality of life, leading to decreased activity, which may further result in reduced blood circulation to the brain and thus impair cognitive function. Another possible mechanism for cognitive impairment relates to a poor muscle-brain axis mediated by myokine secretion imbalances in lower SI groups [33,34]. Myokines are cytokines or other peptides that are produced and released by skeletal muscle fibers and mainly include brain-derived neurotrophic factor (BDNF), insulin growth factor 1 (IGF-1), angiopoietin-like 4, fibroblast growth factor 21, irisin, myostatin, meteorin-like protein, and secreted protein acidic and rich in cysteine (SPARC) [35,36]. Myokines control the process of lipid metabolism and glucose metabolism, act as endocrine mediators of the muscle-brain axis, participate in the homeostasis of the body, prevent the occurrence of neurological diseases and maintain cognitive function [37,38]. In patients with low muscle mass, the balance between muscle catabolism and synthesis is disrupted, resulting in dysfunction of the ability of skeletal muscle to secrete myokines, subsequently upregulating the production of inflammatory cytokines and causing lipid and glucose metabolism impairment, which in turn damages neurons, resulting in cognitive impairment [39,40].
Moreover, there may be common pathophysiological mechanisms associated with inflammatory states, oxidative stress, and hormonal dysregulation between poor muscle health and cognitive impairment. Inflammation, hormonal changes, autophagy and apoptosis, oxidative stress, and insulin resistance have been shown to be associated with the development of cardiovascular and cerebrovascular diseases, poor muscle health and cognitive dysfunction [[41], [42], [43]]. These predisposing conditions may contribute to cognitive impairment in stroke patients with a low SI index. Additionally, people with a low SI index are more likely to experience brain shrinkage, which can contribute to cognitive deterioration. One study has shown that lower muscle mass is associated with lower gray matter volume in the frontal, parietal, and occipital lobes. The extent and speed of gray matter atrophy is more severe in patients with poor muscle health and malnutrition [44]. Moderate to severe medial temporal atrophy and global cortical atrophy were also found to be more common in the sarcopenia population [45]. In addition, low BDNF secretion levels in the sarcopenia population have been reported to be associated with hippocampal atrophy [46]. This may be one of the mechanisms that can explain the increased risk of cognitive impairment in stroke patients with a low SI index.
PSCI, as a common complication of stroke, is more likely to cause disability and death, seriously affect patients’ quality of life and shorten survival time. However, PSCI monitoring is facing current challenges in the field relate to a lack of harmonization of approaches to diagnosis, prevention, and management. The relative contributions of stroke to cognition largely vary between subjects and are often difficult to determine. Currently, the risk monitoring and temporal trajectories of PSCI are determined by a complex interplay of multiple factors, including i) modifiable risk factors: hypertension, diabetes mellitus, hyperlipidemia, smoking, educational level, sedentary lifestyle, malnutrition, etc.; ii) nonmodifiable risk factors: age, genetic variation, prior stroke, cognitive status, etc.; iii) overall brain health: global or focal cerebral atrophy, white matter hyperintensities, cerebral microbleeds, silent brain infarcts/cortical microinfarcts, PET-confirmed AD-pathology; iv) index stroke characteristics: stroke subtype, lesion size/number, lesion topography, clinical stroke severity at onset [3,[47], [48], [49]]. So far, among the risk factors that can be intervened, the evidence for biomarkers to predict PSCI is inconclusive. Therefore, it is necessary to explore potential biomarkers for the diagnosis, prognosis prediction and progression assessment of PSCI.
An efficient, economical and convenient blood biochemical index, the sarcopenia (SI) index, which is defined as the serum Cr (mg/dL) to CysC (mg/L) ratio [13,14,21], was reported reflect poorer skeletal muscle mass and a higher risk of sarcopenia and is associated with poorer functional outcomes and quality of life in hospitalized patients [50]. Both Cr and CysC are serum biomarkers for assessing glomerular filtration rate and renal function. Serum Cr is the derivative of skeletal muscle protein that is freely filtered into the glomerulus and excreted into the urine. Serum Cr production is primarily affected by physiological and clinical conditions that affect skeletal muscle mass, however, Cr is affected not only by muscle mass, but can be influenced by dietary intake and large volume resuscitation [51]. Patients with sarcopenia often have low serum creatinine levels. In contrast, CysC is a cysteine protease inhibitor that is excreted by nucleated cells, has a relatively stable production rate when muscle mass changes and is completely reabsorbed and metabolized by proximal tubule cells, however, CysC concentrations may be affected by thyroid function, inflammation, hypercatabolic states, and corticosteroid use [52]. The sarcopenia index (SI), defined as the serum Cr to CysC ratio eliminates the effect of potential differences in renal function and can be sufficiently dynamic to monitor a patient’s clinical course and response to interventions. However, there is a lack of relevant studies investigating the effect of the SI index on cognitive function in stroke patients. In this study, we firstly focuses on the impact of SI on cognitive function in stroke patients, with the intention of early identification of patients at high risk of cognitive impairment.
However, this study had several limitations. Firstly, we adopted a relatively short follow-up period, which might have underestimated the association of the SI index with PSCI. Therefore, longer follow-up will be considered in the future to give greater breadth and relevance to the results of this study. Secondly, since only patients with TIA and minor ischemic stroke (low NIHSS score) were included in this study, these findings need to be validated in other cohorts. Thirdly, because of certain variables that were not captured in the ICONS database and missing data for some variables of interest, the potential impact of residual confounders such as the location and size of stroke lesions, the collection of some medical histories and biochemical markers, and partial drug use and dosage on the results were not completely excluded. Furthermore, considering that bioelectrical impedance analysis, energy X-ray absorptiometry, magnetic resonance imaging, and computed tomography, which measure low muscle mass, are time-consuming and inconvenient, and some are difficult to implement in routine clinical practice and may cause radiation exposure [[53], [54], [55], [56], [57]], we have not been able to compare the correlation between PSCI and the SI index and the measures obtained from the aforementioned methods. Moreover, SI exhibited only modest accuracy for prediction of muscle mass or sarcopenia depending on the definition utilized. So far, there is no robust evidence that SI can be used as a synonym or a surrogate for sarcopenia. In future studies, we hope to further investigate the relationship between sarcopenia components and cognitive decline, if available.
5. Conclusion
In conclusion, this study showed that a decreased SI index was associated with the risk of PSCI; moreover, adding the SI index into the potential risk factors might provide superior risk stratification. As a reliable, cost-effective and readily measurable marker, the SI index has potential clinical value in predicting cognitive impairment in stroke patients. Therefore, it is necessary to monitor the SI index in patients with stroke or TIA as early as possible to help prevent cognitive deterioration in patients with a low SI index.
Author contributions
SQL designed the study and drafted the manuscript. HYY analyzed the data. YSP contributed to review the statistical problems. SQL and YMZ interpreted the data. YMZ reviewed the manuscript. All authors approved the final version of the manuscript.
Ethics approval and consent to participate
The present study was performed in accordance with the guidelines described by the Helsinki Declaration, and was approved by the Ethics Beijing Tiantan Hospital (No. KY2015-001-01). All participants signed a written informed consent prior to their inclusion in the study.
Conflict of interest statement
The authors have no potential conflicts of interest to declare.
Data availability statement
The analysis data are owned by China National Clinical Research Center for Neurological Diseases (http://paper.ncrcnd.ttctrc.com/). Data of this study are available from the corresponding author upon reasonable request.
Declaration of interests
The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Yumei Zhang reports financial support was provided by National Natural Science Foundation of China. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Acknowledgments
This work was supported by the National Natural Science Foundation of China (grant number. 81972144,82372555). We appreciate all the staff and participants of the ICONS (the Impairment of Cognition and Sleep study of the China National Stroke Registry-3) study for their contribution.
Footnotes
Supplementary material related to this article can be found, in the online version, at doi:https://doi.org/10.1016/j.jnha.2024.100241.
Appendix A. Supplementary data
The following are Supplementary data to this article:
References
- 1.Campbell B.C.V., Khatri P. Stroke. Lancet. 2020;396:129–142. doi: 10.1016/S0140-6736(20)31179-X. [DOI] [PubMed] [Google Scholar]
- 2.Wang Y.J., Li Z.X., Gu H.Q., Zhai Y., Jiang Y., Zhao X.Q., et al. China stroke statistics 2019: a report from the National Center for Healthcare Quality Management in Neurological Diseases, China National Clinical Research Center for Neurological Diseases, the Chinese Stroke Association, National Center for Chronic and Noncommunicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention and Institute for Global Neuroscience and Stroke Collaborations. Stroke Vasc Neurol. 2020;5(3):211–239. doi: 10.1136/svn-2020-000457. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Rost N.S., Brodtmann A., Pase M.P., van Veluw S.J., Biffi A., Duering M., et al. Post-stroke cognitive impairment and dementia. Circ Res. 2022;130(8):1252–1271. doi: 10.1161/CIRCRESAHA.122.319951. [DOI] [PubMed] [Google Scholar]
- 4.Cramer S.C., Richards L.G., Bernhardt J., Duncan P. Cognitive deficits after stroke. Stroke. 2023;54(1):5–9. doi: 10.1161/STROKEAHA.122.041775. [DOI] [PubMed] [Google Scholar]
- 5.Merriman N.A., Sexton E., McCabe G., Walsh M.E., Rohde D., Gorman A., et al. Addressing cognitive impairment following stroke: systematic review and meta-analysis of non-randomised controlled studies of psychological interventions. BMJ Open. 2019;9 doi: 10.1136/bmjopen-2018-024429. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Chinese Stroke Society Vascular Cognitive Disorder Branch Expert consensus on the prevention and treatment of cognitive impairment after stroke in China (in Chinese) Chin J Stroke. 2021;16(4):376–389. doi: 10.3969/j.issn.1673-5765.2021.04.011. [DOI] [Google Scholar]
- 7.Huang Y.Y., Chen S.D., Leng X.Y., Kuo K., Wang Z.T., Cui M., et al. Post-stroke cognitive impairment: epidemiology, risk factors, and management. J Alzheimers Dis. 2022;86(3):983–999. doi: 10.3233/JAD-215644. [DOI] [PubMed] [Google Scholar]
- 8.Barreto E.F., Poyant J.O., Coville H.H., Dierkhising R.A., Kennedy C.C., Gsajic O., et al. Validation of the sarcopenia index to assess muscle mass in the critically ill: a novel application of kidney function markers. Clin Nutr. 2019;38(3):1362–1367. doi: 10.1016/j.clnu.2018.05.031. [DOI] [PubMed] [Google Scholar]
- 9.Rizk J.G., Streja E., Wenziger C., Shlipak M.G., Norris K.C., Crowley S.T., et al. Serum creatinine-to-cystatin-C ratio as a potential muscle mass surrogate and racial differences in mortality. J Ren Nutr. 2023;33(1):69–77. doi: 10.1053/j.jrn.2021.11.005. [DOI] [PubMed] [Google Scholar]
- 10.Lin Y.L., Chen S.Y., Lai Y.H., Wang C.H., Kuo C.H., Liou H.H., et al. Serum creatinine to cystatin C ratio predicts skeletal muscle mass and strength in patients with non-dialysis chronic kidney disease. Clin Nutr. 2020;39(8):2435–2441. doi: 10.1016/j.clnu.2019.10.027. [DOI] [PubMed] [Google Scholar]
- 11.Osaka T., Hamaguchi M., Hashimoto Y., Ushigome E., Tanaka M., Yamazaki M., et al. Decreased the creatinine to cystatin C ratio is a surrogate marker of sarcopenia in patients with type 2 diabetes. Diabetes Res Clin Pract. 2018;139:52–58. doi: 10.1016/j.diabres.2018.02.025. [DOI] [PubMed] [Google Scholar]
- 12.Hirai K., Tanaka A., Homma T., Goto Y., Akimoto K., Uno T., et al. Serum creatinine/cystatin C ratio as a surrogate marker for sarcopenia in patients with chronic obstructive pulmonary disease. Clin Nutr. 2021;40(3):1274–1280. doi: 10.1016/j.clnu.2020.08.010. [DOI] [PubMed] [Google Scholar]
- 13.Barreto E.F., Kanderi T., DiCecco S.R., Lopez-Ruiz A., Poyant J.O., Mara K.C., et al. Sarcopenia index is a simple objective screening tool for malnutrition in the critically ill. JPEN J Parenter Enteral Nutr. 2019;43(6):780–788. doi: 10.1002/jpen.1492. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Ichikawa T., Miyaaki H., Miuma S., Motoyoshi Y., Yamashima M., Yamamichi S., et al. Calculated body muscle mass as a useful screening marker for low skeletal muscle mass and sarcopenia in chronic liver disease. Hepatol Res. 2020;50(6):704–714. doi: 10.1111/hepr.13492. [DOI] [PubMed] [Google Scholar]
- 15.Hashimoto Y., Takahashi F., Okamura T., Osaka T., Okada H., Senmaru T., et al. Relationship between serum creatinine to cystatin C ratio and subclinical atherosclerosis in patients with type 2 diabetes. BMJ Open Diabetes Res Care. 2022;10(3) doi: 10.1136/bmjdrc-2022-002910. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Lu Y.W., Tsai Y.L., Chou R.H., Kuo C.S., Chang C.C., Huang P.H., et al. Serum creatinine to cystatin C ratio is associated with major adverse cardiovascular events in patients with obstructive coronary artery disease. Nutr Metab Cardiovasc Dis. 2021;31(5):1509–1515. doi: 10.1016/j.numecd.2021.01.024. [DOI] [PubMed] [Google Scholar]
- 17.Zhu Y., Tan Z., Li S., Zhu F., Qin C., Zhang Q., et al. Serum creatinine to cystatin C ratio and cognitive function among middle-aged and older adults in China. Front Aging Neurosci. 2022;14 doi: 10.3389/fnagi.2022.919430. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Wang Y., Jing J., Meng X., Pan Y., Wang Y., Zhao X., et al. The Third China national stroke registry (CNSR-III) for patients with acute ischaemic stroke or transient ischaemic attack: design, rationale and baseline patient characteristics. Stroke Vasc Neurol. 2019;4:158–164. doi: 10.1136/svn-2019-000242. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Wang Y., Liao X., Wang C., Zhang N., Zuo L., Yang Y., et al. Impairment of cognition and sleep after acute ischemic stroke or transient ischemic attack in Chinese patients: design, rationale and baseline patient characteristics of a nationwide multicenter prospective registry. Stroke Vasc Neurol. 2021;6(1):139–144. doi: 10.1136/svn-2020-000359. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Hurford R., Li L., Lovett N., Kubiak M., Kuker W., Rothwell P.M. Prognostic value of “tissue-based” definitions of TIA and minor stroke: population-based study. Neurology. 2019;92(21):e2455–e2461. doi: 10.1212/WNL.0000000000007531. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Kashani K.B., Frazee E.N., Kukrálová L., Sarvottam K., Herasevich V., Young P.M., et al. Evaluating muscle mass by using markers of kidney function: development of the sarcopenia index. Crit Care Med. 2017;45(1):e23–e29. doi: 10.1097/CCM.0000000000002013. [DOI] [PubMed] [Google Scholar]
- 22.Liao X.L., Zuo L.J., Zhang N., Yang Y., Pan Y.S., Xiang X.L., et al. The occurrence and longitudinal changes of cognitive impairment after acute ischemic stroke. Neuropsychiatr Dis Treat. 2020;16:807–814. doi: 10.1186/s13054-016-1208-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.Jung H.W., Jang I.Y., Lee C.K., Yu S.S., Hwang J.K., Jeon C., et al. Usual gait speed is associated with frailty status, institutionalization, and mortality in community-dwelling rural older adults: a longitudinal analysis of the aging study of pyeongchang rural Area. Clin Interv Aging. 2018;13:1079–1089. doi: 10.2147/CIA.S166863. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.Cai Y., Cao J., Xu W., Liu H., Wu C. The association between Four gait speed assessments and incident stroke in older adults: the health, aging and body composition study. J Nutr Health Aging. 2020;24(8):888–892. doi: 10.1007/s12603-020-1415-3. [DOI] [PubMed] [Google Scholar]
- 25.Khanittanuphong P., Tipchatyotin S. Correlation of the gait speed with the quality of life and the quality of life classified according to speed-based community ambulation in Thai stroke survivors. NeuroRehabilitation. 2017;41(1):135–141. doi: 10.3233/NRE-171465. [DOI] [PubMed] [Google Scholar]
- 26.Livingston G., Huntley J., Sommerlad A., Ames D., Ballard C., Banerjee S., et al. Dementia prevention, intervention, and care: 2020 report of the Lancet Commission. Lancet. 2020;396(10248):413–446. doi: 10.1016/S0140-6736(20)30367-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Pendlebury S.T., Rothwell P.M. Oxford vascular study. Incidence and prevalence of dementia associated with transient ischaemic attack and stroke: analysis of the population-based Oxford vascular study. Lancet Neurol. 2019;18(3):248–258. doi: 10.1016/S1474-4422(18)30442-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Liu Y., Zhong X., Shen J., Jiao L., Tong J., Zhao W., et al. Elevated serum TC and LDL-C levels in Alzheimer’s disease and mild cognitive impairment: a meta-analysis study. Brain Res. 2020;1727 doi: 10.1016/j.brainres.2019.146554. [DOI] [PubMed] [Google Scholar]
- 29.Kim K.Y., Shin K.Y., Chang K.A. Potential biomarkers for post-stroke cognitive impairment: a systematic review and meta-analysis. Int J Mol Sci. 2022;23(2):602. doi: 10.3390/ijms23020602. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30.Lee H.S., Park K.W., Kang J., Ki Y.J., Chang M., Han J.K., et al. Sarcopenia index as a predictor of clinical outcomes in older patients with coronary artery disease. J Clin Med. 2020;9(10):3121. doi: 10.3390/jcm9103121. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31.Moreira-Pais A., Ferreira R., Oliveira P.A., Duarte J.A. A neuromuscular perspective of sarcopenia pathogenesis: deciphering the signaling pathways involved. Geroscience. 2022;44(3):1199–1213. doi: 10.1007/s11357-021-00510-2. Epub 2022 Jan 4. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32.Lee H., Lee I.H., Heo J., Baik M., Park H., Lee H.S., et al. Impact of sarcopenia on functional outcomes among patients with mild acute ischemic stroke and transient ischemic attack: a retrospective study. Front Neurol. 2022;13 doi: 10.3389/fneur.2022.841945. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 33.Scisciola L., Fontanella R.A., Surina Cataldo V., Paolisso G., Barbieri M. Sarcopenia and cognitive function: role of myokines in muscle brain cross-talk. Life (Basel) 2021;11(2):173. doi: 10.3390/life1w1020173. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 34.Sui S.X., Williams L.J., Holloway-Kew K.L., Hyde N.K., Pasco J.A. Skeletal muscle health and cognitive function: a narrative review. Int J Mol Sci. 2020;22(1):255. doi: 10.3390/ijms22010255. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 35.Whitham M., Febbraio M.A. The ever-expanding myokinome: discovery challenges and therapeutic implications. Nat Rev Drug Discov. 2016;15(10):719–729. doi: 10.1038/nrd.2016.153. [DOI] [PubMed] [Google Scholar]
- 36.Cabett Cipolli G., Sanches Yassuda M., Aprahamian I. Sarcopenia is associated with cognitive impairment in older adults: a systematic review and meta-analysis. J Nutr Health Aging. 2019;23(6):525–531. doi: 10.1007/s12603-019-1188-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 37.Das D.K., Graham Z.A., Cardozo C.P. Myokines in skeletal muscle physiology and metabolism: recent advances and future perspectives. Acta Physiol (Oxf) 2020;228(2) doi: 10.1111/apha.13367. [DOI] [PubMed] [Google Scholar]
- 38.Chen W., Wang L., You W., Shan T. Myokines mediate the cross talk between skeletal muscle and other organs. J Cell Physiol. 2021;236(4):2393–2412. doi: 10.1002/jcp.30033. [DOI] [PubMed] [Google Scholar]
- 39.Jo D., Yoon G., Kim O.Y., Song J. A new paradigm in sarcopenia: cognitive impairment caused by imbalanced myokine secretion and vascular dysfunction. Biomed Pharmacother. 2022;147 doi: 10.1016/j.biopha.2022.112636. [DOI] [PubMed] [Google Scholar]
- 40.He N., Zhang Y., Zhang L., Zhang S., Ye H. Relationship between sarcopenia and cardiovascular diseases in the elderly: an overview. Front Cardiovasc Med. 2021;8 doi: 10.3389/fcvm.2021.743710. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 41.Sui S.X., Hordacre B., Pasco J.A. Are sarcopenia and cognitive dysfunction comorbid after stroke in the context of brain-muscle crosstalk? Biomedicines. 2021;9(2):223. doi: 10.3390/biomedicines9020223. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 42.Du H., Yu M., Xue H., Lu X., Chang Y., Li Z. Association between sarcopenia and cognitive function in older Chinese adults: evidence from the China health and retirement longitudinal study. Front Public Health. 2023;10 doi: 10.3389/fpubh.2022.1078304. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 43.Dziegielewska-Gesiak S. Metabolic syndrome in an aging society - role of oxidant-antioxidant imbalance and inflammation markers in disentangling atherosclerosis. Clin Interv Aging. 2021;16:1057–1070. doi: 10.2147/CIA.S306982. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 44.Yu J.H., Kim R.E.Y., Jung J.M., Park S.Y., Lee D.Y., Cho H.J., et al. Sarcopenia is associated with decreased gray matter volume in the parietal lobe: a longitudinal cohort study. BMC Geriatr. 2021;21(1):622. doi: 10.1186/s12877-021-02581-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 45.Hsu Y.H., Liang C.K., Chou M.Y., Wang Y.C., Liao M.C., Chang W.C., et al. Sarcopenia is independently associated with parietal atrophy in older adults. Exp Gerontol. 2021;151 doi: 10.1016/j.exger.2021.111402. [DOI] [PubMed] [Google Scholar]
- 46.Pedersen B.K. Physical activity and muscle-brain crosstalk. Nat Rev Endocrinol. 2019;15(7):383–392. doi: 10.1038/s41574-019-0174-x. [DOI] [PubMed] [Google Scholar]
- 47.Lo J.W., Crawford J.D., Desmond D.W., Godefroy O., Jokinen H., Mahinrad S., et al. Stroke and cognition (STROKOG) collaboration. Profile of and risk factors for poststroke cognitive impairment in diverse ethnoregional groups. Neurology. 2019;93:e2257–e2271. doi: 10.1212/WNL. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 48.Quinn T.J., Richard E., Teuschl Y., Gattringer T., Hafdi M., O’Brien J.T., et al. European stroke organisation and European academy of neurology joint guidelines on poststroke cognitive impairment. Eur J Neurol. 2021;28:3883–3920. doi: 10.1111/ene. [DOI] [PubMed] [Google Scholar]
- 49.Rost N.S., Meschia J.F., Gottesman R., Wruck L., Helmer K., Greenberg S.M., et al. Cognitive impairment and dementia after stroke: design and rationale for the DISCOVERY study. Stroke. 2021;52:e499–e516. doi: 10.1161/STROKEAHA.120.031611. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 50.Ren C., Su H., Tao J., Xie Y., Zhang X., Guo Q. Sarcopenia index based on serum creatinine and cystatin C is associated with mortality, nutritional risk/malnutrition and sarcopenia in older patients. Clin Interv Aging. 2022;17:211–221. doi: 10.2147/CIA.S351068. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 51.Udy A.A., Scheinkestel C., Pilcher D., Bailey M. Australian and New Zealand intensive care society centre for outcomes and resource evaluation. The association between low admission peak plasma creatinine concentration and in-hospital mortality in patients admitted to intensive care in Australia and New Zealand. Crit Care Med. 2016;44(1):73–82. doi: 10.1097/CCM.0000000000001348. [DOI] [PubMed] [Google Scholar]
- 52.Kim T.J., Kang M.K., Jeong H.G., Kim C.K., Kim Y., Nam K.W., et al. Cystatin C is a useful predictor of early neurological deterioration following ischaemic stroke in elderly patients with normal renal function. Eur Stroke J. 2017;2(1):23–30. doi: 10.1177/2396987316677197. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 53.Chen L.K., Woo J., Assantachai P., Auyeung T.W., Chou M.Y., Iijima K., et al. Asian working group for sarcopenia: 2019 consensus update on sarcopenia diagnosis and treatment. J Am Med Dir Assoc. 2020;21(3):300–307.e2. doi: 10.1016/j.jamda.2019.12.012. [DOI] [PubMed] [Google Scholar]
- 54.Walowski C.O., Braun W., Maisch M.J., Jensen B., Peine S., Norman K., et al. Reference values for skeletal muscle mass - current concepts and methodological considerations. Nutrients. 2020;12(3):755. doi: 10.3390/nu12030755. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 55.Bai A., Xu W., Sun J., Liu J., Deng X., Wu L., et al. Associations of sarcopenia and its defining components with cognitive function in community-dwelling oldest old. BMC Geriatr. 2021;21(1):292. doi: 10.1186/s12877-021-02190-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 56.Yoshimura Y., Wakabayashi H., Bise T., Nagano F., Shimazu S., Shiraishi A., et al. Sarcopenia is associated with worse recovery of physical function and dysphagia and a lower rate of home discharge in Japanese hospitalized adults undergoing convalescent rehabilitation. Nutrition. 2019;61:111–118. doi: 10.1016/j.nut.2018.11.005. [DOI] [PubMed] [Google Scholar]
- 57.Ohtsubo T., Nozoe M., Kanai M., Yasumoto I., Ueno K. Association of sarcopenia and physical activity with functional outcome in older Asian patients hospitalized for rehabilitation. Aging Clin Exp Res. 2022;34(2):391–397. doi: 10.1007/s40520-021-01934-8. [DOI] [PubMed] [Google Scholar]
Associated Data
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Supplementary Materials
Data Availability Statement
The analysis data are owned by China National Clinical Research Center for Neurological Diseases (http://paper.ncrcnd.ttctrc.com/). Data of this study are available from the corresponding author upon reasonable request.