The Triglyceride Glucose Index Is a Risk Factor for Enlarged Perivascular Space

Cai, Yuanwu; Chen, Binxiong; Zeng, Xiaoyi; Xie, Min; Wei, Xiaolan; Cai, Jun

doi:10.3389/fneur.2022.782286

Cited by 12 publications

(12 citation statements)

References 41 publications

(57 reference statements)

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“…In another study, hypertriglyceridemia was found to be associated with reduced leukoaraiosis in patients with a small vessel stroke [3] but not other lipid fractions. A recent study among nondiabetic adults investigated the relationship between total cholesterol, LDL, HDL, and moderate to severe dPVS and, like our study, showed nonsignificant after multivariate comparison [26].…”

Section: Discussionsupporting

confidence: 49%

Association of Dilated Perivascular Spaces With Lipid Indices in Ischemic Stroke Patients

Diker¹,

Gelener²,

Erem³

et al. 2022

Cureus

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BackgroundDilated perivascular spaces (dPVS) in the basal ganglia are associated with aging, vascular risk factors, and other magnetic resonance imaging (MRI) markers of cerebral small vessel disease (cSVD). While high blood lipids are a well-demonstrated risk factor for large artery atherosclerosis, their role in cSVD remains largely elusive. MethodsWe evaluated lipid profiles, cardiovascular risk factors, and brain MRI findings in patients with ischemic stroke or transient ischemic attack. We analyzed the extent of dPVS, cerebral microbleed (CMB), and cerebral white matter hyperintensities (WMHs) as MRI indices of cSVD and investigated associations of dPVS with lipid parameters and other cSVD indices. ResultsOur study enrolled 173 patients with ischemic stroke or transient ischemic attack. The mean age was 68.38±14.31 (range 35-99) years, and 57.8% (n=100) of patients were male. dPVS were detected in 97% (n=168) of the patients. Among the whole population, half of the patients (n=87) had moderate to severe dPVS. According to the univariate analysis, age, hypertension, previous antiaggregant and/or anticoagulant use, and the high-density lipoprotein to low-density lipoprotein (HDL/LDL) ratio but not other lipid profiles, cerebral microbleed load, and cerebral white matter hyperintensities severity were found to be positively associated with dPVS number in the basal ganglia. After multivariate logistic regression analysis, only age and WMH severity remained statistically significant. ConclusionsdPVS are closely associated with other cSVD subtypes and aging. The studied lipid indices were not independently associated with moderate to severe dPVS in basal ganglia in ischemic stroke patients. The association of each lipid and HDL/LDL ratio needs to be further studied with a larger number of participants.

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Section: Discussionsupporting

confidence: 49%

Association of Dilated Perivascular Spaces With Lipid Indices in Ischemic Stroke Patients

Diker¹,

Gelener²,

Erem³

et al. 2022

Cureus

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“…Second, insulin resistance is a key pathological process during the development of CSVD [24,32]. Predictably, TyG index has been proved to be positively associated with a higher prevalence and burden of CSVD [9,[33][34][35][36], which was reported to predict a recurrent ischemic stroke or intracerebral hemorrhage in numerous studies [1,[37][38][39][40][41]. CSVD could make a contribution to the mechanism of increasing ischemic stroke recurrence by a higher TyG index.…”

Section: Discussionmentioning

confidence: 98%

Association of triglyceride-glucose index with ischemic stroke recurrence in nondiabetic patients with small vessel occlusion: a multicenter hospital-based prospective cohort study

Zhu

et al. 2022

Cardiovasc Diabetol

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Background Triglyceride-glucose (TyG) index is a simple and reliable surrogate marker of insulin resistance. Elevated TyG index was related to stroke recurrence. This study aimed to explore the associations between TyG index with ischemic stroke recurrence in nondiabetic patients with small vessel occlusion. Methods From November 1, 2016 to February 28, 2021, consecutive acute ischemic stroke patients admitted within 1 week after onset were screened. The stroke mechanism was determined based on medical history, laboratory examinations, cardiac examinations, vascular examinations and neuroimaging. Nondiabetic patients with small vessel occlusion were enrolled and followed up for 1 year. The primary outcome was ischemic stroke recurrence. Logistic regression and Kaplan–Meier survival curve were used to analyze the association of the TyG index and stroke recurrence. Results A total of 6100 acute ischemic stroke patients were screened, with 1970 nondiabetic patients with small vessel occlusion included and divided into 4 groups according to the TyG index quartiles (Q1: < 8.20; Q2: 8.20–8.53; Q3: 8.54–8.92; Q4: > 8.92). There were significant differences in age, body mass index, systolic blood pression, diastolic blood pressure, lipid-lowering agents, infarct location, fasting blood glucose, total cholesterol, triglyceride, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, uric acid, and stroke recurrence among the 4 groups. In the multi-adjusted models, compared to Q1 of the TyG index, the odds ratio for Q4 of the TyG index for stroke recurrence was 3.100 (1.366–8.019). The Kaplan–Meier survival (ischemic stroke-free) curves by quartiles of the TyG index also showed statistically significant differences (log-rank test, P = 0.004). Conclusions Our findings suggested that the TyG index was associated with ischemic stroke recurrence in nondiabetic patients with small vessel occlusion, and it could be a valuable biomarker for assessing the risk of ischemic stroke recurrence in these patients.

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“…Previous studies [ 15 – 17 ] only discussed LCI-related risk factors such as renal function. However, these studies only identified risk factors closely associated with LCI and did not integrate these risk factors into a model to predict LCI.…”

Section: Discussionmentioning

confidence: 99%

Application of machine learning model to predict lacunar cerebral infarction in elderly patients with femoral neck fracture before surgery

Huang

Tan

et al. 2022

BMC Geriatr

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Background Femoral neck fracture and lacunar cerebral infarction (LCI) are the most common diseases in the elderly. When LCI patients undergo a series of traumas such as surgery, their postoperative recovery results are often poor. Moreover, few studies have explored the relationship between LCI and femoral neck fracture in the elderly. Therefore, this study will develop a ML (machine learning)-based model to predict LCI before surgery in elderly patients with a femoral neck fracture. Methods Professional medical staff retrospectively collected the data of 161 patients with unilateral femoral neck fracture who underwent surgery in the Second Affiliated Hospital of Wenzhou Medical University database from January 1, 2015, to January 1, 2020. Patients were divided into two groups based on LCI (diagnosis based on cranial CT image): the LCI group and the non-LCI group. Preoperative clinical characteristics and preoperative laboratory data were collected for all patients. Features were selected by univariate and multivariate logistic regression analysis, with age, white blood cell (WBC), prealbumin, aspartate aminotransferase (AST), total protein, globulin, serum creatinine (Scr), blood urea nitrogen (Bun)/Scr, lactate dehydrogenase (LDH), serum sodium and fibrinogen as the features of the ML model. Five machine learning algorithms, Logistic regression (LR), Gradient Boosting Machine (GBM), Extreme Gradient Boosting (XGBoost), Random Forest (RF), and Decision tree (DT), were used in combination with preoperative clinical characteristics and laboratory data to establish a predictive model of LCI in patients with a femoral neck fracture. Furthermore, indices like the area under the receiver operating characteristic (AUROC), sensitivity, specificity, and accuracy were calculated to test the models’ performance. Results The AUROC of 5 ML models ranged from 0.76 to 0.95. It turned out that the RF model demonstrated the highest performance in predicting LCI for femoral neck fracture patients before surgery, whose AUROC was 0.95, sensitivity 1.00, specificity 0.81, and accuracy 0.90 in validation sets. Furthermore, the top 4 high-ranking variables in the RF model were prealbumin, fibrinogen, globulin and Scr, in descending order of importance. Conclusion In this study, 5 ML models were developed and validated for patients with femoral neck fracture to predict preoperative LCI. RF model provides an excellent predictive value with an AUROC of 0.95. Clinicians can better conduct multidisciplinary perioperative management for patients with femoral neck fractures through this model and accelerate the postoperative recovery of patients.

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The Triglyceride Glucose Index Is a Risk Factor for Enlarged Perivascular Space

Cited by 12 publications

References 41 publications

Association of Dilated Perivascular Spaces With Lipid Indices in Ischemic Stroke Patients

Association of Dilated Perivascular Spaces With Lipid Indices in Ischemic Stroke Patients

Association of triglyceride-glucose index with ischemic stroke recurrence in nondiabetic patients with small vessel occlusion: a multicenter hospital-based prospective cohort study

Application of machine learning model to predict lacunar cerebral infarction in elderly patients with femoral neck fracture before surgery

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