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Ping Wang,&ast; Mengchao Wang,&ast; Zhe Xie,&ast; Zhongwen Zhi, Yuqian Wang, Fan Liu, Yufeng Liu, Liandong Zhao Department of Neurology, the Affiliated Huai’an Hospital of Xuzhou Medical University, Huai’an, Jiangsu, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Yufeng Liu; Liandong Zhao, Department of Neurology, The Affiliated Huai’an Hospital of Xuzhou Medical University, Huai’an, Jiangsu, People’s Republic of China, Email lyf996226@163.com; zld202109@163.comPurpose: Research suggests that insulin resistance (IR) is associated with acute ischemic stroke (AIS) and depression. The use of insulin-based IR assessments is complicated. Therefore, we explored the relationship between four non-insulin-based IR indices and post-stroke depression (PSD).Patients and Methods: A total of 638 consecutive AIS patients were enrolled in this prospective cohort study. Clinical data were collected to compute indices such as the triglyceride glucose (TyG) index, triglyceride glucose-body mass index (TyG-BMI), insulin resistance metabolic score (METS-IR), and triglyceride/high-density lipoprotein cholesterol ratio (TG/HDL-C). One month post-stroke, neuropsychological assessments were conducted using the 17-item Hamilton Depression Scale. Binary logistic regression analysis was performed to explore the relationship between the four non-insulin-based IR indices and PSD.Results: Ultimately, 381 patients completed the 1-month follow-up, including 112 (29.4%) with PSD. The PSD group exhibited significantly higher levels of the four IR indices compared to the non-PSD group. Logistic regression analysis demonstrated that these indicators were independently associated with PSD occurrence, both before and after adjusting for potential confounders (all P < 0.001). Tertile analyses indicated that the highest tertile group had a greater risk of PSD occurrence than the lowest tertile group for four IR indicators, even after adjusting for potential confounders (all P < 0.05). Restricted cubic spline analysis revealed a linear dose-response relationship between the four IR indices and PSD. In the subgroup analysis, only the TyG index showed a significant interaction with diabetes (P for interaction = 0.014). The area under curve values for the TyG index, TyG-BMI, METS-IR, and TG/HDL-C were 0.700, 0.721, 0.711, and 0.690, respectively.Conclusion: High TyG index, TyG-BMI, METS-IR, and TG/HDL-C at baseline were independent risk factors for PSD in AIS. Each of these indicators exhibits predictive value for PSD occurrence, aiding in the early identification of high-risk groups.Keywords: acute ischemic stroke, post-stroke depression, insulin resistance, non-insulin-based insulin resistance indices

Non-insulin-based insulin resistance (IR) indices serve as the indicators of metabolic syndrome (MetS) but have limited value for predicting clinical outcomes. Whether the obesity paradox affects the predictive value of these indicators in patients with chronic kidney disease (CKD) remains unknown. We investigated whether MetS and non-insulin-based IR indices can predict all-cause mortality and renal outcomes in a prospective observational study with stage 1–4 CKD Asians (N = 2,457). These IR indices were associated with MetS. A Cox regression model including body mass index (BMI) revealed an association between MetS and renal outcomes. Among the IR indices, only high triglyceride–glucose (TyG) index was associated with adverse renal outcomes: the hazard ratio of Q4 quartile of the TyG index was 1.38 (1.12–1.70). All-cause mortality was marginally associated with MetS but not high IR indices. Low TyG and TyG–BMI indices as well as low BMI and triglyceride were paradoxically associated with increased risks of clinical outcomes. The triglyceride-to-high-density lipoprotein cholesterol ratio and metabolic score for IR indices were not associated with clinical outcomes. In conclusion, MetS and TyG index predict renal outcome and obesity paradox affects the prediction of IR indices in patients with stage 1–4 CKD.