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. 2025 Mar 15;17(3):1803-1813.
doi: 10.62347/MXEJ5508. eCollection 2025.

Predictive value of platelet distribution width on organ damage in patients with metabolic syndrome: a retrospective case-control study

Jiaxing Li  1 , Lirong Zhou  2 , Ben Li  2 , Rui Tian  2 , Guangxin Hu  1 , Zhijun Liu  1
Affiliations

Predictive value of platelet distribution width on organ damage in patients with metabolic syndrome: a retrospective case-control study

Jiaxing Li et al. Am J Transl Res. .

Abstract

Objectives: Metabolic syndrome (MS) is a cluster of metabolic disorders characterized by damage to multiple organs. Platelet distribution width (PDW) has been used to assess the progression of several metabolic disorders, including left ventricular hypertrophy (LVH) and diabetic nephropathy (DN). Therefore, this study aimed to evaluate the predictive value of PDW in relation to organ damage in patients with MS.

Methods: The study included 151 patients with MS and 113 healthy controls. Clinicopathological data, including sex, age, abdominal circumference, blood pressure, and body mass index (BMI), were collected. The predictive potential of PDW was assessed by analyzing its correlation with MS progression, LVH, atherosclerosis, and kidney function.

Results: The analysis revealed that patients in the MS group had higher levels of BMI, abdominal circumference, systolic blood pressure (SBP), diastolic blood pressure (DBP), triglycerides (TG), and fasting plasma glucose (FPG), and lower levels of high-density lipoprotein cholesterol (HDL-C), compared with controls. PDW was positively correlated with BMI, abdominal circumference, SBP, DBP, and FPG, and negatively correlated with HDL-C. FPG, SBP, and HDL-C were identified as independent parameters contributing to changes in PDW. Furthermore, heart function was positively related to PDW levels, while kidney function was negatively related. Logistic regression analysis further demonstrated that PDW was an independent risk factor for LVH, atherosclerosis, and kidney dysfunction.

Conclusions: PDW could serve as a promising predictive indicator for organ damage associated with the progression of MS.

Keywords: Atherosclerosis; kidney dysfunction; left ventricular hypertrophy; metabolic syndrome; platelet distribution width.

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Conflict of interest statement

None.

Figures

Figure 1
Figure 1
Flow chart for inclusion and exclusion of the patients.
Figure 2
Figure 2
Univariate logistic regression analysis of factors influencing MS progression and ROC analysis of the predictive value of platelet distribution width (PDW) regarding metabolic syndrome (MS) progression. A. Forest plot of the logistic analysis. B. ROC analysis of PDW the predictive value of PDW regarding MS progression. BMI, body mass index; SBP, systolic blood pressure; DBP, diastolic blood pressure; FPG, fasting plasma glucose; TG, triglycerides; TC, total cholesterol; LDL-C, low-density lipoprotein cholesterol; HDL-C, high-density lipoprotein cholesterol.
Figure 3
Figure 3
Univariate logistic regression analysis of factors influencing left ventricular hypertrophy (LVH), multivariate logistic regression analysis of LVH in relation to platelet distribution width (PDW) and other factors, and receiver operating curve (ROC) analysis of the predictive value of PDW for LVH. A. Forest plot of univariate logistic analysis. B. Forest plot of multivariate logistic analysis. C. ROC analysis of PDW, demonstrating the predictive value of PDW for LVH. BMI, body mass index; SBP, systolic blood pressure; DBP, diastolic blood pressure; TG, triglycerides; TC, total cholesterol; LDL-C, low-density lipoprotein cholesterol; HDL-C, high-density lipoprotein cholesterol; FPG, fasting plasma glucose.
Figure 4
Figure 4
Univariate logistic regression analysis of factors influencing atherosclerosis, multivariate logistic regression analysis of atherosclerosis in relation to platelet distribution width (PDW) and other factors, and receiver operating curve (ROC) analysis of the predictive value of PDW for atherosclerosis. A. Forest plot of the univariate logistic analysis. B. Forest plot of the multivariate logistic analysis. C. ROC analysis showing the predictive value of PDW for atherosclerosis. BMI, body mass index; SBP, systolic blood pressure; DBP, diastolic blood pressure; TG, triglycerides; TC, total cholesterol; LDL-C, low-density lipoprotein cholesterol; HDL-C, high-density lipoprotein cholesterol; FPG, fasting plasma glucose.
Figure 5
Figure 5
Univariate logistic regression analysis of factors influencing renal dysfunction, multivariate logistic regression analysis of renal dysfunction in relation to platelet distribution width (PDW) and other factors, and receiver operating curve (ROC) analysis of the predictive value of PDW for atherosclerosis. A. Forest plot of the univariate logistic analysis. B. Forest plot of the multivariate logistic analysis. C. ROC analysis of PDW, demonstrating the predictive value of PDW for renal insufficiency. BMI, body mass index; SBP, systolic blood pressure; DBP, diastolic blood pressure; TG, triglycerides; TC, total cholesterol; LDL-C, low-density lipoprotein cholesterol; HDL-C, high-density lipoprotein cholesterol; FPG, fasting plasma glucose.
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