STUDY OF CYSTATIN-C AS A CARDIOVASCULAR RISK MARKER IN PATIENTS WITH CHRONIC KIDNEY DISEASE: CROSS-SECTIONAL ANALYTICAL STUDY

Objectives: The objective of this study was to evaluate the relationship between Cystatin C, lipid profile, and cardiovascular risk in patients with chronic kidney disease (CKD), particularly in a rural Indian population. The study aimed to assess whether Cystatin C could serve as a reliable biomarker for predicting cardiovascular events in CKD patients. Methods: This cross-sectional analytical study was conducted in a tertiary care hospital in central India from September 2015 to November 2018. A total of 100 participants were enrolled, including 50 CKD patients (with GFR <60 mL/min/1.73 m²) and 50 age- and sex-matched healthy controls. Fasting blood samples were collected to measure serum Cystatin C and lipid profile parameters (total cholesterol, triglycerides, high-density lipoprotein [HDL], low-density lipoprotein [LDL], and very low-density lipoprotein). CKD patients were followed for 6 months to monitor cardiovascular events. Statistical analyses included unpaired t-tests, Chi-squared tests, and correlation analysis. Results: Serum Cystatin C levels were significantly higher in CKD patients (3.8±0.96 mg/L) compared to controls (0.76±0.11 mg/L, p<0.001). CKD patients exhibited dyslipidemia, characterized by elevated total cholesterol, LDL, and triglyceride levels, and reduced HDL levels. The LDL/HDL ratio was significantly higher in CKD patients (3.73±1.44) compared to controls (2.72±0.79, p<0.001). Among CKD patients, those who developed cardiovascular events during follow-up had significantly higher Cystatin C levels (4.9±0.91 mg/L) compared to those who did not experience cardiovascular events (3.6±0.24 mg/L, p<0.001). Conclusion: Cystatin C is a reliable marker for renal dysfunction and an independent predictor of cardiovascular risk in CKD patients, particularly in rural populations. Elevated Cystatin C levels were closely associated with dyslipidemia and an increased risk of cardiovascular events. These findings suggest that Cystatin C could be a valuable tool for the early identification of CKD patients at high risk for cardiovascular complications. Incorporating Cystatin C into clinical practice could enhance risk stratification and improve management strategies for CKD patients.

Authors

Institutions

Publication Details

Journal
Asian Journal of Pharmaceutical and Clinical Research
Published
2025-02-07
DOI
https://doi.org/10.22159/ajpcr.2025v18i2.53536
Primary Topic
Blood Pressure and Hypertension Studies
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

STUDY OF CYSTATIN-C AS A CARDIOVASCULAR RISK MARKER IN PATIENTS WITH CHRONIC KIDNEY DISEASE: CROSS-SECTIONAL ANALYTICAL STUDY

Avinash Namdeo Jadhao, Umesh Kawalkar, Arun Tadas, HARSHAL PACHPOR
Asian Journal of Pharmaceutical and Clinical Research
Blood Pressure and Hypertension Studies
article

STUDY OF CYSTATIN-C AS A CARDIOVASCULAR RISK MARKER IN PATIENTS WITH CHRONIC KIDNEY DISEASE: CROSS-SECTIONAL ANALYTICAL STUDY

Avinash Namdeo Jadhao, Umesh Kawalkar, Arun Tadas, HARSHAL PACHPOR
article en

Abstract

Objectives: The objective of this study was to evaluate the relationship between Cystatin C, lipid profile, and cardiovascular risk in patients with chronic kidney disease (CKD), particularly in a rural Indian population. The study aimed to assess whether Cystatin C could serve as a reliable biomarker for predicting cardiovascular events in CKD patients. Methods: This cross-sectional analytical study was conducted in a tertiary care hospital in central India from September 2015 to November 2018. A total of 100 participants were enrolled, including 50 CKD patients (with GFR <60 mL/min/1.73 m²) and 50 age- and sex-matched healthy controls. Fasting blood samples were collected to measure serum Cystatin C and lipid profile parameters (total cholesterol, triglycerides, high-density lipoprotein [HDL], low-density lipoprotein [LDL], and very low-density lipoprotein). CKD patients were followed for 6 months to monitor cardiovascular events. Statistical analyses included unpaired t-tests, Chi-squared tests, and correlation analysis. Results: Serum Cystatin C levels were significantly higher in CKD patients (3.8±0.96 mg/L) compared to controls (0.76±0.11 mg/L, p<0.001). CKD patients exhibited dyslipidemia, characterized by elevated total cholesterol, LDL, and triglyceride levels, and reduced HDL levels. The LDL/HDL ratio was significantly higher in CKD patients (3.73±1.44) compared to controls (2.72±0.79, p<0.001). Among CKD patients, those who developed cardiovascular events during follow-up had significantly higher Cystatin C levels (4.9±0.91 mg/L) compared to those who did not experience cardiovascular events (3.6±0.24 mg/L, p<0.001). Conclusion: Cystatin C is a reliable marker for renal dysfunction and an independent predictor of cardiovascular risk in CKD patients, particularly in rural populations. Elevated Cystatin C levels were closely associated with dyslipidemia and an increased risk of cardiovascular events. These findings suggest that Cystatin C could be a valuable tool for the early identification of CKD patients at high risk for cardiovascular complications. Incorporating Cystatin C into clinical practice could enhance risk stratification and improve management strategies for CKD patients.

Asian Journal of Pharmaceutical and Clinical Research
Government Medical College (IN), King Edward Memorial Hospital and Seth G.S. Medical College (IN), Government Medical College and Hospital (IN)
Openalex Percentile: Top 96%
Blood Pressure and Hypertension Studies
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.