ASN's Mission

To create a world without kidney diseases, the ASN Alliance for Kidney Health elevates care by educating and informing, driving breakthroughs and innovation, and advocating for policies that create transformative changes in kidney medicine throughout the world.

learn more

Contact ASN

1401 H St, NW, Ste 900, Washington, DC 20005

email@asn-online.org

202-640-4660

The Latest on X

Kidney Week

Abstract: SA-PO1193

Association Between Acylcarnitine Metabolism and Cardiovascular Disease in Patients with CKD

Session Information

Category: CKD (Non-Dialysis)

  • 2302 CKD (Non-Dialysis): Clinical, Outcomes, and Trials

Authors

  • Xu, Lingling, Center for Kidney Diseases, the Second Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
  • Jiang, Lei, Center for Kidney Diseases, the Second Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
  • Wang, Lulu, Center for Kidney Diseases, the Second Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
  • Dai, Chunsun, Center for Kidney Diseases, the Second Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, China
Background

Acylcarnitines have emerged as important biomarkers in various diseases, including metabolic disorders, neuropsychiatric conditions, and cardiovascular diseases (CVD). However, research on the relationship between acylcarnitine metabolism and CVD in chronic kidney disease (CKD) patients is limited. This study aims to investigate the association between serum acylcarnitine profiles and the presence of CVD in patients with CKD.

Methods

A total of 451 CKD patients were included in this cross-sectional study. Serum levels of 34 acylcarnitine species were measured using liquid chromatography-tandem mass spectrometry (LC-MS/MS). Patients were divided into two groups based on the presence or absence of CVD, and clinical characteristics and serum acylcarnitine levels were compared. Univariate and multivariate logistic regression analyses were performed to identify factors associated with CVD in these patients. Additionally, a predictive risk model was established.

Results

Among the patients, 285 (63.2%) were male, with a median estimated glomerular filtration rate (eGFR) of 51.1 (26.56, 75.94) mL/min/1.73 m2. A total of 87 patients (19.3%) had concurrent CVD. Compared to those without CVD, patients with CVD were older, had lower eGFR, and exhibited lower levels of hemoglobin, total cholesterol, triglycerides, and low-density lipoprotein cholesterol (P < 0.05). Of the 34 acylcarnitine species measured, 16 were significantly elevated in the CVD group. C4, C10:1, C4DC, C8, eGFR, and triglycerides (TG) were identified as independent factors associated with CVD in CKD patients, with increased serum levels of C4 and C10:1 positively correlating with CVD. The composite model exhibited strong performance, with an area under the curve (AUC) of 0.862 (95% CI: 0.790–0.930), demonstrating high sensitivity (80.6%) and specificity (81.2%).

Conclusion

Patients with CKD and CVD exhibit significantly higher serum levels of 16 acylcarnitine species compared to those without CVD. C4, C10:1, C4DC, C8, eGFR, and TG are independently associated with the presence of CVD in CKD patients. The composite prediction model may effectively identifiy high-risk CVD populations among CKD patients.

Funding

  • Government Support – Non-U.S.

Digital Object Identifier (DOI)