Lymphocyte To High-Density Lipoprotein Ratio As A New Indicator Of Inflammation And Metabolic Syndrome

Abstract

Purpose

Metabolic syndrome (MetS), which is a global public health problem, is a state of chronic low-grade inflammation. This study looked at the changes in hematological parameters and the predictive value of the lymphocyte to high-density lipoprotein cholesterol (HDL-C) ratio (LHR) as a new index in subjects with and without MetS in coastal cities in southern China.

Patients and methods

In this cross-sectional study, there were 852 participants (n = 598 with MetS and n = 254 without MetS). MetS was defined in accordance with the National Cholesterol Education Program, Adult Treatment Panel III (NCEP-ATP III) criteria.

Results

MetS was positively correlated with white blood cell count, total lymphocyte count, neutrophil count, red blood cell count, hematocrit, hemoglobin, and high-sensitivity C-reactive protein levels (p<0.05). In addition, there was a positive correlation between LHR and the number of metabolic risk factors for MetS. In a logistic regression analysis, LHR (odds ratio: 4.117; 95% CI: 2.766–6.309; p<0.001) was an independent predictor of MetS. When a receiver operating characteristic (ROC) curve analysis was used to assess the value of LHR for predicting MetS, the area under the curve yielded a cut-off value of 1.657, with a sensitivity of 65% and a specificity of 64% (p<0.0001).

Conclusion

In summary, MetS can involve changes in blood parameters, and LHR may be a useful marker of inflammation to assess the presence and severity of MetS.

Keywords:

• inflammation
• marker
• metabolic syndrome
• lymphocyte to high-density lipoprotein cholesterol ratio

Funding

This study was supported by Risk factors and prediction model of chronic kidney disease caused by metabolic syndrome: A multicentric prospective cohort study Clinical trial training project of Southern Medical University (LC2016PY047, 2016), Science and Technique Program of Guangzhou (201604020015, 2015), and South Wisdom Valley Innovative Research Team Program (CXTD-004, 2014), The National Natural Science Foundation of China (81873620).

Disclosure

The authors report no conflicts of interest in this work.