Is measurement of non-HDL cholesterol an effective way to identify the metabolic syndrome?
OBJECTIVE
The metabolic syndrome (MetS) has been shown to predict coronary heart disease (CHD). Non-high-density lipoprotein cholesterol (non-HDL-C) is also known to predict CHD, and recent evidence indicated non-HDL-C was able to predict MetS in adolescents. The study aim was to determine whether non-HDL-C serves as a useful metabolic marker for MetS in adults.


RESULTS
Fasting non-HDL-C measurements were obtained in 366 non-diabetic adults not on lipid-lowering therapy. In addition to traditional non-HDL-C cut-points based on Adult Treatment Panel III guidelines, receiver-operating characteristic curve analysis was used to identify an optimal cut-point for predicting MetS. A secondary goal was to assess the relationship between non-HDL-C and insulin resistance, defined as the upper tertile of steady-state plasma glucose concentrations measured during the insulin suppression test. Prevalence of MetS was 40% among participants. Those with MetS had higher mean non-HDL-C (4.17 ± 1.0 vs 3.70 ± 0.85 mmol/L, p < 0.001), and the upper vs lower tertile of non-HDL-C concentrations was associated with 1.8-fold increased odds of MetS (p < 0.05). Traditional non-HDL-C cut-points ≥ 4.14 and ≥ 4.92 mmol/L demonstrated respective sensitivities 46% and 24% (specificities 72% and 89%) for identifying MetS. The optimal non-HDL-C cut-point ≥ 4.45 mmol/L had sensitivity 39% (specificity 82%). Comparable results were observed when non-HDL-C was used to identify insulin resistance.


CONCLUSIONS
While MetS was associated with increased non-HDL-C, an effective non-HDL-C threshold to predict MetS in adults was not identified. Dyslipidemic nuances may explain why non-HDL-C may be less useful as a metabolic marker for MetS and/or insulin resistance than for CHD.
Division of Endocrinology, Gerontology, and Metabolism, Department of Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA. Electronic address: aliceliu@stanford.edu.
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Reaven
D000328:Adult; D000465:Algorithms; D015415:Biomarkers / Q000097:blood; D015992:Body Mass Index; D002784:Cholesterol / Q000097:blood*; D008076:Cholesterol, HDL / Q000097:blood; D050171:Dyslipidemias / Q000097:blood / Q000150:complications; D005260:Female; D006801:Humans; D007333:Insulin Resistance; D008297:Male; D024821:Metabolic Syndrome / Q000097:blood / Q000150:complications / Q000175:diagnosis* / Q000453:epidemiology; D008875:Middle Aged; D009765:Obesity / Q000150:complications; D050177:Overweight / Q000150:complications; D015995:Prevalence; D012306:Risk; D012495:San Francisco / Q000453:epidemiology; D012680:Sensitivity and Specificity; D015854:Up-Regulation
D015415:Biomarkers; D008076:Cholesterol, HDL; D002784:Cholesterol
Coronary heart disease risk; Insulin resistance; Metabolic syndrome; Non-high-density lipoprotein cholesterol; Obesity