Frontiers in endocrinology · 2026
Metrnl as a predictive biomarker for postprandial hypertriglyceridemia in overweight and obese populations.
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Wang Xiaoyu, Tang Yale, Zeng Shaojing, Li Luxuan, Hou Yilin, Liu Dandan, Tian Peipei, Song Guangyao
Аннотация
PURPOSE: The relationship between adipokine meteorin-like protein (Metrnl) and postprandial hypertriglyceridemia (PHTG) in overweight and obese populations remains unclear. This study examined the association between serum Metrnl and PHTG with normal fasting lipid profiles, using a standardized oral fat tolerance test (OFTT) to classify fat tolerance. The aim was to explore potential therapeutic targets for early obesity intervention.
PATIENTS AND METHODS: We enrolled 105 adults with normal fasting lipid profiles who met Chinese lipid management criteria for low-risk atherosclerotic cardiovascular disease (ASCVD) prevention. Participants were grouped as control (CON), overweight (OW), or obese (OB). All underwent an OFTT, with venous blood collected fasting serum Metrnl, total cholesterol (TC), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), fasting insulin (FINS). Venous blood samples were collected at 1, 2, 3, and 4 hours postprandially to quantitatively analyze the dynamic changes in serum lipid profiles.
RESULTS: Serum Metrnl showed a significant negative correlation with PHTG (r = -0.473, P < 0.001), fasting TG (r = -0.370, P < 0.001), FINS (r = -0.261, P = 0.007). Multivariate regression identified fasting TG as a risk factor for PHTG. Each 0.1 mmol/L increment in fasting triglycerides was significantly associated with a 76.9% higher risk of PHTG. Metrnl was identified as protective (OR = 0.211, P < 0.001), the protective cutoff for Metrnl was 2.11ng/ml. A combined model of fasting TG and Metrnl improved PHTG prediction over fasting TG or Metrnl alone, with ROC analysis showing an AUC of 0.908, sensitivity of 82.7%, and specificity of 90.6%.
CONCLUSIONS: Overweight and obese adults with normal fasting lipid profiles are at high risk of PHTG. Low serum Metrnl is closely associated with early lipid abnormalities and insulin resistance. Combining Metrnl with TG enhances diagnostic accuracy for PHTG.
PATIENTS AND METHODS: We enrolled 105 adults with normal fasting lipid profiles who met Chinese lipid management criteria for low-risk atherosclerotic cardiovascular disease (ASCVD) prevention. Participants were grouped as control (CON), overweight (OW), or obese (OB). All underwent an OFTT, with venous blood collected fasting serum Metrnl, total cholesterol (TC), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), fasting insulin (FINS). Venous blood samples were collected at 1, 2, 3, and 4 hours postprandially to quantitatively analyze the dynamic changes in serum lipid profiles.
RESULTS: Serum Metrnl showed a significant negative correlation with PHTG (r = -0.473, P < 0.001), fasting TG (r = -0.370, P < 0.001), FINS (r = -0.261, P = 0.007). Multivariate regression identified fasting TG as a risk factor for PHTG. Each 0.1 mmol/L increment in fasting triglycerides was significantly associated with a 76.9% higher risk of PHTG. Metrnl was identified as protective (OR = 0.211, P < 0.001), the protective cutoff for Metrnl was 2.11ng/ml. A combined model of fasting TG and Metrnl improved PHTG prediction over fasting TG or Metrnl alone, with ROC analysis showing an AUC of 0.908, sensitivity of 82.7%, and specificity of 90.6%.
CONCLUSIONS: Overweight and obese adults with normal fasting lipid profiles are at high risk of PHTG. Low serum Metrnl is closely associated with early lipid abnormalities and insulin resistance. Combining Metrnl with TG enhances diagnostic accuracy for PHTG.