Predictive analysis of dyslipidemia in non-obese and lean individuals using body composition analyzer

Fujie Wang; Ting Zhao; Weiwei wang; Qianqian Dai; Qiaoqiao wang; Xianghua Ma; Lin Jiang

doi:10.23751/pn.v27i3.16185

Predictive analysis of dyslipidemia in non-obese and lean individuals using body composition analyzer

Authors

Fujie Wang Nutritional Department, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
Ting Zhao Nutritional Department, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
Weiwei wang Department of Critical Care Medicine, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
Qianqian Dai Nutritional Department, Xuzhou Cancer Hospital, Xuzhou, China
Qiaoqiao wang Neurology Department, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
Xianghua Ma Nutritional Department, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
Lin Jiang Nutritional Department, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China

Keywords:

dyslipidemia, non-alcoholic fatty liver, human body composition

Abstract

Objective: The aim is to examine the predictive value of body composition analysis and anthropometry for dyslipidemia and NAFLD in non-obese and lean individuals.

Method: A descriptive study was conducted to select non-obese and lean individuals who received nutritional counseling at our hospital's nutrition department from January 2023 to December 2023 and were assessed by professional clinical nutritionists for anthropometric measurements. Applying the bioelectrical impedance method to analyze human body composition involves collecting medical history data and laboratory test results. Comprehensively analyze the impact of human body composition analysis and anthropometric indicators on the risk of blood lipids and non-alcoholic fatty liver disease (NAFLD) in non-obese and lean individuals.

Results: The final analysis included 190 individuals. The detection rate of abnormal FPG metabolism was 21.1%, the overall detection rate of abnormal blood lipid metabolism was 58.9%, and the detection rate of abnormal sugar and lipid metabolism was 85.3%. FPG is positively correlated with WHR and the risk of NAFLD. TC is positively correlated with PBF, the risk of NAFLD, and WHR. TG is positively correlated with the risk of NAFLD and negatively correlated with SMM and SMI. LDL-C is positively correlated with PBF, WHR, and the risk of NAFLD. HDL-C is negatively correlated with the risk of NAFLD and WHR, and positively correlated with SMM. PBF is an independent risk factor for increased FPG and TC, and it has a significant impact on HDL (P < 0.05).

Conclusion: Non-obese and lean individuals also have a risk of hyperlipidemia and non-alcoholic fatty liver disease (NAFLD). PBF measured by human body composition analysis and WHR, SMM, and SMI obtained from human body measurements are closely related to each other. They have reference value for alerting to blood lipid abnormalities and monitoring blood lipid levels.

Author Biography

Fujie Wang, Nutritional Department, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China

Nutritional Department

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