Jinling Wang1, Hui-Qi Qu2, Ke Huang1, Wei Wu1, Chunlin Wang3, Li Liang3, Chunxiu Gong4, Feng Xiong5, Feihong Luo6, Geli Liu7, Shaoke Chen8, Lifeng Tian2, Hakon Hakonarson2,9, Junfen Fu1. 1. Department of Endocrinology, The Children's Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, Hangzhou, China. 2. Center for Applied Genomics, The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, United States. 3. Department of Pediatrics, First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China. 4. Department of Pediatric Endocrinology, Genetic and Metabolism, Beijing Children's Hospital of Capital Medical University, National Center for Children's Health, Beijing, China. 5. Department of Endocrinology, Children's Hospital of Chongqing Medical University, Chongqing, China. 6. Department of Endocrinology, Children's Hospital of Shanghai Fudan University, Shanghai, China. 7. Department of Pediatrics, General Hospital of Tianjin Medical University, Tianjin, China. 8. Department of Pediatrics, Maternal and Child Health Hospital of Guangxi Zhuang Autonomous Region, Nanning, China. 9. Department of Pediatrics and Division of Human Genetics, University of Pennsylvania, Philadelphia, Pennsylvania, United States.
Abstract
AIM: This study investigated the pattern of liver enzymes in a large cohort of Chinese children and adolescents, including 16 383 individuals aged 4-18 years old recruited at six medical centres in China. METHODS: Clinical data were collected including weight, height, blood pressure, alanine aminotransferase, aspartate aminotransferase and fasting lipid panel. We used an unsupervised machine learning algorithm, the K-means clustering method, to identify different patterns of increased liver enzymes. RESULTS: Six clusters of elevated enzymes patterns were identified. The most common in 2.18% (325) of youth was elevated transaminases in the absence of features of metabolic syndrome(MetS), and they were thinner, and more likely to be from urban areas. The second cluster, with 1.47% (n = 220) youth had the most notable MetS features. They were older, obese and had central obesity, higher BP, triglycerides cholesterol and lower high-density lipoprotein cholesterol. Cluster 3 (0.6%, N = 90) had mild MetS, and cluster 4 (0.06%, N = 9), 5 (0.03%, N = 5) and 6 (0.007%, N = 1) were not related to MetS. CONCLUSIONS: We identified two distinct groups of children with both increased liver enzymes and MetS features in this population sample of Chinese children. One of the two groups had increased liver enzymes as the predominant clinical features at a younger age, suggesting genetic susceptibility to the condition. Further work to understand the increased MetS risk in cluster 2 is warranted.
AIM: This study investigated the pattern of liver enzymes in a large cohort of Chinese children and adolescents, including 16 383 individuals aged 4-18 years old recruited at six medical centres in China. METHODS: Clinical data were collected including weight, height, blood pressure, alanine aminotransferase, aspartate aminotransferase and fasting lipid panel. We used an unsupervised machine learning algorithm, the K-means clustering method, to identify different patterns of increased liver enzymes. RESULTS: Six clusters of elevated enzymes patterns were identified. The most common in 2.18% (325) of youth was elevated transaminases in the absence of features of metabolic syndrome(MetS), and they were thinner, and more likely to be from urban areas. The second cluster, with 1.47% (n = 220) youth had the most notable MetS features. They were older, obese and had central obesity, higher BP, triglyceridescholesterol and lower high-density lipoprotein cholesterol. Cluster 3 (0.6%, N = 90) had mild MetS, and cluster 4 (0.06%, N = 9), 5 (0.03%, N = 5) and 6 (0.007%, N = 1) were not related to MetS. CONCLUSIONS: We identified two distinct groups of children with both increased liver enzymes and MetS features in this population sample of Chinese children. One of the two groups had increased liver enzymes as the predominant clinical features at a younger age, suggesting genetic susceptibility to the condition. Further work to understand the increased MetS risk in cluster 2 is warranted.
Authors: Jinling Wang; Hu Lin; Valentina Chiavaroli; Binghan Jin; Jinna Yuan; Ke Huang; Wei Wu; Guanping Dong; José G B Derraik; Junfen Fu Journal: Front Endocrinol (Lausanne) Date: 2022-05-19 Impact factor: 6.055