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.
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.
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.
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.

© 2020 Paediatrics and Child Health Division (The Royal Australasian College of Physicians).

References

PubMed