The Effect of Overweight and Obesity on Liver Biochemical Markers in Children and Adolescents

Magnus J. Johansen; Julie Gade; Stefan Stender; Christine Frithioff-Bøjsøe; Morten A. V. Lund; Elizaveta Chabanova; Henrik S. Thomsen; Oluf Pedersen; Cilius E. Fonvig; Torben Hansen; Jens-Christian Holm

Disclosures

J Clin Endocrinol Metab. 2020;105(2) 

In This Article

Materials and Methods

Study Populations

Two cohorts of children and adolescents aged 0.5 years to 26.5 years were invited to participate in the present study: 1) A population-based cohort of children and adolescents, recruited from schools across 11 municipalities in Zealand, Denmark, and 2) a cohort of children and adolescents with overweight or obesity recruited at The Children's Obesity Clinic, Department of Pediatrics, Copenhagen University Hospital Holbæk, Denmark.[24] Informed written consent was obtained from all of the participants aged 18 years and older and was obtained from the parents for participants younger than 18 years. The Ethics Committee of Region Zealand, Denmark (protocol no SJ-104) and the Danish Data Protection Agency approved the study. Phenotyping was performed by trained medical professionals, and included measurements of height and weight, a clinical examination, and a fasting venous blood sample. An extensive questionnaire was completed at home for the population-based cohort prior to the visit and at the hospital for the cohort with overweight or obesity.

Population-based Cohort

The population-based cohort was recruited from October 2010 to February 2015 (N = 2898). The exclusion criteria for this study were: 1) age younger than 6.0 or older than 18.0 years (N = 240), 2) no available data on liver biochemical markers (N = 56), 3) more than 30 days between anthropometrics and blood sampling (N = 94), 4) intake of medication known to influence liver enzymes (N = 10),[25,26] 5) a self-reported alcohol intake > 60 g alcohol per week (N = 43), 6) overweight or obesity (body mass index [BMI] > 90th percentile)[27] (N = 463), and 7) underweight (BMI < 10th percentile)[27] (N = 134).

Cohort of Children and Adolescents With Overweight or Obesity

The cohort of children and adolescents with overweight or obesity was recruited from January 2009 to April 2018 (N = 2846),[24,27] and all exhibited a BMI above the 90th percentile according to Danish BMI charts (corresponding to a BMI standard deviation score (SDS) > 1.28). Exclusion criteria were similar to those used in the population-based cohort, resulting in the following exclusion numbers: 1) age (N = 181); 2) missing data (N = 12); 3) more than 30 days between anthropometrics and blood samples (N = 456); 4) medication (N = 33), and 5) alcohol intake (N = 9).

Anthropometric Measurements

Body weight was measured in light clothing and without shoes to the nearest 100 g on a BC-418 Segmental Body Composition Analyzer (Tanita, Tokyo, Japan). Height was measured to the nearest 1 mm by a stadiometer. BMI-SDS was calculated using the LMS method according to Danish BMI charts.[27]

Biochemical Analyses

Blood samples were obtained by venipuncture of the antecubital vein from 7 am to 9 am after a fast of at least 8 hours. Analysis of plasma concentrations of ALT, AST, LDH, GGT, bilirubin, and ALP were processed immediately in the laboratory of Copenhagen University Hospital Holbæk, Denmark. All analyses were performed on a Cobas®6000 (Roche Diagnostics, Mannheim, Germany) until May 15, 2013, and on a Dimension Vista®1500 (Siemens Healthcare, Erlangen, Germany) using enzymatic colorimetric method from May 16, 2013 onwards.

Pubertal Data

Pubertal staging was performed according to the criteria of Marshall and Tanner (on the basis of breast development in girls and testicular volume in boys).[28] For participants from the population-based cohort, Tanner stage was self-reported using picture pattern recognition. A pediatrician examined the participants from the cohort with overweight and obesity at the first visit to the clinic. Self-reported Tanner-staging has been found adequate for distinguishing between pre- and post-pubertal developmental stage.[29] Accordingly, participants from both cohorts were grouped into either pre- or post-pubertal based on the Tanner staging (1 = prepubertal, 2–5 = postpubertal).[29]

1H-MRS Evaluation

Liver fat content was measured on a subset of both the population-based cohort, and the cohort with overweight or obesity on a 3T Achieva MR imaging system (Philips Medical Systems, Best, the Netherlands). The spectroscopy voxel (11 mm × 11 mm × 11 mm) was placed in the right liver lobe, avoiding major vessels and bile ducts. The acquired spectra were fitted to obtain their areas by an experienced senior magnetic resonance (MR) physicist using a standard postprocessing protocol at the MR imaging system. Details of the applied MR methodology have previously been described.[30,31] We defined hepatic steatosis using 2 different cutoffs of liver fat content: 1) above 5%, a routinely-used cutoff that is based on the upper normal limit of liver fat content in lean adults; and 2) above 1.5%, a cutoff we recently found to more accurately represent the upper normal limit of liver fat content in children.[31]

Statistical Analyses

Statistical analyses were performed using R statistical software (v.3.5.0). Age- and sex-specific percentiles and percentile curves were calculated using the Generalized Additive Models for Location Scale and Shape (GAMLSS) package, using the Box–Cox transformation distribution family. To examine effects of age on biochemical markers of liver damage, we grouped the participants into 3 age groups: 6–9, 10–13, and 14–18 years of age. As most of the biochemical parameters were nonnormally distributed, a Wilcoxon rank-sum test was used to compare differences between age groups. To compare the accuracy of ALT as a diagnostic tool for identifying individuals with and without hepatic steatosis, receiver operating characteristic (ROC) analyses and area under the curve (AUC) were calculated using the R package "pROC".[32] The optimal cutoff for classifying steatosis was determined by the Youden index.

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