Clustering of Metabolic Risk Factors Is Associated with High-Normal Levels of Liver Enzymes Among 6- to 8-Year-Old Children: The PANIC Study

Abstract

Background:

We studied the associations of clustering of metabolic risk factors with plasma levels of alanine aminotransferase (ALT) and gamma-glutamyl transferase (GGT) in healthy prepubertal children.

Methods:

The subjects were a representative population sample of 492 children 6–8 years of age. We assessed body fat percentage (dual-energy X-ray absorptiometry), body mass index, waist circumference, systolic and diastolic blood pressure, glucose, insulin, high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), triglycerides, ALT, GGT, and high-sensitivity C-reactive protein (hsCRP) and calculated a continuous metabolic syndrome score variable. We also used factor analysis to examine whether high-normal liver enzymes are a feature of metabolic syndrome among children.

Results:

Children with overweight or obesity, defined by International Obesity Task Force (IOTF) criteria, had a 2.1-times higher risk of having ALT and a 4.5-times higher risk of having GGT in the highest fifth of its distribution than normal weight children. Children in the highest sex-specific third of metabolic syndrome score, body fat percentage, waist circumference, and insulin had a two to three times higher risk of being in the highest fifth of ALT and GGT. Moreover, children in the highest third of glucose and hsCRP had a 2.5-fold risk of being in the highest fifth of GGT. First-order factor analysis yielded three factors; the first included insulin, glucose, and triglycerides; the second waist circumference, insulin, GGT, and hsCRP; and the third HDL-C, triglycerides, waist circumference, and insulin. Second-order factor analysis yielded a single metabolic syndrome factor, explaining 64.1% of the variance.

Conclusions:

Clustering of metabolic risk factors, particularly excess body fat, is associated with high-normal levels of ALT and GGT in prepubertal children. High-normal levels of liver enzymes, especially GGT, and systemic low-grade inflammation could be considered features of metabolic syndrome among children. Subtle changes in liver function may play an important role in the pathogenesis of metabolic syndrome beginning in childhood.

Introduction

T he epidemic of overweight and obesity in children has been accompanied with increase in the prevalence of pediatric metabolic syndrome, which is a cluster of risk factors for type 2 diabetes and cardiovascular disease, including abdominal obesity, insulin resistance, impaired glucose tolerance, dyslipidemia, and elevated blood pressure.^1,2 Pediatric metabolic syndrome has been defined using artificial cutoff points for its components based on the definitions of adulthood metabolic syndrome.^1,2 Such definitions are problematic, because the levels of metabolic and cardiovascular risk factors above which adverse health consequences start to occur among children are unknown. Therefore, recent studies among children have emphasized clustering of risk factors based on metabolic syndrome scores and factor analysis, using continuous variables for the components of metabolic syndrome.^3
–5 Clustering of risk factors tracks from early childhood into adulthood⁶ and may predict adulthood metabolic syndrome, type 2 diabetes, and cardiovascular disease.^7,8 Thus, there is an urgent need to better understand the pathophysiology and determinants of risk factor clustering in childhood.

The prevalence of pediatric nonalcoholic fatty liver disease (NAFLD) increases with age and the level of overweight or obesity.⁹ Plasma concentrations of liver enzymes, such as alanine aminotransferase (ALT) and gamma-glutamyl transferase (GGT), may be useful tools in the screening of pediatric NAFLD.¹⁰ Overweight and other components of metabolic syndrome have been related to high-normal plasma levels of ALT and GGT among Pima Indian children¹¹ and of ALT in preadolescents and adolescents from Louisiana.¹² However, little is known about the associations of metabolic syndrome and its components with plasma ALT and GGT concentrations and the role of these liver enzymes as features of metabolic syndrome among healthy prepubertal children.

We investigated the associations of clustering of metabolic risk factors, using a continuous metabolic syndrome score variable and continuous variables for the components of metabolic syndrome, with plasma levels of ALT and GGT, in a representative population sample of prepubertal children from Finland. We also studied by factor analysis whether these liver enzymes are a feature of metabolic syndrome among prepubertal children.

Methods

Study population

The present study is part of the Physical Activity and Nutrition in Children (PANIC) Study, which is a 2-year exercise and diet intervention study in a representative population sample of prepubertal children from the city of Kuopio, Finland. Altogether 736 children 6–8 years of age who started the first grade in primary schools of Kuopio in 2007–2009 were invited to participate in the baseline examinations between October, 2007, and November, 2009. Of the 736 invited children, 512 (70%) participated in the baseline examinations. Data on variables of interest were available for 492 children (232 girls, 260 boys), who were included in the final study sample. The study protocol was approved by the Research Ethics Committee of the Hospital District of Northern Savo. Both children and their parents gave their written informed consent.

Assessments of body composition

Body fat mass, fat percentage, and lean mass were measured after voiding, in supine position and light clothing and after removing all metal objects by a Lunar^® dual energy X-ray absorptiometry (DXA) device (Lunar Prodigy Advance; GE Medical Systems, Madison, WI). Body weight was measured twice after overnight fasting, empty bladdered, and standing in light underwear by a calibrated InBody^® 720 bioelectrical impedance device (Biospace, Korea) to an accuracy of 0.1 kg. The mean of these two values was used for the analyses. Body height was measured three times in the Frankfurt plane without shoes by a wall-mounted stadiometer to an accuracy of 0.1 cm. Waist circumference was measured three times after expiration at mid distance between the bottom of the rib cage and the top of the iliac crest with an unstretchable measuring tape to an accuracy of 0.1 cm. The mean of the nearest two values of body height and waist circumference were used for the analyses. Body mass index (BMI) was calculated as body weight (kg) divided by body height (m) squared. BMI-standard deviation score (SDS) was assessed by the national references published recently.¹³ The prevalence of overweight and obesity was assessed using the age and sex specific BMI cutoffs by the International Obesity Task Force (IOTF).¹⁴

Biochemical assessments

The children were asked to fast for 12 h before blood sampling. Biochemical analyses were done using Cobas 6000 analyzers (Hitachi High Technology Co, Tokyo, Japan). A hexokinase method was used to analyze plasma glucose (Roche Diagnostics Co, Mannheim, Germany). Serum insulin was analyzed using an electrochemiluminescence immunoassay with the sandwich principle (Roche Diagnostics Co). A colorimetric enzymatic assay was used to analyze plasma total cholesterol and plasma triglycerides (Roche Diagnostics Co). Homogeneous enzymatic colorimetric assays were used to analyze high-density lipoprotein cholesterol (HDL-C) and low-density lipoprotein cholesterol (LDL-C) (Roche Diagnostics Co). Plasma high-sensitivity C-reactive protein (hsCRP) was measured using enhanced immunoturbidimetric assay with CRP (Latex) High Sensitive Assay reagent (Roche Diagnostics Co). The limit of detection was 0.15–0.20 mg/L. A kinetic method according to International Federation of Clinical Chemistry (IFCC) was used to analyze ALT (Roche Diagnostics Co). A kinetic method according to IFCC was used to analyze GGT (Roche Diagnostics Co).

Assessment of blood pressure

Blood pressure was measured manually by a calibrated aneroid sphygmomanometer (HEINE GAMMA G7, Germany). The measurement protocol included, after a rest of 5 min, three measurements in the sitting position at 2-min intervals. The mean of all three values were used as the systolic (SBP) and diastolic blood pressure (DBP).

Metabolic syndrome score

We calculated a continuous metabolic syndrome score variable similarly to previously published scores¹⁵ using continuous z-score variables by the following formula: Waist circumference+insulin+glucose−HDL-C+triglycerides+the mean of SBP and DBP. A higher metabolic syndrome score indicates a less favorable metabolic risk profile.

Other assessments

Time spent in physical activity and on the computer or watching television was assessed by the PANIC Physical Activity Questionnaire filled out by the parents. Dietary intake was assessed by food records of 4 consecutive days.¹⁶ The presence of chronic diseases and acute infections and the use of medication were assessed by a questionnaire administered by the parents.

Statistical analysis

Statistical analyses were performed with the SPSS software for Windows, version 19.0 (Chicago, IL). Differences in basic characteristics between genders were compared by the independent samples t-test. Associations between the features of metabolic syndrome were analyzed by partial correlations adjusted for age, sex, and body height. The risk of being in the highest fifth of ALT and GGT in the sex-specific thirds of metabolic syndrome and its components were analyzed using binary logistic regression models after adjustment for age and body height. Additional adjustments were also made for other components of metabolic syndrome, body lean mass, time spent in physical activity and on the computer or watching television, the intake of saturated and unsaturated fatty acids, hsCRP, and the presence of acute infections. We also repeated these analyses by excluding children with chronic diseases and medication that could increase liver enzymes. In hsCRP analyses, only values below 10 mg/L were included to exclude children with acute infections or chronic inflammatory diseases.

We included waist circumference, fasting insulin, glucose, HDL-C, triglycerides, SBP, DBP, ALT, GGT, and hsCRP in the factor analyses adjusted for age, sex, and body height. We used natural logarithmic transformation for waist circumference, triglycerides, ALT, GGT, and hsCRP and square root transformation for insulin to normalize skewed distributions. Principal factor analysis was used for the extraction of the initial factors. Only factors with eigenvalues >1.0 were retained in the analysis. We then carried out a promax rotation that allows factors to be correlated to assess possible underlying pathophysiological relationships. For the interpretation of the factors, we considered variables having a correlation coefficient of ≥0.40 to be heavily loaded and those having a correlation coefficient of 0.30–0.39 to be moderately loaded on the factors.^17,18 Associations with a P value of <0.05 were considered statistically significant.

Results

Basic characteristics

Body height, waist circumference, glucose, and HDL-C were higher whereas body fat percentage, insulin, triglycerides, and LDL-C were lower among boys than girls (Table 1). Altogether 14.2% of the girls and 10.4% of the boys were overweight or obese.

Table 1.

Basic Characteristics

	All (n=492)	Girls (n=232)	Boys (n=260)	P value ^a
Age (years)	7.6±0.4	7.6±0.4	7.7±0.4	0.217
Body height (cm)	128.7±5.7	127.7±5.6	129.6±5.6	<0.001
Body weight (kg) ^b	26.8±4.9	26.4±5.0	27.1±4.8	0.068
Body mass index^b	16.1±2.1	16.1±2.1	16.1±2.0	0.850
BMI-SDS^b	−0.20±1.1	−0.18±1.1	−0.22±1.1	0.643
Waist circumference (cm)	56.6±5.6	55.9±5.8	57.2±5.3	0.006
Body fat percentage (%)^c	19.6±8.2	22.4±7.6	17.1±7.8	<0.001
Fasting serum insulin (mU/L)	4.5±2.5	4.8±2.3	4.3±2.6	0.010
Fasting plasma glucose (mmol/L)	4.8±0.4	4.8±0.4	4.9±0.4	0.001
Total cholesterol (mmol/L)	4.3±0.6	4.3±0.6	4.2±0.6	0.155
LDL-C (mmol/L)	2.4±0.5	2.4±0.5	2.3±0.5	0.033
HDL-C (mmol/L)	1.60±0.31	1.56±0.31	1.64±0.31	0.007
Triglycerides (mmol/L)	0.59±0.24	0.62±0.25	0.58±0.23	0.038
Systolic blood pressure (mmHg)	100.1±7.3	99.9±7.6	100.3±7.1	0.507
Diastolic blood pressure (mmHg)^d	61.8±6.6	61.6±6.7	62.0±6.5	0.495
Alanine aminotransferase (U/L)	18.5±6.1	18.1±5.3	18.9±6.7	0.143
Gamma-glutamyltransferase (U/L)	11.7±2.7	11.6±2.6	11.8±2.7	0.397
hsCRP (mg/L)^e	0.64±0.93	0.67±0.84	0.62±0.62	0.365
Metabolic syndrome score	−0.05±3.51	−0.01±3.41	−0.09±3.61	0.712

Differences in means between girls and boys were analyzed with independent samples t-test.

n=491.

n=474.

n=489.

n=484.

BMI-SDS, body mass index standard deviation score, based on Finnish reference values¹³; LDL-C, low-density lipoprotein cholesterol; HDL-C, high-density lipoprotein cholesterol; hsCRP, high-sensitivity C-reactive protein.

Odds ratios with P values < 0.05 and P values for linear trends < 0.05 are bolded.

Risk of being in the highest fifth of ALT and GGT according to metabolic syndrome and its components

Overweight or obese children had 2.1 [95% confidence interval (CI) 1.2–4.0, P=0.016] times higher risk of having ALT and 4.5 (95% CI 2.4–8.2, P<0.001) times higher risk of having GGT in the highest fifth of its distribution than normal weight children. The risk of being in the highest fifth of ALT increased with increasing sex-specific thirds of metabolic syndrome score, body fat percentage, waist circumference, and insulin adjusted for age and body height (Table 2). The risk of having GGT in the highest fifth of its distribution increased with increasing thirds of metabolic syndrome score, body fat percentage, waist circumference, insulin, glucose, and hsCRP. The associations of body fat percentage and waist circumference with ALT and GGT generally remained statistically significant after further adjustment for other components of metabolic syndrome, except that the association of waist circumference with GGT was no longer statistically significant after adjustment for insulin (data not shown). Of the components of metabolic syndrome other than body fat percentage or waist circumference, only glucose remained a statistically significant determinant of GGT after further adjustment for body fat percentage or waist circumference, and only hsCRP remained a statistically significant determinant of GGT after additional adjustment for insulin (data not shown). Other adjustments had little or no effect on these associations (data not shown).

Table 2.

Risk of Having Alanine Aminotransferase and Gamma-Glutamyltransferase in the Highest Fifth in Sex-Specific Thirds of Metabolic Syndrome and Its Components

		Alanine aminotranferase OR (95% CI)	Gamma-glutamyltranferase OR (95% CI)
Metabolic syndrome score	Low (girls <−1.5; boys <−1.9)	1.0 (reference)	1.0 (reference)
	Medium (girls −1.5–0.9; boys −1.9–1.3)	1.0 (0.6–1.9)	1.4 (0.8–2.7)
	High (girls >0.9; boys >1.3)	1.8 (1.0–3.1)	2.6 (1.4–4.8)
	P for trend	0.048	0.002
Body fat percentage	Low (girls <18.4; boys <12.3)	1.0 (reference)	1.0 (reference)
	Medium (girls 18.4–24.2; boys 12.3–18.7)	1.5 (0.8–2.9)	0.7 (0.3–1.3)
	High (girls >24.2; boys >18.7)	2.9 (1.6–5.5)	2.3 (1.3–4.2)
	P for trend	0.001	0.004
Waist circumference	Low (girls ≤53.2; boys ≤54.2)	1.0 (reference)	1.0 (reference)
	Medium (girls 53.3–56.6; boys 54.3–58.4)	1.1 (0.6–2.0)	0.8 (0.4–1.4)
	High (girls >56.6; boys ≥58.5)	2.2 (1.2–4.1)	1.9 (1.0–3.5)
	P for trend	0.008	0.029
Fasting serum insulin	Low (girls ≤3.7; boys <2.9)	1.0 (reference)	1.0 (reference)
	Medium (girls 3.8–5.5; boys 2.9–4.9)	0.8 (0.5–1.5)	1.7 (0.9–3.2)
	High (girls >5.5; boys >4.9)	1.8 (1.0–3.0)	2.4 (1.3–4.4)
	P for trend	0.038	0.006
Fasting plasma glucose	Low (girls ≤4.6; boys ≤4.7)	1.0 (reference)	1.0 (reference)
	Medium (girls 4.7–4.8; boys 4.8–5.0)	1.0 (0.6–1.7)	1.9 (1.0–3.6)
	High (girls ≥4.9; boys ≥5.1)	0.8 (0.5–1.5)	2.5 (1.4–4.5)
	P for trend	0.558	0.003
HDL-C	Low (girls ≤1.44; boys ≤1.48)	1.0 (reference)	1.0 (reference)
	Medium (girls 1.45–1.65; boys 1.49–1.73)	0.6 (0.4–1.1)	0.8 (0.5–1.4)
	High (girls ≥1.66; boys ≥1.74)	0.6 (0.4–1.1)	0.7 (0.4–1.3)
	P for trend	0.085	0.287
Triglycerides	Low (girls ≤0.46; boys ≤0.45)	1.0 (reference)	1.0 (reference)
	Medium (girls 0.47–0.64; boys 0.46–0.59)	0.7 (0.4–1.2)	0.6 (0.3–1.1)
	High (girls ≥0.65; boys ≥0.60)	0.8 (0.5–1.4)	1.3 (0.8–2.2)
	P for trend	0.405	0.303
Systolic blood pressure	Low (girls ≤96.0; boys <96.7)	1.0 (reference)	1.0 (reference)
	Medium (girls 96.1–102.0; boys 96.7–102.0)	1.4 (0.8–2.6)	0.9 (0.5–1.7)
	High (girls ≥102.1; boys >102.0)	1.7 (1.0–3.1)	1.5 (0.8–2.6)
	P for trend	0.064	0.156
hsCRP	Low (girls ≤0.29; boys ≤0.29)	1.0 (reference)	1.0 (reference)
	Medium (girls 0.30–0.92; boys 0.30–0.95)	1.3 (0.8–2.2)	1.4 (0.8–2.4)
	High (girls 0.93–6.60; boys 0.96–9.47)	1.0 (0.5–2.0)	2.5 (1.4–4.6)
	P for trend	0.711	0.004

Data were analyzed by logistic regression models adjusted for age and body height.

OR, odds ratio; CI, confidence interval; HDL-C, high-density lipoprotein cholesterol; hsCRP, high-sensitivity C-reactive protein.

Odds ratios with P values < 0.05 and P values for linear trends < 0.05 are bolded

Factor analysis for metabolic syndrome

The features of metabolic syndrome were generally intercorrelated. Body fat percentage and waist circumference were highly intercorrelated (r=0.76). Body fat percentage, waist circumference, and triglycerides had moderate correlations with insulin (r=0.34–0.40). Insulin and glucose were highly intercorrelated (r=0.52). Body fat percentage and waist circumference had moderate correlations with hsCRP (r=0.34–0.35).

The first-order factor analysis with the promax rotation yielded three factors (Table 3). The first factor that explained the highest variance (24.9%) was heavily loaded by insulin, glucose, and triglycerides. The second factor explaining 16.1% of the variance was highly loaded by waist circumference and insulin and moderately loaded by GGT and hsCRP. The third factor explaining 12.7% of the variance was heavily loaded by HDL-C and triglycerides and moderately by waist circumference and insulin. These three factors were intercorrelated (r=0.31–0.36, P<0.001).

Table 3.

Structure Matrix and Factor Intercorrelations for the Factor Analysis of Features of the Metabolic Syndrome Using a Promax Rotation

	First factor	Second factor	Third factor
Waist circumference	0.23	0.80	0.35
Insulin	0.80	0.43	0.35
Glucose	0.67	0.06	0.14
HDL-C	−0.17	−0.16	−0.61
Triglycerides	0.50	0.16	0.47
(SBP+DBP)/2	0.19	0.26	−0.03
Alanine aminotrensferase	−0.03	0.28	−0.13
Gamma-glutamyltransferase	0.17	0.34	0.08
hsCRP	0.07	0.38	0.16
Second factor	0.31
Third factor	0.36	0.32

All variables were adjusted for age, sex and body height before factor analysis with log transformation or square root transformation, if necessary.

HDL-C, high-density lipoprotein cholesterol; SBP, systolic blood pressure; DBP, diastolic blood pressure; hsCRP, high-sensitivity C-reactive protein.

Odds ratios with P values < 0.05 and P values for linear trends < 0.05 are bolded

The second-order factor analysis that used the three factors generated from the first-order factor analysis yielded a metabolic syndrome factor (Table 4). All three factors from the first-order factor analysis had high loadings on the metabolic syndrome factor that explained 64.1% of the variance. This metabolic syndrome factor was almost the equivalent of the composite metabolic syndrome score (r=0.91), with only minor differences in the correlations with individual components of metabolic syndrome (data not shown).

Table 4.

Second-Order Factor Analysis of the Factors Derived from the First-Order Analysis Shown in Table 3 and Its Correlation with the Metabolic Syndrome Score

	Metabolic syndrome factor
First factor	0.66
Second factor	0.61
Third factor	0.77
Metabolic syndrome score	0.91

We also carried out factor analyses using unadjusted variables and separately in boys and girls. The resulting factors had similar variances and loadings as those shown in Table 3 (data not shown). Therefore, we considered the gender differences negligible and performed the analyses in the entire cohort.

Discussion

The present study in a representative population sample of 492 girls and boys 6–8 years of age showed that the features of metabolic syndrome cluster among apparently healthy prepubertal children. Factor analyses revealed a metabolic syndrome factor characterized by a larger waist circumference; higher levels of fasting insulin, fasting glucose, triglycerides, GGT, and hsCRP; and lower levels of HDL-C. Overweight or obese children had a 2.1 times higher risk of having ALT and a 4.5 times higher risk of having GGT in the highest fifth of their distributions than normal weight children. Children in the highest sex-specific third of the metabolic syndrome score, body fat percentage, waist circumference, and insulin had a 2–3 times higher risk of being in the highest fifth of ALT and GGT. Moreover, children in the highest third of glucose and hsCRP had a 2.5-fold risk of being in the highest fifth of GGT. The associations of body fat percentage and waist circumference with ALT and GGT remained after further adjustment for other features of metabolic syndrome and confounding factors, but the relationships of other features of metabolic syndrome were no longer associated with ALT and GGT after additional adjustment for fat percentage and waist. These findings suggest that the features of metabolic syndrome, especially total and abdominal adiposity, increase the likelihood of having high-normal levels of ALT and GGT.

Most previous studies on the associations of metabolic syndrome and its components with high-normal levels of liver enzymes in children have found associations of the features of metabolic syndrome with higher levels of ALT, and most of these studies have been carried out among obese children. The Bogalusa Heart Study showed that metabolic syndrome and all of its major components were associated with serum levels of ALT within the normal range among apparently healthy preadolescents and adolescents.¹² Other smaller studies among overweight or obese children have provided further evidence for the associations of metabolic syndrome and its components with serum levels of liver enzymes within the reference range.^11,19

The PANIC Study is the first study in a relatively large representative population sample of healthy prepubertal girls and boys who have liver enzymes within normal range and in which the associations of metabolic syndrome and a number of its components with liver enzymes ALT and GGT have been analyzed comprehensively as potential features of metabolic syndrome by factor analysis. Factor analysis can be used to describe the clustering of metabolic and cardiovascular risk factors that seems to exist among children in a corresponding manner as among adults.¹⁷ The commonly used varimax rotation generates uncorrelated factors, which may simplify interpretation of the factors, but may not be biologically relevant.²⁰ Therefore, in the first-order factor analysis, we used the promax rotation, which allows factors to be correlated and makes it possible to assess underlying pathophysiological relationships. The first-order factor analysis yielded three factors: The first factor included insulin, glucose, and triglycerides; the second factor included waist circumference, insulin, GGT, and hsCRP; and the third factor included HDL-C, triglycerides, waist circumference, and insulin. Consistent with our previous findings in adults,¹⁷ the second-order factor analysis generated a single metabolic syndrome factor that was loaded heavily by all three factors from the first-order analysis. The metabolic syndrome factor derived from the second-order factor analysis was essentially the equivalent of a composite metabolic score similar to those widely used in other studies on metabolic risk clustering among children.¹⁵ The present factor analysis suggests that high-normal levels of liver enzymes could be considered a feature of metabolic syndrome in prepubertal children.

All children in the present study had concentrations within the normal range for ALT (10–45 U/L in women and 10–70 U/L in men) and for GGT (10–45 U/L in women and 10–80 U/L in men), as defined by the Nordic Reference Interval Project (NORIP) Study.²¹ There is no single standard cutoff point for abnormal high ALT or GGT levels in children, but the most commonly used limit for ALT is 40 U/L,¹⁰ which was exceeded by only 5 children in the present study. Previous studies in adults suggest that even mildly elevated levels of liver enzymes could indicate a potential dysmetabolic state²² and are associated with an increased risk of developing type 2 diabetes and cardiovascular disease.^23,24 Together with these studies, the present findings suggest that GGT and ALT could be used as continuous biomarkers of metabolic risk not only in adults but also in prepubertal children. In the present study, GGT generally correlated more strongly with the features of metabolic syndrome than ALT. Hence, GGT might be a more specific indicator of metabolic dysfunction, particularly abnormal insulin and glucose metabolism and systemic low-grade inflammation, as assessed by hsCRP, than ALT in children. Moreover, the association between fasting glucose and GGT remained even after controlling for body adiposity, as reported previously in mainly overweight and obese Pima Indian children.¹¹

The associations of features of metabolic syndrome with altered liver enzymes might be due to increased overall and central adiposity and insulin resistance.^23,25 Excess body fat has been associated with fat accumulation and increased metabolic activity in the liver that result in increased gluconeogenesis, lipogenesis, and triglyceride secretion.^9,26 The mechanisms by which fat accumulation in the liver may worsen insulin resistance and dyslipidemia include exacerbation of inflammation and oxidative stress.⁹ Because of the cross-sectional design of most previous studies and the present study, we cannot draw a conclusion about the time order or causality of the relationships between metabolic risk factors and altered liver enzymes.

The strengths of the present study include a rather large and representative population sample of healthy girls and boys, comprehensive and detailed measurements of the features of metabolic syndrome, and the use of factor analysis to identify risk factor clustering. In the present population study, we were not able to measure liver fat content with magnetic resonance imaging (MRI). In obese children, however, higher serum ALT levels have been associated independently with NAFLD and excess hepatic fat content, as assessed by MRI, although ALT alone was not accurate enough to be employed as a marker of NAFLD.^27,28 Some medicines and diseases could increase liver enzyme levels. According to the questionnaire administered by the parents, altogether 9 children used such medicines regularly; 2 of them had rheumatoid arthritis and 1 had inflammatory bowel disease. However, the results remained similar, even after exclusion of these children. We were unable to evaluate the effect of irregular use of pain or allergy medicines. In addition, because plasma levels of liver enzymes fluctuate from day to day, one measurement of ALT and GGT may somewhat underestimate the true associations.

The present study shows that clustering of metabolic risk factors, particularly excess body fat, is associated with high-normal levels of ALT and GGT in prepubertal children. High-normal levels of liver enzymes, especially GGT, and systemic low-grade inflammation could be considered features of metabolic syndrome among children. Subtle changes in liver function may play an important role in the pathogenesis of metabolic syndrome beginning in childhood.

Footnotes

Acknowledgments

This work has been financially supported by Ministry of Social Affairs and Health of Finland 1491/9.02.00/2009, Ministry of Education and Culture of Finland 121/627/2009, Finnish Innovation Fund Sitra, Social Insurance Institution of Finland 22/26/2008, Finnish Cultural Foundation, Juho Vainio Foundation, Foundation for Pediatric Research Paavo Nurmi Foundation, and Kuopio University Hospital EVO 5031343.

Author Disclosure statement

No competing financial interests exist.

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