Volume 62(2); February

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Article Contents

Clin Exp Pediatr > Volume 62(2); 2019
Kim, Baek, Han, Kim, and Shin: Serum alanine aminotransferase levels are closely associated with metabolic disturbances in apparently healthy young adolescents independent of obesity

Abstract

Purpose

Liver metabolism plays a pivotal role in the development of metabolic disorders. We aimed to investigate the clinical and laboratory risk factors associated with alanine aminotransferase (ALT) levels in young adolescents from an urban population in Korea.

Methods

A population of 120 apparently healthy adolescents aged 12–13 years was included in the cross-sectional design study; 58 were overweight or obese and 62 were of normal weight. We estimated anthropometric and laboratory measurements, including waist-to-height ratio, blood pressure, insulin sensitivity, aspartate aminotransferases (AST), ALT, and lipid profiles.

Results

The mean ages of the overweight or obese and normal weight participants were 12.9±0.3 and 13.0±0.3 years, respectively. Height, weight, body mass index, waist circumference, waist-to-height ratio, systolic and diastolic blood pressure, AST, ALT, total cholesterol, low-density lipoprotein-cholesterol, triglyceride, insulin, and the homeostatic model assessment of insulin resistance (HOMA-IR) score were significantly higher and the high-density lipoprotein-cholesterol and quantitative insulin-sensitivity check index were significantly lower in the overweight/obese participants in comparison to the normal-weight participants (all P<0.05). In multivariate linear regression analysis, waist-to-height ratio, systolic blood pressure, and HOMA-IR score were independently and positively associated with serum ALT levels.

Conclusion

Screening for ALT levels in adolescents may help to differentiate those at risk of metabolic abnormalities and thus prevent disease progression at an early age.

Introduction

Obesity has emerged as a large public health concern and is now developing at younger ages around the world [1]. Mounting evidence reports that childhood obesity frequently continues into adulthood and causes the risk of various diseases, such as cardiovascular diseases, diabetes mellitus, and nonalcoholic fatty liver disease (NAFLD), ultimately leading to increased morbidity and mortality [2].
NAFLD is at present the most common cause of liver diseases among children and adolescents [3,4]. With the surging epidemic of childhood and adolescent obesity, NAFLD is recognized as a major health threat in young generations. Previous studies have indicated that NAFLD is the hepatic manifestation of overweight [5], obesity [6], and metabolic syndrome [6,7]. Although several studies [8-10] have investigated the risk of NAFLD among particular pediatric and adolescent groups, there have been scanty studies determining the risk factors associated with this disorder. The lack of data hinders our ability to establish effective and appropriate primary prevention programs and services for young generations at risk of developing liver diseases and obesity-associated disorders. If undiagnosed and untreated, NAFLD in children and adolescents may progress silently and ultimately lead to liver cirrhosis, portal hypertension, and liver-related death in early adulthood [11]. In many countries, liver cirrhosis and other liver diseases are one of the main causes of general mortality and death among adults [12-14]. Determining ways to differentiate subjects at risk of developing NAFLD may be critical to help reduce the burden of liver disease in adolescents.
The current research aimed to determine whether there are differences in serum alanine aminotransferase (ALT) levels between overweight/obese subjects and normal-weight subjects and to identify risk factors associated with ALT concentrations among apparently healthy young adolescents.

Materials and methods

1. Study subjects

One hundred and fifty-one adolescents aged 12–13 years performed Student Health Examinations at a local health check-up clinic in Seoul, Korea between June and August 2015. Subjects who met the following exclusion criteria were not included in the analysis (n=31): any missing covariate data; a medication history of steroids, insulin, glucose regulators, or antihypertension medications; participants who refused the anthropometric measurements or blood test; participants who had signs and/or symptoms of acute infections; or participants who had not fasted for a minimum of 12 hours before blood test. After these exclusions, 120 adolescents (69 boys and 51 girls) were included in the final investigation. Health check-ups were conducted by a single pediatrician in accordance with a systematized process. Body weight and height were quantified to the nearest 0.1 kg and 0.1 cm, respectively, with an automatic height–weight scale while the study participants wore light indoor clothing and no shoes. Body mass index (BMI) was computed as weight (kg)/height (m2). The BMI percentiles for age and sex were calculated according to the 2017 Korea Growth Charts [15]. The participants were categorized into 2 groups according to their BMI (i.e., overweight/obese and normal-weight participants). A certified technician measured blood pressure (BP) a maximum of three times on the right arm with the participants seated after a 5-minute rest using an automatic BP recorder. The development of secondary sexual characteristics was measured in accordance with Tanner stages by the pediatrician. The institutional review board of the CHA Gangnam Medical Center approved the study (IRB No. GCI-115-04) and written informed consent was attained from guardians and consent/assent from the adolescents.

2. Laboratory analyses for obesity-related biomarkers

After a 12-hour overnight fast, blood specimens were attained from the antecubital vein of the participants by venipuncture and were promptly centrifuged, aliquoted, and frozen at -20°C. The frozen serum and plasma samples were accumulated at -80°C until final analysis. Fasting plasma glucose, total cholesterol, triglyceride, and high-density lipoprotein-cholesterol (HDL-C) levels were quantified by enzymatic methods using a Hitachi Modular D2400 automated chemistry analyzer (Hitachi, Tokyo, Japan). Levels of low-density lipoprotein-cholesterol (LDL-C) were determined using the following equation: LDL-C=total cholesterol − HDL-C − (triglyceride/5). Fasting insulin concentrations were assessed using a chemiluminescent microparticle immunoassay (Abbott Architect System, Irving, TX, USA). We also subjected the fasting data to various transformations and ultimately defined quantitative insulin sensitivity check index (QUICKI=1/[log(I0)+log(G0)]), where I0 is the fasting insulin, and G0 is the fasting glucose. Insulin resistance was computed using the homeostatic model assessment of insulin resistance (HOMA-IR) and assessed using the following formula: HOMA-IR=(fasting insulin [μIU/mL]×fasting glucose [mg/dL]/18)/22.5 [16].

3. Serum AST and ALT measurement

Serum aspartate aminotransferases (AST) and ALT levels were measured by the catalytic concentration from the rate of decrease of nicotinamide adenine dinucleotide measured at 340 nm by means of lactate dehydrogenase coupled reaction.

4. Statistical analysis

The normality of continuous variables was evaluated by the Kolmogorov-Smirnov test. All continuous variables are presented as mean±standard deviation, median (interquartile range), or are displayed as scatter plots. Comparisons of group differences for continuous variables were tested by the Mann-Whitney U test or the Student t test. Categorical variables are shown as an absolute number with the corresponding proportion. The significance of differences in proportions was assessed by the chi-square test. Spearman correlation analysis was implemented to determine the association between serum ALT concentrations and anthropometric measurements and obesity-associated biomarkers. The study subjects’ anthropometric and biochemical parameters with regards to ALT quartiles were computed using the analysis of variance for continuous variables and the chi-square test for categorical variables. ALT quartiles were classified separately as follows: Q1, ≤10.0; Q2, 10.0–13.0; Q3, 13.0–20.0; and Q4, ≥20.0. To determine independent correlates of serum ALT concentrations, a multivariate linear regression analysis was utilized with the ALT level as the dependent variable. All statistical analyses were performed using IBM SPSS Statistics ver. 23.0 (IBM Co., Armonk, NY, USA). All statistical tests were 2-sided, with a P value of <0.05 indicating statistical significance.

Results

1. Characteristics of the study participants

There were 58 subjects with overweight/obesity and 62 with normal-weight, respectively (12.9±0.3 years vs. 13.0±0.3 years, P=0.11) (Table 1). There were more boys in the overweight/obese subjects than in the normal-weight subjects (P=0.037) (Table 1).

2. Comparison of clinical and laboratory parameters between overweight/obese and normal-weight subjects

Table 1 indicates the comparison of the clinical and laboratory parameters for overweight/obese and normal-weight subjects. In the anthropometric measurements, height, weight, BMI, waist circumference, waist-to-height ratio, and systolic and diastolic BP were significantly higher in the overweight/obese subjects in comparison with normal-weight subjects (all P<0.05). There were no significant differences in age or Tanner stages between groups. In the laboratory parameters, AST, ALT, total cholesterol, LDL-cholesterol, triglyceride, fasting insulin, and HOMA-IR were significantly higher and HDL-cholesterol and QUICKI were significantly lower in the overweight/obese subjects when compared with normal-weight subjects. There was no difference in fasting glucose between groups.

3. Associations of serum ALT levels with anthropometric measurements and obesity-related biomarkers

Spearman correlation results between ALT and various parameters are depicted in Table 2. ALT level was significantly and positively correlated with weight (rho=0.558, P<0.001), BMI (rho=0.579, P<0.001), waist circumference (rho=0.688, P<0.001), waist-to-height ratio (rho=0.66, P<0.001) (Fig. 1A), systolic BP (rho=0.285, P= 0.002) (Fig. 1B), AST (rho=0.67, P<0.001), fasting insulin (rho=0.539, P<0.001), QUICKI (rho=-0.524, P<0.001), and HOMA-IR (rho=0.524, P<0.001) (Fig. 1C). No significant correlations were detected between ALT levels and age, height, diastolic BP, total cholesterol, HDL-cholesterol, LDL-cholesterol, triglyceride (rho=0.141, P=0.123) (Fig. 1D), or fasting glucose. Weight, BMI, waist circumference, waist-to-height ratio, systolic BP, AST, ALT, fasting insulin, and HOMA-IR increased and QUICKI decreased significantly in accordance with ALT quartiles (all P<0.05) (Table 3). In a multivariate linear regression analysis, waist-to-height ratio, systolic BP, and HOMA-IR were independently and positively associated with serum ALT levels (Table 4).

Discussion

The current study examined whether there are differences in serum ALT concentrations between overweight/obese and normal-weight subjects and to differentiate risk factors associated with ALT concentrations among apparently healthy young adolescents. We found a significant difference in serum ALT levels between overweight/obese and normal-weight subjects and a significant positive correlation between serum ALT levels and waist-to-height ratio, systolic BP, and HOMA-IR in a multivariate linear regression analysis. To the best of our knowledge, this is the first study to determine a significant positive association between ALT concentrations and waist-to-height ratio, systolic BP, and HOMA-IR in a population of apparently healthy young adolescents.
The prevalence of obesity and obesity-related-metabolic disorders are on the rise across the globe [1]. Despite vigorous research effort, the pathogeneses and pathophysiology of these complex diseases remain elusive. In recent times, previous studies have reported that obese children and adolescents have higher serum ALT levels than controls, indicating that ALT may be implicated in the pathogenesis of obesity. A recent representative study of 1,262 participants aged 8–19 years in Mexico has reported that overweight and obese youth have higher serum ALT concentrations than controls and that abdominal obesity and insulin resistance are associated with an increased risk of elevated ALT (>40 U/L) [17]. Another study comprising 5,586 adolescents aged between 12 and 19 years in the U.S. National Health and Nutrition Examination Survey 1999–2004 has reported that ALT level is associated with waist circumference and insulin resistance [18]. Recently, a representative study of 886 subjects with the mean age of 15 years in the 2009–2010 Korea National Health and Nutrition Examination Survey has shown that boys with the highest ALT quartile were more likely to have the highest quartile of insulin resistance [19]. In agreement with previous studies, the present study determined significantly increased serum ALT concentrations in adolescents with overweight/obesity than those of normal-weight. In addition, we found a significant positive correlation between ALT and waist-to-height ratio, systolic BP, and HOMA-IR, even after adjustment for potential confounders. Taken together, these findings indicate that measurement of serum ALT concentrations may provide clinicians with a useful biochemical marker for apparently healthy young adolescents at risk of metabolic deregulations.
Emerging evidence has indicated that the liver metabolism is implicated in the pathogenesis of metabolic syndrome [20] and insulin resistance [18,20]. NAFLD is the accumulation of large droplets of triglycerides in liver cells without a history of chronic alcohol consumption and, therefore, has been recognized as the hepatic manifestation of overweight [5], obesity [6], and metabolic syndrome [6,7]. Despite the fact that liver biopsy is the gold standard for establishing NAFLD, serum biomarkers, particularly ALT levels, have been demonstrated to be highly precise in diagnosing NAFLD. These biomarkers are also well correlated with risk of overweight, obesity, metabolic syndrome, diabetes mellitus, and cardiovascular disease [7,21,22]. Furthermore, previous research have demonstrated that the liver enzymes levels rise prior to the development of nonalcoholic fatty liver, proposing that liver enzymes may be a surrogate marker of NAFLD [6,7,20,23]. Although we did not conduct a liver biopsy study, we found that the serum ALT levels were significantly and positively associated with abdominal obesity estimated using the waist to-height ratio and certain metabolic risk factors including BP and insulin resistance estimated using the HOMA-IR in a group of apparently healthy young adolescents in the multivariate linear regression analysis.
There may be congruent metabolic mechanisms to consider as to why serum ALT is positively associated with waist-to-height ratio and certain metabolic risk factors. One possible underlying mechanism for the current finding is as follows. ALT is a liver enzyme that is released in serum as a consequence of hepatocyte damage. If the aggregation of body fat becomes overloaded, adipose tissue releases free fatty acid into the hepatic portal vein; consequently, this restricts the physiological measure of eradicating insulin resistance. Insulin resistance is known to increase uptake of free fatty acid into liver, and the absorbed hepatic fatty acid is resynthesized into triglyceride, ultimately leading to the development of NAFLD with ALT elevation [24,25]. In this regard, several researchers have suggested that ALT elevation and/or NAFLD may be a hepatic component of metabolic syndrome [6,7].
In the present study, we used waist-to-height ratio instead of BMI in order to determine the association of ALT with anthropometric measures due to the fact that in the current study, ALT level showed a higher correlation coefficient with waist-to-height ratio (rho= 0.66, P<0.001) than BMI (rho=0.579, P<0.001). In this respect, there are studies suggesting that waist-to-height ratio is more useful in screening for cardiovascular risk factors in children and adolescents [26-29]. This measure is much easier to calculate than BMI, does not require sophisticated tables, and can be utilized to determine visceral obesity, even in normal-weight subjects [30]. In addition, waist-to-height ratio integrates waist circumference as a measure of abdominal obesity and adjusts for a subject’s body size by dividing their height [31]. It incorporates the advantages of both BMI and waist-to-hip ratio by accounting for height and abdominal adiposity [32]. Waist-to-height ratio further indicates cardiometabolic risk based on BMI percentile [33]. Furthermore, our study is unique in that we enrolled 12- to 13-year-old young adolescents as subjects and measured their Tanner stages to take the hormonal effect into consideration. Young adolescence, twelve to fourteen years old, is a pivotal stage for constituting healthy lifestyle habits and the majority of the patterns constructed during this expanding period persist well into adulthood [34]. Accordingly, it is of critical significance that pediatricians recognize young adolescents at risk of obesity and/or metabolic abnormalities such as insulin resistance at an early stage of development and adjust their style of living, such as dietary patterns.
However, the present research has a few inherent limitations. First, the epidemiological, cross-sectional method of this research does not allow for a causal inference. Second, we did not implement ultrasonography on participants with elevated ALT for the diagnosis of NAFLD; however, previous research have reported that the association between ALT or AST and fatty liver disease is so precise that elevated ALT and/or AST can be used as a surrogate marker for suspected fatty liver [3,4,35].
In conclusion, we have demonstrated that higher serum ALT concentrations are found in the presence of overweight/obesity in apparently healthy young adolescents and that serum ALT concentrations are significantly and positively associated with waist-to-height ratio, systolic BP, and HOMA-IR. More intensified clinical screening strategies and interventions are warranted for adolescents at risk, particularly those with abdominal obesity, in order to lower the risk of liver disease at an early stage in young adolescents.

Conflicts of interest

No potential conflict of interest relevant to this article was reported.

Fig. 1.
Scatter plots of serum alanine aminotransferase levels vs. waist-to-height ratio, systolic blood pressure, homeostatic model assessment of insulin resistance score, and triglyceride in apparently healthy young adolescents. (A) Alanine aminotransferase vs. waist-to-height ratio; (B) alanine aminotransferase vs. systolic blood pressure; (C) alanine aminotransferase vs. homeostatic model assessment of insulin resistance (HOMA-IR); and (D) alanine aminotransferase vs. triglyceride.
kjp-2018-06639f1.jpg
Table 1.
General characteristics of the study subjects
Characteristic Overweight/obese subjects (n=58) Normal-weight subjects (n=62) P value
Sex, boy:girl 39:9 30:32 0.037
Age (yr) 12.9±0.3 13.0±0.3 0.11
Height (cm) 161.5±6.5 158.8±7.2 0.038
Weight (kg) 68.2±7.9 48.8±7.3 <0.001
BMI (kg/m2) 25.8 (24.6–27.2) 19.3 (17.2–21.2) <0.001
Waist circumference (cm) 89.8 (85.0–93.5) 67.0 (63.8–74.0) <0.001
Waist-to-height ratio 0.56±0.04 0.44±0.05 <0.001
Tanner stage 0.42
 I 5 (8.6) 2 (3.23)
 II 15 (25.9) 9 (14.52)
 III 31 (53.5) 41 (66.13)
 IV 6 (10.3) 10 (16.13)
 V 1 (1.7) 0 (0)
Systolic BP (mmHg) 110.0 (110.0–120.0) 100 (100–110.0) <0.001
Diastolic BP (mmHg) 60.0 (60.0–70.0) 60.0 (60.0–65.0) 0.039
AST (IU/L) 20.5 (17.0–24.0) 18.0 (16.0–21.0) 0.004
ALT (IU/L) 19.0 (13.0–24.0) 11.0 (10.0–13.0) <0.001
Total cholesterol (mg/dL) 170.0±31.3 156.5±27.6 0.014
HDL cholesterol (mg/dL) 54.4±12.0 58.9±12.4 0.047
LDL cholesterol (mg/dL) 92.6±26.9 80.6±22.1 0.009
Triglyceride (mg/dL) 94.0 (66.0–156.0) 68.5 (53.0–101.0) 0.008
Fasting glucose (mg/dL) 90.0 (86.0–95.0) 90.0 (86.0–94.0) 0.707
Fasting Insulin (μIU/mL) 14.4 (11.5–19.1) 8.8 (7.0–12.1) <0.001
QUICKI 0.14 (0.13–0.14) 0.15 (0.14–0.16) <0.001
HOMA-IR 3.2 (2.5–4.3) 2.0 (1.6–2.7) <0.001

Values are presented as the mean±standard deviation, median (interquartile range), or number (%).

BMI, body mass index; BP, blood pressure; AST, aspartate aminotransferase; ALT, alanine aminotransferase; HDL cholesterol, high-density lipoprotein-cholesterol; LDL cholesterol, low-density lipoprotein-cholesterol; QUICKI, quantitative insulin sensitivity check index; HOMA-IR, homeostatic model assessment of insulin resistance.

Table 2.
Correlations of alanine aminotransferase with various parameters
Variable ALT (IU/L)
rho P value
Age -0.174 0.057
Height 0.095 0.301
Weight 0.558 <0.001
BMI 0.579 <0.001
Waist circumference 0.688 <0.001
Waist-to-height ratio 0.66 <0.001
Systolic BP 0.285 0.002
Diastolic BP 0.131 0.162
AST 0.67 <0.001
Total cholesterol 0.09 0.328
HDL-cholesterol -0.109 0.237
LDL-cholesterol 0.098 0.285
Triglyceride 0.141 0.123
Fasting glucose 0.156 0.088
Fasting insulin 0.539 <0.001
QUICKI -0.524 <0.001
HOMA-IR 0.524 <0.001

ALT, alanine aminotransferase; BMI, body mass index; BP, blood pressure; AST, aspartate aminotransferase; HDL cholesterol, high-density lipoprotein-cholesterol; LDL cholesterol, low-density lipoprotein-cholesterol; QUICKI, quantitative insulin sensitivity check index; HOMA-IR, homeostatic model assessment of insulin resistance.

Table 3.
Anthropometric and biochemical parameters according to alanine aminotransferase levels in the study subjects
Variable Q1 (n=21) Q2 (n=34) Q3 (n=33) Q4 (n=32) P value
Sex, boys:girls 7:14 13:21 24:9 25:7 0.0003
Age (yr) 12.9±0.3 12.9±0.2 12.8±0.3 12.8±0.3 0.245
Height (cm) 159.4±6.8 159.1±6.1 160.6±7.5 161.1±7.2 0.651
Weight (kg) 51.9 ±10.0 52.3±10.3 59.0±12.2 67.8±9.8 <0.001
BMI (kg/m2) 19.8 (17.8–23.6) 20.3 (18.4–22.8) 22.9 (18.7–26.0) 25.9 (24.5–27.6) <0.001
Waist circumference (cm) 65.5 (63.0–85.5) 69.5 (65.0–75.0) 80.3 (73.0–90.0) 91.0 (88.5–94.0) <0.001
Waist-to-height ratio 0.46±0.07 0.45±0.05 0.51±0.06 0.56±0.05 <0.001
Tanner stage 0.064
 I 2 (1.7) 0 (0) 0 (0) 5 (4.2)
 II 0 (0) 2 (1.7) 12 (10.0) 10 (8.3)
 III 13 (11.2) 22 (18.3) 24 (20.0) 12 (10.0)
 IV 3 (2.4) 6 (5.0) 2 (1.7) 6 (5.0)
 V 0 (0) 0 (0) 0 (0) 1 (0.8)
Systolic BP (mmHg) 105.0 (100– 115.0) 100.0 (100–110.0) 110.0 (100–120.0) 110.0 (100–120.0) 0.034
Diastolic BP (mmHg) 60.0 (60.0–67.5) 60.0 (55.0–65.0) 62.0 (60.0–70.0) 60.0 (60.0–70.0) 0.297
AST (IU/L) 16.0 (14.0–17.0) 17.5 (16.0–19.0) 19.0 (17.0–22.0) 25.0 (20.0–33.5) <0.001
ALT (IU/L) 8.0 (6.0–8.0) 11.0 (10.0–11.0) 15.0 (13.0–17.0) 28.5 (22.0–44.0) <0.001
Total cholesterol (mg/dL) 159.1±33.6 161.7±26.3 160.9±29.4 169.4±32.5 0.574
HDL-cholesterol (mg/dL) 56.7±11.5 57.5±11.0 59.5±13.8 52.9±12.2 0.182
LDL-cholesterol (mg/dL) 84.7±29.7 83.7±21.2 84.8±24.6 92.0±26.8 0.538
Triglyceride (mg/dL) 76.0 (54.0–101.0) 78.0 (59.0–102.0) 71.0 (47.0–106.0) 95.5 (72.0–170.5) 0.059
Fasting glucose (mg/dL) 88.0 (84.0–92.0) 89.5 (88.0–93.0) 90.0 (86.0–95.0) 91.5 (87.0–96.5) 0.229
Fasting insulin (μIU/mL) 9.2 (8.1–12.5) 8.8 (7.0–11.9) 11.4 (8.5–14.4) 16.30 (12.25–22.65) <0.001
QUICKI 0.15 (0.14–0.15) 0.15 (0.14–0.16) 0.15 (0.14–0.15) 0.14 (0.13–0.14) <0.001
HOMA-IR 2.0 (1.7–2.8) 2.0 (1.5–2.6) 2.5 (2.0–3.2) 3.5 (2.8–5.3) <0.001

Values are presented as the mean±standard deviation, median (interquartile range), or number (%).

Q1, ALT≤10.0; Q2, ALT, 10.0–13.0; Q3, ALT, 13.0–20.0; Q4, ALT≥20.0; ALT, alanine aminotransferase; BMI, body mass index; BP, blood pressure; AST, aspartate aminotransferase; HDL cholesterol, high-density lipoprotein-cholesterol; LDL cholesterol, low-density lipoprotein-cholesterol; QUICKI, quantitative insulin sensitivity check index; HOMA-IR, homeostatic model assessment of insulin resistance.

Table 4.
Assessment of the independent relationships between alanine aminotransferase and clinical and laboratory variables
Variable Parameter, β SE P value
Sex, male vs. female 0.473 3.131 0.88
Waist-to-height ratio 54.84 25.504 0.035
Tanner stage -0.126 0.886 0.888
Systolic BP 0.326 0.115 0.006
Triglyceride 0.029 0.027 0.29
HOMA-IR 4.91 1.235 0.0002

Multivariate linear regression analysis variables included sex (male), waist-to-height ratio, Tanner stage, systolic BP, triglycerides, and HOMA-IR as independent variables.

SE, standard error; BP, blood pressure; HOMA-IR, homeostatic model assessment of insulin resistance.

References

1. Huffman MD, Capewell S, Ning H, Shay CM, Ford ES, Lloyd-Jones DM. Cardiovascular health behavior and health factor changes (1988-2008) and projections to 2020: results from the National Health and Nutrition Examination Surveys. Circulation 2012;125:2595–602.
crossref pmid pmc
2. Reilly JJ, Kelly J. Long-term impact of overweight and obesity in childhood and adolescence on morbidity and premature mortality in adulthood: systematic review. Int J Obes (Lond) 2011;35:891–8.
crossref pmid pdf
3. Schwimmer JB, McGreal N, Deutsch R, Finegold MJ, Lavine JE. Influence of gender, race, and ethnicity on suspected fatty liver in obese adolescents. Pediatrics 2005;115:e561. –5.
crossref pmid
4. Lavine JE, Schwimmer JB. Nonalcoholic fatty liver disease in the pediatric population. Clin Liver Dis 2004;8:549–58. , viii-ix.
crossref pmid
5. Kodhelaj K, Resuli B, Petrela E, Malaj V, Jaze H. Non-alcoholic fatty liver disease and non-alcoholic steatohepatitis in Albanian overweight children. Minerva Pediatr 2014;66:23–30.
pmid
6. Brunt EM. Pathology of nonalcoholic fatty liver disease. Nat Rev Gastroenterol Hepatol 2010;7:195–203.
crossref pmid pdf
7. Takahashi Y, Fukusato T. Pediatric nonalcoholic fatty liver disease: overview with emphasis on histology. World J Gastroenterol 2010;16:5280–5.
crossref pmid pmc
8. Park HK, Hwang JS, Moon JS, Lee JA, Kim DH, Lim JS. Healthy range of serum alanine aminotransferase and its predictive power for cardiovascular risk in children and adolescents. J Pediatr Gastroenterol Nutr 2013;56:686–91.
crossref pmid
9. Park JH, Kim SH, Park S, Park MJ. Alanine aminotransferase and metabolic syndrome in adolescents: the Korean National Health and Nutrition Examination Survey Study. Pediatr Obes 2014;9:411–8.
crossref pmid
10. Park SH, Heo NY, Park JH, Kim TO, Yang SY, Moon YS, et al. Obesity, insulin resistance, and the risk of an elevated alanine aminotransferase activity in the Korean adolescent population. J Pediatr Endocrinol Metab 2012;25:945–9.
crossref pmid pdf
11. Suzuki D, Hashimoto E, Kaneda K, Tokushige K, Shiratori K. Liver failure caused by non-alcoholic steatohepatitis in an obese young male. J Gastroenterol Hepatol 2005;20:327–9.
crossref pmid
12. Kim YS, Um SH, Ryu HS, Lee JB, Lee JW, Park DK, et al. The prognosis of liver cirrhosis in recent years in Korea. J Korean Med Sci 2003;18:833–41.
crossref pmid pmc
13. Schwimmer JB, Behling C, Newbury R, Deutsch R, Nievergelt C, Schork NJ, et al. Histopathology of pediatric nonalcoholic fatty liver disease. Hepatology 2005;42:641–9.
crossref pmid
14. Lozano R, Naghavi M, Foreman K, Lim S, Shibuya K, Aboyans V, et al. Global and regional mortality from 235 causes of death for 20 age groups in 1990 and 2010: a systematic analysis for the Global Burden of Disease Study 2010. Lancet 2012;380:2095–128.
crossref pmid
15. Kim JH, Yun S, Hwang SS, Shim JO, Chae HW, Lee YJ, et al. The 2017 Korean National Growth Charts for children and adolescents: development, improvement, and prospects. Korean J Pediatr 2018;61:135–49.
crossref pmid pmc pdf
16. Matthews DR, Hosker JP, Rudenski AS, Naylor BA, Treacher DF, Turner RC. Homeostasis model assessment: insulin resistance and beta-cell function from fasting plasma glucose and insulin concentrations in man. Diabetologia 1985;28:412–9.
crossref pmid pdf
17. Purcell M, Flores YN, Zhang ZF, Denova-Gutiérrez E, Salmeron J. Prevalence and predictors of alanine aminotransferase elevation among normal weight, overweight and obese youth in Mexico. J Dig Dis 2013;14:491–9.
crossref pmid
18. Fraser A, Longnecker MP, Lawlor DA. Prevalence of elevated alanine aminotransferase among US adolescents and associated factors: NHANES 1999-2004. Gastroenterology 2007;133:1814–20.
crossref pmid pmc
19. Lee Y, Han KD, Jung JJ, Lee KH, Cho KH, Kim YH. Upper normal alanine aminotransferase range and insulin resistance in Korean adolescents: Korean National Health and Nutrition Examination Survey, 2009-2010. Dig Dis Sci 2016;61:1700–6.
crossref pmid pdf
20. Sookoian S, Pirola CJ. Alanine and aspartate aminotransferase and glutamine-cycling pathway: their roles in pathogenesis of metabolic syndrome. World J Gastroenterol 2012;18:3775–81.
crossref pmid pmc
21. Goessling W, Massaro JM, Vasan RS, D'Agostino RB Sr, Ellison RC, Fox CS. Aminotransferase levels and 20-year risk of metabolic syndrome, diabetes, and cardiovascular disease. Gastroenterology 2008;135:1935–44. , 1944.e1.
crossref pmid pmc
22. González-Gil EM, Bueno-Lozano G, Bueno-Lozano O, Moreno LA, Cuadrón-Andres L, Huerta-Blas P, et al. Serum transaminases concentrations in obese children and adolescents. J Physiol Biochem 2009;65:51–9.
crossref pmid pdf
23. Calcaterra V, Muratori T, Klersy C, Albertini R, Caramagna C, Brizzi V, et al. Early-onset metabolic syndrome in prepubertal obese children and the possible role of alanine aminotransferase as marker of metabolic syndrome. Ann Nutr Metab 2011;58:307–14.
crossref pmid
24. Rasouli N, Molavi B, Elbein SC, Kern PA. Ectopic fat accumulation and metabolic syndrome. Diabetes Obes Metab 2007;9:1–10.
crossref
25. Ali AT, Ferris WF, Naran NH, Crowther NJ. Insulin resistance in the control of body fat distribution: a new hypothesis. Horm Metab Res 2011;43:77–80.
crossref pmid pdf
26. Ashwell M, Hsieh SD. Six reasons why the waist-to-height ratio is a rapid and effective global indicator for health risks of obesity and how its use could simplify the international public health message on obesity. Int J Food Sci Nutr 2005;56:303–7.
crossref pmid
27. Nambiar S, Truby H, Davies PS, Baxter K. Use of the waist-height ratio to predict metabolic syndrome in obese children and adolescents. J Paediatr Child Health 2013;49:E281–7.
crossref pmid
28. Savva SC, Tornaritis M, Savva ME, Kourides Y, Panagi A, Silikiotou N, et al. Waist circumference and waist-to-height ratio are better predictors of cardiovascular disease risk factors in children than body mass index. Int J Obes Relat Metab Disord 2000;24:1453–8.
crossref pmid pdf
29. Weili Y, He B, Yao H, Dai J, Cui J, Ge D, et al. Waist-to-height ratio is an accurate and easier index for evaluating obesity in children and adolescents. Obesity (Silver Spring) 2007;15:748–52.
crossref pmid
30. Kuba VM, Leone C, Damiani D. Is waist-to-height ratio a useful indicator of cardio-metabolic risk in 6-10-year-old children? BMC Pediatr 2013;13:91
crossref pmid pmc pdf
31. de Pádua Cintra I, Zanetti Passos MA, Dos Santos LC, da Costa Machado H, Fisberg M. Waist-to-height ratio percentiles and cutoffs for obesity: a cross-sectional study in brazilian adolescents. J Health Popul Nutr 2014;32:411–9.
pmid pmc
32. Mokha JS, Srinivasan SR, Dasmahapatra P, Fernandez C, Chen W, Xu J, et al. Utility of waist-to-height ratio in assessing the status of central obesity and related cardiometabolic risk profile among normal weight and overweight/obese children: the Bogalusa Heart Study. BMC Pediatr 2010;10:73
crossref pmid pmc pdf
33. Khoury M, Manlhiot C, McCrindle BW. Role of the waist/height ratio in the cardiometabolic risk assessment of children classified by body mass index. J Am Coll Cardiol 2013;62:742–51.
crossref pmid
34. Committee on Adolescent Health Care Services and Models of Care for Treatment, Prevention, and Healthy Development; Board on Children, Youth, and Families. Division of Behavioral and Social Sciences and Education. Challenges in adolescent health care. Workshop Report. Washington, DC: National Academies Press, 2007.

35. Quirós-Tejeira RE, Rivera CA, Ziba TT, Mehta N, Smith CW, Butte NF. Risk for nonalcoholic fatty liver disease in Hispanic youth with BMI > or =95th percentile. J Pediatr Gastroenterol Nutr 2007;44:228–36.
crossref pmid


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