Impact of dietary intake, lifestyle and biochemical factors on metabolic health in obese adolescents

Accepted Manuscript Impact of dietary intake, lifestyle and biochemical factors on metabolic health in obese adolescents I. Aldhoon-Hainerová, V. Hainer, H. Zamrazilová PII:

S0939-4753(17)30096-0

DOI:

10.1016/j.numecd.2017.05.002

Reference:

NUMECD 1722

To appear in:

Nutrition, Metabolism and Cardiovascular Diseases

Received Date: 4 January 2017 Revised Date:

9 April 2017

Accepted Date: 8 May 2017

Please cite this article as: Aldhoon-Hainerová I, Hainer V, Zamrazilová H, Impact of dietary intake, lifestyle and biochemical factors on metabolic health in obese adolescents, Nutrition, Metabolism and Cardiovascular Diseases (2017), doi: 10.1016/j.numecd.2017.05.002. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

ACCEPTED MANUSCRIPT Impact of dietary intake, lifestyle and biochemical factors on metabolic health in Czech normal weight and obese adolescents

Aldhoon-Hainerová Ia,b, Hainer Va, Zamrazilová Ha Institute of Endocrinology, Prague, Czech Republic

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Department of Pediatrics, Third Faculty of Medicine, Charles University, Prague, Czech

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Republic

Irena Aldhoon-Hainerová, MD, PhD

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Corresponding author:

Obesity Management Center, Institute of Endocrinology Národní 8, 116 94 Prague 1, Czech Republic

Phone: +420 723 519 898, Fax: +420 224 905 325

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E-mail: [email protected]

Word counts: abstract: 245, text: 3 315, number of references: 46, figures: 0, tables: 2

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Keywords: Adolescents; Determinants of metabolic health; Lifestyle factors; Metabolically healthy obesity; Metabolic syndrome

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List of abbreviations: MHNW, metabolically healthy normal weight; MUO1, metabolically unhealthy obesity with only one cardiometabolic parameter; ALT, alanine aminotransferase; AST, aspartate aminotransferase; BMI, body mass index; COPAT, Childhood Obesity Prevalence And Treatment; EI, Eating Inventory; GGT, gamma-glutamyl transferase; HDL-C, high-density lipoprotein cholesterol; hs-CRP, high-sensitivity C-reactive protein; LDL-C, low-density lipoprotein cholesterol; MHO, metabolically healthy obesity; MUO, metabolically unhealthy obesity; WHtR, waist-to-height ratio 1

ACCEPTED MANUSCRIPT Abstract

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Background and Aims: Some obese individuals lack metabolic abnormalities. This

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phenomenon is - denoted Obesity devoid of metabolic abnormalities is known as

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metabolically healthy obesity (MHO). Limited data on determinants of MHO at adolescence

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are available. The aim of the study was to examine possible determinants of MHO at

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adolescence.

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Methods and Results: From 710 obese adolescents 43 girls and 57 boys were classified as

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Mmetabolically unhealthy obesity was characterized by the presence of (abdominal obesity

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and >2 risk components of metabolic syndrome). MHO (absence of any cardiometabolic risk

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factor) was found in 211 girls and 131 boys (regardless waist circumference) and in 33 girls

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and 27 boys (without abdominal obesity). In 710 obese adolescents studied according to their

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metabolic health status and in 441 metabolically healthy normal weight adolescents,

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lLaboratory and anthropometric parameters, 3-day dietary records, scores of the Eating

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Inventory and various lifestyle factors were compared evaluated between MHO vs. those

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unhealthy MHO was defined as being free of any essential component defining metabolic

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syndrome. The prevalence of MHO regardless waist circumference was higher in girls than in

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boys (53.1 vs. 41.9%, p<0.001) but comparable when abdominal obesity was excluded (8.3

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vs. 8.6%). Most of the aAnthropometric variables,were related to metabolic health. The levels

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of gamma-glutamyl transferase, total and low-density lipoprotein cholesterol in both genders,

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hs-C-reactive protein in girls and alanine aminotransferase in boys differentiated the two

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metabolic phenotypes in obese individuals. Uric acid was related to metabolic health only in

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the analysis of MHO without abdominal obesity. Total hours of sleep, bedtime, and the time

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of the last daily meal, regular meal consumption and protein intake only in obese boys and

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screen time, the score of disinhibition and diet composition only in obese girls were found to

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ACCEPTED MANUSCRIPT impact cardiometabolic health status. Scores of the Eating Inventory were not associated with

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metabolic health.

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Conclusions: In obese adolescents metabolic health was related to anthropometric and

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biochemical parameters and only weak associations were found with most of the studied

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lifestyle factors. Uric acid concentration associated with metabolic health when abdominal

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obesity was excluded.

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ACCEPTED MANUSCRIPT Introduction

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Obesity is associated with higher prevalence of cardiovascular and metabolic

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diseasesmorbidity and total and cardiovascular mortality [1]. There are, however, individuals

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that despite being obese present with favorable metabolic profile [2,3]. This phenomenon is

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known as metabolically healthy obesity (MHO) and its definition is based either on the

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absence of cardiometabolic disturbances or on preservation of insulin sensitivity [4-6].

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Several studies, mainly carried out in adults, have tried to identify determinants and predictors

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of MHO, including dietary and lifestyle factors [3,7–9]. The role of diet in MHO has been

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widely studied but been yielded rather inconsistent results. Comparable macronutrient intakes

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have been reported in individuals with MHO and metabolically unhealthy obesity (MUO) [8-

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11]. Similarly, inconsistent data were observed when physical activity level in MHO was

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studied [8]. On the other hand, metabolic health was positively associated with

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cardiorespiratory fitness [12]. Among children only limited data regarding the role of

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behavioral and lifestyle factors in determining MHO status exist. A dietary fat intake and

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moderate physical activity has been identified as independent predictors of health status in

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children and adolescence [13]. A better compliance to dietary guidelines has been found in

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MHO adolescents and women in comparison to their MUO counterparts [14]. In a study of

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Camhi et al. physical activity differed between MHO and MUO in adults but not in

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adolescents and no effect of screen time was identified [15]. Physical activity was associated

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with MHO only when defined by insulin resistance [8]. We hypothesized that lifestyle factors

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may influence metabolic health status already at adolescence. A treatment of obesity in

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adolescents has so far been mainly focused on promoting a healthy lifestyle. However, it is

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still questionable whether already in adolescence there are specific determinants of metabolic

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health related to lifestyle factors. In our previous study we identified subjects that shared the

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same body weight, height, body mass index (BMI) and waist circumference but differed in

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ACCEPTED MANUSCRIPT cardiometabolic profile [16] we thus speculate that some specific lifestyle behaviors may play

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a role in metabolic health. An identification of particular factors associated with metabolic

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health profile could thus be emphasized in the treatment of adolescent obese patients. The aim

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of our study wasis to reveal potential determinants of MHO particularly with respect to

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dietary and lifestyle behaviors.

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Methods

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Study population and determination of metabolic health status

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Czech individualsSubjects of Caucasian origin aged 13.0–17.9 years were recruited between

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April 2009 and December 2010 within the Childhood Obesity Prevalence And Treatment

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(COPAT) project. This project was conducted in different regions across the Czech Republic

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and included a representative cohort established by the stratified random selection and

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overweight/obese adolescents that had undergone a 4-week weight management program in

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specialized clinical settings.enrolled adolescents across the whole country from general

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pediatricians (representative cohort) and from in-patient or outpatient clinics specialized in

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childhood obesity (obese cohort). We studied created two cohorts based on body mass index

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(BMI): a group Out of 710 obese participants (397 girls, 313 boys) fulfilling the criteria of

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obesity defined as by BMI > 97.0 percentile [17] and age 13.0-17.9 years and a group of 441

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metabolically healthy normal weight individuals (MHNW) with BMI 25.0–75.0 percentile.

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The total cohort of 710 Czech obese adolescents (397 girls, 313 boys) was studied in relation

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according to their metabolic health status and gender. Metabolic health was classified

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according to the number of present cardiometabolic parameters excluding waist

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circumference. 43 girls and 57 boys were classified as MUO. MUO was defined as the

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presence of abdominal obesity (waist circumference 10-16 years: ≥ 90.0 percentile or adult

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cut-off if lower; >16 years: ≥ 94 cm for boys and ≥ 80 cm for girls) and of at least two

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ACCEPTED MANUSCRIPT essential components of the metabolic syndrome [fasting blood glucose > 5.6 mmol/l;

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triglycerides > 1.7 mmol/l; high-density lipoprotein cholesterol (HDL-C) for individuals aged

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13.0–15.9 years: < 1.03 mmol/l and for individuals aged > 16.0 years: girls < 1.29 mmol/l,

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boys < 1.03 mmol/l; increased systolic or/and diastolic blood pressure] as defined by the

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International Diabetes Federation definition. The Fourth Report on Blood Pressure with age

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and gender specific thresholds of blood pressure was used to define increased systolic or/and

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diastolic blood pressure hypertension [18]. Out of 710 adolescents 211 girls and 131 boys had

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The MHO that was characterized by the absence of any above mentioned cardiometabolic

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abnormality regardless of the waist circumference. In order to create MHO cohort

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corresponding to a more strict definition, subjects with abdominal obesity were excluded from

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the initial MHO cohort (leading to a cohort of 33 girls and 27 boys). Obese adolescents with

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only one cardiometabolic risk factor (denoted as MUO1) are presented only for statistical

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analyses but are not discussed within the clinical implication.

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The study protocol was approved by the Ethicsal Committee of the Institute of Endocrinology

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in Prague and was in accordance with the Helsinki declaration II. All participants and their

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parent(s) signed an informed consent.

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Anthropometric and clinical data

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In participants in underwear and without shoes body height, weight, and waist, hip and arm

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circumferences and their z-scores, sum of four skinfolds (biceps, triceps, subscapular and

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suprailiac skinfolds), trunk and total body fat by bioimpedance (Tanita BC-418 MA, Tanita

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Corporation, Tokyo, Japan) were assessed by trained staff using standard protocol. We

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calculated these indexes: BMI and its z-score were assessed according to age- and sex-

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matched Czech reference population [17]; waist-to-height ratio (WHtR) and body adiposity

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index (hip circumference divided by body height1.5 minus 18 [19]). In order to evaluate waist

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circumference z-score we used reference data of the German adolescent population [20]. Two

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ACCEPTED MANUSCRIPT blood pressure measurements assessed by Omron i-C10 (Omron Healthcare Co., Ltd., Kyoto,

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Japan) were averaged and used for its evaluation.

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Lifestyle and dietary data

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Data on personal history (birth weight and length, duration of breastfeeding - never, < 6

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months, > 6 months) and family history (occurrence of diabetes, hypertension, heart disease,

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dyslipidemia disorder, asthma, allergy, cancer in parents and grandparents) were derived from

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questionnaires. All studied individuals completed the Eating Inventory (EI) questionnaire.

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The EI assesses three dimensions of human eating attitudes: cognitive restraint, disinhibition

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and hunger [21,22]. Information on total hours of sleep, bedtime and snoring (“yes” answer if

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one snores at least sometimes), daily screen time (including television viewing and computer

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use, based on the total number of hours during one week a daily screen time was calculated),

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skipping breakfast (“yes” answer if one does not consume breakfast regularly), regular pattern

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of meal intake (“yes” answer if most of the time one consumes breakfast, lunch and dinner

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regularly), presence of the night eating (“yes” answer if one wakes up and eats sometimes at

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night) and time of the last daily meal were collected. The answers “don´t know” were not

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included in the analyses. Each participant with the assistance of a family member filled out a

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dietary record for 3 days (two weekdays and one weekend day). From these self-reported

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records after adjustment for BMI z-score daily intake of energy (megajoules per day),

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macronutrients - carbohydrate, fat and protein (grams per day and a percentage of total energy

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intake), fiber (grams per day) and calcium (milligrams per day) were calculated using

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NutriMaster SE Version 1 (Abbott Park, Illinois, USA).

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Laboratory investigations

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All biochemical samples [blood glucose, lipid profile, liver function tests, C-reactive protein

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(CRP),

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spectrophotometrically (kits and on Cobas 6000 analyzer,; both Roche diagnostics,

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uric

acid]

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collected

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overnight

fast

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determined

ACCEPTED MANUSCRIPT Mannheim, Germany). Blood glucose was measured spectrophotometrically using an

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enzymatic reference method with hexokinase. A high sensitivity C-reactive protein (hs-CRP)

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was measured spectrophotometrically using an immunoturbidimetric assay. Triglycerides,

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total cholesterol, HDL-C, low-density lipoprotein cholesterol (LDL-C), uric acid, gamma-

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glutamyl transferase (GGT), aspartate aminotransferase (AST) and alanine aminotransferase

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(ALT) were measured spectrophotometrically using an enzymatic method.

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Statistical analyses

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Due to a non-normal distribution data were analyzed by non-parametric Mann–Whitney test

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for differences between two independent groups (Table 1). To eliminate skewed data

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distribution and heteroscedasticity, the original data from dietary records were transformed by

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power transformation to attain a constant variance and symmetric distribution of data and

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residuals, and data underwent general linear model ANOVA analysis (Table 2). Data are

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presented as medians with 95 % confidence intervals. In the case of categorical data, Chi-

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squared test was applied and data are presented as percentages (Table 2). The statistical

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software NCSS 2004 (Kaysville, Utah, 190 USA) and Statgraphics Centurion, version XV

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from Statpoint Inc. (Herndon, Virginia, USA) were used.

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Results

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MHO regardless waist circumference

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The prevalence of MHO was significantly higher in girls than in boys (53.1 vs. 41.9 %, p <

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0.001). In both genders the MHO group was significantly younger, had lower sum of four

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skinfolds, WHtR, percentage of total fat and lower z-scores of BMI and of waist

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circumference compared to those with MUO (Table 1). In contrast to girls, the percentage of

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total and trunk (25.7 % vs. 26.7 %) body fat (girls: 32.1 vs 34.7 %, p= 0.004; boys: 26.5 vs.

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27.8 %, p = 0.081) and as well as the body adiposity index did not differ between MHO and

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ACCEPTED MANUSCRIPT MUO boys (Table 1). As expected all anthropometric parameters significantly differed

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between the normal weight group and all categories of obese subjects in both genders. The

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levels of gamma-glutamyl transferase (GGT), total and LDL-Clow-density lipoprotein

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cholesterol in both genders, hs-CRP in girls and alanine aminotransferase (ALT) in boys

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distinguished the two metabolic phenotypes in obese individuals. Our results further showed

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that boys with MUOMHO had significantly higherlower protein intake and girls with

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MUOMHO had significantly lowerhigher intakes of total energy, carbohydrate, and fat and

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calcium in grams than those with MUOMHO (Table 2). A screen time in obese girls and In

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obese boys a total hours of sleep, the bedtime and the time of last daily meal in obese boys

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were found to impact cardiometabolic health status (Table 2). A tendency to regular meal

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consumption in those with MHO was found in boys. Other factors (e.g. night eating, breakfast

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skipping) did not seem to play a role in metabolic health (Table 2).

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MHO without abdominal obesity

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The prevalence of MHO without abdominal obesity dropped to 8.3 % in girls and 8.6 % in

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boys. Apart from the above mentioned biochemical parameters the concentration of uric acid

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in both gender and ALT in girls and AST concentrations in boys differed between the two

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metabolic health phenotypes (Table 1). MHO girls presented lower score of disinhibition and

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lower frequency of snoring when compared to those with metabolically unhealthy abdominal

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obesity (Table 2). A higher number of total hours of sleep and regular meal consumption in

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boys were associated with MHO (Table 2). In both genders uric acid and aspartate

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aminotransferase concentrations were not related to metabolic health status. Laboratory

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findings of MHNW individuals to those with MHO did reach a significant difference except

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for blood glucose in both genders and for triglycerides, total cholesterol and aspartate

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aminotransferase in girls (Table 1).

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ACCEPTED MANUSCRIPT The significant difference was reached for intakes of total energy and carbohydrate between

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MHNW girls and girls with MHO and also those with one cardiometabolic risk factor (Table

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2). A significantly lower intake of fiber was found in MHNW than MHO girls. Similarly,

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MHNW boys presented with significantly higher intake of total energy, fat and carbohydrate

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than obese boys. Scores of EI were not related to cardiometabolic health in obese participants.

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However, differences in scores were only found when obese groups were compared to

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MHNW group. In comparison to normal weight counterparts obese adolescents reported

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higher restraint and disinhibition scores in both genders and higher hunger score only in boys

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(Table 2).

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Significant differences were revealed in the prevalence of snoring in both genders. In the case

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of snoring the significance was, however, only between normal-weight and all obese groups,

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thus not related to metabolic health status of obese individuals (Table 2).

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In both MHO definitions neither birth weight and birth length nor duration of breastfeeding

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did impact metabolic outcomes during adolescence (Table 2). Family history of the

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participants was not found to be related to metabolic health in both boys and girls (data not

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shown).

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Discussion

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This is one of the largest studies related to MHO in pediatric cohort. Of the total of 710 obese

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adolescents 442 were selected either as having MHO or MUO. Our study focused on A broad

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variety of lifestyle factors and analyzing their potential impact on cardiometabolic health was

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studiedin 710 obese adolescentsand 441 normal weight counterparts. Regardless waist

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circumference Among obese adolescents, 41.9 % boys and 53.1 % girls were free of any

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cardiometabolic disturbances characterizing the metabolic syndrome except for increased

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waist circumference. The higher prevalence of MHO in females is widely recognized and is in

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ACCEPTED MANUSCRIPT accordance with previous findings [4,8] including those carried out at adolescence [7,15].

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Several studies by Camhi et al. reported 65 to 68 % prevalence of MHO among obese U.S.

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adolescents based on none or one abnormal cardiometabolic risk factor [8,13]. Girls and boys

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with MHO were significantly younger and had, as expected, more favorable anthropometric

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profile than those with MUO as found in other studies [7,9]. Several studies pointed out that

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MHO children are more likely to be younger and in earlier stage of puberty [3]. It is thus

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questionable whether those identified as MHO in younger age were too young to develop

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features of metabolic syndrome or they were real carriers of MHO phenotype. An interesting

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study of Li et al. looked at the stability of MHO in children with an average follow-up of 24

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years [23]. The adult cardiometabolic profile in subjects identified as MHO in childhood was

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more favorable than that of metabolically unhealthy normal weight individuals and

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comparable to healthy normal weight children. It is thus still to be determined what factors are

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important to achieve and maintain a good metabolic health throughout the life course.

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Apparently visceral fat accumulation represented by waist circumference plays an important

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role in adverse metabolic outcomes already at adolescence. In our cohort both the waist

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circumference and the percentage of total and trunk body fat mass measured by bioimpedance

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was different between the two metabolic phenotype groups of girls but not ofand boys. On the

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other hand, the waist circumference of both boys and girls with MUO was significantly higher

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than in their metabolically healthy counterparts. We are aware of the fact that abdominal

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obesity compared to general obesity is associated with greater cardiovascular risks [24] and is

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a mandatory diagnostic criterion for definition of metabolic syndrome [25]. Lately it has been

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stressed that prevention strategies should be focused on abdominal obesity also in children

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and adolescents [26]. Based on these facts we established a second cohort of MHO in which

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individuals besides being free of any cardiometabolic abnormality also presented with normal

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ACCEPTED MANUSCRIPT waist circumference. The prevalence of MHO then dropped to around 8.0 % in both genders

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which points out to the fact that most of our obese adolescents display abdominal obesity.

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Regardless the definition of MHO Our data showed that the level of GGT in both genders was

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related to metabolic health. A higher level of GGT has been associated with higher

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cardiometabolic risk clustering and exhibited an ability to distinguish between the two

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phenotypes in adolescents [7,27] as well as to a predictor of homeostasis model assessment of

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insulin resistance index in our previously published study [28].

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D´Adamo demonstrated that fatty liver, independent of visceral fat, plays a central role in the

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state of insulin resistance in obese adolescents [29]. It is therefore now recognized that non-

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alcoholic fatty liver disease share the pathophysiologic basis of insulin resistance as found in

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the development of metabolic syndrome and is considered as hepatic manifestation of

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metabolic syndrome [30]. Additionally, tThe levels of hs-CRP in girls and ALT in boys were

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different between MHO and MUO groups in the analysis of MHO regardless waist

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circumference. This is in agreement with In a recent paper in which the level of ALT was

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only in boys associated with higher cardiometabolic risk score also only in boys [27].

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Except for the level of uric acid and aspartate aminotransferase most of the studied

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biochemical parameters were related to metabolically healthy phenotype in both genders. On

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contrary, uUric acid has recently been identified as the best predictor of MUO [7]. This is in

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agreement with the study of Avula and Shenoy, who demonstrated that hyperuricemia, even

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after adjusting for potential confounders, was strongly and independently associated with

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insulin resistance [31]. A significant difference in the level of uric acid was only found in the

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analysis between MUO and MHO without abdominal obesity.

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Shorter sleep duration associated with higher CRP [16]. Some authors even speculated

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whether the negative effect of short sleep duration on the development of cardiometabolic

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ACCEPTED MANUSCRIPT diseases was mediated by CRP [16]. Our results did not confirm this hypothesis either in boys

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or in girls.

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A study by Appannah et al. showed that already at adolescence energy dense, high fat, low

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fiber dietary pattern was positively associated with cardiometabolic risk factors [32]. In our

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study the proportion of nutrients in the diet of obese boys, apart from the intake of protein,

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was comparable between the metabolically healthy and unhealthy individuals. Diet

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composition of obese boys was similar between metabolically healthy and unhealthy

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individuals. This is in line with reports in adults [8,9]. Intakes of total energy, carbohydrate,

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and fat and calcium were paradoxically lower in obese girls with unhealthy phenotype in

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comparison to both the MHO and MHNW girls. With respect to our finding of higher intake

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of calcium in MHO, a study of Camhi showed that adolescents with MHO consumed a higher

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quantity of milk than their MUO counterparts [14]. According to Zemel dietary calcium and

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dairy foods have demonstrated an antiobesity effect in experimental and population studies, as

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well as in randomized clinical trials. In addition, dietary calcium may suppress oxidative and

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inflammatory stress which is related to cardiometabolic risks in obesity [33]. Hur et al. in

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their recent paper did not find any significant associations between total sugar intake and both

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adiposity and continuous metabolic syndrome scores in children and adolescents [34].

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Another paradoxical finding was in agreement with a study by Manu et al. in which MHO

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females presented with lower intake of dietary fiber [10]. We may only speculate about these

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controversial findings. An inaccuracy of self-reported dietary records and possible interaction

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of nutrients with genes may partly explain these trends.

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In adults, restraint and hunger scores of factors of the EI were significant predictors of the

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diseases characterizing metabolic syndrome [35]. Moreover, in a quota sample of Czech

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adults comprised of 1429 men and 1624 women, disinhibition was positively and restraint

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negatively associated with BMI and waist circumference. In our studied cohort of obese

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ACCEPTED MANUSCRIPT adolescents, we did not find any relationships between scores of the EI and metabolic

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health. However, significantly lower disinhibition score in girls with MHO without abdominal

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obesity compared to those with metabolically unhealthy abdominal obesity supports our

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previous finding of a positive association between the dietary disinhibition and abdominal

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obesity observed in adults [35]. In both genders, restraint and disinhibition scores in normal

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weight subjects were significantly lower when compared to scores of all three obese groups.

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This was also true for hunger score, however, only in boys.

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At adolescence screen time was found to be associated with BMI but results concerning an

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association with and cardiovascular risks have been controversial [9,15,36,37]. In line with

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our finding also previously published sStudies in adolescents and adults however did not find

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any association of screen time or sedentary behaviors with metabolically healthy phenotype

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[9,15]. Even though our MHO girls paradoxically showed higher screen time than their MUO

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counterparts, the real time difference is negligible. It could be speculated that girls with MUO

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may be aware of their health status and thus be under pressure to implement healthier

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lifestyle.

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Shorter sleep duration has not only been related to increased body weight [38] but in some

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studies also to cardiovascular risks even in children [39]. In both MHO definitions used we

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found an association of shorter sleep duration with adverse cardiometabolic outcomes only in

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boys. Lee et al. did not find any association of sleep duration with metabolic syndrome except

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for an association of short sleep with elevated blood pressure [23]. An adult study showed that

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women but not men with MHO slept longer than those at metabolic risk [8] although obesity

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risk was higher in sleep-deprived men than in sleep-deprived women [40]. A recent study in

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adolescents and adults has concluded snoring, but not sleep duration, as an

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independent risk factor for dyslipidemia and metabolic syndrome [25]. The prevalence of

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snoring significantly differed between all four studied groups but was not related to metabolic

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ACCEPTED MANUSCRIPT health but to obesity itself. Recently, an inverse relationship between the sleep duration and

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metabolic syndrome was revealed only in subjects under 50 years of age [41].

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A role of skipping breakfast in obesity and cardiovascular risks has also been documented in

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adults and children [42]. According to our results neither skipping breakfast nor eating at

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neither night nor regular meal consumption seemed to play a role in metabolic health. Even

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though the time of last daily meal and bedtime was associated with cardiometabolic health

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status in boys, the time difference less than half an hour has probably little clinical relevance.

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Regular meal consumption in boys was more frequently found in those healthy ones. In an

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adult study an irregular intake of energy appeared to have an increased cardio-metabolic risk

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[43].

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An association of birth weight with metabolic health incl. type 2 diabetes has been

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demonstrated published [44]. Birth weight and length did not relate to metabolic outcomes at

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adolescence in our study. As others we have recently showed that more than birth weight

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itself a weight trajectory during early childhood may impact health later in life [16]. However,

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Tthere are inconsistent results with respect to the duration of breastfeeding and its impact on

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cardiometabolic health outcomes [36,45].

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Family history of the participants related to cardiovascular and metabolic diseases was not

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associated with metabolic health either in boys or in girls as found by others [46]. We have to

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take into account that certain hereditary traits are manifested later in life.

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There are several limitations of the present study. In order to assess various lifestyle factors

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we used self-reported questionnaires instead of more precise tools. This is due to the initial

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design of our COPAT project in which more than 2,000 participants were recruited and thus

23

questionnaires were more accessible. We assume as the level of bias reporting and inaccuracy

24

was similar within the obese participants. We were unable to analyze physical activity in our

25

studied cohort which is also an important aspect for metabolic health. The study was solely

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ACCEPTED MANUSCRIPT performed in Czech Caucasian population and thus we were unable to compare our results to

2

other populations or ethnic groups. At last, body composition in the present study was

3

assessed by bioimpedance and not by more precise imaging techniques. Despite these

4

limitations we were able to examine various determinants of metabolic phenotypes in one of

5

the largest cohort of obese adolescents.

6

In conclusion, the current study demonstrated that MHO regardless waist circumference iswas

7

more common in adolescent girls than boys but comparable when abdominal obesity was

8

excluded. Most of the aAnthropometric variables and the levels of GGT, total and low-density

9

lipoprotein cholesterol in both genders were related to metabolic health. Additional Some of

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the biochemical parameters were found to be gender specific with respect to the ability to

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distinguish metabolic health status (CRP in girls and ALT in boys). Uric acid was related to

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metabolic health only in the analysis of MHO without abdominal obesity. Among lifestyle

13

factors tTotal hours of sleep, bedtime, and the time of the last daily meal and protein intake

14

only in obese boys and screen time and dietary intake only in girls weresignificantly differed

15

between MHO and MUO.found to influence cardiometabolic health. Eating behavior

16

evaluated by the EI was not associated with cardiometabolic health status among adolescents.

17

The score of dietary disinhibition and fat intake in girls and total hours of sleep and regular

18

meal consumption in boys were related to the metabolic health only if the MHO without

19

abdominal obesity was evaluated.

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Acknowledgements

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This study was supported by Ministry of Health of the Czech Republic - DRO (Institute of

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Endocrinology - EÚ, 00023761) and by PRVOUK P31 as they had contributed funding for

24

the project from which data were derived for the present study. We would like to thank the

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study participants and their families, pediatricians and weight management centers, Lenka

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Dušátková and Barbora Sedláčková for laboratory analyses and data storage.

3 References

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Table 1: Characteristics of the study cohort Boys

0.008

< 0.001 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001

0.012

0.014

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0.003

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P2

MHO MHO regardless without waist abdominal circumference obesity (n = 131) (n = 27)

< 0.001 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 0.008

< 0.001

< 0.001 < 0.001 1

MUO (n = 57)

5.0 (4.91, 5.06) 1.29 (1.23, 1.32) 0.89 (0.83, 0.98) 121 (120, 124) 78 (75, 79)

5.0 (4.83, 5.15) 1.29 (1.18, 1.39) 0.87 (0.69, 1.15) 127 (122, 132) 75 (73, 80)

5.25 (5.11, 5.34) 0.91 (0.88, 0.94) 2.18 (1.94, 2.30) 134 (131, 138) 85 (82, 87)

14.7 (14.4, 15.1) 29.5 (28.7, 30.2) 2.48 (2.41, 2.64) 93.5 (92.0, 95.0) 2.20 (2.11, 2.27) 0.54

16.5 (15.3, 17.0) 27.5 (26.8, 27.8) 2.01 (1.94, 2.13) 85.0 (83.0, 88.0) 1.74 (1.55, 2.04) 0.49

15.6 (15.3, 16.0) 33.1 (31.0, 34.4) 3.02 (2.71, 3.15) 103.0 (98.0, 107.0) 2.66 (2.46, 2.88) 0.58

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Girls MHO MHO – regardless without MUO waist abdominal (n = 43) circumference obesity (n = 211) (n = 33) Criteria definition 4.81 4.75 4.94 BG (4.75, 4.87) (4.49, 4.91) (4.74, 5.37) mmol/l 1.37 1.46 1.15 HDL-C (1.34, 1.40) (1.33, 1.60) (0.99, 1.22) mmol/l 0.86 0.68 1.83 TG (0.81, 0.92) (0.58, 0.82) (1.74, 2.0) mmol/l 115 115 129 BPs (113, 116) (109, 121) (122, 134) mm Hg 76 74 83 BPd (74, 77) (72, 77) (80, 89) mm Hg Anthropometric and biochemical characteristics 15.0 15.0 16.1 Age (14.7, 15.2) (14.0, 15.7) (14.6, 16.9) (years) 29.2 27.0 32.4 BMI (28.8, 29.7) (26.5, 27.5) (31.4, 33.0) kg/m2 2.45 2.08 2.89 BMI (2.39, 2.60) (1.97, 2.19) (2.74, 3.02) z-score 86.0 77.0 91.0 Waist (85.0, 88.0) (75.0, 77.5) (89.0, 93.5) cm 2.45 2.01 2.78 Waist (2.32, 2.56) (1.77, 2.40) (2.56, 2.98) z-score 0.52 0.47 0.56 WHtR

P1

P2

< 0.001

0.004

< 0.001 < 0.001 < 0.001 < 0.001 < 0.001

0.011

< 0.001 < 0.001

0.003

0.103

< 0.001 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 0.002

< 0.001

ACCEPTED MANUSCRIPT

(0.52, 0.53) (0.46, 0.48) (0.54, 0.57) (0.53, 0.55) (0.47, 0.51) (0.55, 0.61) 37.1 32.7 39.8 30.8 23.0 33.0 0.011 < 0.001 0.046 < 0.001 (36.2, 38.2) (30.6, 33.9) (38.3, 41.4) (28.4, 32.1) (22.3, 23.9) (29.3, 35.4) 88.0 70.0 101.0 84.0 63.0 91.5 < 0.001 < 0.001 0.008 < 0.001 (86.0, 92.0) (66.0, 76.0) (94.0, 109.0) (78.0, 89.0) (53.5, 71.0) (82.0, 98.0) 33.7 33.3 35.2 29.6 26.8 29.7 0.002 0.001 0.970 0.002 (33.2, 34.1) (31.3, 34.3) (34.0, 36.3) (28.9, 30.7) (25.8, 29.1) (28.5, 30.4) 1.62 0.73 4.01 1.90 0.79 1.37 hs-CRP 0.001 < 0.001 0.481 0.023 (1.32, 2.14) (0.45, 1.48) (2.63, 4.76) (1.24, 2.50) (0.64, 1.54) (1.12, 2.19) mg/l 4.23 4.10 4.95 4.04 3.90 4.50 TC < 0.001 0.001 < 0.001 < 0.001 (4.09, 4.33) (3.96, 4.54) (4.41, 5.20) (3.94, 4.30) (3.34, 4.18) (4.15, 4.93) mmol/l 2.38 2.35 2.84 2.39 2.13 2.76 LDL-C 0.001 0.004 < 0.001 < 0.001 (2.29, 2.50) (2.13, 2.54) (2.50, 3.24) (2.15, 2.49) (1.74, 2.41) (2.45, 3.14) mmol/l 342 320 370 412 374 442 UA 0.271 0.003 0.055 0.001 (332, 354) (281, 334) (331, 384) (383, 427) (301, 412) (413, 464) µmol/l 0.29 0.23 0.31 0.42 0.36 0.57 ALT 0.337 0.003 < 0.001 < 0.001 (0.26, 0.32) (0.22, 0.25) (0.26, 0.36) (0.37, 0.44) (0.28, 0.42) (0.48, 0.75) µkat/l 0.38 0.35 0.37 0.48 0.47 0.52 AST 0.648 0.675 0.160 0.028 (0.36, 0.39) (0.32, 0.40) (0.33, 0.39) (0.46, 0.50) (0.41, 0.49) (0.46, 0.59) µkat/l 0.23 0.18 0.27 0.31 0.26 0.43 GGT 0.008 < 0.001 < 0.001 0.002 (0.21, 0.24) (0.17, 0.21) (0.24, 0.32) (0.29, 0.35) (0.24, 0.38) (0.38, 0.48) µkat/l Abbreviations: ALT, alanine aminotransferase; AST, aspartate aminotransferase; BAI, body adiposity index; BG, blood glucose; BMI, body mass index; BPd, diastolic blood pressure; BPs, systolic blood pressure; hs-CRP, high-sensitivity C-reactive protein; GGT, gamma-glutamyl transferase; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; MHO, metabolically healthy obesity; MUO, metabolically unhealthy obesity; n, number of subjects; SSF, sum of four skinfolds; TC, total cholesterol; TG, triglycerides; UA, uric acid; WHtR, waist-to-height ratio Data are presented as medians (95 % confidence intervals). P1 = MHO – regardless waist circumference vs. MUO P2 = MHO – without abdominal obesity vs. MUO

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Total fat % SSF (mm) BAI

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Table 2: Lifestyle factors and data on early life related to cardiometabolic health status

0.970

M AN U

0.229

P2

SC

P1

0.116

0.982

0.350

0.311

0.010*

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Breastfeeding never † (“yes” < 6 months answer; %) ≥ 6 months Energy intake (MJ per day) Carbohydrate (g per day) Protein (g per day) Fat (g per day) Calcium intake (mg per day) Fiber (g per day) EI - Restraint EI - Disinhibition EI - Hunger

50 (49, 50) 3300 (3100, 3450) 43 43 14 6.3 (5.5, 7.3) 198 (158, 221) 63.4 (55.5, 69.7) 56.1 (45.5, 64.4) 588 (461, 739) 9.5 (9.0, 12.0) 8 (6, 9) 4 (3, 5) 3 (2, 5)

EP

(g)

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Birth length (cm) Birth weight

MUO (n = 43)

0.035*

0.203

0.275*

0.950

0.002*

0.006

0.047*

Boys MHO MHO regardless without MUO waist abdominal (n = 57) (n = 131) obesity (n = 27) 51 51 51 (50, 51) (50, 51) (50, 51) 3430 3600 3600 (3310, (3450, (3450, 3550) 3710) 3710) 47 52 56 33 33 35 20 15 9 8.8 10.1 9.2 (7.9, 9.5) (8.3, 11.6) (8.4, 10.5) 265 323 285 (237, 293) (225, 393) (247, 324) 76.0 91.5 79.8 (69.4, 79.6) (77.8, 95.2) (72.5, 96.0) 75.8 83.2 85.5 (72.5, 80.8) (66.3, 102) (73.4, 95.6) 730 833 760 (667, 843) (661, 984) (631, 886) 12.0 12.0 12.0 (11.0, 13.0) (11.0, 14.0) (10.0, 14.0) 6 (5, 7) 4 (2, 7) 6 (4, 7) 4 (3, 5) 4 (2, 5) 3 (3, 5) 4 (4, 5) 3 (2, 5) 4 (3, 5)

RI PT

Girls MHO MHO – regardless without waist abdominal (n = 211) obesity (n = 33) 50 50 (50, 50) (49, 50) 3400 3200 (3300, (3100, 3450) 3400) 34 28 39 47 28 25 7.8 7.6 (7.0, 8.1) (6.7, 8.5) 227 229 (216, 242) (204, 269) 64.5 63.6 (61.8, 68.1) (59.5, 70.0) 69.8 69.6 (65.4, 75.2) (58.2, 82.7) 713 722 (669, 734) (592, 828) 10.5 10.0 (9.0, 11.0) (8.0, 12.0) 6 (5, 7) 7 (4, 9) 4 (3, 5) 3 (1, 5) 4 (3, 4) 3 (2, 5)

0.061

0.512*

0.757

0.101 0.493 0.633 3

0.578 0.032 0.971

P1

P2

0.141

0.586

0.691

0.079

0.166

0.735

0.071* 0.778* 0.072* 0.719* 0.030* 0.478* 0.259* 0.991* 0.564* 0.701* 0.536* 0.509* 0.177 0.632 0.344

0.336 0.932 0.858

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Skipping breakfast 45 33 40 0.628 0.873 29 30 40 0.127 0.271 †(“yes” answer; %) †Consumption at least 3 meals/day 43 45 36 0.473 0.430 49 63 32 0.058 0.006 (“yes” answer; %) 19.0 19.0 18.5 19.0 19.0 Last meal 20.0 0.171 0.395 0.005 0.380 (19.0, 19.0) (18.5, 19.5) (18.0, 19.0) (19.0, 19.5) (19.0, 20.0) (19.0, 20.0) (time in hours) †Night eating 8 10 11 0.497 0.848 10 12 14 0.246 0.834 (“yes” answer; %) 2.29 2.21 2.14 2.14 2.29 2.0 Screen time 0.027 0.228 0.059 0.097 (2.14, 2.43) (2.0, 2.57) (2.0, 2.29) (2.0, 2.43) (2.14, 2.43) (1.71, 2.29) (hours per day) 8.0 8.0 8.0 8.0 8.0 8.0 Sleep duration 0.541 0.658 0.002 0.021 (8.0, 8.0) (8.0, 8.0) (7.5, 8.0) (8.0, 8.0) (8.0, 8.5) (7.0, 8.0) (hours) 22.0 22.0 22.0 22.0 22.0 22.0 Bedtime 0.616 0.882 0.005 0.616 (22.0, 22.0) (22.0, 22.0) (22.0, 22.5) (22.0, 22.0) (22.0, 22.0) (22.0, 22.5) (at hours) †Snoring 26 9 37 0.255 0.024 43 29 50 0.232 0.114 (“yes” answer; %) Abbreviations: EI, Eating Inventory; MHO, metabolically healthy obesity; MJ, megajoules; MUO, metabolically unhealthy obesity; n, number of subjects †Fisher test was used, data are presented in percentage. Otherwise Mann-Whitney analysis was used. Data are presented as medians (95 % confidence intervals); *After adjustment for BMI z-score. P1 = MHO – regardless waist circumference vs. MUO P2 = MHO – without abdominal obesity vs. MUO

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Highlights About half of obese adolescents exhibited metabolically healthy obesity.

•

Gamma-glutamyl transferase and lipid levels distinguished metabolic phenotypes.

•

Diet composition in obese girls was related to cardiometabolic health status.

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Hours of sleep and time of last meal in obese boys influenced metabolic health.

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The prevalence of MHO in adolescents with normal waist circumference was ~8.5 %.

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Gamma-glutamyl transferase was related to metabolic health status in both genders.

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Uric acid was associated with abdominal obesity in both genders.

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Longer sleep and regular meal consumption was associated with MHO in boys.

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Surprisingly, fat intake was higher in girls with MHO than in those with MUO.

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