Mediated Factors Associated with Metabolic Health among Obese Adolescents

doi:10.21203/rs.3.rs-4699208/v1

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Mediated Factors Associated with Metabolic Health among Obese Adolescents

https://doi.org/10.21203/rs.3.rs-4699208/v1

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background

Obesity is highly associated with metabolic risk. Yet, subset of individuals maintains metabolic health despite obesity. This study aims to identify key factors potentially associated with a metabolically healthy obese (MHO) status in adolescents.

Methods

Cross-sectional study included 31 adolescents with obesity [median age:14 y, median body mass index (BMI) Z-score:2.58] categorized into MHO (n = 16) and metabolically unhealthy obese (MUO, n = 15), based on the presence of at least one metabolic syndrome criteria. A comprehensive evaluation included assessments of abdominal adipose tissue distribution, physiological performance, blood markers, prenatal and sociodemographic characteristics, and dietary habits.

Results

Compared to the MUO, MHO individuals exhibited significantly lower hepatic fat content (HFC, 14.0 ± 9.8% vs. 6.1 ± 3.2%, p = 0.01), liver enzymes (p = 0.004), and experienced a lower risk of complications during birth (p = 0.03). MHO participants consumed fewer total calories (p = 0.04), animal protein (p = 0.005), red meat (p = 0.02), sodium (p = 0.027), palmitic acid (p = 0.04), stearic acid (p = 0.029), arachidonic acid (p = 0.005) and calories from ultra-processed grains (p = 0.049) compared to their MUO counterparts.

Conclusion

HFC emerges as a primary mediator of metabolic health, suggesting its potential as a valuable tool for early risk assessment in adolescents with obesity. In addition, dietary composition exerts a notable influence on metabolic health during early stages of life.

Trial registration:

NCT06032312.

Metabolic health

obesity

adolescents

hepatic fat content

diet

A distinct subgroup of obese youth, known as "metabolically healthy obese" individuals, has recently gained attention

WHAT IS NEW

The presence and extent of fatty liver emerged as the primary health marker distinguishing between metabolically healthy and unhealthy obese adolescents
Diet that contains Ultra processed grains, animal protein and sodium may exerts a notable influence on metabolic health during early stages of life

The global epidemic of childhood obesity is characterized by a rising prevalence of endocrine, metabolic, and cardiovascular comorbidities and represents one of the major public health challenges of our time (1–4). Recent data underscore alarming trends in childhood obesity and its associated metabolic syndrome (MS) features (2, 4–6). MS refers to a cluster of cardio-metabolic risk factors, including high blood pressure, high blood sugar, excess abdominal adiposity, and abnormal lipid profiles (7). The combined presence of multiple risk factors extends the likelihood of developing type 2 diabetes mellitus (T2DM) and cardiovascular diseases. The increasing prevalence of childhood obesity among pediatric populations has raised concerns about potential reductions in life expectancy.

A distinct subgroup of obese youth, known as "metabolically healthy obese" (MHO) individuals, has recently gained attention (8, 9). Despite their obesity status, MHO youngsters exhibit a favorable metabolic profile, characterized by preserved insulin sensitivity, and normal blood pressure, glucose regulation, lipid and liver enzyme levels, and hormonal, inflammatory, and immune profiles (7, 10–13). Although MHO status does not necessarily indicate a benign condition, as it can potentially transition into a metabolically unhealthy obese (MUO) phenotype (7, 14–18), several longitudinal studies, both in adolescent and adults identified a subgroup of individuals with obesity who maintain a preserved metabolic profile and do not exhibit adverse effects on cardiovascular health and mortality (19–21). Therefore, Identifying and understanding this MHO subgroup among adolescents with obesity is essential for elucidating the mechanisms that protect against the clustering of cardiometabolic risk factors and for informed clinical, preventive, and therapeutic decision-making (4, 8, 13, 22, 23).

The MHO phenotype was first described in the 1980s, but its formal definition in pediatric populations emerged only in 2018, as reported by Damanhoury et al. (7). Notably, despite a consensus among 46 experts with an agreement level of ≥ 80%, debates on the definitions of MHO and its criteria are still ongoing (7, 14). Limited studies have examined the predictors and risk factors associated with MUO individuals (22, 23). Furthermore, these studies have yielded mixed results, likely due to variations in the definitions of MHO and criteria used for its classification. Risk factors include lifestyle behaviors, such as insufficient physical activity (24), dietary patterns characterized by increased protein and saturated fat intake, higher consumption of sugary soft drinks, and lower fruit and vegetable consumption in the MUO subgroup (23, 25). Physiological markers, with a specific emphasis on generalized obesity rather than central or combined obesity, alongside a reduced level of hepatic fat content (HFC), are being explored as potential predictors of the MHO phenotype in adolescents (26–28). Research on adults consistently underscores that ectopic adipose tissue accumulation in the liver resulting in fatty liver or metabolic dysfunction-associated steatotic liver disease (MASLD) serves as a robust predictor of the MUO phenotype (29–32). In some cases, this predictive capacity remains significant even when compared to visceral fat and other ectopic fat deposition sites (33). These findings underscore the pivotal role of an elevated HFC in the development of the MUO phenotype and its association with a long-term risk of noncommunicable diseases (14, 29, 30, 33–35).

Noteworthy, previous studies have not comprehensively investigated the interaction among perinatal history, sociodemographic factors, body composition, physical activity, and dietary composition in the obese pediatric population, nor have they attempted to establish relationships between these variables. Therefore, the primary objective of this study was to assess factors associated with metabolically healthy obesity within a framework encompassing sociodemographic, clinical, body composition, nutritional, and physical performance aspects among adolescents with obesity. Given the emerging relevance of HFC in metabolic health, we hypothesize that variations in hepatic fat levels will contribute to the process of distinguishing between metabolically healthy and unhealthy states in the context of childhood obesity.

Study design

This prospective observational study was performed in the Nutrition and Obesity Clinic of the Pediatric Gastroenterology Institute, “Dana Dwek” Children`s Hospital of the Tel Aviv Soursky Medical Center, which is a tertiary care pediatric hospital. Participants under routine care at the Clinic were invited to participate in the study after obtaining parental consent. The study protocol included a clinic visit for anthropometric measurements, and for recording prenatal and sociodemographic characteristics. The participants also underwent a full day of measurements, which included magnetic resonance imaging (MRI) and magnetic resonance spectroscopy (MRS) scans, as well as a series of physiological performance assessments. such as the isometric mid-thigh pull and handgrip dynamometer strength tests and resting metabolic rate (RMR) measurements. Details on self-reported physical activity levels and dietary regimens were also collected. This study was approved by the Institutional Review Board of Tel Aviv Medical Center (TLV-0799-20). All parents provided written informed consent prior to the participant's enrollment (clinical trial registration number: NCT06032312).

Study population

The study cohort consisted of adolescents diagnosed with obesity but devoid of metabolic risk factors. A control group, matched for body mass index (BMI) Z-score and age, was recruited, comprising adolescents with obesity who presented with one or more components of metabolic syndrome (7).

Eligible participants were those treated at the Center from January 2021 to August 2023. Adolescents were excluded from the trial if they had underlying diseases or underwent treatment with medications that could influence body composition or metabolic risk (e.g., inflammatory conditions), or did not complete an MRI scan.

Clinical and demographic variables

Information retrieved from the medical files of the study participants included:

1. Sociodemographic characteristics: age, sex, home address.

2. Medical history: perinatal characteristics (birth weight, gestational age), medications, and family history of cardiometabolic diseases (diabetes, hypertension, dyslipidemia, cardiovascular disease, and cerebrovascular episodes) among first- and second-degree relatives.

3. Physical examination: systolic and diastolic blood pressure and anthropometric measurements (height and weight conducted following standardized protocols). BMI, height and birth weight Z-scores were calculated by means of sex and age-specific BMI reference values from the Center for Disease Control and Prevention growth charts (36).

4. Documented blood markers at study entry included: total cholesterol, low-density lipoprotein cholesterol (LDL-c), high-density lipoprotein-cholesterol (HDL-c), triglycerides (TG), alanine transaminase (ALT), fasting plasma glucose (FPG), and insulin.

Metabolic assessment

RMR was measured in a metabolic unit using an indirect calorimeter device (Quark RMR; Cosmed, Rome, Italy). Indirect calorimetry was based upon the ratio of gas exchange, specifically, carbon dioxide production (VCO₂) versus oxygen consumption (VO₂), which reflects energy metabolism according to the Weir Eq. (37). The 20-minute RMR measurement was performed in a supine position after 8 hours of fasting and a minimum of 24 hours of abstaining from alcohol, caffeine, smoking, and exercise. Gas and turbine calibrations were performed before each test. The first 4 minutes, also known as the adaptation phase, were excluded from the mean RMR, resulting in the utilization of only the final 16 minutes to determine RMR values.

Nutritional assessment

A registered dietitian administered a self-reported semi-quantitative food frequency questionnaire (FFQ) to assess the participant's nutritional intake. The 126-item questionnaire aimed to estimate the participant's dietary habits over the previous year (38). In addition to the measurement of energy intake, the FFQ data underwent a further analysis to generate values for food group consumption, including a macro- and micronutrient breakdown. Ultra-processed foods were classified as falling under NOVA group 4 according to the NOVA food classification system (39). The Mediterranean diet score, developed by Panagiotakos et al., evaluates adherence to the Mediterranean diet, with scores ranging from 0 to 55 (40). It assesses the frequency of consumption of various food groups. including non-refined cereals, fruits, vegetables, legumes, potatoes, fish, meat, poultry, full-fat dairy products, olive oil, and alcohol. Each item within these groups receives a score from 0 to 5 based upon monthly consumption frequency (from never to > 18 servings per month). These analyses utilized the Israeli national nutrient database from the Israeli Ministry of Health ('Tzameret'), as per the Food and Nutrition Services and Public Health Services guidelines [2008] in the Israeli Nutrient Database (41).

Body composition and fat distribution assessment

1. Fat mass, fat-free mass, and skeletal muscle mass were determined by means of a multi-frequency SECA body composition analyzer (mBCA 514 Medical, Hamburg, Germany), utilizing bioelectrical impedance analysis. The calculated bioelectrical impedance analysis variables included appendicular skeletal muscle mass (i.e., skeletal muscle mass of all four limbs) and muscle-to-fat ratio (i.e., appendicular skeletal muscle mass [kg]/total fat mass [kg]). Participants wore light clothing during these assessments and removed all jewelry, socks, and shoes. These measurements were conducted in the morning following an overnight fast.
2. Fatty liver was determined by clinical radiologists via abdominal ultrasonography by a Logic Q700 MR machine (GE, Milwaukee, WI, USA) and a 3.5 MHz probe. The ultrasound images included: i) sagittal view of the right lobe of the liver and right kidney, ii) transverse view of the left lateral segment of the liver and spleen, and iii) transverse view of the liver to assess for altered echo texture. Fatty liver infiltration was identified by an increase in liver echogenicity compared to the echogenicity of the renal cortex, with the diaphragm and intrahepatic vessels appearing normal.
3. Abdominal adipose tissue distribution and HFC were evaluated with MRI and MRS, respectively, by means of the Siemens MAGNETOM Prisma 3-Tesla MRI scanner at the Alfredo Federico Strauss Center at Tel Aviv University. MRI scans were conducted at three axial levels (L5-L4, L4-L3, and L3-L2) to precisely determine visceral fat distribution. The mean values for visceral adipose tissue, deep subcutaneous adipose tissue, and superficial subcutaneous adipose tissue were calculated based upon the three axial slices.

HFC was determined by means of MRS, a well-validated analytical technique that detects radiofrequency electromagnetic signals emitted by atomic nuclei within molecules (42, 43). The total hepatic fat fraction within the image was determined as the ratio of the sum of the area under all fat peaks to the sum of the area under all fat and water peaks.

Performance and physical activity assessments

Muscle strength and function were assessed with an isometric mid-thigh pull and handgrip dynamometer (44). A handgrip strength test was employed to measure upper limb strength by means of a handgrip dynamometer (Baseline® 200lb Capacity, New Jersey, USA) (45). Finally, the participants were instructed to wear an accelerometer (GT9X©; ActiGraph, Pensacola, FL, USA) for seven consecutive days in order to track their sedentary and vigorous activity periods. Data extraction from the device included calibrating the raw data and identifying wear/non-wear episodes to gather validated information on the physical activity levels.

Classification of metabolically unhealthy obese and definition of study variables

Glucose intolerance was defined as FPG ≥ 100 mg/dL (5.5 mmol/L), and insulin resistance was determined with the Homeostatic Model Assessment of Insulin Resistance (HOMA-IR) formula: fasting insulin (µU/mL) × fasting glucose (mmol/L)/22.5 (46). Elevated blood pressure was indicated by systolic and/or diastolic blood pressure ≥ 90th percentile for sex, age, and height (47). Hypertriglyceridemia was characterized by TG ≥ 150 mg/dL (1.69 mmol/L) and HDL-c ≤ 40 mg/dL (1.03 mmol/L) (48). A diagnosis of MASLD was established according to the recent multisociety statement as any case of hepatic steatosis in the setting of cardiometabolic risk factors (49). The consensus-based definition of MUO includes a BMI over the 95th percentile, along with one or more of the following criteria: HDL-c < 40 mg/dL (or < 1.03 mmol/L), TG ≥ 150 mg/dL (or ≥ 1.7 mmol/L), systolic and diastolic blood pressure ≥ 90th percentile, and FPG ≥ 100 mg/dL (or ≥ 5.5 mmol/l) (7). Socioeconomic position (SEP) was determined by the patient’s home address according to the Israel Central Bureau of Statistics’ Characterization and Classification of Statistical Areas within Municipalities and Local Councils by the Socio-Economic Level of the Population 2015 (50). The quality of life (QoL) of the study participants was assessed with the Pediatric Quality of Life Inventory (PedsQL), version 3.0 (51). The PedsQL is a 23-item assessment of physical, emotional, social, and school functioning.

Statistical analysis

A convenient sample of 30 participants was chosen after determining the sample size for assessing trends for metabolic health among adolescents with obesity. This decision was based upon several factors, including feasibility, available resources, and the need to ensure adequate statistical power to detect meaningful trends. Specifically, adolescents were matched for age and BMI Z score to minimize confounding variables and enhance result comparability. Epidemiological data and participant descriptors measured on continuous scales were reported as means and standard deviations, while categorical variables were presented as percentages of the number of participants. Parametric tests, including the Student's t-test for continuous variables, were employed for normally distributed data, while non-parametric tests, such as the Mann-Whitney U test, were utilized for non-normally distributed continuous variables. Levene’s test assessed the equality of group variances for continuous variables. The Chi-squared test was applied for categorical variable analysis. Logistic regression analysis was employed to assess the impact of HFC on metabolic health, adjusting for BMI, sex, age, and total calories. Additionally, linear regression using the enter method was applied to evaluate the independent effect of HFC on FPG, a primary metabolic health criterion, while adjusting for saturated fatty acid (SFA) and animal protein. Significance was determined with a P-value of < 0.05 by applying the Bonferroni corrections for multiple comparisons. Data values were excluded if they deviated more than three times the standard deviation from the mean in order to ensure that extreme values do not unjustifiably influence the statistical results. The statistical analyses were carried out using SPSS V. 25 (SPSS Inc., Chicago, IL, USA) software, and Prism V. 8 (GraphPad, La Jolla, CA, USA).

Nine of the 52 eligible adolescents with obesity were excluded due to chronic kidney disease (n = 1), celiac disease (n = 2), neurologic disability (n = 3), and regular consumption of metformin or risperidone (n = 3). Twelve others declined to participate, leaving a final cohort of 31 participants (15 in the MUO group and 16 in the MHO group).

Clinical factors and sociodemographic data

The sociodemographic and clinical data of all participants are detailed in Table 1. There were 10 females and 6 males in the MHO group and 7 females and 8 males in the MUO group. The mean age and the BMI were similar for both groups. The MHO group had significantly higher HDL-c concentrations compared to the MUO group (44.7 ± 5.7 mg/dL vs. 37.0 ± 5.5 mg/dL, respectively, P = 0.001) as well as lower TG concentrations (96.5 ± 27.0 mg/dL vs. 152.2 ± 66.7 mg/dL; P = 0.01). Additionally, the MHO group had significantly lower insulin (22.0 ± 10.7 µU/mL vs. 48.9 ± 23.4 µU/mL; P = 0.009) and Homa-IR (4.1 ± 2.8 vs. 13.2 ± 10.2; P = 0.02) levels compared to the MUO group, and the prevalence of hypertension was over 25% higher in the MUO group compared to the MHO group (26.6% vs. 0.0%; P = 0.02). The MUO group also had a significantly higher elevation in ALT compared to the MHO group (45.0 ± 23.6 U/L vs 21.3 ± 8.1 U/L. P = 0.004). This latter finding was consistent with the substantially higher incidence of fatty liver observed on the ultrasound scans in the MUO group (MHO: 15.3% vs. MUO: 76.9%; P = 0.002).

Table 1

Clinical and sociodemographic characteristics of the study population
	Metabolically	Metabolically	p
	healthy Obese	unhealthy obese
	(n = 16)	(n = 15)
Age (y)	14.5 ± 1.9	14.2 ± 2.0	0.64
Female, n (%)	10 (62.5)	7 (46.7)	0.37
Weight (kg)	100.0 ± 17.1	108.4 ± 12.1	0.28
Height (m)	1.64 ± 0.1	1.67 ± 0.1	0.41
Height Z-score	0.35 ± 0.93	0.69 ± 0.98	0.33
BMI (kg/m²)	37.1 ± 6.0	38.7 ± 8.3	0.54
BMI Z-score	2.47 ± 0.51	2.77 ± 0.67	0.18
Ultrasonography fatty liver, n (%)	2 (15.3)	10 (76.9)	0.002
Hypertension, n (%)	0 (0.0)	4 (26.6)	0.02
Blood tests
Total Cholesterol (mg/dL)	150.9 ± 20.2	165.4 ± 28.3	0.13
LDL-c (mg/dL)	83.0 ± 18.3	96.4 ± 25.7	0.13
HDL-c (mg/dL)	44.7 ± 5.7	37.0 ± 5.5	0.001
TG (mg/dL)	96.5 ± 27.0	152.2 ± 66.7	0.01
ALT (u/L)	21.3 ± 8.1	45.0 ± 23.6	0.004
FPG (mg/dL)	89.8 ± 6.5	99.0 ± 20.6	0.11
Insulin (µU/mL)	22.0 ± 10.7	48.9 ± 23.4	0.009
Homa-IR (units)	4.1 ± 2.8	13.2 ± 10.2	0.02
Prenatal characteristics
Birth weight (kg)	3.2 ± 0.6	3.0 ± 0.7	0.54
Birth week (weeks)	38.0 ± 1.5	38.5 ± 1.4	0.43
Birth weight-for-birth week Z-score	0.12 ± 1.35	-0.53 ± 1.70	0.24
High-risk pregnancy, n (%)	2 (12.5)	7 (46.7)	0.03
Cesarean section, n (%)	5 (26.3)	3 (21.4)	0.54
Breastfeeding, n (%)	9 (56.2)	6 (42.8)	0.46
Sociodemographic characteristics
SPS status index	0.74 ± 0.54	0.66 ± 0.59	0.71
Physical functioning score	69.0 ± 14.7	68.7 ± 22.5	0.97
Emotional functioning score	60.0 ± 15.8	75.8 ± 21.6	0.06
Social functioning score	68.6 ± 20.8	78.7 ± 15.9	0.19
School functioning score	46.8 ± 8.7	62.5 ± 13.0	0.003
Total QoL score	59.8 ± 11.2	71.4 ± 12.1	0.03
Cardiometabolic risk, n (%)	9 (56.2)	12 (80.0)	0.15
Family history of bariatric surgery, n (%)	3 (18.7)	7 (50.0)	0.07
Values are means ± SD. BMI, body mass index; LDL, low-density lipoprotein; HDL, high-density lipoprotein; ALT, alanine transaminase; FPG, fasting plasma glucose; Homa-IR, Homeostatic Model Assessment for Insulin Resistance; SPS, socioeconomic position; QoL, quality of life. Bold text indicates significant findings p < 0.05 (t-tests for continuous variables; Chi-square for categorical variables).

Significantly more high-risk pregnancies (defined as any pregnancy that required special care, such as intrauterine growth restriction, maternal hypertension, etc.) were reported for the MUO adolescents (46.7%) compared to the MHO adolescents (12.5%) (P = 0.03), However, no differences were noted in terms of birth weight, weeks' gestation, or mode of delivery. In addition, MUO adolescents had significantly higher grades in school functioning and overall QoL compared to their MHO counterparts (P < 0.05).

Body composition, fat distribution, physiological performance and physical activity associated with metabolic health

Physiological and body composition parameters for the two study groups are listed in Table 2. HFC, measured by H-MRS, was significantly elevated in the MUO group compared to the MHO group (14.0 ± 9.8% vs 6.1 ± 3.2%, respectively, P = 0.01) (Fig. 1). However, the levels of fat mass, fat-free mass and muscle-to-fat ratio were comparable for the MHO and MUO groups. The total abdominal fat, visceral adipose tissue, deep subcutaneous adipose tissue and superficial subcutaneous adipose tissue distribution was also similar for the MHO and MUO groups (Table 2). Interestingly, there were no significant differences between the MHO and MUO groups in RMR (1962 ± 427 vs. 2259 ± 379; P = 0.07), nor for the physical activity and strength measures of sedentary time per day (58.2 ± 8.4% vs. 59.1 ± 7.6%; P = 0.81), handgrip max strength (23.1 ± 7.7 kg vs. 25.2 ± 7.3 kg, P = 0.43), and isometric mid-thigh pull max (169.4 ± 38.9 kg vs. 165.9 ± 32.1 kg, P = 0.80) (Table 2).

Table 2

Comparative analysis of metabolic and physiological parameters in healthy and metabolically unhealthy obese adolescents
	Metabolically	Metabolically
	healthy Obese	unhealthy obese
	(n = 16)	(n = 15)
Metabolic
Resting metabolic rate (kcal/day)	1962 ± 427	2259 ± 379	0.07
Respiratory quotient	0.85 ± 0.21	0.75 ± 0.12	0.15
Body composition & fat pools
Body fat (%)	46.2 ± 5.9	43.9 ± 7.2	0.35
Fat mass (kg)	45.6 ± 10.2	48.7 ± 18.2	0.58
Fat-free mass (kg)	53.1 ± 11.6	59.6 ± 9.8	0.11
Skeletal muscle mass (kg)	25.0 ± 6.3	29.3 ± 5.7	0.06
Appendicular skeletal muscle mass (kg)	18.75 ± 4.75	21.99 ± 4.3	0.06
Muscle-to-fat ratio (kg)	0.42 ± 0.1	0.48 ± 0.1	0.23
HFC (%)	6.1 ± 3.2	14.0 ± 9.8	0.01
Total abdominal fat (mm³)	179.5 ± 42.3	209.8 ± 72.1	0.20
Visceral adipose tissue (mm³)	48.6 ± 9.2	53.4 ± 12.7	0.29
Deep subcutaneous adipose tissue (mm³)	50.4 ± 20.0	51.2 ± 12.8	0.91
Superficial subcutaneous adipose tissue (mm³)	80.4 ± 28.6	86.5 ± 37.3	0.64
Physical activity and performance
Days of accelerometer wearing (days)	7.4 ± 0.8	8.0 ± 1.8	0.36
Sedentary time (%/day)	58.2 ± 8.4	59.1 ± 7.6	0.81
Vigorous time (%/day)	0	0
Hand grip strength max (kg)	23.1 ± 7.7	25.2 ± 7.3	0.43
Isometric mid-thigh pull max (kg)	169.4 ± 38.9	165.9 ± 32.1	0.80
Values are means ± SD. Bold text indicates significant findings p < 0.05 (t-tests for continuous variables; ch chi-square for categorical variables).

Differences in dietary patterns between metabolically healthy and unhealthy obese adolescents

Table 3 depicts the self-reported dietary consumption patterns of the MHO and the MUO groups.

Table 3

Differences in self-reported nutrient intake between healthy and metabolically unhealthy obese adolescents.
	Metabolically	Metabolically	p
	healthy Obese	unhealthy obese
	(n = 16)	(n = 15)
Total calories (kcal/day)	1655 ± 475	2071 ± 517	0.04
Carbohydrate (g/day)	177 ± 67	208 ± 65	0.23
Fat (g/day)	66 ± 19	83 ± 24	0.06
Protein (g/day)	75 ± 18	110 ± 32	0.003
Added sugars (g/day)	18 ± 13	21 ± 14	0.66
Fructose (g/day)	18 ± 10	23 ± 10	0.26
Dietary fiber (g/day)	26 ± 15	32.5 ± 11	0.28
Saturated fat (g/day)	20 ± 5	25 ± 9	0.09
Palmitic acid (g/day)	10.5 ± 2.8	13.7 ± 4.9	0.04
Stearic acid (g/day)	4.1 ± 1.1	5.5 ± 2.1	0.029
Arachidonic acid (g/day)	0.14 ± 0.1	0.30 ± 0.1	0.002
Animal protein (g/day)	49 ± 17	76 ± 27	0.005
Red meat calories (kcal/day)	40 ± 50	93 ± 59	0.02
Calcium (mg/day)	847 ± 352	962 ± 368	0.41
Iron (mg/day)	10.2 ± 4.2	15.0 ± 5.1	0.01
Sodium (mg/day)	3050 ± 1090	4084 ± 1056	0.02
Ultra-processed food calories (kcal/day)	364 ± 210	467 ± 247	0.26
Ultra-processed grains calories (kcal/day)	67 ± 37	116 ± 78	0.049
Mediterranean diet score (units)	29 ± 6	27 ± 5	0.22
Values are means ± SD. Bold text indicates significant findings p < 0.05 (t-tests for continuous variables; chi- re square for categorical variables).

MUO individuals had a significantly higher total daily calorie consumption (MHO: 1655 ± 475 kcal/day vs. MUO: 2071 ± 517 kcal/day; p = 0.04). Furthermore, there was a substantial increase in daily protein consumption by MUO individuals (MHO: 75 ± 18 g/day vs. MUO: 110 ± 32 g/day; p = 0.003), along with animal protein (MHO: 49 ± 17 g/day vs. MUO: 76 ± 27 g/day; p = 0.005), red meat calories (MHO: 40 ± 50 kcal/day vs. MUO: 93 ± 59 kcal/day; p = 0.02), and iron intake (MHO: 10.2 ± 4.2 mg/day vs. MUO: 15.0 ± 5.1 mg/day; p = 0.01. Likewise, MUO individuals exhibited significantly higher daily intakes of palmitic acid, stearic acid and arachidonic acid (p < 0.05). In addition, MUO adolescents consumed significantly higher calories from ultra-processed grains (MHO: 67 ± 37 kcal/day vs. MUO: 116 ± 78 kcal/day; p = 0.05) and significantly higher sodium (MHO: 3050 ± 1090 mg/day vs. MUO: 4084 ± 1056 mg/day; p = 0.02).

The independent effect of HFC on metabolic health

Table 4 displays the odds ratios (OR) for MUO status based upon the HFC while adjusting for various covariates. Model 1 represents the unadjusted OR of 1.21 (95% confidence interval [CI]: 1.02–1.41; P = 0.02). Model 2 adjusted for BMI, age and sex revealed an OR of 1.19 (95% CI: 1.00-1.41; P = 0.04). Model 3 included all the covariates and incorporated total calorie intake, resulting in an OR of 1.20 (95% CI: 0.99–1.46; P = 0.06). Additionally, the linear regression analysis depicted in Fig. 2 revealed a significant positive association between HFC and glucose levels (β = 0.419, P = 0.049). Notably, despite saturated fat intake (β = − 0.048, P = 0.852) and animal protein intake (β = 0.190, P = 0.460) showing significant associations with glucose in Pearson’s correlation analysis (Table 5), these relationships did not remain significant after adjusting for HFC.

Table 4

Odds Ratios for Unhealthy Metabolic Obesity According to Hepatic Fat Content percentage, with Adjustments for Covariates.
Models	OR (95% CI)	p
Unadjusted	1.21 (1.02–1.41)	0.02
Adjusted for BMI, age, and sex Adjusted for BMI, age, sex, and total calories	1.19 (1.00-1.41) 1.20 (0.99–1.46)	0.04 0.06
Multivariable logistic regression analysis assessed the association between hepatic fat content and
metabolic health. OR, odds ratio; CI, confidence interval; BMI, body mass index. Bold text indicates
significant findings p < 0.05.

Table 5

Univariate correlation between hepatic fat content, visceral adipose tissue, fasting plasma glucose, dietary animal protein and saturated fat.
	Hepatic fat content	Visceral adipose tissue	Animal protein	Saturated fat
Hepatic fat content			r = 0.21 p = 0.28
Visceral adipose tissue	r = 0.54 p = 0.008
Animal protein	r = 0.21 p = 0.28	r = 0.24 p = 0.27
Saturated fat	r = 0.14 p = 0.47	r = 0.31 p = 0.15	r = 0.71 p = 0.000
Fasting plasma glucose	r = 0.44 p = 0.02	r = 0.37 p = 0.07	r = 0.49 p = 0.01	r = 0.43 p = 0.02
Bold text indicates significant findings p < 0.05

Figure 3 provides a detailed comparison of metabolic differences between MHO and MUO adolescents. It highlights the significance of HFC in the MUO group, indicating increased intake of total calories, salt, animal protein, red meat, long-chain SFA, and ultra-processed grains. It also illustrates a higher prevalence of high-risk pregnancies in the MUO group. Importantly, the levels of fat-free mass, fat distribution, and physical strength are similar for both groups.

In this study, we demonstrated that the presence and extent of fatty liver emerged as the primary health marker, and that it was significantly different between MHO and MUO adolescents. Also significant were the differences in the history of high-risk pregnancies and dietary habits between these two groups. Specifically, adolescents with MUO consumed more calories, animal protein, sodium, arachidonic acid, long-chain saturated fatty acids, and calories from ultra-processed grains compared to their MHO counterparts.

The MUO group exhibited significantly higher HFC, as measured by H-MRS, a reference standard for the noninvasive measurement of liver steatosis (42, 43), and confirmed by elevated liver enzymes and ultrasound findings. This compelling evidence suggests that liver fat may play a pivotal role in distinguishing metabolic phenotypes within the context of obesity, surpassing the influence of visceral fat, which did not significantly differ between the two groups of adolescents. These findings are consistent with previous trials in adults which demonstrated that hepatic and not visceral fat is strongly linked to obesity-related metabolic complications (33, 52). Our regression models reinforce the independent significance of HFC. Even after adjusting for various covariates, our results consistently demonstrated a substantial association between elevated HFC and metabolically unhealthy obesity, as well as with fasting plasma glucose, a primary criterion of the definition of MS (supp. Figure 2). These findings emphasize the potential clinical significance of assessing liver fat as an independent biomarker for early identification and intervention in obese adolescents at risk of metabolic disease. It is worth noting that the consensus-based MHO definition currently does not incorporate hepatic steatosis. Given our findings and those of previous studies (14, 29, 33), there is a growing rationale for considering the inclusion of hepatic steatosis in the definition of MHO.

The precise underlying mechanisms that link metabolic health and hepatic steatosis remain incompletely clarified, but the liver is recognized as playing a pivotal role in governing both carbohydrate and lipid metabolism through an intricate network of metabolic pathways. Primarily, ectopic accumulation of lipids in the liver is closely associated with metabolic dysfunction, leading to MASLD, which exerts a profound impact on the metabolic profile (53, 54). MASLD involves the accumulation of ceramides and diacylglycerols due to excessive free fatty acid accumulation, triggering insulin resistance through insulin receptor dysfunction and downstream signaling pathways (55). The capability of insulin to inhibit hepatic glucose production is compromised in the presence of MASLD-associated insulin resistance, leading to worsened glycemic control. Simultaneously, the suppression of adipose tissue lipolysis is inhibited, perpetuating a vicious cycle of insulin resistance and heightening the risk of cardiovascular complications (56, 57). In line with this perspective, another mechanistic explanation contributing to the metabolically unfavorable profile in MASLD involves the secretion of inflammatory cells and cytokines, known as hepatokines (54). Among these hepatokines are fetuin A, follistatin, HFREP1, LECT2, PEDF, and ectodysplasin, collectively exacerbating insulin resistance in skeletal muscle and adipose tissue through the activation of the c-Jun N-terminal kinase signaling pathway. This pathway is characterized by direct inhibitory phosphorylation of insulin receptor substrates, resulting in diminished insulin signaling and exacerbation of hyperglycemia (54).

Our dietary analysis revealed significant differences in nutrient intake between the two study groups. MUO adolescents had a significantly higher total calorie intake, suggesting overconsumption as a contributing factor to metabolic syndrome among them. Furthermore, their dietary patterns were characterized by elevated protein intake, particularly from animal sources, alongside increased consumption of red meat. The detrimental association of high meat consumption on health is well-documented in adults, contributing to metabolic alterations such as insulin resistance and associated diseases such as T2DM (58, 59), MS (60), cardiovascular disease (61, 62), and colorectal cancer (63, 64). A recent study revealed an association between the intake of total, red, and/or processed meat and the incidence and persistence of MASLD, along with clinically significant fibrosis in adult populations (65). This heightened risk may be attributed to several factors, including the impairment in insulin signaling induced by palmitic acid (16:0), which is the most abundant SFA found in animal-derived foods (66–69). Additionally, the consumption of SFAs has been linked to elevated circulating levels of total lipoprotein and LDL-c (70, 71). While our analysis did not find a significant difference in total SFA intake between groups (P = 0.09), there was a significant increase in the consumption of major long-chain SFAs, specifically, palmitic and stearic acid (18:0), in the MUO group. These long-chain SFAs were observed as being particularly detrimental to metabolic health compared to short-medium SFAs in a recent systematic review (72). Moreover, the detrimental effect of red meat products can be related to specific cooking methods, such as advanced glycation end products, heterocyclic amines, heme iron and other byproducts of muscle protein oxidation, adds to these risks (62, 73–75).

Interestingly, we observed significantly higher consumption of arachidonic acid among adolescents with MUO, which positively correlated with their intake of animal protein and red meat (76). A major role of arachidonic acid is that of a substrate for the synthesis of eicosanoids, which include prostaglandins, thromboxanes, and leukotrienes. These are formed by the metabolism of arachidonic acid by cyclooxygenase, lipoxygenase, and cytochrome P450 pathways (77–79). The resulting metabolites have many roles in inflammation, regulation of the immune response, blood clotting, and smooth muscle contraction (77–79). While the functions of arachidonic acid-derived metabolites are well-established in human health outcomes, recent reviews on the impact of arachidonic acid consumption in adults indicated no adverse effects from their increased intake(80). Also, while there was no significant difference in overall ultra-processed food intake between our two study groups, the MUO group showed a notable increase in the consumption of ultra-processed grains, including white bread and rolls, pastries, sugary breakfast cereals, and more. This observation underscores the negative impact of ultra-processed grains, emphasizing their negative contribution to health outcomes. This effect is evident both independently and when combined with a Western diet that is characterized by high consumption of red and processed meats, sugary snacks and drinks, refined grains, convenience foods, and low intake of fruits, vegetables, and whole grains (81, 82). Sodium intake was significantly higher among our adolescents with MUO compared to those with MHO, and nearly double the recommended daily upper limit of 2,300 milligrams per day (83). Sodium is widely recognized for its substantial role in blood pressure and metabolic health dysregulation, even among adolescent populations (84, 85).

Our MUO adolescents were associated with significantly elevated rates of high-risk pregnancies. Barker's hypothesis posits that adverse conditions during gestation can contribute to increased disease risk in later life (86, 87). Our findings appear to support that hypothesis.

Lastly, our evaluation of physical activity and performance revealed unexpectedly insignificant differences between the MUO and MHO groups. Contrary to prevailing beliefs (24, 88), both groups displayed similar levels of physical activity and sedentary behavior with high levels of sedentary behavior and no regular physical exercise. All participants spent an average of approximately 60% of their day in sedentary pursuits, far exceeding the recognized cutoff that leads to increased cardiovascular disease risk in adults (89). These results highlight the need for further investigation into the relationship between obesity phenotype and physical activity levels. The inclusion of hand grip strength and isometric mid-thigh pull max test in our study can help to establish a normative range for adolescents with obesity and furnish informative benchmarks for future research and clinical evaluations.

One strength of our study is our employment of advanced imaging techniques, including MRI and MRS, which are considered gold standards and references for assessing body fat distribution and HFC, respectively. This approach allowed for precise and reliable measurements, thereby enhancing the validity of our results. Moreover, the study population of adolescents with obesity provides a unique perspective in understanding the early markers of unhealthy metabolic obesity and valuable insights for developing interventions to prevent metabolic abnormalities during development. Another strength lies in our examination of a wide range of variables, from prenatal factors, such as birth weight and pregnancy conditions, to current markers, such as blood parameters, physiological measures, and dietary patterns assessed by the FFQ.

Several limitations include the relatively small sample size, which limited statistical power and may have impacted the precision of the ORs estimated in our logistic regression analysis. While efforts were made to adjust for confounding factors, it is possible that our model did not fully account for all contributors to metabolic obesity as demonstrated in this study. Therefore, caution is warranted when interpreting the independent influence of HFC, since unmeasured covariates may have influenced the observed associations. Additionally, there may be information bias in dietary self-reporting due to recall bias. Future prospective investigations with larger sample sizes in multicenter settings will be instrumental in further understanding the relationship between hepatic fat, metabolic health, and dietary factors.

In conclusion, our study findings shed light on the complex relationship between hepatic fat content and metabolic health in adolescents with obesity. We demonstrate an association between MUO and elevated hepatic fat, increased consumption of total calories, animal protein, red meat, sodium, arachidonic acid, long-chain SFAs and ultra-processed grains, along with a history of high-risk pregnancy. These findings underscore the importance of focusing on this age group for preventing metabolic abnormalities that can have lasting effects on adult health. Larger and longitudinal studies are warranted to establish the causal relationships between hepatic fat content, dietary factors, and metabolic health outcomes. Future interventions should specifically prioritize targeting the reduction of hepatic fat content, while also considering dietary modifications to lower salt intake and reduce consumption of meat and other potential contributors to metabolic abnormalities.

A Data Availability Statement The data that support the findings of this study are available upon request from the corresponding author

Acknowledgments

We acknowledge the participants and their families for their contribution to this research. Additionally, special thanks are extended to the MRI unit at the Alfredo Federico Strauss Center, Tel Aviv University, for their support and assistance throughout the imaging process. We also extend our appreciation to Rivka Albert for her contributions to data collection.

Funding

No funding was received for the current study

Conflict of Interest

The authors declare no conflicts of interest concerning this study.

CRediT authorship contribution statement

Hadar Moran Lev: Conceptualization, Resources,Investigation, Writing - Original Draft, Supervision. Ron Sternfeld*: Formal analysis, Validation, Visualization,Writing – original draft. Rotem Lazmi: Investigation. Rivka Ohayon: Writing - Review & Editing. Rivka Dudi: Investigation. Avivit Brener: Writing - Review & Editing. Shira Zelber Sagi: Writing - Review & Editing. Shlomi Cohen: Resources, Conceptualization. Ronit Lubetzky: Resources, Conceptualization. Yftach Gepner: Resources, Conceptualization, Writing - Review & Editing, Methodology, Funding acquisition, Supervision.

Ethical considerations and conflict of interest

This study strictly adhered to ethical principles outlined in the Declaration of Helsinki and received approval from the Institutional Review Board of Tel Aviv Medical Center (TLV-0799-20). Written informed consent was obtained from all parents before enrollment, and the study was registered with ClinicalTrials.gov under the identifier NCT06032312, ensuring transparency and accessibility to the study protocol and results.

The design, protocols, and procedures underwent thorough review and approval by the ethics committee, to protect participants' rights, privacy, and well-being. Additionally, any conflicts of interest or potential biases were transparently disclosed. The authors declare no financial or non-financial competing interests that could influence the interpretation of results or the presentation of study findings.

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No competing interests reported.

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Mediated Factors Associated with Metabolic Health among Obese Adolescents

Status:

Version 1

Abstract

background

Methods

Results

Conclusion

Trial registration:

Figures

WHAT IS KNOWN

INTRODUCTION

METHODS

Study design

Study population

Clinical and demographic variables

Metabolic assessment

Nutritional assessment

Performance and physical activity assessments

Classification of metabolically unhealthy obese and definition of study variables

Statistical analysis

RESULTS

Clinical factors and sociodemographic data

Body composition, fat distribution, physiological performance and physical activity associated with metabolic health

Differences in dietary patterns between metabolically healthy and unhealthy obese adolescents

The independent effect of HFC on metabolic health

DISCUSSION

Declarations

References

Additional Declarations

Status:

Version 1