Cross-sectional association of fitness, fatness, and dyslipidemia with metabolic syndrome in youth

Abstract

BACKGROUND

The prevalence of metabolic syndrome (MetS) in adolescents is rising, correlating with the global increase in obesity and physical inactivity.

AIM

To examine the individual and combined associations of fitness, fatness, visceral adiposity index (VAI), and lipid ratios with MetS risk in Nigerian adolescents.

METHODS

This cross-sectional study included a sample of 403 adolescents (201 girls and 202 boys) aged 11-19 years. Participants were assessed for cardiorespiratory fitness, body mass index (BMI), VAI, triglyceride-to-high-density lipoprotein cholesterol ratio (TG/HDL-C), and total cholesterol-to-high-density lipoprotein cholesterol ratio (TC/HDL-C). Regression models adjusted for age and sexual maturity were used to determine the associations between these health markers and MetS risk.

RESULTS

Among the 177 high-risk adolescents, 56.6% were at risk of central obesity, 49.1% had low fitness, 33.3% had dyslipidemia, and 11.7% were obese. After controlling for confounding variables, all health markers were independently and jointly associated with MetS risk, with VAI displaying the strongest explanatory power (girls: β = 1.308, P < 0.001; boys: β = 2.300, P < 0.001). Unfit girls were 5.1% more likely to be at risk of MetS, while the odds of unfit boys being at risk of MetS is 3.6. Boys with elevated VAI were 22.3 times more likely to be at risk of MetS, while the likelihood of girls with elevated VAI developing MetS risk is 2.78.

CONCLUSION

Health markers were independently and jointly associated with MetS risk in adolescents, with VAI and dyslipidemia contributing most significantly. Promoting healthy eating and also aerobic activities among adolescents is crucial for improving metabolic health.

Key Words: Adolescents; Dyslipidemia; Fitness; Metabolic health; Visceral obesity

Core Tip: Metabolic syndrome (MetS) is a major global health concern, with its prevalence rising among adolescents in parallel with increasing obesity and physical inactivity. This study identified a clustering of MetS among Nigerian adolescents. Fitness, visceral adiposity index (VAI), total cholesterol-to-high-density lipoprotein cholesterol (HDL-C), and triglycerides-to-HDL-C ratios were uniquely associated with MetS in this population. While the combined contribution of these health markers in predicting MetS was strong, VAI and lipid ratios emerged as the primary determinants. To mitigate the risk of cardiometabolic disease among adolescents, adopting a healthy diet and engaging in regular aerobic physical activity are recommended.

INTRODUCTION

Metabolic syndrome (MetS) is the clustering of adverse cardiovascular disease (CVD) and metabolic risk factors, including central obesity, hypertension, hyperglycemia, and dyslipidemia[1]. The coexistence of any three or more of these CVD risk factors in the same individual constitutes MetS, a major health challenge and an acknowledged precursor of CVD, stroke, and type 2 diabetes (T2D) in adults[2]. Reaven[3] first described MetS, and since then, numerous definitions and diagnostic criteria have been proposed. The International Diabetic Federation (IDF) defines MetS as the presence of any three CVD risk factors, including central obesity[4]. Unlike in the adult population, there is no consensus regarding the cut-off point for the definition of MetS components in adolescents. Consequently, the use of a continuous value of MetS score has been recommended for research in the pediatric population[5].

The prevalence of MetS in adolescents is rising in parallel with the global increase in obesity and sedentary existence[6,7]. Available evidence from the literature indicates that childhood MetS tracks into adulthood[8]. Therefore, early prevention of factors associated with MetS is crucial to guarantee better health prospects during adult life. Early detection and prevention of biological CVD risk factors or health markers [fitness, visceral adiposity index (VAI), triglyceride-to-high-density lipoprotein cholesterol (TG/HDL-C), and total cholesterol-to- HDL-C (TC/HDL-C) ratios], coupled with an appreciation of their interrelationships, are critical strategies for mitigating the risk of MetS. Obesity, physical inactivity, and dyslipidemia are acknowledged predictors of MetS and CVD across the lifespan[9,10]. Pediatric obesity has become a major public health issue with increasing prevalence worldwide in both developed and developing countries. It is an established risk factor for MetS, T2D, and CVD, with consequences on health services and socioeconomic life[9,11]. Body mass index (BMI) has been used as an acceptable proxy measure of body fatness in both youth and adults[11,12]. VAI, a proxy measure of visceral fat distribution and function, is considered more metabolically active and harmful than subcutaneous fat. It has been linked to metabolic abnormalities resulting in the development of MetS and T2D[10,13].

Cardiorespiratory fitness (CRF), the ability of the cardiovascular and respiratory systems to supply oxygen to skeletal muscles during sustained physical exertion, is an important marker of total health and is inversely associated with components of MetS[14,15]. Several studies have shown that higher levels of CRF are associated with a lower prevalence of MetS in adolescents[16,17]. The TC/HDL-C and TG/HDL-C (atherogenic index of the plasma-AIP) ratios are established predictors of CVD risk, insulin resistance, and MetS in the adult population[18] and in adolescents[19,20]. In their study of MetS risk assessment in Korean adolescents, Chu et al[20] concluded that TG/HDL-C ratio and AIP are useful markers of MetS with high predictive values. These ratios are indicators of lipid metabolism and CVD risk, with high ratios associated with an increased risk of MetS and CVD[21].

Adolescence is a critical developmental period characterized by physiological, psychological, and sociological changes, as well as health-compromising behaviors such as physical inactivity and unhealthy eating, which can profoundly impact health outcomes, including MetS, CVD, and associated complications[22], that may persist into adult life[23]. Despite the growing body of evidence linking these health biomarkers to MetS in adults, there is a relative paucity of data in adolescents. Furthermore, their joint association with the risk of MetS is yet to be explored in this population. Understanding the relationship between these health indicators and MetS is crucial, as this can help in early intervention strategies aimed at curbing the rising prevalence of MetS and its sequelae during this formative stage.

This study aims to elucidate the association of adiposity, CRF, VAI, TC/HDL-C, and TG/HDL-C ratios with MetS risk in adolescents. Specifically, the study determined the association of the independent variables individually with MetS risk, examined their combined association with the dependent variable, characterized participants’ MetS risk profile, and determined the cut-off values of the independent variables that are suggestive of MetS risk among adolescents. The present study aims to fill the existing gap in adolescent-focused research, thus providing insights that could serve as public health focused interventions to mitigate the risk of MetS during this early stage of life.

MATERIALS AND METHODS

Study design and setting

This was an observational cross-sectional study involving school girls and boys aged 11 to 19 years from selected secondary schools in Kogi East, North Central Nigeria. Data collection took place over four months (September–December 2019). Kogi State, with its capital in Lokoja, is located in Nigeria’s North-Central geopolitical zone. Secondary schools in Nigeria fall into two main categories: Public and private. Information on adolescent lifestyles, including physical fitness in relation to their health is exiguous.

Study population and sample

Participants included adolescents randomly selected from four secondary schools in the study area. The Slovin sample size determination formula[24] resulted in a minimum sample size of 399 participants, which was increased to 418 to improve representativeness and account for possible dropouts and missing values. Participants were selected using a systematic sampling procedure, with every fourth student on the class list included, starting from a predetermined number. The eligibility criteria included the following: No history of CVD or any reported health issues, no current illness, and not having participated in an organized exercise program for at least six months before data collection. Details of the pilot test have been previously described[25].

Data collection procedure

Visits were made to the participating schools on two separate occasions for data collection. The first visit was for measuring physical attributes, clinical, and biochemical parameters. The second visit included physical fitness testing. All tests were performed in the same order by the same members of the testing team throughout the data collection period to ensure consistency. The measurement procedures are presented below.

Physical and physiological measurements

The participants’ anthropometric characteristics were evaluated using the protocol of the International Society for the Advancement of Kinanthropometry[26]. Using these procedures, stature, body mass, percentage body fat, waist circumference (WC), and BMI were measured. BMI was determined as body mass (kg)/stature (m²). The FitnessGram revised data were used to group the participants into healthy weight and overweight based on their BMI values[27]. Sexual maturity was estimated from chronological age and stature using the formula of Moore and coworkers[28]. Maturity offset (MO: The time before or after a child’s peak height velocity) was estimated directly, and the difference between MO and age was used to determine age at peak height velocity (APHV: Age or period of fastest growth in height in adolescents).

Fitness testing

CRF was assessed using the 20-meter multistage shuttle run test, an aerobic capacity test with progressive intensity. Participants were encouraged verbally to run until volitional fatigue. The test is a valid predictor of peak oxygen uptake in youth (VO_{2 peak})[29]. Participants were classified into high- and low-fitness groups based on the number of laps completed, following the revised sex- and age-specific health-related thresholds of the FitnessGram[27]. The CRF thresholds were set at 23-61 laps for females and 32-94 laps for males. Adolescents who met or exceeded these thresholds were categorized as having 'high fitness,' while those with values below the cut-off points were classified as having 'low fitness’. The specific test administrative procedures based on FitnessGram updated data have been described elsewhere[27].

Blood pressure measurements

Participants’ systolic and diastolic blood pressures were measured using an oscillometric device (HEM-705 CP; Omron, Tokyo, Japan) after a 10-minute rest in a sitting position. The resting blood pressure was monitored on participants’ non-dominant arm, and the average of three readings was retained for statistical analysis. The blood pressure threshold for hypertension was determined using established standards[30].

Biochemical measurements

Fasting plasma TC, low-density lipoprotein cholesterol, HDL-C, TG, and fasting plasma glucose (FPG) were measured from capillary blood samples between 9:00 and 11:00 a.m. using the Cardio-Check Plus Analyzer (CCPA) (PTS Diagnostics, Indianapolis, IN, United States). The protocol details have been previously described with the CCPA reported as a valid instrument for measuring blood lipids[31]. The formula of Amato and Giordano was used for calculating VAI[10]:

Where WC is expressed in centimeters, BMI in kg.m², HDL-C and TG in mmol/L.

Continuous metabolic risk score

MetS was defined in this study according to the diagnostic criteria of the IDF[4], which emphasizes the presence of at least three CVD risk factors including central obesity. Due to the difficulty in identifying variations between individual risk factors among youth, a continuous metabolic risk score (MRS) was derived from the following variables: WC, systolic blood pressure (SBP), FPG, HDL-C (inverted), and TG. Each variable was standardized [z = (value - mean)/SD] separately for each sex group. The z-scores of the individual risk factors were combined to generate a clustered MRS (continuous variable) for each participant, with a lower score suggesting a more favorable risk profile. This approach has been previously used in several research studies involving youth[32,33]. Participants with an MRS +1SD higher than the overall mean were considered to be at risk of clustered MRS[34].

Reference values for risk abnormalities

Participants’ MetS risk abnormalities were determined using the IDF criteria[4]: TG (≥ 1.7 mmol/L); HDL-C (≤ 1.04 mmol/L); FPG (≥ 5.6 mmol/L); SBP (≥ 130 mmHg); and WC (≥ 90th percentile for age and sex). Adolescents who had one or more CVD risks were categorized as "risk", while those with none were classified as "no risk".

Statistical analysis

Descriptive data were presented as means, standard deviations, frequencies, and percentages. Data were checked for normality of distribution using the Kolmogorov-Smirnov test. Significant differences between genders on all study variables were determined with the independent samples t-test or, where appropriate, the Mann-Whitney U test. Bivariate analyses were performed to examine the partial correlations between the dependent and independent variables. The relationships between the health markers as well as their relative importance were determined using hierarchical multiple regression models while adjusting for age and sexual maturation. The logistic regression model assessed the autonomous association of independent variables with MetS risk. Odds ratios of being at risk were computed among categories of the independent variables of MetS, with the models adjusted for confounding variables. The independent variables' predictive capacities were examined using receiver operating characteristic curve (ROC) analysis, with a 95%CI. Values of the area under the curve (AUC), sensitivity, and specificity were used to identify MetS risk thresholds. The AUC values were interpreted using Hosmer and Lemeshow’s criteria[35]. All statistical analyses were conducted using IBM SPSS statistical software (Version 20, IBM Corporation, Armonk, NY, United States), with P values ≤ 0.05 considered significant.

Ethics statement

The present study was approved by the Ethical Review Committee of the College of Health Sciences, Kogi State University, Nigeria (Ref. No. COHS/02/25/2020). Written informed consent and assent of participants were provided by parents/guardians prior to data collection. The study was conducted in compliance with the revised ethical guidelines of the Helsinki Declaration[36].

RESULTS

From a total of 418 adolescents who satisfied the eligibility criteria, 403 were included in the data analysis. Data from 15 adolescents were excluded due to absenteeism and missing data, resulting in a compliance rate of 96%. Participants’ anthropometric, clinical, and biochemical characteristics according to sex are presented in Table 1. Girls exhibited higher values in most variables (P < 0.05) except for the endurance run (P < 0.001), in which boys performed better. Both sexes displayed similar values in stature and the TG/HDL-C ratio.

Table 1 General characteristics of participants stratified by sex (n = 403).

Variable	Total (n = 403)	Girls (n = 201)	Boys (n = 202)	t-value	P value
Age (years)	14.7 ± 2.3	14.8 ± 2.3	14.7 ± 2.2	0.586	0.558
APHV (years)	13.3 ± 1.1	12.5 ± 0.8	14.1 ± 0.8	20.068	< 0.001
MO (years)	1.4 ± 1.3	2.3 ± 1.7	0.6 ± 1.0	10.029	< 0.001
Height (cm)	160.2 ± 9.8	159.6 ± 7.1	160.9 ± 11.9	0.303	0.193
Weight (kg)	53.1 ± 12.5	55.5 ± 12.1	50.8 ± 12.5	3.784	0.016
BMI (Kg.m-2)	20.5 ± 3.5	21.7 ± 4.0	19.3 ± 2.6	6.929	< 0.001
WC (cm)	65.8 ± 8.8	67.2 ± 9.4	64.4 ± 8.0	2.273	0.001
20-MST (lap)	32.2 ± 16.8	24.9 ± 13.3	39.5 ± 16.8	9.697	< 0.001
TG (mMol. L-1)	1.0 ± 0.9	1.1 ± 1.2	0.9 ± 0.4	1.653	0.100
HDL-C (mMol. L-1)	1.3 ± 0.4	1.3 ± 0.3	1.3 ± 0.4	1.090	0.276
TG/HDL-C	0.9 ± 0.6	1.0 ± 0.6	0.8 ± 0.5	1.697	0.091
TC/HDL-C	2.8 ± 0.8	2.9 ± 0.8	2.7 ± 0.7	2.082	0.038
VAI	1.2 ± 0.7	1.6 ± 0.8	0.8 ± 0.6	3.912	< 0.001
FPG (mMol.L-1)	5.1 ± 0.7	5.0 ± 0.7	5.1 ± 0.7	0.401	0.157
MetS	-7.7 ± 3.1	-7.2 ± 3.2	-8.1 ± 3.0	2.839	0.005

BMI: Body mass index; WC: Waist circumference; HDL-C: High-density lipoprotein cholesterol; TG: Triglycerides; FPG: Fasting plasma glucose; VAI: Visceral adiposity index; 20-MST: Multistage shuttle run test.

Table 2 compares the MetS risk status of the participants stratified into “no risk” and “risk” sub-groups. Except for age, in which both sexes were similar, boys generally had a more favorable risk profile than girls. Regarding risk factor prevalence, Clustering was found in 23 participants (5.7%: Girls = 7.0%; boys = 4.5%) with girls being more vulnerable (Figure 1), except for CRF, where a higher proportion of boys were at risk. Overall, more adolescents were at risk of VAI (56.6%) and low fitness (49.1%), as illustrated in Figure 1.

Figure 1 Participants’ metabolic syndrome risk factor profile according to sex. BMI: Body mass index; CRF: Cardiorespiratory fitness; VAI: Visceral adiposity index; TG/HDL: Triglyceride-to-high-density lipoprotein cholesterol; TC/HDL-C: Total cholesterol-to-high-density lipoprotein cholesterol.

Table 2 Comparison of participants according to their metabolic risk status.

Variable	Low (n = 107)	Girls			Boys
		High (n = 94)	Total (n = 201)	Low (n = 119)	High (n = 83)	Total (n = 202)
Age	13.9 ± 2.4	15.9 ± 1.6	14.8 ± 2.3b	13.9 ± 1.9	15.8 ± 2.2	14.7 ± 2.2b
Body mass	53.0 ± 14.0	58.2 ± 8.9	55.5 ± 12.1b	50.2 ± 11.8	51.7 ± 13.8	50.8 ± 12.5
BMI	20.8 ± 4.5	22.7 ± 3.0	21.7 ± 4.0b	19.0 ± 2.2	19.9 ± 3.0	19.9 ± 3.0a
CRF	29.2 ± 12.0	20.0 ± 13.0	24.9 ± 13.3b	48.1 ± 15.1	27.2 ± 10.1	27.2 ± 10.1b
VAI	0.86 ± 0.3	2.5 ± 1.8	1.6 ± 0.7b	0.58 ± 0.2	1.2 ± 0.6	1.2 ± 0.6b
TG/HDL	0.56 ± 0.2	1.5 ± 0.3	1.0 ± 0.6b	0.58 ± 0.2	1.0 ± 0.5	1.0 ± 0.5b
TC/HDL	0.20 ± 0.3	0.50 ± 0.4	0.29 ± 0.8b	0.24 ± 0.4	0.50 ± 0.4	0.46 ± 0.4b

^aP < 0.001.

^bP < 0.05.

VAI: Visceral adiposity index; CRF: Cardiorespiratory fitness; BMI: Body mass index; TG/HDL-C: Triglycerides-to-high-density lipoprotein cholesterol; TC/HDL-C: Total Cholesterol-to-high density lipoprotein cholesterol.

In general, weak to moderate partial correlations (adjusted for age and sexual maturation) were recorded between the dependent and independent variables. Positive correlations were noted in BMI, VAI, TG/HDL-C, and TC/HDL-C for both sexes, while negative relationships were observed in CRF. In girls, the correlation coefficients included: VAI (r = 0.703, P = 0.01), TG/HDL-C (r = 0.646, P = 0.01), TC/HDL-C (r = 0.617, P = 0.01), while in boys they included: VAI (r = 0.636, P = 0.01), TG/HDL-C (r = 0.580, P = 0.01), TC/HDL-C (r = 0.608, P = 0.01). In general, BMI and CRF were weakly correlated with the dependent variable in both sexes.

Results of the hierarchical multiple regression models assessing the ability of the independent variables to predict MRS are presented in Table 3. In both sexes, the dependent variable was significantly (P < 0.05) associated with all independent variables, with VAI (P < 0.001) and the TG/HDL-C ratio (P < 0.001) displaying the greatest explanatory power. In girls, the covariates explained 16.9% of the variance in the dependent variable in step 1. Addition of the health markers in step 2 increased the total variance cumulatively to 85.6%, thus indicating that the independent variables explained an additional variance of 68.7% (P < 0.001) after controlling for the covariates. In boys, the model explained 81% (P < 0.001), with 48.8% contribution from the independent variables. The results further showed that in girls, each unit increase in the TG/HDL-C ratio could result in a mean increase of 1.4 in MRS, while in boys, a unit increase in the TG/HDL-C ratio resulted in an increase of 1.0 in MRS. Regarding VAI, a unit increase resulted in a mean increase of 1.9 in girls and 1.2 in boys.

Table 3 Standardized regression coefficients on the association among health markers and metabolic risk score (n = 403).

Sex	Predictor	r2	r2∆	Model 1		Model 2
				Β	p	β	p
Girls	Age	0.856	0.687	0.498	0.054	0.043	0.708
	MO	-	-	-0.086	0.737	0.117	0.116
	CRF	-	-	-	-	-0.132	< 0.001
	BMI	-	-	-	-	0.126	< 0.001
	VAI	-	-	-	-	1.308	< 0.001
	TG/HDL-C	-	-	-	-	-0.743	< 0.001
	TC/HDL-C	-	-	-	-	0.435	< 0.001
Boys	Age	0.810	0.488	1.561	< 0.001	0.439	0.002
	MO	-	-	-1.161	< 0.001	-0.454	< 0.001
	CRF	-	-	-	-	-0.158	< 0.001
	BMI	-	-	-	-	0.207	< 0.001
	VAI	-	-	-	-	2.300	< 0.001
	TG/HDL-C	-	-	-	-	-1.914	< 0.001
	TC/HDL-C	-	-	-	-	0.428	< 0.001

∆: Change; MO: Maturity off-set; VAI: Visceral adiposity index; CRF: Cardiorespiratory fitness; BMI: Body mass index; WC: Waist circumference; TG/HDL-C: Triglycerides-to-high-density lipoprotein cholesterol; TC/HDL-C: Total cholesterol-to-high density lipoprotein cholesterol.

Results of the binary logistic regression models, after controlling for the covariates indicated that in general, VAI (girls: P = 0.050; boys: P < 0.001) and the CRF (girls: P < 0.001; boys: P = 0.035) displayed unique significant associations in both sexes. Except for BMI (P = 0.556) in girls and TG/HDL-C (P = 0.513) in boys, all other independent variables also turned up significant associations (P < 0.05) in both sexes. Detail results include: CRF [odds ratio (OR) = 5.1, 95%CI: 2.29-11.32, P < 0.001] and TC/HDL-C (OR = 4.4, 95%CI: 1.72-11.43, P = 0.002) were uniquely associated with the dependent variable in girls. In boys, VAI (OR = 22.3, 95%CI: 4.91-101.13, P < 0.001) and TC/HDL-C (OR = 3.7, 95%CI: 1.18-11.43, P = 0.025) were significantly associated with the dependent variable.

Table 4 and Figure 2 display results of the ROC curve analyses. In both sexes, the areas under the curve were significant for all independent variables except for CRF. As diagnostic tests for MRS, the AUCs for VAI (82%), TG/HDL-C (77%), and TC/HDL-C (77%) in girls, as well as VAI (87%), TG/HDL-C (80%), and TC/HDL-C (71%) in boys demonstrated the greatest predictive capacities. In all cases, the sensitivities were high, while the specificities were low, indicating that these tests were effective in identifying most adolescents at risk of MetS, but missed a few at risk.

Figure 2 Sex-specific areas under the receiver operating characteristic curve of metabolic syndrome for females and males. VAI: Visceral adiposity index; TG_HDL: triglyceride-to-high-density lipoprotein cholesterol; TC/HDL-C: total cholesterol-to-high-density lipoprotein cholesterol; BMI: body mass index; CRF: cardiorespiratory fitness.

Table 4 Receiver operating characteristics curve analysis for risk of metabolic syndrome among participants (n = 403).

Sex	Variable	AUC	95%CI	Cut-point	Sen	Spe	P value
Girls	CRF	0.549	0.469-0.630	23.5	0.553	0.542	0.229
	BMI	0.680	0.604-0.755	21.2	0.681	0.402	< 0.001
	VAI	0.817	0.758-0.876	0.811	0.851	0.374	< 0.001
	TG/HDL-C	0.771	0.704-0.838	0.56	0.766	0.318	< 0.001
	TC/HDL-C	0.774	0.710-0.838	2.80	0.766	0.346	< 0.001
Boys	CRF	0.430	0.349-0.510	40.5	0.410	0.513	0.090
	BMI	0.580	0.499-0.662	18.6	0.578	0.546	0.052
	VAI	0.870	0.818-0.922	0.70	0.880	0.252	< 0.001
	TG/HDL-C	0.801	0.738-0.863	0.60	0.795	0.403	< 0.001
	TC/HDL-C	0.708	0.638-0.778	2.60	0.711	0.445	< 0.001

VAI: Visceral adiposity index; CRF: Cardiorespiratory fitness; BMI: Body mass index; WC: Waist circumference; TG/HDL-C: Triglycerides-to-high-density lipoprotein cholesterol; TC/HDL-C: Total cholesterol-to-high density lipoprotein cholesterol.

DISCUSSION

This study investigated the cross-sectional association between specific health markers and the risk of MetS among Nigerian adolescents. The main findings include: First, there is a clustering of CVD risk factors in 5.7% of the sample, with a higher prevalence in girls (7.0%). Second, participants with unfavorable MetS risk had significantly higher mean scores for most health risk factors. Third, there are weak to moderate associations between the dependent variables and health markers. Fourth, the independent variables were both autonomously and jointly associated with MetS risk in both sexes, with the VAI showing the greatest explanatory capacity. Fifth, the VAI also demonstrated the highest diagnostic capacity in identifying MetS risk among the dependent variables in both sexes.

The present study found a MetS prevalence of 5.7% in the study sample, with a higher prevalence among girls. Similar trends have been observed in adolescents from various developed and developing societies, with prevalence rates reported at 5.9%, and 5.0%, respectively in South African[37], and American[38] youth. The observed higher prevalence in girls is at variance with previous findings[39] which reported occurrence rates of 5.0% and 11.1% for American girls and boys respectively. Other discrepancies in MetS prevalence such as the 3.1% in girls and 6.0% in boys have also been reported among South African adolescents[36]. The 5.7% prevalence of MetS documented in this study is higher than the pooled rate of 3.98% among children and adolescents in low- and middle-income countries and the 1.8% reported among Chinese youth[40], both of which used the IDF diagnostic criteria. These findings suggest that MetS is steadily increasing among Nigerian adolescents. Public health initiatives should promote the consumption of healthy diets rich in fruits, vegetables, and whole grains. Additionally, regular participation in aerobic physical activities should be encouraged to prevent and manage this growing health concern.

The greater clustering of MetS in females can be attributed to increased adiposity, fat distribution, and insulin resistance during adolescence[41]. Another possible explanation for the gender difference in clustering is the earlier maturation observed in females in this study. Puberty, or sexual maturation, plays a pivotal role in the development of MetS due to the profound physiological, hormonal, and metabolic changes that occur during adolescence. These changes lead to shifts in body composition, hormone levels, insulin sensitivity, and lipid metabolism, all of which are key risk factors for MetS[42]. The advanced maturation observed among females in this study likely contributes to their greater vulnerability to MetS compared to their male peers. These gender differences underscore the need for gender-specific interventions and preventive measures. Tailored approaches, such as promoting physical activity, a balanced diet, weight management and good sleep hygiene, without disruption in the circadian rhythm are especially crucial for preventing the onset of MetS during and beyond puberty, especially in females.

Our study showed that adolescents at higher risk for MetS had significantly elevated mean scores for most health indicators. This is consistent with findings from other studies[32,33], which have reported that youth at risk of MetS exhibit elevated blood pressure, dyslipidemia, and other biological risk factors. In our study, greater proportions of adolescents were also at risk of VAI and low fitness than other health indicators. To reduce the risk of developing MetS, it is crucial to implement interventions such as health education and lifestyle changes, emphasizing healthy eating and regular physical activity.

We observed weak to moderate associations between health markers and MetS risk, consistent with previous research. For instance, Resaland[32] noted that the strength of the relationship between MetS and various health markers varies, with some markers, such as central adiposity and blood lipids, showing stronger associations. This finding suggests the necessity of a comprehensive assessment of MetS risk factors to identify those most indicative of MetS risk for tailored interventions.

Our results indicate that health markers are independently and collectively associated with MetS risk, with the VAI demonstrating the greatest explanatory power, irrespective of gender. Similar findings have been reported in other studies involving adolescents[19,43]. The VAI, a proxy for visceral fat accumulation, is a significant predictor of MetS risk across diverse populations, including adolescents[10,19]. VAI has been used in previous studies involving both youth[19] and adults[10], supporting its applicability across lifespan. Visceral fat is associated with a higher metabolic risk compared to subcutaneous fat, as individuals with increased visceral adiposity often have elevated pro-inflammatory biomarkers, such as C-reactive protein, interleukin (IL)-6, and tumor necrosis factor-alpha (TNF-α). These biomarkers are indicative of low-grade inflammation, characteristic of obesity-related metabolic disorders such as MetS and T2D[43]. This underscores the need for multiple risk factor interventions in the prevention and management of MetS risk. The prominence of VAI suggests that targeting visceral fat reduction could effectively reduce MetS risk in this population.

In the present study, lipoprotein ratios showed a strong and unique association with MetS risk. Previous research in Italian and Korean adolescents supports this finding[20,21]. These ratios reflect atherogenic dyslipidemia, which is closely linked to visceral adiposity, systemic inflammation, and insulin resistance-key pathophysiological pathways in MetS[44]. These strong associations make lipid ratios valuable markers for early detection and preventive interventions. Poor lipid ratios significantly contribute to the pro-inflammatory state seen in MetS. This promotes chronic low-grade inflammation, a key driver in the pathogenesis of MetS and its complications[20]. Abnormal lipid ratios contribute to MetS by increasing visceral adiposity, which secretes pro-inflammatory adipokines. These include leptin, which promotes insulin resistance; resistin, which is associated with IL-6 and TNF-α; and reduced adiponectin, an anti-inflammatory adipokine that provides protection against MetS[21]. Additionally, dyslipidemia increases the risk of endothelial dysfunction, chronic inflammation, plaque formation, oxidative stress, hypertension, and overall metabolic dysregulation[21,44]. This highlights the importance of lipid management as a strategy to reduce or prevent MetS risk in adolescents. Though CRF was weakly associated with the dependent variable in both sexes, it is still important in health terms, because it is inversely associated with lower prevalence, and various components of MetS[16,17]. Additionally, regular aerobic physical activity confers both molecular and cytopathological benefits on cardiometabolic health, including decreasing vascular oxidative damage, catecholamines levels, renin-angiotensin-aldosterone system activation, arterial stiffness, and vascular inflammation. All these result in better blood pressure and blood lipid regulations[41,44]. This underscores the need to encourage participation in regular physical activity, consistent with the current physical activity guidelines for youth[45] among adolescents designed to promote and maintain metabolic health and reduce risk of chronic diseases.

In this study, the VAI demonstrated the highest diagnostic capacity in identifying MetS risk among participants, corroborated by studies in Iranian and Saudi Arabian youth[19,43], as well as in adults[10]. This finding suggests that visceral adipose tissue dysfunction may be a significant health issue among adolescents. The strong diagnostic capacity of the VAI underscores its potential as a valuable screening tool for the early detection of MetS, combined with appropriate interventions in public health and clinical settings. Furthermore, its accessibility, affordability, and noninvasive nature make it a practical and feasible option, facilitating timely lifestyle modifications that could potentially prevent its progression into adulthood. It is important for future interventions and public health policies to consider incorporating VAI assessment as part of routine health surveillance for adolescents, especially in school- and community-based settings and in regions with rising obesity rates.

The substantial variance in MetS risk explained by health markers in this study carries important public health implications. In this study, the variance accounted for: 69% in females and 49% in males, suggests that MetS risk in adolescents could be reduced by more than half, particularly among females. The need for effective school- and community-based health-promoting effort targeting favorable metabolic health is crucial, and the emphasis should be on healthy diet and also maintaining active lifestyle to mitigate health risk.

The findings from the study offer critical insights into sex-specific determinants of MetS risk among adolescents. Notably, the odds of developing MetS in female adolescents were significantly elevated in those with low CRF, with a 5.1-fold increase in risk compared to their fitter counterparts. This highlights the protective role of CRF, likely due to its association with enhanced insulin sensitivity, improved lipid metabolism, and reduced adiposity. Furthermore, females with an elevated TG/HDL-C ratio were 4.4 times more likely to be at risk of MetS, underscoring the significance of this lipid ratio as a marker of metabolic dysfunction and insulin resistance in adolescent girls. In contrast, the substantially higher odds – 22.3 times of male adolescents with a high VAI developing MetS compared to peers with lower VAI values suggest that central adiposity plays a particularly critical role in the metabolic health of male adolescents, possibly due to sex-specific patterns of fat distribution and hormonal influences during puberty. Additionally, males with an elevated TG/HDL-C ratio were found to have a 3.7-fold increased risk of MetS, reinforcing the importance of this lipid parameter across both sexes.

These results underscore the need for sex-specific preventive strategies. For female adolescents, interventions aimed at improving aerobic fitness may yield substantial metabolic health benefits, while in males, targeting central obesity may be more effective in mitigating MetS risk. The TG/HDL-C ratio emerges as a robust, sex-independent biomarker for early identification of at-risk youth, supporting its integration into routine metabolic screening protocols.

A limitation of this study is its cross-sectional design, which precludes establishing a causal relationship between health markers and MetS risk. The exclusive use of in-school adolescents, excluding those without formal education is another limitation. This sampling bias restricts extrapolation of the findings to all adolescents in the study area. In this study, we did not measure some lifestyle behaviors such as sedentary behavior, sleep, physical activity and dietary patterns. These variables are known to influence MetS development in adolescents[46,47]. VAI was estimated indirectly, which may be less precise than the direct or laboratory-based measures such as the dual-energy X-ray absorptiometry, magnetic resonance imaging, computed tomography, or other imaging techniques. However, a major strength of this study lies in the use of ROC, which provided population-specific thresholds for health markers in evaluating participants' MetS risk as adolescents with elevated VAI and lipid ratios were at increased risk of MetS irrespective of gender. The use of health-related cut-off points rather than median split for BMI and CRF is another strength, because this approach indicated that participants who met the health standards displayed more favorable MetS profile than those who fell below the standards. Lastly, the use of VAI as a proxy for VAT offered a more nuanced assessment compared to WC alone.

CONCLUSION

Clustering of metabolic risk factors exists in Nigerian adolescents, with a striking feature being the unique independent associations between the identified health markers and MetS risk in the adolescents. The joint contribution of the health markers in predicting MetS risk was strong, but VAI and lipid ratios were the major determinants of the dependent variable among adolescents, irrespective of gender. large prospective studies and randomized-control trials are recommended in future studies to clarify the specific roles of visceral adipose tissue, fitness and dyslipidemia in the prevention and management of MetS risk in adolescents.

ACKNOWLEDGEMENTS

The authors wish to thank the study participants, and Heads of the participating schools for making the study possible. The research assistants are also well appreciated.