Age-stratified Characterization of Body Composition and Biochemical Profiles in Cardiometabolic Risk Assessment

Abstract

Background:

Cardiometabolic risk (CMR), encompassing metabolic syndrome, type 2 diabetes mellitus, hypertension, and dyslipidemia, represents a major public health challenge in Bosnia and Herzegovina, where overweight and obesity prevalence is high.

Objective:

The study aimed to: a) examine the prevalence of three or more metabolic risk factors in relation to age; b) analyze biochemical parameters, lipid indices, and body composition indices in association with cardiometabolic risk (QRISK3); and c) identify age-specific thresholds for elevated risk.

Methods:

A cross-sectional, observational, descriptive–analytical study included 203 working-age participants (≤49, 50–54, 55–59, ≥60 years). The research instruments included: laboratory analysis, anthropometric and body composition parameters, and the Q3 risk calculator.

Results:

The prevalence of three or more metabolic risk factors increased with age, from <49 years to ≥60 years. Younger participants exhibited lower HDL and higher visceral fat, whereas older groups showed elevated glucose, ALT, and AST (p<0.05). The Q3 risk score increased significantly across age groups (median 8.15 to 24.80; p<0.001). Visceral fat, BMI, and body fat percentage emerged as strong predictors of risk in younger and middle-aged adults.

Conclusion:

Cardiometabolic risk develops already in early adulthood. Age-specific thresholds for visceral fat and biochemical markers may improve risk stratification, highlighting the importance of early screening and preventive interventions.

1. BACKGROUND

Cardiometabolic risk (CMR) - which includes metabolic syndrome, type 2 diabetes, hypertension, and dyslipidemia - represents a major public health challenge in Bosnia and Herzegovina. Nearly two-thirds of adults in the Federation of Bosnia and Herzegovina are overweight, and more than one-fifth obese (1). According to the World Health Organization (WHO), the main risk factors for cardiovascular disease are high blood pressure, dyslipidemia, physical inactivity, excess weight, smoking, hyperglycemia, alcohol use, and poor diet (2). The Health Status Survey of the Federation showed that respondents attributed the greatest influence on health to diet (72.1%), followed by physical activity and smoking (66.7%), alcohol (62.2%), and social engagement (46.5%) (3).

Although BMI is widely used, it fails to reflect fat distribution or muscle . fat ratio, limiting accuracy in risk assessment. With age, muscle mass declines while visceral adiposity increases, producing sarcopenic obesity - a condition linked to inflammation, insulin resistance, and high cardiometabolic risk but often underdiagnosed. Risk varies across the lifespan: younger adults more often show early disturbances such as insulin resistance and subclinical dyslipidemia, while older groups accumulate multiple risk factors and established disease. Hormonal changes, especially postmenopausal in women and testosterone decline in men, further alter fat distribution and muscle mass. Chronic low-grade inflammation, which increases with age, plays a central role in atherosclerosis and insulin resistance. Biochemical markers like HDL cholesterol and C-reactive protein (CRP) have predictive potential but require age-specific interpretation (4). Despite the rising burden of cardiometabolic disease in Bosnia and Herzegovina, studies on age-specific links between body composition and biochemical markers are lacking. Generating such data is crucial for early detection, targeted prevention, and clinical decision-making. This study therefore examines these associations across adult age groups.

2. OBJECTIVE

The aim of the study were threefold: a) to examine the prevalence of ≥3metabolic risk factors in relation to the participants’ age; b) to analyze biochemical, lipid indices, body mass parameters, body composition indices and to assess cardiovascular risk (Q3) according to the participants age and level of cardiometabolic risk, and c) to identify age-specific thresholds for elevated risk.

3. PATEINTS AND METHODS

Participants

A total of 203 participants from the working-age population were included in the study, divided into four age groups: ≤49 years, 50–54 years, 55–59 years, and ≥60 years. The research was conducted at the Faculty of Health Studies, University of Sarajevo, between September 2021 and June 2022. The sample was obtained using the snowball method. Eligible participants included both sexes, individuals without a diagnosis of cardiovascular disease, those who provided informed consent, and those not taking statins or other lipid-lowering medications, hormone therapy, or estrogen supplements.

Procedure and ethical considerations

Prior to commencing the study, approval was obtained from the management of the University of Sarajevo–Faculty of Health Studies. Participants were informed of the study objectives in accordance with the ethical principles of the Declaration of Helsinki. Participation was voluntary, and all participants signed informed consent before inclusion.

Methods and Measures

The research is an experimental, cross-sectional, observational, descriptive–analytical study.

Laboratory Analysis

Laboratory testing included fasting serum lipid profile (total cholesterol, triglycerides, HDL), enzymes (AST, ALT, CK-MB), CRP, and glucose, analyzed on the Erba Manheim XL 200 biochemical analyzer with routine quality control. LDL, VLDL, Castelli I and II indices, atherogenic coefficient (AC), and atherogenic index of plasma (AIP) were calculated indirectly using empirical formulas.

Cardiovascular Risk Assessment

Cardiovascular risk was assessed using the QRISK3 algorithm, wich includes variables such as age, sex, smoking status, blood pressure, BMI, Castelli I index, family history, comorbidities and medication data.

Blood Pressure Measurement

Blood pressure was measured using the auscultatory method based on Korotkoff sounds with a calibrated mercury sphygmomanometer (Bokang). Both systolic and diastolic blood pressure were recorded.

Anthropometric and Body Composition Measurements

Body composition parameters were assessed using a Tanita body composition analyzer based on the bioelectrical impedance method, following all recommended procedures for accurate measurement. The parameters included body weight, body mass index (BMI), body fat percentage, basal metabolic rate (BMR), visceral fat percentage, and metabolic age. Waist and hip circumferences were measured with a non-stretchable plastic measuring tape, while height was measured with an anthropometer. Waist circumference was measured at the midpoint between the lowest rib and the iliac crest in a standing position, and hip circumference was measured around the widest part of the hips at the level of the trochanter. Based on these values, the Body Adiposity Index (BAI) and Waist-to-Hip Ratio (WHR) were calculated.

Statistical analysis

Statistical analyses were performed using IBM SPSS Statistics version 26.0 (IBM Corporation, Armonk, NY, USA). Statistical significance was set at a p-value of <0.05. Results of the descriptive statistical analysis of risk were presented as mean ± standard deviation or median with interquartile range, depending on data distribution. Statistical tests used: Kruskal–Wallis, chi-square (χ²), and Fisher’s exact test.

4. RESULTS

Analysis of the Presence of Risk Behaviors and Comorbidities

Of the total number of participants, 75.87% were female and 24.14% were male. Among the examined comorbidities, dyslipidemia was the most prevalent, affecting 89.7% participants, followed by hyperglycemia in 87.2% participants and arterial hypertension in 75.9% participants. Excess body weight and obesity were identified in 41.4% and 44.8% respondents, respectively. Type 2 diabetes mellitus was diagnosed in 26.1% participants, rheumatoid arthritis in 27.1% participants. Atrial fibrillation, systemic lupus erythematosus, and severe psychiatric illness were not reported, nor was the use of corticosteroid or antipsychotic therapy. Regarding lifestyle-related factors, 57.6% participants reported active smoking and 1% participants had a family history of myocardial infarction or angina pectoris in first-degree relatives. Chronic kidney disease and migraine were less common, reported in 0.5% and 1.0% respondents.. When classified according to arterial blood pressure, optimal values were recorded in 3.9% participants, normal values in 2.5% participants, and high-normal values in 17.7% participants. Hypertension, however, predominated, being present in 75.9% participants.

Analysis of Lipid Indices, Body Composition Indices, and Cardiovascular Risk across Different Age Groups

Castelli I (3.0–3.5) and Castelli II (4.0) indices were relatively uniform across all age groups, without statistically significant group differences (H=0.802; p=0.849). The values of the atherogenic coefficient were consistent across groups, with median values ranging from 5.0 to 6.0 in all age categories, indicating a high cardiovascular risk without statistically significant differences by age (H=5.927; p=0.115). The waist-to-hip ratio (WHtR) had the highest median values in the <49 years group (0.91) and the 50–54 years group (0.92), with a statistically significant difference observed across age groups (H=9.579; p=0.023). These WHtR values classify participants as being at high health risk. The body adiposity index (BAI) rose slightly with age from 31.06 to 37.57 but without significance (H=4.715; p=0.194). The most pronounced difference was recorded for the Q3 risk, with the median value increasing from 8.15 in the youngest group (<49 years) to 24.80 in participants older than 60 years. This demonstrated a clear age-related rise in risk, with statistically significant differences observed across age groups (H=44.54; p<0.001).

Biochemical Parameters in Relation to Cardiovascular Risk across Different Age Groups

Participants were stratified into three groups: <10% low risk, 10–15% moderate risk, and ≥15% high risk. The highest mean triglyceride value was observed among participants aged 50–54 years with ≥15% high CVD risk (2.43±1.11 mmol/L), without significant differences by age or CVD risk category (F=2.339, p=0.107). A similar pattern was observed for total cholesterol, with values ranging from 5.92 to 7.46 mmol/L, also without significant differences between groups (p=0.468). No statistically significant differences were found for VLDL, CRP or CK-MB (p>0.05).The lowest HDL cholesterol values were found in the younger group (<49 years) with 10–15% moderate CVD risk (X=1.89±0.39 mmol/L), but the differences were not statistically significant (F=0.141; p=0.869). The lowest mean HDL value overall was observed among participants aged 50–54 years (X=1.88±0.35 mmol/L) with a statistically significant difference (F=4.945, p=0.011). In the other age groups, HDL ranged from 1.85 to 2.18 mmol/L without statistical significance. A statistically significant difference in LDL values was observed in the <49 years age group (F=8.704, p<0.001), where elevated LDL levels were present across all CVD risk categories, with the highest values found in the ≥15% high-risk category (X=5.03±1.59 mmol/L). No statistically significant differences were found in the other age groups.

Among participants aged 50–54 years, elevated glucose levels were recorded, with the highest values in the high-risk group (X=8.88±3.27 mmol/L), and a statistically significant difference was confirmed (F=5.98, p=0.005). The highest mean ALT value, although still within the reference range, was noted in participants aged 55–59 years with high CVD risk (X=23.43±8.43 IU/L), with a statistically significant difference (F=3.36, p=0.042). In the 50–54 years group, the highest mean AST value was 23.62±6.74 IU/L, also with a statistically significant difference (F=3.72, p=0.031) (Table 1).

Table 1. Biochemical parameters in relation to cardiovascular risk across different age groups.

Biochemical parameters	Age	<10% risk of cardiovascular disease		10-15% Moderate risk of CVD		15+% High risk of CVD		F	p
		Mean	SD	Mean	SD	Mean	SD
High-density lipoprotein (HDL) mmol/L	<49 year	1.96	0.33	1.89	0.39	1.98	0.24	0.141	0.869
	50-54 years	2.18	0.43	2.23	0.38	1.88	0.35	4.945	0.011
	55-59 years	1.99	0.42	2.18	0.47	2.18	0.44	0.816	0.448
	60+ years	1.97	0.01	2.08	0.37	1.85	0.41	0.738	0.485
Low-density lipoprotein (LDL) mmol/L	<49 year	4.53	1.02	4.98	1.40	5.03	1.59	8.704	<0.001
	50-54 years	4.71	1.44	4.47	1.05	4.77	1.59	0.184	0.833
	55-59 years	4.38	1.21	4.45	1.33	4.60	1.36	0.133	0.876
	60+ years	4.17	0.01	3.44	1.33	4.44	1.52	0.982	0.384
Glucose mmol/L	<49 year	6.95	1.69	6.85	0.82	10.82	4.89	0.871	0.425
	50-54 years	6.30	0.74	7.07	1.24	8.88	3.27	5.98	0.005
	55-59 years	6.92	0.83	7.19	1.57	8.32	3.39	1.50	0.231
	60+ years	4.00	0.01	7.45	1.21	8.35	3.46	0.96	0.39
ALT IU/L	<49 year	23.93	11.78	18.86	5.68	20.97	5.17	2.00	0.146
	50-54 years	21.82	8.57	27.45	10.62	23.47	8.43	1.36	0.265
	55-59 years	20.27	6.19	17.70	4.95	23.43	7.38	3.36	0.042
	60+ years	29.30	0.01	20.42	3.88	23.40	8.30	0.61	0.548
AST IU/L	<49 year	25.94	7.88	21.46	3.59	21.94	5.91	4.10	0.023
	50-54 years	27.62	10.33	32.54	13.34	23.62	6.74	3.72	0.031
	55-59 years	24.86	6.01	24.10	6.61	25.31	6.34	0.15	0.856
	60+ years	27.40	0.01	26.12	7.28	26.23	12.01	0.05	0.995

The analysis showed that certain body composition parameters varied significantly depending on the level of cardiovascular risk. Body mass index (BMI) values were statistically higher among individuals with moderate risk in the 55–59 years age group (F=3.765; p=0.030), while in the younger group (<49 years) differences were observed between participants with low and moderate risk (F=4.054; p=0.024). With respect to body fat percentage, a significant difference was found in the younger group (<49 years), where participants with moderate risk had significantly higher body fat values (F=3.274; p=0.047). Particularly notable was visceral fat, which demonstrated high statistical significance in the younger age category (<49 years), with elevated values among participants with moderate and high risk (F=6.573; p=0.003).In contrast, other parameters, including the body adiposity index (BAI), waist-to-hip ratio (WHtR), basal metabolic rate (BMR), and metabolic age, did not show statistically significant differences between risk groups in most age categories (p>0.05) (Table 2).

Table 2. Body composition parameters in relation to cardiovascular risk across different age groups.

Body composition parameters	Age	<10% risk of cardiovascular disease		10-15% Moderate risk of CVD		15+% High risk of CVD		F	p
		Mean	SD	Mean	SD	Mean	SD
Body Mass Index (BMI)	<49 year	28.3	4.4	34.2	6.3	30.6	5.0	4.054	0.024
	50-54 years	28.9	4.3	32.2	3.6	31.9	5.2	2.456	0.096
	55-59 years	32.1	4.8	28.5	5.5	28.0	3.7	3.765	0.030
	60+ years	25.5	1.0	25.6	2.0	29.6	4.3	2.458	0.099
Body Adiposity Index (BAI)	<49 year	29.9	5.32	32.2	7.8	31.5	5.9	0.558	0.576
	50-54 years	32.5	3.93	31.2	6.8	32.0	6.1	0.158	0.854
	55-59 years	34.8	5.6	30.0	7.1	32.1	5.1	2.153	0.127
	60+ years	26.9	1.1	28.9	3.8	34.5	6.2	2.572	0.090
Waist-to-Hip Ratio (WHR)	<49 year	0.93	0.1	0.9	0.2	0.9	0.1	0.479	0.623
	50-54 years	0.89	0.1	1.0	0.2	0.9	0.1	1.492	0.235
	55-59 years	0.87	0.05	0.9	0.1	0.8	0.7	2.306	0.110
	60+ years	0.77	0.1	0.8	0.9	0.8	0.7	1.694	0.197
Basal Metabolic Rate (BMR) (kcal)	<49 year	1495	190	1631	204	1692	288	3.844	0.290
	50-54 years	1481	261	1695	287	1616	261	2.288	0.112
	55-59 years	1533	135	1611	282	1522	322	0.432	0.651
	60+ years	1358	101	1374	177	1582	307	1.299	0.285
Metabolic age	<49 year	57.7	14.5	60.5	7.06	62.6	9.27	0.699	0.502
	50-54 years	60.1	11.2	65.8	11.1	63.3	9.71	0.803	0.454
	55-59 years	63.5	10.5	60.8	10.6	61.4	12.7	0.216	0.806
	60+ years	45.0	10.9	56.4	11.5	61.1	11.9	1.172	0.321
Fat (%)	<49 year	36.0	7.9	44.6	7.6	37.4	6.3	3.274	0.047
	50-54 years	37.1	5.8	39.2	5.7	38.1	10.1	0.210	0.812
	55-59 years	40.4	6.7	30.5	12.8	33.7	12.4	2.388	0.102
	60+ years	29.3	5.1	30.9	7.6	32.7	12.0	0.085	0.919
Viscelar fat (%)	<49 year	9	3	16	9	13	5	6.573	0.003
	50-54 years	10	3	13	6	13	6	1.921	0.157
	55-59 years	11	3	11	6	10	4	0.055	0.947
	60+ years	7	3	9	4	10	4	0.474	0.626

5. DISCUSSION

The results of this study showed that the prevalence of having three or more metabolic risk factors increased significantly with age, which is consistent with previous studies indicating that aging is accompanied by progressive metabolic burden and the accumulation of risk factors (5, 6). Visceral fat emerged as a particularly strong predictor of risk, with marked differences observed among younger age groups in relation to cardiometabolic risk levels. These findings confirm the results of Ruiz-Castell et al., who demonstrated that visceral adiposity is a stronger and independent predictor of cardiovascular risk than traditional anthropometric measures, including body mass index (BMI) (7).

In our study, BMI showed limited predictive value, with significant differences observed only in specific age categories, whereas measures of body composition, particularly body fat percentage and visceral fat, displayed more consistent associations with risk. Similar conclusions have been reported in recent literature, highlighting that BMI alone cannot adequately differentiate individuals at higher risk, especially among women, and that body composition measures offer a more precise risk stratification (8). Furthermore, our findings suggest that the introduction of age-specific thresholds for parameters such as visceral fat could improve the individualization of risk assessment. A comparable approach was proposed by Bosch et al., who defined sex- and ethnicity-specific visceral adipose tissue cut-offs as useful predictors of cardiometabolic disease (9).

The analysis of biochemical parameters revealed that HDL, glucose, ALT, and AST values differed significantly between age groups and risk categories, pointing to age-specific metabolic alterations. Younger participants presented with lower HDL values and a higher prevalence of moderate risk, while older groups exhibited elevated glucose and liver enzyme levels. These results align with studies emphasizing the importance of integrating traditional risk factors with biochemical and metabolomic markers to achieve greater accuracy in predicting cardiovascular events, offering stronger discriminative power compared to classical models (10).

It is important to emphasize that the combination of body composition indicators and biochemical parameters in our study enabled a clearer differentiation of risk profiles across age groups, supporting the need for a comprehensive approach to cardiometabolic risk assessment. Recent studies have shown that advanced body indices when combined with glucose and lipid biomarkers, provide superior prognostic value compared to BMI, which further supports our findings (11). Accordingly, future risk assessment models should integrate body composition parameters and biochemical markers, while establishing age-stratified thresholds to ensure greater precision in prediction and timely intervention. Early identification and monitoring of key parameters in younger age groups may play a crucial role in preventing and slowing the progression of cardiometabolic diseases. Our results suggest that cardiometabolic risk begins to develop already in early adulthood (<49 years), primarily through reduced HDL and the accumulation of visceral fat, while in later decades the risk intensifies further with rising glucose and liver enzyme levels. These findings are consistent with those of Zhernakova et al., who reported that early metabolic disturbances such as dyslipidemia and insulin resistance are more common in younger adults, while older populations show a clustering of established risk factors and comorbidities (5). Similar results were presented by Min Jeong et al., confirming that early adulthood is a critical period for the initiation of processes leading to the later development of cardiovascular disease (6).

6. CONCLUSION

The prevalence of ≥3 metabolic risk factors increased with age, with younger groups (<49 years) already showing notable risk patterns, while older groups (>60 years) had the highest prevalence. This indicates that cardiometabolic risk develops early and accumulates over time. Age-specific changes in biochemical, lipid, and body composition parameters confirm that combining body and biochemical markers offers more accurate assessment than BMI. Future research should define age-stratified thresholds and validate them across populations, while in practice, early monitoring and intervention in younger adults may help reduce long-term cardiometabolic risk.

Ethics approval:

All procedures performed in studies involving human participants were in accordance with the ethical standards of our institutional research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards

Data Availability Statement:

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Declaration of Patient Consent:

t: All participants provided written informed consent before enrolment in the study. The privacy and confidentiality of patient records were adhered to in managing the clinical information in conducting this research..

Authors’s contribution:

The all authors were involved in all steps of preparation this article. Final proofreading was made by the first author.

Conflict of interest:

There are no conflicts of interest to declare.

Financial support and sponsorship:

The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.