A Cross-Sectional Study of Physical Activity, Body Composition, and Biochemical Parameters in Adolescents from Western Mexico

Introduction

Regular PA is widely recognized as a health-related behavior associated with multiple physiological and psychological benefits across the lifespan (1). Adherence to global PA recommendations has been consistently associated with favorable cognitive outcomes and a lower prevalence of chronic and noncommunicable diseases (2). Accordingly, higher levels of PA have been linked to a lower prevalence of conditions such as cardiovascular disease, hypertension, type 2 diabetes, cancer, and depression (1).

Physical inactivity during adolescence represents a critical public health challenge in Mexico, as this developmental stage is pivotal for solidifying lifelong habits. Insufficient physical activity during this period heightens the risk of developing obesity and chronic metabolic diseases. Consequently, fostering healthy behaviors during these formative years remains the most effective strategy for preventing long-term health complications (3). According to the WHO, adolescents are recommended to engage in at least 60 minutes of moderate-to-vigorous physical activity daily (4). In Mexico, 46.2% of adolescents aged 15–17 years do not meet these recommendations (5), indicating a high prevalence of insufficient PA. Additionally, data from the 2023 National Health and Nutrition Survey (ENSANUT) report that up to 40.4% of adolescents are overweight or obese (6). Physical inactivity has been consistently associated with increased cardiovascular risk, lower cardiorespiratory fitness, and higher levels of adiposity (1).

Observational evidence highlights that PA is an important correlate of cardiometabolic health indicators. Lower levels of PA have been associated with a higher prevalence of risk factors such as obesity, hypertension, reduced high-density lipoprotein cholesterol (HDL-C), and diabetes (10). In Mexico, the prevalence of dyslipidemia has been estimated at 36.7% in the adult population (7). Furthermore, higher levels and intensity of physical activity have been found to be associated with more favorable lipid profiles (8). However, despite these associations, physical activity levels tend to decline during the transition from high school to college, a trend that has been linked to a decline in physical fitness (9).

Given the high prevalence of insufficient PA and its documented associations with adverse health indicators, further research is needed to characterize differences in early metabolic and physiological indicators according to PA level. Therefore, this study aimed to compare body composition and biochemical parameters between adolescents classified as physically active and inactive.

Methods

Participants

A cross-sectional study was conducted. The minimum sample size was estimated using a formula for comparing two proportions, with 95% power and an alpha level of 0.10, based on proportions of prediabetes reported in previous studies (11,12). The sample consisted of 58 adolescents (16.4 ± 0.71 years of age; 67.4 ± 17.00 kg; 168.2 ± 9.67 cm): 28 males (16.4 ± 0.74 years; 74.4 ± 13.56 kg; 175.6 ± 7.71 cm) and 30 females (16.4 ± 0.70 years; 60.8 ± 17.46 kg; 161.3 ± 5.15 cm). High school students were invited to participate. Exclusion criteria included noncommunicable diseases, musculoskeletal limitations, and pregnancy. Before the study, parents and participants were informed of its objectives and procedures. Parents signed an informed consent form, while minors signed an assent form. The study was approved by the Research and Research Ethics Committee of the State Cancer Institute of Colima (registration number CEICANCL220223-ANVSDGL-01).

Protocols

Participants were evaluated in the Ecology Laboratory during the morning under previously instructed conditions. PA levels were assessed using the long-form version of the IPAQ, which consists of 27 items and evaluates five domains: work-related activity, transportation, domestic tasks, outdoor activities, and sedentary behavior. The questionnaire captures three PA dimensions—intensity (light, moderate, or vigorous), frequency (days per week), and duration (time per day)—to estimate total weekly minutes of moderate-to-vigorous physical activity (MVPA). For use in adolescents, minor language adaptations were implemented to improve comprehension without altering the structure or content of the instrument; this modified approach has previously been validated in a multicenter adolescent population (HELENA study) (13). Cut-off points were established according to World Health Organization recommendations for this age group: inactive (<420 minutes/week of MVPA) and active (≥420 minutes/week of MVPA) (4).

Body weight, lean mass (LM), and fat mass (FM) were assessed using a multi-frequency bioelectrical impedance body composition analyzer (InBody 120, South Korea). Participants stood barefoot on two foot electrodes while holding two hand electrodes. Measurements were taken with minimal clothing, without socks or metallic objects, and after an 8-hour fast. Height was measured using a wall-mounted stadiometer (SECA 206, Switzerland). The measurement was performed without shoes, with the back and heels against the wall and feet together, by an ISAK Level I certified technician following the guidelines of the International Society for the Advancement of Kinanthropometry (ISAK) (14). Body mass index (BMI) was calculated using the Quetelet formula (15): BMI = weight (kg)/height² (m²) (Eq. 1).

For the determination of biochemical parameters, blood samples were collected in the morning after an 8-hour fast via brachial venipuncture. Samples were then processed, and serum concentrations of total cholesterol, triglycerides, HDL cholesterol, glucose, uric acid, urea, magnesium, creatinine, calcium, total bilirubin, direct bilirubin, aspartate transaminase (AST), alanine aminotransferase (ALT), alkaline phosphatase (ALP), and albumin were determined using commercial assay kits (Biosystem Laboratories, Barcelona, Spain) and an automated A15 spectrophotometer, following the manufacturer's instructions.

Insulin resistance was estimated using the triglyceride–glucose (TyG) index, calculated as follows (16): TyG = ln [fasting triglycerides (mg/dL) × fasting glucose (mg/dL)] / 2 (Eq. 2).

Statistical Analysis

Participant characteristics and physical activity (PA) levels were summarized using frequencies and percentages. Data distribution was assessed using the Shapiro–Wilk test, and continuous variables are presented as mean ± standard deviation (SD). Differences in body composition and biochemical profiles between active and inactive groups were evaluated using independent-samples t-tests, with two-sided statistical significance set at P ≤ 0.05.

To complement P values, results were also interpreted using 95% confidence intervals (95% CIs) and Cohen’s d effect sizes. Effect sizes were interpreted as small (0.2), medium (0.5), or large (0.8). Given the number of comparisons and the exploratory nature of the study, the findings were interpreted cautiously. All analyses and effect-size estimations were conducted using Jamovi software (version 2.4.8; Sydney, Australia).

Results

A total of 58 adolescents participated in the study, of whom 51.7% (n = 30) were female and 48.3% (n = 28) were male. According to the IPAQ results, 62% (n = 36) were classified as physically active and 38% (n = 22) as physically inactive. Among females, 18 of 30 (60.0%) were active and 12 of 30 (40.0%) were inactive; among males, 18 of 28 (64.3%) were active and 10 of 28 (35.7%) were inactive (Table 1).

BMI was higher in the active group (24.8 ± 5.25 kg/m²) than in the inactive group (21.7 ± 4.13 kg/m²), t(56) = 2.37, P = 0.021, d = 0.640.

Body weight was numerically higher in active (70.5 ± 16.82 kg) than inactive participants (62.2 ± 16.39 kg), but the difference was not statistically significant (P = 0.071). No statistically significant differences were detected in FM between active (20.8 ± 11.79 kg) and inactive groups (17.1 ± 8.04 kg), or in LM between active (27.6 ± 6.19 kg) and inactive groups (24.7 ± 7.66 kg) (Table 2). No statistically significant differences were detected in the assessed biochemical parameters according to PA level (Table 3).

Discussion and Conclusion

In the unadjusted comparisons, BMI was the only body-composition indicator that differed significantly according to the self-reported PA classification; BMI was higher in the active group.

Although scientific evidence supports that PA benefits health and reduces cardiovascular risk factors—mainly through improvements in body composition (10)—the most recent ENSANUT 2023 survey (5) reports that 46.2% of adolescents in Mexico are physically inactive. In line with these findings, our study identified 38% of adolescents as inactive, according to WHO criteria.

Regarding body composition, the mean FM in our sample was 19.42 ± 10.60 kg, compared with 13.72 ± 7.68 kg reported in adolescents in the EVA4YOU study (17). However, comparisons across studies should be interpreted cautiously because of differences in populations and measurement procedures. BMI was higher in physically active (24.8 ± 5.25 kg/m²) than inactive adolescents (21.7 ± 4.13 kg/m²). BMI does not distinguish between fat mass and lean mass and should therefore not be interpreted as a direct measure of adiposity.

The higher BMI observed in the active group should not be interpreted as evidence of greater adiposity because BMI does not distinguish fat mass from lean mass. However, neither FM nor LM differed significantly between groups, and the cross-sectional design does not permit conclusions regarding exercise-induced changes, temporal stability, or visceral adiposity. These findings support interpreting BMI alongside direct body-composition measures and age- and sex-specific reference values (17,18).

Excess fat mass accumulation during adolescence is associated with an increased risk of insulin resistance, hyperglycemia, hyperinsulinemia, and subsequent metabolic and cardiovascular diseases. Previous longitudinal studies have associated greater PA with more favorable fat-mass and lipid trajectories (19–21). In the present cross-sectional study, however, no statistically significant differences were detected in FM or in the assessed biochemical parameters between the active and inactive groups.

This study has several limitations. PA was assessed using the self-reported long-form IPAQ, which is susceptible to recall and social-desirability bias and may lead to misclassification. The cross-sectional design precludes causal inference, the sample was relatively small, and the analyses were not adjusted for potential confounders such as sex, biological maturation, or dietary intake. In addition, multiple outcomes were compared; therefore, the findings should be considered exploratory. Future studies should use larger samples and objective PA measures to improve activity-level classification.

In conclusion, 38% of the adolescents were classified as physically inactive. The active group had a higher raw BMI, whereas no statistically significant between-group differences were detected in the other body-composition or biochemical parameters. Given the cross-sectional design, small sample, self-reported PA assessment, and multiple comparisons, these findings should be interpreted cautiously. Future research should assess PA using objective tools and include age- and sex-adjusted measures of body composition.

Authors’ Contributions

MZF and LJOM designed and directed the project; FRL, PJFM, and MZF conceived and planned the methods; MZF, LJOM, and IPP collected the data; MZF and PJFM analyzed the data; CIGG and JEDV contributed to the interpretation of the results; PJFM and CIGG directed the writing of the manuscript; all authors discussed the results and commented on the manuscript; CIGG, FRL, PJFM, JEDV, and MZF reviewed and edited the final version of the manuscript.

Declaration

No artificial intelligence tools were used.

Transparency Statement

Data are available for research purposes upon reasonable request to the corresponding author.

Acknowledgments

We would like to express our gratitude to everyone who helped us carry out this project, to the University of Colima, and to the Biomedical Research Unit based in Durango.

Declaration of Interest

The authors report no conflict of interest.

Funding

Funding for this work was provided through a postgraduate scholarship awarded by the Mexican Secretariat of Science, Humanities, Technology, and Innovation.

Ethical Considerations

The study protocol adhered to the principles of the Declaration of Helsinki for research involving human participants. Before participation, parents or legal guardians provided written informed consent, and the adolescent participants provided written assent. The protocol was approved by the Research and Research Ethics Committee of the State Cancer Institute of Colima (Approval Code: CEICANCL220223-ANVSDGL-01).