Introduction
The prevalence of obesity and related metabolic disorders is increasing worldwide, posing a significant threat to public health and substantially reducing the quality of life of affected individuals.1 A growing body of evidence suggests that subclinical, low-grade inflammation is not only associated with obesity but may also act as a key biological mechanism in the onset and progression of various metabolic complications, including insulin resistance and type 2 diabetes.2 Chronic inflammatory state, often characterized by elevated levels of markers, such as high-sensitivity C-reactive protein (hs-CRP), is thought to contribute to the development of metabolic dysfunction, which can lead to the abovementioned conditions.3
At the same time, excess adipose tissue itself—reflected by elevated indicators, such as body fat percentage (BF%), body mass index (BMI), and especially waist-to-height ratio (WHtR), which more accurately represents central obesity—can serve as a source of proinflammatory cytokines, thereby initiating or amplifying the inflammatory state.3,4 This creates a complex and potentially bidirectional relationship, where inflammation and adiposity mutually reinforce one another. Rather than a simple cause-and-effect sequence, these mechanisms are likely part of a dynamic network of metabolic and immune interactions.6 Despite this complexity, identifying early markers that could predict a future metabolic risk, such as those related to body composition or low-grade inflammation, is essential for developing effective prevention strategies and mitigating long-term health consequences.
Relationships between body composition, inflammation, and metabolic risks are well documented in adults.7,8 Some scientific reports also indicate that elevated body fat content in adolescents is associated with an increased risk of developing metabolic syndrome in adulthood.9-11 Nevertheless, studies focused on the relationship between adiposity and low-grade inflammation in adolescents remain limited. In particular, no such studies have been conducted in the Central and Eastern European adolescents in the late puberty period. This is particularly important given that adolescence is a critical period, in which significant physiological and hormonal changes take place, influencing fat distribution and inflammatory processes.12,13 Therefore, our study focuses on a young population to help address this research gap.
The aim of the study was to assess the relationship between body composition indicators (BMI, WHtR, and BF%) and the level of subclinical inflammation, measured by hs-CRP concentrations, in Polish adolescents aged 15–18 years.
Methods
The study included data collected from 208 adolescents (52.4% girls), aged between 15 and 18 years (born in the years 2001–2004), participants of the Krakow birth cohort study14 investigating the impact of prenatal exposure to air pollution on the health of infants and children.14,15 All active cohort participants were invited for a follow-up examination in 2019. During the visit, anthropometric and body composition measurements were taken, and blood samples were collected.
The study received approval from the Bioethics Committee of Jagiellonian University (122.6120.190.2015) and was carried out in accordance with the Declaration of Helsinki. Written informed consent was obtained from all participants, as well as from the parents of those underage.
Measurements
Blood samples were collected in the early morning following overnight fasting, using the standard venipuncture procedure. The serum concentration of hs-CRP was determined using an immunonephelometric method on a Siemens BN II analyzer (Munich, Germany).
Anthropometric measurements were performed with the participants wearing light clothing and no shoes. Each measurement was taken twice, and the average was recorded. The height was measured using a wall-mounted stadiometer with 0.1-cm precision. Body circumferences were measured with a nonelastic metric tape, also with an accuracy of 0.1 cm. Waist circumference was measured at the midpoint between the top of the iliac crest and the bottom of the costal margin.
Body weight and BF% were assessed using the bioelectrical impedance analysis method with a Tanita BC-420 electronic analyzer (Amsterdam, the Netherlands). Height, age, and sex data were manually entered into the device before measurement.
Body composition
The body composition status of the participants was classified based on age- and sex-adjusted indicators, using the following criteria: 1) BMI: expressed in kg/m², calculated according to the Polish BMI percentile charts based on OLAF and OLA studies,16 with categories defined as: underweight (<5th percentile), normal weight (5th–85th percentile), overweight (86th–90th percentile), and obesity (>95th percentile); 2) WHtR: determined by dividing waist circumference (cm) by height (cm), and categorized according to the Polish reference chart17 as: underweight (<5th percentile), normal weight (5th–90th percentile), overweight (91st–95th percentile), and obesity (>95th percentile); 3) BF%: assessed using a Tanita analyzer and classified based on sex- and age-specific cut-offs provided by the manufacturer, with categories defined as follows: underfat (<5th percentile), normal (5th–85th percentile), overfat (86th–95th percentile), and obesity (>95th percentile).
Statistical analysis
Data were presented using medians (interquartile ranges [IQRs]) for quantitative variables, because the distributions were skewed, whereas qualitative variables were reported as frequencies and percentages. The Spearman rank correlation coefficient (R) was used to evaluate the relationships between quantitative variables. To compare hs-CRP levels between the groups, the Kruskal–Wallis test was used, and the Dunn test with the Bonferroni correction was applied for post-hoc comparisons. Differences in categorical variables were assessed using the χ² test or the Fisher exact test, as appropriate.
All tests were 2-sided, and a P value below 0.05 was deemed significant. Statistical analyses were conducted using SPSS Statistics software, version 29 (PS IMAGO PRO 10.0, IBM, Armonk, New York, United States).
Results
The study population consisted of 208 adolescents, including 99 boys and 109 girls, aged 15–18 years. Generally, the boys were taller, heavier, and had a greater waist circumference than the girls, whereas the girls had higher body fat content than the boys. Detailed anthropometric characteristics of the study group are presented in Table 1. Excess body weight or adiposity (overweight and obese categories combined) was observed in 17.8% of the participants based on BMI, in 3.8% based on WHtR, and in 11.1% on the basis of BF%. More detailed information on the weight and adiposity status is provided in Table 2.
Parameter | Boys (n = 99) | Girls (n = 109) | P value |
|---|---|---|---|
Age, y | 16 (16–17) | 16 (16–17) | 0.31 |
Weight, kg | 65.2 (61–74.3) | 58.2 (52.9–63.7) | <0.001 |
Height, cm | 178.5 (175.5–183.3) | 165.5 (162–170) | <0.001 |
BMI, kg/m2 | 20.9 (19.2–22.7) | 21 (19.4–23.1) | 0.41 |
Waist circumference, cm | 72 (69–74) | 66 (62–69) | <0.001 |
WHtR | 0.4 (0.39–0.42) | 0.39 (0.38–0.42) | 0.04 |
Body fat percentage, % | 10.5 (8.5–14.8) | 23.6 (19.1–27.1) | <0.001 |
hs-CRP, mg/l | 0.28 (0.16–0.77) | 0.31 (0.16–0.78) | 0.57 |
Data are presented as medians with interquartile ranges. Abbreviations: BMI, body mass index; hs-CRP, high-sensitivity C-reactive protein; WHtR, waist-to-height ratio | |||
Parameter | Boys (n = 99) | Girls (n = 109) | Total (n = 208) | P value | |
|---|---|---|---|---|---|
BMI | Underweight | 7 (7.1) | 5 (4.6) | 12 (5.8) | 0.51 |
Normal | 77 (77.8) | 82 (75.2) | 159 (76.4) | ||
Overweight | 11 (11.1) | 19 (17.4) | 30 (14.4) | ||
Obese | 4 (4) | 3 (2.8) | 7 (3.4) | ||
WHtR | Underweight | 13 (13.1) | 15 (13.8) | 28 (13.5) | 0.33 |
Normal | 81 (81.8) | 91 (83.5) | 172 (82.7) | ||
Overweight | 2 (2) | 3 (2.8) | 5 (2.4) | ||
Obese | 3 (3) | 0 | 3 (1.4) | ||
BF% | Underfat | 46 (46.5) | 7 (6.4) | 53 (25.5) | <0.001 |
Normal | 47 (47.5) | 85 (78) | 132 (63.5) | ||
Overfat | 5 (5.1) | 15 (13.8) | 20 (9.6) | ||
Obese | 1 (1) | 2 (1.8) | 3 (1.4) | ||
Data are presented as numbers (percentages). Abbreviations: BF%, body fat percentage; others, see Table 1 | |||||
In the analyzed group of adolescents, a weak but significant positive correlation was found between the hs-CRP levels and BMI (R = 0.217; P = 0.002; Figure 1A), WHtR (R = 0.231; P <0.001; Figure 1B), and BF% (R = 0.165; P = 0.02; Figure 1C). In the analysis performed in the sex-specific subgroups, this association was significant only among the girls (BMI, R = 0.351; P <0.001; WHtR, R = 0.369; P <0.001; BF%, R = 0.319; P <0.001, respectively), suggesting a sex-specific pattern in the relationship between body composition and low-grade inflammation.
Figure 1. Relationship between high-sensitivity C-reactive protein levels and body mass index (A), waist-to-height ratio (B), and body fat percentage (C)
Abbreviations: see Table 1
The adolescents classified as obese exhibited elevated hs-CRP levels, as compared with their leaner peers, regardless of the classification method used. When defined by BMI centile charts, the participants with obesity had significantly higher median (IQR) hs-CRP concentrations (1.15 [0.87–1.91] mg/l) in comparison with their peers with normal weight or underweight (0.25 [0.16–0.74] mg/l; P = 0.008; 0.22 [0.16–0.38] mg/l; P = 0.02, respectively; Figure 2A). In the analysis of differences in hs-CRP concentrations between the groups classified according to WHtR, the overweight and obese categories were combined into a single group labeled “excess weight” due to the very small number of participants in both subgroups. A notable median (IQR) difference in hs-CRP concentration was observed between the excess weight group (1.14 [0.51–1.91] mg/l) and underweight group (0.18 [0.16–0.34] mg/l; P = 0.003; Figure 2B). When the participants were categorized based on BF%, the group with obesity had a considerably higher median (IQR) hs-CRP level (2.64 [1.90–3.66] mg/l) than both the normal and underfat groups (0.28 [0.16–0.73] mg/l; P = 0.04; 0.23 [0.16–0.75 mg/l]; P = 0.04, respectively; Figure 2C). Due to the limited number of participants in the groups with overweight, obesity, and excess adiposity, additional analyses stratified by sex were not conducted.
Figure 2. Differences in high-sensitivity C-reactive protein levels across body mass index (A), waist-to-height ratio (B), and body fat percentage (C) categories. Boxes represent interquartile ranges, and the lines represent the medians. Whiskers represent the range excluding outliers, which are displayed as individual points.
Abbreviations: see Table 1
To ensure consistency and increase statistical power, we also combined the overweight / obese categories, and the normal / underweight or underfat categories for BMI, WHtR, and BF% analyses. In these analyses, hs-CRP levels were higher among the participants with excess adiposity than those with normal weight or underweight / underfat (BMI, P = 0.007; WHtR, P = 0.01; BF%, P = 0.04, respectively). These data are outlined in Supplementary material, Figure S1.
Discussion
In this study, we demonstrated that indicators of body composition, specifically BMI, WHtR, and BF%, were positively associated with hs-CRP concentration in adolescents aged 15–18 years. Similar associations have been reported in other recent cohorts, including the Spanish GENOBOX study,18 which showed a positive relationship between hs-CRP and multiple body fat parameters in children and adolescents, and the Belgian study conducted by Mol et al,19 which demonstrated that higher total and central adiposity were associated with elevated hs-CRP levels in youths with overweight / obesity. However, our study extended this evidence by examining a relatively narrow age range (15–18 years) corresponding to late puberty. Moreover, it was conducted in an Eastern European population, underrepresented in previous research, and simultaneously evaluated 3 commonly used adiposity indices (BMI, WHtR, and BF%). Therefore, rather than claiming novelty of the association itself, our study should be seen as reinforcing known links in an underrepresented context, namely, an Eastern European cohort of mid-to-late adolescents, thereby filling a geographic and developmental gap in the literature.
Furthermore, adolescents assigned to the group with obesity based on BMI and BF% centile cut-offs, and classified as having excess weight based on WHtR, had markedly higher hs-CRP concentrations than their normal-weight or underweight (underfat) peers. It should be noted, however, that in the correlation analyses, WHtR showed a stronger association with hs-CRP than BF%, while the categorical BF%-defined group with obesity had the highest median hs-CRP levels. This latter finding must be interpreted with caution, given the very small number of participants in the BF% subgroup with obesity, which makes the result highly sensitive to outliers (a similarly limited subgroup size was also observed for WHtR-defined obesity). Additional analyses combining the participants with overweight and obesity into a single category for each indicator showed significant differences in hs-CRP, as compared with the normal / underweight (underfat) group. This suggests that even moderate excess adiposity is associated with higher low-grade inflammation, although the highest values were still observed in the subgroup with obesity. These findings are consistent with previous research, highlighting the link between excess adiposity and chronic low-grade inflammation, even in the pediatric population.20
The positive associations observed between hs-CRP and anthropometric indicators of adiposity are in line with the existing literature demonstrating that excessive fat accumulation plays a key role in triggering systemic low-grade inflammation.21 Adipose tissue is known to secrete pro-inflammatory cytokines, which stimulate the production of hs-CRP and contribute to chronic immune activation.6 Several studies have documented similar findings in young populations. Carvalho et al22 showed a positive relationship between WHtR and CRP levels in adolescents. Kitsios et al23 reported that hs-CRP was significantly higher in children and adolescents with excess weight, as compared with normal-weight individuals. An analogous association was found in a study conducted on Spanish children and adolescents: hsCRP concentration was associated with fat mass, both in the prepubertals and pubertals subgroup, regardless of how body composition was measured.18 These results reinforce the notion that subclinical inflammation may begin early in life and is closely tied to excess body fat.
The strongest correlations were observed in the girls, indicating a potential sex-dependent difference in the strength of association between these markers and low-grade systemic inflammation. Our observation is supported by previous research indicating that sex differences in body fat distribution and hormonal regulation during puberty can influence inflammatory responses.24 It should therefore be noted that sex differences observed in our findings may partially reflect underlying biological and hormonal mechanisms. At the same time, an alternative explanation should be considered: in our cohort, nearly half of the boys were classified as underfat and only a small proportion (6.1% by BF% criteria) had overweight or obesity. This likely reduced statistical power to detect associations, indicating that the lack of differences between the groups in boys may reflect sample limitations rather than a true biological absence of effect. Future studies including larger numbers of boys with overweight / obesity, or spanning the full pubertal transition, are needed to clarify whether sex modifies the adiposity–inflammation relationship.
It is worth noting that in our study, the median hs-CRP serum concentration exceeded 1 mg/l in the subgroups with excess adiposity, regardless of the classification method used. In the adult population, hs-CRP below 1 mg/l is a significant predictor not only of diabetes but also of adverse cardiovascular events.25,26
This study has several limitations that should be acknowledged when interpreting the results. First, it is known that factors other than body composition may also play a role in modulating low-grade inflammation (CRP levels). There is evidence that sleep schedule,27 interpersonal stress,28 diet,29 and socioeconomic status30 can affect inflammation in the youth. Adiposity might then not act independently in driving low-grade inflammation but rather represent a proxy for an overall unhealthy lifestyle. For example, physical inactivity frequently co-occurs with excess adiposity and has been shown to increase CRP levels.19 Therefore, the observed associations should be interpreted as correlations rather than evidence of direct causality between fat mass and inflammation. Moreover, since this study has a cross-sectional design, causal relationships cannot be confirmed. Although associations between adiposity markers and hs-CRP were observed, it is not possible to determine the temporal sequence or directionality of these relationships. It should also be noted that the proportion of individuals classified as obese / with excess weight (based on BMI, WHtR, and BF%) was relatively small, potentially limiting statistical power in subgroup comparisons. Also, this limited number of individuals with excess weight and fat did not allow for meaningful sex-stratified analyses within the subgroups. Nevertheless, subgroup-based classifications, which are adjusted for age and sex, may still offer a more accurate framework for interpreting the results in a developmentally heterogeneous adolescent population—where some individuals have already undergone pubertal changes, while others have not. Unfortunately, these data were not available for the study population. Although participants were in mid-to-late adolescence (15–18 years), variations in biological maturation likely persisted. In contrast, the analyses using continuous values of body composition indicators did not account for differences in biological maturity, which may have affected the clarity of the observed associations. However, it cannot be excluded that part of the observed associations reflects maturational rather than adiposity-related effects. Finally, the use of a single hs-CRP measurement may not adequately reflect the presence of low-grade inflammation. Repeated measurements or measuring other inflammatory biomarkers (eg, interleukin-6 or tumor necrosis factor α) would provide a more comprehensive assessment.
Importantly, our findings demonstrate that signs of low-grade systemic inflammation can already be observed in adolescents without diagnosed metabolic disorders. Even in generally healthy adolescents, substantially higher body fat content is associated with low-grade inflammation. This suggests that inflammation may begin early, before any clear symptoms of metabolic disease appear. Early detection of such state could have considerable preventive value. Subclinical inflammation is a recognized contributor to the development of metabolic and cardiovascular diseases.3 Therefore, introducing simple anthropometric indicators, such as BF% or WHtR, into routine pediatric assessments may offer a practical tool for identifying adolescents at an increased metabolic risk, even in the absence of clinical symptoms. Overall, these findings suggest that all 3 adiposity indicators (BMI, WHtR, and BF%) are useful for identifying adolescents at risk of low-grade inflammation. Consistent with recent evidence,19 central obesity markers, such as WHtR, may offer a slight advantage in predicting the hs-CRP level, but the observed differences in correlation strength were modest. In practical terms, BMI and WHtR are much easier to obtain in routine pediatric or public health settings, while BF% assessment may be valuable primarily in research contexts or when distinguishing highest-risk individuals.
Longitudinal studies are necessary to explore the interplay between body composition and inflammation during adolescence. Tracking hs-CRP and other inflammatory markers over time would allow for identification of patterns that may predict future metabolic problems. Additionally, future studies should consider expanding the panel of inflammatory biomarkers involved in chronic inflammation. These markers could provide a clearer understanding of how excess body fat leads to inflammation and help identify different types of immune responses. Finally, more research could help better understand how lifestyle factors affect hs-CRP levels. Diet, physical activity, sleep, screen time, and stress have all been linked to inflammation in adolescents, but how these factors work together and interact with body fat is still not well understood. Studies using models that combine these elements may help identify modifiable risk factors and support more effective strategies to reduce inflammation and its long-term health effects.
Conclusions
Our findings indicate that excess body weight, especially excess body fat content, is associated with elevated hs-CRP levels in adolescents, even in the absence of clinically manifested metabolic diseases. This association was significant in the overall sample and in the girls, but not the boys. All 3 adiposity indices (BMI, WHtR, and BF%) were associated with hs-CRP, highlighting their utility as screening tools for early low-grade inflammation in adolescence. WHtR showed the strongest correlation coefficients, while the BF%-defined subgroup with obesity consisted of very few individuals, showing particularly high hsCRP levels. Taken together, these measures appear complementary. Since chronic low-grade inflammation contributes to the development of metabolic disorders, using these measures in routine health assessments could support early prevention strategies in the youth.
Agnieszka Pac, MD, PhD, Chair of Epidemiology and Environmental Medicine, Jagiellonian University Medical College, ul. Kopernika 7A, 31-034 Kraków, Poland, phone: +48 12 423 10 03, email: agnieszka.pac@uj.edu.pl
July 15, 2025.
September 19, 2025.
October 3, 2025.
The data were collected in the research “A join project studying the long-term impacts of coal-related air pollutants on children’s health in Kraków” conducted in cooperation between Columbia University, New York, (principal investigator, Professor F.P. Perera) and Jagiellonian University Medical College, Kraków (principal investigator, AP). Blood sample collection and analysis were performed by the team of Diagnostics Department of the University Hospital in Kraków (chair, B. Maziarz, PhD).
This work was supported by the Jagiellonian University Medical College, Kraków, Poland (N41/DBS/000725; to AP).
Study conception and design: AP and ES-T; analysis and interpretation of the results: GŻ, AP, and ES-T; draft manuscript preparation: GŻ, AP, and ES-T. All authors reviewed the results and approved the final version of the manuscript.
Artificial intelligence was not used in the preparation of this manuscript.
None declared.
Żyłka GA, Sochacka-Tatara E, Pac A. Low-grade inflammation and body composition indicators in adolescents aged 15–18 years. Prz Lek Jagiellonian Med Rev. 2025; 77: 20005. doi:10.20452/jmr.2025.20005
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