S-shaped association between hs-CRP/HDL-C index and overweight/obesity in children and adolescents: a population-based study

Abstract

Background

Obesity has become a growing public health problem worldwide, especially in children and adolescents. The ultrasensitive C-reactive protein (hs-CRP)/high-density lipoprotein cholesterol (HDL-C) index is a widely recognized marker of metabolic disorders and inflammatory responses. However, its connection to overweight or obesity in children and adolescents remains unclear. This study aimed to examine the association between the hs-CRP/HDL-C index and overweight or obesity, to identify its possible threshold effect, and to offer a new theoretical basis for prevention and control among youth.

Methods

This study investigated the relationship between hs-CRP/HDL-C index and overweight and obesity in children and adolescents aged 6–19 years, utilizing data from the 2015–2020 National Health and Nutrition Examination Survey (NHANES). This analysis employed t tests, Chi-square tests, multiple logistic regression, restricted cubic spline regression (RCS), and piecewise regression methods to validate the association between the hs-CRP/HDL-C index and overweight/obesity, and to explore potential nonlinear patterns and key thresholds between the two.

Results

This study included 3853 children and adolescents. Multivariate logistic regression analysis revealed a significant positive relationship between the log-transformed indicator and overweight/obesity (OR = 5.81; 95% CI 5.02–6.75; P < 0.001). Furthermore, smoothed curve fitting revealed a nonlinear S-shaped dose–response relationship between the two variables. In addition, segmented regression analysis of hs-CRP/HDL-C identified two inflection points (0.653 and 1.593). Notably, except for household income, interaction p values across subgroups were all greater than 0.05, indicating high stability and consistency among subgroups. However, household income exerted a significant modifying effect.

Conclusion

This study finds that the hs-CRP/HDL-C index is associated with overweight and obesity in children and adolescents in a manner that is not linear. There is a critical range (0.653–1.593) where higher values of the index are related to lower rates of overweight and obesity. Based on these results, it is recommended that pediatric healthcare providers use hs-CRP/HDL-C testing routinely, which may help identify children at risk and support early obesity prevention. Future studies should confirm these findings in additional groups and develop more effective interventions.

Introduction

Obesity has become a growing global public health problem, with prevalence rates rising dramatically since 1990. Obesity rates have more than doubled among adults globally and tripled among children and adolescents [1]. Data from the U.S. Centers for Disease Control and Prevention [2] show that the prevalence of obesity among children and adolescents aged 2–19 years has increased from 16.9% in 2010 to 19.7% in 2020. The economic burden of obesity in the U.S. healthcare system in 2021 exceeds $173 billion [3]. The World Obesity Federation [4] estimates that by 2030, 254 million children and adolescents will be obese. Obesity is associated with chronic diseases such as diabetes, cardiovascular disease, and cancer as the fifth leading risk factor for mortality [5]. Therefore, exploring new indicators related to overweight/obesity is crucial for timely monitoring the progression of obesity and its complications in children and adolescents.

The high-sensitivity protein and high-density lipoprotein cholesterol (hs-CRP/HDL-C) index is a valuable composite index. It has simplicity, utility, and clinical relevance and is recognized for its potential as a clinical marker of cardiovascular disease.[6]. Chronic low-grade inflammation and lipid metabolism disorders are central to the pathomechanisms of obesity [7]. Hs-CRP, a sensitive marker of systemic inflammation, is significantly elevated in obese children and positively correlates with visceral fat accumulation and insulin resistance [8, 9]. Whereas HDL-C has anti-inflammatory, antioxidant, and cholesterol reverse transporter functions, its reduced level is associated with an increased risk of obesity-related atherosclerosis [10, 11]. Recent studies [12, 13] have found that the hs-CRP/HDL-C index may comprehensively reflect the inflammation–lipid metabolism imbalance. In adult studies, this index has been significantly and positively associated with the risk of metabolic syndrome and cardiovascular events. However, its association with overweight/obesity in children and adolescents has not been studied.

Existing studies have shown that non-linearity often characterizes the relationship between obesity-related metabolic markers and clinical outcomes. For example, a clear U- or J-shaped dose–response curve exists between body mass index (BMI) and cardiovascular disease risk [14]. At the same time, inflammatory markers such as hs-CRP may accelerate the progression of endothelial dysfunction and insulin resistance by activating the NF-κB signaling pathway after a specific concentration threshold [15]. It is noteworthy that childhood and adolescence, as critical stages for fat redistribution and fluctuations in sex hormone levels, may significantly influence the synergistic effects of hs-CRP and HDL-C due to dynamic changes in body composition. Evidence from cohort studies [16, 17] suggests that the relationship between visceral fat accumulation and elevated hs-CRP levels is enhanced after puberty. The anti-inflammatory capacity of HDL-C exhibits a gender-specific pattern that varies with estrogen levels [18, 19].

The above information suggests that the association of hs-CRP/HDL-C index with overweight/obesity may exhibit unique dynamic patterns in the child and adolescent population. The data for this study were derived from the National Health and Nutrition Examination Survey (NHANES) conducted between 2015 and 2020, with participants ranging from 6 to 19 years of age. Through cross-sectional analysis, the study aims to investigate the association between the hs-CRP/HDL-C index and overweight/obesity in children and adolescents and its potential threshold effects. This research lays the groundwork for subsequent exploration of the causal relationship between these two factors.

NHANES is a continuous survey using a stratified, multistage sample approach to generate nationally representative estimates of nutrition and health variables estimates [20]. Approximately 5000 Americans participate in the survey each year, which is released on a 2-year cycle. The survey collects five different types of data: demographic profiles, dietary habits, physical examinations, laboratory tests, and individual questionnaires, and these datasets can be evaluated online (URL: http://www.cdc.gov/nchs/nhanes.htm). The study protocols were all approved by the Ethics Review Board of the National Center for Health Statistics, and each participant provided written informed consent. All protocols adhered to the principles outlined in the Declaration of Helsinki and followed the guidelines of the STROBE initiative for reporting observational studies in epidemiology. To date, cross-sectional studies utilizing the NHANES database have spanned various medical disciplines and yielded substantial results [21–24].

Materials and methods

Survey description and study participants

We obtained the relevant data on March 1, 2025, from the NHANES database. We cannot access personally identifiable information about participants during or after data collection. This study used data from the NHANES 2015–2020 cycle. A total of 25,531 individuals participated in NHANES during this period. This study’s inclusion criteria were established: children and adolescents aged 6–19 years (n = 6634). To ensure complete data for statistical analysis, exclusion criteria were standardized after reviewing relevant literature method [25]: (1) missing BMI classification data (n = 526); (2) missing hs-CRP and HDL-C measurements (n = 1210); (3) lack of covariate data (n = 1045). The covariates were selected based on studies examining the relationship between the research variables [26, 27]. Ultimately, 3853 eligible participants were included in the study. The specific screening process for inclusion in the study is shown in Fig. 1.

Fig. 1.

NHANES participant selection flowchart

Definition of overweight and obesity

Professional health technicians at the Mobile Examination Center measured participants' height and weight are using standardized instruments. We used BMI to assess overweight/obesity status in individuals aged 6–19 years, calculated as weight (kg) divided by height (m) squared (kg/m²). Based on the age-sex-specific growth charts published by the Centers for Disease Control and Prevention in 2000, BMI values were categorized by percentile into four categories: underweight (BMI < 5th percentile), normal weight (5th percentile ≤ BMI < 85th percentile), overweight (85th percentile ≤ BMI < 95th percentile), and obese (BMI ≥ 95th percentile). This classification has been widely used in NHANES-related studies to identify overweight and obese adolescents accurately.

Measurement of the hs-CRP/HDL-C index

Fasting blood samples were collected according to established venipuncture protocols and procedures. Hs-CRP levels were quantified using particle-enhanced turbidimetric immunoassay. Specific end-point reactions measured HDL-C levels, and the resulting product was measured photometrically at 600 nm. In this study, the hs-CRP/HDL-C index was calculated as the quotient of hs-CRP (mg/L) and HDL-C (mg/dL/1000).

This study measured the hs-CRP/HDL-C ratio using particle-enhanced turbidimetric immunoassay and photometric methods, which exhibit high accuracy. However, hs-CRP and HDL-C levels may be influenced by various physiological, environmental, and biological factors, leading to measurement variability and biological fluctuations. As an acute-phase reactant protein, hs-CRP levels fluctuate not only due to short-term factors like infection and inflammation but also due to emotional influences during adolescence [28, 29]. HDL-C, meanwhile, may be influenced by factors, such as sex, age, diet, exercise, and hormonal fluctuations, particularly in children and adolescents [30]. Therefore, while the hs-CRP/HDL-C index holds significant application value as an inflammation–metabolic biomarker, its biological variability must be considered in research. It is recommended that future studies employ multiple measurements to enhance data reliability.

Covariate

Based on prior research and in conjunction with clinical practice, we recognized that potential confounders vary significantly in terms of individual impact. To ensure their accuracy, we included the following covariates that may have influenced the study results. Demographic data were collected utilizing a computer-assisted personal interview system as well as household and sample person demographic questionnaires, which primarily incorporated the participant’s sex (male or female), age, ethnicity (Mexican American, other Hispanic, non-Hispanic white, non-Hispanic black, or other ethnicity), and household poverty-to-income ratio (PIR). Dietary intake data were used to estimate the type and amount of food and beverages (including all types of water) consumed in the 24 h prior to the interview, including energy intake, protein intake, carbohydrate intake, and total sugar intake. Whole blood samples were analyzed on NHANES MEC, and laboratory data included serum cotinine, white blood cell count (WBC), percentage of eosinophils (EOS%), percentage of lymphocytes (LYM%), percentage of neutrophils (NEUT%), hemoglobin (Hb), platelets (PLT), and total cholesterol (TC).

Statistical methods

Descriptive analyses were performed on the baseline information of all participants in this study. The statistical analyses involved were performed using R software (version 4.2.2) and Empower Stats (version 2.0), and the test of significance was two-sided with a significance level of P < 0.05. Participants with missing data on independent variables, dependent variables, and covariates were excluded from this study to make the analysis more rigorous. Different methods of representation and testing were used depending on the nature and category of the data. Mean (x̄), standard deviation (SD), or median interquartile range (IQR) were used to represent the collected continuous variables. This study employed the Shapiro–Wilk test to assess the normality of continuous variables. Results indicated that such data conformed to a normal distribution, rendering the t test applicable. Categorical variables were expressed as percentages (%) and analyzed using Chi-square tests. Participants were grouped based on their overweight/obesity status and their hs-CRP/HDL-C levels quartiles. Clinical baseline characteristics were analyzed using the statistical methods described above to reveal associations between study variables and covariates. The selection of quartile stratification is based on its ability to effectively capture complex relationships between biomarkers and health outcomes, particularly when these relationships exhibit nonlinearity. Previous studies [31, 32] have extensively employed quartile stratification to analyze associations between indicators and disease risk. By dividing the hs-CRP/HDL-C index into four groups, the study could more clearly identify relationships between different index levels and obesity risk while effectively controlling for potential confounding factors. Furthermore, quartile stratification helps define critical thresholds, providing more targeted clinical intervention guidance. Consequently, quartile stratification adheres to standard statistical methodology and enhances the scientific rigor and clinical utility of the findings. Consequently, quartile stratification aligns with standard statistical methodology and enhances the findings’ scientific rigor and clinical utility. Subsequently, multivariate logistic regression analyses were performed on the data to explore the association between hs-CRP/HDL-C index and overweight and obesity. Model 1 was not adjusted for any covariates; Model 2 took into account key demographic factors, including sex, age, and ethnicity; and Model 3 was a fully adjusted model with PIR, cotinine, WBC, LYM%, EOS%, NEUT%, Hb, PLT, TC, energy intake, protein intake, carbohydrate intake, and total sugar intake added in. Subgroup analyses based on age, sex, ethnicity, passive smoking, and household income were also performed to explore whether the association between hs-CRP/HDL-C index and overweight/obesity is generalizable in child and adolescent populations. Next, we applied a restricted cubic spline (RCS) fit to more precisely assess the potential relationship between the hs-CRP/HDL-C index and overweight/obesity. RCS is a flexible approach used in regression models to handle nonlinear relationships. It works by setting multiple “nodes” across the range of the independent variable, connecting them with a series of cubic polynomial segments to form a smooth, continuous curve. This method allows for a more accurate representation of the complex relationship between the independent and outcome variables. Finally, segmented models were designed using the R segmented software package, which estimates breakpoints and fits a separate linear model for each segment defined by these breakpoints. Breakpoint values were determined based on model fitting and statistical tests.

Results

Clinical baseline characteristics of the subjects

Table 1 presents the baseline characteristics of the 3853 participants grouped by overweight/obesity. A total of 1567 were overweight/obese, with an incidence of 40.67%. The mean age of all subjects was 12 years, with 50.92% males and 49.08% females. In terms of racial distribution, the most significant number of participants were Non-Hispanic White (31.35%), followed by Non-Hispanic Black (22.66%) and Mexican American (18.89%). The overweight/obese group had higher levels of TC and Cotinine, higher WBC, NEUT, PLT, and hs-CRP/HDL-C, lower levels of PIR, EOS, LYM, and Hb, and lower intake of Energy, Carbohydrate, and Total sugars (P < 0.05). Figure 2 visualizes the sex and age distributions of the study population, which were roughly symmetrical, and both were significantly different in the two groups (P < 0.05).

Table 1.

1Baseline characteristics of study participants according to the presence of overweight/obesity

Characteristic	Overall N = 3853	Overweight/obese N = 1567	Non-overweight/obese N = 2286	P value
Race (%)				< 0.001
Mexican American	728 (18.89%)	370 (23.61%)	358 (15.66%)
Other Hispanic	396 (10.28%)	164 (10.47%)	232 (10.15%)
Non-Hispanic White	1208 (31.35%)	445 (28.40%)	763 (33.38%)
Non-Hispanic Black	873 (22.66%)	384 (24.51%)	489 (21.39%)
Other race	648 (16.82%)	204 (13.02%)	444 (19.42%)
Sex (%)				0.002
Male	1962 (50.92%)	751 (47.93%)	1211 (52.97%)
Female	1891 (49.08%)	816 (52.07%)	1075 (47.03%)
Age (%)	12.0 (9.0, 16.0)	13.0 (10.0, 16.0)	12.0 (9.0, 16.0)	0.002
PIR (%)	1.67 (0.89, 3.13)	1.49 (0.83, 2.72)	1.80 (0.95, 3.46)	< 0.001
TC (mg/dL)	152 (136, 172)	155 (138, 176)	151 (134, 169)	< 0.001
Cotinine (ng/mL)	0.03 (0.01, 0.21)	0.04 (0.01, 0.29)	0.03 (0.01, 0.17)	< 0.001
WBC (1000 cells/uL)	6.80 (5.70, 8.30)	7.30 (6.10, 8.70)	6.50 (5.40, 7.90)	< 0.001
NEUT (%)	52 (45, 58)	53 (47, 59)	50 (43, 58)	< 0.001
EOS (%)	2.50 (1.60, 4.20)	2.40 (1.50, 4.00)	2.60 (1.60, 4.40)	0.019
LYM (%)	36 (30, 42)	35 (30, 40)	37 (31, 44)	< 0.001
Hb (g/dL)	13.50 (12.70, 14.40)	13.40 (12.70, 14.30)	13.50 (12.80, 14.40)	0.005
PLT (1000 cells/uL)	271 (233, 312)	281 (243, 322)	264 (228, 305)	< 0.001
Energy (kcal)	1852 (1408, 2456)	1809 (1364, 2405)	1879 (1433, 2479)	0.003
Protein (gm)	64 (46, 87)	65 (46, 88)	64 (46, 87)	0.960
Carbohydrate (gm)	236 (175, 314)	227 (166, 305)	242 (182, 321)	< 0.001
Total sugars (gm)	99 (66, 144)	96 (63, 143)	102 (68, 145)	0.020
hs-CRP/HDL-C	10 (4, 29)	25 (10, 61)	6 (2, 13)	< 0.001

PIR poverty-to-income ratio, TC total cholesterol, WBC white blood cell, NEUT neutrophils, EOS eosinophils, LYM Lymphocyte, Hb hemoglobin, PLT Platelet

Fig. 2.

Graph of sex and age distribution of participants

Table 2 divides the subjects into four groups based on the quartiles of the hs-CRP/HDL-C index and looks at the differences in other variables between the groups. Group 1 hs-CRP/HDL-C index contained values ranging from 0.741 to 3.7, group 2 contained values ranging from 3.7 to 9.5, group 3 contained values ranging from 9.5 to 28.7, and group 4 contained values ranging from 28.7 to 495. Protein intake was not significantly different between the groups (P > 0.05), consistent with the results in Table 1. Waist circumference, as a simple anthropometric indicator, showed significant differences across different groups (P < 0.001), preliminarily suggesting that the association between the hs-CRP/HDL-C ratio and obesity is not limited to BMI. In addition to this, we were surprised to find that sex and TC levels did not differ significantly among the four subgroups. The prevalence of overweight/obesity in children and adolescents decreased significantly as the hs-CRP/HDL-C index of the subjects increased (Q1:14.48%; Q2:26.09%; Q3:48.81%; Q4:73.24%; P < 0.001).

Table 2.

Baseline characteristics of the hs-CRP/HDL-C-based study population

Characteristic	hs-CRP/HDL-C
	Q1 [0.741,3.7), N = 960	Q2 [3.7,9.5), N = 966	Q3 [9.5,28.7), N = 963	Q4 [28.7,495], N = 964	P value
Race					< 0.001
Mexican American	137 (14.27%)	163 (16.87%)	201 (20.87%)	227 (23.55%)
Other Hispanic	105 (10.94%)	98 (10.14%)	81 (8.41%)	112 (11.62%)
Non-Hispanic White	297 (30.94%)	305 (31.57%)	325 (33.75%)	281 (29.15%)
Non-Hispanic Black	242 (25.21%)	220 (22.77%)	208 (21.60%)	203 (21.06%)
Other race	179 (18.65%)	180 (18.63%)	148 (15.37%)	141 (14.63%)
Sex					0.432
Male	495 (51.56%)	507 (52.48%)	489 (50.78%)	471 (48.86%)
Female	465 (48.44%)	459 (47.52%)	474 (49.22%)	493 (51.14%)
Overweight/obese					< 0.001
Yes	139 (14.48%)	252 (26.09%)	470 (48.81%)	706 (73.24%)
No	821 (85.52%)	714 (73.91%)	493 (51.19%)	258 (26.76%)
Age	11.0 (8.0, 15.0)	12.0 (9.0, 15.0)	13.0 (10.0, 16.0)	14.0 (10.0, 17.0)	< 0.001
PIR	1.88 (0.98, 3.52)	1.75 (0.94, 3.30)	1.56 (0.85, 2.98)	1.50 (0.82, 2.68)	< 0.001
TC (mg/dL)	152 (136, 170)	152 (137, 168)	152 (135, 173)	155 (136, 176)	0.093
Cotinine (ng/mL)	0.02 (0.01, 0.12)	0.03 (0.01, 0.18)	0.04 (0.01, 0.27)	0.04 (0.01, 0.33)	< 0.001
WBC (1000 cells/uL)	6.30 (5.30, 7.70)	6.60 (5.40, 7.80)	6.90 (5.75, 8.20)	7.70 (6.40, 9.20)	< 0.001
NEUT (%)	48 (41, 55)	49 (43, 56)	53 (47, 59)	56 (49, 62)	< 0.001
EOS (%)	2.70 (1.70, 4.40)	2.60 (1.70, 4.60)	2.50 (1.50, 4.20)	2.30 (1.40, 3.90)	< 0.001
LYM (%)	39 (33, 46)	38 (32, 44)	35 (30, 41)	33 (27, 38)	< 0.001
Hb (g/dL)	13.30 (12.70, 14.10)	13.40 (12.70, 14.30)	13.50 (12.70, 14.50)	13.35 (12.60, 14.30)	0.003
PLT (1000 cells/uL)	260 (226, 297)	269 (232, 305)	272 (231, 315)	285 (245, 329)	< 0.001
Energy (kcal)	1906 (1429, 2471)	1879 (1451, 2487)	1855 (1385, 2518)	1780 (1332, 2349)	0.001
Protein (gm)	66 (46, 88)	64 (47, 87)	64 (46, 87)	64 (45, 86)	0.769
Carbohydrate (gm)	242 (184, 321)	242 (181, 324)	239 (171, 318)	224 (164, 293)	< 0.001
Total sugars (gm)	104 (67, 148)	105 (68, 148)	98 (67, 143)	93 (60, 138)	0.004
Waist circumference (cm)	66(58, 73)	69(61, 77)	77(69, 86)	89(75, 102)	< 0.001

Relationship between hs-CRP/HDL-C index and overweight/obesity

Table 3 presents the results of univariate and multivariate logistic regression analyses. Based on preliminary testing, we log-transformed the index before analysis. By comparing effect estimates across different models, we preliminarily established the association between overweight/obesity and the log-transformed hs-CRP/HDL-C ratio. In Model 1 (unadjusted for covariates), log(hs-CRP/HDL-C) showed a significant positive relationship with overweight/obesity incidence (OR = 5.86; 95% CI 5.13–6.71; P < 0.001). This significant positive association persisted in Model 2 (adjusted for major demographic factors) and Model 3 (adjusted for all confounders). We converted the continuous variable log(hs-CRP/HDL-C) into quartiles and subsequently performed a sensitivity analysis using the first quartile as the reference group. Results indicated that in Model 1, participants in the highest quartile of log(hs-CRP/HDL-C) had a 16.16-fold higher incidence of overweight/obesity compared to the reference group (OR = 16.16; 95% CI 12.89–20.39; P < 0.001), demonstrating significant statistical differences between groups. This pronounced intergroup disparity persisted in Models 2 and 3 (P < 0.001), with the fourth quartile exhibiting the highest overweight/obesity risk in Model 2 (OR = 17.59; 95% CI 13.92–22.37; P < 0.001). A trend difference was observed in the positive relationship between the hs-CRP/HDL-C ratio and overweight/obesity across the three groups (P for trend < 0.001), indicating a clear upward trend in risk across different quartiles.

Table 3.

Association between log(hs-CRP/HDL-C) and overweight/obesity based on logistic regression analysis

Exposure	Adjusted OR(95% CI), P value
	Model 1	Model 2	Model 3
Per 1 unit incrementa	5.86 (5.13, 6.71) < 0.001	6.19 (5.40, 7.13) < 0.001	5.81 (5.02, 6.75) < 0.001
Log(hs-CRP/HDL-C) (quartile)
Quartile 1	Reference	Reference	Reference
Quartile 2	2.08 (1.66, 2.63) < 0.001	2.13 (1.69, 2.69) < 0.001	2.10(1.66, 2.66) < 0.001
Quartile 3	5.63 (4.53, 7.03) < 0.001	5.92 (4.74,7.42) < 0.001	5.69 (4.53, 7.17) < 0.001
Quartile 4	16.16 (12.89, 20.39) < 0.001	17.59 (13.92, 22.37) < 0.001	15.53 (12.15, 19.97) < 0.001
P for trend	< 0.001	< 0.001	< 0.001

Model 1: No covariates were adjusted

Model 2: Adjusted for sex, age, and race

Model 3: Adjusted for sex, year, race, PIR, TC, Cotinine, WBC, NEUT%, EOS%, LYM%, Hb, PLT, Energy, Protein, Carbohydrate, and Total sugars

^aIn the sensitivity analysis, Log(hs-CRP/HDL-C) was converted from a continuous variable to a categorical variable (quartiles)

Subgroup analysis

To further validate the association between hs-CRP/HDL-C index and overweight/obesity, we conducted five subgroup analyses using logistic regression modeling based on sex, age, race, household income, and passive smoking. The results are shown in Fig. 3. The hs-CRP/HDL-C index was positively associated with overweight/obesity in the total population (OR = 1.02; 95% CI 1.01–1.02; P < 0.001). The interaction P values for sex, age, race, and passive smoking were all greater than 0.05, indicating that the findings were stable and consistent across these subgroups. Interestingly, we also found a significant interaction P value of slightly less than 0.05 for household income, with a lower OR for higher income, implying that the exposure factor may have a more substantial protective effect for those with higher incomes.

Fig. 3.

Subgroup analysis of the association between hs-CRP/HDL-C index and overweight/obesity

Nonlinear relationship between hs-CRP/HDL-C index and overweight/obesity

To explore the dose–response relationship between the hs-CRP/HDL-C index and overweight/obesity, we performed curve fitting using the RCS model, adjusting for key demographic data. Figure 4 shows an overall positive relationship between hs-CRP/HDL-C index and overweight/obesity (P-nonlinear =  < 0.001). However, this relationship showed a largely S-shaped curve on the graph, with a nonlinear pattern of gentle curve trend in two segments and a steep middle. We further explored the potential threshold effect between them. The segmented logistic regression model (Table 4) identified two breakpoints, which showed a significant but weak relationship with OR = 2.64 (1.15, 6.05), P = 0.022 when the logarithmized hs-CRP/HDL-C index was smaller than the first breakpoint of 0.653. A significant but weak relationship with OR = 2.64 (1.15, 6.05), P = 0.022, when the logarithmized hs- CRP/HDL-C index was above the second breakpoint of 1.593, the relationship became non-significant (OR = 1.53, 95% CI: 0.81–2.87, P = 0.187); whereas the relationship was considerably more substantial when the logarithmized hs-CRP/HDL-C index was in between the two breakpoints (OR = 12.35, 95% CI: 8.48–18.00, with P < 0.001). The likelihood ratio test confirmed that the piecewise model fit was significantly better than the conventional model (P < 0.001). Therefore, we regarded 0.653 and 1.593 as the cut-off values in this nonlinear relationship.

Fig. 4.

Dose–response relationship between hs-CRP/HDL-C index and overweight/obesity

Table 4.

Threshold effect analysis of hs-CRP/HDL-C index on overweight/obesity

	Adjusted OR (95% CI)*	P value
Fitting by standard Logistic regression model
Total	5.96 (5.19, 6.84)	< 0.001
Fitting by piecewise Logistic regression model (break-point = 0.653, 1.593)
hs-CRP/HDL-C < 0.653	2.64 (1.15, 6.05)	0.022
0.653 ≤ hs-CRP/HDL-C < 1.593	12.35 (8.48, 18.00)	< 0.001
hs-CRP/HDL-C ≥ 1.593	1.53(0.81, 2.87)	0.187
Log likelihood ratio		< 0.001

^*Adjusted for: sex, year, and race

Discussion

In this study, based on the NHANES database, we systematically revealed for the first time the nonlinear association of the hs-CRP/HDL-C index with overweight/obesity and its threshold effect in children and adolescents aged 6–19 years. Through multi-model analysis and subgroup validation, we not only confirmed the close association between this ratio and overweight/obesity but also identified its unique S-shaped dose–response pattern, providing an important reference for future clinical applications.

Hs-CRP is a widely used marker of inflammation, and its levels are usually higher in obese individuals, reflecting a chronic low-grade inflammatory response in the body [33]. Obesity, especially visceral obesity, is one of the significant triggers of chronic low-grade inflammation [34]. Studies have shown that adipocytes in obese individuals secrete pro-inflammatory factors (e.g., tumor necrosis factor-alpha, interleukin-6, etc.), which promote an inflammatory response in the body by activating the immune system [35]. As the degree of obesity increases, the accumulation of adipose tissue in the body leads to more inflammatory factors being secreted, promoting elevated levels of hs-CRP. High levels of hs-CRP are closely associated with obesity-related metabolic abnormalities, such as insulin resistance, fat accumulation, and metabolic syndrome [36]. One study found that hs-CRP levels were significantly higher in obese children and adolescents compared to those with normal weight. Additionally, elevated hs-CRP was strongly associated with visceral fat accumulation and the onset of insulin resistance [37].

HDL-C, widely regarded as the 'good cholesterol,' primarily reduces the risk of atherosclerosis. It achieves this by transporting cholesterol from peripheral tissues to the liver via a reverse transporter mechanism [38]. Low HDL-C levels are strongly associated with the development of obesity-related cardiovascular disease, metabolic syndrome, and diabetes mellitus [39, 40]. The anti-inflammatory effect of HDL-C is reflected in its ability to inhibit the release of pro-inflammatory cytokines and reduce intravascular inflammatory responses through interactions with endothelial cells. In addition, HDL-C protects obese individuals from metabolic disorders by modulating lipid metabolism and attenuating oxidative stress [41, 42].

The hs-CRP/HDL-C index has been recognized as a composite biomarker that simultaneously reflects chronic inflammation and lipid metabolism disorder. Recent studies have shown that this index is closely associated with various diseases' pathological progression and prognosis. In cardiovascular research, an elevated hs-CRP/HDL-C index has been demonstrated to significantly predict the risk of cardiovascular disease in the general population. Moreover, mediation analyses indicate that metabolic abnormalities, including hypertension and diabetes, account for 26.2–34.7% of its pathogenic effect [12]. In patients with acute ischemic stroke, the hs-CRP/HDL-C index shows a nonlinear association with poor outcomes at 3 months. When the ratio is below 42.74, each one-unit increase is associated with a 2.4% higher risk of adverse prognosis. This finding suggests that the index may serve as a reference threshold for early intervention [43]. Regarding metabolic diseases, a longitudinal cohort study confirmed that an elevated hs-CRP/HDL-C ratio is significantly associated with the risk of type 2 diabetes mellitus. It is also linked to the progression of diabetes mellitus after renal transplantation and to the development of hyperuricemia. The underlying mechanism may involve the interaction between inflammation and insulin resistance [27, 44, 45]. Long-term follow-up data further demonstrated a 9% increased risk of all-cause mortality in those with an elevated ratio, and the strength of the association was significantly stronger in smokers, with a linear dose–response relationship with mortality, suggesting that it may be a composite of the cumulative detrimental effects of chronic inflammation-lipid metabolism imbalance [6, 46]. Although the available evidence supports that this ratio is more predictive than a single indicator, its molecular mechanism and standardized cut-off value need to be further validated, and the regulatory network of the inflammation-metabolism pathway needs to be clarified by combining with multi-omics studies in the future to promote the application of this ratio in precision medicine.

Although both hs-CRP and HDL-C individually are strongly associated with overweight/obesity, no study has evaluated the combined role of the hs-CRP/HDL-C index in overweight/obesity. We conducted this study for the first time, and the results showed that this index showed a positive relationship with overweight/obesity, which was in line with expectations. The hs-CRP/HDL-C index was significantly elevated in the overweight/obese group, suggesting that a “dual-system imbalance” of chronic inflammation and lipid metabolism disorders may be a central driver of obesity progression [47]. Elevated hs-CRP reflects macrophage infiltration in adipose tissue and activation of the NF-κB pathway, which directly contributes to lipolytic resistance and impaired insulin signaling. In contrast, reduced HDL-C levels weaken anti-inflammatory functions (e.g., inhibition of monocyte adherence and oxidative stress), resulting in an “inflammation-metabolism vicious circle” [48]. More strikingly, however, the association between hs-CRP/HDL-C index and overweight/obesity is not as linear as conventionally recognized. In this study, for the first time in a population of children and adolescents, the relationship between the hs-CRP/HDL-C index and overweight/obesity showed an S-shaped dose–response curve. Specifically, the risk of overweight/obesity showed a steep upward trend when the logarithmized index of the ratio was between 0.653 and 1.593, whereas the increase in risk leveled off outside the threshold range. This nonlinear relationship may reveal a two-stage pathological mechanism in the progression of obesity: in the low-threshold stage (< 0.653), despite some inflammatory–metabolic imbalance in vivo, adipose tissue maintains metabolic homeostasis through dilatation and neovascularization in a compensatory phase, and inflammatory responses and metabolic disturbances are not yet significantly elevated; in the mid-threshold stage (0.653- At the mid-threshold stage (0.653–1.593), further hypertrophy of adipocytes triggered hypoxia and tissue fibrosis, and the polarization of macrophages shifted from anti-inflammatory to pro-inflammatory M2 to pro-inflammatory M1, leading to an increase in insulin resistance and an intensification of the phenomenon of lipid spillage, resulting in a “metabolic collapse” state. However, systemic inflammation and endothelial dysfunction may have entered an irreversible phase when the hs-CRP/HDL-C index exceeds a high threshold (> 1.593). However, because severely obese individuals often have multiple co-morbidities, they are not included in the healthy control group, resulting in a leveling off of the increase in risk for this group, which affects the final assessment of risk. The discovery of this nonlinear pattern has important clinical implications. Similar to the mechanism of J-curve formation for BMI and mortality risk in adult studies, this study emphasizes the uniqueness of the progression of obesity in children. Especially during the critical stage of puberty, fluctuations in fat distribution and sex hormone levels may allow dynamic changes in body composition to significantly impact the mechanisms regulating inflammatory–metabolic imbalance. By identifying this S-shaped dose–response curve, we provide new perspectives for early diagnosis and intervention of childhood obesity, especially in the critical interval of the hs-CRP/HDL-C index (0.653–1.593), where adipose-tissue-induced metabolic alterations should be given special attention, and timely intervention can effectively reduce the long-term health risks associated with obesity. Although this study identifies the association between the hs-CRP/HDL-C index and overweight/obesity, it has not yet been clinically validated for routine practice. Future research should focus on real-world studies, utilizing large-scale, multicenter clinical follow-up across multiple countries to investigate the causal relationship between the two and the predictive value of the index.

The core innovation of this study is to provide a quantifiable critical indicator and precise intervention window for early prevention and control of childhood obesity. Through segmented regression model-based analysis, we identified two critical breakpoints (0.653 and 1.593) that classified the child and adolescent populations into three distinct risk categories: low-risk (logarithmized hs-CRP/HDL-C index < 0.653), critical intervention (0.653 ≤ logarithmized hs-CRP/HDL-C index < 1.593), and high-risk (logarithmized hs-CRP/HDL-C index > 1.593). This stratification approach provides a clear assessment basis for individual health management and opens new pathways for implementing precise intervention strategies. In the critical intervention group, intensive lifestyle management interventions, including an anti-inflammatory diet and intermittent exercise, were recommended to reduce the risk of inflammatory response and lipid metabolism disorders by improving lifestyle. Meanwhile, dynamic monitoring of the hs-CRP/HDL-C index is recommended every three months to adjust the intervention strategy in time to prevent further development of obesity into more serious metabolic diseases. Lifestyle management alone may not effectively reduce obesity-related health risks for the high-risk group, so we suggest combining it with metabolomics screening for a more refined risk assessment. By detecting metabolic markers such as lipid subfractions and markers of inflammatory vesicle activation, possible secondary complications such as type 2 diabetes or cardiovascular disease can be identified early, providing a personalized intervention plan for the clinic. With this combined screening strategy, at-risk children can be more effectively identified and intervened with early intervention, thereby reducing the incidence of obesity-related complications.

In addition, this study revealed the modifying effect of household income on the relationship between the hs-CRP/HDL-C index and obesity risk. This finding provides strong support for public health intervention strategies. The prevalence of obesity and its associated metabolic risks is significantly higher in the child and adolescent population of lower-income families. Therefore, community-based nutrition programs and educational interventions targeting low-income groups to provide more affordable and efficient health management interventions have high socioeconomic benefits. This fits well with the World Health Organization's (WHO) "health equity interventions" strategy [49], which emphasizes providing equitable health resources for disadvantaged groups in different socioeconomic contexts. Finally, it is worth noting that despite controlling for various confounders, the present study found a 1% increase in the risk of overweight/obesity for every 1-unit increase in the hs-CRP/HDL-C index. This small but persistent cumulative effect of risk suggests that the hs-CRP/HDL-C ratio has significant value as a comprehensive index in predicting childhood obesity and its associated metabolic risk. Based on this finding, we recommend incorporating the hs-CRP/HDL-C index into routine pediatric physical examinations to enable early monitoring of obesity-related complications. Regular monitoring of this ratio can provide early warning for the prevention and control of obesity in children and prevent the occurrence and development of obesity through rational interventions.

In summary, by innovatively proposing the hs-CRP/HDL-C ratio as a key indicator for early obesity risk assessment and intervention, the present study provides a new idea for the prevention and management of childhood obesity, especially in precision medicine and personalized interventions, which has an important potential for application. Future studies should further validate the applicability of this index in different populations and explore more effective intervention methods to promote the overall improvement of children's health. Although this study provides valuable insights into the relationship between the hs-CRP/HDL-C index and overweight/obesity in children and adolescents, several limitations exist. First, the cross-sectional design, while revealing a significant association between the index and obesity, cannot establish causality. Future longitudinal studies will help clarify the causal role of this index in obesity development. Second, reliance on self-reported data (e.g., diet and physical activity) may introduce recall and reporting biases. Future studies should therefore prioritize validation using objective biomarkers and dynamic data whenever feasible. Third, the NHANES dataset represents the U.S. population, potentially limiting the external validity of findings due to geographical, cultural, and genetic variations. Future research should expand sample sizes and incorporate data from multiple countries and regions for cross-cultural validation. Finally, while hs-CRP and HDL-C are valid biomarkers, single measurements may exhibit biological variability influenced by factors, such as adolescent hormonal fluctuations and acute-phase responses. Future studies should employ multiple measurements to enhance data reliability.

Conclusion

This study confirms that the hs-CRP/HDL-C index exhibits an S-shaped dose–response relationship with overweight/obesity in children and adolescents, with a significant association within the critical threshold range (0.653–1.593). This not only offers new insights into the dual pathways of inflammatory activation and anti-inflammatory suppression in obesity research but also provides novel theoretical support for early prediction and intervention of obesity-related complications. However, the study's cross-sectional design limits causal inference and may also have limitations in external validity. Based on these findings, we recommend incorporating the hs-CRP/HDL-C index into health screenings for children and adolescents to facilitate the early identification of obesity-related complication risks and enable personalized intervention strategies.

Abbreviations

hs-CRP

High-sensitivity C-reactive protein

HDL-C

High-density lipoprotein cholesterol

BMI

Body mass index

NHANES

National Health and Nutrition Examination Survey

PIR

Poverty-to-income ratio

WBC

Serum cotinine, white blood cell count

EOS

Eosinophil

LYM

Lymphocyte

NEUT

Neutrophil percentage

Hb

Hemoglobin

PLT

Platelets

TC

Total cholesterol

Authors contribution

CZJ designed the study and drafted the manuscript. SJY and ZZY searched strategy. ZS and WYY completed the statistical analysis. YB and JMC reviewed and modified the manuscript. All authors contributed to the article and approved the submitted version.

Funding

This research received no funding.

Data availability

These survey data are free and publicly available, and can be downloaded directly from the NHANES website (http://www.cdc.gov/nchs/nhanes.htm) by users and researchers worldwide.

Declarations

Ethics approval and consent to participate

All participants provided written informed consent and study procedures were approved by the National Center for Health Statistics Research Ethics Review Board.

Competing interest

The authors declare no competing interests.