Non-alcoholic fatty liver disease (NAFLD) is one of the most common chronic liver conditions worldwide, affecting approximately 25% of the global adult population [1] . Characterized by the abnormal accumulation of lipids within hepatocytes, NAFLD is closely linked to metabolic dysfunction and often coexists with other metabolic disorders such as metabolic syndrome, obesity, type 2 diabetes mellitus, and insulin resistance [2] [3] . While NAFLD has traditionally been associated with obesity, recent studies have highlighted its occurrence in individuals with normal body weight or those who are overweight, giving rise to the clinical phenotypes “lean NAFLD” or “non-obese NAFLD” [4] . This growing recognition of non-obese NAFLD is significant, as it often presents with a distinct metabolic phenotype despite the absence of obesity. Studies have shown that non-obese individuals with NAFLD experience impaired glucolipid homeostasis, contributing to a potentially more detrimental metabolic profile [5] - [8] . Although non-obese NAFLD patients generally exhibit a less severe metabolic phenotype than their obese counterparts, they face higher all-cause mortality rates (as high as 28% in a study of over 1000 patients), more rapid disease progression, and significantly greater risks for cardiovascular disease and malignancies [9] - [12] . Evidence further suggests that the relationship between NAFLD and metabolic complications is stronger in non-obese individuals than in obese patients, underscoring the need for more focused attention on this subtype [13] .

Therefore, these findings highlight significant differences in inflammatory and metabolic profiles between obese and non-obese NAFLD patients, underscoring the clinical imperative for precise phenotypic stratification. Implementing such differentiation is crucial for developing tailored management strategies that address the distinct pathophysiological mechanisms underlying each subtype, ultimately advancing toward personalized therapeutic interventions. High-sensitivity CRP (hs-CRP) is one of the most convenient and widely used clinical biomarkers for assessing systemic inflammation [14] . hs-CRP detects low-grade inflammation and is associated with multiple adverse health outcomes, such as cardiovascular disease, metabolic syndrome, insulin resistance, and impaired physical function [15] - [17] . hs-CRP serves as a valid proxy for IL-6 and TNF-α activity, offering a feasible approach for evaluating inflammatory status in large-scale epidemiological studies [14] . Furthermore, the CTI is a composite metric that integrates the TyG index and hs-CRP to concurrently reflect both insulin resistance and the inflammatory pathway [18] . Previous studies have demonstrated the correlation between CTI and conditions such as coronary heart disease [19] , depression [20] , stroke [21] , and NAFLD/liver fibrosis [22] .

However, little is known about how inflammatory and metabolic composite biomarkers distinguish NAFLD phenotypes. This study, therefore, aimed to evaluate the discriminative and complementary roles of hs-CRP and CTI in differentiating obese from non-obese NAFLD using NHANES 2017-2020 data. The combined use of these biomarkers offers a more complete perspective on metabolic and inflammatory contributions to NAFLD, supporting the development of tailored treatment approaches. Our goal is to establish practical biomarkers that improve risk stratification and advance precision medicine in NAFLD management.

This study employed a cross-sectional design using NHANES data to evaluate the health and nutritional status of the U.S. population. NHANES adopts a multistage, stratified sampling strategy to ensure national representativeness. Data are collected biennially through questionnaires, physical examinations, and laboratory tests, covering disease status, nutritional indicators, lifestyle behaviors, and socioeconomic information. The study was approved by the NCHS Institutional Review Board, and all participants provided informed consent. Data from the 2017-2020 NHANES cycles were utilized, with an initial cohort of 15,560 individuals [23] . NAFLD was diagnosed based on a controlled attenuation parameter (CAP) value ≥ 274 dB/m. Based on predefined inclusion and exclusion criteria, the following were excluded: 1) aged < 18 years; 2) lacking vibration-controlled transient elastography (VCTE) data or CAP < 274 dB/m [24] ; 3) positive for hepatitis B surface antigen (HBsAg) or hepatitis C virus RNA (HCV RNA); 4) excessive alcohol consumption (>2 standard drinks/day for men, >1 standard drink/day for women); 5) missing any parameter required for calculating the C-reactive protein-triglyceride glucose index (CTI), including high-sensitivity C-reactive protein (hs-CRP), triglycerides, or fasting glucose.

This study conducted a retrospective analysis using data from two cycles (2017-2020) of the National Health and Nutrition Examination Survey (NHANES). The initial study population consisted of individuals aged 18 years and older (n = 9693). Based on exclusion criteria, we removed participants with missing vibration-controlled transient elastography (VCTE) data or CAP < 274 dB/m (n = 6115), those infected with hepatitis B or C (n = 40), excessive alcohol consumption (>2 standard drinks/day for men, >1 standard drink/day for women) (n = 1116), and those lacking CTI or hs-CRP data, individuals with severe infections (n = 1.251). The final analytical sample comprised 1171 participants. ( Figure 1 )

This study included a total of 1171 patients with non-alcoholic fatty liver disease (NAFLD); the mean age of the sample was 53 years (standard deviation (SD) = 19), with a slightly higher proportion of males compared to females (53.7% vs 46.3%). We identified that 729 (62.3%) participants had obesity. Compared with the non-obese group, participants in the obese group were younger (51 ± 19 vs. 55 ± 19 years, p = 0.002), had a lower proportion of males (51% vs. 60%, p = 0.008), and included more Black/African American individuals (25% vs. 13%, p < 0.001). BMI was higher in the obese group (37 kg/m² ± 7 kg/m²) compared to the non-obese group (27 kg/m² ± 2 kg/m²) (p < 0.001). In terms of laboratory parameters, the obese group showed higher levels of hs-CRP ((6.1 ± 9.5) mg/L vs. (3.0 ± 7.5) mg/L, p < 0.001) and ALT ((26 ± 18) U/L vs. (24 ± 15) U/L, p = 0.017), but lower albumin ((39.4 ± 3.0) g/L vs. (41.1 ± 2.9) g/L, p < 0.001) and HDL cholesterol ((1.20 ± 0.30) mmol/L vs. (1.30 ± 0.36) mmol/L, p < 0.001). No significant differences were detected between groups in fasting glucose, triglycerides, HbA1c, GGT, and AST levels. ( Table 1 )

Both hs-CRP (OR = 1.11, 95% CI: 1.08 - 1.15) and CTI (OR = 1.70, 95% CI: 1.47 - 1.98) were significantly associated with obesity in unadjusted models (both p < 0.001). After adjustment for hs-CRP, CTI, age, and sex, CTI remained strongly predictive (OR = 1.77, 95% CI: 1.48 - 2.12), whereas the association for hs-CRP was modest (OR = 1.04, 95% CI: 1.01 - 1.07). In the fully adjusted model, CTI retained a robust association with obesity (OR = 2.09, 95% CI: 1.68 - 2.62, p < 0.001), while hs-CRP(OR = 1.01, 95% CI: 0.99 - 1.03, p = 0.411) was no longer significant. ( Table 2 )

Abbreviations: BMI, Body Mass Index; ALT, Alanine Aminotransferase; AST, Aspartate Aminotransferase; GGT, Gamma-Glutamyl Transferase; CTI, C-Reactive Protein-Triglyceride Glucose Index; SD, Standard Deviation. The p-value was estimated using chi-square for categorical variables and t-tests for continuous variables.

Notes: Model 1: Crude model, adjusted for hs-CRP. Model 2: Crude model, adjusted for CTI. Model 3: Adjusted for hs-CRP, CTI, age, and gender. Model 4: Adjusted for Model 3 + total cholesterol, HbA1c (%), uric acid, and albumin. Abbreviations: hs-CRP, high-sensitivity C-Reactive Protein; CTI, C-Reactive Protein-Triglyceride Glucose Index; OR, Odds Ratio; NAFLD, Non-Alcoholic Fatty Liver Disease.

The discriminative ability of the hs-CRP model (AUC = 0.711) was significantly superior to that of the CTI model (AUC = 0.628; ΔAUC = −0.082, DeLong’s test p < 0.001). The Brier score for hs-CRP (0.217) was slightly lower than that for CTI (0.224), further supporting its advantage in predictive accuracy and calibration. The optimal cut-off values, as determined by the Youden index, were 2.40 mg/L for hs-CRP and 9.19 for CTI. Furthermore, since the calculation of CTI inherently incorporates hs-CRP, NRI and IDI analyses were not conducted to avoid statistical and interpretative redundancy. ( Table 3 and Figure 2 )

This study indicates that both CTI and hs-CRP possess distinct advantages in differentiating between obese and non-obese NAFLD phenotypes, with their roles being complementary. hs-CRP demonstrates stronger discriminative ability, making it suitable as a primary indicator for rapid clinical screening. In contrast, CTI maintains a stable association with obese phenotypes even after multivariate adjustments, better reflecting metabolic and inflammatory burden, and thus holds unique value in risk stratification. The low collinearity between the two further supports the fact that they provide mutually complementary information.

Therefore, we recommend adopting a tiered application strategy in clinical practice: using hs-CRP as an initial screening tool, followed by combining CTI for metabolic risk assessment and stratified management. This approach not only facilitates more accurate differentiation between obese and non-obese NAFLD patients but also promotes the development of individualized intervention and treatment plans.

*Corresponding author.