Food-specific IgG antibodies and body mass index: multivariate analysis of clinical correlations in underweight populations.

BACKGROUND

This study investigates the relationship between food-specific IgG antibodies and nutritional status in underweight populations, addressing a critical gap in existing research focused predominantly on obesity. It aims to elucidate immune-mediated mechanisms linking food intolerance to abnormal body composition through multidimensional statistical modeling.

METHODS

A retrospective analysis of 1,237 underweight patients (BMI <18.5 kg/m²) included IgG antibody profiling for 14 food antigens (ELISA) and clinical/demographic data. Statistical methods encompassed Spearman correlations, linear regression, factor analysis, and generalized linear models (adjusted for age, gender, comorbidities). Child (n=421) and adult (n=816) cohorts were analyzed separately using R 4.3.0 and GraphPad Prism 9.0.

RESULTS

In children, wheat-specific IgG levels showed a robust inverse correlation with BMI-for-age Z-scores (BAZ) (β = -0.319 to -0.357, p ≤ 0.010), explaining 2.18% of BAZ variance. Factor analysis identified a food sensitivity component (wheat/soy IgG loadings: 0.643-0.654) correlating with BAZ (r = 0.349). Adults exhibited significant inverse associations between soybean IgG and BMI (β = -1.1085, p = 0.0003), explaining 1.67% of variance. Bilirubin metabolism (factor loadings: 0.899-0.991) and hepatic function markers (ALT/GGT: r = 0.372-0.425) showed strong BMI correlations. Cluster analysis revealed distinct IgG profiles, with underweight subgroups demonstrating elevated wheat (p = 0.001) and soybean (OR = 2.4, p < 0.001) sensitization.

CONCLUSION

Food-specific IgG profiles, particularly wheat and soybean antibodies, are independently associated with nutritional status in underweight populations. These findings suggest immune-mediated pathways may contribute to malabsorption and metabolic dysregulation, supporting IgG testing for personalized dietary interventions. Study limitations include small subgroup sizes, underscoring the need for mechanistic research integrating gut microbiota analysis.