Prevalence and Assessment of Malnutrition Using Nutritional Indices and Body Mass Index in Relation to Coronary Artery Disease in Japanese Patients: A Retrospective Real-World Study.

Background Cardiovascular disease (CVD) remains a leading cause of mortality worldwide, with a disproportionate burden in low- and middle-income countries (LMICs). While malnutrition is a recognized clinical issue, its role as a contributing factor to coronary artery disease (CAD) is often overlooked. This study aimed to assess the prevalence of malnutrition and its association with significant coronary artery stenosis (CAS) using routinely available nutritional indices, in combination with body mass index (BMI), in a real-world Japanese patient cohort. Methods This retrospective cross-sectional study included 1,107 patients who underwent coronary angiography (CAG) at Kagawa University Hospital, Kagawa, Japan, between January 2006 and December 2023. Nutritional status was assessed using various indices: controlling nutritional status (CONUT), prognostic nutritional index (PNI), and geriatric nutritional risk index/nutritional risk index (GNRI/NRI). Malnutrition was defined using standard clinical thresholds. CAS was defined as 75% or greater luminal narrowing in at least one coronary artery. Multivariable logistic regression models were developed to assess the association between nutritional indices and CAS, adjusting for age, sex, smoking status, and lipid levels. Interaction terms were included to evaluate the modifying effect of BMI (continuous). Additional risk phenotypes were created by combining nutritional indices and BMI based on the 75th percentile. Results Significant CAS was identified in 45.4% of the study population. Malnutrition prevalence using standard clinical cutoffs ranged from 26.8% using CONUT to over 70% with GNRI/NRI and PNI, with poor inter-index agreement (Fleiss' κ = 0.227). Interaction analysis showed that severe-risk GNRI/NRI scores were positively associated with CAS at higher BMI levels, while mild-risk scores showed inverse associations. Several phenotype-based combinations were also significantly associated with increased CAS risk, including high CONUT + normal BMI (Odds ratio, OR = 1.88), low CONUT + high BMI (OR = 1.75), and high PNI + high BMI (OR = 1.92). Among predictive models, the CONUT + BMI combination demonstrated the best overall discriminative performance (AUC = 0.71; sensitivity = 78.6%), while the PNI + BMI model had the highest positive predictive value (66.7%). Conclusion Malnutrition is prevalent among patients undergoing CAG and is significantly associated with CAS, particularly when assessed in combination with BMI. Our findings suggest that GNRI/NRI, when interacted with BMI as a continuous variable, provides a sensitive gradient of risk, while percentile-based combinations using CONUT or PNI offer clear categorical phenotypes for risk stratification. These accessible, routinely used biomarkers and EMR-based indices can serve as practical tools for early risk identification, especially in resource-limited LMIC settings. Prospective studies are warranted to determine whether targeted nutritional interventions based on these indices can improve cardiovascular outcomes.