An acoustic-only multivariable diagnostic model for Parkinson's disease using bowel sound acoustic signatures

Background Early detection of Parkinson's disease (PD) remains challenging due to the lack of reliable prodromal biomarkers. Non-invasive markers of gastrointestinal dysfunction are lacking. Objectives To characterize bowel sound acoustic phenotypes in PD and evaluate their potential as non-invasive diagnostic markers by developing an acoustic-based predictive model. Methods Standardized 5-min bowel sound recordings were collected from 120 PD patients and 100 controls. Thirteen acoustic features were extracted. LASSO (least absolute shrinkage and selection operator) selected predictors for logistic regression models, internally validated with 1000 bootstrap resamples. The acoustic-only model was evaluated without refitting within the development cohort in 48 patients with Hoehn-Yahr stages 1-2 and 100 controls. Results PD patients exhibited fragmented, low-energy bowel sound patterns compared with controls. Acoustic features did not differ significantly by constipation status, while two duration measures differed by Hoehn-Yahr stage. A full-feature model incorporating acoustic and biochemical markers achieved an area under the curve (AUC) of 0.8659. The acoustic-only model containing six predictors, three of which were statistically significant, yielded an AUC of 0.7999 (95% confidence interval [CI], 0.737–0.852). Optimism-corrected AUCs were 0.839 and 0.784, respectively. In the early-stage subgroup, the AUC was 0.728 (95% CI, 0.644–0.806), with 68.75% sensitivity and 66.00% specificity at the fixed probability threshold of 0.50. Conclusions PD patients exhibit distinct bowel sound acoustic phenotypes. Acoustic features provided discrimination between PD patients and controls, including in early-stage disease. This non-invasive profiling approach offers a promising functional biomarker candidate for multimodal PD assessment.