Objective To investigate the correlation of lipids and inflammatory factors with early diagnosis of Parkinson's disease (PD), and construct and validate its risk prediction model. Methods A total of 96 patients with early-stage Parkinson's disease (Hoehn-Yahr stage 1) from March 2022 to December 2024 were selected as the PD group, and 100 age- and gender-matched healthy individuals underwent physical examinations as the control group. Differences in lipid and inflammatory factor levels between the two groups were compared. Lasso regression and binary logistic regression analysis were used to screen for independent risk factors for early-stage PD, and a nomogram prediction model was constructed. The model's performance was evaluated using ROC curves, calibration curves, and the Hosmer-Lemeshow test. Results Patients in the PD group had significantly lower levels of total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C) compared to the control group, while levels of tumor necrosis factor-α (TNF-α), interleukin-6 (IL-6) were elevated, and the level of in interleukin-1 bota (IL-1β) was decreased (all P＜0.05). Multivariate logistic regression analysis showed that elevated IL-6 levels were an independent risk factor for early PD (OR=2.977, P=0.001), while elevated TC, LDL-C, and IL-1β levels were protective factors (all P＜0.05). The predictive model constructed based on the above indicators demonstrated high diagnostic performance, with an area under the ROC curve of 0.958 (95% CI: 0.93~0.983), sensitivity of 0.896, and specificity of 0.890.Conclusion Elevated IL-6 is an early risk factor for PD, while IL-1β, TC, and LDL-C have a protective effect. The predictive model constructed based on these indicators (AUC=0.958) provides a reliable objective basis for early screening. Further validation is needed to optimize its clinical application