Parkinson’s disease (PD) is a progressive neurodegenerative disorder characterized by motor dysfunction, primarily manifesting as resting tremors, rigidity, bradykinesia, and postural instability (1). It is currently the second most common neurodegenerative disease after Alzheimer’s disease, affecting over 11.77 million people worldwide as of 2021 (2). With increasing life expectancy and global population aging, the prevalence of PD is expected to increase, placing a substantial burden on individuals, caregivers, and healthcare systems globally (3–6). Despite advances in symptom management, no therapies currently exist that can halt or reverse the progression of PD (7, 8). Consequently, identifying modifiable risk factors is crucial for improving early detection and prevention strategies (9).

Obesity has been implicated in various chronic diseases, including neurodegenerative disorders. However, the relationship between obesity, typically assessed using body mass index (BMI), and PD risk remains controversial. Although some studies have suggested that a higher BMI may elevate the risk of PD, others have reported no significant association or even an inverse correlation (10–16). These inconsistencies may be attributed to variations in the study populations, methodologies, and specific anthropometric measures used to assess obesity. Although BMI remains widely used in clinical and epidemiological contexts, it is increasingly recognized as a crude indicator that does not distinguish fat mass from lean mass or account for fat distribution (17). Therefore, there is an urgent need for more precise anthropometric indices to better evaluate the relationship between obesity and the risk of PD.

Recently, alternative anthropometric indices, including the body roundness index (BRI) (18, 19), a body shape index (ABSI) (20, 21), conicity index (CI) (22), waist-to-height ratio (WHtR) (23), and waist-to-weight index (WWI) (24) have been proposed to improve the assessment of fat distribution. These indices have been linked to cardiovascular diseases, type 2 diabetes, and metabolic syndrome (25–27), as they account for different patterns of fat accumulation. However, evidence regarding the association between these alternative anthropometric measures and PD risk remains limited.

This study aimed to explore the relationship between eight anthropometric indices (WWI, CI, ABSI, BRI, WHtR, BMI), waist circumference (WC), and weight (WT) and the prevalence of PD using data from the National Health and Nutrition Examination Survey (NHANES) from 1999 to 2020. By investigating these relationships, this study sought to provide new insights into the role of body fat distribution in PD development, offering potential implications for early identification and prevention strategies targeting high-risk populations.

In this nationally representative study, seven of the eight anthropometric indices examined were significantly associated with a higher prevalence of PD, with BMI being the only index that did not demonstrate a significant association. These findings highlight the relevance of fat distribution rather than general adiposity in PD pathogenesis. Traditional measures, such as BMI, may underestimate neurodegenerative risk by failing to capture visceral fat accumulation, which is more closely linked to metabolic dysfunction and inflammation (36–39).

Although WC and WHtR are widely accepted indicators of central obesity, other indices evaluated in this study, including WWI, CI, ABSI, and BRI, also reflect central fat deposition. Prior research has established strong correlations between these alternative indices, visceral adiposity and both metabolic and neurological health (37, 40–43). Unlike BMI, these indices account for variations in body composition and fat distribution and have demonstrated superior predictive value in identifying individuals with central obesity. Therefore, the consistent associations observed across these measures provide further support for a shared pathophysiological mechanism linking central obesity and PD.

Our findings align with those of several previous studies that have reported a positive association between abdominal adiposity and PD risk. For instance, a large-scale cohort study in Asia involving 6.9 million individuals with an 8.35-year follow-up reported a significant association between higher WC and increased PD risk (OR = 1.09; 95% CI: 1.07–1.12; p < 0.001) (43). Similarly, a meta-analysis conducted by Fang et al. suggested that being overweight may contribute to increased susceptibility to PD (44). Additionally, a recent NHANES-based study demonstrated that the ABSI was positively associated with PD prevalence, particularly in younger male adults (45). Collectively, these results support the hypothesis that central obesity is a critical component in neurodegenerative risk profiling.

However, our findings differ from those of several previous studies, which reported inverse associations between BMI and PD (11, 14, 15, 16). In our analysis, although BMI showed a weak association with PD in the unadjusted and partially adjusted models, this association became non-significant after full adjustment for demographic, lifestyle, and clinical factors. This attenuation may be attributed to several reasons. First, BMI lacks specificity in distinguishing fat mass from muscle mass and fails to capture fat distribution (42, 46). In contrast, indices such as WWI, CI, and WHtR are more sensitive to the visceral fat content, which is metabolically active and implicated in neuroinflammation. Second, sample heterogeneity across the studies may contribute to the divergent outcomes. Our study included a racially and socioeconomically diverse cohort, whereas other investigations may have included more homogeneous groups. Third, survivorship bias in longitudinal cohorts may obscure the true associations, as individuals with higher BMI may die prematurely from cardiovascular diseases or diabetes before developing PD. Lastly, statistical power and variable-covariate adjustments across studies may influence the detection of associations. These factors underscore the importance of methodological rigor and index selection in studies examining adiposity and the risk of PD.

Several biological mechanisms may explain the observed associations. First, central obesity promotes systemic chronic inflammation. Adipose tissue in individuals with excess visceral fat produces proinflammatory cytokines, such as tumor necrosis factor-alpha (TNF-α), interleukin-6 (IL-6), and C-reactive protein (CRP). These mediators can impair the integrity of the blood–brain barrier (47–50), activate microglial cells, and promote neuroinflammation, which collectively may contribute to dopaminergic neuron degeneration in the substantia nigra, ultimately increasing PD risk (51, 52).

Second, central adiposity is closely linked to insulin resistance (IR), a metabolic state known to contribute to neurodegenerative processes. IR may lead to mitochondrial dysfunction, increase oxidative stress, and promote the aggregation of α-synuclein, a pathological hallmark of PD (51, 53–55). Moreover, IR can disrupt key intracellular signaling pathways, such as polo-like kinase 2 (PLK2), which are involved in neuronal survival and function. These alterations may accelerate the degeneration of dopaminergic neurons and exacerbate disease progression (36, 53, 56, 57). Given that central obesity is a well-established predictor of IR, it may serve as a key metabolic link between obesity and PD (58).

Third, obesity is associated with decreased levels of brain-derived neurotrophic factor (BDNF), a protein essential for neuronal development, plasticity, and survival (59, 60). Decreased levels of BDNF expression have been observed in individuals with obesity and may impair the maintenance and function of dopaminergic neurons. This deficiency in neurotrophic support further contributes to the pathology of PD (61, 62).

These biological mechanisms, including inflammation, IR, and impaired neurotrophic signaling, highlight the biological plausibility of the link between central obesity and PD. A simplified schematic is presented in Supplementary Figure 4, summarizing how central obesity may contribute to PD pathogenesis through three interconnected mechanisms.

From a clinical perspective, these findings have important clinical implications for the early detection and targeted prevention strategies. Alternative anthropometric indices could serve as practical tools for early identification of individuals at increased risk of PD, particularly in populations where BMI fails to reflect the true metabolic burden. These indices can be incorporated into routine screening protocols to inform the development of preventive strategies.

This study has several strengths. First, it was based on a large nationally representative sample, which enhanced the generalizability of the findings. Second, it incorporated standardized anthropometric measurements and employed rigorous statistical adjustments for a wide range of potential confounders. Third, the robustness of the associations was verified using subgroup and sensitivity analyses.

Despite these strengths, several limitations of this study must be acknowledged. As this was a cross-sectional study, causal relationships could not be established. To address this, future research should employ prospective cohort designs to clarify the temporal and potentially causal relationships between central obesity and PD. Second, consistent with previously published research (33, 63, 64), PD identification based on antiparkinsonian medication use may not distinguish PD from other forms of parkinsonism. This could lead to potential misclassification and underestimation of true PD prevalence, particularly among untreated individuals. Future studies should incorporate multiple diagnostic approaches, such as clinical interviews, neurological examinations, and biomarker assessments, to enhance classification accuracy. Additionally, the use of secondary data limited our ability to fully account for all confounding variables, thereby introducing the possibility of residual bias. Lastly, although the NHANES dataset is representative of the U.S. population, these findings may not be generalizable to other geographic or ethnic populations. Future validation studies in more diverse international populations are warranted to confirm and extend these results.

This study provided strong evidence that central obesity, as reflected by alternative anthropometric indices such as CI, WHtR, and WWI, is significantly associated with PD prevalence, whereas BMI is not. These results highlight the importance of using alternative anthropometric tools for identifying individuals at an elevated PD risk. Future longitudinal studies should explore whether interventions targeting central obesity reduce the incidence and progression of PD.