Migraine is related to brain energy deficiency. Niacin is a required coenzyme in mitochondrial energy metabolism. However, the relationship between dietary niacin and migraines remains uncertain. We aimed to evaluate the relationship between dietary niacin and migraine. This study used cross-sectional data from people over 20 years old who took part in the National Health and Nutrition Examination Survey between 1999 and 2004, collecting details on their severe headaches or migraines, dietary niacin intake, and several other essential variables. There were 10,246 participants, with 20.1% (2064/10,246) who experienced migraines. Compared with individuals with lower niacin consumption Q1 (≤12.3 mg/day), the adjusted OR values for dietary niacin intake and migraine in Q2 (12.4–18.3 mg/day), Q3 (18.4–26.2 mg/day), and Q4 (≥26.3 mg/day) were 0.83 (95% CI: 0.72–0.97, p = 0.019), 0.74 (95% CI: 0.63–0.87, p < 0.001), and 0.72 (95% CI: 0.58–0.88, p = 0.001), respectively. The association between dietary niacin intake and migraine exhibited an L-shaped curve (nonlinear, p = 0.011). The OR of developing migraine was 0.975 (95% CI: 0.956–0.994, p = 0.011) in participants with niacin intake < 21.0 mg/day. The link between dietary niacin intake and migraine in US adults is L-shaped, with an inflection point of roughly 21.0 mg/day.

Background: Previous studies have explored the associations between obesity and Parkinson’s disease (PD), often using body mass index (BMI) as the main metric. However, findings remain inconsistent. Anthropometric indices—quantitative measures of body shape, size, and fat distribution—offer alternative ways to assess adiposity. This study aimed to evaluate the associations between eight anthropometric indices and PD prevalence.Methods: Data were obtained from the National Health and Nutrition Examination Survey (NHANES), conducted in the U.S. from 1999 to 2020. A total of 41,374 participants aged 20 years and older were included, among whom 354 were diagnosed with PD. Eight anthropometric indices were analyzed: waist-to-weight index (WWI), conicity index (CI), a body shape index (ABSI), body roundness index (BRI), waist-to-height ratio (WHtR), BMI, waist circumference (WC), and weight (WT). Weighted multivariable logistic regression models were used to assess the association between these indices and PD. Restricted cubic spline (RCS) models were employed to examine dose–response relationships. Subgroup and sensitivity analyses were conducted to validate the robustness of the findings.Results: Significant differences were observed between the study groups, with positive and independent correlations identified between PD and all anthropometric measures, except BMI. After full adjustment, each 1-standard deviation increase in WWI, CI, ABSI, BRI, WHtR, WC, and WT was associated with an elevated PD risk by 34, 42, 36, 18, 21, 25, and 16%, respectively. RCS analysis revealed a linear relationship between CI, ABSI, BRI, WtHR, WC, WT, and PD prevalence, whereas WWI exhibited a nonlinear association. The subgroup and sensitivity analyses confirmed the consistency of these associations.Conclusion: Higher values of several anthropometric indices, particularly the ABSI, WWI, and CI, were associated with increased PD prevalence. These findings highlight the potential role of fat distribution rather than overall adiposity in PD pathogenesis. Anthropometric measures may be valuable tools for early PD risk identification and targeted prevention strategies.

The global incidence of hyperuricemia is increasing in parallel with rising obesity rates. This study investigated the relationship between weight change patterns and the risk of hyperuricemia. Utilizing data from the NHANES, we conducted a retrospective cohort analysis of 20,545 U.S. adults aged 40–75 years. We categorized weight changes into six patterns based on BMI measurements at baseline and 10 years prior. Hyperuricemia was defined as uric acid ≥ 7.0 mg/dL in males and ≥ 6.0 mg/dL in females. Multivariable logistic regression models were used to assess the associations between weight change patterns and hyperuricemia. Participants with weight gain or obesity had significantly higher odds of hyperuricemia (ORs = 2.32 [95% CI: 1.93–2.80, P < 0.001] to 4.72 [95% CI: 3.95–5.63, P < 0.001]). Those transitioning from obese to normal weight had reduced odds of hyperuricemia (OR = 0.41 [95% CI: 0.21–0.79, P = 0.008]). A nonlinear relationship between BMI percentage change and hyperuricemia was observed, with a change of ≥ 6% associated with a higher OR of 6.72 (95% CI 3.76–12.02, P < 0.001). The impact was more pronounced in females. This study provides evidence for a significant nonlinear relationship between weight change and hyperuricemia, emphasizing the need for gender-specific, tailored weight management strategies.