Longitudinal Analyses of Body Mass Index and Risk of Parkinson's Disease in 2 million people over 2 decades (BMI and Parkinson´s disease)

Study type

• Study type:
  Non-interventional study
• Scope of the study:
  Assessment of risk minimisation measure implementation or effectiveness

  Disease epidemiology

Non-interventional study

• Non-interventional study design:
  Cohort

Study drug and medical condition

• Medical condition to be studied:
  Obesity

  Parkinson's disease

Population studied

• Short description of the study population:
  2 million patients followed up over 2 decades. Underweight (< 20 kg/m2): 106,716 people Healthy weight (20-24.9 kg/m2):629,126 people Overweight (25-29.9 kg/m2): 727,339 people Obese (≥ 30 kg/m2): 489,406 people With a median follow-up of 9·3 years, PD occurred in 11,616 people.
• Age groups:
  • Adult and elderly population (≥18 years)

    • Adults (18 to < 65 years)

      • Adults (46 to < 65 years)
    • Elderly (≥ 65 years)

      • Adults (65 to < 75 years)
      • Adults (75 to < 85 years)
      • Adults (85 years and over)
• Estimated number of subjects:
  2000000

Study design details

• Main study objective:
  The primary objective is to estimate the age-specific associations between BMI and Parkinson’s disease.Secondary objectives are to assess the shape of the association and Identify potential modifiers and confounders of the risk.The results will be usable for disease prediction models, patient management, the planning of trials, and help in hypotheses of pathophysiological mechanisms of PD
• Outcomes:
  Parkinson´s disease
• Data analysis plan:
  We compared rates of PD across categories of BMI using Poisson regression models. Standardised incidence rates and rate ratios were either adjusted for age (in five-year bands) and sex, or further adjusted for the following covariates, measured at the time of index BMI. We used age at diagnosis, updating the data as people moved through the age categories during follow-up. Incidence rates were standardised to the age and sex distribution of the overall study population. We performed analyses to investigate whether the association between BMI and PD varied depending on the time from the index BMI measurement. We calculated the cumulative incidence of PD using the Kaplan-Meier estimator, with age as the underlying time scale.We performed a sensitivity analysis to investigate whether our findings could be reasonably explained by competing risks usinga novel approach under a hypothetical scenario and using matching