Public Health.

Area of Science:

• Neuroscience
• Public Health
• Gerontology

Background:

• Established risk and protective factors for dementia exist, but current multidomain lifestyle interventions show variable effectiveness.
• Tailoring prevention strategies is crucial for enhancing dementia risk reduction.
• A need exists for research on specific dementia risk profiles beyond single factors or sum scores.

Purpose of the Study:

• To identify distinct dementia risk profiles using the LIfestyle for BRAin Health (LIBRA) index.
• To analyze sociodemographic predictors of these risk profiles.
• To investigate class-specific differences in cognitive functioning.

Main Methods:

• Latent Class Analysis was performed on data from 106,192 participants (aged 40-75) in the German National Cohort (NAKO).
• Ten LIBRA factors (e.g., hypertension, obesity, smoking, physical inactivity) were used as indicators to define risk classes.
• Sociodemographic predictors and cognitive functioning were analyzed in relation to identified classes.

Main Results:

• A four-class model best represented the data, with the largest class (>60%) indicating low overall risk.
• One class (∼16%) was characterized by cardiometabolic risks (hypertension, hypercholesterolemia, heart disease, diabetes).
• Another class (14%) showed high smoking rates and low social participation, with higher physical inactivity, alcohol intake, and depression.
• The smallest class (∼8%) comprised individuals with obesity and high hypertension probability.

Conclusions:

• Identification of four distinct dementia risk profiles enables more targeted prevention strategies.
• Tailored interventions based on specific risk profiles may offer greater benefits than a one-size-fits-all approach.
• These findings can potentially reshape intervention study designs and public health campaigns for more effective dementia risk reduction.