DOI: 10.1177/13872877261498431 ISSN: 1387-2877

Clusters of modifiable dementia risk factors at mid-life and late-life and their association with incident dementia: A prospective cohort study of 246,968 adults

Tessa L. van Baal, Sebastian Köhler, Kay Deckers, Niels Janssen

Background

Modifiable dementia risk factors rarely occur in isolation, yet their co-occurrence and clustering remain poorly characterized, limiting identification of high-risk individuals and development of targeted strategies.

Objective

To identify clusters of ten modifiable risk factors in mid-life (45–65 years) and late-life (>65 years), and to examine their association with incident dementia.

Methods

Data were drawn from the 2012 Dutch Public Health Monitor Survey (mid-life: n = 121,120; late-life: n = 125,848), with dementia follow-up through 2023. Ten modifiable risk factors: obesity, hypertension, diabetes, hearing loss, visual loss, physical inactivity, excessive alcohol consumption, smoking, loneliness, and depression, were used to derive clusters using latent class analysis. Cox proportional hazards models examined the association between late-life clusters and incident dementia.

Results

In mid-life (mean age, 55.7 ± 6.2 years), four risk factor clusters were identified: cardiometabolic, substance use, psychosocial, and lower risk. In late-life (mean age, 73.9 ± 6.4 years), four clusters were identified: cardiometabolic, substance use, psychosocial and physically inactive, and lower risk. After adjustment for sociodemographic factors, the psychosocial and physically inactive cluster was associated with the highest hazard of dementia (hazard ratio (HR), 1.23; 95% CI, 1.16–1.32), followed by the substance use (HR, 1.16; 95% CI, 1.09–1.24) and the cardiometabolic cluster (HR, 1.12; 95% CI, 1.07–1.18).

Conclusions

Distinct risk factor clusters were identified at both mid-life and late-life; the psychosocial and physically inactive cluster carried the highest risk of dementia. These findings underscore the importance of considering risk factor clustering when designing targeted interventions to reduce dementia risk across the life course.

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