Dementia risk scores as surrogate outcomes for lifestyle-based multidomain prevention trials – rationale, preliminary evidence and challenges. Coley et al

Dementia risk scores help to predict the development of dementia over time based generally on lifestyle modifiable factors (e.g. blood pressure, cholesterol, body mass index, physical activity) and/or non-modifiable factors (e.g. age, education, sex). These scoring systems, however, are not designed specifically to measure changes of these factors over time. Moreover, it is unknown whether changes in dementia risk scores over time correlate with the long-term onset of dementia and therefore whether they can be used as surrogate outcomes in dementia prevention trials. However, given the growing need for more preventive interventions for people with dementia and the potential of targeting lifestyle factors earlier in life, Coley and colleagues¹ evaluated the potential use of dementia risk scores as prevention trial outcome measures.

The authors first identified existing dementia risk scores that may be considered suitable for further study as primary outcome measures in dementia prevention trials. Six risk scores were identified:

1. Cardiovascular risk factors, aging and incidence of dementia (CAIDE) risk score
2. Lifestyle for Brain Health (LIBRA) index
3. Australian National University Alzheimer’s Disease Risk Index (ANU-ADRI)
4. Brief dementia screening indicator
5. Dementia Risk Score (DRS)
6. Framingham Risk Score.

Of these six risk scores, the authors focused on the CAIDE and LIBRA scores because these offered the most relevant, complete and validated data sets. Both were assessed for responsiveness (i.e. sensitivity to change) and intervention effects in three large dementia prevention trials (Multidomain Alzheimer Preventive Trial [MAPT],² Prevention of Dementia by Intensive Vascular Care [preDIVA]³ and Healthy Ageing Through Internet Counselling in the Elderly [HATICE]⁴). The original CAIDE and LIBRA scoring system, in addition to a weighted and unweighted modified version of the systems, were evaluated. The proposed modified versions used continuous rather than categorical data because it was hypothesized that this would help the scores detect more modest intervention effects.

The authors concluded that both the original and modified versions of the risk scores were generally responsive and able to detect between-group differences following preventive interventions. However, further study is needed to assess the predictive ability of these scoring systems in terms of absolute dementia risk. Further validation of the proposed alternative scoring system, or variants thereof, is also required. To increase the suitability of dementia risk scores as outcome measures, the authors recommend that any measured risk factors reflected in the scores need to be amenable to change, objectively measurable and strongly predictive of future risk for developing dementia.

References

1. Coley N et al. Dementia risk scores as surrogate outcomes for lifestyle-based multidomain prevention trials – rationale, preliminary evidence and challenges. Alzheimers Dement 2020;16:1674–85.

• Andrieu S et al. Effect of long-term omega 3 polyunsaturated fatty acid supplementation with or without multidomain intervention on cognitive function in elderly adults with memory complaints (MAPT): a randomised, placebo-controlled trial. Lancet Neurol 2017;16:377–89.

• Moll van Charante EP et al. Effectiveness of a 6-year multidomain vascular care intervention to prevent dementia (preDIVA): a cluster-randomised controlled trial. Lancet 2016;388:797–80.

• Richard E et al. Healthy Ageing Through Internet Counselling in the Elderly: the HATICE randomised controlled trial for the prevention of cardiovascular disease and cognitive impairment. BMJ Open 2016;6:e010806.