Review: Designing Prevention Programmes to Reduce Incidence of Dementia: Prospective Cohort Study of Modifiable Risk Factors

The paper reviewed here is ‘Designing Prevention Programmes to Reduce Incidence of Dementia: Prospective Cohort Study of Modifiable Risk Factors’ by Ritchie and colleagues published in the BMJ and freely available here.

The aim of this study was to establish modifiable risk factors for dementia which could be then used in a prevention programme. The authors conclude that the modifiable risk factors most likely to reduce the incidence of dementia include

  • Crystallised intelligence
  • Fruit and vegetable consumption
  • Depression
  • Diabetes
  • Apolipoprotein E Epsilon 4 allele

To get to these conclusions, the authors first of all identified candidate (modifiable) risk factors using a literature search and summarise their results at the beginning of the paper.  They go on to select 1433 subjects over the age of 65 from the Esprit Study. This is a neuropsychiatry study of community dwelling older adults. The researchers have taken a rigorous approach to the collection of data and this is detailed in the methodology section. A panel of neurologists validate the diagnosis of mild cognitive impairment and dementia. Points of interest include the use of an adult reading test as a measure of crystallised intelligence as well as the use of nutritional questionnaires. However depression is gauged simply by use of antidepressants as well as by using a questionnaire and there is a case for a psychiatrist being involved in the assessments given the authors’ conclusions. There is a 7 year follow-up period with several data collection points.

The researchers used the Cox (proportional hazards) model (there is a good explanation of the Cox model here) to establish the likely contribution of the variables to risk of dementia. The calculations are a little more convoluted as they need to make additional assumptions specific to dementia and these are again detailed in the methodology section. Confidence intervals for the likelihood of variables contributing to risk are derived using a bootstrapping method. Briefly, in the statistical analysis they have taken 1000 samples from the population and established estimator parameters using this approach. The hazard ratios and p-values are given in Table 2.

Crystallised intelligence is assumed by the authors to be a proxy marker for exposure to education and is the variable with the largest impact in the analysis. Diabetes, depression and fruit and vegetables combined approximate crystallised intelligence in magnitude of predicted risk. Nevertheless as noted above psychiatric validation of depression diagnosis would be useful. What was also interesting about this study was the absence of a marker of physical activity as there is certainly a lot of good evidence for exercise as a protective factor. What was also curious was an absence of a relationship with blood pressure although there is evidence elsewhere for such a relationship.

Many of the findings I thought were unsurprising. However the magnitude of the effect of crystallised intelligence may be a proxy marker for a ceiling effect on the cognitive tests used in the assessment. If this were the case, then I would expect an artificial increase in the threshold (which is usually normalised) for the cognitive test score (used in diagnosis) to lower the magnitude of the crystallised intelligence variable’s contribution to risk. If this also holds then the implication would be that tests would need to examine a wider range of functions in order to counter the effect of overlearning on test components during educational exposure. This in turn relates to the longstanding issue of whether generalised intelligence or g taps into educational attainment or an underlying marker of general intelligence. The reason this is relevant is that crystallised intelligence is being assessed using a reading test which in turn would correlate with verbal intelligence which also correlates with general intelligence. If one argues that magnitude of effect of fruit consumption, vegetable consumption, depression and diabetes is equivalent to the magnitude of effect of crystallised intelligence implying a biological basis for crystallised intelligence there is a rejoinder. The rejoinder is that the effects of education operate on the biology of the brain as do the other risk factors. This subtle point means that a high level of general intelligence is not required to ensure that a high test performance is achieved but an underlying biology is required in order to maintain performance on the circumscribed tests and this in turn may be impacted by the other variables.

The logical consequence of these conclusions would be to construct an interventional study and this would certainly be an interesting one to follow particularly at a public health level and given the pending global dementia demographics.

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