Affiliations: [a] MRC Unit for Lifelong Health and Ageing at UCL, University College London, London, UK
| [b] Dementia Research Centre, University College London, London, UK
| [c] Division of Psychiatry, University College London, London, UK
| [d] Centre for Medical Image Computing, University College London, London, UK
| [e]
School of Biomedical Engineering & Imaging Sciences, King’s College London, London, UK
Correspondence:
[*]
Correspondence to: Marcus Richards, MRC Unit for Lifelong Health and Ageing at UCL, University College London, 1-19 Torrington Place, London WC1E 7HB, UK. Tel.: +44 20 7670 5700; E-mail: [email protected].
Abstract: Background:Using the British 1946 birth cohort we previously estimated life course paths to the Addenbrooke’s Cognitive Examination (ACE-III). Objective:We now compared those whose ACE-III scores were expected, worse and better than predicted from the path model on a range of independent variables including clinical ratings of cognitive impairment and neuroimaging measures. Methods:Predicted ACE-III scores were categorized into three groups: those with Expected (between –1.5 and 1.5 standard deviation; SD); Worse (< –1.5 SD); and Better (>1.5 SD) scores. Differences in the independent variables were then tested between these three groups. Results:Compared with the Expected group, those in the Worse group showed independent evidence of progressive cognitive impairment: faster memory decline, more self-reported memory difficulties, more functional difficulties, greater likelihood of being independently rated by experienced specialist clinicians as having a progressive cognitive impairment, and a cortical thinning pattern suggestive of preclinical Alzheimer’s disease. Those in the Better group showed slower verbal memory decline and absence of independently rated progressive cognitive impairment compared to the Expected group, but no differences in any of the other independent variables including the neuroimaging variables. Conclusion:The residual approach shows that life course features can map directly to clinical diagnoses. One future challenge is to translate this into a readily usable algorithm to identify high-risk individuals in preclinical state, when preventive strategies and therapeutic interventions may be most effective.
