Machine learning models rank predictive risks for Alzheimer’s disease
Once adults reach age 65, the threshold age for the onset of Alzheimer’s disease, the extent of their genetic risk may outweigh age as a predictor of whether they will develop the fatal brain disorder, a new study suggests.
The study, published recently in the journal Scientific Reports, is one of the first to construct machine learning models with genetic risk scores, non-genetic information and electronic health record data from nearly half a million individuals to rank risk factors in order of how strong their association is with eventual development of Alzheimer’s disease.
Researchers used the models to rank predictive risk factors for two populations from the UK Biobank: White individuals aged 40 and older, and a subset of those adults who were 65 or older.
Results showed that age – which constitutes one-third of total risk by age 85, according to the Alzheimer’s Association – was the biggest risk factor for Alzheimer’s in the entire population, but for the older adults, genetic risk as determined by a polygenic risk score was more predictive.
A low household income also emerged as an important risk factor, ranking either third or fourth after the effects of age and genetics.
Of the 457,936 UK Biobank participants in the sample, 2,177 individuals had developed Alzheimer’s disease and 455,759 had not, and 88,309 were 65 or older.
A few non-genetic risk factors that differed between people with and without Alzheimer’s disease (AD) stood out: Results showed that in people with AD, higher systolic and lower diastolic blood pressure were more common, diabetes was more prevalent, household income and education were lower, and recent falls, hearing difficulty and a mother’s history of having AD were higher.
The top-20 list of risk factors for the full sample of adults also included diagnoses of high blood pressure, urinary tract infection, depressive episodes, fainting, unspecified chest pain, disorientation and abnormal weight loss. Other risk factors in the top 20 for people 65 and older included high cholesterol and gait abnormalities. These findings showed the power of adding condition codes from electronic health records to the models.
