Picture of older, Asian women to represent diseases of aging such as Alzheimer's or other forms of dementia
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Research led by the University of Arizona reveals metabolic signatures for late-onset Alzheimer’s disease linked with sex and APOEe4 genotype, which the investigators believe could help diagnose patients more accurately and develop new targeted therapeutics.

Around 5.8 million Americans were living with Alzheimer’s disease in 2020 and this figure is expected to more than double by 2050. Unfortunately, while there has been a lot of research in this area, many clinical trials have failed and there remains an unmet medical need in terms of effective treatments.

The APOE gene is known to impact Alzheimer’s risk, with the APOEe4 genotype being associated with higher risk, but less is known about how this genotype and other clinical factors impact metabolism in those affected.

To investigate this further, Rui Chang, an associate professor at the University of Arizona and the Center for Innovation in Brain Science, and colleagues analysed changes in 127 metabolites from 656 serum samples taken from individuals in the Alzheimer’s Disease Neuroimaging Initiative cohort, both with and without the condition.

Overall, changes in 13 phosphatidylcholines, three sphingomyelins, four acylcarnitines, and citrulline were linked to late-onset Alzheimer’s disease. The team also identified different profiles linked to Alzheimer’s for APOEe4 carriers and also that differed by sex. For example, presence of APOEe4 creates a phosphatidylcholine-focused metabolic profile regardless of sex-specific differences in serum metabolites.

“One of the most interesting findings of our study is the identification of key drivers of metabolic pathways that discriminate between Alzheimer’s disease and cognitively normal individuals when patient groups were separated by sex and APOE genotype,” said Chang, PhD, who is first author of the Alzheimer’s and Dementia journal paper describing the study, in a press statement.

The team identified a metabolic signature predictive of disease state and cognitive function for eight patient subgroups stratified by sex and/or APOEe4 genotype. The different signatures help identify metabolic drivers of the disease, which could be targeted by new therapies.

“These patient-specific metabolic targets will shed light on the discovery of precision therapeutics for Alzheimer’s patients, which has not been done in previous studies,” noted Chang.

“This study provides an operational strategy to achieve that goal by integrating clinical cognitive assessments, metabolic profiling and a computational network model to identify targeted therapeutics for patients,” added Roberta Diaz Brinton, Regents Professor of Pharmacology and director of the Center for Innovation in Brain Science, who was also a lead study author.

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