This work believes this is the first evidence demonstrating an association between these clinically relevant biomarkers of Alzheimer’s disease and phenotypes of brain aging in nonhuman primates, underscoring their importance as models of aging and neurodegenerative disease.
Abstract
Chimpanzees share a number of age-related brain changes with humans, such as reductions in neurons and increases in neuropathology. To date, there are no published studies of peripheral biomarkers related to Alzheimer’s pathology and their associations with age and cortical atrophy in chimpanzees. Here we examined cross-sectional differences and longitudinal changes in biomarkers of pathological protein aggregation, neuroinflammation, and microglial function measured in serum. We examined the relationships between biomarkers and clinically relevant biomarker ratios with both age and cortical atrophy. We found linear and quadratic relationships between age and several biomarkers and ratios. Most biomarkers increased with age. While controlling for sex, we found significant negative associations between age and sulci surface area, mean depth, and gray matter thickness and a positive association with fold opening. Aβ42 and Aβ40 showed higher biomarker values associated with lower surface area, mean depth, and gray matter thickness and higher fold opening values. The clinically relevant biomarker ratios were also associated with cortical atrophy – Aβ42/Aβ40 was negatively associated with gray matter thickness, and pTau217/Aβ42 (both total and brain-derived) was positively associated with surface area and gray matter thickness and negatively associated with fold opening. Consistent with our hypotheses and previous findings in humans, many peripheral biomarkers associated with neurodegeneration and Alzheimer’s disease increase as chimpanzees age. We believe this is the first evidence demonstrating an association between these clinically relevant biomarkers of Alzheimer’s disease and phenotypes of brain aging in nonhuman primates, underscoring their importance as models of aging and neurodegenerative disease.
This is the first systematic comparative study of age-related changes in neurodegeneration biomarkers in two closely related nonhuman primates using comparable age ranges and sample sizes, and the same multiplex assay.
M. M. Mulholland, Elizabeth R. Magden, H. Scholtzova et al.· bioRxiv· 0 citations
A stratified approach based on amyloid status is essential for the optimal application of blood-based biomarkers in monitoring disease progression and evaluating therapeutic efficacy in future clinical trials and precision medicine.
Keun You Kim, Hyunsun Ham, E. Yoon et al.· The journal of prevention of...· 0 citations
It is found that variation in primate social systems and/or reproductive aging may influence sex and species differences in brain aging, and within the baboons but not the chimpanzees, significant sex differences were found in age‐related differences in cortical folding.
William D Hopkins, Angela M. Achorn, M. M. Mulholland et al.· American Journal of Primatol...· 0 citations
It is demonstrated that the diagnostic accuracy, biomarker–clinical correlation architecture, and optimal analyte selection of plasma p-tau217 vary systematically with age at onset, most markedly for tau-related measures.
Boru Jin, Aitong Li, Feng Xu et al.· Alzheimer's Research & Thera...· 1 citation
Elevated IL-6 is independently linked to cerebrovascular injury and poorer cognition beyond classical AD biomarkers, supporting a vascular–inflammatory pathway distinct from amyloid and tau pathology.
A. Dharmapuri, Soumilee Chaudhuri, J. Contreras et al.· Journal of Alzheimer's Disea...· 0 citations
Serum NfL was associated with anatomically specific WM microstructural changes, with differing patterns across clinical groups, and no significant associations were observed between serum or CSF GFAP concentrations and diffusion tensor imaging metrics.
T. Magalhães, R. Casseb, A. Moraes et al.· Journal of Alzheimer's Disea...· 0 citations
Related blog posts
MIT News · Artificial Intelligence· news.mit.eduAug 27, 2026
A new machine-learning framework aims to improve the success rate of computational protein design while moving away from results that reproduce sequences found in nature.