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New research pinpoints the ages when Alzheimer’s-related brain changes accelerate, offering critical clues to when screening may be most effective.
Study: Breakpoints in Alzheimer’s disease biomarkers and cognition across the aging spectrum: The Mayo Clinic Study of Aging. Image credit: Orawan Pattarawimonchai/Shutterstock.com
A recent study published in Alzheimer’s and Dementia investigated the specific ages at which Alzheimer’s disease biomarkers and cognitive measures experience significant slope changes, providing insight into the timing of early pathological processes across the aging spectrum.
Molecular pathology and biomarker evolution in alzheimer’s disease
Alzheimer’s disease (AD) is a progressive neurodegenerative disorder characterized by gradual cognitive decline, beginning with subtle memory loss and advancing to impairments in orientation, reasoning, language, and daily functioning. As the disease progresses, neuropsychiatric symptoms and loss of independence become increasingly common.
At the molecular level, AD is characterized by the accumulation of amyloid-beta plaques and neurofibrillary tangles composed of hyperphosphorylated tau protein, leading to widespread synaptic dysfunction, neuronal loss, and brain atrophy. These pathological features have catalyzed the development of biomarkers that directly quantify and stage AD pathology in vivo, thereby reshaping both clinical diagnostics and research protocols.
Blood-based biomarker (BBM) assays have become reliable, minimally invasive, and cost-effective tools for detecting molecular changes associated with amyloid, tau, and neurodegeneration, as well as for predicting cognitive decline. When combined with genetic, clinical, and demographic information, BBMs improve the accuracy of Alzheimer’s disease (AD) screening, guide advanced diagnostic procedures, and support individualized treatment strategies. BBM assays are now a standard component of preclinical AD trials, aiding in both participant selection and ongoing disease monitoring.
However, most BBM research has used convenience samples or cohorts with above-average health, limiting generalizability and making it difficult to identify optimal screening windows for the broader population. Population-representative studies are needed to clarify how biomarker trajectories change with age and across different clinical backgrounds. Such data are essential for improving the timing, effectiveness, and equity of AD screening and intervention.
Identifying critical ages for AD-related screening and monitoring
Age-specific breakpoints identify periods of rapid biomarker change that may signal clinical relevance, helping to optimize screening and monitoring strategies. Biomarkers assessed in this study include plasma Aβ42/40, p-tau181, GFAP (glial fibrillary acidic protein), NfL (neurofilament light chain), amyloid positron emission tomography (PET), tau PET, hippocampal volume (adjusted for intracranial volume), and global cognition. In a subset, additional plasma p-tau181, p-tau217, and their ratios to non-phosphorylated tau proteins were analyzed using mass spectrometry.
Participants were drawn from the Mayo Clinic Study of Aging (MCSA), a population-based cohort designed to investigate cognitive decline and dementia risk among Minnesota residents. Recruitment was random, utilizing the Rochester Epidemiology Project to ensure a representative sample.
Each participant attended comprehensive clinical visits that included neuropsychological testing, physician assessments, and age-appropriate blood draws. Neuroimaging procedures were performed on a subset of the cohort. The present analysis focuses on 2,082 individuals for whom plasma AD blood-based biomarkers (BBMs) were available, encompassing cognitively unimpaired individuals, those with mild cognitive impairment (MCI), and those with late-onset dementia. Demographic data, including age and sex, were self-reported.
Age-related patterns in biomarkers and cognition were analyzed using generalized additive models (GAMs) for smooth trends and breakpoint regression to identify key inflection points; the cycle number was adjusted where appropriate. Analyses were focused on ages 45 to 90 to avoid sparse data. As a sensitivity check, models were repeated in cognitively unimpaired subgroups using samples from the Quanterix and C2N biomarker platforms.
Cognitive decline and biomarker changes show age-related inflection points at the population level
The Quanterix sample comprised 2,082 participants (median age: 71 years, 54 % male). The C2N subsample included 462 participants (median age: 73 years, 54 % male), with 93 % cognitively unimpaired and 7.4 % with mild cognitive impairment (MCI).
Median global cognition in the C2N subsample was 0.16, slightly lower than in the full cohort, though still within a generally unimpaired range. Hippocampal volume, amyloid PET SUVR, tau PET SUVR, and other plasma biomarker levels were similar to those found in the full Quanterix cohort.
In the full Quanterix sample, plasma Aβ42/40, hippocampal volume, and global cognition declined with age, while p-tau181, NfL, and GFAP increased, especially after age 70. Amyloid PET increased earlier, around age 60, with NfL showing the greatest age-related change. Tau PET increased with age but did not show a clear breakpoint.
In the C2N subsample, hippocampal volume and global cognition declined with age, with accelerated cognitive decline in older adults. p-tau181, NfL, and GFAP rose more sharply after age 70, while amyloid and tau PET increased steadily. Plasma Aβ42/40 remained stable until approximately 75, increasing thereafter. For tau markers in the C2N subsample, p-tau217 and p-tau181 increased non-linearly with age, especially after age 72, while their ratio measures rose more gradually.
Inflection point analysis in the full sample showed significant breakpoints for plasma Aβ42/40, GFAP, NfL, p-tau181, amyloid PET, hippocampal volume, and global cognition, with sharper changes typically between ages 62–71. Aβ42/40 had an earlier inflection point before age 50. Breakpoint models were strongest for NfL, GFAP, and global cognition.
In the C2N subsample, breakpoints were found for plasma Aβ42/40, GFAP, NfL, and p-tau181, generally at older ages than in the full sample. No breakpoints were observed for hippocampal volume, global cognition, or amyloid PET. NfL again showed the best model fit.
Among plasma biomarkers unique to the C2N subsample, both p-tau217 and p-tau181 showed breakpoints at age 72.6, indicating steeper increases in late life. The Aβ42/40 ratios did not show clear inflection points, and C2N-derived Aβ42/40 measures did not show consistent breakpoint behavior across analyses.
It must be noted that the breakpoints identified in both the Quanterix and C2N groups were partially consistent across platforms, particularly for GFAP and NfL. Other markers, such as Aβ42/40, showed assay variability and cohort composition, and some breakpoints were not replicated across samples. Sensitivity analyses of cognitively unimpaired participants showed that most biomarker breakpoints were similar to those in the full cohort, except that the NfL breakpoint occurred earlier. In the C2N subsample, most breakpoints remained stable, except for p-tau181 and p-tau217, which lost statistical support.
Conclusions
This study demonstrates that breakpoint modeling can identify age thresholds in AD biomarker trajectories, revealing key inflection points, particularly for plasma GFAP, NfL, and p tau markers, at approximately 68–72 years of age. These observed inflection points indicate a late midlife to early older-age acceleration in population-level biomarker changes associated with neurodegeneration. The findings refine our understanding of the optimal timing for screening and monitoring strategies in Alzheimer’s disease.
Importantly, these breakpoint estimates do not imply a precise temporal sequence of disease progression or that biomarker changes occur in a fixed order within individuals. Age explained only a modest proportion of variability in biomarker levels, indicating that other factors, such as underlying pathology and comorbidities, also play substantial roles. These results are based on cross-sectional data and reflect population-level age associations rather than precise biological transition points within individuals or direct predictors of future cognitive decline.
However, interpretation of these results is limited by the cohort’s cognitive and demographic makeup, underrepresentation of advanced dementia, and some missing data, which may restrict generalizability and obscure later-stage associations.
Future research should validate the current findings in more diverse and advanced populations, integrate newer biomarkers, and apply advanced statistical methods to optimize screening and staging.
