A new study pinpoints the age at which brain changes associated with Alzheimer’s disease accelerate, providing important clues to when screening is most effective.
Study: Alzheimer’s disease biomarkers and cognitive breakpoints across the aging spectrum: Mayo Clinic Aging Study. Image credit: Orawan Pattarawimonchai/Shutterstock.com
Recent research published in Alzheimer’s disease and dementia investigated the specific ages at which Alzheimer’s disease biomarkers and cognitive indicators experience significant slope changes, providing insight into the timing of early pathological processes across the aging spectrum.
Evolution of molecular pathology and biomarkers in Alzheimer’s disease
Alzheimer’s disease (AD) is a progressive neurodegenerative disease characterized by gradual cognitive decline, starting with subtle memory loss and ending with impairments in orientation, reasoning, language, and daily living functions. As the disease progresses, neuropsychiatric symptoms and loss of independence become increasingly common.
At the molecular level, Alzheimer’s disease 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 facilitated the development of biomarkers to directly quantify and stage Alzheimer’s disease pathology in vivo, thereby reshaping both clinical diagnosis and research protocols.
Blood-based biomarker (BBM) assays have become reliable, less invasive, and cost-effective tools for detecting molecular changes associated with amyloid, tau, and neurodegeneration and predicting cognitive decline. When combined with genetic, clinical, and demographic information, BBM improves the accuracy of Alzheimer’s disease (AD) screening, guides advanced diagnostic procedures, and supports personalized treatment strategies. BBM assays are now a standard component of preclinical AD trials and are useful for both participant selection and ongoing disease monitoring.
However, most BBM studies use convenience samples or cohorts with above-average health status, which limits generalizability and makes it difficult to identify the optimal screening window for the broader population. Population-representative studies are needed to clarify how biomarker trajectories change with age and across different clinical contexts. Such data are essential to improving the timing, effectiveness, and equity of Alzheimer’s disease screening and intervention.
Identifying critical ages for Alzheimer’s disease-related screening and monitoring
Age-specific breakpoints identify periods of rapid change in biomarkers that may be of clinical relevance and help optimize screening and monitoring strategies. Biomarkers evaluated in this study included 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 ratio to nonphosphorylated tau protein were analyzed using mass spectrometry.
Participants were drawn from the Mayo Clinic Study on Aging (MCSA), a population-based cohort designed to study cognitive decline and dementia risk in Minnesota residents. Recruitment was random, utilizing the Rochester Epidemiology Project to ensure a representative sample.
Each participant participated in a comprehensive clinical visit that included neuropsychological testing, physician evaluation, and age-appropriate blood draws. Neuroimaging procedures were performed on a subset of the cohort. The current analysis focuses on 2,082 people for whom plasma AD blood-based biomarkers (BBM) were available, including those without cognitive impairment, those with mild cognitive impairment (MCI), and those with late-onset dementia. Demographic data such as age and gender were self-reported.
Age-related patterns in biomarkers and cognition were analyzed using generalized additive models (GAM), and major inflection points were identified with smooth trends and breakpoint regression. The number of cycles was adjusted as necessary. To avoid sparse data, the analysis focused on the age group from 45 to 90 years. As a sensitivity check, we repeated the model in the non-cognitively impaired subgroup using samples from the Quanterix and C2N biomarker platforms.
Decline in cognitive function and changes in biomarkers sHow age-related inflection points emerge at the population level
The Quanterix sample included 2,082 participants (median age: 71 years, 54% male). The C2N subsample included 462 participants (median age: 73 years, 54% male), 93% without cognitive impairment and 7.4% with mild cognitive impairment (MCI).
Median overall cognition in the C2N subsample was 0.16, slightly lower than the overall cohort but still generally within the nonimpaired range. Hippocampal volume, amyloid PET SUVR, tau PET SUVR, and other plasma biomarker levels were similar to those seen in the complete Quanterix cohort.
In all Quanterix samples, plasma Aβ42/40, hippocampal volume, and global cognition decreased 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 changes. Tau PET increased with age but did not show a clear breaking point.
In the C2N subsample, hippocampal volume and global cognition decreased with age, and cognitive decline accelerated in older adults. p-tau181, NfL, and GFAP increased more rapidly after age 70, while amyloid and tau PET steadily increased. Plasma Aβ42/40 remained stable until approximately 75 and then increased. Regarding tau markers in the C2N subsample, p-tau217 and p-tau181 increased nonlinearly with age, especially after age 72, whereas their ratio measurements increased more slowly.
Inflection point analysis of the total sample showed significant breakpoints in plasma Aβ42/40, GFAP, NfL, p-tau181, amyloid PET, hippocampal volume, and global cognition, with typically more rapid changes between ages 62 and 71 years. Aβ42/40 had an early inflection point before age 50. The breakpoint model was most powerful for NfL, GFAP, and global cognition.
In the C2N subsample, breakpoints for plasma Aβ42/40, GFAP, NfL, and p-tau181 were generally found at older ages than in the complete sample. No breakpoints were observed in hippocampal volume, global cognition, or amyloid PET. NfL again showed the best model fit.
Among the plasma biomarkers specific to the C2N subsample, p-tau217 and p-tau181 both showed a breakpoint at 72.6 years of age, indicating a sharp increase in later life. The Aβ42/40 ratio did not show a clear inflection point, and the CN-derived Aβ42/40 measurements did not show consistent breakpoint behavior across analyses.
The breakpoints identified in both the Quanterix and C2N groups were It is partially consistent across platforms, especially for GFAP and NfL. Other markers such as Aβ42/40 showed assay variability and cohort composition, with some breakpoints not replicating between samples. Sensitivity analyzes in participants without cognitive impairment 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.
conclusion
This study demonstrates that breakpoint modeling can identify age thresholds in Alzheimer’s disease biomarker trajectories, particularly revealing critical inflection points at approximately 68-72 years of age for plasma GFAP, NfL, and p-tau markers. These observed inflection points indicate that changes in population-level biomarkers associated with neurodegeneration accelerate from late middle age to early old age. This finding furthers our understanding of the optimal timing of screening and monitoring strategies in Alzheimer’s disease.
Importantly, these breakpoint estimates do not imply a precise temporal order of disease progression or that changes in biomarkers occur in a fixed order within an individual. It has been shown that only a small proportion of the variation in biomarker levels is explained by age, and that other factors such as underlying pathology and comorbidities also play an important role. 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 cognitive and demographic composition of the cohort, underestimation of progressive dementia, and some missing data, which may limit generalizability and obscure later-stage associations.
Future studies should validate current findings in more diverse and sophisticated populations, integrate newer biomarkers, and apply advanced statistical methods to optimize screening and staging.
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