Hu, Mingzhao

Abstract Background Despite advances in Alzheimer’s disease (AD) research, limited information exists regarding the absolute risk of mild cognitive impairment (MCI) in cognitively unimpaired (CU) individuals with abnormal AD biomarkers, particularly when accounting for competing risks of death. This knowledge gap is critical as the field addresses disease‐modifying treatments targeting CU individuals with preclinical AD. We focus on the predictive value of AD amyloid stages defined by amyloid PET Centiloid values. Methods We included 5,858 participants from the Mayo Clinic Study of Aging (MCSA) to evaluate AD amyloid stage as a predictor of clinical progression to MCI or dementia. The data includes long‐term follow‐up information on death and dementia beyond active study participation, which mitigates potential bias due to dropout. We predicted 10‐year and lifetime risks of MCI and dementia, accounting for the competing risks of death, given amyloid PET stages, sex, APOE4 status, and baseline age. Results are based on a hidden Markov model. Results AD amyloid staging based on amyloid PET Centiloid values was the strongest predictor of lifetime risk for MCI or for dementia (Figure 1). Higher Centiloid levels amplified age effects on the risk of MCI, whereas for dementia, amyloid stage effects surpassed age effects. For 10‐year risk, age was the dominant factor, whereas for lifetime risk, amyloid stages had a greater influence. While smaller in magnitude than the effects of amyloid stages and age, APOE4 carrier status and (on average) male sex were also associated with increased risk of MCI and dementia. For a female APOE4 carrier at age 65, lifetime risk was 41%/61%/76% for low/moderate/high amyloid PET levels. The 10‐year absolute risk for a female APOE4 carrier with high amyloid PET was 4%/29%/58% when assessed at starting ages 65, 75, and 85. Conclusions AD amyloid staging is a critical predictor of lifetime risk for cognitive impairment, underscoring its importance in guiding treatment decisions if interventions are approved for preclinical AD in the future. Effective patient evaluation should focus on biomarker‐based amyloid staging rather than simple binary biomarker classification, with careful consideration of age, sex, and APOE4 status to ensure a comprehensive benefit‐risk assessment for future therapeutic strategies.

Abstract A workgroup assembled by the Alzheimer’s Association recently described a conceptual framework for Alzheimer’s disease biological staging based on amyloid and tau PET imaging. However, specific tau PET cut points were left to be determined, a step necessary prior to clinical application. We sought to operationalize and evaluate Alzheimer’s disease biological staging by identifying meaningful tau PET cut points to define the four biological stages in a well-characterized participant cohort and describe the features of individuals placed into the different biological stages. The primary analysis included 896 participants in the Mayo Clinic Study of Aging or the Mayo Clinic Alzheimer’s Disease Research Center longitudinal cohorts. A validation cohort consisted of 328 participants in the Alzheimer’s Disease Neuroimaging Initiative. Both cognitively normal and impaired individuals with positive amyloid PET and evaluable tau PET imaging were included. Tau PET cut points were identified with Gaussian mixture models to characterize Alzheimer’s disease biological stage in study participants with different clinical diagnoses and objective degrees of cognitive impairment as measured by Mini-Mental State Examination. A tau PET cut point in the medial temporal region and two cut points in the temporoparietal region were identified to produce, collectively, the four Alzheimer’s disease biological stages described in the revised criteria. Increasing stage was associated with greater likelihood of mild cognitive impairment and dementia diagnosis and worsening cognitive performance on Mini-Mental State Examination and Clinical Dementia Rating Sum of Boxes, a result that was reproduced in the independent Alzheimer’s Disease Neuroimaging Initiative cohort. This study provided empirical validation for the concept of using amyloid PET and tau PET to separate subjects with biomarker-proven Alzheimer’s disease into four biological stages with distinct characteristics.

AbstractBackgroundImaging and plasma markers are used as key indicators of disease for Alzheimer’s disease (AD) but their usefulness in predicting regional tau pathology is relatively understudied. Our objective was to construct predictive models for regional tau pathology measured on postmortem brain tissue using multiple ante‐mortem AD biomarkers. We focused on hippocampal and parietal regions that were immunostained with AT8 and 2E9 that reflect early and advanced aspects of tangle maturity, respectively.MethodsUsing autopsy data from 63 participants enrolled in the Mayo Clinic Study of Aging (MCSA) where the last clinical visit is within three years of death, we analyzed three ante‐mortem plasma markers ‐ phosphorylated tau protein at the threonine 181 residue, glial fibrillary acidic protein, total β‐amyloid Aβ42 relative to Aβ40 ratio (Quanterix Simoa HD‐X Analyzer), and three regional MRI markers computed using SPM12 ‐ parietal thickness, parietal volume relative to total intracranial volume ratio, and hippocampal volume adjusted for total intracranial volume. Based on eight separate cross‐sectional linear regression models weighted by the inverse of time from last visit to death, we estimated the multiple and partial R2 predicting post‐mortem pathology measures, including AT8 and 2E9 in both the hippocampal and parietal regions. All models were adjusted for age, sex, and cardiovascular and metabolic conditions score.ResultsThe MRI models (R2 of 0.26‐0.53) had greater predictive power compared to the plasma models (R2 of 0.17‐0.26). The best multiple R2 was 0.53 for prediction of hippocampal AT8 using antemortem hippocampal volume adjusted for total intracranial volume followed by R2 of 0.43 for prediction of hippocampal 2E9 using hippocampal volume adjusted for total intracranial volume. Though the model prediction of parietal AT8 and 2E9 measurements was low (R2 of 0.21‐0.26), parietal thickness exhibited better predictive performance than parietal volume relative to total intracranial volume ratio.ConclusionsThe data we presented here may be utilized to validate biomarkers using regional pathology. Our results support that regional MRI is sensitive to tau pathology. Newer MRI methods should be tested to examine the specificity of newer quantitative measurements to tau pathology.

AbstractBackgroundThere is increasing need for noninvasive biomarkers of Alzheimer’s Disease (AD) neuropathologic change for early detection and intervention through risk‐factor modification and disease‐modifying therapies. One such biomarker is the prediction of chronological age from routine clinical tests such as an electrocardiogram (EKG) to discriminate between observed biological age from chronological age for healthy aging. Deviation of true age from predicted age has been associated with heart failure, hypertension, and coronary heart disease. However, the association between EKG‐based age and underlying neuropathology has not been studied.MethodWe used a cohort of 336 participants from the Mayo Clinic Study of Aging, a population‐based prospective study of residents in Olmsted County, Minnesota, with antemortem EKG and post‐mortem brain autopsy (Table 1 & 2). Age was predicted from EKG using a validated convolutional neural network algorithm. We used ordinal logistic regression to identify associations between lower neuropathologic scores of AD (Thal phase, neuritic plaque score, Braak stage, and overall AD neuropathologic change) and the Age‐Gap, defined as EKG‐predicted age minus chronological age. Results were controlled for true age, sex, and APOE e4 carrier status. Neuropathologic scales were grouped into none, low, intermediate, and high categories. Observations were weighted by the inverse of years between last EKG and time of death to account for the time difference between acquisition of EKG and autopsy.ResultWe found that an increasing Age‐Gap (i.e. older predicted age) was inversely associated with lower overall AD neuropathologic change (OR 0.93 [0.89, 0.97], p = 0.002). Investigation of the components of AD neuropathologic change showed that an increasing Age‐Gap was also inversely associated with lower amyloid metrics such as Thal phase (OR 0.85 [0.80, 0.89], p < 0.001) and neuritic plaque scores (OR 0.91 [0.87, 0.96], p < 0.001), but not tau metrics like Braak staging (OR 1.05 [1.00, 1.10], p = 0.05).ConclusionIn this work, we show that older predicted age on EKG, a noninvasive biomarker associated with vascular and biological aging, is associated with higher amyloid burden and neuropathologic change of AD.

AbstractBackgroundImaging and plasma markers are used as key indicators of disease for Alzheimer’s disease (AD) but their usefulness in predicting regional tau pathology is relatively understudied. Our objective was to construct predictive models for regional tau pathology measured on postmortem brain tissue using multiple ante‐mortem AD biomarkers. We focused on hippocampal and parietal regions that were immunostained with AT8 and 2E9 that reflect early and advanced aspects of tangle maturity, respectively.MethodsUsing autopsy data from 63 participants enrolled in the Mayo Clinic Study of Aging (MCSA) where the last clinical visit is within three years of death, we analyzed three ante‐mortem plasma markers ‐ phosphorylated tau protein at the threonine 181 residue, glial fibrillary acidic protein, total ß‐amyloid Aß42 relative to Aß40 ratio (Quanterix Simoa HD‐X Analyzer), and three regional MRI markers computed using SPM12 ‐ parietal thickness, parietal volume relative to total intracranial volume ratio, and hippocampal volume adjusted for total intracranial volume. Based on eight separate cross‐sectional linear regression models weighted by the inverse of time from last visit to death, we estimated the multiple and partial R2 predicting post‐mortem pathology measures, including AT8 and 2E9 in both the hippocampal and parietal regions. All models were adjusted for age, sex, and cardiovascular and metabolic conditions score.ResultsThe MRI models (R2 of 0.26‐0.53) had greater predictive power compared to the plasma models (R2 of 0.17‐0.26). The best multiple R2 was 0.53 for prediction of hippocampal AT8 using antemortem hippocampal volume adjusted for total intracranial volume followed by R2 of 0.43 for prediction of hippocampal 2E9 using hippocampal volume adjusted for total intracranial volume. Though the model prediction of parietal AT8 and 2E9 measurements was low (R2 of 0.21‐0.26), parietal thickness exhibited better predictive performance than parietal volume relative to total intracranial volume ratio.ConclusionsThe data we presented here may be utilized to validate biomarkers using regional pathology. Our results support that regional MRI is sensitive to tau pathology. Newer MRI methods should be tested to examine the specificity of newer quantitative measurements to tau pathology.

Abstract Background and Aims Despite prior studies on the clinical impact of COVID-19 and the documented efficacy of COVID-19 vaccinations in hemodialysis (HD) patients, the exact effects of COVID-19 vaccinations on various clinical biomarkers before and after the diagnosis of COVID-19 have not been established. In particular, the longitudinal trajectories of clinical and laboratory parameters remain unknown. We aim to investigate clinical and laboratory variables around the time of COVID-19 diagnosis and how vaccination impacts population trajectories. Method We studied 53,223 HD patients from Fresenius Kidney Care, a large U.S. dialysis organization, who received COVID-19 vaccination between January 1, 2021 and August 2, 2021. Depending on whether or not COVID-19 diagnosis happened after the first dose of vaccination for Johnson & Johnson or after the second dose for all other vaccines, we created two patient cohorts comprising patients infected before (N = 47,443) and after (N = 5780) full vaccination. A nonparametric cubic spline model for flexibility was fitted to the patient data averaged for each day between 60 days before and 60 days after the COVID-19 diagnosis. Clinical parameters include ones that are recorded at each HD treatment (pre-HD temperature, TEMP; pre-HD systolic blood pressure, SBP; pre-HD weight, WT; pre-HD pulse rate, PULSE; intradialytic weight gain, IDWG) and ones that are recorded less frequently (hemoglobin, HGB; white blood cell, WBC; neutrophil-lymphocyte ratio, NLR; platelet count, PLA; albumin, ALB). Results All longitudinal trajectory patterns undergo changes in the days before and after the diagnosis of COVID-19. In both the vaccinated and unvaccinated cohorts, there is an initial increase in TEMP, PULSE, and NLR up to 14 days preceding the diagnosis of COVID-19, followed by a subsequent decrease. Conversely, WT and IDWG exhibit a decline up to 14 days before the diagnosis of COVID-19 and an increase thereafter. ALB maintains a decreasing trend from 14 days prior until 20 days after before increasing again. In the unvaccinated cohort, SBP and WBC start decreasing up to 20 days ahead of diagnosis of COVID-19, whereas the unvaccinated cohort maintains a steady trend, increasing for SBP and decreasing for WBC. The lasting effects of COVID-19 can be found up to 60 days after diagnosis, e.g., WT, PULSE, and HGB. Variations in trend shapes are evident between the vaccinated and unvaccinated cohorts across all variables. Notably, all variables in the vaccinated cohort demonstrate smaller changes around the days of COVID-19 diagnosis. Conclusion We discovered trend changes in multiple variables in the days leading up to COVID-19 diagnosis, corroborating earlier findings. This finding lays the foundation for early detection ahead of regular COVID-19 tests. Prior COVID-19 vaccination mitigates the effect of COVID-19 on several important clinical and laboratory indicators, supporting the need for continued vaccination efforts. Lasting effects from infection are prevalent, extending beyond 60 days after confirmation. Further research is warranted to understand their long-term impact.