Hu, Mingzhao

BACKGROUND: White matter hyperintensities (WMH) are widely used to assess cerebral small vessel disease but reflect late-stage injury. Diffusion magnetic resonance imaging (MRI) biomarkers have been proposed to capture earlier small vessel disease-related microstructural damage but their temporal progression relative to WMH and risk factors associated with progression remain unexplored. METHODS: We identified 2077 participants from the population-based cohort study of the Mayo Clinic Study of Aging in Olmsted County, Minnesota (aged 50–101 years) collected between 05/2005 and 09/2024 with longitudinal neuroimaging. Using multioutput nonlinear mixed-effects models in those with at least 2 fluid-attenuated inversion recovery-MRI and diffusion MRI scans, we characterized the temporal progression of WMH and 4 diffusion MRI biomarkers: fractional anisotropy of the genu of the corpus callosum, peak width of skeletonized mean diffusivity, free water, and Arteriolosclerosis-score, which were automatically estimated. Models incorporated participant-specific time shifts, correlations between biomarkers, and effects of risk factors (sex, education, APOE ε4 status, cardiometabolic conditions). RESULTS: The study population had a mean age of 78 years, 47% were women, 28% were APOE ε4 allele carriers, and 80% were cognitively unimpaired, with an average follow-up of 5.2 years (SD, 4.3 years) for fluid-attenuated inversion recovery-MRI and 4.3 years (SD, 3.9 years) for diffusion MRI. Arteriolosclerosis-score, fractional anisotropy of the genu of the corpus callosum, free water, and peak width of skeletonized mean diffusivity became abnormal in 50% of the study population 16, 12, 10, and 7 years before WMH become abnormal (half-width of CI <1 year), respectively. Global markers (Arteriolosclerosis-score, free water, peak width of skeletonized mean diffusivity, and WMH) were correlated, indicating shared substrates of widespread white matter injury. Fractional anisotropy of the genu of the corpus callosum, a vascular risk microstructural injury biomarker, was weakly coupled with WMH and had an earlier but more linear worsening across adulthood. Cardiometabolic conditions predicted earlier worsening of all biomarkers. Female participants showed earlier WMH, fractional anisotropy of the genu of the corpus callosum, and Arteriolosclerosis-score abnormalities, whereas male participants exhibited earlier peak width of skeletonized mean diffusivity and free water abnormalities. CONCLUSIONS: Diffusion MRI biomarkers were abnormal at least a decade before WMH become abnormal in the population, revealing a prolonged phase of early small vessel disease and highlighting their potential for small vessel disease prevention.

Abstract INTRODUCTION White matter lesions on fluid‐attenuated inversion recovery magnetic resonance imaging (FLAIR‐MRI) reflect small vessel disease (SVD) and aid in identifying vascular contributions to impairment and dementia (VCID). Emerging diffusion MRI (dMRI) biomarkers are sensitive to early SVD but the lack of validated approaches for defining dMRI abnormality has limited their clinical utility. METHODS In 3934 participants (8749 observations) from a population‐based sample, we classified 9% as vascular cognitive impairment versus not (VCI+/VCI−) using abnormal FLAIR‐MRI and cognitive testing. We evaluated four distinct dMRI positivity criteria and assessed performance in an independent clinical sample. RESULTS The young referent criterion performed best and yielded an estimated prevalence of dMRI positivity aligned with pathology literature. About 40% of the population were dMRI positive, and these individuals had lower baseline cognition and faster longitudinal decline. Cutpoints were validated in an independent clinical sample. DISCUSSION The proposed dMRI abnormality framework can detect VCID and aid in identification of individuals at risk of VCI.

Abstract INTRODUCTION We examined when Alzheimer's disease biomarkers become informative by identifying age‐related breakpoints with slope‐changing trajectories. METHODS In 2082 Mayo Clinic Study of Aging participants, we modeled plasma amyloid beta (Aβ)42/40, phosphorylated tau (p‐tau)181, glial fibrillary acidic protein (GFAP), neurofilament light chain (NfL), amyloid/tau positron emission tomography (PET), hippocampal volume (HVa), and global cognition. Generalized additive models described age trends; Davies' test and piecewise linear regression estimated breakpoints. A C2N subsample ( n = 462) provided mass‐spectrometry plasma markers (p‐tau181, p‐tau217, their ratios, Aβ42/40). RESULTS In the full cohort, Aβ42/40, HVa, and cognition declined with age; p‐tau181, NfL, GFAP, and amyloid/tau PET increased. We observed single breakpoints (years, 95% CI): GFAP 68.1 (63.5–71.8), NfL 70.7 (65.9–75.6), p‐tau181 67.2 (60.3–70.3), amyloid PET 62.3 (56.2–69.3), HVa 68.1 (63.1–71.9), cognition 59.8 (55.4–66.0); tau PET showed none. In the mass‐spectrometry subset, p‐tau217 and p‐tau181 broke at 72.6; their ratios and Aβ42/40 showed no breakpoints. DISCUSSION Breakpoints cluster near late midlife, suggesting windows for screening and monitoring.

Abstract Background White matter hyperintensities (WMH) are widely used to assess cerebral small vessel disease (SVD) but reflect late-stage injury. Diffusion MRI based biomarkers have been proposed to capture earlier SVD-related microstructural damage but their temporal progression relative to WMH and the risk factors associated with this progression have not been explored. Methods We analyzed longitudinal neuroimaging data from 2,047 participants from a population-based cohort study (aged 49-101 years, 47% female). Using multi-output nonlinear mixed-effects models, we characterized the temporal progression of WMH and four diffusion MRI based biomarkers: fractional anisotropy of the genu of the corpus callosum (Genu-FA), peak width of skeletonized mean diffusivity (PSMD), free water (FW), and Arteriolosclerosis-score (ARTS). Models incorporated participant-specific time shifts, correlations between biomarkers, and effects of risk factors (sex, education, APOE ε4 status, and cardiometabolic conditions). Results ARTS, Genu-FA, FW, and PSMD became abnormal in 50% of the study population 16, 12, 10, and 7 years before WMH, respectively. Global markers (ARTS, FW, PSMD, WMH) were correlated, indicating shared substrates of widespread white matter injury. Genu-FA, a vascular risk microstructural injury biomarker, was weakly coupled with WMH and had an earlier but more linear worsening across adulthood. Cardiometabolic conditions predicted earlier worsening of all biomarkers. Females showed earlier WMH, Genu-FA, and ARTS abnormalities whereas males exhibited earlier PSMD and FW abnormalities. Conclusions Diffusion MRI based biomarkers capture microstructural injury at least a decade before appearance of WMH, revealing a prolonged phase of early SVD and highlighting their potential for SVD prevention.

Abstract Associations of Alzheimer’s disease biomarker progression with cognitive decline are important to inform patient prognosis. Of particular interest is how newly available plasma biomarkers evolve relative to cognitive decline. The goals of this work are to measure how much earlier versus later an individual’s progression on plasma and PET Alzheimer’s disease biomarkers is associated with earlier versus later cognitive progression and to estimate the average timeline of progression of these processes in the population. In this cohort study of 2369 Mayo Clinic Study of Aging (MCSA) and 1591 Alzheimer’s Disease Neuroimaging Initiative (ADNI) participants, we fit non-linear mixed-effects models to estimate how much earlier versus later each individual progresses on plasma phosphorylated tau (p-tau)217, amyloid PET, tau PET and auditory verbal learning test (AVLT) sum of trials relative to the population mean (individual adjustment), the associations of these individual adjustments among biomarker pairs and how covariates affect the timing of biomarker progression. The association of individual adjustments implies mechanistic associations and the amount of variability in cognitive decline accounted for by each biomarker. By applying cut-off points, we also estimated the relative timing that these biomarkers become abnormal in the population. Associations of individual adjustments were moderate between all biomarkers and AVLT (R = 0.38–0.47) in the MCSA and stronger (R = 0.74–0.81) in ADNI; plasma p-tau217 accounted for 16% of the variability in timing of AVLT decline in the MCSA and 64% in ADNI. APOE ɛ4 carriership was associated with earlier biomarker progression. AVLT became abnormal after the biomarkers up to age 90, after which AVLT was estimated to become abnormal prior to tau biomarkers. The association of the timing of plasma and PET Alzheimer’s disease biomarker progression with cognitive decline was modest in the MCSA population-based sample and stronger in the Alzheimer’s disease-enriched ADNI cohort. The timing of plasma p-tau217 progression explained a similar degree of variability in AVLT progression as amyloid PET, supporting its utility as a marker of disease progression. The estimated temporal ordering of biomarkers and cognitive abnormality was as anticipated (amyloid, tau, cognition) up to the age of 90, beyond which AVLT was estimated to become abnormal prior to tau biomarkers, likely related to the effects of non-Alzheimer’s disease co-pathologies.

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 Background Cerebrovascular disease (CeVD), a common co‐pathology with Alzheimer's Disease, has not been sufficiently operationalized, limiting its usage for accurate estimation of vascular contributions to cognitive impairment (VCI). The proposed CeVD biomarker classification uses more recently developed diffusion MRI markers (fractional anisotropy of Genu (FA), free water (FW), peak width of skeletonized mean diffusivity (PSMD), and ARTS score (ARTS)) as surrogates of early CeVD changes and white matter hyperintensities (WMH) as surrogate of late CeVD changes. Our primary goal was to characterize the temporal progression of these biomarkers to enhance CeVD biomarker staging. Our secondary goal was to establish cut‐points for normal/abnormal CeVD biomarkers. Methods Using CeVD biomarker data from 3,953 Mayo Clinic Study of Aging participants (split 80‐20 for training‐testing with training used for cut‐point estimation), we classified participants into VCI+/VCI‐ (Fazekas≥2 and Clinical Dementia Rating (CDR)≥0.5/not). We evaluated cut‐points using four criteria: specificity‐based, sensitivity‐based, accuracy young referent (VCI+ vs. 30‐49 yrs VCI‐), and accuracy age‐/sex‐matched referent (VCI+ vs. age‐/sex‐matched VCI‐). After selecting a cut‐point based on sensitivity and positive predictive value (PPV), we used nonlinear mixed‐effects (NLME) models to examine temporal progression in 2,047 participants (aged 43.39‐92.51 years, 47% female) with longitudinal data incorporating participant‐specific time shifts, correlations between random effects of each biomarker, and covariates (age, sex, vascular risk, education, APOE4 status). Results The accuracy young referent criterion produced the best sensitivity‐PPV tradeoff across all diffusion MRI markers (Figure 1). Diffusion MRI biomarkers consistently progressed 10 years earlier than WMH (Figure 2). Whole‐brain diffusion MRI measures (FW, PSMD, ARTS) were highly correlated with WMH, showing synchronized progression curves with a time offset. The regional genu measure (interhemispheric disconnection) progressed earlier with a steadier slope across decades and had comparatively lower correlation with WMH. Conclusions We developed cut‐points for diffusion MRI markers using classification of VCI based on Fazekas scale and CDR. Cut‐points from the accuracy young referent (VCI+ vs. 30‐49 yrs VCI‐) outperformed the accuracy age‐/sex‐matched referent (VCI+ vs. age‐/sex‐matched VCI‐), suggesting that older VCI‐ individuals may have unmeasured CeVD changes. Diffusion MRI markers progressed earlier relative to WMH, highlighting their potential for early detection, improved clinical assessment, and targeted interventions.

Abstract Background Chronic disease conditions lead to brain changes elevating dementia risk. We hypothesized that tissue microstructural changes caused by type 2 diabetes mellitus (T2DM) and hypertension could be effectively captured using an advanced diffusion MRI technique, Neurite Orientation Dispersion and Density Imaging (NODDI). Our goal was to evaluate the usefulness of composite NODDI scores, customized for increased hemoglobin (Hb) A1c and systolic blood pressure as predictors of cognition and compare their performance to other measures typically used to assess dementia risk. Method We collected multi‐shell diffusion MR on 1024 Mayo Clinic Study of Aging participants. NODDI measures (neurite density index (NDI), orientation dispersion index (ODI), and free water fraction (FWF)) in gray matter (GM) and white matter (WM) regions were estimated. To identify brain regions susceptible to both conditions, a multiple linear regression model was fitted for each NODDI metric, using hemoglobin (Hb) A1c (in N = 305 participants) or systolic blood pressure ( N = 906) as predictor, with age, sex, and body mass index (BMI) as covariates. Event‐based modeling was performed with the identified NODDI metrics to investigate the chronological pattern along the course of chronic condition development from this cross‐sectional dataset. Composite metrics were created as a weighted sum of susceptible NODDI metrics. We evaluated the impact of both conditions on cognition using gradient boosting models incorporating composite metrics, additional brain measurements, and demographics. Result Increased HbA1c impacts both GM and WM NODDI relatively evenly, whereas increased systolic blood pressure related NODDI changes were mainly seen in WM (data not shown). With increased HbA1c, the earliest changes were observed in GM ODI, followed by changes in the pons and cortical FWF (Figure 1A). With increased systolic blood pressure, the earliest changes occurred in association fibers and middle cerebellar peduncle, followed by FWF change in lobar and projection WM fibers (Figure 1B). Average feature importance from 100 gradient boosting models for cognition prediction suggests that diabetes‐induced GM changes play a moderately important role in cognitive decline (Figure 2). Conclusion Chronic medical conditions, especially T2DM, result in brain changes that are captured by NODDI and contribute moderately to cognitive performance.

Abstract Background Multi‐site aging and dementia studies are challenged by data heterogeneity caused by scanner (manufacturer and protocol) related variability. Quantitative analysis of white matter hyperintensity (WMH) from pooled samples, a traditional marker of cerebrovascular disease, is sensitive to machine variability. Our goal was to compare Deming (symmetric) linear regression and ComBat, a common method used for harmonizing neuroimaging measurements, for harmonizing WMH measurements on participants scanned on two different scanners (GE and Siemens) and protocols (2D FLAIR on GE and 3D FLAIR on Siemens). Methods Cognitively unimpaired (CU) ( n =53, age (mean (SD))=67 (15) years) and cognitively impaired (CI; MCI/dementia) ( n =34, age (mean (SD))=82 (9) years) participants from the Mayo Clinic Study of Aging and Mayo ADRC were scanned on GE and Siemens scanners within a week of each other (typically done on the same day). WMH as a percentage of total intracranial volume was measured using multispectral segmentation of FLAIR and T1‐weighted images (Gunter et. al. AAIC 2023). We used Deming regression as it includes errors in both input dimensions. ComBat was applied using age, sex, and diagnosis as covariates, with scanner vendor and protocol as the batch variable. We assessed the inter‐scanner similarity using Maximum Mean Discrepancy (MMD) analysis, where a value of zero indicates identical distributions, p >0.05 indicates no significant difference under H 0 . (Gretton et al., JMLR 2012). Results Our results showed that the distribution of WMH measurements, which were different in the CU group, were more similar after Deming than ComBat harmonization (Figure 1). This was confirmed by the lower MMD in the CU group from Deming regression (‐0.01 ( p =0.76) from 0.03 ( p =0.05)) compared to ComBat (0.04 ( p =0.02))(Figure 1). However, ComBat and Deming were unable to create distributions as compatible as original measurements for the CI group. Conclusion Using a unique dataset of the same participants scanned close in time on different scanner and protocols, we found that (1) MMD measurements were useful to test the need/improvement due to harmonization; (2) ComBat was not substantially better at creating compatible distributions between scanner vendors than Deming regression, and both were sometimes inadequate. Future work will require alternative models to linear scale and shift transformations.

Abstract Background Cerebrovascular disease (CeVD), a common co‐pathology with Alzheimer’s Disease, has not been sufficiently operationalized, limiting its usage for accurate estimation of vascular contributions to cognitive impairment (VCI). The proposed CeVD biomarker classification uses more recently developed diffusion MRI markers (fractional anisotropy of Genu (FA), free water (FW), peak width of skeletonized mean diffusivity (PSMD), and ARTS score (ARTS)) as surrogates of early CeVD changes and white matter hyperintensities (WMH) as surrogate of late CeVD changes. Our primary goal was to characterize the temporal progression of these biomarkers to enhance CeVD biomarker staging. Our secondary goal was to establish cut‐points for normal/abnormal CeVD biomarkers. Methods Using CeVD biomarker data from 3,953 Mayo Clinic Study of Aging participants (split 80‐20 for training‐testing with training used for cut‐point estimation), we classified participants into VCI+/VCI‐ (Fazekas≥2 and Clinical Dementia Rating (CDR)≥0.5/not). We evaluated cut‐points using four criteria: specificity‐based, sensitivity‐based, accuracy young referent (VCI+ vs. 30‐49 yrs VCI‐), and accuracy age‐/sex‐matched referent (VCI+ vs. age‐/sex‐matched VCI‐). After selecting a cut‐point based on sensitivity and positive predictive value (PPV), we used nonlinear mixed‐effects (NLME) models to examine temporal progression in 2,047 participants (aged 43.39‐92.51 years, 47% female) with longitudinal data incorporating participant‐specific time shifts, correlations between random effects of each biomarker, and covariates (age, sex, vascular risk, education, APOE4 status). Results The accuracy young referent criterion produced the best sensitivity‐PPV tradeoff across all diffusion MRI markers (Figure 1). Diffusion MRI biomarkers consistently progressed 10 years earlier than WMH (Figure 2). Whole‐brain diffusion MRI measures (FW, PSMD, ARTS) were highly correlated with WMH, showing synchronized progression curves with a time offset. The regional genu measure (interhemispheric disconnection) progressed earlier with a steadier slope across decades and had comparatively lower correlation with WMH. Conclusions We developed cut‐points for diffusion MRI markers using classification of VCI based on Fazekas scale and CDR. Cut‐points from the accuracy young referent (VCI+ vs. 30‐49 yrs VCI‐) outperformed the accuracy age‐/sex‐matched referent (VCI+ vs. age‐/sex‐matched VCI‐), suggesting that older VCI‐ individuals may have unmeasured CeVD changes. Diffusion MRI markers progressed earlier relative to WMH, highlighting their potential for early detection, improved clinical assessment, and targeted interventions.