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AbstractBackgroundThe TAR DNA‐binding protein of 43 kDa (TDP‐43) is linked to hippocampal volume loss and faster rates of hippocampal atrophy in Alzheimer's disease and primary age‐related tauopathy (PART). Hence, TDP‐43 is becoming an important player in age‐related neurodegeneration. To advance our understanding of TDP‐43 effect on hippocampus, we conducted an imaging‐pathological study to determine which specific hippocampal subfields are affected by TDP‐43 in cases of PART.MethodOne‐hundred fifteen autopsied cases from Mayo Clinic Alzheimer Disease Center, Study of Aging and Neurodegenerative Research Group were analyzed. All had completed antemortem brain MRIs, underwent neuropathological examination per NIA and Alzheimer’s Association (NIA‐AA) criteria and had additional assessment for TDP‐43 deposition. Only cases meeting criteria for PART (Thal phases 0‐2 and Braak NFT stages I‐IV) were included. Hippocampal subfield segmentation was performed using FreeSurfer‐7. Brain volumes were normalized to total intracranial volume (TIV) when comparing TDP‐43 groups. Statistical analyses including logistic regression analyses were utilized to explore associations between TDP‐43 status and hippocampal subfield volumes.ResultForty‐nine of the 115 cases (43%) with PART were female. Thirty‐seven (32%) cases were TDP‐positive. Mean age at last MRI was 84 years. There were no differences in pathological characteristics between both groups except for presence of hippocampal sclerosis which was more frequent in the TDP(+) group (P <0.001). Whole hippocampal volume was significantly smaller in the TDP(+) group (176 ±31 vs. 188 ±27, p=0.049). After stratification of hippocampus into head, body, and tail, TDP‐positive cases had smaller volumes of hippocampal body (p=0.031) and tail (p=0.021) but did not reach significance for hippocampal head (p=0.129). When regions were subdivided into hippocampal subfields, TDP(+) cases showed smaller volumes of molecular layer of the hippocampal body (p=0.046), CA1 body (p=0.031), subiculum body (p=0.023), and presubiculum body (p=0.046) compared to TDP(‐) cases. Logistic regression analysis adjusting for TIV confirmed these associations.ConclusionThis study underscores the association between TDP‐43 and the hippocampus and provides further evidence that TDP‐43 targets multiple regions and subfields of the hippocampus in patients with PART. Intriguingly, regions targeted are those where TDP‐43 deposition is microscopically observed, suggesting a direct “hit” by TDP‐43 deposition.

AbstractBackgroundProgressive supranuclear palsy (PSP) can present with different clinical variants which show distinct, but partially overlapping, patterns of neurodegeneration and tau deposition in a PSP network of regions, including cerebellar dentate, superior cerebellar peduncle, midbrain, thalamus, basal ganglia, and frontal lobe. We sought to determine whether disruptions in functional connectivity within this PSP network measured using resting‐state functional MRI (rs‐fMRI) differed between PSP‐Richardson’s syndrome and the cortical and subcortical variants of PSP.MethodStructural MRI and rs‐fMRI scans were collected for 40 PSP‐RS, 24 PSP‐cortical (12 speech and language; 10 corticobasal syndrome; 2 frontal) and 36 PSP‐subcortical (18 parkinsonism; 11 progressive gait freezing; 6 postural instability; 1 oculomotor) participants who met the Movement Disorder Society PSP clinical criteria (Table 1). Ninety‐six participants underwent flortaucipir‐PET scans. MRIs were processed using CONN Toolbox. Functional connectivity between seeds placed throughout the PSP network was compared between each PSP group and 83 healthy controls, and between the PSP groups, covarying for age and sex.ResultsConnectivity was reduced throughout the network in PSP‐RS compared to controls (Figure 1A), involving cerebellar dentate, midbrain nuclei, subthalamic nuclei, basal ganglia, thalamus, and frontal regions. Frontal regions showed reduced connectivity to other regions in the network in PSP‐cortical, including substantia nigra and superior cerebellar peduncle. Disruptions in connectivity in PSP‐subcortical were less pronounced, with the strongest disruption between the pallidum and putamen. When PSP groups were compared to each other (Figure 1B), PSP‐RS had lower connectivity from the thalamus and cerebellar dentate to the cortex compared to PSP‐subcortical and from the thalamus to substantia nigra than PSP‐cortical. PSP‐subcortical had lower connectivity from the subthalamic nucleus to caudate than PSP‐RS and from the subthalamic nucleus to substantia nigra than PSP‐cortical and higher connectivity from the thalamus to the prefrontal cortex than both the other variants. Voxel‐based analyses replicated these patterns. There was moderate evidence that tau uptake in the PSP network regions influenced connectivity between these regions in PSP.ConclusionsPatterns of disrupted functional connectivity revealed both variant‐specific and shared disease pathways among PSP variants, providing insight into the disease heterogeneity beyond patterns of atrophy.

AbstractBackgroundCorticobasal syndrome (CBS) is a progressive neurodegenerative syndrome that can result from several underlying neuropathologies, including Alzheimer’s disease (AD). Little is known about the utility of blood plasma measures to predict PET biomarker confirmed AD in CBS.MethodEighteen CBS patients (8 PET Aβ+; 10 PET Aβ‐) and 8 cognitively unimpaired Aβ‐ individuals (CU) were recruited by the Neurodegenerative Research group from the Department of Neurology, Mayo Clinic, Rochester, MN, underwent Aβ (Pittsburgh Compound‐B) and tau (18F‐flortaucipir) PET and provided a blood sample. Blood plasma analysis was performed to assess p‐tau 181, total tau, Aβ 42/40, neurofilament light chain (NfL) and glial fibrillary acidic protein (GFAP) using the Quanterix Simoa platform. Plasma concentrations were compared between Aβ‐, and Aβ+ CBS and controls using Kruskal‐Wallis corrected for multiple comparisons using Dunn’s test, with effect size measured with area under the receiver operating characteristic curve (AUROC) analysis. Spearman correlations were calculated to assess relationships between plasma concentrations and Aβ and tau PET uptake.ResultThe CBS Aβ+ group showed reduced Aβ42/40 ratio, with elevated p‐tau 181, GFAP and NfL plasma concentrations compared to CU, while the CBS Aβ‐ group only showed elevated NfL concentration compared to CU (Figure 1). Both p‐tau 181 (AUROC=0.95) and GFAP (AUROC=0.91) plasma concentrations were able to differentiate CBS Aβ‐ from CBS Aβ+. Plasma p‐tau 181 and GFAP concentrations showed a positive correlation to both Aβ and tau PET uptake, and the Aβ42/40 ratio showed a negative correlation to Aβ PET uptake (Figure 2). No correlations were observed between NfL and Aβ and tau PET.ConclusionThis study supports the use of plasma p‐tau 181 and GFAP to detect AD in CBS, while NfL shows potential as a nonspecific disease biomarker of CBS regardless of underlying pathology. Plasma concentrations of these biomarkers may potentially be incorporated into clinical trials targeting CBS patients with underlying AD pathology.

AbstractBackgroundBlood plasma metrics provide excellent biomarkers of the presence of Alzheimer’s disease (AD) pathology in population cohorts. However, it is less clear whether plasma metrics correlate with PET measures of AD pathology in cohorts of patients with advanced AD disease, particularly in those with atypical clinical presentations of AD.MethodSeventy‐seven patients (visual variant=43, language variant=32, motor variant=1 and dysexecutive variant=1) with PET biomarker‐confirmed atypical AD were recruited by the Neurodegenerative Research group at Mayo Clinic, Rochester, MN, underwent Aβ (Pittsburgh Compound‐B) and tau (18F‐flortaucipir) PET and provided a blood sample. Blood plasma analysis was performed to assess p‐tau 181 and Aβ42/40 ratio concentrations using the Quanterix Simoa platform. Multivariate linear regression models assessed the relationship between plasma concentrations and both global Aβ, atypical AD summary and regional tau PET measures. For each PET measure, two separate models were fit to assess the influence of each plasma biomarker separately, while the third model included all plasma measures to assess the combined influence of all biomarkers on PET uptake. To investigate differences in plasma concentrations between atypical AD phenotypes, we performed Mann‐Whitney’s test across the visual and language variants of AD.ResultPlasma Aβ42/40 ratio showed positive associations with tau PET uptake, specifically within precuneus, parietal and temporal regions and was not associated with Aβ PET. There was no relationship between plasma p‐tau 181 and Aβ or tau PET uptake. However, the model that included both plasma biomarkers showed that with the addition of p‐tau 181, all significant associations between Aβ42/40 ratio and regional tau uptake were weakened to trends. The Aβ42/40 ratio continued to show no relationship with global Aβ‐PET uptake and plasma p‐tau 181 showed no relationship to PET measures (Figure 1). Lastly, no significant difference in plasma Aβ42/40 and p‐tau 181 levels was noted between the visual and language variants of AD (Figure 2).ConclusionThe lack of associations between plasma concentrations and PET measures of Aβ and tau in this relatively advanced AD cohort may suggest that plasma concentrations have greater utility as diagnostic biomarkers of early AD as compared to advanced AD.

AbstractBackgroundTransactive response DNA‐binding protein of 43 kDa (TDP‐43) is implicated in numerous neurodegenerative diseases, including Alzheimer’s disease (AD). Immunohistochemically stained TDP‐43 is linked to faster rates of hippocampal atrophy (HA) in AD. However, it is unclear whether phosphorylated TDP‐43 (pTDP‐43) measured using an immunoassay is also associated with HA. We explored associations between pTDP‐43 levels, TDP‐43 immunohistochemistry, and annual rate of HA in AD. We hypothesized that higher pTDP‐43 levels would be associated with positive TDP‐43 immunohistochemistry, and faster rates of HA.MethodThirty‐seven cognitively impaired participants from the Mayo Clinic Alzheimer’s Disease Research Center underwent serial brain MRIs and brain autopsy. Hippocampal TDP‐43 was detected histochemically, and pTDP‐43 was quantified using biochemical assay. Hippocampal volumes were measured using the longitudinal pipeline in FreeSurfer‐7 and annualized HA rates were calculated. Linear regression analyzed the relationship between HA and pTDP‐43 biochemical assay levels. pTDP‐43 was categorized as positive (≥400) or negative (<400), and logistic regression tested the association of these levels with HA rates. The correlation between TDP‐43 immunohistochemistry status and biochemical assay levels was evaluated using descriptive statistics. All analyses were adjusted for potential confounders.ResultsThe mean age at death was 83.5 (±9.9) years with 49% females. Median scan interval was 3‐years. A negative relationship was observed between age at death and annual HA rate (β=‐3.1; 95% CI [‐5.3,‐0.9]; p=0.007), and a positive relationship was observed between pTDP‐43 level and annual HA rate (β=0.02; 95% CI [0.003,0.03]; p=0.015). A one‐unit increase in HA rate was associated with a 3% increase in the odds of being pTDP‐43 positive (≥400) (odds ratio=1.03; CI [1.007,1.05]; p=0.014). Cases that tested positive for Immunohistochemistry TDP‐43 had significantly higher mean pTDP‐43 immunoassay levels (1508 ± 2072 vs. 202 ± 131; p=0.008). More cases with positive IHC TDP‐43 surpassed the pTDP‐43 immunoassay cut‐off level of 400 vs the negative participants (p=0.003).ConclusionHigher pTDP‐43 levels in the hippocampus are linked to faster HA in AD and correlated with immunohistochemistry TDP‐43 positivity. These findings underscore the role of TDP‐43 in AD neurodegeneration. The biological and clinical implications of these observations necessitate further exploration in subsequent studies.

AbstractBackgroundPopulation dementia prevalence is traditionally estimated using cohort studies, surveys, routinely‐collected administrative data, and registries. Hospital Electronic Health Records (EHRs) are comprised of rich structured and unstructured (text) clinical data that are underutilised for this purpose.We aimed to develop a suite of algorithms using routinely‐collected EHR data to reliably identify cases of dementia, as a key step towards incorporating such data in prevalence estimation. Towards this, we developed a novel predictive framework integrating data‐science and biostatistical methods.MethodTraining data were sourced via the National Centre for Healthy Ageing (NCHA) Data Platform, a linked, curated, EHR‐derived data warehouse. Individuals within the platform catchment aged >60 years with confirmed dementia were identified through hospital specialist dementia clinics. A comparison group of individuals aged >60years with EHR records without dementia was recruited from the community.A panel of clinical experts (Neurology, Geriatric Medicine) informed variable and concept selection and guided data cleaning efforts within both streams. Algorithms were developed via two work‐streams; a traditional biostatistical approach to fit logistic regression models using structured data elements, and a data science stream used Natural Language Processing (NLP) to fit models to the unstructured (text) parts of the EHR, for the same individuals.ResultOf 568 individuals (362 with dementia), 434 had clinical notes available. In the data science stream using unstructured data, among a range of NLP derived models, the Random Forest classifier performed best in assigning dementia status, with Area Under the Curve (AUC) 0.95, specificity 90.2% and sensitivity 88.4%. In the biostatistics stream, 15 structured variables were included in the final model, covering demographics, health service attendance, medications, and ICD‐10 Codes, with AUC 0.94, specificity 85.9% and sensitivity 85.6%.ConclusionArtificial intelligence techniques applied to unstructured electronic health data and guided by human clinical expertise may be powerful tools in capturing the presence of dementia, at least comparable to traditional techniques using structured data, and conferring practical and scientific advantages for dementia prevalence estimation. Future validation is required in less crisply delineated real‐world settings.

AbstractBackgroundPeople living with dementia have high rates of emergency department (ED) use and hospitalizations. Identifying factors that influence acute care utilization is important. We examined the influence of physical activity levels on risk of hospitalization and emergency department (ED) use in a population of older people living with mild cognitive impairment (MCI) or dementia.MethodsWe included people with a diagnosis of MCI or dementia followed in Community Internal Medicine at Mayo Clinic, age 55 years and older, who had a clinic visit between June 1, 2019 and June 30, 2021 and had completed a social determinants of health questionnaire about physical activity levels. Physical activity was classified based on responses to questions about time spent exercising per week and rated as sufficiently active (≥150 minutes per week), insufficiently active (10‐140 minutes) or physically inactive (0 minutes). The risk of hospitalization and ED visits across physical activity levels was examined using quasi‐Poisson regression and presented as rate ratios (95% confidence interval (CI)), with adjustment for demographics, Charlson Comorbidity Index, marital status, living arrangement and body mass index.ResultsA total of 3090 persons living with MCI (n = 1308) or dementia (n = 1782) were included. People who were physically inactive were more likely to be older, female, not married, obese, have higher comorbid burden and a dementia diagnosis (p<0.001). Hospitalization and ED visit rates in those who were physically inactive were approximately double the rates for the sufficiently active group (p<0.001). Compared with those who were sufficiently active, people who were physically inactive had a significantly higher risk of hospitalization (rate ratio 1.73, 95% CI 1.30‐2.31) and ED visits (rate ratio 1.59, 95% CI 1.26‐2.01).ConclusionsPhysical inactivity in older people living with MCI or dementia is associated with an increased risk of ED visits and hospitalization. Targeted physical activity interventions could potentially impact on acute care utilization in this population. Our findings emphasize an area for public health promotion in people living with MCI or dementia.

AbstractBackgroundMidlife vascular risk factors are associated with an increased risk of dementia. However, the overall contribution of modifiable vascular risk factors in midlife and late‐life to dementia remains unclear. In this study, we quantified population attributable fractions, which account for risk factor prevalence and strength of relative risks, of incident dementia from vascular risk factors measured in midlife and early late‐life.MethodWe used 33 years of prospective cohort data in the Atherosclerosis Risk in Communities Study. Incident dementia (community surveillance and algorithmic diagnosis with expert clinical adjudication) was ascertained from baseline (1987‐1989) through December 31, 2020. We quantified population attributable fractions of incident dementia by age 80 from the combination of hypertension (systolic blood pressure ≥130, diastolic blood pressure ≥80, or use of medication), diabetes (fasting glucose ≥126, non‐fasting glucose ≥200, self‐reported physician diagnosis of diabetes, or use of medication for diabetes/insulin), and self‐reported current cigarette smoking (at least one risk factor versus none) measured at different ages (45‐54 [n = 7,731], 55‐64 [n = 12,273]; 65‐74 [n = 6,660]). We also computed attributable fractions stratified by sex, apolipoprotein ε4 genotype, and self‐reported race.ResultThe prevalence of having one or more of the vascular risk factors was 61.6% (95% CI: 60.5, 62.7%) at age 45‐54 and increased to 77.5% (95% CI: 76.5, 78.5%) by age 65‐74. Population attributable fractions of dementia increased from 22.7% (95% CI: 15.3, 30.1%) for vascular risk factors measured at age 45‐54 to 45.4% (95% CI: 32.5, 58.3%) for vascular risk factors assessed at age 65‐74 (see Figure). Attributable fractions were larger in females, participants without apolipoprotein ε4 alleles, and Black participants (Figure).ConclusionBetween 23% to 45% of incident dementia cases by age 80 were attributed to a combination of hypertension, diabetes, or current smoking up through age 74. Attributable fractions for the combined impact of vascular risk varied by key demographics. Public health strategies targeting modifiable vascular risk factors could reduce dementia risk and increase healthy lifespan.

AbstractBackgroundCorticobasal syndrome (CBS) is a progressive neurodegenerative syndrome that can result from several underlying neuropathologies, including Alzheimer’s disease (AD). Little is known about the utility of blood plasma measures to predict PET biomarker confirmed AD in CBS.MethodEighteen CBS patients (8 PET Aß+; 10 PET Aß‐) and 8 cognitively unimpaired Aß‐ individuals (CU) were recruited by the Neurodegenerative Research group from the Department of Neurology, Mayo Clinic, Rochester, MN, underwent Aß (Pittsburgh Compound‐B) and tau (18F‐flortaucipir) PET and provided a blood sample. Blood plasma analysis was performed to assess p‐tau 181, total tau, Aß 42/40, neurofilament light chain (NfL) and glial fibrillary acidic protein (GFAP) using the Quanterix Simoa platform. Plasma concentrations were compared between Aß‐, and Aß+ CBS and controls using Kruskal‐Wallis corrected for multiple comparisons using Dunn’s test, with effect size measured with area under the receiver operating characteristic curve (AUROC) analysis. Spearman correlations were calculated to assess relationships between plasma concentrations and Aß and tau PET uptake.ResultThe CBS Aß+ group showed reduced Aß42/40 ratio, with elevated p‐tau 181, GFAP and NfL plasma concentrations compared to CU, while the CBS Aß‐ group only showed elevated NfL concentration compared to CU (Figure 1). Both p‐tau 181 (AUROC=0.95) and GFAP (AUROC=0.91) plasma concentrations were able to differentiate CBS Aß‐ from CBS Aß+. Plasma p‐tau 181 and GFAP concentrations showed a positive correlation to both Aß and tau PET uptake, and the Aß42/40 ratio showed a negative correlation to Aß PET uptake (Figure 2). No correlations were observed between NfL and Aß and tau PET.ConclusionThis study supports the use of plasma p‐tau 181 and GFAP to detect AD in CBS, while NfL shows potential as a nonspecific disease biomarker of CBS regardless of underlying pathology. Plasma concentrations of these biomarkers may potentially be incorporated into clinical trials targeting CBS patients with underlying AD pathology.

AbstractBackgroundTransactive response DNA‐binding protein of 43 kDa (TDP‐43) is implicated in numerous neurodegenerative diseases, including Alzheimer’s disease (AD). Immunohistochemically stained TDP‐43 is linked to faster rates of hippocampal atrophy (HA) in AD. However, it is unclear whether phosphorylated TDP‐43 (pTDP‐43) measured using an immunoassay is also associated with HA. We explored associations between pTDP‐43 levels, TDP‐43 immunohistochemistry, and annual rate of HA in AD. We hypothesized that higher pTDP‐43 levels would be associated with positive TDP‐43 immunohistochemistry, and faster rates of HA.MethodThirty‐seven cognitively impaired participants from the Mayo Clinic Alzheimer’s Disease Research Center underwent serial brain MRIs and brain autopsy. Hippocampal TDP‐43 was detected histochemically, and pTDP‐43 was quantified using biochemical assay. Hippocampal volumes were measured using the longitudinal pipeline in FreeSurfer‐7 and annualized HA rates were calculated. Linear regression analyzed the relationship between HA and pTDP‐43 biochemical assay levels. pTDP‐43 was categorized as positive (=400) or negative (<400), and logistic regression tested the association of these levels with HA rates. The correlation between TDP‐43 immunohistochemistry status and biochemical assay levels was evaluated using descriptive statistics. All analyses were adjusted for potential confounders.ResultsThe mean age at death was 83.5 (±9.9) years with 49% females. Median scan interval was 3‐years. A negative relationship was observed between age at death and annual HA rate (ß=‐3.1; 95% CI [‐5.3,‐0.9]; p=0.007), and a positive relationship was observed between pTDP‐43 level and annual HA rate (ß=0.02; 95% CI [0.003,0.03]; p=0.015). A one‐unit increase in HA rate was associated with a 3% increase in the odds of being pTDP‐43 positive (=400) (odds ratio=1.03; CI [1.007,1.05]; p=0.014). Cases that tested positive for Immunohistochemistry TDP‐43 had significantly higher mean pTDP‐43 immunoassay levels (1508 ± 2072 vs. 202 ± 131; p=0.008). More cases with positive IHC TDP‐43 surpassed the pTDP‐43 immunoassay cut‐off level of 400 vs the negative participants (p=0.003).ConclusionHigher pTDP‐43 levels in the hippocampus are linked to faster HA in AD and correlated with immunohistochemistry TDP‐43 positivity. These findings underscore the role of TDP‐43 in AD neurodegeneration. The biological and clinical implications of these observations necessitate further exploration in subsequent studies.