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Neurofilament light chain (NfL) is a promising biomarker of active axonal injury and neuronal degeneration. We aimed to characterise cross-sectional and longitudinal plasma NfL measurements and determine the age at which NfL concentrations begin to differentiate between carriers of the presenilin 1 (PSEN1) E280A (Glu280Ala) mutation and age-matched non-carriers from the Colombian autosomal dominant Alzheimer's disease kindred. In this cross-sectional and longitudinal cohort study, members of the familial Alzheimer's disease Colombian kindred aged 8–75 years with no other neurological or health conditions were recruited from the Alzheimer's Prevention Initiative Registry at the University of Antioquia (Medellín, Colombia) between Aug 1, 1995, and Dec 15, 2018. We used a single molecule array immunoassay and log-transformed data to examine the relationship between plasma NfL concentrations and age, and establish the earliest age at which NfL concentrations begin to diverge between mutation carriers and non-carriers. We enrolled a cohort of 1070 PSEN1 E280A mutation carriers and 1074 non-carriers with baseline assessments; of these participants, longitudinal measures (with a mean follow-up of 6 years) were available for 242 mutation carriers and 262 non-carriers. Plasma NfL measurements increased with age in both groups (p<0·0001), and began to differentiate carriers from non-carriers when aged 22 years (22 years before the estimated median age at mild cognitive impairment onset of 44 years), although the ability of plasma NfL to discriminate between carriers and non-carriers only reached high sensitivity close to the age of clinical onset. Our findings further support the promise of plasma NfL as a biomarker of active neurodegeneration in the detection and tracking of Alzheimer's disease and the evaluation of disease-modifying therapies. National Institute on Aging, National Institute of Neurological Disorders and Stroke, Banner Alzheimer's Foundation, COLCIENCIAS, the Torsten Söderberg Foundation, the Swedish Research Council, the Swedish Alzheimer Foundation, the Swedish Brain Foundation, and the Swedish state under the ALF-agreement.

Background Tangle burden, one of the hallmarks of Alzheimer’s Disease, is thought to accumulate and spread throughout the brain in a distinctive pattern starting from the entorhinal cortex following Braak stages as characterized in neuropathological studies. Longitudinal tau PET allows us to investigate in vivo the tau spread in an individual and substantial heterogeneity has been observed in the pattern of tau spread. In this analysis, we examine the statistical power of tau PET measurements in tracking disease progression using data from the ADNI cohort. Method The tau PET measures used in this study included conventional SUVR measures as well as graph theory based network strength measures as we previously reported (Protas 2023). We calculate the slopes of both regional SUVR and strength in 41 MCI and 71 CU participants who had one year, and/or two year follow‐up. Statistical power was assessed as the sample size needed to detect a 25% reduction in the rate of tau accumulation, and the power analysis was done for CU and MCI participants separately. The optimal composite measures (from combining up to 6 FreeSurfer ROIs) were determined based on two criteria: 1) having minimal tau accumulation (calculated sample size > 10000) in the A‐ group to ensure we are looking at AD related accumulation, and 2) having the smallest sample size in the A+ group. Result Strength in amygdala, entorhinal, fusiform, rostral anterior cingulate, frontal pole and transverse temporal regions gives optimal sample sizes of 172 in A+ CU. In CU A+, SUVR in amygdala, lateral occipital and middle temporal produced an optimal sample size of 230. In MCI, for strength, the sample size is 193(A+) in an optimized region including cuneus, inferior temporal, postcentral, rostral anterior cingulate, and superior parietal. With SUVR the optimal region in MCI was entorhinal and middle temporal with a sample size of 263(A+). Conclusion The optimal composite regional SUVR/strength measure varies depending on the target population and typically combines both early and late Braak stages. Further study is needed to confirm this finding using larger datasets.

Background Tangle burden, one of the hallmarks of Alzheimer’s Disease, is thought to accumulate and spread throughout the brain in a distinctive pattern starting from the entorhinal cortex following Braak stages as characterized in neuropathological studies. Longitudinal tau PET allows us to investigate in vivo the tau spread in an individual and substantial heterogeneity has been observed in the pattern of tau spread. In this analysis, we examine the statistical power of tau PET measurements in tracking disease progression using data from the ADNI cohort. Method The tau PET measures used in this study included conventional SUVR measures as well as graph theory based network strength measures as we previously reported (Protas 2023). We calculate the slopes of both regional SUVR and strength in 41 MCI and 71 CU participants who had one year, and/or two year follow‐up. Statistical power was assessed as the sample size needed to detect a 25% reduction in the rate of tau accumulation, and the power analysis was done for CU and MCI participants separately. The optimal composite measures (from combining up to 6 FreeSurfer ROIs) were determined based on two criteria: 1) having minimal tau accumulation (calculated sample size > 10000) in the A‐ group to ensure we are looking at AD related accumulation, and 2) having the smallest sample size in the A+ group. Result Strength in amygdala, entorhinal, fusiform, rostral anterior cingulate, frontal pole and transverse temporal regions gives optimal sample sizes of 172 in A+ CU. In CU A+, SUVR in amygdala, lateral occipital and middle temporal produced an optimal sample size of 230. In MCI, for strength, the sample size is 193(A+) in an optimized region including cuneus, inferior temporal, postcentral, rostral anterior cingulate, and superior parietal. With SUVR the optimal region in MCI was entorhinal and middle temporal with a sample size of 263(A+). Conclusion The optimal composite regional SUVR/strength measure varies depending on the target population and typically combines both early and late Braak stages. Further study is needed to confirm this finding using larger datasets.

Background Different PET tracers can be used to measure neuritic amyloid plaque deposition in human brain. While mean cortical‐to‐cerebellar standard uptake value ratios (SUVRs) generated using different radiotracer methods can be transformed into Centiloid measurements to facilitate comparisons among the resulting amyloid plaque measurements, the level of agreement as measured by the strength of the correlation between the measurements does not change. In this study, paired 18F‐labeled (i.e., florbetapir, florbetaben, flutemetamol, and NAV4694) and 11C PiB radiotracers from the same research participants to test the hypothesis that the correlation between amyloid plaque measurements derived using different radiotracers can be improved by accounting for variability in white matter uptake. Method For all four 18F‐labeled radiotracers, paired 18F‐radiotracer and PiB‐derived PET images were downloaded from the Centiloid project (www.gaain.org/centiloid‐project). An in‐house image‐analysis pipeline was used to generate mean cortical‐to‐cerebellar SUVRs (MC‐SUVRs) from every PET image. Two approaches to account for white matter contributions to MC‐SUVR in the inherently low‐resolution PET images: 1) a linear regression approach to account for white matter contributions using a FreeSurfer‐defined UnsegmentedWhiteMatter SUVR; and 2) a regional spread function (RSF)‐based partial volume correction (PVC) technique. Pearson’s correlation coefficient was used to assess the agreement between F18 tracer‐based measure and PIB. Steiger’s test was used to determine whether accounting for white matter signal significantly improves agreement. Result Accounting for white matter signal improved the agreement between each 18F radiotracer‐based MC‐SUVR and the corresponding PiB‐derived MC‐SUVR. Using the regression approach, the correlations with PiB SUVRs were significantly improved for florbetapir (p<0.0001) and flutemetamol (p=0.03) but did not reach significance for florbetaben and NAV4694. For the PVC‐based approach, correlations with PiB SUVRs were significantly greater for all four 18F‐tracers (p<0.05). Conclusion The relationship between mean cortical measurement of amyloid plaque burden using different PET tracers can be significantly improved by accounting for differences in white matter uptake. Additional studies are needed to optimize this approach and account for any other potential confounds.

INTRODUCTION Longitudinal electronic health records (EHRs), “dementia” diagnostic codes, and genetic data from All of Us were used to see if there is a higher risk of dementia and an attenuated impact of apolipoprotein ε4 (APOE4) on Alzheimer's disease (AD) risk in Black and Hispanic/Latino groups. METHODS Participants included 9,784 Hispanic/Latinos, 14,937 Non‐Hispanic Blacks (NHB), and 60,388 Non‐Hispanic Whites (NHWs) ≥age 60 without an initial dementia diagnosis. RESULTS There was a significantly higher risk of developing a dementia diagnosis in Hispanic/Latino and NHB participants than NHW participants and comparably increased dementia hazard ratios in the Hispanic/Latino, NHB, and NHW APOE4 carriers than non‐carriers (hazard ratio [HR] [95% confidence interval CI] 1.54 [1.25–1.91], 1.25 [1.00–1.57], and 1.55 [1.40–1.72]). DISCUSSION Compared to NHW individuals, Hispanic/Latino and NHB individuals are at higher risk of developing a “dementia diagnosis” and comparably higher risk in APOE4 carriers than non‐carriers. Plasma AD biomarkers could clarify the proportion of dementia cases with and without AD and the impact of APOE4 on AD in these groups. Highlights Hispanic/Latino and non‐Hispanic Black individuals have a higher risk of all‐cause dementia. The impact of apolipoprotein ε4 (APOE4) on dementia risk was similar in the studied ethnoracial groups. Plasma biomarkers could inform the risk of Alzheimer's disease (AD) and impact of APOE4 in these groups.

Background We characterized primary age‐related tauopathy (PART) in individuals without elevated amyloid levels and explored whether levels of this protein differed by sex. Method Tau burden was quantified using PET imaging (18F‐flortaucipir) in cognitively unimpaired participants with amyloid burden below pathological levels (PET SUVR≤17.15, determined via GMM) from the Knight ADRC (n = 251, age: 45‐91) and ADNI (n = 458, age: 50‐94) cohorts. Statistical models were deployed to assess the influence of age, sex, race, and subthreshold amyloid burden on regional tau distribution. Result Significant positive associations between tau PET binding and age occurred in 7 (Knight ADRC1) and 13 (ADNI2) regions, with the largest effects seen in the putamen (t1=9.40; t2=10.50) and pallidum (t1=8.36; t2=7.39). Elevated tracer uptake in females was present in 19 (Knight ADRC1) and 12 (ADNI2) regions, but most prominently in the lateral occipital (t1=6.67; t2=4.31), pars orbitalis (t1=4.93; t2=4.75), rostral middle frontal (t1=4.73; t2=4.85), and the frontal pole (t1=4.59; t2=5.31). No statistically significant influence of race or subthreshold amyloid burden was observed for either cohort. Conclusion Tau PET is sensitive to age‐related tau accumulation and reveals a distribution pattern consistent with the neuropathological features of PART. Women consistently show higher tracer uptake across multiple brain regions suggesting previously reported increases in tau‐burden for females is not merely an implication of AD pathology, but may reflect a widespread, intrinsic phenomenon. Notable age‐associated increases in the basal ganglia are areas known for off target binding. A deeper understanding of the influences of age and sex on tracer uptake is essential for accurate interpretation of imaging data, particularly in distinguishing normal aging from disease related changes.

We characterized primary age-related tauopathy (PART) in individuals without elevated amyloid levels and explored whether levels of this protein differed by sex. Tau burden was quantified using PET imaging (18F-flortaucipir) in cognitively unimpaired participants with amyloid burden below pathological levels (PET SUVR≤17.15, determined via GMM) from the Knight ADRC (n = 251, age: 45-91) and ADNI (n = 458, age: 50-94) cohorts. Statistical models were deployed to assess the influence of age, sex, race, and subthreshold amyloid burden on regional tau distribution. Significant positive associations between tau PET binding and age occurred in 7 (Knight ADRC ) and 13 (ADNI ) regions, with the largest effects seen in the putamen (t =9.40; t =10.50) and pallidum (t =8.36; t =7.39). Elevated tracer uptake in females was present in 19 (Knight ADRC ) and 12 (ADNI ) regions, but most prominently in the lateral occipital (t =6.67; t =4.31), pars orbitalis (t =4.93; t =4.75), rostral middle frontal (t =4.73; t =4.85), and the frontal pole (t =4.59; t =5.31). No statistically significant influence of race or subthreshold amyloid burden was observed for either cohort. Tau PET is sensitive to age-related tau accumulation and reveals a distribution pattern consistent with the neuropathological features of PART. Women consistently show higher tracer uptake across multiple brain regions suggesting previously reported increases in tau-burden for females is not merely an implication of AD pathology, but may reflect a widespread, intrinsic phenomenon. Notable age-associated increases in the basal ganglia are areas known for off target binding. A deeper understanding of the influences of age and sex on tracer uptake is essential for accurate interpretation of imaging data, particularly in distinguishing normal aging from disease related changes.

Background We previously estimated the age of amyloid plaque onset at 28.2 years using florbetapir PET measurements of cerebral amyloid deposition in a cross‐sectional study of a convenience sample of PSEN1 E280A carriers and non‐carriers, ages 20‐56, recruited from the world's largest autosomal dominant Alzheimer’s disease (ADAD) cohort. Here we capitalized on longitudinal data from a different sample than used previously, to estimate the age of amyloid onset (EOA) in carriers who were cognitively unimpaired at baseline and examine differences associated with apolipoprotein ε4 (APOE4) status and between men and women. Method We analyzed baseline, 2‐year, and 5‐year florbetapir PET scans from 161 initially cognitively unimpairedPSEN1 E280A carriers (ages 30–56) enrolled in the Alzheimer’s Prevention Initiative (API) ADAD Colombia Trial of crenezumab vs placebo, in which crenezumab did not lower brain amyloid (NCT01998841). We included PSEN1 E280A carriers with at least 1 amyloid‐positive (A+) PET scan, using cortical‐to‐pontine standard‐uptake value ratios converted to centiloids (CL). Out of the 169 PSEN1 E280A carriers in the trial, 12 were A‐ and 149 were A+ at baseline, we excluded 1 PSEN1 E280A carrier who did not have A+ longitudinal data and 7 who did not have A+ scans at baseline or follow‐up. The Sample Iterative Local Approximation (SILA) model was applied to estimate EOA using a 20 CL threshold for positivity. We estimated amyloid chronicity and compared EOAs, adjusted for baseline age, between A+ male (n =68) and female (n =98) PSEN1 E280A carriers and between A+ PSEN1 E280A APOE4‐carriers (n =35) and non‐carriers (n =126). Result Median EOA was 26.1 years (SD ±7.89, range 1‐54) and estimates were normally distributed. PSEN1 E280A females had a mean EOA of 25.8 years which was not significantly different than the estimates in males (M=27.8 years). PSEN1 E280A APOE4 carriers had an EOA about 3 years younger (M=24.1) than APOE4 non‐carriers (M=27.3, p =0.03). Conclusion This study illustrates the ability to use longitudinal biomarker data to characterize the onset of biomarker changes and impact of putative disease modifiers like APOE4 in ADAD mutation carriers.

We previously estimated the age of amyloid plaque onset at 28.2 years using florbetapir PET measurements of cerebral amyloid deposition in a cross-sectional study of a convenience sample of PSEN1 E280A carriers and non-carriers, ages 20-56, recruited from the world's largest autosomal dominant Alzheimer's disease (ADAD) cohort. Here we capitalized on longitudinal data from a different sample than used previously, to estimate the age of amyloid onset (EOA) in carriers who were cognitively unimpaired at baseline and examine differences associated with apolipoprotein ε4 (APOE4) status and between men and women. We analyzed baseline, 2-year, and 5-year florbetapir PET scans from 161 initially cognitively unimpairedPSEN1 E280A carriers (ages 30-56) enrolled in the Alzheimer's Prevention Initiative (API) ADAD Colombia Trial of crenezumab vs placebo, in which crenezumab did not lower brain amyloid (NCT01998841). We included PSEN1 E280A carriers with at least 1 amyloid-positive (A+) PET scan, using cortical-to-pontine standard-uptake value ratios converted to centiloids (CL). Out of the 169 PSEN1 E280A carriers in the trial, 12 were A- and 149 were A+ at baseline, we excluded 1 PSEN1 E280A carrier who did not have A+ longitudinal data and 7 who did not have A+ scans at baseline or follow-up. The Sample Iterative Local Approximation (SILA) model was applied to estimate EOA using a 20 CL threshold for positivity. We estimated amyloid chronicity and compared EOAs, adjusted for baseline age, between A+ male (n =68) and female (n =98) PSEN1 E280A carriers and between A+ PSEN1 E280A APOE4-carriers (n =35) and non-carriers (n =126). Median EOA was 26.1 years (SD ±7.89, range 1-54) and estimates were normally distributed. PSEN1 E280A females had a mean EOA of 25.8 years which was not significantly different than the estimates in males (M=27.8 years). PSEN1 E280A APOE4 carriers had an EOA about 3 years younger (M=24.1) than APOE4 non-carriers (M=27.3, p =0.03). This study illustrates the ability to use longitudinal biomarker data to characterize the onset of biomarker changes and impact of putative disease modifiers like APOE4 in ADAD mutation carriers.

A cross-sectional study to establish whether a subject's cognitive state can be predicted based on regional values obtained from brain cortical maps of FDDNP Distribution Volume Ratio (DVR), which shows the pattern of beta amyloid and neurofibrillary binding, along with those of early summed FDDNP PET images (reflecting the pattern of perfusion) was performed. Dynamic FDDNP PET studies were performed in a group of 23 subjects (8 control (NL), 8 Mild Cognitive Impairment (MCI) and 7 Alzheimer's Disease (AD) subjects). FDDNP DVR images were mapped to the MR derived hemispheric cortical surface map warped into a common space. A set of Regions of Interest (ROI) values of FDDNP DVR and early summed FDDNP PET (0–6min post tracer injection), were thus calculated for each subject which along with the MMSE score were used to construct a linear mathematical model relating ROI values to MMSE. After the MMSE prediction models were developed, the models' predictive ability was tested in a non-overlapping set of 8 additional individuals, whose cognitive status was unknown to the investigators who constructed the predictive models. Among all possible subsets of ROIs, we found that the standard deviation of the predicted MMSE was 1.8 by using only DVR values from medial and lateral temporal and prefrontal regions plus the early summed FDDNP value in the posterior cingulate gyrus. The root mean square prediction error for the eight new subjects was 1.6. FDDNP scans reflect progressive neuropathology accumulation and can potentially be used to predict the cognitive state of an individual.