Data and code from: Clinical impairment in the most advanced pathological stage of Alzheimer’s disease correlates with quantitative neurofibrillary tangle measures and co-pathologies
Data files
Nov 03, 2026 version files 1.47 MB
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highadnc_paper_data.csv
95.77 KB
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highadnc_paper.ipynb
1.36 MB
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README.md
11.17 KB
Abstract
Objective. The current diagnostic criteria for Alzheimer’s disease define discrete biological stages of the disease. While discrete staging simplifies diagnosis, treatment, and participant stratification in clinical trials, discrete disease categories may mask a considerable range of variability in disease severity, even at what is pathologically considered “end stage" disease. We aimed to characterize the contributions of phosphorylated tau neurofibrillary tangle (NFT) pathology and co-pathologies to variability in dementia severity in brain donors diagnosed with a high level of Alzheimer’s Disease neuropathological change (ADNC).
Methods. In a cohort of 197 donors with a primary diagnosis of high ADNC, we computed association between quantitative measures of neocortical NFT load and an informant-based measure of clinical impairment. We examined the impact of co-pathologies on residuals from the canonical pathology-impairment relationship estimated in donors without known significant co-pathologies.
Results. We found a positive association between cortical NFT load and the clinical impairment measure. Clinical impairment was significantly greater than expected for NFT load in presence of co-pathologies, including limbic-predominant age-related TDP-43 encephalopathy (Stage 2 or 3) and cerebrovascular disease. In donors with limbic and neocortical subtypes of Lewy pathology, neocortical -synuclein burden was associated with greater than expected clinical impairment.
Interpretation. Categorizing high ADNC as a single homogeneous category masks a considerable range of pathological severity that contributes to clinical impairment. Complementing discrete staging with continuous measures of tau pathology and probabilistic measures of co-pathologies will improve clinicopathologic studies needed for a precision approach to diagnosis and treatment.
Data and analysis code accompanying the paper.
highadnc_paper_data.csv: de-identified dataset, one row per donor (n = 197)highadnc_paper.ipynb: analysis notebook that reproduces the figures and tables of the paper (Figures 1–5) and supplement (Supplemental Figures S1, S3–S8 and Tables S1–S4) from the CSV
Cohort
Donors who meet all of the following criteria:
- high Alzheimer's disease neuropathologic change (ADNC)
- no FTLD or non-AD tauopathy diagnosis
- quantitative NFT measurements available
- last Dementia Severity Rating Scale (DSRS) assessment within 5 years of autopsy (primary cohort)
The one-year cohort is the subset with delta_t <= 1.25.
De-identification
- Donor identifiers are replaced by random
SubjectIDs. - Dates are replaced by intervals (
delta_t). - Following HIPAA Safe Harbor,
AgeatDeathis top-coded at 90 years (21 donors). Age is a covariate in the
adjusted models, so some statistics differ slightly from those in the paper: no p-value crosses 0.05 in the main
analyses, and one supplemental comparison (CAA None vs. Moderate, one-year cohort; Supplemental Figure S8 and
Table S2) moves from p = 0.049 to p = 0.051. All other variables are exact: with exact ages, the notebook reproduces
every statistic and table in the paper and supplement. - Following request from DataDryad.org, race/ethnicity identifiers that are outliers were masked
Data dictionary
| Column | Description |
|---|---|
SubjectID |
Random donor identifier |
delta_t |
Years from the last DSRS assessment to autopsy |
FRSTotal |
Total DSRS score at the last assessment (column named after the FRS form in the source database) |
FRSTime |
DSRS time: years since the SILA-estimated crossing of the abnormal DSRS threshold, projected to time of death. Computed with the SILA algorithm in R from all longitudinal DSRS scores |
AgeatDeath |
Age at death in years, top-coded at 90 |
estdtt0 |
SILA-estimated DSRS time at the last DSRS assessment used by SILA (Supplemental Figure S1) |
TestToAutopsy |
Years from that assessment to death (FRSTime = estdtt0 + TestToAutopsy) |
Sex, Race, Education |
Demographics; education in years |
ClinicalDx1, ClinicalDx2 |
Final clinical diagnoses |
ABeta, Braak03, Braak06, CERAD |
NIA-AA A score (Thal phase), B score (Braak 0–3), Braak NFT stage (0–6), C score (CERAD) |
NPDx1–NPDx4 |
Primary to quaternary neuropathological diagnoses |
DLBType |
Lewy body disease subtype (when present) |
LATE_stage |
LATE-NC stage (0–3), derived from regional TDP-43 ratings |
CAA |
Cerebral amyloid angiopathy severity (0–3) |
ArteriolosclerosisOccipitalWM |
Occipital white-matter arteriolosclerosis severity |
<region>aSyn, <region>TDP43, <region>Tau |
Semi-quantitative regional ratings (0, 0.5 = rare, 1–3). Region prefixes follow the CNDR sampling protocol, e.g. Amyg = amygdala, DG = dentate gyrus, CS = CA1/subiculum, EC = entorhinal cortex, MF = medial frontal cortex, Ang = angular gyrus, SMT = superior temporal cortex, Cing = cingulate, OC = occipital cortex, SN = substantia nigra, LC = locus coeruleus, Med = medulla, CB = cerebellum |
cortical_dNFT, hippo_dNFT |
Mean quantitative NFT density (cells/mm³) over cortical (ANG, MFC, STC) and hippocampal (CA1, SUB) measurements |
dNFT_ANG, dNFT_CA1, dNFT_MFC, dNFT_STC, dNFT_SUB |
Mean NFT density per region: angular gyrus, CA1, medial frontal cortex, superior temporal cortex, subiculum |
Running the notebook
Requires Python ≥ 3.12 with numpy, pandas, scipy, statsmodels, matplotlib, seaborn, statannotations,
forestplot, pingouin and tableone. Figures are written to the working directory as PNG files.
Human subjects data
Penn ADRC participants provide broad consent for data sharing. All identifiers, including age greater than 89 have been removed from the dataset.
