In recent years there has been an increasing interest in the neurodegenerative changes in the oldest old. To better understand the full spectrum of neurodegenerative processes in this unique population, we analyzed the brains of patients >90 years of age from the Duke Bryan Brain Bank. In particular, we focused our analysis on the association of limbic-predominant age-related TDP-43 encephalopathy neuropathological change (LATE-NC) with other co-pathologies as well as cognitive status. 98 patients with formalin-fixed tissue were included in our study. Approximately 70% of the cohort had normal cognition at the time of enrollment. Consensus meetings were held yearly to review and update the clinical diagnosis, based upon contemporaneous NIA-AA criteria. Participants maintained a diagnosis of cognitively normal if scores on testing were in expected range for their background and Clinical Dementia Rating (CDR) equaled 0, mild cognitive impairment (MCI) if memory scores were impaired relative to background and function was preserved with a CDR equaling 0.5, and dementia if memory and at least one other cognitive domain was affected, the deficits were slowly progressive over time, no medical cause was identified and there was an impairment in function with CDR≥0.5 [3]. LATE-NC was assessed based on LATE-NC working group recommendations [4]. Alzheimer’s disease neuropathologic change (ADNC), Lewy body disease (LBD), arteriolosclerosis, cerebral amyloid angiopathy (CAA), hippocampal sclerosis, primary age-related tauopathy (PART), and aging-related tau astrogliopathy (ARTAG) were assessed according to established guidelines.
Demographic, clinical, and neuropathologic characteristics of our cohort are summarized in Table 1, online resource. Of note, 41.5% of participants had dementia at time of death, 27.7% had MCI, and 30.8% were cognitively normal. APOE4 allele was present in 25.8% of participants. LATE-NC was present in 31.6% of participants; HS was present in 23.5% of participants. Cerebrovascular diseases were very common in this age group, particularly arteriolosclerosis; 76.5% of participants had arteriolosclerosis in at least one examined brain region. AD was also very common, with 63.2% of participants having intermediate to high level ADNC.
Clinical and neuropathologic characteristics of our cohort were stratified by LATE-NC status; p-values were adjusted for multiple comparisons (Tables 2–4, online resource). Participants with LATE-NC had a higher likelihood of having dementia (64.3% vs. 31.8%, p=0.021), but not carrying an APOE4 allele (39.3% vs. 20.0%, p=0.307) (Table 2, online resource). Participants with LATE-NC had a much higher likelihood of having HS (51.6% vs. 10.4%, p<0.001). LATE-NC was also associated with a higher likelihood of having arteriolosclerosis (93.5% vs. 68.7%, p=0.041) but not CAA, atherosclerosis, microinfarcts, ADNC, LBD, PART, or ARTAG (Tables 3–4, online resource).
We performed additional analysis of the association between arteriolosclerosis and LATE-NC by brain region, graded according to previous guidelines [5] (Table 5, online resource). Participants with LATE-NC were more likely to have arteriolosclerosis in the amygdala (77.4% vs. 37.3%, p=0.001), hippocampus (80.6% vs. 40.3%, p=0.001), and frontal lobe (77.4% vs. 31.3%, p<0.001). These results remained consistent after adjusting for age in a logistic regression model (Table 6, online resource).
We then performed multinomial logistic regression to study the association of cognitive status with potential predictors (Table 1). Due to sample size limitations, we constrained our analysis to ADNC and LATE-NC. Univariable models showed that participants with LATE-NC were more likely to develop dementia than patients without LATE-NC (OR: 5.4, 95% CI [1.6, 18.3], p=0.007). Likewise, patients with intermediate to high degree of ADNC were more likely to develop MCI and dementia than patients with none to low degree of ADNC (OR: 5.0, 95% CI [1.6, 15.6], p=0.006 for MCI, and OR: 14.4, 95% CI [4.4, 47.5], p<0.001 for dementia). We next utilized multivariable analysis to determine whether LATE-NC was an independent predictor of dementia after controlling for the presence of ADNC. We found that patients with LATE-NC were more likely to develop dementia than patients without LATE-NC, even after controlling for levels of ADNC (OR: 8.7, 95% CI [2.0, 37.7], p=.0004).
Table 1:
Univariable analysis | Multivariable analysis | ||||
---|---|---|---|---|---|
Cognitive Status | Predictors | OR (95% CI) | p-value | OR (95% CI) | p-value |
Dementia | LATE-NC: Yes vs No |
5.36 [1.57, 18.31] | 0.007 | 8.66 [1.99, 37.65] | 0.004 |
MCI | LATE-NC: Yes vs No |
1.88 [0.47, 7.57] | 0.377 | 2.52 [0.58, 11.06] | 0.220 |
Dementia | ADNC: Intermediate/High vs None/Low |
14.44 [4.39, 47.53] | <0.001 | 19.82 [5.21, 75.35] | <0.001 |
MCI | ADNC: Intermediate/High vs None/Low |
4.96 [1.57, 15.61] | 0.006 | 5.49 [1.7, 17.76] | 0.005 |
Our study showed a high prevalence of LATE-NC in the oldest old, consistent with previous large autopsy cohorts; similarly, we observed strong associations between LATE-NC and HS and arteriolosclerosis [1, 2]. Our study, unlike some others in the literature, did not find an association between LATE-NC and ADNC or APOE4 genotype. However, given our relatively small study population, lack of association between LATE-NC, ADNC, and other vascular pathologies could be attributed to insufficient statistical power. Finally, our multinomial logistic regression model suggests that LATE-NC is a major contributor to dementia independent of ADNC. Overall, our study demonstrates that LATE-NC appears to be a distinct cause of dementia from AD strongly associated with HS and arteriolosclerosis in the oldest old. Future research is required to understand the pathogenic pathways linking these common co-pathologies in this unique population.
Supplementary Material
Acknowledgements
The authors are indebted to the generous contributions made by study participants and families of the Joseph and Kathleen Bryan Alzheimer’s Disease Research Center at Duke University. We are grateful to K. Welsh-Bohmer, B. Plassman, and J. Burke for their critical reading of the manuscript, and C. Hulette for her leadership of the Duke Bryan Brain Bank. The study was supported by the Duke University School of Medicine Core Voucher Program.
We wish to acknowledge support from the Biostatistics, Epidemiology and Research Design (BERD) Methods Core funded through Grant Award Number UL1TR002553 from the National Center for Advancing Translational Sciences (NCATS), a component of the National Institutes of Health (NIH). The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH.
Footnotes
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Contributor Information
William T. Harrison, Department of Pathology, Duke University Medical Center; Present Address: Department of Pathology, Wake Forest School of Medicine.
Jay B. Lusk, Department of Pathology, Duke University Medical Center.
Beiyu Liu, Department of Biostatistics and Bioinformatics, Duke University Medical Center.
John F. Ervin, Department of Neurology, Duke University Medical Center
Kim G. Johnson, Department of Psychiatry and Department of Neurology, Duke University Medical Center
Cynthia L. Green, Department of Biostatistics and Bioinformatics, Duke University Medical Center
Shih-Hsiu J. Wang, Department of Pathology and Department of Neurology, Duke University Medical Center 214MA Davison Bldg, 40 Duke Medicine Circle, Durham NC 27710, United States of America
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