Mayo Clinic Jacksonville brain bank database was searched for AD and NFTD that had been reviewed and diagnosed by a single neuropathologist (DWD). Classification of AD subtypes using a mathematical algorithm of hippocampal and cortical NFTs has been previously described in detail [30 (link)]. The following exclusion criteria were used for AD: Braak NFT stage ≤ IV, absence of paraffin blocks for immunohistochemical studies, and presence of hippocampal sclerosis (initial sample size HpSp = 97, typical = 665, LP = 127). Cases were further required to have antemortem clinical information available. Given the retrospective nature of this study and the absence of standardized clinical assessments, the sum of both HpSp and LP cases was used to set the number of typical AD for clinical history review. AD cases with three or more Mini-Mental State Exam (MMSE) scores [11 (link)] were included in the final AD subtype cohort (final sample size HpSp = 17, Typical = 52, LP = 19). Clinical parameters and availability are described later in “Materials and methods”.
Neurofibrillary tangle-predominant dementia was objectively diagnosed with a mathematical algorithm that used hippocampal and cortical NFT and SP counts in cases with a gestalt diagnosis of NFTD to set standards. NFTD had to have SP counts of ≤30 according to the minimum level described by Khachaturian criteria [24 (link)] (seeFig. 1 ). Of the 62 NFTD cases identified, we excluded cases with concomitant tau pathology (e.g., corticobasal degeneration, progressive supranuclear palsy, Guam parkinsonism–dementia complex, diffuse argyrophilic grain disease) to avoid confounding immunohistochemical differences in tau burden that may not accurately reflect differences among AD subtypes and when compared with NFTD. Other concurrent postmortem diagnoses that could interfere with data interpretation included frontotemporal lobar degeneration, hippocampal sclerosis, amyotrophic lateral sclerosis, and multiple system atrophy. Of the remaining 20 cases, 1 was excluded because of absence of dementia in the clinical history.
There were two primary sources for study cases: the State of Florida Alzheimer’s Disease Initiative (ADI) (n = 53; 50 %) [4 (link)] and the Mayo Clinic Jacksonville Memory Disorder Clinic (n = 30; 28 %). Of the remaining cases (n = 22), 2 (2 %) were from the Einstein Aging Study (P01 AG03949), 2 (2 %) from the CurePSP Brain Bank, 2 (2 %) from Mayo Clinic Jacksonville Movement Disorder Clinic, 2 (2 %) from the Florida Alzheimer’s Disease Research Center (P50 AG25711), and 15 (14 %) from various referral sources.
Standardized neuropathologic assessment included gross and microscopic evaluation. Senile plaque (SP, 10× objective) and NFT densities (40× objective), using thioflavin-S fluorescent microscopy, were assessed on an Olympus BH2 fluorescent microscope [36 (link)–38 (link)]. SP counts were truncated at 50 (i.e., twice the number needed for neuropathologic AD diagnosis [24 (link)]) and included primitive, neuritic, and cored type of plaques. NFT counts used in the classification algorithm include intracellular and extracellular tangles from two hippocampal sectors (CA1 and subiculum) and three association cortices (middle frontal, inferior parietal, and superior temporal). The detailed algorithm methods have been previously described, including steps, sample sizes, and median values [30 (link)]. To classify NFTD cases objectively, a Microsoft Excel 2003 function was designed. Three algorithm requirements were written to classify NFTD: (1) the hippocampal and average hippocampal NFT densities had to be greater than the minimum NFT found in cases with a gestalt diagnosis of NFTD (minimum of 9 for CA2/3, 5 for CA1, 2 for subiculum, and 10 for hippocampal average); (2) at least three of the cortical and average cortical NFT densities had to be less than the maximum NFT found in cases with a subjective diagnosis of NFTD (maximum of 35 for superior temporal, 4 for inferior parietal, 6 for mid-frontal, and 14 for average cortical); and (3) the SP densities in the association cortex had to be less than or equal to the cutoff according to Khachaturian criteria [24 (link)] (≤30 for middle frontal, inferior parietal, and superior temporal cortices).
Five-micrometer-thick sections of formalin-fixed, paraffin-embedded tissue from the middle frontal gyrus were stained with H&E. Additional serial sections were processed using a DAKO Autostainer (Universal Staining System Carpinteria, CA, USA) using the chromogen 3,3′-diaminobenzidine and immunostained for phospho-tau detecting early neuritic and NFT pathology, including pretangles (CP13, mouse IgG1, 1:1,000, generous gift of Peter Davies, Albert Einstein College of Medicine, Bronx, NY, USA), an antibody to a conformational epitope in NFTs detecting late-stage tangles (Ab39, monoclonal, 1:350, generous gift from Shu-Hui Yen, Mayo Clinic, Jacksonville, FL, USA) [42 (link)], pan-Aβ (33.1.1, 1:1,000, human Aβ1-16 specific) [25 (link)], and Aβ40 (13.1.1, 1:1,000, human Aβ-specific) [25 (link)]. Slides were counterstained with hematoxylin after immunostaining. Stained slides were digitally scanned at 20× on the ScanScope XT (Aperio, Vista, CA, USA) and viewed/annotated using ImageScope v10.2 software (Aperio, Vista, CA, USA). The regions of interest (ROI) for each case were initially drawn on the H&E section and the ROI was transferred to the immunostained slides. Slides were edited if there were any differences between the serial sections. The gray–white matter boundary was evaluated at 20× and marked with arrow tools along a parallel gyrus, neither including the gyral ridge nor depth. Two custom algorithms were designed to specifically detect the optical density of the tau and amyloid [8 (link)]. The result is a percentage burden that reflects the amount of stained pathology out of the total area that was stained.
Immunohistochemical methods for assessment of TAR DNA binding protein 43 (TDP-43) pathology [2 (link)] and Lewy body pathology [38 (link)] were performed as previously described. Non-Alzheimer pathologies are reported as frequencies inTable 1 . Cerebral amyloid angiopathy (CAA) was graded on a four-point semi-quantitative scale: none, mild, moderate, and severe using thioflavin-S microscopy. The severity of NFTs and SPs in the molecular layer of the dentate fascia and basal ganglia was similarly graded on a four-point semi-quantitative scale by retrospectively reviewing neuropathology reports from each case. Vascular calcification was assessed using a four-point semi-quantitative severity scale in the basal ganglia on a routine hematoxylin and eosin (H&E) stained section by extracting information from neuropathology reports. Ab39 immunohistochemistry in the midbrain and pons was assessed using a four-point scale. Midbrain severity was considered: mild if Ab39-immunopositive neurites and<10 NFTs were confined to the substantia nigra; moderate if significant neuritic and extracellular NFT pathology was found in the substantia nigra, with widespread pathology found in the perinigral and periaqueductal gray; severe if neuritic, extracellular NFT were found in substantia nigra, and perinigral and periaqueductal gray. The locus coeruleus and raphe nuclei were considered as: mild, if NFTs were disproportionate to neurons present and contained no extracellular NFTs; moderate, if of the neurons affected a greater proportion were NFTs; severe, if extracelluar NFTs were greater than the number of NFTs. The severity scores were used to determine the absence (sum of none and mild semi-quantitative severity scores) versus the presence (sum of moderate and severe scores) of pathology. The presence of any NFTs in the basal ganglia, whether mild or moderate, was sufficient to be summed as present given the scarcity of NFT pathology.
Clinic reports were reviewed blind to pathologic diagnosis. This study had Mayo Clinic Institutional Review Board approval. Clinical parameters included: education, age of onset, disease duration, and MMSE scores and dates. Elapsed years between age at death and age of onset were used to calculate disease duration. Three or more MMSE testing dates and scores were required to calculate longitudinal decline as a slope, setting MMSE score as the dependent variable and elapsed years between testing and death as the independent variable. Elapsed time between age of onset or death was not a factor used for exclusion of data in longitudinal decline; data available anywhere along the clinical course were included for evaluation. Antemortem clinical diagnoses compatible with dementia included AD, aphasia, Binswanger’s disease, corticobasal syndrome, Creutzfeldt-Jakob disease, dementia with Lewy bodies, frontotemporal dementia, normal pressure hydrocephalus, Parkinson’s disease dementia, Pick’s disease, progressive supranuclear palsy, and semantic dementia. The available clinical parameters in HpSp, typical, LP, and NFTD, respectively, are: education [11 (65 %), 50 (95 %), 17 (89 %), 11 (61 %)], age of onset and disease duration [16 (94 %), 45 (87 %), 14 (74 %), 16 (89 %)], MMSE initial [10 (59 %), 25 (48 %), 8 (42 %), 7 (39 %)], MMSE final [10 (59 %), 16 (31 %), 11 (58 %), 6(33 %)], and longitudinal MMSE [17 (100 %), 52 (100 %), 19 (100 %), 4 (22 %)]. MAPT and APOE genotyping was available for all cases, except one typical AD which did not have frozen tissue available for genotyping. Genomic DNA was extracted from frozen brain tissue according to previously described methods. Each sample was genotyped for MAPT H1/H2 (SNP rs1052553 A/G, A = H1, G = H2) and APOE alleles (SNP rs429358 C/T and rs7412 C/T) using ABI on-demand Taqman assays (Applied Biosystems, Life Technologies Corporation, Carlsbad, CA, USA) and analyzed with SDS 2.2.2 software (also from Applied Biosystems).
SigmaPlot (Ver. 11, San Jose, CA, USA) was used to analyze all statistical data and create graphs. Group comparisons of continuous variables were performed using Kruskal–Wallis one-way analysis of variance on ranks, and pairwise comparisons were performed with the Mann–Whitney rank sum test. Analyses of categorical data were performed using a χ2 test to determine whether the proportions of observations varied between the groups and between the comparison groups. Each group was evaluated for interrelationships of the hippocampal and cortical NFT using a Spearman correlation. A multiple logistic regression model was constructed to control for APOE ε4 allele status when examining pan-Aβ differences between HpSp and LP.
Neurofibrillary tangle-predominant dementia was objectively diagnosed with a mathematical algorithm that used hippocampal and cortical NFT and SP counts in cases with a gestalt diagnosis of NFTD to set standards. NFTD had to have SP counts of ≤30 according to the minimum level described by Khachaturian criteria [24 (link)] (see
There were two primary sources for study cases: the State of Florida Alzheimer’s Disease Initiative (ADI) (n = 53; 50 %) [4 (link)] and the Mayo Clinic Jacksonville Memory Disorder Clinic (n = 30; 28 %). Of the remaining cases (n = 22), 2 (2 %) were from the Einstein Aging Study (P01 AG03949), 2 (2 %) from the CurePSP Brain Bank, 2 (2 %) from Mayo Clinic Jacksonville Movement Disorder Clinic, 2 (2 %) from the Florida Alzheimer’s Disease Research Center (P50 AG25711), and 15 (14 %) from various referral sources.
Standardized neuropathologic assessment included gross and microscopic evaluation. Senile plaque (SP, 10× objective) and NFT densities (40× objective), using thioflavin-S fluorescent microscopy, were assessed on an Olympus BH2 fluorescent microscope [36 (link)–38 (link)]. SP counts were truncated at 50 (i.e., twice the number needed for neuropathologic AD diagnosis [24 (link)]) and included primitive, neuritic, and cored type of plaques. NFT counts used in the classification algorithm include intracellular and extracellular tangles from two hippocampal sectors (CA1 and subiculum) and three association cortices (middle frontal, inferior parietal, and superior temporal). The detailed algorithm methods have been previously described, including steps, sample sizes, and median values [30 (link)]. To classify NFTD cases objectively, a Microsoft Excel 2003 function was designed. Three algorithm requirements were written to classify NFTD: (1) the hippocampal and average hippocampal NFT densities had to be greater than the minimum NFT found in cases with a gestalt diagnosis of NFTD (minimum of 9 for CA2/3, 5 for CA1, 2 for subiculum, and 10 for hippocampal average); (2) at least three of the cortical and average cortical NFT densities had to be less than the maximum NFT found in cases with a subjective diagnosis of NFTD (maximum of 35 for superior temporal, 4 for inferior parietal, 6 for mid-frontal, and 14 for average cortical); and (3) the SP densities in the association cortex had to be less than or equal to the cutoff according to Khachaturian criteria [24 (link)] (≤30 for middle frontal, inferior parietal, and superior temporal cortices).
Five-micrometer-thick sections of formalin-fixed, paraffin-embedded tissue from the middle frontal gyrus were stained with H&E. Additional serial sections were processed using a DAKO Autostainer (Universal Staining System Carpinteria, CA, USA) using the chromogen 3,3′-diaminobenzidine and immunostained for phospho-tau detecting early neuritic and NFT pathology, including pretangles (CP13, mouse IgG1, 1:1,000, generous gift of Peter Davies, Albert Einstein College of Medicine, Bronx, NY, USA), an antibody to a conformational epitope in NFTs detecting late-stage tangles (Ab39, monoclonal, 1:350, generous gift from Shu-Hui Yen, Mayo Clinic, Jacksonville, FL, USA) [42 (link)], pan-Aβ (33.1.1, 1:1,000, human Aβ1-16 specific) [25 (link)], and Aβ40 (13.1.1, 1:1,000, human Aβ-specific) [25 (link)]. Slides were counterstained with hematoxylin after immunostaining. Stained slides were digitally scanned at 20× on the ScanScope XT (Aperio, Vista, CA, USA) and viewed/annotated using ImageScope v10.2 software (Aperio, Vista, CA, USA). The regions of interest (ROI) for each case were initially drawn on the H&E section and the ROI was transferred to the immunostained slides. Slides were edited if there were any differences between the serial sections. The gray–white matter boundary was evaluated at 20× and marked with arrow tools along a parallel gyrus, neither including the gyral ridge nor depth. Two custom algorithms were designed to specifically detect the optical density of the tau and amyloid [8 (link)]. The result is a percentage burden that reflects the amount of stained pathology out of the total area that was stained.
Immunohistochemical methods for assessment of TAR DNA binding protein 43 (TDP-43) pathology [2 (link)] and Lewy body pathology [38 (link)] were performed as previously described. Non-Alzheimer pathologies are reported as frequencies in
Clinic reports were reviewed blind to pathologic diagnosis. This study had Mayo Clinic Institutional Review Board approval. Clinical parameters included: education, age of onset, disease duration, and MMSE scores and dates. Elapsed years between age at death and age of onset were used to calculate disease duration. Three or more MMSE testing dates and scores were required to calculate longitudinal decline as a slope, setting MMSE score as the dependent variable and elapsed years between testing and death as the independent variable. Elapsed time between age of onset or death was not a factor used for exclusion of data in longitudinal decline; data available anywhere along the clinical course were included for evaluation. Antemortem clinical diagnoses compatible with dementia included AD, aphasia, Binswanger’s disease, corticobasal syndrome, Creutzfeldt-Jakob disease, dementia with Lewy bodies, frontotemporal dementia, normal pressure hydrocephalus, Parkinson’s disease dementia, Pick’s disease, progressive supranuclear palsy, and semantic dementia. The available clinical parameters in HpSp, typical, LP, and NFTD, respectively, are: education [11 (65 %), 50 (95 %), 17 (89 %), 11 (61 %)], age of onset and disease duration [16 (94 %), 45 (87 %), 14 (74 %), 16 (89 %)], MMSE initial [10 (59 %), 25 (48 %), 8 (42 %), 7 (39 %)], MMSE final [10 (59 %), 16 (31 %), 11 (58 %), 6(33 %)], and longitudinal MMSE [17 (100 %), 52 (100 %), 19 (100 %), 4 (22 %)]. MAPT and APOE genotyping was available for all cases, except one typical AD which did not have frozen tissue available for genotyping. Genomic DNA was extracted from frozen brain tissue according to previously described methods. Each sample was genotyped for MAPT H1/H2 (SNP rs1052553 A/G, A = H1, G = H2) and APOE alleles (SNP rs429358 C/T and rs7412 C/T) using ABI on-demand Taqman assays (Applied Biosystems, Life Technologies Corporation, Carlsbad, CA, USA) and analyzed with SDS 2.2.2 software (also from Applied Biosystems).
SigmaPlot (Ver. 11, San Jose, CA, USA) was used to analyze all statistical data and create graphs. Group comparisons of continuous variables were performed using Kruskal–Wallis one-way analysis of variance on ranks, and pairwise comparisons were performed with the Mann–Whitney rank sum test. Analyses of categorical data were performed using a χ2 test to determine whether the proportions of observations varied between the groups and between the comparison groups. Each group was evaluated for interrelationships of the hippocampal and cortical NFT using a Spearman correlation. A multiple logistic regression model was constructed to control for APOE ε4 allele status when examining pan-Aβ differences between HpSp and LP.
