Petersen, Ronald C.

AbstractBackgroundGiven that a proportion of mild cognitive impaired (MCI) individuals are amyloid negative and have cerebrovascular disease (CVD), there is a need for biomarkers that can complement AD biomarkers (amyloid and neurodegeneration) for the prediction of future cognitive decline in MCI. The aim of our study was to determine the utility of diffusion tensor imaging (DTI) as a CVD marker for prediction of future cognitive decline in individuals with MCI in conjunction with amyloidosis and neurodegeneration.MethodWe identified 132 MCI individuals ≥60 years old (55 % were amyloid positive and 59 % were male) from the population based sample of Mayo Clinic Study of Aging (MCSA) who had structural MRI, DTI, amyloid PET, and at least one clinical follow‐up after the imaging at the time of their clinical visit as MCI. All imaging scans were acquired and processed using in house developed protocols and pipelines. We used fractional anisotropy in the genu of corpus callosum (FAGCC), as a CVD marker, education/occupation score as a resilience variable, and global cognitive z‐score as the cognitive outcome. Using mixed effect models for cognitive decline, we evaluated three models to test the prognostic ability of (i) DTI marker alone, (ii) DTI marker and amyloid PET, (iii) DTI marker, amyloid PET, and neurodegeneration (cortical thickness). The final parsimonious models included the significant interactions of potential predictors with time, main effects nested within those interactions, and other significant main effects. Since the adjustment for conventional CVD measures like WMH was important to determine the utility DTI over traditional measures, we further assessed the value of FAGCC in conjunction with white matter hyper intensity (WMH).ResultWe found: i) FAGCC was associated with cognitive decline after adjusting for amyloid PET neurodegeneration (Figure 1 and Table 1) and ii) FAGCC was associated with cognitive decline even after controlling for WMH and WMH was no longer significant after accounting for FAGCC (Table 2).ConclusionDTI has significant utility for prediction of cognitive decline in MCI from population‐based samples and will provide complementary information to AD biomarkers.

AbstractBackgroundCharacterization of the changes that occur throughout the transition from asymptomatic to symptomatic for those with familial frontotemporal dementia is essential for planning future disease‐modifying trials.MethodWe identified four related microtubule associated protein tau (MAPT) N279K mutation carriers who have phenoconverted from the asymptomatic to the symptomatic state. Key features were as follows: Case 1 (female) initial exam age 36, onset age 44, current age 49; Case 2 (male) initial exam age 39, onset age 43, current age 48; Case 3 (female) initial exam age 41, onset age 47, current age 53; Case 4 (male) initial exam age 36, onset age 37, current age 46. MRI and neuropsychological data were analyzed.ResultStructural MRI was analyzed using Tensor Based Morphometry Symmetric Diffeomorphic Image Normalization (TBM‐SyN) methodology to quantify lobar volumes, which was then analyzed using a Mixed Effects Model to determine the annual change in lobar volumes. The phenoconverters had a greater estimated annual rate of cortical atrophy in temporal (p <0.001), frontal (p <0.01) and parietal (p <0.05) lobes compared to non‐carriers, with the rate of annual change in symptomatic individuals double that of asymptomatic individuals. Annual change in the parietal and occipital lobes was also analyzed and found to be similar to healthy controls of a similar age. Raw scores on neuropsychological measures were converted to z‐scores for analysis. Category fluency, letter fluency, and Trails A and B were all impaired (z‐score <‐1.5) following phenoconversion, but performance on these and other measures was variable across these four in the late asymptomatic state.ConclusionFor each of the four phenoconverters, structural MRI changes preceded symptom onset, whereas neuropsychological performance was more variable and occurred in closer proximity to the time of phenoconversion. Decrements in verbal fluency and cognitive flexibility were observed earlier than other cognitive domains. These findings suggest that changes in frontal and temporal volume on MRI precede symptom onset, with regional brain volumes most consistently tracking the evolution from the asymptomatic to symptomatic state in N279K MAPT mutation carriers. Supported by: AG063911, AG045390, NS092089, AG016976, AG016574, AG062677.

AbstractBackgroundDementia with Lewy Bodies (DLB) is the second most common cause of degenerative dementia but the evolution of the core clinical features of DLB has not been clearly defined. The objective of this study was to identify the temporal relationship between each core feature from the estimated onset of cognitive symptoms.MethodParticipants include 485 patients with probable DLB who were evaluated at the Mayo Clinic Alzheimer Disease Research Center with neurologic and neurocognitive assessment. Each core clinical feature was compared using Kaplan‐Meier (KM) curves with the sentinel event representing age at which each core feature developed, or time from estimated cognitive onset.ResultThe sample was disproportionately male (76%). Patients were followed longitudinally, with 84% followed until death. Mean MMSE at the last evaluation was 18.3 ± 7.8. KM curves for age of onset and cumulative incidence for DLB core features as time from cognitive onset are provided. REM‐sleep behavior disorder (RBD) developed earliest and in 77% of our sample with a median KM survival estimate of 69 years (95% CI: 67.6–70.8). RBD was present in greater than 50% of patients at cognitive symptom onset. The median KM survival estimate for cognitive symptom onset was 70.2 years (95% CI: 69.3‐71.0). Parkinsonism developed in 89% with a median KM survival estimate of 73.7 years (95% CI: 72.7‐74.7). 76% had fluctuations with a similar median KM survival estimate of 74.6 years (95% CI: 73.7‐75.8). Visual hallucinations (VH) were present in 70% emerging at a median KM survival estimate of 80.2 years (95% CI: 79.5‐ 81.3). Cumulative incidence estimates for median time from cognitive onset to parkinsonism, fluctuations and VH were 2 years (95% CI: 2.0‐2.5), 2.3 years (95% CI: 2.0‐3.2) and 4.3 years (95% CI: 4.0‐4.8 years), respectively.ConclusionIn this DLB cohort, RBD was most likely to precede cognitive onset. The evolution of the other core features after cognitive onset was parkinsonism and fluctuations and then VH. While VH occur about 4 years after cognitive symptom onset, parkinsonism and fluctuations occur within the first 2 years.

AbstractBackgroundPractice effects can obscure interpretation of longitudinal cognitive performance. Reliable change indices (RCIs) provide data for determining a significant change, typically for a single repeat assessment, but multiple follow‐up visits are common clinically and in research cohorts. Further, regression‐based or linear mixed effects models improve prediction of future cognitive decline and have higher correlations with neuroimaging biomarkers than RCI approaches. We present norms for change for the Auditory Verbal Learning Tests (AVLT) with conditional normative z‐scores for up to 7 repeat assessments, and demonstrate application of this method to individuals with preclinical Alzheimer’s disease (AD).MethodNormative Sample: Robust conditional norms were derived from 1001 adults ages 50‐90 from the Mayo Clinic Study of Aging (MCSA) who completed the AVLT across 4‐8 visits approximately 15 months apart and remained cognitively unimpaired (CU) across all available visits. Linear mixed effects models were used to compute a conditional normative z‐score for AVLT 30‐minute recall and sum of trials. Models included adjustment for baseline performance, number of prior test exposures, demographic factors (age, sex, education), and interactions (baseline*time; sex*time). Test Sample: We used a preclinical AD sample with positive amyloid and tau imaging (n= 27 CU A+T+; Jack et al. 2017) expected to have a higher rate of intra‐individual cognitive decline relative to CU A‐T‐ individuals (n = 268) to compare mean performances of conditional normative z‐scores to typical age‐corrected Mayo’s Older Americans Normative Studies (MOANS) norms. Participants had at least 1 and up to 4 follow‐up visits, with their most recent AT data used for classification.ResultAVLT performance based on typical norms did not differ across A+T+ and A‐T‐ groups. Specifically, across 2‐4 follow‐up visits, the typical norms mean z‐score was similar across groups (p=0.945; see Table 1). However, the conditional norms mean z‐score that accounts for practice effects and typical trajectories of AVLT performance was lower in the A+T+ relative to the A‐T‐ group for 30‐minute recall (p=0.055) and sum of trials (p=0.060).ConclusionConditional normative methods that account for practice effects are more sensitive to preclinical AD than typical norms, and show promise for identifying transitional cognitive decline.

AbstractBackgroundNeuropsychiatric symptoms (NPS) are associated with the risk of incident mild cognitive impairment (MCI) and dementia. However, the association between NPS and global and domain‐specific cognitive trajectories among older adults free of dementia remains unclear.MethodWe conducted a longitudinal study derived from the population‐based Mayo Clinic Study of Aging, including individuals aged ≥ 50 years who were free of dementia. Participants completed the Neuropsychiatric Inventory Questionnaire at baseline and underwent neuropsychological testing assessing four cognitive domains (i.e., memory, language, attention, visuospatial skills) every 15 months. We calculated linear mixed‐effect models with random subject‐specific intercepts and slopes to examine the association between presence of twelve different NPS (i.e., delusions, hallucinations, agitation, depression, anxiety, euphoria, apathy, disinhibition, irritability, motor behavior, nighttime behavior, appetite) at baseline and trajectories for individual yearly change in global and domain‐specific cognitive z‐scores. The models were adjusted for age, sex, education, and whether or not this was the first time the cognitive battery had been administered.ResultThe sample included 5081 persons (51% males; 4439 cognitively unimpaired, 642 with MCI). The median [IQR] age was 74 [67, 81] years, and the median [IQR] education was 14 [12, 16] years. Regardless of NPS status, participants declined in cognition over time, on average. However, presence of NPS was significantly associated with an increased decline in cognition, i.e., the annualized change in global cognition z‐scores for participants with compared to without the given NPS ranged from ‐0.018 (p = 0.011) for irritability to ‐0.16 (p = 0.001) for hallucinations. These associations between NPS and annual decline in global cognition were significant for all NPS except euphoria and disinhibition. Similar results were seen for the individual cognitive domains, e.g., participants with depression, apathy and nighttime behavior had a greater longitudinal decline in all domain specific z‐scores, whereas presence of delusions and anxiety was associated with a more pronounced decline in language, attention and visuospatial skills.ConclusionNeuropsychiatric symptoms are associated with greater longitudinal decline of both global and domain‐specific cognitive functions in community‐dwelling older adults free of dementia. More research is needed to confirm these preliminary findings.

AbstractBackgroundThe numeric clinical staging scheme described by the NIA‐AA workgroup proposes evidence of subtle decline on longitudinal cognitive testing as one option to define transitional cognitive decline for individuals in the Alzheimer’s continuum. Few studies have compared longitudinal and cross‐sectional definitions of subtle objective cognitive impairment (sOBJ). The study aim was to compare the diagnostic accuracy of cross‐sectional sOBJ and longitudinal objective cognitive decline (ΔOBJ) for preclinical Alzheimer’s disease (AD) based on Cogstate One Card Learning (OCL) performance.MethodWe included Mayo Clinic Study of Aging cognitively unimpaired (CU) participants who were A+T+ by amyloid and tau PET assessment (n=32) and A‐T‐ (n=211); all had at least two follow‐up visits. sOBJ was defined as performance ≤ ‐1SD on OCL accuracy using age‐corrected normative data. ΔOBJ was measured using two different methods: (1) within subjects’ standard deviation (WSD) using a ≤ ‐1 z‐score for change to define decline based on Cogstate’s normative change data, and (2) extracted subject‐specific slopes from a linear mixed effects model (LME) which can be interpreted as approximate annual change; decline was defined as slope < 10%ile among a reference sample of CU participants aged 50‐65 at baseline (N=732).ResultssOBJ and ΔOBJ‐WSD both showed low sensitivity in detection of A+T+ (9.4% and 12.5%, respectively). Total AUC values did not differ significantly (p=.31) across sOBJ (AUC = .64, CI = .53‐.75) and ΔOBJ‐WSD (AUC = .53, CI = .42‐.65) methods. Sensitivity of the ΔOBJ‐LME method to predict A+T+ was improved (37.5%) when using a conventional cutoff of <10%ile slope and was further improved (56%) when using an optimal derived cutoff equivalent to <17%ile slope within the reference group. Like the sOBJ method, the ΔOBJ‐LME method differentiated groups better than chance (AUC = .69, CI = .58‐.80). Despite increased sensitivity, total AUC values were not significantly different across sOBJ and ΔOBJ‐LME methods (p=.34). However, total AUC of ΔOBJ‐LME was significantly better than ΔOBJ‐WSD (p=.03).ConclusionsΔOBJ may be more sensitive to preclinical AD than sOBJ and may serve as a helpful method for identifying at risk CU individuals if advanced statistical methods are used for defining change.

AbstractBackgroundWe report a group of individuals presenting with a progressive dementia syndrome characterized by predominant dysfunction in core executive functions and positive biomarkers for Alzheimer’s pathophysiology.MethodIn this retrospective review, we report on 55 participants with a clinically defined progressive dysexecutive syndrome with 18F‐fluorodeoxyglucose‐positron emission tomography (FDG) and AD biomarker positivity. A subset of these individuals enrolled in our ADRC were compared to other phenotypic variants of AD.ResultSixty‐two percent were female with a mean of 15.2 years of education. Mean age of symptom onset was 53.8 while mean age at diagnosis was 57.2 years. Multi‐domain cognitive impairment was evident in neuropsychological testing with executive dysfunction most consistently and severely affected. The frontal and/or parietal regions demonstrate clear hypometabolism on positron emission tomography in all cases. Genetic testing for autosomal dominant genes was negative in all 8 participants tested and at least one APOE ε4 allele was present in 14/26. EEG was abnormal in 14/17 cases. CSF and neuroimaging biomarkers (PIB‐PET and Tau‐PET) were consistent with Alzheimer’s disease pathophysiology, although CSF p‐tau was normal in 24% of cases. In two participants that came to autopsy, the hippocampus was relatively spared compared to frontal and parietal cortex. Participants presenting with a progressive dysexecutive syndrome and evidence of AD pathology are labeled dAD. Comparing FDG in dAD relative to other AD phenotypes revealed unique areas of relatively greater hypometabolism in parieto‐frontal cortex and relative sparing of medial temporal (vs typical AD), visual (vs PCA), and left temporal (vs lvPPA) (Figure 1). Controlling for age did not alter patterns of regional differences. This demonstrates regional variability related to clinical phenotype not accountable by variation in age, duration, stage, or severity (Figure 2).ConclusionA progressive dysexecutive syndrome should be recognized as a distinct clinical phenotype. This clinical presentation can be due to Alzheimer’s disease but is not specific for any single etiology (non‐AD cases of progressive dysexecutive syndrome will be discussed). Prominent impairment of executive task performance, young‐age of onset, normal CSF p‐tau, and relative preservation of the hippocampus were commonly encountered in this case series of dAD.

AbstractBackgroundThe NIA‐AA Research Framework for Alzheimer’s disease (AD) includes consideration of two clinical schemes for staging AD: (1) traditional cognitively unimpaired (CU) to MCI to dementia and (2) a new numeric staging (1‐6) for individuals who are on the Alzheimer’s continuum.MethodThe Mayo Clinic Study of Aging is a population‐based longitudinal study of aging and cognitive impairment in Olmsted County, Minnesota. We analyzed CU persons who had completed three consecutive clinical visits and calculated measures for cross‐sectional categories: objective cognitive impairment and function and measures for change including subjective cognitive impairment (SCD), objective cognitive change (ΔOBJ), and new onset of neurobehavioral symptoms (NBS). We calculated the frequencies for Stage 2 by varying the cutoffs for each of the measures above and assessed stability of this stage over 15 months.ResultThe frequency of Stage 2 varied from 5‐48% depending upon the age of the participants and the cutoff scores used. Membership in Stage 2 required abnormal SCD, ΔOBJ, or ΔNBS and having only ΔOBJ abnormal was the primary reason for inclusion in Stage 2 (45‐62%) followed by 10‐21% having only SCD abnormal. The stability of Stage 2 over 15 months ranged from 31‐65% with 5‐13% progressing to Stage 3 or higher and 22‐43% percent reverting to Stage 1.ConclusionStage 2 was characterized as a transitional period between CU and MCI and could be operationalized with a variety of measures. The staging scheme performed well considering the variables evaluated and cutoff scores employed.

AbstractBackgroundWhite matter changes and Alzheimer’s disease pathology frequently co‐exist in the population. Recently we empirically derived subtypes of incident mild cognitive impairment (MCI) and found that there was significant heterogeneity not only in cognitive impairment profiles but also gray matter atrophy suggesting that multiple etiologies may contribute to cognitive impairment in these subtypes. The aim of this study was to examine differences in white matter hyperintensity (WMH) volume in these same MCI subtypes and investigate amyloid deposition in a smaller subset.MethodWe analyzed data from 188 participants with incident MCI enrolled in the population‐based Mayo Clinic Study of Aging (MCSA) identified as having one of four MCI subtypes: subtle cognitive impairment (SCI), amnestic, dysnomic, and dysexecutive. All participants completed 3T imaging at the time of the MCI diagnosis. Using WMH volume scaled by TIV and PIB SUVR, we compared each MCI subtype to 344 cognitively impaired (CU) participants (mean age 78; 46% women) and each other.ResultThe amnestic (n = 83; mean age 80; 43% women), dysnomic (n = 30; mean age 84; 47% women) and dysexecutive subtypes (n = 49; mean age 83; 35% women) had significantly higher WMH volumes compared to CU (p < .02) while the SCI subtype (n = 26; mean age 80; 23% women) did not differ from CU (p = .76). Amongst the MCI subtypes, the amnestic (p = .04) and dysexecutive subtypes (p = .01) had significantly higher WMH volumes than the SCI subtype [Figure 1]. In a smaller subset of individuals who also completed PiB‐PET (17/26 SCI; 39/83 amnestic; 10/30 dysnomic, 28/49 dysexecutive), we found evidence for higher amyloid load in all MCI subgroups except the SCI group (p<0.05) in comparison to CU.ConclusionWe found significantly elevated levels of WMH and amyloid burden in amnestic, dysnomic, and dysexecutive subtypes of incident MCI supporting the notion that both vascular changes and amyloid contribute to the development of clinical symptoms. Surprisingly, WMH and amyloid positivity were not high in SCI suggesting that the pathophysiology in this group needs further investigation.

AbstractBackgroundUsing an objective mathematical algorithm to assess corticolimbic involvement of neurofibrillary tangle pathology, we identified three AD subtypes: hippocampal sparing (HpSp) AD, typical AD, and limbic predominant AD. Typical AD brains were representative of the expected patterns of hippocampal and cortical involvement as outlined by Braak tangle stage. In contrast, we discovered an extreme phenotype that exists outside of the Braakian‐concept of neurofibrillary tangle patterns. Hippocampal sparing AD cases demonstrate unexpected sparing of the hippocampus relative to severely involved association cortices and limbic predominant AD cases demonstrate inundation of the hippocampus relative to mildly involved association cortices. Using these AD subtypes, our objective was to leverage our understanding of disease spectrum to uncover transcriptomic changes that underlie selective vulnerability of the hippocampus in AD.MethodWe performed RNA‐sequencing in 40 neuropathologically diagnosed AD cases (10 HpSp, 20 Typical and 10 Limbic) and 15 controls to uncover gene expression changes associated with phenotype differences allowing us to prioritize 44 genes for further exploration. Validation with NanoString and quantification with digital pathology were performed in an expanded cohort of 158 AD cases (36 HpSp, 79 Typical and 35 Limbic) and 32 controls. We quantified digital pathology measures of early tangle pathology (CP13), mature tangle pathology (Ab39), amyloid‐β (33.1.1). In addition, we measured cellular markers for microglia (CD68), endothelia (CD34), and astroglia (GFAP). Deep learning based prediction models were employed, which nominated the SLC38A2 gene as a predictor of AD.ResultThe SLC38A2 gene was originally prioritized in our RNA‐sequencing data based upon differential expression between controls and typical AD. We observed upregulation of SLC38A2 in all AD subtypes. RNA‐seq gene expression measures validated well with NanoString for SLC38A2 (R=0.97, p<0.001). Regression analysis identified early tangle (p=0.011) and late tangle measures (p=0.026) as significant predictors of gene expression, but not amyloid measures (p=0.74). Microgliosis approached significance (p=0.052), but with no contribution from endothelial cell burden (p=0.17) or astrogliosis (p=0.58). Age approached significance (p=0.06), but not sex (p=0.88) or APOE‐ε4 status (p=0.10).ConclusionOur data supports consideration of intra‐disease divergence with regard to case stratification and may reveal genes previously masked by heterogeneity of cohorts.