Kantarci, Kejal

In vivo MRS is a non‐invasive measurement technique used not only in humans, but also in animal models using high‐field magnets. MRS enables the measurement of metabolite concentrations as well as metabolic rates and their modifications in healthy animals and disease models. Such data open the way to a deeper understanding of the underlying biochemistry, related disturbances and mechanisms taking place during or prior to symptoms and tissue changes. In this work, we focus on the main preclinical 1H, 31P and 13C MRS approaches to study brain metabolism in rodent models, with the aim of providing general experts’ consensus recommendations (animal models, anesthesia, data acquisition protocols). An overview of the main practical differences in preclinical compared with clinical MRS studies is presented, as well as the additional biochemical information that can be obtained in animal models in terms of metabolite concentrations and metabolic flux measurements. The properties of high‐field preclinical MRS and the technical limitations are also described.

Abstract Clinical trials with anti-tau drugs will need to target individuals at risk of accumulating tau. Our objective was to identify variables available in a research setting that predict future rates of tau PET accumulation separately among individuals who were either cognitively unimpaired or cognitively impaired. All 337 participants had: a baseline study visit with MRI, amyloid PET, and tau PET exams, at least one follow-up tau PET exam; and met clinical criteria for membership in one of two clinical diagnostic groups: cognitively unimpaired (n = 203); or cognitively impaired (n = 134, a combined group of participants with either mild cognitive impairment or dementia with Alzheimer’s clinical syndrome). Our primary analyses were in these two clinical groups; however, we also evaluated subgroups dividing the unimpaired group by normal/abnormal amyloid PET and the impaired group by clinical phenotype (mild cognitive impairment, amnestic dementia, and non-amnestic dementia). Linear mixed effects models were used to estimate associations between age, sex, education, APOE genotype, amyloid and tau PET standardized uptake value ratio (SUVR), cognitive performance, cortical thickness, and white matter hyperintensity volume at baseline, and the rate of subsequent tau PET accumulation. Log-transformed tau PET SUVR was used as the response and rates were summarized as annual per cent change. A temporal lobe tau PET meta-region of interest was used. In the cognitively unimpaired group, only higher baseline amyloid PET was a significant independent predictor of higher tau accumulation rates (P < 0.001). Higher rates of tau accumulation were associated with faster rates of cognitive decline in the cognitively unimpaired subgroup with abnormal amyloid PET (P = 0.03), but among the subgroup with normal amyloid PET. In the cognitively impaired group, younger age (P = 0.02), higher baseline amyloid PET (P = 0.05), APOE ε4 (P = 0.05), and better cognitive performance (P = 0.05) were significant independent predictors of higher tau accumulation rates. Among impaired individuals, faster cognitive decline was associated with faster rates of tau accumulation (P = 0.01). While we examined many possible predictor variables, our results indicate that screening of unimpaired individuals for potential inclusion in anti-tau trials may be straightforward because the only independent predictor of high tau rates was amyloidosis. In cognitively impaired individuals, imaging and clinical variables consistent with early onset Alzheimer’s disease phenotype were associated with higher rates of tau PET accumulation suggesting this may be a highly advantageous group in which to conduct proof-of-concept clinical trials that target tau-related mechanisms. The nature of the dementia phenotype (amnestic versus non-amnestic) did not affect this conclusion.

AbstractObjectiveThe Longitudinal Evaluation of Familial Frontotemporal Dementia Subjects evaluates familial frontotemporal lobar degeneration (FTLD) kindreds with MAPT, GRN, or C9orf72 mutations. Objectives were to examine whether health‐related quality of life (HRQoL) correlates with clinical symptoms and caregiver burden, and whether self‐rated and informant‐rated HRQoL would correlate with each other.MethodsIndividuals were classified using the Clinical Dementia Rating (CDR®) Scale plus National Alzheimer's Coordinating Center (NACC) FTLD. HRQoL was measured with DEMQOL and DEMQOL‐proxy; caregiver burden with the Zarit Burden Interview (ZBI). For analysis, Pearson correlations and weighted kappa statistics were calculated.ResultsThe cohort of 312 individuals included symptomatic and asymptomatic individuals. CDR® plus NACC FTLD was negatively correlated with DEMQOL (r = −0.20, P = .001), as were ZBI and DEMQOL (r = −0.22, P = .0009). There was fair agreement between subject and informant DEMQOL (κ = 0.36, P <.0001).ConclusionLower HRQoL was associated with higher cognitive/behavior impairment and higher caregiver burden. These findings demonstrate the negative impact of FTLD on individuals and caregivers.

Abstract Objective: Little is known about how menopausal hormone treatment (HT) may influence the development of white matter hyperintensities (WMHs) in the brain. This study evaluated the associations of changes in levels of pituitary-ovarian hormones during HT and changes in WMH. Methods: Women (n = 78 adherent to treatment) enrolled in the Kronos Early Estrogen Prevention Study underwent brain magnetic resonance imaging, and blood collection before and after 48 months of randomization to 0.45 mg/d oral conjugated equine estrogen (oCEE) daily, 50 μg/d transdermal 17β estradiol (tE2), or placebo pills and patches. Women in the active treatment groups also received oral 200 mg/d micronized progesterone the first 12 days of the month. Estradiol (E2), estrone (E1), follicle-stimulating hormone (FSH), and luteinizing hormone (LH) were measured in serum by high sensitivity liquid chromatography/mass spectrometry at baseline and following 48 months of HT. Longitudinal change in WMH volume was determined from fluid-attenuated inversion recovery magnetic resonance imaging using a semiautomated image segmentation algorithm. Results: Serum levels of FSH, LH, E1, or E2 did not associate with WMH volume at baseline. After 48 months of treatment, smaller increases in WMH associated with decreases in FSH from baseline in the tE2 group and increases in E1 in both tE2 and oCEE groups. Changes in LH did not associate with changes in WMH in any group. Conclusions: Circulating levels of pituitary-ovarian hormones associate with changes in WMH volume in recently menopausal women using HT. Whether these relationships would be influenced by different doses of tE2 or oCEE remains to be determined. Video Summary: http://links.lww.com/MENO/A590.

Abstract Alzheimer’s disease can present clinically with either the typical amnestic phenotype or with atypical phenotypes, such as logopenic progressive aphasia and posterior cortical atrophy. We have recently described longitudinal patterns of flortaucipir PET uptake and grey matter atrophy in the atypical phenotypes, demonstrating a longitudinal regional disconnect between flortaucipir accumulation and brain atrophy. However, it is unclear how these longitudinal patterns differ from typical Alzheimer’s disease, to what degree flortaucipir and atrophy mirror clinical phenotype in Alzheimer’s disease, and whether optimal longitudinal neuroimaging biomarkers would also differ across phenotypes. We aimed to address these unknowns using a cohort of 57 participants diagnosed with Alzheimer’s disease (18 with typical amnestic Alzheimer’s disease, 17 with posterior cortical atrophy and 22 with logopenic progressive aphasia) that had undergone baseline and 1-year follow-up MRI and flortaucipir PET. Typical Alzheimer’s disease participants were selected to be over 65 years old at baseline scan, while no age criterion was used for atypical Alzheimer’s disease participants. Region and voxel-level rates of tau accumulation and atrophy were assessed relative to 49 cognitively unimpaired individuals and among phenotypes. Principal component analysis was implemented to describe variability in baseline tau uptake and rates of accumulation and baseline grey matter volumes and rates of atrophy across phenotypes. The capability of the principal components to discriminate between phenotypes was assessed with logistic regression. The topography of longitudinal tau accumulation and atrophy differed across phenotypes, with key regions of tau accumulation in the frontal and temporal lobes for all phenotypes and key regions of atrophy in the occipitotemporal regions for posterior cortical atrophy, left temporal lobe for logopenic progressive aphasia and medial and lateral temporal lobe for typical Alzheimer’s disease. Principal component analysis identified patterns of variation in baseline and longitudinal measures of tau uptake and volume that were significantly different across phenotypes. Baseline tau uptake mapped better onto clinical phenotype than longitudinal tau and MRI measures. Our study suggests that optimal longitudinal neuroimaging biomarkers for future clinical treatment trials in Alzheimer’s disease are different for MRI and tau-PET and may differ across phenotypes, particularly for MRI. Baseline tau tracer retention showed the highest fidelity to clinical phenotype, supporting the important causal role of tau as a driver of clinical dysfunction in Alzheimer’s disease.