Citations

The study included 1,738 Mayo Clinic Study of Aging participants (≥50 years old; 1,460 cognitively unimpaired and 278 with mild cognitive impairment (MCI)) and examined the cross-sectional association between cerebrovascular (CVD) imaging biomarkers (e.g., white matter hyperintensities (WMH), infarctions) and Beck Depression Inventory-II (BDI-II) and Beck Anxiety Inventory (BAI) scores, as well as their association with MCI. High (abnormal) WMH burden was significantly associated with having BDI-II>13 and BAI > 7 scores, and both (CVD imaging biomarkers and depression/anxiety) were significantly associated with MCI when included simultaneously in the model, suggesting that both were independently associated with the odds of MCI.

Abstract Alzheimer's disease (AD) exhibits spatially heterogeneous 3R/4R tau pathology distributions across participants, making it a challenge to quantify extent of tau deposition. Utilizing Tau-PET from three independent cohorts, we trained and validated a machine learning model to identify visually positive Tau-PET scans from regional SUVR values and developed a novel summary measure, THETA, that accounts for heterogeneity in tau deposition. The model for identification of tau positivity achieved a balanced test accuracy of 95% and accuracy of ≥87% on the validation datasets. THETA captured heterogeneity of tau deposition, had better association with clinical measures, and corresponded better with visual assessments in comparison with the temporal meta-region-of-interest Tau-PET quantification methods. Our novel approach aids in identification of positive Tau-PET scans and provides a quantitative summary measure, THETA, that effectively captures the heterogeneous tau deposition seen in AD. The application of THETA for quantifying Tau-PET in AD exhibits great potential.

Abstract There is a longstanding ambiguity regarding the clinical diagnosis of dementia syndromes predominantly targeting executive functions versus behaviour and personality. This is due to an incomplete understanding of the macro-scale anatomy underlying these symptomatologies, a partial overlap in clinical features and the fact that both phenotypes can emerge from the same pathology and vice versa. We collected data from a patient cohort of which 52 had dysexecutive Alzheimer’s disease, 30 had behavioural variant frontotemporal dementia (bvFTD), seven met clinical criteria for bvFTD but had Alzheimer’s disease pathology (behavioural Alzheimer’s disease) and 28 had amnestic Alzheimer’s disease. We first assessed group-wise differences in clinical and cognitive features and patterns of fluorodeoxyglucose (FDG) PET hypometabolism. We then performed a spectral decomposition of covariance between FDG-PET images to yield latent patterns of relative hypometabolism unbiased by diagnostic classification, which are referred to as ‘eigenbrains’. These eigenbrains were subsequently linked to clinical and cognitive data and meta-analytic topics from a large external database of neuroimaging studies reflecting a wide range of mental functions. Finally, we performed a data-driven exploratory linear discriminant analysis to perform eigenbrain-based multiclass diagnostic predictions. Dysexecutive Alzheimer’s disease and bvFTD patients were the youngest at symptom onset, followed by behavioural Alzheimer’s disease, then amnestic Alzheimer’s disease. Dysexecutive Alzheimer’s disease patients had worse cognitive performance on nearly all cognitive domains compared with other groups, except verbal fluency which was equally impaired in dysexecutive Alzheimer’s disease and bvFTD. Hypometabolism was observed in heteromodal cortices in dysexecutive Alzheimer’s disease, temporo-parietal areas in amnestic Alzheimer’s disease and frontotemporal areas in bvFTD and behavioural Alzheimer’s disease. The unbiased spectral decomposition analysis revealed that relative hypometabolism in heteromodal cortices was associated with worse dysexecutive symptomatology and a lower likelihood of presenting with behaviour/personality problems, whereas relative hypometabolism in frontotemporal areas was associated with a higher likelihood of presenting with behaviour/personality problems but did not correlate with most cognitive measures. The linear discriminant analysis yielded an accuracy of 82.1% in predicting diagnostic category and did not misclassify any dysexecutive Alzheimer’s disease patient for behavioural Alzheimer’s disease and vice versa. Our results strongly suggest a double dissociation in that distinct macro-scale underpinnings underlie predominant dysexecutive versus personality/behavioural symptomatology in dementia syndromes. This has important implications for the implementation of criteria to diagnose and distinguish these diseases and supports the use of data-driven techniques to inform the classification of neurodegenerative diseases.

Abstract Given the prevalence of dementia and the development of pathology-specific disease-modifying therapies, high-value biomarker strategies to inform medical decision-making are critical. In vivo tau-PET is an ideal target as a biomarker for Alzheimer’s disease diagnosis and treatment outcome measure. However, tau-PET is not currently widely accessible to patients compared to other neuroimaging methods. In this study, we present a convolutional neural network (CNN) model that imputes tau-PET images from more widely available cross-modality imaging inputs. Participants (n = 1192) with brain T1-weighted MRI (T1w), fluorodeoxyglucose (FDG)-PET, amyloid-PET and tau-PET were included. We found that a CNN model can impute tau-PET images with high accuracy, the highest being for the FDG-based model followed by amyloid-PET and T1w. In testing implications of artificial intelligence-imputed tau-PET, only the FDG-based model showed a significant improvement of performance in classifying tau positivity and diagnostic groups compared to the original input data, suggesting that application of the model could enhance the utility of the metabolic images. The interpretability experiment revealed that the FDG- and T1w-based models utilized the non-local input from physically remote regions of interest to estimate the tau-PET, but this was not the case for the Pittsburgh compound B-based model. This implies that the model can learn the distinct biological relationship between FDG-PET, T1w and tau-PET from the relationship between amyloid-PET and tau-PET. Our study suggests that extending neuroimaging’s use with artificial intelligence to predict protein specific pathologies has great potential to inform emerging care models.

AbstractBackgroundParticipation in colonoscopies is an essential aspect of endoscopic training. The purpose of this study was to explore the impact of fellow/trainee participation on colonoscopy outcomes.MethodsThis meta‐analysis was registered on the International Prospective Register of Systematic Reviews (PROSPERO). From database inception to July 2022, studies investigating fellow involvement and colonoscopy outcomes were searched across Cochrane library, PubMed, and other databases. The random‐effects model was applied to generate more conservative estimates. Sensitive analysis was conducted to explore whether the result would depend on a particular study. Egger's test and Begg's test were used to estimate the potential for publication bias.ResultsSeventeen studies including 30,062 participants were included. We found that fellow/trainee involvement enhanced the overall rates of adenoma detection and polyp detection (OR = 1.26, 95% CI = 1.14–1.40, p < 0.001; OR = 1.29, 95% CI = 1.02–1.63, p = 0.020, respectively). The mean number of adenoma/polyps per colonoscopy was also higher with fellow/trainee participation (MD = 0.12, 95% CI = 0.08–0.17, p < 0.001; MD = 0.15, 95% CI = 0.02–0.28, p = 0.020, respectively).ConclusionIn addition to its educational purpose, fellow or trainee involvement is associated with beneficial effects on colonoscopy outcomes.

PURPOSE BMI affects breast cancer risk and prognosis. In contrast to cytotoxic chemotherapy, CDK4/6 inhibitors are given at a fixed dose, irrespective of BMI or weight. This preplanned analysis of the global randomized PALLAS trial investigates the impact of BMI on the side-effect profile, treatment adherence, and efficacy of palbociclib. METHODS Patients were categorized at baseline according to WHO BMI categories. Neutropenia rates were assessed with univariable and multivariable logistic regression. Time to early discontinuation of palbociclib was analyzed with Fine and Gray competing risk models. Unstratified Cox models were used to investigate the association between BMI category and time to invasive disease-free survival (iDFS). 95% CIs were derived. RESULTS Of 5,698 patients included in this analysis, 68 (1.2%) were underweight, 2,082 (36.5%) normal weight, 1,818 (31.9%) overweight, and 1,730 (30.4%) obese at baseline. In the palbociclib arm, higher BMI was associated with a significant decrease in neutropenia (unadjusted odds ratio for 1-unit change, 0.93; 95% CI, 0.91 to 0.94; adjusted for age, race ethnicity, region, chemotherapy use, and Eastern Cooperative Oncology Group at baseline, 0.93; 95% CI, 0.92 to 0.95). This translated into a significant decrease in treatment discontinuation rate with higher BMI (adjusted hazard ratio [HR] for 10-unit change, 0.75; 95% CI, 0.67 to 0.83). There was no significant improvement in iDFS with the addition of palbociclib to ET in any weight category (normal weight HR, 0.84; 95% CI, 0.63 to 1.12; overweight HR, 1.10; 95% CI, 0.82 to 1.49; and obese HR, 0.95; 95% CI, 0.69 to 1.30) in this analysis early in follow-up (31 months). CONCLUSION This preplanned analysis of the PALLAS trial demonstrates a significant impact of BMI on side effects, dose reductions, early treatment discontinuation, and relative dose intensity. Additional long-term follow-up will further evaluate whether BMI ultimately affects outcome.

Abstract Previous studies implicated protein arginine methyltransferase 5 (PRMT5) as a synthetic lethal target for MTAP-deleted (MTAP del) cancers; however, the pharmacologic characterization of small-molecule inhibitors that recapitulate the synthetic lethal phenotype has not been described. MRTX1719 selectively inhibited PRMT5 in the presence of MTA, which is elevated in MTAP del cancers, and inhibited PRMT5-dependent activity and cell viability with &gt;70-fold selecti­vity in HCT116 MTAP del compared with HCT116 MTAP wild-type (WT) cells. MRTX1719 demonstrated dose-dependent antitumor activity and inhibition of PRMT5-dependent SDMA modification in MTAP del tumors. In contrast, MRTX1719 demonstrated minimal effects on SDMA and viability in MTAP WT tumor xenografts or hematopoietic cells. MRTX1719 demonstrated marked antitumor activity across a panel of xenograft models at well-tolerated doses. Early signs of clinical activity were observed including objective responses in patients with MTAP del melanoma, gallbladder adenocarcinoma, mesothelioma, non–small cell lung cancer, and malignant peripheral nerve sheath tumors from the phase I/II study. Significance: PRMT5 was identified as a synthetic lethal target for MTAP del cancers; however, previous PRMT5 inhibitors do not selectively target this genotype. The differentiated binding mode of MRTX1719 leverages the elevated MTA in MTAP del cancers and represents a promising therapy for the ∼10% of patients with cancer with this biomarker. See related commentary by Mulvaney, p. 2310. This article is featured in Selected Articles from This Issue, p. 2293

AbstractINTRODUCTIONAtypical variants of Alzheimer's disease (AD) include the visual variant, known as posterior cortical atrophy (PCA), and the language variant, known as logopenic progressive aphasia (LPA). Clinically, rates of disease progression differ between them.METHODSWe evaluated 34 PCA and 29 LPA participants. Structural magnetic resonance imaging and 18F‐flortaucipir positron emission tomography were performed at baseline and at 1‐year follow‐up. Rates of change in tau uptake and grey matter volumes were compared between PCA and LPA with linear mixed‐effects models and voxel‐based analyses.RESULTSPCA had faster rates of occipital atrophy. LPA had faster rates of left temporal atrophy and faster rates of tau accumulation in the parietal, right temporal, and occipital lobes. Age was negatively associated with rates of atrophy and tau accumulation.DISCUSSIONLongitudinal patterns of neuroimaging abnormalities differed between PCA and LPA, although with divergent results for tau accumulation and atrophy.HIGHLIGHTS The language variant of Alzheimer's disease accumulates tau faster than the visual variant. Each variant shows faster rates of atrophy than the other in its signature regions. Age negatively influences rates of atrophy and tau accumulation in both variants.

AbstractBackgroundDementia with Lewy bodies (DLB) is the second most common neurodegenerative dementia, after Alzheimer’s disease (AD). While alpha‐synuclein pathology is the hallmark of DLB, AD and cerebrovascular pathologies are common in DLB patients. However, the contribution of these pathologies towards neurodegeneration in DLB is largely unknown. We investigated associations of amyloid‐beta, tau, and cerebrovascular biomarkers with regional gray matter (GM) volume in DLB patients and cognitively unimpaired (CU) controls.MethodWe included 30 DLB patients (69.3±10.2 years old, 87% men) and 100 CU individuals balanced on age and sex (Table 1). We used 11C‐Pittsburgh Compound‐B (PiB) and 18F‐Flortaucipir positron emission tomography (PET) to assess amyloid‐beta and tau in‐vivo. We used structural MRI to assess white matter hyperintensity (WMH) volume, a marker of cerebrovascular lesion load, and regional GM volume (a marker of neurodegeneration). We used correlations and ANCOVA in the whole dataset and structural equation modelling in the DLB patients to investigate associations of WMH volume and regional amyloid‐beta and tau PET standardized uptake value ratios (SUVRs) with regional GM volume.ResultDLB patients showed lower GM volume across all cortical and subcortical regions except for cuneus, putamen, and pallidum (Fig.1). Greater WMH volume was associated with lower volume in the medial and orbital frontal cortices, insula, fusiform cortex, and thalamus in DLB patients (Fig.2). Greater PiB SUVR was associated with lower volume in the inferior temporal cortex, while Flortaucipir SUVR did not correlate with GM volume (Fig.2). Structural equation modelling showed that higher age and positivity for the APOE e4 genotype were significant predictors of greater WMH, and WMH in turn was a significant predictor of GM volume in medial and orbital frontal cortices, insula, and inferior temporal cortex (Fig.3). In contrast, we observed two distinct paths for the fusiform cortex, with age having an effect through PiB and Flortaucipir SUVRs on the one path, and through WMH on the other path (Fig.3).ConclusionDLB patients have widespread cortical atrophy, most of which likely is influenced by alpha‐synuclein pathology. Although amyloid‐beta, tau, and cerebrovascular pathologies often coexist in DLB, their contributions to neurodegeneration seem to be region specific.