Citations

AbstractAs prevention trials advance with autosomal dominant Alzheimer disease (ADAD) participants, understanding the similarities and differences between ADAD and “sporadic” late‐onset AD (LOAD) is critical to determine generalizability of findings between these cohorts. Cognitive trajectories of ADAD mutation carriers (MCs) and autopsy‐confirmed LOAD individuals were compared to address this question. Longitudinal rates of change on cognitive measures were compared in ADAD MCs (n = 310) and autopsy‐confirmed LOAD participants (n = 163) before and after symptom onset (estimated/observed). LOAD participants declined more rapidly in the presymptomatic (preclinical) period and performed more poorly at symptom onset than ADAD participants on a cognitive composite. After symptom onset, however, the younger ADAD MCs declined more rapidly. The similar but not identical cognitive trajectories (declining but at different rates) for ADAD and LOAD suggest common AD pathologies but with some differences.

Abstract Background: ABO blood group is associated with pancreatic cancer risk. Whether ABO blood group alone or when combined with inherited mutation status of index pancreatic cancer cases (probands) can enhance pancreatic cancer risk estimation in first-degree relatives (FDR) is unclear. We examined FDRs' risk for pancreatic cancer based on probands' ABO blood group and probands' cancer susceptibility gene mutation status. Methods: Data on 23,739 FDRs, identified through 3,268 pancreatic cancer probands, were analyzed. Probands' ABO blood groups were determined serologically or genetically, and 20 cancer susceptibility genes were used to classify probands as “mutation-positive” or “mutation-negative.” SIRs and 95% confidence intervals (CI) were calculated, comparing observed pancreatic cancer cases in the FDRs with the number expected in SEER-21 (reference population). Results: Overall, FDRs had 2-fold risk of pancreatic cancer (SIR = 2.00; 95% CI = 1.79–2.22). Pancreatic cancer risk was higher in FDRs of mutation-positive (SIR = 3.80; 95% CI = 2.81–5.02) than mutation-negative (SIR = 1.79; 95% CI = 1.57–2.04) probands (P < 0.001). The magnitude of risk did not differ by ABO blood group alone (SIRblood-group-O = 1.57; 95% CI = 1.20–2.03, SIRnon-O = 1.83; 95% CI = 1.53–2.17; P = 0.33). Among FDRs of probands with non-O blood group, pancreatic cancer risk was higher in FDRs of mutation-positive (SIR = 3.98; 95% CI = 2.62–5.80) than mutation-negative (SIR = 1.66; 95% CI = 1.35–2.03) probands (P < 0.001), but risk magnitudes were statistically similar when probands had blood group O (SIRmutation-positive = 2.65; 95% CI = 1.09–5.47, SIRmutation-negative = 1.48; 95% CI = 1.06–5.47; P = 0.16). Conclusions: There is a range of pancreatic cancer risk to FDRs according to probands' germline mutation status and ABO blood group, ranging from 1.48 for FDRs of probands with blood group O and mutation-negative to 3.98 for FDRs of probands with non-O blood group and mutation-positive. Impact: Combined ABO blood group and germline mutation status of probands can inform pancreatic cancer risk estimation in FDRs.

AbstractPatients with early‐onset Alzheimer's disease (EOAD) are commonly excluded from large‐scale observational and therapeutic studies due to their young age, atypical presentation, or absence of pathogenic mutations. The goals of the Longitudinal EOAD Study (LEADS) are to (1) define the clinical, imaging, and fluid biomarker characteristics of EOAD; (2) develop sensitive cognitive and biomarker measures for future clinical and research use; and (3) establish a trial‐ready network. LEADS will follow 400 amyloid beta (Aβ)‐positive EOAD, 200 Aβ‐negative EOnonAD that meet National Institute on Aging–Alzheimer's Association (NIA‐AA) criteria for mild cognitive impairment (MCI) or AD dementia, and 100 age‐matched controls. Participants will undergo clinical and cognitive assessments, magnetic resonance imaging (MRI), [18F]Florbetaben and [18F]Flortaucipir positron emission tomography (PET), lumbar puncture, and blood draw for DNA, RNA, plasma, serum and peripheral blood mononuclear cells, and post‐mortem assessment. To develop more effective AD treatments, scientists need to understand the genetic, biological, and clinical processes involved in EOAD. LEADS will develop a public resource that will enable future planning and implementation of EOAD clinical trials.

Background and Aims Acute kidney injury (AKI) has a poor prognosis in cirrhosis. Given the variability of creatinine, the prediction of AKI and dialysis by other markers is needed. The aim of this study is to determine the role of serum and urine metabolomics in the prediction of AKI and dialysis in an inpatient cirrhosis cohort. Approach and Results Inpatients with cirrhosis from 11 North American Consortium of End‐stage Liver Disease centers who provided admission serum/urine when they were AKI and dialysis‐free were included. Analysis of covariance adjusted for demographics, infection, and cirrhosis severity was performed to identify metabolites that differed among patients (1) who developed AKI or not; (2) required dialysis or not; and/pr (3) within AKI subgroups who needed dialysis or not. We performed random forest and AUC analyses to identify specific metabolite(s) associated with outcomes. Logistic regression with clinical variables with/without metabolites was performed. A total of 602 patients gave serum (218 developed AKI, 80 needed dialysis) and 435 gave urine (164 developed AKI, 61 needed dialysis). For AKI prediction, clinical factor–adjusted AUC was 0.91 for serum and 0.88 for urine. Major metabolites such as uremic toxins (2,3‐dihydroxy‐5‐methylthio‐4‐pentenoic acid [DMTPA], N2N2dimethylguanosine, uridine/pseudouridine) and tryptophan/tyrosine metabolites (kynunerate, 8‐methoxykyunerate, quinolinate) were higher in patients who developed AKI. For dialysis prediction, clinical factor–adjusted AUC was 0.93 for serum and 0.91 for urine. Similar metabolites as AKI were altered here. For dialysis prediction in those with AKI, the AUC was 0.81 and 0.79 for serum/urine. Lower branched‐chain amino‐acid (BCAA) metabolites but higher cysteine, tryptophan, glutamate, and DMTPA were seen in patients with AKI needing dialysis. Serum/urine metabolites were additive to clinical variables for all outcomes. Conclusions Specific admission urinary and serum metabolites were significantly additive to clinical variables to predict AKI development and dialysis initiation in inpatients with cirrhosis. These observations can potentially facilitate earlier initiation of renoprotective measures.

Abstract Cognitive resilience is an important modulating factor of cognitive decline in Alzheimer’s disease, but the functional brain mechanisms that support cognitive resilience remain elusive. Given previous findings in normal ageing, we tested the hypothesis that higher segregation of the brain’s connectome into distinct functional networks represents a functional mechanism underlying cognitive resilience in Alzheimer’s disease. Using resting-state functional MRI, we assessed both resting-state functional MRI global system segregation, i.e. the balance of between-network to within-network connectivity, and the alternate index of modularity Q as predictors of cognitive resilience. We performed all analyses in two independent samples for validation: (i) 108 individuals with autosomal dominantly inherited Alzheimer’s disease and 71 non-carrier controls; and (ii) 156 amyloid-PET-positive subjects across the spectrum of sporadic Alzheimer’s disease and 184 amyloid-negative controls. In the autosomal dominant Alzheimer’s disease sample, disease severity was assessed by estimated years from symptom onset. In the sporadic Alzheimer’s sample, disease stage was assessed by temporal lobe tau-PET (i.e. composite across Braak stage I and III regions). In both samples, we tested whether the effect of disease severity on cognition was attenuated at higher levels of functional network segregation. For autosomal dominant Alzheimer’s disease, we found higher functional MRI-assessed system segregation to be associated with an attenuated effect of estimated years from symptom onset on global cognition (P = 0.007). Similarly, for patients with sporadic Alzheimer’s disease, higher functional MRI-assessed system segregation was associated with less decrement in global cognition (P = 0.001) and episodic memory (P = 0.004) per unit increase of temporal lobe tau-PET. Confirmatory analyses using the alternate index of modularity Q revealed consistent results. In conclusion, higher segregation of functional connections into distinct large-scale networks supports cognitive resilience in Alzheimer’s disease.

Background: Cerebral amyloid angiopathy with related inflammation (CAA-ri) is a rare age-associated disorder characterized by an inflammatory response to amyloid in cerebral blood vessels. CAA-ri is often treated with corticosteroids, but response to treatment is variable. Objective: To assess the relationship between clinical and paraclinical measures and outcomes in patients with CAA-ri treated with high doses of methylprednisolone. Methods: Longitudinal clinical course, and results from serum and cerebrospinal fluid (CSF) testing, electroencephalography, and neuroimaging were reviewed from 11 prospectively-accrued CAA-ri patients diagnosed, treated, and followed at Barnes Jewish Hospital (St. Louis, MO, USA). Magnetic resonance imaging (MRI) changes were quantified using a scoring system validated in cases of amyloid related imaging abnormality (ARIA-E). Clinical outcomes were assessed as change in modified Rankin Scale (ΔmRS) from baseline to final assessment (median 175 days from treatment with high doses of methylprednisolone; range, 31–513). Results: Worse outcomes following methylprednisolone treatment were associated with requirement for intensive care unit admission (median ΔmRS, 5 versus 1.5; p = 0.048), CSF pleocytosis (median ΔmRS 4.5 versus 1; p = 0.04), or lower CSF Aβ40 at presentation (rho = –0.83; p = 0.02), and diffusion restriction (median ΔmRS 4 versus 1.5; p = 0.03) or higher late ARIA-E scores (rho = 0.70; p = 0.02) on MRI, but not preexisting cognitive decline (median ΔmRS 2 versus 2; p = 0.66). Conclusion: Clinical and paraclinical measures associated with outcomes may inform clinical counseling and treatment decisions in patients with CAA-ri. Baseline cognitive status was not associated with treatment responsiveness.

AbstractIntroductionMachine learning models were used to discover novel disease trajectories for autosomal dominant Alzheimer's disease.MethodsLongitudinal structural magnetic resonance imaging, amyloid positron emission tomography (PET), and fluorodeoxyglucose PET were acquired in 131 mutation carriers and 74 non‐carriers from the Dominantly Inherited Alzheimer Network; the groups were matched for age, education, sex, and apolipoprotein ε4 (APOE ε4). A deep neural network was trained to predict disease progression for each modality. Relief algorithms identified the strongest predictors of mutation status.ResultsThe Relief algorithm identified the caudate, cingulate, and precuneus as the strongest predictors among all modalities. The model yielded accurate results for predicting future Pittsburgh compound B (R2 = 0.95), fluorodeoxyglucose (R2 = 0.93), and atrophy (R2 = 0.95) in mutation carriers compared to non‐carriers.DiscussionResults suggest a sigmoidal trajectory for amyloid, a biphasic response for metabolism, and a gradual decrease in volume, with disease progression primarily in subcortical, middle frontal, and posterior parietal regions.