Citations

Background: Systolic blood pressure (SBP) fluctuation is linked to increased death or disability in intracerebral hemorrhage (ICH) patients. We analyzed data from the Antihypertensive Treatment of Acute Cerebral Hemorrhage (ATACH)-2 trial to explore whether the association between SBP fluctuation and 90-day outcomes varies with time from randomization. Methods: We calculated hourly SBP fluctuation based on the difference between maximum and minimum SBP during the first 24 hours post-randomization. Logistic regression models assessed the impact of SBP fluctuation on: 1) death or disability (modified Rankin scale [mRS] 4-6 at 90 days); 2) hematoma expansion (>33% increase in volume on 24-hour CT scan); 3) neurological deterioration within 24 hours (defined as ≥2-point decrease on Glasgow Coma Scale or ≥4-point increase on NIH Stroke Scale); and 4) acute kidney injury (AKI) within 72 hours. Adjustments were made for baseline Glasgow Coma Scale score, intraventricular hemorrhage, hematoma volume, and maximum SBP. Results: The hourly SBP fluctuations according to 90-day outcomes (dichotomized into good outcome, mRS 0-3, and poor outcome, mRS 4-6) in 961 patients with ICH are presented in Figure. The SBP fluctuation at 5 hours (p=0.014) and 6 hours (p=0.014) post randomization were significantly associated with death or disability at 90 days, with positive, but not statistically significant associations observed at all other points up to 13 hours after randomization. Risk of neurological deterioration within 24 hours was highly associated with SBP fluctuation, with the largest association observed between 1 (p<0.001) and 5 (p<0.001) hours following randomization, with significant associations observed up to 22 hours following randomization. Risk of hematoma expansion was associated with SBP fluctuation between 3 (p=0.015) and 8 (p=0.002) hours post randomization. Statistically significant associations between SPB fluctuations and risk of AKI were not observed. Conclusions: The first 8 hours following randomization are critical for reducing SBP fluctuations, as this period most strongly influences both short- and long-term outcomes in ICH patients. These findings suggest this time window is crucial for future interventions aimed at controlling SBP in ICH patients.

Abstract Electroconvulsive therapy (ECT) is an effective treatment for severe depression, especially in treatment-resistant cases. However, its potential cognitive side effects necessitate careful dosing to balance therapeutic benefits and cognitive stability. Recent advances in electric field (E-field) modeling offer promising avenues to optimize ECT dosing. This review synthesizes current knowledge on E-field modeling in ECT and explores its clinical applications. It examines the variability in E-field strengths and distributions induced by ECT and their impact on clinical outcomes. Additionally, the relationship between E-field strengths, neuroplasticity, and therapeutic efficacy is discussed. Translational studies of E-field-informed ECT are highlighted, emphasizing individualized optimal amplitude dosing and potential clinical applications. This review provides useful insights into how E-field modeling can improve the effectiveness of ECT while minimizing adverse effects, helping guide future research and clinical practice.

AbstractBackgroundThe immune system is substantially involved in the development and progression of age‐related cognitive decline and Alzheimer’s disease (AD).MethodAs genetic and environmental factors interactively impact these conditions, we investigated how risk factors such as APOE genotype, age, and sex influence immune activation markers and AD biomarkers in cerebrospinal fluid (CSF) in elderly individuals enrolled in the Mayo Clinic Study of Aging cohort. Among cognitively unimpaired individuals aged over 65 at the baseline visit (N=298), we measured 365 CSF immune activation markers using the proximity extension assay.ResultWe found that age, sex, and diabetes status are associated with altered CSF levels of immune activation markers independently of other factors. For CSF AD biomarkers, we observed significant positive correlations between age and total tau, phosphorylated tau‐181 (p‐tau181), neurofilament light (NfL), and YKL40. APOE4 was also associated with lower Aβ42 and higher SNAP25 in CSF. We further examined whether baseline visit variables can predict cognitive decline, represented by the conversion from CDR=0 to CDR>0. We found that age, Aβ42, NfL, and REG4 were independently correlated with CDR conversion risk. When the cohort was dichotomized by their median values, older participants with lower Aβ42, higher NfL, and higher REG4 at baseline developed cognitive impairment during the follow up with a c‐index of 0.762 while age alone had a c‐index of 0.699.ConclusionTogether, our results suggest that assessing CSF immune activation markers and AD biomarkers can improve the prediction of cognitive impairment risk in the elderly.

AbstractBackgroundTwo main risk factors of Alzheimer’s disease (AD) are aging and APOE‐ε4. However, some individuals remain cognitively normal despite having these risk factors. They are considered “cognitively resilient”. This study aimed to identify molecular factors that confer cognitive resilience in APOE‐ε4 carriers ≥ 80 years of age and may serve as biomarkers.MethodWe applied weighted gene co‐expression network analysis (WGCNA) to generate consensus co‐expression networks from blood of participants in two antemortem cohorts, the Mayo Clinic Study of Aging (MCSA, n=105), and the Alzheimer’s Disease Neuroimaging Initiative (ADNI, n=91), using RNA‐sequencing and microarray data, respectively. We associated these networks with resilience (resilient vs non‐resilient), cognitive endophenotypes and hippocampal volume. Preservation between consensus networks from blood and those derived from postmortem brain tissues of AD and control donors from AMP‐AD (n=1174) was evaluated. We validated the human findings in four AD mouse models. Finally, machine learning models were utilized to discriminate cases (AD+mild cognitive impairment (MCI)) from controls in MCSA, ADNI and ANMerge antemortem cohorts.ResultFour consensus networks were significantly correlated with a memory phenotype (logical memory delayed recall=LMDR) and hippocampal volume in both MCSA and ADNI. Among these, blood expression module M3 was most preserved with the brain transcriptome. M3 was enriched with NDUF hub genes that are involved in the mitochondrial respiratory chain. Expression levels of M3 and many blood NDUFs had significant associations with better LMDR and hippocampal volume. In brain, NDUFs were upregulated in controls compared to AD, and their expression levels were associated with better global cognition and decreased AD neuropathology. Many NDUFs were significantly downregulated in the hippocampus or cortex of AD mice compared to wild‐types. Lastly, models that included blood NDUFs improved diagnostic accuracy of AD+MCI compared to models that only included demographic and risk variables (age, sex, APOE‐ε4) in MCSA, ADNI and ANMerge. In MCSA and ADNI, adding NDUFs’ expression to models that included established blood biomarkers (Aβ42/40, ptau181, NFL) further improved diagnostic accuracy.ConclusionOur results suggest that mitochondrial NDUFs are centrally‐linked peripheral molecular signatures that may be resilience factors against AD and serve as both therapeutic targets and novel diagnostic biomarkers.

AbstractBackgroundThe immune system is substantially involved in the development and progression of age‐related cognitive decline and Alzheimer’s disease (AD).MethodAs genetic and environmental factors interactively impact these conditions, we investigated how risk factors such as APOE genotype, age, and sex influence immune activation markers and AD biomarkers in cerebrospinal fluid (CSF) in elderly individuals enrolled in the Mayo Clinic Study of Aging cohort. Among cognitively unimpaired individuals aged over 65 at the baseline visit (N=298), we measured 365 CSF immune activation markers using the proximity extension assay.ResultWe found that age, sex, and diabetes status are associated with altered CSF levels of immune activation markers independently of other factors. For CSF AD biomarkers, we observed significant positive correlations between age and total tau, phosphorylated tau‐181 (p‐tau181), neurofilament light (NfL), and YKL40. APOE4 was also associated with lower Aß42 and higher SNAP25 in CSF. We further examined whether baseline visit variables can predict cognitive decline, represented by the conversion from CDR=0 to CDR>0. We found that age, Aß42, NfL, and REG4 were independently correlated with CDR conversion risk. When the cohort was dichotomized by their median values, older participants with lower Aß42, higher NfL, and higher REG4 at baseline developed cognitive impairment during the follow up with a c‐index of 0.762 while age alone had a c‐index of 0.699.ConclusionTogether, our results suggest that assessing CSF immune activation markers and AD biomarkers can improve the prediction of cognitive impairment risk in the elderly.

Abstract Pharmacogenomics (PGx) is focused on the relationship between an individual's genetic makeup and their response to medications, with the overarching aim of guiding prescribing decisions to improve drug efficacy and reduce adverse events. The PGx and genomic medicine communities have worked independently for over 2 decades, developing separate standards and terminology, making implementation of PGx across all areas of genomic medicine difficult. To address this issue, the Clinical Genome Resource (ClinGen) Pharmacogenomics Working Group (PGxWG) was established by the National Institutes of Health (NIH)-funded ClinGen to initially create frameworks for evaluating gene–drug response clinical validity and actionability aligned with the ClinGen frameworks for evaluating monogenic gene–disease relationships, and a framework for classifying germline PGx variants similar to the American College of Medical Genetics (ACMG) and Association of Molecular Pathology (AMP) system for interpretation of disease-causing variants. These frameworks will leverage decades of work from well-established PGx resources facilitating buy-in among PGx stakeholders. In this report, we describe the background and major activities of the ClinGen PGxWG, and how this initiative will facilitate the critical inclusion of PGx into the larger context of genomic medicine.

AbstractBackgroundAccumulation of the amyloid‐β (Aβ) peptide into amyloid plaque is one of the key pathological markers of Alzheimer’s disease (AD). Apolipoprotein E (APOE) is known to modify AD risk and has been reported to influence Aβ accumulation in the brain in an isoform‐dependent manner. ApoE can be produced by various cell types in the brain, with astrocytes being the main producer. Increasing studies show that altering apoE levels can influence Aβ plaque pathology. However, it is not fully understood how apoE produced by specific cell types, such as astrocytes, contributes to amyloid pathology.MethodWe utilized mouse models expressing APOE isoforms in astrocytes and crossed them with 5xFAD mouse models of amyloidosis. We analyzed the changes to amyloid plaques and assessed the impact on cellular responses to amyloid plaques in condition of astrocytic APOE expression.ResultSpecific expression of APOE3 in astrocytes resulted in compact amyloid plaques and a large increase in plaque deposition, while the total plaque burden was reduced. Astrocytic APOE2 expression led to similar changes to amyloid plaques, but the fold change is lower comparing to that in astrocytic APOE3 models. Intriguingly, the total microglial response increased dramatically in condition of astrocytic APOE3 expression but not in condition of astrocytic APOE2 expression. Further, the ratio of plaque associated Lgals3/Iba1 is higher in condition of astrocytic APOE3 expression, indicating increased microglial activation. Additionally, astrocyte GFAP levels only increased in astrocytic APOE3 models, but not in astrocytic APOE2 models. Finally, APOE isoform effect on dystrophic neurites amount was also observed.ConclusionTogether, our study reveals an important role of astrocytic APOE on the deposition and accumulation of Aβ plaques as well as on Aβ‐associated downstream effects.

AbstractBackgroundAlzheimer’s disease (AD) is a progressive neurodegenerative disease and the most prevalent form of late‐life dementia. The ε2 allele of the APOE gene encoding apolipoprotein E (APOE2) is associated with lower susceptibility to AD among the three genotypes (ε2, ε3, ε4), while APOE4 is the strongest genetic risk factor for late‐onset AD. APOE plays a critical role in maintaining synaptic plasticity and neuronal function by controlling lipid homeostasis, with APOE2 having a superior function. Gene therapy that increases APOE2 levels in the brain has, therefore, emerged as a potential therapeutic strategy to treat AD.MethodWe conjugated PEGylated liposomes with transferrin and Penetratin, a cell‐penetrating peptide, sufficiently deliver chitosan‐APOE2 cDNA plasmid complex into amyloid model AppNL‐G‐F knockin mice at 12‐month‐old. Biochemical studies and brain transcriptome analysis were employed to investigate how brain‐targeting liposome‐based APOE2 gene delivery influences amyloid‐β (Aβ)‐related pathologies in AppNL‐G‐F knockin mice.ResultOne month after APOE2 gene therapy, there was a trend of reduced insoluble Aβ levels in the mouse cortices. Furthermore, in the AppNL‐G‐F knockin mice that received the APOE2 gene therapy, brain transcriptome analysis through RNA‐sequencing identified the upregulation of genes/pathways related to neuronal development. This was supported by increases of Dlg4 and Syp mRNAs coding synaptic proteins in the experimental group. On the other hand, we found that APOE2 gene delivery increased soluble Aβ levels, including oligomers, as well as exacerbated neurite dystrophy and decreased synaptophysin.ConclusionOur results suggest that brain‐targeting liposome‐based APOE2 gene therapy is potentially beneficial for synaptic formation at the transcriptional level. Forced APOE2 expressions, however, may exacerbate Aβ toxicity by increasing the dissociation of Aβ oligomers from aggregates in the presence of considerable amyloid burden.

AbstractBackgroundThe efficacy of Calorie Restriction (CR) in enhancing cognition, promoting healthy aging, and extending lifespan is well‐established. Yet, it remains unclear whether the apolipoprotein E (APOE) genotype, a known modifier for aging and age‐related disorders, influences the beneficial effects of CR in countering aging.MethodsTo investigate this question, we utilized humanized APOE mouse models, which express APOE2, APOE3, or APOE4 alleles systematically (refer to as E2, E3, and E4 mice). These mice were subjected to either ad libitum (AL) feeding or 30% CR feeding, starting from 12 months and continuing up to 20 months of age (N = 11‐20/ genotype /group, mixed sex). We monitored energy expenditure using the Comprehensive Laboratory Animal Monitoring System (CLAMS). Additionally, we evaluated anxiety levels and memory capabilities through various behavioral tests. Finally, we collected brain, blood, and feces samples for transcriptomic, lipidomic, and microbiomic analyses.ResultsCLAMS measurements showed that CR significantly decreased the metabolic rate in E3 and E4 mice, but not E2 mice, during the non‐feeding period of the CR group, compared to the AL group. Furthermore, CR reduced anxiety and enhanced associative memory in E3 and E4 mice relative to the AL group. Brain transcriptomics revealed that CR upregulated the cholesterol synthesis pathway, potentially improving myelination‐related pathways in E3 and E4 mice. This finding was further validated by the increased staining intensity of myelin basic protein (MBP) observed in E3 and E4 mice from an independent cohort. In contrast, these effects were not pronounced in E2 mice. Additionally, while CR’s impact on the brain lipid profile was minor across all APOE genotypes, it significantly altered blood lipid profile of E2 mice, with minimal changes observed in E3 and E4 mice. Finally, in microbiomics, CR led to notable changes in the abundance of Bifidobacterium species in E3 and E4 mice and Lactobacillus species in E2 mice.ConclusionsOur study highlights the role of APOE genotypes in modulating the diverse effects of CR on cognition, brain transcriptomics, lipid metabolism, and the microbiome, providing a crucial foundation for considering APOE genotypes in developing prevention strategies for aging and age‐related disorders.

Introduction: Advances in AI have enabled the generation of synthetic individual patient data (SynthIPD) that mimics the statistical properties of the real IPD, which can help generate subgroup-specific insights in the absence of subgroup-specific IPD. We apply this technology in evaluating Daratumumab treatment benefit in high-risk cytogenetic multiple myeloma subgroup. The addition of daratumumab to standard multiple myeloma (MM) regimens has been associated with improved response rates and clinical outcomes. A systematic review (meta-analysis) enables further evaluation of the effectiveness of daratumumab for MM patients with high-risk cytogenetic factors. However, concerns have been raised about meta-analyses due to the statistical model assumptions they impose. This study aims to address these limitations by employing SynthIPD to provide a more robust analysis. Methods: This study focuses on newly diagnosed multiple myeloma (NDMM) and relapsed or refractory multiple myeloma (RRMM) patients with high-risk cytogenetic profiles. We included six published studies: ALCYONE, CASSIOPEIA, and MAIA for NDMM, and CANDOR, CASTOR, and POLLUX for RRMM. Large language models were used to extract up-to-date clinical evidence from the literature. SynthIPD including cytogenetic risk profile were generated via digitization and an innovative optimization algorithm. Meta-analyses based on random effects models were then conducted, and the results were compared with those from pooled analyses using synthetic data. Results: SynthIPD for cytogenetic high-risk patients were generated for three studies for NDMM: ALCYONE (treatment vs control: n=53 vs n=45), CASSIOPEIA (n=82 vs n=86), MAIA (n=48 vs n=44); and three studies for RRMM: CANDOR(n=48 vs n=26), CASTOR (n=40 vs n=35), POLLUX (n=35 vs n=35). The generated hazard ratios (HRs) for each study closely aligned with the reported HRs for high-risk patients, with a maximum relative error of less than 2%. By pooling these datasets, we obtained SynthIPD datasets for NDMM and RRMM patients. Stratified Cox regression analyses yielded HRs of 0.764 (95% CI 0.529-1.104) for NDMM and 0.431 (95% CI 0.294-0.613) for RRMM. Random-effects meta-analyses showed HRs of 0.764 (95% CI 0.529-1.104) for NDMM and 0.425 (95% CI 0.287-0.621) for RRMM. Compared with traditional meta-analysis techniques, the synthetic data approach provides additional clinical insights that were not available in the original publications. For instance, the clinical results for the six studies within high-risk group (e.g., KM curves in high-risk subgroup) can be reproduced, which were not reported in the original studies. Conclusion: AI-generated synthetic data enhances the robustness of conventional meta-analysis outcomes. The identification of HRs and corresponding confidence intervals through SynthIPD enhances the credibility of our proposed method, offering a reliable approach to evaluating the efficacy of daratumumab in high-risk patients. Since SynthIPD includes individual covariate information, any statistical results derived from the original IPD can be effectively approximated using SynthIPD. Using the SynthIPD, we show that the addition of daratumumab to standard regimens improves PFS for RRMM patients with high-risk cytogenetic factors, although its benefit for NDMM patients is not statistically significant. This analysis can be extended to other multiple myeloma high risk subgroups.