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Importance Bioenergetic failure has been proposed as a driver of amyotrophic lateral sclerosis (ALS). CNM-Au8 is a suspension of gold nanocrystals that catalyzes the conversion of nicotinamide adenine dinucleotide hydride into NAD + , resulting in an increase of cellular adenosine triphosphate production. Objective To determine the effects of CNM-Au8 on ALS disease progression. Design, Setting, and Participants CNM-Au8 was tested as a regimen of the HEALEY ALS Platform Trial, a phase 2/3, multicenter, randomized, double-blind platform trial. The study was conducted at 54 sites in the US from July 2020 to March 2022 (final follow-up, March 17, 2022). A total of 161 participants with ALS were randomized to receive CNM-Au8 (n = 120) or regimen-specific placebo (n = 41). Data from 123 concurrently randomized placebo participants in other regimens were combined for analyses. Interventions Eligible participants were randomized in a 3:3:2 ratio to receive CNM-Au8 60 mg daily (n = 61), CNM-Au8 30 mg daily (n = 59), or matching placebo (n = 41) for 24 weeks. Main Outcomes and Measures The primary efficacy outcome was change from baseline through week 24 in ALS disease severity measured by a bayesian shared parameter model of function (based on the Revised Amyotrophic Lateral Sclerosis Functional Rating Scale) and survival, which provided an estimate of the rate of disease progression measured by the disease rate ratio (DRR), with a DRR of less than 1 indicating treatment benefit. Secondary end points included a Combined Assessment of Function and Survival using a joint-rank test, rate of decline in slow vital capacity (percent predicted), and survival free of permanent assisted ventilation. Results Among 161 participants who were randomized within the CNM-Au8 regimen (mean age, 58.4 years; 61 [37.9%] female), 145 (90%) completed the trial. In the primary analysis comparing the combined CNM-Au8 dosage groups vs the combined placebo groups, the primary end point (DRR, 0.97 [95% credible interval, 0.783-1.175]; posterior probability of DRR <1, 0.65) and the 3 secondary end points suggested no benefit or harm of CNM-Au8. In the active (n = 120) vs placebo (n = 163) groups, the most common adverse events were diarrhea (23 [19%] vs 12 [7%]), nausea (17 [14.2%] vs 14 [8.6%]), fatigue (12 [10.8%] vs 30 [18.4%]), and muscular weakness (24 [20%] vs 45 [27.6%]). Conclusions and Relevance No benefit of CNM-Au8 on ALS disease progression was observed at 24 weeks. Trial Registration ClinicalTrials.gov Identifiers: NCT04297683 , NCT04414345

Background: Artificial intelligence (AI) stroke imaging software is becoming mainstay in many hub-and-spoke hospitals. Brainomix 360 Stroke software is the market leader in Europe focused on leveraging simple imaging (non-contrast computerized topography (NCCT) and CT angiography (CTA)) and has recently been FDA-cleared for use in the USA. We evaluated the implementation of Brainomix 360 AI Stroke software in the 17 spoke, multi-state Mayo Clinic Health System (MCHS) telestroke network. Methods: This prospective study compared decision and treatment times before and after Brainomix AI implementation, as well as clinician feedback and simulated decision making to better understand any changes seen. Patients were included if they underwent telestroke evaluation at an MCHS emergency department within 90 days of implementation (2/9/2024-8/8/2024), and excluded if they were already admitted to the hospital or if video evaluation was not performed. Data collection included demographics, clinical decisions and treatment times. Qualitative surveys were conducted at baseline and evaluation end, as well as simulated decision making in 20 de-identified cases randomized to with/without AI through an online portal. Results: A total of 907 patients were included (444 pre- and 463 post-implementation, 287 (32%) with ischemic stroke final diagnosis). Median NIHSS was 2. IVT was recommended in 20.3% (27/148 ischemic stroke patients) pre- and 25.9% (36/139) post-implementation. EVT was recommended in 16.2% (24/148) pre and 14.4% (20/139) post. AI use was associated with trends of faster telestroke activation to IVT decision (36 vs 32 mins, p=0.6), IVT administration (47 vs 40 minutes, p=0.6), and EVT decision (36 vs 33 minutes, p=0.5). In the simulation, imaging interpretation was significantly faster when randomized to AI use (3.4 vs 2.1 mins, p<0.001) and associated with improved physician confidence (62 vs 88%, p=0.004). Qualitative feedback from 14/24 neurologists showed the greatest perceived value was improved confidence of treatment decisions (43%) and faster triaging (36%). Most value was gained from e-ASPECTS (NCCT) and LVO modules. Conclusion: This service evaluation highlighted the opportunity to improve efficiency of decision making even in the setting of a well-developed telestroke system. The impact was underpinned by the improved speed and confidence of imaging interpretation reflected in an offline simulation and qualitative feedback.

AbstractBackgroundThe NIA Health Disparities Research (NIA‐HD) Framework organizes factors in four domains (i.e., environmental, sociocultural, behavioral, and biological), which work together over the lifetime to influence health and health disparities.MethodIn previous work, we employed a 2‐stage approach using factor and principal component (PC) analyses to identify patterns of factors in the domains of the NIA‐HD Framework. We found three PCs that identified related groupings from more than one NIA‐HD domain. PC1: retirement status, residence type, multivitamin use, heart disease, Charlson comorbidity index, walking/balance difficulties, and Beck Depression Inventory (BDI)‐II questions – mainly from the somatic dimension; PC2: physical activity/exercise, social and group activities, education, occupation, cardiometabolic risk factors, and self‐rated health; PC3: national and state area deprivation index ranking and BDI‐II questions – mainly in the cognitive dimension. Age and sex‐adjusted linear regression models were used to assess the cross‐sectional association of each PC with AD‐plasma biomarkers measured using the Quanterix Simoa platform in the Mayo Clinic Study of Aging (MCSA) participants. AD‐plasma biomarkers included Aβ 42/40, phosphorylated‐tau 181 (p‐tau181), plasma glial fibrillary acidic protein (GFAP), and neurofilament light chain (NfL). All analyses were considered statistically significant at a p <0.05.ResultThere were 1300 cognitively unimpaired participants, 50 years old or older, with available plasma biomarkers (mean age (SD) 68.6 (9.5) years; 49.1% were female) at the current study baseline. PC1 and PC3 were associated with higher NfL and p‐tau 181 values (p<0.05). PC1 and PC2 were associated with higher GFAP values (p<0.05). No PCs were associated with the Aβ 42/40 ratio. Findings were mostly similar after additionally adjusting for global cognitive performance and chronic kidney disease.ConclusionWe found that patterns in a wide range of environmental, sociocultural, behavioral, and biological factors were associated with AD plasma biomarkers. Longitudinal studies are warranted to validate findings and support multi‐faceted approaches to address health disparities and achieve disease delay and prevention.

AbstractBackgroundCerebral small vessel disease (cSVD), as defined by neuroimaging characteristics such as white matter hyperintensities (WMHs), cerebral microhemorrhages (CMHs), and lacunar infarcts, is highly prevalent and has been associated with dementia risk and other clinical sequelae. Although risk factors for cSVD have been identified, little is known about the biological processes and molecular mediators that influence cSVD development and progression.MethodsWithin the Atherosclerosis Risk in Communities (ARIC) study, we used SomaScan Multiplexed Proteomic technology to relate 4,877 plasma proteins to concurrently measured MRI‐defined cSVD characteristics, including WMHs, CMHs, and lacunar infarcts, in late‐life (n=1508; mean age: 76). Candidate protein associations with WMH volume were replicated in the Cardiovascular Health Study (n=765; mean age: 73) and Baltimore Longitudinal Study of Aging (n=902; mean age: 66). Cohort‐specific effect estimates were pooled using a fixed‐effect, inverse variance weighted meta‐analysis. Within 4228 non‐demented older adults in ARIC, we examined the association of WMH‐associated proteins with eight‐year dementia risk (641 incident dementia cases) using multivariate Cox proportional hazard models. All multivariate regression models were adjusted for demographic, kidney function, and cardiovascular risk variables.ResultsThis proteome‐wide analysis of older adults identified 13 WMH‐associated plasma proteins involved in synaptic function, endothelial integrity, and angiogenesis (Figures 1a, 2a). Nine of these proteins replicated in one or more external cohort (Figures 1b, 2b) and two proteins, oligodendrocyte myelin glycoprotein (OMG) and neuronal pentraxin receptor (NPTXR), remained associated with late‐life WMH volume when measured nearly two decades earlier during midlife (Figures 1c, 2c). Eight of the 13 WMH‐associated proteins were associated with lacunar infarcts, 6 with subcortical CMHs, and 4 with lobar CMHs (Figures 1d, 2d). Although none of the genes coding for WMH‐associated proteins have been nominated by WMH GWAS, 7 of these genes have been previously linked to cardiometabolic traits known to influence cSVD, such as blood pressure and cholesterol (Figures 1e, 2e). Finally, 11 candidate proteins were associated with incident dementia, underscoring their clinical relevance (Figures 1f, 2f).ConclusionsThis large‐scale proteomic analysis within longitudinal community‐based samples identified several plasma proteins, including OMG and NPTXR, as top candidate biomarkers for elevated WMH volume and its clinical manifestations.

AbstractBackgroundPlasma Aβ42/40 and p‐tau217 can predict amyloid positivity in cross‐sectional studies. However, it is unclear how plasma biomarkers perform longitudinally, which is important to inform their utility in tracking disease progression. The goal of this study is to describe temporal evolutions of plasma Aβ42/40 and ptau217 ratio (p‐tau217r) measured via mass spectrometry, p‐tau217 measured via an immunoassay, and amyloid PET.MethodsWe included participants in the Mayo Clinic Study of Aging with amyloid PET (PiB) and plasma Aβ42/40 + ptau217r (mass spectrometry, C2N) and/or p‐tau217 (Lilly, MSD). We fit a joint non‐linear mixed effects model with Aβ42/40, p‐tau217r, p‐tau217 (Lilly), and amyloid PET as outcomes and age, sex, APOE ε4 carriership and education as covariates. The model included a per‐participant adjustment (time‐shift) for each biomarker indicating how much earlier or later in time the biomarker was estimated to progress relative to the average participant, a correlation (R) between adjustments for each pair of biomarkers, and the impact of covariates on each biomarker’s timing.ResultsThe study included 1459 participants, age 77 (10) years, 44% female, 82% cognitively unimpaired. Mean (SD) years of followup was 5.6(3.0) years for mass spectrometry plasma measures, 2.5(0.7) years for p‐tau217 (Lilly), and 4.6(3.3) for amyloid PET. Model fits are shown for each biomarker, with greater variability in serial measurements for plasma than PET (Figure 1). Individual adjustments between each of the plasma biomarkers and amyloid PET were highly correlated: p‐tau217r, R=0.73 (95%CI: 0.67, 0.78); p‐tau217 (Lilly), R=0.66 (0.62, 0.7); Aβ42/40, R=0.6 (0.52, 0.68) (Figure 2). The p‐tau217r and Aβ42/40 individual adjustments were modestly correlated, R=0.39 (0.28, 0.49). APOE ε4 carriership had the strongest covariate effect on all biomarkers, e.g. p‐tau217r progression 9.4 (6.7, 12) years earlier (Figure 3).ConclusionsPlasma Aβ42/40 and p‐tau217 measured with currently available assays may both be amenable to longitudinal modeling, although the degree of variability in Aβ42/40 over time may pose challenges. Findings suggest that both plasma Aβ42/40 and p‐tau217r may provide information regarding an individual’s timing of progression on amyloid PET; lower correlations with each other than amyloid PET suggests that each analyte may provide complimentary information.

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.