Biological age scores capture ageing heterogeneity beyond chronological age but are often dominated by lifestyle and environmental exposures, limiting clinical interpretability. We developed an environmentally adjusted metabolic age score (EAmAge) to isolate intrinsic ageing biology relevant to neurodegeneration and chronic disease. Major environmental influences were statistically removed from plasma lipidomic profiles before constructing an age-prediction model using ridge regression. EAmAge was derived in the AusDiab cohort (n = 10,339) and validated across three independent cohorts (BHS, ADNI and ASPREE; total n = 9,835). Compared with an unadjusted lipidomic age model (mAge_orig), EAmAge showed stronger and more consistent associations with incident Alzheimer’s disease-related dementia, cardiovascular events and all-cause mortality. EAmAge was also associated with Alzheimer’s disease-related biomarkers, including amyloid burden, reduced glucose metabolism and hippocampal atrophy. These findings establish EAmAge as a robust and partially modifiable biomarker that improves risk stratification by disentangling intrinsic metabolic ageing from environmental confounding.

Abstract Introduction Alzheimer’s disease (AD) is characterized by pathologies including amyloid, tau, neurodegeneration reflected in established blood biomarkers and track with clinical changes. However, the association between peripheral cell-specific signatures and AD-related phenotypes remain poorly characterized. Methods We analyzed bulk blood transcriptome data from the Mayo Clinic Study of Aging (MCSA) and Emory University Vascular study. We used BayesPrism, CIBERSORTx, and an in-house pipeline CNNreg to deconvolute these data and obtain peripheral cell proportions. Cell type specific transcripts were estimated with BayesPrism. We compared cell proportions between cases (AD/MCI) and controls. Association analysis was performed between cell type specific transcripts and AD-related phenotypes including diagnosis and cognition. Meta-analysis of transcripts associations and gene ontology analysis were conducted to assess the enriched pathways for significant genes. Results In MCSA, B and CD4+ T cells proportions are significantly lower while that of myeloid cells higher in cases. The Emory cohort had similar trends. We identified transcripts associated with AD-related phenotypes in CD4+ T and CD8+ T cells. In CD4+ T cells, transcripts downregulated in case are enriched in response to stimulus pathway, whereas those upregulated in negative regulation of immune response. Downregulation of CD4+ T genes enriched in extracellular matrix disassembly and epigenetic regulation and upregulation of those in protein localization associate with better cognition. In CD8+T cells, genes pertinent to vascular development were downregulated while those to metabolic processes were upregulated in cases. Downregulation of CD8+T genes involved in lipid transportation and upregulation of those in mitochondria associate with better cognition. Conclusion We identified peripheral cellular transcriptional changes associated with AD/MCI phenotypes and involved in important biological pathways, revealing potential disease mechanisms in AD. Funding Source RF1 AG051504 Topic Categories Neuroimmunology (NEUR)

Previously derived polygenic risk scores (PRSs) for Alzheimer's disease (AD) perform inconsistently across diverse ancestries. We developed an APOE-independent multiancestry AD PRS using genome-wide association study summary statistics applied to European ancestry, African American, Caribbean Hispanic and East Asian cohorts. PRS performance was evaluated in a large independent multiancestry dataset and validated in several additional multiancestry cohorts. The PRS was significantly associated with AD in European ancestry, African American, Caribbean Hispanic and Native American Hispanic groups with adjusted odds ratios between 1.14 and 1.52 per PRS standard deviation. PRS performance was validated in the replication cohorts (odds ratios: 1.21-1.65). The PRS was also associated with poorer memory, executive function and language performance, greater AD-related neuropathological burden, reduced hippocampal volume, lower cerebrospinal fluid amyloid-β42 and elevated total tau and phosphorylated tau, with stronger phosphorylated tau associations observed in women. Our findings support the value of ancestry-aware PRSs as a component of broader multimodal risk stratification frameworks.

Abstract INTRODUCTION This study examined veteran‐specific factors affecting interest in Alzheimer's disease and related dementias (ADRD) biomarker research participation. METHODS In this study, 505 community‐dwelling older adults (age ≥55) who had never participated in AD research, including 179 veterans, completed a survey about their perceptions of AD biomarker research. RESULTS Most veterans expressed interest in doing biomarker research (74.0%). They were more likely than non‐veterans to be very concerned about their memory and thinking (31.8% vs 20.6%, p = 0.041) and view research participation as an activity that everyone should do (64.8% vs 52.9%, p = 0.045). However, they were more likely to perceive positron emission tomography scans as unsafe (27.1% vs 14.0%, p = 0.027). DISCUSSION Veterans have distinct facilitators and barriers to AD research participation. Future studies to develop veteran‐centered recruitment strategies should consider differences in motivators, knowledge levels, and hesitancy for specific research procedures.

Alzheimer's disease (AD) patients suffer from consequential diagnostic delay due to the lack of accessible biomarkers. They also show different responses to treatments due to disease heterogeneity and progression. Here, we developed a novel framework to identify disease progression and subtypes by using geometric brain signatures derived from multiple neuroimaging modalities, including [ 18 F]-Florbetapir (AV45) Positron Emission Tomography (PET), [ 18 F]-Fludeoxyglucose (FDG) PET, and structural Magnetic Resonance Imaging (MRI). These signatures were derived by decomposing corresponding maps of amyloid-beta levels, metabolic activity, and cortical thickness in terms of the fundamental, resonant modes--eigenmodes--of cortical geometry, each tied to a specific spatial resolution scale. Our results showed that geometric eigenmode-based features identified trajectories of disease progression, quantified as pseudotime, in distinct subtypes. The disease progression trajectories and subtypes are identified with high stability and are highly related to biological and cognitive measures. These performances are superior to those obtained using conventional localised features and remain robust across datasets, indicating that geometric signatures of brain structure and function can be used to uncover new markers of AD diagnosis and prognosis that are missed by conventional localisation approaches.

Cognitive impairment is increasing with global aging, yet mechanisms linking diet, the gut microbiome, and metabolism to cognitive function remain unclear. To investigate a diet-microbiome-metabolome axis associated with cognition, we integrated fecal metagenomics, diet, and multi-platform plasma metabolomics in 505 older adults from four ADRCs. Several microbes broadly associated with circulating metabolites were also linked to multiple measures of cognitive performance. These taxa exhibited coordinated metabolic signatures, with cognition-positive microbes associated with antioxidant, lipid, and microbial-host co-metabolites, and microbes negatively associated with cognition were linked to inflammatory and aromatic amino acid-derived metabolites. Dietary patterns, particularly the Healthy Eating Index Greens and Beans component, were associated with microbial composition and metabolomic structure. Mediation analyses supported a diet-microbe-metabolite-cognition pathway, while metabolites remained associated with cognition after accounting for microbial features. These findings highlight the metabolome as a central integrator of diet, microbial activity, and cognitive function.

Introduction Treating early cognitive deficits in Mild Cognitive Impairment (MCI) can improve quality of life and may slow or prevent disease progression. Cholinergic system integrity appears to be critical for maintaining normal cognitive functioning in aging. Loss of cholinergic neurons as well as a decline in nicotinic cholinergic receptor number is related to cognitive decline in Alzheimer's disease (AD). Nonspecific cholinergic enhancement via acetylcholinesterase inhibition (AChEI) has modest cognitive benefits in patients with AD and MCI. Treatment directly targeting nicotinic cholinergic receptors may be an additional option. Cognitive improvement is one of the best-established therapeutic effects of nicotinic stimulation. Nicotine improves performance on attention and cognitively demanding vigilance tasks and response inhibition performance, suggesting that nicotine may act to optimize attention/response mechanisms as well as enhancing working memory.A proof of concept 6-month multicenter trial (1) demonstrated that transdermal nicotine provides significant improvement in attention, episodic memory, and global ratings of functioning In MCI with minimal side effects. The MIND trial (Memory Improvement with Nicotine Dosing) was a larger and longer (2-year) trial to determine whether long-term transdermal nicotine treatment results in persistence of cognitive improvement and attenuation of cognitive decline in patients with MCI. Methods At 39 sites, participants with MCI were randomized 1:1 to either transdermal nicotine, beginning at 3.5 mg/day, increasing to 21 mg/day or matching placebo over 5 weeks. Participants were assessed at 0, 3, 6, 12, 18, and 24 months, with a subset undergoing MRI scans at 0, 12, and 24 months, and CSF collection at 0 and 24 months. Participants were allowed concomitant anticholinesterase or disease modifying therapies if initiated after baseline. The primary cognitive outcome was the International Shopping List Test- Total Immediate Recall (ISLT-TIR) score. A key secondary outcome is the MCI-Clinical Global Impression of Change (CGIC). Secondary cognitive outcomes include the Cogstate battery and the Continuous Performance Test (CPT). Secondary clinical measures include behavioral/functional scales and the Clinical Dementia Rating Scale Sum of Boxes (CDR-SB). An optional MRI and CSF sub-study was designed to examine AD-associated structural, biochemical, and functional effects of nicotine therapy. Results 692 participants were screened and 348 were randomized including 151 females (43%) and 197 males (57%). Mean age was 73.7± 7 (55-90), education 15.9± 2.9 (4-30) years. Mean MMSE was 27.3± 2 (20-30) and CDR Global was 0.5. Randomization of populations of special interest was 7.2% (Black/African-American 6.3%, Asian 2%, Hispanic/Latino 3%). 197 participants completed the study. Treatment and/or study discontinuations were higher than expected at 43%. Adverse events and study withdrawal due to other reasons were the most common reasons for discontinuation. In the nicotine arm discontinuation due to adverse events was higher (40.7% vs 27%).Topline analyses showed that transdermal nicotine treatment did not result in a significant difference in the ISLT-TIR score (0.37; 95% CI,-0.783, 1.516 p=0.53) or the MCI-CGIC (0.59; 95% CI, 0.27, 1.291 p=0.187). Secondary outcomes show no significant difference in CDR-SB score (-0.09; 95% CI, -0.501, 0.317), Mini-Mental State Examination (MMSE) (0.61; 95% CI, -0.143, 1.354), International Shopping List Task - Delayed Recall (ISRL) (0.14; 95% CI, -0.454, 0.741) or Activities of Daily Living (ADL) (-0.05, 95% CI, -1.787, 1.684). The pattern of means favored transdermal nicotine treatment on several secondary cognitive measures including the CPT, but these did not reach statistical significance.Safety data indicate that transdermal nicotine treatment was generally well tolerated. The most frequent adverse events associated with treatment discontinuation included dizziness, insomnia, and nausea. No serious adverse events were definitively or probably tied to study participation or investigational product. No drug withdrawal symptoms were reported following treatment cessation. Conclusions Topline study results show that up to 2 years of treatment with transdermal nicotine did not demonstrate a benefit on cognitive or functional outcomes. Dropout rate was higher than expected and was exacerbated by the COVID pandemic but was not different by treatment arms. Accounting for APOE genotype, ptau217 status, and AChEI cotreatment did not alter the primary results. Analysis of nicotine blood concentration effects on treatment outcomes is ongoing. Treatment was generally well tolerated, and SAEs did not differ between treatment arms. There was no evidence of cardiovascular complications, withdrawal, or dependence symptoms. Future studies assessing the potential of combination of nicotinic and muscarinic receptor stimulation along with disease-modifying agents are to achieve long-term cognitive benefit are under consideration.

Environmental exposures influence the risk of psychiatric disorders, yet the biological mechanisms by which such experiences become embedded in brain structure remain poorly understood. The human cerebral cortex is crucial for cognition and emotional regulation, and variation in cortical thickness (CT) and surface area (SA) is linked to various behavioural and psychiatric traits. Here, we present a large-scale epigenome-wide association study that combines peripheral blood DNA methylation (DNAm) with MRI-derived cortical measures in over 7,400 individuals across 20 cohorts within the ENIGMA consortium. We identify mostly non-overlapping DNAm signatures associated with CT and SA, consistent with their distinct developmental and regulatory architectures. CT-associated CpGs are replicated across independent cohorts and are enriched for environmentally responsive regulatory elements and pathways associated with stress, metabolism, and immune signalling. In contrast, SA-associated CpGs cluster within chromatin-regulatory regions involved in early cortical development. Phenome-wide and Mendelian randomisation analyses reveal pleiotropic associations between DNAm, cortical structure, and psychiatric and cognitive traits. These findings suggest that peripheral DNAm captures environmentally sensitive biological processes that link exposure, cortical organisation, and behavioural vulnerability.

The functional network architecture of the aging brain undergoes significant systematic and idiosyncratic changes. Emergent individualized network mapping approaches may yield better or more sensitive explanatory insight about age-related neural and behavioral variability, although most applications have focused on young adults. In the current study, we tested the validity and impact of mapping individual-specific topography in two fMRI datasets comprising 112 young (18-35 years) and 176 older adults (60-92 years). Older adults had more idiosyncratic network topography than young adults. Individualized maps from resting-state fMRI improved network homogeneity and fidelity to social cognitive task fMRI activations and exhibited intra-individual stability and inter-individual discriminability over a 2-year interval. Last, traditional group-averaged (vs. individualized) network mapping had a moderate-to-large impact on individual-level estimates of network segregation, a widely-studied measure of functional brain aging. Therefore, individualized network mapping captures important heterogeneity in older adulthood and may yield more precise characterization of neurocognitive aging.

AbstractINTRODUCTIONThe exponential growth of genomic datasets necessitates advanced analytical tools to effectively identify genetic loci from large‐scale high throughput sequencing data. This study presents Deep‐Block, a multi‐stage deep learning framework that incorporates biological knowledge into its AI architecture to identify genetic regions as significantly associated with Alzheimer's disease (AD). The framework employs a three‐stage approach: (1) genome segmentation based on linkage disequilibrium (LD) patterns, (2) selection of relevant LD blocks using sparse attention mechanisms, and (3) application of TabNet and Random Forest algorithms to quantify single nucleotide polymorphism (SNP) feature importance, thereby identifying genetic factors contributing to AD risk.METHODSThe Deep‐Block was applied to a large‐scale whole genome sequencing (WGS) dataset from the Alzheimer's Disease Sequencing Project (ADSP), comprising 7416 non‐Hispanic white (NHW) participants (3150 cognitively normal older adults (CN), 4266 AD).RESULTS30,218 LD blocks were identified and then ranked based on their relevance with Alzheimer's disease. Subsequently, the Deep‐Block identified novel SNPs within the top 1500 LD blocks and confirmed previously known variants, including APOE rs429358 and rs769449. Expression Quantitative Trait Loci (eQTL) analysis across 13 brain regions provided functional evidence for the identified variants. The results were cross‐validated against established AD‐associated loci from the European Alzheimer's and Dementia Biobank (EADB) and the GWAS catalog.DISCUSSIONThe Deep‐Block framework effectively processes large‐scale high throughput sequencing data while preserving SNP interactions during dimensionality reduction, minimizing bias and information loss. The framework's findings are supported by tissue‐specific eQTL evidence across brain regions, indicating the functional relevance of the identified variants. Additionally, the Deep‐Block approach has identified both known and novel genetic variants, enhancing our understanding of the genetic architecture and demonstrating its potential for application in large‐scale sequencing studies.Highlights Growing genomic datasets require advanced tools to identify genetic loci in sequencing. Deep‐Block, a novel AI framework, was used to process large‐scale ADSP WGS data. Deep‐Block identified both known and novel AD‐associated genetic loci. rs429358 (APOE) was key; rs11556505 (TOMM40), rs34342646 (NECTIN2) were significant. The AI framework uses biological knowledge to enhance detection of Alzheimer's loci.

Large-scale genome-wide association studies (GWAS) of Alzheimer’s disease (AD) from European ancestry identified many genetic variants associated with clinical diagnosis of AD dementia. However, it remains unclear whether these AD-related variants are associated with AD biomarkers, particularly hippocampal atrophy, a well-known neurodegeneration biomarker of AD in a Korean population. In this study, we investigated the association between known AD risk single nucleotide polymorphisms (SNPs) and hippocampal atrophy along the AD continuum in older Korean adults. A total of 487 participants (258 cognitively normal olde adults [CN], 144 mild cognitive impairment [MCI], 85 AD dementia) from the Korean Brain Aging Study for the Early Diagnosis and Prediction of Alzheimer’s disease (KBASE) were included for analysis. All participants underwent 11 C-PiB-PET/MRI. Hippocampal volume adjusted for intracranial volume (HVa) was obtained from 3D T1-weighted MRI scans using FreeSurfer and used as a neurodegeneration marker of AD. Global beta-amyloid (Aβ) deposition was calculated from PiB uptake in the global cortical region-of-interest using SPM12. From the genetic evidence gathered by the AD Sequencing Project (ADSP), which consists of 76 SNPs associated with AD, we selected 38 SNPs with a minor allele frequency (MAF) greater than 1% from the genotyping data imputed using the TOPMed imputation server in the KBASE cohort. Among 38 known AD-related SNPs, three SNPs (rs6966331 in EPDR1 , rs2242595 in MYO15A , and rs17125924 in FERMT2 ) were associated with HVa in an initial exploratory analysis ( p <0.05). In a subsequent confirmatory analysis, the associations of rs6966331 in EPDR1 and rs2242595 in MYO15A with HVa remained significant after controlling for age, sex, and APOE4 carrier status, as well as global Aβ deposition ( p <0.001 and p = 0.009 for rs6966331 and rs2242595, respectively) (Table 1). Our study identified associations of rs6966331 in EPDR1 and rs2242595 in MYO15A with hippocampal volume in Korean older adults, and these associations were independent of cerebral Aβ deposition and APOE4 carrier status. These findings suggest that these AD-related loci may contribute to the development of AD dementia via Aβ-independent neurodegeneration.

INTRODUCTION:White matter (WM) microstructure is essential for brain function but deteriorates with age and in neurodegenerative conditions such as Alzheimer's disease (AD). Diffusion MRI, enhanced by advanced bi-tensor models accounting for free water (FW), enables in vivo quantification of WM microstructural differences. METHODS:To evaluate how AD genetic risk factors affect limbic WM microstructure - crucial for memory and early impacted in disease - we conducted linear regression analyses in a cohort of 2,614 non-Hispanic White aging adults (aged 50.12 to 100.85 years). The study evaluated 36 AD risk variants across 26 genes, the association between AD polygenic scores (PGSs) and WM metrics, and interactions with cognitive status. RESULTS:AD PGSs, variants in TMEM106B, PTK2B, WNT3, and apolipoprotein E (APOE), and interactions involving MS4A6A were significantly linked to WM microstructure. DISCUSSION:These findings implicate AD-related genetic factors related to neurodevelopment (WNT3), lipid metabolism (APOE), and inflammation (TMEM106B, PTK2B, MS4A6A) that contribute to alternations in WM microstructure in older adults. HIGHLIGHTS:AD risk variants in TMEM106B, PTK2B, WNT3, and APOE genes showed distinct associations with limbic FW-corrected WM microstructure metrics. Interaction effects were observed between MS4A6A variants and cognitive status. PGS for AD was associated with higher FW content in the limbic system.

Some evidence supports an association between traumatic brain injury (TBI) and greater risk of dementia, but the role of cognitive resilience in this association is poorly understood. 2,050 participants from the Framingham Heart Study Offspring cohort who were aged ≥60 year and had a plasma total tau (t-tau) measure at Exam 8 (2005-2008), and a neuropsychological (NP) exam visit within five years were included. Plasma t-tau was measured using the Simoa assay (Quanterix). NP factor scores were previously derived for memory, language, and executive function using confirmatory factor analysis. Information on TBIs was collected by comprehensive review of medical records, health history updates, exams, and self-report. TBI occurrence and severity were operationalized using modified ACRM & VA/DoD criteria, respectively. Cognitive resilience was operationalized using a residual approach by regressing each NP factor score on the plasma t-tau measure, adjusting for age at Exam 8, sex, education, time from blood draw, and APOE ε4 genotype. The adjusted residuals were then regressed on history of TBI (yes versus no), and severity of TBI (moderate-to-severe versus mild versus none). The sample was, on average, 67 years of age at Exam 8, 54% female, and college educated. No differences were observed in plasma t-tau levels between those with and without TBI. Having a history of TBI was significantly associated with a reduction in resilience in executive function (β: -0.110; 95% CI: -0.175, -0.044; p: 0.001) as compared to not having a history of TBI. No significant associations were observed between history of TBI and resilience in memory or language. Greater TBI severity was significantly associated with worse resilience in executive function in a dose-response manner (P trend : <0.001), with the association being strongest in the moderate-to-severe TBI group (β: -0.209; 95% CI: -0.340, -0.078; p: 0.002) followed by the mild TBI group (β: -0.082; 95% CI: -0.155, -0.010; p: 0.026). Having a TBI was associated with worse resilience to neurodegeneration in executive function, and most strongly among individuals with moderate-to-severe TBI. These results suggest that having a TBI may increase vulnerability to late-life executive dysfunction after accounting for a primary neurodegenerative disease process.

Previous studies suggest limited knowledge about Alzheimer’s Disease (AD) is a barrier to underrepresented group participation in AD research. Connections between knowledge of AD and factors like social determinants of health or confidence in biomarker research have not been carefully examined. We hypothesized perceived knowledge about AD would be associated with research hesitancy independent of sociodemographics and trust of researchers. The AD-REACH study surveyed 399 research-naïve non-Hispanic Black and white adults 55 years and older living in Indianapolis, Indiana. Participants reported perceived knowledge of AD on a 5-point scale (5 = “I know what [AD] is, what causes it, and how to manage and prevent it;” 1 = “I know nothing at all”). Knowledge was dichotomized into higher (≥ 3) and lower (< 3) levels. Demographics were compared using chi-squared tests and t-tests. Ordinal logistic regression models examined the association between perceived knowledge about AD and outcome variables (e.g., hesitancy towards research participation) and were adjusted for sociodemographics and very high trust of researchers. Those who reported being Black, male, from a higher Area Deprivation Index, or having less than 16 years of education had lower perceived knowledge about AD ( Table 1 ). Trust of researchers was not associated with perceived knowledge. Those reporting lower AD knowledge described more hesitancy to participate in biomarker research (53.0% vs 72.7%, OR 2.50, 95% CI 4.35-1.39, p = 0.002) and blood draw (62.3% vs 80.9%, OR 2.17, 95% CI 4.00-1.19, p = 0.012) but not neuroimaging procedures ( Table 2 ). Those with lower perceived knowledge of AD were more likely to need additional information to make decision about whether to participate in research (58.1% vs 29.9%, OR 3.70, 95% CI 9.09-1.49, p = 0.005) ( Table 3 ). Populations with health disparities report lower knowledge of AD, and lower perceived knowledge is associated with research hesitancy, especially for biomarker procedures. Perceived AD knowledge is also independent from trust, suggesting the feasibility of a two-pronged intervention to foster diverse participation in AD biomarker research. Future studies will need to confirm these findings in other cohorts and examine how culturally tailored strategies to increase AD knowledge may reduce research hesitancy among underrepresented groups.

Despite recognition of the need to increase underrepresented groups (URG) engagement in Alzheimer’s disease and related dementias (ADRD) studies, enrollment remains low. As a first step in examining these disparities, these analyses aimed to compare referral sources for Alzheimer’s Disease Research Centers (ADRC) enrollment of URG participants. These analyses included data from 48,330 participants across 46 ADRCs, obtained through the National Alzheimer’s Coordinating Center Uniform Data Set. Generalized logistic regression models with generalized estimating equations were used to examine the association of racial/ethnic group and professional vs non-professional referral source. The ‘professional’ category included referrals made by healthcare professionals or ADRC staff, while the ‘non-professional’ category included referrals made by self, family or friends. This association was examined across the entire sample, and then individuals who had completed magnetic resonance imaging (MRI). The analyses were adjusted for age, gender, education, visit year, and categorical CDR with random site effect to adjust for study site. Descriptive statistics are shown in Table 1. Non-Hispanic Black and Asian participants were less likely to have completed an MRI. Across the entire sample, Non-Hispanic Black and Non-Hispanic Asian participants were less likely to be referred by a professional contact than Non-Hispanic White participants (Table 2). In those who had completed an MRI, there were no significant differences across the racial groups, although we note that the sample sizes for those with MRI were much smaller (Table 3). Results for both analyses were similar when only participants who had a diagnosis of MCI or dementia and a global CDR of 0.5 or 1 at baseline were included. One major factor leading to lower rates of URG participation in ADRD research is disproportionately fewer healthcare professional referrals. To develop and optimize ADRC recruitment strategies, future studies are needed to explore reasons for differences in URG referrals by healthcare professionals and non-professionals.

The Mediterranean diet has been associated with decreased brain atrophy (Staubo et al. 2016, Alz&Dem ), but the MIND (Mediterranean-Dietary Approaches to Stop Hypertension (DASH) Intervention for Neurodegenerative Delay) diet, designed for dementia prevention (Morris et al. 2015, Alz&Dem ), remains underexplored for its impact on brain atrophy. We investigated the MIND diet’s association with cortical thickness (CT) in the Indiana Alzheimer’s Disease Research Center (IADRC) sample. 134 participants (49 CN, 45 SCD, 30 MCI, 10 AD/other) completed a self-report MIND diet questionnaire at the IADRC, which was coded into high, medium, or low intake groups for each food (5 ‘unhealthy’ food groups were reverse scored) and completed an MRI scan on a 3T scanner. The cortical surface was parcellated using FreeSurfer v6. We selected two regions of interest (ROIs) reflecting AD-associated neurodegeneration: temporal and global CT. We examined the association of MIND diet scores (0-15) and food groups with CT using regression models adjusted for age, sex, race, education, and diagnosis. Higher MIND diet scores were associated with greater mean temporal CT (r = 0.269, p = 0.002) and greater mean global CT (r = 0.230, p = 0.008). In multivariable-adjusted models, the association persisted for temporal but not global CT. Among the 15 food components, greater olive oil (r = 0.034, p<0.001), fish (r = 0.181, p = 0.040), beans (r = 0.237, p = 0.008), and nuts (r = 0.214, p = 0.014), and reduced fast food intake (r = 0.188, p = 0.035) were significantly associated with temporal CT. These associations, except for nuts, remained significant in multivariable-adjusted models, with an additional relationship found for chicken (r = 0.189, p = 0.038). Among the 15 food components, greater olive oil (r = 0.243, p = 0.008), and beans (0.180, p = 0.044), and reduced fast food (r = 0.212, p = 0.017) were significantly associated with global CT. Only reduced fast food retained significance in the multivariable-adjusted models. Greater adherence to the MIND diet was associated with greater CT in both global and temporal regions. Specific components, including increased olive oil, beans, nuts, fish, and reduced fast food, showed significant associations with CT, suggesting elements within the diet driving this association. These findings highlight the potential neuroprotective effects of the MIND diet, emphasizing the importance of dietary patterns in preserving brain health during aging.

Understanding the relationship between genetic variations and brain imaging phenotypes is an important issue in Alzheimer's disease (AD) research. As an alternative to GWAS univariate analyses, canonical correlation analysis (CCA) and its deep learning extension (DCCA) are widely used to identify associations between multiple genetic variants such as SNPs and multiple imaging traits such as brain ROIs from PET/MRI. However, with the recent availability of numerous genetic variants from genotyping and whole genome sequencing data for AD, these approaches often suffer from severe overfitting when dealing with ‘fat’ genetics data, e.g. large numbers of SNPs with much smaller numbers of samples. Here, we propose to tackle the challenge by integrating an efficient model parameterization approach from Mila’s Diet Network architecture into DCCA to handle high dimensional SNP data in AD imaging-genetics study (Figure 1). The new method, DietDCCA, was applied to nine datasets derived from 955 subjects in the ADNI data. Each dataset contains 68 FreeSurfer cortical ROIs from the florbetapir (AV45) PET imaging and varied numbers of SNPs from 810 to 11,938 based on different significance thresholds derived from previous studies. Firstly, we compared our DietDCCA with DCCA on each of the nine datasets to demonstrate the improvement on test correlations (Figure 2). DietDCCA outperformed DCCA by a large margin on all datasets even when the SNPs are as many as ∼10k. Next, we sought to verify if the detected correlations were contributed by meaningful SNPs and extracted the SNP feature that has the largest weight in each neuron of the diet net layer (Figure 3). DietDCCA successfully selected the APOE4 SNP (rs429358) for most cases and also picked out SNPs in other genes (ABCA7, APOC2, CLPTM1, NECTIN2) that were previously reported to associate with AD. We introduced a novel method, DietDCCA, to handle high-dimensional SNP features in AD imaging-genetics study. The initial investigation of DietDCCA on the ADNI data showed promises in detecting correlation signals with AV45 ROIs from biologically meaningful SNPs. The study supplies a novel and effective tool to study the genetic basis of AD imaging phenotypes for future analyses.

Impaired glucose uptake in the brain is an early presymptomatic manifestation of Alzheimer's disease (AD), with symptom-free periods of varying duration that likely reflect individual differences in metabolic resilience. We propose a systemic "bioenergetic capacity", the individual ability to maintain energy homeostasis under pathological conditions. Using fasting serum acylcarnitine profiles from the AD Neuroimaging Initiative as a blood-based readout for this capacity, we identified subgroups with distinct clinical and biomarker presentations of AD. Our data suggests that improving beta-oxidation efficiency can decelerate bioenergetic aging and disease progression. The estimated treatment effects of targeting the bioenergetic capacity were comparable to those of recently approved anti-amyloid therapies, particularly in individuals with specific mitochondrial genotypes linked to succinylcarnitine metabolism. Taken together, our findings provide evidence that therapeutically enhancing bioenergetic health may reduce the risk of symptomatic AD. Furthermore, monitoring the bioenergetic capacity via blood acylcarnitine measurements can be achieved using existing clinical assays.

Shared genetic risk between Alzheimer’s disease (AD) and concussion may help explain the association between concussion and elevated risk for dementia. However, there has been little investigation into whether AD risk genes also associate with concussion severity/recovery, and the limited findings are mixed. We used AD polygenic risk scores (PRS) and APOE genotypes to investigate associations between AD genetic risk and concussion severity/recovery in the NCAA-DoD Grand Alliance CARE Consortium (CARE) dataset. There were 1,917 injuries in the dataset upon project initiation. After removing repeated injuries, related participants, and those without genetic/outcome data, we had 931 participants. Outcomes were number of days to return to play (RTP) as a recovery measure, and four severity measures (scores on SAC and BESS, SCAT symptom severity and total number of symptoms). We calculated PRS using a published score (de Rojas et al., 2021) and performed a linear regression (MLR) of RTP by PRS in normal (<24 days) and long (>24 days) RTP subgroups. We then compared severity measures by PRS using MLR. Next, we used t-tests to examine outcomes by APOE genotype in military and civilian subgroups. We also performed chi-squared tests of RTP category (normal vs. long) by APOE genotype. Finally, we analyzed outcomes by PRS in European or African genetic ancestry subgroups using MLR. Higher PRS was associated with longer injury to RTP interval in the normal RTP (<24 days) subgroup (estimate = 0.0412, SE = 0.182, p = 0.0237). 1 SD increase in PRS resulted in a 0.412 day (9.89 hours) increase to the interval. This may be clinically meaningful in the collegiate athlete environment. We did not identify any other significant differences. Our preliminary results provide limited evidence for an impact of AD PRS on concussion recovery, though the pattern was inconsistent and its clinical significance is uncertain. Future studies should attempt to replicate these findings in larger samples with longer follow-up using PRS calculated from multiple/diverse populations, which will be especially relevant for diverse datasets like CARE.

Despite significant advancements in the development of blood biomarkers for AD, challenges persist due to the complex interplay of genetic and environmental risk factors in AD pathogenesis. Epigenetic processes, including non-coding RNAs and especially microRNAs (miRs), have emerged as important players in the molecular mechanisms underlying neurodegenerative diseases. MiRs have the ability to fine-tune gene expression and proteostasis, and microRNAome profiling in liquid biopsies is gaining increasing interest since changes in miR levels can indicate the presence of multiple pathologies. We have profiled blood samples via smallRNA sequencing for 1056 individuals of the DELCODE and 847 individuals of the ANDI cohort. We profiled blood samples via smallRNA sequencing for 1056 individuals of the DELCODE (German Longitudinal Cognitive Impairment and Dementia Study) and 847 individuals of the ANDI (Aging and Dementia in the Community) cohort, consisting of individuals diagnosed with SCD, MCI, AD, or control. By applying differential expression, WGCNA, as well as linear and non-linear machine learning approaches, we identify microRNA signatures that can help identify patients at distinct stages of disease progression, as well as signatures that can predict the course of the disease. These data are compared with phenotyping data, such as cognitive function and ATN biomarkers. We will also discuss the role of other non-coding RNAs besides microRNAs and provide a framework for developing RNA-based point-of-care assays.