Sokratis Charisis

BACKGROUND: Accelerated vascular aging, assessed as artificial intelligence–based vascular age (AIVA), is associated with small vessel disease that may impact brain structure and neuropsychological function. METHODS: In a cross-section of Framingham Heart Study participants, AIVA was estimated using a validated convolutional neural network trained to predict carotid-femoral pulse wave velocity from a normalized pressure waveform. Brain structure was assessed using magnetic resonance imaging with diffusion tensor imaging, and neuropsychological function was assessed using a standardized test battery. Analyses included magnetic resonance imaging (N=2313) and neuropsychological (N=3001) samples. We used multivariable linear and logistic regression to relate AIVA to brain structural and neuropsychological functional measures. RESULTS: The mean±SD age across participants was 62±11 years; 56% were women. In multivariable models, higher AIVA was associated with worse markers of cerebral small vessel disease (mean white matter free water: β [per SD], 0.09 [95% CI, 0.04–0.14]; P β , 0.05 [95% CI, 0.00–0.10]; P =0.045; white matter hyperintensity volume: β , 0.08 [95% CI, 0.03–0.12]; P P =0.03; presence of brain infarcts: odds ratio, 1.44 [95% CI, 1.02–2.04]; P =0.04). In addition, higher AIVA was associated with worse performance on Trails B-A ( β , −0.05 [95% CI, −0.10 to −0.01]; P =0.03), similarities ( β , −0.06 [95% CI, −0.11 to −0.01]; P =0.02), and global cognition ( β , − 0.07 [95% CI, −0.12 to −0.03]; P =0.002) and higher odds of prevalent depressive symptoms (odds ratio, 1.24 [95% CI, 1.08–1.41]; P =0.002) and high Center for Epidemiologic Studies Depression Scale score (odds ratio, 1.20 [95% CI, 1.00–1.44]; P =0.047). Vascular brain injury markers partially mediated the associations of AIVA with global cognition score and presence of depressive symptoms. CONCLUSIONS: Peripheral pressure waveform AIVA may be a novel, noninvasive indicator of subclinical vascular brain injury and neuropsychological function.

Dementia, a degenerative disease affecting millions globally, is projected to triple by 2050. Early and precise diagnosis is essential for effective treatment and improved quality of life. However, current diagnostic approaches often show inconsistent performance across multi-racial and multi-ethnic groups, raising concerns about fairness and clinical reliability. This study investigates performance discrepancies in dementia classification among 6584 Non-Hispanic White, 1263 Non-Hispanic African American, and 713 Hispanic White populations. We observed significant cross-group bias, particularly when models trained on one group are tested on another. To address this, we evaluated RegAlign, a few-shot domain adaptation objective that combines source-side focal learning, target-side class-weighted supervision, and class-conditional alignment to improve adaptation to underrepresented populations. Our results show that this approach substantially reduces inter-group performance gaps, especially between Non-Hispanic White and Hispanic populations. Here, we show the importance of fairness-aware learning strategies and diverse training data for improving the accuracy and equity of MRI-based dementia classification.

Peripheral blood measurements dominate oxidative stress research, yet whether they reflect central nervous system (CNS) redox status remains untested in humans. We simultaneously profiled five biomarkers, total antioxidant capacity (TAC), glutathione (GSH), thiobarbituric acid-reactive substances (TBARS), ferric reducing antioxidant power (FRAP), and hydroxyl radical scavenging activity (HRSA), in paired blood and cerebrospinal fluid (CSF) from 140 adults in the ALBION cohort. Only FRAP showed a significant positive cross-compartment correlation (ρ = +0.49, FDR-p < 0.001), supporting its role as a systemic antioxidant signal. TBARS showed a significant inverse cross-compartment association (ρ = -0.20, FDR-p = 0.042), suggesting compartmental compensation in lipid peroxidation regulation rather than parallel dynamics. TAC and GSH showed no meaningful intercompartmental alignment. Individual biomarker levels were largely stable across the 40-85 year age range in both compartments, suggesting that age effects operate through coordinated latent networks rather than single-marker trajectories. Principal component extraction with varimax rotation identified four latent factors explaining 66.6% of total variance, dominated by a coherent CSF-centred redox axis alongside multiple partially opposing peripheral components. Age stratification revealed progressive fragmentation: middle-aged adults retained four coherent cross-compartment factors, whereas older adults exhibited five more dispersed components. Sex-stratified analyses showed that females exhibited four-factor modular organisation centred on glutathione, while males showed a simpler three-factor structure with tighter cross-compartment coupling anchored by FRAP. Blood and CSF oxidative stress biomarkers are not interchangeable, a finding with direct implications for biomarker selection in clinical trials targeting neurological conditions.

Background: Previous studies have leveraged machine learning (ML) analytics to detect brain atrophy patterns that are specific to Alzheimer's disease (AD) and to distinguish them from structural changes related to brain aging. These tools have been primarily developed and validated in White individuals; therefore, their applicability in diverse samples remains uncertain. Herein, we explore the generalizability of ML indices in a racially and ethnically diverse neuroimaging sample. Method: MRI data from four studies (FHS, NACC, CHS, NOMAS) including a total of 6,700 scans were processed using robust statistical harmonization pipelines to remove scanner‐related differences and were utilized to derive ML indices that quantify AD‐like (SPARE‐AD) and aging‐related (SPARE‐BA) brain atrophy patterns. A global cognitive score was computed as a weighted sum of each study's cognitive tests, using the first principal component loadings from a PCA containing the normalized scores of the cognitively normal participants as weights. We examined the associations of SPARE‐AD with cognitive impairment (defined as prevalent mild cognitive impairment or dementia) and the associations of SPARE‐BA with global cognition in the total sample and across race and ethnicity using mixed effects models adjusted for age and sex. Result: Mean age (SD) was 70.58 (10.97) years; 59% of participants were women (Table 1). A >50% probability for spatial abnormalities consistent with early AD on brain MRI based on the SPARE‐AD index was associated with a tenfold increase in the odds for cognitive impairment (OR[95%CI] = 10.37[8.76,12.27]). Similar associations were observed across race and ethnicity (Table 2). A 1‐year increase in SPARE‐BA‐predicted brain age was associated with an 8.6% of a standard deviation decrease in global cognitive score (β[95%CI] = ‐0.086[‐0.142,‐0.03]). This association was not significant in Hispanic individuals (Table 3). In individuals <75 years of age, associations between SPARE‐BA and global cognition were more consistent across race and ethnicity (Table 3). Conclusion: Robust harmonization techniques and ML model training using diverse neuroimaging datasets can facilitate the expansion of ML indices to different racial and ethnic groups. Older Hispanic individuals may have a different relationship between aging‐related brain atrophy patterns and cognition, which might indicate differences in underlying neuropathologies or other unidentified factors.

Background: Proteins involved in lipoprotein metabolism could lie in the causal pathway between cardiovascular health and Alzheimer's disease (AD). The strongest body of evidence exists for Apolipoproteins (Apo) E and J; however, other lipoproteins may be involved. To further explore the physiological links between cardiovascular health and AD risk, we studied the relationships between various blood lipoprotein concentrations and AD incidence in a community‐based sample. Method: This analysis included 822 participants ≥60 years of age without prevalent dementia from the Framingham Heart Study. Concentrations of high‐density lipoprotein cholesterol (HDL‐C), low‐density lipoprotein cholesterol (LDL‐C), small dense LDL‐C (sdLDL‐C), lipoprotein a (Lp(a)), apolipoprotein B (ApoB), and the ApoB isoform ApoB48 – a specific marker of chylomicron metabolism – were measured by immunoturbidimetric assays and ELISA kits in blood samples obtained from 1985 to 1988. Participants were under surveillance for incident AD until 2020. AD diagnosis was based on standard clinical criteria. The relationships between blood lipoprotein concentrations and AD incidence were explored using Cox proportional hazards regression adjusting for baseline age and sex (Model 1). Additional models were further adjusted for education, vascular risk factors, and APOE genotype (Model 2). Result: Mean baseline age (standard deviation) was 72.5 (3.7) years; 538 (65.5%) participants were women (Table 1). Over a median (interquartile range) of 12.55 (7.34, 15) years of follow‐up, 128 participants (99 women) developed AD. A standard deviation unit (SDU) increase in ln(sdLDL‐C) concentration was associated with a 21% increase in the risk for incident AD (hazard ratio‐HR [95% CI] = 1.21 [1.01, 1.45]), while a SDU increase in ln(ApoB48) concentration was associated with a 22% decrease in the risk for incident AD (HR [95% CI] = 0.78 [0.66, 0.93]) – Model 1 (Table 2). In Model 2, the association with sdLDL‐C remained in the same direction but did not reach statistical significance, likely due to the smaller sample size (Table 3). Conclusion: Lower sdLDL‐C and higher ApoB48 concentrations were associated with a reduced risk for AD. These findings highlight a potential role of blood lipoprotein markers in AD risk stratification and of lipid modification strategies in dementia prevention.

Elevated levels of total tau (t-tau) are a key biomarker of neurodegeneration, often seen in Alzheimer disease (AD). Identifying individuals at increased risk of AD using minimally invasive biomarkers can enable early intervention. We developed a polygenic risk score (PRS) for plasma t-tau and examined its association with the risk for developing clinical endophenotypes of AD pathology. This longitudinal cohort study used data from the Hellenic Longitudinal Investigation of Aging and Diet (HELIAD) study, involving individuals aged 65 years or older, free of AD, or amnestic mild cognitive impairment (aMCI-an AD prodrome) at baseline. Our primary exposure was PRStau, a PRS based on common genetic variants linked to plasma tau levels. The primary outcome was aMCI or AD incidence. We assessed the association between PRStau levels and aMCI/AD risk using Cox regression models adjusted for age, sex, education, APOE ε4 allele carriership, and population structure. We sought replication in a sample of UK Biobank (UKB) participants aged 60 years or older without prevalent dementia. In the HELIAD sample, among 618 cognitively healthy participants (mean age 73.37 years, 58.4% female), followed for 2.92 ± 0.80 years, 73 developed AD/aMCI. A 1 SD increase in PRStau was linked to a 29% higher AD/aMCI risk (hazard ratio [HR] 1.290, 95% CI 1.006-1.654). Stratified analyses revealed greater effect estimates in women (HR 1.451, 95% CI 1.023-2.058) and younger participants (HR 1.866, 95% CI 1.175-2.962), whereas results in men and older participants did not reach statistical significance. In the UKB sample (n = 142,637, mean age 64.2 years, 52% female), 2,737 participants developed AD over 12.9 ± 2.4 years of follow-up. Higher PRStau was also linked to increased AD risk (HR 1.046, 95% CI 1.007-1.086). These results support the potential utility of PRS for plasma t-tau in predicting AD/aMCI incidence. The relationship between genetic predisposition for elevated plasma t-tau levels and AD pathology might be influenced by sex and age, suggesting that these factors should be considered in AD genetic risk modeling. PRS could serve as an early indicator of genetic propensity for tau pathology, enhancing existing AD diagnostic and risk stratification algorithms.

Cardiovascular risk factors are important contributors to the risk of Alzheimer disease (AD). To further explore the physiologic links between cardiovascular health and AD risk, we studied the associations between various blood lipoprotein levels and AD risk in community-dwelling older adults. This longitudinal analysis included participants aged 60 years or older without prevalent dementia and with available cognitive follow-up and lipoprotein marker data from the Framingham Heart Study. Levels of high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), small dense LDL-C (sdLDL-C), lipoprotein a (Lp(a)), apolipoprotein B (ApoB), and the ApoB isoform ApoB48 were measured in blood samples obtained from 1985 to 1988. Participants were under surveillance for incident AD until 2020. AD diagnosis was based on standard clinical criteria. The relationships between blood lipoprotein levels (expressed as both continuous variables and quartiles) and AD incidence were examined using Cox proportional hazard models adjusted for baseline age and sex. A total of 822 participants (mean [SD] age 72.5 [3.7] years, 538 [65.5%] women) were followed for a median (interquartile range) of 12.55 (7.34-15) years, during which 128 participants developed incident AD. An increase of 1 standard deviation unit (SDU) in ln(sdLDL-C) concentration was associated with a 21% increase in the risk of incident AD (hazard ratio [HR] 1.21, 95% CI 1.01-1.45), whereas a 1-SDU increase in ln(ApoB48) concentration was associated with a 22% decrease in the risk of incident AD (HR 0.78, 95% CI 0.66-0.93). Participants in the first HDL-C quartile were 44% less likely to develop AD compared with those in the second, third, and fourth HDL-C quartiles (HR 0.56, 95% CI 0.33-0.95). Participants with sdLDL-C concentrations below the median were 38% less likely to develop AD compared with those with sdLDL-C concentrations above the median (HR 0.62, 95% CI 0.44-0.86). Lower sdLDL-C and higher ApoB48 concentrations were associated with a lower AD risk. In addition, individuals with the lowest HDL-C concentrations were less likely to develop AD compared with the remaining sample. These findings underscore links between lipoprotein metabolism pathways and AD risk, emphasizing the potential role of blood lipoprotein markers in AD risk stratification and of lipid modification strategies in dementia prevention.

Dementia, a degenerative disease affecting millions globally, is projected to triple by 2050. Early and precise diagnosis is essential for effective treatment and improved quality of life. However, current diagnostic approaches frequently demonstrate inconsistent precision and impartiality, particularly among diverse cultural groups. This study investigates performance discrepancies in dementia classification among White American, African American, and Hispanic populations. We reveal significant cross-group bias, particularly when models trained on one group are tested on another. To address this, we introduce a novel combination of few-shot learning and domain alignment to improve model adaptability across underrepresented populations. Our results show that these techniques substantially reduce inter-group performance gaps, especially between White American and Hispanic cohorts. This finding highlights the crucial need for fairness-aware strategies and the inclusion of diverse populations in training data to ensure accurate and equitable dementia diagnoses.

INTRODUCTION: We aimed to examine the global impact of brain small vessel disease (SVD) on cognitive performance.METHODS: In 892 participants from the Multi‐Ethnic Study of Atherosclerosis (MESA), we derived perivascular spaces (PVS), white matter hyperintensities (WMH), microbleeds (MB), and white matter fractional anisotropy (FA) and trace (TR). Cognitive function was assessed with a comprehensive neuropsychological battery.RESULTS: A composite SVD measure was constructed as a linear combination of basal ganglia PVS, thalamus PVS, periventricular WMH, subcortical WMH, and white matter FA and TR, and exhibited associations with worse global and domain‐specific cognitive performance. Additionally, SVD mediated the effect of age and cardiovascular disease risk on global cognitive function, both directly and through smaller gray matter (GM) volume.DISCUSSION: Integrating multiple individual SVD endophenotypes may more accurately reflect the neurobiology of SVD and capture its global impact on cognition. SVD mediates the effects of age and cardiovascular disease risk on cognition through both atrophy‐related and non–atrophy‐related pathways.Highlights: Associations between individual magnetic resonance imaging (MRI) markers of brain small vessel disease and cognitive outcomes might not fully capture the global impact of small vessel disease on cognition. We modeled small vessel disease as a latent construct, integrating multiple MRI endophenotypes in strategic brain regions. The small vessel disease construct was associated with worse global and domain‐specific cognitive performance. The small vessel disease construct exhibited mediating effects in the relationships of aging and cardiovascular disease risk with cognition through pathways that both involve and are independent of brain atrophy. Integrating information from multiple relevant imaging endophenotypes could open new avenues in small vessel disease research, broadening our understanding of its risk factors and clinical correlates.