👤 Leandro Z Agudelo

Chemokines C-X-C Motif Chemokine Ligand 9 (CXCL9) and C-C Motif Chemokine Ligand 2 (CCL2) were previously linked to incident cognitive impairment and dementia in the Northern Manhattan Study (NOMAS). We investigated whether circulating CXCL9 and CCL2 are independently associated with the cerebral white matter disease (WMD) burden and whether WMD mediates their association with prospective cognitive outcomes. In the stroke-free, prospective, community-dwelling NOMAS cohort (age≥50) we examined white matter hyperintensity volume (WMHV) on brain MRI and serum chemokine levels. WMHV was normalized, log-transformed, and standardized. Cognitive status was assessed at MRI and again 12.2±1.3 years later to adjudicate incident cognitive decline and dementia. Multivariable linear regression models with either CXCL9 or CCL2 (in quartiles) as exposures and WMHV as the outcome were adjusted for socio-demographics and key contributors to WMD, including vascular risk factors (Model 1), kidney function (2), and APOE ε4 status (3). Mediation of the CXCL9-cognitive outcome association by WMHV was tested using Monte Carlo integration. Among 1,179 participants (mean age 70±9 years; 60% female), elevated CXCL9 (Q4 vs. Q1) was associated with greater WMHV (Model 1: β=0.20, 95%CI 0.06-0.34). This association persisted even after adjusting for kidney function (Model 2: β=0.17, 95%CI 0.03-0.34) and APOE ε4 status (Model 3: β=0.19, 95%CI 0.04-0.33). CXCL9 (Q4 vs. Q1) effect magnitude in Model 3 approximated ~4 years of aging (β=0.05/year, 95%CI 0.04-0.06), exceeding that of hypertension (β=0.16, 95%CI 0.05-0.27), with a stepwise trend present across quartiles (β/quartile increase=0.07, 95%CI 0.02-0.12, p=0.003). Among 1,166 participants (dementia-free at MRI), the indirect, WMHV-mediated pathway was statistically significant for the association of CXCL9 with incident cognitive decline (ACME 0.009, 95%CI 0.002-0.018, p=0.016) and with dementia (ACME 0.008, 95%CI 0.003-0.016, p=0.004). CCL2 showed no association with WMHV. Greater CXCL9 levels were associated with greater white matter lesion load, independent of vascular, renal, and genetic factors, suggesting a role in WMD pathogenesis. WMHV mediated CXCL9's association with cognitive decline and dementia risk. This IFN-γ-induced monokine (MIG) warrants further evaluation as a biomarker of white matter and cognitive health as well as a potentially modifiable therapeutic target. Show less

The purpose of this study was to estimate the prevalence and number of cerebral microbleeds (CMBs) in a Hispanic and Latino cohort from various self-identified backgrounds and test associations with age, vascular risk factors, APOE (apolipoprotein E), and cognitive function. The 3T brain magnetic resonance imaging exams were obtained on SOL-INCA-MRI (Study of Latinos-Investigation of Neurocognitive Aging-MRI) magnetic resonance imaging study participants, a community-based study. CMB number was counted and categorized as: (1) any CMB, (2) lobar only, (3) deep only, (4) mixed, (5) deep+mixed, and (6) lobar+mixed. We examined whether prevalence of CMBs varied by age, sex, education, Hispanic background, cardiovascular risk factors (hypertension, diabetes, Framingham Risk Score), APOE genotype, and cognition. A total of 2455 participants were included who were 63.0±8.4 years of age, 67.9% women, and 62.2% high school education or higher. CMBs prevalence was 11.7% (8.3% lobar only, 2.0% deep only, 1.4% mixed locations). After adjusting for age, sex, and education, a high Framingham Risk Score was associated with the presence of CMBs of all types, except lobar only. Prevalent stroke/transient ischemic attack was associated with higher likelihood of deep-only CMBs. For participants with cognitive impairment, the adjusted prevalence of mixed CMBs (2.2% versus 1.1%, High vascular risk scores, self-reported history of stroke/transient ischemic attack, and cognitive status were associated with a higher likelihood of CMBs, especially in deep regions. Show less

To delineate organ-specific and systemic drivers of metabolic dysfunction-associated steatotic liver disease (MASLD), we applied integrative causal inference across clinical, imaging, and proteomic domains in individuals with and without type 2 diabetes (T2D). Bayesian network analyses and complementary two-sample Mendelian randomization were used to quantify causal pathways linking adipose distribution, glycemia, and insulin dynamics with liver fat in the IMI-DIRECT prospective cohort study. Data included frequently sampled metabolic challenge tests, MRI-derived abdominal and hepatic fat content, serological biomarkers, and Olink plasma proteomics from 331 adults with new-onset T2D and 964 adults without diabetes, with harmonized protocols enabling replication. High basal insulin secretion rate (BasalISR), estimated via C-peptide deconvolution, emerged as the primary potential causal driver of liver fat accumulation in both cohorts. BasalISR, a clearance-independent measure of β-cell insulin output distinct from peripheral insulin levels, was independently linked to hepatic steatosis. Visceral adipose tissue exhibited bidirectional associations with liver fat, suggesting a self-reinforcing metabolic loop. Of 446 analyzed proteins, 34 mapped to these metabolic networks (27 in the non-diabetes network, 18 in the T2D network, and 11 shared). Key proteins directly associated with liver fat included GUSB, ALDH1A1, LPL, IGFBP1/2, CTSD, HMOX1, FGF21, AGRP, and ACE2. Sex-stratified analyses identified GUSB in females and LEP in males as the strongest protein predictors of liver fat. BasalISR may better capture early β-cell-driven disturbances contributing to MASLD. These findings outline a multifactorial, sex- and disease stage-specific proteo-metabolic architecture of hepatic steatosis and identify potential biomarkers or therapeutic targets. Show less

As dementia cases continue to rise, effective prevention strategies are urgently needed. However, objective biomarkers that directly reflect lifestyle factors remain limited. Life's Essential 8 (LE8) is a composite of modifiable cardiovascular health metrics, and lower LE8 has been consistently associated with increased risk of dementia. In this study, we aimed to identify DNA methylation biomarkers associated with LE8 scores and investigate their relevance for dementia risk. We performed an epigenome-wide association study of 273 stroke-free, self-identified Hispanic adults aged 40 and older from the Northern Manhattan Study (NOMAS), a community-based urban cohort study. DNA methylation (DNAm) was assessed using Illumina MethylationEPIC arrays. Robust linear models identified CpGs associated with LE8 score, a composite score on eight health metrics including diet quality, physical activity, nicotine exposure, sleep health, body mass index, blood lipids, blood glucose, and blood pressure. Differentially methylated regions were identified by combining P-values in sliding windows while accounting for spatial correlations across the genome. We also performed functional annotation, pathway analyses, and integrative analyses with gene expression, genetic variants, brain-blood correlations, and comparisons with previous dementia studies to identify the most biologically meaningful DNAm sites. After adjusting for age, sex, APOE ε4, immune cell composition, and ancestry, we found 11 CpGs with suggestive evidence of association with LE8 (P-value < 1 × 10 Our comparison with published results showed that a number of LE8-associated DNA methylation sites are associated with dementia, highlighting the possible connection between cardiovascular health and dementia risk and pointing to potential actionable targets for dementia prevention. Moreover, DNAm biomarkers have clinical potential as objective measures to identify individuals at elevated risk, stratify participants based on biologically informed risk profiles, and monitor epigenetic responses to lifestyle interventions in dementia prevention trials. Future studies in larger and more diverse cohorts are needed to validate and refine these methylation biomarkers for clinical applications. Show less

To delineate organ-specific and systemic drivers of metabolic dysfunction-associated steatotic liver disease (MASLD), we applied integrative causal inference across clinical, imaging, and proteomic domains in individuals with and without type 2 diabetes (T2D). We used Bayesian network analyses to quantify causal pathways linking adipose distribution, glycemia, and insulin dynamics with fatty liver using data from the IMI-DIRECT prospective cohort study. Measurements were made of glucose and insulin dynamics (using frequently-sampled metabolic challenge tests), MRI-derived abdominal and liver fat content, serological biomarkers, and Olink plasma proteomics from 331 adults with new-onset T2D and 964 adults free from diabetes at enrolment. The common protocols used in these two cohorts provided the opportunity for replication analyses to be performed. When the direction of the effect could not be determined with high probability through Bayesian networks, complementary two-sample Mendelian randomization (MR) was employed. High basal insulin secretion rate (BasalISR) was identified as the primary causal driver of liver fat accumulation in both diabetes and non-diabetes. Excess visceral adipose tissue (VAT) was bidirectionally associated with liver fat, indicating a self-reinforcing metabolic loop. Basal insulin clearance (Clinsb) worsened as a consequence of liver fat accumulation to a greater degree before the onset of T2D. Out of 446 analysed proteins, 34 mapped to these metabolic networks and 27 were identified in the non-diabetes network, 18 in the diabetes network, and 11 were common between the two networks. Key proteins directly associated with liver fat included GUSB, ALDH1A1, LPL, IGFBP1/2, CTSD, HMOX1, FGF21, AGRP, and ACE2. Sex-stratified analyses revealed distinct proteomic drivers: GUSB and LEP were most predictive of liver fat in females and males, respectively. Basal insulin hypersecretion is a modifiable, causal driver of MASLD, particularly prior to glycaemic decompensation. Our findings highlight a multifactorial, sex- and disease-stage-specific proteo-metabolic architecture of hepatic steatosis. Proteins such as GUSB, ALDH1A1, LPL, and IGFBPs warrant further investigation as potential biomarkers or therapeutic targets for MASLD prevention and treatment. Show less