The association of microvascular disease and endothelial dysfunction with vertebral trabecular bone mineral density : The MESA study.

Retinopathy and albuminuria are associated with hip fracture risk. We investigated whether these disorders and endothelial dysfunction (which underlies microvascular diseases) were associated with low trabecular bone density. No significant associations were found, suggesting that microvascular diseases are not related to fracture risk through low trabecular bone density. PURPOSE: Microvascular diseases of the eye, kidney, and brain are associated with endothelial dysfunction and increased hip fracture risk. To explore the basis for higher hip fracture risk, we comprehensively examined whether markers of microvascular disease and/or endothelial dysfunction are related to trabecular bone mineral density (BMD), a proximate risk factor for osteoporotic fractures. METHODS: Among 6814 participants in the Multi-Ethnic Study of Atherosclerosis study (MESA), we derived thoracic vertebral trabecular BMD from computed tomography of the chest and measured urine albumin to creatinine ratios (UACR), retinal arteriolar and venular widths, flow mediated dilation (FMD) of the brachial artery after 5 min of ischemia; and levels of five soluble endothelial adhesion markers (ICAM-1, VCAM-1, L-selectin, P-selectin, and E-selectin). Linear regression models were used to examine the association of trabecular BMD with markers of microvascular disease and with markers of endothelial dysfunction. RESULTS: We observed no significant associations of UACR, retinal arteriolar or venular widths, or FMD with BMD. We also observed no statistically significant association of spine trabecular BMD with levels of endothelial adhesion markers. Men and women had largely similar results. CONCLUSION: We conclude that there is little evidence to connect thoracic spine trabecular BMD to microvascular disorders or to endothelial dysfunction among multi-ethnic middle-aged and older adults. Other factors beyond trabecular BMD (e.g., bone quality or predisposition to falling) may be responsible for the associations of microvascular disease with osteoporotic fractures.

Plasma proteomic signature of human longevity.

The identification of protein targets that exhibit anti-aging clinical potential could inform interventions to lengthen the human health span. Most previous proteomics research has been focused on chronological age instead of longevity. We leveraged two large population-based prospective cohorts with long follow-ups to evaluate the proteomic signature of longevity defined by survival to 90 years of age. Plasma proteomics was measured using a SOMAscan assay in 3067 participants from the Cardiovascular Health Study (discovery cohort) and 4690 participants from the Age Gene/Environment Susceptibility-Reykjavik Study (replication cohort). Logistic regression identified 211 significant proteins in the CHS cohort using a Bonferroni-adjusted threshold, of which 168 were available in the replication cohort and 105 were replicated (corrected p value <0.05). The most significant proteins were GDF-15 and N-terminal pro-BNP in both cohorts. A parsimonious protein-based prediction model was built using 33 proteins selected by LASSO with 10-fold cross-validation and validated using 27 available proteins in the validation cohort. This protein model outperformed a basic model using traditional factors (demographics, height, weight, and smoking) by improving the AUC from 0.658 to 0.748 in the discovery cohort and from 0.755 to 0.802 in the validation cohort. We also found that the associations of 169 out of 211 proteins were partially mediated by physical and/or cognitive function. These findings could contribute to the identification of biomarkers and pathways of aging and potential therapeutic targets to delay aging and age-related diseases.

Large-Scale Proteomics Identifies Novel Biomarkers and Circulating Risk Factors for Aortic Stenosis.

BACKGROUND: Limited data exist regarding risk factors for aortic stenosis (AS). The plasma proteome is a promising phenotype for discovery of novel biomarkers and potentially causative mechanisms. OBJECTIVES: The aim of this study was to discover novel biomarkers with potentially causal associations with AS. METHODS: We measured 4,877 plasma proteins (SomaScan aptamer-affinity assay) among ARIC (Atherosclerosis Risk In Communities) study participants in mid-life (visit 3 [V3]; n = 11,430; age 60 ± 6 years) and in late-life (V5; n = 4,899; age 76 ± 5 years). We identified proteins cross-sectionally associated with aortic valve (AV) peak velocity (AVmax) and dimensionless index by echocardiography at V5 and with incident AV-related hospitalization after V3 with the use of multivariable linear and Cox proportional hazard regression. We assessed associations of candidate proteins with changes in AVmax over 6 years and with AV calcification with the use of cardiac computed tomography, replicated analysis in an independent sample, performed Mendelian randomization, and evaluated gene expression in explanted human AV tissue. RESULTS: Fifty-two proteins cross-sectionally were associated with AVmax and dimensionless index at V5 and with risk of incident AV-related hospitalization after V3. Among 3,413 participants in the Cardiovascular Health Study, 6 of those proteins were significantly associated with adjudicated moderate or severe AS, including matrix metalloproteinase 12 (MMP12), complement C1q tumor necrosis factor-related protein 1 (C1QTNF1), and growth differentiation factor-15. MMP12 was also associated with greater increase in AVmax over 6 years, greater degree of AV calcification, and greater expression in calcific compared with normal or fibrotic AV tissue. C1QTNF1 had consistent potential causal effects on both AS and AVmax according to Mendelian randomization analysis. CONCLUSIONS: These findings identify MMP12 as a potential novel circulating biomarker of AS risk and C1QTNF1 as a new putative target to prevent AS progression.

A plasma protein-based risk score to predict hip fractures.

As there are effective treatments to reduce hip fractures, identification of patients at high risk of hip fracture is important to inform efficient intervention strategies. To obtain a new tool for hip fracture prediction, we developed a protein-based risk score in the Cardiovascular Health Study using an aptamer-based proteomic platform. The proteomic risk score predicted incident hip fractures and improved hip fracture discrimination in two Trøndelag Health Study validation cohorts using the same aptamer-based platform. When transferred to an antibody-based proteomic platform in a UK Biobank validation cohort, the proteomic risk score was strongly associated with hip fractures (hazard ratio per s.d. increase, 1.64; 95% confidence interval 1.53-1.77). The proteomic risk score, but not available polygenic risk scores for fractures or bone mineral density, improved the C-index beyond the fracture risk assessment tool (FRAX), which integrates information from clinical risk factors (C-index, FRAX 0.735 versus FRAX + proteomic risk score 0.776). The developed proteomic risk score constitutes a new tool for stratifying patients according to hip fracture risk; however, its improvement in hip fracture discrimination is modest and its clinical utility beyond FRAX with information on femoral neck bone mineral density remains to be determined.

Large-scale circulating proteome association study (CPAS) meta-analysis identifies circulating proteins and pathways predicting incident hip fractures.

Hip fractures are associated with significant disability, high cost, and mortality. However, the exact biological mechanisms underlying susceptibility to hip fractures remain incompletely understood. In an exploratory search of the underlying biology as reflected through the circulating proteome, we performed a comprehensive Circulating Proteome Association Study (CPAS) meta-analysis for incident hip fractures. Analyses included 6430 subjects from two prospective cohort studies (Cardiovascular Health Study and Trøndelag Health Study) with circulating proteomics data (aptamer-based 5 K SomaScan version 4.0 assay; 4979 aptamers). Associations between circulating protein levels and incident hip fractures were estimated for each cohort using age and sex-adjusted Cox regression models. Participants experienced 643 incident hip fractures. Compared with the individual studies, inverse-variance weighted meta-analyses yielded more statistically significant associations, identifying 23 aptamers associated with incident hip fractures (conservative Bonferroni correction 0.05/4979, P < 1.0 × 10-5). The aptamers most strongly associated with hip fracture risk corresponded to two proteins of the growth hormone/insulin growth factor system (GHR and IGFBP2), as well as GDF15 and EGFR. High levels of several inflammation-related proteins (CD14, CXCL12, MMP12, ITIH3) were also associated with increased hip fracture risk. Ingenuity pathway analysis identified reduced LXR/RXR activation and increased acute phase response signaling to be overrepresented among those proteins associated with increased hip fracture risk. These analyses identified several circulating proteins and pathways consistently associated with incident hip fractures. These findings underscore the usefulness of the meta-analytic approach for comprehensive CPAS in a similar manner as has previously been observed for large-scale human genetic studies. Future studies should investigate the underlying biology of these potential novel drug targets.

Hip fractures are associated with significant disability, high cost, and mortality. However, the exact biological mechanisms underlying susceptibility to hip fractures remain incompletely understood. To increase the understanding of the underlying mechanisms, we performed a meta-analysis of the associations between 4860 circulating proteins and risk of fractures using two large cohorts, including 6430 participants with 643 incident hip fractures. We identified 23 proteins/aptamers associated with incident hip fractures. Two proteins of the growth hormone/insulin growth factor system (GHR and IGFBP2), as well as GDF15 and EGFR were most strongly associated with hip fracture risk. High levels of several inflammation-related proteins were also associated with increased hip fracture risk. Pathway analysis identified reduced LXR/RXR activation and increased acute phase response signaling to be overrepresented among those proteins associated with increased hip fracture risk. Future mechanistic studies should investigate the underlying biology of these novel protein biomarkers which may be potential drug targets.

Brain aging patterns in a large and diverse cohort of 49,482 individuals.

Brain aging process is influenced by various lifestyle, environmental and genetic factors, as well as by age-related and often coexisting pathologies. Magnetic resonance imaging and artificial intelligence methods have been instrumental in understanding neuroanatomical changes that occur during aging. Large, diverse population studies enable identifying comprehensive and representative brain change patterns resulting from distinct but overlapping pathological and biological factors, revealing intersections and heterogeneity in affected brain regions and clinical phenotypes. Herein, we leverage a state-of-the-art deep-representation learning method, Surreal-GAN, and present methodological advances and extensive experimental results elucidating brain aging heterogeneity in a cohort of 49,482 individuals from 11 studies. Five dominant patterns of brain atrophy were identified and quantified for each individual by respective measures, R-indices. Their associations with biomedical, lifestyle and genetic factors provide insights into the etiology of observed variances, suggesting their potential as brain endophenotypes for genetic and lifestyle risks. Furthermore, baseline R-indices predict disease progression and mortality, capturing early changes as supplementary prognostic markers. These R-indices establish a dimensional approach to measuring aging trajectories and related brain changes. They hold promise for precise diagnostics, especially at preclinical stages, facilitating personalized patient management and targeted clinical trial recruitment based on specific brain endophenotypic expression and prognosis.

Genetic and Clinical Correlates of AI-Based Brain Aging Patterns in Cognitively Unimpaired Individuals.

Importance: Brain aging elicits complex neuroanatomical changes influenced by multiple age-related pathologies. Understanding the heterogeneity of structural brain changes in aging may provide insights into preclinical stages of neurodegenerative diseases. Objective: To derive subgroups with common patterns of variation in participants without diagnosed cognitive impairment (WODCI) in a data-driven manner and relate them to genetics, biomedical measures, and cognitive decline trajectories. Design, Setting, and Participants: Data acquisition for this cohort study was performed from 1999 to 2020. Data consolidation and harmonization were conducted from July 2017 to July 2021. Age-specific subgroups of structural brain measures were modeled in 4 decade-long intervals spanning ages 45 to 85 years using a deep learning, semisupervised clustering method leveraging generative adversarial networks. Data were analyzed from July 2021 to February 2023 and were drawn from the Imaging-Based Coordinate System for Aging and Neurodegenerative Diseases (iSTAGING) international consortium. Individuals WODCI at baseline spanning ages 45 to 85 years were included, with greater than 50 000 data time points. Exposures: Individuals WODCI at baseline scan. Main Outcomes and Measures: Three subgroups, consistent across decades, were identified within the WODCI population. Associations with genetics, cardiovascular risk factors (CVRFs), amyloid ß (Aß), and future cognitive decline were assessed. Results: In a sample of 27 402 individuals (mean [SD] age, 63.0 [8.3] years; 15 146 female [55%]) WODCI, 3 subgroups were identified in contrast with the reference group: a typical aging subgroup, A1, with a specific pattern of modest atrophy and white matter hyperintensity (WMH) load, and 2 accelerated aging subgroups, A2 and A3, with characteristics that were more distinct at age 65 years and older. A2 was associated with hypertension, WMH, and vascular disease-related genetic variants and was enriched for Aß positivity (ages ≥65 years) and apolipoprotein E (APOE) ε4 carriers. A3 showed severe, widespread atrophy, moderate presence of CVRFs, and greater cognitive decline. Genetic variants associated with A1 were protective for WMH (rs7209235: mean [SD] B = -0.07 [0.01]; P value = 2.31 × 10-9) and Alzheimer disease (rs72932727: mean [SD] B = 0.1 [0.02]; P value = 6.49 × 10-9), whereas the converse was observed for A2 (rs7209235: mean [SD] B = 0.1 [0.01]; P value = 1.73 × 10-15 and rs72932727: mean [SD] B = -0.09 [0.02]; P value = 4.05 × 10-7, respectively); variants in A3 were associated with regional atrophy (rs167684: mean [SD] B = 0.08 [0.01]; P value = 7.22 × 10-12) and white matter integrity measures (rs1636250: mean [SD] B = 0.06 [0.01]; P value = 4.90 × 10-7). Conclusions and Relevance: The 3 subgroups showed distinct associations with CVRFs, genetics, and subsequent cognitive decline. These subgroups likely reflect multiple underlying neuropathologic processes and affect susceptibility to Alzheimer disease, paving pathways toward patient stratification at early asymptomatic stages and promoting precision medicine in clinical trials and health care.

Five dominant dimensions of brain aging are identified via deep learning: associations with clinical, lifestyle, and genetic measures.

Brain aging is a complex process influenced by various lifestyle, environmental, and genetic factors, as well as by age-related and often co-existing pathologies. MRI and, more recently, AI methods have been instrumental in understanding the neuroanatomical changes that occur during aging in large and diverse populations. However, the multiplicity and mutual overlap of both pathologic processes and affected brain regions make it difficult to precisely characterize the underlying neurodegenerative profile of an individual from an MRI scan. Herein, we leverage a state-of-the art deep representation learning method, Surreal-GAN, and present both methodological advances and extensive experimental results that allow us to elucidate the heterogeneity of brain aging in a large and diverse cohort of 49,482 individuals from 11 studies. Five dominant patterns of neurodegeneration were identified and quantified for each individual by their respective (herein referred to as) R-indices. Significant associations between R-indices and distinct biomedical, lifestyle, and genetic factors provide insights into the etiology of observed variances. Furthermore, baseline R-indices showed predictive value for disease progression and mortality. These five R-indices contribute to MRI-based precision diagnostics, prognostication, and may inform stratification into clinical trials.