The journals of gerontology. Series A, Biological sciences and medical sciences最新文献

Alzheimer's disease is strongly linked to metabolic abnormalities. We aimed to distinguish amyloid-positive people who progressed to cognitive decline from those who remained cognitively intact. We performed untargeted metabolomics of blood samples from amyloid-positive individuals, before any sign of cognitive decline, to distinguish individuals who progressed to cognitive decline from those who remained cognitively intact. A plasma-derived metabolite signature was developed from Supercritical Fluid chromatography coupled with high-resolution mass spectrometry (SFC-HRMS) and nuclear magnetic resonance (NMR) metabolomics. The 2 metabolomics data sets were analyzed by Data Integration Analysis for Biomarker discovery using Latent approaches for Omics studies (DIABLO), to identify a minimum set of metabolites that could describe cognitive decline status. NMR or SFC-HRMS data alone cannot predict cognitive decline. However, among the 320 metabolites identified, a statistical method that integrated the 2 data sets enabled the identification of a minimal signature of 9 metabolites (3-hydroxybutyrate, citrate, succinate, acetone, methionine, glucose, serine, sphingomyelin d18:1/C26:0 and triglyceride C48:3) with a statistically significant ability to predict cognitive decline more than 3 years before decline. This metabolic fingerprint obtained during this exploratory study may help to predict amyloid-positive individuals who will develop cognitive decline. Due to the high prevalence of brain amyloid-positivity in older adults, identifying adults who will have cognitive decline will enable the development of personalized and early interventions.

Background: Despite significant support system disruptions during the coronavirus 2019 (COVID-19) pandemic, little is known about the relationship between social support and symptom burden among older adults following COVID-19 hospitalization.

Methods: From a prospective cohort of 341 community-living persons aged ≥60 years hospitalized with COVID-19 between June 2020 and June 2021 who underwent follow-up at 1, 3, and 6 months after discharge, we identified 311 participants with ≥1 follow-up assessment. Social support prehospitalization was ascertained using a 5-item version of the Medical Outcomes Study Social Support Survey (range, 5-25), with low social support defined as a score ≤15. At hospitalization and each follow-up assessment, 14 physical symptoms were assessed using a modified Edmonton Symptom Assessment System inclusive of COVID-19-relevant symptoms. Mental health symptoms were assessed using Patient Health Questionnaire-4. Longitudinal associations between social support and physical and mental health symptoms, respectively, were evaluated through multivariable regression.

Results: Participants' mean age was 71.3 years (standard deviation, 8.5), 52.4% were female, and 34.2% were of Black race or Hispanic ethnicity. 11.8% reported low social support. Over the 6-month follow-up period, low social support was independently associated with higher burden of physical symptoms (adjusted rate ratio [aRR], 1.26; 95% confidence interval [CI], 1.05-1.52), but not mental health symptoms (aRR, 1.14; 95% CI, 0.85-1.53).

Conclusions: Low social support is associated with greater physical, but not mental health, symptom burden among older survivors of COVID-19 hospitalization. Our findings suggest a potential need for social support screening and interventions to improve post-COVID-19 symptom management in this vulnerable group.

Advanced age is an independent risk factor for coronary artery disease (CAD), the leading global cause of mortality. Senescent vascular cells in the atherosclerotic plaques exhibit senescence-associated secretory phenotype (SASP). How SASP contributes to atherosclerosis and CAD, however, remains unclear. Here, we integrated RNA-array datasets of senescent human coronary arterial endothelial cells (HCAECs) and aortic smooth muscle cells (HASMCs) as well as genome-wide association data for CAD. We identified 26 genes from HCAECs and 6 genes from HASMCs related to SASP and CAD in both in-house and published datasets. Of which, Cystatin C (CST3), a CAD susceptibility gene, was found to be expressed in both HCAECs and HASMCs, thus, it was prioritized for further investigation. We demonstrated it was significantly elevated in senescent vascular cells, aged arteries, and early atherosclerosis. In vitro experiments showed that CST3 enhances the monocyte-endothelial cell adhesion. Additionally, ligand-receptor pairing analyses revealed two important pathways, COL4A1-ITGA1 and LPL-LRP1 pathways, linked to the critical processes in the development of atherosclerosis, including cell adhesion, inflammation response, extracellular matrix organization, and lipid metabolism. We further demonstrated a reduced monocyte-endothelial cell adhesion following the knockdown of COL4A1 or ITGA1 and a significantly increased expression of COL4A1, ITGA1, and LPL in arterial intima of aged mice and ApoE-/- mice. Our findings demonstrate that vascular cell-derived SASP proteins increase the CAD susceptibility and identify CST3 functionally contributing to atherosclerosis.

The morbidity and mortality associated with vascular cognitive impairment (VCI) generally increase steeply, and health systems will face increasing demand for services. The present study aims to screen key genes to give new insight into the mechanisms and treatment of VCI based on bioinformatic approaches combined with biological experiments in rats. The gene expression data of VCI patients contained in the GSE122063 data set were downloaded from the Gene Expression Omnibus. We performed a weighted gene co-expression network analysis to identify a hub module and 44 hub genes. Two hundred and seventy-seven differentially expressed genes (DEGs) were analyzed using R software by the "limma" package. STRING database was used to construct protein-protein interaction (PPI) network, after which 36 hub genes were identified through Cytoscape. Functional enrichment analysis revealed that these genes from the yellow module and 277 DEGs were mainly associated with these pathways, such as Staphylococcus aureus infection, complement, and coagulation cascades. These biological functions are related to inflammatory cell activation and inflammatory response. The key genes of VCI were the overlapping hub genes from the yellow module and the PPI network. The expressions of hub genes in rats were determined by quantitative reverse transcription-polymerase chain reaction, Western blot, immunohistochemistry, and immunofluorescence. In conclusion, C1QA, C1QB, C1QC, CD163, and FCGR2A were highly expressed in the hippocampus of VCI rats, and they can serve as candidate biomarkers for the diagnosis and prognosis of VCI. Finally, molecular docking results suggested that 5 genes interact with Bisphenol A. These findings open a new avenue to investigate molecular mechanisms for preventing or treating VCI.

In the brain, environmental changes, such as neuroinflammation, can induce senescence, characterized by the decreased proliferation of neurons and dendrites and synaptic and vascular damage, resulting in cognitive decline. Senescence promotes neuroinflammatory disorders by senescence-associated secretory phenotypes and reactive oxygen species. In human brain microvascular endothelial cells (HBMVECs), we demonstrate that chronological aging and irradiation increase death-associated protein kinase 3 (DAPK3) expression. To confirm the role of DAPK3 in HBMVEC senescence, we disrupted DAPK3 activity using small interfering RNA (siRNA) or a dominant-negative mutant (DAPK3-P216S), which reduced cellular senescence phenotypes, as assessed by changes in tube formation, senescence-associated beta-galactosidase activity, and cell proliferation. In endothelial cells, DAPK3 promotes cellular senescence by regulating the phosphorylation and inactivation of peroxisome proliferator-activated receptor gamma coactivator 1 alpha (PGC1α) via the protein kinase B pathway, resulting in the decreased expression of mitochondrial metabolism-associated genes, such as ATP5G1, BDNF, and COX5A. Our studies show that DAPK3 is involved in cellular senescence and PGC1α regulation, suggesting that DAPK3 regulation may be important for treating aging-related brain diseases or the response to radiation therapy.

Background: It is imperative for public health to identify the factors that contribute to the progression of sarcopenia among middle-aged and older adults. Our study aimed to investigate the association between pain characteristics and the progression to sarcopenia and its subcomponents among middle-aged and older adults in China.

Methods: We included 5 568 participants from the China Health and Retirement Longitudinal Study. All participants completed assessments for pain characteristics and sarcopenia. Pain assessment included pain status (baseline pain, incident pain, and pain persistence) and pain distribution (single-site pain and multisite pain) using a self-report questionnaire. Diagnosis of sarcopenia followed The Asian Working Group for Sarcopenia 2019 consensus. The odds ratios (ORs) and 95% confidence intervals (CIs) were obtained by logical regression analysis.

Results: Participants who reported baseline pain, multisite pain, pain persistence, or multisite pain persistence were more likely to progress to sarcopenia than those without pain, with ORs of 1.33 (95% CI: 1.08-1.65), 1.44 (95% CI: 1.15-1.80), 1.63 (95% CI: 1.23-2.14), and 1.59 (95% CI: 1.19-2.11), respectively. Even after adjusting for other covariates such as gender, age, residential area, education level, marital status, smoking, alcohol consumption, comorbidities, and falls, these associations remained significant. Additionally, pain persistence and multisite pain persistence were significantly associated with low grip strength and clinically meaningful Short Physical Performance Battery decline, but not with low muscle mass.

Conclusions: Our study showed that pain, especially pain persistence, was closely correlated to the increased risk of progression to sarcopenia in Chinese middle-aged and older adults.

Background: Muscle mass loss may be associated with liver fat accumulation, yet scientific consensus is lacking and evidence in older adults is scant. It is unclear which muscle characteristics might contribute to this association in older adults.

Methods: We associated comprehensive muscle-related phenotypes including muscle mass normalized to body weight (D3-creatine dilution), muscle fat infiltration (magnetic resonance imaging), carbohydrate-supported muscle mitochondrial maximal oxidative phosphorylation (respirometry), and cardiorespiratory fitness (VO2 peak) with liver fat among older adults. Linear regression models adjusted for age, gender, technician (respirometry only), daily minutes of moderate-to-vigorous physical activity, and prediabetes/diabetes status tested main effects and interactions of each independent variable with waist circumference (high: women-≥88 cm, men-≥102 cm) and gender.

Results: Among older adults aged 75 (interquartile range: 73, 79 years; 59.8% women), muscle mass and liver fat were not associated overall (N = 362) but were positively associated among participants with a high waist circumference (β: 25.2; 95% confidence intervals [95% CI]: 11.7, 40.4; p = .0002; N = 160). Muscle fat infiltration and liver fat were positively associated (β: 15.2; 95% CI: 6.8, 24.3; p = .0003; N = 378). Carbohydrate-supported maximum oxidative phosphorylation (before adjustment) and VO2 peak (after adjustment; β: -12.9; 95% CI: -20.3, -4.8; p = .003; N = 361) were inversely associated with liver fat; adjustment attenuated the estimate for maximum oxidative phosphorylation although the point estimate remained negative (β: -4.0; 95% CI: -11.6, 4.2; p = .32; N = 321).

Conclusions: Skeletal muscle-related characteristics are metabolically relevant factors linked to liver fat in older adults. Future research should confirm our results to determine whether trials targeting mechanisms common to liver and muscle fat accumulation are warranted.

Aging is a multifactorial process characterized by an age-related decline in organismal fitness. This deterioration is the major risk factor for chronic diseases such as cardiovascular pathologies, neurodegeneration, or cancer, and it represents one of the main challenges of modern society. Therefore, understanding why and how we age would be a fundamental pillar to design strategies to promote a healthy aging. In the last decades, the study of the molecular bases of disease has been revolutionized by the discovery of different types of noncoding RNAs (ncRNAs) with regulatory potential. In this work, we will review the implication of ncRNAs in aging, with the aim to provide a first approach to the different aging-associated ncRNAs, their mechanism of action, and their potential relevance as therapeutic targets and disease biomarkers.

Background: The relationship between subjective and objective health is complex and not always matched. Although frailty and self-rated health (SRH) have been separately associated with adverse outcomes, their joint effects remained unclear.

Methods: Participants were 5 300 adults ≥60 years from the China Health and Retirement Longitudinal Study in 2011. Frailty, measured by the validated physical frailty phenotype approach, was classified as nonfrail, prefrail, and frail. SRH was categorized into 3 groups: excellent/very good/good, fair, and poor/very poor. We used the Cox models to examine the independent and joint association of frailty and SRH with mortality. We used the interaction approach to determine whether the association of SRH with mortality differed by frailty. Subgroup analyses were conducted by depression and cognitive impairment.

Results: About 8.1% of frail participants reported excellent/very good/good health; 21.2% of the nonfrail reported poor/very poor health. Prefrailty and frailty were associated with a 1.63- and 2.38-fold increase in the hazard of mortality than the nonfrail, respectively, after adjusting for SRH. Reporting fair and poor/very poor health was associated with a 29% and 100% increase in the hazard of mortality, respectively, after adjusting for frailty. No significant interaction was found. Prefrail and frail older adults with excellent/very good/good health had a similar mortality as the nonfrail with poor/very poor SRH. The association of SRH with mortality was less pronounced among individuals with depression or cognitive impairment.

Conclusions: SRH is a potential marker of resilience among people living with frailty that may be a target for ameliorating health risks induced by frailty.

Sarcopenia is among the most common musculoskeletal illnesses, yet its underlying biochemical mechanisms remain incompletely understood. In this study, we used Mendelian randomization (MR) to investigate the causal relationship between the genetically determined blood metabolites and sarcopenia, with the overall objective of identifying likely molecular pathways for sarcopenia. We used 2-sample MR to investigate the effects of blood metabolites on sarcopenia-related traits. 452 metabolites were exposure, and 3 sarcopenia-related traits as the outcomes: handgrip strength, appendicular lean mass, and walking pace. The inverse-variance weighted (IVW) causal estimates were determined. For sensitivity analysis, methods such as MR-Egger regression, the weighted median, the weighted mode, and the heterogeneity test were used. Additionally, for complementation, we performed replication, meta-analysis, and metabolic pathway analyses. Candidate biomarkers were defined by meeting one of the following criteria: (1) significant metabolites are defined as pIVW < pBonferroni [1.11 × 10-4 (.05/452)]; (2) strong metabolites are defined as 4 MR methods p < .05; and (3) suggestive metabolites are defined as passing sensitivity analysis. Three metabolites (creatine, 1-arachidonoylglycerophosphocholine, and pentadecanoate [15:0]) with significant causality, 3 metabolites (glycine, 1-arachidonoylglycerophosphocholine, and epiandrosterone sulfate) with strong causality, and 25 metabolites (including leucylleucin, pyruvic acid, etc.) with suggestive causality were associated with sarcopenia-related traits. After further replication analyses and meta-analysis, these metabolites maintained substantial effects on sarcopenia-related traits. We additionally identified 14 important sarcopenia-related trait metabolic pathways. By combining metabolomics with genomics, these candidate metabolites and metabolic pathways identified in our study may provide new clues regarding the mechanisms underlying sarcopenia.