Biological aging for risk prediction of first-ever intracerebral hemorrhage and cerebral infarction in advanced age

Identification of Senescence-Related Biomarkers and Regulatory Networks in Intracerebral Hemorrhage

OBJECTIVES

Intracerebral hemorrhage (ICH) is a severe neurological disorder with substantial societal implications. Cellular senescence plays a critical role in ICH pathogenesis. This study aims to identify senescence-related biomarkers in ICH for diagnostic and therapeutic purposes.

METHODS

Raw data from GSE24265 in Gene Expression Omnibus was downloaded. Senescence-related genes were acquired from CellAge. Differential gene analysis was done between patients with ICH and controls. The intersection of ICH differentially expressed genes and senescence-related genes for senescence-related ICH genes. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses were performed. Protein-protein interaction network was constructed through the Search Tool for the Retrieval of Interacting Genes. Single sample gene set enrichment analysis was done for immune cell infiltration and function evaluation in control and ICH groups. miRWalk2.0 database was used for microRNA predictions targeting ICH biomarkers. Transcriptional regulatory relationships unraveled by sentence-based text mining database was employed to predict transcription factors regulating identified biomarkers.

RESULTS

Thirteen senescence-related ICH genes were identified. They were primarily enriched in the positive regulation of angiogenesis and the Advanced Glycation End Product -Receptor for AGE signaling pathway in diabetic complications. Validation in the GSE149317 data set and receiver operating characteristic analysis highlighted Caveolin 1, C-X-C Motif Chemokine Ligand 1, ETS proto-oncogene 1, transcription factor, and Serpin Family E Member 1 as potential ICH biomarkers. Single sample gene set enrichment analysis revealed increased Type 2 T helper cell 2_cells, Treg cells, and immune functions like Antigen-presenting cells_co_stimulation in patients with ICH. Fourteen microRNA, including has-miR-6728-3p, were predicted to regulate these biomarkers. transcription factors such as PPARG, RARA, HMGA1, and NFKB1 were identified as potential regulators of the ICH biomarkers.

CONCLUSION

Caveolin 1, C-X-C Motif Chemokine Ligand 1, ETS proto-oncogene 1, transcription factor, and Serpin Family E Member 1 may serve as valuable biomarkers in ICH. Targeting these genes could contribute to ICH prevention and treatment.

Connections between cross-tissue and intra-tissue biomarkers of aging biology in older adults

Background

Saliva measures are generally more accessible than blood, especially in vulnerable populations. However, connections between aging biology biomarkers in different body tissues remain unknown.

Methods

The present study included individuals (N = 2406) who consented for saliva and blood draw in the Health and Retirement Telomere length study in 2008 and the Venous blood study in 2016 who had complete data for both tissues. We assessed biological aging based on telomere length in saliva and DNA methylation and physiology measures in blood. DNA methylation clocks combine information from CpGs to produce the aging measures representative of epigenetic aging in humans. We analyzed DNA methylation clocks proposed by Horvath (353 CpG sites), Hannum (71 CpG sites), Levine or PhenoAge, (513 CpG sites), GrimAge, (epigenetic surrogate markers for select plasma proteins), Horvath skin and blood (391 CpG sites), Lin (99 CpG sites), Weidner (3 CpG sites), and VidalBralo (8 CpG sites). Physiology measures (referred to as phenotypic age) included albumin, creatinine, glucose, [log] C-reactive protein, lymphocyte percent, mean cell volume, red blood cell distribution width, alkaline phosphatase, and white blood cell count. The phenotypic age algorithm is based on parametrization of Gompertz proportional hazard models. Average telomere length was assayed using quantitative PCR (qPCR) by comparing the telomere sequence copy number in each patient's sample (T) to a single-copy gene copy number (S). The resulting T/S ratio was proportional to telomere length, mean. Within individual, relationships between aging biology measures in blood and saliva and variations according to sex were assessed.

Results

Saliva-based telomere length showed inverse associations with both physiology-based and DNA methylation-based aging biology biomarkers in blood. Longer saliva-based telomere length was associated with 1 to 4 years slower biological aging based on blood-based biomarkers with the highest magnitude being Weidner (β = - 3.97, P = 0.005), GrimAge (β = - 3.33, P < 0.001), and Lin (β = - 3.45, P = 0.008) biomarkers of DNA methylation.

Conclusions

There are strong connections between aging biology biomarkers in saliva and blood in older adults. Changes in telomere length vary with changes in DNA methylation and physiology biomarkers of aging biology. We observed variations in the relationship between each body system represented by physiology biomarkers and biological aging, particularly at the DNA methylation level. These observations provide novel opportunities for integration of both blood-based and saliva-based biomarkers in clinical care of vulnerable and clinically difficult to reach populations where either or both tissues would be accessible for clinical monitoring purposes.