Metabolomics analysis reveals serum biomarkers in patients with diabetic sarcopenia

Aging-Related Sarcopenia: Metabolic Characteristics and Therapeutic Strategies

The proportion of the elderly population is gradually increasing as a result of medical care advances, leading to a subsequent surge in geriatric diseases that significantly impact quality of life and pose a substantial healthcare burden. Sarcopenia, characterized by age-related decline in skeletal muscle mass and quality, affects a considerable portion of older adults, particularly the elderly, and can result in adverse outcomes such as frailty, fractures, bedridden, hospitalization, and even mortality. Skeletal muscle aging is accompanied by underlying metabolic changes. Therefore, elucidating these metabolic profiles and specific mechanisms holds promise for informing prevention and treatment strategies for sarcopenia. This review provides a comprehensive overview of the key metabolites identified in current clinical studies on sarcopenia and their potential pathophysiological alterations in metabolic activity. Besides, we examine potential therapeutic strategies for sarcopenia from a perspective focused on metabolic regulation.

The causal relationship of human blood metabolites with the components of Sarcopenia: a two-sample Mendelian randomization analysis

BACKGROUND

Sarcopenia is a progressive loss of muscle mass and function. Since skeletal muscle plays a critical role in metabolic homeostasis, identifying the relationship of blood metabolites with sarcopenia components would help understand the etiology of sarcopenia.

METHODS

A two-sample Mendelian randomization study was conducted to examine the causal relationship of blood metabolites with the components of sarcopenia. Summary genetic association data for 309 known metabolites were obtained from the Twins UK cohort and KORA F4 study (7824 participants). The summary statistics for sarcopenia components [hand grip strength (HGS), walking pace (WP), and appendicular lean mass (ALM)] were obtained from the IEU Open GWAS project (461,089 participants). The inverse variance weighted method was used, and the MR-Egger, weighted median, and MR-PRESSO were used for the sensitivity analyses. Metabolic pathways analysis was further performed.

RESULTS

Fifty-four metabolites associated with sarcopenia components were selected from 275 known metabolites pool. Metabolites that are causally linked to the sarcopenia components were mainly enriched in amino sugar and nucleotide sugar metabolism, galactose metabolism, fructose and mannose metabolism, carnitine synthesis, and biotin metabolism. The associations of pentadecanoate (15:0) with ALM, and 3-dehydrocarnitine and isovalerylcarnitine with HGS were significant after Bonferroni correction with a threshold of P < 1.82 × 10- 4 (0.05/275). Meanwhile, the association of hyodeoxycholate and glycine with the right HGS, and androsterone sulfate with ALM were significant in the sensitivity analyses.

CONCLUSION

Blood metabolites from different metabolism pathways were causally related to the components of sarcopenia. These findings might benefit the understanding of the biological mechanisms of sarcopenia and targeted drugs development for muscle health.

From Data to Cure: A Comprehensive Exploration of Multi-omics Data Analysis for Targeted Therapies

In the dynamic landscape of targeted therapeutics, drug discovery has pivoted towards understanding underlying disease mechanisms, placing a strong emphasis on molecular perturbations and target identification. This paradigm shift, crucial for drug discovery, is underpinned by big data, a transformative force in the current era. Omics data, characterized by its heterogeneity and enormity, has ushered biological and biomedical research into the big data domain. Acknowledging the significance of integrating diverse omics data strata, known as multi-omics studies, researchers delve into the intricate interrelationships among various omics layers. This review navigates the expansive omics landscape, showcasing tailored assays for each molecular layer through genomes to metabolomes. The sheer volume of data generated necessitates sophisticated informatics techniques, with machine-learning (ML) algorithms emerging as robust tools. These datasets not only refine disease classification but also enhance diagnostics and foster the development of targeted therapeutic strategies. Through the integration of high-throughput data, the review focuses on targeting and modeling multiple disease-regulated networks, validating interactions with multiple targets, and enhancing therapeutic potential using network pharmacology approaches. Ultimately, this exploration aims to illuminate the transformative impact of multi-omics in the big data era, shaping the future of biological research.

Metabolic clues to aging: exploring the role of circulating metabolites in frailty, sarcopenia and vascular aging related traits and diseases

Background: Physical weakness and cardiovascular risk increase significantly with age, but the underlying biological mechanisms remain largely unknown. This study aims to reveal the causal effect of circulating metabolites on frailty, sarcopenia and vascular aging related traits and diseases through a two-sample Mendelian Randomization (MR) analysis. Methods: Exposures were 486 metabolites analyzed in a genome-wide association study (GWAS), while outcomes included frailty, sarcopenia, arterial stiffness, atherosclerosis, peripheral vascular disease (PAD) and aortic aneurysm. Primary causal estimates were calculated using the inverse-variance weighted (IVW) method. Methods including MR Egger, weighted median, Q-test, and leave-one-out analysis were used for the sensitive analysis. Results: A total of 125 suggestive causative associations between metabolites and outcomes were identified. Seven strong causal links were ultimately identified between six metabolites (kynurenine, pentadecanoate (15:0), 1-arachidonoylglycerophosphocholine, androsterone sulfate, glycine and mannose) and three diseases (sarcopenia, PAD and atherosclerosis). Besides, metabolic pathway analysis identified 13 significant metabolic pathways in 6 age-related diseases. Furthermore, the metabolite-gene interaction networks were constructed. Conclusion: Our research suggested new evidence of the relationship between identified metabolites and 6 age-related diseases, which may hold promise as valuable biomarkers.

Integrative analysis of transcriptome and proteome in primary Sjögren syndrome

OBJECTIVE

Primary Sjögren's syndrome (pSS) is a intricate autoimmune disease mainly characterized of immune-mediated destruction of exocrine tissues, such as salivary and lacrimal glands, occurring dry mouth and eyes. Although some breakthroughs in understanding pSS have been uncovered, many questions remain about its pathogenesis, especially the internal relations between exocrine glands and secretions.

METHOD

Transcriptomic and proteomic analyses were conducted on salivary tissues and saliva in experimental Sjögren syndrome (ESS). The ESS model was established by immunization with salivary gland protein. The expression of mRNAs and proteins in salivary tissues and saliva were determined by high-throughput sequencing transcriptomic analysis and LC-MS/MS-based proteome, respectively. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were used to recognize dysregulated genes and proteins. The association between RNA and protein abundance was investigated to provides a comprehensive understanding of RNA-protein correlations in the pathogenesis of pSS.

RESULTS

As a result, we successfully established the ESS model. We recognized 3221 differentially expressed genes (DEGs) and 253 differentially expressed proteins (DEPs). The sample analysis showed that 61 proteins overlapped through the integrative analysis of transcriptomics and proteomics data. The enrichment pathway analysis of DEGs and DEPs in samples showed alterations in renin-angiotensin-system (RAS), lysosome, and apoptosis. Notably, we found that some genes, such as AGT, FN1, Klk1b26, Klk1, Klk1b5, Klk1b3 had a consistent trend in the regulation at the RNA and protein levels and might be potential diagnostic biomarkers of pSS.

CONCLUSION

Herein, we found critical processes and potential biomakers that may contribute to pSS pathogenesis by analyzing dysregulated genes and pathways. Additionally, the integrative multi-omics datasets provided additional insight into understanding complicated disease mechanisms.