PubMed HealthSearch

PubMed · 41904425

Metabolic pathways linked to sarcopenia in the Bushehr Elderly Health Program: kynurenine, nicotinamide, B-vitamins, and sulfur amino acids.

Abstract

BACKGROUND: Sarcopenia, characterized by the loss of muscle mass and function, is a common condition in the elderly, associated with increased morbidity and mortality. Metabolic pathways, including the kynurenine, nicotinamide, B-vitamins, and sulfur amino acid pathways, may play a significant role in the development and progression of sarcopenia. This study investigates the relationship between metabolic pathways and sarcopenia, aiming to identify potential therapeutic targets. METHOD: Four hundred participants over 60 years were randomly selected from the second stage of the Bushehr Elderly Health Program (BEH). Frozen plasma samples were used to measure metabolomics. We used factor analysis and logistic regression analysis to determine the kynurenine-tryptophan metabolites associated with sarcopenia and its components. RESULT: Study participants included 89 sarcopenic subjects aged 72.92 ± 7.32 years and 307 non-sarcopenic subjects aged 68.12 ± 5.56 years. In full model adjustment, factor 3, which included methionine, tryptophan, 3-hydroxyanthranilic acid, picolinic acid, and xanthurenic acid, was associated with 38.3% lower risk of sarcopenia (OR = 0.617 [95%CI = 0.436–0.875]); Factor 6, which included methylmalonic acid and total homocysteine, was associated with a 33.7% increased risk of sarcopenia (OR = 1.337 [95%CI = 1.031–1.735]); and factor 7, consisting of nicotinamide, were related to a 25.2% lower risk of developing sarcopenia (OR = 0.748 [95%CI = 0.571–0.979]). Additionally, factor 1, which included quinolinic acid, kynurenine, 3-hydroxykynurenine, neopterin, kynurenic acid, anthranilic acid, cystathionine, and total cysteine, was linked to a 49.2% higher risk of low muscle strength, while factors 3 and 7 were associated with approximately a 24% decrease in risk of low muscle strength. Factors 5, consisting of serine and glycine, and factor 7 were related to 43% and 27.7% lower risk of low skeletal muscle index, respectively. While factor 6 was related to a 32.8% higher risk of low skeletal muscle index. Factor 1 was also related to a 32.9% higher risk of low walking speed, while factor 3 was related to a 28.5% lower risk of low walking speed. CONCLUSION: Specific metabolites from the kynurenine, nicotinamide, B-vitamin, and sulfur amino acid pathways are significantly associated with sarcopenia and its key parameters, such as muscle strength, skeletal muscle index, and walking speed. These findings suggest that metabolic profiling could offer valuable insights for early detection and targeted interventions for sarcopenia in elderly populations.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Narges Zargar Balajam, Hojat Dehghanbanadaki, Ramin Heshmat, Heydar Ali Mardani-Fard, Amir Kasaeian, Ziba Majidi, Fatemeh Bandarian, Noushin Fahimfar, Farshad Farzadfar, Afshin Ostovar, Iraj Nabipour, Farideh Razi, Gita Shafiee. 2026-03-28. Metabolic pathways linked to sarcopenia in the Bushehr Elderly Health Program: kynurenine, nicotinamide, B-vitamins, and sulfur amino acids.. https://doi.org/10.1186/s12877-026-07058-w

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

Global Genomic Surveillance.

Global genomic surveillance has emerged as a foundational pillar of public health in the twenty-first century, enabling real-time tracking of pathogen evolution and informing outbreak response. This chapter examines the strategic architecture of global genomic surveillance, focusing on its application to arboviruses such as chikungunya virus (CHIKV). It explores the integration of genomic data with epidemiological, clinical, and environmental information within a One Health framework, while addressing critical challenges in governance, equity, and interoperability. The discussion covers the entire genomic surveillance workflow, from sample collection and sequencing to bioinformatic analysis and phylogenetic inference, and highlights the transformative role of artificial intelligence (AI) in predictive surveillance. By analyzing global initiatives, operational barriers, and emerging technologies, this chapter underscores the necessity of sustainable, equitable, and interoperable genomic systems to proactively address current and future infectious disease threats.

Humans

Systematic Dissection of Key Driver Perturbation Signatures in Single Cells via ECCITE-seq.

CRISPR screens, such as expanded CRISPR-compatible cellular indexing of transcriptomes and epitopes by sequencing (ECCITE-seq), enable the simultaneous measurement of transcriptomes, gRNA identity, and cell-surface protein expression at single-cell resolution to systematically interrogate gene function. This platform provides a powerful and scalable experimental approach for validating disease-associated regulators identified by large-scale association studies and other computational methods, including network-based analyses of multi-omics data. Here, as an example application, we describe an ECCITE-seq framework to characterize the transcriptomic consequences of perturbing multiple neuronal key driver genes associated with Alzheimer's disease (AD) in human-induced pluripotent stem cell (hiPSC)-derived neurons. More broadly, by integrating customized pooled gRNA libraries with different CRISPR effectors across multiple cell types, this approach allows for the assessment of the regulatory impact of candidate genes implicated in development and disease processes.

Humans

Identification of Genome-Wide Chromatin Structural Aberration in Cancer by Hi-C Analysis.

Aberrant three-dimensional genome organization is a hallmark of cancer, often driving oncogene activation through mechanisms such as enhancer hijacking. High-throughput chromosome conformation capture (Hi-C) maps these interactions on a genome-wide scale. Unlike earlier dilution-based methods, in situ Hi-C performs proximity ligation within intact nuclei, minimizing random ligation noise and enabling fine-scale structure detection. This chapter describes an optimized in situ Hi-C protocol tailored for cancer cell lines using MboI digestion and biotin-mediated pull-down to generate high-complexity libraries. We further outline a computational workflow that extends beyond standard topological mapping of compartments and topologically associating domains to identify cancer-specific aberrations. Specifically, we focus on detecting chromosomal rearrangements (structural variants) and characterizing the distinct circular topology of extrachromosomal DNA. This integrated experimental and analytical framework provides the necessary tools to dissect the spatial dysregulation underlying tumor evolution.

Humans