This prospective observational study aimed to evaluate the association of metabolomic alterations with weight loss outcomes following sleeve gastrectomy (SG). We evaluated the metabolomic profile of serum and feces prior to SG and three months post-SG, along with weight loss outcomes in 45 adults with obesity. The percent total weight loss for the highest versus the lowest weight loss tertiles (T3 vs. T1) was 17.0 ± 1.3% and 11.1 ± 0.8%, p < 0.001. Serum metabolite alterations specific to T3 at three months included a decrease in methionine sulfoxide concentration as well as alterations to tryptophan and methionine metabolism (p < 0.03). Fecal metabolite changes specific to T3 included a decrease in taurine concentration and perturbations to arachidonic acid metabolism, and taurine and hypotaurine metabolism (p < 0.002). Preoperative metabolites were found to be highly predictive of weight loss outcomes in machine learning algorithms, with an average area under the curve of 94.6% for serum and 93.4% for feces. This comprehensive metabolomics analysis of weight loss outcome differences post-SG highlights specific metabolic alterations as well as machine learning algorithms predictive of weight loss. These findings could contribute to the development of novel therapeutic targets to enhance weight loss outcomes after SG.

Background: Huntington’s disease (HD) is a multifaceted neurological disorder characterized by the progressive deterioration of motor, cognitive, and psychiatric functions. Despite a limited understanding of its pathogenesis, research has implicated abnormal trinucleotide cytosine-adenine-guanine CAG repeat expansion in the huntingtin gene (HTT) as a critical factor. The development of innovative strategies is imperative for the early detection of predictive biomarkers, enabling timely intervention and mitigating irreversible cellular damage. Lipidomics, a comprehensive analytical approach, has emerged as an indispensable tool for systematically characterizing lipid profiles and elucidating their role in disease pathology. Method: A MedLine search was performed to identify studies that use lipidomics for the characterization of HD. Search terms included “Huntington disease”; “lipidomics”; “biomarker discovery”; “NMR”; and “Mass spectrometry”. Results: This review highlights the significance of lipidomics in HD diagnosis and treatment, exploring changes in brain lipids and their functions. Recent breakthroughs in analytical techniques, particularly mass spectrometry and NMR spectroscopy, have revolutionized brain lipidomics research, enabling researchers to gain deeper insights into the complex lipidome of the brain. Conclusions: A comprehensive understanding of the broad spectrum of lipidomics alterations in HD is vital for precise diagnostic evaluation and effective disease management. The integration of lipidomics with artificial intelligence and interdisciplinary collaboration holds promise for addressing the clinical variability of HD.

The impact of environmental factors on epigenetic changes is well established, and cellular function is determined not only by the genome but also by interacting partners such as metabolites. Given the significant impact of metabolism on disease progression, exploring the interaction between the metabolome and epigenome may offer new insights into Huntington’s disease (HD) diagnosis and treatment. Using fourteen post-mortem HD cases and fourteen control subjects, we performed metabolomic profiling of human postmortem brain tissue (striatum and frontal lobe), and we performed DNA methylome profiling using the same frontal lobe tissue. Along with finding several perturbed metabolites and differentially methylated loci, Aminoacyl-tRNA biosynthesis (adj p-value = 0.0098) was the most significantly perturbed metabolic pathway with which two CpGs of the SEPSECS gene were correlated. This study improves our understanding of molecular biomarker connections and, importantly, increases our knowledge of metabolic alterations driving HD progression.

Postoperative delirium (POD) is a common complication in older surgical patients, linked to long-term cognitive decline and progression to dementia, yet its mechanisms remain unclear. We investigated arginine-related metabolites (ARMs) in cerebrospinal fluid (CSF) from 248 patients undergoing elective-surgery: 25 developed POD. Targeted mass spectrometry, gene expression profiling, and machine learning were applied to identify metabolic predictors. POD patients showed significant correlations with citrulline, ornithine, and glutamine, while models highlighted glutamine, glutamic acid, putrescine, N1-acetylspermidine, and spermidine as key biomarkers, achieving >77% predictive accuracy. Cluster and pathway analyses revealed POD-specific shifts in GABA synthesis and polyamine metabolism, contrasting with urea cycle dominance in non-POD cases. Associations persisted after adjusting for age and CSF Aβ42. Preoperative profiles in polyamine metabolism, ammonia detoxification, and neurotransmitter regulation suggest underlying neuroinflammatory and oxidative stress vulnerabilities that reduce resilience. Targeting polyamine biosynthesis may offer novel preventative and therapeutic strategies to mitigate POD and dementia risk.