Effects of Resistant Starch Interventions on Metabolic Biomarkers in Pre-Diabetes and Diabetes Adults

Simple lifestyle changes can prevent or delay the onset of type 2 diabetes mellitus (T2DM). In addition to maintaining a physically active way of life, the diet has become one of the bases in managing TD2M. Due to many studies linking the ability of resistant starch (RS) to a substantial role in enhancing the nutritional quality of food and disease prevention, the challenge of incorporating RS into the diet and increasing its intake remains. Therefore, we conducted this review to assess the potential benefits of RS on metabolic biomarkers in pre-diabetes and diabetes adults based on available intervention studies over the last decade. Based on the conducted review, we observed that RS intake correlates directly to minimize possible effects through different mechanisms for better control of pre-diabetic and diabetic conditions. In most studies, significant changes were evident in the postprandial glucose and insulin incremental area under the curve (iAUC). Comparative evaluation of RS consumption and control groups also showed differences with inflammatory markers such as TNF-α, IL-1β, MCP-1, and E-selectin. Only RS2 and RS3 were extensively investigated and widely reported among the five reported RS types. However, a proper comparison and conclusion are deemed inappropriate considering the variations observed with the study duration, sample size, subjects and their metabolic conditions, intervention doses, and the intervention base products. In conclusion, this result provides interesting insights into the potential use of RS as part of a sustainable diet in diabetes management and should be further explored in terms of the mechanism involved.

Identification of biomarkers related to metabolically unhealthy obesity in Korean obese adolescents: A cross-sectional study

BackgroundObesity is classified as metabolically unhealthy obesity (MUO) and metabolically healthy obesity (MHO). The current study aimed to screen for relationships and different potential metabolic biomarkers involved between MHO and MUO in adolescents.MethodsThe study included 148 obese adolescents aged between 14 and 16. The study participants were divided into MUO and MHO groups based on the age-specific adolescent metabolic syndrome (MetS) criteria of the International Diabetes Federation. The current study was conducted to investigate the clinical and metabolic differences (AbsoluteIDQ™ p180 kit) between adolescents in the MHO group and those in the MUO group. Multivariate analyses were conducted to investigate the metabolites as independent predictors for the odds ratio and the presence of the MetS in adolescents.ResultsThere were significant differences in the 3 acylcarnitines, 5 amino acids, glutamine/glutamate ratio, 3 biogenic amines, and 2 glycerophospholipids between the obese adolescents in the MUO group and those in the MHO group. Moreover, several metabolites were associated with the prevalence of MUO in adolescents. Additionally, several metabolites were inversely correlated with MHO in adolescents of the MUO group.ConclusionsWe observed that histidine, lysine, PCaaC34:1, and several clinical factors in adolescents of the MUO group were reverse correlated with the results in adolescents of the MHO group. In addition, the triglyceride-glucose index was related to MUO in adolescents, compared with the homeostasis model assessment of insulin resistance. Thus, the biomarkers found in this study have a potential to reflect the clinical outcomes of MUO in adolescents. These biomarkers will lead to a better understanding of MetS in obese adolescents.

Metabolomics of a cell line-derived xenograft model reveals circulating metabolic signatures for malignant mesothelioma

Background Malignant mesothelioma (MM) is a rare and highly aggressive cancer. Despite advances in multidisciplinary treatments for cancer, the prognosis for MM remains poor with no effective diagnostic biomarkers currently available. The aim of this study was to identify plasma metabolic biomarkers for better MM diagnosis and prognosis by use of a MM cell line-derived xenograft (CDX) model. Methods The MM CDX model was confirmed by hematoxylin and eosin staining and immunohistochemistry. Twenty female nude mice were randomly divided into two groups, 10 for the MM CDX model and 10 controls. Plasma samples were collected two weeks after tumor cell implantation. Gas chromatography-mass spectrometry analysis was conducted. Both univariate and multivariate statistics were used to select potential metabolic biomarkers. Hierarchical clustering analysis, metabolic pathway analysis, and receiver operating characteristic (ROC) analysis were performed. Additionally, bioinformatics analysis was used to investigate differential genes between tumor and normal tissues, and survival-associated genes. Results The MM CDX model was successfully established. With VIP > 1.0 and P-value < 0.05, a total of 23 differential metabolites were annotated, in which isoleucine, 5-dihydrocortisol, and indole-3-acetamide had the highest diagnostic values based on ROC analysis. These were mainly enriched in pathways for starch and sucrose metabolism, pentose and glucuronate interconversions, galactose metabolism, steroid hormone biosynthesis, as well as phenylalanine, tyrosine and tryptophan biosynthesis. Further, down-regulation was observed for amino acids, especially isoleucine, which is consistent with up-regulation of amino acid transporter genes SLC7A5 and SLC1A3 in MM. Overall survival was also negatively associated with SLC1A5, SLC7A5, and SLC1A3. Conclusion We found several altered plasma metabolites in the MM CDX model. The importance of specific metabolic pathways, for example amino acid metabolism, is herein highlighted, although further investigation is warranted.

Assessing the contribution of rare-to-common protein-coding variants to circulating metabolic biomarker levels via 412,394 UK Biobank exome sequences

Genome-wide association studies have established the contribution of common and low frequency variants to metabolic biomarkers in the UK Biobank (UKB); however, the role of rare variants remains to be assessed systematically. We evaluated rare coding variants for 198 metabolic biomarkers, including metabolites assayed by Nightingale Health, using exome sequencing in participants from four genetically diverse ancestries in the UKB (N=412,394). Gene-level collapsing analysis, that evaluated a range of genetic architectures, identified a total of 1,303 significant relationships between genes and metabolic biomarkers (p<1x10-8), encompassing 207 distinct genes. These include associations between rare non-synonymous variants in GIGYF1 and glucose and lipid biomarkers, SYT7 and creatinine, and others, which may provide insights into novel disease biology. Comparing to a previous microarray-based genotyping study in the same cohort, we observed that 40% of gene-biomarker relationships identified in the collapsing analysis were novel. Finally, we applied Gene-SCOUT, a novel tool that utilises the gene-biomarker association statistics from the collapsing analysis to identify genes having similar biomarker fingerprints and thus expand our understanding of gene networks.

Phenylacetylglutamine, a Novel Biomarker in Acute Ischemic Stroke

Background: To discover novel metabolic biomarkers of ischemic stroke (IS), we carried out a two-stage metabolomic profiling of IS patients and healthy controls using untargeted and targeted metabolomic approaches.Methods: We applied untargeted liquid chromatography-mass spectrometry (LC-MS) to detect the plasma metabolomic profiles of 150 acute IS patients and 50 healthy controls. The candidate differential microbiota-derived metabolite phenylacetylglutamine (PAGln) was validated in 751 patients with IS and 200 healthy controls. We evaluated the associations between PAGln levels and the severity and functional outcomes of patients with IS. Clinical mild stroke was defined as the National Institutes of Health Stroke Scale (NIHSS) score 0–5, and moderate-severe stroke as NIHSS score &gt;5. A favorable outcome at 3 months after IS was defined as the modified Rankin Scale (mRS) score 0–2, and unfavorable outcome as mRS score 3–6.Results: In untargeted metabolomic analysis, we detected 120 differential metabolites between patients with IS and healthy controls. Significantly altered metabolic pathways were purine metabolism, TCA cycle, steroid hormone biosynthesis, and pantothenate and CoA biosynthesis. Elevated plasma PAGln levels in IS patients, compared with healthy controls, were observed in untargeted LC-MS analysis and confirmed by targeted quantification (median 2.0 vs. 1.0 μmol/L; p &lt; 0.001). Patients with moderate-severe stroke symptoms and unfavorable short-term outcomes also had higher levels of PAGln both in discovery and validation stage. After adjusting for potential confounders, high PAGln levels were independently associated with IS (OR = 3.183, 95% CI 1.671–6.066 for the middle tertile and OR = 9.362, 95% CI 3.797–23.083 for the highest tertile, compared with the lowest tertile) and the risk of unfavorable short-term outcomes (OR = 2.286, 95% CI 1.188–4.401 for the highest tertile).Conclusions: IS patients had higher plasma levels of PAGln than healthy controls. PAGln might be a potential biomarker for IS and unfavorable functional outcomes in patients with IS.

Diagnostic Model of Alzheimer’s Disease in the Elderly Based on Protein and Metabolic Biomarkers

Background: With the accelerating aging process, the number of participants with Alzheimer’s disease (AD) is rising sharply, causing a huge economic burden. Objective: This study aimed to identify blood protein and metabolic biomarkers and explore the diagnostic model for AD among elderly in southeast China. Methods: We established a cohort among population with high risk AD in Zhejiang Province in 2018. Case and control groups each consisting of 45 subjects, matched for gender and age, were randomly selected from the cohort. Based on bioinformatics research, PRM/MRM technology was used to detect candidate biomarkers. Ensemble-based feature selection and machine learning methods was used to screen important variables as risk indicators for AD. Based on the risk biomarkers, the risk diagnostic model of AD in the elderly was constructed and evaluated. Results: Cystine and CPB2 were evaluated as biomarkers. The diagnostic model is constructed using logistic regression algorithm with the best cutoff value, sensitivity, specificity, and accuracy of 0.554, 0.895, 0.976, and 0.938, respectively, which determined by Youden’s index. The results showed that the model with protein and metabolite had a high efficiency. Conclusion: It showed that the diagnostic model constructed by Cystine and CPB2 had a good performance on sample classification. This study was of great significance for the early screening and diagnosis of AD, timely intervention, control and delay the development of dementia in southeast China.

Metabolomic Profiling Identifies Exogenous and Microbiota-Derived Metabolites as Markers of Methotrexate Efficacy in Juvenile Idiopathic Arthritis

Variability in methotrexate (MTX) efficacy represents a barrier to early and effective disease control in the treatment of juvenile idiopathic arthritis (JIA). This work seeks to understand the impact of MTX on the plasma metabolome and to identify metabolic biomarkers of MTX efficacy in a prospective cohort of children with JIA. Plasma samples from a cohort of children with JIA (n = 30) collected prior to the initiation of MTX and after 3 months of therapy were analyzed using a semi-targeted global metabolomic platform detecting 673 metabolites across a diversity of biochemical classes. Disease activity was measured using the 71-joint count juvenile arthritis disease activity score (JADAS-71) and clinical response to MTX was based on achievement of ACR Pedi 70 response. Metabolomic analysis identified 50 metabolites from diverse biochemical classes that were altered following the initiation of MTX (p &lt; 0.05) with 15 metabolites reaching a false-discovery rate adjusted p-value (q-value) of less than 0.05. Enrichment analysis identified a class-wide reduction in unsaturated triglycerides following initiation of MTX (q = 0.0009). Twelve of the identified metabolites were significantly associated with disease activity by JADAS-71. Reductions in three metabolites were found to be associated with clinical response by ACR Pedi 70 response criteria and represented several microbiota and exogenously derived metabolites including: dehydrocholic acid, biotin, and 4-picoline. These findings support diverse metabolic changes following initiation of MTX in children with JIA and identify metabolites associated with microbial metabolism and exogenous sources associated with MTX efficacy.

Impact of Different Operational Definitions of Sarcopenia on Prevalence in a Population-Based Sample: The Salus in Apulia Study

Background: In 2010, the European Working Group on Sarcopenia in Older People (EWGSOP1) issued its first operational definition to diagnose sarcopenia. This was updated in 2019 with a revised sequence of muscle mass and muscle strength (EWGSOP2). The aim of the study was to investigate the impact of these different operational definitions on sarcopenia prevalence in a representative population-based sample. Methods: For each algorithm, the prevalence of sarcopenia-related categories was calculated and related to sociodemographic and lifestyle variables, anthropometric parameters, and laboratory biomarkers. The present analysis used data from the Salus in Apulia Study (Italy, 740 subjects, mean age 75.5 ± 5.9 years, 54% women). Results: The application of the EWGSOP1 adapted algorithm resulted in 85% [95% confidence intervals (CI): 82–88%] non-sarcopenic subjects, 10% (95% CI: 8–12%) pre-sarcopenic subjects, and 5% (95% CI: 3–7%) sarcopenic/severe sarcopenic subjects. The sarcopenia-related categories were inversely related to weight and body mass index (BMI), particularly in overweight/obese subjects, and these categories showed favorable metabolic biomarkers. The EWGSOP2 algorithm yielded 73% (95% CI: 69–76%) non-sarcopenic subjects, 24% (95% CI: 21–27%) probably sarcopenic subjects, and 4% (95% CI: 2–5%) sarcopenic subjects. Conclusions: The present study identified BMI as a potential confounder of the prevalence estimates of sarcopenia-related categories in population-based settings with different EWGSOP operational definitions.

Fat metabolism is associated with telomere length in six population-based studies

Telomeres are repetitive DNA sequences located at the end of chromosomes, which are associated to biological aging, cardiovascular disease, cancer, and mortality. Lipid and fatty acid metabolism have been associated with telomere shortening. We have conducted an in-depth study investigating the association of metabolic biomarkers with telomere length (LTL). We performed an association analysis of 226 metabolic biomarkers with LTL using data from 11 775 individuals from six independent population-based cohorts (BBMRI-NL consortium). Metabolic biomarkers include lipoprotein lipids and subclasses, fatty acids, amino acids, glycolysis measures and ketone bodies. LTL was measured by quantitative polymerase chain reaction or FlowFISH. Linear regression analysis was performed adjusting for age, sex, lipid-lowering medication and cohort-specific covariates (model 1) and additionally for body mass index (BMI) and smoking (model 2), followed by inverse variance-weighted meta-analyses (significance threshold pmeta = 6.5x10−4). We identified four metabolic biomarkers positively associated with LTL, including two cholesterol to lipid ratios in small VLDL (S-VLDL-C % and S-VLDL-ce %) and two omega-6 fatty acid ratios (FAw6/FA and LA/FA). After additionally adjusting for BMI and smoking, these metabolic biomarkers remained associated with LTL with similar effect estimates. In addition, cholesterol esters in very small VLDL (XS-VLDL-ce) became significantly associated with LTL (p = 3.6x10−4). We replicated the association of FAw6/FA with LTL in an independent dataset of 7845 individuals (p = 1.9x10−4). To conclude, we identified multiple metabolic biomarkers involved in lipid and fatty acid metabolism that may be involved in LTL biology. Longitudinal studies are needed to exclude reversed causation.