Targeting systemic and gut microbial metabolism in ER+ breast cancer.

Estrogen receptor-positive (ER+) breast tumors have a better overall prognosis than ER- tumors; however, there is a sustained risk of recurrence. Mounting evidence indicates that genetic and epigenetic changes associated with resistance impact critical signaling pathways governing cell metabolism. This review delves into recent literature concerning the metabolic pathways regulated in ER+ breast tumors by the availability of nutrients and endocrine therapies and summarizes research on how changes in systemic and gut microbial metabolism can affect ER activity and responsiveness to endocrine therapy. As targeting of metabolic pathways using dietary or pharmacological approaches enters the clinic, we provide an overview of the supporting literature and suggest future directions.

Gestational Diabetes Is Characterized by Decreased Medium-Chain Acylcarnitines and Elevated Purine Degradation Metabolites across Pregnancy: A Case-Control Time-Course Analysis.

Gestational Diabetes Mellitus (GDM) results in complications affecting both mothers and their offspring. Metabolomic analysis across pregnancy provides an opportunity to better understand GDM pathophysiology. The objective was to conduct a metabolomics analysis of first and third trimester plasma samples to identify metabolic differences associated with GDM development. Forty pregnant women with overweight/obesity from a multisite clinical trial of a lifestyle intervention were included. Participants who developed GDM (n = 20; GDM group) were matched with those who did not develop GDM (n = 20; Non-GDM group). Plasma samples collected at the first (10-16 weeks) and third (28-35 weeks) trimesters were analyzed with ultra-performance liquid chromatography-mass spectrometry (UPLC-MS). Cardiometabolic risk markers, dietary recalls, and physical activity metrics were also assessed. Four medium-chain acylcarnitines, lauroyl-, octanoyl-, decanoyl-, and decenoylcarnitine, significantly differed over the course of pregnancy in the GDM vs Non-GDM group in a group-by-time interaction (p < 0.05). Hypoxanthine and inosine monophosphate were elevated in the GDM group (p < 0.04). In both groups over time, bile acids and sorbitol increased while numerous acylcarnitines and α-hydroxybutyrate decreased (p < 0.05). Metabolites involved in fatty acid oxidation and purine degradation were altered across the first and third trimesters of GDM-affected pregnancies, providing insight into metabolites and metabolic pathways altered with GDM development.

Identification of potential biomarkers and metabolic insights for gestational diabetes prevention: A review of evidence contrasting gestational diabetes versus weight loss studies that may direct future nutritional metabolomics studies.

Gestational diabetes mellitus (GDM) significantly increases maternal health risks and adverse effects for the offspring. Observational studies suggest that weight loss before pregnancy may be a promising GDM prevention method. Still, biochemical pathways linking preconception weight changes with subsequent development of GDM among women who are overweight or obese remain unclear. Metabolomic assessment is a powerful approach for understanding the global biochemical pathways linking preconception weight changes and subsequent GDM. We hypothesize that many of the alterations of metabolite levels associated with GDM will change in one direction in GDM studies but will change in the opposite direction in studies focusing on lifestyle interventions for weight loss. The present review summarizes available evidence from 21 studies comparing women with GDM with healthy participants and 12 intervention studies that investigated metabolite changes that occurred during weight loss using caloric restriction and behavioral interventions. We discuss 15 metabolites, including amino acids, lipids, amines, carbohydrates, and carbohydrate derivatives. Of particular note are the altered levels of branched-chain amino acids, alanine, palmitoleic acid, lysophosphatidylcholine 18:1, and hypoxanthine because of their mechanistic links to insulin resistance and weight change. Mechanisms that may explain how these metabolite modifications contribute to GDM development in those who are overweight or obese are proposed, including insulin resistance pathways. Future nutritional metabolomics preconception intervention studies in overweight or obese are necessary to investigate whether weight loss through lifestyle intervention can reduce GDM occurrence in association with these metabolite alterations and to test the value of these metabolites as potential diagnostic biomarkers of GDM development.

Autism-related language preferences of English-speaking individuals across the globe: A mixed methods investigation.

Over the past two decades, there have been increasing discussions around which terms should be used to talk about autism. Whilst these discussions have largely revolved around the suitability of identity-first language and person-first language, more recently this debate has broadened to encompass other autism-related terminology (e.g., 'high-functioning'). To date, academic studies have not investigated the language preferences of autistic individuals outside of the United Kingdom or Australia, nor have they compared levels of endorsement across countries. Hence, the current study adopted a mixed-methods approach, employing both quantitative and qualitative techniques, to explore the linguistic preferences of 654 English-speaking autistic adults across the globe. Despite variation in levels of endorsement between countries, we found that the most popular terms were similar-the terms 'Autism', 'Autistic person', 'Is autistic', 'Neurological/Brain Difference', 'Differences', 'Challenges', 'Difficulties', 'Neurotypical people', and 'Neurotypicals' were consistently favored across countries. Despite relative consensus across groups, both our quantitative and qualitative data demonstrate that there is no universally accepted way to talk about autism. Our thematic analysis revealed the reasons underlying participants' preferences, generating six core themes, and illuminated an important guiding principle-to respect personal preferences. These findings have significant implications for informing practice, research and language policy worldwide.

Predictive Gestational Diabetes Biomarkers With Sustained Alterations Throughout Pregnancy.

Gestational diabetes mellitus (GDM) results in an increased risk of pre- and postpartum health complications for both mother and child. Metabolomics analysis can potentially identify predictive biomarkers and provide insight into metabolic alterations associated with GDM pathogenesis and progression, but few metabolomics studies investigate alterations observed across the first and third trimester. We hypothesize that metabolites altered in first-trimester GDM that remain altered in late pregnancy may best inform interventions. Metabolomic studies comparing plasma and serum metabolite alterations in GDM vs non-GDM pregnancies were retrieved by searching PubMed, Medline, and CINAHL Plus databases. The present scoping review summarizes the metabolites found to be consistently altered throughout the course of GDM and proposes mechanisms that explain how these metabolic perturbations relate to GDM development and progression. Metabolites involved in fatty acid metabolism, reductive carboxylation, branched-chain amino acid metabolism, cell membrane lipid metabolism, purine degradation, and the gut microbiome were found to be altered throughout GDM pregnancies, with many of these pathways showing mechanistic links to insulin resistance, inflammation, and impaired cell signaling. Future studies are required to investigate if normalization of these perturbed pathways can be the targets of interventions.

Metabolites involved in purine degradation, insulin resistance, and fatty acid oxidation are associated with prediction of Gestational diabetes in plasma.

INTRODUCTION: Gestational diabetes mellitus (GDM) significantly increases maternal and fetal health risks, but factors predictive of GDM are poorly understood. OBJECTIVES: Plasma metabolomics analyses were conducted in early pregnancy to identify potential metabolites associated with prediction of GDM. METHODS: Sixty-eight pregnant women with overweight/obesity from a clinical trial of a lifestyle intervention were included. Participants who developed GDM (n = 34; GDM group) were matched on treatment group, age, body mass index, and ethnicity with those who did not develop GDM (n = 34; Non-GDM group). Blood draws were completed early in pregnancy (10-16 weeks). Plasma samples were analyzed by UPLC-MS using three metabolomics assays. RESULTS: One hundred thirty moieties were identified. Thirteen metabolites including pyrimidine/purine derivatives involved in uric acid metabolism, carboxylic acids, fatty acylcarnitines, and sphingomyelins (SM) were different when comparing the GDM vs. the Non-GDM groups (p < 0.05). The most significant differences were elevations in the metabolites' hypoxanthine, xanthine and alpha-hydroxybutyrate (p < 0.002, adjusted p < 0.02) in GDM patients. A panel consisting of four metabolites: SM 14:0, hypoxanthine, alpha-hydroxybutyrate, and xanthine presented the highest diagnostic accuracy with an AUC = 0.833 (95% CI: 0.572686-0.893946), classifying as a "very good panel". CONCLUSION: Plasma metabolites mainly involved in purine degradation, insulin resistance, and fatty acid oxidation, were altered in early pregnancy in connection with subsequent GDM development.

Postprandial Dried Blood Spot-Based Nutritional Metabolomic Analysis Discriminates a High-Fat, High-Protein Meat-Based Diet from a High Carbohydrate Vegan Diet: A Randomized Controlled Crossover Trial.

BACKGROUND: Due to the challenges associated with accurate monitoring of dietary intake in humans, nutritional metabolomics (including food intake biomarkers) analysis as a complementary tool to traditional dietary assessment methods has been explored. Food intake biomarker assessment using postprandial dried blood spot (DBS) collection can be a convenient and accurate means of monitoring dietary intake vs 24-hour urine collection. OBJECTIVE: The objective of this study was to use nutritional metabolomics analysis to differentiate a high-fat, high-protein meat (HFPM) diet from a high-carbohydrate vegan (HCV) diet in postprandial DBS and 24-hour urine. DESIGN: This was a randomized controlled crossover feeding trial. PARTICIPANTS/SETTING: Participants were healthy young adult volunteers (n = 8) in California. The study was completed in August 2019. INTERVENTION: The standardized isocaloric diet interventions included an HFPM and an HCV diet. Participants attended 2 intervention days, separated by a 2-week washout. MAIN OUTCOME MEASURES: During each intervention day, a finger-prick blood sample was collected in the fasting state, 3 hours post breakfast, and 3 hours post lunch. Participants also collected their urine for 24 hours. DBS and urine samples were analyzed by ultra-performance liquid chromatography mass spectrometry to identify potential food intake biomarkers. STATISTICAL ANALYSES PERFORMED: Principal component analysis for discriminatory analysis and univariate analysis using paired t tests were performed. RESULTS: Principal component analysis found no discrimination of baseline DBS samples. In both the postprandial DBS and 24-hour urine, post-HFPM consumption had higher (P < 0.05) levels of acylcarnitines, creatine, and cis-trans hydroxyproline, and the HCV diet was associated with elevated sorbitol (P < 0.05). The HFPM diet had higher concentrations of triacylglycerols with fewer than 54 total carbons in DBS, and 24-hour urine had higher nucleoside mono- and di-phosphates (P < 0.05). CONCLUSIONS: Nutritional metabolomics profiles of postprandial DBS and 24-hour urine collections were capable of differentiating the HFPM and HCV diets. The potential use of postprandial DBS-based metabolomic analysis deserves further investigation for dietary intake monitoring.