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Background: Diabetic kidney disease (DKD) is a major complication of diabetes leading to kidney failure. Methods: This study investigates lipid metabolism profiles of long-standing DKD (LDKD, diabetes duration > 10 years) by integrative analysis of available single-cell RNA sequencing and spatial multi-omics data (focusing on spatial continuity samples) from the Kidney Precision Medicine Project. Results: Two injured cell types, an injured thick ascending limb (iTAL) and an injured proximal tubule (iPT), were identified and significantly elevated in LDKD samples. Both iTAL and iPT exhibit increased lipid metabolic and biosynthetic activities and decreased lipid and fatty acid oxidative processes compared to TAL/PT cells. Notably, compared to PT, iPT shows significant upregulation of specific injury and fibrosis-related genes, including FSHR and BMP7. Meanwhile, comparing iTAL to TAL, inflammatory-related genes such as ANXA3 and IGFBP2 are significantly upregulated. Furthermore, spatial metabolomics analysis reveals regionally distributed clusters in the kidney and notably differentially expressed lipid metabolites, such as triglycerides, glycerophospholipids, and sphingolipids, particularly pronounced in the inner medullary regions. Conclusions: These findings provide an integrative description of the lipid metabolism landscape in LDKD, highlighting injury-associated cellular processes and potential molecular mechanisms. [ABSTRACT FROM AUTHOR]

Background: Lactation is known to improve insulin resistance, but this phenomenon remains poorly understood. Our goal was to evaluate whether subclinical inflammation could mediate the association between breastfeeding (BF) and improvement in glucose metabolism and markers of insulin resistance (MIRs) in the postpartum. Methods: A total of 95 adult women (≥18 years) with a BMI ≥ 25 kg/m2 from the outpatient clinic of the Federal University of São Paulo were followed from early pregnancy until 60 to 180 days postpartum. The patients were divided based on their BF status: BF and non-BF groups. A latent variable termed SubInf was created incorporating inflammation-related biomarkers: adiponectin, E-selectin, branched-chain amino acids, zonulin, copeptin, and lipopolysaccharides. The association of BR with MIRs in the postpartum was evaluated through linear regression analysis, and mediation analysis was performed to evaluate the role of SubInf in this association. Results: The groups were similar regarding gestational diabetes mellitus (GDM) prevalence, pre-gestational BMI, caloric intake, physical activity, and postpartum weight loss. The BF group presented lower levels of triglycerides (TGs), fasting glucose, fasting insulin, TG/HDLcholesterol ratio (TG/HDL), TyG index, and HOMA-IR compared to the non-BF group. A linear regression analysis adjusted for scholarity, parity, pre-gestational BMI, GDM, weight gain during pregnancy, and mode of delivery revealed an inverse association between BF and fasting glucose [−6.30 (−10.71 to −1.89), p = 0.005), HOMA-IR [−0.28 (−0.50 to −0.05), p = 0.017], TyG index [−0.04 (−0.06 to −0.01), p = 0.002], and TG/HDL ratio [−0.23 (−0.46 to −0.01), p = 0.001]. In the mediation analysis, SubInf did not mediate the indirect effect of BF on MIRs. Conclusions: In overweight and obese women, an association between BF and improvement in MIRs in the postpartum was seen, corroborating that BF should be stimulated, especially in these cardiometabolic high-risk women. Subclinical inflammation did not seem to mediate this association. [ABSTRACT FROM AUTHOR]

Background/Objectives: Recent data indicate the involvement of skeletal muscles in the regulation of metabolism and in the pathogenesis of chronic noncommunicable diseases. The goal of our study was to describe the serum concentrations of myokines in patients with metabolic dysfunction-associated steatotic liver disease (MASLD) and hypertension (HTN) and their correlation with laboratory parameters, blood pressure (BP), and MASLD severity. Methods: A total of 67 patients with MASLD and HTN underwent anthropometric measurements, laboratory tests, and point shear-wave elastography. The serum concentrations of myokines were measured using enzyme-linked immunosorbent assay (ELISA). Results: Patients with detectable serum myonectin concentrations had significantly higher maximum systolic blood pressure (p = 0.022) and higher blood levels of uric acid (p = 0.029). Serum irisin concentration ≥ 6.1 μg/mL was associated with higher FLI values (p = 0.042) and liver stiffness (p = 0.034), as well as with slightly higher waist circumference (p = 0.082) and triglyceride level (p = 0.062). Patients with serum myostatin concentration ≥ 4.98 ng/mL were significantly older (p = 0.033) and had a lower blood albumin level (p = 0.043). Conclusions: In conclusion, the myokine profile in patients with MASLD and HTN correlates both with the severity of MASLD and the parameters characteristic of metabolic health, suggesting the possible contribution of altered irisin, myonectin, and myostatin concentrations to the occurrence of cardiometabolic risks in patients with MASLD. [ABSTRACT FROM AUTHOR]

Non-alcoholic fatty liver disease (NAFLD) is the most common cause of abnormal liver function tests worldwide, with an estimated prevalence ranging between 19–46% in the general population. Of note, NAFLD is also expected to become a leading cause of end-stage liver disease in the next decades. Given the high prevalence and severity of NAFLD, especially in high-risk populations (i.e., patients with type-2 diabetes mellitus and/or obesity), there is a major interest in early detection of the disease in primary care. Nevertheless, substantial uncertainties still surround the development of a screening policy for NAFLD, such as limitations in currently used non-invasive markers of fibrosis, cost-effectiveness and the absence of a licensed treatment. In this review, we summarise current knowledge and try to identify the limitations surrounding the screening policy for NAFLD in primary care. [ABSTRACT FROM AUTHOR]

Diet is currently considered one of the most important adjustable determinants of human health. The gut microbiota, the collection of microorganisms that inhabit (mainly) the distal bowel, has recently been shown to ensure critical physiological functions, such as immune, metabolic and neuropsychiatric. Many of these biological effects result from the production of bacterial metabolites that may target host cells, tissues and organs. In line with this rationale, epigenetics has brought new insights to our understanding of how environmental factors influence gene expression and, interestingly, gut microbiota metabolites have recently been proposed as novel and significant inducers of epigenetic modifications. Efforts have been dedicated to unveil how the production of specific metabolites influences the activity of epigenetic writers and erasers in order to establish a mechanistic link between gut microbiota, epigenetic modifications and health. Recent data is now evidencing how specific microbial metabolites shape the epigenetic landscape of eukaryotic cells, paving new avenues for innovative therapeutic strategies relying on diet-driven microbiota: epigenetic interactions. Herein is discussed the impact of diet on gut microbiota and the molecular mechanisms underlying microbiota–host interactions, highlighting the influence of diet on microbiota metabolome and how this may induce epigenetic modifications in host cells. Furthermore, it is hypothesized that epigenetics may be a key process transducing the effects of diet on gut microbiota with consequences for health and disease. Accordingly, innovating strategies of disease prevention based on a "precision diet", a personalized dietary planning according to specific epigenetic targets, are discussed. [ABSTRACT FROM AUTHOR]

We here characterize changes in metabolite patterns in glioblastoma patients undergoing surgery and concurrent chemoradiation using machine learning (ML) algorithms to characterize metabolic changes during different stages of the treatment protocol. We examined 105 plasma specimens (before surgery, 2 days after surgical resection, before starting concurrent chemoradiation, and immediately after chemoradiation) from 36 patients with isocitrate dehydrogenase (IDH) wildtype glioblastoma. Untargeted GC-TOF mass spectrometry-based metabolomics was used given its superiority in identifying and quantitating small metabolites; this yielded 157 structurally identified metabolites. Using Multinomial Logistic Regression (MLR) and GradientBoostingClassifier (GB Classifier), ML models classified specimens based on metabolic changes. The classification performance of these models was evaluated using performance metrics and area under the curve (AUC) scores. Comparing post-radiation to pre-radiation showed increased levels of 15 metabolites: glycine, serine, threonine, oxoproline, 6-deoxyglucose, gluconic acid, glycerol-alpha-phosphate, ethanolamine, propyleneglycol, triethanolamine, xylitol, succinic acid, arachidonic acid, linoleic acid, and fumaric acid. After chemoradiation, a significant decrease was detected in 3-aminopiperidine 2,6-dione. An MLR classification of the treatment phases was performed with 78% accuracy and 75% precision (AUC = 0.89). The alternative GB Classifier algorithm achieved 75% accuracy and 77% precision (AUC = 0.91). Finally, we investigated specific patterns for metabolite changes in highly correlated metabolites. We identified metabolites with characteristic changing patterns between pre-surgery and post-surgery and post-radiation samples. To the best of our knowledge, this is the first study to describe blood metabolic signatures using ML algorithms during different treatment phases in patients with glioblastoma. A larger study is needed to validate the results and the potential application of this algorithm for the characterization of treatment responses. [ABSTRACT FROM AUTHOR]

Metabolic dysfunction-associated fatty liver disease (MAFLD) is a complex disorder that is implicated in dysregulations in multiple biological pathways, orchestrated by interactions between genetic predisposition, metabolic syndromes and environmental factors. The limited knowledge of its pathogenesis is one of the bottlenecks in the development of prognostic and therapeutic options for MAFLD. Moreover, the extent to which metabolic pathways are altered due to ongoing hepatic steatosis, inflammation and fibrosis and subsequent liver damage remains unclear. To uncover potential MAFLD pathogenesis in humans, we employed an untargeted nuclear magnetic resonance (NMR) spectroscopy- and high-resolution mass spectrometry (HRMS)-based multiplatform approach combined with a computational multiblock omics framework to characterize the plasma metabolomes and lipidomes of obese patients without (n = 19) or with liver biopsy confirmed MAFLD (n = 63). Metabolite features associated with MAFLD were identified using a metabolome-wide association study pipeline that tested for the relationships between feature responses and MAFLD. A metabolic pathway enrichment analysis revealed 16 pathways associated with MAFLD and highlighted pathway changes, including amino acid metabolism, bile acid metabolism, carnitine shuttle, fatty acid metabolism, glycerophospholipid metabolism, arachidonic acid metabolism and steroid metabolism. These results suggested that there were alterations in energy metabolism, specifically amino acid and lipid metabolism, and pointed to the pathways being implicated in alerted liver function, mitochondrial dysfunctions and immune system disorders, which have previously been linked to MAFLD in human and animal studies. Together, this study revealed specific metabolic alterations associated with MAFLD and supported the idea that MAFLD is fundamentally a metabolism-related disorder, thereby providing new perspectives for diagnostic and therapeutic strategies. [ABSTRACT FROM AUTHOR]

Weight loss and increased physical activity may promote beneficial modulation of the metabolome, but limited evidence exists about how very low-level weight loss affects the metabolome in previously non-obese active individuals. Following a weight loss period (21.1 ± 3.1 weeks) leading to substantial fat mass loss of 52% (−7.9 ± 1.5 kg) and low body fat (12.7 ± 4.1%), the liquid chromatography-mass spectrometry-based metabolic signature of 24 previously young, healthy, and normal weight female physique athletes was investigated. We observed uniform increases (FDR < 0.05) in bile acids, very-long-chain free fatty acids (FFA), and oxylipins, together with reductions in unsaturated FFAs after weight loss. These widespread changes, especially in the bile acid profile, were most strongly explained (FDR < 0.05) by changes in android (visceral) fat mass. The reported changes did not persist, as all of them were reversed after the subsequent voluntary weight regain period (18.4 ± 2.9 weeks) and were unchanged in non-dieting controls (n = 16). Overall, we suggest that the reported changes in FFA, bile acid, and oxylipin profiles reflect metabolic adaptation to very low levels of fat mass after prolonged periods of intense exercise and low-energy availability. However, the effects of the aforementioned metabolome subclass alteration on metabolic homeostasis remain controversial, and more studies are warranted to unravel the complex physiology and potentially associated health implications. In the end, our study reinforced the view that transient weight loss seems to have little to no long-lasting molecular and physiological effects. [ABSTRACT FROM AUTHOR]

Signalling between the tissues integrating synthesis, transformation and utilization of energy substrates and their regulatory hormonal axes play a substantial role in the development of metabolic disorders. Interactions between cytokines, particularly liver derived hepatokines and adipokines, secreted from adipose tissue, constitute one of major areas of current research devoted to metabolic dysregulation. The thyroid exerts crucial influence on the maintenance of basal metabolic rate, thermogenesis, carbohydrate and lipid metabolism, while its dysfunction promotes the development of metabolic disorders. In this review, we discuss the interplay between three adipokines: fatty acid binding protein type 4, fetuin-A, retinol binding protein type 4 and thyroid hormones, that shed a new light onto mechanisms underlying atherosclerosis, cardiovascular complications, obesity, insulin resistance and diabetes accompanying thyroid dysfunction. Furthermore, we summarize clinical findings on those cytokines in the course of thyroid disorders. [ABSTRACT FROM AUTHOR]

Consumption of a high-fat diet (HFD) links obesity to colon cancer in humans. Our data show that a HFD (45% energy fat versus 16% energy fat in an AIN-93 diet (AIN)) promotes azoxymethane (AOM)-induced colonic aberrant crypt foci (ACF) formation in a mouse cancer model. However, the underlying metabolic basis remains to be determined. In the present study, we hypothesize that AOM treatment results in different plasma metabolomic responses in diet-induced obese mice. An untargeted metabolomic analysis was performed on the plasma samples by gas chromatography time-of-flight mass spectrometry (GC-TOF-MS). We found that 53 of 144 identified metabolites were different between the 4 groups of mice (AIN, AIN + AOM, HFD, HFD + AOM), and sparse partial least-squares discriminant analysis showed a separation between the HFD and HFD + AOM groups but not the AIN and AIN + AOM groups. Moreover, the concentrations of dihydrocholesterol and cholesterol were inversely associated with AOM-induced colonic ACF formation. Functional pathway analyses indicated that diets and AOM-induced colonic ACF modulated five metabolic pathways. Collectively, in addition to differential plasma metabolomic responses, AOM treatment decreases dihydrocholesterol and cholesterol levels and alters the composition of plasma metabolome to a greater extent in mice fed a HFD compared to the AIN. [ABSTRACT FROM AUTHOR]

The identification of insulin resistance and hyperinsulinemia in polycystic ovary syndrome (PCOS) is not a minor issue. The homeostasis model assessment of insulin resistance index (HOMA) is the most used index of IR (Insulin Resistance), validated in overweight and obese patients but not in normal-weight PCOS subjects, who can still present with increased insulin secretion by an oral glucose tolerance test (OGTT). The evaluation of insulin secretion and resistance represents a still unresolved problem. The aim of this study is to identify a possible yet noninvasive method to properly evaluate the insulin metabolism in young non-diabetic subjects. Girls aged 14–22 years, afferent to the center of Gynecological Diseases in Childhood and Adolescence of Cagliari (Italy), were screened for PCOS. A total of 42 subjects comprised the study group. Hormonal assays, OGTT, transabdominal (TA) or transvaginal (TV) US, and urine collection for 1H-NMR analysis were assayed in the early follicular phase. A 1H-NMR coupled multivariate statistical analysis was performed. The OPLS model indicated that the NMR profile of urine had a good fit and prediction ability for the AUC OGTT with R2 = 0.813. Metabolomics can be a promising tool to the potential identification of biomarkers of an exaggerated insulin response to OGTT and can encourage substantial progress for a more accurate and early diagnosis in PCOS. [ABSTRACT FROM AUTHOR]

Metabolomics has found numerous applications in the study of liver metabolism in health and disease. Metabolomics studies can be conducted in a variety of biological matrices ranging from easily accessible biofluids such as urine, blood or feces, to organs, tissues or even cells. Sample collection and storage are critical steps for which standard operating procedures must be followed. Inappropriate sample collection or storage can indeed result in high variability, interferences with instrumentation or degradation of metabolites. In this review, we will first highlight important general factors that should be considered when planning sample collection in the study design of metabolomic studies, such as nutritional status and circadian rhythm. Then, we will discuss in more detail the specific procedures that have been described for optimal pre-analytical handling of the most commonly used matrices (urine, blood, feces, tissues and cells). [ABSTRACT FROM AUTHOR]

Despite the tremendous success, pitfalls have been observed in every step of a clinical metabolomics workflow, which impedes the internal validity of the study. Furthermore, the demand for logistics, instrumentations, and computational resources for metabolic phenotyping studies has far exceeded our expectations. In this conceptual review, we will cover inclusive barriers of a metabolomics-based clinical study and suggest potential solutions in the hope of enhancing study robustness, usability, and transferability. The importance of quality assurance and quality control procedures is discussed, followed by a practical rule containing five phases, including two additional "pre-pre-" and "post-post-" analytical steps. Besides, we will elucidate the potential involvement of machine learning and demonstrate that the need for automated data mining algorithms to improve the quality of future research is undeniable. Consequently, we propose a comprehensive metabolomics framework, along with an appropriate checklist refined from current guidelines and our previously published assessment, in the attempt to accurately translate achievements in metabolomics into clinical and epidemiological research. Furthermore, the integration of multifaceted multi-omics approaches with metabolomics as the pillar member is in urgent need. When combining with other social or nutritional factors, we can gather complete omics profiles for a particular disease. Our discussion reflects the current obstacles and potential solutions toward the progressing trend of utilizing metabolomics in clinical research to create the next-generation healthcare system. [ABSTRACT FROM AUTHOR]