Similar Metabolic Health in Overweight/Obese Individuals With Contrasting Metabolic Flexibility to an Oral Glucose Tolerance Test.
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ABSTRACT:
SUBMITTER: Fernandez-Verdejo R
PROVIDER: S-EPMC8637191 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Insulin secretion following ingestion of a carbohydrate load affects a multitude of metabolic pathways that simultaneously change direction and quantity of interorgan fluxes of sugars, lipids and amino acids. In the present study, we aimed at identifying markers associated with differential responses to an OGTT a population of healthy adults. By use of three metabolite profiling platforms, we assessed these postprandial responses of a total of 202 metabolites in plasma of 72 healthy volunteers undergoing comprehensive phenotyping and of which half enrolled into a weight-loss program over a three-month period. A standard oral glucose tolerance test (OGTT) served as dietary challenge test to identify changes in postprandial metabolite profiles. Despite classified as healthy according to WHO criteria, two discrete clusters (A and B) were identified based on the postprandial glucose profiles with a balanced distribution of volunteers based on gender and other measures. Cluster A individuals displayed 26% higher postprandial glucose levels, delayed glucose clearance and increased fasting plasma concentrations of more than 20 known biomarkers of insulin resistance and diabetes previously identified in large cohort studies. The volunteers identified by canonical postprandial responses that form cluster A may be called pre-pre-diabetics and defined as "at risk" for development of insulin resistance. Moreover, postprandial changes in selected fatty acids and complex lipids, bile acids, amino acids, acylcarnitines and sugars like mannose revealed marked differences in the responses seen in cluster A and cluster B individuals that sustained over the entire challenge test period of 240 min. Almost all metabolites, including glucose and insulin, returned to baseline values at the end of the test (at 240 min), except a variety of amino acids and here those that have been linked to diabetes development. Analysis of the corresponding metabolite profile in a fasting blood sample may therefore allow for early identification of these subjects at risk for insulin resistance without the need to undergo an OGTT.
| S-EPMC9237474 | biostudies-literature
Project description:ObjectiveThe objective of the present study was to compare 24-hour glycemic levels between obese pregnant women with normal glucose tolerance and non-obese pregnant women.MethodsIn the present observational, longitudinal study, continuous glucose monitoring was performed in obese pregnant women with normal oral glucose tolerance test with 75 g of glucose between the 24th and the 28th gestational weeks. The control group (CG) consisted of pregnant women with normal weight who were selected by matching the maternal age and parity with the same characteristics of the obese group (OG). Glucose measurements were obtained during 72 hours.ResultsBoth the groups were balanced in terms of baseline characteristics (age: 33.5 [28.7-36.0] vs. 32.0 [26.0-34.5] years, p = 0.5 and length of pregnancy: 25.0 [24.0-25.0] vs. 25.5 [24.0-28.0] weeks, p = 0.6 in the CG and in the OG, respectively). Pre-breakfast glycemic levels were 77.77 ± 10.55 mg/dL in the CG and 82.02 ± 11.06 mg/dL in the OG (p<0.01). Glycemic levels at 2 hours after breakfast were 87.31 ± 13.10 mg/dL in the CG and 93.48 ± 18.74 mg/dL in the OG (p<0.001). Daytime blood glucose levels were 87.6 ± 15.4 vs. 93.1 ± 18.3 mg/dL (p<0.001) and nighttime blood glucose levels were 79.3 ± 15.8 vs. 84.7 ± 16.3 mg/dL (p<0.001) in the CG and in the OG, respectively. The 24-hour, daytime, and nighttime values of the area under the curve were higher in the OG when compared with the CG (85.1 ± 0.16 vs. 87.9 ± 0.12, 65.6 ± 0.14 vs. 67.5 ± 0.10, 19.5 ± 0.07 vs. 20.4 ± 0.05, respectively; p<0.001).ConclusionThe results of the present study showed that obesity in pregnancy was associated with higher glycemic levels even in the presence of normal findings on glucose tolerance test.
| S-EPMC8191902 | biostudies-literature
Project description:BackgroundInsulin resistance (IR) is predictive for type 2 diabetes and associated with various metabolic abnormalities in fasting conditions. However, limited data are available on how IR affects metabolic responses in a non-fasting setting, yet this is the state people are mostly exposed to during waking hours in the modern society. Here, we aim to comprehensively characterise the metabolic changes in response to an oral glucose test (OGTT) and assess the associations of these changes with IR.MethodsBlood samples were obtained at 0 (fasting baseline, right before glucose ingestion), 30, 60, and 120 min during the OGTT. Seventy-eight metabolic measures were analysed at each time point for a discovery cohort of 4745 middle-aged Finnish individuals and a replication cohort of 595 senior Finnish participants. We assessed the metabolic changes in response to glucose ingestion (percentage change in relative to fasting baseline) across the four time points and further compared the response profile between five groups with different levels of IR and glucose intolerance. Further, the differences were tested for covariate adjustment, including gender, body mass index, systolic blood pressure, fasting, and 2-h glucose levels. The groups were defined as insulin sensitive with normal glucose (IS-NGT), insulin resistant with normal glucose (IR-NGT), impaired fasting glucose (IFG), impaired glucose tolerance (IGT), and new diabetes (NDM). IS-NGT and IR-NGT were defined as the first and fourth quartile of fasting insulin in NGT individuals.ResultsGlucose ingestion induced multiple metabolic responses, including increased glycolysis intermediates and decreased branched-chain amino acids, ketone bodies, glycerol, and triglycerides. The IR-NGT subgroup showed smaller responses for these measures (mean + 23%, interquartile 9-34% at 120 min) compared to IS-NGT (34%, 23-44%, P < 0.0006 for difference, corrected for multiple testing). Notably, the three groups with glucose abnormality (IFG, IGT, and NDM) showed similar metabolic dysregulations as those of IR-NGT. The difference between the IS-NGT and the other subgroups was largely explained by fasting insulin, but not fasting or 2 h glucose. The findings were consistent after covariate adjustment and between the discovery and replication cohort.ConclusionsInsulin-resistant non-diabetic individuals are exposed to a similar adverse postprandial metabolic milieu, and analogous cardiometabolic risk, as those with type 2 diabetes. The wide range of metabolic abnormalities associated with IR highlights the necessity of diabetes diagnostics and clinical care beyond glucose management.
| S-EPMC6883544 | biostudies-literature
Project description:The concept of "metabolic inflexibility" was first introduced to describe the failure of insulin-resistant human subjects to appropriately adjust mitochondrial fuel selection in response to nutritional cues. This phenomenon has since gained increasing recognition as a core component of the metabolic syndrome, but the underlying mechanisms have remained elusive. Here, we identify an essential role for the mitochondrial matrix enzyme, carnitine acetyltransferase (CrAT), in regulating substrate switching and glucose tolerance. By converting acetyl-CoA to its membrane permeant acetylcarnitine ester, CrAT regulates mitochondrial and intracellular carbon trafficking. Studies in muscle-specific Crat knockout mice, primary human skeletal myocytes, and human subjects undergoing L-carnitine supplementation support a model wherein CrAT combats nutrient stress, promotes metabolic flexibility, and enhances insulin action by permitting mitochondrial efflux of excess acetyl moieties that otherwise inhibit key regulatory enzymes such as pyruvate dehydrogenase. These findings offer therapeutically relevant insights into the molecular basis of metabolic inflexibility.
| S-EPMC3348515 | biostudies-literature
Project description:BackgroundSeveral studies point to a correlation between obesity and the severity of depressive and anxiety symptoms in children and adults, but there are still some controversial points about this association. The aim of this study is to investigate the relationship between body composition and the severity of anxiety/depressive symptoms in overweight and obese individuals with Metabolic Syndrome (MS).MethodsFifty patients, 18-50 years old, overweight or obese and with the diagnosis of MS based on the International Diabetes Federation (IDF) criteria were selected for this study. Body composition was evaluated using Dual Energy X-ray Absorptiometry (DXA). Depressive symptoms were evaluated using the Hospital Anxiety and Depression Scale (HADS-Depression) and the Beck Depression Inventory (BDI). Anxiety symptoms were evaluated using HADS-Anxiety.ResultsNo correlation was found between depressive symptoms (HADS-Depression or BDI) and Body Mass Index (BMI) (r = 0.01; p = 0.94 and r = -0.12, p = 0.38; respectively), Waist Circumference (WC) (r = -0.06, p = 0.67 and r = -0.22, p = 0.12; respectively), and Waist-to-Hip Ratio (WHR) (r = -0.12, p = 0.40 and r = -0.17, p = 0.23; respectively). Additionally, no correlation was found among anxiety symptoms (HADS-Anxiety) and BMI (r = -0.15, p = 0.27), and WHR (r = -0.17, p = 0.24). In contrast, a significant correlation was found between percentage of total fat (DXA) and HADS-Depression (r = 0.34, p = 0.019) and HADS-Anxiety (r = 0.30, p = 0.039). Additionally, an inverse and strong correlation was found between lean mass (in grams) and HADS-Depression (r = -0.42, p = 0.004), HADS anxiety (r = -0.57, p < 0.0001), and BDI (r = -0.44, p = 0.026).ConclusionsIn individuals with MS, the percentage of body fat, and not central fat, BMI, WC, or WHR, was associated with an increased severity of anxiety and depressive symptoms. In contrast, total lean mass was strongly associated with fewer anxiety/depressive symptoms, suggesting that body composition might be related to psychiatric comorbidity in overweight individuals with MS.
| S-EPMC3913787 | biostudies-literature
Project description:Understanding the role of white adipose tissue (WAT) in the occurrence and progression of metabolic syndrome is of considerable interest; among the metabolic syndromes are obesity and type 2 diabetes (T2D). Insulin resistance is a key factor in the development of T2D. When the target cells become resistant to insulin, the pancreas responds by producing more insulin to try to lower blood glucose. Over time, this can lead to a state of hyperinsulinemia (high levels of insulin in the blood), which can further exacerbate insulin resistance and contribute to the development of T2D. In order to understand the difference between healthy and unhealthy obese individuals, we have used published transcriptomic profiling to compare differences between the WAT obtained from obese diabetics and subjects who are obese with normal glucose tolerance and insulin resistance. The identification of aberrantly expressed messenger RNA (mRNA) and the resulting molecular interactions and signaling networks is essential for a better understanding of the progression from normal glucose-tolerant obese individuals to obese diabetics. Computational analyses using Ingenuity Pathway Analysis (IPA) identified multiple activated signaling networks in obesity progression from insulin-resistant and normal glucose-tolerant (IR-NGT) individuals to those with T2D. The pathways affected are: Tumor Necrosis Factor (TNF), Extracellular signal-Regulated protein Kinase 1/2 ERK1/2, Interleukin 1 A (IL1A), Protein kinase C (Pkcs), Convertase C5, Vascular endothelial growth factor (Vegf), REL-associated protein (RELA), Interleukin1/1 B (IL1/1B), Triggering receptor expressed on myeloid cells (TREM1) and Nuclear factor KB1 (NFKB1) networks, while functional annotation highlighted Liver X Receptor (LXR) activation, phagosome formation, tumor microenvironment pathway, LPS/IL-1 mediated inhibition of RXR function, TREM1 signaling and IL-6 signaling. Together, by conducting a thorough bioinformatics study of protein-coding RNAs, prospective targets could be exploited to clarify the molecular pathways underlying the development of obesity-related type 2 diabetes.
| S-EPMC10093815 | biostudies-literature
Project description:The objective of this study was to examine the effects of a gastrointestinal microbiome modulator (GIMM) containing inulin, ?-glucan, blueberry anthocyanins, and blueberry polyphenols on metabolic parameters, fecal markers of gut microbiota, and satiety.Thirty overweight or obese individuals aged 18 to 70years, were enrolled in a randomized controlled trial. Participants consumed the test product or placebo daily for four weeks. Stool samples were collected and blood was drawn at baseline and week four for assessments of gut microbiota, satiety hormones, glucose control, and lipid measures. Subjective satiety was assessed weekly. Linear models were used to compare differences from baseline to week four.GIMM consumption improved blood glucose tolerance (p=0.008), and increased satiety (p=0.03). There were no statistically significant differences in insulin sensitivity, fecal markers of gut microbiota, plasma satiety hormones, or serum lipid concentrations between the groups. However, plasma satiety hormones and fecal short chain fatty acid concentrations increased in the test group compared to the placebo.GIMM consumption for four weeks, increases satiety, and improves glucose tolerance possibly through insulin-independent pathways.
| S-EPMC4656110 | biostudies-literature
Project description:Background and objectivesHigher body mass index (BMI) is paradoxically associated with lower mortality in persons with CKD, but whether cardiometabolic abnormalities modulate this association is unclear.Design, setting, participants, & measurementsParticipants with CKD from the Reasons for Geographic and Racial Differences in Stroke (REGARDS) study (n=4374) were analyzed. The harmonized criteria for metabolic syndrome were used to define metabolic health, and participants were categorized into one of six mutually exclusive categories defined by combined measures of metabolic health (metabolically healthy, <3 criteria for metabolic syndrome; metabolically unhealthy, ?3 criteria) and weight status (normal weight, BMI 18.5-24.9 kg/m(2); overweight, BMI 25-29.9 kg/m(2); obese, BMI ?30 kg/m(2)). Cox models were used to estimate the hazard ratio (HR) of death as a function of each category.ResultsA total of 683 deaths were observed over a mean 4.5 years of follow-up. In analyses adjusted for age, race, sex, and geographic region of residence, compared with metabolically healthy normal weight persons, the HRs of mortality in metabolically healthy overweight and obese persons were 0.68 (95% confidence interval [95% CI], 0.53 to 0.87) and 0.71 (95% CI, 0.51 to 0.98), respectively, whereas there were no statistically significant differences in survival among metabolically unhealthy overweight or obese individuals. After further adjustment for lifestyle, clinical and laboratory factors including markers of kidney function, the HR of mortality remained lower in metabolically healthy overweight individuals compared with metabolically healthy normal weight individuals (HR, 0.74; 95% CI, 0.57 to 0.96).ConclusionsMetabolic abnormalities may attenuate the magnitude and strength of survival benefits associated with higher BMI in individuals with CKD.
| S-EPMC3848389 | biostudies-literature
Project description:BackgroundThe prevalence of overweight is increasing globally and has become a serious health problem. Low-grade chronic inflammation in overweight subjects is thought to play an important role in disease development. Novel tools to understand these processes are needed. Metabolic profiling is one such tool that can provide novel insights into the impact of treatments on metabolism.MethodologyTo study the metabolic changes induced by a mild anti-inflammatory drug intervention, plasma metabolic profiling was applied in overweight human volunteers with elevated levels of the inflammatory plasma marker C-reactive protein. Liquid and gas chromatography mass spectrometric methods were used to detect high and low abundant plasma metabolites both in fasted conditions and during an oral glucose tolerance test. This is based on the concept that the resilience of the system can be assessed after perturbing a homeostatic situation.ConclusionsMetabolic changes were subtle and were only detected using metabolic profiling in combination with an oral glucose tolerance test. The repeated measurements during the oral glucose tolerance test increased statistical power, but the metabolic perturbation also revealed metabolites that respond differentially to the oral glucose tolerance test. Specifically, multiple metabolic intermediates of the glutathione synthesis pathway showed time-dependent suppression in response to the glucose challenge test. The fact that this is an insulin sensitive pathway suggests that inflammatory modulation may alter insulin signaling in overweight men.
| S-EPMC2643463 | biostudies-literature
Project description:Impaired glucose tolerance (IGT) is diagnosed by a standardized oral glucose tolerance test (OGTT). However, the OGTT is laborious, and when not performed, glucose tolerance cannot be determined from fasting samples retrospectively. We tested if glucose tolerance status is reasonably predictable from a combination of demographic, anthropometric, and laboratory data assessed at one time point in a fasting state.Given a set of 22 variables selected upon clinical feasibility such as sex, age, height, weight, waist circumference, blood pressure, fasting glucose, HbA1c, hemoglobin, mean corpuscular volume, serum potassium, fasting levels of insulin, C-peptide, triglyceride, non-esterified fatty acids (NEFA), proinsulin, prolactin, cholesterol, low-density lipoprotein, HDL, uric acid, liver transaminases, and ferritin, we used supervised machine learning to estimate glucose tolerance status in 2,337 participants of the TUEF study who were recruited before 2012. We tested the performance of 10 different machine learning classifiers on data from 929 participants in the test set who were recruited after 2012. In addition, reproducibility of IGT was analyzed in 78 participants who had 2 repeated OGTTs within 1?year.The most accurate prediction of IGT was reached with the recursive partitioning method (accuracy?=?0.78). For all classifiers, mean accuracy was 0.73?±?0.04. The most important model variable was fasting glucose in all models. Using mean variable importance across all models, fasting glucose was followed by NEFA, triglycerides, HbA1c, and C-peptide. The accuracy of predicting IGT from a previous OGTT was 0.77.Machine learning methods yield moderate accuracy in predicting glucose tolerance from a wide set of clinical and laboratory variables. A substitution of OGTT does not currently seem to be feasible. An important constraint could be the limited reproducibility of glucose tolerance status during a subsequent OGTT.
| S-EPMC5868129 | biostudies-literature