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Plasma metabolites associated with homeostatic model assessment of insulin resistance: metabolite-model design and external validation.

ABSTRACT: Different plasma metabolites have been related to insulin resistance (IR). However, there is a lack of metabolite models predicting IR with external validation. The aim of this study is to identify a multi-metabolite model associated to the homeostatic model assessment (HOMA)-IR values. We performed a cross-sectional metabolomics analysis of samples collected from overweight and obese subjects from two independent studies. The training step was performed in 236 subjects from the SATIN study and validated in 102 subjects from the GLYNDIET study. Plasma metabolomics profile was analyzed using three different approaches: GC/quadrupole-TOF, LC/quadrupole-TOF, and nuclear magnetic resonance (NMR). Associations between metabolites and HOMA-IR were assessed using elastic net regression analysis with a leave-one-out cross validation (CV) and 100 CV runs. HOMA-IR was analyzed both as linear and categorical (median or lower versus higher than the median). Receiver operating characteristic curves were constructed based on metabolites' weighted models. A set of 30 metabolites discriminating extremes of HOMA-IR were consistently selected. These metabolites comprised some amino acids, lipid species and different organic acids. The area under the curve (AUC) for the discrimination between HOMA-IR extreme categories was 0.82 (95% CI: 0.74-0.90), based on the multi-metabolite model weighted with the regression coefficients of metabolites in the validation dataset. We identified a set of metabolites discriminating between extremes of HOMA-IR and able to predict HOMA-IR with high accuracy.

SUBMITTER: Hernandez-Alonso P

PROVIDER: S-EPMC6761105 | biostudies-literature | 2019 Sep

REPOSITORIES: biostudies-literature

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Plasma metabolites associated with homeostatic model assessment of insulin resistance: metabolite-model design and external validation.

Hernández-Alonso Pablo P García-Gavilán Jesús J Camacho-Barcia Lucía L Sjödin Anders A Hansen Thea T TT Harrold Jo J Salas-Salvadó Jordi J Halford Jason C G JCG Canudas Silvia S Bulló Mònica M

Scientific reports 20190925 1

Different plasma metabolites have been related to insulin resistance (IR). However, there is a lack of metabolite models predicting IR with external validation. The aim of this study is to identify a multi-metabolite model associated to the homeostatic model assessment (HOMA)-IR values. We performed a cross-sectional metabolomics analysis of samples collected from overweight and obese subjects from two independent studies. The training step was performed in 236 subjects from the SATIN study and ...[more]

PMID: 31554919

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Project description:ObjectiveTo evaluate the associations between homeostatic model assessment for insulin resistance (HOMA-IR) and pregnancy outcomes in non-dyslipidemic infertile women undergoing in vitro fertilization/intracytoplasmic sperm injection-embryo transfer (IVF/ICSI-ET).Materials and methodsThis is a retrospective study involving 3,615 non-dyslipidemic infertile women who attend to the Reproductive Medicine Center of Xiangya Hospital, Central South University (CSU) between January 2014 and October 2021. Eligible participants were divided into three groups according to the quartiles of HOMA-IR: Group 1 (HOMA-IR <1.46), Group 2 (1.46 to <2.71) and Group 3 (HOMA-IR ≥2.71). Baseline data, clinical characteristics during the assisted reproductive technology (ART) procedure, pregnancy, and neonatal outcomes were compared among the three groups. Subgroup analysis based on presence or absence of the polycystic ovary syndrome (PCOS) status was also performed to analyze the effects of HOMA-IR among non-PCOS populations.ResultsThe late miscarriage rate and percentage of macrosomia increased with the HOMA-IR group (for late miscarriage rate: 2.23% vs. 3.04% vs. 7.35%, P<0.001; for macrosomia: 0.21% vs. 1.70% vs. 3.23%, P=0.002). Increased HOMA-IR (HOMA-IR≥2.71) was positively associated with late miscarriage (crude OR 3.50, 95% CI 1.64-7.47, P=0.001; adjusted OR 3.56, 95% CI 1.56-8.15, P=0.003). In the subgroup analysis, there were 3,165 participants in the non-PCOS group and 450 were assigned to the PCOS group. Late miscarriage rate increased with the HOMA-IR group among non-PCOS populations (2.20% vs. 3.03% vs. 7.67%, P<0.001). Late miscarriage rate of PCOS women were comparable among the three HOMA-IR groups (2.50% vs. 3.06% vs. 5.71%, P=0.634). Among non-PCOS women, increased HOMA-IR (HOMA-IR≥2.71) was positively associated with late miscarriage (crude OR 3.71, 95% CI 1.66-8.30, P=0.001; adjusted OR 3.82, 95% CI 1.59-9.17, P=0.003).ConclusionsLate miscarriage rate and prevalence of macrosomia increased with the HOMA-IR index. Preconception HOMA-IR is an independent risk factor for late miscarriage in normolipidemic women undergoing IVF/ICSI-ET. Controlling insulin resistance before ART might prevent the occurrence of late miscarriage and macrosomia.

Project description:IntroductionAmong industrialized countries, endometrial cancer is a common malignancy with generally an excellent outcome. To personalize medicine, we ideally compile as much information as possible concerning patient prognosis prior to effecting an appropriate treatment decision. Endometrial cancer preoperative risk stratification (ENDORISK) is a machine learning-based computational Bayesian networks model that predicts lymph node metastasis and 5-year disease-specific survival potential with percentual probability. Our objective included validating ENDORISK effectiveness in our patient cohort, assessing its application in the current use of sentinel node biopsy, and verifying its accuracy in advanced stages.MethodsThe ENDORISK model was evaluated with a retrospective cohort of 425 patients from the University Hospital Brno, Czech Republic. Two hundred ninety-nine patients were involved in our disease-specific survival analysis; 226 cases with known lymph node status were available for lymph node metastasis analysis. Patients were included undergoing either pelvic lymph node dissection (N = 84) or sentinel node biopsy (N =70) to explore the accuracy of both staging procedures.ResultsThe area under the curve was 0.84 (95% confidence interval [CI], 0.77-0.9) for lymph node metastasis analysis and 0.86 (95% CI, 0.79-0.93) for 5-year disease-specific survival evaluation, indicating quite positive concordance between prediction and reality. Calibration plots to visualize results demonstrated an outstanding predictive value for low-risk cancers (grades 1-2), whereas outcomes were underestimated among high-risk patients (grade 3), especially in disease-specific survival. This phenomenon was even more obvious when patients were subclassified according to FIGO clinical stages.ConclusionsOur data confirmed ENDORISK model's laudable predictive ability, particularly among patients with a low risk of lymph node metastasis and expected favorable survival. For high-risk and/or advanced stages, the ENDORISK network needs to be additionally trained/improved.

Project description:BackgroundTissue-specific insulin resistance (IR) predominantly in muscle (muscle IR) or liver (liver IR) has previously been linked to distinct fasting metabolite profiles, but postprandial metabolite profiles have not been investigated in tissue-specific IR yet. Given the importance of postprandial metabolic impairments in the pathophysiology of cardiometabolic diseases, we compared postprandial plasma metabolite profiles in response to a high-fat mixed meal between individuals with predominant muscle IR or liver IR.MethodsThis cross-sectional study included data from 214 women and men with BMI 25-40 kg/m2, aged 40-75 years, and with predominant muscle IR or liver IR. Tissue-specific IR was assessed using the muscle insulin sensitivity index (MISI) and hepatic insulin resistance index (HIRI), which were calculated from the glucose and insulin responses during a 7-point oral glucose tolerance test. Plasma samples were collected before (T = 0) and after (T = 30, 60, 120, 240 min) consumption of a high-fat mixed meal and 247 metabolite measures, including lipoproteins, cholesterol, triacylglycerol (TAG), ketone bodies, and amino acids, were quantified using nuclear magnetic resonance spectroscopy. Differences in postprandial plasma metabolite iAUCs between muscle and liver IR were tested using ANCOVA with adjustment for age, sex, center, BMI, and waist-to-hip ratio. P-values were adjusted for a false discovery rate (FDR) of 0.05 using the Benjamini-Hochberg method.ResultsSixty-eight postprandial metabolite iAUCs were significantly different between liver and muscle IR. Liver IR was characterized by greater plasma iAUCs of large VLDL (p = 0.004), very large VLDL (p = 0.002), and medium-sized LDL particles (p = 0.026), and by greater iAUCs of TAG in small VLDL (p = 0.025), large VLDL (p = 0.003), very large VLDL (p = 0.002), all LDL subclasses (all p < 0.05), and small HDL particles (p = 0.011), compared to muscle IR. In liver IR, the postprandial plasma fatty acid (FA) profile consisted of a higher percentage of saturated FA (p = 0.013), and a lower percentage of polyunsaturated FA (p = 0.008), compared to muscle IR.ConclusionPeople with muscle IR or liver IR have distinct postprandial plasma metabolite profiles, with more unfavorable postprandial metabolite responses in those with liver IR compared to muscle IR.

Project description:BACKGROUND:Current risk assessment models (RAMs) for prediction of venous thromboembolism (VTE) risk in the outpatient cancer population have shown poor predictive value in many of the most common cancers. The Comparison of Methods for Thromboembolic Risk Assessment with Clinical Perceptions and AwareneSS in Real Life Patients-Cancer Associated Thrombosis (COMPASS-CAT) RAM was derived in this patient population and predicted patients at high risk for VTE even after initiation of chemotherapy. We sought to externally validate this RAM. MATERIALS AND METHODS:Patients aged ?18?years who presented to a tertiary care center between January 1, 2014, and December 31, 2016, with invasive breast, ovarian, lung, or colorectal cancers were included. The COMPASS-CAT RAM was applied using our health system's tumor registry and variables that were identified by International Statistical Classification of Diseases and Related Health Problems-9 and -10 codes of the electronic health record and independent chart review. The primary endpoint at 6-month study follow-up was documented VTE. RESULTS:A total of 3,814 patients were included. Documented VTE at 6-month follow-up occurred in 5.85% of patients. Patients stratified into low/intermediate- and high-risk groups had VTE rates of 2.27% and 6.31%, respectively. The sensitivity, specificity, and negative and positive predictive value of the RAM were 95%, 12%, 97.73%, and 6.31%, respectively. Diagnostic accuracy via receiver operating characteristic curve was calculated at 0.62 of the area under the curve. CONCLUSION:In this large retrospective external validation study of the COMPASS-CAT RAM for VTE in patients with cancer undergoing active treatment, model discrimination was moderate and calibration was poor. The model had good negative predictive value. Further prospective validation studies-especially within 6 months of cancer diagnosis-are needed before the model can be implemented into routine clinical practice for primary thromboprophylaxis of high-VTE-risk patients with cancer with solid tumors. IMPLICATIONS FOR PRACTICE:This study provides further guidance for researchers and clinicians in determining clinical and laboratory risk factors associated with development of venous thromboembolism among the ambulatory population of patients being treated for lung, breast, colorectal, or ovarian cancer. It validates the COMPASS-CAT risk model that was developed in this cancer population and suggests that further prospective validation of the model, with more focus on patients within 6 months of their index cancer diagnosis, would likely enhance the accuracy and usefulness of this model as a clinical prediction tool.

Dataset Information

Plasma metabolites associated with homeostatic model assessment of insulin resistance: metabolite-model design and external validation.

Publications

Plasma metabolites associated with homeostatic model assessment of insulin resistance: metabolite-model design and external validation.

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