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Browsing by Author "Temprosa, Marinella"
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Item Metabolite Profiles of Incident Diabetes and Heterogeneity of Treatment Effect in the Diabetes Prevention Program(American Diabetes Association, 2019-12) Chen, Zsu-Zsu; Liu, Jinxi; Morningstar, Jordan; Heckman-Stoddard, Brandy M.; Lee, Christine G.; Dagogo-Jack, Samuel; Ferguson, Jane F.; Hamman, Richard F.; Knowler, William C.; Mather, Kieren J.; Perreault, Leigh; Florez, Jose C.; Wang, Thomas J.; Clish, Clary; Temprosa, Marinella; Gerszten, Robert E.; Medicine, School of MedicineNovel biomarkers of type 2 diabetes (T2D) and response to preventative treatment in individuals with similar clinical risk may highlight metabolic pathways that are important in disease development. We profiled 331 metabolites in 2,015 baseline plasma samples from the Diabetes Prevention Program (DPP). Cox models were used to determine associations between metabolites and incident T2D, as well as whether associations differed by treatment group (i.e., lifestyle [ILS], metformin [MET], or placebo [PLA]), over an average of 3.2 years of follow-up. We found 69 metabolites associated with incident T2D regardless of treatment randomization. In particular, cytosine was novel and associated with the lowest risk. In an exploratory analysis, 35 baseline metabolite associations with incident T2D differed across the treatment groups. Stratification by baseline levels of several of these metabolites, including specific phospholipids and AMP, modified the effect that ILS or MET had on diabetes development. Our findings highlight novel markers of diabetes risk and preventative treatment effect in individuals who are clinically at high risk and motivate further studies to validate these interactions.Item Non-traditional biomarkers and incident diabetes in the Diabetes Prevention Program: comparative effects of lifestyle and metformin interventions(Springer Verlag, 2018-10-17) Goldberg, Ronald B.; Bray, George A.; Marcovina, Santica M.; Mather, Kieren J.; Orchard, Trevor J.; Perreault, Leigh; Temprosa, Marinella; Medicine, School of MedicineWe compared the associations of circulating biomarkers of inflammation, endothelial and adipocyte dysfunction and coagulation with incident diabetes in the placebo, lifestyle and metformin intervention arms of the Diabetes Prevention Program, a randomised clinical trial, to determine whether reported associations in general populations are reproduced in individuals with impaired glucose tolerance, and whether these associations are independent of traditional diabetes risk factors. We further investigated whether biomarker-incident diabetes associations are influenced by interventions that alter pathophysiology, biomarker concentrations and rates of incident diabetes. METHODS: The Diabetes Prevention Program randomised 3234 individuals with impaired glucose tolerance into placebo, metformin (850 mg twice daily) and intensive lifestyle groups and showed that metformin and lifestyle reduced incident diabetes by 31% and 58%, respectively compared with placebo over an average follow-up period of 3.2 years. For this study, we measured adiponectin, leptin, tissue plasminogen activator (as a surrogate for plasminogen activator inhibitor 1), high-sensitivity C-reactive protein, IL-6, monocyte chemotactic protein 1, fibrinogen, E-selectin and intercellular adhesion molecule 1 at baseline and at 1 year by specific immunoassays. Traditional diabetes risk factors were defined as family history, HDL-cholesterol, triacylglycerol, BMI, fasting and 2 h glucose, HbA1c, systolic blood pressure, inverse of fasting insulin and insulinogenic index. Cox proportional hazard models were used to assess the effects of each biomarker on the development of diabetes assessed semi-annually and the effects of covariates on these. RESULTS: E-selectin, (HR 1.19 [95% CI 1.06, 1.34]), adiponectin (0.84 [0.71, 0.99]) and tissue plasminogen activator (1.13 [1.03, 1.24]) were associated with incident diabetes in the placebo group, independent of diabetes risk factors. Only the association between adiponectin and diabetes was maintained in the lifestyle (0.69 [0.52, 0.92]) and metformin groups (0.79 [0.66, 0.94]). E-selectin was not related to diabetes development in either lifestyle or metformin groups. A novel association appeared for change in IL-6 in the metformin group (1.09 [1.021, 1.173]) and for baseline leptin in the lifestyle groups (1.31 [1.06, 1.63]). CONCLUSIONS/INTERPRETATION: These findings clarify associations between an extensive group of biomarkers and incident diabetes in a multi-ethnic cohort with impaired glucose tolerance, the effects of diabetes risk factors on these, and demonstrate differential modification of associations by interventions. They strengthen evidence linking adiponectin to diabetes development, and argue against a central role for endothelial dysfunction. The findings have implications for the pathophysiology of diabetes development and its prevention.Item Predictive utilities of lipid traits, lipoprotein subfractions and other risk factors for incident diabetes: a machine learning approach in the Diabetes Prevention Program(BMJ, 2021-03) Varga, Tibor V.; Liu, Jinxi; Goldberg, Ronald B.; Chen, Guannan; Dagogo-Jack, Samuel; Lorenzo, Carlos; Mather, Kieren J.; Pi-Sunyer, Xavier; Brunak, Søren; Temprosa, Marinella; Medicine, School of MedicineIntroduction: Although various lipid and non-lipid analytes measured by nuclear magnetic resonance (NMR) spectroscopy have been associated with type 2 diabetes, a structured comparison of the ability of NMR-derived biomarkers and standard lipids to predict individual diabetes risk has not been undertaken in larger studies nor among individuals at high risk of diabetes. Research design and methods: Cumulative discriminative utilities of various groups of biomarkers including NMR lipoproteins, related non-lipid biomarkers, standard lipids, and demographic and glycemic traits were compared for short-term (3.2 years) and long-term (15 years) diabetes development in the Diabetes Prevention Program, a multiethnic, placebo-controlled, randomized controlled trial of individuals with pre-diabetes in the USA (N=2590). Logistic regression, Cox proportional hazards model and six different hyperparameter-tuned machine learning algorithms were compared. The Matthews Correlation Coefficient (MCC) was used as the primary measure of discriminative utility. Results: Models with baseline NMR analytes and their changes did not improve the discriminative utility of simpler models including standard lipids or demographic and glycemic traits. Across all algorithms, models with baseline 2-hour glucose performed the best (max MCC=0.36). Sophisticated machine learning algorithms performed similarly to logistic regression in this study. Conclusions: NMR lipoproteins and related non-lipid biomarkers were associated but did not augment discrimination of diabetes risk beyond traditional diabetes risk factors except for 2-hour glucose. Machine learning algorithms provided no meaningful improvement for discrimination compared with logistic regression, which suggests a lack of influential latent interactions among the analytes assessed in this study.Item Statin use and risk of developing diabetes: results from the Diabetes Prevention Program(BMJ Journals, 2017-10-10) Crandall, Jill P; Mather, Kieren; Rajpathak, Swapnil N; Goldberg, Ronald B; Watson, Karol; Foo, Sandra; Ratner, Robert; Barrett-Connor, Elizabeth; Temprosa, Marinella; Medicine, School of MedicineObjective Several clinical trials of cardiovascular disease prevention with statins have reported increased risk of type 2 diabetes (T2DM) with statin therapy. However, participants in these studies were at relatively low risk for diabetes. Further, diabetes was often based on self-report and was not the primary outcome. It is unknown whether statins similarly modify diabetes risk in higher risk populations. Research design and methods During the Diabetes Prevention Program Outcomes Study (n=3234), the long-term follow-up to a randomized clinical trial of interventions to prevent T2DM, incident diabetes was assessed by annual 75 g oral glucose tolerance testing and semiannual fasting glucose. Lipid profile was measured annually, with statin treatment determined by a participant’s own physician outside of the protocol. Statin use was assessed at baseline and semiannual visits. Results At 10 years, the cumulative incidence of statin initiation prior to diabetes diagnosis was 33%–37% among the randomized treatment groups (p=0.36). Statin use was associated with greater diabetes risk irrespective of treatment group, with pooled HR (95% CI) for incident diabetes of 1.36 (1.17 to 1.58). This risk was not materially altered by adjustment for baseline diabetes risk factors and potential confounders related to indications for statin therapy. Conclusions In this population at high risk for diabetes, we observed significantly higher rates of diabetes with statin therapy in all three treatment groups. Confounding by indication for statin use does not appear to explain this relationship. The effect of statins to increase diabetes risk appears to extend to populations at high risk for diabetes. Trial registration number NCT00038727; Results.