Fatty liver index as a predictor for type 2 diabetes in subjects with normoglycemia in a nationwide cohort study

Our aim was to evaluate whether fatty liver index (FLI) is associated with the risk of type 2 diabetes (T2DM) development within the Spanish adult population and according to their prediabetes status; additionally, to examine its incremental predictive value regarding traditional risk factors. A total of 2260 subjects (Prediabetes: 641 subjects, normoglycemia: 1619 subjects) from the [email protected] cohort study were studied. Socio-demographic, anthropometric, clinical data and survey on habits were recorded. An oral glucose tolerance test was performed and fasting determinations of glucose, lipids and insulin were made. FLI was calculated and classified into three categories: Low ( 60). In total, 143 people developed diabetes at follow-up. The presence of a high FLI category was in all cases a significant independent risk factor for the development of diabetes. The inclusion of FLI categories in prediction models based on different conventional T2DM risk factors significantly increase the prediction power of the models when all the population was considered. According to our results, FLI might be considered an early indicator of T2DM development even under normoglycemic condition. The data also suggest that FLI could provide additional information for the prediction of T2DM in models based on conventional risk factors

Chronic Kidney Disease – Where Next? : Predicting Outcomes And Planning Care Pathways

Peer reviewedPublisher PD

Risk models and scores for type 2 diabetes: Systematic review

This article is published under a Creative Commons Attribution Non Commercial (CC BY-NC 3.0) licence that allows reuse subject only to the use being non-commercial and to the article being fully attributed (http://creativecommons.org/licenses/by-nc/3.0).Objective - To evaluate current risk models and scores for type 2 diabetes and inform selection and implementation of these in practice. Design - Systematic review using standard (quantitative) and realist (mainly qualitative) methodology. Inclusion - criteria Papers in any language describing the development or external validation, or both, of models and scores to predict the risk of an adult developing type 2 diabetes. Data sources - Medline, PreMedline, Embase, and Cochrane databases were searched. Included studies were citation tracked in Google Scholar to identify follow-on studies of usability or impact. Data extraction - Data were extracted on statistical properties of models, details of internal or external validation, and use of risk scores beyond the studies that developed them. Quantitative data were tabulated to compare model components and statistical properties. Qualitative data were analysed thematically to identify mechanisms by which use of the risk model or score might improve patient outcomes. Results - 8864 titles were scanned, 115 full text papers considered, and 43 papers included in the final sample. These described the prospective development or validation, or both, of 145 risk prediction models and scores, 94 of which were studied in detail here. They had been tested on 6.88 million participants followed for up to 28 years. Heterogeneity of primary studies precluded meta-analysis. Some but not all risk models or scores had robust statistical properties (for example, good discrimination and calibration) and had been externally validated on a different population. Genetic markers added nothing to models over clinical and sociodemographic factors. Most authors described their score as “simple” or “easily implemented,” although few were specific about the intended users and under what circumstances. Ten mechanisms were identified by which measuring diabetes risk might improve outcomes. Follow-on studies that applied a risk score as part of an intervention aimed at reducing actual risk in people were sparse. Conclusion - Much work has been done to develop diabetes risk models and scores, but most are rarely used because they require tests not routinely available or they were developed without a specific user or clear use in mind. Encouragingly, recent research has begun to tackle usability and the impact of diabetes risk scores. Two promising areas for further research are interventions that prompt lay people to check their own diabetes risk and use of risk scores on population datasets to identify high risk “hotspots” for targeted public health interventions.Tower Hamlets, Newham, and City and Hackney primary care trusts and National Institute of Health Research

Predicting erythropoietin resistance in hemodialysis patients with type 2 diabetes

<p>Background: Resistance to ESAs (erythropoietin stimulating agents) is highly prevalent in hemodialysis patients with diabetes and associated with an increased mortality. The aim of this study was to identify predictors for ESA resistance and to develop a prediction model for the risk stratification in these patients.</p> <p>Methods: A post-hoc analysis was conducted of the 4D study, including 1015 patients with type 2 diabetes undergoing hemodialysis. Determinants of ESA resistance were identified by univariate logistic regression analyses. Subsequently, multivariate models were performed with stepwise inclusion of significant predictors from clinical parameters, routine laboratory and specific biomarkers.</p> <p>Results: In the model restricted to clinical parameters, male sex, shorter dialysis vintage, lower BMI, history of CHF, use of ACE-inhibitors and a higher heart rate were identified as independent predictors of ESA resistance. In regard to routine laboratory markers, lower albumin, lower iron saturation, higher creatinine and higher potassium levels were independently associated with ESA resistance. With respect to specific biomarkers, higher ADMA and CRP levels as well as lower Osteocalcin levels were predictors of ESA resistance.</p> <p>Conclusions: Easily obtainable clinical parameters and routine laboratory parameters can predict ESA resistance in diabetic hemodialysis patients with good discrimination. Specific biomarkers did not meaningfully further improve the risk prediction of ESA resistance. Routinely assessed data can be used in clinical practice to stratify patients according to the risk of ESA resistance, which may help to assign appropriate treatment strategies.</p&gt