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Preanalytical, analytical, and computational factors affect homeostasis model assessment estimates.

ABSTRACT: OBJECTIVE: We investigated how beta-cell function and insulin sensitivity or resistance are affected by the type of blood sample collected or choice of insulin assay and homeostatis model assessment (HOMA) calculator (http://www.dtu.ox.ac.uk). RESEARCH DESIGN AND METHODS: Insulin was measured using 11 different assays in serum and 1 assay in heparinized plasma. Fasting subjects with normoglycemia (n = 12), pre-diabetes, i.e., impaired fasting glucose or impaired glucose tolerance (n = 18), or type 2 diabetes (n = 67) were recruited. Patients treated with insulin or those who were insulin antibody-positive were excluded. HOMA estimates were calculated using specific insulin (SI) or radioimmunoassay (RIA) calculators (version 2.2). RESULTS: All glucose values were within model (HOMA) limits but not all insulin results, as 4.3% were <20 pmol/l and 1% were >300 pmol/l. beta-Cell function derived from different insulin assays ranged from 67 to 122% (median) for those with normoglycemia (P = 0.026), from 89 to 138% for those with pre-diabetes (P = 0.990), and from 50 to 81% for those with type 2 diabetes (P < 0.0001). Furthermore, insulin resistance ranged from 0.8 to 2.0 (P = 0.0007), from 1.9 to 3.2 (P = 0.842), and from 1.5 to 2.9 (P < 0.0001), respectively. This twofold variation in HOMA estimates from the various insulin assays studied in serum may be significant metabolically. Insulin was 15% lower in heparinized plasma (used in the original HOMA study) compared with serum, which is now more commonly used. beta-Cell function differed by 11% and insulin resistance by 15% when estimates derived from specific insulin were calculated using the RIA rather than the SI calculator. CONCLUSIONS: To enable comparison of HOMA estimates among individuals and different research studies, preanalytical factors and calculator selection should be standardized with insulin assays traceable to an insulin reference method procedure.

SUBMITTER: Manley SE

PROVIDER: S-EPMC2518363 | biostudies-literature | 2008 Sep

REPOSITORIES: biostudies-literature

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Preanalytical, analytical, and computational factors affect homeostasis model assessment estimates.

Manley Susan E SE Luzio Stephen D SD Stratton Irene M IM Wallace Tara M TM Clark Penelope M S PM

Diabetes care 20080605 9

<h4>Objective</h4>We investigated how beta-cell function and insulin sensitivity or resistance are affected by the type of blood sample collected or choice of insulin assay and homeostatis model assessment (HOMA) calculator (http://www.dtu.ox.ac.uk).<h4>Research design and methods</h4>Insulin was measured using 11 different assays in serum and 1 assay in heparinized plasma. Fasting subjects with normoglycemia (n = 12), pre-diabetes, i.e., impaired fasting glucose or impaired glucose tolerance (n ...[more]

PMID: 18535197

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Project description:The article presents an assessment of the ability of the thirty-seven model quality assessment (MQA) methods participating in CASP10 to provide an a priori estimation of the quality of structural models, and of the 67 tertiary structure prediction groups to provide confidence estimates for their predicted coordinates. The assessment of MQA predictors is based on the methods used in previous CASPs, such as correlation between the predicted and observed quality of the models (both at the global and local levels), accuracy of methods in distinguishing between good and bad models as well as good and bad regions within them, and ability to identify the best models in the decoy sets. Several numerical evaluations were used in our analysis for the first time, such as comparison of global and local quality predictors with reference (baseline) predictors and a ROC analysis of the predictors' ability to differentiate between the well and poorly modeled regions. For the evaluation of the reliability of self-assessment of the coordinate errors, we used the correlation between the predicted and observed deviations of the coordinates and a ROC analysis of correctly identified errors in the models. A modified two-stage procedure for testing MQA methods in CASP10 whereby a small number of models spanning the whole range of model accuracy was released first followed by the release of a larger number of models of more uniform quality, allowed a more thorough analysis of abilities and inabilities of different types of methods. Clustering methods were shown to have an advantage over the single- and quasi-single- model methods on the larger datasets. At the same time, the evaluation revealed that the size of the dataset has smaller influence on the global quality assessment scores (for both clustering and nonclustering methods), than its diversity. Narrowing the quality range of the assessed models caused significant decrease in accuracy of ranking for global quality predictors but essentially did not change the results for local predictors. Self-assessment error estimates submitted by the majority of groups were poor overall, with two research groups showing significantly better results than the remaining ones.

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Preanalytical, analytical, and computational factors affect homeostasis model assessment estimates.

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Preanalytical, analytical, and computational factors affect homeostasis model assessment estimates.

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