Detecting Undiagnosed Diabetes: Proof-of-Concept Based on the Health-Information Exchange System of the Veneto Region (North-East Italy).

Diabetes is a chronic illness characterised by elevated blood glucose levels, driving excess mortality. Its prompt detection and accurate management are critical for delaying complications. Nevertheless, diabetes can remain undiagnosed for years from the onset. The identification of undiagnosed diabetes is a public health priority: in Italy, it is estimated that up to 30% of diabetes cases remain undetected, i.e., that ~1.8 million citizens may be unaware they need medical help. Sometimes, this happens even though these subjects undergo routine or emergency check-ups. Veneto, a region in North-East Italy with 4.9 million residents, implements a regional Health Information Exchange system (rHIE) to collect healthcare data, including laboratory reports, and integrate them with administrative claims. Their combination may be instrumental in finding otherwise undetected cases of diabetes. On the one hand, known diabetic patients should have disease management-generated claims; on the other, laboratory test results can be independently evaluated against diagnostic criteria. In the present work, we examined the anonymised claims and laboratory data, extracted from the rHIE, of 23,376 citizens of the Veneto region. We compared their exemptions, diabetes-related hospitalisation discharge codes, and antidiabetic drugs between 2012 and 2018 to the results of their fasting glucose, glycated haemoglobin, and oral glucose tolerance tests in 2017-2018. We identified 1,407 (6.02%) subjects who, according to administrative claims, appear to be free from diabetes, but met at least one laboratory diagnostic criterion. Such a discrepancy suggests that these people may be undiagnosed diabetic patients. To the best of our knowledge, this is the first proof of concept of an automatic system for the detection of undiagnosed diabetes in Italy. Its full integration in the rHIE and its consequent capillary application could potentially reveal thousands of hidden cases throughout Veneto.

Importance of Recalibrating Models for Type 2 Diabetes Onset Prediction: Application of the Diabetes Population Risk Tool on the Health and Retirement Study.

A timely prediction of type 2 diabetes (T2D) onset is important for early intervention to prevent, or at least postpone, its incidence. Several models to predict T2D onset according to individual risk factors were proposed. However, their practical applicability is limited by the fact that they often perform suboptimally when applied to a different population. A solution to overcome this limitation is model recalibration, which consists in updating the model parameters. The aim of this work is to demonstrate the benefits of T2D predictive model recalibration. For the purpose, we considered as case study the Diabetes Population Risk Tool (DPoRT), originally tuned for the Canadian population, and we applied it to data collected in older Americans in the Health and Retirement Study (HRS). A subset of 30,274 subjects was extracted from HRS and divided into a training (N=24,219) and a test set (N=6,055) stratifying for sex and diabetes incidence. The DPoRT was recalibrated by re-estimating all model coefficients on the training set, and then assessed on the test set by comparing the performance of recalibrated vs original model. Model discriminatory ability and calibration were assessed by the concordance index (C-index) and the expected to observed event probability ratio (E/O), respectively. Results show that the recalibrated DPoRT presents similar discriminatory ability to the original model, with C-index equal to 0.68 vs. 0.67 in men, 0.73 vs. 0.73 in women, and better calibration than the original model, with E/O ratio equal to 0.75 vs. 4.57 in men, 0.81 vs. 2.53 in women. Results confirm that recalibration is a key step to be performed before the application of predictive models to different populations in order to guarantee an accurate prediction of diabetes incidence.

Wide-transcriptome analysis and cellularity of bone marrow CD34+/lin- cells of patients with chronic-phase chronic myeloid leukemia at diagnosis vs. 12 months of first-line nilotinib treatment.

BACKGROUND: Chronic myeloid leukemia (CML) is a clonal myeloproliferative disorder with heterogeneous biological and clinical features. The biomolecular mechanisms of CML response to tyrosine-kinase inhibitors are not fully defined. OBJECTIVE: We undertook a gene expression profiling (GEP) study of selected bone marrow (BM) CD34+/lin- cells of chronic-phase CML patients at diagnosis and after 12 months of TKI nilotinib to investigate molecular signatures characterizing both conditions.

Knowledge Driven Variable Selection (KDVS) - a new approach to enrichment analysis of gene signatures obtained from high-throughput data.

BACKGROUND: High-throughput (HT) technologies provide huge amount of gene expression data that can be used to identify biomarkers useful in the clinical practice. The most frequently used approaches first select a set of genes (i.e. gene signature) able to characterize differences between two or more phenotypical conditions, and then provide a functional assessment of the selected genes with an a posteriori enrichment analysis, based on biological knowledge. However, this approach comes with some drawbacks. First, gene selection procedure often requires tunable parameters that affect the outcome, typically producing many false hits. Second, a posteriori enrichment analysis is based on mapping between biological concepts and gene expression measurements, which is hard to compute because of constant changes in biological knowledge and genome analysis. Third, such mapping is typically used in the assessment of the coverage of gene signature by biological concepts, that is either score-based or requires tunable parameters as well, limiting its power. RESULTS: We present Knowledge Driven Variable Selection (KDVS), a framework that uses a priori biological knowledge in HT data analysis. The expression data matrix is transformed, according to prior knowledge, into smaller matrices, easier to analyze and to interpret from both computational and biological viewpoints. Therefore KDVS, unlike most approaches, does not exclude a priori any function or process potentially relevant for the biological question under investigation. Differently from the standard approach where gene selection and functional assessment are applied independently, KDVS embeds these two steps into a unified statistical framework, decreasing the variability derived from the threshold-dependent selection, the mapping to the biological concepts, and the signature coverage. We present three case studies to assess the usefulness of the method. CONCLUSIONS: We showed that KDVS not only enables the selection of known biological functionalities with accuracy, but also identification of new ones. An efficient implementation of KDVS was devised to obtain results in a fast and robust way. Computing time is drastically reduced by the effective use of distributed resources. Finally, integrated visualization techniques immediately increase the interpretability of results. Overall, KDVS approach can be considered as a viable alternative to enrichment-based approaches.