Pasquale De Cata

Thyroidal effect of metformin treatment in patients with polycystic ovary syndrome

Objective  Metformin is widely used for the treatment of type 2 diabetes. Growing evidence supports the beneficial effects of metformin also in patients with polycystic ovary syndrome (PCOS). It was recently reported that metformin has a TSH‐lowering effect in hypothyroid patients with diabetes being treated with metformin.

A dashboard-based system for supporting diabetes care

Objective To describe the development, as part of the European Union MOSAIC (Models and Simulation Techniques for Discovering Diabetes Influence Factors) project, of a dashboard-based system for the management of type 2 diabetes and assess its impact on clinical practice. Methods The MOSAIC dashboard system is based on predictive modeling, longitudinal data analytics, and the reuse and integration of data from hospitals and public health repositories. Data are merged into an i2b2 data warehouse, which feeds a set of advanced temporal analytic models, including temporal abstractions, care-flow mining, drug exposure pattern detection, and risk-prediction models for type 2 diabetes complications. The dashboard has 2 components, designed for (1) clinical decision support during follow-up consultations and (2) outcome assessment on populations of interest. To assess the impact of the clinical decision support component, a pre-post study was conducted considering visit duration, number of screening examinations, and lifestyle interventions. A pilot sample of 700 Italian patients was investigated. Judgments on the outcome assessment component were obtained via focus groups with clinicians and health care managers. Results The use of the decision support component in clinical activities produced a reduction in visit duration (P ≪ .01) and an increase in the number of screening exams for complications (P < .01). We also observed a relevant, although nonstatistically significant, increase in the proportion of patients receiving lifestyle interventions (from 69% to 77%). Regarding the outcome assessment component, focus groups highlighted the systems capability of identifying and understanding the characteristics of patient subgroups treated at the center. Conclusion Our study demonstrates that decision support tools based on the integration of multiple-source data and visual and predictive analytics do improve the management of a chronic disease such as type 2 diabetes by enacting a successful implementation of the learning health care system cycle.

Metformin-induced thyrotropin suppression is not associated with cardiac effects

OBJECTIVEMetformin treatment may induce a decrease/suppression in serum TSH levels, mimicking sub-clinical hyperthyroidism (SHT). The aim of the present study was to retrospectively evaluate changes in several electrocardiographic indices in euthyroid subjects with diabetes who, after starting metformin treatment, developed a low serum TSH as compared to patients with SHT resulting from an underlying thyroid disease or TSH suppressive treatment with L-thyroxine.DESIGNHeart rate, P wave duration, P wave dispersion, QTmax, QTmin and QT-dispersion were assessed in 23 patients with diabetes treated with metformin before and after 6 months of TSH-suppression and in 31 control patients with SHT.RESULTSNo significant changes in electrocardiographic parameters were observed from baseline to the TSH-suppression measurement. A significant difference in P wave duration (102.9 ±7.4 vs. 92.1 ±5.8 ms, p<0.001), P wave dispersion (13.1 ±3.4 vs. 7.1 ±3.5 ms, p<0.001), QTmax (399±18 vs. 388 ±16 ms, p = 0.024), QTmin (341 ±14 vs. 350±17 ms, p = 0.038) and QT dispersion (49.9 ±9.6 vs. 30.9 ±9.2 ms, p< 0.001) were observed between the control group with SHT and the group of diabetic patients with low serum levels of TSH.CONCLUSIONSOur results show that the TSH-suppressive effect observed in patients taking metformin is not associated with peripheral markers of thyroid hormone excess, at least at the cardiac level.

Machine Learning Methods to Predict Diabetes Complications

One of the areas where Artificial Intelligence is having more impact is machine learning, which develops algorithms able to learn patterns and decision rules from data. Machine learning algorithms have been embedded into data mining pipelines, which can combine them with classical statistical strategies, to extract knowledge from data. Within the EU-funded MOSAIC project, a data mining pipeline has been used to derive a set of predictive models of type 2 diabetes mellitus (T2DM) complications based on electronic health record data of nearly one thousand patients. Such pipeline comprises clinical center profiling, predictive model targeting, predictive model construction and model validation. After having dealt with missing data by means of random forest (RF) and having applied suitable strategies to handle class imbalance, we have used Logistic Regression with stepwise feature selection to predict the onset of retinopathy, neuropathy, or nephropathy, at different time scenarios, at 3, 5, and 7 years from the first visit at the Hospital Center for Diabetes (not from the diagnosis). Considered variables are gender, age, time from diagnosis, body mass index (BMI), glycated hemoglobin (HbA1c), hypertension, and smoking habit. Final models, tailored in accordance with the complications, provided an accuracy up to 0.838. Different variables were selected for each complication and time scenario, leading to specialized models easy to translate to the clinical practice.

Risk factors for the development of micro-vascular complications of type 2 diabetes in a single-centre cohort of patients:

Aims: In type 2 diabetes, we aimed at clarifying the role of glycated haemoglobin variability and other risk factors in the development of the main micro-vascular complications: peripheral neuropathy, nephropathy and retinopathy. Methods: In a single-centre cohort of 900 patients, glycated haemoglobin variability was evaluated as intra-individual standard deviation, adjusted standard deviation and coefficient of variation of serially measured glycated haemoglobin in the 2-year period before a randomly selected index visit. We devised four models considering different aspects of glycated haemoglobin evolution. Multivariate stepwise logistic regression analysis was performed including the following covariates at the index visit: age, disease duration, body mass index, total cholesterol, high-density lipoprotein cholesterol, triglycerides, sex, smoking habit, hypertension, dyslipidemia, treatment with anti-diabetic drugs, occurrence of macro-vascular events and the presence of another micro-vascular complication. Results: Males with high mean glycated haemoglobin, long duration of diabetes, presence of macro-vascular events and retinopathy emerged at higher risk for peripheral neuropathy. Development of nephropathy was independently associated with higher glycated haemoglobin variability, older age, male sex, current smoking status, presence of retinopathy, of peripheral neuropathy and of hypertension. Higher mean glycated haemoglobin, younger age, longer duration of diabetes, reduced estimated glomerular filtration rate and the presence of peripheral neuropathy were significantly associated with increased incidence of retinopathy. Conclusion: Glycated haemoglobin variability was associated with increased incidence of nephropathy, while mean glycated haemoglobin emerged as independent risk factor for the development of retinopathy and peripheral neuropathy. The presence of macro-vascular events was positively correlated with peripheral neuropathy. Finally, the occurrence of another micro-vascular complication was found to be a stronger risk factor for developing another micro-vascular complication than the mean or variability of glycated haemoglobin.

Disabling portosystemic encephalopathy in a non-cirrhotic patient: Successful endovascular treatment of a giant inferior mesenteric-caval shunt via the left internal iliac vein

Hepatic encephalopathy is suspected in non-cirrhotic cases of encephalopathy because the symptoms are accompanied by hyperammonaemia. Some cases have been misdiagnosed as psychiatric diseases and consequently patients hospitalized in psychiatric institutions or geriatric facilities. Therefore, the importance of accurate diagnosis of this disease should be strongly emphasized. A 68-year-old female patient presented to the Emergency Room with confusion, lethargy, nausea and vomiting. Examination disclosed normal vital signs. Neurological examination revealed a minimally responsive woman without apparent focal deficits and normal reflexes. She had no history of hematologic disorders or alcohol abuse. Her brain TC did not demonstrate any intracranial abnormalities and electroencephalography did not reveal any subclinical epileptiform discharges. Her ammonia level was > 400 mg/dL (reference range < 75 mg/dL) while hepatitis viral markers were negative. The patient was started on lactulose, rifaximin and low-protein diet. On the basis of the doppler ultrasound and abdomen computed tomography angiography findings, the decision was made to attempt portal venography which confirmed the presence of a giant portal-systemic venous shunt. Therefore, mechanic obliteration of shunt by interventional radiology was performed. As a consequence, mesenteric venous blood returned to hepatopetally flow into the liver, metabolic detoxification of ammonia increased and hepatic encephalopathy subsided. It is crucial that physicians immediately recognize the presence of non-cirrhotic encephalopathy, in view of the potential therapeutic resolution after accurate diagnosis and appropriate treatments.