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OBJECTIVE -To report the cardiac events in type 2 diabetic outpatients screened for unknown asymptomatic coronary heart disease (CHD) and followed for 5 years.RESEARCH DESIGN AND METHODS -During 1993, 925 subjects aged 40 -65 years underwent an exercise treadmill test (ETT). If it was abnormal, the subjects then underwent an exercise scintigraphy. Of the 925 subjects, 735 were followed for 5 years and cardiac events were recorded.RESULTS -At the entry of the study, 638 of the 735 followed subjects had normal ETT, 45 had abnormal ETT with normal scintigraphy, and 52 had abnormal ETT and abnormal scintigraphy. The 52 subjects with abnormal scintigraphy and ETT underwent a cardiological and diabetological follow-up; the subjects with just abnormal ETT had a diabetological follow-up only. During the follow-ups, 42 cardiac events occurred: 1 fatal myocardial infarction (MI), 20 nonfatal MIs, and 10 cases of angina in the 638 subjects with normal ETT; 1 fatal MI in the 45 subjects with normal scintigraphy; and 1 fatal MI and 9 cases of angina in the 52 subjects with abnormal scintigraphy. In these 52 subjects all cardiac events were significantly more frequent ( 2 ϭ 21.40, P Ͻ 0.0001) but the ratio of major (cardiac death and MI) to minor (angina) cardiac events was significantly lower (P ϭ 0.002). Scintigraphy abnormality (hazard ratio 5.47; P Ͻ 0.001; 95% CI 2.43-12.29), diabetes duration (1.06; P ϭ 0.021; 1.008 -1.106), and diabetic retinopathy (2.371; P ϭ 0.036; 1.059 -5.307) were independent predictors of cardiac events on multivariate analysis. CONCLUSIONS -The low ratio of major to minor cardiac events in the positive scintigraphy group may suggest, although it does not prove, that the screening program followed by appropriate management was effective for the reduction of risk of major cardiac events.

The SINERGIA model is effective in improving metabolic control and major cardiovascular risk factors, while allowing diabetologists to dedicate more time to patients with more acute disease.

We evaluated gender-differences in quality of type 1 diabetes (T1DM) care. Starting from electronic medical records of 300 centers, 5 process indicators, 3 favorable and 6 unfavorable intermediate outcomes, 6 treatment intensity/appropriateness measures and an overall quality score were measured. The likelihood of women vs. men (reference class) to be monitored, to reach outcomes, or to be treated has been investigated through multilevel logistic regression analyses; results are expressed as Odd Ratios (ORs) and 95% confidence intervals (95%CIs). The inter-center variability in the achievement of the unfavorable outcomes was also investigated. Overall, 28,802 subjects were analyzed (45.5% women). Women and men had similar age (44.5±16.0 vs. 45.0±17.0 years) and diabetes duration (18.3±13.0 vs. 18.8±13.0 years). No between-gender differences were found in process indicators. As for intermediate outcomes, women showed 33% higher likelihood of having HbA1c ≥8.0% (OR = 1.33; 95%CI: 1.25–1.43), 29% lower risk of blood pressure ≥140/90 mmHg (OR = 0.71; 95%CI: 0.65–0.77) and 27% lower risk of micro/macroalbuminuria (OR = 0.73; 95%CI: 0.65–0.81) than men, while BMI, LDL-c and GFR did not significantly differ; treatment intensity/appropriateness was not systematically different between genders; overall quality score was similar in men and women. Consistently across centers a larger proportion of women than men had HbA1c ≥8.0%, while a smaller proportion had BP ≥140/90 mmHg. No gender-disparities were found in process measures and improvements are required in both genders. The systematic worse metabolic control in women and worse blood pressure in men suggest that pathophysiologic differences rather than the care provided might explain these differences.

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SMBG is underutilized in patients with T2DM treated or not with insulin. In all treatment groups, PPG is seldom investigated. Poor metabolic control and rates of hyper- and hypoglycemia deserve consideration in all treatment groups.

IntroductionThe aim of this study was to investigate the factors (clinical, organizational or doctor-related) involved in a timely and effective achievement of metabolic control, with no weight gain, in type 2 diabetes.Research design and MethodsOverall, 5.5 million of Hab1c and corresponding weight were studied in the Associazione Medici Diabetologi Annals database (2005–2017 data from 1.5 million patients of the Italian diabetes clinics network). Logic learning machine, a specific type of machine learning technique, was used to extract and rank the most relevant variables and to create the best model underlying the achievement of HbA1c<7 and no weight gain.ResultsThe combined goal was achieved in 37.5% of measurements. High HbA1c and fasting glucose values and slow drop of HbA1c have the greatest relevance and emerge as first, main, obstacles the doctor has to overcome. However, as a second line of negative factors, markers of insulin resistance, microvascular complications, years of observation and proxy of duration of disease appear to be important determinants. Quality of assistance provided by the clinic plays a positive role. Almost all the available oral agents are effective whereas insulin use shows positive impact on glucometabolism but negative on weight containment. We also tried to analyze the contribution of each component of the combined endpoint; we found that weight gain was less frequently the reason for not reaching the endpoint and that HbA1c and weight have different determinants. Of note, use of glucagon-like peptide-1 receptor agonists (GLP1-RA) and glifozins improves weight control.ConclusionsTreating diabetes as early as possible with the best quality of care, before beta-cell deterioration and microvascular complications occurrence, make it easier to compensate patients. This message is a warning against clinical inertia. All medications play a role in goal achievements but use of GLP1-RAs and glifozins contributes to overweight prevention.

Since the last decade, most of our daily activities have become digital. Digital health takes into account the ever-increasing synergy between advanced medical technologies, innovation, and digital communication. Thanks to machine learning, we are not limited anymore to a descriptive analysis of the data, as we can obtain greater value by identifying and predicting patterns resulting from inductive reasoning. Machine learning software programs that disclose the reasoning behind a prediction allow for “what-if” models by which it is possible to understand if and how, by changing certain factors, one may improve the outcomes, thereby identifying the optimal behavior. Currently, diabetes care is facing several challenges: the decreasing number of diabetologists, the increasing number of patients, the reduced time allowed for medical visits, the growing complexity of the disease both from the standpoints of clinical and patient care, the difficulty of achieving the relevant clinical targets, the growing burden of disease management for both the health care professional and the patient, and the health care accessibility and sustainability. In this context, new digital technologies and the use of artificial intelligence are certainly a great opportunity. Herein, we report the results of a careful analysis of the current literature and represent the vision of the Italian Association of Medical Diabetologists (AMD) on this controversial topic that, if well used, may be the key for a great scientific innovation. AMD believes that the use of artificial intelligence will enable the conversion of data (descriptive) into knowledge of the factors that “affect” the behavior and correlations (predictive), thereby identifying the key aspects that may establish an improvement of the expected results (prescriptive). Artificial intelligence can therefore become a tool of great technical support to help diabetologists become fully responsible of the individual patient, thereby assuring customized and precise medicine. This, in turn, will allow for comprehensive therapies to be built in accordance with the evidence criteria that should always be the ground for any therapeutic choice.