Determinants of good metabolic control without weight gain in type 2 diabetes management: a machine learning analysis.

INTRODUCTION: The 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 METHODS: Overall, 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. RESULTS: The 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. CONCLUSIONS: Treating 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.

Artificial Intelligence and Big Data in Diabetes Care: A Position Statement of the Italian Association of Medical Diabetologists.

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.

[Corticosteroid induced diabetes mellitus: diagnosis and management]. / Il diabete steroideo: inquadramento e gestione.

Steroid diabetes occurs in 20% (range 10-60%) of the persons treated with corticosteroid drugs. Steroid diabetes diagnosis often is omitted or late because the diagnostic sensitivity of fasting blood sugar is low, so the postprandial blood glucose must be monitored and the diagnosis should be made clinically, based on 2 hours after lunch blood glucose or OGTT. Steroid diabetes causes increased hospitalizations for acute diabetic complications; there are few data on the chronic complications. Steroid therapy increases the macrovascular complications in diabetic people, while globally does not increase the mortality. However, in solid organ transplant recipients steroid diabetes causes 60% increase of rejections, 90% of mortality and 150% of the annual costs and considerably worsens the prognosis of AGVHD in bone marrow transplants. The corticosteroids have negative actions on insulin resistance in muscle, liver and adipose tissue and on insulin secretion; hyperglycemia is mainly postprandial, in the afternoon and in the evening, also related to the pharmacokinetics of the drugs. There is insufficient evidence of the efficacy of specific treatments in randomized controlled trials and the treatment is based on pathophysiology, mechanisms of action of drugs and experience. The antidiabetic drug choosing criteria are the body weight, the underlying disease, the type and dose of the corticosteroid drugs, the way of administration, the blood glucose levels, the possible contraindications. New antidiabetic drugs can open therapeutic perspectives, yet still to be explored with ad hoc studies. Insulin is frequently needed, in single or multiple doses with different combinations.

Algorithms for personalized therapy of type 2 diabetes: results of a web-based international survey.

OBJECTIVE: In recent years increasing interest in the issue of treatment personalization for type 2 diabetes (T2DM) has emerged. This international web-based survey aimed to evaluate opinions of physicians about tailored therapeutic algorithms developed by the Italian Association of Diabetologists (AMD) and available online, and to get suggestions for future developments. Another aim of this initiative was to assess whether the online advertising and the survey would have increased the global visibility of the AMD algorithms. RESEARCH DESIGN AND METHODS: The web-based survey, which comprised five questions, has been available from the homepage of the web-version of the journal Diabetes Care throughout the month of December 2013, and on the AMD website between December 2013 and September 2014. Participation was totally free and responders were anonymous. RESULTS: Overall, 452 physicians (M=58.4%) participated in the survey. Diabetologists accounted for 76.8% of responders. The results of the survey show wide agreement (>90%) by participants on the utility of the algorithms proposed, even if they do not cover all possible needs of patients with T2DM for a personalized therapeutic approach. In the online survey period and in the months after its conclusion, a relevant and durable increase in the number of unique users who visited the websites was registered, compared to the period preceding the survey. CONCLUSIONS: Patients with T2DM are heterogeneous, and there is interest toward accessible and easy to use personalized therapeutic algorithms. Responders opinions probably reflect the peculiar organization of diabetes care in each country.

Personalized therapy algorithms for type 2 diabetes: a phenotype-based approach.

Type 2 diabetes is a progressive disease with a complex and multifactorial pathophysiology. Patients with type 2 diabetes show a variety of clinical features, including different "phenotypes" of hyperglycemia (eg, fasting/preprandial or postprandial). Thus, the best treatment choice is sometimes difficult to make, and treatment initiation or optimization is postponed. This situation may explain why, despite the existing complex therapeutic armamentarium and guidelines for the treatment of type 2 diabetes, a significant proportion of patients do not have good metabolic control and at risk of developing the late complications of diabetes. The Italian Association of Medical Diabetologists has developed an innovative personalized algorithm for the treatment of type 2 diabetes, which is available online. According to the main features shown by the patient, six algorithms are proposed, according to glycated hemoglobin (HbA1c, ≥9% or ≤9%), body mass index (≤30 kg/m(2) or ≥30 kg/m(2)), occupational risk potentially related to hypoglycemia, chronic renal failure, and frail elderly status. Through self-monitoring of blood glucose, patients are phenotyped according to the occurrence of fasting/preprandial or postprandial hyperglycemia. In each of these six algorithms, the gradual choice of treatment is related to the identified phenotype. With one exception, these algorithms contain a stepwise approach for patients with type 2 diabetes who are metformin-intolerant. The glycemic targets (HbA1c, fasting/preprandial and postprandial glycemia) are also personalized. This accessible and easy to use algorithm may help physicians to choose a personalized treatment plan for each patient and to optimize it in a timely manner, thereby lessening clinical inertia.

Management of hyperglycemia in type 2 diabetes: evidence and uncertainty.

The panoply of treatment algorithms, periodically released to improve guidance, is one mean to face therapeutic uncertainty in pharmacological management of hyperglycemia in type 2 diabetes, especially after metformin failure. Failure of recent guidelines to give advice on the use of specific antidiabetic drugs in patients with co-morbidity may generate further uncertainty, given the frequent association of type 2 diabetes with common comorbidity, including, although not limited to obesity, cardiovascular disease, impaired renal function, and frailty. The Italian Association of Diabetologists (Associazione Medici Diabetologi, AMD) recognized the need to develop personalized treatment plans for people with type 2 diabetes, taking into account the patients' individual profile (phenotype), with the objective of the safest possible glycemic control. As not every subject with type 2 diabetes benefits from intensive glycemic control, flexible regimens of treatment with diabetes drugs (including insulin) are needed for reaching individualized glycemic goals. Whether personalized diabetology will improve the quality healthcare practice of diabetes management is unknown, but specific research has been launched.

Personalizing treatment in type 2 diabetes: a self-monitoring of blood glucose inclusive innovative approach.

A strong correlation exists between improved blood glucose control, obtained from the earliest stages of diabetes, and the prevention of complications. However, tight glycometabolic control does not always translate into an advantage for every patient. Because the characteristics of individual patients play an important role in diabetes care, there is a need to develop personalized action plans. This article suggests tailored therapeutic algorithms for some of the commonest type 2 diabetes phenotypes, taking into consideration age, body mass index, presence of micro- and macrovascular complications, hypoglycemia risk, and the co-existence of chronic renal failure. Particular emphasis is placed on exploiting information supplied through the rational use of self-monitoring of blood glucose as a tool for optimizing diabetes management, according to the prevalence of fasting/preprandial or postprandial hyperglycemia.

Simulacro de emergencia: Rescate y atención de una víctima en actividades submarinas / A shan of emergency: Rescue and service to victims in submarine activities

Simulacro en el que se representó el rescate y atención de una víctima de buceo deportivo en mar abierto, con el objetivo de evaluar la coordinación de actividades y comunicaciones.

Exocrine pancreatic cancer, cigarette smoking, and diabetes mellitus: a case-control study in northern Italy.

The role of cigarette smoking and diabetes mellitus as risk factors for exocrine pancreatic cancer (PC) was investigated in a hospital based case-control study. Current smokers were at increased risk for PC (OR = 2.36, 95% CI 1.53-3.63): the magnitude of the risk was related to the lifetime amount of smoking (chi2(trend) = 17.00; P < 0.0001). Among former smokers, after 15 years from ceasing smoking, the risk for PC dropped to the level of a lifetime non-smoker, whichever the lifetime smoking amount. Diabetes was associated with a 2.89-fold increased risk for PC (95% CI 1.71-4.86): the risk was 4.76 (95% CI 1.99-11.53) for diabetes diagnosed up to 2 years before the diagnosis of PC and dropped to 2.07 (95% CI 1.02-4.20) for diabetes diagnosed more than 5 years before PC. The risk for PC was estimated according to the treatment used to control diabetes: it was 6.49 (95% CI 2.28-18.48) for insulin treated diabetes and 2.12 (95% CI 1.16-3.87) for diabetes treated with oral hypoglycemic drugs. The risk of PC for diabetes treated for more than 5 years before the diagnosis of PC was 6.21 (95% CI 1.61-23.96) for patients treated with insulin and 1.21 (95% CI 0.50-2.92) for those treated with oral hypoglycemic drugs: the type of treatment needed to control the disease may discriminate between the diabetes that represents a consequence of cancer from the diabetes that could represent an etiological co-factor. More studies are needed to clarify whether long-lasting insulin-treated diabetes is an etiological co-factor in PC.

Calcium-dependent cleavage of endogenous wild-type huntingtin in primary cortical neurons.

Huntington's disease (HD) is caused by a polyglutamine expansion in the amino-terminal region of huntingtin. Mutant huntingtin is proteolytically cleaved by caspases, generating amino-terminal aggregates that are toxic for cells. The addition of calpains to total brain homogenates also leads to cleavage of wild-type huntingtin, indicating that proteolysis of mutant and wild-type huntingtin may play a role in HD. Here we report that endogenous wild-type huntingtin is promptly cleaved by calpains in primary neurons. Exposure of primary neurons to glutamate or 3-nitropropionic acid increases intracellular calcium concentration, leading to loss of intact full-length wild-type huntingtin. This cleavage could be prevented by calcium chelators and calpain inhibitors. Degradation of wild-type huntingtin by calcium-dependent proteases thus occurs in HD neurons, leading to loss of wild-type huntingtin neuroprotective activity.