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

2020 ◽  
Vol 8 (1) ◽  
pp. e001362
Author(s):  
Carlo Bruno Giorda ◽  
Federico Pisani ◽  
Alberto De Micheli ◽  
Paola Ponzani ◽  
Giuseppina Russo ◽  
...  
Keyword(s):  
Machine Learning ◽  
Type 2 Diabetes ◽  
Weight Gain ◽  
Metabolic Control ◽  
Diabetes Management ◽  
Microvascular Complications ◽  
Clinical Inertia ◽  
Oral Agents

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.

scholarly journals Depression and microvascular complications predict poor goal achievement among Colombian patients with type 2 diabetes

2017 ◽  
Vol 3 (2) ◽  
pp. 36-47
Author(s):  
Catalina Sanmiguel ◽  
María C. Luna ◽  
William Kattah ◽  
Carlos O. Mendivil
Keyword(s):  
Type 2 Diabetes ◽  
Metabolic Control ◽  
Latin American ◽  
Diabetes Management ◽  
Microvascular Complications ◽  
Good Control ◽  
Therapeutic Success ◽  
Poor Metabolic Control

Aims: Many patients with type 2 diabetes (DM2) in Latin American countries remain insufficiently controlled. We aimed to identify factors associated with persistent poor metabolic control in Colombian patients with DM2.Methods: Retrospective one-year follow-up cohort study of adult patients with DM2. The primary outcome was persistent poor metabolic control (PPMC): HbA1c level >8% in all measurements during follow-up. Secondary outcomes were intermittent poor metabolic control (IPMC) and good control (GC: simultaneous achievement of HbA1c, blood pressure and LDL cholesterol goals). Multiple demographic, clinical and laboratory variables were predictors in multivariable logistical models. Results: Of 399 patients included, 50 had the primary endpoint during follow-up. Older age was negatively associated with PPMC (OR 0.40, 95%CI 0.17-0.92 for extreme quartiles), even after multivariate adjustment. Depression and the presence of multiple microvascular complications were strongly associated with the secondary endpoint IPMC (multivariate OR respectively 4.2, 95%CI 1.08-16.4 for depression; 5.61, 95%CI 1.03-30.6 for microvascular complications). Being unemployed was associated with significantly less odds of achieving GC (multivariate OR 0.19, 95%CI 0.04-0.95). Conclusions: Age, depression, the presence of microvascular complications and employment status were associated with the success or failure of diabetes management. These factors were better correlates of therapeutic success than the pharmacological agent employed.

1311-P: Machine Learning (ML) Application to Predict Patient Risk of Nonadherence in Type 2 Diabetes Management Using U.S. Claims Databases

Diabetes ◽  
2019 ◽  
Vol 68 (Supplement 1) ◽  
pp. 1311-P
Author(s):  
XIN CHEN ◽  
GAIL FERNANDES ◽  
JIE CHEN ◽  
ZHIWEN LIU ◽  
RICHARD BAUMGARTNER
Keyword(s):  
Machine Learning ◽  
Type 2 Diabetes ◽  
Diabetes Management ◽  
Patient Risk

“Position Paper” Timely Insulinisation in Type 2 Diabetes Patients

2012 ◽  
Vol 19 (4) ◽  
pp. 441-444
Author(s):  
László Barkai ◽  
Nicolae Hâncu ◽  
György Jermendy ◽  
Maya Konstantinova ◽  
Radu Lichiardopol ◽  
...  
Keyword(s):  
Quality Of Life ◽  
Type 2 Diabetes ◽  
Life Expectancy ◽  
Insulin Therapy ◽  
Metabolic Control ◽  
Current Medical ◽  
Position Paper ◽  
Medical Evidence

AbstractThe objective of this position paper is to review the current medical evidence andguidelines regarding the treatment of type 2 diabetes (T2DM) and to issue medicalrecommendations strengthening the timely use of insulin in patients with T2DMuncontrolled on noninsulin therapy. When noninsulin therapy fails to achieve or tomaintain HbA1c targets, insulin therapy is required. Timely insulin therapy couldprovide proper metabolic control that might prevent complications, lead toimprovement of life expectancy and quality of life.

scholarly journals The Model of Self Care Behaviour and the Relationship with Quality Of Life, Metabolic Control and Lipid Control of Type 2 Diabetes Mellitus Patients in Binjai City, Indonesia

2018 ◽  
Vol 6 (9) ◽  
pp. 1762-1767 ◽  
Author(s):  
Rina Amelia
Keyword(s):  
Quality Of Life ◽  
Diabetes Mellitus ◽  
Type 2 Diabetes ◽  
Metabolic Control ◽  
Self Care ◽  
The Self ◽  
Lipid Control ◽  
Diabetes Patients

BACKGROUND: Diabetes is a type of chronic disease with exceptional medical care for a patient's lifetime, which ultimately requires lifestyle and behavioural adjustments to prevent complications to death. Patients with good self-care behaviour will cause diabetes to be controlled to avoid complications to death and make patients have a better quality of life. AIM: This study aims (1) to determine the model of self-care behaviour in Type 2 diabetes patients in Binjai City (2) to analyse the effect of self-care behaviour on quality of life, metabolic control and lipid control of Type 2 diabetes patients in Binjai City. METHODS: This type of research is survey-based and explanatory using a cross-sectional approach. The study population was Type 2 Diabetes Mellitus (T2DM) patients who remained patients in 8 primary health centres in Binjai City. The consecutive sampling yielded a sample size of 115 people. Data analysis method uses descriptive statistics and Structural Equation Modeling (SEM) using SPSS and Amos 16.0. RESULTS: The results showed that all factors that build T2DM patient self-care behaviour were able to be predictors that shape the patient's self-care behaviour. The self-care behaviour model consists of knowledge, attitudes, communication, financing, family support, motivation, and self-efficacy. Motivation is the most significant predictor of its contribution to the self-care behaviour of Type 2 diabetes patients. Self-care behaviour was also known to be significantly related to the quality of life, metabolic control and lipid control of T2DM patients (p < 0.05). CONCLUSION: Self-care behaviour in T2DM patients can have a substantial and significant impact on quality of life, metabolic control and lipid control possessed by Type 2 Diabetes patients.

scholarly journals Type 2 diabetes in women of reproductive age

2021 ◽  
Vol 9 (2) ◽  
Author(s):  
Donna M. Williams ◽  
Kathryn Evans Kreider
Keyword(s):  
Type 2 Diabetes ◽  
Nurse Practitioners ◽  
Diabetes Management ◽  
Reproductive Age ◽  
Current Evidence ◽  
Evidence Based ◽  
Women Of Reproductive Age

The prevalence of type 2 diabetes in women of reproductive age ranges from 3% to 7%. Due to the common nature of this condition, it is imperative for nurse practitioners who care for women to understand evidence-based methods of diabetes management. The aim of this article is to describe current screening and diagnostic criteria for type 2 diabetes, discuss current evidence-based management and patient education necessary to prevent long-term complications, and provide the tools for a healthy reproductive plan in women with type 2 diabetes from puberty until menopause. Diabetes management extends well beyond glucose control, and nurse practitioners should be attuned to all factors that can impact cardiovascular risk and quality of life.

The Old and the New in the Treatment of Type 2 Diabetes: Focus on the Combination Therapy with Dipeptidyl Peptidase-4 Inhibitors and Metformin

2010 ◽  
Vol 2 ◽  
pp. CMT.S3420 ◽  
Author(s):  
Giovanni Anfossi ◽  
Isabella Russo ◽  
Katia Bonomo ◽  
Mariella Trovati
Keyword(s):  
Type 2 Diabetes ◽  
Cell Function ◽  
Microvascular Complications ◽  
Chronic Complications ◽  
Dipeptidyl Peptidase 4 ◽  
Chronic Hyperglycemia ◽  
Oral Agents ◽  
Dipeptidyl Peptidase 4 Inhibitors

Type 2 diabetes mellitus (T2DM) is a complex multifactorial disease which affects the length and quality of life by severe chronic complications. Chronic hyperglycemia, which is the main alteration in T2DM, is strictly related to microvascular complications (such as retinopathy and nephropathy) and neuropathy, whereas large vessel atherosclerosis is also dependent on lipid and hemostasis abnormalities, arterial hypertension and other known cardiovascular risk factors. An early intervention to control hyperglycemia and to prevent deterioration of β-cell function is considered mandatory in patients with T2DM to minimize the risk of chronic complications. Recently, the availability of new pharmacological agents with different targets, including the activation of the incretin system has allowed the proposal of more effective strategies for early treatment of metabolic alterations in patients with T2DM. This commentary will focus on the role of new oral agents influencing the incretin system and the putative advantages of their co-administration with metformin, an old, effective anti-hyperglycemic agent also able to exert beneficial actions on arterial vessels, reducing the risk of macrovascular-related events. The vasoprotective role of metformin is largely independent of its hypoglycemic action, and has been ascribed to pleiotropic effects.

Can Blood Glucose Monitoring be Simplified/Modified in Order to Achieve Better Diabetes Management—Perspectives for the Future

2010 ◽  
Vol 06 (01) ◽  
pp. 48
Author(s):  
Robert M Cuddihy ◽  
Keyword(s):  
Type 2 Diabetes ◽  
Blood Glucose ◽  
Diabetes Management ◽  
Healthcare Providers ◽  
Glucose Monitoring ◽  
Blood Glucose Monitoring ◽  
Clear Understanding ◽  
Major Advance ◽  
Oral Agents

Self-monitoring of blood glucose (SMBG) with reflectance meters was heralded as a major advance in the management of diabetes and has been available to individuals with diabetes for home use since the late 1970s. This tool was put to use in the landmark Diabetes Control and Complications Trial (DCCT), which revolutionized care for individuals with type 1 diabetes, enabling these individuals to intensify their glucose control. SMBG has similar benefit in individuals with type 2 diabetes requiring insulin therapy. Its use in other individuals with type 2 diabetes treated with oral agents or non-insulin therapies is less clear. While SMBG is a potentially powerful tool to aid in the daily management of diabetes, to be used effectively, SMBG must be optimized to ensure the information derived from it can be acted on to modify physical activity, dietary intake, or medications to improve glycemic control. Recently, studies looking at this population have called into question the utility of SMBG in the management of individuals with type 2 diabetes treated with non-insulin therapies. However, these studies are lacking in the specifics of how such information was used to modify therapies. In addition to this, the lack of a universally accepted output for SMBG data significantly impedes its uptake and appropriate use by healthcare providers and patients. To maximize the effectiveness of SMBG, both patients and providers need to have a clear understanding of when and how to use SMBG data and, most importantly, act upon the data to effect a change in their diabetes management.

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