Prediction of excessive weight gain in insulin treated patients with type 2 diabetes

Quality of life, patient satisfaction, and cardiovascular outcomes of the randomised 2 x 3 factorial Copenhagen insulin and Metformin therapy (CIMT) trial - A detailed statistical analysis plan

Background

The evidence on the effects of metformin and insulin in type 2 diabetes patients on quality of life, patient satisfaction, and cardiovascular outcomes is unclear.

Methods

The Copenhagen Insulin and Metformin Therapy (CIMT) trial is an investigator-initiated multicentre, randomised, placebo-controlled trial with a 2 × 3 factorial design conducted at eight hospitals in Denmark. Participants with type 2 diabetes were randomised to metformin (n = 206) versus placebo (n = 206); in combination with open-label biphasic insulin aspart one to three times daily (n = 137) versus insulin aspart three times daily in combination with insulin detemir once daily (n = 138) versus insulin detemir once daily (n = 137).We present a detailed description of the methodology and statistical analysis of the clinical CIMT outcomes including a detailed description of tests of the assumptions behind the statistical analyses. The outcomes are quality of life (Short Form Health Survey (SF-36)), Diabetes Medication Satisfaction Questionnaire, and Insulin Treatment Satisfaction Questionnaire (assessed at entry and 18 months after randomisation) and cardiovascular outcomes including time to a composite of either myocardial infarction, stroke, peripheral amputation, coronary revascularisation, peripheral revascularisation, or death.

Discussions

This statistical analysis plan ensure the highest possible quality of the subsequent post-hoc analyses.

Trial registration

The protocol was approved by the Regional Committee on Biomedical Research Ethics (H-D-2007-112), the Danish Medicines Agency (EudraCT: 2007-006665-33 CIMT), and registered within ClinicalTrials.gov (NCT00657943, 8^th of April 2008).

Course of body weight before and after the initiation of insulin therapy in type 2 diabetes mellitus: Retrospective inception cohort study (ZODIAC 58)

Aims

The aim of this study was to explore the effect of insulin treatment initiation on weight by taking weight change prior to initiation into account.

Materials and methods

We performed an observational retrospective inception cohort study, concerning Dutch primary care. We identified all patients that initiated insulin treatment (n = 7967) and individually matched patients with a reference patient (n = 5213 pairs). We obtained estimated mean weight changes in the five years prior to five years post insulin therapy. We applied linear regression analysis on weight change in the first year after insulin therapy (T0 to T+1), with matched group as primary determinant adjusted for pre-insulin weight change and additional covariates.

Results

Estimated mean weight increased in the five consecutive years prior to insulin therapy (-0.23 kg in year T-5 to T-4, 0.01 kg in year T-4 to T-3, 0.07 kg in year T-3 to T-2, 0.24 kg in year T-2 to T-1, and 0.46 kg in year T-1 to T0) and continued to increase in the first year after, that is T0 to T+1, at a slightly lower rate (0.31 ± 3.9 kg). Pre-insulin weight change had the highest explained variance and was inversely and independently associated with weight change (p < .001). Starting insulin was associated with weight increase, independent of pre-insulin weight change (β-adjusted 1.228, p < .001). Stratification revealed that despite having a more or less similar baseline BMI, patients with substantial weight increase showed higher estimated mean BMI's followed by weight loss pre-insulin. In matched references, estimated mean weight changes were negative in all years concerning the study period, indicating consistent weight loss.

Conclusions

Initiation of insulin therapy was independently associated with weight increase; however, overall effect on weight was small and subject to substantial variation. Pre-insulin weight change is identified as a relatively strong inverse determinant of weight change after insulin initiation.

Improvements in health parameters of a diabetic and hypertensive patient with only 40 minutes of exercise per week: a case study

Purpose: The present study aimed to report the effects of 15 weeks of a minimal dose resistance training on blood glucose levels, blood pressure, heart rate, physical capacity, and quality of life of a 67 old woman with type 2 diabetes mellitus, cardiopathy and hypertension.Method: The Patient was sedentary with a family history of heart failure and coronary artery disease. She suffered a heart attack in 2013 and was submitted to six cardiac catheterizations and one angioplasty between the 2015 and 2017. On April 2017, she was initially untrained in resistance training and was followed for 15 weeks with the performance of 31 resistance training sessions. Resistance training followed a minimal dose approach with 4 exercises performed with 2 sets to muscle failure, twice a week.Results: After follow up, the Patient presented a reduction in blood glucose, systolic and diastolic blood pressure, and heart rate at rest. There were also improvements on body mass index, cardiorespiratory fitness and quality of life. These results were accompanied by a reduction in the amount of antihypertensive and anti-diabetic medications.Conclusions: These results suggest that only 40 min of resistance training per week might help to improve general health and quality of life in a patient with hypertension and type 2 diabetes mellitus.Implications for RehabilitationMinimal dose resistance training improved health parameters in a diabetic and hypertensive patient.Resistance training promoted a reduction in the drugs used to control blood pressure and blood glucose levels.

PITUITARY STALK THICKENING IN A LARGE COHORT: TOWARD MORE ACCURATE PREDICTORS OF PITUITARY DYSFUNCTION AND ETIOLOGY

Objective: To summarize the characteristics of patients with pituitary stalk thickening, analyze the association between pituitary stalk width and hypopituitarism, and develop a diagnostic model to differentiate neoplastic and inflammatory origins. Methods: A total of 325 patients with pituitary stalk thickening in a tertiary teaching hospital between January 2012 and February 2018 were enrolled. Basic characteristics and hormonal status were evaluated. Indicators to predict etiology in patients with histologic diagnoses were analyzed. Results: Of the 325 patients, 62.5% were female. Deficiency in gonadotropin was most common, followed by corticotropin, growth hormone, and thyrotropin. The increase in pituitary stalk width was associated with a risk of central diabetes insipidus (odds ratio [OR], 3.57; P<.001) and with a combination of central diabetes insipidus and anterior pituitary deficiency (OR, 2.28; P = .029). The cut-off pituitary stalk width of 4.75 mm had a sensitivity of 69.2% and a specificity of 71.4% for the presence of central diabetes insipidus together with anterior pituitary deficiency. Six indicators (central diabetes insipidus, pattern of pituitary stalk thickening, pituitary stalk width, neutrophilic granulocyte percentage, serum sodium level, and gender) were used to develop a model having an accuracy of 95.7% to differentiate neoplastic from inflammatory causes. Conclusion: Pituitary stalk width could indicate the presence of anterior pituitary dysfunction, especially in central diabetes insipidus patients. With the use of a diagnostic model, the neoplastic and inflammatory causes of pituitary stalk thickening could be preliminarily differentiated. Abbreviations: APD = anterior pituitary dysfunction; AUC = area under the curve; CDI = central diabetes insipidus; GH = growth hormone; MRI = magnetic resonance imaging; OR = odd ratio; PHBS = posterior hypophyseal bright spots; PST = pituitary stalk thickening; PSW = pituitary stalk width.

Toward Big Data Analytics: Review of Predictive Models in Management of Diabetes and Its Complications

Diabetes is one of the top priorities in medical science and health care management, and an abundance of data and information is available on these patients. Whether data stem from statistical models or complex pattern recognition models, they may be fused into predictive models that combine patient information and prognostic outcome results. Such knowledge could be used in clinical decision support, disease surveillance, and public health management to improve patient care. Our aim was to review the literature and give an introduction to predictive models in screening for and the management of prevalent short- and long-term complications in diabetes. Predictive models have been developed for management of diabetes and its complications, and the number of publications on such models has been growing over the past decade. Often multiple logistic or a similar linear regression is used for prediction model development, possibly owing to its transparent functionality. Ultimately, for prediction models to prove useful, they must demonstrate impact, namely, their use must generate better patient outcomes. Although extensive effort has been put in to building these predictive models, there is a remarkable scarcity of impact studies.