Zoekresultaten

Obese older Type 2 Diabetes Mellitus patients with muscle insulin resistance benefit from an enriched protein drink during combined lifestyle intervention

(1) Background: Recent research showed that subtypes of patients with type 2 diabetes may differ in response to lifestyle interventions based on their organ-specific insulin resistance (IR). (2) Methods: 123 Subjects with type 2 diabetes were randomized into 13-week lifestyle intervention, receiving either an enriched protein drink (protein+) or an isocaloric control drink (control). Before and after the intervention, anthropometrical and physiological data was collected. An oral glucose tolerance test was used to calculate indices representing organ insulin resistance (muscle, liver, and adipose tissue) and β-cell functioning. In 82 study-compliant subjects (per-protocol), we retrospectively examined the intervention effect in patients with muscle IR (MIR, n = 42) and without MIR (no-MIR, n = 40). (3) Results: Only in patients from the MIR subgroup that received protein+ drink, fasting plasma glucose and insulin, whole body, liver and adipose IR, and appendicular skeletal muscle mass improved versus control. Lifestyle intervention improved body weight and fat mass in both subgroups. Furthermore, for the MIR subgroup decreased systolic blood pressure and increased VO2peak and for the no-MIR subgroup, a decreased 2-h glucose concentration was found. (4) Conclusions: Enriched protein drink during combined lifestyle intervention seems to be especially effective on increasing muscle mass and improving insulin resistance in obese older, type 2 diabetes patients with muscle IR.

Benefits of exercise in addition to diet in people with type 2 diabetes: a systematic review and meta-analysis

We can only keep hoping

The studies in this thesis aim to increase understanding of the effects of various characteristics of scientific news about a common chronic disease, i.e., diabetes, on the cognitive responses (e.g., emotions, attitudes, intentions) of diabetes patients. The research questions presented in this thesis are guided by the Health Belief Model, a theoretical framework developed to explain and predict healthrelated behaviours based on an individual’s beliefs and attitudes. The model asserts that perceived barriers to a recommended health behavior, advantages of the behavior, self-efficacy in executing the behavior, and disease severity and personal susceptibility to the disease are important predictors of a health behavior. Communication is one of the cues to action (i.e., stimuli) that may trigger the decision-making process relating to accepting a medical or lifestyle recommendation.

Diabetes mellitus type II as a risk factor for depression: a lower than expected risk in a general practice setting.

The aim of the present study was to determine whether a diagnosis of diabetes mellitus (DM) in a primary setting is associated with an increased risk of subsequent depression. A retrospective cohort design was used based on the Registration Network Family Practice (RNH) database. Patients diagnosed with diabetes mellitus at or after the age of 40 and who were diagnosed between 01-01-1980 and 01-01-2007 (N = 6,140), were compared with age-matched controls from a reference group (N = 18,416) without a history of diabetes. Both groups were followed for an emerging first diagnosis of depression (and/or depressive feelings) until January 1, 2008. 2.0% of the people diagnosed with diabetes mellitus developed a depressive disorder, compared to 1.6% of the reference group. After statistical correction for confounding factors diabetes mellitus was associated with an increased risk of developing subsequent depression (HR 1.26; 95% CI: 1.12-1.42) and/or depressive feelings (HR 1.33; 95% CI: 1.18-1.46). After statistical adjustment practice identification code, age and depression preceding diabetes, were significantly related to a diagnosis of depression. Patients with diabetes mellitus are more likely to develop subsequent depression than persons without a history of diabetes. Results from this large longitudinal study based on a general practice population indicate that this association is weaker than previously found in cross-sectional research using self-report surveys. Several explanations for this dissimilarity are discussed

Effective lifestyle interventions to improve type II diabetes self-management for those with schizophrenia or schizoaffective disorder:

Patiënten met schizofrenie of een schizo-affectieve stoornis hebben een verhoogde kans op diverse somatische ziektes, zoals cardiovasculaire aandoeningen, virale ziektes, diabetes, respiratoire aandoeningen en seksuele stoornissen. Deze ziektes dragen ertoe bij dat de levensverwachting van deze patiëntengroep naar schatting 20 jaar lager ligt dan die van de normale bevolking. Verschillende factoren dragen bij aan deze hoge morbiditeit en mortaliteit, waaronder genetische kwetsbaarheid, de ongezonde leefstijl van deze patiënten en de bijwerkingen van de anti psychotische medicatie.

Patient and disease characteristics associated with activation for self-management in patients with diabetes, chronic obstructive pulmonary disease, chronic heart failure and chronic renal disease

A substantial proportion of chronic disease patients do not respond to self-management interventions, which suggests that one size interventions do not fit all, demanding more tailored interventions. To compose more individualized strategies, we aim to increase our understanding of characteristics associated with patient activation for self-management and to evaluate whether these are disease-transcending. A cross-sectional survey study was conducted in primary and secondary care in patients with type-2 Diabetes Mellitus (DM-II), Chronic Obstructive Pulmonary Disease (COPD), Chronic Heart Failure (CHF) and Chronic Renal Disease (CRD). Using multiple linear regression analysis, we analyzed associations between self-management activation (13-item Patient Activation Measure; PAM-13) and a wide range of socio-demographic, clinical, and psychosocial determinants. Furthermore, we assessed whether the associations between the determinants and the PAM were disease-transcending by testing whether disease was an effect modifier. In addition, we identified determinants associated with low activation for self-management using logistic regression analysis. We included 1154 patients (53% response rate); 422 DM-II patients, 290 COPD patients, 223 HF patients and 219 CRD patients. Mean age was 69.6±10.9. Multiple linear regression analysis revealed 9 explanatory determinants of activation for selfmanagement: age, BMI, educational level, financial distress, physical health status, depression, illness perception, social support and underlying disease, explaining a variance of 16.3%. All associations, except for social support, were disease transcending. This study explored factors associated with varying levels of activation for self-management. These results are a first step in supporting clinicians and researchers to identify subpopulations of chronic disease patients less likely to be engaged in self-management. Increased scientific efforts are needed to explain the greater part of the factors that contribute to the complex nature of patient activation for self-management.