Association between peripheral arterial and muscle function and quality of life in patients with type 2 diabetes mellitus

Abstract

Introduction: Diabetes Mellitus is a group of metabolic disorders characterized by hyperglycaemia caused by dysfunction of insulin and/or its secretion. Type-2 Diabetes Mellitus (T2DM) is the most common form of diabetes and has implications in several target organs. Vascular disease is responsible for most cases of hospitalization, morbidity, and mortality that occur in patients with T2DM. Additionally, reduction of muscle mass and function is another known complication in these individuals, being a consequence of micro and macrovascular changes, reducing the overall functional capacity and quality of life in this population. Objective: To evaluate the correlation between peripheral arterial function, peripheral muscle function and quality of life of patients with T2DM. Methods: In this cross-sectional study, 19 subjects with T2DM from the ambulatory clinic of endocrinology of the Newton Bethlem Polyclinic were included. The participants were submitted to the evaluation of the peripheral arterial function through the analysis of the pulse wave morphology – including pulse wave velocity (PWV), arterial compliance (AC), and reflection index (IR1,2) – and ankle-brachial index (ABI). Peripheral muscle function was assessed by the handgrip strength (HGS). The International Physical Activity Questionnaire (IPAQ) questionnaire was applied to assess participants' level of physical activity. Health-related quality of life was investigated using both specific (Diabetes Quality of life Measure, DQOL) and generic questionnaires (Medical Outcomes Study 36-item Short-Form Health Survey, SF-36). Results: The bivariate correlation analysis showed an inverse correlation of the DQOL questionnaire with the time of DM (r = -0.471; p = 0.042), as well as the physical (r = -0.632; p = 0.004) and mental components of the SF-36 (r = -0.608; p = 0.006). Conversely the mental component of SF-36 was directly correlated with age (r = 0.564; p = 0.012) and the physical component of SF-36 (r = 0.629, p = 0.004). Weak, nonsignificant correlations were observed between quality of life scores and HGS (r < 0.317, p > 0.186). Also, there were weak, no significant correlations between the PWV, AC, IR1,2 and ABI with quality of life scores (r < 0.376, p > 0.113). The best prediction of the physical component of SF-36 was achieved with a set of variables including age, PWV, handgrip, and the mental component of SF-26, which explained 65% of the variance. Age and PWV were inversely associated, being the former an independent predictor; handgrip and mental health were directly associated, both of which are independent predictors. The best prediction of DQOL score, the set of variables included age, time of T2DM, HGS, physical and mental components of the SF-36, with an explained variance of 78.6%. Age, time of T2DM, and physical component of SF-36 were inversely associated with DQOL; the time of T2DM and the physical component being independent predictors of DQOL; HGS and mental health were positively associated; being HGS an independent predictor. Conclusion: Both generic and specific assessments of quality of life can be explained by personal (age), clinical (time since T2DM diagnosis), and variables related to impaired skeletal muscle (HGS) and vascular (PWV) functions in patients with T2DM.