Variability of risk factors and diabetes complications

Abstract

Several studies suggest that, together with glucose variability, the variability of other risk factors, as blood pressure, plasma lipids, heart rate, body weight, and serum uric acid, might play a role in the development of diabetes complications. Moreover, the variability of each risk factor, when contemporarily present, may have additive effects. However, the question is whether variability is causal or a marker. Evidence shows that the quality of care and the attainment of the target impact on the variability of all risk factors. On the other hand, for some of them causality may be considered. Although specific studies are still lacking, it should be useful checking the variability of a risk factor, together with its magnitude out of the normal range, in clinical practice. This can lead to an improvement of the quality of care, which, in turn, could further hesitate in an improvement of risk factors variability.

Keywords: Blood pressure variability; Body weight variability; Cardiovascular complications; Diabetes mellitus; Glucose variability; Heart rate variability; Lipids variability; Microvascular complications; Oxidative stress; Uric acid variability.

Fig. 1

Recursive partitioning techniques (RECPAM) analysis of developing albuminuria in a cohort of 4231 patients with T2D followed up for a median of 3.4 years and with 5 subsequent measurements of risk factors [36]. The tree-growing algorithm resumes the hazard of developing albuminuria according to a multivariable Cox regression analysis. At each step, the method proceeds forward using the covariate with the highest difference in risk. The algorithm proceeds until user-defined conditions are met. Variables used to build the model were quartiles of variability in HbA1c, systolic blood pressure (SBP) and diastolic blood pressure (DBP), serum uric acid (UA), total, high-density lipoprotein (HDL), low-density lipoprotein (LDL) cholesterol and triglycerides., while additional baseline parameters were considered in the model as global variables, i.e. age, gender, duration of diabetes, smoking, hypertension, baseline HbA1c, blood pressure, UA, lipid parameters and estimated glomerular filtration rate (eGFR) values. The variable determining patient’s assignment to the subsequent group is evidenced on the branch proceeding to the following subgroup, while rectangles represent the REPCAM class. The numbers in the circles and rectangles represent the patients who develop albuminuria compared with the total number of patients in the subgroup, respectively (Reproduced with permission from Ref. [36])

Fig. 2

RECPAM analysis of developing a decrease in glomerular filtration rate (GFR). The RECPAM tree-growing algorithm models the hazard of developing GFR < 60 mL/min/1.73 m² using the same approach and the same variables described for Fig. 1 in the same population (Reproduced with permission from Ref. [36])