Dataset Information

Visit-to-visit systolic blood pressure variability and microvascular complications among patients with diabetes.

ABSTRACT:

Aims

To examine the relationship between systolic blood pressure (SBP) variability and the risk of microvascular complications in a non-elderly diabetic population.

Methods

This is a retrospective cohort study of individuals aged ≤60years treated for diabetes in 2003 in the US Department of Veterans Affairs healthcare system. Individuals were followed for five years for any new diagnosis of diabetic nephropathy, retinopathy, or neuropathy. In each year of follow-up, individuals were classified into quartiles based on their SBP variability.

Results

We identified 208,338 patients with diabetes without diabetic nephropathy, retinopathy, or neuropathy at baseline. Compared to individuals with the least SBP variability (Quartile 1), those with most variability (Quartile 4) had 81% (OR=1.81; 95% CI, 1.72-1.91), 17% (OR=1.17; 95% CI, 1.13-1.21), 30% (OR=1.30; 95% CI, 1.25-1.35), and 19% (OR=1.19; 95% CI, 1.15-1.23) higher incidence of nephropathy, retinopathy, neuropathy, and any complication, respectively, after adjusting for mean SBP, demographic and clinical factors.

Conclusions

We found a significant graded relationship between SBP variability and the incidence of each complication and of any combined endpoint. This is the first study showing a significant association between SBP variability and the risk of diabetic retinopathy and neuropathy.

SUBMITTER: Sohn MW

PROVIDER: S-EPMC5209256 | biostudies-literature | 2017 Jan

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Visit-to-visit systolic blood pressure variability and microvascular complications among patients with diabetes.

Sohn Min-Woong MW Epstein Noam N Huang Elbert S ES Huo Zhiping Z Emanuele Nicholas N Stukenborg George G Guihan Marylou M Li Junping J Budiman-Mak Elly E

Journal of diabetes and its complications 20160914 1

<h4>Aims</h4>To examine the relationship between systolic blood pressure (SBP) variability and the risk of microvascular complications in a non-elderly diabetic population.<h4>Methods</h4>This is a retrospective cohort study of individuals aged ≤60years treated for diabetes in 2003 in the US Department of Veterans Affairs healthcare system. Individuals were followed for five years for any new diagnosis of diabetic nephropathy, retinopathy, or neuropathy. In each year of follow-up, individuals we ...[more]

PMID: 27671535

Similar Datasets

Project description:BackgroundIdentification of prognostic factors for diabetes complications are crucial. Glucose variability (GV) and its association with diabetes have been studied extensively but the inclusion of measures of glucose variability (GVs) in prognostic models is largely lacking. This study aims to assess which GVs (i.e., coefficient of variation (CV), standard deviation (SD), and time-varying) are better in predicting diabetic complications, including cardiovascular disease (CVD), diabetic retinopathy (DR), and chronic kidney disease (CKD). The model performance between traditional statistical models (adjusting for covariates) and machine learning (ML) models were compared.MethodsA retrospective cohort of type 2 diabetes (T2D) patients between 2010 and 2019 in Ramathibodi Hospital was created. Complete case analyses were used. Three GVs using HbA1c and fasting plasma glucose (FPG) were considered including CV, SD, and time-varying. Cox proportional hazard regression, ML random survival forest (RSF) and left-truncated, right-censored (LTRC) survival forest were compared in two different data formats (baseline and longitudinal datasets). Adjusted hazard ratios with 95% confidence intervals were used to report the association between three GVs and diabetes complications. Model performance was evaluated using C-statistics along with feature importance in ML models.ResultsA total of 40,662 T2D patients, mostly female (61.7%), with mean age of 57.2 years were included. After adjusting for covariates, HbA1c-CV, HbA1c-SD, FPG-CV and FPG-SD were all associated with CVD, DR and CKD, whereas time-varying HbA1c and FPG were associated with DR and CKD only. The CPH and RSF for DR (C-indices: 0.748-0.758 and 0.774-0.787) and CKD models (C-indices: 0.734-0.750 and 0.724-0.740) had modestly better performance than CVD models (C-indices: 0.703-0.730 and 0.698-0.727). Based on RSF feature importance, FPG GV measures ranked higher than HbA1c GV, and both GVs were the most important for DR prediction. Both traditional and ML models had similar performance.ConclusionsWe found that GVs based on HbA1c and FPG had comparable performance. Thus, FPG GV may be used as a potential monitoring parameter when HbA1c is unavailable or less accessible.

Dataset Information

Visit-to-visit systolic blood pressure variability and microvascular complications among patients with diabetes.

Aims

Methods

Results

Conclusions

Publications

Visit-to-visit systolic blood pressure variability and microvascular complications among patients with diabetes.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets