Dataset Information

Prognostic significance of visit-to-visit variability, and maximum and minimum LDL cholesterol in diabetes mellitus.

ABSTRACT:

Background

Current guidelines for dyslipidemia management recommend that the LDL-C goal be lower than 70 mg/dL. The present study investigated the prognostic significance of visit-to-visit variability in LDL-C, and minimum and maximum LDL-C during follow-up in diabetes mellitus.

Methods

The risk of outcomes in relation to visit-to-visit LDL-C variability was investigated in the Action to Control Cardiovascular Risk in Diabetes (ACCORD) Lipid trial. LDL-C variability indices were coefficient of variation (CV), variability independent of the mean (VIM), and average real variability (ARV). Multivariable Cox proportional hazards models were employed to estimate the adjusted hazard ratio (HR) and 95% confidence interval (CI).

Results

Compared with the placebo group (n=2667), the fenofibrate therapy group (n=2673) had a significantly (P<0.01) lower mean plasma triglyceride (152.5 vs. 178.6 mg/dL), and total cholesterol (158.3 vs.162.9 mg/dL) but a similar mean LDL-C during follow-up (88.2 vs. 88.6 mg/dL, P>0.05). All three variability indices were associated with primary outcome, total mortality and cardiovascular mortality both in the total population and in the fenofibrate therapy group but only with primary outcome in the placebo group. The minimum LDL-C but not the maximum during follow-up was significantly associated with various outcomes in the total population, fenofibrate therapy and placebo group. The minimum LDL-C during follow-up ≥70 mg/dL was associated with an increased risk for various outcomes.

Conclusions

Visit-to-visit variability in LDL-C was a strong predictor of outcomes, independent of mean LDL-C. Patients with LDL-C controlled to less than 70 mg/dL during follow-up might have a benign prognosis. ClinicalTrials.gov number: NCT00000620.

SUBMITTER: Sheng CS

PROVIDER: S-EPMC8832816 | biostudies-literature | 2022 Feb

REPOSITORIES: biostudies-literature

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Publications

Prognostic significance of visit-to-visit variability, and maximum and minimum LDL cholesterol in diabetes mellitus.

Sheng Chang-Sheng CS Miao Ya Y Ding Lili L Cheng Yi Y Wang Dan D Yang Yulin Y Tian Jingyan J

Lipids in health and disease 20220210 1

<h4>Background</h4>Current guidelines for dyslipidemia management recommend that the LDL-C goal be lower than 70 mg/dL. The present study investigated the prognostic significance of visit-to-visit variability in LDL-C, and minimum and maximum LDL-C during follow-up in diabetes mellitus.<h4>Methods</h4>The risk of outcomes in relation to visit-to-visit LDL-C variability was investigated in the Action to Control Cardiovascular Risk in Diabetes (ACCORD) Lipid trial. LDL-C variability indices were c ...[more]

PMID: 35144636

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Project description:BackgroundAtrial fibrillation (AF) is prevalent in patients with type 2 diabetes mellitus (T2DM). Glycemic variability (GV) is associated with risk of micro- and macrovascular diseases. However, whether the GV can increase the risk of AF remains unknown.MethodsThe cohort study used a database from National Taiwan University Hospital, a tertiary medical center in Taiwan. Between 2014 and 2019, a total of 27,246 adult patients with T2DM were enrolled for analysis. Each individual was assessed to determine the coefficients of variability of fasting glucose (FGCV) and HbA1c variability score (HVS). The GV parameters were categorized into quartiles. Multivariate Cox regression models were employed to estimate the relationship between the GV parameters and the risk of AF, transient ischemic accident (TIA)/ischemic stroke and mortality in patients with T2DM.ResultsThe incidence rates of AF and TIA/ischemic stroke were 21.31 and 13.71 per 1000 person-year respectively. The medium follow-up period was 70.7 months. In Cox regression model with full adjustment, the highest quartile of FGCV was not associated with increased risk of AF [Hazard ratio (HR): 1.12, 95% confidence interval (CI) 0.96-1.29, p = 0.148] or TIA/ischemic stroke (HR: 1.04, 95% CI 0.83-1.31, p = 0.736), but was associated with increased risk of total mortality (HR: 1.33, 95% CI 1.12-1.58, p < 0.001) and non-cardiac mortality (HR: 1.41, 95% CI 1.15-1.71, p < 0.001). The highest HVS was significantly associated with increased risk of AF (HR: 1.29, 95% CI 1.12-1.50, p < 0.001), total mortality (HR: 2.43, 95% CI 2.03-2.90, p < 0.001), cardiac mortality (HR: 1.50, 95% CI 1.06-2.14, p = 0.024) and non-cardiac mortality (HR: 2.80, 95% CI 2.28-3.44, p < 0.001) but was not associated with TIA/ischemic stroke (HR: 0.98, 95% CI 0.78-1.23, p = 0.846). The Kaplan-Meier analysis showed significantly higher risk of AF, cardiac and non-cardiac mortality according to the magnitude of GV (log-rank test, p < 0.001).ConclusionsOur data demonstrate that high GV is independently associated with the development of new-onset AF in patients with T2DM. The benefit of maintaining stable glycemic levels to improve clinical outcomes warrants further studies.

Project description:BackgroundThere are limited data on the association of diabetes mellitus (DM) and levels of glycated hemoglobin (HbA1c) with outcomes in patients with atrial fibrillation (AF).MethodsThis retrospective cohort study included patients who were recently hospitalized with a primary or secondary diagnosis of AF from December 2015 through June 2018. Kaplan-Meier curves and Cox-regression adjusted hazard ratios (aHR) were calculated for the primary outcome of all-cause mortality and for the secondary outcomes of cardiovascular (CV) mortality and the composite outcome of CV death or hospitalization. Competing-risk regression analyses were performed to calculate the cumulative risk of stroke, major bleeding, AF- or HF-hospitalizations adjusted for the competing risk of all-cause death. Spline curve models were fitted to investigate associations of HbA1c values and mortality among patients with AF and DM.ResultsIn total 1109 AF patients were included, of whom 373 (33.6%) had DM. During a median follow-up of 2.6 years, 414 (37.3%) patients died. The presence of DM was associated with a higher risk of all-cause mortality (aHR = 1.40 95% confidence intervals [CI] 1.11-1.75), CV mortality (aHR = 1.39, 95% CI 1.07-1.81), sudden cardiac death (aHR = 1.73, 95% CI 1.19-2.52), stroke (aHR = 1.87, 95% CI 1.01-3.45) and the composite outcome of hospitalization or CV death (aHR = 1.27, 95% CI 1.06-1.53). In AF patients with comorbid DM, the spline curves showed a positive linear association between HbA1c levels and outcomes, with values 7.6-8.2% being independent predictors of increased all-cause mortality, and values < 6.2% predicting significantly decreased all-cause and CV mortality.ConclusionsThe presence of DM on top of AF was associated with substantially increased risk for all-cause or CV mortality, sudden cardiac death and excess morbidity. HbA1c levels lower than 6.2% were independently related to better survival rates suggesting that optimal DM control could be associated with better clinical outcomes in AF patients with DM.

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.

Project description:BackgroundHigh blood pressure is a major risk factor for cardiovascular disease. Visit-to-visit blood pressure variability (BPV) has recently been shown to predict cardiovascular outcomes. We investigated the predictive value of BPV for major adverse cardiovascular events (MACE) among patients with coronary artery disease (CAD), with and without type 2 diabetes mellitus (T2DM).MethodsPatients with stable CAD were enrolled and monitored for new MACE. Visit-to-visit BPV was defined as the coefficient of variation (CV) of systolic and diastolic BP across clinic visits. Multivariable logistic regression analysis was performed to evaluate the association of BPV with MACE. Area under the receiver operating characteristic curve (AUC) was used to assess its predictive ability.ResultsAmong 1140 Chinese patients with stable CAD, 192 (17%) experienced a new MACE. In multivariable analyses, the risk of MACE was significantly associated with CV of systolic BP (odds ratio [OR] for highest versus lowest quartile, 3.30; 95% CI 1.97-5.54), and diastolic BP (OR for highest versus lowest quartile, 2.39; 95% CI 1.39-4.11), after adjustment for variables of the risk factor model (age, gender, T2DM, hypertension, antihypertensive agents, number of BP measurements) and mean BP. The risk factor model had an AUC of 0.70 for prediction of MACE. Adding systolic/diastolic CV into the risk factor model with mean BP significantly increased the AUC to 0.73/0.72 (P = 0.002/0.007). In subgroup analyses, higher CV of systolic BP remained significantly associated with an increased risk for MACE in patients with and without T2DM, whereas the association of CV of diastolic BP with MACE was observed only in those without T2DM.ConclusionsVisit-to-visit variability of systolic BP and of diastolic BP was an independent predictor of new MACE and provided incremental prognostic value beyond mean BP and conventional risk factors in patients with stable CAD. The association of BPV in CAD patients without T2DM with subsequent risk for MACE was stronger than in those with T2DM.

Dataset Information

Prognostic significance of visit-to-visit variability, and maximum and minimum LDL cholesterol in diabetes mellitus.

Background

Methods

Results

Conclusions

Publications

Prognostic significance of visit-to-visit variability, and maximum and minimum LDL cholesterol in diabetes mellitus.

Similar Datasets

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