Long-term tracking of fasting blood glucose variability and peripheral artery disease in people without diabetes

Glycemic Variability as a Predictor of Graft Failure Following Infrainguinal Bypass for Peripheral Arterial Disease: A Retrospective Cohort Study

BACKGROUND

Glycemic variability (GV), measured as the change in visit-to-visit glycated hemoglobin (HbA1c), increases the risk of multiple adverse outcomes. However, the impact of GV on graft patency following infrainguinal bypass (IIB) is unknown. A retrospective cohort study was undertaken to assess the impact of GV on graft patency.

METHODS

A 3-year single-center retrospective case notes analysis of all people undergoing IIB between 2017 and 2019. Rutherford stage, graft conduit, level of bypass, procedure details, baseline demographics, comorbidities, and GV were assessed. Time to reintervention, ipsilateral amputation, or death was recorded to determine primary patency (PP).

RESULTS

One hundred six IIB outcomes were analyzed: mean (± standard deviation) age 68.0 (9.2) years; 69 (65.1%) male, 37 (33.9%), 75 (70.8%) had diabetes mellitus; and 46 (43.4%) underwent elective procedures. GV > 9.1% was associated with significantly lower median PP than GV < 9.1%, 198 (97-753.5) vs. 713 (166.5-1,044.5) days (P = 0.045). On univariate analysis, GV > 9.1% vs. < 9.1% was significantly associated with PP (hazard ratio [HR] 1.85 [confidence interval {CI} 1.091-3.136], P = 0.022). Bypass level was also a univariate predictor, with below knee bypasses (HR 2.31 [CI 1.164-4.564], P = 0.017), and tibial (HR 2.00 [CI 1.022-3.090], P < 0.043) having lower PP than above knee bypasses. On multivariate adjustment, GV > 9.1% and level of bypass remained independent predictors of PP, HR 1.96 (95% CI: 1.12-3.42, P = 0.018) and HR 2.54 (95% CI: 1.24-5.22, P = 0.011), respectively.

CONCLUSIONS

GV is an independent predictor of PP following infrainguinal bypass, thus optimizing GV should be a therapeutic target.

Longitudinal profiling and tracking stability in the Singapore study of macro-angiopathy and microvascular reactivity in type 2 diabetes cohort

INTRODUCTION

The Singapore Study of Macro-Angiopathy and microvascular Reactivity in Type 2 Diabetes (SMART2D) is a prospective cohort study which was started in 2011 to investigate the effect of risk factors on vascular function and diabetes-related complications in Asians. We aimed to compare the longitudinal change in risk factors by accounting for batch effect and assess the tracking stability of risk factors over time in patients recruited for SMART2D. In this study, we (1) described batch effect and its extent across a heterogenous range of longitudinal data parameters; (2) mitigated batch effect through statistical approach; and (3) assessed the tracking stability of the risk factors over time.

METHODS

A total of 2258 patients with type 2 diabetes mellitus (T2DM) were recruited at baseline. The study adopted a three-wave longitudinal design with intervals of 3 years between consecutive waves. The changes in a few selected risk factors were assessed after calibration, assuming patients with similar demographic and anthropometry profile had similar physiology. The tracking pattern of the risk factors was determined with stability coefficients derived from generalised estimating equations.

RESULTS

The medians of the longitudinal differences in risk factors between the waves were mostly modest at <10%. Larger increases in augmentation index (AI), aortic systolic blood pressure (BP) and aortic mean BP were consistently observed after calibration. The medians of the longitudinal differences in AI, aortic systolic BP and aortic mean BP between the waves were <2% before calibration, but increased slightly to <5% after calibration. Most of the risk factors had moderate to high tracking stability. Muscle mass and serum creatinine were among those with relatively high tracking stability.

CONCLUSIONS

The longitudinal differences in parameters between the waves were overall modest after calibration, suggesting that calibration may attenuate longitudinal differences inflated by non-biological factors such as systematic drift due to batch effect. Changes of the hemodynamic parameters are robust over time and not entirely attributable to age. Our study also demonstrated moderate to high tracking stability for most of the parameters.

Identifying peripheral arterial disease in the elderly patients using machine-learning algorithms

BACKGROUND

Peripheral artery disease (PAD) is a common syndrome in elderly people. Recently, artificial intelligence (AI) algorithms, in particular machine-learning algorithms, have been increasingly used in disease diagnosis.

AIM

In this study, we designed an effective diagnostic model of PAD in the elderly patients using artificial intelligence.

METHODS

The study was performed with 539 participants, all over 80 years in age, who underwent the measurements of Doppler ultrasonography and ankle-brachial pressure index (ABI). Blood samples were collected. ABI and two machine-learning algorithms (MLAs)-logistic regression and a random forest (RF) model-were established to diagnose PAD. The sensitivity and specificity of the models were analyzed. An additional RF model was designed based on the most significant features of the original RF model and a prospective study was conducted to demonstrate its external validity.

RESULTS

Thirteen of the 28 features introduced to the MLAs differed significantly between PAD and non-PAD participants. The respective sensitivities and specificities of logistic regression, RF, and ABI were as follows: logistic regression (81.5%, 83.8%), RF (89.3%, 91.6%) and ABI (85.1%, 84.5%). In the prospective study, the newly designed RF model based on the most significant seven features exhibited an acceptable performance rate for the diagnosis of PAD with 100.0% sensitivity and 90.3% specificity.

CONCLUSIONS

An RF model was a more effective method than the logistic regression and ABI for the diagnosis of PAD in an elderly cohort.

Implications of fasting plasma glucose variability on the risk of incident peripheral artery disease in a population without diabetes: a nationwide population-based cohort study

Background

Diabetes have been known as a traditional risk factor of developing peripheral artery disease (PAD). However, the study evaluating the impact of long-term glycemic variability on the risk of developing PAD is limited, especially in a general population without diabetes.

Methods

We included 152,931 individuals without diabetes from the Korean National Health Insurance Service–Health Screening Cohort. Fasting plasma glucose (FPG) variability was measured using coefficient variance (FPG-CV), standard deviation (FPG-SD), and variability independent of the mean (FPG-VIM).

Results

A total of 16,863 (11.0%) incident cases of PAD were identified during a median follow-up of 8.3 years. Kaplan–Meier curves showed a progressively increasing risk of PAD in the higher quartile group of FPG variability than in the lowest quartile group (log rank P < 0.001). Multivariable Cox proportional hazard analysis showed the hazard ratio for PAD prevalence as 1.11 (95% CI 1.07–1.16, P < 0.001) in the highest FPG-CV quartile than in the lowest FPG-CV quartile after adjusting for confounding variables, including mean FPG. Similar degree of association was shown in the FPG-SD and FPG-VIM. In sensitivity analysis, the association between FPG variability and the risk of developing PAD persisted even after the participants were excluded based on previously diagnosed diseases, including stroke, coronary artery disease, congestive heart failure, chronic kidney disease, or current smokers or drinkers. Subgroup analysis demonstrated that the effects of FPG variability on the risk of PAD were more powerful in subgroups of younger age, regular exercisers, and those with higher income.

Conclusions

Increased long-term glycemic variability may have a significant prognostic effect for incident PAD in individuals without diabetes.

Graphical Abstract

Supplementary Information

The online version contains supplementary material available at 10.1186/s12933-022-01448-1.

Variability in Annual Fasting Glucose and the Risk of Peripheral Artery Disease in Patients with Diabetes Mellitus

PURPOSE

High glucose concentrations and swings are associated with endothelial dysfunction. We examined the effects of variability in fasting plasma glucose on peripheral artery disease (PAD) in patients with diabetes mellitus (DM).

PATIENTS AND METHODS

In this screening study for the risk factors of PAD, we retrospectively collected data on the ankle-brachial index (ABI) and the percentage of mean arterial pressure (%MAP) at the ankle between August 01, 2016 and July 31, 2017. We defined low ABI ≤0.90, high %MAP ≥45%, or both as high-risk PAD and others as low-risk PAD. We compared the standard deviation (SD) of the first fasting plasma glucose data available each year after January 01, 2007.

RESULTS

In 2577 patients, a higher SD of annual fasting glucose was observed in those with an ABI ≤0.90 than in patients with an ABI >0.90 (2.6 ± 2.1 vs 2.2 ± 2.3, P = 0.009), and in patients with %MAP ≥45% than in those with %MAP <45% (2.4 ± 2.1 vs 2.2 ± 2.3, P = 0.034). A high-risk PAD was significantly associated with the SD (P = 0.032) but not with the mean (P = 0.338) of annual fasting glucose. The former was an independent risk factor for high-risk PAD (odds ratio = 1.424; 95% CI = 1.118‒1.814; P = 0.004).

CONCLUSION

Variability but not mean of annual fasting plasma glucose was significantly associated with a high risk of PAD in patients with DM.