Arterial stiffness in type 2 diabetes: determinants and indication of a discriminative value

Discriminative value of pulse wave velocity for arterial stiffness and cardiac injury in prediabetic patients

Background

Prediabetes (PD) is defined as impaired fasting glucose and/or impaired glucose tolerance (IGT) and may be associated with high risk of cardiovascular injury. It is recommended that PD patients be screened for signs of arterial stiffness and cardiovascular injury to reinforce therapeutic strategies.

Objectives

To identify pulse wave velocity values discriminative for arterial stiffness and cardiovascular injury in PD patients.

Methods

A cross-sectional study was conducted with PD (N=43) and normoglycemic (N=37) patients who underwent clinical evaluation, arterial stiffness assessment by carotid-femoral pulse wave velocity (cfPWV) using SphygmoCor, laboratory blood analysis, investigation of morphological and functional cardiac variables by transthoracic echocardiogram, and assessment of carotid intima-media-thickness (CIMT) by carotid ultrasonography. A statistical analysis was performed using SPSS software and values of p<0.05 were considered significant.

Results

A cfPWV cut-off value of 6.9 m/s was identified for IGT (Sensitivity [SE]: 74% and Specificity [SP]: 51%). Comparison of general data and risk factors between subsets with values above and below this cutoff value revealed higher rates of fasting glucose (p=0.02), obesity (p=0.03), dyslipidemia (p=0.004), early signs of left ventricle (p=0.017) and right ventricle (p=0.03) impaired diastolic function, and elevated CIMT in subjects with cfPWV ≥ 6.9m/s (p=0.04).

Conclusions

In PD patients, a cfPWV cutoff of 6.9 m/s was considered a discriminative value for arterial stiffness. These findings highlight the value of early investigation of cardiovascular injury and aggressive therapy strategies with good control of risk factors in PD.

Association of baseline and dynamic arterial stiffness status with dyslipidemia: a cohort study

Background and aims

Dyslipidemia is known to contribute to arterial stiffness, while the inverse association remains unknown. This study aimed to explore the association of baseline arterial stiffness and its changes, as determined by brachial-ankle pulse wave velocity (baPWV), with dyslipidemia onset in the general population.

Methods

This study enrolled participants from Beijing Health Management Cohort using measurements of the first visit from 2012 to 2013 as baseline, and followed until the dyslipidemia onset or the end of 2019. Unadjusted and adjusted Cox proportional regression models were used to evaluate the associations of baseline baPWV and baPWV transition (persistent low, onset, remitted and persistent high) with incident dyslipidemia.

Results

Of 4362 individuals (mean age: 55.5 years), 1490 (34.2%) developed dyslipidemia during a median follow-up of 5.9 years. After adjusting for potential confounders, participants with elevated arterial stiffness at baseline had an increased risk of dyslipidemia (HR, 1.194; 95% CI, 1.050-1.358). Compared with persistent low baPWV, new-onset and persistent high baPWV were associated with a 51.2% and 37.1% excess risk of dyslipidemia.

Conclusion

The findings indicated that arterial stiffness is an early risk factor of dyslipidemia, suggesting a bidirectional association between arterial stiffness and lipid metabolism.

Different Home Blood Pressure Thresholds to Predict Perfect 24-Hour Ambulatory Blood Pressure Control in Treated Hypertension Based on an "All-in-One" Device

BACKGROUND

Home blood pressure (BP) is an important component of digital strategies for hypertension management. However, no studies have used the same device to investigate 24-hour BP control status in relation to different home BP control thresholds.

METHODS

Participants in the general practitioner-based, multicenter HI-JAMP study (Home-Activity Information and Communication Technology-Based Japan Ambulatory Blood Pressure Monitoring Prospective) underwent office BP measurement, then 24-hour ambulatory BP monitoring, then home BP monitoring for 5 days. A validated all-in-one BP monitoring device was used to measure office, home, and ambulatory BP. Baseline data were used to investigate ambulatory BP control status in individuals with well-controlled home BP based on the different guideline thresholds (125/75 mm Hg, 130/80 mm Hg, and 135/85 mm Hg).

RESULTS

Data from 2269 patients were analyzed. For individuals with well-controlled home BP <135/85 mm Hg (59.5% of the total population), the prevalence of uncontrolled 24-hour (≥130/80 mm Hg), daytime (≥135/85 mm Hg), and nighttime ambulatory BP (≥120/70 mm Hg) was 19.9%, 18.5%, and 33.6%, respectively. Corresponding prevalence rates in the 42.7% of participants with well-controlled home BP <130/80 mm Hg were 13.4%, 12.9%, and 26.0%, and when well-controlled home BP was strictly defined as <125/75 mm Hg (23.9% of the population), prevalence of rates of uncontrolled 24-hour, daytime, and nighttime ambulatory BP were 7.0%, 9.0%, and 15.3%, respectively.

CONCLUSIONS

Home BP control status defined using different thresholds could predict 24-hour ambulatory BP control status in treated hypertension. One-third of individuals still had uncontrolled nocturnal hypertension when home BP was controlled to <135/85 mm Hg, but ambulatory BP was quite well controlled when home BP was <125/75 mm Hg.

An environment-wide association study for the identification of non-invasive factors for type 2 diabetes mellitus: Analysis based on the Henan Rural Cohort study

AIM

To explore the influencing factors of Type 2 diabetes mellitus (T2DM) in the rural population of Henan Province and evaluate the predictive ability of non-invasive factors to T2DM.

METHODS

A total of 30,020 participants from the Henan Rural Cohort Study in China were included in this study. The dataset was randomly divided into a training set and a testing set with a 50:50 split for validation purposes. We used logistic regression analysis to investigate the association between 56 factors and T2DM in the training set (false discovery rate < 5 %) and significant factors were further validated in the testing set (P < 0.05). Gradient Boosting Machine (GBM) model was used to determine the ability of the non-invasive variables to classify T2DM individuals accurately and the importance ranking of these variables.

RESULTS

The overall population prevalence of T2DM was 9.10 %. After adjusting for age, sex, educational level, marital status, and body measure index (BMI), we identified 13 non-invasive variables and 6 blood biochemical indexes associated with T2DM in the training and testing dataset. The top three factors according to the GBM importance ranking were pulse pressure (PP), urine glucose (UGLU), and waist-to-hip ratio (WHR). The GBM model achieved a receiver operating characteristic (AUC) curve of 0.837 with non-invasive variables and 0.847 for the full model.

CONCLUSIONS

Our findings demonstrate that non-invasive variables that can be easily measured and quickly obtained may be used to predict T2DM risk in rural populations in Henan Province.

Determinants of arterial stiffness in patients with type 2 diabetes mellitus: a cross sectional analysis

In patients with type 2 diabetes mellitus (T2DM) arterial stiffness is associated with increased cardiovascular and total mortality. Little is known about determinants of arterial stiffness in clinical routine. Identification of potential determinants of arterial stiffness will help to address treatment targets for patients in the early state of T2DM. This is a cross-sectional analysis of arterial stiffness in 266 patients in the early stage of T2DM who did not have cardiovascular or renal complications. Parameters of arterial stiffness such as central systolic blood pressure (cSBP), central pulse pressure (cPP) and pulse wave velocity (PWV) were measured with the SphygmoCor System (AtCor Medical). We investigated the influence of parameters of glucose metabolism, lipid status, body constitution, blood pressure (BP) and inflammation on the stiffness parameters using multivariate regression analysis. The study cohort consisted of male and female patients aged 61 ± 8 years with mean diabetes duration of 6.4 ± 5.1 years, mean HbA1c 7.1 ± 0.9%, mean cSBP 121 ± 12 mmHg, mean cPP 44 ± 10 mmHg and mean PWV 8.9 ± 1.8 m/s. Multiple regression analysis identified waist circumference (WC) (beta = 0.411, p = 0.026), LDL-cholesterol (beta = 0.106, p = 0.006), systolic office BP (beta = 0.936, p < 0.001) and diabetes duration (beta = 0.233, p = 0.043) as potential determinants of cSBP. cPP was determined by sex (beta = 0.330, p = 0.008), age (beta = 0.383, p < 0.001), systolic office BP (beta = 0.370, p < 0.001) and diabetes duration (beta = 0.231, p = 0.028) whereas for PWV the following determinants could be identified: age (beta = 0.405, p < 0.001), systolic office BP (beta = 0.421, p < 0.001) and diabetes duration (beta = 0.073, p = 0.038). In addition to the known parameters age, sex and systolic office BP serum LDL-cholesterol, WC and diabetes duration have been identified as determinants of arterial stiffness in patients with T2DM. Treatment of patients in the early stage of T2DM should focus on these clinical parameters to prevent progression of arterial stiffness and as a consequence reduce cardiovascular mortality.Trial registration: The patients included in the analysis participated in one of the following clinical trials NCT02752113 (registered 26.4.2016), NCT02383238 (09.03.2015), NCT02471963 (15.06.2015), NCT01319357 (21.03.2011) ( http://www.clinicaltrials.gov ).

Association of Pulse Pressure Difference and Diabetes Mellitus in Chinese People: A Cohort Study

Purpose

Here, we sought to determine the association between pulse pressure difference and the incidence of type 2 diabetes mellitus (T2DM) in Chinese people.

Methods

This study involved 211,814 participants among whom 4156 had been diagnosed with T2DM. The correlation between pulse pressure difference and T2DM incidence in Chinese people was determined by multivariate analysis. A smooth curve fitting diagram was then used to explore correlation between pulse pressure difference and T2DM incidence. Finally, the inflection point in the correlation between pulse pressure difference and the T2DM incidence was located by piecewise linear regression.

Results

To understand the relationship, adjustments were made for sex, age, total serum cholesterol (TC), fasting blood glucose (FPG), triglyceride (TG), alanine aminotransferase (ALT), family history of diabetes, body mass index (BMI), blood urea nitrogen (BUN), drinking status, and smoking status. Diabetes incidence increased by 0.3% [HR 1.003 (1.001, 1.005), p = <0.05] for every 1mmHg increase in pulse pressure difference. Smooth curve analysis showed that, when pulse pressure difference was ≤35mmHg, diabetes incidence negatively correlated to pulse pressure difference [HR 0.972 (0.953, 0.972) p = 0.053]. However, when pulse pressure difference was >35mmHg, diabetes incidence increased with increasing pulse pressure difference [HR 1.044 (1.042, 1.047) p = <0.001]. And between pulse pressure difference and fasting blood glucose in the final visit, the blood glucose level increased with the elevation of pulse pressure.

Conclusion

The risk of diabetes was lowest at about 35mmHg pulse pressure difference.