Estimated pulse wave velocity is associated with all-cause mortality and cardiovascular mortality among adults with diabetes

Abstract

AimsWe aim to examine the association of estimated pulse wave velocity (ePWV) with all-cause and cardiovascular mortality in patients with diabetes.MethodsAll of adult participants with diabetes from the National Health and Nutrition Examination Survey (NHANES) (1999–2018) were enrolled. ePWV was calculated according to the previously published equation based on age and mean blood pressure. The mortality information was obtained from the National Death Index database. Weighted Kaplan-Meier (KM) plot and weighted multivariable Cox regression was used to investigate the association of ePWV with all-cause and cardiovascular mortality risks. Restricted cubic spline was adopted to visualize the relationship between ePWV and mortality risks.Results8,916 participants with diabetes were included in this study and the median follow-up duration was ten years. The mean age of study population was 59.0 ± 11.6 years, 51.3% of the participants were male, representing 27.4 million patients with diabetes in weighted analysis. The increment of ePWV was closely associated with increased risks of all-cause mortality (HR: 1.46, 95% CI: 1.42–1.51) and cardiovascular mortality (HR: 1.59, 95% CI: 1.50–1.68). After adjusting for cofounding factors, for every 1 m/s increase in ePWV, there was a 43% increased risk of all-cause mortality (HR: 1.43, 95% CI: 1.38–1.47) and 58% increased of cardiovascular mortality (HR: 1.58, 95% CI: 1.50–1.68). ePWV had positive linear associations with all-cause and cardiovascular mortality. KM plots also showed that the risks of all-cause and cardiovascular mortality were significantly elevated in patients with higher ePWV.ConclusionsePWV had a close association with all-cause and cardiovascular mortality risks in patients with diabetes.

Gender differences in the association between healthy eating index-2015 and hypertension in the US population: evidence from NHANES 1999–2018

“…Anyone who provided a previous diagnosis of diabetes by a physician or health professional was defined as patients with diagnosed diabetes, while those without diagnosed diabetes but with a HbA1c level 6.5% (47.5 mmol/mol) or higher, FPG level 126 mg/dL (7.0 mmol/L) or higher, or 2-h oral glucose tolerance test (OGTT) plasma glucose 200 mg/dL or higher (11.1 mmol/L) tested by laboratory examinations were classified as having undiagnosed diabetes. Participants with diagnosed diabetes or undiagnosed diabetes were both considered diabetic patients [ 33 , 34 ].…”

Relationship of body mass index and visceral fat area combination with arterial stiffness and cardiovascular risk in cardiovascular disease-free people: NHANES (2011–2018)

“…Age was in years. MBP was calculated using the formula MBP = DBP + (0:4 × (SBP − DBP)) ( 2 , 20 , 21 ). Blood pressure was measured by a trained health technician, and participants obtained three consecutive sphygmomanometric counts after 5 min of sitting still, and SDP and DBP were determined by calculating the average of all the blood pressure readings obtained.…”

“…Higher cf-PWV and faster conduction velocity indicate less vascular elasticity and more severe sclerosis. However, cf-PWV is not widely used due to the limitations of personnel and equipment ( 2 ). Estimated pulse wave velocity (ePWV) can effectively predict cf-PWV in response to arterial stiffness ( 3 ).…”

“…The aims of this study were as follows: ( 1 ) to explore the relationship between obesity and vascular stiffness; ( 2 ) to redefine obesity by combining the general obesity index (BMI) and central obesity index (VAT) to identify the primary prevention population of CVD; ( 3 ) to explore risk factors related to vascular stiffness and identify targets for vascular stiffness treatment; ( 4 ) to assess early diagnosis, early prevention, and personalized treatment for CVD populations to reduce the global burden of CVD worldwide.…”

Application of machine learning algorithms to construct and validate a prediction model for coronary heart disease risk in patients with periodontitis: a population-based study

“…NHANES employs a stratified multistage probability sampling technique, wherein specific sampling weights are assigned to each participant based on the primary sampling unit, thereby facilitating the generation of nationally representative estimates ( 30 ). Following the NHANES analysis guidelines, new sample weights were generated by combining continuous data from three two-year cycles (original two-year sample weights divided by 2).…”