Cf-PWV and TyG Index Study

Introduction

Hypertension remains a major global health problem and a leading contributor to cardiovascular morbidity and mortality. Large epidemiological studies have demonstrated a continuous and graded relationship between blood pressure levels and cardiovascular events, including myocardial infarction, stroke, and heart failure. However, traditional office blood pressure measurements do not fully capture the complexity of cardiovascular risk.

Arterial stiffness has emerged as a key marker of vascular aging and cardiovascular risk. Carotid-femoral pulse wave velocity (cf-PWV) is considered the gold standard non-invasive method for assessing aortic stiffness. Increased cf-PWV reflects cumulative vascular damage resulting from structural and functional alterations in the arterial wall, including elastin degradation, collagen deposition, vascular calcification, and endothelial dysfunction.

Insulin resistance is a central mechanism linking metabolic disorders to vascular dysfunction. It is associated with reduced nitric oxide bioavailability, increased sympathetic activity, chronic low-grade inflammation, and vascular remodeling. The triglyceride-glucose (TyG) index has been validated as a reliable surrogate marker of insulin resistance and has shown consistent associations with cardiometabolic outcomes.

Derived indices incorporating anthropometric parameters, such as TyG-WC, TyG-BMI, and TyG-WHtR, have been proposed to better capture the interaction between metabolic dysfunction and central adiposity. In addition, composite indices such as the body roundness index (BRI), metabolic score for insulin resistance (METS-IR), and cardiometabolic index (CMI) integrate multiple physiological domains and may provide incremental value in cardiovascular risk stratification.

Circadian blood pressure variation represents another important dimension of cardiovascular physiology. Blood pressure follows a daily rhythm, with a physiological increase in the early morning associated with sympathetic activation and hormonal changes. This phenomenon, known as the morning blood pressure surge (MBPS), has been associated with increased risk of cardiovascular events when exaggerated.

Arterial stiffness may amplify this morning increase in blood pressure due to reduced arterial compliance and impaired buffering of pulsatile flow. In addition, metabolic dysfunction may alter autonomic regulation and baroreflex sensitivity, further influencing circadian blood pressure patterns.

Despite strong biological plausibility, the integrated relationship between metabolic indices, arterial stiffness, and morning blood pressure parameters remains insufficiently explored. Understanding these interactions may improve early identification of individuals at increased cardiovascular risk.

Objectives

Primary Objective

To evaluate the association between arterial stiffness, measured by carotid-femoral pulse wave velocity, and the triglyceride-glucose (TyG) index.

Secondary Objectives

To assess associations between cf-PWV and TyG-derived indices (TyG-WC, TyG-BMI, TyG-WHtR); To evaluate associations between cf-PWV and composite metabolic indices (METS-IR, CMI, BRI); To investigate relationships between metabolic indices and morning blood pressure levels; To assess associations between metabolic indices and morning blood pressure surge (MBPS); To compare the performance of different metabolic indices in identifying increased arterial stiffness and morning hemodynamic load.

Methods Study Design and Data Sources

This is an observational, cross-sectional study with a prospective component, designed to evaluate the integrated relationship between metabolic indices, arterial stiffness, and circadian blood pressure patterns. The study adopts a hybrid design, combining data from two complementary sources: (1) previously collected data from an ethically approved research protocol and (2) prospectively collected data obtained under a standardized protocol.

Previously collected data will be used as secondary data, ensuring full anonymization and confidentiality. Prospectively recruited participants will provide written informed consent prior to inclusion. To ensure comparability, identical measurement protocols and definitions will be applied across both datasets.

Study Population

The study will include adults aged 18 to 65 years referred for ambulatory blood pressure monitoring (ABPM) as part of routine clinical evaluation in a specialized cardiovascular center. Participants will be included regardless of sex.

Only individuals not receiving antihypertensive treatment at the time of evaluation will be eligible for inclusion, in order to minimize the confounding effects of pharmacological therapy on arterial stiffness, metabolic indices, and circadian blood pressure patterns.

Participants will be classified according to 24-hour ABPM results into:

Normotensive individuals Untreated hypertensive individuals (defined as 24-hour mean blood pressure ≥130/80 mmHg) Clinical and Anthropometric Assessment

Baseline clinical data will include age, sex, and relevant medical history. Anthropometric measurements will be obtained using standardized procedures:

Body weight and height will be measured with participants wearing light clothing and no shoes; Body mass index (BMI) will be calculated as weight (kg) divided by height squared (m²); Waist circumference will be measured at the midpoint between the lowest rib and the iliac crest using a flexible tape measure.

In addition, the Body Roundness Index (BRI) will be calculated using a validated geometric formula incorporating waist circumference and height, providing an estimate of body fat distribution and central adiposity.

Office Blood Pressure Measurement

Office blood pressure (OBP) will be measured using a validated automated oscillometric device under standardized conditions. Measurements will be obtained after at least 5 minutes of seated rest, with the participant in a quiet environment.

Three consecutive readings will be recorded at short intervals, and the average of these measurements will be used for analysis. Heart rate will be recorded simultaneously.

Ambulatory Blood Pressure Monitoring (ABPM)

All participants will undergo 24-hour ABPM using a validated oscillometric device, applied to the non-dominant arm with an appropriately sized cuff.

The device will be programmed to obtain measurements:

Every 20 minutes during daytime (awake period) Every 30 minutes during nighttime (sleep period)

Daytime and nighttime periods will be defined based on individual sleep diaries. Only recordings meeting established quality criteria will be considered valid.

Mean systolic and diastolic blood pressure values will be calculated for:

24-hour period Daytime period Nighttime period

Hypertension will be defined based on a 24-hour mean blood pressure ≥130/80 mmHg.

Morning Blood Pressure and Morning Surge Definitions

Morning systolic and diastolic blood pressure will be defined as the average of readings obtained during the first two hours after awakening.

Morning blood pressure surge (MBPS) will be assessed using two complementary definitions:

Sleep-through MBPS (ST-MBPS): difference between morning systolic BP and the lowest nocturnal systolic BP value (including adjacent readings); Prewaking MBPS (PW-MBPS): difference between morning systolic BP and the average systolic BP during the two hours preceding awakening.

These indices will be analyzed as continuous variables and may also be evaluated across quartiles of metabolic indices.

Arterial Stiffness Assessment

Arterial stiffness will be assessed using carotid-femoral pulse wave velocity (cf-PWV), measured with a validated device under standardized conditions.

Participants will be evaluated in the supine position after at least 10 minutes of rest. Pulse waveforms will be recorded simultaneously at carotid and femoral sites.

At least three consecutive measurements will be obtained. For analysis, the mean of the two closest values will be used, provided that the difference between them is ≤0.5 m/s, ensuring measurement reproducibility and quality control.

Laboratory Data and Metabolic Indices

Laboratory data will be obtained from routine clinical tests performed under fasting conditions (minimum 8 hours), as requested by the treating physician.

The following parameters will be collected:

Fasting glucose Triglycerides HDL cholesterol

These values will be used to calculate metabolic indices:

Primary metabolic index TyG index: Ln [fasting triglycerides (mg/dL) × fasting glucose (mg/dL) / 2] Derived indices TyG-WC = TyG × waist circumference TyG-BMI = TyG × BMI TyG-WHtR = TyG × waist-to-height ratio Composite indices METS-IR: Ln [(2 × glucose) + triglycerides] × BMI / Ln (HDL-C) CMI: (waist-to-height ratio) × (triglycerides / HDL-C) BRI: geometric index of body adiposity

All indices will be analyzed both as continuous variables and categorized into quartiles.

Study Variables Primary exposure: TyG index and derived metabolic indices Primary outcome: cf-PWV

Secondary outcomes:

Morning systolic and diastolic blood pressure ST-MBPS and PW-MBPS

Potential confounders will include:

Age Sex BMI 24-hour blood pressure Hypertension status Sample Size Considerations

The sample size was defined based on the primary association between cf-PWV and the TyG index, as well as the need to perform stratified analyses.

Given planned analyses involving quartiles of metabolic indices and stratification by hypertension status, a total sample size of approximately 800 participants was established to ensure adequate statistical power and stability of subgroup estimates.

Statistical Analysis

Continuous variables will be expressed as mean ± standard deviation or median (interquartile range), depending on distribution. Categorical variables will be presented as frequencies and percentages.

Participants will be categorized into quartiles according to metabolic indices. Comparisons across quartiles will be performed using analysis of variance (ANOVA) or appropriate non-parametric tests, with evaluation of linear trends.

Associations between metabolic indices and cf-PWV, as well as between metabolic indices and morning blood pressure parameters, will be assessed using linear regression models.

Multivariable models will be adjusted for predefined confounders based on biological plausibility. Interaction terms (e.g., metabolic indices × hypertension status) will be explored.

Additional analyses may include:

Concordance correlation coefficient (Lin's CCC) Bland-Altman analysis for bias assessment

A two-sided p-value <0.05 will be considered statistically significant.