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Integrated Ambulatory Blood Pressure, Electrocardiographic, and Inflammatory Markers for Cross-sectional Discrimination of Echocardiographically Defined Left Ventricular Diastolic Dysfunction

This cross-sectional study demonstrates that integrating diastolic morning surge, T-peak–to–T-end interval, and neutrophil-to-lymphocyte ratio significantly improves the discriminatory accuracy for identifying echocardiographically defined left ventricular diastolic dysfunction beyond standard clinical and ambulatory blood pressure parameters.

Original authors: Ayhan Cosgun, Huseyin Oren

Published 2026-06-24

📖 5 min read🧠 Deep dive

CC BY 4.0

Original authors: Ayhan Cosgun, Huseyin Oren

Original paper licensed under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). ✨ This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer

The Big Picture: Checking the Heart's "Morning Rush"

Imagine your heart is a house. Usually, doctors check the house's foundation (the heart muscle) and its plumbing (blood flow) using an ultrasound (echocardiogram). This study asked a new question: Can we tell if the house is having trouble relaxing by looking at three specific "stress signals" that happen before the ultrasound even shows a problem?

The researchers looked at three specific clues:

The Morning Pressure Spike: How much your blood pressure jumps up right after you wake up.
The Electrical "Echo": A tiny delay in the heart's electrical reset signal.
The Body's "Fire Alarm": A blood test showing low-level inflammation.

They wanted to see if combining these three clues helps doctors spot "Left Ventricular Diastolic Dysfunction" (LVDD)—a condition where the heart muscle gets stiff and can't relax properly to fill with blood.

The Three Clues (The "Detective Tools")

1. The Morning Pressure Spike (Diastolic Morning Surge)

The Analogy: Imagine your blood vessels are like garden hoses. Most of the night, the water pressure is low and steady. But when you wake up, your body naturally wakes up the "pump," causing a sudden rush of water pressure.
What they found: In people with stiff hearts, this morning rush was much more violent. It wasn't just about the average pressure during the day; it was about that specific, repetitive jolt of pressure hitting the heart every single morning. The study found that a bigger "morning jolt" was a strong sign that the heart was struggling to relax.

2. The Electrical "Echo" (T-peak–to–T-end Interval)

The Analogy: Think of your heart's electrical system like a conductor leading an orchestra. When the music stops, the musicians need to reset before the next note. The "T-peak-to-T-end" interval is like measuring how long it takes for the last violinist to stop playing before the next song starts.
What they found: In people with stiff hearts, this "reset time" was longer. It's like the orchestra is taking too long to quiet down. This suggests the heart muscle is electrically "messy" or uneven, which often happens when the muscle itself is stiff or scarred.

3. The Body's "Fire Alarm" (Neutrophil-to-Lymphocyte Ratio)

The Analogy: Imagine your blood is a city. "Neutrophils" are the firefighters rushing to a small, smoldering fire, and "Lymphocytes" are the police officers keeping the peace. The "Ratio" is just counting how many firefighters there are compared to police.
What they found: People with stiff hearts had a higher number of "firefighters" relative to "police." This doesn't mean there is a giant fire (like a heart attack), but rather a low-grade, smoldering inflammation inside the body that is making the heart muscle stiffer over time.

How the Study Was Done

The researchers looked at 700 adults who had already gone through a full check-up, including:

Wearing a blood pressure monitor for 24 hours (to catch the morning spike).
Getting an ECG (to check the electrical reset time).
Giving a blood sample (to check the inflammation ratio).
Getting an ultrasound (the "gold standard" to see if the heart was actually stiff).

They split the group into two:

Group A (560 people): Hearts that were relaxing perfectly.
Group B (140 people): Hearts that were definitely stiff (LVDD).

They then built a "prediction model" in three steps, like adding ingredients to a cake:

Step 1: Used only basic info (age, diabetes, smoking). Result: Okay, but not great.
Step 2: Added the 24-hour blood pressure numbers. Result: Better.
Step 3: Added the Morning Spike, the Electrical Echo, and the Inflammation Ratio. Result: This was the winner.

The Results

When they added those three special clues to the basic check-up, the model got much better at spotting the stiff hearts.

Without the new clues, the model was about 72% accurate.
With the new clues (Morning Spike + Electrical Echo + Inflammation), the model jumped to 87% accurate.

The study also found that these clues worked best together. It's like a three-legged stool: if you have a big morning spike and a slow electrical reset and high inflammation, the heart is very likely to be stiff.

What the Paper Does NOT Say

It is very important to stick to what the authors actually claimed:

It is not a replacement for ultrasound: The authors explicitly state this model does not replace the echocardiogram (the ultrasound). The ultrasound is still the boss. This model just adds extra information to help understand the heart better right now.
It is not a crystal ball: This was a "snapshot" study (cross-sectional). They looked at people on one day. They cannot say that these people will definitely get heart failure in the future. They only proved that these three clues are linked to the problem today.
It is not a new drug: They didn't test any treatments. They just identified the signals.

The Bottom Line

This study suggests that the heart's struggle to relax isn't just about how hard it pumps; it's also about how hard it gets hit every morning, how messy its electrical reset is, and how much low-grade inflammation is brewing.

By looking at these three things together, doctors can get a clearer, more detailed picture of a patient's heart health than by looking at blood pressure or age alone. However, this is still a research finding that needs to be tested on more people in different places before it becomes a standard tool in every doctor's office.

Technical Summary: Integrated Ambulatory Blood Pressure, Electrocardiographic, and Inflammatory Markers for Cross-sectional Discrimination of Echocardiographically Defined Left Ventricular Diastolic Dysfunction

Problem Statement
Left ventricular diastolic dysfunction (LVDD) is a structural and functional abnormality often identified via echocardiography, serving as an early precursor to heart failure with preserved ejection fraction (HFpEF). While LVDD is defined by imaging, its underlying phenotype likely reflects a convergence of ambulatory hemodynamic stress, ventricular repolarization heterogeneity, and systemic inflammation. Current diagnostic approaches rely heavily on echocardiography, but it remains uncertain whether integrating dynamic circadian blood pressure measures, specifically diastolic morning surge (DMS), alongside electrocardiographic repolarization markers (T-peak–to–T-end interval, or Tp-e) and inflammatory indices (neutrophil-to-lymphocyte ratio, or NLR), provides incremental discriminatory value for identifying definite LVDD beyond conventional clinical and ambulatory blood pressure variables.

Methodology
This retrospective cross-sectional study analyzed data from 700 adults evaluated between January 2025 and March 2026 at Ankara Bilkent City Hospital. The cohort was derived from an initial screen of 5,145 individuals, with exclusions applied for incomplete data, reduced ejection fraction (<50%), prior heart failure, valvular disease, arrhythmias, chronic kidney disease, or inflammatory/autoimmune conditions.

Outcome Definition: Definite LVDD was classified using the 2016 ASE/EACVI integrative algorithm based on a majority rule (>50% of available parameters abnormal) involving septal/lateral e′ velocities, E/e′ ratio, left atrial volume index (LAVI), and tricuspid regurgitation velocity. Participants with indeterminate diastolic function were excluded from the primary analysis to ensure outcome specificity.
Data Collection:
- Hemodynamics: 24-hour ambulatory blood pressure monitoring (ABPM) was used to derive mean pressures, nocturnal dipping, and Diastolic Morning Surge (DMS), defined as the difference between mean DBP in the 2 hours post-awakening and the lowest nighttime DBP.
- Electrocardiography: 12-lead ECGs were analyzed offline for the Tp-e interval (measured in lead V5 or V4/V6) as a marker of ventricular repolarization heterogeneity.
- Laboratory: Neutrophil-to-lymphocyte ratio (NLR) was calculated from differential blood counts.
Statistical Analysis: A prespecified hierarchical logistic regression framework was employed to build three sequential models:
1. Model 1 (Clinical): Age, sex, BMI, diabetes, and smoking status.
2. Model 2 (Clinical + ABPM): Model 1 plus 24-hour mean SBP/DBP and nocturnal systolic dipping.
3. Model 3 (Multidomain): Model 2 plus DMS, Tp-e interval, and NLR.
  Model performance was evaluated using the Area Under the Curve (AUC), Brier score, and calibration slopes. Internal validation was performed via bootstrap resampling (1,000 iterations). Sensitivity analyses addressed the handling of indeterminate cases and the inclusion of systolic morning surge.

Key Results
The final cohort comprised 560 participants with definite normal diastolic function and 140 with definite LVDD.

Baseline Differences: The LVDD group exhibited significantly higher prevalence of diabetes and hypertension history, higher 24-hour systolic and diastolic pressures, attenuated nocturnal dipping, and greater DBP load.
Specific Markers: Compared to the normal group, the LVDD group demonstrated:
- Higher DMS: 15.4 ± 6.5 mmHg vs. 9.5 ± 4.6 mmHg ( $p < 0.001$ ).
- Prolonged Tp-e: 92.5 ± 12.9 ms vs. 80.2 ± 9.5 ms ( $p < 0.001$ ).
- Elevated NLR: 2.69 ± 0.94 vs. 1.96 ± 0.71 ( $p < 0.001$ ).
Model Performance:
- The clinical model (Model 1) yielded an AUC of 0.72.
- Adding ABPM parameters (Model 2) improved the AUC to 0.77.
- The final multidomain model (Model 3), incorporating DMS, Tp-e, and NLR, achieved an AUC of 0.87 (95% CI, 0.83–0.90).
- The addition of the three new markers to Model 2 resulted in a statistically significant improvement ( $\Delta$ AUC = +0.10, $p < 0.001$ ).
- Internal validation confirmed stability with an optimism-corrected AUC of 0.86 and a calibration slope of 0.97.
Interaction Analysis: Exploratory analyses suggested that the associations of Tp-e and NLR with LVDD were stronger in the presence of elevated DMS, indicating a potential context-dependent interaction between hemodynamic stress, electrical heterogeneity, and inflammation.

Significance and Claims
The paper claims that integrating DMS, Tp-e interval, and NLR provides incremental cross-sectional discriminatory information for identifying definite echocardiographic LVDD. The authors posit a multimodal pathophysiological framework where prevalent LVDD is characterized by the convergence of:

Circadian diastolic hemodynamic stress (represented by exaggerated DMS).
Ventricular repolarization heterogeneity (represented by prolonged Tp-e).
Systemic inflammatory activation (represented by elevated NLR).

The study emphasizes that these markers improve model discrimination and accuracy without replacing echocardiography. The authors explicitly state that the model is a framework for the discrimination of prevalent LVDD within the derivation cohort, not a prospective prediction tool for future HFpEF or arrhythmic events. The findings support the hypothesis that early-morning diastolic wall stress, when combined with electrical and inflammatory substrates, identifies a distinct myocardial vulnerability phenotype.

Limitations Acknowledged
The authors note the study's retrospective, cross-sectional nature limits causal inference. Generalizability is constrained by the single-center, tertiary care setting. Furthermore, the study did not assess specific biomarkers of myocardial fibrosis or oxidative stress, and external validation in distinct cohorts is required before the model can be considered transportable. The proposed framework is not intended as a substitute for echocardiography or a validated clinical decision tool.