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May 27, 2026Cureus0 citationsOpen Access

The Ateq Protocol: A Novel Mathematical Model for Predicting ECG Voltage and Detecting Early Metabolic Hypertension

AAAmany H Ateq

Key Result

The Ateq Gap significantly predicted metabolic-mediated cardiac changes with an OR of 1.659 per 1 mm increase, achieving 74% sensitivity compared to 26% for traditional blood pressure monitoring.

Study Design

Type

Cross-Sectional (n=680)

Multicenter

Yes

Structured PICO

Does the Ateq Equation integrating fasting proinsulin and SBP improve the prediction of ECG voltage and detection of early metabolic hypertension compared to SBP alone in young adults?

P
Population
Young adults with essential hypertension and a metabolic phenotype defined by hyperinsulinemia and a Sokolow-Lyon Index > 35 mm, from harmonized population data
I
Intervention
Ateq Equation / Ateq Gap (a novel mathematical model integrating fasting proinsulin and systolic blood pressure)
C
Comparator
Systolic blood pressure (SBP) alone
O
Outcome
Prediction of ECG voltage and diagnostic accuracy for detecting early structural changessurrogate

Fasting proinsulin is a stronger predictor of increased ECG voltage than systolic blood pressure in young adults, suggesting early structural cardiac changes may be driven by underlying metabolic dysfunction rather than hemodynamics alone.

Main Result

Effect estimate: OR 1.659

p-value: p=<0.001

Limitations

  • Cross-sectional design
  • Need for future longitudinal studies to determine if correcting the Ateq Gap directly reverses ECG changes

Abstract

Background: The diagnosis of "essential hypertension" in young adults often masks underlying metabolic dysfunctions. Traditional blood pressure monitoring frequently fails to explain early structural cardiac changes. This study aims to isolate a distinct "metabolic hypertension" phenotype driven by proinsulin-mediated pathways, utilizing a novel predictive model to assess the "hormonal-hemodynamic-voltage axis." Materials and methods: We conducted a retrospective cross-sectional analysis using harmonized population data. A specific metabolic phenotype was defined by hyperinsulinemia and a Sokolow-Lyon Index > 35 mm. We utilized linear regression to develop the Ateq Equation, integrating fasting proinsulin and systolic blood pressure (SBP) as primary predictors. Diagnostic accuracy was evaluated using receiver operating characteristic (ROC) curve analysis and the assessment of standardized beta coefficients to determine the relative impact of metabolic versus mechanical stressors. Results: The final model confirmed that proinsulin is a superior predictor of ECG voltage compared to SBP alone (p < 0.001). Standardized coefficients revealed that proinsulin exerts a significantly stronger influence on cardiac voltage (β = 0.690) than SBP (β = 0.173). Furthermore, proinsulin demonstrated a powerful correlation with SBP (R = 0.912, R2 = 0.832), identifying it as a primary driver of blood pressure elevation. The Ateq Gap demonstrated strong diagnostic power (area under the curve (AUC) = 0.766). Using a cut-off of 2.5 mm, the criteria achieved a sensitivity of 74% and specificity of 71% in detecting early structural changes unexplained by hemodynamics alone. Conclusion: Hyperproinsulinemia is the primary independent predictor of increased ECG voltage and elevated SBP in young patients, suggesting that hypertension is a hemodynamic symptom of an underlying metabolic disorder. The Ateq Gap provides a quantifiable metric to identify this phenotype. These findings provide the foundational logic for the Ateq Chip, a proposed biosensor for real-time monitoring of proinsulin-driven cardiac risks, enabling intervention years before overt clinical complications.

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Cite This Study

Amany H Ateq (2026) conducted a cross-sectional in Metabolic hypertension (n=680). Ateq Protocol (Ateq Gap) vs. Traditional sphygmomanometry was evaluated on Risk of metabolic-mediated cardiac changes per 1 mm increase in the Ateq Gap (OR 1.659, p=<0.001). The Ateq Gap significantly predicted metabolic-mediated cardiac changes with an OR of 1.659 per 1 mm increase, achieving 74% sensitivity compared to 26% for traditional blood pressure monitoring.

synapsesocial.com/papers/6a19091b0666c170ed4f7053https://doi.org/10.7759/cureus.109720
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