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

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.