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Phenotyping and Validating Central Sensitization in Painful and Non-Painful Benign Urologic Disease Using Machine Learning

Award Number: R15AT014052
ORGANIZATION: CENTER FOR DRUG EVALUATION AND RESEARCH, NIH
OPDIV: NIH
AWARD CLASS: DISCRETIONARY
AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)
PERIOD OF PERFORMANCE START DATE: 08/17/2026
PERIOD OF PERFORMANCE END DATE: 07/31/2029

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Phenotyping and Validating Central Sensitization in Painful and Non-Painful Benign Urologic Disease Using Machine Learning - Interstitial cystitis/bladder pain syndrome (IC/BPS) and overactive bladder (OAB) are prevalent, debilitating urologic disorders marked by lower urinary tract symptoms—including pain, urgency, and frequency—but lack mechanistically informed treatments. Evidence implicates central sensitization (CS)—heightened central nervous system responsiveness—in persistent urologic symptoms, yet no validated CS phenotype exists. We aim to extend the putative CS framework by employing mechanism-based phenotyping to inform precision interventions for urologic disorders. The project uses secondary analyses of existing NIH-R01-funded datasets collected at Vanderbilt University Medical Center (VUMC) to clarify the role of CS in urinary hypersensitivity, and establishes a sustainable undergraduate-centered collaborative research infrastructure at Fairleigh Dickinson University (FDU). Aim 1: Apply machine learning to existing datasets to identify a CS phenotype (or dimension) of urologic patients using quantitative sensory testing (QST). We will apply latent growth mixture modeling to ten-trial pain ratings from a thermal QST paradigm across OAB and IC/BPS participants (N ≈ 500) to identify a CS subgroup via positive temporal summation of pain (TSP) slopes. Aim 2: Harness interdisciplinary expertise across research sites to characterize CS through objective, ecological, and treatment data. We will characterize the CS phenotype/dimension with objective (Sensation Meter), ecological (daily surveys), and treatment response data. H2a: CS phenotype/dimension will predict bladder sensation meter metrics. H2b: In OAB, CS will predict average and variability in momentary lower urinary symptoms and anxiety/stress. H2c: In IC/BPS, baseline CS will enhance benefit of cognitive behavioral therapy (compared to physical therapy) for urinary and psychological outcomes. Aim 3: Establish a sustainable, interdisciplinary pain-science environment and professional research pipeline advancing research skills and grant-funded, biomedical science engagement for students at FDU. Paid interns will be trained with emphasis on education, immersion, and dissemination of research . Faculty and graduate mentors at FDU and VUMC will facilitate research training. Impact. This project will yield the first QST-based CS phenotype in urologic disorders, characterize CS in objective and real-world cases, clarify treatment moderators with precision medicine implications, and expand FDU’s undergraduate translational research ecosystem.


Issue Date FY	Funding FY	Legal Entity Name	Legal Entity Address	Legal Entity City	Legal Entity State	Legal Entity Zip Code	Legal Entity COUNTY	Legal Entity COUNTRY	Assistance Listing	Award Code	Budget Year	Action Date	Action Type	Action Amount

Issue Date FY: 2026 ( Subtotal = $560,491 )
2026	2026	FAIRLEIGH DICKINSON UNIVERSITY	1000 RIVER RD	TEANECK	NJ	07666	BERGEN	USA	The National Institute of Neurological Disorders and Stroke (NINDS) Helping to End Addiction Long-term® (HEAL) Initiative	000	1	8/18/2026	NEW	$560,491
														Subtotal = $560,491

Grand Total All Awards = $560,491

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Phenotyping and Validating Central Sensitization in Painful and Non-Painful Benign Urologic Disease Using Machine Learning

Award Number: R15AT014052

ORGANIZATION: CENTER FOR DRUG EVALUATION AND RESEARCH, NIH

OPDIV: NIH

AWARD CLASS: DISCRETIONARY

AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)

PERIOD OF PERFORMANCE START DATE: 08/17/2026

PERIOD OF PERFORMANCE END DATE: 07/31/2029

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