# The optimal loop diuretic: mechanistic insights from longitudinal changes in blood and urine proteins to explain efficacy and safety of torsemide vs furosemide after a heart failure hospitalization

> **NIH NIH R01** · INOVA HEALTH CARE SERVICES · 2020 · $418,800

## Abstract

Loop diuretics including furosemide and torsemide are among the most commonly used drugs for heart failure
(HF) and remain the foundation of therapy for these patients, but it remains uncertain if one loop diuretic
should be used preferentially. The manner by which torsemide and furosemide may differentially affect
outcomes for patients with HF remains undetermined, and whether the effects are homogenous across
important subgroups including gender, race, and ejection fraction (EF) is unknown. The NIH-funded pragmatic
TRANFORM-HF trial is studying whether torsemide is associated with reduced mortality and hospitalizations
and improved quality of life compared to furosemide, but contains no mechanistic aims. This 750-patient
mechanistic ancillary study is designed to fill a critical knowledge gap, complementing the clinical findings of
TRANSFORM-HF by potentially augmenting uptake of the study findings by providing mechanistic plausibility
to support the outcome results. Serial blood and urine specimens will be to collected at baseline and 90-days
and then longitudinal targeted discovery proteomics along with biomarkers with known prognostic and
mechanistic roles will be used to elucidate the unique systems biology underlying the potential differential
effects of the two loop diuretics studied in the trial. Longitudinal proteomic measurements within blood and
urine will provide the opportunity to simultaneously asses multiple similarities and differences of the two
diuretics on cardiac, renal and systemic pathophysiology. Recent advances in proteomic technology have
overcome prior limitations of mechanistic studies embedded within clinical trials that were limited by a small
portfolio of immunoassays, by now including precise repeated measures of 100 or more proteins which can be
clustered according to biological roles. Our prospective pilot data utilizing these hybrid ELISA-oligonucleotide
proximal extension assays to simultaneously measure 184 proteins suggests that many differences in
inflammation and fibrosis mediating protein levels are present between patients using torsemide vs
furosemide. The aims of this appropriately powered study based on our pilot data will describe how the
trajectory of proteins and biomarkers clustered to multiple biologic roles are influenced by diuretic strategy in
the entire ancillary study population and important subgroups including gender, race, and baseline EF. This
study will also determine the trajectory of renal function decline post HF hospitalization, estimate the effect of
diuretic strategy on renal function and determine the association of renal function decline with urinary
biomarker evidence of tubular injury. In aggregate, the focused mechanistic insights obtained from this
ancillary study will ultimately allow clinicians to better understand the physiologic implications of loop diuretic
use in the contemporary polydrug management of HF and assimilate the potential clinical implications
identified by ...

## Key facts

- **NIH application ID:** 10074852
- **Project number:** 1R01HL154768-01
- **Recipient organization:** INOVA HEALTH CARE SERVICES
- **Principal Investigator:** Lauren Beth Cooper
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $418,800
- **Award type:** 1
- **Project period:** 2020-09-01 → 2024-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10074852

## Citation

> US National Institutes of Health, RePORTER application 10074852, The optimal loop diuretic: mechanistic insights from longitudinal changes in blood and urine proteins to explain efficacy and safety of torsemide vs furosemide after a heart failure hospitalization (1R01HL154768-01). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10074852. Licensed CC0.

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