Longevity & AgingArticle de rechercheAccès payant

A Precision Framework Uses Biomarkers to Target Heart, Kidney and Metabolic Drugs

A new three-dimensional biomarker framework aims to match the right drugs to CKM syndrome patients, similar to companion diagnostics in oncology.

jeudi 1 octobre 2026 1 vue
Publié dans Pharmacol Ther
Molecular diagram of interconnected heart, kidney, and metabolic pathways with glowing biomarker nodes and drug molecule icons overlaid.

Résumé

Cardiovascular-kidney-metabolic (CKM) syndrome affects roughly 90% of US adults at stage 1 or higher, yet choosing among SGLT2 inhibitors, GLP-1 agonists, finerenone, and newer biologics remains complex. This 2026 review proposes a three-dimensional biomarker-guided framework to match drugs to patients more precisely. The first dimension maps organ-specific biomarkers to drug targets. The second identifies cross-system inflammatory and metabolic biomarkers like hs-CRP, IL-6, galectin-3, GDF-15, and FGF21 to shared druggable pathways. The third uses serial biomarker trajectories as real-time pharmacodynamic readouts to separate drug effects from disease progression. The authors also introduce a drug-biomarker interaction matrix and survey an emerging pipeline including RNA-based Lp(a) therapies, FGF21 analogues, and anti-fibrotic CAR-T approaches.

Résumé détaillé

Cardiovascular-kidney-metabolic syndrome was formally defined by the American Heart Association in 2023, yet it already touches nearly nine in ten American adults at measurable stages. Managing it requires navigating an expanding arsenal of drug classes whose mechanisms overlap across the heart, kidneys, and metabolic tissues — a complexity that demands smarter decision tools.

This comprehensive review from researchers at Sun Yat-sen University, Zhejiang University, Shanxi Medical University, and Temple University proposes a three-dimensional biomarker framework for precision pharmacotherapy. Rather than treating biomarkers as passive diagnostic flags, the authors position them as active guides for drug selection, mechanistic interpretation, and therapeutic monitoring.

The organ-specific dimension clarifies molecular mechanisms: SGLT2 inhibitors shift myocardial fuel use toward ketone bodies and may inhibit NHE1, explaining cardioprotection; sacubitril/valsartan's neprilysin selectivity accounts for differential natriuretic peptide responses; and tubuloglomerular feedback underlies SGLT2 inhibitors' renoprotective hemodynamic effects. The pathway-specific dimension identifies cross-organ biomarkers — hs-CRP, IL-6, galectin-3, GDF-15, FGF21 — that point to shared targets including the NLRP3 inflammasome axis (canakinumab, colchicine), IL-6 trans-signaling (ziltivekimab), and FGF21/β-klotho metabolic pathways. The temporal dimension shows how serial biomarker changes, such as the transient eGFR dip after SGLT2 inhibitor initiation or natriuretic peptide shifts during combination therapy, distinguish expected pharmacodynamic effects from true disease worsening.

The authors coin the term 'pharmacological phenotyping' — using multi-biomarker panels to define drug-responsive physiological states analogous to oncology companion diagnostics. They pair this with a drug-biomarker interaction matrix and highlight pipeline agents including RNA-based Lp(a) therapies, FGF21 analogues, galectin-3 inhibitors, and in vivo CAR-T anti-fibrotic strategies.

As a narrative review relying primarily on existing literature and preclinical data, prospective validation of this framework in clinical populations will be essential before widespread adoption.

Principales conclusions

  • A three-dimensional biomarker framework (organ-specific, pathway-specific, temporal) guides drug selection and monitoring in CKM syndrome.
  • SGLT2 inhibitors protect the heart via ketone body metabolism and possible NHE1 inhibition, not solely glucose lowering.
  • Cross-system biomarkers like hs-CRP, IL-6, galectin-3, and FGF21 map to shared druggable pathways across heart, kidney, and metabolism.
  • Serial biomarker trajectories can distinguish intended pharmacodynamic drug effects from underlying disease progression.
  • An emerging pipeline includes RNA-based Lp(a) therapies, FGF21 analogues, and anti-fibrotic in vivo CAR-T approaches.

Méthodologie

This is a narrative review synthesizing published clinical trial data, mechanistic studies, and preclinical evidence. The authors construct a conceptual three-dimensional framework and a drug-biomarker interaction matrix rather than conducting original experimental or meta-analytic research. No patient-level data were generated.

Limites de l'étude

As a review-based conceptual framework, no prospective validation cohort is presented, limiting direct applicability. Much of the mechanistic evidence for specific drug-biomarker interactions (e.g., NHE1 inhibition by SGLT2 inhibitors) remains preclinical. The 'pharmacological phenotyping' approach requires multi-biomarker panel standardization before clinical implementation.

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