Introduction
Heart failure (HF) remains a leading cause of morbidity and mortality worldwide, affecting millions and imposing a substantial burden on healthcare systems. While overt HF is characterized by debilitating symptoms such as shortness of breath, fatigue, and fluid retention, subclinical heart failure—also known as stage B HF—represents a preclinical phase where structural or functional cardiac abnormalities exist without overt clinical manifestations. This silent progression underscores the critical need for early detection and risk stratification to enable preventive interventions, such as lifestyle modifications, pharmacotherapy, or enrollment in clinical trials.
Biomarker-based risk scoring has emerged as a promising tool in this domain. By leveraging measurable biological indicators in blood or other fluids, these scores quantify the likelihood of progressing from subclinical dysfunction to symptomatic HF. This approach integrates multiple biomarkers reflecting myocardial stress, injury, inflammation, and fibrosis, offering a more nuanced risk assessment than traditional clinical factors alone. Recent studies, particularly in high-risk populations like those with prediabetes, diabetes, or atherothrombotic disease, demonstrate that such scores can identify individuals warranting targeted therapies, potentially averting thousands of HF events.
Understanding Subclinical Heart Failure
Subclinical HF encompasses detectable cardiac remodeling—such as left ventricular hypertrophy (LVH), diastolic dysfunction, or reduced global longitudinal strain (GLS)—in the absence of symptoms. Echocardiography often reveals these changes, including elevated E/e’ ratios (indicating diastolic stiffness), impaired e’ velocity, or increased left atrial volume index (LAVi). In community-based cohorts, subclinical left ventricular dysfunction (LVD) affects up to 20-30% of adults over 65, with progression rates to clinical HF estimated at 1-2% per year.
The pathophysiology involves chronic hemodynamic stress, inflammation, and fibrosis, often exacerbated by comorbidities like type 2 diabetes mellitus (T2DM) or hypertension. Early identification is vital, as interventions like sodium-glucose cotransporter-2 inhibitors (SGLT-2i) can halt progression. However, routine echocardiography is resource-intensive, making non-invasive biomarkers essential for scalable screening.
Key Biomarkers in Subclinical Heart Failure
Biomarkers for subclinical HF are categorized by their pathophysiological roles: myocardial stretch (natriuretic peptides), injury (troponins), inflammation (C-reactive protein), and structural changes (electrocardiographic markers). These are detectable via high-sensitivity assays, enabling low-threshold identification of risk.
- N-terminal pro-B-type natriuretic peptide (NT-proBNP): Released in response to ventricular wall stress, elevated levels (>125 pg/mL) signal subclinical hemodynamic overload. It is the most established marker, with prognostic value for incident HF in asymptomatic individuals.
- High-sensitivity cardiac troponin T (hs-cTnT): Indicates myocyte injury, even at subclinical levels (≥6 ng/L). Chronically elevated hs-cTnT reflects ongoing microvascular ischemia or fibrosis, predicting HF hospitalization (HHF) independently of other factors.
- High-sensitivity C-reactive protein (hs-CRP): A marker of systemic inflammation (≥3 mg/L), linking metabolic disorders like T2DM to cardiac remodeling. It correlates with endothelial dysfunction and oxidative stress in early HF stages.
- Electrocardiographic left ventricular hypertrophy (ECG-LVH): Detected via Sokolow-Lyon criteria, this non-invasive marker of structural remodeling adds value to biochemical scores.
Emerging biomarkers include growth differentiation factor-15 (GDF-15) for fibrosis, soluble ST2 (sST2) for remodeling, and galectin-3 for extracellular matrix turnover, though their integration into scores is nascent. In general populations, these novel markers of inflammation and fibrosis predict incident HF with hazard ratios up to 2-3 fold.
Development of Biomarker-Based Risk Scores
Risk scores combine these biomarkers with clinical variables to generate a composite probability of HF progression. Construction typically involves Cox proportional hazards models from large cohorts, assigning points based on hazard ratios or simple counts for ease of use.
One prominent example is the biomarker score from the Women’s Health Initiative and Multi-Ethnic Study of Atherosclerosis (MESA), targeting prediabetes and T2DM patients without cardiovascular disease (CVD). It assigns 1 point each for abnormal hs-cTnT, NT-proBNP, hs-CRP, or ECG-LVH, yielding a 0-4 range. Scores of 0-1 indicate very low risk (5-year HF incidence ~0.5-1.3%, akin to euglycemic individuals), while ≥3 denotes high risk (up to 12% in T2DM). This score achieved C-indices of 0.70-0.75 for 5- and 10-year HF prediction, outperforming clinical models alone.
In T2DM-specific contexts, the ARIC-HF score incorporates age, race, smoking, BMI, diabetes duration, and prior HF, augmented by NT-proBNP and hs-cTnT. Similarly, the WATCH-DM score emphasizes T2DM complications like albuminuria. A dedicated HHF score for T2DM uses NT-proBNP, hs-cTnT, and prior HF history, with points weighted by their predictive strength (e.g., NT-proBNP >300 pg/mL = 2 points). Validated in cohorts like SAVOR-TIMI 53 and EXAMINE, it yielded C-statistics of 0.75-0.80 and net reclassification improvements (NRI) of 10-15% over clinical scores.
For broader atherothrombotic populations, multivariable models integrating NT-proBNP, hs-cTnT, and hs-CRP predict HHF with hazard ratios of 1.5-2.0 per standard deviation increase, independent of ejection fraction.
| Biomarker/Score | Components | Target Population | Performance (C-index) | Key Reference |
|---|---|---|---|---|
| WHI/MESA Biomarker Score | hs-cTnT, NT-proBNP, hs-CRP, ECG-LVH (1 pt each) | Pre-DM/T2DM without CVD | 0.70-0.75 (5-10 yr HF) | |
| ARIC-HF Score | Age, race, smoking, BMI, DM duration + NT-proBNP, hs-cTnT | General/T2DM | 0.65-0.70 (incident HF) | |
| WATCH-DM Score | Age, BMI, albuminuria, prior MI + biomarkers | T2DM | 0.68 (HHF) | |
| HHF Score for T2DM | NT-proBNP, hs-cTnT, prior HF | T2DM | 0.75-0.80 (HHF) |
Clinical Applications and Evidence
These scores excel in risk stratification, particularly in primary care or diabetes clinics. In prediabetes/T2DM cohorts, high-score individuals (<10% of the population) account for 30-40% of HF events, guiding selective SGLT-2i use—potentially preventing 44 events per 1,000 treated over 5 years versus 11 population-wide. In stable CVD patients, elevated biomarkers independently predict HHF, supporting serial monitoring.
However, evidence for subclinical LVD detection is mixed. In a T2DM cohort (n=804), NT-proBNP and hs-cTnT associated with GLS abnormalities (β=0.26-0.32 per SD, p<0.001), but AUCs were low (0.54-0.67) for diastolic or hypertrophy markers, with specificities <50% at 90% sensitivity. Thus, scores aid broad screening but falter in pinpointing echocardiography candidates.
In HF trials, biomarkers enrich enrollment: FDA guidance endorses NT-proBNP for risk enrichment, improving event rates by 20-50%. Multi-biomarker panels (e.g., NT-proBNP + sST2 + GDF-15) further enhance NRI by 20-30% for prognosis.
Challenges and Limitations
Despite promise, challenges persist. Biomarker elevations can stem from non-cardiac causes (e.g., renal dysfunction inflating NT-proBNP), necessitating adjustment for confounders. Assay variability and cost limit accessibility in low-resource settings. Discrimination wanes in low-prevalence subclinical stages, as seen in poor AUCs for LVD. Additionally, scores like ARIC-HF underperform in diverse ethnic groups due to derivation biases.
Longitudinal validation remains sparse; most data derive from observational cohorts, with few randomized trials testing score-guided interventions.
Future Directions
Advancements in multi-omics—integrating genetic scores (e.g., polygenic risk for HF) with proteins—could refine predictions. Machine learning models combining biomarkers, imaging, and wearables may boost AUCs to >0.80. Trials like EMPEROR-Preserved explore biomarker-enriched designs for subclinical cohorts. Ultimately, point-of-care assays and AI-driven scores could democratize risk assessment, shifting HF management toward prevention.
Conclusion
Biomarker-based risk scoring represents a paradigm shift in tackling subclinical heart failure, empowering precise, proactive care. By harnessing NT-proBNP, hs-cTnT, and companions, these tools stratify risk effectively in vulnerable groups, paving the way for therapies that interrupt silent progression. As research evolves, integrating these scores into guidelines will be key to curbing the HF epidemic, underscoring that in cardiology, foresight is the ultimate lifesaver.
