Key result
Biomarker profiling favors a two-class phenotype over a single continuous reserve construct.
Why the study?
It is unclear whether routine biomarkers in acute and chronic cardiorenal settings can be represented by a single physiological reserve score or if distinct biomarker-defined subgroups exist relevant to individualized care.
Does an exploratory two-class biomarker phenotype structure capture patient heterogeneity better than a single continuous physiological reserve score in acute and chronic cardiometabolic patients?
Cross-Sectional (n=571)
No
Does an exploratory two-class biomarker phenotype structure capture patient heterogeneity better than a single continuous physiological reserve score in acute and chronic cardiometabolic patients?
Routine clinical biomarkers in cardiometabolic patients cluster into distinct phenotypic subgroups rather than reflecting a single linear scale of physiological reserve, underscoring the need for phenotype-driven individualized monitoring.
Answers are grounded in this paper and cite their sources.
Challenges single reserve models in cardiometabolic assessment; leaves open validation of two-class phenotypes in prospective studies.
Background: Patient heterogeneity in routine biomarkers is often collapsed into a single physiological reserve score, but whether that one-dimensional representation holds across acute and chronic settings—or whether it obscures biomarker-defined subgroups relevant to individualized care—is unclear. Methods: We harmonized routine biomarkers (BMI, blood pressure, eGFR, LDL, total cholesterol, and HbA1c) across two cohorts from the same health system: an acute cardiorenal syndrome cohort and a chronic outpatient cohort treated with dapagliflozin and/or semaglutide, excluding 15 patients common to both. We tested whether these biomarkers supported a one-factor structure via exploratory factor analysis, then a categorical alternative via Gaussian mixture modeling, assessed six-month class persistence in the chronic cohort, and applied the derived classes to the acute cohort for descriptive independent cohort application. Results: A one-factor representation was not supported (KMO = 0.485; covariance concentrated in two definitionally related variable pairs). Gaussian mixture modeling favored a two-class solution, distinguished mainly by LDL, total cholesterol, HbA1c, and diastolic blood pressure. Class assignment showed moderate six-month agreement (κ = 0.46) in a subgroup enriched for closer monitoring. In the acute cohort, class structure was not clearly associated with age, BNP, ejection fraction, length of stay, CKD stage, HF phenotype, NYHA class, or comorbidities; associations with outcomes were descriptive, not causal. Conclusions: Rather than reducing to a single reserve score, these biomarkers were better described by an exploratory two-class, biomarker-defined phenotype structure that captured observable patient heterogeneity, showed moderate short-term persistence, and was not clearly related to acute illness severity. These findings are specific to the biomarkers and cohorts studied and should not be generalized without independent replication.
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AlTaweel et al. (2026) conducted a cross-sectional in Acute cardiorenal syndrome and chronic outpatient cardiometabolic disease (n=571). Two-class biomarker-defined phenotype structure vs. Single continuous physiological reserve score (one-factor structure) was evaluated on Latent dimensionality of routine biomarkers via exploratory factor analysis and Gaussian mixture modeling (KMO = 0.485 (one-factor not supported); GMM favored a two-class solution (BIC = 7569.7)). Cross-sectional analysis of routine biomarkers did not support a single continuous physiological reserve construct (KMO = 0.485), instead favoring a two-class exploratory phenotype structure driven mainly by lipid-glycemic and diastolic blood pressure patterns.
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