What is the clinical evidence from this study?

Study design: Case-Control. Population: Hypertrophic Cardiomyopathy (HCM) (n=147). Intervention: Plasma proteomics profiling (17-protein model) vs. No late gadolinium enhancement (LGE). Primary outcome: Discriminative ability (AUC) of the 17-protein model to detect LGE in the independent test set (AUC 0.71, 95% CI 0.54-0.88, p=0.03).

June 17, 2022Open Access

Comprehensive Proteomics Profiling Identifies Patients With Late Gadolinium Enhancement on Cardiac Magnetic Resonance Imaging in the Hypertrophic Cardiomyopathy Population

Q: What does this research mean for the field?

A 17-protein plasma proteomics model can successfully distinguish hypertrophic cardiomyopathy patients with and without late gadolinium enhancement, achieving an AUC of 0.71 in independent validation. Novelty: ClaimNovelty.NOVEL_FINDING. Consensus alignment: ConsensusAlignment.NEUTRAL.

BLBradley LanderUniversity Hospitals of Cleveland YZYanling ZhaoChinese PLA General Hospital KHKohei HasegawaBoston University

Key Result

A 17-protein plasma proteomics model successfully distinguished hypertrophic cardiomyopathy patients with and without late gadolinium enhancement, achieving an AUC of 0.71 in an independent test set.

Study Design

Type

Case-Control (n=147)

Blinding

Single-blind (CMR readers blinded to proteomics results)

Multicenter

Yes

Structured PICO

Does plasma proteomics profiling distinguish patients with and without late gadolinium enhancement on cardiac magnetic resonance imaging in the hypertrophic cardiomyopathy population?

Population

147 patients with hypertrophic cardiomyopathy (HCM)

Intervention

Plasma proteomics profiling (17-protein discrimination model)

Outcome

Discriminative ability (AUC, sensitivity, specificity) to distinguish patients with and without late gadolinium enhancement (LGE) on cardiac magnetic resonance imaging (CMR)surrogate

A 17-protein plasma biomarker panel demonstrates good discriminative ability to identify hypertrophic cardiomyopathy patients with late gadolinium enhancement on CMR, potentially guiding imaging utilization.

Main Result

Effect estimate: AUC 0.71 (95% CI 0.54-0.88)

p-value: p=0.03

Limitations

LGE was a binary variable and quantification was not performed to identify the extent of LGE
No association with subsequent clinical outcomes (e.g., sudden cardiac death) was evaluated
The study sample consisted of patients enrolled at tertiary care centers who underwent CMR, limiting generalizability
Temporality or causality between differentially regulated pathways and LGE in HCM was not assessed
Not all patients with HCM underwent genetic testing
Myocardial samples were not available for direct analysis with tissue specimens
The number of patients included in the test set was relatively small and the negative predictive value was modest
Possibility of false positive discovery remains despite statistical measures

Abstract

Introduction In hypertrophic cardiomyopathy (HCM), late gadolinium enhancement (LGE) on cardiac magnetic resonance imaging (CMR) represents myocardial fibrosis and is associated with sudden cardiac death. However, CMR requires particular expertise and is expensive and time-consuming. Therefore, it is important to specify patients with a high pre-test probability of having LGE as the utility of CMR is higher in such cases. The objective was to determine whether plasma proteomics profiling can distinguish patients with and without LGE on CMR in the HCM population. Materials and Methods We performed a multicenter case-control (LGE vs. no LGE) study of 147 patients with HCM. We performed plasma proteomics profiling of 4,979 proteins. Using the 17 most discriminant proteins, we performed logistic regression analysis with elastic net regularization to develop a discrimination model with data from one institution (the training set; n = 111) and tested the discriminative ability in independent samples from the other institution (the test set; n = 36). We calculated the area under the receiver-operating-characteristic curve (AUC), sensitivity, and specificity. Results Overall, 82 of the 147 patients (56%) had LGE on CMR. The AUC of the 17-protein model was 0.83 (95% confidence interval CI, 0.75–0.90) in the training set and 0.71 in the independent test set for validation (95% CI, 0.54–0.88). The sensitivity of the training model was 0.72 (95% CI, 0.61–0.83) and the specificity was 0.78 (95% CI, 0.66–0.90). The sensitivity was 0.71 (95% CI, 0.49–0.92) and the specificity was 0.74 (95% CI, 0.54–0.93) in the test set. Based on the discrimination model derived from the training set, patients in the test set who had high probability of having LGE had a significantly higher odds of having LGE compared to those who had low probability (odds ratio 29.6; 95% CI, 1.6–948.5; p = 0.03). Conclusions In this multi-center case-control study of patients with HCM, comprehensive proteomics profiling of 4,979 proteins demonstrated a high discriminative ability to distinguish patients with and without LGE. By identifying patients with a high pretest probability of having LGE, the present study serves as the first step to establishing a panel of circulating protein biomarkers to better inform clinical decisions regarding CMR utilization.

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