Human myocardial tissue samples were obtained from seven patients with HCM who underwent surgical myectomy at Guangdong Provincial People’s Hospital. The diagnosis of HCM was established according to contemporary guideline criteria based on comprehensive clinical evaluation, including echocardiography and/or cardiac magnetic resonance imaging. Control myocardial samples were obtained from five healthy organ donors with no history of cardiovascular disease, confirmed through standard donor screening. Samples were collected between 2018 and 2020. This retrospective study analyzed frozen myocardial tissue specimens. This is a cross-sectional study, as all samples were collected at a single time point without longitudinal follow-up. Among the seven HCM patients, five were male and two were female, with an age range of 9–52 years. Due to the anonymous nature of organ donor protocols and confidentiality agreements, detailed demographic information (including age and sex) for the five healthy control donors was not available for disclosure. All samples were collected with written informed consent from participants or their legal guardians. The study protocol was approved by the Ethics Committee of Guangdong Provincial People’s Hospital (approval No. GDREC2016255H) and conducted in accordance with the Declaration of Helsinki. Immediately after excision, myocardial specimens were rinsed in cold saline, snap frozen in liquid nitrogen, and stored at −80°C until proteomic and metabolomic analysis. For each case, adjacent tissue fragments from the same sample block were used in parallel for protein and metabolite extraction to ensure matched multi-omics profiling.

For proteomic and metabolomic analyses, adjacent pieces of frozen myocardial tissue (~50 mg each) from the same HCM and control samples were processed in parallel to enable matched multi-omics profiling. Samples were kept on dry ice throughout preparation.

For proteomics, myocardial tissue was lysed in urea buffer with protease inhibitors, and protein concentrations were determined by bicinchoninic acid assay. Equal amounts of protein from each sample were reduced, alkylated, and trypsin-digested, and the resulting peptides were desalted and quantified prior to Liquid Chromatography-Tandem Mass Spectrometry (LC–MS/MS) analysis.

For metabolomics, aliquots of the same tissue samples were extracted with cold methanol/water containing 0.02 mg/mL of internal standard (L-2-chlorophenylalanine, CAS: 10389-97-6, exact molecular weight: 199.04 Da), and the supernatants were used for LC–MS/MS profiling. A pooled quality control (QC) sample, prepared by mixing equal volumes of all extracts, was analyzed at regular intervals to monitor analytical stability.

Tryptic peptides were reconstituted in 0.1% formic acid in 2% acetonitrile containing indexed retention time (iRT) standards and analyzed by an EASY nLC system (Thermo, USA) coupled to a Trapped Ion Mobility Spectrometry Time-Of-Flight (timsTOF) Pro2 mass spectrometer (Bruker, Germany) in data independent acquisition (DIA PASEF) mode at Majorbio Bio Pharm Technology Co. Ltd. (Shanghai, China). Briefly, a C18 reversed-phase column (75 μm × 25 cm, IonOpticks, USA) was equilibrated with solvent A (2% acetonitrile with 0.1% formic acid) and solvent B (80% acetonitrile with 0.1% formic acid). The peptides were eluted using the following gradient at a flow rate of 250 nL/min: 0–45 min, 3%–28% B; 45–50 min, 28%–44% B; 50–55 min, 44%–90% B; followed by a washing and re-equilibration step. DIA data were acquired over an m/z range of 400–1200 with 64 isolation windows using standard timsTOF Pro2 DIA PASEF settings.

Raw DIA PASEF data were processed with Spectronaut (version 14, Biognosys INC. Switzerland). Retention times were calibrated using iRT peptides. Protein identification and quantification were performed against the human UniProt database with a peptide and protein false discovery rate (FDR) ≤ 1%. Only proteins with at least one unique peptide were retained. Label-free quantitative intensities were log2 transformed and normalized. Differentially expressed proteins (DEPs) between HCM and control groups were defined using |fold change (FC)| > 1.0 and p < 0.05.

The LC-MS/MS analysis was conducted on a Thermo UHPLC-Q Exactive HF-X system equipped with an ACQUITY HSS T3 column (100 mm × 2.1 mm i.d., 1.8 μm; Waters, USA) at Majorbio Bio-Pharm Technology Co. Ltd. (Shanghai, China). The mobile phases consisted of 0.1% formic acid in water: acetonitrile (95:5, v/v) (solvent A) and 0.1% formic acid in acetonitrile:isopropanol:water (47.5:47.5:5, v/v/v) (solvent B). The flow rate was 0.40 mL/min, and the column temperature was 40°C. Positive ion mode gradient: 0–3 min, B increased from 0% to 20%; 3–4.5 min, B increased from 20% to 35%; 4.5–5 min, B increased from 35% to 100%; 5–6.3 min, B held at 100%; 6.3–6.4 min, B decreased from 100% to 0%; 6.4–8 min, B held at 0%. Negative ion mode gradient: 0–1.5 min, B increased from 0% to 5%; 1.5–2 min, B increased from 5% to 10%; 2–4.5 min, B increased from 10% to 30%; 4.5–5 min, B increased from 30% to 100%; 5–6.3 min, B held at 100%; 6.3–6.4 min, B decreased from 100% to 0%; 6.4–8 min, B held at 0%.

Raw LC–MS data were processed using Progenesis QI (Waters Corporation) for peak detection, alignment, and intensity extraction. Metabolites were annotated by matching accurate mass, retention time, and MS/MS spectra against the Human Metabolome Database. Data preprocessing on the Majorbio Cloud platform (China, https://www.majorbio.com/tools) included filtering of features detected in <80% of samples, normalization, QC-based filtering, and log10 transformation [6]. Principal Component Analysis (PCA) and Orthogonal Partial Least Squares Discrimination Analysis (OPLS-DA) were performed using the ropls package in R, and differential metabolites between HCM and control groups were defined as those with variable importance in projection (VIP) > 1 and p < 0.05 (Student’s t-test).

Functional enrichment analyses were performed to characterize the biological processes and pathways associated with the observed proteomic and metabolomic changes. For proteomic data, DEPs were subjected to Gene Ontology (GO) enrichment (biological process, cellular component, and molecular function) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis using the clusterProfiler package in R [7]. For metabolomic data, differential metabolites were mapped to KEGG pathways and tested for over representation using the KEGG database (http://www.genome.jp/kegg/) [8]. Enriched GO terms and KEGG pathways with p < 0.05 were considered significant and were visualized as bar plots or bubble plots. The Pathview package in R was used to project DEPs and metabolites onto the KEGG pathway [9].

All statistical analyses were performed in R (version 4.3.0). Differential proteins and metabolites were identified using standard univariate tests combined with multiple significance threshold p < 0.05. For multivariate analysis, orthogonal partial least squares discriminant analysis (OPLS-DA) and partial least squares discriminant analysis (PLS-DA) were performed using the ropls package in R to visualize group separation and identify variables contributing to discrimination. Model performance was evaluated by R² and Q² values, with 200-fold permutation tests to avoid overfitting.

Using a threshold of |log2FC| > 1 and p < 0.05 (false discovery rate was not applied to the p-values, as this exploratory study prioritized sensitivity to detect potential protein changes), a total of 165 DEPs were identified between HCM and control myocardial tissue, including 100 upregulated and 65 downregulated proteins (Fig. 1A; detailed in Supplementary Table S1). Hierarchical clustering heatmaps (Fig. 1B) and OPLS-DA (Fig. 1C), together with PLS-DA (Fig. 1D), showed clear separation between groups, indicating a distinct proteomic signature in HCM.

GO enrichment analysis revealed that these proteins were predominantly localized to mitochondrial structures. The top enriched cellular component terms included “mitochondrial part”, “mitochondrial membrane”, and “mitochondrial inner membrane”, underscoring extensive remodeling of mitochondrial compartments (Fig. 2A). Molecular function analysis highlighted NADH dehydrogenase- and oxidoreductase-related activities, further supporting disruption of electron transport and redox balance (Fig. 2B). At the biological process level, consistently, altered proteins were mainly involved in “mitochondrial ATP synthesis coupled electron transport”, “oxidative phosphorylation”, “respiratory electron transport chain”, and “cellular respiration”, indicating pervasive changes in mitochondrial energy metabolism (Fig. 3A).

KEGG pathway analysis was in line with these observations (Fig. 3B). Results were detailed in Supplementary Table S2. Differential proteins were significantly enriched in “Oxidative phosphorylation”, “Carbon metabolism”, “Metabolic pathways”, “Fatty acid degradation” and “Fatty acid metabolism”, pointing to coordinated remodeling of central energy and substrate utilization pathways. In addition, enrichment in “Cardiac muscle contraction” and “Hypertrophic cardiomyopathy” links these metabolic alterations to structural and functional remodeling of the myocardium.

Together, these proteomic findings suggest that HCM myocardium is characterized by pronounced mitochondrial and metabolic reprogramming, particularly affecting oxidative phosphorylation, fatty acid utilization, and contractile function, providing a mechanistic basis for subsequent metabolomic and integrative analyses.

Untargeted metabolomic profiling identified extensive metabolic alterations in HCM myocardium compared with controls. In positive ion mode, 93 differential metabolites were detected (50 upregulated and 43 downregulated) (Fig. 4A), and in negative ion mode, 98 differential metabolites were identified (60 upregulated and 38 downregulated), based on predefined thresholds (Fig. 4B). Results were detailed in Supplementary Table S3. OPLS-DA (Fig. 4C,D), together with hierarchical clustering heatmaps (Fig. 4E), showed clear separation between HCM and control samples, indicating a distinct metabolic signature associated with HCM.

KEGG enrichment analysis of differential metabolites (p < 0.05) highlighted several pathways related to energy metabolism, nucleotide turnover and signaling regulation (Fig. 5A). Results are detailed in Supplementary Table S4. Notably, “Purine metabolism” and “Pantothenate and CoA biosynthesis” were enriched, suggesting altered high-energy phosphate handling and CoA-dependent metabolic processes. Pathways such as “Glycerophospholipid metabolism” and “Choline metabolism” pointed to remodeling of membrane lipid composition. In addition, several signaling and stress-related pathways were significantly enriched, including the “AMPK signaling pathway”, “cGMP-PKG signaling pathway”, and “FoxO signaling pathway”, which are closely linked to cellular energy sensing, survival and stress adaptation (Fig. 5B). The presence of “Oxidative phosphorylation” and “Thermogenesis” among enriched pathways further supports the notion of mitochondrial functional reprogramming, consistent with our proteomic findings (Fig. 5B).

Collectively, these metabolomic changes suggest that HCM myocardium undergoes coordinated alterations in nucleotide, lipid, and amino acid metabolism, together with disruption of energy-sensing and mitochondrial pathways, providing a metabolic context for the proteomic evidence of impaired oxidative phosphorylation and mitochondrial remodeling. These metabolic changes, particularly those related to oxidative phosphorylation and energy-sensing pathways, prompted us to perform an integrated proteomic–metabolomic pathway analysis.

To identify pathways that were consistently perturbed at both the protein and metabolite levels, we intersected the KEGG enrichment results from proteomics (77 pathways with p < 0.05) and metabolomics (38 pathways with p < 0.05). This analysis yielded seven overlapping pathways: “Purine metabolism”, “cGMP-PKG signaling pathway”, “Parkinson disease”, “Thermogenesis”, “Sulfur metabolism”, “Oxidative phosphorylation”, and “Retrograde endocannabinoid signaling”. These shared pathways highlight biological processes in which enzyme expression and metabolite abundance are simultaneously altered (Fig. 6A).

Given its multi-omics support and central relevance to myocardial energy production, we further examined the oxidative phosphorylation pathway in detail. In total, 24 DEPs mapped to this pathway. Strikingly, 23 of these proteins were downregulated in HCM myocardium compared with controls, whereas only ATP5PO, a subunit of ATP synthase (complex V), was upregulated. This pattern suggests a broad suppression of electron transport chain components accompanied by a relative increase in a single ATP synthase subunit, consistent with a globally impaired but potentially compensatory regulated oxidative phosphorylation system.

At the metabolite level, two key intermediates associated with mitochondrial energy metabolism, NAD and ADP, were both significantly decreased in HCM samples. The concomitant downregulation of multiple complex I–IV proteins together with reduced levels of NAD, a critical electron carrier, and ADP, a substrate for ATP synthase, indicates that both electron transfer capacity and ATP production potential are compromised. When DEPs and metabolites were mapped onto the oxidative phosphorylation pathway, the coordinated changes in respiratory chain subunits and energy-related metabolites outlined a consistent picture of impaired mitochondrial oxidative phosphorylation in HCM myocardium (Fig. 6B).

These integrated proteomic and metabolomic alterations in the oxidative phosphorylation pathway provide mechanistic support for mitochondrial dysfunction as a key feature of HCM, and link structural changes in respiratory complexes to functional deficits in high-energy phosphate turnover.