Evaluation of Anthracycline-Induced Cardiotoxicity Using Proton Magnetic Resonance Spectroscopy Compared With Cardiac Functional Parameters　― An Animal Study ―

Abstract

Background: Anthracycline-induced cardiotoxicity (AIC) poses significant challenges due to its severe adverse effects, limiting the use of anthracycline drugs (ATC). Early detection and intervention are pivotal, yet current diagnostic methods lack sensitivity.

Methods and Results: In a prospective animal study, 20 rabbits were administered adriamycin weekly and underwent cardiac magnetic resonance (CMR) scanning every 2 weeks. Ventricular function and myocardial metabolite content were assessed. Using a linear mixed model, we determined the earliest CMR-sensitive time and diagnostic thresholds for AIC detection via proton magnetic resonance spectroscopy (¹H-MRS). Results showed that Lipid1 increased significantly earlier at week 6 compared to the decreased left ventricular ejection fraction (LVEF) at week 8 (P<0.05). ROC analysis revealed that a Lipid1 cutoff value of 2.60 had the best diagnostic accuracy for AIC at week 6, with an area under the curve of 0.745, specificity of 0.71, and sensitivity of 0.80 (95% CI: 0.575–0.916). Lipid1 also demonstrated a moderately negative correlation with LVEF (r=–0.418, P<0.01).

Conclusions: ¹H-MRS-detected Lipid1 increased at week 6 after anthracycline injection, offering earlier diagnosis of AIC compared to conventional LVEF biomarkers.

Anthracycline drugs (ATC) are mature chemotherapy drugs used to treat solid tumors and hematologic malignancies.¹ However, the well-recognized major adverse reaction of anthracycline-induced cardiotoxicity (AIC) ultimately limits the clinical application of ATC, resulting in left ventricular (LV) dysfunction, heart failure (HF), myocarditis, and even death.² AIC may occur anytime and cannot be predicted, thus affecting the quality of life and long-term prognosis of cancer patients.^3,4 Nevertheless, research has shown that prompt initiation of HF treatment by early detection of cardiac dysfunction can lead to the recovery of LV ejection fraction (LVEF) and reduce cardiac adverse events in cancer patients with AIC.^4–6 Therefore, early detection of AIC and timely intervention are of particular importance.

Editorial p ????

Continuous and meticulous monitoring of the cardiovascular system is the traditional method of detecting cardiac toxicity early.⁷ LVEF and/or global longitudinal strain (GLS) are the recommended diagnostic criteria for cardiac toxicity in the latest guidelines,⁸ although LVEF has limited sensitivity in detecting early changes in cardiac contractile function and cannot reflect subtle changes in the early stages of AIC.^9,10 GLS is utilized to predict the occurrence of clinical events such as HF, hospitalization, and death.¹¹ Nevertheless, cardiac motion varies in each segment of the left ventricle, which may cause a delay in overall myocardial strain. Moreover, studies have shown that patient prognosis, based on global strain, did not improve,¹² possibly due to the late detection of cardiac injury assessed by global strain. Therefore, there is an urgent need for additional sensitive methods to detect AIC early.

ATC drugs induce cardiac toxicity by effecting changes in myocardial metabolism.¹³ It is well known that the main source of energy for myocardial cells is primarily derived from the oxidation of fatty acids.¹⁴ Under normal physiological conditions, the lipid concentration in myocardial tissue is low,¹⁵ but abnormal regulation of fatty acid intake or alterations in lipid metabolism can lead to abnormal lipid accumulation within myocardial cells, a condition known as cardiac lipotoxicity and shown to impair cardiac function.^16,17 In addition to fatty acids, creatine (Cr) also plays a significant role in myocardial energy metabolism and energy storage. Changes in the kinetics of cardiac Cr kinase, an enzyme involved in Cr metabolism, can result in myocardial dysfunction.¹⁸ Therefore, monitoring the changes in these metabolites may have significant importance for diagnosing AIC.

Although ³¹P magnetic resonance spectroscopy (³¹P-MRS) has demonstrated early bioenergetic disruption in AIC through PCr/ATP ratio decline,¹⁹ its clinical application remains limited by the requirement for specialized hardware and spectral analysis expertise. Compared with ³¹P-MRS and ¹³C-NMR, ¹H-MRS is more widely used in clinical practice, even though it can detect fewer metabolites. Its key benefits are simplicity, cost-effectiveness, and relatively less complex equipment needs,²⁰ making it an excellent choice for AIC screening. ¹H-MRS could leverage widely available clinical MRI systems to detect lipid metabolism alterations, a hallmark of anthracycline-induced lipotoxicity. To address this, our study established an experimental rabbit model with the injection of adriamycin and applied ¹H-MRS to assess the occurrence and progression of AIC. Our principal objective was to characterize the dynamic alterations in cardiac metabolites, with a focused aim of identifying the earliest manifestation of AIC detectable via ¹H-MRS. Based on this foundation, we aspired to delineate crucial threshold parameters that serve as diagnostic markers for AIC, and subsequently leverage these experimental findings to advance the development of a novel monitoring methodology for AIC. This approach may hold significant potential for enhancing the early detection and management of AIC, ultimately improving patient outcomes.

Methods

Animal Preparation

This experiment was approved by the Institutional Review Board of Southwest Medical University. A total of 20 New Zealand rabbits (weight 2.0–2.5 kg, male, ≈3 months old) were obtained and kept in the animal experiment center for 2 weeks to acclimatize. All rabbits underwent cardiac magnetic resonance (CMR) scanning as the baseline blank control before modeling. After the baseline CMR scan, 3 rabbits were randomly selected for euthanasia for baseline histological evaluation, and the remaining rabbits were injected with adriamycin (doxorubicin hydrochloride, Aladdin, Shanghai, China) once weekly in the marginal ear vein. Based on the human dose of adriamycin (60 mg/m²) recommended by the Chinese Society of Clinical Oncology, the rabbit dose was calculated according to the human and animal body surface area (BSA) conversion formula of 100 × BSA [m²] = 11.0 × body weight [kg] × 2 / 3. CMR scans were performed every 2 weeks until the end of week 10 (i.e., baseline, weeks 2, 4, 6, 8, 10). After each CMR scan, approximately 2–5 rabbits were randomly selected for euthanasia for histopathological reference at the corresponding time points.

All animal experiments were performed in compliance with the institutional guidelines for the care and use of laboratory animals (Southwest Medical University Animal Ethics Committee, approval no. SWMU20210384) and the National Regulations on the Management of Laboratory Animals (China). Efforts were made to minimize animal suffering, including the use of anesthesia and euthanasia protocols.

The study inclusion criteria were that rabbits had completed the entire CMR sequence scan with appropriate image quality for analysis. Exclusion criteria were: (1) pneumonia and (2) cardiac disease or myocardial injury detected in the baseline CMR scan and pathologic findings. Finally, 16 rabbits were included in this study. Figure 1 shows the flowchart of the experiment.

Figure 1.

Flowchart of the experiment. A total of 20 rabbits were recruited and 16 rabbits were injected with adriamycin every week and scanned CMR every 2 weeks until week 10. After each scan, 2–5 randomly chosen rabbits were humanely killed as a reference for pathology: 3, 2, 2, 2, 3, 5 rabbits in weeks 0, 2, 4, 6, 8, 10, respectively.

CMR Scanning

Rabbits were weighed and anesthetized using intramuscular injection of isoproterenol (2.0 mg/kg) into the lumbar muscles on one side of the spine 1 h before each CMR examination (alternating sides for sequential procedures). Prior to each anesthesia, isoproterenol was prepared temporarily under sterile conditions using 5% dextrose solution diluted in a ratio of 1 : 4 and had to be used within 6 h. Prepared rabbits were given metoprolol hydrochloride (0.3 mg/kg) intravenously in the ear vein to reduce heart rate. After induction of anesthesia, animals were intubated and anesthesia was maintained with mechanical ventilation using a mixture of oxygen and isoflurane.

CMR was performed on a 3.0 Tesla magnetic resonance scanner (MAGNETOM Prisma, Siemens Healthineers, Erlangen, Germany). All experimental animals were positioned in the supine position and placed in an 18-channel abdominal coil (nucleated at ¹H), fitted with ECG gating. During the CMR examination, standard segmented steady-state free-flow (SSFP) cine images were acquired in various views, including short-axis from the base to the apex, and 2-, 3- and 4-chamber cardiac views. These images were acquired to comprehensively analyze the cardiac function by covering the entire left ventricle. The main scanning parameters were: repetition time (TR) of 40.39 ms, echo time (TE) of 3.46 ms, flip angle of 12°, 4 averages, and slice thickness of 3.0 mm.

After completing the movie sequence, a cardiac ¹H-MRS scan was performed using a single point-resolved selective spectroscopy (PRESS) sequence, and ECG gating was used to ensure that each scan was captured during the cardiac plateau phase. The main parameters were: TR of 390 ms, TE of 30 ms, 20 averages, bandwidth of 2,500 Hz, flip angle of 90°, and signal-to-noise ratio of 1. A region of interest (ROI) measuring 3×5×7 was carefully positioned within the interventricular septum in both the standard short-axis and 4-chamber views, ensuring avoidance of the blood pool to minimize signal contamination (Figure 2). To ensure optimal quality, rabbits underwent 4–6 consecutive scans to acquire ¹H-MRS spectra with the best fit.

Figure 2.

Schematic of measuring myocardial metabolic substances in typical ¹H-MRS images with a region of interest (red outline) for performing analysis and of the chemical shifts of each metabolite on a typical ¹H-MRS spectrum, with peaks that correspond to water, creatine (Cr), and myocardial lipids, especially the methylene (Lipid1, MYCL-CH2) and methyl (Lipid2, MYCL-CH3) groups.

Image Analysis

The cine images were evaluated by 2 radiologists, each with >3 years of experience in diagnostic CMR, using the post-processing software CVI42 (version 5.12.4, Circulation Cardiovascular Imaging, Calgary, Canada).

The cine images were imported into a specialized cardiac analysis module, whereupon they were processed in accordance with guideline recommendations. Subsequently, the software automatically calculated a range of cardiac function parameters, including LV end-diastolic volume (LVEDV), LV endsystolic volume (LVESV), LV stroke volume (LVSV), LV cardiac output (LVCO), LVEF, LV mass, etc. Additionally, GLS was obtained following standardized processing protocols.

The ¹H-MRS data were processed using the jMRUI software (developed by the MRUI Consortium). The software performed waveform correction, toe-cutting, noise suppression, peak shifting, and water suppression on the ¹H-MRS spectra. Additionally, the software automatically calculated peak heights and areas for water, choline, Cr, lactate (Lac), and lipids in the spectra. In the water-suppressed spectra, the lipid signals were located at 0.78 ppm (Lipid1, spectral line of methylene, MYCL-CH₂) and 1.45 ppm (Lipid2, spectral line of methyl, MYCL-CH₃). We excluded peaks with a signal-to-noise ratio <3 from the statistical analysis.²¹

Histological Analysis

After each CMR scan, 2–5 rabbits were randomly selected and euthanazed by inhalation of excess isoflurane. The entire heart was immediately removed following euthanasia. The interventricular septal myocardial tissue was then cut, fixed in 10% formalin, dehydrated, and sectioned with a thickness of 5 μm for pathology. Subsequently, the sections were stained with hematoxylin-eosin, as well as Sirius red, following standard procedures. Images of the sections were collected using a digital section scanner (Panoramic 250, 3D Histech, Budapest, Hungary). Histological data were analyzed and evaluated by 2 pathologists with 5 years of experience. Myocardial injury was assessed using HE staining, specifically observing vacuoles or disappearance in the cytoplasm of myocardial fibers, disorganization of myocardial tissue structure, and changes in the morphology of cardiomyocytes. Additionally, histological scoring was conducted based on criteria from previous studies.^22,23 Collagen volume fraction (CVF) was determined by Sirius red staining. Manual drawing of regions of interest (ROIs) and calculation of CVF were performed on Sirius red-stained sections corresponding to the CMR ROIs of the ventricular septal myocardium.

Statistical Analysis

Statistical analysis was conducted using SPSS 26.0 software (IBM, Armonk, NY, USA). Normally distributed data are presented as mean ± standard deviation, while non-normally distributed continuous variables are expressed as medians. One-way analysis of variance (ANOVA) was used to compare the body weights among groups across different weeks. To determine the earliest time of CMR diagnosis of AIC, a linear mixed model with restricted maximum likelihood estimation was utilized to assess the parameters of LV function, spectral metabolites and GLS parameters for each period. Time was treated as a categorical variable, and equal covariances were assumed between all time points. Furthermore, combined with the pathological findings in the early CMR scan period, receiver operating characteristic (ROC) curves for diagnosis of AIC were used to calculate the area under the curve (AUC), critical value, sensitivity, and specificity for establishing the ¹H-MRS parameter thresholds for AIC diagnosis. Correlation analyses were conducted separately between: (1) myocardial metabolites and cardiac functional parameters (including LVEF), and (2) myocardial metabolites and CVFs. P<0.05 was considered statistically significant.

Results

Clinical Data of Rabbits

CMR images of rabbits in week 0 (n=16), week 2 (n=14), week 4 (n=12), week 6 (n=10), week 8 (n=8) and week 10 (n=5) were included in the study. During this experiment, 2 rabbits died due to diarrhea before the baseline CMR scan, and 2 were excluded from the experiment after developing pneumonia. The weights of the rabbits exhibited a slight upward trend in correlation with the duration of the modeling period (P<0.01).

The LV function parameters are presented in Table 1. LVESV was elevated at week 6 and LVEDV at week 8 (P<0.05), while LVEF showed a decrease at week 8 (P<0.05). No significant differences were observed in other cardiac function parameters or GLS (P>0.05).

Table 1.

Cardiac Function Parameters at Each Time Point of the Study

	Baseline (n=16)	Week 2 (n=14)	Week 4 (n=12)	Week 6 (n=10)	Week 8 (n=8)	Week 10 (n=5)
LVEDV (mL)	3.69 (0.95)	3.56 (0.95)	4.10 (1.82)	4.20 (0.53)	4.95 (1.41)*	3.07 (1.88)
LVESV (mL)	1.80 (0.53)	1.70 (0.32)	2.32 (0.82)	2.48 (0.48)*	2.86 (0.98)*	1.69 (1.36)
LVSV (mL)	1.92 (0.52)	1.82 (0.56)	1.99 (0.79)	1.66 (0.46)	2.21 (0.52)	1.38 (0.51)
LVEF (%)	52.41 (6.99)	50.63 (5.02)	49.77 (7.43)	47.58 (5.83)	41.74 (5.59)*	43.82 (7.58)*
LVCO (L/min)	0.30 (0.13)	0.36 (0.14)	0.38 (0.13)	0.30 (0.14)	0.36 (0.12)	0.30 (0.07)

All data are presented as median and interquartile range (IQR). *P<0.05 compared to baseline. LVCO, left ventricular cardiac output; LVEDV, left ventricular end-diastolic volume; LVESV, left ventricular endsystolic volume; LVEF, left ventricular ejection fraction; LVSV, left ventricular stroke volume.

Pathological Findings

Figure 3 shows the changes in myocardial pathology. The mean myocardial damage score and CVF in the baseline group were 0 and 4.4%, respectively. At week 2, histological examination revealed a small inflammatory cell infiltrate and vacuolated cells in the LV free wall, but no significant pathological changes in the interventricular septum. The mean myocardial injury score was 1, and CVF was 5.3% at week 2. However, starting at week 6, myocardial fibers in the septal wall degenerated and showed cytoplasmic vacuoles of varying sizes, with localized areas of lymphocyte aggregation in the interstitium. The mean myocardial injury score was 1, and the CVF was 10.6%. By week 10, extensive myocardial fiber degeneration and necrosis had occurred in the interventricular septum, and the cardiac myocardial injury score reached its highest point (3), as did the CVF (17.9%).

Figure 3.

Histologic images of myocardial injury and changes in myocardial injury score and collagen volume fraction (CVF). Hematoxylin-eosin staining (×200): (A) normal myocardium of control subjects; (B) slight myocardial injury (myocardial injury score=1) in the left ventricular septum of the 2-week model; (C) model at week 6 shows mild myocardial injury in the septal wall (myocardial injury score=1). (D) Myocardial cell injury score for each model point. Sirius red staining (×200): (E) small number of collagen fibers in the interstitium of baseline subjects (CVF=4.4%); (F) no significant increase in collagen fibers in the 2-week model (CVF=5.3%); (G) significant deposition of collagen fibers in the week-6 model (CVF=10.6%). (H) CVFs for each model point.

¹H-MRS Results

Table 2 presents the ¹H-MRS data obtained at different time points. Lipid1 (methylene groups of myocardial lipids) of the LV septal wall increased at week 6 (P<0.05). Combined with the pathological findings of AIC at week 6, a critical value of 2.60 was determined for cardiotoxicity through ROC analysis, with an AUC of 0.745 (specificity=0.71, sensitivity=0.80, 95% confidence interval (CI): 0.575–0.916). Both the Lipid1/Water and Cr2/Water ratio showed an increased trend at week 10 (P<0.05). However, no statistically significant differences were observed for Lac, Cr, Lipid2/W or Cr1/W (P>0.05). In addition, Lipid1 showed a moderate negative correlation with LVEF (r=−0.418, P<0.01), and a moderate positive correlation with CVF (r=0.508, P<0.05).

Table 2.

¹H-MRS Metabolite Parameters at Each Time Point of the Study

	Baseline (n=16)	Week 2 (n=14)	Week 4 (n=12)	Week 6 (n=10)	Week 8 (n=8)	Week 10 (n=5)
Lipid1	2.18 (1.14)	2.93 (3.74)	2.39 (1.79)	5.48 (7.89)*	3.53 (5.46)	5.09 (11.29)
Lipid2	2.73 (3.37)	2.56 (4.09)	2.42 (1.94)	4.57 (6.62)	2.16 (3.47)	1.66 (9.30)
Cr1	2.36 (1.92)	1.86 (2.68)	1.04 (1.55)	0.81 (1.72)	1.85 (1.39)	1.53 (4.62)
Cr2	1.84 (3.29)	3.23 (3.77)	3.12 (3.66)	1.60 (3.13)	3.21 (2.85)	2.25 (3.71)
Cho	2.05 (2.36)	3.06 (2.58)	2.93 (1.51)	2.24 (3.50)	2.27 (1.62)	1.73 (14.64)
Lac	2.41 (2.66)	2.26 (4.16)	4.45 (3.17)	2.31 (2.71)	3.52 (1.87)	2.33 (2.97)
Lipid1/W	1.02 (1.03)	1.84 (2.02)	1.40 (2.72)	3.02 (9.34)	1.39 (5.22)	5.17 (29.68)*
Lipid2/W	1.37 (1.69)	1.71 (4.18)	0.98 (0.85)	3.37 (5.57)	1.08 (1.99)	5.97 (8.29)
Cr1/W	1.02 (0.97)	1.22 (1.86)	0.52 (1.85)	0.37 (0.78)	0.88 (0.64)	1.55 (2.92)
Cr2/W	0.93 (1.46)	1.03 (5.96)	0.84 (3.55)	0.77 (2.79)	1.21 (1.18)	1.57 (50.48)*

All data are presented as median and interquartile range (IQR). *P<0.05 compared to baseline. Cr, creatine; Cr/W, Cr-to-water ratio; Cho, choline; Lac, lactate; Lipid/W, lipid-to-water ratio.

Discussion

Main Findings

Interestingly, changes in the LV myocardial lipid levels were detected by ¹H-MRS at week 6, preceding the 8th week when the earliest changes in LVEF emerged. This suggests that ¹H-MRS holds promising potential for the early diagnosis of AIC compared with the conventional biomarker of LVEF/GLS. Additionally, our study revealed a correlation between changes in Lipid1 content and LVEF and CVF, indicating elevated myocardial lipid content may lead to decreased cardiac function and myocardial fibrosis.^24–27 Consequently, monitoring the Lipid1 threshold value via ¹H-MRS may be a clinically actionable biomarker to initiate intensified surveillance or preventive cardioprotective strategies prior to the manifestation of overt systolic dysfunction, particularly at the critical 6-week post-anthracycline timepoint.

Characteristics Changes in Myocardial Metabolites by AIC

Fatty acids and Cr are essential myocardial energy substrates, whereas Lac accumulates during ischemia/hypoxia. Quantifying these metabolites provides insights into cardiac metabolic status and aids in diagnosing myocardial ischemia, injury, and hypertrophy.^17,28

Our study demonstrated a significant increase in Lipid1 as early as week 6, preceding the observed LVEF decline at week 8. This finding aligns with established mechanisms of anthracycline-induced lipotoxicity, where oxidative stress and mitochondrial dysfunction, likely through disruption of fatty acid metabolism pathways, lead to rapid lipid accumulation prior to functional impairment.^24,29 Adriamycin reduces the oxidation of long-chain fatty acids,²³ but the mechanisms underlying AIC are still unknown, although several potential mechanisms have been proposed, including oxidative stress-induced molecular changes, altered cell death pathways, and epigenetic modifications.^25,26,29 Furthermore, we only observed changes in Lipid1 levels at week 6, but not in the metabolism of other substances such as Cr and Lac. It was not until week 10 that we observed an increase in the Cr2/Water ratio, which could be attributed to several factors: (1) a temporal hierarchy of metabolic responses; anthracyclines preferentially disrupt lipid metabolism through oxidative stress and transcriptional repression of fatty acid oxidation genes,^27,29 while Cr kinase impairment and Lac accumulation require prolonged energetic stress; (2) technical sensitivity limitations: ¹H-MRS detects lipids (≈mmol/g tissue) at significantly higher concentrations than Cr (≈μmol/g),²⁰ and Lac signals may be obscured by overlapping metabolite peaks.³⁰

Moreover, the pathological findings of myocardial tissue in our study, such as vacuolar degeneration and fibrosis, support the metabolic changes detected by ¹H-MRS, especially the increase in lipid content. Lipid1 moderately positively correlated with CVF (r=0.508, P<0.05), which provides stronger evidence for the role of lipid metabolism in early AIC.

Value of ¹H-MRS in Diagnosis of Cardiotoxicity

Diagnosis of myocardial injury is typically based on a decrease in LVEF ≥10% or <50%. However, by this point, it often progresses to an irreversible stage.⁸ Relying solely on monitoring LVEF to determine myocardial injury does not adequately meet clinical needs.³¹ Our findings align with this, as we observed that the cardiac functional response time at week 8 was too slow to effectively diagnose cardiotoxicity. Consequently, using LVEF as a diagnostic measure has limitations and delays in detecting myocardial injury, potentially affecting the quality of life and prognosis of patients undergoing chemotherapy. On the other hand, based on the results of this study, ¹H-MRS can detect the onset of cardiotoxicity earlier than LVEF, as early as week 6.

Our results showed moderate diagnostic efficacy for Lipid1 at week 6 (AUC=0.745, 95% CI: 0.575–0.916), which is comparable to established biomarkers such as GLS and troponins.^32,33 Although this suggests that Lipid1 alone may not achieve high standalone accuracy, its early detection capability (week 6 vs. week 8 for LVEF) supports its role as a complementary metabolic biomarker in AIC screening. Future studies with larger cohorts could further refine the AUC and validate the optimal Lipid1 threshold (2.60 in our study).

Notably, current guideline-recommended parameters (LVEF and GLS) still exhibit limitations in sensitivity,^12,33 underscoring the need for alternative biomarkers such as ¹H-MRS-detected lipid changes. A multimodal approach combining metabolic and functional markers might improve diagnostic accuracy, though our current data did not support GLS integration due to its nonsignificant changes. Further research should explore whether combining Lipid1 with other emerging biomarkers (e.g., T1 mapping, extracellular volume fraction) could enhance early AIC detection.

The clinical implementation of ¹H-MRS shows particular promise for monitoring high-risk patients receiving anthracycline-based chemotherapy, especially those undergoing high-dose regimens. As a noninvasive imaging modality that avoids ionizing radiation, ¹H-MRS offers distinct advantages for serial assessment of chemotherapy-induced cardiotoxicity. Our experimental findings suggest the optimal timing for ¹H-MRS measurements occurs during or shortly after treatment completion, with the 6th post-treatment week emerging as a particularly informative window for detecting early metabolic changes that precede LV dysfunction. This early detection capability, achieved through monitoring characteristic lipid profile alterations, provides clinicians with a critical opportunity to initiate cardioprotective interventions before irreversible cardiac damage occurs. To validate and extend these findings, we are preparing clinical studies that will systematically evaluate the diagnostic performance of ¹H-MRS in anthracycline-treated patients, with a specific focus on establishing optimal screening timepoints, characterizing the evolution of metabolic changes, and determining their predictive value for subsequent development of clinically significant cardiotoxicity (defined as LVEF decline below 50%). These investigations aim to translate our experimental observations into clinically actionable protocols that may ultimately improve outcomes for cancer patients receiving potentially cardiotoxic therapies.

Building on the clinical potential of ¹H-MRS discussed earlier, it is noteworthy that this technique has already gained widespread clinical acceptance in neuro-oncology due to its relatively modest equipment requirements and proven diagnostic value for brain tumors. This established clinical track record suggests strong potential for cardiac applications in AIC detection. Although current metabolic research in AIC has primarily focused on ³¹P-MRS (e.g., Maslov et al.¹⁹ demonstrated that PCr/ATP reduction preceded LV dysfunction in adriamycin-treated mice, with detectable changes emerging at week 6, coinciding with our ¹H-MRS lipid changes at the same timepoint), ¹H-MRS offers 2 distinct advantages: (1) eliminates the need for specialized ³¹P coils, significantly enhancing clinical feasibility, and (2) detects lipid accumulation, a potentially earlier metabolic disturbance (e.g., lipotoxicity) than the energetic deficits reflected by the PCr/ATP ratio. Should cost-effective ¹H-MRS prove comparable to ³¹P-MRS for early AIC detection, it would represent a significant advancement in practical cardiotoxicity monitoring, particularly for resource-constrained clinical settings.

Study Limitations

There are several to note. Firstly, the sample size in this study was small, with only 20 rabbits participating. However, scanning every 2 weeks for each rabbit allowed for continuous dynamic observation with expanded sample size and self-control, which will be expanded in future studies to include a larger sample size. Secondly, the total observation period of this experiment was 10 weeks. Although most myocardial metabolites showed changes during this period, some remained unchanged, possibly due to the relatively short observation period. To gain a deeper understanding of cardiac metabolic changes in AIC, future studies will extend the observation period. Additionally, variations in timing and physiological states can influence myocardial metabolites. To minimize the impact of external factors, we made efforts to scan the rabbit myocardium at consistent time periods and maintain consistent feeding conditions. Furthermore, rabbits of the same sex were selected to exclude the potential influence of sex on myocardial metabolites. Finally, ¹H-MRS requires relatively specialized equipment and expertise, which poses challenges for its widespread clinical adoption. However, this method could still be recommended as a viable option for monitoring AIC.

In conclusion, myocardial lipids increased with anthracycline injection at week 6, which were detected using ¹H-MRS for an earlier diagnosis of AIC than with the conventional biomarker of LVEF. Moreover, lipid threshold parameters for the diagnosis of AIC were obtained by ¹H-MRS, providing an experimental reference for subsequent patient studies. Among these metabolic substances, myocardial lipids were affected firstly by anthracycline and related to cardiac dysfunction. Therefore, to improve prognosis it might be useful for monitoring and managing lipid metabolism in patients using of anthracycline.

Acknowledgments

The authors extend their profound gratitude to the joint program of Luzhou City and Southwest Medical University (Grant No. 2021LZXNYD-J20) and the Sichuan Science and Technology Program (Grant No. 2024NSFSC1793) for their generous financial support of this research.

The article complied with Ethical Standards. There are no conflicts of interest. This research was partly supported by the joint program of Luzhou city and Southwest Medical University (2021LZXNYD-J20) and Sichuan Science and Technology Program (2024NSFSC1793).

Disclosure

The authors declare that they have no competing interests or financial conflicts related to the content of this work. Additionally, all data and sources used in this study are accurately cited and comply with appropriate ethical guidelines.

All authors have seen the manuscript and approved submission. Neither the entire paper nor any part of its contents has been published or been accepted elsewhere. It is not being submitted to any other journal. And agreement to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work was appropriately investigated and resolved.

IRB Information

The Institutional Review Board of Southwest Medical University approved the application for exemption of patients’ informed consent (ethics committee approval SWMU20210384).

References

• 1.   Jain D, Aronow W. Cardiotoxicity of cancer chemotherapy in clinical practice. Hosp Pract (1995) 2019; 47: 6–15, doi:10.1080/21548331.2018.1530831.
• 2.   Dong J, Chen H. Cardiotoxicity of Anticancer Therapeutics. Front Cardiovasc Med 2018; 5: 9, doi:10.3389/fcvm.2018.00009.
• 3.   Inoue K, Machino-Ohtsuka T, Nakazawa Y, Iida N, Sasamura R, Bando H, et al. Early detection and prediction of anthracycline-induced cardiotoxicity: A prospective cohort study. Circ J 2024; 88: 751–759, doi:10.1253/circj.CJ-24-0065.
• 4.   Curigliano G, Lenihan D, Fradley M, Ganatra S, Barac A, Blaes A, et al. Management of cardiac disease in cancer patients throughout oncological treatment: ESMO consensus recommendations. Ann Oncol 2020; 31: 171–190, doi:10.1016/j.annonc.2019.10.023.
• 5.   Lustberg MB, Kuderer NM, Desai A, Bergerot C, Lyman GH. Mitigating long-term and delayed adverse events associated with cancer treatment: Implications for survivorship. Nat Rev Clin Oncol 2023; 20: 527–542, doi:101038/s41571-023-00776-9.
• 6.   Liu B, Xie H, Du X, Zhou Y, Huang J. Catalpol inhibits autophagy to ameliorate doxorubicin-induced cardiotoxicity via the AKT-mTOR pathway. Int Heart J 2023; 64: 910–917, doi:10.1536/ihj.23-062.
• 7.   Mitani I, Jain D, Joska TM, Burtness B, Zaret BL. Doxorubicin cardiotoxicity: Prevention of congestive heart failure with serial cardiac function monitoring with equilibrium radionuclide angiocardiography in the current era. J Nucl Cardiol 2003; 10: 132–139, doi:10.1067/mnc.2003.7.
• 8.   Lyon AR, López-Fernández T, Couch LS, Asteggiano R, Aznar MC, Bergler-Klein J, et al. 2022 ESC Guidelines on cardio-oncology developed in collaboration with the European Hematology Association (EHA), the European Society for Therapeutic Radiology and Oncology (ESTRO) and the International Cardio-Oncology Society (IC-OS). Eur Heart J 2022; 43: 4229–4361, doi:10.1093/eurheartj/ehac244. [Published correction appears in Eur Heart J 2023; 44: 1621, doi:10.1093/eurheartj/ehad196.]
• 9.   Meléndez GC, Jordan JH, D’Agostino RB Jr, Vasu S, Hamilton CA, Hundley WG. Progressive 3-month increase in LV myocardial ECV after anthracycline-based chemotherapy. JACC Cardiovasc Imaging 2017; 10: 708–709, doi:10.1016/j.jcmg.2016.06.006.
• 10.   Ferreira de Souza T, Quinaglia A C Silva T, Osorio Costa F, Shah R, Neilan TG, Velloso L, et al. Anthracycline therapy is associated with cardiomyocyte atrophy and preclinical manifestations of heart disease. JACC Cardiovasc Imaging 2018; 11: 1045–1055, doi:10.1016/j.jcmg.2018.05.012.
• 11.   Fischer K, Obrist SJ, Erne SA, Stark AW, Marggraf M, Kaneko K, et al. Feature tracking myocardial strain incrementally improves prognostication in myocarditis beyond traditional CMR imaging features. JACC Cardiovasc Imaging 2020; 13: 1891–1901, doi:10.1016/j.jcmg.2020.04.025.
• 12.   Houbois CP, Nolan M, Somerset E, Shalmon T, Esmaeilzadeh M, Lamacie MM, et al. Serial cardiovascular magnetic resonance strain measurements to identify cardiotoxicity in breast cancer: Comparison with echocardiography. JACC Cardiovasc Imaging 2021; 14: 962–974, doi:10.1016/j.jcmg.2020.09.039.
• 13.   da Cunha Menezes Souza L, Fernandes FH, Presti PT, Anjos Ferreira AL, Fávero Salvadori DM. Effect of doxorubicin on cardiac lipid metabolism-related transcriptome and the protective activity of Alda-1. Eur J Pharmacol 2021; 898: 173955, doi:10.1016/j.ejphar.2021.173955.
• 14.   Taegtmeyer H, McNulty P, Young ME. Adaptation and maladaptation of the heart in diabetes. Part I: General concepts. Circulation 2002; 105: 1727–1733, doi:10.1161/01.cir.0000012466.50373.e8.
• 15.   Chang KF, Lin G, Huang PC, Juan YH, Wang CH, Tsai SY, et al. Left ventricular function and myocardial triglyceride content on 3T cardiac MR predict major cardiovascular adverse events and readmission in patients hospitalized with acute heart failure. J Clin Med 2020; 9: 169, doi:10.3390/jcm9010169.
• 16.   Okere IC, Chandler MP, McElfresh TA, Rennison JH, Sharov V, Sabbah HN, et al. Differential effects of saturated and unsaturated fatty acid diets on cardiomyocyte apoptosis, adipose distribution, and serum leptin. Am J Physiol Heart Circ Physiol 2006; 291: H38–H44, doi:10.1152/ajpheart.01295.2005.
• 17.   Einarsen E, Saeed S, Cramariuc D, Chambers JB, Midtbø H, Gerdts E. Impact of obesity on persistent left ventricular hypertrophy after aortic valve replacement for aortic stenosis. Am J Cardiol 2019; 123: 942–947, doi:10.1016/j.amjcard.2018.12.012.
• 18.   Osbakken M, Douglas PS, Ivanics T, Zhang DN, Van Winkle T. Creatinine kinase kinetics studied by phosphorus-31 nuclear magnetic resonance in a canine model of chronic hypertension-induced cardiac hypertrophy. J Am Coll Cardiol 1992; 19: 223–228, doi:10.1016/0735-1097(92)90076-y.
• 19.   Maslov MY, Chacko VP, Hirsch GA, Akki A, Leppo MK, Steenbergen C, et al. Reduced in vivo high-energy phosphates precede adriamycin-induced cardiac dysfunction. Am J Physiol Heart Circ Physiol 2010; 299: H332–H337, doi:10.1152/ajpheart.00727.2009.
• 20.   Faller KM, Lygate CA, Neubauer S, Schneider JE. (1)H-MR spectroscopy for analysis of cardiac lipid and creatine metabolism. Heart Fail Rev 2013; 18: 657–668, doi:10.1007/s10741-012-9341-z.
• 21.   Kuo YT, Li CW, Chen CY, Jao J, Wu DK, Liu GC. In vivo proton magnetic resonance spectroscopy of large focal hepatic lesions and metabolite change of hepatocellular carcinoma before and after transcatheter arterial chemoembolization using 3.0-T MR scanner. J Magn Reson Imaging 2004; 19: 598–604, doi:10.1002/jmri.20046.
• 22.   Hong YJ, Park HS, Park JK, Han K, Park CH, Kim TK, et al. Early detection and serial monitoring of anthracycline-induced cardiotoxicity using T1-mapping cardiac magnetic resonance imaging: An animal study. Sci Rep 2017; 7: 2663, doi:10.1038/s41598-017-02627-x. [Published correction appears in Sci Rep 2022; 12: 9544, doi:10.1038/s41598-022-13822-w.]
• 23.   Lai RC, Wang XD, Zhang X, Lin WQ, Rong TH. Heart fatty acid-binding protein may not be an early biomarker for anthracycline-induced cardiotoxicity in rabbits. Med Oncol 2012; 29: 2303–2308, doi:10.1007/s12032-011-9843-x.
• 24.   Volkova M, Russell R 3rd. Anthracycline cardiotoxicity: prevalence, pathogenesis and treatment. Curr Cardiol Rev 2011; 7: 214–220, doi:10.2174/157340311799960645.
• 25.   Di Lisi D, Leggio G, Vitale G, Arrotti S, Iacona R, Inciardi RM, et al. Chemotherapy cardiotoxicity: Cardioprotective drugs and early identification of cardiac dysfunction. J Cardiovasc Med (Hagerstown) 2016; 17: 270–275, doi:10.2459/JCM.0000000000000232.
• 26.   Russell RR, Alexander J, Jain D, Poornima IG, Srivastava AV, Storozynsky E, et al. The role and clinical effectiveness of multimodality imaging in the management of cardiac complications of cancer and cancer therapy. J Nucl Cardiol 2016; 23: 856–884, doi:10.1007/s12350-016-0538-8.
• 27.   Christoffersen C, Bollano E, Lindegaard ML, Bartels ED, Goetze JP, Andersen CB, et al. Cardiac lipid accumulation associated with diastolic dysfunction in obese mice. Endocrinology 2003; 144: 3483–3490, doi:10.1210/en.2003-0242.
• 28.   Szczepaniak LS, Dobbins RL, Metzger GJ, Sartoni-D’Ambrosia G, Arbique D, Vongpatanasin W, et al. Myocardial triglycerides and systolic function in humans: In vivo evaluation by localized proton spectroscopy and cardiac imaging. Magn Reson Med 2003; 49: 417–423, doi:10.1002/mrm.10372.
• 29.   Tokarska-Schlattner M, Zaugg M, Zuppinger C, Wallimann T, Schlattner U. New insights into doxorubicin-induced cardiotoxicity: The critical role of cellular energetics. J Mol Cell Cardiol 2006; 41: 389–405, doi:10.1016/j.yjmcc.2006.06.009.
• 30.   Dellegrottaglie S, Scatteia A, Pascale CE, Renga F, Perrone-Filardi P. Evaluation of cardiac metabolism by magnetic resonance spectroscopy in heart failure. Heart Fail Clin 2019; 15: 421–433, doi:10.1016/j.hfc.2019.02.010.
• 31.   Nagai S, Nishimori M, Kintsu M, Todo S, Ota E, Odajima S, et al. Left Ventricular longitudinal myocardial function of heart failure patients with transition from reduced to preserved ejection fraction and of those with preserved ejection fraction. Circ Rep 2024; 6: 161–167, doi:10.1253/circrep.CR-24-0025.
• 32.   Kang Y, Xu X, Cheng L, Li L, Sun M, Chen H, et al. Two-dimensional speckle tracking echocardiography combined with high-sensitive cardiac troponin T in early detection and prediction of cardiotoxicity during epirubicine-based chemotherapy. Eur J Heart Fail 2014; 16: 300–308, doi:10.1002/ejhf.8.
• 33.   Thavendiranathan P, Poulin F, Lim KD, Plana JC, Woo A, Marwick TH. Use of myocardial strain imaging by echocardiography for the early detection of cardiotoxicity in patients during and after cancer chemotherapy: A systematic review. J Am Coll Cardiol 2014; 63: 2751–2768, doi:10.1016/j.jacc.2014.01.073.