The mechanisms that lead to pMIHF are currently under elucidation.Considering the unique role of lipid disorders in the pathogenesis of pMIHF, there is substantial interest in understanding the changes in lipid metabolism pathways as this may aid in biomarker screening. Accordingly, lipidomics assays were used to delineate the metabolic profiles of numerous lipids and elucidate the lipid characteristics associated with pMIHF, thus, providing reference data for the identification of early diagnostic biomarkers.The pMIHF disease progression lacks early biomarkers for diagnosis, and lipidomics study in pMIHF has not revealed the novel biomarkers fully. There is an urgent need to discover new lipid markers that will help in the early diagnosis of pMIHF and provide a powerful aid in the clinical management and prognosis of patients with pMIHF. In this study, 18 MI patients and 24 pMIHF were included for metabolic profile analysis, further reveal the potential biomarkers and disorder metabolic pathway, finally ROC and correlation analysis were utilized to obtain the potential biomarkers in pMIHF diagnosis.
Baseline characteristics analysis
In this study, 42 subjects were included in the serum lipidomic analysis. The baseline characteristics demonstrated that patient age and BNP, BUN, and TC levels were obviously different between the MI and pMIHF groups, indicating that elderly patients with MI and renal function impairment may be more susceptible to HF.
Aging is a major risk factor for HF, and a sizeable percentage of elderly patients with HF have cardiac amyloidosis, which is an HF precipitator [19, 20]. BNP is an endogenous cardiac peptide and an established HF predictor, which is proportional to the severity of HF [21]. In addition, patients with HF should be monitored for changes in BNP levels over the first month after discharge since they are a useful prognostic indicator of rehospitalization [22]. It is important for patients with HF to monitor BNP levels over the first month after discharge, as they can be used as a predictor of rehospitalization [23]. Relatively high BNP levels in chronic kidney disease, a major player in the heart-renal connection [24]. Our results demonstrated that BNP and BUN levels are elevated in patients with pMIHF.Conversely, TC has been suggested to increase the risk of CAD [25], and our results showed that TC levels were low in patients with pMIHF. Furthermore, low serum TC levels can be a predictor of adverse outcomes, advanced HF patients are significantly more likely to die from these complications [26-28]. Increased BNP and BUN levels are commonly used as indicators to predict HF. Therefore, a reduction in TC can be a prognostic indicator or marker of dynamic changes in pMIHF development.
Lipid disorders
A total of 88 lipids, including 76 (86.36%) downregulated lipids, were detected between 18 patients with MI and 24 patients with pMIHF. Lipids are inhibited in patients with pMIHF, leading to disorders in glycerophospholipid and sphingolipid metabolism. The total levels of PC and TG were decreased, and that of PE was dramaticly downregulated in patients with pMIHF.
As a critical component of the membrane, lipids play an essential role in energy storage and metabolism [29]. TG is a TG-rich lipoprotein that causes lipotoxic cardiomyopathy and cardiac steatosis [30]. Progressively higher TG concentrations are associated with a progressively higher risk of HF and MI [31, 32]. PC is a key source of bioactive eicosanoids, including leukotrienes, prostaglandins, and lipoxins, Increasing free fatty acid accumulation and inflammation. Mammalian cells mainly contain PEs in their plasma and mitochondrial membranes [33]. Molecular PC/PE ratios influence mitochondrial energy metabolism and contribute to disease progression in cells [34]. In the present study, 19 PCs (90.48%), 23 TGs (85.19%), and 12 PEs (100%) were downregulated, which may contribute to lipotoxic cardiomyopathy, disruptions to mitochondrial function, and inflammation in patients with HF and subsequent MI lipotoxic cardiomyopathy.
Potential lipid biomarkers
To better screen for early warning biomarkers of pMIHF, lipid biomarkers were identified for ROC analysis. Based on the ROC results, valuable biomarkers were further subjected to correlation analyses with TC, BUN, and BNP levels. PA (44:5), PE (12:1e_22:0), SiE (16:0), PC (22:4_14:1), and CL (78:6) showed higher AUC values. PAs are bioactive lipids that are activated by a variety of inflammatory mediators, including bradykinin, ATP and glutamate; Pas also contribute to the modulation of cardiac contractility [35]. Our results showed that PA lipid content was the lowest in patients with pMIHF.
Correlation analysis showed that PC (22:4_14:1), PE (12:1e_22:0), and CL (78:6) were strongly correlated with TC, BUN, and BNP. Notably, PC and PE are involved in key glycerophospholipid and sphingolipid metabolism pathways. PC disorders can disturb myocardial metabolism and cellular signaling [36]. Thus, PCs, in particular, the metabolism of PCs in the failing heart may be associated with alterations in myocardial lipid homeostasis.[37]. PC (20:0/18:4), PC (20:4/20:0), PC (40:4), PC (20:4/18:0),and PC (34:4) were increased, whereas PC(32_0), PC(C34:4), and PC (36:5) were decreased in HF, and thus, PC has a diagnostic value similar to that of BNP [11, 38, 39]. PE levels are significantly increased in serum and cardiac samples during HF [16, 40], and PE (O-18:1(1Z)/20:4(5Z,8Z,11Z,14Z)) levels can be improved by higenamine in doxorubicin-induced HF rats [41]. The study observed the upregulation of PC (22:4_14:1) and downregulation of PE (12:1e_22:0), which exhibited a close correlation with the indicators of HF, including BNP. Therefore, PC and PE may be key potential biomarkers in patients with HF with subsequent MI.
The study showed a positive correlation between pMIHF and mortality among patients with MI. It is important to keep in mind that optimal management of patients with pMIHF depends on the time since their infarction began. A poor prognosis was found in patients with pMIHF. An observational study of 4,825 in-hospital deaths among patients who suffered non-ST-segment elevation MIs in Canada from 1999 to 2003 found that the presence of HF on admission significantly increased in-hospital mortality [42]. HF complicated by MI requires early intervention owing to its high mortality rate. However, the known biomarkers are expensive and exhibit low specificity, thereby hindering their use for early diagnosis. This makes the treatment challenging. There are also two emerging biomarkers for ischemic cardiomyopathy, angiopoietin-2 and thrombospondin-2, in addition to BNP and cardiac troponin [43]. Additionally, miRNAs contribute significantly to the exploration of novel pMIHF biomarkers for future applications [44]. Higher circulating LIPCAR levels have been found in patients with pMIHF [45]. Our study has revealed several specific lipid disorders in patients that were highly related to clinical diagnosis indicators. The emergence of lipidomics tools has rapidly altered the process of discovery of biomarkers. Thus, the upregulation of PC (22:4_14:1) and downregulation of PE (12:1e_22:0) will provide a novel perspective from which to explore potential serum lipid biomarkers in patients.
Strengths and limitations
This study has several strengths. First, the study successfully obtained potential serum lipid biomarkers using the UHPLC-Q-Exactive MS lipidomics approach, to screen the metabolic profile changes between patients with MI and pMIHF, and combining it with ROC analysis. Second, correlation analysis revealed that PC (22:4_14:1) and PE (12:1e_22:0) were highly correlated with BNP, and could, thus, be novel biomarkers for early heart failure diagnosis. This study also had several limitations. First, targeted lipidomic analysis was not performed. Second, clinical samples were not sufficiently large and were not externally validated. Finally, middle-aged to older and yellow populations were obtained individually for detection in this study.
Future studies should use integrative untargeted and targeted metabolomics and lipidomics. Multicenter studies involving other races, ethnicities, and cultures within Asia are needed with a larger sample size. These findings should also be externally validated to identify their generalizability. Through combining proteomics and metabolomics, lipidomics, and urine analysis, potential biomarkers can be identified more specifically.