Association between Serum Milk Fat Globule-EGF Factor 8 and Prognosis in Patients with Heart Failure

doi:10.21203/rs.3.rs-4951954/v1

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Association between Serum Milk Fat Globule-EGF Factor 8 and Prognosis in Patients with Heart Failure

https://doi.org/10.21203/rs.3.rs-4951954/v1

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Objective: Investigating the relationship between serum milk fat globule-EGF factor 8 (MFGE8) and prognosis in patients with heart failure (HF).

Methods:This research conducted a prospective observational analysis on patients with HF at Huai’an TCM Hospital, which is affiliated with Nanjing University of Chinese Medicine between September 2021 and September 2022. The main focus was determined as combined result of being readmitted to the hospital for HF or all-cause death within a year. Various methods were utilized to evaluate the relationship between MFGE8 and prognosis, such as least absolute shrinkage and selection operator regression (LASSO), logistic regression, generalized additive model (GAM), multivariate adaptive regression splines (MARS), and random forest.

Results:A total of 153 patients were enrolled, and 44 patients had outcome events (28.76%). The MFGE8 at hospital release were significantly lower in patients with primary the main outcome (398 [258, 762] vs. 600 [36, 892], P = 0.002), as was the fold change of MFGE8 at discharge and admission (ΔMFGE8) (4.37 [-73.34, 69.1] vs. 58.6 [30.2, 88.2], P < 0.001). Four variables were chosen using by LASSO regression: hypertension, body mass index, MFGE8 at discharge, and ΔMFGE8. Multivariate logistic regression was implemented using the chosen variables. The results showed that MFGE8 at discharge (beta = -0.001, OR = 1.000, P = 0.039) and ΔMFGE8 (beta = -3.786, OR = 0.022, P = 0.003) were independently associated with primary outcome. The random forest model determined that the ΔMFGE8 is the variable with the highest level of significance (GINI coefficient: 10.64).

Conclusion: The presence of Serum MFGE8 is inversely associated with the likelihood of clinical outcome in HF patients, suggesting that it might be a valuable indicator of the prognosis for HF patients.

MFGE8

Heart Failure

Prognosis

Biomarker

Prospective Observational Study

Despite the rapid development in contemporary medical technology, heart failure (HF) reminds a prominent cause of morbidity and mortality worldwide[1, 2]. Being a progressive disease, it presents multiple difficulties in accurately predicting the outcomes for patients[3, 4]. Multiple risk factors have been demonstrated to be associated with prognosis of HF, including age, body mass index (BMI), glycemic status, smoking, and genetic factors[5, 6]. Currently, several biomarkers such as brain natriuretic peptide (BNP), N-terminal proBNP (NT-proBNP), and high-sensitivity C-reactive protein (hs-CRP) are advised to support therapeutic therapy of HF[7, 8]. Nevertheless, the categorization of risk based on current clinical signs only accounts for a portion of the variation in individual prognosis of HF patients. Study clinically relevant biomarkers that predict specific therapy targets and individual prognosis remains crucial.

Milk fat globule-EGF factor 8, or lactadherin, is a glycoprotein that is released into the body and was first discovered in milk and mammary epithelial cells. It is found throughout the human body in various locations[9, 10]. Recent studies have investigated the molecular mechanisms and clinical applications of MFGE8 in different organs. According to reports, MFGE8 was found to be implicated in the progression of obesity and played a significant role in promoting insulin signaling by interacting with integrin β5[11]. Furthermore, previous studies have indicated a negative correlation between MFGE8 the severity of coronary artery stenosis, as well as the risk of experiencing vascular complications[12]. The Finnish biobank study, FinnGen discovered a correlation between an inframe insertion rs534125149 in MFGE8 gene (which encodes lactadherin) with a reduced risk of coronary atherosclerosis[13]. Administering recombinant human MFGE8 to rats reduced atrial fibrosis (AF) and remodeling, and further decreased AF vulnerability induced by Ang-II in vivo[14]. Additional study demonstrated a significant decrease in circulating MFGE8 levels in HF patients with dilated cardiomyopathy[15], which aligns with the experimental results[15, 16]. However further clinical evidence is required to substantiate these findings. Therefore, it is necessary to conduct ongoing clinical studies to examine the correlation between the serum level of MFGE8 and the prognosis of disease development in HF patients.

HF is a complicated clinical illness that exhibits variability in multiple generic or disease-specific measurements. The connection between indicators and outcomes are not completely linear[17]. Nonlinear calibration models have recently become more popular due to their ability to address both linear and nonlinear multivariate relationships[18, 19]. This study aimed to explore the correlation between MFGE8 and specific clinical metrics to HF, and to demonstrate the predictive significance of MFGE8 in HF patients using multiple approaches.

Study design and participants

This prospective observational study was conducted at the Huai’an TCM Hospital Affiliated to Nanjing University of Chinese Medicine from September 2021 to September 2022, enrolling patients with HF. Conditions for inclusion: 1) Aged ≥ 18 years; 2) Diagnosed with HF in accordance with the Guidelines for Diagnosis and Treatment of Heart Failure in China[20]; 3) Have a New York Heart Association (NYHA) II–IV at during the screening process. Conditions for exclusion: 1) Life expectancy of less than 30 days; 2) Stroke or transient ischemic attack within the past 3 months; 3) Heart transplant recipients or patients who were listed for heart transplant; 4) Cognitive dysfunction, or using antidepressants and other antipsychotics; 5) Coagulation dysfunction and blood system diseases; 6) Malignancy; or 7) Participation in other clinical studies within the past 3 months. We obtained Informed consent from each patient. The study was approved by the Ethics Committee of the Huai’an TCM Hospital and the ethical approval number was K2020-25.

Data collection

Demographic information of hospitalized patients was collected, including gender, age and body mass index (BMI). Clinical data, such as blood pressure, NYHA classification, history of PCI, and comorbidities (hypertension, type 2 diabetes (T2D), coronary artery disease (CAD)), along with routine laboratory parameters, including NT-proBNP, Hs-CRP, Apolipoprotein A (ApoA), ApoB, fasting plasma glucose (FPG), uric acid (UA), serum creatinine (SCr), epidermal growth factor receptor (eGFR), D-Dimer, were also collected. In additionally, an echocardiogram was conducted within the initial 48h of admission to determine the left ventricular ejection fraction (LVEF), left ventricular end diastolic dimension (LVDD), left anterior descending artery (LAD) was measured.

Serum MFGE8 detection

The concentration of MFGE8 in the serum of patients was measured using a human MFGE8 ELISA Kit (CSB-E12637h) in duplicate, following the instructions provided by the manufacturer. The serum was incubated on coated plates, and then the MFGE8 detection antibody was introduced, forming a bond with streptavidin that had been conjugated with horseradish peroxidase (HRP). The level of MFGE8 expression was quantified by incubating the samples with tetramethylbenzidine (TMB) and measuring the absorbance of optical density (OD) at 450 nm using a microplate reader.

Follow-up and outcomes

Patients were monitored at 1, 6, and 12 months. The main outcome was defined as the combined outcome of being readmitted to the hospital for HF or all-cause death during a period of 1 year. The secondary outcomes consisted of individual outcomes for each unique outcome: 1) hospital readmission for HF; 2) all-cause death.

Statistical analysis

Statistical analysis was carried out with the R programming language (version 4.3.0). Normally distributed continuous variables were reported as means ± standard deviations (SD) and differences between groups were assessed using Student’s t-test. Skewed continuous variables as median (interquartile range), and differences were tested using Mann-Whitney U test. Categorical variables were given as n (percentage), and tested using chi-square or Fisher’s exact tests for unordered variables and the Cochran-Armitage test for ordered multi-categorical variables. Propensity score matching (PSM) analysis was used to eliminate potential selection bias with the following covariates: age, gender, BMI, and NYHA. Spearman correlation analysis was used analyze the data. A two‐sided P < 0.05 was judged statistically significant.

Given that some of the independent variables in our dataset may be highly correlated, we used east absolute shrinkage and selection operator (LASSO) regression to filter feature variables by constructing penalty functions and compressing coefficients, followed 10-fold cross-validation to fine-tune the optimal penalty parameter. The screened-in variables were then used in multivariable logistic regression models or the generalized additive model (GAM). The random forest method was applied to rank the importance of variables. Furthermore, we attempted to evaluate potential connections between patients features and prognosis using a multivariate adaptive regression splines (MARS) model.

A total of 153 patients were enrolled, and all of them completed their follow-ups. After 1–12 months of follow-up, 44 patients (28.76%) experienced outcome events, including 42 (27.45%) readmissions for HF, and 10 (6.54%) all‐cause deaths. Patients with primary outcomes (case group) exhibited higher BMI (26.0 ± 2.56 vs. 24.7 ± 3.13, P = 0.010), and higher rates of hypertension (90.9% vs. 75.2%, P = 0.044), and higher D-Dimer levels (0.45 [0.31, 0.86] vs. 0.30 [0.21, 0.52], P = 0.012) compared to those without outcomes (control group). After admission, there was no significant difference in serum MFGE8 between the two groups (P = 0.114). However, after hospital discharge, serum MFGE8 was significantly lower in patients with main outcome (398 [258, 762] vs. 600 [36, 892], P = 0.002). Comparing to the control group, patients with main outcome had a smaller fold change of MFGE8 at discharge and admission (ΔMFGE8 = (MFGE8 at discharge - MFGE8 at admission) / MFGE8 at admission) (0.10 [0.05, 0.18] vs. 0.01 [-0.15, 0.17], P = 0.004) (Table 1). In the secondary outcome analysis, readmitted patients had lower serum MFGE8 at discharge and ΔMFGE8 compared to those without hospital readmission for HF. The results were similarly comparable when comparing patients with and without mortality outcomes.

Table 1

Characteristics of patients.
Variables	Control group (n = 109)	Case group (n = 44)	P
Sex, n (%)			1.000
Female	47 (43.1%)	19 (43.2%)
Male	62 (56.9%)	25 (56.8%)
Age, years	70.0 [64.0, 79.0]	71.0 [63.8, 80.2]	0.881
BMI, kg/m²	24.7 ± 3.13	26.0 ± 2.56	0.010
NYHA, n (%)			0.102
II	25 (22.9%)	7 (15.9%)
III	62 (56.9%)	22 (50.0%)
IV	22 (20.2%)	15 (34.1%)
CAD, n (%)			0.674
Yes	103 (94.5%)	43 (97.7%)
No	6 (5.50%)	1 (2.27%)
PCI, n (%)			0.149
Yes	39 (35.8%)	22 (50.0%)
No	70 (64.2%)	22 (50.0%)
Hypertension, n (%)			0.044
Yes	82 (75.2%)	40 (90.9%)
No	27 (24.8%)	4 (9.09%)
T2D, n (%)			0.821
Yes	36 (33.0%)	13 (29.5%)
No	73 (67.0%)	31 (70.5%)
NT-proBNP, pg/ml	499 [120, 1235]	820 [232, 1938]	0.115
Hs-CRP, mg/L	6.25 [1.74, 9.08]	3.04 [1.13, 9.42]	0.217
LVEF, %	0.56 [0.49, 0.63]	0.57 [0.46, 0.62]	0.722
LVDD, mm	46.0 [42.0, 51.0]	46.0 [43.0, 52.5]	0.291
LAD, mm	35.0 [32.0, 42.0]	37.0 [32.0, 42.0]	0.597
ApoA, mmol/L	1.07 [0.92, 1.24]	1.08 [0.83, 1.21]	0.290
ApoB, mmol/L	0.74 [0.64, 0.83]	0.76 [0.68, 0.84]	0.255
FPG, mmol/L	5.80 [5.00, 7.10]	5.60 [5.00, 6.55]	0.687
UA, umol/L	331 [257, 423]	337 [269, 429]	0.489
SCr, umol/L	81.0 [68.0, 96.0]	82.0 [66.8, 101]	0.700
D-Dimer, mg/L	0.30 [0.21, 0.52]	0.45 [0.31, 0.86]	0.012
eGFR, ml/min/1.73m²	74.6 ± 25.8	72.4 ± 25.5	0.625
SBP, mmHg	134 [130, 148]	130 [120, 139]	0.088
DBP, mmHg	80.0 [71.0, 90.0]	80.0 [72.0, 85.0]	0.735
MFGE8 at admission	541 [343, 827]	331 [279, 722]	0.114
MFGE8 at discharge	600 [368, 892]	398 [258, 762]	0.002
ΔMFGE8	0.10 [0.05, 0.18]	0.01 [-0.15, 0.17]	0.004

After adjusting age, gender, BMI and NYHA using PSM analysis, 25 propensity score-matched pairs between the case and control groups were identified (Fig. 1). The clinical results were comparable between the two groups, except for serum ΔMFGE8 (0.13 ± 0.10 vs. 0.02 ± 0.19, P = 0.015) (Table 2). Correlation analysis revealed a negative correlation (r = -0.508, P < 0.001) between NT-proBNP and MFGE8 at admission. However, no correlation was found between Hs-CRP and MFGE8 at admission (r = -0.291, P > 0.05) (Table 3).

Table 2

Characteristics of the patients after propensity score matching.
Variables	Control group (n = 25)	Case group (n = 25)	P
Sex, n (%)			0.363
Female	6 (24.0)	10 (40.0)
Male	19 (76.0)	15 (60.0)
Age, years	68.3 ± 12.2	69.4 ± 10.4	0.738
BMI, kg/m²	25.7 ± 2.17	25.4 ± 2.47	0.706
NYHA, n (%)			0.735
II	7 (28.0)	5 (20.0)
III	13 (52.0)	16 (64.0)
IV	5 (20.0)	4 (16.0)
CHD, n (%)			0.609
Yes	22 (88.0)	24 (96.0)
No	3 (12.0)	1 (4.00)
PCI, n (%)			0.232
Yes	6 (24.0)	11 (44.0)
No	19 (76.0)	14 (56.0)
Hypertension, n (%)			1.000
Yes	22 (88.0)	22 (88.0)
No	3 (12.0)	3 (12.0)
DM, n (%)			0.537
Yes	9 (36.0)	6 (24.0)
No	16 (64.0)	19 (76.0)
NT-proBNP, pg/ml	373 [118, 903]	783 [240, 1902]	0.277
Hs-CRP, mg/L	5.32 [0.94, 8.23]	3.00 [0.78, 7.93]	0.521
EF, %	0.56 ± 0.09	0.54 ± 0.11	0.515
LVDD, mm	45.9 ± 5.35	48.2 ± 7.27	0.198
LAD, mm	35.9 ± 4.90	37.9 ± 5.52	0.182
ApoA, mmol/L	1.09 [0.92, 1.23]	1.10 [0.87, 1.26]	0.977
ApoB, mmol/L	0.71 [0.64, 0.75]	0.72 [0.66, 0.83]	0.240
FPG, mmol/L	5.90 [4.70, 6.70]	5.30 [4.80, 6.50]	0.683
UA, umol/L	339 [256, 457]	355 [251, 429]	0.778
SCR, umol/L	78 [69, 95]	82.0 [66, 106]	0.497
D-Dimer, mg/L	0.24 [0.16, 0.43]	0.35 [0.27, 0.48]	0.101
eGRF, ml/min/1.73m²	83.2 ± 27.0	72.2 ± 25.3	0.144
SBP, mmHg	134 [130, 147]	130 [120, 138]	0.311
DBP, mmHg	80.60 ± 9.65	80.10 ± 10.60	0.856
MFGE8 at admission	560 [443, 787]	371 [283, 748]	0.327
MFGE8 at discharge	706 ± 318	550 ± 301	0.082
ΔMFGE8	0.13 ± 0.10	0.02 ± 0.19	0.015

Table 3

Correlations between MFGE8 and other indicators.
Variables	Before propensity score matching			After propensity score matching
Variables	MFGE8 at admission	MFGE8 at discharge	ΔMFGE8	MFGE8 at admission	MFGE8 at discharge	ΔMFGE8
Age	-0.034	-0.068	-0.003	-0.100	-0.124	0.029
NT-proBNP	-0.372***	-0.371***	0.130	-0.508**	-0.476***	0.211
Hs-CRP	-0.286***	-0.250***	0.058	-0.291*	-0.270	0.011
EF	0.035	0.069*	0.040	0.179	0.153	0.092
LVDD	-0.162*	-0.175	0.004	-0.300*	-0.247	0.064
LAD	-0.281***	-0.270***	0.188*	-0.361**	-0.354*	0.133
ApoA	-0.140	-0.105	-0.063	-0.157	-0.144	-0.052
ApoB	-0.092	-0.170*	-0.099	-0.120	-0.118	-0.075
BMI	-0.132	-0.194*	-0.124	0.029	-0.006	-0.172
FPG	0.056	0.041	0.070	0.126	0.109	-0.125
UA	-0.076	-0.098	0.007	-0.319*	-0.266	0.180
SCr	0.017	-0.006	0.026	-0.194	-0.169	0.099
DD	-0.111	-0.093	0.083	-0.133	-0.117	0.018
eGFR	-0.062	-0.022	0.030	-0.016	-0.004	-0.036
SBP	0.047	0.028	-0.019	0.184	0.227	0.173
DBP	-0.009	-0.007	-0.027	0.149	0.163	-0.001
Note: () indicated P < 0.05, () indicated P < 0.01, and (**) indicated P < 0.001.

Four models were created to evaluate the results’ stability, LASSO regression with10-fold cross-validation (model 1) was used to select candidate variable after adjusting the penalty coefficient λ. The optimum model value was λ (lambda. min = 0.061), with four variables selected: hypertension, BMI, MFGE8 at discharge, and ΔMFGE8 (Fig. 2). The four variables were added to the multivariate logistic regression model (model 2). The results revealed that MFGE8 at discharge (beta = -0.001, OR = 1.000, P = 0.039) and ΔMFGE8 (beta = -3.786, OR = 0.022, P = 0.003) were independently associated with the main outcome (Table 4). We then created the GAM (model 3) using the four variables listed above, taking into account any non-linear correlations that could exist. The study found the substantial link between ΔMFGE8 and primary outcomes events (Fig. 3). The random forest model (model 4) identifies ΔMFGE8 as the most relevant variable (GINI coefficient: 10.64) (Fig. 4). The MARS analysis (model 5) was instead of the standard linear model. Among all the variables, it selected ΔMFGE8 and MFGE8 upon admission. The results revealed a significant relationship between serum ΔMFGE8 and the prognosis of HF patients, which could be verified using several approaches.

Table 4

Multivariate logistic regression
Variables	Beta	OR	P value
BMI	0.123	1.131	0.069
Hypertension	1.200	3.320	0.049
MFGE8 at discharge	-0.001	0.999	0.039
ΔMFGE8	-3.786	0.023	0.003

This study explored the relationship between serum MFGE8 and prognosis in HF patients. The results demonstrated a correlation between ΔMFGE8 and the prognosis of HF patients, suggesting that ΔMFGE8 could potentially be used as a predictive biomarker for HF. The findings may have implications for the development and improvement of therapeutic interventions for patients with HF.

HF is a developing health issue with substantial morbidity and mortality. Thus, it is critical to find biomarkers related with the prognosis of HF patients. A previous study has found that recurrent HF admission was a risk factor for mortality among HF patients[21]. This study focused on hospital readmission and mortality, which shared numerous risk factors[22, 23]. In this study, serum MFGE8 after hospital discharge and ΔMFGE8 were significantly lower in patients with main outcome. After PSM analysis all the clinical features were comparable. However, the change in ΔMFGE8 remained statistically significant. It was consistent with the recent findings that circulating MFGE8 levels decreased dramatically in failing hearts from patients with dilated cardiomyopathy[15]. NT-proBNP is an important predictor for the diagnosis and prognosis of HF, and it is regularly used to evaluate the severity of HF[24, 25]. This study indicated that during admission, MFGE8 and NT-proBNP were modestly correlated both before and after PSM analysis, indicating the possible role of serum MFGE8 in HF patients’ prognosis.

Recently, MFGE8-mediated diagnostic or therapeutic potential has been discovered in several cardiovascular disorders. In mice, MFGE8 was an age‑related inflammatory factor for both aortic remodeling and the induction of angiotensin II-dependent in younger mice, with an aortic inflammatory phenotype reported in advanced age[26]. Clinical studies found a negative correlated between circulating MFGE8 levels and the severity of atherosclerosis[27], as well as cardiac dysfunction and remodeling in afflicted patients[15]. This study found a negative correlated between serum MFGE8 and clinical events in HF patients. Multiple approaches might be used to assess this statistically significant association. Thus, MFGE8 may have the potential to be a biomarker for the prognosis of HF.

There were some limitations to this study. Despite considering 10-fold EPV (Events Per Variable), this single-center study with tiny sample size cannot rele out single-centered effects. Secondly, the study focused on whether or not clinical outcomes occurred, without taking into account the time of occurrence. Moreover, previous studies have found association between MFGE8 and AF, which it not been discussed in this study. Finally, information on the impact of post discharge rehabilitation training for was unavailable. Subsequent multicenter studies with larger sample sizes are still require to corroborate the findings of this study.

Overall, the presence of serum MFGE8 is negatively associated with the risk of clinical events in HF patients. Additionally, the alteration of MFGE8 from admission to discharge is particularly. MFGE8 could potentially function as prognosis markers for patients with HF.

Acknowledgements

We extend our great thanks to the enrolled patients for their devotion to the study.

Contributors: Feng Lu helped to formulate the hypothesis, and edited the manuscript. Lei Zhou conceived the project, supervised all experiments. Feng Lu collected the data. Jie Hou, Xueyun Huang and Shuguang Zhang were involved in data cleaning, follow-up, and verification. Lei Zhou analyzed the data. Feng Lu drafted the manuscript. Lei Zhou and Feng Lu contributed to the interpretation of the results and critical revision of the manuscript for important intellectual content and approved the final version of the manuscript. All authors have read and approved the final manuscript. Fneg Lu and Lei Zhou are the study guarantors.

Fundings: This study was supported by Hubei Provincial Natural Science Foundation （No.2022CFB930 to Lei Zhou）.

Ethical approval: This study was approved by the ethical review committee of Huai’an TCM Hospital Affiliated to Nanjing University of Chinese Medicine and Xiangyang No. 1 People′s Hospital, Hubei University of Medicine.

Competing interest: The authors declare that they have no conflicts of interest.

Data sharing: No additional data are available.

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No competing interests reported.

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Editorial decision: Revision requested
03 Sep, 2024
Editor assigned by journal
30 Aug, 2024
Submission checks completed at journal
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21 Aug, 2024

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Association between Serum Milk Fat Globule-EGF Factor 8 and Prognosis in Patients with Heart Failure

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Abstract

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Introduction

Methods

Study design and participants

Data collection

Serum MFGE8 detection

Follow-up and outcomes

Statistical analysis

Results

Discussion

Declarations

References

Additional Declarations

Status:

Version 1