
==== Front
Heliyon
Heliyon
Heliyon
2405-8440
Elsevier

S2405-8440(24)10606-8
10.1016/j.heliyon.2024.e34575
e34575
Research Article
Serum Raman spectroscopy: Unearthing the snapshot of distinct metabolic profile in patients with congenital heart defects (CHDs)
Joshi Radha a1
Goswami Debosmita b1
Saha Panchali bc1
Hole Arti b
Mandhare Poonam a
Wadke Rishikesh d
Murthy Prabhatha Rashmi d
Borgohain Shyamdeep d
C Murali Krishna mchilakapati@actrec.gov.in
bc⁎
Kapoor Sudhir drsudhir.kapoor@srisathyasaisanjeevani.org
a⁎⁎
a Sri Sathya Sai Sanjeevani Research Centre, Sri Sathya Sai Sanjeevani Research Foundation, Plot No. 2, Sector 38, Kharghar, Navi Mumbai, 410210, Maharashtra, India
b Advanced Centre for Treatment, Research and Education in Cancer (ACTREC), Sector 22, Utsav Chowk - CISF Road, Owe Camp, Kharghar, Navi Mumbai, 410210, Maharashtra, India
c Training School Complex, Homi Bhabha National Institute, Anushakti Nagar, Maharashtra, India
d Sri Sathya Sai Sanjeevani Centre for Child Heart Care & Training in Pediatric Cardiac Skills, Plot No. 2, Sector 38, Kharghar, Navi Mumbai, 410210, Maharashtra, India
⁎ Corresponding author. Advanced Centre for Treatment Research and Education in Cancer, Tata Memorial Centre (TMC), Kharghar, Navi Mumbai, 410210, India. mchilakapati@actrec.gov.in
⁎⁎ Corresponding author. Sri Sathya Sai Sanjeevani Research Foundation, Sector 38, Kharghar, Navi Mumbai, 410210, India. drsudhir.kapoor@srisathyasaisanjeevani.org
1 These authors contributed equally.

14 7 2024
30 8 2024
14 7 2024
10 16 e3457516 3 2024
10 7 2024
11 7 2024
© 2024 The Authors
2024
https://creativecommons.org/licenses/by-nc/4.0/ This is an open access article under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
In the present study, efficacy of minimally-invasive serum Raman spectroscopy (SRS) in stratification of congenital heart diseases was explored. Blood was collected from 62 subjects [42 congenital heart defect (CHD) patients (19 with atrial septal defect, 13 with ventricular septal defect and 10 with tetralogy of fallot) and 20 controls], and serum separated. Raman spectra of sera were recorded, pre-processed and subjected to spectral and multivariate analyses. Multivariate curve resolution-alternating least squares (MCR-ALS) analyses indicated alterations in lipid and protein levels between the study groups. Principal Component Analysis (PCA) and Principal Component based Linear Discriminant Analysis (PC-LDA), cross-validated with Leave-one-out cross validation (LOOCV), were employed to study stratification between the different groups. CHD could be classified from controls with 76 % efficiency. The different CHD subtypes could be distinguished with efficiencies as high as ∼90 %. To the best of our knowledge, differentiation between controls and CHDs as well as the stratification between controls and CHDs subtypes was for the first time successfully accomplished by serum-based Raman spectroscopy.

Graphical abstract

Image 1

HIGHLIGHTS

• Serum based Raman spectroscopy is used for the first time to detect congenital heart diseases (CHDs.)

• Raman spectroscopy in conjunction with multivariate tools can be exploited for stratification of CHD.

• CHD could be classified from controls with 76 % efficiency.

• The different CHD subtypes could be distinguished with efficiencies as high as ∼90 %.

Keywords

Raman spectroscopy
Congenital heart disease
Acyanotic
Cyanotic
==== Body
pmc1 Introduction

Congenital Heart Defects (CHDs) are amongst the most common birth defects, with significant global burden [[1], [2], [3], [4], [5], [6], [7], [8], [9], [10], [11], [12]]. Prevalence of CHD in India is 9.3/1000 live births. Limited access to quality medical care is one of the reasons for this high prevalence rate observed in developing countries [2]. Realizing the impact and burden of CHD, in addition to other neonatal diseases, on the nations the UN in 2016 has adopted “The Sustainable Development Goals (SDGs)”. The main aim of SDGs is to reduce the mortality of neonates to less than 12 deaths per 1000 live births and the mortality of children to less than 25 deaths per 1000 live births. Estimating the burden of CHD on nations is a daunting task. Hence, researchers from various countries have formed a consortium “The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD)”, and share data to understand the variation in the trend of CHD prevalence and mortality throughout the world. A survey from 1990 to 2017 showed that CHD mortality rates declined with increasing socio-demographic Index (SDI); most deaths occurred in countries in the low and low-middle SDI quintiles. The report suggested global prevalence of CHD at birth, in 2017, is estimated to be nearly 1.8 cases per 100 live births [13,14]. Within India the prevalence of CHD at birth varies across different regions. The reasons for this include lack of awareness, financial limitations, unequal geographic distribution of tertiary pediatric cardiac care centres, etc [2].

Abnormal formation of heart structure during embryonic development results in CHD which contributes to nearly one third of congenital defects [15]. CHDs vary in complexity, impacting the childhood mortality and morbidity and quality of life [2]. Lack of prompt medical intervention for CHD results in impaired physiology, reflected by increased cardiac load and lower cardiac output. Effective increased pulmonary blood flow results in elevated mean pulmonary arterial pressure and eventual pulmonary arterial hypertension (PAH), which may prove irreversible and fatal in advanced stage [16]. Cardiac catheterization is gold standard in detecting PAH, but it is invasive technique. Thoracic ECHO is non-invasive technique, but its main limitation is the detection window of the trachea. Also, the extra cardiac features are not clear [16].

Efficient screening techniques for early detection, diagnosis and management of CHD have been greatly explored. Biomarkers serve as guiding light in overall disease assessment, clinical diagnosis, disease prognosis, recurrence risk and therapeutics [17]. Factors like genetic basis, epigenetic factors, specific hemodynamics, volume and pressure overload, altered metabolism and many more can pose a molecular signature specific for CHD and even CHD subtypes [18]. The varied molecular signatures captured by various techniques throw light on etiological and/or physiological basis of disease. To note a few, cardiac markers like troponin I, procollagen III peptide levels, BNP and N-terminal proBNP have been reviewed in detail with respect to CHD subtypes [18]. Serum levels of betaine, choline, acetylcholine, xanthosine, S-adenosyl methionine (SAM), guanine, inosine and guanosine were reported to be significantly changed in CHD with PAH as compared to CHD [16]. Apart from these, there are several molecular markers of heart failure, myocardial injury, hypoxia and tissue damage, inflammation and so on which are potentially relevant in CHD. All these markers result in disease specific molecular signatures.

Spectroscopic techniques have been widely used on medical and research fields for disease diagnosis and discovery of novel biomarkers. Out of various such techniques, Raman spectroscopy can analyze complex molecular compositions. This is an extremely efficient tool of clinical importance. It is based on the principle of inelastic photon scattering, and is beneficial for global analysis. This highly sensitive technique when used for blood analysis can yield a complex spectrum detecting multiple biomarkers simultaneously. Raman spectroscopy is a vibrational spectroscopy method that detects molecular vibration and molecular rotation energy levels. The characteristic peaks in Raman spectrum are called “fingerprints,” as they are specific to molecules. The simple process of sample preparation, the non-destructive detection process preserving sample integrity, amenability to repeated sampling, specificity, and high sensitivity, makes Raman spectroscopy an ideal technique to analyze the molecular composition of the bio-samples. It was envisaged that diseases and other pathological anomalies can lead to chemical and structural changes therefore changing vibrational spectra which can be used as sensitive, phenotypic markers of the disease. Due to this Raman spectroscopy has been widely explored for diagnosing a diverse range of diseases [[19], [20], [21], [22], [23], [24], [25], [26], [27], [28], [29]], as it detects components on the biomolecule level with a high sensitivity for distinguishing vibrations and conformations of proteins, peptides, and nucleic acid [30]. It is known that the metabolic changes in the heart induce the changes in the metabolome of biofluids [31], and serum is an essential source for metabolic profiling [32]. Also, the serum is not interfered with by anticoagulants molecules [33]. It has been suggested that, serum sample, free from blood corpuscles and coagulating factors is a superior choice of biological fluids for marker detection.

In this study, we have presented comparative analysis of serum Raman spectra in CHD cases and healthy controls in order to explore potential variations in spectral signatures. To the best of our knowledge this is first ever study to explore Raman spectroscopy in identifying potential spectral signatures of CHD. In this study, the efficacy of minimally-invasive serum Raman spectroscopy (SRS) in stratification of CHD cases and healthy controls as well as in distinguishing acyanotic CHD (ACHD) and cyanotic CHD (CCHD), was explored. Raman spectra of serum samples were subjected to spectral and multivariate analyses. Multivariate curve analysis alternating least squares (MCR-ALS) was employed to uncover the underlying biochemical variations. This approach has achieved noteworthy results in numerous RS studies of biological samples [34,35]. Spectral variations among the four CHD subtypes and controls were examined by MCR. Classification models were developed by Principal Component based Linear Discriminant Analysis (PC-LDA) and validated by leave one out cross validation (LOOCV). PC-LDA, combined with LOOCV, is a widely used classification method in RS studies, and has been known to yield good results [36,37].

2 Experimental

2.1 Materials

This study was approved by the Sri Sathya Sai Sanjeevani Centre, Kharghar, Institutional Ethics Committee (Registration No: EC/NEW/INST/2022/2782; registered with NECRBHR, DHR, Govt. of India). Written informed consent was obtained from parents of the CHD patients and healthy control subjects, before recruitment to the study. Informed consent was obtained from them after explaining the nature of study, methodology, voluntary participation, their rights, benefits, and study confidentiality.

CHD patients in the age group of 0–30 years (both genders inclusive) were included in the study during the period April 2022–March 2023. Total n = 42 CHD patients were recruited with complete data collection. Patients with severe cardiac and other illnesses were excluded from the study. All the participants were of Indian origin. The cases (n = 42) included 18 males (42.85 %) and 24 females (57.14 %) in the age group of 0–30 years (6.07 ± 6.61 years, mean ± SD). CHD evaluation in preoperative period was done by echocardiography (ECHO) and cardiac catheterization in selected cases. All preoperative diagnoses were confirmed intra operatively by surgical findings. All the patients were grouped into broad categories of cyanotic (tetralogy of fallot, TOF) and acyanotic CHDs (atrial septal defect, ASD, and ventricular septal defect, VSD).

Control (C) samples were obtained from the normal healthy pediatric beneficiaries of the well-baby program at the public health department of Sri Sathya Sai Sanjeevani Hospital. The C group (n = 20) included 10 males (50 %) and 10 females (50 %) in the age group of 0–12 years (3.96 ± 2.06 years, mean ± SD). Normal health status of controls was confirmed by thorough physical examination by the physicians and self-declared medical history by parents. C subjects exhibited no illnesses of any degree to minor illnesses like mild cough/cold at presentation. There was no history or presentation of any major illness or birth defect or CHD in C. The demographic and clinical features of the C and CHD groups have been summarized in Table 1, Table 2, respectively.Table 1 Demographic and clinical features of enrolled subjects.

Table 1Parameters	CHD (Cases n = 42)	Healthy controls(C) (n = 20)	
Age group (years)	
Average age (years)	6.07 ± 6.61 years	3.96 ± 2.06 years	
0–1	12	1	
1–5	15	14	
5–12	7	5	
12–18	6	0	
18–30	2	0	
Gender	
Male	18	10	
Female	24	10	
CHD subtypes	
Acyanotic- ASD	19	NA	
Acyanotic- VSD	13	NA	
Cyanotic- TOF	10	NA	
*NA: Not applicable.

Table 2 Age group wise classification of CHD cases.

Table 2Age group	ASD	VSD	TOF	
0–1	1	6	4	
1–5	7	6	2	
5–12	4	1	3	
12–18	5	0	1	
18–30	2	0	0	
Total cases	19	13	10	

2.2 Sample preparation and recording

The sample processing was as per the standard procedure mentioned in literature earlier (38). ∼2 mL peripheral blood samples were collected by venepuncture from all the subjects in plain vacutainers and were kept at room temperature for 1 h for clot formation. All biobank SOPs based on Indian Council of Medical Research (ICMR) guidelines & institutional guidelines were strictly followed, in order to maintain quality of samples. Serum was separated by centrifugation at 1800 rpm for 5 min at 4 °C and stored in separate aliquots at −80° C within 24 h of collection. A working aliquot of serum was passively thawed on ice (∼4° C) and only once used for Raman spectroscopy analysis to avoid repeated freeze-thawing and sample loss. 8 μl serum was used for Raman spectra acquisitions.

A WITec 300 R alpha (WITec GmbH in Germany) confocal Raman instrument was employed. The spectral acquisition conditions were: 532 nm laser source, approximate power output of 30 mW, 100× objective lens (Zeiss, NA 0.75), grating: 600 grooves/mm, 5 s acquisitions over 10 integrations. To evaluate intra sample variability, Raman spectra (n = 10) were obtained from distinct regions of the sample. The spectrograph was calibrated by utilising the 520 cm−1 Raman line of silica.

2.3 Data pre-processing and analysis

All spectra acquired were interpolated in the 600 to 1800 cm−1 range. Following this, all spectra were subjected to smoothing for noise reduction, baseline correction and vector normalization, effectively eliminating variations related to intensity and experimental conditions. To mitigate intra-sample variations, the ten pre-processed spectra for each sample were averaged, resulting in a single representative mean spectrum. The sample mean spectra were then subjected to multivariate analyses (Principal Component Analysis and PC-LDA). MCR-ALS analysis was employed to investigate the molecular level differences between the groups.

For multivariate analysis, Unscrambler X software (v.10.4.1, CAMO Software AS, Oslo, Norway) was employed, which included MCR-ALS, unsupervised PCA and supervised PC-LDA. MCR-ALS unravels the underlying sources of data variation and is used to obtain pure or mixtures of chemical components. SPSS was used for statistical significance tests. Unsupervised PCA allows for the identification of trends and patterns in spectral data by reducing the dimensionality of the data. PC-LDA is a supervised method that aims to maximize the differences between classes while minimizing the variability within each class. PC-LDA models were validated by LOOCV, the results of which have been presented below.

3 Results and discussion

3.1 Exploration of Raman spectral features

Fig. 1 shows the mean spectra of each of the study groups. Tyrosine (Tyr, 830, 850 cm−1), phenylalanine (Phe, 1005, 1033 cm−1), amide III (1247 cm−1), nucleic acids (1097 and 1340 cm−1), CH2 bending (1452 cm−1) and amide I (1663 cm−1) bands were observed in the spectra of all the groups.Fig. 1 Mean Raman spectrum with tentative band assignments(a) C, (b) TOF, (c) VSD, (d) ASD. Shaded regions represent standard deviation.

Fig. 1

To gain a more comprehensive understanding of the biomolecular differences between the study groups, exploratory MCR-ALS analysis was employed. Based on the spectral profiles, five spectral components were identified (Fig. 2A). Tentative band assignments of the components and abundance have been discussed below. Protein-related bands were observed in component 1 at 833, 854 (Tyr doublet), 1010 (Phe), 1039 (Phe), 1246 (amide III), 1457 (CH2 bending), 1621 (Tyr), 1673 cm−1 (amide I) and indicated β-fold structures. Component 2 constitutedprotein bands at 1005 (Phe), 1243 (amide III), 1455 (CH2 bending) and 1673 cm−1 (amide I). The bands of components 1 and 2 might be indicative of β-sheet structures which have been correlated with heart diseases in literature [[38], [39]]. Protein bands were observed at 1005 (Phe), 1248 (amide I), 1450 (CH2 bending) and 1668 cm−1 (amide I) in component 3. Component 5 showed features indicative of globular proteins at 939 (C–C), 1006 (Phe), 1319 (CH2), 1455 (CH2 bending) and 1656 cm−1 (amide I). Lipid-related bands at 1086, 1304, 1745 (phospholipids), 1445 (CH2 bending), and 1663 cm−1 were observed in component 4. Fig. 2 B illustrates the corresponding abundance profiles for each component. Average relative abundance, along with standard errors in each group is illustrated in Fig. 2C. Kruskal Wallis test followed by pairwise comparison of groups were employed to test for statistical significance. Component 1 was found to be significantly different (p = 0.017) in TOF and ASD groups with higher abundance of the same in ASD. Components 3 (p = 0.042) and 4 (p = 0.046) showed significant difference in C and TOF groups with higher abundance of component 3 in C and component 4 in TOF. Components 2 and 5 did not exhibit not significant difference across any of the groups.Fig. 2 (A) MCR-extracted spectral components for ASD, VSD, TOF and C (B) MCR extracted abundance profiles (C) Average relative abundance bar graphs (i) Component 1, (ii) Component 2, (iii) Component 3 (iv) Component 4, (v) Component 5. Error bars represent standard error. Kruskal Wallis test was employed for statistical significance analysis.

Fig. 2

3.2 Investigation of stratification

The mean spectra of the samples were subjected to unsupervised PCA and supervised PC-LDA.

3.2.1 CHD and C

In the initial 2-model system comparing all CHD subtypes with C, the PCA scatter plot (Fig. 3a) demonstrated some overlaps between the two groups. In PC-LDA with 5 Principal Components (PCs), which accounted for approximately 90 % of the variance, distinct clusters with a few overlaps were observed (Fig. 4a). The PC-LDA confusion matrix (Table 3a) summarized correct classifications and misclassifications, revealing that 32 out of 42 CHD cases and 14 out of 20C were identified correctly. This PC-LDA model demonstrated a sensitivity of 76.19 %, specificity of 70 %, a positive predictive value of 84.2 %, and a negative predictive value of 58.3 %.Fig. 3 PCA scatter plots (a) CHD and C, (b) ASD, VSD, TOF and C, (c) ACN and CYN, (d) VSD, ASD and TOF, (e) ASD and VSD, (f) ASD and TOF, (g) VSD and TOF.

Fig. 3

Fig. 4 PC-LDA scatter plots (a) CHD and C, (b) ASD, VSD, TOF and C, (c) ACN and CYN, (d) VSD, ASD and TOF, (e) ASD and VSD, (f) ASD and TOF, (g) VSD and TOF.

Fig. 4

Table 3 PC-LDA confusion matrices of (a) CHD vs C, (b) ASD vs TOF vs VSD vs C, (c) ACN vs CYN, (d) ASD vs VSD vs TOF, (e) ASD vs VSD, (f) ASD vs TOF, and (g) VSD vs TOF. Diagonal elements are true predictions and Ex-diagonal elements are false predictions.

Table 3

Predicted Class

	True class	
a)		CHD	C		
	CHD	32 (76 %)	6		
	C	10	14 (70 %)		
					
b)	ASD	TOF	VSD	C	
ASD	13 (68 %)	2	5	4	
TOF	1	8 (80 %)	0	1	
VSD	3	0	6 (46 %)	5	
C	2	0	2	10 (50 %)	
					
c)		ACN	CYN		
	ACN	25 (78 %)	2		
	CYN	7	8 (80 %)		
					
d)	VSD	ASD	TOF		
VSD	8 (61 %)	2	0		
ASD	4	14 (74 %)	3		
TOF	1	3	7 (70 %)		
					
e)		ASD	VSD		
	ASD	16 (84 %)	1		
	VSD	3	12 (92 %)		
					
f)		ASD	TOF		
	ASD	15 (79 %)	2		
	TOF	4	8 (80 %)		
					
g)		VSD	TOF		
	VSD	12 (92 %)	1		
	TOF	1	9 (90 %)		

3.2.2 ASD, VSD, TOF and C

Expanding the analysis to include all CHD subtypes and C in a 4-model system, it was observed that TOF formed a distinct group in PCA (Fig. 3b), while overlaps were observed within the other groups. PC-LDA using 5 PCs revealed overlapping clusters for ASD, VSD, and C but a distinct cluster for TOF (Fig. 4b). CHD cases 13/19 (68.42 %) ASD, 8/10 (80 %) TOF, 6/11 (46.1 %) VSD and 10/20 (50 %) C were correctly classified, as seen from the PC-LDA confusion matrix (Table 3b).

3.2.3 Acyanotic and cyanotic CHD

Next, the two major CHD subtypes acyanotic (ACN, VSD + ASD) and cyanotic (CYN, TOF) were compared via a 2-model system. Both PCA (Fig. 3c) and PC-LDA (Fig. 4c) scatter plots exhibited reasonably distinct clusters with minor overlaps. 25/32 (78.125 %) ACN, 8/10 (80 %) CYN, were correctly classified, as seen from the PC-LDA confusion matrix (Table 3c). The sensitivity, specificity, positive predictive value and negative predictive value of this PC-LDA model are 78.125 %, 80 %, 92.5 % and 53.3 % respectively.

3.2.4 VSD, ASD and TOF

In a 3-model system, VSD, ASD, and TOF subtypes were evaluated. Both PCA (Fig. 3d) and PC-LDA (Fig. 4d) scatter plots revealed reasonably distinct clusters for TOF but overlaps between ASD and VSD. PC-LDA correctly classified 8/13 (61.5 %) VSD, 14/19(73.6 %) ASD and 7/10 (70 %) TOF (Table 3d).

3.2.5 ASD and VSD

Next, the two subtypes of ACN were compared in a 2-model system. Some overlaps between the two groups were observed in the PCA scatter plot (Fig. 3e). PC-LDA scatter plots exhibited reasonably distinct clusters (Fig. 4e). Table 3e summarises the correct classifications which are 16/19 (84.2 %) for ASD and 12/13 (92.3 %) for VSD. The sensitivity, specificity, positive predictive value and negative predictive value of this PC-LDA model were 84.2 %, 92.3 %, 94.11 % and 80 % respectively.

3.2.6 ASD and TOF

In this 2-model system, ASD and TOF were compared. Both PCA (Fig. 3f) and PC-LDA scatter plots displayed distinct clusters (Fig. 4f). 15/19 (78.9 %) ASD, 8/10 (80 %) TOF, were correctly classified, as seen from the PC-LDA confusion matrix (Table 3f). The sensitivity, specificity, positive predictive value and negative predictive value of this PC-LDA model were 78.9 %, 80 %, 88.23 % and 66.66 %, respectively.

3.2.7 VSD and TOF

Finally, VSD and TOF were evaluated in a 2-model system. Both PCA (Fig. 3g) and PC-LDA scatter plots showed distinct clusters (Fig. 4g). 12/13 (92.3 %) VSD, 9/10 (90 %) TOF, were correctly classified, as seen from the PC-LDA confusion matrix (Table 3g). This PC-LDA model achieved sensitivity, specificity, positive predictive value, and negative predictive value rates of 92.3 %, 90 %, 92.3 %, and 90 %, respectively.

CHDs are serious birth defects caused due to abnormal development of heart; presenting pre- or post-natally. With advances in medical field, nowadays pediatric patients receive effective corrective surgeries or interventions; yet there is persistent risk of re-surgeries, arrhythmias, heart failures and associated neurological conditions post operatively in large number of cases [32]. Despite availability of advanced diagnostic tools like cardiac computerized tomography (CT) scan, magnetic resonance imaging (MRI) and echocardiography; biomarkers like B-type Natriuretic Peptide (BNP) have promising diagnostic application. This highlights the importance of search for more clinically relevant biomarkers for CHD for more insights in etiology and physiology of CHD, and the development of a rapid, minimal-invasive, label free and objective tool for the detection of CHD.

Disease detection at early stages drastically increases treatment success. Although metabolomics studies have provided leads for the identification of potential serum biomarkers for pediatric patients with CHD [32] as well as maternal biomarkers [[40], [41], [42]] for the detection of fetal CHDs [32,[40], [41], [42], [43], [44]], none are in routine clinical use. In recent years, extensive efforts have been devoted to the development of rapid and cost-efficient diagnostic methods that detect minute biochemical alterations with high accuracy and with minimal sample processing steps [[45], [46], [47], [48], [49]]. To this end several studies have reported the SRS's efficacy in the detection of a variety of disease conditions, and biomolecular alterations relevant to these diseases [[47], [48], [49], [50]]. Consistent with these studies, our results unveiled statistically significant alterations in lipids and protein related Raman bands of CHD patients compared to those of healthy children.

Using NMR spectroscopy variation in the five metabolites (i.e.valine, glucose, glutamine, creatinine and PUFA) were identified to differentiate between cyanotic CHDs from controls [50]. In another report the variation in CHD biomarkers troponin I and amino-terminal procollagen type III peptide in patients with ASDs, VSDs and TOF [18] was studied using assay method. Though, above studies report findings with higher specificity the methodology used was laborious as they need pre-treatment of samples. Several RS investigations on heart and cardiovascular diseases have been reported in literature. A study by Khristoforova et al. explored chronic heart failure detection by RS of skin tissues. Alterations in the intensities of 1445, 1087, 1157, 1275, 1335, and 1650 cm−1 were observed [51]. Another study examined vibrational spectroscopy imaging in following the disease progression and pathological changes in heart valves. A number of tissue RS studies of aorta [52], coronary [[53], [54]], brachiocephalic [55] and carotid arteries [56] for atherosclerosis have reported prominent protein features at 1650, 1250, and 1450 cm−1 [57]. Surface-enhanced Raman spectroscopy (SERS) study on human urine for the detection of coronary heart disease have documented intensity differences in 1223, 1243, 1272, 1463, 1481, 1516, 1536, 1541 and 1550 cm−1 Raman bands between coronary heart diseases and controls were reported [58]. The present study is the first to report SRS examination of CHD, to the best of our knowledge. MCR results of the present study yielded three statistically significant components. Component 1 composed of protein related bands at 833, 854,1010, 1039, 1246, 1457, 1621, and 1673 cm−1 indicative of β-fold structures was significantly different among ASD and TOF. Component 3, with prominent bands attributable to proteins, at 1005, 1248, 1450 and 1668 cm−1, was significantly altered among C and TOF. 1086, 1304, 1745, 1445, and 1663 cm−1, attributable to lipids, were observed in component 4, which was found to be significantly altered among C and TOF. TOF is amongst the most common and serious cyanotic CHDs (CCHD) [2]. Chronic hypoxia results in complex changes in whole blood composition as well as coagulation profile in children affected with CCHD. Prolonged hypoxia in TOF may potentially affect aerobic respiration pathway, amino acid and nucleobases metabolism leading to distinct plasma metabolic profile in TOF as compared to normal. Significant elevation in levels of many intermediates of purines and amino acids products but marked reduction in electron carriers and energy substrates in TOF as compared to normal have been reported [59]. These molecular markers in CHD patients can explain the variations in Raman spectral signatures between CHD and C in our study and to some extent even the spectral differences in CHD subtypes.

PC-LDA was employed to explore classification among the different groups. The initial 2-model system achieved a 76.19 % sensitivity and 70 % specificity in distinguishing all CHD cases from C. Comparing acyanotic (VSD + ASD) and cyanotic (TOF) CHD subtypes in a 2-model system yielded accuracies of 78.125 % for acyanotic and 80 % for cyanotic cases. Subsequent analyses of ASD vs. VSD, ASD vs. TOF, and VSD vs. TOF consistently demonstrated high accuracies of classification, reaching up to 92 %. The present study suggests that Raman spectroscopy could be a minimally invasive, safe and efficient technique for distinguishing not only CHD and control cases but also acyanotic and cyanotic cases.

Despite all the discussed merits, this preliminary study faces certain limitations. The presented findings need further validation in a more systematic study with large statistically significant sample size and uniform age group. Fasting sample collection needs to be explored carefully in future studies, as several dietary factors like food items containing carotenoids may significantly skew the Raman spectral features. The leads from this analysis will be useful for further biomarker characterization using NMR, HPLC and/or Mass Spectroscopy.

Raman spectroscopy can detect minute changes underlying the disease etiology or physiology as a reflection of disease specific biochemical alterations in tissues. This could be the plausible reason for seeing differences among the CHD sub types. The high classification efficiencies of the PC-LDA models highlight the clinical relevance of the method as rapid CHD detection tool. The present work corroborates the earlier findings mentioning the importance of Raman spectroscopy in bio-applications. In summary, the results obtained in this preliminary study inspire confidence for large scale validations with more discrete study groups. Additionally, metabolomics studies to gain insights into the disease biology of CHD and establishment of novel biomarkers are warranted, in view of the promising leads from this study.

4 Conclusion

This is the first report best to our knowledge to explore the efficacy of serum RS in CHD detection and stratification of CHD subtypes. In this study, 4-, 3- and 2- model systems comparing the various groups were constructed to explore stratification. PCA and PC-LDA were employed for dimensionality reduction and classification, respectively. The different subtypes of CHD, when grouped together in a 2-model system against C, achieved sensitivity of 76 %, specificity of 70 %, a positive predictive value of 84 %, and a negative predictive value of 58.3 %. The methodology provides greater efficiency in the detection of CCHD (TOF) as observed in the two and three model systems. >70 % TOF cases were accurately predicted in the 3 model PC-LDA, and were as high as ∼90 % in the two model systems against ASD and VSD. ACN groups, when clubbed together were identified with 78 % accuracy. On comparing the two ACN groups ASD and VSD, in a two-model system, PC-LDA identified 84 % ASD and 92 % VSD correctly. MCR extracted components attributable protein and lipid vary significantly (p < 0.05) among C and TOF, and ASD and VSD. Molecular reflection of distinct functional, molecular and cellular remodelling attributable to various CHDs was observed in dissimilar Raman signatures. Raman spectroscopy can pave a sound foundation for future studies aimed at the identification of CHD specific markers. Additionally, SRS signatures in CHD coupled with efficient AI tools can augment overall diagnostic efficiency of Raman spectroscopy.

Data availability statement

The data that has been used is confidential.

CRediT authorship contribution statement

Radha Joshi: Writing – original draft, Methodology, Investigation. Debosmita Goswami: Visualization, Methodology, Investigation. Panchali Saha: Writing – review & editing, Writing – original draft, Visualization, Validation, Methodology, Investigation, Formal analysis. Arti Hole: Validation, Methodology. Poonam Mandhare: Visualization, Methodology. Rishikesh Wadke: Methodology, Investigation. Prabhatha Rashmi Murthy: Methodology, Investigation. Shyamdeep Borgohain: Methodology, Investigation. Murali Krishna C: Writing – review & editing, Supervision, Software, Resources, Project administration, Conceptualization. Sudhir Kapoor: Writing – review & editing, Visualization, Supervision, Resources, Project administration, Conceptualization.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgment

The authors would like to thank Dr. C. Sreenivas, Chairman; Sri Sathya Sai Sanjeevani Research Foundation & Sri Sathya Sai Health and Education Trust, India; and Dr Sudeep Gupta, Director, 10.13039/501100007179 ACTREC , Navi Mumbai for their constant encouragement and support. The authors would also like to thank Dr. Sonal Raut, lab technicians, clinical & nonclinical support staff of Sri Sathya Sai Sanjeevani Centre for Child Heart Care & Training in Pediatric Cardiac Skills, Kharghar, Navi Mumbai, Maharashtra for their help in coordination for sample collection.
==== Refs
References

1 Hoffman J.I. Kaplan S. The incidence of congenital heart disease J. Am. Coll. Cardiol. 39 12 2002 1890 1900 10.1016/s0735-1097(02)01886-7 12084585
2 Saxena A. Congenital heart disease in India: a status report Indian Pediatr. 55 12 2018 1075 1082 30745481
3 Bravo-valenzuela N.J. B Peixoto A. Araujo J.E. Prenatal diagnosis of congenital heart disease: a review of current knowledge Indian Heart J. 70 1 2018 150 164 10.1016/j.ihj.2017.12.005 29455772
4 Pierpont M.E. Brueckner M. Chung W.K. Garg V. Lacro R.V. McGuire A.L. Mital S. Priest J.R. Pu W.T. Roberts A. Ware S.M. Gelb B.D. Russell M.W. Genetic basis for congenital heart disease: Revisited: A scientific statement from the American heart association Circulation 138 21 2018 e653 e711 10.1161/CIR.0000000000000606 30571578
5 Nees S.N. Chung W.K. Genetic basis of human congenital heart disease Cold Spring HarbPerspect Biol 12 9 2020 a036749 10.1101/cshperspect.a036749
6 Oyen N. Poulsen G. Boyd H.A. Wohlfahrt J. A Jensen P.K. Melbye M. Recurrence of congenital heart defects in families Circulation 120 4 2009 295 301 10.1161/CIRCULATIONAHA.109.85798 19597048
7 Ellesoe S.G. Workman C.T. Bouvagnet P. Loffredo C.A. McBride K.L. Hinton R.B. V Engelen K. Gertsen E.C. Mulder B.J.M. Postma A.V. Anderson R.H. Hjortdal V.E. Brunak S. Larsen L.A. Familial co-occurrence of congenital heart defects follows distinct patterns Eur. Heart J. 39 12 2018 1015 1022 10.1093/eurheartj/ehx314 29106500
8 Mullen M. Zhang A. Lui G.K. Romfh A.W. Rhee J.W. Wu J.C. Race and genetics in congenital heart disease: application of iPSCs, omics, and machine learning technologies Frontiers in Cardiovascular Medicine 8 2021 635280 10.3389/fcvm.2021.635280
9 Peng J. Meng Z. Zhou S. Zhou Y. Wu Y. Wang Q. Wang J. Sun K. The non-genetic paternal factors for congenital heart defects: a systematic review and meta-analysis Clin. Cardiol. 42 7 2019 684 691 10.1002/clc.23194 31073996
10 Joshi R.O. Chellappan S. Kukshal P. Exploring the role of maternal nutritional epigenetics in congenital heart disease Curr. Dev. Nutr. 4 11 2020 nzaa166 10.1093/cdn/nzaa166
11 Joshi R.O. Kukshal P. Kumar A. Murthy P.R. Chellappan S. Manohar K. Sathe Y. Guhathakurta S. Kapoor S. Congenital heart defects and environmental factors: a snapshot of CHD cohort from a tertiary cardiac care centre in India Ann. Pediatr. 6 2023 1123
12 Kukshal P. Joshi R.O. Kumar A. Ahamad S. Murthy P.R. Sathe Y. Manohar K. Guhathakurta S. Chellappan S. Case–control association study of congenital heart disease from a tertiary paediatric cardiac centre from North India BMC Pediatr. 23 1 2023 290 10.1186/s12887-023-04095-x 37322441
13 Zimmerman M.S. Smith A.G.C. Sable C.A. Echko M.M. Wilner L.B. Olsen H.E. Atalay H.T. Awasthi A. Bhutta Z.A. Boucher J.L. Castro F. Cortesi P.A. Dubey M. Fischer F. Hamidi S. Hay S.I. Hoang C.L. Hugo-Hamman C.T. Jenkins K.J. Kar A. Khalil I.A. Kumar R.K. Kwan G.F. Mengistu D.T. Mokdad A.H. Naghavi M. Negesa L. Negoi I. Negoi R.I. Nguyen C.T. Nguyen H.L.T. Nguyen L.H. Nguyen S.H. Nguyen T.H. Nixon M.R. Noubiap J.J. Patel S. Peprah E.K. Reiner R.C. Roth G.A. Temsah M.H. Tovani-Palone M.R. Towbin J.A. Tran B.X. T Tran T. T Truong N. Vos T. Vosoughi K. Weintraub R.G. Weldegwergs K.G. Zaidi Z. Zheleva B. Zuhlke L.J. Murray C.J.L. Martin G.R. Kassebaum N.J. Global, regional, and national burden of congenital heart disease, 1990-2017: a systematic analysis for the Global Burden of Disease Study 2017 Lancet Child and Adolesc Health 4 3 2020 185 200 10.1016/S2352-4642(19)30402-X
14 European Commission European network of population-based registries for the epidemiological surveillance of congenital anomalies (EUROCAT) prevalence charts and Tables https://eu-rd-platform.jrc.ec.europa.eu/eurocat/eurocat-data/prevalence_en 2019
15 Linde D. Konings E.E.M. Slager M.A. Witsenburg M. Helbing W.A. Takkenberg J.J.M. Roos-Hesselink J.W. Birth prevalence of congenital heart disease worldwide: a systematic review and meta-analysis J. Am. Coll. Cardiol. 58 21 2011 2241 2247 10.1016/j.jacc.2011.08.025 22078432
16 Jin N. Yu M. Du X. Wu Z. Zhai C. Pan H. Guand J. Xie B. Identification of potential serum biomarkers for congenital heart disease children with pulmonary arterial hypertension by metabolomics BMC Cardiovasc. Disord. 23 1 2023 167 10.1186/s12872-023-03171-5 36991345
17 Upadhyay R.K. Emerging biomarkers of congenital heart diseasesand disorders J. Stem Cell Res. Ther. 1 3 2016 108 115 10.15406/jsrt.2016.01.00021
18 Sugimoto M. Kuwata S. Kurishima C. Hye Kim J. Iwamoto Y. Senzaki H. Cardiac biomarkers in children with congenital heart disease World J Pediatr 11 4 2015 309 315 10.1007/s12519-015-0039-x 26454435
19 Naseer K. Amin A. Saleem M. Qazi J. Identification of new spectral signatures from hepatitis C virus infected human sera Spectrochim. Acta Mol. Biomol. Spectrosc. 222 2019 117181 10.1016/j.saa.2019.117181
20 Khan S. Ullah R. Khan A. Ashraf R. Ali H. Bilal M. Saleem M. Analysis of hepatitis B virus infection in blood sera using Raman spectroscopy and machine learning Photodiagnosis Photodyn. Ther. 23 2018 89 10.1016/j.pdpdt.2018.05.010 29787817
21 Lee J.H. Kim B.C. Oh B.K. Choi J.W. Rapid and sensitive determination of HIV-1 virus based on surface enhanced Raman spectroscopy Biomed. Nanotechnol. 11 12 2015 2223 10.1166/jbn.2015.2117
22 Ralbovsky N.M. Halamkova L. Wall K. Anderson-Hanley C. Lednev I.K. Screening for alzheimer's disease using saliva: a new approach based on machine learning and Raman hyperspectroscopy J. Alzheimers Dis. 71 4 2019 1351 10.3233/JAD-190675 31524171
23 Parlatan U. Inanc M.T. Ozgor B.Y. Oral E. Bastu E. Unlu M.B. Basar G. Raman spectroscopy as a non-invasive diagnostic technique for endometriosis Sci. Rep. 9 1 2019 19795 10.1038/s41598-019-56308-y
24 Feng S. Chen R. Lin J. Pan J. Chen G. Li Y. Cheng M. Huang Z. Chen J. Zeng H. Nasopharyngeal cancer detection based on blood plasma surface-enhanced Raman spectroscopy and multivariate analysis Biosens. Bioelectron. 25 11 2010 2414 10.1016/j.bios.2010.03.033 20427174
25 Lin D. Feng S. Pan J. Chen Y. Lin J. Chen G. Xie S. Zeng H. Chen R. Colorectal cancer detection by gold nanoparticle based surface-enhanced Raman spectroscopy of blood serum and statistical analysis Opt Express 19 14 2011 13565 10.1364/OE.19.013565
26 Desai S. Mishra S.V. Joshi A. Sarkar D. Hole A. Mishra R. Dutt S. Chilakapati M.K. Gupta S. Dutt A. Raman spectroscopy-based detection of RNA viruses in saliva: a preliminary report J. Biophot. 13 20 2020 e202000189 10.1002/jbio.202000189
27 Mehta K. Atak A. Sahu A. Srivastava S. Murali K.C. An early investigative serum Raman spectroscopy study of meningioma Analyst 143 8 2018 1916 10.1039/c8an00224j 29620771
28 Perez A. Pradaa Y.A. Cabanzoa R. Gonzalezb C.I. Mejia-Ospino E. Diagnosis of chagas disease from human blood serum using surface enhanced Raman scattering (SERS) spectroscopy and chemometric methods Sens. Bio-Sens. Res 21 2018 40 45 10.1016/j.sbsr.2018.10.003
29 Krafft C. Sergo V. Biomedical applications of Raman and infrared spectroscopy to diagnose tissues Spectroscopy 2006 20 195 218 10.1155/2006/738186
30 Duguid J.G. Bloomfield V.A. Benevides J.M. Thomas G.J. Jr. Raman spectroscopy of DNA-Metal Complexes. I The thermal denaturation of DNA in the presence of Sr2+ , Ba2+, Mg2+, Ca2+, Mn2+, Co2+, Ni2+, and Cd2+ Biophys. J. 69 6 1995 2623 10.1016/S0006-3495(95)80133-5 8599669
31 Sabatine M.S. Liu E. Morrow D.A. Heller E. McCarroll R. Wiegand R. Berriz G.F. Roth F.P. Gerszten R.E. Metabolomic identification of novel biomarkers of myocardial ischemia Circulation 112 5 2005 3868 3875 10.1161/CIRCULATIONAHA.105.569137 16344383
32 Yu M. Sun S. Yu J. Du F. Zhang S. Yang W. Xiao J. Xie B. Discovery and validation of potential serum biomarkers for pediatric patients with congenital heart diseases by metabolomics J. Prot Res. 17 10 2018 3517 3525 10.1021/acs.jproteome.8b00466
33 Hernandes V.V. Barbas C. Dudzik D. A review of blood sample handling and pre-processing for metabolomics studies Electrophoresis 38 18 2017 2232 2241 10.1002/elps.201700086 28543881
34 Mojidra Rahul Araki A. Krishna C.M. Maruyama R. Yamamoto T. Noothalapati H. DNA fingerprint analysis of Raman spectra captures global genomic alterations in imatinib-resistant chronic myeloid leukemia: a potential single assay for screening imatinib resistance Cells 10 10 2021 2506 10.3390/cells10102506 34685486
35 Iwasaki Keita Identification of molecular basis for objective discrimination of breast cancer cells (MCF-7) from normal human mammary epithelial cells by Raman microspectroscopy and multivariate curve resolution analysis Int. J. Mol. Sci. 22 2 2021 800 10.3390/ijms22020800 33466869
36 Sahu Aditi Sawant S. Mamgain H. Krishna C.M. Raman spectroscopy of serum: an exploratory study for detection of oral cancers Analyst 138 14 2013 4161 4174 10.1039/c3an00308f 23736856
37 Chaturvedi Deepika Balaji S.A. Bn V.K. Ariese F. Umapathy S. Rangarajan A. Different phases of breast cancer cells: Raman study of immortalized, transformed, and invasive cells Biosensors 6 4 2021 57 10.3390/bios6040057
38 Li Y.L. Xie J.Y. Lu B. Sun X.D. Chen F.F. Tong Z.J. Sai W.W. Zhang W. Wang Z.H. Zhong M. β-sheets in serum protein are independent risk factors for coronary lesions besides LDL-C in coronary heart disease patients Front. Cardiovasc. Med. 9 2022 911358 10.3389/fcvm.2022.911358
39 Derbala M.H. Guo A.S. Mohler P.J. Smith S.A. The role of βII spectrin in cardiac health and disease Life Sci. 192 2018 278 285 10.1016/j.lfs.2017.11.009 29128512
40 Bahado-Singh R.O. Ertl R. Mandal R. Bjorndahl T.C. Syngelaki A. Han B. Dong E. Liu P.B. Alpay-Savasan Z. Wishart D.S. Nicolaides K.H. Metabolomic Prediction of fetal congenital heart defect in the first trimester AJOG 211 3 2014 240.e1 240.e14 10.1016/j.ajog.2014.03.056
41 Friedman P. Yilmaz A. Ugur Z. Jafar F. Whitten A. Ustun I. Turkoglu O. Graham S. Bahado Singh R.O. Urine metabolomic biomarkers for prediction of isolated fetal congenital heart defect J. Matern. Fetal Neonatal Med. 35 25 2022 6380 6387 10.1080/14767058.2021.1914572 33944672
42 Radhakrishna U. Vishweswaraiah S. Veerappa A.M. Zafra R. Albayrak S. H Sitharam P. Saiyed N.M. Mishra N.K. Guda C. Bahado-Singh R.O. Newborn blood DNA epigenetic variations and signaling pathway genes associated with Tetralogy of Fallot (TOF) PLoS One 13 9 2018 e0203893 10.1371/journal.pone.0203893
43 Hakuno D. Hamba Y. Toya T. Adachi T. Plasma amino acid profiling identifies specific amino acid associations with cardiovascular function in patients with systolic heart failure PLoS One 10 2 2015 e0117325 10.1371/journal.pone.0117325
44 Fukushima A. Zhang L. Huqi A. Lam V.H. Rawat S. Altamimi T. Wagg C.S. Dhaliwal K.K. Hornberger L.K. Kantor P.F. Rebeyka I.M. Lopaschuk G.D. Acetylation contributes to hypertrophy-caused maturational delay of cardiac energy metabolism JCI Insight 3 10 2018 e99239 10.1172/jci.insight.99239
45 Yang L.G. Song Z.X. Yin H. Wang Y.Y. Shu G.F. Lu H.X. Wang S.K. Sun G.J. Low n-6/n-3 PUFA ratio improves lipid metabolism, inflammation, oxidative stress and endothelial function in rats using plant oils as n-3 fatty acid source Lipids 51 1 2016 49 59 10.1007/s11745-015-4091-z 26526061
46 Kanoh M. Inai K. Shinohara T. Tomimatsu H. Nakanishi T. Clinical implications of eicosapentaenoic acid/arachidonic acid ratio (EPA/AA) in adult patients with congenital heart disease Heart Ves. 32 12 2017 1513 1522 10.1007/s00380-017-1015-2
47 Pichardo-Molina J.L. Frausto-Reyes C. Barbosa-García O. Huerta-Franco R. González-Trujillo J.L. Ramírez-Alvarado C.A. …Medina-Gutiérrez C. Raman spectroscopy and multivariate analysis of serum samples from breast cancer patients Laser Med. Sci. 22 4 2007 229 236 10.1007/s10103-006-0432-8
48 Yin G. Li L. Lu S. Yin Y. Su Y. Zeng Y. …Lang J. An efficient primary screening of COVID‐19 by serum Raman spectroscopy J. Raman Spectrosc. 52 5 2021 949 958 10.1002/jrs.6080 33821082
49 Mehta K. Atak A. e A. Srivastava S. An early investigative serum Raman spectroscopy study of meningioma Analyst 143 8 2018 1916 1923 10.1039/c8an00224j 29620771
50 Saha P. Sawant S. Deshmukh A. Hole A. Krishna C.M. Serum Raman spectroscopy: prognostic applications in oral cancers Head Neck 45 5 2023 1244 1254 10.1002/hed.27338 36919570
51 Vimal S. Ranjan R. Yadav S. Majumdar G. Mittal B. Sinha N. Agarwal S.K. Serum metabolomics profiling to identify novel biomarkers for Cyanotic Heart Disease Biomed. pharmacol. J. 14 2021 81 94 10.13005/bpj/2101
52 Khristoforova Yulia A. Bratchenko L.A. Skuratova M.A. Lebedeva E.A. Lebedev P.A. Bratchenko I.A. Raman spectroscopy in chronic heart failure diagnosis based on human skin analysis J. Biophot. 16 7 2023 e202300016 10.1002/jbio.202300016
53 Rygula A. Pacia M.Z. Mateuszuk L. Kaczor A. Kostogrys R.B. Chlopicki S. Baranska M. Identification of a biochemical marker for endothelial dysfunction using Raman spectroscopy Analyst 140 7 2015 2185 2189 10.1039/c4an01998a 25664353
54 Römer T.J. Brennan J.F. Schut T.C. Wolthuis R. van den Hoogen R.C. Emeis J.J. van der Laarse A. Bruschke A.V. Puppels G.J. Raman spectroscopy for quantifying cholesterol in intact coronary artery wall Atherosclerosis 141 1 1998 117 124 10.1016/s0021-9150(98)00155-5 9863544
55 Römer T.J. Brennan J.F. Fitzmaurice M. Feldstein M.L. Deinum G. Myles J.L. Kramer J.R. Lees R.S. Feld M.S. Histopathology of human coronary atherosclerosis by quantifying its chemical composition with Raman spectroscopy Circulation 97 9 1998 878 885 10.1161/01.cir.97.9.878 9521336
56 Marzec Katarzyna M. Visualization of the biochemical markers of atherosclerotic plaque with the use of Raman, IR and AFM J. Biophot. 7 9 2014 744 756 10.1002/jbio.201400014
57 Sćepanović O.R. Fitzmaurice M. Miller A. Kong C.R. Volynskaya Z. Dasari R.R. Kramer J.R. Feld M.S. Multimodal spectroscopy detects features of vulnerable atherosclerotic plaque J. Biomed. Opt. 16 1 2011 011009 10.1117/1.3525287
58 Li B. Ding H. Wang Z. Liu Z. Cai X. Yang H. Research on the difference between patients with coronary heart disease and healthy controls by surface enhanced Raman spectroscopy Spectrochim. Acta Mol. Biomol. Spectrosc. 2022 272 10.1016/j.saa.2022.120997 120997
59 Wang W. Cui H. Ran G. Du C. Chen X. Dong S. Huang S. Yan J. Chu J. Song J. Plasma metabolic profiling of patients with tetralogy of fallot Clin. Chim. Acta 548 2023 117522 10.1016/j.cca.2023.117522
