High-Wavenumber Infrared Spectroscopy of Blood Plasma for Pre-Eclampsia Detection with Machine Learning

Abstract

Early detection of pre-eclampsia is challenging due to the low sensitivity and specificity of current clinical methods and biomarkers. This study investigates the potential of high-wavenumber FTIR spectroscopy (region between 2800 and 3600 cm⁻¹) as an innovative diagnostic approach capable of providing comprehensive biochemical insights with minimal sample preparation. Blood samples were collected from 33 pregnant women and their corresponding 33 newborns during induction or spontaneous labor. By analyzing the dried blood plasma samples, we identified biomarkers associated with FTIR vibrational modes, including 2853.6 cm⁻¹ (CH₂ stretching in lipids), 2873.0 cm⁻¹ (CH₃ stretching in lipids and proteins), and 3279.7 cm⁻¹ (O–H stretching related to water and proteins). Machine learning classification revealed 76.3% ± 3.5% sensitivity and 56.1% ± 4.4% specificity in distinguishing between pre-eclamptic and non-pre-eclamptic pregnant women, along with 79.0% ± 3.5% sensitivity and 76.9% ± 6.2% specificity for newborns. The overall accuracy for classifying all pregnant women and newborns was 71.8% ± 2.5%. The results indicate that high-wavenumber FTIR spectroscopy can enhance classification performance when combined with other analytical methods. Our findings suggest that investigating hydrophilic sites may complement plasma analysis in clinical settings.

1. Introduction

This pregnancy-specific condition contributes to maternal and perinatal mortality worldwide. The timely diagnosis of pre-eclampsia is crucial for implementing treatment; however, a concrete form of diagnosis is still being discussed. A first-trimester screening algorithm has been developed and validated to predict preterm pre-eclampsia. The discussion for predicting term disease, where the majority of cases occur, is poor. Clinically established biomarkers such as soluble fms-like tyrosine kinase-1 (sFlt-1) and placental growth factor (PlGF) are utilized in cases of suspected preterm pre-eclampsia, providing a high negative predictive value for confidently excluding disease in women with normal results, but their sensitivity remains modest [1]. While these methods are widely accepted and form the backbone of prenatal screening, they have limitations in terms of sensitivity and specificity, particularly in identifying cases of nonproteinuric or early-onset pre-eclampsia. Additionally, screening approaches that rely solely on clinical markers may fail to detect the complex molecular and biochemical changes associated with pre-eclampsia, contributing to delayed diagnosis and management. Considering these challenges, there is a growing interest in exploring more advanced diagnostic tools, such as Fourier Transform Infrared (FTIR) spectroscopy, which can detect subtle biomolecular alterations in blood plasma, potentially improving the early detection and differentiation of pre-eclampsia [2].

These methods are useful while lacking sensitivity and specificity, leading to delayed diagnosis and inadequate treatment [3]. Limitations include variability in biomarkers and reliance on symptoms that may overlap with other conditions including gestational and chronic hypertension, urinary tract infections, renal diseases, liver disorders, autoimmune diseases, diabetes, thrombophilia, and placental abruption. Given these limitations, there is a pressing need for novel diagnostic approaches. Fourier-transform infrared (FTIR) spectroscopy has emerged as a promising method for biochemical analysis, offering rapid, non-invasive insights into the molecular composition of biological samples. Previous studies have indicated that FTIR can detect biochemical changes associated with pre-eclampsia, potentially providing a complementary approach to conventional screening methods. In this context, it is essential to compare the results obtained through FTIR spectroscopy with those from established routine screening methods to assess its effectiveness and clinical applicability. This paper aims to explore the diagnostic potential of FTIR in the context of pre-eclampsia, examining how its findings correlate with existing biomarkers and clinical screening practices, thereby contributing to the development of more effective predictive tools in obstetric care.

FTIR spectroscopy provides information on the composition of substances according to their molecular content. This fact has been used in different research and topics since the early 1990s to distinguish the difference between normal and diseased tissues. Some initial studies were conducted for in vivo applications using ATR-IR combined with optical fibers [4,5,6]. FTIR spectroscopy offers distinct advantages over Raman spectroscopy, particularly in the analysis of biological fluids such as plasma. FTIR provides higher sensitivity in detecting biomolecular changes, with fewer issues related to fluorescence interference that often complicate Raman spectra. This makes FTIR an ideal tool for identifying biomarkers in complex biological samples, which is crucial in the diagnosis of conditions such as pre-eclampsia [7].

Fourier-transform infrared (FTIR) spectroscopy offers significant advantages compared to other spectroscopic techniques, such as mass spectrometry, nuclear magnetic resonance (NMR) spectroscopy, and UV–Vis spectroscopy. Mass spectrometry is highly sensitive and capable of identifying specific molecular compounds but requires complex sample preparation and may not be suitable for rapid analyses in clinical settings. Conversely, NMR spectroscopy provides detailed structural information about analysed molecules but is limited by high operational costs and the need for large sample volumes. UV–Vis spectroscopy, while useful for analyzing compounds that absorb ultraviolet or visible light, faces challenges when applied to complex biological fluids like blood plasma, where the presence of multiple components can interfere with readings. In contrast, FTIR stands out for its ability to perform rapid, non-invasive analyses, providing a comprehensive view of the biochemical composition of samples, making it a promising tool for early detection of pre-eclampsia and other pathological conditions [6,8].

In comparison to other methods, Fourier Transform Infrared (FTIR) spectroscopy offers rapid, non-destructive, and high-throughput analysis for faster diagnosis, timely treatment and provides additional information to conventional exams that can be used for further clinical decision-making once the first symptoms of pre-eclampsia have been controlled. FTIR can detect specific biochemical changes in biofluids and biological tissues by identifying molecular vibrations corresponding to different chemical functional groups within the analyzed sample. These molecular vibrations are associated with proteins, lipids, and other metabolites, making FTIR a good alternative for detailed biochemical fingerprints of biofluids [9]. The cFTIR capability to analyze multiple metabolites simultaneously is promising for early and accurate pre-eclampsia detection [10]. Clinically, this means that FTIR could facilitate earlier diagnosis and better monitoring of pre-eclampsia, potentially improving maternal and fetal outcomes [11].

The use of the high-wavenumber (HW) region, between 2600 and 3800 cm⁻¹, for the diagnosis of pathologies offers several advantages over the fingerprint region (400 to 1800 cm⁻¹). This spectral region mostly comprises vibrational modes related to CH₂, CH₃ and OH, which are biochemically within certain lipids, proteins and confined water. Recent studies have shown that the HW region contains diagnostic information similar to the fingerprint region, making it effective in discriminating tissue structures with different molecular compositions, as observed in brain and bladder tumors. The use of FTIR spectroscopy in the HW region can be defined as high-wavenumber infrared spectroscopy or HWIR spectroscopy. When investigating biological samples, HWIR spectroscopy captures molecular vibrations of the hydrophilic sites within biomolecules such as lipids, proteins, carbohydrates and nucleic acids. Given the maturity of FTIR as a technology, not only portable equipment can be produced, but also biofluid analysis and sample classification can be achieved in minutes, limited mostly by the sample drying time. Little to no professional training is required to use FTIR equipment. In addition, clinical translation of bound water analysis can be facilitated by automatic analysis of the HW region of infrared spectra by using machine learning. Thus, HW spectroscopy presents a more practical and economical alternative for diagnostics that can complement biochemical analysis and potentially introduce new methods for time-sensitive clinical decision-making [12].

Machine learning classification applied to FTIR spectroscopy data analysis has shown great potential in detecting conditions such as pre-eclampsia by identifying specific biomarkers in biological samples. For example, previous studies have demonstrated that machine learning algorithms, such as Neural Networks and Support Vector Machines (SVM), can be used to analyze FTIR spectra, allowing the distinction between healthy and diseased tissues as well as the identification of biochemical changes in fluids such as plasma. This approach not only improves diagnostic accuracy but also enables the simultaneous analysis of multiple metabolites, which is crucial for the early detection of pathologies. The integration of machine learning with FTIR provides additional information that can complement traditional clinical examinations, contributing to more informed decision-making in clinical practice [13,14].

Various physical and chemical analytical methods have been utilized for pre-eclampsia detection. Techniques such as mass spectrometry, nuclear magnetic resonance (NMR) spectroscopy, and enzyme-linked immunosorbent assays (ELISA) have been employed in studies involving patient cohorts ranging from small groups of 20–50 patients to larger studies involving hundreds of participants [15]. However, these methods require extensive sample preparation, are time-consuming, and may not be feasible for routine clinical use. In addition, current analytical methods may not capture the full spectrum of biochemical changes associated with pre-eclampsia [16] and may benefit from additional biomolecular information associated with the onset of pre-eclampsia or monitoring its potential development.

Despite advancements in pre-eclampsia detection, a method that can provide comprehensive biochemical insights with minimal sample preparation is still needed. FTIR spectroscopy addresses this gap by enabling detailed molecular analysis that could provide a more holistic view of the patient’s condition.

Our proposed analysis using FTIR in the high-wavenumber region involves detecting pre-eclampsia by capturing critical biochemical information associated with the interaction between bound water and other blood plasma metabolites. The combination of FTIR spectroscopy and machine learning to evaluate the usefulness of the high-wavenumber region in pre-eclampsia diagnostics. For interpretation of potentially associated biological and biochemical processes, we identified biomarkers associated with the FTIR spectroscopy information used for the classification of control and pre-eclampsia pregnant patients, as well as control and pre-eclampsia newborns. By providing details on biomarkers and the associated diagnostic relevance of high-wavenumber FTIR spectroscopy, we showed its potential to be translated into clinics and/or to complement other clinical and laboratory tests.

To the best of our knowledge, we performed the first study using high-wavenumber Fourier-transform infrared (FTIR) spectroscopy for pre-eclampsia detection, as well as evaluation of bound water to in hydrophilic sites of blood plasma biomolecules including lipids, proteins, carbohydrates and nucleic acids. Furthermore, we achieved one of the first studies across any disease or condition to analyze the usefulness of the high-wavenumber region and evaluate the molecular binding to water by examining the information that can be extracted by several combinations of data processing methods including spectral derivative and machine learning classification.

2. Materials and Methods

2.1. Clinical Protocol

Our study included 33 pregnant women and corresponding 33 newborns undergoing induction of labor or spontaneous labor at Municipal University Hospital of Taubaté (HMUT) enrolled in a retrospective observational pilot study. This study was approved by Research Ethics Committee of the University of Taubaté under protocol approval number CAAE: 34126120.9.0000.5501. All procedures followed the guidelines of the Declaration of Helsinki and respective local institutional guidelines also respecting international regulations.

2.2. Inclusion Criteria

All study participants included in our study were over 18 years old, were non-smokers, and had a single pregnancy. Also, all participants were pregnant women who began prenatal care during the first half of pregnancy (gestational age ≤ 20 weeks), and had at least one risk factor for the development of pre-eclampsia. These risk factors included obstetric history, primiparity, nulliparity, history of pre-eclampsia in a previous pregnancy, age ≥ 35 years, obesity (BMI ≥ 30 kg/m²), or chronic arterial hypertension. Women without any of these risk factors but diagnosed with pre-eclampsia at the time of peripartum were also included.

Gestational age was determined based on the last menstrual period using Nagele’s rule and/or corrected through an ultrasound examination performed in the first half of pregnancy. Additionally, the definition of chronic arterial hypertension during pregnancy followed the criteria of the American College of Obstetrics and Gynecology.

2.3. Exclusion Criteria

From our study, we excluded pregnant women with conditions during pregnancy that could either interfere with the interpretation of the biochemical values of interest, or who did not reach the second half of pregnancy. The excluded cases had medical history of gestational diabetes, autoimmune diseases, and miscarriages. Also, loss of follow-up was determined as voluntary withdrawal from our study or the inability to access birth data.

2.4. Sample Collection and Processing

Blood was collected either during the patient’s hospitalization for clinical follow-up at the pathological obstetrics ward at HMUT or at the time of delivery. Antecubital fossa was the preferably selected site for vacuum blood collection with a 4 mL Vacuette^® tube containing EDTA (ethylenediaminetetraacetic acid; anticoagulant). Newborn blood was collected from the umbilical cord. After collection, blood tubes were identified and stored at 4 °C until the sample processing step at the Dental Research Center. The samples remained cooled at 4 °C for about 3 h until processing for plasma extraction by centrifuging them at 3000× g for 10 min and the supernatant was stored at –80 °C until FTIR spectroscopy analysis.

2.5. FTIR Spectroscopy

FTIR spectra of blood plasma samples were collected using a Bruker Alpha II spectrometer equipped with an ATR-FTIR diamond crystal and heating functions to facilitate sample drying. We pipetted samples directly onto the spectrometer crystal without additives and allowed to dry for 1–3 min before data collection. Each sample’s spectrum was recorded in triplicate. To prevent cross-contamination, the crystal was sanitized with 70% alcohol and allowed to dry completely after each spectral data collection and before measuring the next sample.

2.6. Study Groups

A division of samples into 4 study groups was made: control newborns, control pregnant patients, pre-eclampsia newborns and pre-eclampsia pregnant patients. The control pregnant group consisted of 17 pregnant people and their respective newborns in which the pregnant people only had their blood collected from the antecubital fossa and newborn blood was collected from the umbilical cords of each pregnant patient. Similarly, 16 pre-eclampsia pregnant patients and their corresponding newborns had their blood collected with the same procedure and pregnant patients were followed up either at the HMUT high-risk prenatal care department or, in the case of pre-eclampsia diagnosis, in prenatal care at other locations.

2.7. Spectral Data Analysis

We performed the spectral pre- and post-processing using MATLAB (R2018a version, MathWorks, Natick, Massachusetts, United States) scripts. Spectral pre-processing of the raw FTIR spectra included smoothing with a Savitsky–Golay filter (second polynomial order, 11-point frame window) and vector normalization. The vibrational modes and corresponding biochemical compounds were utilized to assign the primary constituents of our blood plasma samples. The second derivative of the normalized FTIR spectra was subsequently employed for plasma classification into the study groups by utilizing various supervised machine learning algorithms. These algorithms included Support Vector Machine (SVM), fine, medium, coarse, weighted, and cosine k-Nearest Neighbors, as well as Linear Discriminant Analysis and Quadratic Discriminant Analysis.

To ensure clarity and consistency, we presented only the classification performance metrics of the most accurate classifier across all study groups or pairs of groups. The most important metrics for our feasibility study were sensitivity, specificity, accuracy and area under the receiver operating characteristic curve (AUC). These metrics were calculated as per the definitions and equations below:

• True positive (TP): number of pre-eclampsia pregnant patients or newborns correctly classified;
• False positive (FP): healthy pregnant patients or newborns incorrectly classified;
• True negative (TN): healthy pregnant patients or newborns correctly classified;
• False negative (FN): number of pre-eclampsia pregnant patients or newborns incorrectly classified.

$$\mathrm{S}\mathrm{e}\mathrm{n}\mathrm{s}\mathrm{i}\mathrm{t}\mathrm{i}\mathrm{v}\mathrm{i}\mathrm{t}\mathrm{y}={\displaystyle \frac{\mathrm{T}\mathrm{P}}{\mathrm{T}\mathrm{P}+\mathrm{F}\mathrm{N}}}$$

(1)

$$\mathrm{S}\mathrm{p}\mathrm{e}\mathrm{c}\mathrm{i}\mathrm{f}\mathrm{i}\mathrm{c}\mathrm{i}\mathrm{t}\mathrm{y}={\displaystyle \frac{\mathrm{T}\mathrm{N}}{\mathrm{T}\mathrm{N}+\mathrm{F}\mathrm{P}}}$$

(2)

$$\mathrm{A}\mathrm{c}\mathrm{c}\mathrm{u}\mathrm{r}\mathrm{a}\mathrm{c}\mathrm{y}={\displaystyle \frac{\mathrm{T}\mathrm{P}+\mathrm{T}\mathrm{N}}{\mathrm{T}\mathrm{P}+\mathrm{F}\mathrm{N}+\mathrm{T}\mathrm{N}+\mathrm{F}\mathrm{P}}}$$

(3)

It is worth noting that sensitivity and specificity are only pertinent for classification using two groups (one healthy group and one diseased group). The receiver operating characteristic curve (also known as ROC curve) features the true positive rate (TPR) on the Y-axis, and false positive rate (FPR) on the X-axis. In the classification using two groups, the TPR equals sensitivity and FPR equals (1-specificity). However, in the classification using more than two groups (e.g., our classification including all four study groups referred to in Section 2.6), TP and FP considered the positive group as the selected group for calculating the AUC (e.g., AUC_{Control newborn} considered the newborn controls as positive group), while TN and FN included the remaining groups (e.g., when calculating AUC_{Control newborn} for the classification of all four study groups, the negative group included the control pregnant patients, pre-eclampsia newborns and pre-eclampsia pregnant patients. Accuracy could still be calculated for any number of study groups because it equals the number of correctly classified spectra (observations of this study) divided by the total number of correctly classified spectra.

We reported the mean and standard deviation of these metrics calculated over 20 iterations of 5-fold cross-validation (CV). Each iteration applied random sampling, i.e., the dataset was randomly divided into training and validation sets with 80% and 20% of the total data, respectively. At each iteration, machine learning model training and validation were repeated five times until all parts of the dataset were tested using all five validation sets. Therefore, the mean of 20 iterations reflects the average classification performance, and the standard deviation indicates the stability of the model based on the dataset’s distribution. Figure 1 below indicates the steps for machine learning classification and validation based on processed FTIR spectra and their second derivative.

3. Results

Figure 2 shows that the mean of the processed FTIR spectra (Figure 2A,B) and the mean of the second derivative of all processed FTIR spectra (Figure 2C,D) for each study group. The main bands and peaks of the mean FTIR spectra were used for the assignment of the main biochemical constituents of blood plasma, as shown in

Table 1. Assignment of the main vibrational modes and structural components to FTIR peaks evidenced in blood plasma spectra of our study groups [16,17].

Bands (cm−1)	Vibrational Modes	Biomolecular Components	Study Groups Where Vibrational Modes Can Be Observed
2854	νs CH2 of lipids, νC–H, CH2 symmetric stretching, Asymmetric CH2 stretching mode of the methylene chains in membrane lipids	Lipids and small contribution of carbohydrates, nucleic acids and proteins with C-H bonds	All
2873	νs CH3, Stretching C–H and N–H, CH3 symmetric stretching, Symmetric stretching vibration of CH3 of acyl chains	Lipids, peptides/proteins, and contribution from carbohydrates, nucleic acids with C-H and N-H bonds	All
2927	νC–H, νas CH2 and CH3, Stretching C–H	Lipids, proteins, carbohydrates and nucleic acids	Pregnant (control and pre-eclampsia)
2932	νas CH2 and CH3, Stretching C–H	Lipids, proteins, carbohydrates and nucleic acids	Newborn (control and pre-eclampsia)
2959	CH3 asymmetric stretching, CH stretching, νas CH3, asymmetric stretching mode of the methyl groups	Lipids, proteins, carbohydrates and nucleic acids	Newborn (pre-eclampsia)
2960	CH3 asymmetric stretching, CH stretching, νas CH3, asymmetric stretching mode of the methyl groups	Lipids, proteins, carbohydrates and nucleic acids	All
3011	ν =CH	Pregnant (control and pre-eclampsia)	Newborn (control and pre-eclampsia)
3013	ν =CH	Unsaturated lipids and cholesterol esters	All
3065	C2c–H2 aromatic stretching	Major contribution: Steroids and carbohydrates, Small contribution: side chains of lipids and proteins	All
3083	C–H ring	Major: Steroids and carbohydrates, Small contribution: side chains of lipids and proteins	All
3114	C–H ring	Major: Steroids and carbohydrates, Small contribution: side chains of lipids and proteins	All
3192	N–H stretching bands of mainly cis-ordered substructures, Stretching N–H symmetric	Peptides/proteins	Newborn (control and pre-eclampsia)
3194	N–H stretching bands of mainly cis-ordered substructures, Stretching N–H symmetric	Peptides/proteins	Pregnant (control and pre-eclampsia)
3280	νO–H (water), νN–H (protein)	Water and proteins	All
3288	Stretching OH symmetric	Water	All
3456	Stretching OH symmetric	Water	All
3483	OH bonds	Water	All

and discussed in Section 4.

Table 2. Comparison between classification performance metrics achieved for 4-class models using either processed FTIR spectra or their second derivative. Please note that sensitivity and specificity cannot be defined when two control groups are present.

Classification Performance Metric	FTIR Spectra	Second Derivative of FTIR Spectra
Accuracy (%)	71.8 ± 2.5	63.8 ± 2.0
$\mathit{A}\mathit{U}{\mathit{C}}_{\mathit{C}\mathit{o}\mathit{n}\mathit{t}\mathit{r}\mathit{o}\mathit{l}\mathit{n}\mathit{e}\mathit{w}\mathit{b}\mathit{o}\mathit{r}\mathit{n}}$	0.918 ± 0.013	0.923 ± 0.008
$\mathit{A}\mathit{U}{\mathit{C}}_{\mathit{C}\mathit{o}\mathit{n}\mathit{t}\mathit{r}\mathit{o}\mathit{l}\mathit{p}\mathit{r}\mathit{e}\mathit{g}\mathit{n}\mathit{a}\mathit{n}\mathit{t}}$	0.901 ± 0.009	0.886 ± 0.009
$\mathit{A}\mathit{U}{\mathit{C}}_{\mathit{P}\mathit{r}\mathit{e}\mathit{c}\mathit{l}\mathit{a}\mathit{m}\mathit{p}\mathit{s}\mathit{i}\mathit{a}\mathit{n}\mathit{e}\mathit{w}\mathit{b}\mathit{o}\mathit{r}\mathit{n}}$	0.933 ± 0.016	0.864 ± 0.015
$\mathit{A}\mathit{U}{\mathit{C}}_{\mathit{P}\mathit{r}\mathit{e}\mathit{c}\mathit{l}\mathit{a}\mathit{m}\mathit{p}\mathit{s}\mathit{i}\mathit{a}\mathit{p}\mathit{r}\mathit{e}\mathit{g}\mathit{n}\mathit{a}\mathit{n}\mathit{t}}$	0.837 ± 0.020	0.810 ± 0.013

AUC: area under curve.

and

Table 3. Comparison between classification performance metrics achieved for 2-class models using either processed FTIR spectra or their second derivative. Please note that sensitivity and specificity cannot be defined when two control groups are present.

	Pregnant		Newborn
Spectral Type	FTIR Spectra	Second Derivative of FTIR Spectra	FTIR Spectra	Second Derivative of FTIR Spectra
Classification performance metric
Sensitivity (%)	76.3 ± 3.5	63.7 ± 5.9	79.0 ± 3.5	74.5 ± 4.2
Specificity (%)	56.1 ± 4.4	54.1 ± 5.1	76.9 ± 6.2	68.8 ± 4.0
Accuracy (%)	66.3 ± 2.9	58.9 ± 3.5	78.0 ± 3.8	71.6 ± 2.8
$\mathit{A}\mathit{U}{\mathit{C}}_{\mathit{C}\mathit{o}\mathit{n}\mathit{t}\mathit{r}\mathit{o}\mathit{l}}$	0.692 ± 0.036	0.613 ± 0.032	0.83 ± 0.04	0.792 ± 0.025
$\mathit{A}\mathit{U}{\mathit{C}}_{\mathit{P}\mathit{r}\mathit{e}\mathit{e}\mathit{c}\mathit{l}\mathit{a}\mathit{m}\mathit{p}\mathit{s}\mathit{i}\mathit{a}}$	0.692 ± 0.036	0.613 ± 0.032	0.83 ± 0.04	0.792 ± 0.025

compare the classification performance metrics for four-class models and two-class models using either processed FTIR spectra or their second derivative. The overall most accurate model for all study groups. For the classification of all groups (i.e., four-class model, Table 2 and Figure 3), the highest accuracy was observed when using the processed FTIR spectra instead of its second derivative. A similar behavior was observed for two-class models only including the control and pre-eclampsia groups for pregnant patients and newborns separately (Table 3, Figure 4 and Figure 5).

Tables S1–S6 (Supplementary Materials) indicate the sample classification results based on either the mean FTIR spectra or their second derivative. Based on the higher classification performance metrics achieved by classifiers using the mean FTIR spectra compared to classifiers using the second derivative FTIR spectra, our results suggest that low-frequency spectral components are important for sample classification. The importance of these low-frequency components may be associated with interactions between bound water and other plasma components of dried blood plasma, as these will have minor contributions to the vibrational modes while distributed over a wide range of wavenumbers

It is important to note that the classification of newborn plasma samples was more accurate in both four-class and two-class models, i.e., including or excluding pregnant plasma samples when training classifiers. A higher accuracy may suggest that newborn blood plasma exhibited more biochemical changes compared to pregnant plasma. These changes may be associated with the potential effects reflected in each child’s growth due to the biochemical changes assigned in Table 1. Similarly, changes in pregnant plasma assigned in Table 1 can be related to maternity complications and mortality.

4. Discussion

By analyzing the results obtained from the FTIR spectroscopy on the blood plasma from pre-eclamptic pregnant women compared to the controls, we used the high-wavenumber region to (1) find its usefulness for sample classification and (2) investigate a deeper association between the metabolic processes due to interactions between bound water with other biomolecules such as proteins, nucleic acids, and lipids within blood plasma samples.

In terms of clinical diagnostics, pre-eclampsia is a complex systemic disorder with multiple factors involved in its pathogenesis, including hypoxia, shallow placentation, endothelial cell damage, and immunological factors. These factors contribute to the altered levels of various substrates, many of which are in the early stages of understanding their roles in the development of pre-eclampsia. The condition is characterized by hypertension, proteinuria, and/or edema occurring after 20 weeks of gestation [13]. Maternal clinical risk factors include advanced maternal age, previous history of pre-eclampsia, family history of pre-eclampsia, nulliparity, obesity, multifetal gestation, diabetes mellitus, chronic hypertension, and chronic renal disorders. To aid in early diagnosis, various analyses using FTIR have been conducted [18,19,20].

FTIR spectroscopy has potential for pre-eclampsia diagnostics due to its significant advantages in precision and minimal sample preparation [10,11]. The high-wavenumber FTIR analysis of pregnant women’s plasma enables the identification of significant differences in biochemical composition, highlighting biomolecular changes mainly in lipids, bound water, proteins and nucleic acids. FTIR spectroscopy can be made accessible and practical even in resource-limited settings, due to its capabilities of real-time extraction of digital spectral features associated with sample biochemistry, the minimal sample preparation and the absence of additional requirements for specialized reagents. Therefore, FTIR’s accessibility can significantly improve prenatal monitoring capabilities [11].

For instance, routine biomarkers, including serum creatinine and uric acid, provide valuable information, but they can miss subtle biochemical changes occurring in the early stages of the disorder. FTIR spectroscopy, with its real-time extraction of digital spectral features, presents a promising complementary approach, allowing for a more comprehensive understanding of metabolic disruptions associated with pre-eclampsia. This method could facilitate early diagnosis and improve prenatal monitoring capabilities, particularly in resource-limited settings due to its minimal sample preparation and the absence of specialized reagents [6,8,12,15].

Comparative studies between FTIR and Raman spectroscopy demonstrate that both offer similar practical advantages, particularly in prenatal diagnostics [13]. In particular, FTIR spectroscopy is a more mature and cost-effective technology, hence making equipment more compact and easier to translate to clinics. The FTIR spectroscopy’s ease of implementation and low operational cost reinforce its practical feasibility as a valuable tool for diagnosing and monitoring complex conditions such as pre-eclampsia.

In terms of biomarkers, high-wavenumber FTIR analysis is useful to probe the interaction between bound water and other biomolecules [11,13]. This interaction is only possible because bound water still remains in the dried blood plasma, indicating the quantity of water that is still bound to the hydrophilic molecular sites in each plasma sample. Our study correlated pre-eclampsia biochemical changes with high-wavenumber vibrational modes from bound water (occurring mainly between 3100 and 3600 cm⁻¹), and other molecules such as lipids, proteins, carbohydrates and nucleic acids (mainly between 2800 and 3100 cm⁻¹) [18].

To understand the biological processes associated with pre-eclampsia plasma biomarkers, it is important to consider pre-eclampsia biochemical changes as a placental disease [19,20]. Placentas from women with pre-eclampsia show increased frequencies of villous infarctions, villous-free placental lakes, inflammation, fibrin deposition, syncytial knots, and abnormal cytotrophoblast proliferation. Additionally, it is associated with changes in genetic expression and DNA methylation in the placenta. Factors released from the placenta, including exosomes, pro-inflammatory cytokines, cell-free fetal DNA, and anti-angiogenic agents, disrupt maternal endothelial function, leading to the multi-systemic clinical syndrome of pre-eclampsia [21,22]. However, the exact cause of this pathology remains undefined.

Many studies aim to identify biomarkers and maternal characteristics associated with hypertensive pathology through predictive algorithms [23]. Yet, these biomarkers have not been translated into clinical practice. As a result, the diagnostic steps for pre-eclampsia remain unchanged. This underscores the potential of vibrational spectroscopy, specifically Raman spectroscopy and FTIR spectroscopy [13,20,24].

To interpret FTIR analysis results, it is essential to understand the metabolic characteristics of pre-eclampsia, which occurs in two phases. The first phase involves inadequate implantation and placentation, leading to poor uteroplacental perfusion, tissue hypoxia, and oxidative stress. This triggers the release of anti-angiogenic factors into the maternal circulation, causing a systemic inflammatory response. In the second phase, these factors induce widespread endothelial dysfunction, which is responsible for the hypertensive syndrome [25,26].

This study on high-wavenumber FTIR spectroscopy for pre-eclampsia detection aligns with the findings of Macdonald et al. (2022), who emphasize the ongoing challenges in achieving sensitivity and specificity in clinical tools for predicting this condition. They highlight the potential of combining biomarkers, such as placental growth factor (PlGF) and fatty acid-binding protein (FABP), to enhance predictive accuracy. Additionally, Macdonald et al. stress the importance of non-invasive blood analysis to detect biochemical changes early in pregnancy. Our research contributes to this need by providing a detailed molecular assessment of blood plasma, which may reveal critical biochemical interactions related to pre-eclampsia [27].

Changes in protein concentrations and specific expression patterns in coagulation cascades can favor thrombophilia and inflammation, linking pre-eclampsia to cardiovascular disease development [23]. Identifying peptides associated with pre-eclampsia is crucial for determining cardiovascular alterations that may persist long-term, as changes in the proteome suggest cardiovascular and thrombotic risks in symptomatic and asymptomatic individuals six months post-pre-eclampsia [28].

Lipid concentration changes during pregnancy are another factor. Normal, uncomplicated pregnancies exhibit a progressive physiological increase in maternal serum lipid concentrations, essential for fetal development [29]. Near the end of pregnancy, fatty acid storage in maternal adipose tissue increases due to physiological insulin resistance [30,31]. Evidence suggests that pre-eclampsia is associated with dyslipidemia, an imbalance in lipid regulation [30,31,32].

Women with pre-eclampsia have insufficient adipose tissue expansion compared to healthy pregnant women [32]. Additionally, their adipocytes become more insulin-resistant, increasing lipolysis [30,33]. These processes result in ectopic fat accumulation in the liver and other tissues [29]. Serum triglyceride levels are significantly higher in women with pre-eclampsia compared to those with normal pregnancies. Furthermore, LDL levels increase, HDL levels decrease, and serum-free fatty acids rise [20,30,32,33,34]. These lipid concentration changes are similar to those observed in obese patients [30,35].

In the high-wavenumber region, the spectrum is primarily attributed to lipid absorption for bands at 2849, 2917, and 3008 cm⁻¹, and dominated by water absorption bands at 3350 cm⁻¹. The spectral region 3050–2800 cm⁻¹ corresponds to asymmetric CH₃ (ν_asCH₃), asymmetric CH₂ (ν_asCH₂), symmetric CH₃ (ν_sCH₃), and symmetric CH₂ (ν_sCH₂) distribution, observable in the control and pre-eclampsia sample results [14]. This factor is essential for fetal development, especially in the late gestation period, a critical time for pre-eclampsia patients [22].

Alterations in proteases, enzymes that catalyze the hydrolysis of peptide bonds, can be observed during the pathology’s development, disrupting cellular homeostasis. Previous studies reported inconclusive protein levels due to the low sensitivity of ELISA in measuring ultra-low protein levels and inflammatory cytokines with low positive predictive values for accurate pre-eclampsia diagnosis [25].

According to the absorbance range found for peptides in the high-wavenumber region, the bands between 3280 cm⁻¹ (H–O–H stretch), 2957 cm⁻¹ (asymmetric CH₃ stretch), 2920 cm⁻¹ (asymmetric CH₂ stretch), 2872 cm⁻¹ (symmetric CH₃ stretch), 1536 cm⁻¹ (amide II of proteins), 1453 cm-1 (CH₂ scissor), 1394 cm⁻¹ (C=O stretch of COO–), 1242 cm⁻¹ (asymmetric PO₂ stretch), 1171 cm⁻¹ (asymmetric C–O ester stretch), and 1080 cm⁻¹ (C–O stretch) were observed [26].

These protein patterns are important for diagnosing various pathologies. In pre-eclampsia, studies identifying molecular alterations causing endothelial dysfunction found amino acids, protein secondary structures, lipids, and fatty acids in higher amounts in the blood or urine of women with pre-eclampsia [11].

As a guide for complementary and novel biochemical methods, we described potential biochemical changes through FTIR band assignment (Table 1) that can be associated with effects on child growth and maternity complications/mortality associated with pre-eclampsia. Since our machine learning classification was more accurate for newborns than for pregnant patients, our results indicated that changes in the FTIR high-wavenumber region are more prominent in newborn plasma compared to pregnant plasma. Hence, our results may suggest that newborns are highly affected at the time of birth. Our future work will comprise of new analysis with increased number of patients and association with 24 h urine sample analysis for further correlation with plasma biochemical changes and understanding the origin of such changes. This future work may include feature selection or dimensionality reduction techniques to identify the most relevant biomarkers for the classification of control and pre-eclampsia samples based on FTIR spectra (instead of the two aims of this work: 1. optimizing the machine learning classification of control and pre-eclampsia samples, and 2. reporting the most prominent biochemical components from the blood plasma of pregnant patients and newborns in the control and pre-eclampsia groups).

5. Limitations of Our Study

Our pilot study included approximately 15 patients per study group (33 pregnant people and their respective newborns, totaling 66 patients). Further research with larger sample sizes is needed to validate the potential variability in blood plasma over a heterogeneous and diverse population. However, our study is the first study to perform high-wavenumber Fourier-transform infrared (FTIR) spectroscopy for pre-eclampsia detection. We believe that our findings can inspire further study on the evaluation of bound water in the hydrophilic sites of blood plasma biomolecules, as well as the contribution of the high-wavenumber region to clinical decision-making based on the understanding of the systemic effects of pre-eclampsia in pregnant people and newborns. Thus, our pilot study is the first step towards showing the usefulness of high-wavenumber FTIR for future diagnostics and biomarker discovery associated with bound water in blood plasma.

6. Conclusions

Based on our findings, not only could high-wavenumber FTIR spectroscopy increase the overall classification performance when combined with other analytical methods but also the biomolecular interactions appear to involve a wide range of hydrophilic sites within the molecules of dried blood plasma. Our findings indicate that novel methods looking for these hydrophilic sites may find useful information to complement plasma analysis in the clinic.