A Plasma Proteomics-Based
Model for Identifying the
Risk of Postpartum Depression Using Machine Learning
Introduction Postpartum depression (PPD) is commonly defined as an episode of major depressive disorder that occurs in the postpartum period. PPD affects approximately 17.2% of women globally and is characterized by emotional fluctuations, negative attitudes, and psychological changes, with severe cases potentially involving suicidal ideation. , During the COVID-19 pandemic, the prevalence of clinically significant depressive and anxiety symptoms among mothers of young children rose significantly, reaching 26.9% for depression and 41.9% for anxiety. PPD causes personal distress and impairs women’s ability to function effectively in various aspects of life, and ongoing depression can interfere with parenting, leading to a range of negative outcomes for children in both the short and long term. − Currently, the primary tools for screening for PPD risk are questionnaires administered by clinicians, with the Edinburgh Postnatal Depression Scale (EPDS) being the most frequently used. Questionnaire symptom screening can only identify the risk of PPD, but it cannot provide an accurate diagnosis. The Structured Clinical Interview for DSM-V (SCID) can provide an accurate diagnosis of PPD, but it is often not used due to its time-consuming nature and the need for professional skills. In relevant studies, it has been found that NCAM1, NRCAM, and NTRK3 are associated with the occurrence of perinatal depression. Mechanistic research has clarified that the impairment of neurotransmitter function related to inflammation and γ-aminobutyric acid (GABA) is considered a contributing factor to the pathophysiological conditions underlying PPD. Previous studies have assessed the roles of molecules associated with stress, immune dysfunction, and neuronal signaling imbalance. However, there is currently no clinically established physiological method, such as a reliable biomarker, to assess the risk of PPD. Proteomics analysis, as an objective approach, focuses on studying the variations of proteins related to specific tissues, organs, or diseases. Proteomics provides critical foundational information for understanding complex diseases and, by uncovering numerous potential biomarkers, offers new perspectives for disease diagnosis and the elucidation of pathological mechanisms. Machine learning is a scientific discipline focused on how computers learn from data, broadly defined as the process of fitting predictive models to data or identifying groups of information within data. Many studies have utilized machine learning to establish predictive or diagnostic models for diseases. , In recent years, correlating proteomic data with corresponding clinical information to identify molecular markers associated with diseases has become a new research focus. These methods hold significant practical value in the discovery of biomarkers and therapeutic targets. Utilizing Data-Independent Acquisition (DIA) proteomics technology, we screened for proteins with differential abundance in the plasma of the PPD risk group and developed a new proteomic model to differentiate between the PPD risk group and the healthy control group. The application of predictive biomarkers holds promise for providing more quantitative and objective criteria for assessing PPD risk and identifying potential pathophysiological mechanisms. This provides a theoretical basis for formulating PPD treatment strategies, contributing to mental health prevention and control, and enhancing the quality of life for affected individuals.   Materials and Methods 2.1 Patient Information and Sample Collection This study was approved by the Ethics Committee of the Jinshan Hospital of Fudan University (Approved ID: JIEC-2021-S05). The study was conducted at the Jinshan Hospital of Fudan University. The hospital is located in the city and delivers approximately 1500 babies annually. All participants, aged 20–45, signed informed consent prior to their inclusion in the study. The assessments were carried out on day 42 after childbirth. Participants in both groups were face-to-face orally interviewed to fill out the paper-based EPDS questionnaire. The results were used as a screening tool to assess the risk of PPD, based on symptoms, rather than as a prospective structured diagnostic or diagnostic result from chart review. For the healthy group, individuals scored 0 on the Edinburgh Postnatal Depression Scale (EPDS), indicating the absence of depressive symptoms. In contrast, the PPD risk group scored 13 or higher on the EPDS, indicating a higher risk of PPD, but we do not include those who endorsed EPDS item #10 (suicidal ideation) in the at-risk group. To establish exclusion criteria to ensure the reliability of the study, we reviewed electronic medical records to exclude pregnant women with other conditions, such as premature birth, low birth weight, or gestational diabetes, and those who had not been admitted to the neonatal intensive care unit (NICU). The pregnant women needed to have the ability to accurately understand the questionnaire. None of the participants were taking any medications, including psychotropic drugs. We collected plasma samples from 70 healthy postpartum women and 51 PPD-risk participants. IBM SPSS Statistics (version 27) was used to match the sample’s confounders with propensity scores. All 70 cases in the control group and 51 cases in the PPD risk group were included. A total of 121 cases were included. The commonly used confounding factors for matching were included: age, height, and weight, and the matching tolerance was set at 0.01. All matches were performed once, and the matching groups (control group vs PPD risk group) were matched, and 34 vs 34 matching results were obtained. Abnormal plasma samples (liposemia or hemolysis) were removed. We selected 30 plasma samples from healthy postpartum women and 30 from PPD-risk participants for proteomic analysis using DIA technology. 2.2 Sample Preparation Enrichment of samples was carried out using superparamagnetic iron oxide nanoparticles. A 20-μL sample was taken and diluted with loading buffer. The diluted sample was then mixed with a suspension of nanoparticles and incubated at 37 °C for 1 h. The magnetic beads were washed twice with the loading buffer and then washed once with loading buffer without CHAPS. The magnetic beads were then immobilized using a magnetic rack, and the supernatant was discarded, resulting in the enrichment of protein-bound nanoparticles. To the sample, lysis buffer was added, followed by a 30-min incubation at 60 °C for reduction and alkylation reactions. An equal volume of ddH2O was added to dilute the SDC concentration to below 0.5%. Subsequently, 1 μg of trypsin was added, and the mixture was incubated overnight at 37 °C. The next day, the reaction was terminated by adding TFA. The supernatant was desalted using an SDB-RPS desalting column, vacuum-dried, and stored at −20 °C for future use. 2.3 Liquid Chromatography–Tandem Mass Spectrometry (LC-MS/MS) Analysis The sample mass spectrometry analysis utilized an UltiMate 3000 RSLCnano liquid chromatography system (Thermo) coupled with a timsTOF Pro mass spectrometer (Bruker). Peptide samples were automatically injected through an autosampler and loaded onto a C18 trapping column, followed by separation on an analytical column. The chromatographic separation was achieved by using mobile phase A (0.1% formic acid) and mobile phase B (0.1% formic acid in ACN) with a flow rate of 300 nL/min. The mass spectrometry data acquisition was performed in diaPASEF mode, with a capillary voltage set at 1400 V. The scan range for both MS1 and MS2 spectra was set to 100–1700 m / z , and the ion mobility range was set to 0.6–1.6 Vs/cm 2 . Accumulation time and ramp time were set at 100 ms. For diaPASEF window settings, the timsControl software was employed based on the distribution pattern of mass-to-charge ratio and ion mobility. Collision energy was linearly increased from 59 eV at 1/ K 0 = 1.6 Vs/cm 2 to 20 eV at 1/ K 0 = 0.6 Vs/cm 2 , following the ion mobility settings. 2.4 Library Search The DIA raw data was analyzed using the library-free mode of DIA-NN software (version 1.8.1). The retrieval was performed against the HUMAN protein sequence database obtained from UniProt (release date: 20230619, comprising 20,423 protein entries). The retrieval parameters largely followed the default settings, and key parameters are described as follows: precursor ion generation option was enabled for predicting theoretical spectral libraries; trypsin/P was used with a maximum allowance of 2 missed cleavage sites; carbamidomethyl on C as a fixed modification; oxidation on M and protein N-terminal acetylation as variable modifications; MS1 and MS2 mass tolerance was set at 15 ppm; MBR and Heuristic protein inference were enabled; and FDR was set to 1%. Protein quantification information was normalized by using the MaxLFQ algorithm. 2.5 Data Processing For processing the proteomics data, initially, we excluded proteins affected by contamination and performed a logarithmic transformation. Subsequently, missing values were addressed by imputing values modeled after a normal distribution centered around the mass spectrometer’s detection limit. In essence, the mean and standard deviation of the actual intensity distribution were determined, creating a new distribution with a downward shift of 1.8 s and a width of 0.25 standard deviations. Imputation of missing values was then carried out using these parameters, facilitating subsequent statistical analysis. Finally, any protein exhibiting a missing ratio of >50% in both experimental groups was eliminated. The remaining proteins were chosen for further analysis. 2.6 Biological Information Analysis The log2 ratios of spectral counts between the control group and the postpartum depression group were utilized to calculate the fold change (FC) representing changes in protein abundance. In this study, enrichment analysis of the DEPs was conducted using a two-sided hypergeometric test. The ratios and p-values were computed through the hypergeometric distribution. Identified proteins were mapped to the corresponding databases downloaded from the official websites to collect annotation information for GO, KEGG, and DO. For constructing the network diagram of DEPs and further identifying hub proteins, protein–protein interaction (PPI) data from the STRING database were utilized. The DEPs network was built using Cytoscape software and the “CytoHubba” plugin, followed by the additional filtering of hub proteins. 2.7 Statistical Analysis For the comparison between two groups of data, a t -test is employed for data conforming to a normal distribution, while a rank-sum test is utilized for data that do not adhere to a normal distribution. In the screening of biomarkers, differential fold change (FC) and statistical tests ( p ≤ 0.05) are chosen as initial screening criteria. Further biomarker selection involves the application of LASSO regression and machine learning methods such as eXtreme Gradient Boosting (XGBoost). Ultimately, selected biomarkers are then subjected to suitable modeling methods based on practical considerations. Regarding the modeling data processing, the intention is to split the modeling data, with 75% used as the training set and 25% as the testing set. Model evaluation is anticipated to occur from three perspectives: discrimination is assessed through ROC area under the curve (AUC); calibration is evaluated through methods such as calibration curves; and clinical impact is assessed through Decision Curve Analysis (DCA) or line plots. Statistical analysis and modeling methods are carried out using R.   Patient Information and Sample Collection This study was approved by the Ethics Committee of the Jinshan Hospital of Fudan University (Approved ID: JIEC-2021-S05). The study was conducted at the Jinshan Hospital of Fudan University. The hospital is located in the city and delivers approximately 1500 babies annually. All participants, aged 20–45, signed informed consent prior to their inclusion in the study. The assessments were carried out on day 42 after childbirth. Participants in both groups were face-to-face orally interviewed to fill out the paper-based EPDS questionnaire. The results were used as a screening tool to assess the risk of PPD, based on symptoms, rather than as a prospective structured diagnostic or diagnostic result from chart review. For the healthy group, individuals scored 0 on the Edinburgh Postnatal Depression Scale (EPDS), indicating the absence of depressive symptoms. In contrast, the PPD risk group scored 13 or higher on the EPDS, indicating a higher risk of PPD, but we do not include those who endorsed EPDS item #10 (suicidal ideation) in the at-risk group. To establish exclusion criteria to ensure the reliability of the study, we reviewed electronic medical records to exclude pregnant women with other conditions, such as premature birth, low birth weight, or gestational diabetes, and those who had not been admitted to the neonatal intensive care unit (NICU). The pregnant women needed to have the ability to accurately understand the questionnaire. None of the participants were taking any medications, including psychotropic drugs. We collected plasma samples from 70 healthy postpartum women and 51 PPD-risk participants. IBM SPSS Statistics (version 27) was used to match the sample’s confounders with propensity scores. All 70 cases in the control group and 51 cases in the PPD risk group were included. A total of 121 cases were included. The commonly used confounding factors for matching were included: age, height, and weight, and the matching tolerance was set at 0.01. All matches were performed once, and the matching groups (control group vs PPD risk group) were matched, and 34 vs 34 matching results were obtained. Abnormal plasma samples (liposemia or hemolysis) were removed. We selected 30 plasma samples from healthy postpartum women and 30 from PPD-risk participants for proteomic analysis using DIA technology.   Sample Preparation Enrichment of samples was carried out using superparamagnetic iron oxide nanoparticles. A 20-μL sample was taken and diluted with loading buffer. The diluted sample was then mixed with a suspension of nanoparticles and incubated at 37 °C for 1 h. The magnetic beads were washed twice with the loading buffer and then washed once with loading buffer without CHAPS. The magnetic beads were then immobilized using a magnetic rack, and the supernatant was discarded, resulting in the enrichment of protein-bound nanoparticles. To the sample, lysis buffer was added, followed by a 30-min incubation at 60 °C for reduction and alkylation reactions. An equal volume of ddH2O was added to dilute the SDC concentration to below 0.5%. Subsequently, 1 μg of trypsin was added, and the mixture was incubated overnight at 37 °C. The next day, the reaction was terminated by adding TFA. The supernatant was desalted using an SDB-RPS desalting column, vacuum-dried, and stored at −20 °C for future use.   Liquid Chromatography–Tandem Mass Spectrometry (LC-MS/MS) Analysis The sample mass spectrometry analysis utilized an UltiMate 3000 RSLCnano liquid chromatography system (Thermo) coupled with a timsTOF Pro mass spectrometer (Bruker). Peptide samples were automatically injected through an autosampler and loaded onto a C18 trapping column, followed by separation on an analytical column. The chromatographic separation was achieved by using mobile phase A (0.1% formic acid) and mobile phase B (0.1% formic acid in ACN) with a flow rate of 300 nL/min. The mass spectrometry data acquisition was performed in diaPASEF mode, with a capillary voltage set at 1400 V. The scan range for both MS1 and MS2 spectra was set to 100–1700 m / z , and the ion mobility range was set to 0.6–1.6 Vs/cm 2 . Accumulation time and ramp time were set at 100 ms. For diaPASEF window settings, the timsControl software was employed based on the distribution pattern of mass-to-charge ratio and ion mobility. Collision energy was linearly increased from 59 eV at 1/ K 0 = 1.6 Vs/cm 2 to 20 eV at 1/ K 0 = 0.6 Vs/cm 2 , following the ion mobility settings.   Library Search The DIA raw data was analyzed using the library-free mode of DIA-NN software (version 1.8.1). The retrieval was performed against the HUMAN protein sequence database obtained from UniProt (release date: 20230619, comprising 20,423 protein entries). The retrieval parameters largely followed the default settings, and key parameters are described as follows: precursor ion generation option was enabled for predicting theoretical spectral libraries; trypsin/P was used with a maximum allowance of 2 missed cleavage sites; carbamidomethyl on C as a fixed modification; oxidation on M and protein N-terminal acetylation as variable modifications; MS1 and MS2 mass tolerance was set at 15 ppm; MBR and Heuristic protein inference were enabled; and FDR was set to 1%. Protein quantification information was normalized by using the MaxLFQ algorithm.   Data Processing For processing the proteomics data, initially, we excluded proteins affected by contamination and performed a logarithmic transformation. Subsequently, missing values were addressed by imputing values modeled after a normal distribution centered around the mass spectrometer’s detection limit. In essence, the mean and standard deviation of the actual intensity distribution were determined, creating a new distribution with a downward shift of 1.8 s and a width of 0.25 standard deviations. Imputation of missing values was then carried out using these parameters, facilitating subsequent statistical analysis. Finally, any protein exhibiting a missing ratio of >50% in both experimental groups was eliminated. The remaining proteins were chosen for further analysis.   Biological Information Analysis The log2 ratios of spectral counts between the control group and the postpartum depression group were utilized to calculate the fold change (FC) representing changes in protein abundance. In this study, enrichment analysis of the DEPs was conducted using a two-sided hypergeometric test. The ratios and p-values were computed through the hypergeometric distribution. Identified proteins were mapped to the corresponding databases downloaded from the official websites to collect annotation information for GO, KEGG, and DO. For constructing the network diagram of DEPs and further identifying hub proteins, protein–protein interaction (PPI) data from the STRING database were utilized. The DEPs network was built using Cytoscape software and the “CytoHubba” plugin, followed by the additional filtering of hub proteins.   Statistical Analysis For the comparison between two groups of data, a t -test is employed for data conforming to a normal distribution, while a rank-sum test is utilized for data that do not adhere to a normal distribution. In the screening of biomarkers, differential fold change (FC) and statistical tests ( p ≤ 0.05) are chosen as initial screening criteria. Further biomarker selection involves the application of LASSO regression and machine learning methods such as eXtreme Gradient Boosting (XGBoost). Ultimately, selected biomarkers are then subjected to suitable modeling methods based on practical considerations. Regarding the modeling data processing, the intention is to split the modeling data, with 75% used as the training set and 25% as the testing set. Model evaluation is anticipated to occur from three perspectives: discrimination is assessed through ROC area under the curve (AUC); calibration is evaluated through methods such as calibration curves; and clinical impact is assessed through Decision Curve Analysis (DCA) or line plots. Statistical analysis and modeling methods are carried out using R.   Result 3.1 Demographics Data of Participants displays clinical information for 30 healthy postpartum women and 30 PPD-risk participants, including age, height, weight, BMI values, EPDS scores, previous births, socioeconomic status (SES), marital status, and newborn babies. 3.2 Mass Spectrometry Data LC-MS/MS data acquisition was carried out on an atimsTOF Promass spectrometer system. We acquired plasma proteome profiles of all participants ( n = 60) using a data-independent acquisition (DIA) strategy. In addition, we analyzed 60 proteomes in the whole cohort with a DIA method, quantifying 1967 proteins and 17969 peptides in total ( A,B). The raw MS data were processed using DIA-NN software (1.8.1) using a library-free method. The filter parameters for precursor and protein were set as 1% FDR. 3.3 Differential Protein Analysis Differential abundance analysis in the entire cohort proteome revealed 98 proteins significantly dysregulated (|log2FC| ≥ 0.26, p ≤ 0.05) in the PPD-risk group compared to the healthy group. In total, 29 proteins were upregulated and 69 proteins were downregulated in the PPD-risk group ( C). Principal component analysis (PCA) plot with loadings representing the intensities of the 98 differential proteins revealed a clear separation of G1 and G0 ( D). The expression of differential proteins (DEPs) was used to make a heatmap ( E); we found that the expression of DEPs was significantly different between the G1 group and the G0 group. 3.4 Functional Enrichment Analysis In order to accurately evaluate the biological function of prognostic proteins, we performed functional enrichment analysis of 98 prognostic proteins, including Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, and Disease Ontology (DO) enrichment analysis. In this study, the BP categories of GO were regulation of catalytic activity, cellular component assembly, cell activation, G protein-coupled receptor signaling pathway, and postsynaptic signal transduction ( A). The CC categories of GO were organelle, extracellular organelle, extracellular matrix, and neuronal cell body ( B). The MF categories of GO were protein-containing complex binding, signaling receptor activity, and protein self-association ( C). The top 5 KEGG enrichments were pathways in cancer, cysteine and methionine metabolism, HIF-1 signaling pathway, chemical carcinogenesis-receptor activation, and parathyroid hormone synthesis, secretion, and action. The top 5 DO enrichment groups were vascular disease, cognitive disorder, melanoma, musculoskeletal system cancer, and connective tissue cancer. 3.5 Protein–Protein Interaction Network Analysis of DEPs CytoScape software was used to establish network maps of DEPs with protein–protein interaction (PPI) data from the STRING database ( A). In addition, using the “CytoHubba” plug-in, we further calculated the hub protein under four algorithms (DEGREE, DMNC, MCC, and MNC) ( B–E). The top 10 proteins in each algorithm were selected as hub proteins, and by cross-analysis, mapped to the Venn diagram, FN1, ANXA5, EEF2, CD44, TCP1, and AHCY were identified as the genes corresponding to the hub proteins ( F). 3.6 Prediction of Disease Risk First, 25 important proteins were screened from the 98 differential proteins in the above differential analysis using the XGBOOST machine learning model, and A shows the predictive importance of proteins in the training cohort ( A). The 10-fold cross-validation LASSO regression model was then used to further screen out the 17 DEPs protein signatures ( B, C), followed by a random combination of proteins and a 5-fold cross-validation screen; the logistic regression model was established by selecting the optimal combination by indicators such as AUC value. The optimal combination of variables included P13797 (PLS3), P56750 (CLDN17), O43173 (ST8SIA3), P01593 (IGKV1D–33), and P43243 (MATR3). Finally, the proteins selected in the final screening were utilized to construct the risk prediction model. D illustrates the coefficients of the proteins in the optimal model. 3.7 Model Validation The ROC curve is used to evaluate the discrimination of the model. The AUC (95% CI) of the training set is 0.9483 (0.8776–1), and the AUC (95% CI) of the test set is 0.9375 (0.8065–1), indicating that the model has excellent discrimination ability ( A). The confusion matrix represents the classification of the test set. The horizontal axis represents the actual category, and the vertical axis represents the predicted category, which can show the prediction ability of the model ( B). The calibration curve is used to evaluate the calibration of the model. The dotted line is the ideal curve; the red line is the actual curve of the model, and the blue line is the curve after resampling calibration. The calibration curve is close to the ideal curve, manifesting that the model is great ( C). The decision curve is used to evaluate the clinical utility of the model. The net return rate reflects the clinical value of the model. The greater the net return rate, the better the model’s effect. The red curve is the net income curve of the current model, indicating that the model has greater clinical utility ( D). The nomogram provides a visual display of model results to facilitate the prediction of disease risk. After the individual scores (points) corresponding to each variable at different values are added, the total score (total points) is obtained, which is then mapped to the risk score, which is the probability of event occurrence. The total points from the sum of five proteins can determine the possibility of PPD ( E).   Demographics Data of Participants displays clinical information for 30 healthy postpartum women and 30 PPD-risk participants, including age, height, weight, BMI values, EPDS scores, previous births, socioeconomic status (SES), marital status, and newborn babies.   Mass Spectrometry Data LC-MS/MS data acquisition was carried out on an atimsTOF Promass spectrometer system. We acquired plasma proteome profiles of all participants ( n = 60) using a data-independent acquisition (DIA) strategy. In addition, we analyzed 60 proteomes in the whole cohort with a DIA method, quantifying 1967 proteins and 17969 peptides in total ( A,B). The raw MS data were processed using DIA-NN software (1.8.1) using a library-free method. The filter parameters for precursor and protein were set as 1% FDR.   Differential Protein Analysis Differential abundance analysis in the entire cohort proteome revealed 98 proteins significantly dysregulated (|log2FC| ≥ 0.26, p ≤ 0.05) in the PPD-risk group compared to the healthy group. In total, 29 proteins were upregulated and 69 proteins were downregulated in the PPD-risk group ( C). Principal component analysis (PCA) plot with loadings representing the intensities of the 98 differential proteins revealed a clear separation of G1 and G0 ( D). The expression of differential proteins (DEPs) was used to make a heatmap ( E); we found that the expression of DEPs was significantly different between the G1 group and the G0 group.   Functional Enrichment Analysis In order to accurately evaluate the biological function of prognostic proteins, we performed functional enrichment analysis of 98 prognostic proteins, including Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, and Disease Ontology (DO) enrichment analysis. In this study, the BP categories of GO were regulation of catalytic activity, cellular component assembly, cell activation, G protein-coupled receptor signaling pathway, and postsynaptic signal transduction ( A). The CC categories of GO were organelle, extracellular organelle, extracellular matrix, and neuronal cell body ( B). The MF categories of GO were protein-containing complex binding, signaling receptor activity, and protein self-association ( C). The top 5 KEGG enrichments were pathways in cancer, cysteine and methionine metabolism, HIF-1 signaling pathway, chemical carcinogenesis-receptor activation, and parathyroid hormone synthesis, secretion, and action. The top 5 DO enrichment groups were vascular disease, cognitive disorder, melanoma, musculoskeletal system cancer, and connective tissue cancer.   Protein–Protein Interaction Network Analysis of DEPs CytoScape software was used to establish network maps of DEPs with protein–protein interaction (PPI) data from the STRING database ( A). In addition, using the “CytoHubba” plug-in, we further calculated the hub protein under four algorithms (DEGREE, DMNC, MCC, and MNC) ( B–E). The top 10 proteins in each algorithm were selected as hub proteins, and by cross-analysis, mapped to the Venn diagram, FN1, ANXA5, EEF2, CD44, TCP1, and AHCY were identified as the genes corresponding to the hub proteins ( F).   Prediction of Disease Risk First, 25 important proteins were screened from the 98 differential proteins in the above differential analysis using the XGBOOST machine learning model, and A shows the predictive importance of proteins in the training cohort ( A). The 10-fold cross-validation LASSO regression model was then used to further screen out the 17 DEPs protein signatures ( B, C), followed by a random combination of proteins and a 5-fold cross-validation screen; the logistic regression model was established by selecting the optimal combination by indicators such as AUC value. The optimal combination of variables included P13797 (PLS3), P56750 (CLDN17), O43173 (ST8SIA3), P01593 (IGKV1D–33), and P43243 (MATR3). Finally, the proteins selected in the final screening were utilized to construct the risk prediction model. D illustrates the coefficients of the proteins in the optimal model.   Model Validation The ROC curve is used to evaluate the discrimination of the model. The AUC (95% CI) of the training set is 0.9483 (0.8776–1), and the AUC (95% CI) of the test set is 0.9375 (0.8065–1), indicating that the model has excellent discrimination ability ( A). The confusion matrix represents the classification of the test set. The horizontal axis represents the actual category, and the vertical axis represents the predicted category, which can show the prediction ability of the model ( B). The calibration curve is used to evaluate the calibration of the model. The dotted line is the ideal curve; the red line is the actual curve of the model, and the blue line is the curve after resampling calibration. The calibration curve is close to the ideal curve, manifesting that the model is great ( C). The decision curve is used to evaluate the clinical utility of the model. The net return rate reflects the clinical value of the model. The greater the net return rate, the better the model’s effect. The red curve is the net income curve of the current model, indicating that the model has greater clinical utility ( D). The nomogram provides a visual display of model results to facilitate the prediction of disease risk. After the individual scores (points) corresponding to each variable at different values are added, the total score (total points) is obtained, which is then mapped to the risk score, which is the probability of event occurrence. The total points from the sum of five proteins can determine the possibility of PPD ( E).   Discussion Currently, the identification of risk for PPD relies on screening through questionnaires; however, due to individual subjective tendencies, questionnaire scores may not accurately identify PPD risk. Furthermore, there are no blood-related biomarkers for predicting PPD risk. Therefore, utilizing proteomics, we have revealed alterations in peripheral blood protein profiles associated with PPD. Using machine learning models such as XGBoost and LASSO regression, we identified a combination of 5 plasma protein biomarkers (PLS3, CLDN17, ST8SIA3, IGKV1D–33, and MATR3) that can accurately predict the risk of PPD. All five biomarkers were significantly downregulated in the PPD-risk group. The model’s performance was evaluated using ROC curves, confusion matrices, calibration curves, and decision curves, demonstrating its excellent ability to differentiate between healthy postpartum women and those with PPD. Plastin-3 (PLS3) is a crucial actin-bundling protein, thereby influencing cell shape, migration, and signal transduction, including in neurons and muscle systems, highlighting its importance in various biological processes. Studies have reported that the PLS3 protein plays a crucial role in neuronal migration and the establishment of brain morphology by regulating cell migration speed and actin organization. PLS3 is a crucial protein associated with various neurodegenerative diseases. Studies have shown that overexpression of PLS3 has a protective effect on spinal muscular atrophy (SMA) and may have similar effects on other diseases such as amyotrophic lateral sclerosis (ALS). Therefore, overexpression of PLS3 may become a potential cross-disease therapeutic strategy, targeting common neurodegenerative pathways. Currently, there is no research indicating the relationship between PLS3 and the onset or treatment of depression. However, with the uncovering of the complex interaction between cytoskeletal proteins and neuronal health, PLS3 has a potential role in the development of targeted therapies for neurological diseases. Claudin-17 (CLDN17) is a member of the claudin family of the protein family. Claudin proteins are integral membrane proteins, crucial for the formation of tight junctions in epithelial and endothelial cells. These tight junctions play a significant role in maintaining the barrier and fence functions of cells, thus regulating paracellular transport and ensuring cell polarity. The expression and function of CLDN17 in various tissues, including the liver and kidneys, have been studied, highlighting its importance in maintaining tissue integrity and function. Research on CLDN17 has revealed its involvement in fundamental cell functions and its association with diseases such as cancer and kidney damage, emphasizing its significance in cell biology and disease pathology. , Although not directly related to neurodegenerative diseases, the role of CLDN17 in maintaining cell barriers makes it a protein of interest in the blood–brain barrier (BBB) and neurological diseases. The integrity of the blood–brain barrier is crucial for the normal functioning of the nervous system, protecting it from toxins and pathogens. It is speculated that any disruption in tight junction proteins like CLDN17 could affect the integrity of the BBB, thereby impacting the health of the nervous system. Understanding the role of CLDN17 in barrier integrity and its potential impact on the nervous system can provide valuable insights into the pathophysiology of neurological diseases and the development of new therapeutic strategies. ST8SIA3 has been identified as an essential α-2,8-sialyltransferase, playing a key role in the biosynthesis of sialylated glycoconjugates, thereby influencing neural development, synaptic plasticity, and cell communication. , Its activity is crucial in the brain, underscoring its significance in neural development and function. ST8SIA3, by regulating the degree of cell surface sialylation, may affect tumor cell migration, invasion, and immune evasion. , Although no studies have directly linked ST8SIA3 with specific diseases such as depression, its critical role in regulating neuronal migration, brain structure, and signal transduction provides valuable insights for understanding and addressing neurological disorders. Immunoglobulin Kappa Variable 1D-33, or IGKV1D-33, is a protein that plays a key role in the immune system. It is part of the immunoglobulin genes, involved in the V region of the variable domain of immunoglobulin light chains that participate in antigen recognition. Immunoglobulin genes are crucial for the production of antibodies that help to resist pathogens. This gene has been studied for its expression across various tissues, displaying a specific expression pattern that is enhanced in certain tissues, such as the gallbladder, lymphoid tissue, stomach, and bladder. The protein is secreted and participates in immune responses, playing a significant role in antigen binding. Antigen binding is a crucial aspect of the immune response, where antibodies recognize and bind to specific parts of pathogens. Matrin-3 is a multifunctional nuclear protein with two zinc finger domains and two RNA recognition motifs (RRMs). One study indicates that Matrin-3 engages in RNA-dependent interactions with various RNA processing proteins, playing a key role in the regulation of mRNA stability. Matrin-3 is associated with the pathogenesis of neuromuscular and neurodegenerative diseases within the nervous system. Mutations in the MATR3 gene have been identified as a cause of familial amyotrophic lateral sclerosis (FALS) and frontotemporal dementia (FTD). The expression levels of MATR3 have a bidirectional impact on neuronal survival, with pathological MATR3 mutations exhibiting enhanced toxicity. Its association with Alzheimer’s Disease (AD) suggests a broader relevance of MATR3 dysfunction across various neurological diseases, highlighting the protein’s importance in disease mechanisms and its prospects as a target for therapeutic intervention. Currently, there is no research indicating the relationship between MATR3 and depression. However, our study suggests that MATR3 may be involved in the onset and development of PPD and could potentially serve as one of the therapeutic targets for treating postpartum depression. Strengths, Limitations, and Future Directions of the Study The primary strength of our study lies in the establishment of a novel prediction model for assessing PPD risk using the proteomic analysis of plasma proteins. This model involves five key proteins: PLS3, CLDN17, ST8SIA3, IGKV1D–33, and MATR3. Given the current lack of objective predictive markers for PPD risk, our research provides a new avenue for improving PPD risk prediction and highlights proteins closely associated with PPD risk, which may facilitate the identification of therapeutic targets for this condition. However, our study has several limitations. All collected samples were derived from a single ethnic group, limiting the generalizability of our findings to diverse populations. Additionally, we did not employ the Structured Clinical Interview for DSM Disorders (SCID) to confirm the PPD diagnosis. Furthermore, the five proteins selected for the prediction model have not been previously studied in relation to depression, leaving their associations with the condition unclear. In future research, we aim to incorporate SCID to ensure accurate PPD diagnosis and refine participant selection. We also plan to collect samples from PPD patients of diverse ethnic backgrounds to enhance the generalizability of our findings. Molecular biology studies will be conducted on the five proteins identified by our model to elucidate their roles in the pathogenesis of depression, further advancing our understanding of its mechanisms and potential therapeutic targets.   