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Journal of Peking University (Health Sciences) ›› 2026, Vol. 58 ›› Issue (3): 479-489. doi: 10.19723/j.issn.1671-167X.2026.03.006

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Association between umbilical cord blood proteome and early infant neurodevelopmental risk

Jingxian MO1, Haijun WANG1, Jue LIU2, Qin LI1, Tao SU3,*(), Yuelong JI1,*()   

  1. 1. Department of Maternal and Child Health, Peking University School of Public Health, Beijing 100191, China
    2. Department of Epidemiology and Biostatistics, Peking University School of Public Health, Beijing 100191, China
    3. Tongzhou Maternal & Child Health Hospital of Beijing, Beijing 101100, China
  • Received:2026-02-25 Online:2026-06-18 Published:2026-04-24
  • Contact: Tao SU, Yuelong JI
  • Supported by:
    the National Natural Science Foundation of China(82204055)

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Abstract:

Objective: To systematically investigate the associations between umbilical cord blood protein expression profiles and early infant neurodevelopment using a prospective birth cohort, to identify potential early biomarkers through high-throughput proteomics, and to explore underlying biological mechanisms, thereby providing scientific evidence for early identification of neurodevelopmental risks and understanding the molecular basis of neurodevelopmental deviations in general populations. Methods: Based on the Peking University Birth Cohort in Tongzhou, this study enrolled 96 children who completed ages and stages questionnaires, third edition (ASQ-3) assessments at 1 and 3 years of age. Participants were classified into an abnormal group (n=42) and a control group (n=54) according to ASQ-3 screening results. Non-targeted quantitative proteomics was performed on cryopreserved umbilical cord blood plasma samples collected at birth. Differential expression analysis, principal component analysis (PCA), orthogonal partial least squares discriminant analysis (OPLS-DA), and weighted gene co-expression network analysis (WGCNA) were conducted to identify differentially expressed proteins, followed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional enrichment analyses. The fold change (FC) was calculated. Independent samples t-test was used for statistical comparison, with Benjamini-Hochberg method applied to calculate false discovery rate (FDR) for multiple testing correction. Results: Proteomic analysis identified 8 214 common proteins, among which 385 proteins were differentially expressed (P < 0.05, |log2FC| >0.585), including 189 proteins upregulated and 196 proteins downregulated in the abnormal group. PCA and OPLS-DA revealed systematic differences in protein expression patterns between the two groups. WGCN A identified 10 co-expression modules, with the yellow module showing significant negative correlation with ASQ-3 abnormal grouping (r=-0.233, P=0.024) and the pink module positively correlating with communication domain scores (r=0.342, P=0.003). Enrichment analyses demonstrated that differential proteins and key modules were primarily enriched in two functional categories: (1) genetic information processing pathways, including ribosome, spliceosome, and mRNA processing; and (2) cytoskeleton organization and Wnt signaling pathways. These pathways held significant biological relevance in the pathogenesis of neurodevelopmental disorders. Conclusion: Perturbations in proteins associated with genetic information processing and cytoskeleton/Wnt signaling pathways in umbilical cord blood may represent important molecular characteristics of early neurodevelopmental screening abnormalities in infants. This study provides potential peripheral blood biomarker combinations for early identification of neurodevelopmental risks in general populations and offers novel insights into the biological mechanisms underlying neurodevelopmental deviations. Future research should validate these findings in larger-scale cohorts and elucidate specific functional mechanisms of key proteins through experimental studies.

Key words: Umbilical cord blood, Proteomics, Neurodevelopment, Birth cohort

CLC Number: 

  • R174.6

Table 1

Baseline characteristics of the proteomics study population"

Characteristics Total (n=96) Control (n=54) Abnormal (n=42) P Statistics
Age/years，M(P25, P75) 29.0 (27.0, 31.0) 28.0 (26.8, 32.0) 30.0 (27.0, 31.0) 0.655 Z=983.00
Age category, n (%) 0.177 -
   < 35 years 82 (89.1) 44 (84.6) 38 (95.0)
  ≥35 years 10 (10.9) 8 (15.4) 2 (5.0)
Parity, n (%) 0.833 -
  Nulliparous 60 (62.5) 33 (61.1) 27 (64.3)
  Multiparous 36 (37.5) 21 (38.9) 15 (35.7)
BMI/(kg/m2), M(P25，P75) 21.3 (19.7, 23.8) 21.3 (19.7, 23.6) 21.3 (19.7, 23.9) 0.813 Z =1 048.50
BMI category, n (%) 0.691 χ2=0.74
   < 18.5 kg/m2 7 (7.4) 5 (9.3) 2 (5.0)
  ≥18.5- < 22.5 kg/m2 50 (53.2) 29 (53.7) 21 (52.5)
  ≥22.5 kg/m2 37 (39.4) 20 (37.0) 17 (42.5)
Education level, n (%) 0.073 χ2=5.24
  High school/technical secondary 12 (16.4) 4 (10.0) 8 (24.2)
  Junior college 32 (43.8) 22 (55.0) 10 (30.3)
  Bachelor’s degree or above 29 (39.7) 14 (35.0) 15 (45.5)
Annual household income, n (%) 0.899 χ2=0.59
   < 30 000 yuan 2 (2.2) 1 (2.0) 1 (2.5)
  ≥30 000- < 80 000 yuan 20 (22.0) 10 (19.6) 10 (25.0)
  ≥80 000-15 000 yuan 29 (31.9) 16 (31.4) 13 (32.5)
  ≥150 000 yuan 40 (44.0) 24 (47.1) 16 (40.0)
Infant feeding mode, n (%) 0.033 χ2=6.81
  Artificial feeding 6 (7.8) 2 (4.2) 4 (13.8)
  Exclusive breastfeeding 60 (77.9) 42 (87.5) 18 (62.1)
  Mixed feeding 11 (14.3) 4 (8.3) 7 (24.1)
Gestational diabetes mellitus, n (%) 29 (30.2) 17 (31.5) 12 (28.6) 0.825 -
Gestational hypertension, n (%) 3 (3.1) 3 (5.6) 0 (0.0) 0.254 -
Alcohol consumption, n (%) 0 (0.00) 0 (0.00) 0 (0.00) >0.999 χ2=0.00
Smoking and/or passive smoking, n (%) 35 (36.5) 16 (29.6) 19 (45.2) 0.137 -
Folic acid supplementation, n (%) 80 (84.2) 46 (85.2) 34 (82.9) 0.783 -
Gestational age at delivery/ weeks 39.0 (38.0, 40.0) 39.0 (38.2, 40.0) 39.0 (38.0, 40.0) 0.216 Z =1 296.00
Birth weight/g, ${\bar x}$±s 3 385.3±376.7 3 282.0±274.7 3 518.1±446.3 0.004 t=-3.01
Male, n (%) 45 (46.9) 22 (40.7) 23 (54.8) 0.217 -

Figure 1

PCA and OPLS-DA score plots of umbilical cord blood protein expression profiles A, PCA score plot of cord blood proteome profiles; B, OPLS-DA score plot of cord blood proteome profiles. Values in parentheses indicate the explained variance. PC1, first principal component; PC2, second principal component; PCA, principal component analysis; OPLS-DA, orthogonal partial least squares discriminant analysis."

Figure 2

Volcano plot analysis showing differential protein expression profiles in cord blood"

Figure 3

Module-trait correlation heatmap of WGCNA protein modules A, correlations between WGCNA gene co-expression modules and group status; B, correlations between modules and ASQ-3 developmental domains. The left labels indicate module names (number of proteins within each module), and the labels within the heatmap represent Pearson correlation coefficients (P values). ASQ-3, ages & stages questionnaires, third edition; WGCNA, weighted gene co-expression network analysis."

Table 2

Enrichment analysis results of significantly associated modules"

Module GO biological process GO molecular function GO cellular component KEGG pathway
Pink
Cortical actin cytoskeleton organization Spectrin binding Spectrin-associated cytoskeleton Mineral absorption
Heme biosynthetic process Calmodulin binding Basolateral plasma membrane Collecting duct acid secretion
Cell cycle Structural constituent of cytoske- leton Integral component of plasma membrane Mitophagy-animal
Cellular iron ion homeostasis Cytoskeletal anchor activity Protein kinase CK2 complex Porphyrin metabolism
Iron ion homeostasis Proton-transporting ATPase activity, rotational mechanism Cell junction Wnt signaling pathway
Yellow
Translation Structural constituent of ribosome Nucleolus Ribosome
mRNA splicing (via spliceosome) DNA binding Nuclear speck Spliceosome
mRNA processing mRNA binding Focal adhesion COVID-19
Cytoplasmic translation Chromatin binding Endoplasmic reticulum Neutrophil extracellular trap formation
RNA splicing Zinc ion binding Cytosolic large ribosomal subunit mRNA surveillance pathway

Figure 4

Bubble plot of KEGG pathway enrichment analysis for differentially expressed proteins Rich factor=(differential proteins in pathway/total differential proteins) ÷ (background proteins in pathway/total background proteins). ATP, adenosine triphosphate; COVID-19, coronavirus disease-2019; cAMP, cyclic adenosine monophosphate; KEGG, Kyoto encyclopedia of genes and genomes."

Figure 5

Bubble plot of GO pathway enrichment analysis for differentially expressed proteins mRNA, messenger ribonucleic acid; DNA, deoxyribonucleic acid; snRNP, small nuclear ribonucleoprotein; RNA, ribonucleic acid; FDR, false discovery rate; GO, gene ontology."

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