Identification of Putative Biomarkers in Cerebral Palsy: A Meta-Analysis and Meta-Regression

Background: Cerebral palsy (CP) is a neurological disorder that impairs motor abilities. Identifying maternal biomarker derangements can facilitate further evaluation for early diagnosis, potentially leading to improved clinical outcomes. This study investigates the association between maternal biomarker derangements and CP development during the antenatal period. Methods: A systematic search was conducted in MEDLINE, EMBASE, and Cochrane databases, following MOOSE guidelines. Data on participants exceeding biomarker thresholds (95th and 5th percentiles) were extracted for combined odds ratio estimation. Geometric mean differences, reported as multiples of the median (MoMs), were used to analyze changes in marker levels. Trimesterwise subgroup analysis and metaregression assessed the impact of variables on outcomes. Results: Five observational studies (1552 cases, 484,985 controls) revealed lower maternal pregnancyassociated plasma protein A levels were associated with CP (pooled odds ratio [OR] = 1.60, 95% confidence interval [CI] = 1.22 to 2.09; I = 0%), with a -0.04 MoM geometric mean difference. Lower maternal beta-human chorionic gonadotropin (HCG) levels in first and second trimesters indicated a pooled OR = 1.18 (95% CI = 0.85 to 1.63; I = 57%). Sensitivity analysis showed an OR =1.40 (95% CI =1.08 to 1.82; I = 0%), with a -0.07 MoM geometric mean difference. Metaregression identified primigravida status as negatively influencing beta-HCG levels. Elevated nuchal translucency values and CP presented a pooled OR = 1.06 (95% CI = 0.77 to 1.44; I = 0%). Conclusion: Lower maternal pregnancy-associated plasma protein A levels during the first trimester and lower beta-HCG levels in the first and second trimesters are associated with CP development in children. Future research should validate the predictive utility of these biomarkers and explore novel ones through large-scale cohort studies. (c) 2024 Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies.