TY - JOUR
T1 - Metabolomic data presents challenges for epidemiological meta-analysis
T2 - a case study of childhood body mass index from the ECHO consortium
AU - Prince, Nicole
AU - Liang, Donghai
AU - Tan, Youran
AU - Alshawabkeh, Akram
AU - Angel, Elizabeth Esther
AU - Busgang, Stefanie A.
AU - Chu, Su H.
AU - Cordero, José F.
AU - Curtin, Paul
AU - Dunlop, Anne L.
AU - Gilbert-Diamond, Diane
AU - Giulivi, Cecilia
AU - Hoen, Anne G.
AU - Karagas, Margaret R.
AU - Kirchner, David
AU - Litonjua, Augusto A.
AU - Manjourides, Justin
AU - McRitchie, Susan
AU - Meeker, John D.
AU - Pathmasiri, Wimal
AU - Perng, Wei
AU - Schmidt, Rebecca J.
AU - Watkins, Deborah J.
AU - Weiss, Scott T.
AU - Zens, Michael S.
AU - Zhu, Yeyi
AU - Lasky-Su, Jessica A.
AU - Kelly, Rachel S.
N1 - Publisher Copyright:
© 2024, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
PY - 2024/2
Y1 - 2024/2
N2 - Introduction: Meta-analyses across diverse independent studies provide improved confidence in results. However, within the context of metabolomic epidemiology, meta-analysis investigations are complicated by differences in study design, data acquisition, and other factors that may impact reproducibility. Objective: The objective of this study was to identify maternal blood metabolites during pregnancy (> 24 gestational weeks) related to offspring body mass index (BMI) at age two years through a meta-analysis framework. Methods: We used adjusted linear regression summary statistics from three cohorts (total N = 1012 mother–child pairs) participating in the NIH Environmental influences on Child Health Outcomes (ECHO) Program. We applied a random-effects meta-analysis framework to regression results and adjusted by false discovery rate (FDR) using the Benjamini–Hochberg procedure. Results: Only 20 metabolites were detected in all three cohorts, with an additional 127 metabolites detected in two of three cohorts. Of these 147, 6 maternal metabolites were nominally associated (P < 0.05) with offspring BMI z-scores at age 2 years in a meta-analytic framework including at least two studies: arabinose (Coefmeta = 0.40 [95% CI 0.10,0.70], Pmeta = 9.7 × 10–3), guanidinoacetate (Coefmeta = − 0.28 [− 0.54, − 0.02], Pmeta = 0.033), 3-ureidopropionate (Coefmeta = 0.22 [0.017,0.41], Pmeta = 0.033), 1-methylhistidine (Coefmeta = − 0.18 [− 0.33, − 0.04], Pmeta = 0.011), serine (Coefmeta = − 0.18 [− 0.36, − 0.01], Pmeta = 0.034), and lysine (Coefmeta = − 0.16 [− 0.32, − 0.01], Pmeta = 0.044). No associations were robust to multiple testing correction. Conclusions: Despite including three cohorts with large sample sizes (N > 100), we failed to identify significant metabolite associations after FDR correction. Our investigation demonstrates difficulties in applying epidemiological meta-analysis to clinical metabolomics, emphasizes challenges to reproducibility, and highlights the need for standardized best practices in metabolomic epidemiology.
AB - Introduction: Meta-analyses across diverse independent studies provide improved confidence in results. However, within the context of metabolomic epidemiology, meta-analysis investigations are complicated by differences in study design, data acquisition, and other factors that may impact reproducibility. Objective: The objective of this study was to identify maternal blood metabolites during pregnancy (> 24 gestational weeks) related to offspring body mass index (BMI) at age two years through a meta-analysis framework. Methods: We used adjusted linear regression summary statistics from three cohorts (total N = 1012 mother–child pairs) participating in the NIH Environmental influences on Child Health Outcomes (ECHO) Program. We applied a random-effects meta-analysis framework to regression results and adjusted by false discovery rate (FDR) using the Benjamini–Hochberg procedure. Results: Only 20 metabolites were detected in all three cohorts, with an additional 127 metabolites detected in two of three cohorts. Of these 147, 6 maternal metabolites were nominally associated (P < 0.05) with offspring BMI z-scores at age 2 years in a meta-analytic framework including at least two studies: arabinose (Coefmeta = 0.40 [95% CI 0.10,0.70], Pmeta = 9.7 × 10–3), guanidinoacetate (Coefmeta = − 0.28 [− 0.54, − 0.02], Pmeta = 0.033), 3-ureidopropionate (Coefmeta = 0.22 [0.017,0.41], Pmeta = 0.033), 1-methylhistidine (Coefmeta = − 0.18 [− 0.33, − 0.04], Pmeta = 0.011), serine (Coefmeta = − 0.18 [− 0.36, − 0.01], Pmeta = 0.034), and lysine (Coefmeta = − 0.16 [− 0.32, − 0.01], Pmeta = 0.044). No associations were robust to multiple testing correction. Conclusions: Despite including three cohorts with large sample sizes (N > 100), we failed to identify significant metabolite associations after FDR correction. Our investigation demonstrates difficulties in applying epidemiological meta-analysis to clinical metabolomics, emphasizes challenges to reproducibility, and highlights the need for standardized best practices in metabolomic epidemiology.
KW - Childhood obesity
KW - Maternal metabolites
KW - Metabolomic epidemiology
KW - Metabolomic meta-analysis
UR - http://www.scopus.com/inward/record.url?scp=85183334249&partnerID=8YFLogxK
U2 - 10.1007/s11306-023-02082-y
DO - 10.1007/s11306-023-02082-y
M3 - Article
C2 - 38267770
AN - SCOPUS:85183334249
SN - 1573-3882
VL - 20
JO - Metabolomics
JF - Metabolomics
IS - 1
M1 - 16
ER -