Hemoglobin normalization outperforms other methods for standardizing dried blood spot metabolomics: A comparative study

Authors

Abhishek Jain, Department of Environmental Health Sciences, Yale School of Public Health, New Haven, CT, United States.Follow
Montana Morris, Yale University School of Medicine, New Haven, CT, United States.Follow
Elizabeth Z. Lin, Department of Environmental Health Sciences, Yale School of Public Health, New Haven, CT, United States.
Sajid A. Khan, Division of Surgical Oncology, Department of Surgery, Yale University School of Medicine, New Haven, CT, United States.Follow
Xiaomei Ma, Department of Chronic Disease Epidemiology, Yale School of Public Health, New Haven, CT, United States.
Nicole C. Deziel, Department of Environmental Health Sciences, Yale School of Public Health, New Haven, CT, United States; Yale Center for Perinatal, Pediatric, and Environmental Epidemiology, Yale School of Public Health, New Haven, CT, United States.Follow
Krystal J. Godri Pollitt, Department of Environmental Health Sciences, Yale School of Public Health, New Haven, CT, United States.Follow
Caroline H. Johnson, Department of Environmental Health Sciences, Yale School of Public Health, New Haven, CT, United States. Electronic address: caroline.johnson@yale.edu.Follow

Document Type

Article

Publication Title

The Science of the total environment

Abstract

Dried blood spot (DBS) metabolomics has numerous applications in newborn health screening, exposomics, and biomonitoring of environmental chemicals in pregnant women and the elderly. However, accurate metabolite quantification is hindered by several challenges: notably the "hematocrit effect" and unknown blood-spotting volumes. Different techniques have been employed to overcome these issues but there is no consensus on the optimal normalization method for DBS metabolomics, and in some cases no normalization is used. We compared five normalization methods (hemoglobin (Hb), specific gravity (SG), protein, spot weight, potassium (K+)) to unnormalized data, and assessed sex-related differences in the DBS metabolome in 21 adults (group 1, n = 10 males, n = 11 females). The performance of each normalization method was evaluated using multiple criteria: (a) reduction of intragroup variation (pooled median absolute deviation, pooled estimate of variance, pooled coefficient of variation, NMDS and principal component analysis), (b) effect on differential metabolic analysis (dendrogram, heatmap, p-value distribution), and (c) influence on classification accuracy (partial least squares discriminant analysis, sparse partial least squares discriminant analysis error rates, receiver operating curve, random forest out of bag error rate). Our results revealed that Hb normalization outperformed all the other methods based on the three criteria and 13 different parameters; the performance of Hb was further demonstrated in an independent group of DBS from 18 neonates (group 2, n = 9 males, n = 9 females). Furthermore, we showed that SG and Hb are correlated in adults (r = 0.86, p < 0.001), and validated this relationship in an independent group of 18 neonates and infants (group 3) (r = 0.84, p < 0.001). Using the equation, SG = -0.4814Hb + 2.44Hb + 0.005, SG can be used as a surrogate for normalization by Hb. This is the first comparative study to concurrently evaluate multiple normalization methods for DBS metabolomics which will serve as a robust methodological platform for future environmental epidemiological studies.

First Page

158716

DOI

10.1016/j.scitotenv.2022.158716

Publication Date

1-1-2023

Recommended Citation

Jain, Abhishek; Morris, Montana; Lin, Elizabeth Z.; Khan, Sajid A.; Ma, Xiaomei; Deziel, Nicole C.; Godri Pollitt, Krystal J.; and Johnson, Caroline H., "Hemoglobin normalization outperforms other methods for standardizing dried blood spot metabolomics: A comparative study" (2023). Surgery. 202.
https://scholar.bridgeporthospital.org/surgery/202

Identifier

36113793 (pubmed); 10.1016/j.scitotenv.2022.158716 (doi); S0048-9697(22)05815-6 (pii)