The translation of lipid profiles to nutritional biomarkers in the study of infant metabolism

Abstract

IntroductionLinks between early life exposures and later health outcomes may, in part, be due to nutritional programming in infancy. This hypothesis is supported by observed long-term benefits associated with breastfeeding, such as better cognitive development in childhood, and lower risks of obesity and high blood pressure in later life. However, the possible underlying mechanisms are expected to be complex and may be difficult to disentangle due to the lack of understanding of the metabolic processes that differentiate breastfed infants compared to those receiving just formula feed.ObjectiveOur aim was to investigate the relationships between infant feeding and the lipid profiles and to validate specific lipids in separate datasets so that a small set of lipids can be used as nutritional biomarkers.MethodWe utilized a direct infusion high-resolution mass spectrometry method to analyse the lipid profiles of 3.2 mm dried blood spot samples collected at age 3 months from the Cambridge Baby Growth Study (CBGS-1), which formed the discovery cohort. For validation two sample sets were profiled: Cambridge Baby Growth Study (CBGS-2) and Pregnancy Outcome Prediction Study (POPS). Lipidomic profiles were compared between infant groups who were either exclusively breastfed, exclusively formula-fed or mixed-fed at various levels. Data analysis included supervised Random Forest method with combined classification and regression mode. Selection of lipids was based on an iterative backward elimination procedure without compromising the class error in the classification mode.ConclusionFrom this study, we were able to identify and validate three lipids: PC(35:2), SM(36:2) and SM(39:1) that can be used collectively as biomarkers for infant nutrition during early development. These biomarkers can be used to determine whether young infants (3–6 months) are breast-fed or receive formula milk.Electronic supplementary materialThe online version of this article (doi:10.1007/s11306-017-1166-2) contains supplementary material, which is available to authorized users.

The validation of biomarkers of metabolic efficacy in infant nutrition

“…Through the validation we were able to show that these biomarkers can be used to determine whether young infants (3–6 months) are breastfed or receive formula milk, and even whether an infant is fed a mixture of both (Acharjee et al . ).…”

“…From this work, we were able to identify and validate three lipids: a phosphatidylcholine (PC) with two fatty acid residues that comprise 35 carbons and two double bonds [PC(35:2)] and two sphingomyelins (SM) SM(36:2) and SM(39:1) that could be used collectively as biomarkers for infant nutrition during early development. Through the validation we were able to show that these biomarkers can be used to determine whether young infants (3-6 months) are breastfed or receive formula milk, and even whether an infant is fed a mixture of both (Acharjee et al 2017).…”

High Peak Density Artifacts in Fourier Transform Mass Spectra and their Effects on Data Analysis

“…Pooled lithium heparin treated plasma (Seralab) was extracted using the methods described by Koulman et al, 2017(Acharjee et al, 2017. Briefly, 15 uL of plasma was extracted with 100 uL of ultra-pure H2O in a glass vial (2 mL was also added to the well.…”

Lipid extraction from dried blood spots and dried milk spots for high throughput lipidomics

“…Several studies have used DBSs for testing hypotheses about lipid metabolism 7,16 and work investigating the analytical limits of this sample type for lipidomics has also been reported 5,17,18 . Early work on DMSs has also been reported 19 .…”

“…Early work on DMSs has also been reported 19 . These studies have been made possible by considerable progress in laboratory infrastructure and methods for the high throughput lipidomics, including Direct Infusion Mass Spectrometry (DI-MS) 16,20,21 . The increasing demand for this sample type in high throughput studies, and the desire for high quality data, led us to answer unresolved questions about the analytical issues associated with using either DBSs or DMSs.…”