Article activity feed

  1. Evaluation Summary:

    This paper will be of interest to a broad audience of epidemiologists and early childhood researchers as it provides data on the relationship between infection burden in infancy and metabolomic/lipidomic profiles at 12 months of age from a unique cohort of 555 mother-infant dyads. The paper also examines potential biologic pathways that may inform prevention of cardiovascular disease. The series of analyses presented support the preliminary associations outlined and require validation and interventional studies to support the causal relationship between infection, cumulative inflammation burden, and atherosclerosis.

    (This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. Reviewer #3 agreed to share their name with the authors.)

  2. Reviewer #1 (Public Review):

    In this paper, Mansell et al investigated the relationship between infection and plasma metabolomic and lipidomic profiles at 12 months of age, and link between of these associations and inflammation. The authors generated matched infection, metabolomics and lipidomics data from 555 infants in a pre-birth longitudinal cohort. their data demonstrate that frequent infant infection is associated with adverse metabolomic profile, characterized by elevated inflammation markers, triglycerides, phenylalanine, and lower HDL, apolipoprotein A1, and omega-3 fatty acids, and lipidomic profiles, characterized by elevated phosphatidylethanolamines and lower hexosylceramides, trihexosylceramides, and cholesteryl esters. Similar profiles were noted with higher GlycA, but not hsCRP. They concluded that "Infants with a greater infection burden from birth to 12 months had pro-inflammatory and pro-atherogenic plasma metabolomic/lipid profiles, indicative of heightened risk of cardiovascular disease, obesity, and type 2 diabetes in adults. These findings suggest potentially modifiable pathways linking early life infection and inflammation with subsequent cardiometabolic risk."

    The paper is interesting and the data is based on a very large cohort. The paper contains a large amount of data and addresses an important and an understudied area of science. However, there are a couple of areas that need to be improved. First, the paper contains a number of errors that make the paper hard to follow. The authors are strongly encouraged to review the paper carefully prior to the next submission. Second, the authors need to better define the significance of their findings. For example, although the link between inflammation and metabolome change is known, the authors need to provide a better explanation on why their specific findings are important for infection in infants. Additionally, it is hard to read and understand some of the graphs that are provided in the paper. Finally, the authors need to study whether environmental factors during early infancy (such as exposure to second hand smoke or body weight, etc) also correlate with a change in serum lipids and metabolome.

    The paper will likely have a high impact on the field, as it identifies changes in lipids and metabolome with infection.

  3. Reviewer #2 (Public Review):

    Mansell et. al. report on a highly unique cohort (Barwon Infant Study) of 555 mother-child dyads with extensive phenotyping data including parental report of infant infections from birth to 12 months, metabolomic, and lipidomic profiles from cord blood at brith and at 12 months of age, and markers of inflammation at 12 months (GlycA and hsCRP).

    Major strengths of the study include the study of a population-based pre-birth longitudinal cohort with detailed phenotyping maternal and child information. Data from birth records were abstracted including infant gestational age and birth weight, which are major predictors of child outcomes, including cardiovascular disease risk.

    Weaknesses of the study include the potential for residual confounding, particularly due to shared upstream risk factors that may confer increased risk for both infant infections and adverse metabolic/lipid profiles, such as adverse intrauterine environment (e.g., gestational diabetes) and preterm delivery between 32-37 weeks gestational age.

    The results support the conclusions of associations and do not confer causation. The mediation analysis proposes a biologic pathway that is hypothesis generating given the cross-sectional nature of the mediator and outcome at the same time point. This work will have significant impact on the field due to the deep phenotyping performed in children age 12 months in the context of a pre-brith cohort.

  4. Reviewer #3 (Public Review):

    Mansell et al. examined the link between early-life infections (during the first year of life) and markers indicative of poor cardiometabolic health when the infants are 1 year old. They drew on an impressive dataset of 555 infants that were part of a longitudinal cohort, which included parent-reported infection data from birth to 12 months, and paired this with peripheral inflammatory markers and metabolomic and lipidomic profiles at 12 months. They found that more frequent infections were associated with adverse metabolomic and lipidomic profiles at 12 months of age, which might indicate heightened risk for cardiometabolic diseases later in life. They also thoughtfully compared the predictive ability of two inflammatory markers, hsCRP and GlycA, and found that the less-commonly-used marker, GlycA, was more predictive of 'omics profiles at 12 months of age as well as more strongly associated with parent-reported infection burden. This is a useful methodological advancement/validation that suggests that GlycA is a better marker of cumulative infection burden/inflammation, while CRP primarily reflects acute inflammatory responses. Together, the findings provide novel insights into how early-life exposures, like infections, might set individuals on different health trajectories that have long-lasting effects. These data might provide an opportunity to identify the most at-risk individuals using single markers (like GlycA) in order to intervene and potentially monitor the success of the intervention.

    The paper is thoughtfully written and the conclusions mostly justified by the data, but a bit of clarification is needed-especially with regard to the mediation analysis/conclusion:

    1. One technical concern is the variation in the amount of time the blood samples spent between post-processing and storage at -80C (and how they were held during that interval). The authors should be commended for their sensitivity analysis looking at just those samples that were stored for < 4 hours. However, the justification for using only those < 4 hours is not clear. It is also unclear and a bit difficult for a reader to look at Supplementary files (1A and 1B) and draw the authors' conclusion that "this had little difference on the estimated effect sizes observed in analyses with the full cohort". Could the authors make more direct comparisons between the effect sizes to hammer this point home? Given that the sample size is smaller for this analysis, the p-values should be higher (less power), but the effect sizes should be unbiased and relatively similar. Perhaps calculating the correlation between effect sizes would help improve this sensitivity analysis and be good to report in the main text.

    2. The authors show that while the correlation between GlycA (or CRP) and number of infections is relatively low (albeit a bit higher for GlycA), the effect sizes of GlycA and infections on the metabolome and lipidome are strongly correlated (and to a lesser extent between CRP and infections). A third comparison here that would be very useful, would be between GlycA and CRP effects on the metabolome and lipidome.

    3. The mediation analysis is the least convincing part of the manuscript. It is appreciated that the authors are trying to identify how infections might be translating to adverse metabolomic and lipidomic profiles. However, the justification for GlycA or hsCRP being the mediating mechanisms is not convincing. The authors are implying (statistically through their mediation models) that the effect of the number of infections on many metabolites and lipids is due to the changes in GlycA (or hsCRP). Since both GlycA and number of infections are cumulative measures at 12mo, it is unlikely that one (e.g., GlycA) precedes the other. Rather, as the authors state, GlycA is a marker of cumulative infections. This would preclude it from being a mediator unless the authors have data from earlier timepoints (like previous months) that would provide more support for a mediation effect.