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The metabolic data was subjected to multivariate statistical analysis (PLS-DA) which could differentiate the metabolic profiles of infested tissues from those of un-infested tissues at all the three distinct time points.

The putative identification of metabolites was based on two dimensions i.e. retention index (RI) for GC and similarity index (SI) for MS. The processed data was exported to MS Excel (Microsoft, USA) and the statistical analyses of the metabolic data was carried out using Metaboanalyst (Xia et al. 2015).

During tests, metabolic data was collected on a breath-by-breath basis using portable open circuit spirometry (Jaeger Oxycon Mobile, Viasys, CA).

Realistic metabolic data was successfully simulated using a 4-step process.

The study period ended and acquisition of hemodynamic and metabolic data was stopped after the last measurement following sternal spread.

Metabolic data was statistically analyzed for changes over time and between dietary groups using Analysis of Variance (ANOVA) with a linear model [ 16].

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Metabolic data were collected from each pup on alternate days beginning on P2 and ending on either P6 (Experiment 2) or P14 (Experiment 1).

Metabolic data are presented in Table 3 and Figures 3 and 4. ISI and HDL-cholesterol were lower in South Asians than Europeans in unadjusted analysis; this persisted after adjustment for age, BMI and fat mass.

Body weight, composition and metabolic data are shown in Table 1-wrap>.

The normalized metabolic data is provided as an Additional file (Additional file 1).

All metabolic data were tested for normality using the Shapiro-Wilk test.

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