Metabolomics analysis generates vast arrays of data, necessitating comprehensive workflows involving expertise in analytics, biochemistry and bioinformatics in order to provide coherent and high-quality data that enable discovery of robust and biologically significant metabolic findings. In this protocol article, we introduce notame, an analytical workflow for non-targeted metabolic profiling approaches, utilizing liquid chromatography–mass spectrometry analysis. We provide an overview of lab protocols and statistical methods that we commonly practice for the analysis of nutritional metabolomics data. The paper is divided into three main sections: the first and second sections introducing the background and the study designs available for metabolomics research and the third section describing in detail the steps of the main methods and protocols used to produce, preprocess and statistically analyze metabolomics data and, finally, to identify and interpret the compounds that have emerged as interesting.

Aims/hypothesis We studied for the first time the long-term effects of a combined physical activity and dietary intervention on insulin resistance and fasting plasma glucose in a general population of predominantly normal-weight children. Methods We carried out a 2 year non-randomised controlled trial in a population sample of 504 children aged 6–9 years at baseline. The children were allocated to a combined physical activity and dietary intervention group (306 children at baseline, 261 children at 2-year follow-up) or a control group (198 children, 177 children) without blinding. We measured fasting insulin and fasting glucose, calculated HOMA-IR, assessed physical activity and sedentary time by combined heart rate and body movement monitoring, assessed dietary factors by a 4 day food record, used the Finnish Children Healthy Eating Index (FCHEI) as a measure of overall diet quality, and measured body fat percentage (BF%) and lean body mass by dual-energy x-ray absorptiometry. The intervention effects on insulin, glucose and HOMA-IR were analysed using the intention-to-treat principle and linear mixed-effects models after adjustment for sex, age at baseline, and pubertal status at baseline and 2 year follow-up. The measures of physical activity, sedentary time, diet and body composition at baseline and 2 year follow-up were entered one-by-one as covariates into the models to study whether changes in these variables might partly explain the observed intervention effects. Results Compared with the control group, fasting insulin increased 4.65 pmol/l less (absolute change +8.96 vs +13.61 pmol/l) and HOMA-IR increased 0.18 units less (+0.31 vs +0.49 units) over 2 years in the combined physical activity and dietary intervention group. The intervention effects on fasting insulin (regression coefficient β for intervention effect −0.33 [95% CI −0.62, −0.04], p = 0.026) and HOMA-IR (β for intervention effect −0.084 [95% CI −0.156, −0.012], p = 0.023) were statistically significant after adjustment for sex, age at baseline, and pubertal status at baseline and 2 year follow-up. The intervention had no effect on fasting glucose, BF% or lean body mass. Changes in total physical activity energy expenditure, light physical activity, moderate-to-vigorous physical activity, total sedentary time, the reported consumption of high-fat (≥60%) vegetable oil-based spreads, and FCHEI, but not a change in BF% or lean body mass, partly explained the intervention effects on fasting insulin and HOMA-IR. Conclusions/interpretation The combined physical activity and dietary intervention attenuated the increase in insulin resistance over 2 years in a general population of predominantly normal-weight children. This beneficial effect was partly mediated by changes in physical activity, sedentary time and diet but not changes in body composition. Trial registration ClinicalTrials.gov NCT01803776

Scope The article investigates the FADS1 rs174550 genotype interaction with dietary intakes of high linoleic acid (LA) and high alpha‐linolenic acid (ALA) on the response of fatty acid composition of plasma phospholipids (PLs), and of markers of low‐grade inflammation and glucose‐insulin homeostasis. Methods and results One‐hundred thirty homozygotes men for FADS1 rs174550 SNP (TT and CC genotypes) were randomized to an 8‐week intervention with either LA‐ or ALA‐enriched diet (13 E% PUFA). The source of LA and ALA are 30–50 mL of sunflower oil (SFO, 62–63% LA) and Camelina sativa oil (CSO, 30– are randomized to an 35% ALA), respectively. In the SFO arm, there is a significant genotype x diet interaction for the proportion of arachidonic acid in plasma phospholipids (p < 0.001), disposition index (DI30) (p = 0.039), and for serum high‐sensitive c‐reactive protein (hs‐CRP, p = 0.029) after excluding the participants with hs‐CRP concentration of >10 mg L‐1 and users of statins or anti‐inflammatory therapy. In the CSO arm, there are significant genotype x diet interactions for n‐3 polyunsaturated fatty acids, but not for the clinical characteristics. Conclusions The FADS1 genotype modifies the response to high PUFA diets, especially to high‐LA diet. These findings suggest that approaches considering FADS variation may be useful in personalized dietary counseling.

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Purpose We studied the effects of a physical activity and dietary intervention on plasma lipids in a general population of children. We also investigated how lifestyle changes contributed to the intervention effects. Methods We carried out a 2-year controlled, non-randomized lifestyle intervention study among 504 mainly prepubertal children aged 6-9 years at baseline. We assigned 306 children to the intervention group and 198 children to the control group. We assessed plasma concentrations of total, LDL, HDL, and VLDL cholesterol, triglycerides, HDL triglycerides, and VLDL triglycerides. We evaluated the consumption of foods using 4-day food records and physical activity using a movement and heart rate sensor. We analyzed data using linear mixed-effect models adjusted for age at baseline, sex, and pubertal stage at both time points. Furthermore, specific lifestyle variables were entered in these models. Results Plasma LDL cholesterol decreased in the intervention group but did not change in the control group (− 0.05 vs. 0.00 mmol/L, regression coefficient (β) = − 0.0385, p = 0.040 for group*time interaction). This effect was mainly explained by the changes in the consumption of high-fat vegetable oil-based spreads (β = − 0.0203, + 47% change in β) and butter-based spreads (β = − 0.0294, + 30% change in β), moderate-to-vigorous physical activity (β = − 0.0268, + 30% change in β), light physical activity (β = − 0.0274, + 29% change in β) and sedentary time (β = − 0.0270, + 30% change in β). The intervention had no effect on other plasma lipids. Conclusion Lifestyle intervention resulted a small decrease in plasma LDL cholesterol concentration in children. The effect was explained by changes in quality and quantity of dietary fat and physical activity. Clinical Trial Registry Number NCT01803776, ClinicalTrials.gov