Elin Chorell

Impact of probiotic feeding during weaning on the serum lipid profile and plasma metabolome in infants.

The gut microbiome interacts with the host in the metabolic response to diet, and early microbial aberrancies may be linked to the development of obesity and metabolic disorders later in life. Probiotics have been proposed to affect metabolic programming and blood lipid levels, although studies are lacking in infants. Here, we report on the lipid profile and global metabolic response following daily feeding of probiotics during weaning. A total of 179 healthy, term infants were randomised to daily intake of cereals with (n 89) or without (n 90) the addition of Lactobacillus paracasei ssp. paracasei F19 (LF19) 108 colony-forming units per serving from 4 to 13 months of age. Weight, length and skinfold thickness were monitored. Venous blood was drawn at 5·5 and 13 months of age for analysis of the serum lipid profile. In a subsample, randomly selected from each group, GC-time-of-flight/MS was used to metabolically characterise plasma samples from thirty-seven infants. A combination of multi- and univariate analysis was applied to reveal differences related to LF19 treatment based on 228 putative metabolites, of which ninety-nine were identified or classified. We observed no effects of probiotic feeding on anthropometrics or the serum lipid profile. However, we detected significantly lower levels of palmitoleic acid (16 : 1) (P< 0·05) and significantly higher levels of putrescine (P< 0·01) in LF19-treated infants. Palmitoleic acid is a major MUFA strongly linked to visceral obesity, while putrescine is a polyamine with importance for gut integrity. Whether the observed differences will have long-term health consequences are being followed.

Validated and predictive processing of gas chromatography-mass spectrometry based metabolomics data for large scale screening studies, diagnostics and metabolite pattern verification.

The suggested approach makes it feasible to screen large metabolomics data, sample sets with retained data quality or to retrieve significant metabolic information from small sample sets that can be verified over multiple studies. Hierarchical multivariate curve resolution (H-MCR), followed by orthogonal partial least squares discriminant analysis (OPLS-DA) was used for processing and classification of gas chromatography/time of flight mass spectrometry (GC/TOFMS) data characterizing human serum samples collected in a study of strenuous physical exercise. The efficiency of predictive H-MCR processing of representative sample subsets, selected by chemometric approaches, for generating high quality data was proven. Extensive model validation by means of cross-validation and external predictions verified the robustness of the extracted metabolite patterns in the data. Comparisons of extracted metabolite patterns between models emphasized the reliability of the methodology in a biological information context. Furthermore, the high predictive power in longitudinal data provided proof for the potential use in clinical diagnosis. Finally, the predictive metabolite pattern was interpreted physiologically, highlighting the biological relevance of the diagnostic pattern.

Pregnancy to postpartum transition of serum metabolites in women with gestational diabetes

CONTEXT Gestational diabetes is commonly linked to development of type 2 diabetes mellitus (T2DM). There is a need to characterize metabolic changes associated with gestational diabetes in order to find novel biomarkers for T2DM. OBJECTIVE To find potential pathophysiological mechanisms and markers for progression from gestational diabetes mellitus to T2DM by studying the metabolic transition from pregnancy to postpartum. DESIGN The metabolic transition profile from pregnancy to postpartum was characterized in 56 women by mass spectrometry-based metabolomics; 11 women had gestational diabetes mellitus, 24 had normal glucose tolerance, and 21 were normoglycaemic but at increased risk for gestational diabetes mellitus. Fasting serum samples collected during trimester 3 (gestational week 32±0.6) and postpartum (10.5±0.4months) were compared in diagnosis-specific multivariate models (orthogonal partial least squares analysis). Clinical measurements (e.g., insulin, glucose, lipid levels) were compared and models of insulin sensitivity and resistance were calculated for the same time period. RESULTS Women with gestational diabetes had significantly increased postpartum levels of the branched-chain amino acids (BCAAs) leucine, isoleucine, and valine, and their circulating lipids did not return to normal levels after pregnancy. The increase in BCAAs occurred postpartum since the BCAAs did not differ during pregnancy, as compared to normoglycemic women. CONCLUSIONS Postpartum levels of specific BCAAs, notably valine, are related to gestational diabetes during pregnancy.

Glucocorticoid receptor gene expression in adipose tissue and associated metabolic risk in black and white South African women

Background:Black women have lower visceral adipose tissue (VAT) but are less insulin sensitive than white women; the mechanisms responsible are unknown.Objective:The study aimed to test the hypothesis that variation in subcutaneous adipose tissue (SAT) sensitivity to glucocorticoids might underlie these differences.Methods:Body fatness (dual energy X-ray absorptiometry) and distribution (computerized tomography), insulin sensitivity (SI, intravenous and oral glucose tolerance tests), and expression of 11β-hydroxysteroid dehydrogenase-1 (11HSD1), hexose-6-phosphate dehydrogenase and glucocorticoid receptor-α (GRα), as well as genes involved in adipogenesis and inflammation were measured in abdominal deep SAT, superficial SAT and gluteal SAT (GLUT) depots of 56 normal-weight or obese black and white premenopausal South African (SA) women. We used a combination of univariate and multivariate statistics to evaluate ethnic-specific patterns in adipose gene expression and related body composition and insulin sensitivity measures.Results:Although 11HSD1 activity and mRNA did not differ by ethnicity, GRα mRNA levels were significantly lower in SAT of black compared with white women, particularly in the GLUT depot (0.52±0.21 vs 0.91±0.26 AU, respectively, P<0.01). In black women, lower SAT GRα mRNA levels were associated with increased inflammatory gene transcript levels and abdominal SAT area, and reduced adipogenic gene transcript levels, VAT/SAT ratio and SI. Abdominal SAT 11HSD1 activity associated with increased VAT area and decreased SI in white, but not in black women.Conclusions:In black SA women, downregulation of GRα mRNA levels with obesity and reduced insulin sensitivity, possibly via increased SAT inflammation, is associated with reduced VAT accumulation.

Plasma metabolomic response to postmenopausal weight loss induced by different diets

BackgroundMenopause is associated with increased abdominal fat and increased risk of developing diabetes and cardiovascular disease.ObjectivesThe present study evaluated the plasma metabolic response in relation to insulin sensitivity after weight loss via diet intervention.MethodsThis work includes two studies; i) Ten women on a 5 weeks Paleolithic-type diet (PD, 30 energy percent (E%) protein, 40 E% fat, 30 E% carbohydrates), ii) 55 women on 6 months of either PD or Nordic Nutrition Recommendations diet (NNR, 15 E% protein, 30 E% fat, and 55 E% carbohydrates). Plasma metabolic profiles were acquired at baseline and post diet using gas chromatography time-of-flight/mass spectrometry and investigated in relation to insulin sensitivity using multivariate bioinformatics.ResultsBoth the PD and NNR diet resulted in significant weight loss, reduced waist circumference, improved serum lipid profiles, and improved insulin sensitivity. We detected a baseline metabolic profile that correlated significantly with insulin sensitivity, and of which components increased significantly in the PD group compared to NNR. Specifically, a significant increase in myo-inositol (MI), a second messenger of insulin action, and β-hydroxybutyric acid (β-HB) increased while dihomo-gamma-linoleic acid (DGLA) decreased in PD compared to NNR, which correlated with improved insulin sensitivity. We also detected a significant decrease in tyrosine and tryptophan, potential markers of insulin resistance when elevated in the circulation, with the PD but not the NNR.ConclusionsUsing metabolomics, we detected changes in the plasma metabolite profiles associated with weight loss in postmenopausal women by different diets. The metabolic profiles following 6 months of PD were linked to beneficial effects on insulin sensitivity compared to NNR.Graphical Abstract

Attenuated Low-Grade Inflammation Following Long-Term Dietary Intervention in Postmenopausal Women with Obesity

Abdominal fat accumulation after menopause is associated with low‐grade inflammation and increased risk of metabolic disorders. Effective long‐term lifestyle treatment is therefore needed.

Statistical loadings and latent significance simplify and improve interpretation of multivariate projection models

Multivariate projection methods are unique in being both multivariable by combining many variables into stronger predictive features (latent variables), and multivariate for being able to model systematic variation both related and orthogonal to an observed response. Orthogonal partial least squares (OPLS) is a versatile multivariate projection method for analysis of correlation, discrimination and effect changes. However, currently OPLS is not fully using its multivariate potential since orthogonal systematic variation is not considered in model interpretation, resulting in univariate interpretation of variable significance. We present a strategy for improved interpretation of OPLS models based upon a post-hoc linear regression analysis that can be used with or without the orthogonal OPLS score(s) as a covariate to make the interpretation multivariate or univariate respectively. By selecting the observed response y or estimated response yhat as a one of the factors in the linear regression the results are related to either of the OPLS loadings w or p. Furthermore, converting the OPLS loading values to statistical t-values creates a direct link to statistical significance. Finally, by applying three different Boolean loadings W, P and W∧P variable significance can be summarized based on three criteria. W and P reveal if the values in w or p respectively are outside the statistical limits with W∧P being the logical conjunction of W and P (significant if outside limits in both W and P). Two examples are used to verify the proposed strategy. First, a synthetic example, simulating a mix of mass spectra, and second a clinical metabolomics study of a dietary intervention. In the simulated example we show that multivariate interpretation gives higher accuracy for estimation of true differences, mainly due to higher true positive rate. Furthermore, we highlight how application of W∧P for summarizing variable significance leads to higher accuracy. For the metabolomics example, we show that a more detailed interpretation, i.e. larger number of significant metabolites of relevance, is obtained using the multivariate interpretation. In summary, the suggested strategy provides means for facilitated interpretation of OPLS models, beyond univariate statistics, and offers a multivariate tool for discovery of biomarker patterns, i.e. latent biomarkers.