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 ± 0.4 months) 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

Adiposity Mediates the Association Between the Dietary Inflammatory Index and Markers of Type 2 Diabetes Risk in Middle-Aged Black South African Women

The dietary inflammatory index (DII®), a validated tool used to measure the inflammatory potential of the diet, has been associated with metabolic disorders in various settings, but not in African populations. The aim of this study was to investigate whether the DII is associated with markers of type 2 diabetes (T2D) risk, and if this association is mediated by adiposity and/or low-grade inflammation, in black South Africa women. Energy-adjusted-DII (E-DII) scores were calculated in 190 women (median age, 53 years) from the Birth-to-Twenty plus cohort using a validated food frequency questionnaire. Fasting glucose, insulin, HbA1c, and inflammatory cytokines were measured, and an oral glucose tolerance test performed. Basic anthropometry and dual-energy x-ray absorptiometry-derived body fat, including estimate of visceral adipose tissue (VAT) area, were measured. E-DII scores were associated with all markers of T2D risk, namely, fasting glucose and insulin, HbA1c, HOMA2-IR, two-hour glucose and Matsuda index (all p \u3c 0.05). After adjusting for age, measures of adiposity, but not inflammatory cytokines, mediated the association between E-DII and markers of T2D risk (p \u3c 0.05). Measures of central obesity had proportionally higher (range: 23.5–100%) mediation effects than total obesity (range: 10–60%). The E-DII is associated with T2D risk through obesity, in particular central obesity, among black middle-aged South African women

Circulating and Adipose Tissue Fatty Acid Composition in Black South African Women with Obesity: A Cross-Sectional Study

Background and Aims: During positive energy balance, excess lipid storage in subcutaneous adipose tissue (SAT) is associated with increased lipolysis. Elevated circulating fatty acid (FA) concentrations from both SAT lipolysis and dietary fat intake may result in visceral adipose tissue (VAT) accumulation, impairment of glucose metabolism, altogether increasing obesity-associated metabolic risks. We aimed to test the hypothesis that FA composition of red blood cell total phospholipids (RBC-TPL) and SAT is associated with body fat centralisation (VAT/SAT ratio) and insulin sensitivity (SI) in black South African women with obesity. Methods: Participants’ (n = 41) body fat composition and distribution, SI, and RBC-TPL, abdominal and gluteal SAT (gSAT) FA composition (gas-liquid chromatography) were measured. Results: RBC-TPL contained higher proportions of saturated fatty acids (SFAs) than SAT (p < 0.001), which were associated with lower SI (p < 0.05). Mono-unsaturated fatty acids (MUFAs) and stearoyl-CoA desaturase-1 (SCD1)-16 were lower, while poly-unsaturated fatty acids (PUFAs), and delta-5 and delta-6 desaturase indices were higher in RBC-TPL than SAT (p < 0.001). Interestingly, FA profiles differed between SAT depots with higher SFAs and lower MUFAs, SCD1-16 and SCD1-18 indices in abdominal compared to gluteal SAT (p < 0.01). In both SAT depots, higher SFAs and lower PUFAs (n-3 and n-6) correlated with lower VAT/SAT ratio; and lower PUFAs (n-3 and n-6) and higher total MUFA correlated with higher SI. Conclusion: Our findings confirm the relationships between the FA composition of RBC-TPL and SAT and metabolic risk in black women with obesity, which are dependent on both the FA class, and the tissue type/blood compartment in which they are distributed

Mapping the consequenses of physical exercise and nutrition on human health : A predictive metabolomics approach

Human health is a complex and wide-ranging subject far beyond nutrition and physical exercise. Still, these factors have a huge impact on global health by their ability to prevent diseases and thus promote health. Thus, to identify health risks and benefits, it is necessary to reveal the underlying mechanisms of nutrition and exercise, which in many cases follows a complex chain of events. As a consequence, current health research is generating massive amounts of data from anthropometric parameters, genes, proteins, small molecules (metabolites) et cetera, with the intent to understand these mechanisms. For the study of health responses, especially related to physical exercise and nutrition, alterations in small molecules (metabolites) are in most cases immediate and located close to the phenotypic level and could therefore provide early signs of metabolic imbalances. Since there are roughly as many different responses to exercise and nutrients as there are humans, this quest is highly multifaceted and will benefit from an interpretation of treatment effects on a general as well as on an individual level. This thesis involves the application of chemometric methods to the study of global metabolic reactions, i.e. metabolomics, in a strategy coined predictive metabolomics. Via the application of predictive metabolomics an extensive hypothesis-free biological interpretation has been carried out of metabolite patterns in blood, acquired using gas chromatography-mass spectrometry (GC-MS), related to physical exercise, nutrition and diet, all in the context of human health. In addition, the chemometrics methodology have computational benefits concerning the extraction of relevant information from information-rich data as well as for interpreting general treatment effects and individual responses, as exemplified throughout this work. Health concerns all lifestages, thus this thesis presents a strategic framework in combination with comprehensive interpretations of metabolite patterns throughout life. This includes a broad range of human studies revealing metabolic patterns related to the impact of physical exercise, macronutrient modulation and different fitness status in young healthy males, short and long term dietary treatments in overweight post menopausal women as well as metabolic responses related to probiotics treatment and early development in infants. As a result, the studies included in the thesis have revealed metabolic patterns potentially indicative of an anti-catabolic response to macronutrients in the early recovery phase following exercise. Moreover, moderate differences in the metabolome associated with cardiorespiratory fitness level were detected, which could be linked to variation in the inflammatory and antioxidaive defense system. This work also highlighted mechanistic information that could be connected to dietary related weight loss in overweight and obese postmenopausal women in relation to short as well as long term dietary effects based on different macronutrient compositions. Finally, alterations were observed in metabolic profiles in relation to probiotics treatment in the second half of infancy, suggesting possible health benefits of probiotics supplementation at an early age.  Embargo until 2012-06-0

Lysophospholipids as Predictive Markers of ST-Elevation Myocardial Infarction (STEMI) and Non-ST-Elevation Myocardial Infarction (NSTEMI)

The present study explored patterns of circulating metabolites and proteins that can predict future risk for ST-elevation myocardial infarction (STEMI) and non-ST-elevation myocardial infarction (NSTEMI). We conducted a prospective nested case-control study in northern Sweden in individuals who developed STEMI (N = 50) and NSTEMI (N = 50) within 5 years and individually matched controls (N = 100). Fasted plasma samples were subjected to multiplatform mass spectrometry-based metabolomics and multiplex protein analyses. Multivariate analyses were used to elucidate infarction-specific metabolite and protein risk profiles associated with future incident STEMI and NSTEMI. We found that altered lysophosphatidylcholine (LPC) to lysophosphatidylethanolamine (LPE) ratio predicted STEMI and NSTEMI events in different ways. In STEMI, lysophospholipids (mainly LPEs) were lower, whereas in NSTEMI, lysophospholipids (mainly LPEs) were higher. We found a similar response for all detected lysophospholipids but significant alterations only for those containing linoleic acid (C18:2, p &lt; 0.05). Patients with STEMI had higher secretoglobin family 3A member 2 and tartrate-resistant acid phosphate type 5 and lower platelet-derived growth factor subunit A, which are proteins associated with atherosclerosis severity and plaque development mediated via altered phospholipid metabolism. In contrast, patients with NSTEMI had higher levels of proteins associated with inflammation and macrophage activation, including interleukin 6, C-reactive protein, chemerin, and cathepsin X and D. The STEMI risk marker profile includes factors closely related to the development of unstable plaque, including a higher LPC:LPE ratio, whereas NSTEMI is characterized by a lower LPC:LPE ratio and increased inflammation

Genetic Mimicry Analysis Reveals the Specific Lipases Targeted by the ANGPTL3-ANGPTL8 Complex and ANGPTL4

Angiopoietin-like proteins, ANGPTL3, ANGPTL4, and ANGPTL8, are involved in regulating plasma lipids. In vitro and animal-based studies point to LPL and endothelial lipase (EL, LIPG) as key targets of ANGPTLs. To examine the ANGPTL mechanisms for plasma lipid modulation in humans, we pursued a genetic mimicry analysis of enhancing or suppressing variants in the LPL, LIPG, lipase C hepatic type (LIPC), ANGPTL3, ANGPTL4, and ANGPTL8 genes using data on 248 metabolic parameters derived from over 110,000 nonfasted individuals in the UK Biobank and validated in over 13,000 overnight fasted individuals from 11 other European populations. ANGPTL4 suppression was highly concordant with LPL enhancement but not HL or EL, suggesting ANGPTL4 impacts plasma metabolic parameters exclusively via LPL. The LPL-independent effects of ANGPTL3 suppression on plasma metabolic parameters showed a striking inverse resemblance with EL suppression, suggesting ANGPTL3 not only targets LPL but also targets EL. Investigation of the impact of the ANGPTL3-ANGPTL8 complex on plasma metabolite traits via the ANGPTL8 R59W substitution as an instrumental variable showed a much higher concordance between R59W and EL activity than between R59W and LPL activity, suggesting the R59W substitution more strongly affects EL inhibition than LPL inhibition. Meanwhile, when using a rare and deleterious protein-truncating ANGPTL8 variant as an instrumental variable, the ANGPTL3-ANGPTL8 complex was very LPL specific. In conclusion, our analysis provides strong human genetic evidence that the ANGPTL3-ANGPTL8 complex regulates plasma metabolic parameters, which is achieved by impacting LPL and EL. By contrast, ANGPTL4 influences plasma metabolic parameters exclusively via LPL

Genetic mimicry analysis reveals the specific lipases targeted by the ANGPTL3-ANGPTL8 complex and ANGPTL4

Angiopoietin-like proteins, ANGPTL3, ANGPTL4, and ANGPTL8, are involved in regulating plasma lipids. In vitro and animal-based studies point to LPL and endothelial lipase (EL, LIPG) as key targets of ANGPTLs. To examine the ANGPTL mechanisms for plasma lipid modulation in humans, we pursued a genetic mimicry analysis of enhancing or suppressing variants in the LPL, LIPG, lipase C hepatic type (LIPC), ANGPTL3, ANGPTL4, and ANGPTL8 genes using data on 248 metabolic parameters derived from over 110,000 nonfasted individuals in the UK Biobank and validated in over 13,000 overnight fasted individuals from 11 other European populations. ANGPTL4 suppression was highly concordant with LPL enhancement but not HL or EL, suggesting ANGPTL4 impacts plasma metabolic parameters exclusively via LPL. The LPL-independent effects of ANGPTL3 suppression on plasma metabolic parameters showed a striking inverse resemblance with EL suppression, suggesting ANGPTL3 not only targets LPL but also targets EL. Investigation of the impact of the ANGPTL3-ANGPTL8 complex on plasma metabolite traits via the ANGPTL8 R59W substitution as an instrumental variable showed a much higher concordance between R59W and EL activity than between R59W and LPL activity, suggesting the R59W substitution more strongly affects EL inhibition than LPL inhibition. Meanwhile, when using a rare and deleterious protein-truncating ANGPTL8 variant as an instrumental variable, the ANGPTL3-ANGPTL8 complex was very LPL specific. In conclusion, our analysis provides strong human genetic evidence that the ANGPTL3-ANGPTL8 complex regulates plasma metabolic parameters, which is achieved by impacting LPL and EL. By contrast, ANGPTL4 influences plasma metabolic parameters exclusively via LPL

Lysophospholipids as Predictive Markers of ST-Elevation Myocardial Infarction (STEMI) and Non-ST-Elevation Myocardial Infarction (NSTEMI)

The present study explored patterns of circulating metabolites and proteins that can predict future risk for ST-elevation myocardial infarction (STEMI) and non-ST-elevation myocardial infarction (NSTEMI). We conducted a prospective nested case-control study in northern Sweden in individuals who developed STEMI (N = 50) and NSTEMI (N = 50) within 5 years and individually matched controls (N = 100). Fasted plasma samples were subjected to multiplatform mass spectrometry-based metabolomics and multiplex protein analyses. Multivariate analyses were used to elucidate infarction-specific metabolite and protein risk profiles associated with future incident STEMI and NSTEMI. We found that altered lysophosphatidylcholine (LPC) to lysophosphatidylethanolamine (LPE) ratio predicted STEMI and NSTEMI events in different ways. In STEMI, lysophospholipids (mainly LPEs) were lower, whereas in NSTEMI, lysophospholipids (mainly LPEs) were higher. We found a similar response for all detected lysophospholipids but significant alterations only for those containing linoleic acid (C18:2, p &lt; 0.05). Patients with STEMI had higher secretoglobin family 3A member 2 and tartrate-resistant acid phosphate type 5 and lower platelet-derived growth factor subunit A, which are proteins associated with atherosclerosis severity and plaque development mediated via altered phospholipid metabolism. In contrast, patients with NSTEMI had higher levels of proteins associated with inflammation and macrophage activation, including interleukin 6, C-reactive protein, chemerin, and cathepsin X and D. The STEMI risk marker profile includes factors closely related to the development of unstable plaque, including a higher LPC:LPE ratio, whereas NSTEMI is characterized by a lower LPC:LPE ratio and increased inflammation

Constrained randomization and multivariate effect projections improve information extraction and biomarker pattern discovery in metabolomics studies involving dependent samples

Analytical drift is a major source of bias in mass spectrometry based metabolomics confounding interpretation and biomarker detection. So far, standard protocols for sample and data analysis have not been able to fully resolve this. We present a combined approach for minimizing the influence of analytical drift on multivariate comparisons of matched or dependent samples in mass spectrometry based metabolomics studies. The approach is building on a randomization procedure for sample run order, constrained to independent randomizations between and within dependent sample pairs (e.g. pre/post intervention). This is followed by a novel multivariate statistical analysis strategy allowing paired or dependent analyses of individual effects named OPLS-effect projections (OPLS-EP). We show, using simulated data that OPLS-EP gives improved interpretation over existing methods and that constrained randomization of sample run order in combination with an appropriate dependent statistical test increase the accuracy and sensitivity and decrease the false omission rate in biomarker detection. We verify these findings and prove the strength of the suggested approach in a clinical data set consisting of LC/MS data of blood plasma samples from patients before and after radical prostatectomy. Here OPLS-EP compared to traditional (independent) OPLS-discriminant analysis (OPLS-DA) on constrained randomized data gives a less complex model (3 versus 5 components) as well a higher predictive ability (Q2 = 0.80 versus Q2 = 0.55). We explain this by showing that paired statistical analysis detects 37 unique significant metabolites that were masked for the independent test due to bias, including analytical drift and inter-individual variation

Constrained randomization and multivariate effect projections improve information extraction and biomarker pattern discovery in metabolomics studies involving dependent samples

Analytical drift is a major source of bias in mass spectrometry based metabolomics confounding interpretation and biomarker detection. So far, standard protocols for sample and data analysis have not been able to fully resolve this. We present a combined approach for minimizing the influence of analytical drift on multivariate comparisons of matched or dependent samples in mass spectrometry based metabolomics studies. The approach is building on a randomization procedure for sample run order, constrained to independent randomizations between and within dependent sample pairs (e.g. pre/post intervention). This is followed by a novel multivariate statistical analysis strategy allowing paired or dependent analyses of individual effects named OPLS-effect projections (OPLS-EP). We show, using simulated data that OPLS-EP gives improved interpretation over existing methods and that constrained randomization of sample run order in combination with an appropriate dependent statistical test increase the accuracy and sensitivity and decrease the false omission rate in biomarker detection. We verify these findings and prove the strength of the suggested approach in a clinical data set consisting of LC/MS data of blood plasma samples from patients before and after radical prostatectomy. Here OPLS-EP compared to traditional (independent) OPLS-discriminant analysis (OPLS-DA) on constrained randomized data gives a less complex model (3 versus 5 components) as well a higher predictive ability (Q2 = 0.80 versus Q2 = 0.55). We explain this by showing that paired statistical analysis detects 37 unique significant metabolites that were masked for the independent test due to bias, including analytical drift and inter-individual variation