Metabolomic Profiles Associated with BMI, Waist Circumference, and Diabetes and Inflammation Biomarkers in Women

Lipids as potential mediators linking body mass index to diabetes: evidence from a mediation analysis based on the NAGALA cohort

BACKGROUND

Body mass index (BMI) and lipid disorders are both known to be strongly associated with the development of diabetes, however, the indirect effect of lipid parameters in the BMI-related diabetes risk is currently unknown. This study aimed to investigate the mediating role of lipid parameters in the association of BMI with diabetes risk.

METHODS

We assessed the association of diabetes risk with BMI, as well as lipid parameters including high-density lipoprotein cholesterol(HDL-C), low-density lipoprotein cholesterol(LDL-CF and LDL-CS), triglycerides(TG), total cholesterol(TC), remnant cholesterol(RC), non-HDL-C, and combined indices of lipid parameters with HDL-C (RC/HDL-C ratio, TG/HDL-C ratio, TC/HDL-C ratio, non-HDL/HDL-C ratio, LDL/HDL-C ratio) using data from 15,453 subjects in the NAGALA project. Mediation models were used to explore the mediating role of lipid parameters in the association of BMI with diabetes risk, and mediation percentages were calculated for quantifying the strength of the indirect effects. Finally, receiver operating characteristic curve (ROC) analysis was used to compare the accuracy of BMI and BMI combined with lipid parameters in predicting incident diabetes.

RESULTS

Multivariate regression models, adjusted for confounding factors, demonstrated robust associations of lipid parameters, BMI, with diabetes risk, with the exception of TC, LDL-CF, LDL-CS, and non-HDL-C. Mediation analysis showed that lipid parameters except TC, LDL-CF, LDL-CS, and Non-HDL-C were involved in and mediated the association of BMI with diabetes risk, with the largest mediation percentage being the RC/HDL-C ratio, which was as high as 40%; it is worth mentioning that HDL-C and HDL-C-related lipid ratio parameters also play an important mediating role in the association between BMI and diabetes, with the mediator proportion being greater than 30%. Finally, based on the ROC results, we found that the prediction performance of all lipid parameters in the current study except TC was significantly improved when combined with BMI.

CONCLUSION

Our fresh findings suggested that lipid parameters partially mediated the association of BMI with diabetes risk; this result indicated that in the context of diabetes risk screening and disease management, it is important to not only monitor BMI but also pay attention to lipid parameters, particularly HDL-C and HDL-C-related lipid ratio parameters.

Metabolomic Biomarkers of Dietary Approaches to Stop Hypertension (DASH) Dietary Patterns in Pregnant Women

Objective: the aim of this study was to identify plasma metabolomic markers of Dietary Approaches to Stop Hypertension (DASH) dietary patterns in pregnant women. Methods: This study included 186 women who had both dietary intake and metabolome measured from a nested case-control study within the NICHD Fetal Growth Studies-Singletons cohort (FGS). Dietary intakes were ascertained at 8-13 gestational weeks (GW) using the Food Frequency Questionnaire (FFQ) and DASH scores were calculated based on eight food and nutrient components. Fasting plasma samples were collected at 15-26 GW and untargeted metabolomic profiling was performed. Multivariable linear regression models were used to examine the association of individual metabolites with the DASH score. Least absolute shrinkage and selection operator (LASSO) regression was used to select a panel of metabolites jointly associated with the DASH score. Results: Of the total 460 known metabolites, 92 were individually associated with DASH score in linear regressions, 25 were selected as a panel by LASSO regressions, and 18 were identified by both methods. Among the top 18 metabolites, there were 11 lipids and lipid-like molecules (i.e., TG (49:1), TG (52:2), PC (31:0), PC (35:3), PC (36:4) C, PC (36:5) B, PC (38:4) B, PC (42:6), SM (d32:0), gamma-tocopherol, and dodecanoic acid), 5 organic acids and derivatives (i.e., asparagine, beta-alanine, glycine, taurine, and hydroxycarbamate), 1 organic oxygen compound (i.e., xylitol), and 1 organoheterocyclic compound (i.e., maleimide). Conclusions: our study identified plasma metabolomic markers for DASH dietary patterns in pregnant women, with most of being lipids and lipid-like molecules.

The metabolic profile of waist to hip ratio-A multi-cohort study

BACKGROUND

The genetic background of general obesity and fat distribution is different, pointing to separate underlying physiology. Here, we searched for metabolites and lipoprotein particles associated with fat distribution, measured as waist/hip ratio adjusted for fat mass (WHRadjfatmass), and general adiposity measured as percentage fat mass.

METHOD

The sex-stratified association of 791 metabolites detected by liquid chromatography-mass spectrometry (LC-MS) and 91 lipoprotein particles measured by nuclear magnetic spectroscopy (NMR) with WHRadjfatmass and fat mass were assessed using three population-based cohorts: EpiHealth (n = 2350) as discovery cohort, with PIVUS (n = 603) and POEM (n = 502) as replication cohorts.

RESULTS

Of the 193 LC-MS-metabolites being associated with WHRadjfatmass in EpiHealth (false discovery rate (FDR) <5%), 52 were replicated in a meta-analysis of PIVUS and POEM. Nine metabolites, including ceramides, sphingomyelins or glycerophosphatidylcholines, were inversely associated with WHRadjfatmass in both sexes. Two of the sphingomyelins (d18:2/24:1, d18:1/24:2 and d18:2/24:2) were not associated with fat mass (p>0.50). Out of 91, 82 lipoprotein particles were associated with WHRadjfatmass in EpiHealth and 42 were replicated. Fourteen of those were associated in both sexes and belonged to very-large or large HDL particles, all being inversely associated with both WHRadjfatmass and fat mass.

CONCLUSION

Two sphingomyelins were inversely linked to body fat distribution in both men and women without being associated with fat mass, while very-large and large HDL particles were inversely associated with both fat distribution and fat mass. If these metabolites represent a link between an impaired fat distribution and cardiometabolic diseases remains to be established.

Metabolic Signatures Elucidate the Effect of Body Mass Index on Type 2 Diabetes

Obesity plays an important role in the development of insulin resistance and diabetes, but the molecular mechanism that links obesity and diabetes is still not completely understood. Here, we used 146 targeted metabolomic profiles from the German KORA FF4 cohort consisting of 1715 participants and associated them with obesity and type 2 diabetes. In the basic model, 83 and 51 metabolites were significantly associated with body mass index (BMI) and T2D, respectively. Those metabolites are branched-chain amino acids, acylcarnitines, lysophospholipids, or phosphatidylcholines. In the full model, 42 and 3 metabolites were significantly associated with BMI and T2D, respectively, and replicate findings in the previous studies. Sobel mediation testing suggests that the effect of BMI on T2D might be mediated via lipids such as sphingomyelin (SM) C16:1, SM C18:1 and diacylphosphatidylcholine (PC aa) C38:3. Moreover, mendelian randomization suggests a causal relationship that BMI causes the change of SM C16:1 and PC aa C38:3, and the change of SM C16:1, SM C18:1, and PC aa C38:3 contribute to T2D incident. Biological pathway analysis in combination with genetics and mice experiments indicate that downregulation of sphingolipid or upregulation of phosphatidylcholine metabolism is a causal factor in early-stage T2D pathophysiology. Our findings indicate that metabolites like SM C16:1, SM C18:1, and PC aa C38:3 mediate the effect of BMI on T2D and elucidate their role in obesity related T2D pathologies.

A prospective case-cohort analysis of plasma metabolites and breast cancer risk

BACKGROUND

Breast cancer incidence rates have not declined despite an improvement in risk prediction and the identification of modifiable risk factors, suggesting the need to identify novel risk factors and etiological pathways involved in this cancer. Metabolomics has emerged as a promising tool to find circulating metabolites associated with breast cancer risk.

METHODS

Untargeted metabolomic analysis was done on prediagnostic plasma samples from a case-cohort study of 1695 incident breast cancer cases and a 1983 women subcohort drawn from Cancer Prevention Study 3. The associations of 868 named metabolites (per one standard deviation increase) with breast cancer were determined using Prentice-weighted Cox proportional hazards regression modeling.

RESULTS

A total of 11 metabolites were associated with breast cancer at false discovery rate (FDR) < 0.05 with the majority having inverse association [ranging from RR = 0.85 (95% CI 0.80-0.92) to RR = 0.88 (95% CI 0.82-0.94)] and one having a positive association [RR = 1.14 (95% CI 1.06-1.23)]. An additional 50 metabolites were associated at FDR < 0.20 with inverse associations ranging from RR = 0.88 (95% CI 0.81-0.94) to RR = 0.91 (95% CI 0.85-0.98) and positive associations ranging from RR = 1.13 (95% CI 1.05-1.22) to RR = 1.11 (95% CI 1.02-1.20). Several of these associations validated the findings of previous metabolomic studies. These included findings that several progestogen and androgen steroids were associated with increased risk of breast cancer in postmenopausal women and four phospholipids, and the amino acids glutamine and asparagine were associated with decreased risk of this cancer in pre- and postmenopausal women. Several novel associations were also identified, including a positive association for syringol sulfate, a biomarker for smoked meat, and 3-methylcatechol sulfate and 3-hydroxypyridine glucuronide, which are metabolites of xenobiotics used for the production of pesticides and other products.

CONCLUSIONS

Our study validated previous metabolite findings and identified novel metabolites associated with breast cancer risk, demonstrating the utility of large metabolomic studies to provide new leads for understanding breast cancer etiology. Our novel findings suggest that consumption of smoked meats and exposure to catechol and pyridine should be investigated as potential risk factors for breast cancer.

The Sexual Dimorphism of Human Adipose Depots

The amount and the distribution of body fat exhibit trajectories that are sex- and human species-specific and both are determinants for health. The enhanced accumulation of fat in the truncal part of the body as a risk factor for cardiovascular and metabolic diseases is well supported by epidemiological studies. In addition, a possible independent protective role of the gluteofemoral fat compartment and of the brown adipose tissue is emerging. The present narrative review summarizes the current knowledge on sexual dimorphism in fat depot amount and repartition and consequences on cardiometabolic and reproductive health. The drivers of the sex differences and fat depot repartition, considered to be the results of complex interactions between sex determination pathways determined by the sex chromosome composition, genetic variability, sex hormones and the environment, are discussed. Finally, the inter- and intra-depot heterogeneity in adipocytes and progenitors, emphasized recently by unbiased large-scale approaches, is highlighted.

BMI and lipidomic biomarkers with risk of gestational diabetes in pregnant women

OBJECTIVE

The study aimed to identify BMI-related lipids and to explore the role of lipids linking BMI and gestational diabetes mellitus (GDM).

METHODS

Plasma lipidome, height, and weight were measured in early pregnancy among 1008 women. Pearson correlation analyses and least absolute shrinkage and selection operator regression (LASSO) were performed to identify BMI-associated lipids. Based on these lipids, a lipid score was created using LASSO, and its association with GDM risk was evaluated by conditional logistic regression. The causal relationships between BMI and lipids were tested by Mendelian randomization analysis with genotyping data. Mediation analysis was conducted to evaluate the mediating effect of lipids on the association of BMI with GDM.

RESULTS

Of 366 measured lipids, BMI was correlated with 28 lipids, which mainly belong to glycerophospholipids and glycerolipids. A total of 10 lipid species were associated with BMI, and a lipid score was established. A causal relationship between BMI and lysophosphatidylcholine 14:0 was observed. The lipid score was associated with a 1.69-fold increased risk of GDM per 1-point increment (95% CI: 1.33-2.15). Furthermore, BMI-associated lipids might explain 66.4% of the relationship between BMI and GDM.

CONCLUSIONS

Higher BMI in early pregnancy was associated with altered lipid metabolism that may contribute to the increased risk of GDM.

Peroxisomal regulation of energy homeostasis: Effect on obesity and related metabolic disorders

BACKGROUND

Peroxisomes are single membrane-bound organelles named for their role in hydrogen peroxide production and catabolism. However, their cellular functions extend well beyond reactive oxygen species (ROS) metabolism and include fatty acid oxidation of unique substrates that cannot be catabolized in mitochondria, and synthesis of ether lipids and bile acids. Metabolic functions of peroxisomes involve crosstalk with other organelles, including mitochondria, endoplasmic reticulum, lipid droplets and lysosomes. Emerging studies suggest that peroxisomes are important regulators of energy homeostasis and that disruption of peroxisomal functions influences the risk for obesity and the associated metabolic disorders, including type 2 diabetes and hepatic steatosis.

SCOPE OF REVIEW

Here, we focus on the role of peroxisomes in ether lipid synthesis, β-oxidation and ROS metabolism, given that these functions have been most widely studied and have physiologically relevant implications in systemic metabolism and obesity. Efforts are made to mechanistically link these cellular and systemic processes.

MAJOR CONCLUSIONS

Circulating plasmalogens, a form of ether lipids, have been identified as inversely correlated biomarkers of obesity. Ether lipids influence metabolic homeostasis through multiple mechanisms, including regulation of mitochondrial morphology and respiration affecting brown fat-mediated thermogenesis, and through regulation of adipose tissue development. Peroxisomal β-oxidation also affects metabolic homeostasis through generation of signaling molecules, such as acetyl-CoA and ROS that inhibit hydrolysis of stored lipids, contributing to development of hepatic steatosis. Oxidative stress resulting from increased peroxisomal β-oxidation-generated ROS in the context of obesity mediates β-cell lipotoxicity. A better understanding of the roles peroxisomes play in regulating and responding to obesity and its complications will provide new opportunities for their treatment.

Adipocyte lysoplasmalogenase TMEM86A regulates plasmalogen homeostasis and protein kinase A-dependent energy metabolism

Dysregulation of adipose tissue plasmalogen metabolism is associated with obesity-related metabolic diseases. We report that feeding mice a high-fat diet reduces adipose tissue lysoplasmalogen levels and increases transmembrane protein 86 A (TMEM86A), a putative lysoplasmalogenase. Untargeted lipidomic analysis demonstrates that adipocyte-specific TMEM86A-knockout (AKO) increases lysoplasmalogen content in adipose tissue, including plasmenyl lysophosphatidylethanolamine 18:0 (LPE P-18:0). Surprisingly, TMEM86A AKO increases protein kinase A signalling pathways owing to inhibition of phosphodiesterase 3B and elevation of cyclic adenosine monophosphate. TMEM86A AKO upregulates mitochondrial oxidative metabolism, elevates energy expenditure, and protects mice from metabolic dysfunction induced by high-fat feeding. Importantly, the effects of TMEM86A AKO are largely reproduced in vitro and in vivo by LPE P-18:0 supplementation. LPE P-18:0 levels are significantly lower in adipose tissue of human patients with obesity, suggesting that TMEM86A inhibition or lysoplasmalogen supplementation might be therapeutic approaches for preventing or treating obesity-related metabolic diseases.

Total Th1/Th2 cytokines profile from peripheral blood lymphocytes in normal pregnancy and preeclampsia syndrome

To evaluate total Th1/Th2 cytokines in CD3⁺ cells (immunocompetent T-lymphocytes) and peripheral blood lymphocytes, mostly CD4+ (T helper cells) and CD8+ (T-cytotoxic cells) subpopulations in preeclampsia. Total blood leukocytes and lymphocytes counts, percent cells: CD3⁺, INF-g⁺/CD3⁺, IL-4⁺/CD3⁺, and IL-10⁺/CD3⁺, CD4⁺/CD8⁺ were determined by flow-cytometry. Preeclampsia (n= 26) and normal pregnancy (n= 25) participants were age and gestational age matched. CD4⁺ lymphocytes count was higher in preeclampsia, compared with normal pregnancy (43.6 ± 5.8 vs 37.6 ± 5.6%; P< 0.001). CD3⁺ cells Th1/Th2 shift was not detected in preeclampsia, yet may be present in other cell types, such as CD4⁺ and CD3 - lymphocytes.

Body Composition and Metabolomics in the Alberta Physical Activity and Breast Cancer Prevention Trial

BACKGROUND

Obesity is correlated with many biomarkers, but the extent to which these correlate with underlying body composition is poorly understood.

OBJECTIVES

Our objectives were to 1) describe/compare distinct contributions of fat/lean mass with BMI-metabolite correlations and 2) identify novel metabolite biomarkers of fat/lean mass.

METHODS

The Alberta Physical Activity and Breast Cancer Prevention Trial was a 2-center randomized trial of healthy, inactive, postmenopausal women (n = 304). BMI (in kg/m2) was calculated using weight and height, whereas DXA estimated fat/lean mass. Ultra-performance liquid chromatography and mass spectrometry measured relative concentrations of serum metabolite concentrations. We estimated partial Pearson correlations between 1052 metabolites and BMI, adjusting for age, smoking, and site. Fat mass index (FMI; kg/m2) and lean mass index (LMI; kg/m2) correlations were estimated similarly, with mutual adjustment to evaluate independent effects.

RESULTS

Using a Bonferroni-corrected α level <4.75 × 10-5,  we observed 53 BMI-correlated metabolites (|r| = 0.24-0.42). Of those, 21 were robustly correlated with FMI (|r| > 0.20), 25 modestly (0.10 ≤ |r| ≤ 0.20), and 7 virtually null (|r| < 0.10). Ten of 53 were more strongly correlated with LMI than with FMI. Examining non-BMI-correlated metabolites, 6 robustly correlated with FMI (|r| = 0.24-0.31) and 2 with LMI (r = 0.25-0.26). For these, correlations for fat and lean mass were in opposing directions compared with BMI-correlated metabolites, in which correlations were mostly in the same direction.

CONCLUSIONS

Our results demonstrate how a thorough evaluation of the components of fat and lean mass, along with BMI, provides a more accurate assessment of the associations between body composition and metabolites than BMI alone. Such an assessment makes evident that some metabolites correlated with BMI predominantly reflect lean mass rather than fat, and some metabolites related to body composition are not correlated with BMI. Correctly characterizing these relations is important for an accurate understanding of how and why obesity is associated with disease.

Metabolic Profiling of Plasma in Different Calving Body Condition Score Cows Using an Untargeted Liquid Chromatography-Mass Spectrometry Metabolomics Approach

This study was undertaken to identify metabolite differences in plasma of dairy cows with a normal or high calving body condition score (CBCS), using untargeted liquid chromatography-mass spectrometry (LC-MS) metabolomics. Sixteen multiparous dairy cows were assigned to one of two groups based on CBCS (0 to 5 scale): Normal group (NBCS, 3.25 ≤ BCS ≤ 3.5, n = 8), and high BCS group (HBCS, BCS ≥ 4, n = 8). Plasma samples were collected for metabolomics analysis and evaluation of biomarkers of lipid metabolism (nonesterified fatty acid (NEFA) and β-hydroxybutyrate (BHB)), and cytokines (leptin, adiponectin, tumor necrosis factor-α (TNF-α) and interleukin 6 (IL-6)). A total of 23 differential metabolites were identified, and functional analyses were performed using the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. Among these metabolites, the concentrations of six lysophosphatidylcholines and one phosphatidylethanolamine, were lower in the HBCS group than in the NBCS group (p < 0.01). Furthermore, these metabolites were involved in these four pathways, among others: glycerophospholipid metabolism, retrograde endocannabinoid signaling, autophagy, and glycosylphosphatidylinositol (GPI)-anchor biosynthesis (p < 0.05). In addition, plasma concentrations of leptin (p = 0.06) and TNF-α (p = 0.08) tended to be greater while adiponectin (p = 0.09) lower in HBCS cows than in NBCS cows. The concentrations of NEFA, BHB, or IL-6 did not differ between NBCS and HBCS groups. More importantly, based on the results of the Spearman's correlation analysis, the seven important metabolites were negatively correlated with indices of lipid metabolisms, proinflammatory cytokines, and leptin, but positively correlated with adiponectin. These results demonstrate that CBCS has a measurable impact on the plasma metabolic profile, even when NEFA and BHB are not different. In addition, the identified differential metabolites were significantly correlated to lipid metabolism and inflammation in the over-conditioned fresh cows, which are expected to render a metabolic basis for the diseases associated with over-conditioned dry cows.