Metabolic signatures and risk of type 2 diabetes in a Chinese population: an untargeted metabolomics study using both LC-MS and GC-MS

Comparative Metabolomics Analysis of Cervicitis in Human Patients and a Phenol Mucilage-Induced Rat Model Using Liquid Chromatography Tandem Mass Spectrometry

Cervicitis is an exceedingly common gynecological disorder that puts women at high risk of sexually transmitted infections and induces a series of reproductive system diseases. This condition also has a significant impact on quality of life and is commonly misdiagnosed in clinical practice due to its complicated pathogenesis. In the present study, we performed non-targeted plasma metabolomics analysis of cervicitis in both plasma samples obtained from human patients and plasma samples from a phenol mucilage induced rat model of cervicitis, using ultra-performance liquid chromatography coupled to quadrupole time-of-flight tandem mass spectrometry. In addition to differences in histopathology, we identified differences in the metabolic profile between the cervicitis and control groups using unsupervised principal component analysis and orthogonal projections to latent structures discriminant analysis. These results demonstrated changes in plasma metabolites, with 27 and 22 potential endogenous markers identified in rat and human samples, respectively. The metabolic pathway analysis showed that linoleic acid, arachidonic acid, ether lipid, and glycerophospholipid metabolism are key metabolic pathways involved in cervicitis. This study showed the rat model was successfully created and applied to understand the pathogenesis of cervicitis.

Plasma metabolites associated with type 2 diabetes in a Swedish population: a case-control study nested in a prospective cohort

AIMS/HYPOTHESIS

The aims of the present work were to identify plasma metabolites that predict future type 2 diabetes, to investigate the changes in identified metabolites among individuals who later did or did not develop type 2 diabetes over time, and to assess the extent to which inclusion of predictive metabolites could improve risk prediction.

METHODS

We established a nested case-control study within the Swedish prospective population-based Västerbotten Intervention Programme cohort. Using untargeted liquid chromatography-MS metabolomics, we analysed plasma samples from 503 case-control pairs at baseline (a median time of 7 years prior to diagnosis) and samples from a subset of 187 case-control pairs at 10 years of follow-up. Discriminative metabolites between cases and controls at baseline were optimally selected using a multivariate data analysis pipeline adapted for large-scale metabolomics. Conditional logistic regression was used to assess associations between discriminative metabolites and future type 2 diabetes, adjusting for several known risk factors. Reproducibility of identified metabolites was estimated by intra-class correlation over the 10 year period among the subset of healthy participants; their systematic changes over time in relation to diagnosis among those who developed type 2 diabetes were investigated using mixed models. Risk prediction performance of models made from different predictors was evaluated using area under the receiver operating characteristic curve, discrimination improvement index and net reclassification index.

RESULTS

We identified 46 predictive plasma metabolites of type 2 diabetes. Among novel findings, phosphatidylcholines (PCs) containing odd-chain fatty acids (C19:1 and C17:0) and 2-hydroxyethanesulfonate were associated with the likelihood of developing type 2 diabetes; we also confirmed previously identified predictive biomarkers. Identified metabolites strongly correlated with insulin resistance and/or beta cell dysfunction. Of 46 identified metabolites, 26 showed intermediate to high reproducibility among healthy individuals. Moreover, PCs with odd-chain fatty acids, branched-chain amino acids, 3-methyl-2-oxovaleric acid and glutamate changed over time along with disease progression among diabetes cases. Importantly, we found that a combination of five of the most robustly predictive metabolites significantly improved risk prediction if added to models with an a priori defined set of traditional risk factors, but only a marginal improvement was achieved when using models based on optimally selected traditional risk factors.

CONCLUSIONS/INTERPRETATION

Predictive metabolites may improve understanding of the pathophysiology of type 2 diabetes and reflect disease progression, but they provide limited incremental value in risk prediction beyond optimal use of traditional risk factors.

Lysophosphatidylinositols, from Cell Membrane Constituents to GPR55 Ligands

Lysophosphatidylinositols (LPIs) are membrane constituents that alter the properties of said membranes. However, recent data showing that the once orphan receptor, GPR55, can act as a receptor for LPIs has sparked a renewed interest in LPIs as bioactive lipids. As evidence supporting the importance of LPIs and/or GPR55 is continuously accumulating and because LPI levels are altered in a number of pathologies such as obesity and cancer, the coming years should bring new, exciting discoveries to this field. In this review, we discuss the recent work on LPIs and on their molecular target, the GPR55 receptor. First, we summarize the metabolism of LPIs before outlining the cellular pathways activated by GPR55. Then, we review the actions of LPIs and GPR55 that could have potential pharmacological or therapeutic applications in several pathophysiological settings, such as cancer, obesity, pain, and inflammation.

Diet and Cardiovascular Disease: Advances and Challenges in Population-Based Studies

In this Minireview, we provide an epidemiologist's perspective on the debate and recent advances in determining the relationship between diet and cardiovascular health. We conclude that, in order to reduce the global burden of cardiovascular disease, there should be a greater emphasis on improving overall diet quality and food sources of macronutrients, such as dietary fats and carbohydrates. In addition, building a strong evidence base through high-quality intervention and observational studies is crucial for effective policy changes, which can greatly improve the food environment and population health.

Serum Lipids in Association With Type 2 Diabetes Risk and Prevalence in a Chinese Population

CONTEXT

We previously reported an association between lysophosphatidylinositol (LPI) (16:1) and risk for type 2 diabetes in a Chinese population using an untargeted analysis.

OBJECTIVE

To examine the overall associations of LPIs and their related metabolites, such as nonesterified fatty acids (NEFAs) and acylcarnitines, with incident and prevalent type 2 diabetes using a targeted approach.

DESIGN AND SETTING

A case-control study was nested within the Singapore Chinese Health Study. Cases and controls were individually matched by age, sex, and date of blood collection. We used both liquid and gas chromatography tandem mass spectrometry to measure serum metabolite levels at baseline, including 8 LPIs, 19 NEFAs, and 34 acylcarnitines. Conditional logistic regression models were used to estimate the associations between metabolites and diabetes risk.

PARTICIPANTS

Participants included 160 incident and 144 prevalent cases with type 2 diabetes and 304 controls.

MAIN OUTCOME MEASURE

Incident and prevalent type 2 diabetes.

RESULTS

On the basis of a false discovery rate <0.1, we identified 37 metabolites associated with prevalent type 2 diabetes, including 7 LPIs, 18 NEFAs, and 12 acylcarnitines, and 11 metabolites associated with incident type 2 diabetes, including 2 LPIs and 9 NEFAs. Two metabolites, LPI (16:1) and dihomo-γ-linolenic acid, showed independent associations with incident type 2 diabetes and significantly enhanced the risk prediction.

CONCLUSIONS

We found several LPIs and NEFAs that were associated with risk for type 2 diabetes and may improve our understanding of the pathogenesis. The findings suggest that lipid profiles could aid in diabetes risk assessment in Chinese populations.

Serum metabolic profiling of type 2 diabetes mellitus in Chinese adults using an untargeted GC/TOFMS

BACKGROUND

Type 2 diabetes mellitus (T2DM) is a huge burden in China. The Chinese patients with T2DM have several special clinical characteristics. Metabolomics studies predominantly have identified several distinguishing metabolites associated with T2DM in Western ancestry population. However, few previous metabolomics studies were conducted in Chinese populations.

METHODS

We performed untargeted serum metabolic profiling between 30 T2DM patients and 30 healthy controls based on GC/TOFMS. Multivariate data analyses were applied to identify the distinguishing metabolites.

RESULTS

Excellent separation was obtained between the two cases. And overall 54 distinguishing metabolites were identified with VIP>1 and P<0.05, which were involved in metabolic pathways of amino acid, carbohydrate, lipids, membrane transport and nucleotides. To further analyze the correlation between the identified metabolites and T2DM, 17 metabolites were selected with FC>2.0, including gentisic acid, citraconic acid, succinic acid, 2-hydroxybutanoic acid and 3-hydroxy-l-proline, the corresponding FC were respectively 5.44, 2.21, 2.10, 2.21 and -2.04.

CONCLUSION

Our results demonstrated that untargeted GC/TOFMS-based metabolic approach processed well performance to identify serum distinguishing metabolites of T2DM in Chinese adults, which may be as potential biomarkers in diagnose and treatment of diabetes. And the results also provided new insight into understand the underlying molecular mechanism.

Disturbance in Plasma Metabolic Profile in Different Types of Human Cytomegalovirus-Induced Liver Injury in Infants

Human cytomegalovirus (HCMV) infection in infants is a global problem and the liver is a target organ of HCMV invasion. However, the mechanism by which HCMV causes different types of liver injury is unclear, and there are many difficulties in the differential diagnosis of HCMV infantile cholestatic hepatopathy (ICH) and extrahepatic biliary atresia (EHBA). We established a non-targeted gas chromatography-mass spectrometry metabolomics method in conjunction with orthogonal partial least squares-discriminate analysis based on 127 plasma samples from healthy controls, and patients with HCMV infantile hepatitis, HCMV ICH, and HCMV EHBA to explore the metabolite profile of different types of HCMV-induced liver injury. Twenty-nine metabolites related to multiple amino acid metabolism disorder, nitrogen metabolism and energy metabolism were identified. Carbamic acid, glutamate, L-aspartic acid, L-homoserine, and noradrenaline for HCMV ICH vs. HCMV EHBA were screened as potential biomarkers and showed excellent discriminant performance. These results not only revealed the potential pathogenesis of HCMV-induced liver injury, but also provided a feasible diagnostic tool for distinguishing EHBA from ICH.

Furan fatty acids - Beneficial or harmful to health?

Furan fatty acids are found in plants, algae, and fish, and reported to have some positive health benefits, including anti-oxidant and anti-inflammatory activities, and inhibition of non-enzymatic lipid peroxidation. A major metabolite of furan fatty acids, 3-carboxy-4-methyl-5-propyl-2-furanpropanoic acid (CMPF), has been reported to be increased in patients who progress from prediabetes to type 2 diabetes, although CMPF is not necessarily associated with impaired glucose metabolism. Other studies report that CMPF levels are lower in subjects with diabetes than control subjects. Plasma CMPF levels increase in subjects who consume fish or fish oil, and in patients with renal failure. It is not known where furan fatty acids are converted to CMPF and it is speculated that this might be a result of microbiome activity. The plasma levels reported for CMPF in healthy, diabetic and patients with renal disease vary by factors of more than 100-fold within each of these three groups, so measurement error appears to be limiting the ability to interpret studies. This review explores these controversies and raises questions about whether CMPF is a marker for healthy diets or indeed associated with diabetes and renal health. The review concludes that, on balance, furan fatty acids are beneficial for health.

Multi-factors including Inflammatory/Immune, Hormones, Tumor-related Proteins and Nutrition associated with Chronic Prostatitis NIH IIIa+b and IV based on FAMHES project

Chronic prostatitis (CP) is a complex disease. Fragmentary evidence suggests that factors such as infection and autoimmunity might be associated with CP. To further elucidate potential risk factors, the current study utilized the Fangchenggang Area Male Health and Examination Survey (FAMHES) project; where 22 inflammatory/immune markers, hormone markers, tumor-related proteins, and nutrition-related variables were investigated. We also performed baseline, regression, discriminant, and receiver operating characteristic (ROC) analyses. According to NIH-Chronic Prostatitis Symptom Index (NIH-CPSI), participants were divided into chronic prostatitis/chronic pelvic pain syndrome (CP/CPPS, pain ≥ 4; divided into IIIa and IIIb sub-groups) and non-CPPS (pain = 0; divided into IV and normal sub-groups). Analyses revealed osteocalcin as a consistent protective factor for CP/CPPS, NIH-IIIb, and NIH-IV prostatitis. Further discriminant analysis revealed that ferritin (p = 0.002) and prostate-specific antigen (PSA) (p = 0.010) were significantly associated with NIH-IIIa and NIH-IV prostatitis, respectively. Moreover, ROC analysis suggested that ferritin was the most valuable independent predictor of NIH-IIIa prostatitis (AUC = 0.639, 95% CI = 0.534–0.745, p = 0.006). Together, our study revealed inflammatory/immune markers [immunoglobulin E, Complement (C3, C4), C-reactive protein, anti-streptolysin, and rheumatoid factors], hormone markers (osteocalcin, testosterone, follicle-stimulating hormone, and insulin), tumor-related proteins (carcinoembryonic and PSA), and a nutrition-related variable (ferritin) were significantly associated with CP or one of its subtypes.

Metabolomics based markers predict type 2 diabetes in a 14-year follow-up study

BACKGROUND

The growing field of metabolomics has opened up new opportunities for prediction of type 2 diabetes (T2D) going beyond the classical biochemistry assays.

OBJECTIVES

We aimed to identify markers from different pathways which represent early metabolic changes and test their predictive performance for T2D, as compared to the performance of traditional risk factors (TRF).

METHODS

We analyzed 2776 participants from the Erasmus Rucphen Family study from which 1571 disease free individuals were followed up to 14-years. The targeted metabolomics measurements at baseline were performed by three different platforms using either nuclear magnetic resonance spectroscopy or mass spectrometry. We selected 24 T2D markers by using Least Absolute Shrinkage and Selection operator (LASSO) regression and tested their association to incidence of disease during follow-up.

RESULTS

The 24 markers i.e. high-density, low-density and very low-density lipoprotein sub-fractions, certain triglycerides, amino acids, and small intermediate compounds predicted future T2D with an area under the curve (AUC) of 0.81. The performance of the metabolic markers compared to glucose was significantly higher among the young (age < 50 years) (0.86 vs. 0.77, p-value <0.0001), the female (0.88 vs. 0.84, p-value =0.009), and the lean (BMI < 25 kg/m²) (0.85 vs. 0.80, p-value =0.003). The full model with fasting glucose, TRFs, and metabolic markers yielded the best prediction model (AUC = 0.89).

CONCLUSIONS

Our novel prediction model increases the long-term prediction performance in combination with classical measurements, brings a higher resolution over the complexity of the lipoprotein component, increasing the specificity for individuals in the low risk group.