The Phospholipid Linoleoylglycerophosphocholine as a Biomarker of Directly Measured Insulin Resistance

Identification of potential serum biomarkers associated with HbA1c levels in Indian type 2 diabetic subjects using NMR-based metabolomics

BACKGROUND

The prevalence of type 2 diabetes mellitus (T2DM), a progressive metabolic disorder characterized by chronic hyperglycemia and the development of insulin resistance, has increased globally, with worrying statistics coming from children, adolescents, and young adults from developing countries like India. Here, we investigated unique circulating metabolic signatures associated with prediabetes and T2DM in an Indian cohort using NMR-based metabolomics.

MATERIALS AND METHODS

The study subjects included healthy volunteers (N = 101), prediabetic subjects (N = 75), and T2DM patients (N = 108). Serum metabolic profiling was performed using 1H NMR spectroscopy and major perturbed metabolites were identified by multivariate analysis and receiver operating characteristic (ROC) modules.

RESULTS

Of the 36 aqueous abundant metabolites, 24 showed a statistically significant difference between healthy volunteers, prediabetics, and established T2DM subjects. On performing multivariate ROC curve analysis with 5 commonly dysregulated metabolites (namely, glucose, pyroglutamate, o-phosphocholine, serine, and methionine) in prediabetes and T2DM, AUC values obtained were 0.96 (95 % confidence interval (CI) = 0.93, 0.98) for T2DM; and 0.88 (95 % CI = 0.81, 0.93) for prediabetic subjects, respectively.

CONCLUSION

We propose that the identified metabolite panel can be used in the future as a biomarker for clinical diagnosis, patient surveillance, and for predicting individuals at risk for developing diabetes.

Comparative Metabolomic Profiling of L-Histidine and NEFA Treatments in Bovine Mammary Epithelial Cells

Non-esterified fatty acids (NEFAs) are pivotal in energy metabolism, yet high concentrations can lead to ketosis, a common metabolic disorder in cattle. Our laboratory observed lower levels of L-histidine in cattle suffering from ketosis, indicating a potential interaction between L-histidine and NEFA metabolism. This relationship prompted us to investigate the metabolomic alterations in bovine mammary epithelial cells (BMECs) induced by elevated NEFA levels and to explore L-histidine's potential mitigating effects. Our untargeted metabolomic analysis revealed 893 and 160 metabolite changes in positive and negative models, respectively, with VIP scores greater than 1 and p-values below 0.05. Notable metabolites like 9,10-epoxy-12-octadecenoic acid were upregulated, while 9-Ethylguanine was downregulated. A pathway analysis suggested disruptions in fatty acid and steroid biosynthesis pathways. Furthermore, L-histidine treatment altered 61 metabolites in the positive model and 34 in the negative model, with implications for similar pathways affected by NEFA. Overlaying differential metabolites from both conditions uncovered a potential key mediator, 1-Linoleoylglycerophosphocholine, which was regulated in opposite directions by NEFA and L-histidine. Our study uncovered that both NEFA L- and histidine metabolomics analyses pinpoint similar lipid biosynthesis pathways, with 1-Linoleoylglycerophosphocholine emerging as a potential key metabolite mediating their interaction, a discovery that may offer insights for therapeutic strategies in metabolic diseases.

Assessing the causal effect of genetically predicted metabolites and metabolic pathways on stroke

BACKGROUND

Stroke is a common neurological disorder that disproportionately affects middle-aged and elderly individuals, leading to significant disability and mortality. Recently, human blood metabolites have been discovered to be useful in unraveling the underlying biological mechanisms of neurological disorders. Therefore, we aimed to evaluate the causal relationship between human blood metabolites and susceptibility to stroke.

METHODS

Summary data from genome-wide association studies (GWASs) of serum metabolites and stroke and its subtypes were obtained separately. A total of 486 serum metabolites were used as the exposure. Simultaneously, 11 different stroke phenotypes were set as the outcomes, including any stroke (AS), any ischemic stroke (AIS), large artery stroke (LAS), cardioembolic stroke (CES), small vessel stroke (SVS), lacunar stroke (LS), white matter hyperintensities (WMH), intracerebral hemorrhage (ICH), subarachnoid hemorrhage (SAH), transient ischemic attack (TIA), and brain microbleeds (BMB). A two-sample Mendelian randomization (MR) study was conducted to investigate the causal effects of serum metabolites on stroke and its subtypes. The inverse variance-weighted MR analyses were conducted as causal estimates, accompanied by a series of sensitivity analyses to evaluate the robustness of the results. Furthermore, a reverse MR analysis was conducted to assess the potential for reverse causation. Additionally, metabolic pathway analysis was performed using the web-based MetOrigin.

RESULTS

After correcting for the false discovery rate (FDR), MR analysis results revealed remarkable causative associations with 25 metabolites. Further sensitivity analyses confirmed that only four causative associations involving three specific metabolites passed all sensitivity tests, namely ADpSGEGDFXAEGGGVR* for AS (OR: 1.599, 95% CI 1.283-1.993, p = 2.92 × 10-5) and AIS (OR: 1.776, 95% CI 1.380-2.285, p = 8.05 × 10-6), 1-linoleoylglycerophosph-oethanolamine* for LAS (OR: 0.198, 95% CI 0.091-0.428, p = 3.92 × 10-5), and gamma-glutamylmethionine* for SAH (OR: 3.251, 95% CI 1.876-5.635, p = 2.66 × 10-5), thereby demonstrating a high degree of stability. Moreover, eight causative associations involving seven other metabolites passed both sensitivity tests and were considered robust. The association result of one metabolite (glutamate for LAS) was considered non-robust. As for the remaining metabolites, we speculate that they may potentially possess underlying causal relationships. Notably, no common metabolites emerged from the reverse MR analysis. Moreover, after FDR correction, metabolic pathway analysis identified 40 significant pathways across 11 stroke phenotypes.

CONCLUSIONS

The identified metabolites and their associated metabolic pathways are promising circulating metabolic biomarkers, holding potential for their application in stroke screening and preventive strategies within clinical settings.

Causality of genetically determined metabolites and metabolic pathways on osteoarthritis: a two-sample mendelian randomization study

BACKGROUND

Osteoarthritis (OA) is one of the most prevalent musculoskeletal diseases and is the leading cause of pain and disability in the aged population. However, the underlying biological mechanism has not been fully understood. This study aims to reveal the causal effect of circulation metabolites on OA susceptibility.

METHODS

A two-sample Mendelian Randomization (MR) analysis was performed to estimate the causality of GDMs on OA. A genome-wide association study (GWAS) of 486 metabolites was used as the exposure, whereas 8 different OA phenotypes, including any-site OA (All OA), knee and/or hip OA (knee/hip OA), knee OA, hip OA, spine OA, finger and/or thumb OA (hand OA), finger OA, thumb OA, were set the outcomes. Inverse-variance weighted (IVW) was used for calculating causal estimates. Methods including weight mode, weight median, MR-egger, and MR-PRESSO were used for the sensitive analysis. Furthermore, metabolic pathway analysis was performed via the web-based Metaconflict 4.0. All statistical analyses were performed in R software.

RESULTS

In this MR analysis, a total of 235 causative associations between metabolites and different OA phenotypes were observed. After false discovery rate (FDR) correction and sensitive analysis, 9 robust causative associations between 7 metabolites (e.g., arginine, kynurenine, and isovalerylcarnitine) and 5 OA phenotypes were finally identified. Additionally, eleven significant metabolic pathways in 4 OA phenotypes were identified by metabolic pathway analysis.

CONCLUSION

The finding of our study suggested that identified metabolites and metabolic pathways can be considered useful circulating metabolic biomarkers for OA screening and prevention in clinical practice, and can also serve as candidate molecules for future mechanism exploration and drug target selection.

Is insulin resistance tissue-dependent and substrate-specific? The role of white adipose tissue and skeletal muscle

Insulin resistance (IR) refers to a reduction in the ability of insulin to exert its metabolic effects in organs such as adipose tissue (AT) and skeletal muscle (SM), leading to chronic diseases such as type 2 diabetes, hepatic steatosis, and cardiovascular diseases. Obesity is the main cause of IR, however not all subjects with obesity develop clinical insulin resistance, and not all clinically insulin-resistant people have obesity. Recent evidence implies that IR onset is tissue-dependent (AT or SM) and/or substrate-specific (glucometabolic or lipometabolic). Therefore, the aims of the present review are 1) to describe the glucometabolic and lipometabolic activities of insulin in AT and SM in the maintenance of whole-body metabolic homeostasis, 2) to discuss the pathophysiology of substrate-specific IR in AT and SM, and 3) to highlight novel validated tests to assess tissue and substrate-specific IR that are easy to perform in clinical practice. In AT, glucometabolic IR reduces glucose availability for glycerol and fatty acid synthesis, thus decreasing the esterification and synthesis of signaling bioactive lipids. Lipometabolic IR in AT impairs the antilipolytic effect of insulin and lipogenesis, leading to an increase in circulating FFAs and generating lipotoxicity in peripheral tissues. In SM, glucometabolic IR reduces glucose uptake, whereas lipometabolic IR impairs mitochondrial lipid oxidation, increasing oxidative stress and inflammation, all of which lead to metabolic inflexibility. Understanding tissue-dependent and substrate-specific IR is of paramount importance for early detection before clinical manifestations and for the development of more specific treatments or direct interventions to prevent chronic life-threatening diseases.

Discrete Correlation Summation Clustering Reveals Differential Regulation of Liver Metabolism by Thrombospondin-1 in Low-Fat and High-Fat Diet-Fed Mice

Thrombospondin-1 (TSP1) is a matricellular protein with many important roles in mediating carcinogenesis, fibrosis, leukocyte recruitment, and metabolism. We have previously shown a role of diet in the absence of TSP1 in liver metabolism in the context of a colorectal cancer model. However, the metabolic implications of TSP1 regulation by diet in the liver metabolism are currently understudied. Therefore Discrete correlation summation (DCS) was used to re-interrogate data and determine the metabolic alterations of TSP1 deficiency in the liver, providing new insights into the role of TSP1 in liver injury and the progression of liver pathologies such as nonalcoholic fatty liver disease (NAFLD). DCS analysis provides a straightforward approach to rank covariance and data clustering when analyzing complex data sets. Using this approach, our previous liver metabolite data was re-analyzed by comparing wild-type (WT) and Thrombospondin-1 null (Thbs1-/-) mice, identifying changes driven by genotype and diet. Principal component analysis showed clustering of animals by genotype regardless of diet, indicating that TSP1 deficiency alters metabolite handling in the liver. High-fat diet consumption significantly altered over 150 metabolites in the Thbs1-/- livers versus approximately 90 in the wild-type livers, most involved in amino acid metabolism. The absence of Thbs1 differentially regulated tryptophan and tricarboxylic acid cycle metabolites implicated in the progression of NAFLD. Overall, the lack of Thbs1 caused a significant shift in liver metabolism with potential implications for liver injury and the progression of NAFLD.

Lipidomics in diabetes

PURPOSE OF REVIEW

Multiple studies have shown a strong association between lipids and diabetes. These are usually described through the effects of cholesterol content of lipid particles and in particular low-density lipoprotein. However, lipoprotein particles contain other components, such as phospholipids and more complex lipid species, such as ceramides and sphingolipids. Ceramides, such as sphingolipids are also produced intracellularly and have signalling actions in regulating cell metabolism including effects on inflammation, and potentially have a mechanistic role in the development of insulin resistance.

RECENT FINDINGS

Recently, techniques have been developed to analyse detailed molecular profiles of lipid particles - lipidomics. Proteomics has confirmed the different proteins associated with different particles but far less is known about the relationship of individual lipid species with diabetes and cardiovascular risk. A number of studies have now shown that the plasma lipidome, and in particular, ceramides and sphingolipids may predict the development of diabetes.

SUMMARY

Lipidomics had identified ceramides and sphingolipids as potential mediators of cellular dysfunction in diabetes. Further work is required to ascertain whether they have clinical utility.

Recent Developments in Biomarkers for Diagnosis and Screening of Type 2 Diabetes Mellitus

PURPOSE OF REVIEW

Diabetes mellitus is a complex, chronic illness characterized by elevated blood glucose levels that occurs when there is cellular resistance to insulin action, pancreatic β-cells do not produce sufficient insulin, or both. Diabetes prevalence has greatly increased in recent decades; consequently, it is considered one of the fastest-growing public health emergencies globally. Poor blood glucose control can result in long-term micro- and macrovascular complications such as nephropathy, retinopathy, neuropathy, and cardiovascular disease. Individuals with diabetes require continuous medical care, including pharmacological intervention as well as lifestyle and dietary changes.

RECENT FINDINGS

The most common form of diabetes mellitus, type 2 diabetes (T2DM), represents approximately 90% of all cases worldwide. T2DM occurs more often in middle-aged and elderly adults, and its cause is multifactorial. However, its incidence has increased in children and young adults due to obesity, sedentary lifestyle, and inadequate nutrition. This high incidence is also accompanied by an estimated underdiagnosis prevalence of more than 50% worldwide. Implementing successful and cost-effective strategies for systematic screening of diabetes mellitus is imperative to ensure early detection, lowering patients' risk of developing life-threatening disease complications. Therefore, identifying new biomarkers and assay methods for diabetes mellitus to develop robust, non-invasive, painless, highly-sensitive, and precise screening techniques is essential. This review focuses on the recent development of new clinically validated and novel biomarkers as well as the methods for their determination that represent cost-effective alternatives for screening and early diagnosis of T2DM.

Metabolic Dysfunction Biomarkers as Predictors of Early Diabetes

During the pathophysiological course of type 2 diabetes (T2D), several metabolic imbalances occur. There is increasing evidence that metabolic dysfunction far precedes clinical manifestations. Thus, knowing and understanding metabolic imbalances is crucial to unraveling new strategies and molecules (biomarkers) for the early-stage prediction of the disease's non-clinical phase. Lifestyle interventions must be made with considerable involvement of clinicians, and it should be considered that not all patients will respond in the same manner. Individuals with a high risk of diabetic progression will present compensatory metabolic mechanisms, translated into metabolic biomarkers that will therefore show potential predictive value to differentiate between progressors/non-progressors in T2D. Specific novel biomarkers are being proposed to entrap prediabetes and target progressors to achieve better outcomes. This study provides a review of the latest relevant biomarkers in prediabetes. A search for articles published between 2011 and 2021 was conducted; duplicates were removed, and inclusion criteria were applied. From the 29 studies considered, a survey of the most cited (relevant) biomarkers was conducted and further discussed in the two main identified fields: metabolomics, and miRNA studies.

Serum metabolomics analysis of mice that received repeated airway exposure to a water-soluble PM2.5 extract

BACKGROUND

Air pollutant exposure negatively affects human health; however, the molecular mechanisms causing disease remain largely unclear.

OBJECTIVES

To explore the effects of respiratory particulate matter (PM2.5) exposure on the serum metabolome and to identify biomarkers for risk assessment of PM2.5 exposure.

METHODS

PM2.5 from Nanjing, China, was collected, and its water-soluble extract was subjected to component analysis. BALB/c mice received acute or prolonged exposure to insoluble PM2.5 particles or its water-soluble extract, and lung tissue was submitted to histopathological analyses. Serum samples were collected pre- and post-PM2.5 exposure and analyzed by liquid chromatography/mass spectrometry.

RESULTS

Component analysis revealed that metals and inorganic ions were the most abundant components in the soluble PM2.5 samples. Acute exposure to insoluble PM2.5 particles and prolonged exposure to the water-soluble PM2.5 extract both induced severe lung injury, and the lung histopathological scores were significantly associated with PM2.5 exposure. Metabolomics analysis showed that prolonged exposure to the water-soluble PM2.5 extract was associated with statistically significant metabolite changes; the serum concentrations of 30 known metabolites, including metabolites of phospholipids, amino acids and sphingolipids, differed significantly between the control and PM2.5 exposure group. Pathway analysis identified an association of the tricarboxylic acid cycle (TCA) and the phospholipase metabolism pathway with PM2.5 exposure. The most influential metabolites for discriminating between the PM2.5-exposure group serum and the control serum were LysoPE, LysoPC, LGPC, citric acid, PAF C-18, NeuAcalpha2-3Galbeta-Cer, Lyso-PAF C-16, ganglioside GA2, 1-sn-glycero-3-phosphocholine, PC and L-tryptophan.

CONCLUSIONS

Respiratory exposure to water-soluble PM2.5 extract has developmental consequences affecting not only the respiratory system but also metabolism.