Lipidomic risk score independently and cost-effectively predicts risk of future type 2 diabetes: results from diverse cohorts

Screening for Type 2 Diabetes Mellitus: A Systematic Review of Recent Economic Evaluations

OBJECTIVES

To examine recent economic evaluations and understand whether any type 2 diabetes mellitus (T2DM) screening designs may represent better value for money and to rate their methodological qualities.

METHODS

We systematically searched 3 concepts (economic evaluations [EEs], T2DM, screening) in 5 databases (Medline, Embase, EconLit, Web of Science, and Cochrane) for EEs published between 2010 and 2023. Two independent reviewers screened for and rated their methodological quality (using the Consensus on Health Economics Criteria Checklist-Extended).

RESULTS

Of 32 EEs, a majority were from high-income countries (69%). Half used single biomarkers (50%) to screen adults ≥30 to <60 years old (60%) but did not report locations (69%), treatments for those diagnosed (66%), diagnostic methods (57%), or screening intervals (54%). Compared with no screening, T2DM screening using single biomarkers was found to be not cost-effective (23/54 comparisons), inconclusive (16/54), dominant (11/54), or cost-effective (4/54). Compared with no screening, screening with a risk score and single biomarkers was found to be cost-effective (21/40) or dominant (19/40). The risk score alone was mostly dominant (6/10). Compared with universal screening, targeted screening among obese, overweight, or older people may be cost-effective or dominant. Compared with fasting plasma glucose or fasting capillary glucose, screening using risk scores was found to be mostly dominant or cost-effective. Expanding screening locations or lowering HbA1c or fasting plasma glucose thresholds was found to be dominant or cost-effective. Each EE had 4 to 17 items (median 13/20) on Consensus on Health Economics Criteria Checklist-Extended rated "Yes/Rather Yes."

CONCLUSIONS

EE findings varied based on screening tools, intervals, locations, minimum screening age, diagnostic methods, and treatment. Future EEs should more comprehensively report screening designs and evaluate T2DM screening in low-income countries.

A lipidomic based metabolic age score captures cardiometabolic risk independent of chronological age

BACKGROUND

Metabolic ageing biomarkers may capture the age-related shifts in metabolism, offering a precise representation of an individual's overall metabolic health.

METHODS

Utilising comprehensive lipidomic datasets from two large independent population cohorts in Australia (n = 14,833, including 6630 males, 8203 females), we employed different machine learning models, to predict age, and calculated metabolic age scores (mAge). Furthermore, we defined the difference between mAge and age, termed mAgeΔ, which allow us to identify individuals sharing similar age but differing in their metabolic health status.

FINDINGS

Upon stratification of the population into quintiles by mAgeΔ, we observed that participants in the top quintile group (Q5) were more likely to have cardiovascular disease (OR = 2.13, 95% CI = 1.62-2.83), had a 2.01-fold increased risk of 12-year incident cardiovascular events (HR = 2.01, 95% CI = 1.45-2.57), and a 1.56-fold increased risk of 17-year all-cause mortality (HR = 1.56, 95% CI = 1.34-1.79), relative to the individuals in the bottom quintile group (Q1). Survival analysis further revealed that men in the Q5 group faced the challenge of reaching a median survival rate due to cardiovascular events more than six years earlier and reaching a median survival rate due to all-cause mortality more than four years earlier than men in the Q1 group.

INTERPRETATION

Our findings demonstrate that the mAge score captures age-related metabolic changes, predicts health outcomes, and has the potential to identify individuals at increased risk of metabolic diseases.

FUNDING

The specific funding of this article is provided in the acknowledgements section.

Circulating Sphingolipids in Insulin Resistance, Diabetes and Associated Complications

Sphingolipids play an important role in the development of diabetes, both type 1 and type 2 diabetes, as well as in the development of both micro- and macro-vascular complications. Several reviews have been published concerning the role of sphingolipids in diabetes but most of the emphasis has been on the possible mechanisms by which sphingolipids, mainly ceramides, contribute to the development of diabetes. Research on circulating levels of the different classes of sphingolipids in serum and in lipoproteins and their importance as biomarkers to predict not only the development of diabetes but also of its complications has only recently emerged and it is still in its infancy. This review summarizes the previously published literature concerning sphingolipid-mediated mechanisms involved in the development of diabetes and its complications, focusing on how circulating plasma sphingolipid levels and the relative content carried by the different lipoproteins may impact their role as possible biomarkers both in the development of diabetes and mainly in the development of diabetic complications. Further studies in this field may open new therapeutic avenues to prevent or arrest/reduce both the development of diabetes and progression of its complications.

Exploring Metabolomic Patterns in Type 2 Diabetes Mellitus and Response to Glucose-Lowering Medications-Review

The spectrum of information related to precision medicine in diabetes generally includes clinical data, genetics, and omics-based biomarkers that can guide personalized decisions on diabetes care. Given the remarkable progress in patient risk characterization, there is particular interest in using molecular biomarkers to guide diabetes management. Metabolomics is an emerging molecular approach that helps better understand the etiology and promises the identification of novel biomarkers for complex diseases. Both targeted or untargeted metabolites extracted from cells, biofluids, or tissues can be investigated by established high-throughput platforms, like nuclear magnetic resonance (NMR) and mass spectrometry (MS) techniques. Metabolomics is proposed as a valuable tool in precision diabetes medicine to discover biomarkers for diagnosis, prognosis, and management of the progress of diabetes through personalized phenotyping and individualized drug-response monitoring. This review offers an overview of metabolomics knowledge as potential biomarkers in type 2 diabetes mellitus (T2D) diagnosis and the response to glucose-lowering medications.

Replacement of dietary saturated with unsaturated fatty acids is associated with beneficial effects on lipidome metabolites: a secondary analysis of a randomized trial

BACKGROUND

The effects of replacing dietary saturated fatty acids (SFAs) with monounsaturated fatty acids (MUFAs) and/or polyunsaturated fatty acids (PUFAs) on the plasma lipidome in relation to the cardiometabolic disease (CMD) risk are poorly understood.

OBJECTIVES

We aimed to assess the impact of substituting dietary SFAs with unsaturated fatty acids (UFAs) on the plasma lipidome and examine the relationship between lipid metabolites modulated by diet and CMD risk.

METHODS

Plasma fatty acid (FA) concentrations among 16 lipid classes (within-class FAs) were measured in a subgroup from the Dietary Intervention and VAScular function (DIVAS) parallel randomized controlled trial (n = 113/195), which consisted of three 16-wk diets enriched in SFAs (target SFA:MUFA:n-6PUFA ratio = 17:11:4% total energy [TE]), MUFAs (9:19:4% TE), or a MUFA/PUFA mixture (9:13:10% TE). Similar lipidomics analyses were conducted in the European investigation into Cancer and Nutrition (EPIC)-Potsdam prospective cohort study (specific case/cohorts: n = 775/1886 for type 2 diabetes [T2D], n = 551/1671 for cardiovascular disease [CVD]). Multiple linear regression and multivariable Cox models identified within-class FAs sensitive to replacement of dietary SFA with UFA in DIVAS and their association with CMD risk in EPIC-Potsdam. Elastic-net regression models identified within-class FAs associated with changes in CMD risk markers post-DIVAS interventions.

RESULTS

DIVAS high-UFA interventions reduced plasma within-class FAs associated with a higher CVD risk in EPIC-Potsdam, especially SFA-containing glycerolipids and sphingolipids (e.g., diacylglycerol (20:0) z-score = -1.08; SE = 0.17; P value < 10^-8), whereas they increased those inversely associated with CVD risk. The results on T2D were less clear. Specific sphingolipids and phospholipids were associated with changes in markers of endothelial function and ambulatory blood pressure, whereas higher low-density lipoprotein cholesterol concentrations were characterized by higher plasma glycerolipids containing lauric and stearic acids.

CONCLUSIONS

These results suggest a mediating role of plasma lipid metabolites in the association between dietary fat and CMD risk. Future research combining interventional and observational findings will further our understanding of the role of dietary fat in CMD etiology. This trial was registered in ClinicalTrials.gov as NCT01478958.

[Diabetes mellitus prediction based on the triglyceride and glucose index]

INTRODUCTION

In Mexico, type 2 Diabetes mellitus (DM2) presents epidemiological levels with a prevalence rate of 9.12% and with the highest overweight and obesity rates worldwide. To overcome this situation, strategies must be created focused on the identification of subjects at risk. The Triglyceride and Glucose (TyG) index, was created for the detection of insulin resistance, has recently been used in the prediction of DM. The objective of the present study was to determine the predictive power of the TyG index in a cohort from Mexico City.

METHODS

3195 patients were selected from a cohort of patients from the chronic degenerative area of the Health Centers of the Public Health Services of Mexico City. The ability of the TyG index in predicting diabetes was evaluated as: ln [Fasting triglycerides (mg/dl) x fasting glucose (mg/dl)/2]. after a follow-up of at least 4.5 years. A CHAID test was determined that was corroborated by a ROC test.

RESULTS

the value of the TyG index was significantly higher for patients who develop DM2. Values of AUC=0.934, 95% CI: 0.924-0.924. Obtaining a cut-off point of 9.45 in women; in men: DM2 AUC=0.824, 95% CI: 0.824-0.873, and cut-off point 9.12.

CONCLUSIONS

The TyG index is a good marker in the prediction of DM2. The CHAID determination is a useful tool in the prediction of DM2.

Comprehensive Lipidomic Workflow for Multicohort Population Phenotyping Using Stable Isotope Dilution Targeted Liquid Chromatography-Mass Spectrometry

Dysregulated lipid metabolism underpins many chronic diseases including cardiometabolic diseases. Mass spectrometry-based lipidomics is an important tool for understanding mechanisms of lipid dysfunction and is widely applied in epidemiology and clinical studies. With ever-increasing sample numbers, single batch acquisition is often unfeasible, requiring advanced methods that are accurate and robust to batch-to-batch and interday analytical variation. Herein, an optimized comprehensive targeted workflow for plasma and serum lipid quantification is presented, combining stable isotope internal standard dilution, automated sample preparation, and ultrahigh performance liquid chromatography-tandem mass spectrometry with rapid polarity switching to target 1163 lipid species spanning 20 subclasses. The resultant method is robust to common sources of analytical variation including blood collection tubes, hemolysis, freeze-thaw cycles, storage stability, analyte extraction technique, interinstrument variation, and batch-to-batch variation with 820 lipids reporting a relative standard deviation of <30% in 1048 replicate quality control plasma samples acquired across 16 independent batches (total injection count = 6142). However, sample hemolysis of ≥0.4% impacted lipid concentrations, specifically for phosphatidylethanolamines (PEs). Low interinstrument variability across two identical LC-MS systems indicated feasibility for intra/inter-lab parallelization of the assay. In summary, we have optimized a comprehensive lipidomic protocol to support rigorous analysis for large-scale, multibatch applications in precision medicine. The mass spectrometry lipidomics data have been deposited to massIVE: data set identifiers MSV000090952 and 10.25345/C5NP1WQ4S.

Integrated Analysis of Metabolomics and Lipidomics in Plasma of T2DM Patients with Diabetic Retinopathy

Diabetic retinopathy (DR) is a major cause of blindness worldwide and may be non-proliferative (NPDR) or proliferative (PDR). To Investig.gate the metabolomic and lipidomic characteristics of plasma in DR patients, plasma samples were collected from patients with type 2 diabetes mellitus (DR group) with PDR (n = 27), NPDR (n = 18), or no retinopathy (controls, n = 21). Levels of 54 and 41 metabolites were significantly altered in the plasma of DR patients under positive and negative ion modes, respectively. By subgroup analysis, 74 and 29 significantly changed plasma metabolites were detected in PDR patients compared with NPDR patients under positive and negative ion modes, respectively. KEGG analysis indicated that pathways such as biosynthesis of amino acids and neuroactive ligand-receptor interaction were among the most enriched pathways in altered metabolites in the DR group and PDR subgroup. Moreover, a total of 26 and 41 lipids were significantly changed in the DR group and the PDR subgroup, respectively. The panel using the 29-item index could discriminate effectively between diabetic patients with and without retinopathy, and the panel of 22 items showed effective discrimination between PDR and NPDR. These results provide a basis for further research into the therapeutic targets associated with these metabolite and lipid alterations.

Sphingomyelin profiling in patients with diabetes could be potentially useful as differential diagnostics biomarker: A pilot study

PURPOSE

Autoimmune diabetes (AD) in adults includes both the classical form of type 1 diabetes mellitus (T1DM) and latent autoimmune diabetes in adults (LADA). LADA shares clinical and metabolic features with type 1 and type 2 diabetes mellitus (T2DM). Ceramide (Cer) levels negatively correlate with insulin sensitivity in humans and animal models. However, only a few studies have focused on other sphingolipids, including sphingomyelin (SM). Therefore, we determined sphingolipids in patients with newly diagnosed diabetes as possible diagnostic biomarkers.

MATERIALS AND METHODS

We evaluated sphingolipids in a cohort of 59 adults with newly diagnosed diabetes without prior hypoglycemic pharmacotherapy to distinguish diabetes mellitus types and for precise LADA definition. All patients with newly diagnosed diabetes were tested for the concentrations of individual Cer and SM species by gas-liquid chromatography. The study included healthy controls and patients with T1DM, T2DM and LADA.

RESULTS

SM species were significantly altered in patients with newly diagnosed diabetes compared to healthy controls. SM-C16:0, C16:1, -C18:0, -C18:1, -C18:2, -C18:3, -C20:4, and -C22:6 species were found to be significantly elevated in LADA patients. In contrast, significant differences were observed for Cer species with saturated acyl chains, especially Cer-C14:0, -C16:0, -C18:0 (AD and T2DM), -C22:0, and -C24:0 (T1DM). Following ROC analysis, SM-C16:0, and particularly -C18:1, and -C20:4 may be supportive diagnostic markers for LADA.

CONCLUSION

SM profiling in patients with newly diagnosed diabetes could be potentially helpful for differential diagnosis of LADA, T1DM, and T2DM in more challenging cases.

A Lipid Signature with Perturbed Triacylglycerol Co-Regulation, Identified from Targeted Lipidomics, Predicts Risk for Type 2 Diabetes and Mediates the Risk from Adiposity in Two Prospective Cohorts of Chinese Adults

BACKGROUND

The roles of individual and co-regulated lipid molecular species in the development of type 2 diabetes (T2D) and mediation from metabolic risk factors remain unknown.

METHODS

We conducted profiling of 166 plasma lipid species in 2 nested case-control studies within 2 independent cohorts of Chinese adults, the Dongfeng-Tongji and the Jiangsu non-communicable disease cohorts. After 4.61 (0.15) and 7.57 (1.13) years' follow-up, 1039 and 520 eligible participants developed T2D in these 2 cohorts, respectively, and controls were 1:1 matched to cases by age and sex.

RESULTS

We found 27 lipid species, including 10 novel ones, consistently associated with T2D risk in the 2 cohorts. Differential correlation network analysis revealed significant correlations of triacylglycerol (TAG) 50:3, containing at least one oleyl chain, with 6 TAGs, at least 3 of which contain the palmitoyl chain, all downregulated within cases relative to controls among the 27 lipids in both cohorts, while the networks also both identified the oleyl chain-containing TAG 50:3 as the central hub. We further found that 13 of the 27 lipids consistently mediated the association between adiposity indicators (body mass index, waist circumference, and waist-to-height ratio) and diabetes risk in both cohorts (all P < 0.05; proportion mediated: 20.00%, 17.70%, and 17.71%, and 32.50%, 28.73%, and 33.86%, respectively).

CONCLUSIONS

Our findings suggested notable perturbed co-regulation, inferred from differential correlation networks, between oleyl chain- and palmitoyl chain-containing TAGs before diabetes onset, with the oleyl chain-containing TAG 50:3 at the center, and provided novel etiological insight regarding lipid dysregulation in the progression from adiposity to overt T2D.

Metabolomics and Type 2 Diabetes Risk: An Updated Systematic Review and Meta-analysis of Prospective Cohort Studies

BACKGROUND

Due to the rapidly increasing availability of metabolomics data in prospective studies, an update of the meta evidence on metabolomics and type 2 diabetes risk is warranted.

PURPOSE

To conduct an updated systematic review and meta-analysis of plasma, serum, and urine metabolite markers and incident type 2 diabetes.

DATA SOURCES

We searched PubMed and Embase until 6 March 2021.

STUDY SELECTION

We selected prospective observational studies where investigators used high-throughput techniques to investigate the relationship between plasma, serum, or urine metabolites and incident type 2 diabetes.

DATA EXTRACTION

Baseline metabolites per-SD risk estimates and 95% CIs for incident type 2 diabetes were extracted from all eligible studies.

DATA SYNTHESIS

A total of 61 reports with 71,196 participants and 11,771 type 2 diabetes cases/events were included in the updated review. Meta-analysis was performed for 412 metabolites, of which 123 were statistically significantly associated (false discovery rate-corrected P < 0.05) with type 2 diabetes risk. Higher plasma and serum levels of certain amino acids (branched-chain, aromatic, alanine, glutamate, lysine, and methionine), carbohydrates and energy-related metabolites (mannose, trehalose, and pyruvate), acylcarnitines (C4-DC, C4-OH, C5, C5-OH, and C8:1), the majority of glycerolipids (di- and triacylglycerols), (lyso)phosphatidylethanolamines, and ceramides included in meta-analysis were associated with higher risk of type 2 diabetes (hazard ratio 1.07-2.58). Higher levels of glycine, glutamine, betaine, indolepropionate, and (lyso)phosphatidylcholines were associated with lower type 2 diabetes risk (hazard ratio 0.69-0.90).

LIMITATIONS

Substantial heterogeneity (I2 > 50%, τ2 > 0.1) was observed for some of the metabolites.

CONCLUSIONS

Several plasma and serum metabolites, including amino acids, lipids, and carbohydrates, are associated with type 2 diabetes risk.

Serum lipidomics profiles reveal potential lipid markers for prediabetes and type 2 diabetes in patients from multiple communities

OBJECTIVE

Dyslipidemia is a hallmark of diabetes mellitus (DM). However, specific lipid molecules closely associated with the initiation and progression of diabetes remain unclear. We used a pseudotargeted lipidomics approach to evaluate the complex lipid changes that occurred long before the diagnosis of type 2 diabetes mellitus (T2DM) and to identify novel lipid markers for screening prediabetes mellitus (PreDM) and T2DM in patients from multiple communities.

METHODS

Four hundred and eighty-one subjects consisting of T2DM, three subtypes of PreDM, and normal controls (NC) were enrolled as discovery cohort. Serum lipidomic profiles of 481 subjects were analyzed using an ultrahigh performance liquid chromatography-triple quadrupole mass spectrometry (UHPLC-QqQ-MS)-based pseudotargeted lipidomics method. The differential lipid molecules were further validated in an independent case-control study consisting of 150 PreDM, 234 T2DM and 94 NC.

RESULTS

Multivariate discriminative analyses show that lipidomics data have considerable potential for identifying lipidome differences among T2DM, subtypes of PreDM and NC. Statistical associations of lipid (sub)species display significant variations in 11 lipid (sub)species levels for T2DM and distinctive differences in 8 lipid (sub)species levels between prediabetic and normoglycemic individuals, with further differences in 8 lipid (sub)species levels among subtypes of PreDM. Adjusted for sex, age and BMI, only two lipid (sub)species of fatty acid (FA) and phosphatidylcholine (PC) were associated at p< 0.05 for PreDM (all) and subtypes of PreDM. The defined lipid markers not only significantly improve the diagnostic accuracy of PreDM and T2DM but also effectively evaluating the risk of developing into each subtype of PreDM and T2DM when addition of age, sex, BMI, and FPG, respectively.

CONCLUSIONS

Our findings improve insights into the lipid metabolic complexity and interindividual variations among subtypes of PreDM and T2DM, beyond the well-known differences in dyslipidemia in clinic.

Longitudinal Plasma Lipidome and Risk of Type 2 Diabetes in a Large Sample of American Indians With Normal Fasting Glucose: The Strong Heart Family Study

OBJECTIVE

Comprehensive assessment of alterations in lipid species preceding type 2 diabetes (T2D) is largely unknown. We aimed to identify plasma molecular lipids associated with risk of T2D in American Indians.

RESEARCH DESIGN AND METHODS

Using untargeted liquid chromatography-mass spectrometry, we repeatedly measured 3,907 fasting plasma samples from 1,958 participants who attended two examinations (∼5.5 years apart) and were followed up to 16 years in the Strong Heart Family Study. Mixed-effects logistic regression was used to identify lipids associated with risk of T2D, adjusting for traditional risk factors. Repeated measurement analysis was performed to examine the association between change in lipidome and change in continuous measures of T2D, adjusting for baseline lipids. Multiple testing was controlled by false discovery rate at 0.05.

RESULTS

Higher baseline level of 33 lipid species, including triacylglycerols, diacylglycerols, phosphoethanolamines, and phosphocholines, was significantly associated with increased risk of T2D (odds ratio [OR] per SD increase in log2-transformed baseline lipids 1.50-2.85) at 5-year follow-up. Of these, 21 lipids were also associated with risk of T2D at 16-year follow-up. Aberrant lipid profiles were also observed in prediabetes (OR per SD increase in log2-transformed baseline lipids 1.30-2.19 for risk lipids and 0.70-0.78 for protective lipids). Longitudinal changes in 568 lipids were significantly associated with changes in continuous measures of T2D. Multivariate analysis identified distinct lipidomic signatures differentiating high- from low-risk groups.

CONCLUSIONS

Lipid dysregulation occurs many years preceding T2D, and novel molecular lipids (both baseline level and longitudinal change over time) are significantly associated with risk of T2D beyond traditional risk factors. Our findings shed light on the mechanisms linking dyslipidemia to T2D and may yield novel therapeutic targets for early intervention tailored to American Indians.

Effect of Metabolite Levels on Type 2 Diabetes Mellitus and Glycemic Traits: A Mendelian Randomization Study

OBJECTIVE

To assess the causal associations of plasma levels of metabolites with type 2 diabetes mellitus (T2DM) and glycemic traits.

METHODS

Two-sample mendelian randomization (MR) was conducted to assess the causal associations. Genetic variants strongly associated with metabolites at genome-wide significance level (P < 5 × 10-8) were selected from public genome-wide association studies, and single-nucleotide polymorphisms of outcomes were obtained from the Diabetes Genetics Replication and Meta-analysis consortium for T2DM and from the Meta-Analyses of Glucose and Insulin-related Traits Consortium for fasting glucose, insulin, and glycated hemoglobin (HbA1c). The Wald ratio and inverse-variance weighted methods were used for analyses, and MR-Egger was used for sensitivity analysis.

RESULTS

The β estimates per 1-SD increase of arachidonic acid (AA) level was 0.16 (95% CI, 0.078-0.242; P < 0.001). Genetic predisposition to higher plasma AA levels were associated with higher fasting glucose levels (β 0.10 [95% CI, 0.064-0.134], P < 0.001), higher HbA1c levels (β 0.04 [95% CI, 0.027-0.061]), and lower fasting insulin levels (β -0.025 [95% CI, -0.047 to -0.002], P = 0.033). Besides, 2-hydroxybutyric acid (2-HBA) might have a positive causal effect on glycemic traits.

CONCLUSIONS

Our findings suggest that AA and 2-HBA may have causal associations on T2DM and glycemic traits. This is beneficial for clarifying the pathogenesis of T2DM, which would be valuable for early identification and prevention for T2DM.

Uncovering the shared lipidomic markers of subclinical osteoporosis-atherosclerosis comorbidity: The Young Finns Study

BACKGROUND

Osteoporosis and atherosclerosis are complex multifactorial diseases sharing common risk factors and pathophysiological mechanisms suggesting that these are comorbidities. Omics studies identifying joint molecular markers associated with these diseases are sparse.

SUBJECTS AND METHODS

Using liquid chromatography-tandem mass spectrometry, we quantified 437 molecular lipid species from the Young Finns Study cohort (aged 30-45 years and 57% women) and performed lipidome-wide multivariate analysis of variance (MANOVA) with early markers for both diseases. Carotid intima-media thickness for atherosclerosis measured with ultrasound and bone mineral density from distal radius and tibia for osteoporosis measured with peripheral quantitative computed tomography were used as early markers of the diseases.

RESULTS

MANOVA adjusted with age, sex and body mass index, identified eight statistically significant (adjusted p-value (p_adj) < 0.05) and 15 suggestively significant (p_adj < 0.25) molecular lipid species associated with the studied markers. Similar analysis adjusted additionally for smoking habit, physical activity and alcohol consumption identified four significant and six suggestively significant molecular lipid species. These most significant lipid classes/species jointly associated with the studied markers were glycerolipid/TAG(18:0/18:0/18:1), glycerophospholipid/PC(40:3), sphingolipid/Gb3(d18:1/22:0), and sphingolipid/Gb3(d18:1/24:0).

CONCLUSION

Our results support the osteoporosis-atherosclerosis comorbidity hypothesis and present potential new joint lipid biomarkers for these diseases.

A Randomized Controlled Dietary Intervention Improved the Serum Lipid Signature towards a Less Atherogenic Profile in Patients with Rheumatoid Arthritis

Diet is a major modifiable risk factor for cardiovascular disease (CVD). One explanation for this is its effect on specific lipids. However, knowledge on how the lipidome is affected is limited. We aimed to investigate if diet can change the new ceramide- and phospholipid-based CVD risk score CERT2 and the serum lipidome towards a more favorable CVD signature. In a crossover trial (ADIRA), 50 patients with rheumatoid arthritis (RA) had 10 weeks of a Mediterranean-style diet intervention or a Western-style control diet and then switched diets after a 4-month wash-out-period. Five hundred and thirty-eight individual lipids were measured in serum by liquid chromatography-tandem mass spectrometry (LC-MS/MS). Lipid risk scores were analyzed by Wilcoxon signed-rank test or mixed model and lipidomic data with multivariate statistical methods. In the main analysis, including the 46 participants completing ≥1 diet period, there was no significant difference in CERT2 after the intervention compared with the control, although several CERT2 components were changed within periods. In addition, triacylglycerols, cholesteryl esters, phosphatidylcholines, alkylphosphatidylcholines and alkenylphosphatidylcholines had a healthier composition after the intervention compared to after the control diet. This trial indicates that certain dietary changes can improve the serum lipid signature towards a less atherogenic profile in patients with RA.

Multi-Omics Analysis Reveals Disturbance of Nanosecond Pulsed Electric Field in the Serum Metabolic Spectrum and Gut Microbiota

Nanosecond pulsed electric field (nsPEF) is a novel ablation technique that is based on high-intensity electric voltage to achieve tumour-killing effect in the target region, and increasingly considered for treating tumours of the liver, kidneys and other organs with rich blood supply. This study aims to observe effect of nsPFE treatment on serum metabolites and gut microbiota. The serum and faecal specimens of the pigs were collected pre- and post-treatment. The gut microbiota of pigs was sequenced by Illumina Miseq platform for analysing the diversity and alterations of gut microbiota. Liquid chromatography-mass spectrometry (LC-MS)-based metabonomic analysis and Pearson coefficient method were also used to construct the interaction system of different metabolites, metabolic pathways and flora. A total of 1,477 differential metabolites from the serum were identified by four cross-comparisons of different post-operative groups with the control group. In addition, an average of 636 OTUs per sample was detected. Correlation analysis also revealed the strong correlation between intestinal bacteria and differential metabolites. The nsPEF ablation of the liver results in a degree of liver damage that affects various metabolic pathways, mainly lipid metabolism, as well as gut microbiota. In conclusion, our study provided a good point for the safety and feasibility of applying nsPEF on liver through the integrated analysis of metabolomics and microbiomes, which is beneficial for the improvement of nsPEF in clinical use.

Multi-omics profiling of living human pancreatic islet donors reveals heterogeneous beta cell trajectories towards type 2 diabetes

Most research on human pancreatic islets is conducted on samples obtained from normoglycaemic or diseased brain-dead donors and thus cannot accurately describe the molecular changes of pancreatic islet beta cells as they progress towards a state of deficient insulin secretion in type 2 diabetes (T2D). Here, we conduct a comprehensive multi-omics analysis of pancreatic islets obtained from metabolically profiled pancreatectomized living human donors stratified along the glycemic continuum, from normoglycemia to T2D. We find that islet pools isolated from surgical samples by laser-capture microdissection display remarkably more heterogeneous transcriptomic and proteomic profiles in patients with diabetes than in non-diabetic controls. The differential regulation of islet gene expression is already observed in prediabetic individuals with impaired glucose tolerance. Our findings demonstrate a progressive, but disharmonic, remodelling of mature beta cells, challenging current hypotheses of linear trajectories toward precursor or transdifferentiation stages in T2D. Furthermore, through integration of islet transcriptomics with preoperative blood plasma lipidomics, we define the relative importance of gene coexpression modules and lipids that are positively or negatively associated with HbA1c levels, pointing to potential prognostic markers.

Lipidomics characterization of the mechanism of Cynomorium songaricum polysaccharide on treating type 2 diabetes

Although Cynomorium songaricum Rupr. polysaccharide (CSP) has been examined for its effects on glucose regulation, its underlying mechanism is still unclear. To address this issue, a MS-based lipidomics strategy was developed to gain a system-level understanding of the mechanism of CSP on improving type 2 diabetes mellitus (T2DM). UPLC-QTOF/MS and multivariate statistical tools were used to identify the alteration of serum metabolites associated with T2DM and responses to CSP treatment. As a result, 35 potential biomarkers were found and identified in serum, amongst which 26 metabolites were regulated to normal like levels after the administration of CSP. By analyzing the metabolic pathways, glycerophospholipid metabolism was suggested to be closely involved. These results indicated that the intake of CSP exhibited promising anti-diabetic activity, largely due to the regulation of phospholipid metabolism, including phosphatidylcholines, lysophosphatydylcholines, phosphtatidylethanolamines and sphingomyelins.

Predictive utilities of lipid traits, lipoprotein subfractions and other risk factors for incident diabetes: a machine learning approach in the Diabetes Prevention Program

INTRODUCTION

Although various lipid and non-lipid analytes measured by nuclear magnetic resonance (NMR) spectroscopy have been associated with type 2 diabetes, a structured comparison of the ability of NMR-derived biomarkers and standard lipids to predict individual diabetes risk has not been undertaken in larger studies nor among individuals at high risk of diabetes.

RESEARCH DESIGN AND METHODS

Cumulative discriminative utilities of various groups of biomarkers including NMR lipoproteins, related non-lipid biomarkers, standard lipids, and demographic and glycemic traits were compared for short-term (3.2 years) and long-term (15 years) diabetes development in the Diabetes Prevention Program, a multiethnic, placebo-controlled, randomized controlled trial of individuals with pre-diabetes in the USA (N=2590). Logistic regression, Cox proportional hazards model and six different hyperparameter-tuned machine learning algorithms were compared. The Matthews Correlation Coefficient (MCC) was used as the primary measure of discriminative utility.

RESULTS

Models with baseline NMR analytes and their changes did not improve the discriminative utility of simpler models including standard lipids or demographic and glycemic traits. Across all algorithms, models with baseline 2-hour glucose performed the best (max MCC=0.36). Sophisticated machine learning algorithms performed similarly to logistic regression in this study.

CONCLUSIONS

NMR lipoproteins and related non-lipid biomarkers were associated but did not augment discrimination of diabetes risk beyond traditional diabetes risk factors except for 2-hour glucose. Machine learning algorithms provided no meaningful improvement for discrimination compared with logistic regression, which suggests a lack of influential latent interactions among the analytes assessed in this study.

TRIAL REGISTRATION NUMBER

Diabetes Prevention Program: NCT00004992; Diabetes Prevention Program Outcomes Study: NCT00038727.

Lipid Traffic Analysis reveals the impact of high paternal carbohydrate intake on offsprings' lipid metabolism

In this paper we present an investigation of parental-diet-driven metabolic programming in offspring using a novel computational network analysis tool. The impact of high paternal carbohydrate intake on offsprings’ phospholipid and triglyceride metabolism in F1 and F2 generations is described. Detailed lipid profiles were acquired from F1 neonate (3 weeks), F1 adult (16 weeks) and F2 neonate offspring in serum, liver, brain, heart and abdominal adipose tissues by MS and NMR. Using a purpose-built computational tool for analysing both phospholipid and fat metabolism as a network, we characterised the number, type and abundance of lipid variables in and between tissues (Lipid Traffic Analysis), finding a variety of reprogrammings associated with paternal diet. These results are important because they describe the long-term metabolic result of dietary intake by fathers. This analytical approach is important because it offers unparalleled insight into possible mechanisms for alterations in lipid metabolism throughout organisms.

Furse et al. use a purpose-built computational tool called Lipid Traffic Analysis to determine the spatial distribution of lipids throughout an organism. They use it to show that high paternal carbohydrate intake influences lipid metabolism in offspring two generations hence.

Dihydroceramides in Triglyceride-Enriched VLDL Are Associated with Nonalcoholic Fatty Liver Disease Severity in Type 2 Diabetes

Plasma dihydroceramides are predictors of type 2 diabetes and related to metabolic dysfunctions, but the underlying mechanisms are not characterized. We compare the relationships between plasma dihydroceramides and biochemical and hepatic parameters in two cohorts of diabetic patients. Hepatic steatosis, steatohepatitis, and fibrosis are assessed by their plasma biomarkers. Plasma lipoprotein sphingolipids are studied in a sub-group of diabetic patients. Liver biopsies from subjects with suspected non-alcoholic fatty liver disease are analyzed for sphingolipid synthesis enzyme expression. Dihydroceramides, contained in triglyceride-rich very-low-density lipoprotein (VLDL), are associated with steatosis and steatohepatitis. Expression of sphingolipid synthesis enzymes is correlated with histological steatosis and inflammation grades. In conclusion, association of plasma dihydroceramides with nonalcoholic fatty liver might explain their predictive character for type 2 diabetes. Our results suggest a relationship between hepatic sphingolipid metabolism and steatohepatitis and an involvement of dihydroceramides in the synthesis/secretion of triglyceride-rich VLDL, a hallmark of NAFLD and type 2 diabetes dyslipidemia.

Plasma dihydroceramides are associated with NAFLD severity in type 2 diabetic patients

Plasma dihydroceramides are found in triglyceride-enriched VLDL

A role for dihydroceramide in triglyceride-rich VLDL synthesis/secretion is suggested

Expression of enzymes of hepatic sphingolipid synthesis increases with NAFLD severity

Metabolomic profiles associated with subtypes of prediabetes among Mexican Americans in Starr County, Texas, USA

AIMS/HYPOTHESIS

To understand the complex metabolic changes that occur long before the diagnosis of type 2 diabetes, we investigated differences in metabolomic profiles in plasma between prediabetic and normoglycaemic individuals for subtypes of prediabetes defined by fasting glucose, 2 h glucose and HbA1c measures.

METHODS

Untargeted metabolomics data were obtained from 155 plasma samples from 127 Mexican American individuals from Starr County, TX, USA. None had type 2 diabetes at the time of sample collection and 69 had prediabetes by at least one criterion. We tested statistical associations of amino acids and other metabolites with each subtype of prediabetes.

RESULTS

We identified distinctive differences in amino acid profiles between prediabetic and normoglycaemic individuals, with further differences in amino acid levels among subtypes of prediabetes. When testing all named metabolites, several fatty acids were also significantly associated with 2 h glucose levels. Multivariate discriminative analyses show that untargeted metabolomic data have considerable potential for identifying metabolic differences among subtypes of prediabetes.

CONCLUSIONS/INTERPRETATION

People with each subtype of prediabetes have a distinctive metabolomic signature, beyond the well-known differences in branched-chain amino acids.

DATA AVAILABILITY

Metabolomics data are available through the NCBI database of Genotypes and Phenotypes (dbGaP, accession number phs001166; www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs001166.v1.p1).

Plasma Lipidome and Prediction of Type 2 Diabetes in the Population-Based Malmö Diet and Cancer Cohort

OBJECTIVE

Type 2 diabetes mellitus (T2DM) is associated with dyslipidemia, but the detailed alterations in lipid species preceding the disease are largely unknown. We aimed to identify plasma lipids associated with development of T2DM and investigate their associations with lifestyle.

RESEARCH DESIGN AND METHODS

At baseline, 178 lipids were measured by mass spectrometry in 3,668 participants without diabetes from the Malmö Diet and Cancer Study. The population was randomly split into discovery (n = 1,868, including 257 incident cases) and replication (n = 1,800, including 249 incident cases) sets. We used orthogonal projections to latent structures discriminant analyses, extracted a predictive component for T2DM incidence (lipid-PC_DM), and assessed its association with T2DM incidence using Cox regression and lifestyle factors using general linear models.

RESULTS

A T2DM-predictive lipid-PC_DM derived from the discovery set was independently associated with T2DM incidence in the replication set, with hazard ratio (HR) among subjects in the fifth versus first quintile of lipid-PC_DM of 3.7 (95% CI 2.2-6.5). In comparison, the HR of T2DM among obese versus normal weight subjects was 1.8 (95% CI 1.2-2.6). Clinical lipids did not improve T2DM risk prediction, but adding the lipid-PC_DM to all conventional T2DM risk factors increased the area under the receiver operating characteristics curve by 3%. The lipid-PC_DM was also associated with a dietary risk score for T2DM incidence and lower level of physical activity.

CONCLUSIONS

A lifestyle-related lipidomic profile strongly predicts T2DM development beyond current risk factors. Further studies are warranted to test if lifestyle interventions modifying this lipidomic profile can prevent T2DM.

Lipidomic architecture shared by subclinical markers of osteoporosis and atherosclerosis: The Cardiovascular Risk in Young Finns Study

BACKGROUND

Studies have shown that osteoporosis and atherosclerosis are comorbid conditions sharing common risk factors and pathophysiological mechanisms. Understanding these is crucial in order to develop shared methods for risk stratification, prevention, diagnosis and treatment. The aim of this study was to apply a system-level bioinformatics approach to lipidome-wide data in order to pinpoint the lipidomic architecture jointly associated with surrogate markers of these complex comorbid diseases.

SUBJECTS AND METHODS

The study was based on the Cardiovascular Risk in Young Finns Study cohort from the 2007 follow-up (n = 1494, aged 30-45 years, women: 57%). Liquid chromatography-tandem mass spectrometry (LC-MS/MS) was used to analyse the serum lipidome, involving 437 molecular lipid species. The subclinical osteoporotic markers included indices of bone mineral density and content, measured using peripheral quantitative computer tomography from the distal and shaft sites of both the tibia and the radius. The subclinical atherosclerotic markers included carotid and bulbus intima media thickness measured with high-resolution ultrasound. Weighted co-expression network analysis was performed to identify networks of densely interconnected lipid species (i.e. lipid modules) associated with subclinical markers of both osteoporosis and atherosclerosis. The levels of lipid species (lipid profiles) of each of the lipid modules were summarized by the first principal component termed as module eigenlipid. Then, Pearson's correlation (r) was calculated between the module eigenlipids and the markers. Lipid modules that were significantly and jointly correlated with subclinical markers of both osteoporosis and atherosclerosis were considered to be related to the comorbidities. The hypothesis that the eigenlipids and profiles of the constituent lipid species in the modules have joint effects on the markers was tested with multivariate analysis of variance (MANOVA).

RESULTS

Among twelve studied molecular lipid modules, we identified one module with 105 lipid species significantly and jointly associated with both subclinical markers of both osteoporosis (r = 0.24, p-value = 2 × 10^-20) and atherosclerosis (r = 0.16, p-value = 2 × 10^-10). The majority of the lipid species in this module belonged to the glycerolipid (n = 60), glycerophospholipid (n = 13) and sphingolipid (n = 29) classes. The module was also enriched with ceramides (n = 20), confirming their significance in cardiovascular outcomes and suggesting their joint role in the comorbidities. The top three of the 37 statistically significant (adjusted p-value < 0.05) lipid species jointly associated with subclinical markers of both osteoporosis and atherosclerosis within the module were all triacylglycerols (TAGs) - TAG(18:0/18:0/18:1) with an adjusted p-value of 8.6 × 10^-8, TAG(18:0/18:1/18:1) with an adjusted p-value of 3.7 × 10^-6, and TAG(16:0/18:0/18:1) with an adjusted p-value of 8.5 × 10^-6.

CONCLUSION

This study identified a novel lipid module associated with both surrogate markers of both subclinical osteoporosis and subclinical atherosclerosis. Alterations in the metabolism of the identified lipid module and, more specifically, the TAG related molecular lipids within the module may provide potential new biomarkers for testing the comorbidities, opening avenues for the emergence of dual-purpose prevention measures.

Rare DEGS1 variant significantly alters de novo ceramide synthesis pathway

The de novo ceramide synthesis pathway is essential to human biology and health, but genetic influences remain unexplored. The core function of this pathway is the generation of biologically active ceramide from its precursor, dihydroceramide. Dihydroceramides have diverse, often protective, biological roles; conversely, increased ceramide levels are biomarkers of complex disease. To explore the genetics of the ceramide synthesis pathway, we searched for deleterious nonsynonymous variants in the genomes of 1,020 Mexican Americans from extended pedigrees. We identified a Hispanic ancestry-specific rare functional variant, L175Q, in delta 4-desaturase, sphingolipid 1 (DEGS1), a key enzyme in the pathway that converts dihydroceramide to ceramide. This amino acid change was significantly associated with large increases in plasma dihydroceramides. Indexes of DEGS1 enzymatic activity were dramatically reduced in heterozygotes. CRISPR/Cas9 genome editing of HepG2 cells confirmed that the L175Q variant results in a partial loss of function for the DEGS1 enzyme. Understanding the biological role of DEGS1 variants, such as L175Q, in ceramide synthesis may improve the understanding of metabolic-related disorders and spur ongoing research of drug targets along this pathway.

New evidence from plasma ceramides links apoE polymorphism to greater risk of coronary artery disease in Finnish adults

apoE, a key regulator of plasma lipids, mediates altered functionalities in lipoprotein metabolism and thus affects the risk of coronary artery disease (CAD). The significance of different apoE polymorphisms remains unclear; although the ε4 allele is clearly associated with increased cholesterol levels (which inform CAD risk), direct studies about apoE polymorphisms on CAD risk and development have yielded controversial results. Furthermore, certain species of ceramides-complex lipids abundant in plasma LDL-are markers of increased risk of myocardial infarction and cardiovascular death. Using a high-throughput MS approach, we quantified 30 molecular plasma ceramide species from a cohort of 2,160 apoE-genotyped (rs7412, rs429358) young adults enrolled in the population-based Cardiovascular Risk in Young Finns Study. We then searched this lipidome data set to identify new indications of pathways influenced by apoE polymorphisms and possibly related to CAD risk. This approach revealed a previously unreported association between apoE polymorphism and a consistently documented high-risk CAD marker, Cer(d18:1/16:0). Compared with the apoE ε3/3 reference group, plasma levels of apoE ε4 were elevated and those of apoE ε2 were lowered in all subjects without evidence of apoE-by-sex interactions. apoE associated with seven ceramides that are connected to atherogenically potent macrophages and/or lipoprotein particles; these associations could indicate a plausible linkage between apoE polymorphism and ceramide metabolism, leading to adverse plasma LDL metabolism and atherogenesis. In conclusion, new evidence from plasma ceramides links apoE polymorphism with an increased risk of CAD and extends our understanding of the role of apoE in health and disease.

Challenges of the heterogeneous nutrition response: interpreting the group mean

Extensive research demonstrates unequivocally that nutrition plays a fundamental role in maintaining health and preventing disease. In parallel nutrition research provides evidence that the risks and benefits of diet and lifestyle choices do not affect people equally, as people are inherently variable in their responses to nutrition and associated interventions to maintain health and prevent disease. To simplify the inherent complexity of human subjects and their nutrition, with the aim of managing expectations for dietary guidance required to ensure healthy populations and individuals, nutrition researchers often seek to group individuals based on commonly used criteria. This strategy relies on demonstrating meaningful conclusions based on comparison of group mean responses of assigned groups. Such studies are often confounded by the heterogeneous nutrition response. Commonly used criteria applied in grouping study populations and individuals to identify mechanisms and determinants of responses to nutrition often contribute to the problem of interpreting the results of group comparisons. Challenges of interpreting the group mean using diverse populations will be discussed with respect to studies in human subjects, in vivo and in vitro model systems. Future advances in nutrition research to tackle inter-individual variation require a coordinated approach from funders, learned societies, nutrition scientists, publishers and reviewers of the scientific literature. This will be essential to develop and implement improved study design, data recording, analysis and reporting to facilitate more insightful interpretation of the group mean with respect to population diversity and the heterogeneous nutrition response.

Common Fatty Markers in Diseases with Dysregulated Lipogenesis

Recent studies have reported the upregulation of a subgroup of triacylglycerides as markers of different diseases with dysregulated lipogenesis, which means that these markers are not selective. This observation has a deep impact on their use as diagnostic tools in clinical practice (e.g., markers of risk of type 2 diabetes).

Linking of metabolomic biomarkers with cardiometabolic health in Chinese population

Due to rapid nutrition transitions, the prevalence of cardiometabolic diseases, such as metabolic syndrome, type 2 diabetes, and cardiovascular diseases, has been increasing at an alarming rate in the Chinese population. Moreover, Asians, including Chinese, have been hypothesized to have a higher susceptibility to cardiometabolic diseases than Caucasians. Early prediction and prevention are key to controlling this epidemic trend; to this end, the identification of novel biomarkers is critical to reflect environmental exposure, as well as to reveal endogenous metabolic and pathophysiologic mechanisms. The emerging "omics" technologies, especially metabolomics, offer a unique opportunity to provide novel signatures or fingerprints to understand the effects of genetic and non-genetic factors on cardiometabolic health. During the past two decades, metabolomic approaches have been increasingly used in various epidemiological studies, primarily in Western populations. Although the field is still in its early stages, some studies have tried to identify novel compounds or confirm their metabolites and associations with cardiometabolic diseases in Chinese populations, including amino acids, fatty acids, acylcarnitines and other metabolites. Despite major efforts to discover novel biomarkers for disease prediction or intervention, the limits in current study design, analytical platforms, and data processing approaches are challenges in metabolomic research worldwide. Therefore, future research with more advanced technologies, rigorous study designs, standardized detection and analytic approaches, and integrated data from multiomics approaches are essential to evaluate the feasibility of using metabolomics in clinical settings. Finally, the functional roles and underlying biological mechanisms of metabolomic biomarkers should be elucidated by future mechanistic research.

The role of dihydrosphingolipids in disease

Dihydrosphingolipids refer to sphingolipids early in the biosynthetic pathway that do not contain a C4-trans-double bond in the sphingoid backbone: 3-ketosphinganine (3-ketoSph), dihydrosphingosine (dhSph), dihydrosphingosine-1-phosphate (dhS1P) and dihydroceramide (dhCer). Recent advances in research related to sphingolipid biochemistry have shed light on the importance of sphingolipids in terms of cellular signalling in health and disease. However, dihydrosphingolipids have received less attention and research is lacking especially in terms of their molecular mechanisms of action. This is despite studies implicating them in the pathophysiology of disease, for example dhCer in predicting type 2 diabetes in obese individuals, dhS1P in cardiovascular diseases and dhSph in hepato-renal toxicity. This review gives a comprehensive summary of research in the last 10-15 years on the dihydrosphingolipids, 3-ketoSph, dhSph, dhS1P and dhCer, and their relevant roles in different diseases. It also highlights gaps in research that could be of future interest.

Serum Metabolomics of Activity Energy Expenditure and its Relation to Metabolic Syndrome and Obesity

Modifiable lifestyle factors, including exercise and activity energy expenditure (AEE), may attenuate the unfavorable health effects of obesity, such as risk factors of metabolic syndrome (MetS). However, the underlying mechanisms are not clear. In this study we sought to investigate whether the metabolite profiles of MetS and adiposity assessed by body mass index (BMI) and central obesity are inversely correlated with AEE and physical activity. We studied 35 men and 47 women, aged 30-60 years, using doubly labeled water to derive AEE and the Sedentary Time and Activity Reporting Questionnaire (STAR-Q) to determine the time spent in moderate and vigorous physical activity. Proton nuclear magnetic resonance spectroscopy was used for serum metabolomics analysis. Serine and glycine were found in lower concentrations in participants with more MetS risk factors and greater adiposity. However, serine and glycine concentrations were higher with increasing activity measures. Metabolic pathway analysis and recent literature suggests that the lower serine and glycine concentrations in the overweight/obese state could be a consequence of serine entering de novo sphingolipid synthesis. Taken together, higher levels of AEE and physical activity may play a crucial part in improving metabolic health in men and women with and without MetS risk factors.

Lipidome as a predictive tool in progression to type 2 diabetes in Finnish men

BACKGROUND

There is a need for early markers to track and predict the development of type 2 diabetes mellitus (T2DM) from the state of normal glucose tolerance through prediabetes. In this study we tested whether the plasma molecular lipidome has biomarker potential to predicting the onset of T2DM.

METHODS

We applied global lipidomic profiling on plasma samples from well-phenotyped men (107 cases, 216 controls) participating in the longitudinal METSIM study at baseline and at five-year follow-up. To validate the lipid markers, an additional study with a representative sample of adult male population (n=631) was also conducted. A total of 277 plasma lipids were analyzed using the lipidomics platform based on ultra-performance liquid chromatography coupled to time-of-flight mass spectrometry. Lipids with the highest predictive power for the development of T2DM were computationally selected, validated and compared to standard risk models without lipids.

RESULTS

A persistent lipid signature with higher levels of triacylglycerols and diacyl-phospholipids as well as lower levels of alkylacyl phosphatidylcholines was observed in progressors to T2DM. Lysophosphatidylcholine acyl C18:2 (LysoPC(18:2)), phosphatidylcholines PC(32:1), PC(34:2e) and PC(36:1), and triacylglycerol TG(17:1/18:1/18:2) were selected to the full model that included metabolic risk factors and FINDRISC variables. When further adjusting for BMI and age, these lipids had respective odds ratios of 0.32, 2.4, 0.50, 2.2 and 0.31 (all p<0.05) for progression to T2DM. The independently-validated predictive power improved in all pairwise comparisons between the lipid model and the respective standard risk model without the lipids (integrated discrimination improvement IDI>0; p<0.05). Notably, the lipid models remained predictive of the development of T2DM in the fasting plasma glucose-matched subset of the validation study.

CONCLUSION

This study indicates that a lipid signature characteristic of T2DM is present years before the diagnosis and improves prediction of progression to T2DM. Molecular lipid biomarkers were shown to have predictive power also in a high-risk group, where standard risk factors are not helpful at distinguishing progressors from non-progressors.

Predicting the presence of colon cancer in members of a health maintenance organisation by evaluating analytes from standard laboratory records

Background:

A valid risk prediction model for colorectal cancer (CRC) could be used to identify individuals in the population who would most benefit from CRC screening. We evaluated the potential for information derived from a panel of blood tests to predict a diagnosis of CRC from 1 month to 3 years in the future.

Methods:

We abstracted information on 1755 CRC cases and 54 730 matched cancer-free controls who had one or more blood tests recorded in the electronic records of Maccabi Health Services (MHS) during the period 30–180 days before diagnosis. A scoring model (CRC score) was constructed using the study subjects' blood test results. We calculated the odds ratio for being diagnosed with CRC after the date of blood draw, according to CRC score and time from blood draw.

Results:

The odds ratio for having CRC detected within 6 months for those with a score of four or greater (vs three or less) was 7.3 (95% CI: 6.3–8.5) for men and was 7.8 (95% CI: 6.7–9.1) for women.

Conclusions:

Information taken from routine blood tests can be used to predict the risk of being diagnosed with CRC in the near future.

Lipidomics-Reshaping the Analysis and Perception of Type 2 Diabetes

As a consequence of a sedentary lifestyle as well as changed nutritional behavior, today's societies are challenged by the rapid propagation of metabolic disorders. A common feature of diseases, such as obesity and type 2 diabetes (T2D), is the dysregulation of lipid metabolism. Our understanding of the mechanisms underlying these diseases is hampered by the complexity of lipid metabolic pathways on a cellular level. Furthermore, overall lipid homeostasis in higher eukaryotic organisms needs to be maintained by a highly regulated interplay between tissues, such as adipose tissue, liver and muscle. Unraveling pathological mechanisms underlying metabolic disorders therefore requires a diversified approach, integrating basic cellular research with clinical research, ultimately relying on the analytical power of mass spectrometry-based techniques. Here, we discuss recent progress in the development of lipidomics approaches to resolve the pathological mechanisms of metabolic diseases and to identify suitable biomarkers for clinical application. Due to its growing impact worldwide, we focus on T2D to highlight the key role of lipidomics in our current understanding of this disease, discuss remaining questions and suggest future strategies to address them.