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

Lipid metabolic links between serum pyrethroid levels and the risk of incident type 2 diabetes: A mediation study in the prospective design

Emerging evidence revealed that pyrethroids and circulating lipid metabolites are involved in incident type 2 diabetes (T2D). However, the pyrethroid-associated lipid profile and its potential role in the association of pyrethroids with T2D remain unknown. Metabolome-wide association or mediation analyses were performed among 1006 pairs of T2D cases and matched controls nested within the prospective Dongfeng-Tongji cohort. We identified 59 lipid metabolites significantly associated with serum deltamethrin levels, of which eight were also significantly associated with serum fenvalerate (false discovery rate [FDR] < 0.05). Pathway enrichment analysis showed that deltamethrin-associated lipid metabolites were significantly enriched in the glycerophospholipid metabolism pathway (FDR = 0.02). Furthermore, we also found that several deltamethrin-associated lipid metabolites (i.e., phosphatidylcholine [PC] 32:0, PC 34:4, cholesterol ester 20:0, triacylglycerol 52:5 [18:2]), and glycerophosphoethanolamine-enriched latent variable mediated the association between serum deltamethrin levels and T2D risk, with the mediated proportions being 44.81%, 15.92%, 16.85%, 16.66%, and 22.86%, respectively. Serum pyrethroids, particularly deltamethrin, may lead to an altered circulating lipid profile primarily in the glycerophospholipid metabolism pathway represented by PCs and lysophosphatidylcholines, potentially mediating the association between serum deltamethrin and T2D. The study provides a new perspective in elucidating the potential mechanisms through which pyrethroid exposure might induce T2D.

Evaluation of Lipid Extraction Protocols for Untargeted Analysis of Mouse Tissue Lipidome

Lipidomics refers to the full characterization of lipids present within a cell, tissue, organism, or biological system. One of the bottlenecks affecting reliable lipidomic analysis is the extraction of lipids from biological samples. An ideal extraction method should have a maximum lipid recovery and the ability to extract a broad range of lipid classes with acceptable reproducibility. The most common lipid extraction relies on either protein precipitation (monophasic methods) or liquid-liquid partitioning (bi- or triphasic methods). In this study, three monophasic extraction systems, isopropanol (IPA), MeOH/MTBE/CHCl3 (MMC), and EtOAc/EtOH (EE), alongside three biphasic extraction methods, Folch, butanol/MeOH/heptane/EtOAc (BUME), and MeOH/MTBE (MTBE), were evaluated for their performance in characterization of the mouse lipidome of six different tissue types, including pancreas, spleen, liver, brain, small intestine, and plasma. Sixteen lipid classes were investigated in this study using reversed-phase liquid chromatography/mass spectrometry. Results showed that all extraction methods had comparable recoveries for all tested lipid classes except lysophosphatidylcholines, lysophosphatidylethanolamines, acyl carnitines, sphingomyelines, and sphingosines. The recoveries of these classes were significantly lower with the MTBE method, which could be compensated by the addition of stable isotope-labeled internal standards prior to lipid extraction. Moreover, IPA and EE methods showed poor reproducibility in extracting lipids from most tested tissues. In general, Folch is the optimum method in terms of efficacy and reproducibility for extracting mouse pancreas, spleen, brain, and plasma. However, MMC and BUME methods are more favored when extracting mouse liver or intestine.

Dietary Quality and Circulating Lipidomic Profiles in 2 Cohorts of Middle-Aged and Older Male Finnish Smokers and American Populations

BACKGROUND

Higher dietary quality is associated with lower disease risks and has not been examined extensively with lipidomic profiles.

OBJECTIVES

Our goal was to examine associations of the Healthy Eating Index (HEI)-2015, Alternate HEI-2010 (AHEI-2010), and alternate Mediterranean Diet Index (aMED) diet quality indices with serum lipidomic profiles.

METHODS

We conducted a cross-sectional analysis of HEI-2015, AHEI-2010, and aMED with lipidomic profiles from 2 nested case-control studies within the Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial (n = 627) and the Alpha-Tocopherol, Beta-Carotene Cancer Prevention Study (n = 711). We used multivariable linear regression to determine associations of the indices, derived from baseline food-frequency questionnaires (Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial: 1993-2001, Alpha-Tocopherol, Beta-Carotene Cancer Prevention Study: 1985-1988) with serum concentrations of 904 lipid species and 252 fatty acids (FAs) across 15 lipid classes and 28 total FAs, within each cohort and meta-analyzed results using fixed-effect models for lipids significant at Bonferroni-corrected threshold in common in both cohorts.

RESULTS

Adherence to HEI-2015, AHEI-2010, or aMED was associated positively with 31, 41, and 54 lipid species and 8, 6, and 10 class-specific FAs and inversely with 2, 8, and 34 lipid species and 1, 3, and 5 class-specific FAs, respectively. Twenty-five lipid species and 5 class-specific FAs were common to all indices, predominantly triacylglycerols, FA22:6 [docosahexaenoic acid (DHA)]-containing species, and DHA. All indices were positively associated with total FA22:6. AHEI-2010 and aMED were inversely associated with total FA18:1 (oleic acid) and total FA17:0 (margaric acid), respectively. The identified lipids were most associated with components of seafood and plant proteins and unsaturated:saturated fat ratio in HEI-2015; eicosapentaenoic acid plus DHA in AHEI-2010; and fish and monounsaturated:saturated fat ratio in aMED.

CONCLUSIONS

Adherence to HEI-2015, AHEI-2010, and aMED is associated with serum lipidomic profiles, mostly triacylglycerols or FA22:6-containing species, which are related to seafood and plant proteins, eicosapentaenoic acid-DHA, fish, or fat ratio index components.

Longitudinal Lipidomic Profile of Hypertension in American Indians: Findings From the Strong Heart Family Study

BACKGROUND

Dyslipidemia is an important risk factor for hypertension and cardiovascular disease. Standard lipid panel cannot reflect the complexity of blood lipidome. The associations of individual lipid species with hypertension remain to be determined in large-scale epidemiological studies, especially in a longitudinal setting.

METHODS

Using liquid chromatography-mass spectrometry, we repeatedly measured 1542 lipid species in 3699 fasting plasma samples at 2 visits (1905 at baseline, 1794 at follow-up, ~5.5 years apart) from 1905 unique American Indians in the Strong Heart Family Study. We first identified baseline lipids associated with prevalent and incident hypertension, followed by replication of top hits in Europeans. We then conducted repeated measurement analysis to examine the associations of changes in lipid species with changes in systolic blood pressure, diastolic blood pressure, and mean arterial pressure. Network analysis was performed to identify lipid networks associated with the risk of hypertension.

RESULTS

Baseline levels of multiple lipid species, for example, glycerophospholipids, cholesterol esters, sphingomyelins, glycerolipids, and fatty acids, were significantly associated with both prevalent and incident hypertension in American Indians. Some lipids were confirmed in Europeans. Longitudinal changes in multiple lipid species, for example, acylcarnitines, phosphatidylcholines, fatty acids, and triacylglycerols, were significantly associated with changes in blood pressure measurements. Network analysis identified distinct lipidomic patterns associated with the risk of hypertension.

CONCLUSIONS

Baseline plasma lipid species and their longitudinal changes are significantly associated with hypertension development in American Indians. Our findings shed light on the role of dyslipidemia in hypertension and may offer potential opportunities for risk stratification and early prediction of hypertension.

Identification of biomarkers for glycaemic deterioration in type 2 diabetes

We identify biomarkers for disease progression in three type 2 diabetes cohorts encompassing 2,973 individuals across three molecular classes, metabolites, lipids and proteins. Homocitrulline, isoleucine and 2-aminoadipic acid, eight triacylglycerol species, and lowered sphingomyelin 42:2;2 levels are predictive of faster progression towards insulin requirement. Of ~1,300 proteins examined in two cohorts, levels of GDF15/MIC-1, IL-18Ra, CRELD1, NogoR, FAS, and ENPP7 are associated with faster progression, whilst SMAC/DIABLO, SPOCK1 and HEMK2 predict lower progression rates. In an external replication, proteins and lipids are associated with diabetes incidence and prevalence. NogoR/RTN4R injection improved glucose tolerance in high fat-fed male mice but impaired it in male db/db mice. High NogoR levels led to islet cell apoptosis, and IL-18R antagonised inflammatory IL-18 signalling towards nuclear factor kappa-B in vitro. This comprehensive, multi-disciplinary approach thus identifies biomarkers with potential prognostic utility, provides evidence for possible disease mechanisms, and identifies potential therapeutic avenues to slow diabetes progression.

Metabolic signature of healthy lifestyle and risk of rheumatoid arthritis: observational and Mendelian randomization study

BACKGROUND

While substantial evidence reveals that healthy lifestyle behaviors are associated with a lower risk of rheumatoid arthritis (RA), the underlying metabolic mechanisms remain unclear.

OBJECTIVES

This study aimed to identify the metabolic signature reflecting a healthy lifestyle and investigate its observational and genetic linkage with RA risk.

METHODS

This study included 87,258 UK Biobank participants (557 cases of incident RA) aged 37 to 73 years with complete lifestyle, genotyping and nuclear magnetic resonance (NMR) metabolomics data. A healthy lifestyle was assessed based on five factors: healthy diet, regular exercise, not smoking, moderate alcohol consumption, and normal body mass index. The metabolic signature was developed by summing selected metabolites' concentrations weighted by the coefficients using elastic net regression. We used multivariate Cox model to assess the associations between metabolic signatures and RA risk, and examined the mediating role of the metabolic signature in the impact of a healthy lifestyle on RA. We performed genome-wide association analysis (GWAS) to obtain genetic variants associated with the metabolic signature, then conducted Mendelian randomization (MR) analyses to detect causality.

RESULTS

The metabolic signature comprised of 81 metabolites, robustly correlated with healthy lifestyle ( r = 0.45, P = 4.2 × 10^-15). The metabolic signature was inversely associated with RA risk (HR per SD increment: 0.76, 95% CI: 0.70-0.83), and largely explained protective effects of healthy lifestyle on RA with 64% (95%CI: 50.4-83.3) mediation proportion. One and two-sample MR analyses also consistently showed the associations of genetically inferred per SD increment in metabolic signature with a reduction in RA risk (HR: 0.84, 95% CI: 0.75-0.94, P = 0.002 and OR: 0.84, 95% CI: 0.73-0.97, P = 0.02 respectively).

CONCLUSION

Our findings implicate the metabolic signature reflecting healthy lifestyle as a potential causal mediator in the development of RA, highlighting the importance of early lifestyle intervention and metabolic tracking for precise prevention of RA.

Specific triacylglycerol, diacylglycerol, and lyso-phosphatidylcholine species for the prediction of type 2 diabetes: a ~ 16-year prospective study in Chinese

Background

Bioactive lipids play an important role in insulin secretion and sensitivity, contributing to the pathophysiology of type 2 diabetes (T2D). This study aimed to identify novel lipid species associated with incident T2D in a nested case–control study within a long-term prospective Chinese community-based cohort with a median follow-up of ~ 16 years.

Methods

Plasma samples from 196 incident T2D cases and 196 age- and sex-matched non-T2D controls recruited from the Hong Kong Cardiovascular Risk Factor Prevalence Study (CRISPS) were first analyzed using untargeted lipidomics. Potential predictive lipid species selected by the Boruta analysis were then verified by targeted lipidomics. The associations between these lipid species and incident T2D were assessed. Effects of novel lipid species on insulin secretion in mouse islets were investigated.

Results

Boruta analysis identified 16 potential lipid species. After adjustment for body mass index (BMI), triacylglycerol/high-density lipoprotein (TG/HDL) ratio and the presence of prediabetes, triacylglycerol (TG) 12:0_18:2_22:6, TG 16:0_11:1_18:2, TG 49:0, TG 51:1 and diacylglycerol (DG) 18:2_22:6 were independently associated with increased T2D risk, whereas lyso-phosphatidylcholine (LPC) O-16:0, LPC P-16:0, LPC O-18:0 and LPC 18:1 were independently associated with decreased T2D risk. Addition of the identified lipid species to the clinical prediction model, comprised of BMI, TG/HDL ratio and the presence of prediabetes, achieved a 3.8% improvement in the area under the receiver operating characteristics curve (AUROC) (p = 0.0026). Further functional study revealed that, LPC O-16:0 and LPC O-18:0 significantly potentiated glucose induced insulin secretion (GSIS) in a dose-dependent manner, whereas neither DG 18:2_22:6 nor TG 12:0_18:2_22:6 had any effect on GSIS.

Conclusions

Addition of the lipid species substantially improved the prediction of T2D beyond the model based on clinical risk factors. Decreased levels of LPC O-16:0 and LPC O-18:0 may contribute to the development of T2D via reduced insulin secretion.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12933-022-01677-4.

The association between plasma metabolites and future risk of all-cause mortality

BACKGROUND

Metabolite profiles provide snapshots of the overall effect of numerous exposures accumulated over life courses, which may lead to health outcomes in the future.

OBJECTIVE

We hypothesized that the risk of all-cause mortality is linked to alterations in metabolism earlier in life, which are reflected in plasma metabolite profiles. We aimed to identify plasma metabolites associated with future risk of all-cause mortality.

METHODS

Through metabolomics, 110 metabolites were measured in 3833 individuals from the Malmö Diet and Cancer-Cardiovascular Cohort (MDC-CC). A total of 1574 deaths occurred within an average follow-up time of 22.2 years. Metabolites that were significantly associated with all-cause mortality in MDC-CC were replicated in 1500 individuals from Malmö Preventive Project re-examination (MPP), among whom 715 deaths occurred within an average follow-up time of 11.3 years.

RESULTS

Twenty two metabolites were significantly associated with all-cause mortality in MDC-CC, of which 13 were replicated in MPP. Levels of trigonelline, glutamate, dimethylglycine, C18-1-carnitine, C16-1-carnitine, C14-1-carnitine, and 1-methyladenosine were associated with an increased risk, while levels of valine, tryptophan, lysine, leucine, histidine, and 2-aminoisobutyrate were associated with a decreased risk of all-cause mortality.

CONCLUSION

We used metabolomics in two Swedish prospective cohorts and identified replicable associations between 13 metabolites and future risk of all-cause mortality. Novel associations between five metabolites-C18-1-carnitine, C16-1-carnitine, C14-1-carnitine, trigonelline, and 2-aminoisobutyrate-and all-cause mortality were discovered. These findings suggest potential new biomarkers for the prediction of mortality and provide insights for understanding the biochemical pathways that lead to mortality.

Sources of Variability in Serum Lipidomic Measurements and Implications for Epidemiologic Studies

Epidemiological studies using lipidomic approaches can identify lipids associated with exposures and diseases. We evaluated the sources of variability of lipidomic profiles measured in blood samples and the implications when designing epidemiologic studies. We measured 918 lipid species in nonfasting baseline serum from 693 participants in the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial, with 570 participants having serial blood samples separated by 1-5 years and 72 blinded replicate quality control samples. Blood samples were collected during 1993-2006. For each lipid species, we calculated the between-individual, within-individual, and technical variances, and we estimated the statistical power to detect associations in case-control studies. The technical variability was moderate, with a median intraclass correlation coefficient of 0.79. The combination of technical and within-individual variances accounted for most of the variability in 74% of the lipid species. For an average true relative risk of 3 (comparing upper and lower quartiles) after correction for multiple comparisons at the Bonferroni significance threshold (α = 0.05/918 = 5.45 ×10-5), we estimated that a study with 500, 1,000, and 5,000 total participants (1:1 case-control ratio) would have 19%, 57%, and 99% power, respectively. Epidemiologic studies examining associations between lipidomic profiles and disease require large samples sizes to detect moderate effect sizes associations.

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.

The emerging role of lipidomics in prediction of diseases

A recent paper published in PLoS Biology reported the application of lipidomics in predicting the incidence of diabetes and cardiovascular diseases in a population cohort. The study is clearly remarkable in demonstrating the role of lipidomics in prediction of diseases and translational research. We believe it comes to an era with quantitative lipidomics.

Serum Metabolomics of Incident Diabetes and Glycemic Changes in a Population With High Diabetes Burden: The Hispanic Community Health Study/Study of Latinos

Metabolomic signatures of incident diabetes remain largely unclear for the U.S. Hispanic/Latino population, a group with high diabetes burden. We evaluated the associations of 624 known serum metabolites (measured by a global, untargeted approach) with incident diabetes in a subsample (n = 2,010) of the Hispanic Community Health Study/Study of Latinos without diabetes and cardiovascular disease at baseline (2008-2011). Based on the significant metabolites associated with incident diabetes, metabolite modules were detected using topological network analysis, and their associations with incident diabetes and longitudinal changes in cardiometabolic traits were further examined. There were 224 incident cases of diabetes after an average 6 years of follow-up. After adjustment for sociodemographic, behavioral, and clinical factors, 134 metabolites were associated with incident diabetes (false discovery rate-adjusted P < 0.05). We identified 10 metabolite modules, including modules comprising previously reported diabetes-related metabolites (e.g., sphingolipids, phospholipids, branched-chain and aromatic amino acids, glycine), and 2 reflecting potentially novel metabolite groups (e.g., threonate, N-methylproline, oxalate, and tartarate in a plant food metabolite module and androstenediol sulfates in an androgenic steroid metabolite module). The plant food metabolite module and its components were associated with higher diet quality (especially higher intakes of healthy plant-based foods), lower risk of diabetes, and favorable longitudinal changes in HOMA for insulin resistance. The androgenic steroid module and its component metabolites decreased with increasing age and were associated with a higher risk of diabetes and greater increases in 2-h glucose over time. We replicated the associations of both modules with incident diabetes in a U.S. cohort of non-Hispanic Black and White adults (n = 1,754). Among U.S. Hispanic/Latino adults, we identified metabolites across various biological pathways, including those reflecting androgenic steroids and plant-derived foods, associated with incident diabetes and changes in glycemic traits, highlighting the importance of hormones and dietary intake in the pathogenesis of diabetes.

Metabolome-Defined Obesity and the Risk of Future Type 2 Diabetes and Mortality

OBJECTIVE

Obesity is a key risk factor for type 2 diabetes; however, up to 20% of patients are normal weight. Our aim was to identify metabolite patterns reproducibly predictive of BMI and subsequently to test whether lean individuals who carry an obese metabolome are at hidden high risk of obesity-related diseases, such as type 2 diabetes.

RESEARCH DESIGN AND METHODS

Levels of 108 metabolites were measured in plasma samples of 7,663 individuals from two Swedish and one Italian population-based cohort. Ridge regression was used to predict BMI using the metabolites. Individuals with a predicted BMI either >5 kg/m2 higher (overestimated) or lower (underestimated) than their actual BMI were characterized as outliers and further investigated for obesity-related risk factors and future risk of type 2 diabetes and mortality.

RESULTS

The metabolome could predict BMI in all cohorts (r2 = 0.48, 0.26, and 0.19). The overestimated group had a BMI similar to individuals correctly predicted as normal weight, had a similar waist circumference, were not more likely to change weight over time, but had a two times higher risk of future type 2 diabetes and an 80% increased risk of all-cause mortality. These associations remained after adjustments for obesity-related risk factors and lifestyle parameters.

CONCLUSIONS

We found that lean individuals with an obesity-related metabolome have an increased risk for type 2 diabetes and all-cause mortality compared with lean individuals with a healthy metabolome. Metabolomics may be used to identify hidden high-risk individuals to initiate lifestyle and pharmacological interventions.

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.

Lipidomic risk scores are independent of polygenic risk scores and can predict incidence of diabetes and cardiovascular disease in a large population cohort

Type 2 diabetes (T2D) and cardiovascular disease (CVD) represent significant disease burdens for most societies and susceptibility to these diseases is strongly influenced by diet and lifestyle. Physiological changes associated with T2D or CVD, such has high blood pressure and cholesterol and glucose levels in the blood, are often apparent prior to disease incidence. Here we integrated genetics, lipidomics, and standard clinical diagnostics to assess future T2D and CVD risk for 4,067 participants from a large prospective population-based cohort, the Malmö Diet and Cancer-Cardiovascular Cohort. By training Ridge regression-based machine learning models on the measurements obtained at baseline when the individuals were healthy, we computed several risk scores for T2D and CVD incidence during up to 23 years of follow-up. We used these scores to stratify the participants into risk groups and found that a lipidomics risk score based on the quantification of 184 plasma lipid concentrations resulted in a 168% and 84% increase of the incidence rate in the highest risk group and a 77% and 53% decrease of the incidence rate in lowest risk group for T2D and CVD, respectively, compared to the average case rates of 13.8% and 22.0%. Notably, lipidomic risk correlated only marginally with polygenic risk, indicating that the lipidome and genetic variants may constitute largely independent risk factors for T2D and CVD. Risk stratification was further improved by adding standard clinical variables to the model, resulting in a case rate of 51.0% and 53.3% in the highest risk group for T2D and CVD, respectively. The participants in the highest risk group showed significantly altered lipidome compositions affecting 167 and 157 lipid species for T2D and CVD, respectively. Our results demonstrated that a subset of individuals at high risk for developing T2D or CVD can be identified years before disease incidence. The lipidomic risk, which is derived from only one single mass spectrometric measurement that is cheap and fast, is informative and could extend traditional risk assessment based on clinical assays.

Associations of plasma glycerophospholipid profile with modifiable lifestyles and incident diabetes in middle-aged and older Chinese

AIMS/HYPOTHESIS

Glycerophospholipid (GPL) perturbance was linked to the pathogenesis of diabetes in animal studies but prospective studies in humans are rare, particularly in Asians. We aimed to investigate the associations between plasma GPLs and incident diabetes and to explore effects of lifestyle on the associations in a Chinese population.

METHODS

The study included 1877 community-dwelling Chinese individuals aged 50-70 years (751 men and 1126 women), free of diabetes at baseline and followed for 6 years. A total of 160 GPL species were quantified in plasma at baseline by using high-throughput targeted lipidomics. Log-Poisson regression was used to assess the associations between GPLs and incidence of diabetes.

RESULTS

Over the 6 years of follow-up, 499 participants (26.6%) developed diabetes. After multivariable adjustment, eight GPLs were positively associated with incident diabetes (RR_{per SD} 1.13-1.25; all false-discovery rate [FDR]-corrected p < 0.05), including five novel GLPs, namely phosphatidylcholines (PCs; 16:0/18:1, 18:0/16:1, 18:1/20:3), lysophosphatidylcholine (LPC; 20:3) and phosphatidylethanolamine (PE; 16:0/16:1), and three reported GPLs (PCs 16:0/16:1, 16:0/20:3 and 18:0/20:3). In network analysis, a PC-containing module was positively associated with incident diabetes (RR_{per SD} 1.16 [95% CI 1.06, 1.26]; FDR-corrected p < 0.05). Notably, three of the diabetes-associated PCs (16:0/16:1, 16:0/18:1 and 18:0/16:1) and PE (16:0/16:1) were associated not only with fatty acids in the de novo lipogenesis (DNL) pathway, especially 16:1n-7 (Spearman correlation coefficients = 0.35-0.62, p < 0.001), but also with an unhealthy dietary pattern high in refined grains and low in fish, dairy and soy products (|factor loadings| ≥0.2). When stratified by physical activity levels, the associations of the eight GPLs and the PC module with incident diabetes were stronger in participants with lower physical activity (RR_{per SD} 1.24-1.49, FDR-corrected p < 0.05) than in those with the median and higher physical activity levels (RR_{per SD} 1.03-1.12, FDR-corrected p ≥ 0.05; FDR-corrected p_interaction < 0.05).

CONCLUSIONS/INTERPRETATION

Eight GPLs, especially PCs associated with the DNL pathway, were positively associated with incident diabetes in a cohort of Chinese men and women. The associations were most prominent in participants with a low level of physical activity.

Effects of a 12-week whole-grain or refined wheat intervention on plasma acylcarnitines, bile acids and signaling lipids, and association with liver fat: A post-hoc metabolomics study of a randomized controlled trial

BACKGROUND

We previously showed that whole-grain wheat (WGW) consumption had beneficial effects on liver fat accumulation, as compared to refined wheat (RW). The mechanisms underlying these effects remain unclear.

OBJECTIVE

In this study, we investigated the effects of WGW vs. RW consumption on plasma metabolite levels to explore potential underlying mechanisms of the preventive effect of WGW consumption on liver fat accumulation.

METHODS

Targeted metabolomics of plasma obtained from a concluded 12-week double-blind, randomized controlled trial was performed. Fifty overweight or obese men and women aged 45-70 years with mildly elevated levels of plasma cholesterol were randomized to either 98 g/d of WGW or RW products. Before and after the intervention, a total of 89 fasting plasma metabolite concentrations including acylcarnitines, trimethylamine-N-oxide (TMAO), choline, betaine, bile acids, and signaling lipids were quantified by UPLC-MS/MS. Intrahepatic triglycerides (IHTG) were quantified by ¹H-MRS, and multiple liver markers, including circulating levels of β-hydroxybutyrate, alanine transaminase (ALT), aspartate transaminase (AST), γ-glutamyltransferase (γ-GT), serum amyloid A (SAA), and C-reactive protein, were assessed.

RESULTS

The WGW intervention increased plasma concentrations of four out of 52 signaling lipids-lysophosphatidic acid C18:2, lysophosphatidylethanolamine C18:1 and C18:2, and platelet-activating factor C18:2-and decreased concentrations of the signaling lipid lysophosphatidylglycerol C20:3 as compared to RW intervention, although these results were no longer statistically significant after false discovery rate (FDR) correction. Plasma concentrations of the other metabolites that we quantified were not affected by WGW or RW intervention. Changes in the above-mentioned metabolites were not correlated to change in IHTG upon the intervention.

CONCLUSION

Plasma acylcarnitines, bile acids, and signaling lipids were not robustly affected by the WGW or RW interventions, which makes them less likely candidates to be directly involved in the mechanisms that underlie the protective effect of WGW consumption or detrimental effect of RW consumption on liver fat accumulation.

CLINICAL TRIAL REGISTRATION

[www.ClinicalTrials.gov], identifier [NCT02385149].

The Role of Changes in Cumulative Lipid Parameter Burden in the Pathogenesis of Type 2 Diabetes Mellitus: A Cohort Study of People Aged 35-65 Years in Rural China

PURPOSE

The main purpose of this study was to examine the effect of the cumulative exposure of blood lipid parameters on type 2 diabetes mellitus (T2DM). Another purpose was to explore whether the cumulative burden of blood lipid parameters plays a certain role in the pathogenesis of diet affecting T2DM.

PATIENTS AND METHODS

A total of 63 cases of diabetes occurred from 2017 to 2020, with an incidence density of 3.71 person-years. The dietary intake of the residents was obtained by using a dietary frequency questionnaire (FFQ). Cumulative lipid parameter burden was calculated according to the number of years (2016-2020) multiplied by total cholesterol (TC), high density lipoprotein (HDL), low density lipoprotein (LDL) and triglyceride (TG). A Cox proportional hazard model was used to estimate the effect of cumulative lipid burden on T2DM. A mediating analysis of accelerated failure time (AFT) was used to investigate the mediating effects of certain foods, the cumulative lipid parameter burden and T2DM.

RESULTS

A higher cumulative TG load corresponded to a higher risk of T2DM onset (P_trend =0.021). After adjusting for covariates, the highest quartile cumulative TG burden had a 3.462 times higher risk of T2DM than that in the lowest quartile (HR=3.462, 95% CI: 1.297-9.243). Moreover, a higher cumulative HDL load corresponded to a lower risk of T2DM onset (P_trend =0.006). After adjusting for covariates, the risk of T2DM was 0.314-fold lower in the highest quartile of cumulative HDL burden than that in the lowest quartile (HR=0.314, 95% CI: 0.131-0.753). Cumulative TG burden partially mediated the association between red meat and T2DM.

CONCLUSION

The increase in cumulative HDL burden and the decrease in cumulative HDL burden are related to the incidence of T2DM. Cumulative TG burden was shown to play a partial mediating role in the pathogenesis of red meat and diabetes.

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.

Mouse lipidomics reveals inherent flexibility of a mammalian lipidome

Lipidomics has become an indispensable method for the quantitative assessment of lipid metabolism in basic, clinical, and pharmaceutical research. It allows for the generation of information-dense datasets in a large variety of experimental setups and model organisms. Previous studies, mostly conducted in mice (Mus musculus), have shown a remarkable specificity of the lipid compositions of different cell types, tissues, and organs. However, a systematic analysis of the overall variation of the mouse lipidome is lacking. To fill this gap, in the present study, the effect of diet, sex, and genotype on the lipidomes of mouse tissues, organs, and bodily fluids has been investigated. Baseline quantitative lipidomes consisting of 796 individual lipid molecules belonging to 24 lipid classes are provided for 10 different sample types. Furthermore, the susceptibility of lipidomes to the tested parameters is assessed, providing insights into the organ-specific lipidomic plasticity and flexibility. This dataset provides a valuable resource for basic and pharmaceutical researchers working with murine models and complements existing proteomic and transcriptomic datasets. It will inform experimental design and facilitate interpretation of lipidomic datasets.

LipidSig: a web-based tool for lipidomic data analysis

With the continuing rise of lipidomic studies, there is an urgent need for a useful and comprehensive tool to facilitate lipidomic data analysis. The most important features making lipids different from general metabolites are their various characteristics, including their lipid classes, double bonds, chain lengths, etc. Based on these characteristics, lipid species can be classified into different categories and, more interestingly, exert specific biological functions in a group. In an effort to simplify lipidomic analysis workflows and enhance the exploration of lipid characteristics, we have developed a highly flexible and user-friendly web server called LipidSig. It consists of five sections, namely, Profiling, Differential Expression, Correlation, Network and Machine Learning, and evaluates lipid effects on cellular or disease phenotypes. One of the specialties of LipidSig is the conversion between lipid species and characteristics according to a user-defined characteristics table. This function allows for efficient data mining for both individual lipids and subgroups of characteristics. To expand the server's practical utility, we also provide analyses focusing on fatty acid properties and multiple characteristics. In summary, LipidSig is expected to help users identify significant lipid-related features and to advance the field of lipid biology. The LipidSig webserver is freely available at http://chenglab.cmu.edu.tw/lipidsig

Circulating Glycerolipids, Fatty Liver Index, and Incidence of Type 2 Diabetes: A Prospective Study Among Chinese

CONTEXT

Few lipidomic studies have specifically investigated the association of circulating glycerolipids and type 2 diabetes (T2D) risk, especially among Asian populations. It remains unknown whether or to what degree fatty liver could explain the associations between glycerolipids and T2D.

OBJECTIVE

We aimed to assess associations between plasma glycerolipids and incident T2D and to explore a potential role of liver fat accumulation in the associations.

METHODS

This was a prospective cohort study with 6 years of follow-up. The study population included 1781 Chinese participants aged 50 to 70 years. The main outcome measure was incident T2D.

RESULTS

At the 6-year resurvey, 463 participants had developed T2D. At the false discovery rate (FDR) of 5%, 43 of 104 glycerolipids were significantly associated with incident T2D risk after multivariate adjustment for conventional risk factors. After further controlling for glycated hemoglobin (HbA1c), 9 of the 43 glycerolipids remained significant, including 2 diacylglycerols (DAGs) (16:1/20:4, 18:2/20:5) and 7 triacylglycerols (TAGs) (46:1, 48:0, 48:1, 50:0, 50:1, 50:2, and 52:2), with relative risks (RRs) (95% CIs) ranging from 1.16 (1.05-1.27) to 1.23 (1.11-1.36) per SD increment of glycerolipids. However, additional adjustment for fatty liver index largely attenuated these findings (RR [95% CI] 0.88 [0.81 to 0.95] to 1.10 [1.01 to 1.21]). Mediation analyses suggested that the fatty liver index explained 12% to 28% of the glycerolipids-T2D associations (all P < 0.01).

CONCLUSION

Higher plasma levels of DAGs and TAGs were associated with increased incident T2D risk in this Chinese population, which might be partially explained by liver fat accumulation.

A Panel of 6 Biomarkers Significantly Improves the Prediction of Type 2 Diabetes in the MONICA/KORA Study Population

CONTEXT

Improved strategies to identify persons at high risk of type 2 diabetes are important to target costly preventive efforts to those who will benefit most.

OBJECTIVE

This work aimed to assess whether novel biomarkers improve the prediction of type 2 diabetes beyond noninvasive standard clinical risk factors alone or in combination with glycated hemoglobin A1c (HbA1c).

METHODS

We used a population-based case-cohort study for discovery (689 incident cases and 1850 noncases) and an independent cohort study (262 incident cases, 2549 noncases) for validation. An L1-penalized (lasso) Cox model was used to select the most predictive set among 47 serum biomarkers from multiple etiological pathways. All variables available from the noninvasive German Diabetes Risk Score (GDRSadapted) were forced into the models. The C index and the category-free net reclassification index (cfNRI) were used to evaluate the predictive performance of the selected biomarkers beyond the GDRSadapted model (plus HbA1c).

RESULTS

Interleukin-1 receptor antagonist, insulin-like growth factor binding protein 2, soluble E-selectin, decorin, adiponectin, and high-density lipoprotein cholesterol were selected as the most relevant biomarkers. The simultaneous addition of these 6 biomarkers significantly improved the predictive performance both in the discovery (C index [95% CI], 0.053 [0.039-0.066]; cfNRI [95% CI], 67.4% [57.3%-79.5%]) and the validation study (0.034 [0.019-0.053]; 48.4% [35.6%-60.8%]). Significant improvements by these biomarkers were also seen on top of the GDRSadapted model plus HbA1c in both studies.

CONCLUSION

The addition of 6 biomarkers significantly improved the prediction of type 2 diabetes when added to a noninvasive clinical model or to a clinical model plus HbA1c.

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.

A plasma lipid signature predicts incident coronary artery disease

BACKGROUND

Dyslipidemia is a hallmark of cardiovascular disease but is characterized by crude measurements of triglycerides, HDL- and LDL cholesterol. Lipidomics enables more detailed measurements of plasma lipids, which may help improve risk stratification and understand the pathophysiology of cardiovascular disease.

METHODS

Lipidomics was used to measure 184 lipids in plasma samples from the Malmö Diet and Cancer - Cardiovascular Cohort (N = 3865), taken at baseline examination. During an average follow-up time of 20.3 years, 536 participants developed coronary artery disease (CAD). Least absolute shrinkage and selection operator (LASSO) were applied to Cox proportional hazards models in order to identify plasma lipids that predict CAD.

RESULTS

Eight plasma lipids improved prediction of future CAD on top of traditional cardiovascular risk factors. Principal component analysis of CAD-associated lipids revealed one principal component (PC2) that was associated with risk of future CAD (HR per SD increment =1.46, C·I = 1.35-1.48, P < 0.001). The risk increase for being in the highest quartile of PC2 (HR = 2.33, P < 0.001) was higher than being in the top quartile of systolic blood pressure. Addition of PC2 to traditional risk factors achieved an improvement (2%) in the area under the ROC-curve for CAD events occurring within 10 (P = 0.03), 15 (P = 0.003) and 20 (P = 0.001) years of follow-up respectively.

CONCLUSIONS

A lipid pattern improve CAD prediction above traditional risk factors, highlighting that conventional lipid-measures insufficiently describe dyslipidemia that is present years before CAD. Identifying this hidden dyslipidemia may help motivate lifestyle and pharmacological interventions early enough to reach a substantial reduction in absolute risk.