| 1. |
- Bowden, John A., et al.
(författare)
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Harmonizing lipidomics : NIST interlaboratory comparison exercise for lipidomics using SRM 1950-Metabolites in Frozen Human Plasma
- 2017
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Ingår i: Journal of Lipid Research. - 0022-2275 .- 1539-7262. ; 58:12, s. 2275-2288
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Tidskriftsartikel (refereegranskat)abstract
- As the lipidomics field continues to advance, self-evaluation within the community is critical. Here, we performed an interlaboratory comparison exercise for lipidomics using Standard Reference Material (SRM) 1950-Metabolites in Frozen Human Plasma, a commercially available reference material. The interlaboratory study comprised 31 diverse laboratories, with each laboratory using a different lipidomics workflow. A total of 1,527 unique lipids were measured across all laboratories and consensus location estimates and associated uncertainties were determined for 339 of these lipids measured at the sum composition level by five or more participating laboratories. These evaluated lipids detected in SRM 1950 serve as community-wide benchmarks for intra-and interlaboratory quality control and method validation. These analyses were performed using nonstandardized laboratory-independent workflows. The consensus locations were also compared with a previous examination of SRM 1950 by the LIPID MAPS consortium.jlr While the central theme of the interlaboratory study was to provide values to help harmonize lipids, lipid mediators, and precursor measurements across the community, it was also initiated to stimulate a discussion regarding areas in need of improvement.
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| 2. |
- Fernandez, Céline, et al.
(författare)
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Plasma Lipidome and Prediction of Type 2 Diabetes in the Population-Based Malmö Diet and Cancer Cohort
- 2020
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Ingår i: Diabetes Care. - : American Diabetes Association. - 1935-5548 .- 0149-5992. ; 43:2, s. 366-373
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Tidskriftsartikel (refereegranskat)abstract
- 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-PCDM), and assessed its association with T2DM incidence using Cox regression and lifestyle factors using general linear models. RESULTS: A T2DM-predictive lipid-PCDM 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-PCDM 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-PCDM to all conventional T2DM risk factors increased the area under the receiver operating characteristics curve by 3%. The lipid-PCDM 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.
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| 3. |
- Kammer, Michael, et al.
(författare)
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Integrative analysis of prognostic biomarkers derived from multiomics panels helps discrimination of chronic kidney disease trajectories in people with type 2 diabetes
- 2019
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Ingår i: Kidney International. - : Elsevier BV. - 0085-2538. ; 96:6, s. 1381-1388
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Tidskriftsartikel (refereegranskat)abstract
- Clinical risk factors explain only a fraction of the variability of estimated glomerular filtration rate (eGFR) decline in people with type 2 diabetes. Cross-omics technologies by virtue of a wide spectrum screening of plasma samples have the potential to identify biomarkers for the refinement of prognosis in addition to clinical variables. Here we utilized proteomics, metabolomics and lipidomics panel assay measurements in baseline plasma samples from the multinational PROVALID study (PROspective cohort study in patients with type 2 diabetes mellitus for VALIDation of biomarkers) of patients with incident or early chronic kidney disease (median follow-up 35 months, median baseline eGFR 84 mL/min/1.73 m2, urine albumin-to-creatinine ratio 8.1 mg/g). In an accelerated case-control study, 258 individuals with a stable eGFR course (median eGFR change 0.1 mL/min/year) were compared to 223 individuals with a rapid eGFR decline (median eGFR decline -6.75 mL/min/year) using Bayesian multivariable logistic regression models to assess the discrimination of eGFR trajectories. The analysis included 402 candidate predictors and showed two protein markers (KIM-1, NTproBNP) to be relevant predictors of the eGFR trajectory with baseline eGFR being an important clinical covariate. The inclusion of metabolomic and lipidomic platforms did not improve discrimination substantially. Predictions using all available variables were statistically indistinguishable from predictions using only KIM-1 and baseline eGFR (area under the receiver operating characteristic curve 0.63). Thus, the discrimination of eGFR trajectories in patients with incident or early diabetic kidney disease and maintained baseline eGFR was modest and the protein marker KIM-1 was the most important predictor.
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| 4. |
- Kjellqvist, Sanela, et al.
(författare)
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Identification of Shared and Unique Serum Lipid Profiles in Diabetes Mellitus and Myocardial Infarction
- 2016
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Ingår i: Journal of the American Heart Association. - 2047-9980. ; 5:12
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Tidskriftsartikel (refereegranskat)abstract
- Background-Diabetes mellitus (DM) and cardiovascular disease are associated with dyslipidemia, but the detailed lipid molecular pattern in both diseases remains unknown. Methods and Results-We used shotgun mass spectrometry to determine serum levels of 255 molecular lipids in 316 controls, 171 DM, and 99 myocardial infarction (MI) events from a cohort derived from the Malmo Diet and Cancer study. Orthogonal projections to latent structures analyses were conducted between the lipids and clinical parameters describing DM or MI. Fatty acid desaturases (FADS) and elongation of very long chain fatty acid protein 5 (ELOVL5) activities were estimated by calculating product to precursor ratios of polyunsaturated fatty acids in complex lipids. FADS genotypes encoding these desaturases were then tested for association with lipid levels and ratios. Differences in the levels of lipids belonging to the phosphatidylcholine and triacylglyceride (TAG) classes contributed the most to separating DM from controls. TAGs also played a dominating role in discriminating MI from controls. Levels of C18:2 fatty acids in complex lipids were lower both in DM and MI versus controls (DM, P=0.004; MI, P=6.0E-06) at least due to an acceleration in the metabolic flux from C18: 2 to C20:4 (eg, increased estimated ELOVL5: DM, P=0.02; MI, P=0.04, and combined elongase-desaturase activities: DM, P=3.0E-06; MI, P=2.0E-06). Minor allele carriers of FADS genotypes were associated with increased levels of C18: 2 (P <= 0.007) and lower desaturase activity (P <= 0.002). Conclusions-We demonstrate a possible relationship between decreased levels of C18: 2 in complex lipids and DM or MI. We thereby highlight the importance of molecular lipids in the pathogenesis of both diseases.
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| 5. |
- Lauber, Chris, et al.
(författare)
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Lipidomic risk scores are independent of polygenic risk scores and can predict incidence of diabetes and cardiovascular disease in a large population cohort
- 2022
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Ingår i: PLoS Biology. - : Public Library of Science (PLoS). - 1544-9173 .- 1545-7885. ; 20:3
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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| 6. |
- Slieker, Roderick C, et al.
(författare)
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Distinct Molecular Signatures of Clinical Clusters in People with Type 2 Diabetes : an IMIRHAPSODY Study
- 2021
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Ingår i: Diabetes. - : American Diabetes Association. - 1939-327X .- 0012-1797. ; 70:11, s. 2683-2693
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Tidskriftsartikel (refereegranskat)abstract
- Type 2 diabetes is a multifactorial disease with multiple underlying aetiologies. To address this heterogeneity a previous study clustered people with diabetes into five diabetes subtypes. The aim of the current study is to investigate the aetiology of these clusters by comparing their molecular signatures. In three independent cohorts, in total 15,940 individuals were clustered based on five clinical characteristics. In a subset, genetic- (N=12828), metabolomic- (N=2945), lipidomic- (N=2593) and proteomic (N=1170) data were obtained in plasma. In each datatype each cluster was compared with the other four clusters as the reference. The insulin resistant cluster showed the most distinct molecular signature, with higher BCAAs, DAG and TAG levels and aberrant protein levels in plasma enriched for proteins in the intracellular PI3K/Akt pathway. The obese cluster showed higher cytokines. A subset of the mild diabetes cluster with high HDL showed the most beneficial molecular profile with opposite effects to those seen in the insulin resistant cluster. This study showed that clustering people with type 2 diabetes can identify underlying molecular mechanisms related to pancreatic islets, liver, and adipose tissue metabolism. This provides novel biological insights into the diverse aetiological processes that would not be evident when type 2 diabetes is viewed as a homogeneous disease.
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| 7. |
- Slieker, Roderick C, et al.
(författare)
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Identification of biomarkers for glycaemic deterioration in type 2 diabetes
- 2023
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Ingår i: Nature Communications. - 2041-1723. ; 14, s. 1-18
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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| 8. |
- Slieker, Roderick C, et al.
(författare)
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Replication and cross-validation of type 2 diabetes subtypes based on clinical variables : an IMI-RHAPSODY study
- 2021
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Ingår i: Diabetologia. - : Springer Science and Business Media LLC. - 0012-186X .- 1432-0428. ; 64:9, s. 1982-1989
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Tidskriftsartikel (refereegranskat)abstract
- Aims/hypothesis: Five clusters based on clinical characteristics have been suggested as diabetes subtypes: one autoimmune and four subtypes of type 2 diabetes. In the current study we replicate and cross-validate these type 2 diabetes clusters in three large cohorts using variables readily measured in the clinic. Methods: In three independent cohorts, in total 15,940 individuals were clustered based on age, BMI, HbA1c, random or fasting C-peptide, and HDL-cholesterol. Clusters were cross-validated against the original clusters based on HOMA measures. In addition, between cohorts, clusters were cross-validated by re-assigning people based on each cohort’s cluster centres. Finally, we compared the time to insulin requirement for each cluster. Results: Five distinct type 2 diabetes clusters were identified and mapped back to the original four All New Diabetics in Scania (ANDIS) clusters. Using C-peptide and HDL-cholesterol instead of HOMA2-B and HOMA2-IR, three of the clusters mapped with high sensitivity (80.6–90.7%) to the previously identified severe insulin-deficient diabetes (SIDD), severe insulin-resistant diabetes (SIRD) and mild obesity-related diabetes (MOD) clusters. The previously described ANDIS mild age-related diabetes (MARD) cluster could be mapped to the two milder groups in our study: one characterised by high HDL-cholesterol (mild diabetes with high HDL-cholesterol [MDH] cluster), and the other not having any extreme characteristic (mild diabetes [MD]). When these two milder groups were combined, they mapped well to the previously labelled MARD cluster (sensitivity 79.1%). In the cross-validation between cohorts, particularly the SIDD and MDH clusters cross-validated well, with sensitivities ranging from 73.3% to 97.1%. SIRD and MD showed a lower sensitivity, ranging from 36.1% to 92.3%, where individuals shifted from SIRD to MD and vice versa. People belonging to the SIDD cluster showed the fastest progression towards insulin requirement, while the MDH cluster showed the slowest progression. Conclusions/interpretation: Clusters based on C-peptide instead of HOMA2 measures resemble those based on HOMA2 measures, especially for SIDD, SIRD and MOD. By adding HDL-cholesterol, the MARD cluster based upon HOMA2 measures resulted in the current clustering into two clusters, with one cluster having high HDL levels. Cross-validation between cohorts showed generally a good resemblance between cohorts. Together, our results show that the clustering based on clinical variables readily measured in the clinic (age, HbA1c, HDL-cholesterol, BMI and C-peptide) results in informative clusters that are representative of the original ANDIS clusters and stable across cohorts. Adding HDL-cholesterol to the clustering resulted in the identification of a cluster with very slow glycaemic deterioration. Graphical abstract: [Figure not available: see fulltext.]
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