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- Beijer, Kristina, et al.
(författare)
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In search of causal pathways in diabetes : a study using proteomics and genotyping data from a cross-sectional study
- 2019
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Ingår i: Diabetologia. - : SPRINGER. - 0012-186X .- 1432-0428. ; 62:11, s. 1998-2006
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Tidskriftsartikel (refereegranskat)abstract
- Aims/hypothesis: The pathogenesis of type 2 diabetes is not fully understood. We investigated whether circulating levels of preselected proteins were associated with the outcome 'diabetes' and whether these associations were causal.Methods: In 2467 individuals of the population-based, cross-sectional EpiHealth study (45-75 years, 50% women), 249 plasma proteins were analysed by the proximity extension assay technique. DNA was genotyped using the Illumina HumanCoreExome-12 v1.0 BeadChip. Diabetes was defined as taking glucose-lowering treatment or having a fasting plasma glucose of >= 7.0 mmol/l. The associations between proteins and diabetes were assessed using logistic regression. To investigate causal relationships between proteins and diabetes, a bidirectional two-sample Mendelian randomisation was performed based on large, genome-wide association studies belonging to the DIAGRAM and MAGIC consortia, and a genome-wide association study in the EpiHealth study.Results: Twenty-six proteins were positively associated with diabetes, including cathepsin D, retinal dehydrogenase 1, alpha-l-iduronidase, hydroxyacid oxidase 1 and galectin-4 (top five findings). Three proteins, lipoprotein lipase, IGF-binding protein 2 and paraoxonase 3 (PON-3), were inversely associated with diabetes. Fourteen of the proteins are novel discoveries. The Mendelian randomisation study did not disclose any significant causal effects between the proteins and diabetes in either direction that were consistent with the relationships found between the protein levels and diabetes.Conclusions/interpretation: The 29 proteins associated with diabetes are involved in several physiological pathways, but given the power of the study no causal link was identified for those proteins tested in Mendelian randomisation. Therefore, the identified proteins are likely to be biomarkers for type 2 diabetes, rather than representing causal pathways.
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| 3. |
- Iggman, David, et al.
(författare)
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Adipose tissue fatty acids and insulin sensitivity in elderly men.
- 2010
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Ingår i: Diabetologia. - : Springer Science and Business Media LLC. - 0012-186X .- 1432-0428. ; 53:5, s. 850-857
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Tidskriftsartikel (refereegranskat)abstract
- AIMS/HYPOTHESIS Dietary fatty acids may affect insulin sensitivity. Adipose tissue fatty acid composition partly reflects long-term dietary intake, but data from large studies regarding relationships with insulin sensitivity are lacking. We aimed to determine the association between adipose tissue fatty acids and insulin sensitivity in elderly Swedish men. METHODS In a cross-sectional analysis of the community-based Uppsala Longitudinal Study of Adult Men (n = 795, mean age 71 years), adipose tissue biopsies were obtained and fatty acid composition was determined by gas-liquid chromatography. Insulin sensitivity was measured directly by a euglycaemic clamp. RESULTS Palmitic acid (16:0), the major saturated fatty acid (SFA) in the diet and in adipose tissue, was negatively correlated with insulin sensitivity (r = -0.14), as were 16:1 n-7 (r = -0.15), 20:3 n-6 (r = -0.31), 20:4 n-6 (r = -0.38), 22:4 n-6 (r = -0.37) and 22:5 n-3 (r = -0.24; p < 0.001 for all). Some minor SFAs were positively correlated; 12:0 (r = 0.46), 14:0 (r = 0.32), 17:0 (r = 0.21) and 18:0 (r = 0.41; p < 0.001 for all), as were essential polyunsaturated fatty acids (PUFAs) 18:2 n-6 (r = 0.10, p < 0.01) and 18:3 n-3 (r = 0.16, p < 0.001). Docosahexaenoic acid (22:6 n-3) was negatively correlated (r = -0.11, p < 0.01), whereas eicosapentaenoic acid (20:5 n-3) was not (r = -0.02, NS). Most associations diminished or disappeared in lean individuals, indicating an effect of obesity. CONCLUSIONS/INTERPRETATION Adipose tissue enriched with palmitic acid and depleted of essential PUFAs is associated with insulin resistance. The positive association between minor SFAs and insulin sensitivity merits further investigation.
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| 4. |
- Nowak, Christoph, et al.
(författare)
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Multiplex proteomics for prediction of major cardiovascular events in type 2 diabetes
- 2018
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Ingår i: Diabetologia. - : SPRINGER. - 0012-186X .- 1432-0428. ; 61:8, s. 1748-1757
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Tidskriftsartikel (refereegranskat)abstract
- Aims/hypothesis Multiplex proteomics could improve understanding and risk prediction of major adverse cardiovascular events (MACE) in type 2 diabetes. This study assessed 80 cardiovascular and inflammatory proteins for biomarker discovery and prediction of MACE in type 2 diabetes. Methods We combined data from six prospective epidemiological studies of 30-77-year-old individuals with type 2 diabetes in whom 80 circulating proteins were measured by proximity extension assay. Multivariable-adjusted Cox regression was used in a discovery/replication design to identify biomarkers for incident MACE. We used gradient-boosted machine learning and lasso regularised Cox regression in a random 75% training subsample to assess whether adding proteins to risk factors included in the Swedish National Diabetes Register risk model would improve the prediction of MACE in the separate 25% test subsample. Results Of 1211 adults with type 2 diabetes (32% women), 211 experienced a MACE over a mean (+/- SD) of 6.4 +/- 2.3 years. We replicated associations (< 5% false discovery rate) between risk of MACE and eight proteins: matrix metalloproteinase (MMP)-12, IL-27 subunit alpha (IL-27a), kidney injury molecule (KIM)-1, fibroblast growth factor (FGF)-23, protein S100-A12, TNF receptor (TNFR)-1, TNFR-2 and TNF-related apoptosis-inducing ligand receptor (TRAIL-R)2. Addition of the 80-protein assay to established risk factors improved discrimination in the separate test sample from 0.686 (95% CI 0.682, 0.689) to 0.748 (95% CI 0.746, 0.751). A sparse model of 20 added proteins achieved a C statistic of 0.747 (95% CI 0.653, 0.842) in the test sample. Conclusions/interpretation We identified eight protein biomarkers, four of which are novel, for risk of MACE in community residents with type 2 diabetes, and found improved risk prediction by combining multiplex proteomics with an established risk model. Multiprotein arrays could be useful in identifying individuals with type 2 diabetes who are at highest risk of a cardiovascular event.
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| 7. |
- Xu, Hong, et al.
(författare)
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Dietary acid load, insulin sensitivity and risk of type 2 diabetes in community-dwelling older men
- 2014
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Ingår i: Diabetologia. - : Springer Science and Business Media LLC. - 0012-186X .- 1432-0428. ; 57:8, s. 1561-1568
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Tidskriftsartikel (refereegranskat)abstract
- Aims/hypothesis We tested the hypothesis that dietary acid load may increase the risk of type 2 diabetes, and studied the association between acid load and insulin sensitivity as a possible mechanism involved. Methods An observational survey with prospective follow-up including 911 non-diabetic Swedish men aged 70-71 years was carried out. The gold standard euglycaemic-hyperinsulinaemic clamp technique and the OGTT were used to determine insulin sensitivity and beta cell function, respectively. Diabetes incidence was assessed during 18 years of follow-up. Renal function was estimated from serum cystatin C concentrations. Dietary acid load was calculated as potential renal acid load (PRAL) and net endogenous acid production (NEAP) algorithms from 7 day food records. Adequate dietary reporters were identified by Goldberg cut-offs. Results PRAL and NEAP were not associated with insulin sensitivity or beta cell function. Underlying kidney function or consideration of dietary adequate reporters did not modify these null findings. During follow-up, 115 new cases of diabetes were validated. Neither PRAL nor NEAP was associated with diabetes incidence. Conclusios/interpretation Our results do not support the hypothesis that dietary acid load influences insulin sensitivity, beta cell function or diabetes risk. Interventional studies modifying acid-base dietary intake are needed to further elucidate a possible role of acid load in the development of type 2 diabetes.
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| 8. |
- Zanetti, Daniela, et al.
(författare)
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Plasma proteomic signatures of a direct measure of insulin sensitivity in two population cohorts
- 2023
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Ingår i: Diabetologia. - : Springer. - 0012-186X .- 1432-0428. ; 66:9, s. 1643-1654
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Tidskriftsartikel (refereegranskat)abstract
- Aims/hypothesis The euglycaemic-hyperinsulinaemic clamp (EIC) is the reference standard for the measurement of whole-body insulin sensitivity but is laborious and expensive to perform. We aimed to assess the incremental value of high-through-put plasma proteomic profiling in developing signatures correlating with the M value derived from the EIC.Methods We measured 828 proteins in the fasting plasma of 966 participants from the Relationship between Insulin Sensitivity and Cardiovascular disease (RISC) study and 745 participants from the Uppsala Longitudinal Study of Adult Men (ULSAM) using a high-throughput proximity extension assay. We used the least absolute shrinkage and selection operator (LASSO) approach using clinical variables and protein measures as features. Models were tested within and across cohorts. Our primary model performance metric was the proportion of the M value variance explained (R-2).Results A standard LASSO model incorporating 53 proteins in addition to routinely available clinical variables increased the M value R-2 from 0.237 (95% CI 0.178, 0.303) to 0.456 (0.372, 0.536) in RISC. A similar pattern was observed in ULSAM, in which the M value R-2 increased from 0.443 (0.360, 0.530) to 0.632 (0.569, 0.698) with the addition of 61 proteins. Models trained in one cohort and tested in the other also demonstrated significant improvements in R-2 despite differences in baseline cohort characteristics and clamp methodology (RISC to ULSAM: 0.491 [0.433, 0.539] for 51 proteins; ULSAM to RISC: 0.369 [0.331, 0.416] for 67 proteins). A randomised LASSO and stability selection algorithm selected only two proteins per cohort (three unique proteins), which improved R-2 but to a lesser degree than in standard LASSO models: 0.352 (0.266, 0.439) in RISC and 0.495 (0.404, 0.585) in ULSAM. Reductions in improvements of R-2 with randomised LASSO and stability selection were less marked in cross-cohort analyses (RISC to ULSAM R-2 0.444 [0.391, 0.497]; ULSAM to RISC R-2 0.348 [0.300, 0.396]). Models of proteins alone were as effective as models that included both clinical variables and proteins using either standard or randomised LASSO. The single most consistently selected protein across all analyses and models was IGF-binding protein 2.Conclusions/interpretation A plasma proteomic signature identified using a standard LASSO approach improves the cross-sectional estimation of the M value over routine clinical variables. However, a small subset of these proteins identified using a stability selection algorithm affords much of this improvement, especially when considering cross-cohort analyses. Our approach provides opportunities to improve the identification of insulin-resistant individuals at risk of insulin resistance-related adverse health consequences.
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