Dataset Information

Protein markers and risk of type 2 diabetes and prediabetes: a targeted proteomics approach in the KORA F4/FF4 study.

ABSTRACT: The objective of the present study was to identify proteins that contribute to pathophysiology and allow prediction of incident type 2 diabetes or incident prediabetes. We quantified 14 candidate proteins using targeted mass spectrometry in plasma samples of the prospective, population-based German KORA F4/FF4 study (6.5-year follow-up). 892 participants aged 42-81 years were selected using a case-cohort design, including 123 persons with incident type 2 diabetes and 255 persons with incident WHO-defined prediabetes. Prospective associations between protein levels and diabetes, prediabetes as well as continuous fasting and 2 h glucose, fasting insulin and insulin resistance were investigated using regression models adjusted for established risk factors. The best predictive panel of proteins on top of a non-invasive risk factor model or on top of HbA1c, age, and sex was selected. Mannan-binding lectin serine peptidase (MASP) levels were positively associated with both incident type 2 diabetes and prediabetes. Adiponectin was inversely associated with incident type 2 diabetes. MASP, adiponectin, apolipoprotein A-IV, apolipoprotein C-II, C-reactive protein, and glycosylphosphatidylinositol specific phospholipase D1 were associated with individual continuous outcomes. The combination of MASP, apolipoprotein E (apoE) and adiponectin improved diabetes prediction on top of both reference models, while prediabetes prediction was improved by MASP plus CRP on top of the HbA1c model. In conclusion, our mass spectrometric approach revealed a novel association of MASP with incident type 2 diabetes and incident prediabetes. In combination, MASP, adiponectin and apoE improved type 2 diabetes prediction beyond non-invasive risk factors or HbA1c, age and sex.

SUBMITTER: Huth C

PROVIDER: S-EPMC6451724 | biostudies-literature | 2019 Apr

REPOSITORIES: biostudies-literature

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Protein markers and risk of type 2 diabetes and prediabetes: a targeted proteomics approach in the KORA F4/FF4 study.

Huth Cornelia C von Toerne Christine C Schederecker Florian F de Las Heras Gala Tonia T Herder Christian C Kronenberg Florian F Meisinger Christa C Rathmann Wolfgang W Koenig Wolfgang W Waldenberger Melanie M Roden Michael M Peters Annette A Hauck Stefanie M SM Thorand Barbara B

European journal of epidemiology 20181231 4

The objective of the present study was to identify proteins that contribute to pathophysiology and allow prediction of incident type 2 diabetes or incident prediabetes. We quantified 14 candidate proteins using targeted mass spectrometry in plasma samples of the prospective, population-based German KORA F4/FF4 study (6.5-year follow-up). 892 participants aged 42-81 years were selected using a case-cohort design, including 123 persons with incident type 2 diabetes and 255 persons with incident WH ...[more]

PMID: 30599058

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Project description:BackgroundHigh N-terminal pro-brain-type natriuretic peptide levels have been associated with a lower risk of type 2 diabetes mellitus (T2D). However, less is known about other cardiac stress biomarkers in this context. Here we evaluated the association of mid-regional pro-atrial natriuretic peptide (MR-proANP), C-terminal pro-arginine vasopressin (copeptin), C-terminal pro-endothelin-1 (CT-proET-1) and mid-regional pro-adrenomedullin (MR-proADM) with incident T2D and changes in glucose metabolism.MethodsWe performed a prospective cohort study using data from the population-based KORA F4/FF4 study. 1773 participants (52.3% women) with MR-proANP measurements and 960 (52.7% women) with copeptin, CT-proET-1 and MR-proADM measurements were included. We examined associations of circulating plasma levels of MR-proANP, copeptin, CT-proET-1 and MR-proADM with incident T2D, the combined endpoint of incident prediabetes/T2D and with fasting and 2 h-glucose, fasting insulin, HOMA-IR, HOMA-B and HbA1c at follow-up. Logistic and linear regression models adjusted for age, sex, waist circumference, height, hypertension, total/HDL cholesterol ratio, triglycerides, smoking, physical activity and parental history of diabetes were used to compute effect estimates.ResultsDuring a median follow-up time of 6.4 years (25th and 75th percentiles: 6.0 and 6.6, respectively), 119 out of the 1773 participants and 72 out of the 960 participants developed T2D. MR-proANP was inversely associated with incident T2D (odds ratio [95% confidence interval]: 0.75 [0.58; 0.96] per 1-SD increase of log MR-proANP). Copeptin was positively associated with incident prediabetes/T2D (1.29 [1.02; 1.63] per 1-SD increase of log copeptin). Elevated levels of CT-proET-1 were associated with increased HOMA-B at follow-up, while elevated MR-proADM levels were associated with increased fasting insulin, HOMA-IR and HOMA-B at follow-up. These associations were independent of previously described diabetes risk factors.ConclusionsHigh plasma concentrations of MR-proANP contributed to a lower risk of incident T2D, whereas high plasma concentrations of copeptin were associated with an increased risk of incident prediabetes/T2D. Furthermore, high plasma concentrations of CT-proET-1 and MR-proADM were associated with increased insulin resistance. Our study provides evidence that biomarkers implicated in cardiac stress are associated with incident T2D and changes in glucose metabolism.

Project description:BackgroundAir pollution contributes to type 2 diabetes and cardiovascular diseases, but its relevance for other complications of diabetes, in particular distal sensorimotor polyneuropathy (DSPN), is unclear. Recent studies have indicated that DSPN is also increasingly prevalent in obesity.ObjectivesWe aimed to assess associations of air pollutants with prevalent and incident DSPN in a population-based study of older individuals with high rates of type 2 diabetes and obesity.MethodsCross-sectional analyses on prevalent DSPN were based on 1,075 individuals 62-81 years of age from the German Cooperative Health Research in the Region of Augsburg (KORA) F4 survey (2006-2008). Analyses on incident DSPN included 424 individuals without DSPN at baseline (KORA F4), of whom 188 had developed DSPN by the KORA FF4 survey (2013-2014). Associations of annual average air pollutant concentrations at participants' residences with prevalent and incident DSPN were estimated using Poisson regression models with a robust error variance adjusting for multiple confounders.ResultsHigher particle number concentrations (PNCs) were associated with higher prevalence [risk ratio (RR) per interquartile range (IQR) increase=1.10 (95% CI: 1.01, 1.20)] and incidence [1.11 (95% CI: 0.99, 1.24)] of DSPN. In subgroup analyses, particulate (PNC, PM10, PMcoarse, PM2.5, and PM2.5abs) and gaseous (NOx, NO2) pollutants were positively associated with prevalent DSPN in obese participants, whereas corresponding estimates for nonobese participants were close to the null [e.g., for an IQR increase in PNC, RR=1.17 (95% CI: 1.05, 1.31) vs. 1.06 (95% CI: 0.95, 1.19); pinteraction=0.22]. With the exception of PM2.5abs, corresponding associations with incident DSPN were positive in obese participants but null or inverse for nonobese participants, with pinteraction≤0.13 [e.g., for PNC, RR=1.28 (95% CI: 1.08, 1.51) vs. 1.03 (95% CI: 0.90, 1.18); pinteraction=0.03].DiscussionBoth particulate and gaseous air pollutants were positively associated with prevalent and incident DSPN in obese individuals. Obesity and air pollution may have synergistic effects on the development of DSPN. https://doi.org/10.1289/EHP7311.

Project description:BackgroundDue to the asymptomatic nature of the early stages, chronic kidney disease (CKD) is usually diagnosed at late stages and lacks targeted therapy, highlighting the need for new biomarkers to better understand its pathophysiology and to be used for early diagnosis and therapeutic targets. Given the close relationship between CKD and cardiovascular disease (CVD), we investigated the associations of 233 CVD- and inflammation-related plasma proteins with kidney function decline and aimed to assess whether the observed associations are causal.MethodsWe included 1140 participants, aged 55-74 years at baseline, from the Cooperative Health Research in the Region of Augsburg (KORA) cohort study, with a median follow-up time of 13.4 years and 2 follow-up visits. We measured 233 plasma proteins using a proximity extension assay at baseline. In the discovery analysis, linear regression models were used to estimate the associations of 233 proteins with the annual rate of change in creatinine-based estimated glomerular filtration rate (eGFRcr). We further investigated the association of eGFRcr-associated proteins with the annual rate of change in cystatin C-based eGFR (eGFRcys) and eGFRcr-based incident CKD. Two-sample Mendelian randomization was used to infer causality.ResultsIn the fully adjusted model, 66 out of 233 proteins were inversely associated with the annual rate of change in eGFRcr, indicating that higher baseline protein levels were associated with faster eGFRcr decline. Among these 66 proteins, 21 proteins were associated with both the annual rate of change in eGFRcys and incident CKD. Mendelian randomization analyses on these 21 proteins suggest a potential causal association of higher tumor necrosis factor receptor superfamily member 11A (TNFRSF11A) level with eGFR decline.ConclusionsWe reported 21 proteins associated with kidney function decline and incident CKD and provided preliminary evidence suggesting a potential causal association between TNFRSF11A and kidney function decline. Further Mendelian randomization studies are needed to establish a conclusive causal association.

Project description:Relationships between endogenous female sex hormones and glycemic traits remain understudied, especially in men. We examined whether endogenous 17α-hydroxyprogesterone (17-OHP), progesterone, estradiol (E2), and free estradiol (fE2) were associated with glycemic traits and glycemic deterioration. 921 mainly middle-aged and elderly men and 390 perimenopausal/postmenopausal women from the German population-based Cooperative Health Research in the Region of Augsburg (KORA) F4/FF4 cohort study were followed up for a median of 6.4 years. Sex hormones were measured at baseline using mass spectrometry. We calculated regression coefficients (β) and ORs with 95% CIs using multivariable-adjusted linear and logistic regression models for Z-standardized hormones and glycemic traits or glycemic deterioration (ie, worsening of categorized glucose tolerance status), respectively. In the cross-sectional analysis (n=1222 men and n=594 women), in men, 17-OHP was inversely associated with 2h-glucose (2hG) (β=-0.067, 95% CI -0.120 to -0.013) and fasting insulin (β=-0.074, 95% CI -0.118 to -0.030), and positively associated with Quantitative Insulin Sensitivity Check Index (QUICKI) (β=0.061, 95% CI 0.018 to 0.105). Progesterone was inversely associated with fasting insulin (β=-0.047, 95% CI -0.088 to -0.006) and positively associated with QUICKI (β=0.041, 95% CI 0.001 to 0.082). E2 was inversely associated with fasting insulin (β=-0.068, 95% CI -0.116 to -0.020) and positively associated with QUICKI (β=0.059, 95% CI 0.012 to 0.107). fE2 was positively associated with glycated hemoglobin (HbA1c) (β=0.079, 95% CI 0.027 to 0.132). In women, 17-OHP was positively associated with fasting glucose (FG) (β=0.068, 95% CI 0.014 to 0.123). fE2 was positively associated with FG (β=0.080, 95% CI 0.020 to 0.141) and HbA1c (β=0.121, 95% CI 0.062 to 0.180). In the sensitivity analyses restricted to postmenopausal women, we observed a positive association between 17-OHP and glycemic deterioration (OR=1.518, 95% CI 1.033 to 2.264). Inter-relations exist between female sex hormones and glucose-related traits among perimenopausal/postmenopausal women and insulin-related traits among men. Endogenous progestogens and estrogens appear to be involved in glucose homeostasis not only in women but in men as well. Further well-powered studies assessing causal associations between endogenous female sex hormones and glycemic traits are warranted.

Project description:BackgroundThe coexistence of low muscle mass and high fat mass, two interrelated conditions strongly associated with declining health status, has been characterized by only a few protein biomarkers. High-throughput proteomics enable concurrent measurement of numerous proteins, facilitating the discovery of potentially new biomarkers.MethodsData derived from the prospective population-based Cooperative Health Research in the Region of Augsburg S4/FF4 cohort study (median follow-up time: 13.5 years) included 1478 participants (756 men and 722 women) aged 55-74 years in the cross-sectional and 608 participants (315 men and 293 women) in the longitudinal analysis. Appendicular skeletal muscle mass (ASMM) and body fat mass index (BFMI) were determined through bioelectrical impedance analysis at baseline and follow-up. At baseline, 233 plasma proteins were measured using proximity extension assay. We implemented boosting with stability selection to enable false positives-controlled variable selection to identify new protein biomarkers of low muscle mass, high fat mass, and their combination. We evaluated prediction models developed based on group least absolute shrinkage and selection operator (lasso) with 100× bootstrapping by cross-validated area under the curve (AUC) to investigate if proteins increase the prediction accuracy on top of classical risk factors.ResultsIn the cross-sectional analysis, we identified kallikrein-6, C-C motif chemokine 28 (CCL28), and tissue factor pathway inhibitor as previously unknown biomarkers for muscle mass [association with low ASMM: odds ratio (OR) per 1-SD increase in log2 normalized protein expression values (95% confidence interval (CI)): 1.63 (1.37-1.95), 1.31 (1.14-1.51), 1.24 (1.06-1.45), respectively] and serine protease 27 for fat mass [association with high BFMI: OR (95% CI): 0.73 (0.61-0.86)]. CCL28 and metalloproteinase inhibitor 4 (TIMP4) constituted new biomarkers for the combination of low muscle and high fat mass [association with low ASMM combined with high BFMI: OR (95% CI): 1.32 (1.08-1.61), 1.28 (1.03-1.59), respectively]. Including protein biomarkers selected in ≥90% of group lasso bootstrap iterations on top of classical risk factors improved the performance of models predicting low ASMM, high BFMI, and their combination [delta AUC (95% CI): 0.16 (0.13-0.20), 0.22 (0.18-0.25), 0.12 (0.08-0.17), respectively]. In the longitudinal analysis, N-terminal prohormone brain natriuretic peptide (NT-proBNP) was the only protein selected for loss in ASMM and loss in ASMM combined with gain in BFMI over 14 years [OR (95% CI): 1.40 (1.10-1.77), 1.60 (1.15-2.24), respectively].ConclusionsProteomic profiling revealed CCL28 and TIMP4 as new biomarkers of low muscle mass combined with high fat mass and NT-proBNP as a key biomarker of loss in muscle mass combined with gain in fat mass. Proteomics enable us to accelerate biomarker discoveries in muscle research.

Dataset Information

Protein markers and risk of type 2 diabetes and prediabetes: a targeted proteomics approach in the KORA F4/FF4 study.

Publications

Protein markers and risk of type 2 diabetes and prediabetes: a targeted proteomics approach in the KORA F4/FF4 study.

Similar Datasets

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