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. Electronic supplementary material The online version of this article (10.1007/s10654-018-0475-8) contains supplementary material, which is available to authorized users.

Objective Sex hormone-binding globulin (SHBG) and androgens have been associated with mortality in women and men, but controversy still exists. Our objective was to investigate associations of SHBG and androgens with all-cause and cause-specific mortality in men and women. Design 1006 men and 709 peri- and postmenopausal women (age range: 45–82 years) from the German population-based KORA F4 cohort study were followed-up for a median of 8.7 years. Methods SHBG was measured with an immunoassay, total testosterone (TT) and dihydrotestosterone (DHT) with mass-spectrometry in serum samples and we calculated free testosterone (cFT). To assess associations between SHBG and androgen levels and mortality, we calculated hazard ratios (HRs) with 95% CIs using Cox proportional-hazards models. Results In the cohort, 128 men (12.7%) and 70 women (9.9%) died. In women, we observed positive associations of SHBG with all-cause (HR: 1.54, 95% CI: 1.16–2.04) and with other disease-related mortality (HR: 1.86, 95% CI: 1.08–3.20) and for DHT with all-cause mortality (HR: 1.32, 95% CI: 1.00–1.73). In men, we found a positive association of SHBG (HR: 1.24 95% CI: 1.00–1.54) and inverse associations of TT (HR: 0.87, 95% CI: 0.77–0.97) and cFT (HR: 0.84, 95% CI: 0.73–0.97) with all-cause mortality. No other associations were found for cause-specific mortality. Conclusions Higher SHBG levels were associated with increased risk of all-cause mortality in men and women. Lower TT and cFT levels in men and higher DHT levels in women were associated with increased risk of all-cause mortality. Future, well-powered population-based studies should further investigate cause-specific mortality risk.

ObjectivesThis study aimed to assess the impact of using different weighting procedures for the German Index of Multiple Deprivation (GIMD) investigating their link to mortality rates.Design and settingIn addition to the original (normative) weighting of the GIMD domains, four alternative weighting approaches were applied: equal weighting, linear regression, maximization algorithm and factor analysis. Correlation analyses to quantify the association between the differently weighted GIMD versions and mortality based on district-level official data from Germany in 2010 were applied (n=412 districts).Outcome measuresTotal mortality (all age groups) and premature mortality (<65 years).ResultsAll correlations of the GIMD versions with both total and premature mortality were highly significant (p<0.001). The comparison of these associations using Williams’s t-test for paired correlations showed significant differences, which proved to be small in respect to absolute values of Spearman’s rho (total mortality: between 0.535 and 0.615; premature mortality: between 0.699 and 0.832).ConclusionsThe association between area deprivation and mortality proved to be stable, regardless of different weighting of the GIMD domains. The theory-based weighting of the GIMD should be maintained, due to the stability of the GIMD scores and the relationship to mortality.

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.