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Hypertension before and during early pregnancy has been associated with an increased risk of gestational diabetes mellitus (GDM) in retrospective analyses. We aimed to investigate the prospective blood pressure trackings in a population-based cohort of pregnant women, who were stratified according to their metabolic status in early third trimester.We recorded blood pressure longitudinally during pregnancy in 1230 women from the Odense Child Cohort, Denmark. Fasting glucose and insulin were measured at gestational weeks 28-30. Metabolic status was evaluated according to the WHO 2013 threshold for GDM (GDM-WHO: fasting plasma glucose ≥5.1 mmol/l), insulin and homeostatic model assessment of insulin resistance (HOMA-IR). Relationships between metabolic status in third trimester and blood pressure trajectories were evaluated with adjusted linear mixed models. Trajectory was defined as blood pressure records in pregnancy per 4 weeks interval.Prevalence of GDM-WHO was 40% (498/1230). GDM-WHO was associated with 1.46 (0.22-2.70) mmHg higher SBP and 1.04 (0.07-2.01) mmHg higher DBP trajectories in the overall cohort. The associations were driven by differences in the overweight group, with 3.14 (1.05-5.25) mmHg higher SBP and 1.94 (0.42-3.47) mmHg higher DBP per 4 weeks in women with GDM-WHO compared with women without GDM-WHO. GDM-WHO was not associated with blood pressure in women with normal weight. Blood pressure trajectories were elevated across quartiles of insulin resistance.GDM-WHO is associated with higher blood pressure in pregnancy, and there appears to be a stronger effect in overweight women.

OBJECTIVEEvidence on plasma n-6 polyunsaturated fatty acids (PUFAs) and type 2 diabetes risk is inconsistent. We examined the associations of lipid class–specific PUFA concentrations with type 2 diabetes risk.RESEARCH DESIGN AND METHODSIn the prospective European Prospective Investigation into Cancer and Nutrition (EPIC)-Potsdam cohort (nested case-cohort study: subcohort 1,084 participants, 536 participants with type 2 diabetes, median follow-up 6.5 years), we measured plasma 18:2, 20:3, and 20:4 concentrations in 12 lipid (sub)classes, likely reflecting the plasma concentrations of linoleic acid (18:2n-6), dihomo-γ-linolenic acid (20:3n-6), and arachidonic acid (20:4n-6). The Δ-5 desaturase (D5D) activity was estimated as the 20:4/20:3 ratio. Associations with diabetes were estimated with Cox proportional hazards models.RESULTSHigher concentrations of 18:2 were inversely associated with type 2 diabetes risk, particularly in lysophosphatidylcholines (hazard ratio [HR] per 1 SD 0.53; 95% CI 0.23–1.26) and monoacylglycerols (HR 0.59; 0.38–0.92). Higher concentrations of 20:3 in phospholipid classes phosphatidylcholines (HR 1.63; 1.23–2.14), phosphatidylethanolamines (HR 1.87; 1.32–2.65), and phosphatidylinositol (HR 1.40; 1.05–1.87); free fatty acids (HR 1.44; 1.10–1.90); and cholesteryl esters (HR 1.47; 1.09–1.98) were linked to higher type 2 diabetes incidence, and these associations remained statistically significant after correction for multiple testing. Higher 20:4 concentrations were not associated with risk. The estimated D5D activity in phospholipids and cholesteryl esters was associated with lower type 2 diabetes risk. Single nucleotide polymorphisms in the D5D-encoding FADS genes explained relatively high proportions of variation of estimated D5D activity in those lipid classes.CONCLUSIONSPlasma n-6 PUFAs were associated differently with type 2 diabetes, depending on fatty acid and the lipid class.

Peer reviewed: True, Funder: University of New South Wales; FundRef: http://dx.doi.org/10.13039/501100001773, OBJECTIVE: To assess the prospective associations of circulating levels of omega 3 polyunsaturated fatty acid (n-3 PUFA) biomarkers (including plant derived α linolenic acid and seafood derived eicosapentaenoic acid, docosapentaenoic acid, and docosahexaenoic acid) with incident chronic kidney disease (CKD). DESIGN: Pooled analysis. DATA SOURCES: A consortium of 19 studies from 12 countries identified up to May 2020. STUDY SELECTION: Prospective studies with measured n-3 PUFA biomarker data and incident CKD based on estimated glomerular filtration rate. DATA EXTRACTION AND SYNTHESIS: Each participating cohort conducted de novo analysis with prespecified and consistent exposures, outcomes, covariates, and models. The results were pooled across cohorts using inverse variance weighted meta-analysis. MAIN OUTCOME MEASURES: Primary outcome of incident CKD was defined as new onset estimated glomerular filtration rate

OBJECTIVE N-glycosylation is a functional posttranslational modification of immunoglobulins (Igs). We hypothesized that specific IgG N-glycans are associated with incident type 2 diabetes and cardiovascular disease (CVD). RESEARCH DESIGN AND METHODS We performed case-cohort studies within the population-based European Prospective Investigation into Cancer and Nutrition (EPIC)–Potsdam cohort (2,127 in the type 2 diabetes subcohort [741 incident cases]; 2,175 in the CVD subcohort [417 myocardial infarction and stroke cases]). Relative abundances of 24 IgG N-glycan peaks (IgG-GPs) were measured by ultraperformance liquid chromatography, and eight glycosylation traits were derived based on structural similarity. End point–associated IgG-GPs were preselected with fractional polynomials, and prospective associations were estimated in confounder-adjusted Cox models. Diabetes risk associations were validated in three independent studies. RESULTS After adjustment for confounders and multiple testing correction, IgG-GP7, IgG-GP8, IgG-GP9, IgG-GP11, and IgG-GP19 were associated with type 2 diabetes risk. A score based on these IgG-GPs was associated with a higher diabetes risk in EPIC-Potsdam and independent validation studies (843 total cases, 3,149 total non-cases, pooled estimate per SD increase 1.50 [95% CI 1.37–1.64]). Associations of IgG-GPs with CVD risk differed between men and women. In women, IgG-GP9 was inversely associated with CVD risk (hazard ratio [HR] per SD 0.80 [95% CI 0.65–0.98]). In men, a weighted score based on IgG-GP19 and IgG-GP23 was associated with higher CVD risk (HR per SD 1.47 [95% CI 1.20–1.80]). In addition, several derived traits were associated with cardiometabolic disease incidence. CONCLUSIONS Selected IgG N-glycans are associated with cardiometabolic risk beyond classic risk factors, including clinical biomarkers.

OBJECTIVE Circulating N-terminal pro B-type natriuretic peptide (NT-proBNP) is a classic diagnostic and prognostic marker for heart failure. However, it is inversely associated with diabetes risk. We aimed to investigate relationships of NT-proBNP with risk of diabetes-related complications in initially healthy individuals. RESEARCH DESIGN AND METHODS We performed a case-cohort study within the European Prospective Investigation Into Cancer and Nutrition (EPIC)-Potsdam cohort including a random subcohort (n = 1,294) and incident cases of type 2 diabetes (n = 649) and cardiovascular diseases (n = 478). Incident cases of type 2 diabetes (n = 545) were followed up for microvascular (n = 133) and macrovascular (n = 50) complications. Plasma NT-proBNP was measured at baseline in initially healthy participants. RESULTS In multivariable models, NT-proBNP was linearly inversely associated with incident type 2 diabetes with a hazard ratio (HR) (95% CI) per doubling in NT-proBNP of 0.91 (0.86, 0.98). The association was only observable in women (0.80 [0.72, 0.90]) compared with men (0.98 [0.91, 1.07]). Among people with incident diabetes, NT-proBNP was positively associated with diabetes complications: overall, 1.31 (1.13, 1.53); microvascular complications, 1.20 (1.01, 1.43); and macrovascular complications, 1.37 (1.03, 1.83). CONCLUSIONS Although higher NT-proBNP levels are associated with lower diabetes risk, NT-proBNP is a biomarker for vascular complications in people who develop diabetes independent of potential confounders. Thus, NT-proBNP might be informative to monitor risk for diabetes-related microvascular and macrovascular complications, which should be further explored in future prospective studies.

Background: In blood and tissues, dietary and endogenously generated fatty acids (FAs) occur in free form or as part of complex lipid molecules that collectively represent the lipidome of the respective tissue. We assessed associations of plasma lipids derived from high-resolution lipidomics with incident cardiometabolic diseases and subsequently tested if the identified risk-associated lipids were sensitive to dietary fat modification. Methods: The EPIC Potsdam cohort study (European Prospective Investigation into Cancer and Nutrition) comprises 27 548 participants recruited within an age range of 35 to 65 years from the general population around Potsdam, Germany. We generated 2 disease-specific case cohorts on the basis of a fixed random subsample (n=1262) and all respective cohort-wide identified incident primary cardiovascular disease (composite of fatal and nonfatal myocardial infarction and stroke; n=551) and type 2 diabetes (n=775) cases. We estimated the associations of baseline plasma concentrations of 282 class-specific FA abundances (calculated from 940 distinct molecular species across 15 lipid classes) with the outcomes in multivariable-adjusted Cox models. We tested the effect of an isoenergetic dietary fat modification on risk-associated lipids in the DIVAS randomized controlled trial (Dietary Intervention and Vascular Function; n=113). Participants consumed either a diet rich in saturated FAs (control), monounsaturated FAs, or a mixture of monounsaturated and n-6 polyunsaturated FAs for 16 weeks. Results: Sixty-nine lipids associated (false discovery rate Conclusions: We identified several lipids associated with cardiometabolic disease risk. A subset was beneficially altered by a dietary fat intervention that supports the substitution of dietary saturated FAs with unsaturated FAs as a potential tool for primary disease prevention.

Aims/hypothesis Studies suggest decreased mortality risk among people who are overweight or obese compared with individuals with normal weight in type 2 diabetes (obesity paradox). However, the relationship between body weight or weight change and microvascular vs macrovascular complications of type 2 diabetes remains unresolved. We investigated the association between BMI and BMI change with long-term risk of microvascular and macrovascular complications in type 2 diabetes in a prospective cohort study. Methods We studied participants with incident type 2 diabetes from the European Prospective Investigation into Cancer and Nutrition (EPIC)-Potsdam cohort, who were free of cancer, cardiovascular disease and microvascular disease at diagnosis (n = 1083). Pre-diagnosis BMI and relative annual change between pre- and post-diagnosis BMI were evaluated in multivariable-adjusted Cox models. Results There were 85 macrovascular (myocardial infarction and stroke) and 347 microvascular events (kidney disease, neuropathy and retinopathy) over a median follow-up of 10.8 years. Median pre-diagnosis BMI was 29.9 kg/m2 (IQR 27.4–33.2), and the median relative annual BMI change was −0.4% (IQR −2.1 to 0.9). Higher pre-diagnosis BMI was positively associated with total microvascular complications (multivariable-adjusted HR per 5 kg/m2 [95% CI]: 1.21 [1.07, 1.36], kidney disease 1.39 [1.21, 1.60] and neuropathy 1.12 [0.96, 1.31]) but not with macrovascular complications (HR 1.05 [95% CI 0.81, 1.36]). Analyses according to BMI categories corroborated these findings. Effect modification was not evident by sex, smoking status or age groups. In analyses according to BMI change categories, BMI loss of more than 1% indicated a decreased risk of total microvascular complications (HR 0.62 [95% CI 0.47, 0.80]), kidney disease (HR 0.57 [95% CI 0.40, 0.81]) and neuropathy (HR 0.73 [95% CI 0.52, 1.03]), compared with participants with a stable BMI; no clear association was observed for macrovascular complications (HR 1.04 [95% CI 0.62, 1.74]). The associations between BMI gain compared with stable BMI and diabetes-related vascular complications were less apparent. Associations were consistent across strata of sex, age, pre-diagnosis BMI or medication but appeared to be stronger among never-smokers compared with current or former smokers. Conclusions/interpretation Among people with incident type 2 diabetes, pre-diagnosis BMI was positively associated with microvascular complications, while a reduced risk was observed with weight loss when compared with stable weight. The relationships with macrovascular disease were less clear. Graphical abstract

Inclusion of clinical parameters limits the application of most cardiovascular disease (CVD) prediction models to clinical settings. We developed and externally validated a non-clinical CVD risk score with a clinical extension and compared the performance to established CVD risk scores. We derived the scores predicting CVD (non-fatal and fatal myocardial infarction and stroke) in the European Prospective Investigation into Cancer and Nutrition (EPIC)-Potsdam cohort (n = 25,992, cases = 683) using competing risk models and externally validated in EPIC-Heidelberg (n = 23,529, cases = 692). Performance was assessed by C-indices, calibration plots, and expected-to-observed ratios and compared to a non-clinical model, the Pooled Cohort Equation, Framingham CVD Risk Scores (FRS), PROCAM scores, and the Systematic Coronary Risk Evaluation (SCORE). Our non-clinical score included age, gender, waist circumference, smoking, hypertension, type 2 diabetes, CVD family history, and dietary parameters. C-indices consistently indicated good discrimination (EPIC-Potsdam 0.786, EPIC-Heidelberg 0.762) comparable to established clinical scores (thereof highest, FRS: EPIC-Potsdam 0.781, EPIC-Heidelberg 0.764). Additional clinical parameters slightly improved discrimination (EPIC-Potsdam 0.796, EPIC-Heidelberg 0.769). Calibration plots indicated very good calibration with minor overestimation in the highest decile of predicted risk. The developed non-clinical 10-year CVD risk score shows comparable discrimination to established clinical scores, allowing assessment of individual CVD risk in physician-independent settings.

Purpose We aimed to examine the prospective association between manganese, iron, copper, zinc, iodine, selenium, selenoprotein P, free zinc, and their interplay, with incident type 2 diabetes (T2D), cardiovascular disease (CVD) and colorectal cancer (CRC). Methods Serum trace element (TE) concentrations were measured in a case-cohort study embedded within the EPIC-Potsdam cohort, consisting of a random sub-cohort (n = 2500) and incident cases of T2D (n = 705), CVD (n = 414), and CRC (n = 219). TE patterns were investigated using principal component analysis. Cox proportional hazard models were fitted to examine the association between TEs with T2D, CVD and CRC incidence. Results Higher manganese, zinc, iodine and selenium were associated with an increased risk of developing T2D (HR Q5 vs Q1: 1.56, 1.09–2.22; HR per SD, 95% CI 1.18, 1.05–1.33; 1.09, 1.01–1.17; 1.19, 1.06–1.34, respectively). Regarding CVD, manganese, copper and copper-to-zinc ratio were associated with an increased risk (HR per SD, 95% CI 1.13, 1.00–1.29; 1.22, 1.02–1.44; 1.18, 1.02–1.37, respectively). The opposite was observed for higher selenium-to-copper ratio (HR Q5 vs Q1, 95% CI 0.60, 0.39–0.93). Higher copper and zinc were associated with increasing risk of developing CRC (HR per SD, 95% CI 1.29, 1.05–1.59 and 1.14, 1.00–1.30, respectively). Selenium, selenoprotein P and selenium-to-copper-ratio were associated to decreased risk (HR per SD, 95% CI 0.82, 0.69–0.98; 0.81, 0.72–0.93; 0.77, 0.65–0.92, respectively). Two TE patterns were identified: manganese–iron–zinc and copper–iodine–selenium. Conclusion Different TEs were associated with the risk of developing T2D, CVD and CRC. The contrasting associations found for selenium with T2D and CRC point towards differential disease-related pathways.

Recent studies suggest that insulin-like growth factor binding protein 2 (IGFBP-2) may protect against type 2 diabetes, but population-based human studies are scarce. We aimed to investigate the prospective association of circulating IGFBP-2 concentrations and of differential methylation in the IGFBP-2 gene with type 2 diabetes risk.

Summary Background Cardiovascular disease risk among individuals across different categories of BMI might depend on their metabolic health. It remains unclear to what extent metabolic health status changes over time and whether this affects cardiovascular disease risk. In this study, we aimed to examine the association between metabolic health and its change over time and cardiovascular disease risk across BMI categories. Methods Between June and December, 1976, 121 701 female nurses were recruited to the Nurses' Health Study (NHS) of whom 103 298 returned a questionnaire in 1980 used as baseline in this study. After excluding women with a history of cardiovascular disease or cancer, with missing body weight and with underweight. 90 257 women were followed-up from 1980 to 2010 for incident cardiovascular disease. Participants were cross-classified by BMI categories, metabolic health (defined by absence of diabetes, hypertension and hypercholesterolaemia), and change in metabolic health status during follow-up. The cardiovascular component of the NHS is registered with ClinicalTrials.gov, number NCT00005152. Findings During 2 127 391 person-years of follow-up with a median follow-up of 24 years, we documented 6306 cases of cardiovascular disease including 3304 myocardial infarction cases and 3080 strokes. Cardiovascular disease risk of women with metabolically healthy obesity was increased compared with women with metabolically healthy normal weight (HR 1·39, 95% CI 1·15–1·68), but risk was considerably higher in women with metabolically unhealthy normal weight (2·43, 2·19–2·68), overweight (2·61, 2·36–2·89) and obesity (3·15, 2·83–3·50). The majority of metabolically healthy women converted to unhealthy phenotypes (2555 [84%] of 3027 women with obesity, 22 215 [68%] of 32 882 women with normal-weight after 20 years). Women who maintained metabolically healthy obesity during follow-up were still at a higher cardiovascular disease risk compared with women with stable healthy normal weight (HR 1·57, 1·03–2·38), yet this risk was lower than for initially metabolically healthy women who converted to an unhealthy phenotype (normal-weight 1·90, 1·66–2·17 vs obesity 2·74, 2·30–3·27). Particularly incident diabetes and hypertension increased the risk among women with initial metabolic health. Interpretation Even when metabolic health is maintained during long periods of time, obesity remains a risk factor for cardiovascular disease. However, risks are highest for metabolically unhealthy women across all BMI categories. A large proportion of metabolically healthy women converted to an unhealthy phenotype over time across all BMI categories, which is associated with an increased cardiovascular disease risk. Funding US National Institutes of Health, German Federal Ministry of Education and Research.

Objective To compare weighting methods for Cox regression and multiple imputation (MI) in a case–cohort study in the context of risk prediction modeling. Study Design and Setting Based on the European Prospective Investigation into Cancer and Nutrition Potsdam study, we estimated risk scores to predict incident type-2 diabetes using full cohort data and case–cohort data assuming missing information on waist circumference outside the case–cohort (∼90%). Varying weighting approaches and MI were compared with regard to the calculation of relative risks, absolute risks, and predictive abilities including C -index, the net reclassification improvement, and calibration. Results The full cohort comprised 21,845 participants, and the case–cohort comprised 2,703 participants. Relative risks were similar across all methods and compatible with full cohort estimates. Absolute risk estimates showed stronger disagreement mainly for Prentice and Self & Prentice weighting. Barlow and Langholz & Jiao weighting methods and MI were in good agreement with full cohort analysis. Predictive abilities were closest to full cohort estimates for MI or for Barlow and Langholz & Jiao weighting. Conclusions MI seems to be a valid method for deriving or extending a risk prediction model from case–cohort data and might be superior for absolute risk calculation when compared to weighted approaches.

OBJECTIVE Plasma protein N-glycan profiling integrates information on enzymatic protein glycosylation, which is a highly controlled ubiquitous posttranslational modification. Here we investigate the ability of the plasma N-glycome to predict incidence of type 2 diabetes and cardiovascular diseases (CVDs; i.e., myocardial infarction and stroke). RESEARCH DESIGN AND METHODS Based on the prospective European Prospective Investigation of Cancer (EPIC)-Potsdam cohort (n = 27,548), we constructed case-cohorts including a random subsample of 2,500 participants and all physician-verified incident cases of type 2 diabetes (n = 820; median follow-up time 6.5 years) and CVD (n = 508; median follow-up time 8.2 years). Information on the relative abundance of 39 N-glycan groups in baseline plasma samples was generated by chromatographic profiling. We selected predictive N-glycans for type 2 diabetes and CVD separately, based on cross-validated machine learning, nonlinear model building, and construction of weighted prediction scores. This workflow for CVD was applied separately in men and women. RESULTS The N-glycan–based type 2 diabetes score was strongly predictive for diabetes risk in an internal validation cohort (weighted C-index 0.83, 95% CI 0.78–0.88), and this finding was externally validated in the Finland Cardiovascular Risk Study (FINRISK) cohort. N-glycans were moderately predictive for CVD incidence (weighted C-indices 0.66, 95% CI 0.60–0.72, for men; 0.64, 95% CI 0.55–0.73, for women). Information on the selected N-glycans improved the accuracy of established and clinically applied risk prediction scores for type 2 diabetes and CVD. CONCLUSIONS Selected N-glycans improve type 2 diabetes and CVD prediction beyond established risk markers. Plasma protein N-glycan profiling may thus be useful for risk stratification in the context of precisely targeted primary prevention of cardiometabolic diseases.

Plasma proteinBased on the prospective European Prospective Investigation of Cancer (EPIC)-Potsdam cohort (TheSelected

Aims/hypothesis This study aimed to evaluate associations of height as well as components of height (sitting height and leg length) with risk of type 2 diabetes and to explore to what extent associations are explainable by liver fat and cardiometabolic risk markers. Methods A case-cohort study within the European Prospective Investigation into Cancer and Nutrition (EPIC)-Potsdam study comprising 26,437 participants who provided blood samples was designed. We randomly selected a subcohort of 2500 individuals (2029 diabetes-free at baseline and with anamnestic, anthropometrical and metabolic data for analysis). Of the 820 incident diabetes cases identified in the full cohort during 7 years of follow-up, 698 remained for analyses after similar exclusions. Results After adjustment for age, potential lifestyle confounders, education and waist circumference, greater height was related to lower diabetes risk (HR per 10 cm, men 0.59 [95% CI 0.47, 0.75] and women 0.67 [0.51, 0.88], respectively). Leg length was related to lower risk among men and women, but only among men if adjusted for total height. Adjustment for liver fat and triacylglycerols, adiponectin and C-reactive protein substantially attenuated associations between height and diabetes risk, particularly among women. Conclusions/interpretation We observed inverse associations between height and risk of type 2 diabetes, which was largely related to leg length among men. The inverse associations may be partly driven by lower liver fat content and a more favourable cardiometabolic profile.

Purpose Several studies implicated a crosstalk between bone and fat in the pathogenesis of osteoporosis. Few studies indicated an association between adiponectin and omentin-1 on the bone remodeling process and bone mineral density, and suggested osteoprotegerin (OPG) as a mediator of this relationship. However, only limited evidence on this relationship is available in humans. Therefore, this study aimed to investigate the association between omentin-1, adiponectin and broadband ultrasound attenuation (BUA) in peri-/premenopausal and postmenopausal women, and to assess the role of OPG as a possible mediator. Methods Data from the German population-based EPIC-Potsdam cohort comprising 637 women were analyzed. Multivariable-adjusted ANCOVA including age, BMI, waist circumference, smoking status, education, physical activity, adiponectin or omentin-1 and hormone use was used to investigate potential relationships between the adipokines and BUA levels. A mediation analysis assessed the mediating effect of OPG on the association of BUA and omentin-1 levels. Results Peri-/premenopausal women had higher BUA levels (112.5 ± 10.1 dB/MHz), compared to postmenopausal women (106.3 ± 10.0 dB/MHz). In peri-/premenopausal women neither adiponectin nor omentin-1 was significantly associated with BUA. In postmenopausal women, adiponectin was not associated with BUA, but 10 % increase in the omentin-1 concentration was significantly associated with 0.44 dB/MHz lower BUA levels (p = 0.01). Omentin-1 was positively associated with OPG (p = 0.02); however, OPG was not significantly related to BUA (p = 0.62). Conclusion Our study provides evidence for an inverse association between circulating omentin-1 and BUA levels in postmenopausal women. However, the present findings do not support a mediating effect of OPG in the adipose tissue–bone pathway. Electronic supplementary material The online version of this article (doi:10.1007/s40618-016-0544-3) contains supplementary material, which is available to authorized users.

BACKGROUND: Habitual red meat consumption was consistently related to a higher risk of type 2 diabetes in observational studies. Potentially underlying mechanisms are unclear. OBJECTIVE: This study aimed to identify blood metabolites that possibly relate red meat consumption to the occurrence of type 2 diabetes. DESIGN: Analyses were conducted in the prospective European Prospective Investigation into Cancer and Nutrition-Potsdam cohort (n = 27,548), applying a nested case-cohort design (n = 2681, including 688 incident diabetes cases). Habitual diet was assessed with validated semiquantitative food-frequency questionnaires. Total red meat consumption was defined as energy-standardized summed intake of unprocessed and processed red meats. Concentrations of 14 amino acids, 17 acylcarnitines, 81 glycerophospholipids, 14 sphingomyelins, and ferritin were determined in serum samples from baseline. These biomarkers were considered potential mediators of the relation between total red meat consumption and diabetes risk in Cox models. The proportion of diabetes risk explainable by biomarker adjustment was estimated in a bootstrapping procedure with 1000 replicates. RESULTS: After adjustment for age, sex, lifestyle, diet, and body mass index, total red meat consumption was directly related to diabetes risk [HR for 2 SD (11 g/MJ): 1.26; 95% CI: 1.01, 1.57]. Six biomarkers (ferritin, glycine, diacyl phosphatidylcholines 36:4 and 38:4, lysophosphatidylcholine 17:0, and hydroxy-sphingomyelin 14:1) were associated with red meat consumption and diabetes risk. The red meat-associated diabetes risk was significantly (P < 0.001) attenuated after simultaneous adjustment for these biomarkers [biomarker-adjusted HR for 2 SD (11 g/MJ): 1.09; 95% CI: 0.86, 1.38]. The proportion of diabetes risk explainable by respective biomarkers was 69% (IQR: 49%, 106%). CONCLUSION: In our study, high ferritin, low glycine, and altered hepatic-derived lipid concentrations in the circulation were associated with total red meat consumption and, independent of red meat, with diabetes risk. The red meat-associated diabetes risk was largely attenuated after adjustment for selected biomarkers, which is consistent with the presumed mediation hypothesis.

The Net Reclassification Improvement (NRI) has become a popular metric for evaluating improvement in disease prediction models through the past years. The concept is relatively straightforward but usage and interpretation has been different across studies. While no thresholds exist for evaluating the degree of improvement, many studies have relied solely on the significance of the NRI estimate. However, recent studies recommend that statistical testing with the NRI should be avoided. We propose using confidence ellipses around the estimated values of event and non-event NRIs which might provide the best measure of variability around the point estimates. Our developments are illustrated using practical examples from EPIC-Potsdam study.