Anna Floegel

Identification of Serum Metabolites Associated With Risk of Type 2 Diabetes Using a Targeted Metabolomic Approach

Metabolomic discovery of biomarkers of type 2 diabetes (T2D) risk may reveal etiological pathways and help to identify individuals at risk for disease. We prospectively investigated the association between serum metabolites measured by targeted metabolomics and risk of T2D in the European Prospective Investigation into Cancer and Nutrition (EPIC)-Potsdam (27,548 adults) among all incident cases of T2D (n = 800, mean follow-up 7 years) and a randomly drawn subcohort (n = 2,282). Flow injection analysis tandem mass spectrometry was used to quantify 163 metabolites, including acylcarnitines, amino acids, hexose, and phospholipids, in baseline serum samples. Serum hexose; phenylalanine; and diacyl-phosphatidylcholines C32:1, C36:1, C38:3, and C40:5 were independently associated with increased risk of T2D and serum glycine; sphingomyelin C16:1; acyl-alkyl-phosphatidylcholines C34:3, C40:6, C42:5, C44:4, and C44:5; and lysophosphatidylcholine C18:2 with decreased risk. Variance of the metabolites was largely explained by two metabolite factors with opposing risk associations (factor 1 relative risk in extreme quintiles 0.31 [95% CI 0.21–0.44], factor 2 3.82 [2.64–5.52]). The metabolites significantly improved T2D prediction compared with established risk factors. They were further linked to insulin sensitivity and secretion in the Tübingen Family study and were partly replicated in the independent KORA (Cooperative Health Research in the Region of Augsburg) cohort. The data indicate that metabolic alterations, including sugar metabolites, amino acids, and choline-containing phospholipids, are associated early on with a higher risk of T2D.

Novel biomarkers for pre-diabetes identified by metabolomics

Type 2 diabetes (T2D) can be prevented in pre‐diabetic individuals with impaired glucose tolerance (IGT). Here, we have used a metabolomics approach to identify candidate biomarkers of pre‐diabetes. We quantified 140 metabolites for 4297 fasting serum samples in the population‐based Cooperative Health Research in the Region of Augsburg (KORA) cohort. Our study revealed significant metabolic variation in pre‐diabetic individuals that are distinct from known diabetes risk indicators, such as glycosylated hemoglobin levels, fasting glucose and insulin. We identified three metabolites (glycine, lysophosphatidylcholine (LPC) (18:2) and acetylcarnitine) that had significantly altered levels in IGT individuals as compared to those with normal glucose tolerance, with P‐values ranging from 2.4 × 10−4 to 2.1 × 10−13. Lower levels of glycine and LPC were found to be predictors not only for IGT but also for T2D, and were independently confirmed in the European Prospective Investigation into Cancer and Nutrition (EPIC)‐Potsdam cohort. Using metabolite–protein network analysis, we identified seven T2D‐related genes that are associated with these three IGT‐specific metabolites by multiple interactions with four enzymes. The expression levels of these enzymes correlate with changes in the metabolite concentrations linked to diabetes. Our results may help developing novel strategies to prevent T2D.

Trend in obesity prevalence in European adult cohort populations during follow-up since 1996 and their predictions to 2015.

Objective To investigate trends in obesity prevalence in recent years and to predict the obesity prevalence in 2015 in European populations. Methods Data of 97 942 participants from seven cohorts involved in the European Prospective Investigation into Cancer and Nutrition (EPIC) study participating in the Diogenes project (named as “Diogenes cohort” in the following) with weight measurements at baseline and follow-up were used to predict future obesity prevalence with logistic linear and non-linear (leveling off) regression models. In addition, linear and leveling off models were fitted to the EPIC-Potsdam dataset with five weight measures during the observation period to find out which of these two models might provide the more realistic prediction. Results During a mean follow-up period of 6 years, the obesity prevalence in the Diogenes cohort increased from 13% to 17%. The linear prediction model predicted an overall obesity prevalence of about 30% in 2015, whereas the leveling off model predicted a prevalence of about 20%. In the EPIC-Potsdam cohort, the shape of obesity trend favors a leveling off model among men (R2 = 0.98), and a linear model among women (R2 = 0.99). Conclusion Our data show an increase in obesity prevalence since the 1990ies, and predictions by 2015 suggests a sizeable further increase in European populations. However, the estimates from the leveling off model were considerably lower.

Reliability of Serum Metabolite Concentrations over a 4-Month Period Using a Targeted Metabolomic Approach

Metabolomics is a promising tool for discovery of novel biomarkers of chronic disease risk in prospective epidemiologic studies. We investigated the between- and within-person variation of the concentrations of 163 serum metabolites over a period of 4 months to evaluate the metabolite reliability expressed by the intraclass-correlation coefficient (ICC: the ratio of between-person variance and total variance). The analyses were performed with the BIOCRATES AbsoluteIDQ™ targeted metabolomics technology, including acylcarnitines, amino acids, glycerophospholipids, sphingolipids and hexose in 100 healthy individuals from the European Prospective Investigation into Cancer and Nutrition (EPIC)-Potsdam study who had provided two fasting blood samples 4 months apart. Overall, serum reliability of metabolites over a 4-month period was good. The median ICC of the 163 metabolites was 0.57. The highest ICC was observed for hydroxysphingomyelin C14:1 (ICC = 0.85) and the lowest was found for acylcarnitine C3:1 (ICC = 0). Reliability was high for hexose (ICC = 0.76), sphingolipids (median ICC = 0.66; range: 0.24–0.85), amino acids (median ICC = 0.58; range: 0.41–0.72) and glycerophospholipids (median ICC = 0.58; range: 0.03–0.81). Among acylcarnitines, reliability of short and medium chain saturated compounds was good to excellent (ICC range: 0.50–0.81). Serum reliability was lower for most hydroxyacylcarnitines and monounsaturated acylcarnitines (ICC range: 0.11–0.45 and 0.00–0.63, respectively). For most of the metabolites a single measurement may be sufficient for risk assessment in epidemiologic studies with healthy subjects.

Estimation of Antioxidant Intakes from Diet and Supplements in U.S. Adults

The importance of antioxidants in reducing risks of chronic diseases has been well established; however, antioxidant intakes by a free-living population have not yet been estimated adequately. In this study, we aimed to estimate total antioxidant intakes from diets and supplement sources in the U.S. population. The USDA Flavonoid Database, food consumption data, and dietary supplement use data of 8809 U.S. adults aged >/=19 y in NHANES 1999-2000 and 2001-2002 were used in this study. Daily total antioxidant intake was 208 mg vitamin C (46 and 54% from diets and supplements, respectively), 20 mg alpha-tocopherol (36 and 64), 223 mug retinol activity equivalents carotenes (86 and 14), 122 mug selenium (89 and 11), and 210 mg flavonoids (98 and 2). Antioxidant intakes differed among sociodemographic subgroups and lifestyle behaviors. Energy-adjusted dietary antioxidant intakes were higher in women, older adults, Caucasians, nonconsumers of alcohol (only for vitamin C and carotenes), nonsmokers (only for vitamin C, vitamin E, and carotenes), and in those with a higher income and exercise level (except for flavonoids) than in their counterparts (P < 0.05). Consumption of fruits, vegetables, and whole grains may be a good strategy to increase antioxidant intake. The possible association between antioxidant intake and the prevalence of chronic diseases should be investigated further.

Coffee consumption and risk of chronic disease in the European Prospective Investigation into Cancer and Nutrition (EPIC)–Germany study

BACKGROUND Early studies suggested that coffee consumption may increase the risk of chronic disease. OBJECTIVE We investigated prospectively the association between coffee consumption and the risk of chronic diseases, including type 2 diabetes (T2D), myocardial infarction (MI), stroke, and cancer. DESIGN We used data from 42,659 participants in the European Prospective Investigation into Cancer and Nutrition (EPIC)-Germany study. Coffee consumption was assessed by self-administered food-frequency questionnaire at baseline, and data on medically verified incident chronic diseases were collected by active and passive follow-up procedures. HRs and 95% CIs were calculated with multivariate Cox regression models and compared by competing risk analysis. RESULTS During 8.9 y of follow-up, we observed 1432 cases of T2D, 394 of MI, 310 of stroke, and 1801 of cancer as first qualifying events. Caffeinated (HR: 0.94; 95% CI: 0.84, 1.05) or decaffeinated (HR: 1.05; 95% CI: 0.84, 1.31) coffee consumption (≥4 cups/d compared with <1 cup/d; 1 cup was defined as 150 mL) was not associated with the overall risk of chronic disease. A lower risk of T2D was associated with caffeinated (HR: 0.77; 95% CI: 0.63, 0.94; P-trend 0.009) and decaffeinated (HR: 0.70; 95% CI: 0.46, 1.06; P-trend: 0.043) coffee consumption (≥4 cups/d compared with <1 cup/d), but cardiovascular disease and cancer risk were not. The competing risk analysis showed no significant differences between the risk associations of individual diseases. CONCLUSION Our findings suggest that coffee consumption does not increase the risk of chronic disease, but it may be linked to a lower risk of T2D.

Variation of serum metabolites related to habitual diet: a targeted metabolomic approach in EPIC-Potsdam

Background/objective:Serum metabolites have been linked to higher risk of chronic diseases but determinants of serum metabolites are not clear. We aimed to investigate the association between habitual diet as a modifiable risk factor and relevant serum metabolites.Subjects/methods:This cross-sectional study comprised 2380 EPIC-Potsdam participants. Intake of 45 food groups was assessed by food frequency questionnaire and concentrations of 127 serum metabolites were measured by targeted metabolomics. Reduced rank regression was used to find dietary patterns that explain the maximum variation of metabolites.Results:In the multivariable-adjusted model, the proportion of explained variation by habitual diet was ranked as follows: acyl-alkyl-phosphatidylcholines (5.7%), sphingomyelins (5.1%), diacyl-phosphatidylcholines (4.4%), lyso-phosphatidylcholines (4.1%), acylcarnitines (3.5%), amino acids (2.2%) and hexose (1.6%). A pattern with high intake of butter and low intake of margarine was related to acylcarnitines, acyl-alkyl-phosphatidylcholines, lyso-phosphatidylcholines and hydroxy-sphingomyelins, particularly with saturated and monounsaturated fatty acid side chains. A pattern with high intake of red meat and fish and low intake of whole-grain bread and tea was related to hexose and phosphatidylcholines. A pattern consisting of high intake of potatoes, dairy products and cornflakes particularly explained methionine and branched chain amino acids. Dietary patterns related to type 2 diabetes-relevant metabolites included high intake of red meat and low intake of whole-grain bread, tea, coffee, cake and cookies, canned fruits and fish.Conclusions:Dietary patterns characterized by intakes of red meat, whole-grain bread, tea and coffee were linked to relevant metabolites and could be potential targets for chronic disease prevention.

Linking diet, physical activity, cardiorespiratory fitness and obesity to serum metabolite networks: findings from a population-based study

Objective:It is not yet resolved how lifestyle factors and intermediate phenotypes interrelate with metabolic pathways. We aimed to investigate the associations between diet, physical activity, cardiorespiratory fitness and obesity with serum metabolite networks in a population-based study.Methods:The present study included 2380 participants of a randomly drawn subcohort of the European Prospective Investigation into Cancer and Nutrition-Potsdam. Targeted metabolomics was used to measure 127 serum metabolites. Additional data were available including anthropometric measurements, dietary assessment including intake of whole-grain bread, coffee and cake and cookies by food frequency questionnaire, and objectively measured physical activity energy expenditure and cardiorespiratory fitness in a subsample of 100 participants. In a data-driven approach, Gaussian graphical modeling was used to draw metabolite networks and depict relevant associations between exposures and serum metabolites. In addition, the relationship of different exposure metabolite networks was estimated.Results:In the serum metabolite network, the different metabolite classes could be separated. There was a big group of phospholipids and acylcarnitines, a group of amino acids and C6-sugar. Amino acids were particularly positively associated with cardiorespiratory fitness and physical activity. C6-sugar and acylcarnitines were positively associated with obesity and inversely with intake of whole-grain bread. Phospholipids showed opposite associations with obesity and coffee intake. Metabolite networks of coffee intake and obesity were strongly inversely correlated (body mass index (BMI): r=−0.57 and waist circumference: r=−0.59). A strong positive correlation was observed between metabolite networks of BMI and waist circumference (r=0.99), as well as the metabolite networks of cake and cookie intake with cardiorespiratory fitness and intake of whole-grain bread (r=0.52 and r=0.50; respectively).Conclusions:Lifestyle factors and phenotypes seem to interrelate in various metabolic pathways. A possible protective effect of coffee could be mediated via counterbalance of pathways of obesity involving hepatic phospholipids. Experimental studies should validate the biological mechanisms.

Amino acids, lipid metabolites, and ferritin as potential mediators linking red meat consumption to type 2 diabetes

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.

Evaluation of various biomarkers as potential mediators of the association between coffee consumption and incident type 2 diabetes in the EPIC-Potsdam Study

BACKGROUND The inverse association between coffee consumption and the risk of type 2 diabetes (T2D) is well established; however, little is known about potential mediators of this association. OBJECTIVE We aimed to investigate the association between coffee consumption and diabetes-related biomarkers and their potential role as mediators of the association between coffee consumption and T2D. DESIGN We analyzed a case-cohort study (subcohort: n = 1610; verified incident T2D cases: n = 417) nested within the European Prospective Investigation into Cancer and Nutrition-Potsdam study involving 27,548 middle-aged participants. Habitual coffee consumption was assessed with a validated, semiquantitative food-frequency questionnaire. We evaluated the association between coffee consumption and several T2D-related biomarkers, such as liver markers (reflected by γ-glutamyltransferase, fetuin-A, and sex hormone-binding globulin), markers of dyslipidemia (high-density lipoprotein cholesterol and triglycerides), inflammation [C-reactive protein (CRP)], an adipokine (adiponectin), and metabolites, stratified by sex. RESULTS Coffee consumption was inversely associated with diacyl-phosphatidylcholine C32:1 in both sexes and with phenylalanine in men, as well as positively associated with acyl-alkyl-phosphatidylcholines C34:3, C40:6, and C42:5 in women. Furthermore, coffee consumption was inversely associated with fetuin-A (P-trend = 0.06) and CRP in women and γ-glutamyltransferase and triglycerides in men. Coffee consumption tended to be inversely associated with T2D risk in both sexes, reaching significance only in men [HR (95% CI): women: ≥4 compared with >0 to <2 cups coffee/d: 0.78 (0.46, 1.33); men: ≥5 compared with >0 to <2 cups coffee/d: 0.40 (0.19, 0.81)]. The association between coffee consumption and T2D risk in men was slightly reduced after adjustment for phenylalanine or lipid markers. CONCLUSIONS Coffee consumption was inversely associated with a diacyl-phosphatidylcholine and liver markers in both sexes and positively associated with certain acyl-alkyl-phosphatidylcholines in women. Furthermore, coffee consumption showed an inverse trend with CRP in women and with triglycerides and phenylalanine in men. However, these markers explained only to a small extent the inverse association between long-term coffee consumption and T2D risk.