Higher plasma levels of lysophosphatidylcholine 18:0 are related to a lower risk of common cancers in a prospective metabolomics study

EPIC-Heidelberg was established as part of the European Prospective Investigation into Cancer and Nutrition (EPIC) between 1994 and 1998 [6]. Overall, 25,540 adults (53.3 % females) aged 35–65 years from the local general population entered the study [7]. The vast majority of participants are white. Detailed information on habitual diet, smoking, alcohol consumption, physical activity, and socio-economic status was obtained by questionnaires and during interviews. In addition, anthropometric measurements were taken by trained personnel. Blood was drawn, processed, and aliquoted into 0.5 ml straws of serum (8 straws), plasma (12 straws), buffy coat (4 straws), and erythrocytes (4 straws), which were stored in liquid nitrogen at −196 °C. Plasma straws used for the present study were retrieved for the first time, i.e. they had not undergone freeze-and-thaw cycles.

Since the baseline, participants are followed-up by active and passive procedures [8]. Self-reported and registry-derived incident cases of cancer were validated by study physicians who accessed diagnostic records provided by treating physicians and hospitals. A case-cohort design [9] was chosen for the present metabolomics study and metabolites were measured in pre-diagnostic blood samples of cancer cases and a random subcohort [10]. Individuals in the case-cohort selection were free of diabetes at baseline, as the subcohort was initially drawn for the EPIC-InterAct study on incident diabetes [10]. All incident primary cases of breast (ICD-10 C50), prostate (ICD-10 C61), and colorectal cancer (ICD-10 C18–20) as well as cases secondary to non-melanoma skin cancer that had occurred before 31 December 2006 were selected. After exclusion of participants with missing covariate information (n = 4) and prevalent cancer cases (n = 65), the subcohort comprised 774 individuals. The final numbers of incident cases of breast, prostate, and colorectal cancer were 362, 310, and 163, respectively, after exclusions due to missing covariate information (breast cancer: n = 3; colorectal cancer: n = 2).

The present study was approved by the Ethics Committee of the Medical Faculty of the University of Heidelberg (Heidelberg, Germany). The study participants provided written consent for the use of their blood samples and data.

Metabolites, i.e. 40 acylcarnitines, 21 amino acids, 14 biogenic amines and creatinine, 76 phosphatidylcholines (PCs), 14 lysophosphatidylcholines (lysoPCs), 15 sphingomyelins, and overall hexose were quantified using the MetaDisIDQ™ Kit (Biocrates, Innsbruck, Austria). Sample preparation and measurements were carried out according to the suppliers’ protocol and have been described in detail elsewhere [11]. In short, 10 μl of standards, quality controls, and plasma samples, which had been thawed for 30 minutes at −4 °C, were pipetted onto filter paper within each well of the 96-well plate that had been commercially prepared. Nitrogen gas was used to dry the samples followed by application of phenylisothiocyanate (PITC) and extraction of metabolites from the filter paper. The eluate was then dried under flowing nitrogen gas and the wells sealed. Kit plates were shipped to the partner laboratory at the Department of Proteomics, Helmholtz Centre for Environmental Research (Leipzig, Germany), where liquid FIA-MS/MS (amino acids and biogenic amines) and LC-MS/MS (lipids and hexoses) analyses were carried out on each kit. Samples were blinded to the laboratory team. Metabolites were detected using an Agilent 1100 HPLC (Agilent Technologies, Böblingen, Germany) coupled to an API 5000 triple quadrupole mass spectrometer (AB Sciex, Darmstadt, Germany).

The MetaDisIDQ™ software (Biocrates) was used to randomize samples of the study population so that samples of cases and non-cases were stratified within and across batches. Each analytical batch contained three quality control (QC) samples of lyophilized human EDTA-plasma with standardized low, medium (spiked), and high (spiked) concentrations of internal standards to monitor the validity of measurements. Two blinded pooled QC plasma samples were included in duplicate to independently verify the consistency of measurements within and across batches. In addition to a commercially available pooled QC sample (NIST SRM 1950), we used a pooled sample from our own population. One blank without internal standard was included on each batch to calculate background levels and to check the system for contamination. Three additional zero samples with internal standards, but no analytes were integrated to calculate limits of detection.

Metabolites with more than 25 % missing values (n = 2), within batch coefficients of variation greater than 25 % (n = 12), or more than 25 % of values below the limit of detection (n = 47) were excluded so that 120 out of 181 metabolites were used for statistical evaluation (Additional file 1). All metabolite values were then log2-tranformed and normalized by metabolite-wise batch-standardization [12]. Multivariable outliers were identified by robust principal components analyses and excluded [13]. Spearman’s rho adjusted for age and sex was calculated to detect correlations between metabolites. Associations between metabolite concentrations and epidemiological covariates were assessed by calculating Spearman’s correlations (continuous covariates) and geometric mean values across categories (categorical covariates). Cox proportional hazards regression analyses were carried out to evaluate the relationships between circulating metabolites and cancer risks. Metabolite concentrations were divided into quartiles and linear trends were tested for using log2-transformed metabolite values on the continuous scale. The so-called Prentice method was used to account for the case-cohort design of the study [14]. Age was used as the underlying timescale, with age at recruitment as age at entry and age at the end of follow-up, death, or cancer diagnosis, whichever came first, as age at exit. The proportional hazards assumption was tested based on Schoenfeld residuals [15]. With respect to the vast majority of metabolites, and particularly those which showed significant associations with cancer risk, no strong violations of the proportional hazards assumption were noted. Adjustment factors for multivariable Cox regression models were selected based on literature review. The Bonferroni method was used to adjust for multiple testing, i.e. P values of single tests were divided by the total number of tests (n = 120). All statistical tests were two-sided and P values below 0.05 were considered statistically significant. All analyses were performed using SAS 9.3 (SAS Institute, Cary, NC, USA).