Metabolomics data complemented drug use information in epidemiological databases: pilot study of potential kidney donors

High-abundance peaks and peak clusters associate with pharmaceutical polymers and excipients in urinary untargeted clinical metabolomics data: exploration of their origin and possible impact on label-free quantification

Pharmaceutical polymers and excipients represent interesting but often overlooked chemical classes in clinical exposure and bioanalytical research. These chemicals may cause hypersensitivity reactions, they can be useful to confirm exposure to pharmaceuticals, and they may pose bioanalytical challenges, including ion suppression in liquid chromatography-mass spectrometry (LC-MS-)based workflows. In this work, we assessed these chemicals in light of a rather surprising finding presented in two previously published studies, namely that usage of cyclosporine A, an immunosuppressive drug which is known to be cleared through excretion in the bile, explained the largest amount of variance in principal component analysis of urinary LC-SWATH/MS small-molecule profiling data. Specifically, we examined the freely-accessible 24-hour urine metabolomics data of 570 kidney transplant recipients included in the TransplantLines Biobank and Cohort Study (NCT03272841). These data unveiled thousands of high-abundance polymer peaks in some samples, which were associated with the use of the macrogol (i.e., polyethylene glycol) 3350 oral laxative agent. In addition, we found multiple clusters of high-abundance peaks which were linked to the exposure to two pharmaceutical excipients, namely short-chain polyethylene glycol (molecular weight <1000 Da) and polyethoxylated castor oil (also known as Kolliphor® EL or Cremophor® EL). Respectively, these excipients are used in temazepam capsules and cyclosporine A capsules, and the latter provides a plausible explanation for the rather surprising finding that instigated our work. Moreover, such explanation and our findings in general put emphasis on taking into consideration these and other pharmaceutical polymers and excipients when exploring, processing, and interpreting clinical small-molecule profiling data.

Matching Drug Metabolites from Non-Targeted Metabolomics to Self-Reported Medication in the Qatar Biobank Study

Modern metabolomics platforms are able to identify many drug-related metabolites in blood samples. Applied to population-based biobank studies, the detection of drug metabolites can then be used as a proxy for medication use or serve as a validation tool for questionnaire-based health assessments. However, it is not clear how well detection of drug metabolites in blood samples matches information on self-reported medication provided by study participants. Here, we curate free-text responses to a drug-usage questionnaire from 6000 participants of the Qatar Biobank (QBB) using standardized WHO Anatomical Therapeutic Chemical (ATC) Classification System codes and compare the occurrence of these ATC terms to the detection of drug-related metabolites in matching blood plasma samples from 2807 QBB participants for which we collected non-targeted metabolomics data. We found that the detection of 22 drug-related metabolites significantly associated with the self-reported use of the corresponding medication. Good agreement of self-reported medication with non-targeted metabolomics was observed, with self-reported drugs and their metabolites being detected in a same blood sample in 79.4% of the cases. On the other hand, only 29.5% of detected drug metabolites matched to self-reported medication. Possible explanations for differences include under-reporting of over-the-counter medications from the study participants, such as paracetamol, misannotation of low abundance metabolites, such as metformin, and inability of the current methods to detect them. Taken together, our study provides a broad real-world view of what to expect from large non-targeted metabolomics measurements in population-based biobank studies and indicates areas where further improvements can be made.

Association of diuretic use with increased risk for long-term post-transplantation diabetes mellitus in kidney transplant recipients

Background

Post-transplantation diabetes mellitus (PTDM) is a major clinical problem in kidney transplant recipients (KTRs). Diuretic-induced hyperglycaemia and diabetes have been described in the general population. We aimed to investigate whether diuretics also increase PTDM risk in KTRs.

Methods

We included 486 stable outpatient KTRs (with a functioning graft ≥1 year) without diabetes from a prospective cohort study. Participants were classified as diuretic users and non-users based on their medication use verified by medical records.

Results

At the baseline study, 168 (35%) KTRs used a diuretic (thiazide, n = 74; loop diuretic, n = 76; others, n = 18) and 318 KTRs did not use a diuretic. After 5.2 years [interquartile range (IQR) 4.0‒5.9] of follow up, 54 (11%) KTRs developed PTDM. In Cox regression analyses, diuretic use was associated with incident PTDM, independent of age, sex, fasting plasma glucose (FPG) and haemoglobin A_1c (HbA_1c) {hazard ratio [HR] 3.28 [95% confidence interval (CI) 1.84–5.83]; P <0.001}. Further adjustment for potential confounders, including lifestyle, family history of cardiovascular disease, use of other medication, kidney function, transplantation-specific parameters, BMI, lipids and blood pressure did not materially change the association. Moreover, in Cox regression analyses, both thiazide and loop diuretics associated with the development of PTDM, independent of age, sex, FPG and HbA_1c [HR 2.70 (95% CI 1.24–5.29); P = 0.012 and HR 5.08 (95% CI 2.49–10.34); P <0.001), respectively].

Conclusions

This study demonstrates that diuretics overall are associated with an increased risk of developing PTDM in KTRs, independent of established risk factors for PTDM development. The association was present for both thiazide and loop diuretics.

A Checklist for Reproducible Computational Analysis in Clinical Metabolomics Research

Clinical metabolomics emerged as a novel approach for biomarker discovery with the translational potential to guide next-generation therapeutics and precision health interventions. However, reproducibility in clinical research employing metabolomics data is challenging. Checklists are a helpful tool for promoting reproducible research. Existing checklists that promote reproducible metabolomics research primarily focused on metadata and may not be sufficient to ensure reproducible metabolomics data processing. This paper provides a checklist including actions that need to be taken by researchers to make computational steps reproducible for clinical metabolomics studies. We developed an eight-item checklist that includes criteria related to reusable data sharing and reproducible computational workflow development. We also provided recommended tools and resources to complete each item, as well as a GitHub project template to guide the process. The checklist is concise and easy to follow. Studies that follow this checklist and use recommended resources may facilitate other researchers to reproduce metabolomics results easily and efficiently.

Boron Intake and decreased risk of mortality in kidney transplant recipients

Purpose

In a search for potentially modifiable factors to improve long-term outcome among kidney transplant recipients (KTR), we hypothesized that boron exposure is associated with improved long-term outcome in KTR.

Methods

We determined 24 h urinary boron excretion using inductively coupled plasma mass spectrometry as a measure of boron exposure in 693 stable KTR (57% male, mean age 53y), enrolled in the TransplantLines Food and Nutrition Biobank and Cohort Study. Dietary intake was assessed using validated food-frequency questionnaires.

Results

Linear regression analyses showed that dietary intake of fruit, wine and nuts were key determinants of boron excretion. In addition, boron excretion was negatively correlated with homocysteine and inflammatory parameters. In total, 73 (32%), 47 (20%) and 30 (13%) KTR died among the lowest, middle and highest tertiles of 24 h urinary boron excretion, respectively (P_log-rank < 0.001). Cox regression analyses showed that high boron excretion was strongly associated with lower risk of mortality, independent of age, sex, estimated glomerular filtration rate and history of cardiovascular disease (HR per doubling: 0.51, 95% CI: 0.40 to 0.66, P < 0.001).

Conclusion

Boron may be an overlooked target to improve long-term survival among KTR and potentially other patients, likely through pathways other than inflammation or the methionine-homocysteine cycle that were previously suggested. Interventional trials are warranted to confirm the potential of dietary boron supplementation in KTR and other patient populations.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00394-021-02702-0.

Food Metabolites as Tools for Authentication, Processing, and Nutritive Value Assessment

Secondary metabolites are molecules with unlimited applications that have been gaining importance in various industries and studied from many angles. They are mainly used for their bioactive capabilities, but due to the improvement of sensibility in analytical chemistry, they are also used for authentication and as a quality control parameter for foods, further allowing to help avoid food adulteration and food fraud, as well as helping understand the nutritional value of foods. This manuscript covers the examples of secondary metabolites that have been used as qualitative and authentication molecules in foods, from production, through processing and along their shelf-life. Furthermore, perspectives of analytical chemistry and their contribution to metabolite detection and general perspectives of metabolomics are also discussed.