Urinary Cannabis Metabolite Concentrations in Cannabis Hyperemesis Syndrome

2021 ◽  
Vol Publish Ahead of Print ◽  
Author(s):  
Jonathan Cordova ◽  
Vincent Biank ◽  
Elizabeth Black ◽  
Jerrold Leikin
1982 ◽  
Vol 14 (4-5) ◽  
pp. 43-58 ◽  
Author(s):  
M Rizet ◽  
J Mouchet

This study was conducted in order to understand the taste and odour problems that occurred in the Seine and the Marne rivers during the severe drought of 1976. Samples were taken every 15 days from several locations in the rivers themselves and from storage reservoirs upstream from Paris. Algae and actinomycetes were identified and counted. Metabolite concentrations were measured. These data were correlated with threshold odor numbers and bacteriological water quality parameters.


2021 ◽  
Vol 197 ◽  
pp. 110891
Author(s):  
Genoa R. Warner ◽  
Diana C. Pacyga ◽  
Rita S. Strakovsky ◽  
Rebecca Smith ◽  
Tamarra James-Todd ◽  
...  

Author(s):  
Maxim Kuzin ◽  
Franziskos Xepapadakos ◽  
Isabel Scharrer ◽  
Marc Augsburger ◽  
Chin‐Bin Eap ◽  
...  

Metabolites ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 8
Author(s):  
Michiel Bongaerts ◽  
Ramon Bonte ◽  
Serwet Demirdas ◽  
Edwin H. Jacobs ◽  
Esmee Oussoren ◽  
...  

Untargeted metabolomics is an emerging technology in the laboratory diagnosis of inborn errors of metabolism (IEM). Analysis of a large number of reference samples is crucial for correcting variations in metabolite concentrations that result from factors, such as diet, age, and gender in order to judge whether metabolite levels are abnormal. However, a large number of reference samples requires the use of out-of-batch samples, which is hampered by the semi-quantitative nature of untargeted metabolomics data, i.e., technical variations between batches. Methods to merge and accurately normalize data from multiple batches are urgently needed. Based on six metrics, we compared the existing normalization methods on their ability to reduce the batch effects from nine independently processed batches. Many of those showed marginal performances, which motivated us to develop Metchalizer, a normalization method that uses 10 stable isotope-labeled internal standards and a mixed effect model. In addition, we propose a regression model with age and sex as covariates fitted on reference samples that were obtained from all nine batches. Metchalizer applied on log-transformed data showed the most promising performance on batch effect removal, as well as in the detection of 195 known biomarkers across 49 IEM patient samples and performed at least similar to an approach utilizing 15 within-batch reference samples. Furthermore, our regression model indicates that 6.5–37% of the considered features showed significant age-dependent variations. Our comprehensive comparison of normalization methods showed that our Log-Metchalizer approach enables the use out-of-batch reference samples to establish clinically-relevant reference values for metabolite concentrations. These findings open the possibilities to use large scale out-of-batch reference samples in a clinical setting, increasing the throughput and detection accuracy.


2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Luke Whiley ◽  
◽  
Katie E. Chappell ◽  
Ellie D’Hondt ◽  
Matthew R. Lewis ◽  
...  

Abstract Background Both serotonergic signalling disruption and systemic inflammation have been associated with the pathogenesis of Alzheimer’s disease (AD). The common denominator linking the two is the catabolism of the essential amino acid, tryptophan. Metabolism via tryptophan hydroxylase results in serotonin synthesis, whilst metabolism via indoleamine 2,3-dioxygenase (IDO) results in kynurenine and its downstream derivatives. IDO is reported to be activated in times of host systemic inflammation and therefore is thought to influence both pathways. To investigate metabolic alterations in AD, a large-scale metabolic phenotyping study was conducted on both urine and serum samples collected from a multi-centre clinical cohort, consisting of individuals clinically diagnosed with AD, mild cognitive impairment (MCI) and age-matched controls. Methods Metabolic phenotyping was applied to both urine (n = 560) and serum (n = 354) from the European-wide AddNeuroMed/Dementia Case Register (DCR) biobank repositories. Metabolite data were subsequently interrogated for inter-group differences; influence of gender and age; comparisons between two subgroups of MCI - versus those who remained cognitively stable at follow-up visits (sMCI); and those who underwent further cognitive decline (cMCI); and the impact of selective serotonin reuptake inhibitor (SSRI) medication on metabolite concentrations. Results Results revealed significantly lower metabolite concentrations of tryptophan pathway metabolites in the AD group: serotonin (urine, serum), 5-hydroxyindoleacetic acid (urine), kynurenine (serum), kynurenic acid (urine), tryptophan (urine, serum), xanthurenic acid (urine, serum), and kynurenine/tryptophan ratio (urine). For each listed metabolite, a decreasing trend in concentrations was observed in-line with clinical diagnosis: control > MCI > AD. There were no significant differences in the two MCI subgroups whilst SSRI medication status influenced observations in serum, but not urine. Conclusions Urine and serum serotonin concentrations were found to be significantly lower in AD compared with controls, suggesting the bioavailability of the neurotransmitter may be altered in the disease. A significant increase in the kynurenine/tryptophan ratio suggests that this may be a result of a shift to the kynurenine metabolic route due to increased IDO activity, potentially as a result of systemic inflammation. Modulation of the pathways could help improve serotonin bioavailability and signalling in AD patients.


Author(s):  
Hongsoo Kim ◽  
Seyune Lee ◽  
Young-Il Jung ◽  
Yun-Chul Hong

Only a few studies have examined the impacts of environmental exposure on frailty. This study investigated the association between phthalates and frailty among community-dwelling older adults. The Korean Elderly Environmental Panel II (KEEP II) study is a repeated panel data study of 800 community-dwelling older adults in South Korea. Frailty was measured with five items defined by Fried and colleagues. Environmental pollutants in the form of two types of metabolites for Di-ethylhexyl phthalate (DEHPs)—Mono (2-ethyl-5-hydroxyhexyl) phthalate (MEHHP) and Mono (2-ethyl-5-oxohexyl) phthalate (MEOHP)—were obtained from urine specimens. Analyses were performed using repeated linear mixed models. The concentration levels of both MEOHP and MEHHP in urine were significantly higher in the pre-frail or frail group than its counterparts. While adjusting for covariates, MEOHP level was positively associated with the likelihood of being pre-frail or frail in both males and females; the concentration level of MEHHP also had a positive impact on the likelihood of being pre-frail or frail in females. The DEHP metabolite concentrations were significantly lower among adults with daily fruit consumption in both males and females. DEHPs, measured by metabolite concentrations, may increase the risk of frailty among older men and women; further studies are necessary. The preventive effects of nutrition on DEHP risk should also be further investigated.


2021 ◽  
Vol 156 ◽  
pp. 106633
Author(s):  
Catheryne Chiang ◽  
Diana C. Pacyga ◽  
Rita S. Strakovsky ◽  
Rebecca L. Smith ◽  
Tamarra James-Todd ◽  
...  

Sign in / Sign up

Export Citation Format

Share Document