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Detecting and quantifying marijuana metabolites in serum and urine of 19 dogs affected by marijuana toxicity.

ABSTRACT: Veterinarians diagnose marijuana toxicity based on clinical signs and history, or in conjunction with an over-the-counter (OTC) human urine drug screen. With the legalization of recreational marijuana use becoming more prevalent in the United States, a more accurate test to aid in the diagnosis of canine marijuana toxicity is needed. We collected urine and serum samples from 19 dogs with confirmed or suspected marijuana toxicosis from multiple veterinary hospitals and analyzed them with a novel UPLC-MS/MS method. Calibrations from 0.1 to 100 ng/mL and QC materials were prepared. Samples were extracted, purified, and eluted with solid-phase extraction. Urine samples were tested with an OTC human urine drug screen. The limit of detection (LOD) and lower limit of quantification (LLOQ) ranges for marijuana metabolites in serum were 0.05-0.25 ng/mL and 0.1-0.5 ng/mL, respectively. In urine, the LOD and LLOQ ranges for the metabolites were 0.05-0.1 ng/mL and 0.1-0.5 ng/mL, respectively. In serum, median and range of metabolite concentrations (ng/mL) detected included: THC, 65.0 (0.14-160); 11-OH-Δ⁹-THC, 4.78 (1.15-17.8); 11-nor-9-carboxy-Δ⁹-THC, 2.18 (0.71-7.79); CBD, 0.28 (0.11-82.5); and THC-glucuronide, 2.05 (0.72-18.3). In the 19 urine samples, metabolite: creatinine (ng: mg) values detected included: THC, 0.22 (0.05-0.74); 11-OH-Δ⁹-THC, 0; 11-nor-9-carboxy-Δ⁹-THC, 1.32 (0.16-11.2); CBD, 0.19 (0.12-0.26); THC-COOH-glucuronide, 0.08 (0.04-0.11); and THC-glucuronide, 0.98 (0.25-10.7). Twenty of 21 urine samples tested negative for THC on the urine drug screen. All 19 serum samples contained quantifiable concentrations of THC using our novel UPLC-MS/MS method. Utilizing a UPLC-MS/MS method can be a useful aid in the diagnosis of marijuana toxicosis in dogs, whereas using an OTC human urine drug test is not a useful test for confirming marijuana exposure in dogs because of the low concentration of THC-COOH in urine.

SUBMITTER: Fitzgerald AH

PROVIDER: S-EPMC8366259 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Obesity is a complex multifactorial phenotype that influences several metabolic pathways. Yet, few studies have examined the relations of different body fat compartments to urinary and serum metabolites. Anthropometric phenotypes (visceral adipose tissue (VAT), subcutaneous adipose tissue (SAT), the ratio between VAT and SAT (VSR), body mass index (BMI), waist circumference (WC)) and urinary and serum metabolite concentrations measured by nuclear magnetic resonance spectroscopy were measured in a population-based sample of 228 healthy adults. Multivariable linear and logistic regression models, corrected for multiple testing using the false discovery rate, were used to associate anthropometric phenotypes with metabolites. We adjusted for potential confounding variables: age, sex, smoking, physical activity, menopausal status, estimated glomerular filtration rate (eGFR), urinary glucose, and fasting status. In a fully adjusted logistic regression model dichotomized for the absence or presence of quantifiable metabolite amounts, VAT, BMI and WC were inversely related to urinary choline (ß = -0.18, p = 2.73*10-3), glycolic acid (ß = -0.20, 0.02), and guanidinoacetic acid (ß = -0.12, p = 0.04), and positively related to ethanolamine (ß = 0.18, p = 0.02) and dimethylamine (ß = 0.32, p = 0.02). BMI and WC were additionally inversely related to urinary glutamine and lactic acid. Moreover, WC was inversely associated with the detection of serine. VAT, but none of the other anthropometric parameters, was related to serum essential amino acids, such as valine, isoleucine, and phenylalanine among men. Compared to other adiposity measures, VAT demonstrated the strongest and most significant relations to urinary and serum metabolites. The distinct relations of VAT, SAT, VSR, BMI, and WC to metabolites emphasize the importance of accurately differentiating between body fat compartments when evaluating the potential role of metabolic regulation in the development of obesity-related diseases, such as insulin resistance, type 2 diabetes, and cardiovascular disease.

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Detecting and quantifying marijuana metabolites in serum and urine of 19 dogs affected by marijuana toxicity.

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