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Metabolomics for Prediction of Relapse in Graves' Disease: Observational Pilot Study.

ABSTRACT: Background: There is a lack of biochemical markers for early prediction of relapse in patients with Graves' disease [GD], which may help to direct treatment decisions. We assessed the prognostic ability of a high-throughput proton NMR metabolomic profile to predict relapse in a well characterized cohort of GD patients. Methods: Observational study investigating patients presenting with GD at a Swiss hospital endocrine referral center and an associated endocrine outpatient clinic. We measured 227 metabolic markers in the blood of patients before treatment initiation. Main outcome was relapse of hyperthyroidism within 18 months of stopping anti-thyroid drugs. We used ROC analysis with AUC to assess discrimination. Results: Of 69 included patients 18 (26%) patients had a relapse of disease. The clinical GREAT score had an AUC of 0.68 (95% CI 0.63-0.70) to predict relapse. When looking at the metabolomic markers, univariate analysis revealed pyruvate and triglycerides in medium VLDL as predictors with AUCs of 0.73 (95% CI 0.58-0.84) and 0.67 (95% CI 0.53-0.80), respectively. All other metabolomic markers had lower AUCs. Conclusion: Overall, metabolomic markers in our pilot study had low to moderate prognostic potential for prediction of relapse of GD, with pyruvate and triglycerides being candidates with acceptable discriminatory abilities. Our data need validation in future larger trials.

SUBMITTER: Struja T

PROVIDER: S-EPMC6199355 | biostudies-other | 2018

REPOSITORIES: biostudies-other

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Metabolomics for Prediction of Relapse in Graves' Disease: Observational Pilot Study.

Struja Tristan T Eckart Andreas A Kutz Alexander A Huber Andreas A Neyer Peter P Kraenzlin Marius M Mueller Beat B Meier Christian C Bernasconi Luca L Schuetz Philipp P

Frontiers in endocrinology 20181017

<b>Background:</b> There is a lack of biochemical markers for early prediction of relapse in patients with Graves' disease [GD], which may help to direct treatment decisions. We assessed the prognostic ability of a high-throughput proton NMR metabolomic profile to predict relapse in a well characterized cohort of GD patients. <b>Methods:</b> Observational study investigating patients presenting with GD at a Swiss hospital endocrine referral center and an associated endocrine outpatient clinic. W ...[more]

PMID: 30386302

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Project description:IntroductionIntravenous glucocorticoids pulse therapy is the first-line treatment for moderate-to-severe and active Graves' ophthalmopathy, with a large proportion of patients having poor efficacy and exposed to the risk of glucocorticoids adverse effects. We introduce a novel protocol to develop a prediction model designed to identify patients with Graves' ophthalmopathy who are not likely to benefit from intravenous glucocorticoids pulse therapy before administration, so that these patients can advance the time to receive appropriate treatment. Existing prediction models for prognosis of Graves' ophthalmopathy have usually focused on traditional clinical indicators without adequate consideration of orbital soft tissue changes. Our protocol for model development will address this limitation by using artificial intelligence models to quantify facial morphological changes.Methods and analysisThis study is a single-centre, prospective and observational study. A sample size of 278 patients with moderate-to-severe and active Graves' ophthalmopathy will be prospectively recruited at ophthalmology clinic of Shanghai Ninth People's Hospital to collect clinical and artificial intelligence model's baseline data as potential variables to develop the prediction model. They will receive 12-week intravenous glucocorticoids pulse therapy according to the 2021 European Group on Graves' Orbitopathy treatment guideline. After standard medication course and following 12-week observation, patients will be evaluated for the effectiveness of treatment in our ophthalmology clinic and divided into glucocorticoids-sensitive and glucocorticoids-insensitive groups. The model will be developed by means of multivariate logistic regression to select the best variables for the prediction of glucocorticoids treatment efficacy before administration. The result of the study will provide evidence for the use of a prediction model to personalise treatment options for patients with moderate-to-severe and active Graves' ophthalmopathy.Ethics and disseminationThe study received approval from the Ethics Committee of Shanghai Ninth People's Hospital (ethical approval number: SH9H-2020-T211-1. Findings will be disseminated via peer-reviewed publications and conference presentations.Trial registration numberChiCTR2000036584 (Pre-results).

Project description:BackgroundPatients with hematological cancer who experience relapse or progressive disease often face yet another line of treatment and continued mortality risk that could increase their physical and emotional trauma and worsen their health-related quality of life. Healthcare professionals who use patient-reported outcomes to identify who will have specific sensitivities in particular health-related quality of life domains may be able to individualize and target treatment and supportive care, both features of precision medicine. Here, in a cohort of patients with relapsed or progressive hematological cancer, we sought to identify health-related quality of life domains in which they experienced deterioration after relapse treatment and to investigate health-related quality of life patterns.MethodPatients were recruited in connection with a precision medicine study at the Department of Hematology, Aalborg University Hospital. They completed the European Organization for Research and Treatment of Cancer questionnaire and the Hospital Anxiety and Depression Scale at baseline and at 3, 6, 9, and 12 months after the relapse diagnosis or progressive cancer. Modes of completion were electronically or on paper. Clinically relevant changes from baseline to 12 months were interpreted according to Cocks' guidelines. We quantified the number of patients with moderate or severe symptoms and functional problems and the number who experienced improvements or deterioration from baseline to 12 months.ResultsA total of 104 patients were included, of whom 90 (87%) completed baseline questionnaires and 50 (56%) completed the 12-month assessments. The three symptoms that patients most often reported as deteriorating were fatigue (18%), insomnia (18%), and diarrhea (18%). The three functions that patients most often reported as deteriorating were role (16%) and emotional (16%) and cognitive (16%) functioning.ConclusionIn this study, patient-reported outcome data were useful for identifying negatively affected health-related quality of life domains in patients with relapsed or progressive hematological cancer. We identified patients experiencing deterioration in health-related quality of life during treatment and characterized a potential role for patient-reported outcomes in precision medicine to target treatment and supportive care in this patient group.

Dataset Information

Metabolomics for Prediction of Relapse in Graves' Disease: Observational Pilot Study.

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Metabolomics for Prediction of Relapse in Graves' Disease: Observational Pilot Study.

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