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Neurogastroenterol Motil. 2009 Oct;21(10):1039-e83. doi: 10.1111/j.1365-2982.2009.01340.x. Epub 2009 Jun 22.

Influence of clinical parameters on the results of 13C-octanoic acid breath tests: examination of different mathematical models in a large patient cohort.

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Israelitic Hospital, Hamburg, Germany.


It is assumed, although not proven, that 13CO2-excretion following ingestion of 13C-octanoic acid (13C-OA) does not only depend on gastric emptying (GE) but also on absorption and metabolism of 13C-OA and endogenous CO2-production. Our aims were (i) to test the effects of patient characteristics and of diseases that may impair 13C-OA-metabolism on GE parameters. (ii) To compare different GE endpoints. Therefore, we investigated effects of age, gender, BMI and diseases with potential impact on 13C-OA-metabolism (including pancreatic, liver and lung disease, diabetes, IBD) on cumulative 4h-13CO2-excretion (4h-CUM) and T1/2 calculated by non-linear regression model (NL, determined by shape of breath test curve) and generalized linear regression model (GLR, reflects absolute 13CO2-excretion) in 1279 patients and 19 healthy controls who underwent a standardized 13C-OA-breath test. Digestive and metabolic disturbances hardly influenced 4h-CUM or T1/2 calculated by NL or GLR models. In the multivariate linear regression models, 4h-CUM was significantly predicted by diabetes adjusted for age, gender and IBD but influence of these parameters was small (R2 = 0.028, P < 0.0001). T1/2(NL) and 4h-CUM were weakly correlated, even after exclusion of tests with unrealistically high estimates for T1/2(NL) (n = 1095, R(2) = 0.029, P < 0.0001). Conversely, 4h-CUM was closely associated with T(1/2)(GLR) (exponential correlation, R(2) = 0.774, P < 0.00001, n = 1279). We conclude that influences of digestive and metabolic disturbances on 13CO2-excretion following 13C-OA-application are generally low. Thus, our findings resolve an important criticism of methods using absolute 13CO2-excretion for evaluation of 13C-OA-breath tests and suggest that such models may correctly identify T1/2 in a mixed patient population.

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