Dilmaghani Saam, Lupianez-Merly Camille, BouSaba Joelle, Vijayvargiya Priya, Busciglio Irene, Ferber Monique, Carlson Paula, Donato Leslie J, Camilleri Michael
Clinical Enteric Neuroscience Translational and Epidemiological Research, Mayo Clinic, Rochester, Minnesota.
Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota.
Clin Gastroenterol Hepatol. 2025 May 14. doi: 10.1016/j.cgh.2025.02.031.
Diagnosis of bile acid diarrhea (BAD) has been based on 48-hour fecal BA excretion; serum 7αC4 (C4) has been used to screen for BAD. Optimal diagnostic cutoffs for C4 and biochemical measurements in a single stool sample are unknown. We sought to examine the relationship between total BA concentration (TBAc) and percent primary BA (%PBA) in a single stool sample and serum C4 in patients with and without BAD and explore performance characteristics of stool consistency and biochemical (serum C4 and single-stool BA) parameters for diagnosis of BAD compared with gold standard 48-hour fecal BA.
Based on data from patients with BAD, irritable bowel syndrome with diarrhea (IBS-D), and healthy control subjects, we assessed correlations among stool and serum measurements. Machine learning models (based on data from 30 patients with BAD, 8 patients with IBS-D, and 26 healthy control subjects) were trained on 25 bootstrapped random samples, the superior model was identified, and optimal cutoffs of biological measurements to diagnose BAD were summarized.
There were correlations between serum C4 and %PBA (R = 0.284, P < .001), and between %PBA and TBAc (R = 0.49, P < .001). Using a %PBA of 1.05% (25th percentile in BAD), the %PBA distinguished BAD from IBS-D (odds ratio, 3.06; 95% confidence interval, 1.35-7.46; P = .01). The multivariate logistic regression model had superior balance of variance and bias. Optimal cutoffs for predicting BAD using logistic regression were 4.5% PBA (P = .023) and 1.88 μmol/g TBAc (P = .016). Serum C4 >24 ng/mL and PBA >4.6% individually had 57% and 75.8% positive predictive value, respectively, but together had a 90.1% positive predictive value. Stool consistency was less informative.
New diagnostic cutoffs based on serum C4 and single-stool TBAc and % PBA provide potential alternatives for diagnosing BAD. Further validation is warranted.
胆汁酸腹泻(BAD)的诊断一直基于48小时粪便胆汁酸排泄量;血清7αC4(C4)已被用于筛查BAD。单一粪便样本中C4的最佳诊断临界值以及生化检测指标尚不清楚。我们试图研究单一粪便样本中总胆汁酸浓度(TBAc)和初级胆汁酸百分比(%PBA)与有无BAD患者血清C4之间的关系,并探讨与金标准48小时粪便胆汁酸相比,粪便稠度和生化指标(血清C4和单一粪便胆汁酸)对BAD诊断的性能特征。
基于BAD患者、腹泻型肠易激综合征(IBS-D)患者和健康对照者的数据,我们评估了粪便和血清检测指标之间的相关性。机器学习模型(基于30例BAD患者、8例IBS-D患者和26例健康对照者的数据)在25个自抽样随机样本上进行训练,确定了最优模型,并总结了诊断BAD的生物学检测指标的最佳临界值。
血清C4与%PBA之间存在相关性(R = 0.284,P <.001),%PBA与TBAc之间也存在相关性(R = 0.49,P <.001)。使用1.05%的%PBA(BAD患者的第25百分位数),%PBA可将BAD与IBS-D区分开来(比值比,3.06;95%置信区间,1.35 - 7.46;P =.01)。多变量逻辑回归模型在方差和偏差的平衡方面表现更优。使用逻辑回归预测BAD的最佳临界值为4.5% PBA(P =.023)和1.88 μmol/g TBAc(P =.016)。血清C4>24 ng/mL和PBA>4.6%的阳性预测值分别为57%和75.8%,但两者结合时阳性预测值为90.1%。粪便稠度提供的信息较少。
基于血清C4、单一粪便TBAc和%PBA的新诊断临界值为BAD的诊断提供了潜在的替代方法。有必要进行进一步验证。