Digital Therapeutics Care Utilizing Genetic and Gut Microbiome Signals for the Management of Functional Gastrointestinal Disorders: Results From a Preliminary Retrospective Study.
Digital Therapeutics Care Utilizing Genetic and Gut Microbiome Signals for the Management of Functional Gastrointestinal Disorders: Results From a Preliminary Retrospective Study.
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DOI：
10.3389/fmicb.2022.826916
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发表时间：
2022
影响因子：
5.2
通讯作者：
Sinha R
中科院分区：
文献类型：
--
作者：
Kumbhare SV;Francis-Lyon PA;Kachru D;Uday T;Irudayanathan C;Muthukumar KM;Ricchetti RR;Singh-Rambiritch S;Ugalde J;Dulai PS;Almonacid DE;Sinha R
与饮食和生活方式相关的疾病，包括功能性胃肠道疾病 (FGID) 和肥胖症，正在全球范围内迅速出现的健康问题。研究重点是通过面对面的认知行为疗法、饮食调节和药物干预来解决 FGID。然而，关于数字治疗护理如何实现减肥和减轻 FGID 症状严重程度，以及对 FGID 状态和症状严重程度减轻（包括个性化基因组 SNP 和肠道微生物组信号）进行建模的研究报告却很少。我们这项研究的目的是评估针对基因组 SNP 和肠道微生物组信号的个性化数字治疗干预在减轻成功减肥个体的 FGID 症状方面的效果如何。我们还旨在利用人口统计学、基因组 SNP 和肠道微生物组变量对 FGID 状态和 FGID 症状严重程度减轻进行建模。本研究试图使用人口统计、遗传和基线微生物组数据训练逻辑回归模型来区分参加数字治疗护理计划的受试者的 FGID 状态。我们还训练了线性回归模型，以确定与基线相比体重减轻 5% 或更多时受试者 FGID 症状严重程度的变化。为此，我们利用了 177 名在 Digbi Health 个性化数字护理计划中体重减轻了 5% 或更多的成年人，对他们进行了回顾性调查，了解计划前后 FGID 症状严重程度和其他合并症的变化。肠道微生物群分类群和人口统计学是 FGID 状态的最强预测因素。实施的数字治疗计划降低了 89.42% (93/104) 报告 FGID 的用户症状的总体严重程度。 FGID 症状严重程度和 IBS 症状严重程度的降低最好通过基因组和微生物组预测因子的混合来建模，而腹泻和便秘症状严重程度的降低最好仅通过微生物组预测因子来建模。这项初步回顾性研究产生了 FGID 状态的诊断模型以及减轻 FGID 症状严重程度的治疗模型。此外，这些治疗模型针对许多生物标志物在 FGID 症状预后中的关联产生了可检验的假设。
Diet and lifestyle-related illnesses including functional gastrointestinal disorders (FGIDs) and obesity are rapidly emerging health issues worldwide. Research has focused on addressing FGIDs via in-person cognitive-behavioral therapies, diet modulation and pharmaceutical intervention. Yet, there is paucity of research reporting on digital therapeutics care delivering weight loss and reduction of FGID symptom severity, and on modeling FGID status and symptom severity reduction including personalized genomic SNPs and gut microbiome signals. Our aim for this study was to assess how effective a digital therapeutics intervention personalized on genomic SNPs and gut microbiome signals was at reducing symptomatology of FGIDs on individuals that successfully lost body weight. We also aimed at modeling FGID status and FGID symptom severity reduction using demographics, genomic SNPs, and gut microbiome variables. This study sought to train a logistic regression model to differentiate the FGID status of subjects enrolled in a digital therapeutics care program using demographic, genetic, and baseline microbiome data. We also trained linear regression models to ascertain changes in FGID symptom severity of subjects at the time of achieving 5% or more of body weight loss compared to baseline. For this we utilized a cohort of 177 adults who reached 5% or more weight loss on the Digbi Health personalized digital care program, who were retrospectively surveyed about changes in symptom severity of their FGIDs and other comorbidities before and after the program. Gut microbiome taxa and demographics were the strongest predictors of FGID status. The digital therapeutics program implemented, reduced the summative severity of symptoms for 89.42% (93/104) of users who reported FGIDs. Reduction in summative FGID symptom severity and IBS symptom severity were best modeled by a mixture of genomic and microbiome predictors, whereas reduction in diarrhea and constipation symptom severity were best modeled by microbiome predictors only. This preliminary retrospective study generated diagnostic models for FGID status as well as therapeutic models for reduction of FGID symptom severity. Moreover, these therapeutic models generate testable hypotheses for associations of a number of biomarkers in the prognosis of FGIDs symptomatology.
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影响因子：
4.6
作者：
Polster A;Öhman L;Tap J;Derrien M;Le Nevé B;Sundin J;Törnblom H;Cvijovic M;Simrén M
通讯作者：
Simrén M
影响因子：
29.4
作者：
Clouse, Ray E.;Mayer, Emeran A.;Naliboff, Bruce D.
通讯作者：
Naliboff, Bruce D.
影响因子：
5.9
作者：
Elli L;Tomba C;Branchi F;Roncoroni L;Lombardo V;Bardella MT;Ferretti F;Conte D;Valiante F;Fini L;Forti E;Cannizzaro R;Maiero S;Londoni C;Lauri A;Fornaciari G;Lenoci N;Spagnuolo R;Basilisco G;Somalvico F;Borgatta B;Leandro G;Segato S;Barisani D;Morreale G;Buscarini E
通讯作者：
Buscarini E
影响因子：
3.1
作者：
Agnello, Melissa;Carroll, Lauren N.;Hoaglin, Michael C.
通讯作者：
Hoaglin, Michael C.
影响因子：
15.5
作者：
Fernandes AD;Reid JN;Macklaim JM;McMurrough TA;Edgell DR;Gloor GB
通讯作者：
Gloor GB