Strain-Specificity and Disease-Specificity of Probiotic Efficacy: A Systematic Review and Meta-Analysis

Background

As the use and diversity of probiotic products expands, the choice of an appropriate type of probiotic is challenging for both medical care professionals and the public alike. Two vital factors in choosing the appropriate probiotic are often ignored, namely, the probiotic strain-specificity and disease-specificity for efficacy. Reviews and meta-analyses often pool together different types of probiotics, resulting in misleading conclusions of efficacy.

Methods

A systematic review of the literature (1970-2017) assessing strain-specific and disease-specific probiotic efficacy was conducted. Trials were included for probiotics with an identifiable strain (either single strain or mixtures of strains) that had at least two randomized, controlled trials for each type of disease indication. The goal was to determine if probiotic strains have strain and/or disease-specific efficacy.

Results

We included 228 trials and found evidence for both strain specificity and disease specificity for the efficacy of specific probiotic strains. Significant efficacy evidence was found for 7 (70%) of probiotic strain(s) among four preventive indications and 11 (65%) probiotic strain(s) among five treatment indications. Strain-specific efficacy for preventing adult antibiotic-associated diarrhea was clearly demonstrated within the Lactobacillus species [e.g., by the mixture of Lactobacillus acidophilus CL1285, Lactobacillus casei LBC80R, and Lactobacillus rhamnosus CLR2 (Bio-K+^®), by L. casei DN114001 (Actimel^®) and by Lactobacillus reuteri 55730], while other Lactobacillus strains did not show efficacy. Significant disease-specific variations in efficacy was demonstrated by L. rhamnosus GG and Saccharomyces boulardii CNCM I-745, as well as other probiotic strains.

Conclusion

Strong evidence was found supporting the hypothesis that the efficacy of probiotics is both strain-specific and disease-specific. Clinical guidelines and meta-analyses need to recognize the importance of reporting outcomes by both specific strain(s) of probiotics and the type of disease. The clinical relevance of these findings indicates that health-care providers need to take these two factors into consideration when recommending the appropriate probiotic for their patient.

Fecal Microbiota Composition, Their Interactions, and Metagenome Function in US Adults with Type 2 Diabetes According to Enterotypes

T2DM etiology differs among Asians and Caucasians and may be associated with gut microbiota influenced by different diet patterns. However, the association between fecal bacterial composition, enterotypes, and T2DM susceptibility remained controversial. We investigated the fecal bacterial composition, co-abundance network, and metagenome function in US adults with T2DM compared to healthy adults based on enterotypes. We analyzed 1911 fecal bacterial files of 1039 T2DM and 872 healthy US adults from the Human Microbiome Projects. Operational taxonomic units were obtained after filtering and cleaning the files using Qiime2 tools. Machine learning and network analysis identified primary bacteria and their interactions influencing T2DM incidence, clustered into enterotypes, Bacteroidaceae (ET-B), Lachnospiraceae (ET-L), and Prevotellaceae (ET-P). ET-B showed higher T2DM incidence. Alpha-diversity was significantly lower in T2DM in ET-L and ET-P (p < 0.0001), but not in ET-B. Beta-diversity revealed a distinct separation between T2DM and healthy groups across all enterotypes (p < 0.0001). The XGBoost model exhibited high accuracy and sensitivity. Enterocloster bolteae, Facalicatena fissicatena, Clostridium symbiosum, and Facalibacterium prausnitizii were more abundant in the T2DM group than in the healthy group. Bacteroides koreensis, Oscillibacter ruminantium, Bacteroides uniformis, and Blautia wexlerae were lower in the T2DM than in the healthy group regardless of the enterotypes in the XGBoost model (p < 0.0001). However, the patterns of microbial interactions varied among different enterotypes affecting T2DM risk. The interaction between fecal bacteria was more tightly regulated in the ET-L than in the ET-B and ET-P groups (p < 0.001). Metagenomic analysis revealed an inverse association between bacteria abundance in T2DM, energy utility, butanoate and propanoate metabolism, and the insulin signaling pathway (p < 0.0001). In conclusion, fecal bacteria play a role in T2DM pathogenesis, particularly within different enterotypes, providing valuable insights into the link between gut microbiota and T2DM in the US population.

A guide for researchers seeking training in retrospective data harmonization for population neuroscience studies of Alzheimer's disease and related dementias

Due to needs surrounding rigor and reproducibility, subgroup specific disease knowledge, and questions of external validity, data harmonization is an essential tool in population neuroscience of Alzheimer's disease and related dementias (ADRD). Systematic harmonization of data elements is necessary to pool information from heterogeneous samples, and such pooling allows more expansive evaluations of health disparities, more precise effect estimates, and more opportunities to discover effective prevention or treatment strategies. The key goal of this Tutorial in Population Neuroimaging Curriculum, Instruction, and Pedagogy article is to guide researchers in creating a customized population neuroscience of ADRD harmonization training plan to fit their needs or those of their mentees. We provide brief guidance for retrospective data harmonization of multiple data types in this area, including: (1) clinical and demographic, (2) neuropsychological, and (3) neuroimaging data. Core competencies and skills are reviewed, and resources are provided to fill gaps in training as well as data needs. We close with an example study in which harmonization is a critical tool. While several aspects of this tutorial focus specifically on ADRD, the concepts and resources are likely to benefit population neuroscientists working in a range of research areas.

Measuring the Impact of Health on Economic Growth Using Pooling Data in Regions of Asia: Evidence From a Quantile-On-Quantile Analysis

Health improvement has become a significant social priority since a moderately good human capital condition improves the workforce's abilities, efficiency, and quality of life. A rapid increase in healthcare expenditure is a trend in major developing and developed countries; however, healthcare expenditure widely varies among most Asian countries. Asian countries contribute a significant amount of output to economic development worldwide. The statistical test power is more efficient for the pooling of national data than individual national data because of the economic value and trade integration of regional nations. This is the first study that applies the quantile-on-quantile approach to investigate the influence of the quantiles of healthcare on the quantiles of the economy's growth for pooling forty countries in the Asian region. As the quantile of healthcare expenditure increases in the countries, the impact of healthcare expenditure on the economy's growth does not guarantee an increase. The positive and negative effects of healthcare expenditure on developing the economic relationship will repeatedly occur when the quantiles of the economy's growth increase in the countries. One implication is that the governments should account for problems such as corruption, bureaucracy, underinvestment, and inefficiency in health-related resource utilization.

Group testing regression analysis with covariates and specimens subject to missingness

We develop parametric estimators of a conditional prevalence in the group testing context. Group testing is applied when a binary outcome variable, often a disease indicator, is assessed by testing a specimen for the presence of the disease. Instead of testing all individual specimens separately, these are pooled in groups and the grouped specimens are tested for the disease, which permits to significantly reduce the number of tests to be performed. Various techniques have been developed in the literature for estimating a conditional prevalence from group testing data, but most of them are not valid when the data are subject to missingness. We consider this problem in the case where the specimen and the covariates are subject to nonmonotone missingness. We propose parametric estimators of the conditional prevalence, establish identifiability conditions for a logistic missing not at random model, and introduce an ignorable missing at random model. In theory, our estimators could be applied with multiple covariates missing, but in practice, they face numerical challenges when more than one covariate is missing for given individuals. We illustrate the method on simulated data and on a dataset from the Demographics and Health Survey.