| Literature DB >> 26442272 |
David B Allison1, Josep Bassaganya-Riera2, Barbara Burlingame3, Andrew W Brown4, Johannes le Coutre5, Suzanne L Dickson6, Willem van Eden7, Johan Garssen8, Raquel Hontecillas2, Chor San H Khoo9, Dietrich Knorr10, Martin Kussmann11, Pierre J Magistretti12, Tapan Mehta13, Adrian Meule14, Michael Rychlik15, Claus Vögele16.
Abstract
Entities:
Keywords: behavior; big data analysis; brain health; food; food safety; human microbiome; nutrition; sustainable development
Year: 2015 PMID: 26442272 PMCID: PMC4563164 DOI: 10.3389/fnut.2015.00026
Source DB: PubMed Journal: Front Nutr ISSN: 2296-861X
Common errors noted in the published literature.
| Error | Example(s) of error |
|---|---|
| Errors involving or resulting from poor measurement | • Self-reported energy intake ( |
| • Self-reported weights ( | |
| Errors involving inappropriate choice of or incorrect study design | • Cluster randomized trials with no degrees of freedom ( |
| • Lack of control for non-specific factors, i.e., failure to isolate the independent variable of interest ( | |
| • Non-random assignment in self-described RCTs ( | |
| Errors involving replication | • Not validating prediction models in fresh samples ( |
| • Gratuitous replication ( | |
| Errors in statistical analyses | • Inappropriate baseline testing in parallel groups RCTs ( |
| • Failure to appropriately manage missing data ( | |
| • Not accounting for clustering in cluster randomized trials ( | |
| Errors involving insufficient transparency in choices made about how to analyze and present the data | • Changing endpoints in a study ( |
| • Excessive or unacknowledged multiple testing [called p-hacking ( | |
| Errors of misleadingly describing past literature | • Selectively citing only the part of a study that supports a hypothesis ( |
| • Perpetuating citations from previous research without confirming the original source ( | |
| Errors that distort the scientific record by publishing studies as a function of study outcomes | • Publication bias ( |
| Errors of interpretation or communication | • Inappropriate use of causal language ( |
| • Exaggerating or mis-describing results ( | |
| • Highlighting benefits of treatment when the effects were non-significant (i.e., spin) ( | |
| • Issuing misleading press-releases ( | |
| Errors of logic and mathematics | • Unreasonable linear extrapolations (e.g., 3500 kcal rule) ( |
.
.
.
.
Variation in microbial ecology among individuals (.
| Each person’s microbiome is unique and no two individuals have the same microbiome ( |
|---|
| Microbial communities across varying body regions may predict some characteristics such as breast fed history and educational level |
| Microbial communities from different body regions from an individual were predictive for others. For example, the oral community can be used to predict the gut community |
| Overall, low relative numbers of pathogens have been observed |
| Strong site specialization but considerable variation in diversity and abundance of each habitat’s signature microbes among subjects |
| Strong functional stability. This means that while the microbial compositions were widely different, the functionality is similar. This suggests flexibility to develop microbial communities that can provide similar performance |
| Wide variation in patterns of alpha and beta diversity (alpha-diversity within a site; beta diversity among subjects) |
| Correlations between ethnicity and microbiome composition across all body habitats |
| A positive correlation of vaginal pH to microbial diversity (higher pH having higher diversity) |
| An association of age with skin microbiome-associated metabolic pathways and oral microbiome composition |