Epidemiologic studies of the human microbiome and cancer.
Ontology highlight
ABSTRACT:
SUBMITTER: Vogtmann E
PROVIDER: S-EPMC4742587 | biostudies-other | 2016 Feb
REPOSITORIES: biostudies-other
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Project description:The microbiome has been hypothesized to play a role in cancer development. Because of the diversity of published data, an overview of available epidemiologic evidence linking the microbiome with cancer is now needed. We conducted a systematic review using a tailored search strategy in Medline and EMBASE databases to identify and summarize the current epidemiologic literature on the relationship between the microbiome and different cancer outcomes published until December 2019. We identified 124 eligible articles. The large diversity of parameters used to describe microbial composition made it impossible to harmonize the different studies in a way that would allow meta-analysis, therefore only a qualitative description of results could be performed. Fifty studies reported differences in the gut microbiome between patients with colorectal cancer and various control groups. The most consistent findings were for Fusobacterium, Porphyromonas, and Peptostreptococcus being significantly enriched in fecal and mucosal samples from patients with colorectal cancer. For the oral microbiome, significantly increased and decreased abundance was reported for Fusobacterium and Streptococcus, respectively, in patients with oral cancer compared with controls. Overall, although there was a large amount of evidence for some of these alterations, most require validation in high-quality, preferably prospective, epidemiologic studies.
| S-EPMC7541789 | biostudies-literature
Project description:Temporal variation in microbiome measurements can reduce statistical power in research studies. Quantification of this variation is essential for designing studies of chronic disease. We analyzed 16S ribosomal RNA profiles in paired biological specimens separated by 6 months from 3 studies conducted during 1985-2013 (a National Cancer Institute colorectal cancer study, a Costa Rica study, and the Human Microbiome Project). We evaluated temporal stability by calculating intraclass correlation coefficients (ICCs). Sample sizes needed in order to detect microbiome differences between equal numbers of cases and controls for a nested case-control design were calculated on the basis of estimated ICCs. Across body sites, 12 phylum-level ICCs were greater than 0.5. Similarly, 11 alpha-diversity ICCs were greater than 0.5. Fecal beta-diversity estimates had ICCs over 0.5. For a single collection with most microbiome metrics, detecting an odds ratio of 2.0 would require 300-500 cases when matching 1 case to 1 control at P = 0.05. Use of 2 or 3 sequential specimens reduces the number of required subjects by 40%-50% for low-ICC metrics. Relative abundances of major phyla and alpha-diversity metrics have low temporal stability. Thus, detecting associations of moderate effect size with these metrics will require large sample sizes. Because beta diversity for feces is reasonably stable over time, smaller sample sizes can detect associations with community composition. Sequential prediagnostic specimens from thousands of prospectively ascertained cases are required to detect modest disease associations with particular microbiome metrics.
| S-EPMC5982812 | biostudies-literature
Project description:Vaginal bacterial communities are thought to help prevent sexually transmitted infections. Bacterial vaginosis (BV) is a common clinical syndrome in which the protective lactic acid-producing bacteria (mainly species of the Lactobacillus genus) are supplanted by a diverse array of anaerobic bacteria. Epidemiologically, BV has been shown to be an independent risk factor for adverse outcomes including preterm birth, development of pelvic inflammatory disease, and acquisition of sexually transmitted infections. Longitudinal studies of the vaginal microbiome using molecular techniques such as 16S ribosomal DNA analysis may lead to interventions that shift the vaginal microbiota toward more protective states.
| S-EPMC3225992 | biostudies-other
Project description:BackgroundCirculating melatonin is a good candidate biomarker for studies of circadian rhythms and circadian disruption. However, epidemiologic studies on circulating melatonin are limited because melatonin is secreted at night, yet most epidemiologic studies collect blood during the day when melatonin levels are very low, and assays are lacking that are ultrasensitive to detect low levels of melatonin reliably.ObjectiveTo assess the performance of a refined radioimmunoassay in measuring morning melatonin among women.MethodsWe used morning serum samples from 47 postmenopausal women ages 48-80 years without a history of breast cancer who participated in the San Francisco Bay Area Breast Cancer Study, including 19 women who had duplicate measurements. The coefficient of variation (CV) and intraclass coefficient (ICC) were estimated using the random effect model.ResultsReproducibility for the assay was satisfactory, with a CV of 11.2% and an ICC of 98.9%; correlation between the replicate samples was also high (R = 0.96). In the 47 women, serum melatonin levels ranged from 0.6 to 62.6 pg/ml, with a median of 7.0 pg/ml.ConclusionOur results suggest that it is possible to reliably measure melatonin in postmenopausal women in morning serum samples in large epidemiologic studies to evaluate the role of melatonin in cancer etiology or prognosis.
| S-EPMC5895067 | biostudies-literature
Project description:The obesity epidemic has emerged over the past few decades and is thought to be a result of both genetic and environmental factors. A newly identified factor, the gut microbiota, which is a bacterial ecosystem residing within the gastrointestinal tract of humans, has now been implicated in the obesity epidemic. Importantly, this bacterial community is impacted by external environmental factors through a variety of undefined mechanisms. We focus this review on how the external environment may impact the gut microbiota by considering, the host's geographic location 'human geography', and behavioral factors (diet and physical activity). Moreover, we explore the relationship between the gut microbiota and obesity with these external factors. And finally, we highlight here how an epidemiologic model can be utilized to elucidate causal relationships between the gut microbiota and external environment independently and collectively, and how this will help further define this important new factor in the obesity epidemic.
| S-EPMC4710045 | biostudies-literature
Project description:BackgroundWhile the primary role of central cancer registries in the United States is to provide vital information needed for cancer surveillance and control, these registries can also be leveraged for population-based epidemiologic studies of cancer survivors. This study was undertaken to assess the feasibility of using the NCI's Surveillance, Epidemiology, and End Results (SEER) Program registries to rapidly identify, recruit, and enroll individuals for survivor research studies and to assess their willingness to engage in a variety of research activities.MethodsIn 2016 and 2017, six SEER registries recruited both recently diagnosed and longer-term survivors with early age-onset multiple myeloma or colorectal, breast, prostate, or ovarian cancer. Potential participants were asked to complete a survey, providing data on demographics, health, and their willingness to participate in various aspects of research studies.ResultsResponse rates across the registries ranged from 24.9% to 46.9%, with sample sizes of 115 to 239 enrolled by each registry over a 12- to 18-month period. Among the 992 total respondents, 90% answered that they would be willing to fill out a survey for a future research study, 91% reported that they would donate a biospecimen of some type, and approximately 82% reported that they would consent to have their medical records accessed for research.ConclusionsThis study demonstrated the feasibility of leveraging SEER registries to recruit a geographically and racially diverse group of cancer survivors.ImpactCentral cancer registries are a source of high-quality data that can be utilized to conduct population-based cancer survivor studies.
| S-EPMC7484198 | biostudies-literature
Project description:Exploring spatial-temporal patterns of disease incidence identifies areas of significantly elevated risk and can lead to discoveries of disease risk factors. One popular way to investigate patterns in risk over space and time is spatial-temporal cluster detection analysis. The identification of significant clusters may lead to etiological hypotheses to explain the pattern of elevated risk and to additional epidemiologic studies to explore these hypotheses. Several methodological issues and data challenges that arise in space-time cluster analysis of chronic diseases, such as cancer, include poor spatial precision of residence locations, long disease latencies, and adjustment for known risk factors. This paper reviews the key challenges faced when performing cluster analyses of chronic diseases and presents a spatial-temporal analysis of non-Hodgkin lymphoma (NHL) risk addressing these challenges. Residential histories, collected as part of a population-based case-control study of NHL (the National Cancer Institute [NCI]-Surveillance, Epidemiology, and End Results [SEER] NHL study) in four SEER centers (Detroit metropolitan area, Los Angeles, California, Seattle metropolitan area, and Iowa) were geocoded. In this analysis, we explored previously detected spatial-temporal clusters and adjusted for exposure to polychlorinated biphenyls (PCBs) and genetic polymorphisms in four genes, previously found to be associated with NHL, using a generalized additive model framework. We found that the genetic factors and PCB exposure did not fully explain previously detected areas of elevated risk.
| S-EPMC6450537 | biostudies-literature
Project description:We review approaches for characterizing "peak" exposures in epidemiologic studies and methods for incorporating peak exposure metrics in dose-response assessments that contribute to risk assessment. The focus was on potential etiologic relations between environmental chemical exposures and cancer risks. We searched the epidemiologic literature on environmental chemicals classified as carcinogens in which cancer risks were described in relation to "peak" exposures. These articles were evaluated to identify some of the challenges associated with defining and describing cancer risks in relation to peak exposures. We found that definitions of peak exposure varied considerably across studies. Of nine chemical agents included in our review of peak exposure, six had epidemiologic data used by the U.S. Environmental Protection Agency (US EPA) in dose-response assessments to derive inhalation unit risk values. These were benzene, formaldehyde, styrene, trichloroethylene, acrylonitrile, and ethylene oxide. All derived unit risks relied on cumulative exposure for dose-response estimation and none, to our knowledge, considered peak exposure metrics. This is not surprising, given the historical linear no-threshold default model (generally based on cumulative exposure) used in regulatory risk assessments. With newly proposed US EPA rule language, fuller consideration of alternative exposure and dose-response metrics will be supported. "Peak" exposure has not been consistently defined and rarely has been evaluated in epidemiologic studies of cancer risks. We recommend developing uniform definitions of "peak" exposure to facilitate fuller evaluation of dose response for environmental chemicals and cancer risks, especially where mechanistic understanding indicates that the dose response is unlikely linear and that short-term high-intensity exposures increase risk.
| S-EPMC6850123 | biostudies-literature
Project description:BackgroundDifferent analytical approaches can influence the associations estimated in observational studies. We assessed the variability of effect estimates reported within and across observational studies evaluating the impact of alcohol on breast cancer.MethodsWe abstracted largest harmful, largest protective and smallest (closest to the null value of 1.0) relative risk estimates in studies included in a recent alcohol-breast cancer meta-analysis, and recorded how they differed based on five model specification characteristics, including exposure definition, exposure contrast levels, study populations, adjustment covariates and/or model approaches. For each study, we approximated vibration of effects by dividing the largest by the smallest effect estimate [i.e. ratio of odds ratio (ROR)].ResultsAmong 97 eligible studies, 85 (87.6%) reported both harmful and protective relative effect estimates for an alcohol-breast cancer relationship, which ranged from 1.1 to 17.9 and 0.0 to 1.0, respectively. The RORs comparing the largest and smallest estimates in value ranged from 1.0 to 106.2, with a median of 3.0 [interquartile range (IQR) 2.0-5.2]. One-third (35, 36.1%) of the RORs were based on extreme effect estimates with at least three different model specification characteristics; the vast majority (87, 89.7%) had different exposure definitions or contrast levels. Similar vibrations of effect were observed when only extreme estimates with differences based on study populations and/or adjustment covariates were compared.ConclusionsMost observational studies evaluating the impact of alcohol on breast cancer report relative effect estimates for the same associations that diverge by >2-fold. Therefore, observational studies should estimate the vibration of effects to provide insight regarding the stability of findings.
| S-EPMC7266551 | biostudies-literature
Project description:BackgroundA major challenge in studies of etiologic heterogeneity in breast cancer has been the limited throughput, accuracy, and reproducibility of measuring tissue markers. Computerized image analysis systems may help address these concerns, but published reports of their use are limited. We assessed agreement between automated and pathologist scores of a diverse set of immunohistochemical assays done on breast cancer tissue microarrays (TMA).MethodsTMAs of 440 breast cancers previously stained for estrogen receptor (ER)-alpha, progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2), ER-beta, and aromatase were independently scored by two pathologists and three automated systems (TMALab II, TMAx, and Ariol). Agreement between automated and pathologist scores of negative/positive was measured using the area under the receiver operating characteristics curve (AUC) and weighted kappa statistics for categorical scores. We also investigated the correlation between immunohistochemical scores and mRNA expression levels.ResultsAgreement between pathologist and automated negative/positive and categorical scores was excellent for ER-alpha and PR (AUC range = 0.98-0.99; kappa range = 0.86-0.91). Lower levels of agreement were seen for ER-beta categorical scores (AUC = 0.99-1.0; kappa = 0.80-0.86) and both negative/positive and categorical scores for aromatase (AUC = 0.85-0.96; kappa = 0.41-0.67) and HER2 (AUC = 0.94-0.97; kappa = 0.53-0.72). For ER-alpha and PR, there was a strong correlation between mRNA levels and automated (rho = 0.67-0.74) and pathologist immunohistochemical scores (rho = 0.67-0.77). HER2 mRNA levels were more strongly correlated with pathologist (rho = 0.63) than automated immunohistochemical scores (rho = 0.41-0.49).ConclusionsAutomated analysis of immunohistochemical markers is a promising approach for scoring large numbers of breast cancer tissues in epidemiologic investigations. This would facilitate studies of etiologic heterogeneity, which ultimately may allow improved risk prediction and better prevention approaches.
| S-EPMC2852578 | biostudies-literature