0
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Causal analysis of the gut microbiota in differentiated thyroid carcinoma: a two-sample Mendelian randomization study

      research-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Objective: Numerous studies have highlighted an association between the gut microbiota (GM) and thyroid tumors. Employing Mendelian randomization methodology, we seek to elucidate the causal link between the gut microbiota and thyroid neoplasms.

          Methods: We procured data from the Mibiogen database encompassing 211 distinct gut microbiota taxa, alongside extensive genome-wide association studies (GWAS) summary data for differentiated thyroid carcinoma (DTC). Our principal analytical approach involved the application of the Inverse-Variance Weighted method (IVW) within the framework of Mendelian randomization. Simultaneously, we conducted sensitivity analyses to assess result heterogeneity, horizontal pleiotropy, and outcome stability.

          Results: IVW analysis revealed a dual role of the GM in thyroid carcinoma. The phylum Actinobacteria (OR, 0.249 [95% CI, 0.121–0.515]; p < 0.001) was associated with a decreased risk of DTC. Conversely, the genus Ruminiclostridium9 (OR, 11.276 [95% CI, 4.406–28.860]; p < 0.001), class Mollicutes (OR, 5.902 [95% CI, 1.768–19.699]; p = 0.004), genus RuminococcaceaeUCG004 (OR, 3.831 [95% CI, 1.516–9.683]; p = 0.005), genus Paraprevotella (OR, 3.536 [95% CI, 1.330–9.401]; p = 0.011), and phylum Tenericutes (OR, 5.902 [95% CI, 1.768–19.699]; p = 0.004) were associated with an increased risk of DTC.

          Conclusion: Our findings underscore that the presence of genus Ruminiclostridium9, class Mollicutes, genus RuminococcaceaeUCG004, genus Paraprevotella, and phylum Tenericutes is associated with an elevated risk of DTC, whereas the presence of the phylum Actinobacteria is linked to a decreased risk. These discoveries enhance our comprehension of the relationship between the GM and DTC.

          Related collections

          Most cited references77

          • Record: found
          • Abstract: found
          • Article: not found

          Global Cancer Statistics 2018: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries

          This article provides a status report on the global burden of cancer worldwide using the GLOBOCAN 2018 estimates of cancer incidence and mortality produced by the International Agency for Research on Cancer, with a focus on geographic variability across 20 world regions. There will be an estimated 18.1 million new cancer cases (17.0 million excluding nonmelanoma skin cancer) and 9.6 million cancer deaths (9.5 million excluding nonmelanoma skin cancer) in 2018. In both sexes combined, lung cancer is the most commonly diagnosed cancer (11.6% of the total cases) and the leading cause of cancer death (18.4% of the total cancer deaths), closely followed by female breast cancer (11.6%), prostate cancer (7.1%), and colorectal cancer (6.1%) for incidence and colorectal cancer (9.2%), stomach cancer (8.2%), and liver cancer (8.2%) for mortality. Lung cancer is the most frequent cancer and the leading cause of cancer death among males, followed by prostate and colorectal cancer (for incidence) and liver and stomach cancer (for mortality). Among females, breast cancer is the most commonly diagnosed cancer and the leading cause of cancer death, followed by colorectal and lung cancer (for incidence), and vice versa (for mortality); cervical cancer ranks fourth for both incidence and mortality. The most frequently diagnosed cancer and the leading cause of cancer death, however, substantially vary across countries and within each country depending on the degree of economic development and associated social and life style factors. It is noteworthy that high-quality cancer registry data, the basis for planning and implementing evidence-based cancer control programs, are not available in most low- and middle-income countries. The Global Initiative for Cancer Registry Development is an international partnership that supports better estimation, as well as the collection and use of local data, to prioritize and evaluate national cancer control efforts. CA: A Cancer Journal for Clinicians 2018;0:1-31. © 2018 American Cancer Society.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: found
            Is Open Access

            Global, regional, and national life expectancy, all-cause mortality, and cause-specific mortality for 249 causes of death, 1980–2015: a systematic analysis for the Global Burden of Disease Study 2015

            Summary Background Improving survival and extending the longevity of life for all populations requires timely, robust evidence on local mortality levels and trends. The Global Burden of Disease 2015 Study (GBD 2015) provides a comprehensive assessment of all-cause and cause-specific mortality for 249 causes in 195 countries and territories from 1980 to 2015. These results informed an in-depth investigation of observed and expected mortality patterns based on sociodemographic measures. Methods We estimated all-cause mortality by age, sex, geography, and year using an improved analytical approach originally developed for GBD 2013 and GBD 2010. Improvements included refinements to the estimation of child and adult mortality and corresponding uncertainty, parameter selection for under-5 mortality synthesis by spatiotemporal Gaussian process regression, and sibling history data processing. We also expanded the database of vital registration, survey, and census data to 14 294 geography–year datapoints. For GBD 2015, eight causes, including Ebola virus disease, were added to the previous GBD cause list for mortality. We used six modelling approaches to assess cause-specific mortality, with the Cause of Death Ensemble Model (CODEm) generating estimates for most causes. We used a series of novel analyses to systematically quantify the drivers of trends in mortality across geographies. First, we assessed observed and expected levels and trends of cause-specific mortality as they relate to the Socio-demographic Index (SDI), a summary indicator derived from measures of income per capita, educational attainment, and fertility. Second, we examined factors affecting total mortality patterns through a series of counterfactual scenarios, testing the magnitude by which population growth, population age structures, and epidemiological changes contributed to shifts in mortality. Finally, we attributed changes in life expectancy to changes in cause of death. We documented each step of the GBD 2015 estimation processes, as well as data sources, in accordance with Guidelines for Accurate and Transparent Health Estimates Reporting (GATHER). Findings Globally, life expectancy from birth increased from 61·7 years (95% uncertainty interval 61·4–61·9) in 1980 to 71·8 years (71·5–72·2) in 2015. Several countries in sub-Saharan Africa had very large gains in life expectancy from 2005 to 2015, rebounding from an era of exceedingly high loss of life due to HIV/AIDS. At the same time, many geographies saw life expectancy stagnate or decline, particularly for men and in countries with rising mortality from war or interpersonal violence. From 2005 to 2015, male life expectancy in Syria dropped by 11·3 years (3·7–17·4), to 62·6 years (56·5–70·2). Total deaths increased by 4·1% (2·6–5·6) from 2005 to 2015, rising to 55·8 million (54·9 million to 56·6 million) in 2015, but age-standardised death rates fell by 17·0% (15·8–18·1) during this time, underscoring changes in population growth and shifts in global age structures. The result was similar for non-communicable diseases (NCDs), with total deaths from these causes increasing by 14·1% (12·6–16·0) to 39·8 million (39·2 million to 40·5 million) in 2015, whereas age-standardised rates decreased by 13·1% (11·9–14·3). Globally, this mortality pattern emerged for several NCDs, including several types of cancer, ischaemic heart disease, cirrhosis, and Alzheimer's disease and other dementias. By contrast, both total deaths and age-standardised death rates due to communicable, maternal, neonatal, and nutritional conditions significantly declined from 2005 to 2015, gains largely attributable to decreases in mortality rates due to HIV/AIDS (42·1%, 39·1–44·6), malaria (43·1%, 34·7–51·8), neonatal preterm birth complications (29·8%, 24·8–34·9), and maternal disorders (29·1%, 19·3–37·1). Progress was slower for several causes, such as lower respiratory infections and nutritional deficiencies, whereas deaths increased for others, including dengue and drug use disorders. Age-standardised death rates due to injuries significantly declined from 2005 to 2015, yet interpersonal violence and war claimed increasingly more lives in some regions, particularly in the Middle East. In 2015, rotaviral enteritis (rotavirus) was the leading cause of under-5 deaths due to diarrhoea (146 000 deaths, 118 000–183 000) and pneumococcal pneumonia was the leading cause of under-5 deaths due to lower respiratory infections (393 000 deaths, 228 000–532 000), although pathogen-specific mortality varied by region. Globally, the effects of population growth, ageing, and changes in age-standardised death rates substantially differed by cause. Our analyses on the expected associations between cause-specific mortality and SDI show the regular shifts in cause of death composition and population age structure with rising SDI. Country patterns of premature mortality (measured as years of life lost [YLLs]) and how they differ from the level expected on the basis of SDI alone revealed distinct but highly heterogeneous patterns by region and country or territory. Ischaemic heart disease, stroke, and diabetes were among the leading causes of YLLs in most regions, but in many cases, intraregional results sharply diverged for ratios of observed and expected YLLs based on SDI. Communicable, maternal, neonatal, and nutritional diseases caused the most YLLs throughout sub-Saharan Africa, with observed YLLs far exceeding expected YLLs for countries in which malaria or HIV/AIDS remained the leading causes of early death. Interpretation At the global scale, age-specific mortality has steadily improved over the past 35 years; this pattern of general progress continued in the past decade. Progress has been faster in most countries than expected on the basis of development measured by the SDI. Against this background of progress, some countries have seen falls in life expectancy, and age-standardised death rates for some causes are increasing. Despite progress in reducing age-standardised death rates, population growth and ageing mean that the number of deaths from most non-communicable causes are increasing in most countries, putting increased demands on health systems. Funding Bill & Melinda Gates Foundation.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: found
              Is Open Access

              Reading Mendelian randomisation studies: a guide, glossary, and checklist for clinicians

              Mendelian randomisation uses genetic variation as a natural experiment to investigate the causal relations between potentially modifiable risk factors and health outcomes in observational data. As with all epidemiological approaches, findings from Mendelian randomisation studies depend on specific assumptions. We provide explanations of the information typically reported in Mendelian randomisation studies that can be used to assess the plausibility of these assumptions and guidance on how to interpret findings from Mendelian randomisation studies in the context of other sources of evidence
                Bookmark

                Author and article information

                Contributors
                URI : https://loop.frontiersin.org/people/1450683/overviewRole: Role:
                URI : https://loop.frontiersin.org/people/1452446/overviewRole: Role:
                URI : https://loop.frontiersin.org/people/2416657/overviewRole: Role: Role: Role:
                Role: Role: Role:
                Journal
                Front Genet
                Front Genet
                Front. Genet.
                Frontiers in Genetics
                Frontiers Media S.A.
                1664-8021
                13 December 2023
                2023
                : 14
                : 1299930
                Affiliations
                [1] 1 Department of Oncology Surgery , The Affiliated Hospital of Northwest University , Xi’an, China
                [2] 2 Department of Intensive Care Unit , The First Affiliated Hospital of Xi’an Jiaotong University , Xi’an, China
                [3] 3 Department of Surgical Oncology , The First Affiliated Hospital of Xi’an Jiaotong University , Xi’an, China
                Author notes

                Edited by: Eleonore Fröhlich, Medical University of Graz, Austria

                Reviewed by: Le Liu, Southern Medical University, China

                Kaijian Hou, Shantou University, China

                *Correspondence: Yong Meng, mengyong@ 123456nwu.edu.cn
                [ † ]

                These authors have contributed equally to this work and share first authorship

                Article
                1299930
                10.3389/fgene.2023.1299930
                10753834
                38155712
                ddb30786-54a5-4ac0-b723-d82e8aa297a6
                Copyright © 2023 Quan, Zhang, Wang and Meng.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 23 September 2023
                : 04 December 2023
                Funding
                The authors declare financial support was received for the research, authorship, and/or publication of this article. This work was funded by the Xi’an NO. 3 Hospital Institutional Medical Research Fund (Grant NO. Y2023yxyj09), Clinical Research Award of the First Affiliated Hospital of Xi’an Jiaotong University (Grant NO. XJTU1AF2021CRF-009).
                Categories
                Genetics
                Original Research
                Custom metadata
                Statistical Genetics and Methodology

                Genetics
                gut microbiota,differentiated thyroid carcinoma,heterogeneity assessment,mendelian randomization study,causal relationship

                Comments

                Comment on this article