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1.
Appl Environ Microbiol ; 82(19): 6057-67, 2016 10 01.
Artigo em Inglês | MEDLINE | ID: mdl-27520811

RESUMO

UNLABELLED: Periodontal disease ranges from gingival inflammation (gingivitis) to the inflammation and loss of tooth-supporting tissues (periodontitis). Previous research has focused mainly on subgingival plaque, but supragingival plaque composition is also known to be associated with disease. Quantitative modeling of bacterial abundances across the natural range of periodontal severities can distinguish which features of disease are associated with particular changes in composition. We assessed a cross-sectional cohort of 962 Malawian women for periodontal disease and used 16S rRNA gene amplicon sequencing (V5 to V7 region) to characterize the bacterial compositions of supragingival plaque samples. Associations between bacterial relative abundances and gingivitis/periodontitis were investigated by using negative binomial models, adjusting for epidemiological factors. We also examined bacterial cooccurrence networks to assess community structure. The main differences in supragingival plaque compositions were associated more with gingivitis than periodontitis, including higher bacterial diversity and a greater abundance of particular species. However, even after controlling for gingivitis, the presence of subgingival periodontitis was associated with an altered supragingival plaque. A small number of species were associated with periodontitis but not gingivitis, including members of Prevotella, Treponema, and Selenomonas, supporting a more complex disease model than a linear progression following gingivitis. Cooccurrence networks of periodontitis-associated taxa clustered according to periodontitis across all gingivitis severities. Species including Filifactor alocis and Fusobacterium nucleatum were central to this network, which supports their role in the coaggregation of periodontal biofilms during disease progression. Our findings confirm that periodontitis cannot be considered simply an advanced stage of gingivitis even when only considering supragingival plaque. IMPORTANCE: Periodontal disease is a major public health problem associated with oral bacteria. While earlier studies focused on a small number of periodontal pathogens, it is now accepted that the whole bacterial community may be important. However, previous high-throughput marker gene sequencing studies of supragingival plaque have largely focused on high-income populations with good oral hygiene without including a range of periodontal disease severities. Our study includes a large number of low-income participants with poor oral hygiene and a wide range of severities, and we were therefore able to quantitatively model bacterial abundances as functions of both gingivitis and periodontitis. A signal associated with periodontitis remains after controlling for gingivitis severity, which supports the concept that, even when only considering supragingival plaque, periodontitis is not simply an advanced stage of gingivitis. This suggests the future possibility of diagnosing periodontitis based on bacterial occurrences in supragingival plaque.


Assuntos
Fenômenos Fisiológicos Bacterianos , Placa Dentária/microbiologia , Gengivite/microbiologia , Periodontite/microbiologia , Adolescente , Adulto , Bactérias/classificação , Estudos de Coortes , Estudos Transversais , Placa Dentária/classificação , Feminino , Gengivite/classificação , Humanos , Malaui , Periodontite/classificação , RNA Bacteriano/análise , RNA Ribossômico 16S/análise , Análise de Sequência de RNA , Adulto Jovem
2.
Preprint em Inglês | PREPRINT-MEDRXIV | ID: ppmedrxiv-20083287

RESUMO

BackgroundCancer and multiple non-cancer conditions are considered by the Centers for Disease Control and Prevention (CDC) as high risk conditions in the COVID-19 emergency. Professional societies have recommended changes in cancer service provision to minimize COVID-19 risks to cancer patients and health care workers. However, we do not know the extent to which cancer patients, in whom multi-morbidity is common, may be at higher overall risk of mortality as a net result of multiple factors including COVID-19 infection, changes in health services, and socioeconomic factors. MethodsWe report multi-center, weekly cancer diagnostic referrals and chemotherapy treatments until April 2020 in England and Northern Ireland. We analyzed population-based health records from 3,862,012 adults in England to estimate 1-year mortality in 24 cancer sites and 15 non-cancer comorbidity clusters (40 conditions) recognized by CDC as high-risk. We estimated overall (direct and indirect) effects of COVID-19 emergency on mortality under different Relative Impact of the Emergency (RIE) and different Proportions of the population Affected by the Emergency (PAE). We applied the same model to the US, using Surveillance, Epidemiology, and End Results (SEER) program data. ResultsWeekly data until April 2020 demonstrate significant falls in admissions for chemotherapy (45-66% reduction) and urgent referrals for early cancer diagnosis (70-89% reduction), compared to pre-emergency levels. Under conservative assumptions of the emergency affecting only people with newly diagnosed cancer (incident cases) at COVID-19 PAE of 40%, and an RIE of 1.5, the model estimated 6,270 excess deaths at 1 year in England and 33,890 excess deaths in the US. In England, the proportion of patients with incident cancer with [≥]1 comorbidity was 65.2%. The number of comorbidities was strongly associated with cancer mortality risk. Across a range of model assumptions, and across incident and prevalent cancer cases, 78% of excess deaths occur in cancer patients with Harry [≥]1 comorbidity. ConclusionWe provide the first estimates of potential excess mortality among people with cancer and multimorbidity due to the COVID-19 emergency and demonstrate dramatic changes in cancer services. To better inform prioritization of cancer care and guide policy change, there is an urgent need for weekly data on cause-specific excess mortality, cancer diagnosis and treatment provision and better intelligence on the use of effective treatments for comorbidities.

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