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Abstract
Asthma is a heterogenous disease, and its prevalence and severity are different in
males
versus females through various ages. As children, boys have an increased prevalence of asthma.
As adults, women have an increased prevalence and severity of asthma. Sex hormones,
genetic and epigenetic variations, social and environmental factors, and responses
to asthma therapeutics are important factors in the sex differences observed in asthma
incidence, prevalence and severity. For women, fluctuations in sex hormone levels
during puberty, the menstrual cycle and pregnancy are associated with asthma pathogenesis.
Further, sex differences in gene expression and epigenetic modifications and responses
to environmental factors, including SARS-CoV-2 infections, are associated with differences
in asthma incidence, prevalence and symptoms. We review the role of sex hormones,
genetics and epigenetics, and their interactions with the environment in the clinical
manifestations and therapeutic response of asthma.
Abstract
Dysanaptic lung growth through life, and hormonal and genetic differences affect phenotypic
manifestations of asthma and response to therapy between males and females. These
sex and gender differences in asthma are discussed in this article.
https://bit.ly/3xkaiPW
COVID-19 has rapidly impacted on mortality worldwide. 1 There is unprecedented urgency to understand who is most at risk of severe outcomes, requiring new approaches for timely analysis of large datasets. Working on behalf of NHS England we created OpenSAFELY: a secure health analytics platform covering 40% of all patients in England, holding patient data within the existing data centre of a major primary care electronic health records vendor. Primary care records of 17,278,392 adults were pseudonymously linked to 10,926 COVID-19 related deaths. COVID-19 related death was associated with: being male (hazard ratio 1.59, 95%CI 1.53-1.65); older age and deprivation (both with a strong gradient); diabetes; severe asthma; and various other medical conditions. Compared to people with white ethnicity, black and South Asian people were at higher risk even after adjustment for other factors (HR 1.48, 1.29-1.69 and 1.45, 1.32-1.58 respectively). We have quantified a range of clinical risk factors for COVID-19 related death in the largest cohort study conducted by any country to date. OpenSAFELY is rapidly adding further patients’ records; we will update and extend results regularly.
Summary Background As mortality rates decline, life expectancy increases, and populations age, non-fatal outcomes of diseases and injuries are becoming a larger component of the global burden of disease. The Global Burden of Diseases, Injuries, and Risk Factors Study 2016 (GBD 2016) provides a comprehensive assessment of prevalence, incidence, and years lived with disability (YLDs) for 328 causes in 195 countries and territories from 1990 to 2016. Methods We estimated prevalence and incidence for 328 diseases and injuries and 2982 sequelae, their non-fatal consequences. We used DisMod-MR 2.1, a Bayesian meta-regression tool, as the main method of estimation, ensuring consistency between incidence, prevalence, remission, and cause of death rates for each condition. For some causes, we used alternative modelling strategies if incidence or prevalence needed to be derived from other data. YLDs were estimated as the product of prevalence and a disability weight for all mutually exclusive sequelae, corrected for comorbidity and aggregated to cause level. We updated the Socio-demographic Index (SDI), a summary indicator of income per capita, years of schooling, and total fertility rate. GBD 2016 complies with the Guidelines for Accurate and Transparent Health Estimates Reporting (GATHER). Findings Globally, low back pain, migraine, age-related and other hearing loss, iron-deficiency anaemia, and major depressive disorder were the five leading causes of YLDs in 2016, contributing 57·6 million (95% uncertainty interval [UI] 40·8–75·9 million [7·2%, 6·0–8·3]), 45·1 million (29·0–62·8 million [5·6%, 4·0–7·2]), 36·3 million (25·3–50·9 million [4·5%, 3·8–5·3]), 34·7 million (23·0–49·6 million [4·3%, 3·5–5·2]), and 34·1 million (23·5–46·0 million [4·2%, 3·2–5·3]) of total YLDs, respectively. Age-standardised rates of YLDs for all causes combined decreased between 1990 and 2016 by 2·7% (95% UI 2·3–3·1). Despite mostly stagnant age-standardised rates, the absolute number of YLDs from non-communicable diseases has been growing rapidly across all SDI quintiles, partly because of population growth, but also the ageing of populations. The largest absolute increases in total numbers of YLDs globally were between the ages of 40 and 69 years. Age-standardised YLD rates for all conditions combined were 10·4% (95% UI 9·0–11·8) higher in women than in men. Iron-deficiency anaemia, migraine, Alzheimer’s disease and other dementias, major depressive disorder, anxiety, and all musculoskeletal disorders apart from gout were the main conditions contributing to higher YLD rates in women. Men had higher age-standardised rates of substance use disorders, diabetes, cardiovascular diseases, cancers, and all injuries apart from sexual violence. Globally, we noted much less geographical variation in disability than has been documented for premature mortality. In 2016, there was a less than two times difference in age-standardised YLD rates for all causes between the location with the lowest rate (China, 9201 YLDs per 100 000, 95% UI 6862–11943) and highest rate (Yemen, 14 774 YLDs per 100 000, 11 018–19 228). Interpretation The decrease in death rates since 1990 for most causes has not been matched by a similar decline in age-standardised YLD rates. For many large causes, YLD rates have either been stagnant or have increased for some causes, such as diabetes. As populations are ageing, and the prevalence of disabling disease generally increases steeply with age, health systems will face increasing demand for services that are generally costlier than the interventions that have led to declines in mortality in childhood or for the major causes of mortality in adults. Up-to-date information about the trends of disease and how this varies between countries is essential to plan for an adequate health-system response.
Measurement of fractional nitric oxide (NO) concentration in exhaled breath (Fe(NO)) is a quantitative, noninvasive, simple, and safe method of measuring airway inflammation that provides a complementary tool to other ways of assessing airways disease, including asthma. While Fe(NO) measurement has been standardized, there is currently no reference guideline for practicing health care providers to guide them in the appropriate use and interpretation of Fe(NO) in clinical practice. To develop evidence-based guidelines for the interpretation of Fe(NO) measurements that incorporate evidence that has accumulated over the past decade. We created a multidisciplinary committee with expertise in the clinical care, clinical science, or basic science of airway disease and/or NO. The committee identified important clinical questions, synthesized the evidence, and formulated recommendations. Recommendations were developed using pragmatic systematic reviews of the literature and the GRADE approach. The evidence related to the use of Fe(NO) measurements is reviewed and clinical practice recommendations are provided. In the setting of chronic inflammatory airway disease including asthma, conventional tests such as FEV(1) reversibility or provocation tests are only indirectly associated with airway inflammation. Fe(NO) offers added advantages for patient care including, but not limited to (1) detecting of eosinophilic airway inflammation, (2) determining the likelihood of corticosteroid responsiveness, (3) monitoring of airway inflammation to determine the potential need for corticosteroid, and (4) unmasking of otherwise unsuspected nonadherence to corticosteroid therapy.
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History
Date
received
: 10
March
2021
Date
accepted
: 26
June
2021
Related
Funding
Funded by: AstraZeneca, doi 10.13039/100004325;
Funded by: GlaxoSmithKline, doi 10.13039/100004330;
Funded by: Teva Pharmaceutical Industries, doi 10.13039/100006259;
Funded by: Sanofi, doi 10.13039/100004339;
Funded by: NIH
Award ID: HL122554 (DCN), HL136664 (DCN), DK020593 (DCN), T3
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