Deprivation effects on length of stay and death of hospitalised COVID-19 patients in Greater Manchester.

Introduction: The World Health Organisation declared a global pandemic in March 2020. The impact of COVID-19 has not been felt equally by all regions and sections of society. The extent to which socio-demographic and deprivation factors have adversely impacted on outcomes is of concern to those looking to 'level-up' and decrease widening health inequalities. Objectives: In this paper we investigate the impact of deprivation on the outcomes for hospitalised COVID-19 patients in Greater Manchester during the first wave of the pandemic in the UK (30/12/19-2/1/21), controlling for proven risk factors from elsewhere in the literature. Methods: We fitted Negative Binomial and logistic regression models to NHS administrative data to investigate death from COVID in hospital and length of stay for surviving patients in a sample of adult patients admitted within Greater Manchester (N = 10,372, spell admission start dates from 30/12/2019 to 02/01/2021 inclusive). Results: Deprivation was associated with death risk for hospitalised patients but not with length of stay. Male sex, co-morbidities and older age was associated with higher death risk. Male sex and co-morbidities were associated with increased length of stay. Black and other ethnicities stayed longer in hospital than White and Asian patients. Period effects were detected in both models with death risk reducing over time, but the length of stay increasing. Conclusion: Deprivation is important for death risk; however, the picture is complex, and the results of this analysis suggest that the reported COVID related mortality and deprivation linked reductions in life expectancy, may have occurred in the community, rather than in acute settings. Highlights: Older age and male sex are predictive of longer hospital stays and higher death risk for hospitalised cases in this analysis.Deprivation is associated with death risk but not length of stay for hospitalised patients.Ethnicity is associated with length of stay, but not with death risk.There is a social gradient in health, but these data would suggest that once in the care of an NHS hospital in an acute health episode, outcomes are more equal.

Toward a common standard for data and specimen provenance in life sciences.

Open and practical exchange, dissemination, and reuse of specimens and data have become a fundamental requirement for life sciences research. The quality of the data obtained and thus the findings and knowledge derived is thus significantly influenced by the quality of the samples, the experimental methods, and the data analysis. Therefore, a comprehensive and precise documentation of the pre-analytical conditions, the analytical procedures, and the data processing are essential to be able to assess the validity of the research results. With the increasing importance of the exchange, reuse, and sharing of data and samples, procedures are required that enable cross-organizational documentation, traceability, and non-repudiation. At present, this information on the provenance of samples and data is mostly either sparse, incomplete, or incoherent. Since there is no uniform framework, this information is usually only provided within the organization and not interoperably. At the same time, the collection and sharing of biological and environmental specimens increasingly require definition and documentation of benefit sharing and compliance to regulatory requirements rather than consideration of pure scientific needs. In this publication, we present an ongoing standardization effort to provide trustworthy machine-actionable documentation of the data lineage and specimens. We would like to invite experts from the biotechnology and biomedical fields to further contribute to the standard.

Harmonization of multi-site diffusion tensor imaging data for cervical and thoracic spinal cord at 1.5 T and 3 T using longitudinal ComBat.

MRI scanner hardware, field strengths, and sequence parameters are major variables in diffusion studies of the spinal cord. Reliability between scanners is not well known, particularly for the thoracic cord. DTI data was collected for the entire cervical and thoracic spinal cord in thirty healthy adult subjects with different MR vendors and field strengths. DTI metrics were extracted and averaged for all slices within each vertebral level. Metrics were examined for variability and then harmonized using longitudinal ComBat (longComBat). Four scanners were used: Siemens 3 T Prisma, Siemens 1.5 T Avanto, Philips 3 T Ingenia, Philips 1.5 T Achieva. Average full cord diffusion values/standard deviation for all subjects and scanners were FA: 0.63, σ = 0.10, MD: 1.11, σ = 0.12 × 10-3 mm2/s, AD: 1.98, σ = 0.55 × 10-3 mm2/s, RD: 0.67, σ = 0.31 × 10-3 mm2/s. FA metrics averaged for all subjects by level were relatively consistent across scanners, but large variability was found in diffusivity measures. Coefficients of variation were lowest in the cervical region, and relatively lower for FA than diffusivity measures. Harmonized metrics showed greatly improved agreement between scanners. Variability in DTI of the spinal cord arises from scanner hardware differences, pulse sequence differences, physiological motion, and subject compliance. The use of longComBat resulted in large improvement in agreement of all DTI metrics between scanners. This study shows the importance of harmonization of diffusion data in the spinal cord and potential for longitudinal and multisite clinical research and clinical trials.

Leadless Cardiac Pacing: New Horizons.

Since the introduction of transvenous cardiac pacing leads, pacemaker system design has remained similar for several decades. Progressive miniaturisation of electronic circuitry and batteries has enabled a smaller, single pacing unit comprising the intracardiac electrodes, generator and computer. This review explores the development of leadless pacing, the clinical trials comparing leadless to transvenous pacing in addition to the future developments of multi-chamber leadless pacing.

Federated learning for generating synthetic data: a scoping review.

Introduction: Federated Learning (FL) is a decentralised approach to training statistical models, where training is performed across multiple clients, producing one global model. Since the training data remains with each local client and is not shared or exchanged with other clients the use of FL may reduce privacy and security risks (compared to methods where multiple data sources are pooled) and can also address data access and heterogeneity problems. Synthetic data is artificially generated data that has the same structure and statistical properties as the original but that does not contain any of the original data records, therefore minimising disclosure risk. Using FL to produce synthetic data (which we refer to as "federated synthesis") has the potential to combine data from multiple clients without compromising privacy, allowing access to data that may otherwise be inaccessible in its raw format. Objectives: The objective was to review current research and practices for using FL to generate synthetic data and determine the extent to which research has been undertaken, the methods and evaluation practices used, and any research gaps. Methods: A scoping review was conducted to systematically map and describe the published literature on the use of FL to generate synthetic data. Relevant studies were identified through online databases and the findings are described, grouped, and summarised. Information extracted included article characteristics, documenting the type of data that is synthesised, the model architecture and the methods (if any) used to evaluate utility and privacy risk. Results: A total of 69 articles were included in the scoping review; all were published between 2018 and 2023 with two thirds (46) in 2022. 30% (21) were focussed on synthetic data generation as the main model output (with 6 of these generating tabular data), whereas 59% (41) focussed on data augmentation. Of the 21 performing federated synthesis, all used deep learning methods (predominantly Generative Adversarial Networks) to generate the synthetic data. Conclusions: Federated synthesis is in its early days but shows promise as a method that can construct a global synthetic dataset without sharing any of the local client data. As a field in its infancy there are areas to explore in terms of the privacy risk associated with the various methods proposed, and more generally in how we measure those risks.

Measuring the association between fathers' involvement and risky behaviours in adolescence.

Understanding the drivers of adolescent risky behaviour is important given the costs that such behaviours impose on society and young people themselves. Evidence shows that early parent-child relations shape children's development and behaviour, however we know relatively little about the potential causal pathways that lead to risky behaviour and the differential impact of fathers' and mothers' childcare involvement. This study tests whether paternal involvement in childcare at age 11 is associated with adolescent risky behaviour at age 14. Accounting for likely mediators, we run a Structural Equation model on three sweeps (2008-2015) of the UK's Millennium Cohort Study. Results show significant associations between father's involvement and reduction in adolescent risky behaviour. Father's involvement and father-adolescent closeness are shown to be stronger predictors of risky behaviours relative to mother's involvement and mother-adolescent closeness. This has implications for delinquency prevention programs that should be (re)designed to encourage healthy father-child bonds in adolescence.

Domiciliary noninvasive ventilation for chronic respiratory diseases.

Patients with chronic respiratory diseases, including chronic obstructive pulmonary disease (COPD), neuromuscular diseases, kyphoscoliosis and obstructive sleep apnoea-obesity hypoventilation syndrome (OSA-OHS), are at a higher risk of decompensation in the form of hypercapnic respiratory failure leading to intensive care unit (ICU) admission and increased mortality. This article reviews the evidence of role of domiciliary noninvasive ventilation (NIV) in patients with diseases with chronic ventilatory failure, including the mechanism of the effect of (NIV).

Attitudes to climate change risk: classification of and transitions in the UK population between 2012 and 2020.

Strategies for achieving carbon emissions goals presuppose changes in individual behaviour, which can be indirectly nudged by interventions or tailored information but ultimately depend upon individual attitudes. Specifically, the perception that climate change is low risk has been identified as a barrier to participation in climate change adaptation and mitigation efforts. Therefore, understanding public attitudes towards climate change risk is an important element of reducing emissions. We applied k-means cluster analysis to explore attitudes to climate change risk in the UK population using data from the UK Household Longitudinal Study, a national survey running from 2009 to present. We identified three distinct attitude clusters: "Sceptical", "Concerned", and "Paradoxical" in both waves 4 (from 2012 to 2014) and 10 (from 2018 to 2020) of this survey. The Sceptical cluster tended to deny the seriousness of climate change and the urgency or even the necessity of dealing with it. The Concerned cluster displayed anxiety about climate change risks and supported action to reduce them. The Paradoxical cluster acknowledged the reality of climate change impacts but did not support actions to mitigate them. We further observed statistical associations between cluster membership and the social characteristics of the participants, including sex, age, income, education, and political affiliation. We also found a temporal stability of cluster structure between the two waves. However, the transition matrices indicated a general transition away from the Sceptical and Paradoxical clusters, and toward the Concerned cluster between wave 4 to wave 10. The findings suggest that more tailored public information campaigns regarding climate change risk may be necessary.

An analysis of changes in wellbeing during the COVID-19 pandemic in the UK.

Purpose: We investigated the trajectory of wellbeing over the course of the first wave and sought to determine whether the change in wellbeing is distributed equally across the population. Specifically we investigated pre-existing medical conditions, social isolation, financial stress and deprivation as a predictor for wellbeing and whether there were community level characteristics which protect against poorer wellbeing. Methods: Using online survey responses from the COVID-19 modules of Understanding society, we linked 8379 English cases across five waves of data collection to location based deprivation statistics. We used ordinary least squares regression to estimate the association between deprivation, pre-existing conditions and socio-demographic factors and the change in well-being scores over time, as measured by the GHQ-12 questionnaire. Results: A decline in wellbeing was observed at the beginning of the first lock down period at the beginning of March 2020. This was matched with a corresponding recovery between April and July as restrictions were gradually lifted. There was no association between the decline and deprivation, nor between deprivation and recovery. The strongest predictor of wellbeing during the lock down, was the baseline score, with the counterintuitive finding that for those will pre-existing poor wellbeing, the impact of pandemic restrictions on mental health were minimal, but for those who had previously felt well, the restrictions and the impact of the pandemic on well-being were much greater. Conclusions: These data show no evidence of a social gradient in well-being related to the pandemic. In fact, well-being was shown to be highly elastic in this period indicating a national level of resilience which cut across the usually observed health inequalities.

Integrating 1H MRS and deuterium labeled glucose for mapping the dynamics of neural metabolism in humans.

In the technique presented here, dubbed 'qMRS', we quantify the change in 1H MRS signal following administration of 2H-labeled glucose. As in recent human DMRS studies, we administer [6,6'-2H2]-glucose orally to healthy subjects. Since 2H is not detectable by 1H MRS, the transfer of the 2H label from glucose to a downstream metabolite leads to a reduction in the corresponding 1H MRS resonance of the metabolite, even if the total concentration of both isoforms remains constant. Moreover, introduction of the deuterium label alters the splitting pattern of the proton resonances, making indirect detection of the deuterated forms- as well as the direct detection of the decrease in unlabeled form- possible even without a 2H coil. Because qMRS requires only standard 1H MRS acquisition methods, it can be performed using commonly implemented single voxel spectroscopy (SVS) and chemical shift imaging (CSI) sequences. In this work, we implement qMRS in semi-LASER based CSI, generating dynamic maps arising from the fitted spectra, and demonstrating the feasibility of using qMRS and qCSI to monitor dynamic metabolism in the human brain using a 7T scanner with no auxiliary hardware.

Regional left ventricle scar detection from routine cardiac computed tomography angiograms using latent space classification.

OBJECTIVES: The aim of this study is to develop an automated method of regional scar detection on clinically standard computed tomography angiography (CTA) using encoder-decoder networks with latent space classification. BACKGROUND: Localising scar in cardiac patients can assist in diagnosis and guide interventions. Magnetic resonance imaging (MRI) with late gadolinium enhancement (LGE) is the clinical gold standard for scar imaging; however, it is commonly contraindicated. CTA is an alternative imaging modality that has fewer contraindications and is widely used as a first-line imaging modality of cardiac applications. METHODS: A dataset of 79 patients with both clinically indicated MRI LGE and subsequent CTA scans was used to train and validate networks to classify septal and lateral scar presence within short axis left ventricle slices. Two designs of encoder-decoder networks were compared, with one encoding anatomical shape in the latent space. Ground truth was established by segmenting scar in MRI LGE and registering this to the CTA images. Short axis slices were taken from the CTA, which served as the input to the networks. An independent external set of 22 cases (27% the size of the cross-validation set) was used to test the best network. RESULTS: A network classifying lateral scar only achieved an area under ROC curve of 0.75, with a sensitivity of 0.79 and specificity of 0.62 on the independent test set. The results of septal scar classification were poor (AUC < 0.6) for all networks. This was likely due to a high class imbalance. The highest AUC network encoded anatomical shape information in the network latent space, indicating it was important for the successful classification of lateral scar. CONCLUSIONS: Automatic lateral wall scar detection can be performed from a routine cardiac CTA with reasonable accuracy, without any scar specific imaging. This requires only a single acquisition in the cardiac cycle. In a clinical setting, this could be useful for pre-procedure planning, especially where MRI is contraindicated. Further work with more septal scar present is warranted to improve the usefulness of this approach.

Investigating the impact of distance on the use of primary care extended hours.

INTRODUCTION: Poor access to general practice services has been attributed to increasing pressure on the health system more widely and low satisfaction among patients. Recent initiatives in England have sought to expand access by the provision of appointments in the evening and at weekends. Services are provided using a hub model. NHS national targets mandate extended opening hours as a mechanism for increasing access to primary care, based on the assumption that unmet need is caused by a lack of appointments at the right time. However, research has shown that other factors affect access to healthcare and it may not simply be appointment availability that limits an individual's ability to access general practice services. OBJECTIVES: To determine whether distance and deprivation impact on the uptake of extended hours GP services that use a hub practice model. METHODS: We linked a dataset (N = 25,408) concerning extended access appointments covering 158 general practice surgeries in four Clinical Commissioning Groups (CCGs) to the General Practice Patient Survey (GPPS) survey, deprivation statistics and primary care registration data. We used negative binomial regression to estimate associations between distance and deprivation on the uptake of extended hours GP services in the Greater Manchester City Region. Distance was defined as a straight line between the extended hours provider location and the patient's home practice, the English Indices of Multiple Deprivation were used to determine area deprivation based upon the home practice, and familiarity was defined as whether the patient's home practice provided an extended hours service. RESULTS: The number of uses of the extended hours service at a GP practice level was associated with distance. After allowing for distance, the number of uses of the service for hub practices was higher than for non-hub practices. Deprivation was not associated with rates of use. CONCLUSION: The results indicate geographic inequity in the extended hours service. There may be many patients with unmet need for whom the extension of hours via a hub and spoke model does not address barriers to access. Findings may help to inform the choice of hub practices when designing an extended access service. Providers should consider initiatives to improve access for those patients located in practices furthest away from hub practices. This is particularly of importance in the context of closing health inequality gaps.

Hospital length of stay for COVID-19 patients: Data-driven methods for forward planning.

BACKGROUND: Predicting hospital length of stay (LoS) for patients with COVID-19 infection is essential to ensure that adequate bed capacity can be provided without unnecessarily restricting care for patients with other conditions. Here, we demonstrate the utility of three complementary methods for predicting LoS using UK national- and hospital-level data. METHOD: On a national scale, relevant patients were identified from the COVID-19 Hospitalisation in England Surveillance System (CHESS) reports. An Accelerated Failure Time (AFT) survival model and a truncation corrected method (TC), both with underlying Weibull distributions, were fitted to the data to estimate LoS from hospital admission date to an outcome (death or discharge) and from hospital admission date to Intensive Care Unit (ICU) admission date. In a second approach we fit a multi-state (MS) survival model to data directly from the Manchester University NHS Foundation Trust (MFT). We develop a planning tool that uses LoS estimates from these models to predict bed occupancy. RESULTS: All methods produced similar overall estimates of LoS for overall hospital stay, given a patient is not admitted to ICU (8.4, 9.1 and 8.0 days for AFT, TC and MS, respectively). Estimates differ more significantly between the local and national level when considering ICU. National estimates for ICU LoS from AFT and TC were 12.4 and 13.4 days, whereas in local data the MS method produced estimates of 18.9 days. CONCLUSIONS: Given the complexity and partiality of different data sources and the rapidly evolving nature of the COVID-19 pandemic, it is most appropriate to use multiple analysis methods on multiple datasets. The AFT method accounts for censored cases, but does not allow for simultaneous consideration of different outcomes. The TC method does not include censored cases, instead correcting for truncation in the data, but does consider these different outcomes. The MS method can model complex pathways to different outcomes whilst accounting for censoring, but cannot handle non-random case missingness. Overall, we conclude that data-driven modelling approaches of LoS using these methods is useful in epidemic planning and management, and should be considered for widespread adoption throughout healthcare systems internationally where similar data resources exist.

Length of Stay in ICU of Covid-19 patients in England, March - May 2020.

INTRODUCTION: Length of Stay (LoS) in Intensive Care Units (ICUs) is an important measure for planning beds capacity during the Covid-19 pandemic. However, as the pandemic progresses and we learn more about the disease, treatment and subsequent LoS in ICU may change. OBJECTIVES: To investigate the LoS in ICUs in England associated with Covid-19, correcting for censoring, and to evaluate the effect of known predictors of Covid-19 outcomes on ICU LoS. DATA SOURCES: We used retrospective data on Covid-19 patients, admitted to ICU between 6 March and 24 May, from the "Covid-19 Hospitalisation in England Surveillance System" (CHESS) database, collected daily from England's National Health Service, and collated by Public Health England. METHODS: We used Accelerated Failure Time survival models with Weibull and log-normal distributional assumptions to investigate the effect of predictors, which are known to be associated with poor Covid-19 outcomes, on the LoS in ICU. RESULTS: Patients admitted before 25 March had significantly longer LoS in ICU (mean = 18.4 days, median = 12), controlling for age, sex, whether the patient received Extracorporeal Membrane Oxygenation, and a co-morbid risk factors score, compared with the period after 7 April (mean = 15.4, median = 10). The periods of admission reflected the changes in the ICU admission policy in England. Patients aged 50-65 had the longest LoS, while higher co-morbid risk factors score led to shorter LoS. Sex and ethnicity were not associated with ICU LoS. CONCLUSIONS: The skew of the predicted LoS suggests that a mean LoS, as compared with median, might be better suited as a measure used to assess and plan ICU beds capacity. This is important for the ongoing second and any future waves of Covid-19 cases and potential pressure on the ICU resources. Also, changes in the ICU admission policy are likely to be confounded with improvements in clinical knowledge of Covid-19.

Homonegativity in Britain: Changing Attitudes Towards Same-Sex Relationships.

Over recent decades, British attitudes towards same-sex relationships have become more accepting. However, results from the National Survey of Sexual Attitudes and Lifestyles show that, in 2010, around a fifth of 16-59-year-olds still viewed sex between two men or two women as 'always wrong'. Using data from each edition of this survey (1990, 2000, 2010), we investigated which individuals are more likely to regard same-sex relationships as wrong and how this has changed over time. Using various measures of individual characteristics, the results showed sex, religiosity, ethnicity, education and whether someone has ever experienced same-sex attraction were most strongly associated with homonegative attitudes. We show that religiosity and ethnicity became more strongly associated with homonegativity between 1990 and 2010, with religiosity replacing education as the characteristic most associated with homonegativity by 2010; explanations for these changes are offered. Further results show that attitudes towards one night stands are also associated with attitudes towards same-sex relationships. This suggests that falling rates of homonegativity might (in part) be explained by a general liberalization in attitudes towards non-traditional sexual relationships.

Using latent class analysis to produce a typology of environmental concern in the UK.

Factor analysis is often used to study environmental concern. This choice of methodology is driven by predominant theories that tie environmental attitudes to the multidimensional construct of environmental concern. This paper demonstrates that using a clustering method such as latent class analysis can be a valuable tool for studying environmental attitudes as they exist within a given population. In making the case for the value of latent class analysis in this context, we examine UK public concern for the environment and how this concern is associated with pro-environmental behaviours. To do this we use responses to DEFRA's 2009 Survey of Public Attitudes and Behaviours towards the Environment, which is still the most nationally representative survey of its type in the UK. Grouping respondents according to homogenous response patterns, we identify four classes of people, defined by their concern for the environment: Pro-environment, Neutral Majority, Disengaged and Paradoxical. To understand how these attitude classes are associated with behaviour and socio-economic status, class membership probability is regressed onto education, income and social grade, as well as 16 measures of environmental behaviour related to transport, food, recycling and home energy conservation. The results contradict most previous research with the environmental attitude classes by being highly predictive of environmental behaviour.

Reduced denitration activity in peripheral lung of chronic obstructive pulmonary disease.

BACKGROUND: Accumulation of nitrated protein is seen in peripheral lung and cells from patients with chronic obstructive pulmonary disease (COPD). Nitrated protein causes abnormal protein function, but the nitration was believed to be an irreversible process. However, there are accumulating evidences that this process is reversible by an active denitration pathway. The aim of this study is to detect denitration activity in protein extracts from peripheral lung tissue of COPD and to compare with those in healthy subjects. MATERIALS AND METHODS: Peripheral lung tissue from 4 healthy, 4 smokers without COPD, 4 GOLD stage 1 and 4 GOLD stage 2 were used for denitration assay. Denitration activity was determined as reduction of nitro-tyrosine level of nitrated histone protein after incubation with protein extracts from peripheral lung, which was determined by western blotting. In addition, RNA is extracted from peripheral lung of 8 healthy, 7 smoking control, 8 stage 1 and 2 COPD and 10 stage 3 and 4 COPD and nitrate reductase mRNA expression was determined by real time RT-PCR. RESULTS: Peripheral lung protein extracts from healthy subjects reduced nitro-tyrosine level of nitrated histone. Thus, we were able to show denitration activity in peripheral lungs. The denitration activity was slightly reduced in smoking controls, and significantly reduced in COPD patients. We also showed that the expression of the human homologue of nitrate reductase (chytochrome ß2 reductase), a potential candidate of denitrase, was significanty reduced in COPD lung. CONCLUSION: This study suggests that accumulation of nitrated protein in lung tissue of COPD may, at least in part, be induced by a reduction in denitration activity or nitrate reductase.

Health utility and survival after hospital admission with acute cardiogenic pulmonary oedema.

BACKGROUND: The aim of this study was to measure health utility and survival in patients with acute cardiogenic pulmonary oedema (ACPO), identify predictors of outcome and determine the effect of initial treatment with non-invasive ventilation (NIV) upon outcomes. METHODS: A randomised controlled trial was conducted at 26 hospitals in the UK. 1069 adults with ACPO were randomised to continuous positive airway pressure (CPAP), non-invasive positive pressure ventilation (NIPPV) or standard oxygen therapy. The main outcome measures were survival to 1-5 years, health utility measured using the EQ-5D survey at 1, 3 and 6 months, and quality-adjusted life years (QALYs). RESULTS: Median survival was 771 days (95% CI 669 to 875), with no difference between the three treatment groups (p = 0.827). Age (HR 1.042, 95% CI 1.031 to 1.052), chronic obstructive pulmonary disease (HR 1.13, 95% CI 1.06 to 1.62), cerebrovascular disease (HR 1.41, 95% CI 1.14 to 1.73) and diabetes mellitus (HR 1.31, 95% CI 1.01 to 1.63) independently predicted mortality. Mean EQ-5D scores were 0.578, 0.576 and 0.582 at 1, 3 and 6 months, respectively, with no significant difference between the treatment groups. Male gender (+0.045 QALYs, 95% CI 0.009 to 0.081) and cerebrovascular disease (-0.080 QALYs, 95% CI -0.131 to -0.029) independently predicted health utility. CONCLUSION: Patients with ACPO have high mortality and reduced health utility. Initial treatment with CPAP or NIPPV does not alter subsequent survival or health utility.

A protein deacetylase SIRT1 is a negative regulator of metalloproteinase-9.

Inappropriate elevation of matrix metalloproteinase-9 (MMP9) is reported to be involved in the pathogenesis of chronic obstructive pulmonary disease (COPD). The object of this study was to identify the molecular mechanism underlying this increase of MMP9 expression, and here we show that oxidative stress-dependent reduction of a protein deacetylase, SIRT1, known as a putative antiaging enzyme, causes elevation of MMP9 expression. A sirtuin inhibitor, splitomycin, and SIRT1 knockdown by RNA interference led an increase in MMP9 expression in human monocytic U937 cells and in primary sputum macrophages, which was detected by RT-PCR, Western blot, activity assay, and zymography. In fact, the SIRT1 level was significantly decreased in peripheral lungs of patients with COPD, and this increase was inversely correlated with MMP9 expression and MMP9 promoter activation detected by a chromatin immunoprecipitation assay. H(2)O(2) reduced SIRT1 expression and activity in U937 cells; furthermore, cigarette smoke exposure also caused reduction of SIRT1 expression in lung tissue of A/J mice, with concomitant elevation of MMP9. Intranasal treatment of a selective and novel SIRT1 small molecule activator, SRT2172, blocked the increase of MMP9 expression in the lung as well as pulmonary neutrophilia and the reduction in exercise tolerance. Thus, SIRT1 is a negative regulator of MMP9 expression, and SIRT1 activation is implicated as a novel therapeutic approach to treating chronic inflammatory diseases, in which MMP9 is abundant.