Finerenone cardiovascular and kidney outcomes by age and sex: FIDELITY post hoc analysis of two phase 3, multicentre, double-blind trials.

OBJECTIVES: This study aimed to evaluate the efficacy and safety of finerenone, a selective, non-steroidal mineralocorticoid receptor antagonist, on cardiovascular and kidney outcomes by age and/or sex. DESIGN: FIDELITY post hoc analysis; median follow-up of 3 years. SETTING: FIDELITY: a prespecified analysis of the FIDELIO-DKD and FIGARO-DKD trials. PARTICIPANTS: Adults with type 2 diabetes and chronic kidney disease receiving optimised renin-angiotensin system inhibitors (N=13 026). INTERVENTIONS: Randomised 1:1; finerenone or placebo. PRIMARY AND SECONDARY OUTCOME MEASURES: Cardiovascular (cardiovascular death, non-fatal myocardial infarction, non-fatal stroke or hospitalisation for heart failure (HHF)) and kidney (kidney failure, sustained ≥57% estimated glomerular filtration rate (eGFR) decline or renal death) composite outcomes. RESULTS: Mean age was 64.8 years; 45.2%, 40.1% and 14.7% were aged <65, 65-74 and ≥75 years, respectively; 69.8% were male. Cardiovascular benefits of finerenone versus placebo were consistent across age (HR 0.94 (95% CI 0.81 to 1.10) (<65 years), HR 0.84 (95% CI 0.73 to 0.98) (65-74 years), HR 0.80 (95% CI 0.65 to 0.99) (≥75 years); Pinteraction=0.42) and sex categories (HR 0.86 (95% CI 0.77 to 0.96) (male), HR 0.89 (95% CI 0.35 to 2.27) (premenopausal female), HR 0.87 (95% CI 0.73 to 1.05) (postmenopausal female); Pinteraction=0.99). Effects on HHF reduction were not modified by age (Pinteraction=0.70) but appeared more pronounced in males (Pinteraction=0.02). Kidney events were reduced with finerenone versus placebo in age groups <65 and 65-74 but not ≥75; no heterogeneity in treatment effect was observed (Pinteraction=0.51). In sex subgroups, finerenone consistently reduced kidney events (Pinteraction=0.85). Finerenone reduced albuminuria and eGFR decline regardless of age and sex. Hyperkalaemia increased with finerenone, but discontinuation rates were <3% across subgroups. Gynaecomastia in males was uncommon across age subgroups and identical between treatment groups. CONCLUSIONS: Finerenone improved cardiovascular and kidney composite outcomes with no significant heterogeneity between age and sex subgroups; however, the effect on HHF appeared more pronounced in males. Finerenone demonstrated a similar safety profile across age and sex subgroups. TRIAL REGISTRATION NUMBERS: NCT02540993, NCT02545049.

Role of Diuretics in Cardiovascular Events and Mortality in Systolic Blood Pressure Intervention Trial: A Post Hoc Analysis.

BACKGROUND: In a post hoc analysis, we examined whether postrandomization diuretics use can explain and/or mediate the beneficial effects of intensive systolic BP lowering on cardiovascular disease and all-cause mortality in the Systolic Blood Pressure Intervention Trial (SPRINT). METHODS: SPRINT was a randomized, controlled trial of 9361 participants comparing the effects of intensive (systolic BP target <120 mm Hg) versus standard (systolic BP target <140 mm Hg) BP control on a primary composite cardiovascular end point in participants aged 50 years or older with systolic BP of 130-180 mm Hg. In time-varying multivariable Cox analyses, we assessed hazard ratios (HRs) of cardiovascular end points and all-cause mortality in participants on thiazide type, loop and/or potassium (K) sparing, or no diuretics. We also conducted mediation analysis to formally assess the role of diuretics in the effects of intensive systolic BP lowering. RESULTS: At baseline, diuretics were prescribed in 46% and 48% of participants in standard and intensive systolic BP-lowering groups, respectively, and in 46% and 74% in the corresponding groups during the trial. The lower risk of cardiovascular end points in the intensive group (HR, 0.75; 95% confidence interval [CI], 0.64 to 0.89) persisted after adjustment for postrandomization time-varying diuretics use (HR, 0.74; 95% CI, 0.62 to 0.89). Across the entire study population, time-varying diuretics use was not associated with cardiovascular end points (compared with no diuretics, HR for thiazide type, 0.89; 95% CI, 0.73 to 1.10, and loop/K sparing, 1.29; 95% CI, 0.97 to 1.73). However, thiazide-type diuretics were associated with lower risk of cardiovascular end points in the intensive (HR, 0.62; 95% CI, 0.46 to 0.85) but not in the standard (HR, 1.07; 95% CI, 0.82 to 1.39) group. In mediation analysis, HRs for total effect, direct effect (not mediated through diuretics use), and indirect effect (mediated through diuretics) of the intervention on cardiovascular end points were 0.66 (95% CI, 0.54 to 0.79), 0.67 (95% CI, 0.54 to 0.81), and 0.98 (95% CI, 0.88 to 1.10), respectively. The results were largely similar for all-cause mortality. CONCLUSIONS: The favorable effects of intensive systolic BP lowering on cardiovascular end points and all-cause mortality in SPRINT were independent of and not mediated by time-varying diuretics use. However, thiazide-type diuretics use associated with benefit if intensive systolic BP lowering was targeted.

The impact of spatial connectivity on NPIs effectiveness.

BACKGROUND: France implemented a combination of non-pharmaceutical interventions (NPIs) to manage the COVID-19 pandemic between September 2020 and June 2021. These included a lockdown in the fall 2020 - the second since the start of the pandemic - to counteract the second wave, followed by a long period of nighttime curfew, and by a third lockdown in the spring 2021 against the Alpha wave. Interventions have so far been evaluated in isolation, neglecting the spatial connectivity between regions through mobility that may impact NPI effectiveness. METHODS: Focusing on September 2020-June 2021, we developed a regionally-based epidemic metapopulation model informed by observed mobility fluxes from daily mobile phone data and fitted the model to regional hospital admissions. The model integrated data on vaccination and variants spread. Scenarios were designed to assess the impact of the Alpha variant, characterized by increased transmissibility and risk of hospitalization, of the vaccination campaign and alternative policy decisions. RESULTS: The spatial model better captured the heterogeneity observed in the regional dynamics, compared to models neglecting inter-regional mobility. The third lockdown was similarly effective to the second lockdown after discounting for immunity, Alpha, and seasonality (51% vs 52% median regional reduction in the reproductive number R0, respectively). The 6pm nighttime curfew with bars and restaurants closed, implemented in January 2021, substantially reduced COVID-19 transmission. It initially led to 49% median regional reduction of R0, decreasing to 43% reduction by March 2021. In absence of vaccination, implemented interventions would have been insufficient against the Alpha wave. Counterfactual scenarios proposing a sequence of lockdowns in a stop-and-go fashion would have reduced hospitalizations and restriction days for low enough thresholds triggering and lifting restrictions. CONCLUSIONS: Spatial connectivity induced by mobility impacted the effectiveness of interventions especially in regions with higher mobility rates. Early evening curfew with gastronomy sector closed allowed authorities to delay the third wave. Stop-and-go lockdowns could have substantially lowered both healthcare and societal burdens if implemented early enough, compared to the observed application of lockdown-curfew-lockdown, but likely at the expense of several labor sectors. These findings contribute to characterize the effectiveness of implemented strategies and improve pandemic preparedness.

Comparative approaches in social network ecology.

Social systems vary enormously across the animal kingdom, with important implications for ecological and evolutionary processes such as infectious disease dynamics, anti-predator defence, and the evolution of cooperation. Comparing social network structures between species offers a promising route to help disentangle the ecological and evolutionary processes that shape this diversity. Comparative analyses of networks like these are challenging and have been used relatively little in ecology, but are becoming increasingly feasible as the number of empirical datasets expands. Here, we provide an overview of multispecies comparative social network studies in ecology and evolution. We identify a range of advancements that these studies have made and key challenges that they face, and we use these to guide methodological and empirical suggestions for future research. Overall, we hope to motivate wider publication and analysis of open social network datasets in animal ecology.

Heart Failure With Preserved Ejection Fraction With CKD: A Narrative Review of a Multispecialty Disorder.

Heart failure with preserved ejection fraction (HFpEF) is a heterogenous syndrome with varying phenotypic expression. The phenotype chronic kidney disease (CKD) associated HFpEF is increasing in prevalence globally and is associated with increased morbidity and mortality compared to other HFpEF variants. These 2 conditions share common risk factors, including obesity, diabetes, and metabolic syndrome, as well as similar pathophysiology, including systemic inflammation, oxidative stress, elevated neurohormones, mineralocorticoid-receptor activation, and venous congestion. Given the coexistence of CKD and HFpEF, the diagnosis of HFpEF can be difficult. Moreover, treatment options for HFpEF have remained limited despite the success seen in its counterpart, heart failure with reduced ejection fraction. HFpEF encompasses complex multisystem pathophysiological perturbations beyond neurohormones, it is unlikely that a single agent can have significant benefit in this population. Recent data on sodium-glucose cotransporter 2 (SGLT2) inhibitors in HFpEF and CKD, and on glucagon-like peptide-1 (GLP-1) agonists and mineralocorticoid-receptor antagonists in metabolic syndrome, which target multiple pathways simultaneously, have led to promising therapeutics for HFpEF and CKD. In this perspective, our goal is to increase awareness of HFpEF as a multisystem disorder that shares the same disease processes seen in CKD and to emphasize that its management in individuals with CKD warrants a collective and multidisciplinary approach.

Contrast-Associated Acute Kidney Injury: Definitions, Epidemiology, Pathophysiology, and Implications.

Acute kidney injury (AKI) is a common occurrence after contrast media administration. Hemodynamic changes, direct tubular injury, and reactive oxygen species are the proposed mechanisms involved in AKI. However, in most scenarios, it is not possible to establish causality despite extensive clinical evaluation, therefore, contrast-associated AKI (CA-AKI) has become a widely accepted term to define AKI postcontrast. CA-AKI is associated with worse clinical outcomes including cardiovascular events and mortality; however, discussions are ongoing whether CA-AKI is a marker of an increased risk of adverse outcomes or a mediator of such outcomes.

Perplexity of Preoperative Multimodal Investigations in a Case of Parotid MALToma.

Mucosa-associated lymphoid tissue(MALT) lymphoma is a distinct subtype of lymphoma, presenting as a diffuse gland involvement or a discrete mass. Pre-operative diagnosis is a challenge as Fine Needle Aspiration Cytology is often inconclusive and presently no radiological investigation is confirmatory, therefore final diagnosis is made after surgical resection and immunohistopathology. We report a case of MALT lymphoma, which clinically presented as a unilateral large diffuse swelling of the parotid gland with a diagnostic dilemma eventually underwent total parotidectomy to be finally diagnosed as MALT lymphoma of parotid gland and received field radiotherapy with complete cure and no recurrence in a 2-year follow-up.

Charting the spatial dynamics of early SARS-CoV-2 transmission in Washington state.

The spread of SARS-CoV-2 has been geographically uneven. To understand the drivers of this spatial variation in SARS-CoV-2 transmission, in particular the role of stochasticity, we used the early stages of the SARS-CoV-2 invasion in Washington state as a case study. We analysed spatially-resolved COVID-19 epidemiological data using two distinct statistical analyses. The first analysis involved using hierarchical clustering on the matrix of correlations between county-level case report time series to identify geographical patterns in the spread of SARS-CoV-2 across the state. In the second analysis, we used a stochastic transmission model to perform likelihood-based inference on hospitalised cases from five counties in the Puget Sound region. Our clustering analysis identifies five distinct clusters and clear spatial patterning. Four of the clusters correspond to different geographical regions, with the final cluster spanning the state. Our inferential analysis suggests that a high degree of connectivity across the region is necessary for the model to explain the rapid inter-county spread observed early in the pandemic. In addition, our approach allows us to quantify the impact of stochastic events in determining the subsequent epidemic. We find that atypically rapid transmission during January and February 2020 is necessary to explain the observed epidemic trajectories in King and Snohomish counties, demonstrating a persisting impact of stochastic events. Our results highlight the limited utility of epidemiological measures calculated over broad spatial scales. Furthermore, our results make clear the challenges with predicting epidemic spread within spatially extensive metropolitan areas, and indicate the need for high-resolution mobility and epidemiological data.

Estimating county-level flu vaccination in the United States.

In the United States, influenza vaccines are an important part of public health efforts to blunt the effects of seasonal influenza epidemics. This in turn emphasizes the importance of understanding the spatial distribution of influenza vaccination coverage. Despite this, high quality data at a fine spatial scale and spanning a multitude of recent flu seasons are not readily available. To address this gap, we develop county-level counts of vaccination across five recent, consecutive flu seasons and fit a series of regression models to these data that account for bias. We find that the spatial distribution of our bias-corrected vaccination coverage estimates is generally consistent from season to season, with the highest coverage in the Northeast and Midwest but is spatially heterogeneous within states. We also observe a negative relationship between a county's vaccination coverage and social vulnerability. Our findings stress the importance of quantifying flu vaccination coverage at a fine spatial scale, as relying on state or region-level estimates misses key heterogeneities.

Disentangling the rhythms of human activity in the built environment for airborne transmission risk: An analysis of large-scale mobility data.

Background: Since the outset of the COVID-19 pandemic, substantial public attention has focused on the role of seasonality in impacting transmission. Misconceptions have relied on seasonal mediation of respiratory diseases driven solely by environmental variables. However, seasonality is expected to be driven by host social behavior, particularly in highly susceptible populations. A key gap in understanding the role of social behavior in respiratory disease seasonality is our incomplete understanding of the seasonality of indoor human activity. Methods: We leverage a novel data stream on human mobility to characterize activity in indoor versus outdoor environments in the United States. We use an observational mobile app-based location dataset encompassing over 5 million locations nationally. We classify locations as primarily indoor (e.g. stores, offices) or outdoor (e.g. playgrounds, farmers markets), disentangling location-specific visits into indoor and outdoor, to arrive at a fine-scale measure of indoor to outdoor human activity across time and space. Results: We find the proportion of indoor to outdoor activity during a baseline year is seasonal, peaking in winter months. The measure displays a latitudinal gradient with stronger seasonality at northern latitudes and an additional summer peak in southern latitudes. We statistically fit this baseline indoor-outdoor activity measure to inform the incorporation of this complex empirical pattern into infectious disease dynamic models. However, we find that the disruption of the COVID-19 pandemic caused these patterns to shift significantly from baseline and the empirical patterns are necessary to predict spatiotemporal heterogeneity in disease dynamics. Conclusions: Our work empirically characterizes, for the first time, the seasonality of human social behavior at a large scale with a high spatiotemporal resolutio and provides a parsimonious parameterization of seasonal behavior that can be included in infectious disease dynamics models. We provide critical evidence and methods necessary to inform the public health of seasonal and pandemic respiratory pathogens and improve our understanding of the relationship between the physical environment and infection risk in the context of global change. Funding: Research reported in this publication was supported by the National Institute of General Medical Sciences of the National Institutes of Health under award number R01GM123007.

Spatial distribution and determinants of childhood vaccination refusal in the United States.

Parental refusal and delay of childhood vaccination has increased in recent years in the United States. This phenomenon challenges maintenance of herd immunity and increases the risk of outbreaks of vaccine-preventable diseases. We examine US county-level vaccine refusal for patients under five years of age collected during the period 2012-2015 from an administrative healthcare dataset. We model these data with a Bayesian zero-inflated negative binomial regression model to capture social and political processes that are associated with vaccine refusal, as well as factors that affect our measurement of vaccine refusal. Our work highlights fine-scale socio-demographic characteristics associated with vaccine refusal nationally, finds that spatial clustering in refusal can be explained by such factors, and has the potential to aid in the development of targeted public health strategies for optimizing vaccine uptake.

COVID-19 and preeclampsia: The unique and the mutually nonexclusive clinical manifestations.

Preeclampsia (PE) is a serious, unpredictable hypertensive disorder of pregnancy present in around 8-10% of all pregnancies resulting in high rate of maternal and fetal morbidity and mortality. With the pathophysiology partially known, delivery is the only cure for PE. The disease sets due to multiple pathologic processes involving endothelial cell activation, inflammation, multiorgan damage and syncytiotrophoblast stress.  Though the primary target organ is lungs in COVID-19, other systemic manifestations which include endothelial dysfunction, dysregulated angiogenesis, thrombosis, liver injury, thrombocytopenia, hypertension and kidney damage overlap with PE. COVID-19 patients show a higher incidence of PE as compared to their noninfected counterparts and vice versa. Similar pathophysiology and clinical features make differential diagnosis challenging. For effective and specific management, it is important to differentiate actual PE from COVID-19 with PE like features. There are contradictory reports about the accuracy of diagnostic tools in distinguishing PE from severe COVID-19 with PE like features. With the available data, it can only be stated that PE is a common adverse pregnancy event, which may be exacerbated by, or may exacerbate, COVID-19. Future research should focus on cohesive understanding of the pathophysiology of the clinical manifestations, and preventive strategies during pregnancy.

Spatiotemporal Trends in Self-Reported Mask-Wearing Behavior in the United States: Analysis of a Large Cross-sectional Survey.

BACKGROUND: Face mask wearing has been identified as an effective strategy to prevent the transmission of SARS-CoV-2, yet mask mandates were never imposed nationally in the United States. This decision resulted in a patchwork of local policies and varying compliance, potentially generating heterogeneities in the local trajectories of COVID-19 in the United States. Although numerous studies have investigated the patterns and predictors of masking behavior nationally, most suffer from survey biases and none have been able to characterize mask wearing at fine spatial scales across the United States through different phases of the pandemic. OBJECTIVE: Urgently needed is a debiased spatiotemporal characterization of mask-wearing behavior in the United States. This information is critical to further assess the effectiveness of masking, evaluate the drivers of transmission at different time points during the pandemic, and guide future public health decisions through, for example, forecasting disease surges. METHODS: We analyzed spatiotemporal masking patterns in over 8 million behavioral survey responses from across the United States, starting in September 2020 through May 2021. We adjusted for sample size and representation using binomial regression models and survey raking, respectively, to produce county-level monthly estimates of masking behavior. We additionally debiased self-reported masking estimates using bias measures derived by comparing vaccination data from the same survey to official records at the county level. Lastly, we evaluated whether individuals' perceptions of their social environment can serve as a less biased form of behavioral surveillance than self-reported data. RESULTS: We found that county-level masking behavior was spatially heterogeneous along an urban-rural gradient, with mask wearing peaking in winter 2021 and declining sharply through May 2021. Our results identified regions where targeted public health efforts could have been most effective and suggest that individuals' frequency of mask wearing may be influenced by national guidance and disease prevalence. We validated our bias correction approach by comparing debiased self-reported mask-wearing estimates with community-reported estimates, after addressing issues of a small sample size and representation. Self-reported behavior estimates were especially prone to social desirability and nonresponse biases, and our findings demonstrated that these biases can be reduced if individuals are asked to report on community rather than self behaviors. CONCLUSIONS: Our work highlights the importance of characterizing public health behaviors at fine spatiotemporal scales to capture heterogeneities that may drive outbreak trajectories. Our findings also emphasize the need for a standardized approach to incorporating behavioral big data into public health response efforts. Even large surveys are prone to bias; thus, we advocate for a social sensing approach to behavioral surveillance to enable more accurate estimates of health behaviors. Finally, we invite the public health and behavioral research communities to use our publicly available estimates to consider how bias-corrected behavioral estimates may improve our understanding of protective behaviors during crises and their impact on disease dynamics.