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Summary: Reliably detecting potentially misleading patterns in automated diagnostic assistance systems, such as those powered by artificial intelligence (AI), is crucial for instilling user trust and ensuring reliability. Current techniques fall short in visualizing such confounding factors. We propose DiffChest, a self-conditioned diffusion model trained on 515,704 chest radiographs from 194,956 patients across the US and Europe. DiffChest provides patient-specific explanations and visualizes confounding factors that might mislead the model. The high inter-reader agreement, with Fleiss’ kappa values of 0.8 or higher, validates its capability to identify treatment-related confounders. Confounders are accurately detected with 10%–100% prevalence rates. The pretraining process optimizes the model for relevant imaging information, resulting in excellent diagnostic accuracy for 11 chest conditions, including pleural effusion and heart insufficiency. Our findings highlight the potential of diffusion models in medical image classification, providing insights into confounding factors and enhancing model robustness and reliability.

Investigation of health inequities tend to be examined, in human neurosciences, as biological factors at the level of the individual. In actuality, health inequities arise, due largely in part, to deep-seated structural factors. Structural inequality refers to the systemic disadvantage of one social group compared to others with whom they coexist. The term encompasses policy, law, governance, and culture and relates to race, ethnicity, gender or gender identity, class, sexual orientation, and other domains. These structural inequalities include but are not limited to social segregation, the intergenerational effects of colonialism and the consequent distribution of power and privilege. Principles to address inequities influenced by structural factors are increasingly prevalent in a subfield of the neurosciences, i.e., cultural neurosciences. Cultural neuroscience articulates the bidirectional relationship between biology and environmental contextual factors surrounding research participants. However, the operationalization of these principles may not have the intended spillover effect on the majority of human neurosciences: this limitation is the overarching focus of the present piece. Here, we provide our perspective that these principles are missing and very much needed in all human neuroscience subdisciplines to accelerate our understanding of the human brain. Furthermore, we provide an outline of two key tenets of a health equity lens necessary for achieving research equity in human neurosciences: the social determinants of health (SDoH) framework and how to deal with confounders using counterfactual thinking. We argue that these tenets should be prioritized across future human neuroscience research more generally, and doing so is a pathway to further gain an understanding of contextual background intertwined with the human brain, thus improving the rigor and inclusivity of human neuroscience research.

Programs to protect biodiversity on private land are increasingly being used worldwide. To understand the efficacy of such programs, it is important to determine their impact: the difference between the program's outcome and what would have happened without the program. Typically, these programs are evaluated by estimating the average program‐level impact, which readily allows comparisons between programs or regions, but masks important heterogeneity in impact across the individual conservation interventions. We used synthetic control design, statistical matching, and time‐series data to estimate the impact of individual protected areas over time and combined individual‐level impacts to estimate program‐level impact with a meta‐analytic approach. We applied the method to private protected areas governed by conservation covenants (legally binding on‐title agreements to protect biodiversity) in the Goldfields region of Victoria, Australia using woody vegetation cover as our outcome variable. We compared our results with traditional approaches to estimating program‐level impact based on a subset of covenants that were the same age. Our results showed an overall program‐level impact of a 0.3–0.8% increase in woody vegetation cover per year. However, there was significant heterogeneity in the temporal pattern of impact for individual covenants, ranging from −4 to +7% change in woody vegetation cover per year. Results of our approach were consistent with results based on traditional approaches to estimating program‐level impact. Our study provides a transparent and robust workflow to estimate individual and program‐level impacts of private protected areas. [ABSTRACT FROM AUTHOR]

Non-destructive assessments are required for the quality control of tissue-engineered constructs and the optimization of the tissue culture process. Near-infrared (NIR) spectroscopy coupled with machine learning (ML) provides a promising approach for such assessment. However, due to its nonspecific nature, each spectrum incorporates information on both neotissue and non-neotissue constituents of the construct; the effect of these constituents on the NIR-based assessments of tissue-engineered constructs has been overlooked in previous studies. This study investigates the effect of scaffolds, growth factors, and buffers on NIR-based assessments of tissue-engineered constructs. To determine if these non-neotissue constituents have a measurable effect on the NIR spectra of the constructs that can introduce bias in their assessment, nine ML algorithms were evaluated in classifying the NIR spectra of engineered cartilage according to the scaffold used to prepare the constructs, the growth factors added to the culture media, and the buffers used for storing the constructs. The effect of controlling for these constituents was also evaluated using controlled and uncontrolled NIR-based ML models for predicting tissue maturity as an example of neotissue-related properties of interest. Samples used in this study were prepared using norbornene-modified hyaluronic acid scaffolds with or without the conjugation of an N-cadherin mimetic peptide. Selected samples were supplemented with transforming growth factor-beta1 or bone morphogenetic protein-9 growth factor. Some samples were frozen in cell lysis buffer, while the remaining samples were frozen in PBS until required for NIR analysis. The ML models for classifying the spectra of the constructs according to the four constituents exhibited high to fair performances, with F1 scores ranging from 0.9 to 0.52. Moreover, controlling for the four constituents significantly improved the performance of the models for predicting tissue maturity, with improvement in F1 scores ranging from 0.09 to 0.77. In conclusion, non-neotissue constituents have measurable effects on the NIR spectra of tissue-engineered constructs that can be detected by ML algorithms and introduce bias in the assessment of the constructs by NIR spectroscopy. Therefore, controlling for these constituents is necessary for reliable NIR-based assessments of tissue-engineered constructs.

Demographic and clinical factors influence the metabolome. The discovery and validation of disease biomarkers are often challenged by potential confounding effects from such factors. To address this challenge, we investigated the magnitude of the correlation between serum and urine metabolites and demographic and clinical parameters in a well-characterized observational cohort of 444 post-menopausal women participating in the Women's Health Initiative (WHI). Using LC-MS and lipidomics, we measured 157 aqueous metabolites and 756 lipid species across 13 lipid classes in serum, along with 195 metabolites detected by GC-MS and NMR in urine and evaluated their correlations with 29 potential disease risk factors, including demographic, dietary and lifestyle factors, and medication use. After controlling for multiple testing (FDR < 0.01), we found that log-transformed metabolites were mainly associated with age, BMI, alcohol intake, race, sample storage time (urine only), and dietary supplement use. Statistically significant correlations were in the absolute range of 0.2–0.6, with the majority falling below 0.4. Incorporation of important potential confounding factors in metabolite and disease association analyses may lead to improved statistical power as well as reduced false discovery rates in a variety of data analysis settings. [ABSTRACT FROM AUTHOR]

Investigation of health inequities tend to be examined, in human neurosciences, as biological factors at the level of the individual. In actuality, health inequities arise, due largely in part, to deep-seated structural factors. Structural inequality refers to the systemic disadvantage of one social group compared to others with whom they coexist. The term encompasses policy, law, governance, and culture and relates to race, ethnicity, gender or gender identity, class, sexual orientation, and other domains. These structural inequalities include but are not limited to social segregation, the intergenerational effects of colonialism and the consequent distribution of power and privilege. Principles to address inequities influenced by structural factors are increasingly prevalent in a subfield of the neurosciences, i.e., cultural neurosciences. Cultural neuroscience articulates the bidirectional relationship between biology and environmental contextual factors surrounding research participants. However, the operationalization of these principles may not have the intended spillover effect on the majority of human neurosciences: this limitation is the overarching focus of the present piece. Here, we provide our perspective that these principles are missing and very much needed in all human neuroscience subdisciplines to accelerate our understanding of the human brain. Furthermore, we provide an outline of two key tenets of a health equity lens necessary for achieving research equity in human neurosciences: the social determinants of health (SDoH) framework and how to deal with confounders using counterfactual thinking. We argue that these tenets should be prioritized across future human neuroscience research more generally, and doing so is a pathway to further gain an understanding of contextual background intertwined with the human brain, thus improving the rigor and inclusivity of human neuroscience research.

Background: Establishing a large study network to conduct influenza vaccine effectiveness (IVE) studies while collecting appropriate variables to account for potential bias is important; the most relevant variables should be prioritized. We explored the impact of potential confounders on IVE in the DRIVE multi‐country network of sites conducting test‐negative design (TND) studies. Methods: We constructed a directed acyclic graph (DAG) to map the relationship between influenza vaccination, medically attended influenza infection, confounders, and other variables. Additionally, we used the Development of Robust and Innovative Vaccines Effectiveness (DRIVE) data from the 2018/2019 and 2019/2020 seasons to explore the effect of covariate adjustment on IVE estimates. The reference model was adjusted for age, sex, calendar time, and season. The covariates studied were presence of at least one, two, or three chronic diseases; presence of six specific chronic diseases; and prior healthcare use. Analyses were conducted by site and subsequently pooled. Results: The following variables were included in the DAG: age, sex, time within influenza season and year, health status and comorbidities, study site, health‐care‐seeking behavior, contact patterns and social precautionary behavior, socioeconomic status, and pre‐existing immunity. Across all age groups and settings, only adjustment for lung disease in older adults in the primary care setting resulted in a relative change of the IVE point estimate >10%. Conclusion: Our study supports a parsimonious approach to confounder adjustment in TND studies, limited to adjusting for age, sex, and calendar time. Practical implications are that necessitating fewer variables lowers the threshold for enrollment of sites in IVE studies and simplifies the pooling of data from different IVE studies or study networks. [ABSTRACT FROM AUTHOR]

Alzheimer's disease is a progressive form of dementia that results in problems with memory, thinking, and behavior. It often starts with abnormal aggregation and deposition of β amyloid and tau, followed by neuronal damage such as atrophy of the hippocampi, leading to Alzheimer's disease (AD). The aim of this article is to map the genetic-imaging-clinical pathway for AD in order to delineate the genetically-regulated brain changes that drive disease progression based on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. We develop a novel two-step approach to delineate the association between high-dimensional 2D hippocampal surface exposures and the Alzheimer's Disease Assessment Scale (ADAS) cognitive score, while taking into account the ultra-high dimensional clinical and genetic covariates at baseline. Analysis results suggest that the radial distance of each pixel of both hippocampi is negatively associated with the severity of behavioral deficits conditional on observed clinical and genetic covariates. These associations are stronger in Cornu Ammonis region 1 (CA1) and subiculum subregions compared to Cornu Ammonis region 2 (CA2) and Cornu Ammonis region 3 (CA3) subregions. for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement. [ABSTRACT FROM AUTHOR]

Background and Aims: Surgical Pleth Index (SPI) provides an objective assessment of nociception - anti-nociception balance but is influenced by multiple confounders. The effect of change of position on SPI, has not been studied extensively. The aim of the study was to observe the effect of prone positioning on SPI and its correlation with hemodynamic variables, in patients undergoing lumbar and thoracic spine surgery. Material and Methods: This prospective observational pilot study included 14 patients. In addition to hemodynamic monitoring, SPI, entropy and pulse pressure variability (PPV) were monitored. Propofol and Fentanyl infusions were used for maintenance of anesthesia. The patients were made prone on bolsters and all the variables were recorded every 5 minutes in supine position and after making prone for 20 minutes, before and after incision, muscle splitting and laminectomy. Results: Comparing the last value of the variables in the supine position with those immediately after making prone, SPI increased by 16.36 units (P = 0.003), followed by gradual reduction over the next 20 minutes. Mean arterial pressure and heart rate increased transiently (Pvalue = 0.028 and 0.025, respectively) without any significant change in PPV. Surgical incision also led to a significant increase in SPI. Conclusion: Prone positioning leads to significant increase in SPI, probably due to increased sympathetic tone.

Abstract We would like to thank authors Reece and Hulse (2022) for their three-part article titled “Geotemporospatial and causal inferential epidemiological overview and survey of USA cannabis, cannabidiol and cannabinoid genotoxicity expressed in cancer incidence 2003-2017”, in which the authors infer that cannabis use has a causal role in the development of various cancer types. While the authors use reputable datasets and a well-established epidemiological methodology, the authors’ conclusion of a causal association is limited due to biases inherent in ecological epidemiological studies. Though the researchers attempt to overcome these biases through validation and statistical manipulations, their approaches are insufficient to create conditions suitable for causal inferencing upon examination. There are also concerns in the practical and conceptual application of the studies’ dataset that further question the validity of the authors’ inferences. Further research exploring the potential benefits and harm of cannabinoids in the context of cancer must be performed before a distinct relationship can be defined.

High-dimensional mediation analysis has been developed to study whether epigenetic phenotype in a high-dimensional data form would mediate the causal pathway of exposure to disease. However, most existing models are designed based on the assumption that there are no confounders between the exposure, the mediators, and the outcome. In practice, this assumption may not be feasible since high-dimensional mediation analysis (HIMA) tends to be observational where a randomized controlled trial (RCT) cannot be conducted for some economic or ethical reasons. Thus, to deal with the confounders in HIMA cases, we proposed three propensity score-related approaches named PSR (propensity score regression), PSW (propensity score weighting), and PSU (propensity score union) to adjust for the confounder bias in HIMA, and compared them with the traditional covariate regression method. The procedures mainly include four parts: calculating the propensity score, sure independence screening, MCP (minimax concave penalty) variable selection, and joint-significance testing. Simulation results show that the PSU model is the most recommended. Applying our models to the TCGA lung cancer dataset, we find that smoking may lead to lung disease through the mediation effect of some specific DNA-methylation sites, including site Cg24480765 in gene RP11-347H15.2 and site Cg22051776 in gene KLF3. [ABSTRACT FROM AUTHOR]

Background and Aims: Surgical Pleth Index (SPI) provides an objective assessment of nociception - anti-nociception balance but is influenced by multiple confounders. The effect of change of position on SPI, has not been studied extensively. The aim of the study was to observe the effect of prone positioning on SPI and its correlation with hemodynamic variables, in patients undergoing lumbar and thoracic spine surgery. Material and Methods: This prospective observational pilot study included 14 patients. In addition to hemodynamic monitoring, SPI, entropy and pulse pressure variability (PPV) were monitored. Propofol and Fentanyl infusions were used for maintenance of anesthesia. The patients were made prone on bolsters and all the variables were recorded every 5 minutes in supine position and after making prone for 20 minutes, before and after incision, muscle splitting and laminectomy. Results: Comparing the last value of the variables in the supine position with those immediately after making prone, SPI increased by 16.36 units (P = 0.003), followed by gradual reduction over the next 20 minutes. Mean arterial pressure and heart rate increased transiently (Pvalue = 0.028 and 0.025, respectively) without any significant change in PPV. Surgical incision also led to a significant increase in SPI. Conclusion: Prone positioning leads to significant increase in SPI, probably due to increased sympathetic tone. [ABSTRACT FROM AUTHOR]

Competing Interests: Conflict of interest: none declared.

Unsupervised learning, particularly clustering, plays a pivotal role in disease subtyping and patient stratification, especially with the abundance of large-scale multi-omics data. Deep learning models, such as variational autoencoders (VAEs), can enhance clustering algorithms by leveraging inter-individual heterogeneity. However, the impact of confounders-external factors unrelated to the condition, e.g. batch effect or age-on clustering is often overlooked, introducing bias and spurious biological conclusions. In this work, we introduce four novel VAE-based deconfounding frameworks tailored for clustering multi-omics data. These frameworks effectively mitigate confounding effects while preserving genuine biological patterns. The deconfounding strategies employed include (i) removal of latent features correlated with confounders, (ii) a conditional VAE, (iii) adversarial training, and (iv) adding a regularization term to the loss function. Using real-life multi-omics data from The Cancer Genome Atlas, we simulated various confounding effects (linear, nonlinear, categorical, mixed) and assessed model performance across 50 repetitions based on reconstruction error, clustering stability, and deconfounding efficacy. Our results demonstrate that our novel models, particularly the conditional multi-omics VAE (cXVAE), successfully handle simulated confounding effects and recover biologically driven clustering structures. cXVAE accurately identifies patient labels and unveils meaningful pathological associations among cancer types, validating deconfounded representations. Furthermore, our study suggests that some of the proposed strategies, such as adversarial training, prove insufficient in confounder removal. In summary, our study contributes by proposing innovative frameworks for simultaneous multi-omics data integration, dimensionality reduction, and deconfounding in clustering. Benchmarking on open-access data offers guidance to end-users, facilitating meaningful patient stratification for optimized precision medicine., (© The Author(s) 2024. Published by Oxford University Press.)

Reliably detecting potentially misleading patterns in automated diagnostic assistance systems, such as those powered by artificial intelligence (AI), is crucial for instilling user trust and ensuring reliability. Current techniques fall short in visualizing such confounding factors. We propose DiffChest, a self-conditioned diffusion model trained on 515,704 chest radiographs from 194,956 patients across the US and Europe. DiffChest provides patient-specific explanations and visualizes confounding factors that might mislead the model. The high inter-reader agreement, with Fleiss' kappa values of 0.8 or higher, validates its capability to identify treatment-related confounders. Confounders are accurately detected with 10%-100% prevalence rates. The pretraining process optimizes the model for relevant imaging information, resulting in excellent diagnostic accuracy for 11 chest conditions, including pleural effusion and heart insufficiency. Our findings highlight the potential of diffusion models in medical image classification, providing insights into confounding factors and enhancing model robustness and reliability., Competing Interests: Declaration of interests J.N.K. declares consulting services for Owkin, France; DoMore Diagnostics, Norway; Panakeia, UK; and Scailyte, Basel, Switzerland. Furthermore, J.N.K. holds shares in Kather Consulting, Dresden, Germany and StratifAI GmbH, Dresden, Germany and has received honoraria for lectures and advisory board participation by AstraZeneca, Bayer, Eisai, MSD, BMS, Roche, Pfizer, and Fresenius. D.T. received honoraria for lectures by Bayer and holds shares in StratifAI GmbH, Germany., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)

Abstract Background To evaluate and determine the protective role of statins in COVID-19 patients. Methods This is a retrospective cohort study conducted across five hospitals in India. Patients diagnosed with COVID-19 and hospitalized with existing and valid medical documentation were included. Results This study comprised 3252 COVID-19 patients, of whom 1048 (32.2%) were on statins, with 52.4% being males. The comorbidity prevalence of hypertension was 75%, followed by diabetes 62.51% and coronary artery disease being 47.5%. At the time of hospitalization, statin users had a higher incidence of dyspnea, cough, and fatigue (95.8, 93.3, and 92.7%). The laboratory results revealed a lower mean of WBC count (7.8 × 103/μL), D-dimer (2.4 μg/mL), and C-reactive protein (103 mg/L) among statin users. They also had lower mortality rates (17.1%), a lesser requirement for mechanical ventilation (20%), and hemodialysis (5.4%). Conclusion This observation study elaborates on the beneficial effects of statins in COVID-19 patients. However, the inferences from this study should be viewed with caution due to the impending effect of confounding factors on its statistical results.

Despite considerable morbidity and mortality, numerous cases of endocrine hypertension (EHT) forms, including primary aldosteronism (PA), pheochromocytoma and functional paraganglioma (PPGL), and Cushing's syndrome (CS), remain undetected. We aimed to establish signatures for the different forms of EHT, investigate potentially confounding effects and establish unbiased disease biomarkers. Plasma samples were obtained from 13 biobanks across seven countries and analyzed using untargeted NMR metabolomics. We compared unstratified samples of 106 PHT patients to 231 EHT patients, including 104 PA, 94 PPGL and 33 CS patients. Spectra were subjected to a multivariate statistical comparison of PHT to EHT forms and the associated signatures were obtained. Three approaches were applied to investigate and correct confounding effects. Though we found signatures that could separate PHT from EHT forms, there were also key similarities with the signatures of sample center of origin and sample age. The study design restricted the applicability of the corrections employed. With the samples that were available, no biomarkers for PHT vs. EHT could be identified. The complexity of the confounding effects, evidenced by their robustness to correction approaches, highlighted the need for a consensus on how to deal with variabilities probably attributed to preanalytical factors in retrospective, multicenter metabolomics studies. [ABSTRACT FROM AUTHOR]

Demographic and clinical factors influence the metabolome. The discovery and validation of disease biomarkers are often challenged by potential confounding effects from such factors. To address this challenge, we investigated the magnitude of the correlation between serum and urine metabolites and demographic and clinical parameters in a well-characterized observational cohort of 444 post-menopausal women participating in the Women’s Health Initiative (WHI). Using LC-MS and lipidomics, we measured 157 aqueous metabolites and 756 lipid species across 13 lipid classes in serum, along with 195 metabolites detected by GC-MS and NMR in urine and evaluated their correlations with 29 potential disease risk factors, including demographic, dietary and lifestyle factors, and medication use. After controlling for multiple testing (FDR < 0.01), we found that log-transformed metabolites were mainly associated with age, BMI, alcohol intake, race, sample storage time (urine only), and dietary supplement use. Statistically significant correlations were in the absolute range of 0.2–0.6, with the majority falling below 0.4. Incorporation of important potential confounding factors in metabolite and disease association analyses may lead to improved statistical power as well as reduced false discovery rates in a variety of data analysis settings.

Cerebral autoregulation is a complex mechanism that serves to keep cerebral blood flow relatively constant within a wide range of cerebral perfusion pressures. The mean flow index (Mx) is one of several methods to assess dynamic cerebral autoregulation, but its reliability and validity have never been assessed systematically. The purpose of the present systematic review was to evaluate the methodology, reliability and validity of Mx. Based on 128 studies, we found inconsistency in the pre-processing of the recordings and the methods for calculation of Mx. The reliability in terms of repeatability and reproducibility ranged from poor to excellent, with optimal repeatability when comparing overlapping recordings. The discriminatory ability varied depending on the patient populations; in general, those with acute brain injury exhibited a higher Mx than healthy volunteers. The prognostic ability in terms of functional outcome and mortality ranged from chance result to moderate accuracy. Since the methodology was inconsistent between studies, resulting in varying reliability and validity estimates, the results were difficult to compare. The optimal method for deriving Mx is currently unknown. [ABSTRACT FROM AUTHOR]

I would like to congratulate James Woodward for another landmark accomplishment, after publishing his Making Things Happen: A Theory of Causal Explanation (Woodward, 2003). Makes Things Happens gives an elegant interventionist theory for understanding explanation and causation. The new contribution (Woodward, 2022) relies on that theory and further makes a big step towards empirical inference of causal relations from nonexperimental data. In this paper, I will focus on some of the emerging computational methods for finding causal relations from non-experimental data and attempt to complement Woodward’s contribution with discussions on 1) how these methods are connected to the interventionist theory of causality, 2) how informative the output of the methods is, including whether they output directed causal graphs and how they deal with confounders (unmeasured common causes of two measured variables), and 3) the assumptions underlying the asymptotic correctness of the output of the methods about causal relations. Different causal discovery methods may rely on different aspects of the joint distribution of the data, and this discussion aims to provide a technical account of the assumptions. [ABSTRACT FROM AUTHOR]

This is an introductory article which explains the importance of explicit consideration and modeling of causality, contrary to current econometric practice, in order to use data set for extraction of meaningful information. One of the easiest to understand approaches to causality is via Simpson’s paradox. We will use this paradox, framed in different real-world contexts, to provide an introduction to basic concepts of causality.

Abstract Background Several studies have shown that maternal HIV infection is associated with adverse pregnancy outcomes such as low birth weight and perinatal mortality. However, the association is conflicted with the effect of antiretroviral therapy (ART) on the pregnancy outcomes and it remains unexamined. If the association is confirmed then it would guide policy makers towards more effective prevention of mother to child HIV transmission interventions. Using methods for matching possible confounders, the objectives of the study were to assess the effect of maternal HIV infection on birth weight and perinatal mortality and to investigate the effect of ART on these two pregnancy outcomes in HIV-infected women. Methods Data on 4111 and 4759 children, born within five years of the 2010 and 2015-16 Malawi Demographic and Health Surveys (MDHS) respectively, whose mothers had an HIV test result, were analysed. A best balancing method was chosen from a set of covariate balance methods namely, the 1:1 nearest neighbour (NN) matching, matching on the propensity score (PS) and inverse weighting on the PS. HIV and ART data were only available in the MDHS 2010, permitting an assessment of the moderating effect of ART on the association between maternal HIV infection and birth weight and perinatal mortality. Results The overall average birth weight was 3227.9g (95% CI: 3206.4, 3249.5) in 2010 and 3226.4g (95%: 3205.6, 3247.2) in 2015-16 and perinatal mortality was 3.8% (95%: 3.2, 4.3) in 2010 and 3.5% (95%: 2.8, 3.8) in 2015-16. The prevalence of HIV among the mothers was 11.1% (95%: 10.1, 12.0) and 9.2% (95% CI: 8.4, 10.1) in 2010 and 2015-16, respectively. In 2010, maternal HIV infection was negatively associated with birth weight (mean= -25.3g, 95% CI:(-95.5, -7.4)) and in 2015-16 it was positively associated with birth weight (mean= 116.3g, 95% CI:(27.8, 204.7)). Perinatal mortality was higher in infants of HIV-infected mothers compared to infants of HIV-uninfected mothers (OR = 1.5, 95% CI:(1.1 - 3.1)) in 2010, while there was no difference in the rate in 2015-16 (OR = 1.0, 95% CI:(0.4, 1.6)). ART was not associated with birth weight, however, it was associated with perinatal mortality (OR=3.9, 95% CI:(1.1, 14.8)). Conclusion The study has found that maternal HIV infection had an adverse effect on birth weight and perinatal mortality in 2010. Birth weight was not dependent on ART uptake but perinatal mortality was higher among infants of HIV-infected mothers who were not on ART. The higher birth weight among HIV-infected mothers and similarity in perinatal mortality with HIV-uninfected mothers in 2015-16 may be indicative of successes of interventions within the PMTCT program in Malawi.

Abstract Background Although shoulder arthroplasty is less common than knee or hip arthroplasty, the number of procedures being performed is increasing rapidly. The treatment effect is a simple method to measure outcome of joint replacement. The method was applied to measure results of total hip/knee arthroplasty but not yet for shoulder arthroplasty. Methods Included were patients with unilateral cuff arthropathy (Hamada grades > = 2) treated with reversed total shoulder arthroplasty (RSA) in this prospective multicenter study. The patients were assessed with the ASES questionnaire. The treatment effects (TE) was calculated for each patient. TE = score reduction/baseline score. A positive TE means amelioration, TE = 0 unchanged, and a negative TE means worse. The primary aim was to calculate the TE’s for RSA at 6, 12, 24, and 60 months postoperatively. The secondary aim was to analyze the influence of confounders (preoperative Hamada grade, age, gender, dominance, side of the affected shoulder, general co-morbidities measured using ASA grade). Results Two hundred three patients were included for this analysis of whom 183 patients had a complete 2 year follow up. Two years postoperatively the mean ASES score augmented significant from 20.5 to 78.7 (p

Abstract Background With the explosion in the number of methods designed to analyze bulk and single-cell RNA-seq data, there is a growing need for approaches that assess and compare these methods. The usual technique is to compare methods on data simulated according to some theoretical model. However, as real data often exhibit violations from theoretical models, this can result in unsubstantiated claims of a method’s performance. Results Rather than generate data from a theoretical model, in this paper we develop methods to add signal to real RNA-seq datasets. Since the resulting simulated data are not generated from an unrealistic theoretical model, they exhibit realistic (annoying) attributes of real data. This lets RNA-seq methods developers assess their procedures in non-ideal (model-violating) scenarios. Our procedures may be applied to both single-cell and bulk RNA-seq. We show that our simulation method results in more realistic datasets and can alter the conclusions of a differential expression analysis study. We also demonstrate our approach by comparing various factor analysis techniques on RNA-seq datasets. Conclusions Using data simulated from a theoretical model can substantially impact the results of a study. We developed more realistic simulation techniques for RNA-seq data. Our tools are available in the seqgendiff R package on the Comprehensive R Archive Network: https://cran.r-project.org/package=seqgendiff .

Interest in investigating gene--environment (GxE) interactions has rapidly increased over the last decade. Although GxE interactions have been extremely investigated in large studies, few such effects have been identified and replicated, highlighting the need to develop statistical GxE tests with greater statistical power. The reverse test has been proposed for testing the interaction effect between continuous exposure and genetic variants in relation to a binary disease outcome, which leverages the idea of linear discriminant analysis, significantly increasing statistical power comparing to the standard logistic regression approach. However, this reverse approach did not take into consideration adjustment for confounders. Since GxE interaction studies are inherently nonexperimental, adjusting for potential confounding effects is critical for valid evaluation of GxE interactions. In this study, we extend the reverse test to allow for confounders. The proposed reverse test also allows for exposure measurement errors as typically occurs. Extensive simulation experiments demonstrated that the proposed method not only provides greater statistical power under most simulation scenarios but also provides substantive computational efficiency, which achieves a computation time that is more than sevenfold less than that of the standard logistic regression test. In an illustrative example, we applied the proposed approach to the Veterans Aging Cohort Study (VACS) to search for genetic susceptibility loci modifying the smoking-HIV status association. [ABSTRACT FROM AUTHOR]

BackgroundFew large epidemiologic studies have used rigorous case criteria, individual-level exposure measurements, and appropriate control for confounders to examine associations between workplace psychosocial and biomechanical factors and carpal tunnel syndrome (CTS).MethodsPooling data from five independent research studies, we assessed associations between prevalent CTS and personal, work psychosocial, and biomechanical factors while adjusting for confounders using multivariable logistic regression.ResultsPrevalent CTS was associated with personal factors of older age, obesity, female sex, medical conditions, previous distal upper extremity disorders, workplace measures of peak forceful hand activity, a composite measure of force and repetition (ACGIH Threshold Limit Value for Hand Activity Level), and hand vibration.ConclusionsIn this cross-sectional analysis of production and service workers, CTS prevalence was associated with workplace and biomechanical factors. The findings were similar to those from a prospective analysis of the same cohort with differences that may be due to recall bias and other factors.

Mediation analysis is a common statistical method for investigating the mechanism of environmental exposures on health outcomes. Previous studies have extended mediation models with a single mediator to high-dimensional mediators selection. It is often assumed that there are no confounders that influence the relations among the exposure, mediator, and outcome. This is not realistic for the observational studies. To accommodate the potential confounders, we propose a concise and efficient high-dimensional mediation analysis procedure using the propensity score for adjustment. Results from simulation studies demonstrate the proposed procedure has good performance in mediator selection and effect estimation compared with methods that ignore all confounders. Of note, as the sample size increases, the performance of variable selection and mediation effect estimation is as well as the results shown in the method which include all confounders as covariates in the mediation model. By applying this procedure to a TCGA lung cancer data set, we find that lung cancer patients who had serious smoking history have increased the risk of death via the methylation markers cg21926276 and cg20707991 with significant hazard ratios of 1.2093 (95% CI: 1.2019–1.2167) and 1.1388 (95% CI: 1.1339–1.1438), respectively.

Mediation analysis is a common statistical method for investigating the mechanism of environmental exposures on health outcomes. Previous studies have extended mediation models with a single mediator to high-dimensional mediators selection. It is often assumed that there are no confounders that influence the relations among the exposure, mediator, and outcome. This is not realistic for the observational studies. To accommodate the potential confounders, we propose a concise and efficient high-dimensional mediation analysis procedure using the propensity score for adjustment. Results from simulation studies demonstrate the proposed procedure has good performance in mediator selection and effect estimation compared with methods that ignore all confounders. Of note, as the sample size increases, the performance of variable selection and mediation effect estimation is as well as the results shown in the method which include all confounders as covariates in the mediation model. By applying this procedure to a TCGA lung cancer data set, we find that lung cancer patients who had serious smoking history have increased the risk of death via the methylation markers cg21926276 and cg20707991 with significant hazard ratios of 1.2093 (95% CI: 1.2019–1.2167) and 1.1388 (95% CI: 1.1339–1.1438), respectively. [ABSTRACT FROM AUTHOR]

Objective To build a prediction model for uveitis in children with JIA for use in current clinical practice. Methods Data from the international observational Pharmachild registry were used. Adjusted risk factors as well as predictors for JIA-associated uveitis (JIA-U) were determined using multivariable logistic regression models. The prediction model was selected based on the Akaike information criterion. Bootstrap resampling was used to adjust the final prediction model for optimism. Results JIA-U occurred in 1102 of 5529 JIA patients (19.9%). The majority of patients that developed JIA-U were female (74.1%), ANA positive (66.0%) and had oligoarthritis (59.9%). JIA-U was rarely seen in patients with systemic arthritis (0.5%) and RF positive polyarthritis (0.2%). Independent risk factors for JIA-U were ANA positivity [odds ratio (OR): 1.88 (95% CI: 1.54, 2.30)] and HLA-B27 positivity [OR: 1.48 (95% CI: 1.12, 1.95)] while older age at JIA onset was an independent protective factor [OR: 0.84 (9%% CI: 0.81, 0.87)]. On multivariable analysis, the combination of age at JIA onset [OR: 0.84 (95% CI: 0.82, 0.86)], JIA category and ANA positivity [OR: 2.02 (95% CI: 1.73, 2.36)] had the highest discriminative power among the prediction models considered (optimism-adjusted area under the receiver operating characteristic curve = 0.75). Conclusion We developed an easy to read model for individual patients with JIA to inform patients/parents on the probability of developing uveitis. [ABSTRACT FROM AUTHOR]

We would like to thank authors Reece and Hulse (2022) for their three-part article titled "Geotemporospatial and causal inferential epidemiological overview and survey of USA cannabis, cannabidiol and cannabinoid genotoxicity expressed in cancer incidence 2003-2017", in which the authors infer that cannabis use has a causal role in the development of various cancer types. While the authors use reputable datasets and a well-established epidemiological methodology, the authors' conclusion of a causal association is limited due to biases inherent in ecological epidemiological studies. Though the researchers attempt to overcome these biases through validation and statistical manipulations, their approaches are insufficient to create conditions suitable for causal inferencing upon examination. There are also concerns in the practical and conceptual application of the studies' dataset that further question the validity of the authors' inferences. Further research exploring the potential benefits and harm of cannabinoids in the context of cancer must be performed before a distinct relationship can be defined. [ABSTRACT FROM AUTHOR]

Despite considerable morbidity and mortality, numerous cases of endocrine hypertension (EHT) forms, including primary aldosteronism (PA), pheochromocytoma and functional paraganglioma (PPGL), and Cushing’s syndrome (CS), remain undetected. We aimed to establish signatures for the different forms of EHT, investigate potentially confounding effects and establish unbiased disease biomarkers. Plasma samples were obtained from 13 biobanks across seven countries and analyzed using untargeted NMR metabolomics. We compared unstratified samples of 106 PHT patients to 231 EHT patients, including 104 PA, 94 PPGL and 33 CS patients. Spectra were subjected to a multivariate statistical comparison of PHT to EHT forms and the associated signatures were obtained. Three approaches were applied to investigate and correct confounding effects. Though we found signatures that could separate PHT from EHT forms, there were also key similarities with the signatures of sample center of origin and sample age. The study design restricted the applicability of the corrections employed. With the samples that were available, no biomarkers for PHT vs. EHT could be identified. The complexity of the confounding effects, evidenced by their robustness to correction approaches, highlighted the need for a consensus on how to deal with variabilities probably attributed to preanalytical factors in retrospective, multicenter metabolomics studies.

ObjectivesGut dysbiosis is associated with chronic kidney disease (CKD), and serum free immunoglobulin light chains (FLCs) are biomarkers for CKD. This study aims to assess the CKD gut microbiome and to determine its impact on serum FLC levels.MethodsTo control for confounders, 100 patients and sex- and age-matched healthy controls (HCs) were recruited. The gut microbiome was assessed by sequencing 16S rRNA gene V3-V4 hypervariable regions. Phylogenetic Investigation of Communities by Reconstruction of Unobserved States was applied to infer functional metabolic pathways. When observing group differences in the microbiome and predicted metabolic pathways, demographic confounders were adjusted using binary logistic regression; when examining impacts of the gut microbiome and metabolic pathways on serum FLCs, factors influencing FLC levels were adjusted using multiple regression.ResultsPrincipal coordinate analysis revealed a significantly different bacterial community between the CKD and HC groups (P < 0.05). After adjusting for confounders, lower Chao 1, observed species and Shannon indices based on binary logistic regression predicted CKD prevalence. Actinobacteria, Alistipes, Bifidobacterium and Bifidobacterium longum enrichment, upregulation of metabolic pathways of bacterial toxin, chloroalkane and chloroalkene degradation, and Staphylococcus aureus infection also predicted CKD prevalence (P < 0.05). Furthermore, depletion of Actinobacteria and Bifidobacterium and reduced chloroalkane and chloroalkene degradation predicted high levels of FLC λ (P < 0.05).ConclusionsGut dysbiosis in CKD patients was confirmed by controlling for confounders in the present study. Additionally, the association between gut dysbiosis and FLC λ levels demonstrates the existence of crosstalk between the microbiome and immune response in CKD.

Objectives: Gut dysbiosis is associated with chronic kidney disease (CKD), and serum free immunoglobulin light chains (FLCs) are biomarkers for CKD. This study aims to assess the CKD gut microbiome and to determine its impact on serum FLC levels. Methods: To control for confounders, 100 patients and sex- and age-matched healthy controls (HCs) were recruited. The gut microbiome was assessed by sequencing 16S rRNA gene V3-V4 hypervariable regions. Phylogenetic Investigation of Communities by Reconstruction of Unobserved States was applied to infer functional metabolic pathways. When observing group differences in the microbiome and predicted metabolic pathways, demographic confounders were adjusted using binary logistic regression; when examining impacts of the gut microbiome and metabolic pathways on serum FLCs, factors influencing FLC levels were adjusted using multiple regression. Results: Principal coordinate analysis revealed a significantly different bacterial community between the CKD and HC groups (P < 0.05). After adjusting for confounders, lower Chao 1, observed species and Shannon indices based on binary logistic regression predicted CKD prevalence. Actinobacteria, Alistipes, Bifidobacterium and Bifidobacterium longum enrichment, upregulation of metabolic pathways of bacterial toxin, chloroalkane and chloroalkene degradation, and Staphylococcus aureus infection also predicted CKD prevalence (P < 0.05). Furthermore, depletion of Actinobacteria and Bifidobacterium and reduced chloroalkane and chloroalkene degradation predicted high levels of FLC λ (P < 0.05). Conclusions: Gut dysbiosis in CKD patients was confirmed by controlling for confounders in the present study. Additionally, the association between gut dysbiosis and FLC λ levels demonstrates the existence of crosstalk between the microbiome and immune response in CKD. [ABSTRACT FROM AUTHOR]

Aims Recent technical developments have allowed the study of the human microbiome to accelerate at an unprecedented pace. Methodological differences may have considerable impact on the results obtained. Thus, we investigated how different storage, isolation, and DNA extraction methods can influence the characterization of the intestinal microbiome, compared to the impact of true biological signals such as intraindividual variability, nutrition, health, and demographics. Methods and results An observative cohort study in 27 healthy subjects was performed. Participants were instructed to collect stool samples twice spaced by a week, using six different methods (naive and Zymo DNA/RNA Shield on dry ice, OMNIgene GUT, RNALater, 95% ethanol, Zymo DNA/RNA Shield at room temperature). DNA extraction from all samples was performed comparatively using QIAamp Power Fecal and ZymoBIOMICS DNA Kits. 16S rRNA sequencing of the gut microbiota as well as qPCRs were performed on the isolated DNA. Metrics included alpha diversity as well as multivariate and univariate comparisons of samples, controlling for covariate patterns computationally. Interindividual differences explained 7.4% of overall microbiome variability, whereas the choice of DNA extraction method explained a further 5.7%. At phylum level, the tested kits differed in their recovery of Gram-positive bacteria, which is reflected in a significantly skewed enterotype distribution. Conclusion DNA extraction methods had the highest impact on observed microbiome variability, and were comparable to interindividual differences, thus may spuriously mimic the microbiome signatures of various health and nutrition factors. Conversely, collection methods had a relatively small influence on microbiome composition. The present study provides necessary insight into the technical variables which can lead to divergent results from seemingly similar study designs. We anticipate that these results will contribute to future efforts towards standardization of microbiome quantification procedures in clinical research. [ABSTRACT FROM AUTHOR]

Replication crisis and debates about p-values have raised doubts about what we can statistically infer from research findings, both in experimental and observational studies. With a view to the ongoing debate on inferential errors, this paper systematizes and discusses experimental designs with regard to the inferences that can and – perhaps more important – that cannot be made from particular designs.

Background: Although shoulder arthroplasty is less common than knee or hip arthroplasty, the number of procedures being performed is increasing rapidly. The treatment effect is a simple method to measure outcome of joint replacement. The method was applied to measure results of total hip/knee arthroplasty but not yet for shoulder arthroplasty.Methods: Included were patients with unilateral cuff arthropathy (Hamada grades > = 2) treated with reversed total shoulder arthroplasty (RSA) in this prospective multicenter study. The patients were assessed with the ASES questionnaire. The treatment effects (TE) was calculated for each patient. TE = score reduction/baseline score. A positive TE means amelioration, TE = 0 unchanged, and a negative TE means worse. The primary aim was to calculate the TE's for RSA at 6, 12, 24, and 60 months postoperatively. The secondary aim was to analyze the influence of confounders (preoperative Hamada grade, age, gender, dominance, side of the affected shoulder, general co-morbidities measured using ASA grade).Results: Two hundred three patients were included for this analysis of whom 183 patients had a complete 2 year follow up. Two years postoperatively the mean ASES score augmented significant from 20.5 to 78.7 (p < 0.001). The 2 year TE's ranged from 1 to 0.09. We had no patient with a negative TE. A higher Hamada grade was associated with better TE's (Hamada grade 4+ vs. 2, p-value 0.042). For age and dominant side there were weak associations where those aged 80+ and dominant side had better TE's. The patients with higher ASA grade had lower TE's (ASA grade 4+ vs. 1, p-value 0.013). The mean TE's were 0.77 at 6-months, 0.81 at 1 year, 0.76 at 2 years and 0.73 at 5 years.Conclusions: The outcome for reverse shoulder arthroplasty can be measured with the treatment effect method; the 2 years TE's vary from 1 to 0.09. The mean treatment effects change little in the first five postoperative years (from 0.73 to 0.81). The confounders for better TE's were: higher severity of cuff arthropathy (Hamada grade 3, 4 and 5), less co-morbidities (ASA Grade 1), higher age (80+) and dominant side. Gender did not influence the 2-year TE's.Trial Registration: Comité intercantonal d'éthique (Jura, Fribourg, Neuchâtel), number 01/2008, 24.09.2008. [ABSTRACT FROM AUTHOR]

Background: Several studies have shown that maternal HIV infection is associated with adverse pregnancy outcomes such as low birth weight and perinatal mortality. However, the association is conflicted with the effect of antiretroviral therapy (ART) on the pregnancy outcomes and it remains unexamined. If the association is confirmed then it would guide policy makers towards more effective prevention of mother to child HIV transmission interventions. Using methods for matching possible confounders, the objectives of the study were to assess the effect of maternal HIV infection on birth weight and perinatal mortality and to investigate the effect of ART on these two pregnancy outcomes in HIV-infected women.Methods: Data on 4111 and 4759 children, born within five years of the 2010 and 2015-16 Malawi Demographic and Health Surveys (MDHS) respectively, whose mothers had an HIV test result, were analysed. A best balancing method was chosen from a set of covariate balance methods namely, the 1:1 nearest neighbour (NN) matching, matching on the propensity score (PS) and inverse weighting on the PS. HIV and ART data were only available in the MDHS 2010, permitting an assessment of the moderating effect of ART on the association between maternal HIV infection and birth weight and perinatal mortality.Results: The overall average birth weight was 3227.9g (95% CI: 3206.4, 3249.5) in 2010 and 3226.4g (95%: 3205.6, 3247.2) in 2015-16 and perinatal mortality was 3.8% (95%: 3.2, 4.3) in 2010 and 3.5% (95%: 2.8, 3.8) in 2015-16. The prevalence of HIV among the mothers was 11.1% (95%: 10.1, 12.0) and 9.2% (95% CI: 8.4, 10.1) in 2010 and 2015-16, respectively. In 2010, maternal HIV infection was negatively associated with birth weight (mean= -25.3g, 95% CI:(-95.5, -7.4)) and in 2015-16 it was positively associated with birth weight (mean= 116.3g, 95% CI:(27.8, 204.7)). Perinatal mortality was higher in infants of HIV-infected mothers compared to infants of HIV-uninfected mothers (OR = 1.5, 95% CI:(1.1 - 3.1)) in 2010, while there was no difference in the rate in 2015-16 (OR = 1.0, 95% CI:(0.4, 1.6)). ART was not associated with birth weight, however, it was associated with perinatal mortality (OR=3.9, 95% CI:(1.1, 14.8)).Conclusion: The study has found that maternal HIV infection had an adverse effect on birth weight and perinatal mortality in 2010. Birth weight was not dependent on ART uptake but perinatal mortality was higher among infants of HIV-infected mothers who were not on ART. The higher birth weight among HIV-infected mothers and similarity in perinatal mortality with HIV-uninfected mothers in 2015-16 may be indicative of successes of interventions within the PMTCT program in Malawi. [ABSTRACT FROM AUTHOR]

Background: With the explosion in the number of methods designed to analyze bulk and single-cell RNA-seq data, there is a growing need for approaches that assess and compare these methods. The usual technique is to compare methods on data simulated according to some theoretical model. However, as real data often exhibit violations from theoretical models, this can result in unsubstantiated claims of a method's performance. Results: Rather than generate data from a theoretical model, in this paper we develop methods to add signal to real RNA-seq datasets. Since the resulting simulated data are not generated from an unrealistic theoretical model, they exhibit realistic (annoying) attributes of real data. This lets RNA-seq methods developers assess their procedures in non-ideal (model-violating) scenarios. Our procedures may be applied to both single-cell and bulk RNA-seq. We show that our simulation method results in more realistic datasets and can alter the conclusions of a differential expression analysis study. We also demonstrate our approach by comparing various factor analysis techniques on RNA-seq datasets. Conclusions: Using data simulated from a theoretical model can substantially impact the results of a study. We developed more realistic simulation techniques for RNA-seq data. Our tools are available in the seqgendiff R package on the Comprehensive R Archive Network: https://cran.r-project.org/package=seqgendiff. [ABSTRACT FROM AUTHOR]

Abstract Recently, Okada et al. reported an association between low pH value before the implementation of extracorporeal cardiopulmonary resuscitation (ECPR) and 1-month unfavorable neurological outcome among out-of-hospital cardiac arrest (OHCA) patients treated with ECPR. Nevertheless, we believe that some methodological flaws deserve their conclusions. The time duration between OHCA occurrence and blood gas analysis (BGA), a major confounder for misinterpretation, was not taken into account. It is not reported whether the result of BGA analysis was considered and/or treated, and if ECPR implementation decision had been influenced by the results analysis. Furthermore, the no-flow duration and the in-hospital phase confounders for neurological outcome are not included as covariates in the logistic regression. Therefore, we believe that causes and consequences should not be confused: the longer is the no-flow duration, the greater are the metabolic consequences.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants with enhanced transmissibility and immune escape have emerged periodically throughout the coronavirus disease 2019 (COVID-19) pandemic, but the impact of these variants on disease severity has remained unclear. In this single-center, retrospective cohort study, we examined the association between SARS-CoV-2 clade and patient outcome over a two-year period in Chicago, Illinois. Between March 2020 and March 2022, 14,252 residual diagnostic specimens were collected from SARS-CoV-2-positive inpatients and outpatients alongside linked clinical and demographic metadata, of which 2,114 were processed for viral whole-genome sequencing. When controlling for patient demographics and vaccination status, several viral clades were associated with risk for hospitalization, but this association was negated by the inclusion of population-level confounders, including case count, sampling bias, and shifting standards of care. These data highlight the importance of integrating non-virological factors into disease severity and outcome models for the accurate assessment of patient risk., Competing Interests: Declaration of interests M.G.I. has received research support, paid to Northwestern University, from AiCuris, GlaxoSmithKline Janssen, and Shire. M.G.I. is a paid consultant for Adagio, AlloVir, Celltrion, Cidara, Genentech, Roche, Janssen, Shionogi, Takeda, and Viracor Eurofins. M.G.I. is a paid member of the data and safety monitoring boards (DSMBs) of CSL Berhring, Janssen, Merck, SAB Biotherapeutics, Sequiris, and Takeda. J.F.H. has received research support, paid to Northwestern University, from Gilead Sciences and is a paid consultant for Merck., (Copyright © 2023 The Author(s). Published by Elsevier Inc. All rights reserved.)

The conduct of research requires a systematic approach involving diligent planning and its execution as planned. It comprises various essential predefined components such as aims, population, conduct/technique, outcome and statistical considerations. These need to be objective, reliable and in a repeatable format. Hence, the understanding of the basic aspects of methodology is essential for any researcher. This is a narrative review and focuses on various aspects of the methodology for conduct of a clinical research. The relevant keywords were used for literature search from various databases and from bibliographies of the articles.