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Despite significant advances in the diagnosis and treatment of cardiovascular diseases, recent calls have emphasized the unmet need to improve precision-based approaches in cardiovascular disease. Although some studies provide preliminary evidence of the diagnostic and prognostic potential of circulating coding and non-coding RNAs, the complex RNA biology and lack of standardization have hampered the translation of these markers into clinical practice. In this position paper of the CardioRNA COST action CA17129, we provide recommendations to standardize the RNA development process in order to catalyse efforts to investigate novel RNAs for clinical use. We list the unmet clinical needs in cardiovascular disease, such as the identification of high-risk patients with ischaemic heart disease or heart failure who require more intensive therapies. The advantages and pitfalls of the different sample types, including RNAs from plasma, extracellular vesicles, and whole blood, are discussed in the sample matrix, together with their respective analytical methods. The effect of patient demographics and highly prevalent comorbidities, such as metabolic disorders, on the expression of the candidate RNA is presented and should be reported in biomarker studies. We discuss the statistical and regulatory aspects to translate a candidate RNA from a research use only assay to an in-vitro diagnostic test for clinical use. Optimal planning of this development track is required, with input from the researcher, statistician, industry, and regulatory partners. [ABSTRACT FROM AUTHOR]

Few biomarker-based validation studies have examined error in online self-report dietary assessment instruments, and food records (FRs) have been considered less than food frequency questionnaires (FFQs) and 24-hour recalls (24HRs). We investigated measurement error in online and paper-based FFQs, online 24HRs, and paper-based FRs in 3 samples drawn primarily from 3 cohorts, comprising 1,393 women and 1,455 men aged 45–86 years. Data collection occurred from January 2011 to October 2013. Attenuation factors and correlation coefficients between reported and true usual intake for energy, protein, sodium, potassium, and respective densities were estimated using recovery biomarkers. Across studies, average attenuation factors for energy were 0.07, 0.07, and 0.19 for a single FFQ, 24HR, and FR, respectively. Correlation coefficients for energy were 0.24, 0.23, and 0.40, respectively. Excluding energy, the average attenuation factors across nutrients and studies were 0.22 for a single FFQ, 0.22 for a single 24HR, and 0.51 for a single FR. Corresponding correlation coefficients were 0.31, 0.34, and 0.53, respectively. For densities (nutrient expressed relative to energy), the average attenuation factors across studies were 0.37, 0.17, and 0.50, respectively. The findings support prior research suggesting different instruments have unique strengths that should be leveraged in epidemiologic research. [ABSTRACT FROM AUTHOR]

The coronavirus disease 2019 (COVID-19) pandemic has been as unprecedented as unexpected, affecting more than 105 million people worldwide as of 8 February 2020 and causing more than 2.3 million deaths according to the World Health Organization (WHO). Not only affecting the lungs but also provoking acute respiratory distress, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is able to infect multiple cell types including cardiac and vascular cells. Hence a significant proportion of infected patients develop cardiac events, such as arrhythmias and heart failure. Patients with cardiovascular comorbidities are at highest risk of cardiac death. To face the pandemic and limit its burden, health authorities have launched several fast-track calls for research projects aiming to develop rapid strategies to combat the disease, as well as longer-term projects to prepare for the future. Biomarkers have the possibility to aid in clinical decision-making and tailoring healthcare in order to improve patient quality of life. The biomarker potential of circulating RNAs has been recognized in several disease conditions, including cardiovascular disease. RNA biomarkers may be useful in the current COVID-19 situation. The discovery, validation, and marketing of novel biomarkers, including RNA biomarkers, require multi-centre studies by large and interdisciplinary collaborative networks, involving both the academia and the industry. Here, members of the EU-CardioRNA COST Action CA17129 summarize the current knowledge about the strain that COVID-19 places on the cardiovascular system and discuss how RNA biomarkers can aid to limit this burden. They present the benefits and challenges of the discovery of novel RNA biomarkers, the need for networking efforts, and the added value of artificial intelligence to achieve reliable advances. [ABSTRACT FROM AUTHOR]

Several Eastern Harris County communities lie near the now enclosed San Jacinto River (SJR) Waste Pit Superfund, a dumping ground for chlorinated dioxins and other paper mill waste products. Currently, no active monitoring of the SJR is conducted for these toxins with the exact concentration and health impact to the area unknown. As such, remediation and monitoring efforts outside of the Superfund itself could be necessary. To better understand the possible environmental fate of these aromatics, here we provide a shotgun metagenomic analysis of the structural and putative functional diversity of the SJR microbiome from two impacted Channelview, Texas communities bordering the Superfund. Results show that the underlying SJR microbiome possesses a core of metabolic enzymes related to the β-ketoadipate and benzoate degradation pathways. This suggests possible endpoints for many aromatics found deposited in the SJR including dioxin-like compounds. However, degradation biomarkers related to the priming and initial cleavage of chlorinated dioxin-like aromatics while present, are poorly concentrated across sampled sites. This may be due in part to decreased coverage of low abundance bacterial species, but also be a contributing factor leading to increased recalcitrance of these compounds in this environment compared to other aromatics. [ABSTRACT FROM AUTHOR]

Background There has been increasing interest in both suicide-specific diagnoses within the psychiatric nomenclature and related biomarkers. Because the Suicide Crisis Syndrome—an emotional crescendo of several interrelated symptoms—seems to be promising for the identification of individuals at risk of suicide, the aim of the present paper is to review the putative biological underpinnings of the Suicide Crisis Syndrome symptoms (entrapment, affective disturbance, loss of cognitive control, hyperarousal, social withdrawal). Methods A PubMed literature search was performed to identify studies reporting a link between each of the 5 Suicide Crisis Syndrome symptoms and biomarkers previously reported to be associated with suicidal outcomes. Results Disturbances in the hypothalamic-pituitary-adrenal axis, with dysregulated corticotropin-releasing hormone and cortisol levels, may be linked to a sense of entrapment. Affective disturbance is likely mediated by alterations in dopaminergic circuits involved in reward and antireward systems as well as endogenous opioids. Loss of cognitive control is linked to altered neurocognitive function in the areas of executive function, attention, and decision-making. Hyperarousal is linked to autonomic dysregulation, which may be characterized by a reduction in both heart rate variability and electrodermal activity. Social withdrawal has been associated with oxytocin availability. There is also evidence that inflammatory processes may contribute to individual Suicide Crisis Syndrome symptoms. Conclusion The Suicide Crisis Syndrome is a complex syndrome that is likely the consequence of distinct changes in interconnected neural, neuroendocrine, and autonomic systems. Available clinical and research data allow for development of empirically testable hypotheses and experimental paradigms to scrutinize the biological substrates of the Suicide Crisis Syndrome. [ABSTRACT FROM AUTHOR]

Carcinoid heart disease (CaHD) is an important complication among patients with metastatic neuroendocrine tumors and carcinoid syndrome (CS). CS patients (25%-65%) eventually develop CaHD; these patients face a significantly increased risk of morbidity and mortality. Guidance papers (eg, clinical practice guidelines, consensus guidelines, and expert statements) have been established by major organizations across the disciplines of cardiology and oncology; however, these recommendations are not routinely implemented. The aim of this article is to encourage the integration of current recommendations from national societies into clinical practice. Early screening upon recognition of CS and prior to the development of CaHD symptoms is paramount, as no existing therapies are approved to reverse the fibrotic damage to the heart once it occurs. Valvular replacement is the only definitive treatment for CaHD once it has developed. When patients are noted to have urinary 5-hydroxyindoleacetic acid (5-HIAA) levels ≥300 µmol/24 h and/or serum N-terminal pro B-type natriuretic peptide (NT-proBNP) levels >260 pg/mL, echocardiography is recommended. Systemic approaches to control tumor growth and hormonal secretion include somatostatin analogs (SSAs), followed by options including peptide receptor radiotherapy (PRRT), everolimus and liver embolization. Telotristat is the primary choice for control of diarrhea refractory to SSA. Diuretics are the mainstay of heart failure symptom management for patients who develop CaHD. Considerations for future research are discussed, including the ongoing TELEHEART (TELotristat Ethyl in a HEART biomarker study) trial involving telotristat and not yet activated CHARRT (Carcinoid Heart disease And peptide Receptor Radiotargetted Therapy) study involving PRRT with lutetium 177 (177Lu) dotatate. [ABSTRACT FROM AUTHOR]

If one wanted to link the hypotheses in (1) and (2), HT ht could be expressed as HT ht , where HT ht represents the probability density function of the biomarker in the population of patients who are otherwise eligible for the trial. Keywords: adaptive enrichment; continuous biomarker; monotonicity EN adaptive enrichment continuous biomarker monotonicity 23 25 3 03/24/23 20230301 NES 230301 INTRODUCTION I would like to congratulate Professor Stallard on an important contribution to the literature on adaptive enrichment trials (Stallard, [6]). CONTRIBUTION OF THE PAPER As pointed out in the paper, most methods for adaptive enrichment trials assume there is a prespecified biomarker that divides patients into two categories: "biomarker-negative" and "biomarker-positive.". [Extracted from the article]

Aims There has been an increased interest in studying the association between microbial communities and different diseases and in discovering microbiome biomarkers. This association is pivotal to discover such biomarkers. In this paper, we present a unified modelling approach that can be used to detect and develop microbiome biomarkers for different clinical responses of interest at different levels of the microbiome ecosystem. Methods and results We extended the methodology rooted in the information theory and joint modelling approaches for the evaluation of surrogate endpoints in randomized clinical trials to the high-dimensional microbiome setting. The unified modelling approach introduced in this paper allows for detecting biomarkers associated with a clinical response of interest, adjusting for the intervention applied to the subjects. For some microbiome features, the association is driven by the treatment, while for others, the association reflects the correlation between the microbiome biomarker and the clinical response of interest. Conclusions The results have demonstrated that biomarkers can be identified at different levels of the microbiome phylogenetic tree using various measures as biomarkers. [ABSTRACT FROM AUTHOR]

This paper discusses the use of biomarkers in clinical practice and biomedical research. Biomarkers are measurable characteristics that can be used to indicate the presence or absence of a disease or to track the progression of a disease. They can also be used to predict how a patient will respond to a particular treatment. Biomarkers have enriched clinical practice and disease prognosis by providing measurable characteristics that indicate biological processes. They offer valuable insights into disease susceptibility, progression, and treatment response, aiding drug development and personalized medicine. However, developing and implementing biomarkers come with challenges that must be addressed. Rigorous testing, standardization of assays, and consideration of ethical factors are crucial in ensuring the reliability and validity of biomarkers. Reliability is vital in biomarker research. It ensures accurate measurements by preventing biases and facilitating robust correlations with outcomes. Conversely, validation examines which and how many biomarkers correspond to theoretical constructs and external criteria, establishing their predictive value. Multiple biomarkers are sometimes necessary to represent the complex relationship between exposure and disease outcomes accurately. Susceptibility factors are pivotal in disease states' complex interaction among genetic and environmental factors. Gaining a comprehensive understanding of these factors is essential for effectively interpreting biomarker data and maximizing their clinical usefulness. Using well-validated biomarkers can improve diagnoses, more effective treatment evaluations, and enhanced disease prediction. This, in turn, will contribute to better patient outcomes and drive progress in medicine. [ABSTRACT FROM AUTHOR]

In clinical studies of chronic diseases, the effectiveness of an intervention is often assessed using "high cost" outcomes that require long-term patient follow-up and/or are invasive to obtain. While much progress has been made in the development of statistical methods to identify surrogate markers, that is, measurements that could replace such costly outcomes, they are generally not applicable to studies with a small sample size. These methods either rely on nonparametric smoothing which requires a relatively large sample size or rely on strict model assumptions that are unlikely to hold in practice and empirically difficult to verify with a small sample size. In this paper, we develop a novel rank-based nonparametric approach to evaluate a surrogate marker in a small sample size setting. The method developed in this paper is motivated by a small study of children with nonalcoholic fatty liver disease (NAFLD), a diagnosis for a range of liver conditions in individuals without significant history of alcohol intake. Specifically, we examine whether change in alanine aminotransferase (ALT; measured in blood) is a surrogate marker for change in NAFLD activity score (obtained by biopsy) in a trial, which compared Vitamin E (⁠|$n=50$|⁠) versus placebo (⁠|$n=46$|⁠) among children with NAFLD. [ABSTRACT FROM AUTHOR]

Connections between circular RNAs (circRNAs) and microRNAs (miRNAs) assume a pivotal position in the onset, evolution, diagnosis and treatment of diseases and tumors. Selecting the most potential circRNA-related miRNAs and taking advantage of them as the biological markers or drug targets could be conducive to dealing with complex human diseases through preventive strategies, diagnostic procedures and therapeutic approaches. Compared to traditional biological experiments, leveraging computational models to integrate diverse biological data in order to infer potential associations proves to be a more efficient and cost-effective approach. This paper developed a model of Convolutional Autoencoder for CircRNA–MiRNA Associations (CA-CMA) prediction. Initially, this model merged the natural language characteristics of the circRNA and miRNA sequence with the features of circRNA–miRNA interactions. Subsequently, it utilized all circRNA–miRNA pairs to construct a molecular association network, which was then fine-tuned by labeled samples to optimize the network parameters. Finally, the prediction outcome is obtained by utilizing the deep neural networks classifier. This model innovatively combines the likelihood objective that preserves the neighborhood through optimization, to learn the continuous feature representation of words and preserve the spatial information of two-dimensional signals. During the process of 5-fold cross-validation, CA-CMA exhibited exceptional performance compared to numerous prior computational approaches, as evidenced by its mean area under the receiver operating characteristic curve of 0.9138 and a minimal SD of 0.0024. Furthermore, recent literature has confirmed the accuracy of 25 out of the top 30 circRNA–miRNA pairs identified with the highest CA-CMA scores during case studies. The results of these experiments highlight the robustness and versatility of our model. [ABSTRACT FROM AUTHOR]

As a dynamical system, complex disease always has a sudden state transition at the tipping point, which is the result of the long-term accumulation of abnormal regulations. This paper proposes a novel approach to detect the early-warning signals of influenza A (H3N2 and H1N1) outbreaks by dysregulated dynamic network biomarkers (dysregulated DNBs) for individuals. The results of cross-validation show that our approach can detect early-warning signals before the symptom appears successfully. Unlike the traditional DNBs, our dysregulated DNBs are anchored and very few, which is essential for disease early diagnosis in clinical practice. Moreover, the genes of dysregulated DNBs are significantly enriched in the influenza-related pathways. The source code of this paper can be freely downloaded from https://github.com/YanhaoHuo/dysregulated-DNBs.git. [ABSTRACT FROM AUTHOR]

Motivation Many ophthalmic disease biomarkers have been identified through comprehensive multiomics profiling, and hold significant potential in advancing the diagnosis, prognosis, and management of diseases. Meanwhile, the eye itself serves as a natural biomarker for several systemic diseases including neurological, renal, and cardiovascular systems. We aimed to collect and standardize this eye biomarkers information and construct the eye biomarker database (EBD) to provide ophthalmologists with a platform to search, analyze, and download these eye biomarker data. Results In this study, we present the EBD < http://www.eyeseeworld.com/ebd/index.html >, a world-first online compilation comprising 889 biomarkers for 26 ocular diseases and 939 eye biomarkers for 181 systemic diseases. The EBD also includes the information of 78 "nonbiomarkers"—the objects that have been proven cannot be biomarkers. Biological function and network analysis were conducted for these ocular disease biomarkers, and several hub pathways and common network topology characteristics were newly identified, which may promote future ocular disease biomarker discovery and characterizes the landscape of biomarkers for eye diseases at the pathway and network level. The EBD is expected to yield broader utility among developmental biologists and clinical scientists in and outside of the eye field by assisting in the identification of biomarkers linked to eye disorders and related systemic diseases. Availability and implementation EBD is available at http://www.eyeseeworld.com/ebd/index.html. [ABSTRACT FROM AUTHOR]

Micronucleus (MN) formation is routinely used as a biodosimeter for radiation exposures and has historically been used as a measure of DNA damage in cells. Strongly correlating with dose, MN are also suggested to indicate radiation quality, differentiating between particle and photon irradiation. The "gold standard" for measuring MN formation is Fenech's cytokinesis-block micronucleus (CBMN) cytome assay, which uses the cytokinesis blocking agent cytochalasin-B. Here, we present a comprehensive analysis of the literature investigating MN induction trends in vitro , collating 193 publications, with 2476 data points. Data were collected from original studies that used the CBMN assay to quantify MN in response to ionizing radiation in vitro. Overall, the meta-analysis showed that individual studies mostly have a linear increase of MN with dose [85% of MN per cell (MNPC) datasets and 89% of percentage containing MN (PCMN) datasets had an R 2 greater than 0.90]. However, there is high variation between studies, resulting in a low R 2 when data are combined (0.47 for MNPC datasets and 0.60 for PCMN datasets). Particle type, species, cell type, and cytochalasin-B concentration were suggested to influence MN frequency. However, variation in the data meant that the effects could not be strongly correlated with the experimental parameters investigated. There is less variation between studies when comparing the PCMN rather than the number of MNPC. Deviation from CBMN protocol specified timings did not have a large effect on MN induction. However, further analysis showed less variation between studies following Fenech's protocol closely, which provided more reliable results. By limiting the cell type and species as well as only selecting studies following the Fenech protocol, R 2 was increased to 0.64 for both measures. We therefore determine that due to variation between studies, MN are currently a poor predictor of radiation-induced DNA damage and make recommendations for futures studies assessing MN to improve consistency between datasets. [ABSTRACT FROM AUTHOR]

Idiopathic inflammatory myopathies (IIMs) are a heterogeneous group of muscle disorders including adult and juvenile dermatomyositis, polymyositis, immune-mediated necrotising myopathy and sporadic inclusion body myositis, all of which present with variable symptoms and disease progression. The identification of effective biomarkers for IIMs has been challenging due to the heterogeneity between IIMs and within IIM subgroups, but recent advances in machine learning (ML) techniques have shown promises in identifying novel biomarkers. This paper reviews recent studies on potential biomarkers for IIM and evaluates their clinical utility. We also explore how data analytic tools and ML algorithms have been used to identify biomarkers, highlighting their potential to advance our understanding and diagnosis of IIM and improve patient outcomes. Overall, ML techniques have great potential to revolutionize biomarker discovery in IIMs and lead to more effective diagnosis and treatment. [ABSTRACT FROM AUTHOR]

Selecting informative features, such as accurate biomarkers for disease diagnosis, prognosis and response to treatment, is an essential task in the field of bioinformatics. Medical data often contain thousands of features and identifying potential biomarkers is challenging due to small number of samples in the data, method dependence and non-reproducibility. This paper proposes a novel ensemble feature selection method, named Filter and Wrapper Stacking Ensemble (FWSE), to identify reproducible biomarkers from high-dimensional omics data. In FWSE, filter feature selection methods are run on numerous subsets of the data to eliminate irrelevant features, and then wrapper feature selection methods are applied to rank the top features. The method was validated on four high-dimensional medical datasets related to mental illnesses and cancer. The results indicate that the features selected by FWSE are stable and statistically more significant than the ones obtained by existing methods while also demonstrating biological relevance. Furthermore, FWSE is a generic method, applicable to various high-dimensional datasets in the fields of machine intelligence and bioinformatics. [ABSTRACT FROM AUTHOR]

The era of modern antiretroviral therapy (ART) has markedly improved health and survival among persons with human immunodeficiency virus (HIV) (PWH). In the pre-ART era, wasting was associated with HIV disease progression to acquired immunodeficiency syndrome and death. Effective ART has reduced the prevalence and incidence of this pre-ART form of HIV-associated wasting. However, a subgroup of ART-treated virally suppressed PWH continue to lose weight, often accompanied by aging-related comorbidities and/or functional deficits. For this subgroup of patients, the older definition of HIV-associated wasting (HIVAW) cannot and should not be applied. An expert panel comprising the authors of this white paper convened to review the existing definition of HIVAW and to create an updated definition that they termed HIV-associated weight loss, based on clinically defined parameters among contemporary PWH receiving ART. Here, clinical features and laboratory biomarkers associated with HIV-associated weight loss are reviewed and approaches to screening and treatment are considered. Available management approaches, including the use of current US Food and Drug Administration–approved medications for HIVAW and other available therapies are discussed. The expert panel also identified knowledge gaps and provided recommendations for clinicians, payers, and researchers. [ABSTRACT FROM AUTHOR]

In celebration of the National Institute on Aging's (NIA) 50th anniversary, this paper highlights the significant advances in cognitive aging research and the promotion of cognitive health among older adults. Since its inception in 1974, the NIA has played a pivotal role in understanding cognitive aging, including cognitive epidemiology, interventions, and methods, for measuring cognitive change. Key milestones include the shift toward understanding cognitive impairment and Alzheimer's disease and Alzheimer's disease-related dementias (AD/ADRD), the development of large-scale longitudinal studies, and the incorporation of AD/ADRD-related biomarkers in cognitive aging cohorts. Additionally, NIA has championed diversifying the scientific workforce through initiatives, such as the Resource Centers for Minority Aging Research and the Butler-Williams Scholars Program. The next 50 years will continue to emphasize the importance of inclusion, innovation, and impactful research to enhance the cognitive health and well-being of older adults. [ABSTRACT FROM AUTHOR]

When there is a predictive biomarker, enrichment can focus the clinical trial on a benefiting subpopulation. We describe a two‐stage enrichment design, in which the first stage is designed to efficiently estimate a threshold and the second stage is a "phase III‐like" trial on the enriched population. The goal of this paper is to explore design issues: sample size in Stages 1 and 2, and re‐estimation of the Stage 2 sample size following Stage 1. By treating these as separate trials, we can gain insight into how the predictive nature of the biomarker specifically impacts the sample size. We also show that failure to adequately estimate the threshold can have disastrous consequences in the second stage. While any bivariate model could be used, we assume a continuous outcome and continuous biomarker, described by a bivariate normal model. The correlation coefficient between the outcome and biomarker is the key to understanding the behavior of the design, both for predictive and prognostic biomarkers. Through a series of simulations we illustrate the impact of model misspecification, consequences of poor threshold estimation, and requisite sample sizes that depend on the predictive nature of the biomarker. Such insight should be helpful in understanding and designing enrichment trials. [ABSTRACT FROM AUTHOR]

Quantitatively predicting phenotypic variables using biomarkers is a challenging task for several reasons. First, the collected biological observations might be heterogeneous and correspond to different biological mechanisms. Second, the biomarkers used to predict the phenotype are potentially highly correlated since biological entities (genes, proteins, and metabolites) interact through unknown regulatory networks. In this paper, we present a novel approach designed to predict multivariate quantitative traits from biological data which address the 2 issues. The proposed model performs well on prediction but it is also fully parametric, with clusters of individuals and regulatory networks, which facilitates the downstream biological interpretation. [ABSTRACT FROM AUTHOR]

Identifying effective and valid surrogate markers to make inference about a treatment effect on long‐term outcomes is an important step in improving the efficiency of clinical trials. Replacing a long‐term outcome with short‐term and/or cheaper surrogate markers can potentially shorten study duration and reduce trial costs. There is sizable statistical literature on methods to quantify the effectiveness of a single surrogate marker. Both parametric and nonparametric approaches have been well developed for different outcome types. However, when there are multiple markers available, methods for combining markers to construct a composite marker with improved surrogacy remain limited. In this paper, building on top of the optimal transformation framework of Wang et al. (2020), we propose a novel calibrated model fusion approach to optimally combine multiple markers to improve surrogacy. Specifically, we obtain two initial estimates of optimal composite scores of the markers based on two sets of models with one set approximating the underlying data distribution and the other directly approximating the optimal transformation function. We then estimate an optimal calibrated combination of the two estimated scores which ensures both validity of the final combined score and optimality with respect to the proportion of treatment effect explained by the final combined score. This approach is unique in that it identifies an optimal combination of the multiple surrogates without strictly relying on parametric assumptions while borrowing modeling strategies to avoid fully nonparametric estimation which is subject to the curse of dimensionality. Our identified optimal transformation can also be used to directly quantify the surrogacy of this identified combined score. Theoretical properties of the proposed estimators are derived, and the finite sample performance of the proposed method is evaluated through simulation studies. We further illustrate the proposed method using data from the Diabetes Prevention Program study. [ABSTRACT FROM AUTHOR]

In studies that require long‐term and/or costly follow‐up of participants to evaluate a treatment, there is often interest in identifying and using a surrogate marker to evaluate the treatment effect. While several statistical methods have been proposed to evaluate potential surrogate markers, available methods generally do not account for or address the potential for a surrogate to vary in utility or strength by patient characteristics. Previous work examining surrogate markers has indicated that there may be such heterogeneity, that is, that a surrogate marker may be useful (with respect to capturing the treatment effect on the primary outcome) for some subgroups, but not for others. This heterogeneity is important to understand, particularly if the surrogate is to be used in a future trial to replace the primary outcome. In this paper, we propose an approach and estimation procedures to measure the surrogate strength as a function of a baseline covariate W and thus examine potential heterogeneity in the utility of the surrogate marker with respect to W. Within a potential outcome framework, we quantify the surrogate strength/utility using the proportion of treatment effect on the primary outcome that is explained by the treatment effect on the surrogate. We propose testing procedures to test for evidence of heterogeneity, examine finite sample performance of these methods via simulation, and illustrate the methods using AIDS clinical trial data. [ABSTRACT FROM AUTHOR]

Psychological stress is a significant public health concern as it is associated with various comorbidities and long-term health implications. Music interventions are emerging therapies for alleviating psychological stress and improving one's physical and mental well-being. We conducted a systematic literature review in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) Statement guidelines for reporting to identify all neuroendocrine biomarkers used to evaluate psychological stress in randomized control trials involving music interventions. We identified 18 unique biomarkers of stress from 14 full-text randomized controlled trials studies. Only one of the 14 music studies included a music therapy intervention. The most frequently used biomarkers across the studies were plasma cortisol, salivary cortisol, and salivary α-amylase. Of the 14 studies, 12 included in this review assessed at least one of these three biomarkers. Of these 12 studies, five papers reported p-values for changes in both stress biomarkers and psychological stress outcome measures. Four of the five studies found significant p-values for the reduction of both stress biomarkers and psychological stress in music intervention groups. The variety of stress biomarkers used and the variance in study protocols makes it difficult to assess the magnitude of effect of music interventions on psychological stress. However, our findings suggest that music interventions have the potential for reducing both stress biomarker levels and psychological stress in acute stress situations. [ABSTRACT FROM AUTHOR]

We congratulate Dr. Nigel Stallard on his stimulating paper on adaptive enrichment designs with a continuous biomarker. Dr. Stallard details a framework for a large and interesting class of enrichment procedures. His work has motivated us to offer some thoughts in response. Dr. Stallard's strategy is to use the maximum of a test statistic over a set of possible threshold values to define the enriched population to be sampled in a second stage. This reminds us of procedures for identifying a change point, a biomarker value beyond which the effect of treatment is increased. For simplicity we focus our comments on Dr. Stallard's Rule 1 for selecting the second‐stage sampling threshold. Using this rule, we present the likelihood ratio approach for adaptive testing and compare it to Dr. Stallard's approach for a few scenarios. [ABSTRACT FROM AUTHOR]

Continuous biomarkers are common for disease screening and diagnosis. To reach a dichotomous clinical decision, a threshold would be imposed to distinguish subjects with disease from nondiseased individuals. Among various performance metrics, specificity at a controlled sensitivity level (or vice versa) is often desirable because it directly targets the clinical utility of the intended clinical test. Meanwhile, covariates, such as age, race, as well as sample collection conditions, could impact the biomarker distribution and may also confound the association between biomarker and disease status. Therefore, covariate adjustment is important in such biomarker evaluation. Most existing covariate adjustment methods do not specifically target the desired sensitivity/specificity level, but rather do so for the entire biomarker distribution. As such, they might be more prone to model misspecification. In this paper, we suggest a parsimonious quantile regression model for the diseased population, only locally at the controlled sensitivity level, and assess specificity with covariate‐specific control of the sensitivity. Variance estimates are obtained from a sample‐based approach and bootstrap. Furthermore, our proposed local model extends readily to a global one for covariate adjustment for the receiver operating characteristic (ROC) curve over the sensitivity continuum. We demonstrate computational efficiency of this proposed method and restore the inherent monotonicity in the estimated covariate‐adjusted ROC curve. The asymptotic properties of the proposed estimators are established. Simulation studies show favorable performance of the proposal. Finally, we illustrate our method in biomarker evaluation for aggressive prostate cancer. [ABSTRACT FROM AUTHOR]

The coexistence of osteoporosis and chronic kidney disease (CKD) is an evolving healthcare challenge in the face of increasingly aging populations. Globally, accelerating fracture incidence causes disability, impaired quality of life and increased mortality. Consequently, several novel diagnostic and therapeutic tools have been introduced for treatment and prevention of fragility fractures. Despite an especially high fracture risk in CKD, these patients are commonly excluded from interventional trials and clinical guidelines. While management of fracture risk in CKD has been discussed in recent opinion-based reviews and consensus papers in the nephrology literature, many patients with CKD stages 3–5D and osteoporosis are still underdiagnosed and untreated. The current review addresses this potential treatment nihilism by discussing established and novel approaches to diagnosis and prevention of fracture risk in patients with CKD stages 3–5D. Skeletal disorders are common in CKD. A wide variety of underlying pathophysiological processes have been identified, including premature aging, chronic wasting, and disturbances in vitamin D and mineral metabolism, which may impact bone fragility beyond established osteoporosis. We discuss current and emerging concepts of CKD–mineral and bone disorders (CKD-MBD) and integrate management of osteoporosis in CKD with current recommendations for management of CKD-MBD. While many diagnostic and therapeutic approaches to osteoporosis can be applied to patients with CKD, some limitations and caveats need to be considered. Consequently, clinical trials are needed that specifically study fracture prevention strategies in patients with CKD stages 3–5D. [ABSTRACT FROM AUTHOR]

Saliva as a non-invasive diagnostic fluid has immense potential as a tool for early diagnosis and prognosis of patients. The information about salivary biomarkers is broadly scattered across various resources and research papers. It is important to bring together all the information on salivary biomarkers to a single platform. This will accelerate research and development in non-invasive diagnosis and prognosis of complex diseases. We collected widespread information on five types of salivary biomarkers—proteins, metabolites, microbes, micro-ribonucleic acid (miRNA) and genes found in humans. This information was collected from different resources that include PubMed, the Human Metabolome Database and SalivaTecDB. Our database SalivaDB contains a total of 15 821 entries for 201 different diseases and 48 disease categories. These entries can be classified into five categories based on the type of biomolecules; 6067, 3987, 2909, 2272 and 586 entries belong to proteins, metabolites, microbes, miRNAs and genes, respectively. The information maintained in this database includes analysis methods, associated diseases, biomarker type, regulation status, exosomal origin, fold change and sequence. The entries are linked to relevant biological databases to provide users with comprehensive information. We developed a web-based interface that provides a wide range of options like browse, keyword search and advanced search. In addition, a similarity search module has been integrated which allows users to perform a similarity search using Basic Local Alignment Search Tool and Smith–Waterman algorithm against biomarker sequences in SalivaDB. We created a web-based database—SalivaDB, which provides information about salivary biomarkers found in humans. A wide range of web-based facilities have been integrated to provide services to the scientific community. https://webs.iiitd.edu.in/raghava/salivadb/ [ABSTRACT FROM AUTHOR]

The stratification of patients at risk of progression of COVID-19 and their molecular characterization is of extreme importance to optimize treatment and to identify therapeutic options. The bioinformatics community has responded to the outbreak emergency with a set of tools and resource to identify biomarkers and drug targets that we review here. Starting from a consolidated corpus of 27 570 papers, we adopt latent Dirichlet analysis to extract relevant topics and select those associated with computational methods for biomarker identification and drug repurposing. The selected topics span from machine learning and artificial intelligence for disease characterization to vaccine development and to therapeutic target identification. Although the way to go for the ultimate defeat of the pandemic is still long, the amount of knowledge, data and tools generated so far constitutes an unprecedented example of global cooperation to this threat. [ABSTRACT FROM AUTHOR]

Introduction: A new law in the United States gives the Food and Drug Administration (FDA) wide latitude to regulate tobacco products for the first time. Given the need for science to serve as a foundation for FDA actions, it is critical that a scientific review of the literature relevant to the proposed legislation be undertaken by experts in the field of nicotine and tobacco research in order to develop research priorities. Methods: This paper describes an initiative that was implemented to identify research opportunities under “The Family Smoking Prevention and Tobacco Control Act” and summarizes the conclusions and future directions derived from that initiative. Results: Multiple research and surveillance needs were identified, such as characterization of biomarkers and increased analysis of risk perception. It was also recognized that science will play a critical role in policy determinations such as what constitutes “substantial equivalence” and that there will be considerable infrastructure needs (e.g., laboratories for product testing). Conclusions: Science must drive FDA's decision making regarding tobacco regulation. This article provides a summary of research opportunities identified through literature reviews related to various provisions of the new law. However, the science required by the law requires a transdisciplinary approach because of its complexity, so one of the challenges facing the FDA will be to connect the silos of research in recognition that the “system” of tobacco regulation is greater than the sum of its parts. [ABSTRACT FROM AUTHOR]

The Reporting Recommendations for Tumor Marker Prognostic Studies (REMARK) were developed to address widespread deficiencies in the reporting of such studies. The REMARK checklist consists of 20 items to report for published tumor marker prognostic studies. A detailed paper was published explaining the rationale behind checklist items, providing positive examples and giving empirical evidence of the quality of reporting. REMARK provides a comprehensive overview to educate on good reporting and provide a valuable reference for the many issues to consider when designing, conducting, and analyzing tumor marker studies and prognostic studies in medicine in general. Despite support for REMARK from major cancer journals, prognostic factor research studies remain poorly reported. To encourage dissemination and uptake of REMARK, we have produced this considerably abridged version of the detailed explanatory manuscript, which may also serve as a brief guide to key issues for investigators planning tumor marker prognostic studies. To summarize the current situation, more recent papers investigating the quality of reporting and related reporting guidelines are cited, but otherwise the literature is not updated. Another important impetus for this paper is that it serves as a basis for literal translations into other languages. Translations will help to bring key information to a larger audience world-wide. Many more details can be found in the original paper. [ABSTRACT FROM AUTHOR]

Summary: Background: Biologics are indicated for treating moderate‐to‐severe psoriasis. As the number of biologics registered for the treatment of psoriasis increases, so does the need for biomarkers to guide personalized therapeutic decisions. Genetic variants might serve as predictors for treatment response, a field of research known as pharmacogenetics. Objectives: To assess which genetic variants are associated with response to biologics in patients with psoriasis according to current literature. Methods: A systematic search was performed in Embase, MEDLINE, the Cochrane Library and Web of Science. In total, 26 papers were included in this systematic review; 24 original studies and two meta‐analyses. Quality was assessed using a predesigned form and risk of bias was assessed using the Newcastle–Ottawa Scale. Results: The majority of studies reported a candidate gene approach, focusing on polymorphisms in genes related to the therapeutic target or to psoriasis susceptibility. Studied populations were small and results were divergent, especially for studies investigating tumour necrosis factor inhibitors. The evidence for the role of HLA‐Cw6 in ustekinumab efficacy shows minimal heterogeneity, with a higher response rate among patients who were positive for HLA‐Cw6 reported across three of five studies. Conclusions: Pharmacogenetic studies in psoriasis have generated divergent results. Replication of findings in larger cohorts is required. Large‐scale hypothesis‐free searches for genetic biomarkers are needed to uncover the complete genetic background of outcomes for treatment with biologics. [ABSTRACT FROM AUTHOR]

The association between the compositions of microbial communities and various host phenotypes is an important research topic. Microbiome association research addresses multiple domains, such as human disease and diet. Statistical methods for testing microbiome–phenotype associations have been studied recently to determine their ability to assess longitudinal microbiome data. However, existing methods fail to detect sparse association signals in longitudinal microbiome data. In this paper, we developed a novel method, namely aGEEMIHC, which is a data-driven adaptive microbiome higher criticism analysis based on generalized estimating equations to detect sparse microbial association signals from longitudinal microbiome data. aGEEMiHC adopts generalized estimating equations framework that fully considers the correlation among different observations from the same subject in longitudinal data. To be robust to diverse correlation structures for longitudinal data, aGEEMiHC integrates multiple microbiome higher criticism analyses based on generalized estimating equations with different working correlation structures. Extensive simulation experiments demonstrate that aGEEMiHC can control the type I error correctly and achieve superior performance according to a statistical power comparison. We also applied it to longitudinal microbiome data with various types of host phenotypes to demonstrate the stability of our method. aGEEMiHC is also utilized for real longitudinal microbiome data, and we found a significant association between the gut microbiome and Crohn's disease. In addition, our method ranks the significant factors associated with the host phenotype to provide potential biomarkers. [ABSTRACT FROM AUTHOR]

Context Poor diet quality and unhealthy dietary patterns have been linked to poor bone health, yet few studies have investigated the role of diet quality in bone health in pediatric populations. Objective This systematic review aims to assess the available evidence on the association between diet quality and bone health markers in children and adolescents. Data Sources The PubMed, Scopus, and Virtual Health Library databases were searched electronically from October to November 2022, without any restrictions on date or language. The STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) checklist was used to assess the quality of the studies. Data Extraction Published observational studies in children and adolescents (ages 2 to 19 years) that investigated the association between diet quality and bone health were eligible for inclusion. Two researchers independently analyzed and selected all articles using the Rayyan app. Initially, 965 papers were identified. A total of 12 observational studies qualified, including 8 cross-sectional and 4 longitudinal studies. The sample comprised 7130 individuals aged 3 to 17.9 years, representing both sexes. Bone health was evaluated by measures of bone mineral density and bone mineral content. Data Analysis Seven studies (58.3%) showed significant associations between diet quality and bone health markers, all of which evaluated diet quality by identifying dietary patterns. Diet quality as evaluated by all dietary indexes was not associated with bone health markers. Conclusions Adherence to a healthy diet may benefit bone health in children and adolescents. These findings emphasize the importance of developing effective public health policies that encourage healthy eating habits from childhood to preserve bone health. Longitudinal research using a specific tool to assess diet quality in relation to bone health is warranted. Future studies should also measure bone-regulating hormones and markers of bone turnover. Systematic Review Registration PROSPERO registration no. CRD42022368610. [ABSTRACT FROM AUTHOR]

In this paper, we advocate and expand upon a previously described monitoring strategy for efficient and robust estimation of disease prevalence and case numbers within closed and enumerated populations such as schools, workplaces, or retirement communities. The proposed design relies largely on voluntary testing, which is notoriously biased (e.g. in the case of coronavirus disease 2019) due to nonrepresentative sampling. The approach yields unbiased and comparatively precise estimates with no assumptions about factors underlying selection of individuals for voluntary testing, building on the strength of what can be a small random sampling component. This component enables the use of a recently proposed "anchor stream" estimator, a well-calibrated alternative to classical capture-recapture (CRC) estimators based on 2 data streams. We show that this estimator is equivalent to a direct standardization based on "capture," that is, selection (or not) by the voluntary testing program, made possible by means of a key parameter identified by design. This equivalency simultaneously allows for novel 2-stream CRC-like estimation of general mean values (e.g. means of continuous variables like antibody levels or biomarkers). For inference, we propose adaptations of Bayesian credible intervals when estimating case counts and bootstrapping when estimating means of continuous variables. We use simulations to demonstrate significant precision benefits relative to random sampling alone. [ABSTRACT FROM AUTHOR]

Introduction Neuromuscular electrical stimulation (NMES) is a treatment to prevent or reverse acquired disability in hospitalised adults. We conducted a systematic review and meta-analysis of its effectiveness. Method We searched MEDLINE, EMBASE, Cumulative Index to Nursing & Allied Health (CINAHL) and the Cochrane library. Inclusion criteria: randomised controlled trials of hospitalised adult patients comparing NMES to control or usual care. The primary outcome was muscle strength. Secondary outcomes were muscle size, function, hospital length of stay, molecular and cellular biomarkers, and adverse effects. We assessed risk of bias using the Cochrane risk-of-bias tool. We used Review Manager (RevMan) software for data extraction, critical appraisal and synthesis. We assessed certainty using the Grading of Recommendations Assessment, Development and Evaluation tool. Results A total of 42 papers were included involving 1,452 participants. Most studies had unclear or high risk of bias. NMES had a small effect on muscle strength (moderate certainty) (standardised mean difference (SMD) = 0.33; P  < 0.00001), a moderate effect on muscle size (moderate certainty) (SMD = 0.66; P  < 0.005), a small effect on walking performance (moderate certainty) (SMD = 0.48; P  < 0.0001) and a small effect on functional mobility (low certainty) (SMD = 0.31; P  < 0.05). There was a small and non-significant effect on health-related quality of life (very low certainty) (SMD = 0.35; P  > 0.05). In total, 9% of participants reported undesirable experiences. The effects of NMES on length of hospital stay, and molecular and cellular biomarkers were unclear. Conclusions NMES is a promising intervention component that might help to reduce or prevent hospital-acquired disability. [ABSTRACT FROM AUTHOR]

Saliva is a complex physiologic fluid that contains an abundance of biological analytes, or biomarkers. Recent research has shown that these biomarkers may be able to convey the physiologic health of a person. Work has been done linking derangements in these salivary biomarkers to a wide variety of pathologic disorders ranging from oncologic diseases to atopic conditions. The specific area of interest for this review paper is esophageal disorders. Particularly because the diagnosis and management of esophageal disorders often includes invasive testing such as esophagogastroduodenoscopy, prolonged pH monitoring, and biopsy. The aim of this review will be to explore salivary biomarkers (pepsin, bile, epidermal growth factor, and micro-RNA) that are being studied as they relate specifically to esophageal disorders. Finally, it will explore the benefits of salivary testing and identify areas of possible future research. [ABSTRACT FROM AUTHOR]

Motivation A gradient boosting decision tree (GBDT) is a powerful ensemble machine-learning method that has the potential to accelerate biomarker discovery from high-dimensional molecular data. Recent algorithmic advances, such as extreme gradient boosting (XGB) and light gradient boosting (LGB), have rendered the GBDT training more efficient, scalable and accurate. However, these modern techniques have not yet been widely adopted in discovering biomarkers for censored survival outcomes, which are key clinical outcomes or endpoints in cancer studies. Results In this paper, we present a new R package 'Xsurv' as an integrated solution that applies two modern GBDT training frameworks namely, XGB and LGB, for the modeling of right-censored survival outcomes. Based on our simulations, we benchmark the new approaches against traditional methods including the stepwise Cox regression model and the original gradient boosting function implemented in the package 'gbm'. We also demonstrate the application of Xsurv in analyzing a melanoma methylation dataset. Together, these results suggest that Xsurv is a useful and computationally viable tool for screening a large number of prognostic candidate biomarkers, which may facilitate future translational and clinical research. Availability and implementation 'Xsurv' is freely available as an R package at: https://github.com/topycyao/Xsurv. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Precision medicine relies on the idea that, for a particular targeted agent, only a subpopulation of patients is sensitive to it and thus may benefit from it therapeutically. In practice, it is often assumed based on preclinical data that a treatment‐sensitive subpopulation is known, and moreover that the agent is substantively efficacious in that subpopulation. Due to important differences between preclinical settings and human biology, however, data from patients treated with a new targeted agent often show that one or both of these assumptions are false. This paper provides a Bayesian randomized group sequential enrichment design that compares an experimental treatment to a control based on survival time and uses early response as an ancillary outcome to assist with adaptive variable selection and enrichment. Initially, the design enrolls patients under broad eligibility criteria. At each interim decision, submodels for regression of response and survival time on a baseline covariate vector and treatment are fit; variable selection is used to identify a covariate subvector that characterizes treatment‐sensitive patients and determines a personalized benefit index, and comparative superiority and futility decisions are made. Enrollment of each cohort is restricted to the most recent adaptively identified treatment‐sensitive patients. Group sequential decision cutoffs are calibrated to control overall type I error and account for the adaptive enrollment restriction. The design provides a basis for precision medicine by identifying a treatment‐sensitive subpopulation, if it exists, and determining whether the experimental treatment is superior to the control in that subpopulation. A simulation study shows that the proposed design reliably identifies a sensitive subpopulation, yields much higher generalized power compared to several existing enrichment designs and a conventional all‐comers group sequential design, and is robust. [ABSTRACT FROM AUTHOR]

In biomedical practices, multiple biomarkers are often combined using a prespecified classification rule with tree structure for diagnostic decisions. The classification structure and cutoff point at each node of a tree are usually chosen on an ad hoc basis, depending on decision makers' experience. There is a lack of analytical approaches that lead to optimal prediction performance, and that guide the choice of optimal cutoff points in a pre‐specified classification tree. In this paper, we propose to search for and estimate the optimal decision rule through an approach of rank correlation maximization. The proposed method is flexible, theoretically sound, and computationally feasible when many biomarkers are available for classification or prediction. Using the proposed approach, for a prespecified tree‐structured classification rule, we can guide the choice of optimal cutoff points at tree nodes and estimate optimal prediction performance from multiple biomarkers combined. [ABSTRACT FROM AUTHOR]

Over the past few years, meta-analysis has become popular among biomedical researchers for detecting biomarkers across multiple cohort studies with increased predictive power. Combining datasets from different sources increases sample size, thus overcoming the issue related to limited sample size from each individual study and boosting the predictive power. This leads to an increased likelihood of more accurately predicting differentially expressed genes/proteins or significant biomarkers underlying the biological condition of interest. Currently, several meta-analysis methods and tools exist, each having its own strengths and limitations. In this paper, we survey existing meta-analysis methods, and assess the performance of different methods based on results from different datasets as well as assessment from prior knowledge of each method. This provides a reference summary of meta-analysis models and tools, which helps to guide end-users on the choice of appropriate models or tools for given types of datasets and enables developers to consider current advances when planning the development of new meta-analysis models and more practical integrative tools. [ABSTRACT FROM AUTHOR]

We evaluated the performance of a semiquantitative food frequency questionnaire (SFFQ), the Automated Self-Administered 24-Hour Dietary Recall (ASA24), and 7-day dietary records (7DDRs), in comparison with biomarkers, in the estimation of nutrient intakes among 627 women in the Women's Lifestyle Validation Study (United States, 2010-2012). Two paper SFFQs, 1 Web-based SFFQ, 4 ASA24s (beta version), 2 7DDRs, 4 24-hour urine samples, 1 doubly labeled water measurement (repeated among 76 participants), and 2 fasting blood samples were collected over a 15-month period. The dietary variables evaluated were energy, energy-adjusted intakes of protein, sodium, potassium, and specific fatty acids, carotenoids, a-tocopherol, retinol, and folate. In general, relative to biomarkers, averaged ASA24s had lower validity than the SFFQ completed at the end of the data-collection year (SFFQ2); SFFQ2 had slightly lower validity than 1 7DDR; the averaged SFFQs had validity similar to that of 1 7DDR; and the averaged 7DDRs had the highest validity. The deattenuated correlation of energy-adjusted protein intake assessed by SFFQ2 with its biomarker was 0.46, similar to its correlation with 7DDRs (deattenuated r = 0.54). These data indicate that the SFFQ2 provides reasonably valid measurements of energy-adjusted intake for most of the nutrients assessed in our study, consistent with earlier conclusions derived using 7DDRs as the comparison method. The ASA24 needs further evaluation for use in large population studies, but an average of 3 days of measurement will not be sufficient for some important nutrients. [ABSTRACT FROM AUTHOR]

Background: The function of the gestational sac (GS) and the placenta in the closely related processes of embryogenesis and teratogenicity in the first trimester has been minimally described. The prevailing assumption is that direct teratogenic effects are mediated by the critical extraembryonic organ, the placenta, which either blocks or transfers exposures to the foetus. Placental transfer is a dominant mechanism, but there are other paradigms by which the placenta can mediate teratogenic effects. Knowledge of these paradigms and first trimester human developmental biology can be useful to the epidemiologist in the conduct of biomarker-based studies of both maternal and child health.Objective and Rationale: Our aim is to provide a causal framework for modelling the teratogenic effects of first trimester exposures on child health outcomes mediated by the GS and placenta using biomarker data collected in the first trimester. We initially present first trimester human developmental biology for the sake of informing and strengthening epidemiologic approaches. We then propose analytic approaches of modelling placental mechanisms by way of causal diagrams using classical non-embryolethal teratogens (diethylstilboestrol [DES], folic acid deficiency and cytomegalovirus [CMV]) as illustrative examples. We extend this framework to two chronic exposures of particular current interest, phthalates and maternal adiposity.Search Methods: Information on teratogens was identified by a non-systematic, narrative review. For each teratogen, we included papers that answered the five following questions: (i) why were these exposures declared teratogens? (ii) is there a consensus on biologic mechanism? (iii) is there reported evidence of a placental mechanism? (iv) can we construct a theoretical model of a placental mechanism? and (v) can this knowledge inform future work on measurement and modelling of placental-foetal teratogenesis? We prioritized literature specific to human development, the organogenesis window in the first trimester and non-embryolethal mechanisms.Outcomes: As a result of our review of the literature on five exposures considered harmful in the first trimester, we developed four analytic strategies to address first trimester placental mechanisms in birth cohort studies: placental transfer and direct effects on the foetus (DES and maternal adiposity), indirect effects through targeted placental molecular pathways (DES and phthalates), pre-placental effects through disruptions in embryonic and extraembryonic tissue layer differentiation (folic acid deficiency), and multi-step mechanisms that involve maternal, placental and foetal immune function and inflammation (DES and CMV).Wider Implications: The significance of this review is to offer a causal approach to classify the large number of potentially harmful exposures in pregnancy when the exposure occurs in the first trimester. Our review will facilitate future research by advancing knowledge of the first trimester mechanisms necessary for researchers to effectively associate environmental exposures with child health outcomes. [ABSTRACT FROM AUTHOR]

Gene expression data provide the expression levels of tens of thousands of genes from several hundred samples. These data are analyzed to detect biomarkers that can be of prognostic or diagnostic use. Traditionally, biomarker detection for gene expression data is the task of gene selection. The vast number of genes is reduced to a few relevant ones that achieve the best performance for the respective use case. Traditional approaches select genes based on their statistical significance in the data set. This results in issues of robustness, redundancy and true biological relevance of the selected genes. Integrative analyses typically address these shortcomings by integrating multiple data artifacts from the same objects, e.g. gene expression and methylation data. When only gene expression data are available, integrative analyses instead use curated information on biological processes from public knowledge bases. With knowledge bases providing an ever-increasing amount of curated biological knowledge, such prior knowledge approaches become more powerful. This paper provides a thorough overview on the status quo of biomarker detection on gene expression data with prior biological knowledge. We discuss current shortcomings of traditional approaches, review recent external knowledge bases, provide a classification and qualitative comparison of existing prior knowledge approaches and discuss open challenges for this kind of gene selection. [ABSTRACT FROM AUTHOR]

The UK Biobank (UKB) is a health research resource of major international importance, incorporating comprehensive characterization of >500 000 men and women recruited between 2006 and 2010 from across the UK. There is prospective tracking of health outcomes for all participants through linkages with national cohorts (death registers, cancer registers, electronic hospital records, and primary care records). The dataset has been enhanced with the UKB imaging study, which aims to scan a subset of 100 000 participants. The imaging protocol includes magnetic resonance imaging of the brain, heart, and abdomen, carotid ultrasound, and whole-body dual X-ray absorptiometry. Since its launch in 2015, over 48 000 participants have completed the imaging study with scheduled completion in 2023. Repeat imaging of 10 000 participants has been approved and commenced in 2019. The cardiovascular magnetic resonance (CMR) scan provides detailed assessment of cardiac structure and function comprising bright blood anatomic assessment (sagittal, coronal, and axial), left and right ventricular cine images (long and short axes), myocardial tagging, native T1 mapping, aortic flow, and imaging of the thoracic aorta. The UKB is an open access resource available to health researchers across all scientific disciplines from both academia and industry with no preferential access or exclusivity. In this paper, we consider how we may best utilize the UKB CMR data to advance cardiovascular research and review notable achievements to date. [ABSTRACT FROM AUTHOR]

Modern machine learning techniques (such as deep learning) offer immense opportunities in the field of human biological aging research. Aging is a complex process, experienced by all living organisms. While traditional machine learning and data mining approaches are still popular in aging research, they typically need feature engineering or feature extraction for robust performance. Explicit feature engineering represents a major challenge, as it requires significant domain knowledge. The latest advances in deep learning provide a paradigm shift in eliciting meaningful knowledge from complex data without performing explicit feature engineering. In this article, we review the recent literature on applying deep learning in biological age estimation. We consider the current data modalities that have been used to study aging and the deep learning architectures that have been applied. We identify four broad classes of measures to quantify the performance of algorithms for biological age estimation and based on these evaluate the current approaches. The paper concludes with a brief discussion on possible future directions in biological aging research using deep learning. This study has significant potentials for improving our understanding of the health status of individuals, for instance, based on their physical activities, blood samples and body shapes. Thus, the results of the study could have implications in different health care settings, from palliative care to public health. [ABSTRACT FROM AUTHOR]

This paper reviews developments regarding the use of plasma-free amino acid (PFAA) profiles as biomarkers for detecting and predicting disease risk. This work was initiated and first published in 2006 and was subsequently developed by Ajinomoto Co. Inc. After commercialization in 2011, PFAA-based tests were adopted in over 1500 clinics and hospitals in Japan, and numerous clinician-led studies have been performed to validate these tests. Evidence is accumulating that PFAA profiles can be used for diabetes prediction and evaluation of frailty; in particular, decreased plasma essential amino acids could contribute to the pathophysiology of severe frailty. Integration of PFAA evaluation as a biomarker and effective essential amino acid supplementation, which improves physical and mental functions in the elderly, could facilitate the development of precision nutrition, including personalized solutions. This present review provides the background for the technology as well as more recent clinical findings, and offers future possibilities regarding the implementation of precision nutrition. [ABSTRACT FROM AUTHOR]

AKI biomarkers (…) appear to have value, (…) informing on the pathobiology of AKI. However, their performance is influenced by patient characteristics, co-morbid illness, inciting kidney injury event, timing of measurement and selected thresholds for diagnosis.Background Acute kidney injury (AKI) remains associated with high morbidity and mortality, despite progress in medical care. Although the RIFLE (Risk, Injury, Failure, Loss, End-Stage Kidney Disease) and AKIN (Acute Kidney Injury Network) criteria, based on serum creatinine and urine output, were a step forward in diagnosing AKI, a reliable tool to differentiate between true parenchymal and pre-renal azotaemia in clinical practice is still lacking. In the last decade, many papers on the use of new urinary and serum biomarkers for the diagnosis and prognostication of AKI have been published. Thus, the question arises which biomarker is a reliable differential diagnostic tool under which circumstances. Methods We searched Medline from inception to April 2012 using medical subject heading and text words for AKI and biomarkers [neutrophil gelatinase-associated lipocalin (NGAL), kidney injury molecule-1 (KIM-1), Cystatin C, interleukin-6 (IL-6), interleukin-8 (IL-8), interleukin-18 (IL-18), N-acetyl-glucosaminidase (NAG), glutathione transferases (GST) and liver fatty acid binding protein (LFABP)] to identify relevant papers in five different settings (paediatrics, cardiac surgery, emergency department, critically ill and contrast-induced nephropathy). Results We included 87 relevant papers, reporting on 74 studies. Depending upon the setting, 7–27 different definitions of AKI were used. Reported diagnostic performance of the different biomarkers was variable from poor to excellent, and no consistent generalizable conclusions can be drawn on their diagnostic value. Conclusions Early diagnosing of AKI in clinical conditions by using new serum and urinary biomarkers remains cumbersome, especially in those settings where timing and aetiology of AKI are not well defined. Putting too much emphasis on markers that have not convincingly proven reliability might lead to incorrect interpretation of clinical trials. Further research in this field is warranted before biomarkers can be introduced in clinical practice. [ABSTRACT FROM AUTHOR]

BACKGROUND Endometriosis is usually diagnosed by an invasive procedure such as a laparoscopy. Great interest therefore lies in the potential to identify biomarkers which may be surrogates of disease presence or activity, especially relating to the effects of therapy. We have reviewed the existing literature on endometrial differences in women with endometriosis, and assess their potential use as disease biomarkers. METHODS We used QUADAS (Quality Assessment of Diagnostic Accuracy Studies) criteria to conduct a systematic review of published papers over the past 25 years on the subject of endometrial differences in endometriosis. We searched for all studies assessing differences between eutopic endometrium of women with and without endometriosis. RESULTS We identified 182 relevant articles that are summarized in the review. These studies assess over 200 potential biomarkers, including hormones and their receptors (n = 29), cytokines (n = 25), factors identified using proteomics (n = 8) and histological analysis (n = 10) of endometrial tissue. Sensitivity and specificity were reported or could be calculated for only 32 articles, and ranged from 0 to 100%. Of the nine highest quality studies, six identified putative biomarkers related to nerve fibre growth or cell cycle control, highlighting these areas as promising candidates for future biomarker research. CONCLUSIONS This systematic review identified several reports of endometrial differences which have the potential to be biomarkers of endometriosis. However, larger studies in well-defined populations are clearly required to determine their true usefulness. [ABSTRACT FROM AUTHOR]

Introduction The dearth of biomarkers limits the precision of our research into pathogenesis of psychiatric disorders and has slowed down the development of new drugs. In clinical practice, it undermines the validity of psychiatric diagnoses and hampers the delivery of personalized treatment. Sources of data The data quoted in this paper are gathered from a range of sources encompassing scientific and journalistic both in print and electronic. Areas of agreement Availability of clinically useful biomarkers will improve the prognosis and outcome of psychiatric patients by helping in early diagnosis and delivery of individualized treatment. Areas of controversy The cross-sectional and longitudinal observation of psychopathology is the bedrock of current clinical practice. Are psychiatric biomarkers advanced enough to supplant it? Growing points The need for biomarkers of psychiatric disorders has become more acute with the advent of new treatments which require precision and an individualized approach. Areas timely for developing research Identification and deployment of intermediate phenotypes in classification, research and clinical practice of psychiatry. [ABSTRACT FROM AUTHOR]