penalizedclr: an R package for penalized conditional logistic regression for integration of multiple omics layers.

BACKGROUND: The matched case-control design, up until recently mostly pertinent to epidemiological studies, is becoming customary in biomedical applications as well. For instance, in omics studies, it is quite common to compare cancer and healthy tissue from the same patient. Furthermore, researchers today routinely collect data from various and variable sources that they wish to relate to the case-control status. This highlights the need to develop and implement statistical methods that can take these tendencies into account. RESULTS: We present an R package penalizedclr, that provides an implementation of the penalized conditional logistic regression model for analyzing matched case-control studies. It allows for different penalties for different blocks of covariates, and it is therefore particularly useful in the presence of multi-source omics data. Both L1 and L2 penalties are implemented. Additionally, the package implements stability selection for variable selection in the considered regression model. CONCLUSIONS: The proposed method fills a gap in the available software for fitting high-dimensional conditional logistic regression models accounting for the matched design and block structure of predictors/features. The output consists of a set of selected variables that are significantly associated with case-control status. These variables can then be investigated in terms of functional interpretation or validation in further, more targeted studies.

Meropenem pharmacokinetic/pharmacodynamic target attainment and clinical response in ICU patients: A prospective observational study.

BACKGROUND: Several studies report lack of meropenem pharmacokinetic/pharmacodynamic (PK/PD) target attainment (TA) and risk of therapeutic failure with intermittent bolus infusions in intensive care unit (ICU) patients. The aim of this study was to describe meropenem TA in an ICU population and the clinical response in the first 72 h after therapy initiation. METHODS: A prospective observational study of ICU patients ≥18 years was conducted from 2014 to 2017. Patients with normal renal clearance (NRC) and augmented renal clearance (ARC) and patients on continuous renal replacement therapy (CRRT) were included. Meropenem was administered as intermittent bolus infusions, mainly at a dose of 1 g q6h. Peak, mid, and trough levels were sampled at 24, 48, and 72 h after therapy initiation. TA was defined as 100% T > 4× MIC or trough concentration above 4× MIC. Meropenem PK was estimated using traditional calculation methods and population pharmacokinetic modeling (P-metrics®). Clinical response was evaluated by change in C-reactive protein (CRP), Sequential Organ Failure Assessment (SOFA) score, leukocyte count, and defervescence. RESULTS: Eighty-seven patients were included, with a median Simplified Acute Physiology (SAPS) II score 37 and 90 days mortality rate of 32%. Median TA was 100% for all groups except for the ARC group with 45.5%. Median CRP fell from 175 (interquartile range [IQR], 88-257) to 70 (IQR, 30-114) (p < .001) in the total population. A reduction in SOFA score was observed only in the non-CRRT groups (p < .001). CONCLUSION: Intermittent meropenem bolus infusion q6h gives satisfactory TA in an ICU population with variable renal function and CRRT modality, except for ARC patients. No consistent relationship between TA and clinical endpoints were observed.

Computed tomography for visible haematuria - a single nephrographic phase is sufficient for detecting renal cell carcinoma.

OBJECTIVES: No previous studies have compared two computed tomography (CT) protocols in patients presenting with visible haematuria, and most patients undergo a multiphase CT in order to detect upper tract malignancies. We aimed to prospectively compare the diagnostic performance of single- and four-phase CT for detecting renal cell carcinoma (RCC) in patients with visible haematuria. MATERIALS & METHODS: 'A Prospective Trial for Examining Hematuria using Computed Tomography' (PROTEHCT) was a single-centre prospective paired diagnostic study in patients referred for CT due to painless visible haematuria between September 2019 and June 2021. All patients underwent four-phase CT (control) from which a single nephrographic phase dual energy CT (experimental) was extracted. Both were independently assessed for RCC by randomised radiologists. Histologically verified RCC defined a positive reference standard. Follow-up ascertainment of RCC diagnosis was completed in May 2022. Descriptive statistics were used to calculate the accuracies. Inter-reader agreement was assessed by kappa statistics. RESULTS: A total of 308 patients (median age, 68 years [interquartile range 53-77, range 18-96], 250 males) were included for analysis. RCC was diagnosed in seven (2.3%) patients during a median follow-up time of 19 months (interquartile range: 15-25). For the control and experimental CT, sensitivity was 100% versus 100%, specificity was 97% versus 98% and accuracy 97% versus 97%. The positive predictive value was 44% versus 50%, and the negative predictive value was 100% versus 100%. The agreement between the control and experimental CT was 98% (k = 0.79). CONCLUSION: A single nephrographic phase dual energy CT is sufficient for detecting RCC in patients with visible haematuria.

Preventing hypothermia in pediatric neurosurgery in Africa-A randomized controlled non-inferiority trial of insulation versus active warming.

PURPOSE: The objective of this study was to compare the efficacy of a low-cost heat-preserving method in preventing intraoperative hypothermia with that of forced-air warming in a resource-limited setting. METHODS: In this randomized controlled non-inferiority trial, we recruited children younger than 12 years scheduled for cranial neurosurgery in a large East-African hospital. Patients were block-randomized by age to intraoperative warming measures using Hibler's method (intervention) or warm air (comparator). Hibler's group patients were circumferentially wrapped in transparent plastic sheeting (providing a vapor-trap) over a layer of cotton blankets, then laid on an insulating foam mattress. Warm air group patients were treated with forced-air convection via an underlying Snuggle Warm™ Pediatric Full Body mattress. Allocated warming measures were initiated in the operating theatre and discontinued upon anesthesia emergence. Perioperative temperatures were measured using noninvasive forehead probes (SpotOn™). The primary outcome was incidence of hypothermia (core temperature < 36.0° for longer than 5 min). Our null hypothesis was that Hibler's method is inferior in efficacy to the warm air method by a margin exceeding 20%. Among secondary outcomes were duration of hypothermia as proportion of surgical duration, incidence of postoperative shivering and rescue measure requirements. RESULTS: We analyzed data for 77 participants (Hibler's = 38; warm air = 39). There was no significant difference between the Hibler's and warm air arms of the study in the primary outcome of incidence of hypothermia (59.0% vs. 60.5% respectively; OR 1.07; 95% CI 0.43-2.65; p = .890). However, the risk difference (1.55%; 95% CI -0.20 to -0.24) exceeded the 0.2 margin and non-inferiority could not be declared. There was considerable need for rescue measures in both groups (71.1 0% vs. 69.2%; OR 1.09; 95% CI 0.41-2.90; p = .861). There was no statistically significant difference between groups for any prespecified secondary outcome. CONCLUSION: Although perioperative core temperatures were not significantly different, we could not declare an inexpensive heat-preserving method non-inferior to warm air convection in preventing intraoperative hypothermia in children undergoing anesthesia for cranial neurosurgery in a resource-limited setting. The extensive need for rescue measures may have masked important differences. TRIAL REGISTRATION: US National Institutes of Health Clinicaltrials.gov database (ID no. NCT02975817).

Pleconaril and ribavirin in new-onset type 1 diabetes: a phase 2 randomized trial.

Previous studies showed a low-grade enterovirus infection in the pancreatic islets of patients with newly diagnosed type 1 diabetes (T1D). In the Diabetes Virus Detection (DiViD) Intervention, a phase 2, placebo-controlled, randomized, parallel group, double-blind trial, 96 children and adolescents (aged 6-15 years) with new-onset T1D received antiviral treatment with pleconaril and ribavirin (n = 47) or placebo (n = 49) for 6 months, with the aim of preserving ß cell function. The primary endpoint was the mean stimulated C-peptide area under the curve (AUC) 12 months after the initiation of treatment (less than 3 weeks after diagnosis) using a mixed linear model. The model used longitudinal log-transformed serum C-peptide AUCs at baseline, at 3 months, 6 months and 1 year. The primary endpoint was met with the serum C-peptide AUC being higher in the pleconaril and ribavirin treatment group compared to the placebo group at 12 months (average marginal effect = 0.057 in the linear mixed model; 95% confidence interval = 0.004-0.11, P = 0.037). The treatment was well tolerated. The results show that antiviral treatment may preserve residual insulin production in children and adolescent with new-onset T1D. This provides a rationale for further evaluating antiviral strategies in the prevention and treatment of T1D. European Union Drug Regulating Authorities Clinical Trials identifier: 2015-003350-41 .

Is a Single Nephrographic Phase Computed Tomography Sufficient for Detecting Urothelial Carcinoma in Patients with Visible Haematuria? A Prospective Paired Noninferiority Comparison.

Background: There is uncertainty about the utility of multiphase computed tomography (CT) compared with single-phase CT in the routine examination of patients with visible haematuria (VH). Objective: To compare the accuracies of single nephrographic phase (NP) CT and four-phase CT in detecting urothelial carcinoma (UC). Design setting and participants: This was a single-centre, prospective, paired, noninferiority study of patients with painless VH referred for CT before cystoscopy between September 2019 and June 2021. Patients were followed up for 1 yr to ascertain UC diagnosis. Intervention: All patients underwent four-phase CT (control), from which single NP CT (experimental) was extracted. Both were independently assessed for UC. Outcome measurements and statistical analysis: The primary outcome was the difference in accuracy between the control and experimental CT using a 7.5% noninferiority limit. Histologically verified UC defined a positive reference standard. Secondary outcomes included differences in sensitivity, specificity, negative (NPV) and positive (PPV) predictive values, and area under the curve (AUC). All results are reported per patient. Results and limitations: Of the 308 patients included, UC was diagnosed in 45 (14.6%). The difference in accuracy between the control and experimental CT was 1.9% (95% confidence interval -2.8 to 6.7), demonstrating noninferiority. Sensitivity was 93.3% versus 91.1%, specificity was 83.7% versus 81.8%, NPV was 98.7% versus 98.2%, PPV was 49.4% versus 46.1%, and AUC was 0.96 versus 0.94 for the control versus experimental CT. Limitations included a low number of UC cases and no definite criteria for selecting a noninferiority limit. Conclusions: The accuracy of NP CT is not inferior to that of four-phase CT for detecting UC. Patient summary: This study shows that a computed tomography (CT) examination with only one contrast phase is no worse than a more complex CT examination for detecting cancer in the urinary tract among patients presenting with visible blood in the urine.

Integrative, multi-omics, analysis of blood samples improves model predictions: applications to cancer.

BACKGROUND: Cancer genomic studies often include data collected from several omics platforms. Each omics data source contributes to the understanding of the underlying biological process via source specific ("individual") patterns of variability. At the same time, statistical associations and potential interactions among the different data sources can reveal signals from common biological processes that might not be identified by single source analyses. These common patterns of variability are referred to as "shared" or "joint". In this work, we show how the use of joint and individual components can lead to better predictive models, and to a deeper understanding of the biological process at hand. We identify joint and individual contributions of DNA methylation, miRNA and mRNA expression collected from blood samples in a lung cancer case-control study nested within the Norwegian Women and Cancer (NOWAC) cohort study, and we use such components to build prediction models for case-control and metastatic status. To assess the quality of predictions, we compare models based on simultaneous, integrative analysis of multi-source omics data to a standard non-integrative analysis of each single omics dataset, and to penalized regression models. Additionally, we apply the proposed approach to a breast cancer dataset from The Cancer Genome Atlas. RESULTS: Our results show how an integrative analysis that preserves both components of variation is more appropriate than standard multi-omics analyses that are not based on such a distinction. Both joint and individual components are shown to contribute to a better quality of model predictions, and facilitate the interpretation of the underlying biological processes in lung cancer development. CONCLUSIONS: In the presence of multiple omics data sources, we recommend the use of data integration techniques that preserve the joint and individual components across the omics sources. We show how the inclusion of such components increases the quality of model predictions of clinical outcomes.

Accounting for measurement error to assess the effect of air pollution on omic signals.

Studies on the effects of air pollution and more generally environmental exposures on health require measurements of pollutants, which are affected by measurement error. This is a cause of bias in the estimation of parameters relevant to the study and can lead to inaccurate conclusions when evaluating associations among pollutants, disease risk and biomarkers. Although the presence of measurement error in such studies has been recognized as a potential problem, it is rarely considered in applications and practical solutions are still lacking. In this work, we formulate Bayesian measurement error models and apply them to study the link between air pollution and omic signals. The data we use stem from the "Oxford Street II Study", a randomized crossover trial in which 60 volunteers walked for two hours in a traffic-free area (Hyde Park) and in a busy shopping street (Oxford Street) of London. Metabolomic measurements were made in each individual as well as air pollution measurements, in order to investigate the association between short-term exposure to traffic related air pollution and perturbation of metabolic pathways. We implemented error-corrected models in a classical framework and used the flexibility of Bayesian hierarchical models to account for dependencies among omic signals, as well as among different pollutants. Models were implemented using traditional Markov Chain Monte Carlo (MCMC) simulative methods as well as integrated Laplace approximation. The inclusion of a classical measurement error term resulted in variable estimates of the association between omic signals and traffic related air pollution measurements, where the direction of the bias was not predictable a priori. The models were successful in including and accounting for different correlation structures, both among omic signals and among different pollutant exposures. In general, more associations were identified when the correlation among omics and among pollutants were modeled, and their number increased when a measurement error term was additionally included in the multivariate models (particularly for the associations between metabolomics and NO2).

Relationships between airborne pollutants, serum albumin adducts and short-term health outcomes in an experimental crossover study.

Exposure to air pollution can have both short-term and long-term effects on health. However, the relationships between specific pollutants and their effects can be obscured by characteristics of both the pollution and the exposed population. One way of elucidating the relationships is to link exposures and internal changes at the level of the individual. To this end, we combined personal exposure monitoring (59 individuals, Oxford Street II crossover study) with mass-spectrometry-based analyses of putative serum albumin adducts (fixed-step selected reaction monitoring). We attempted to infer adducts' identities using data from another, higher-resolution mass spectrometry method, and were able to detect a semi-synthetic standard with both methods. A generalised least squares regression method was used to test for associations between amounts of adducts and pollution measures (ambient concentrations of nitrogen dioxide and particulate matter), and between amounts of adducts and short-term health outcomes (measures of lung health and arterial stiffness). Amounts of some putative adducts (e.g., one with a positive mass shift of ∼143 Da) were associated with exposure to pollution (11 associations), and amounts of other adducts were associated with health outcomes (eight associations). Adducts did not appear to provide a link between exposures and short-term health outcomes.

Impact of short-term traffic-related air pollution on the metabolome - Results from two metabolome-wide experimental studies.

Exposure to traffic-related air pollution (TRAP) has been associated with adverse health outcomes but underlying biological mechanisms remain poorly understood. Two randomized crossover trials were used here, the Oxford Street II (London) and the TAPAS II (Barcelona) studies, where volunteers were allocated to high or low air pollution exposures. The two locations represent different exposure scenarios, with Oxford Street characterized by diesel vehicles and Barcelona by normal mixed urban traffic. Levels of five and four pollutants were measured, respectively, using personal exposure monitoring devices. Serum samples were used for metabolomic profiling. The association between TRAP and levels of each metabolic feature was assessed. All pollutant levels were significantly higher at the high pollution sites. 29 and 77 metabolic features were associated with at least one pollutant in the Oxford Street II and TAPAS II studies, respectively, which related to 17 and 30 metabolic compounds. Little overlap was observed across pollutants for metabolic features, suggesting that different pollutants may affect levels of different metabolic features. After observing the annotated compounds, the main pathway suggested in Oxford Street II in association with NO2 was the acyl-carnitine pathway, previously found to be associated with cardio-respiratory disease. No overlap was found between the metabolic features identified in the two studies.

Heritability, selection, and the response to selection in the presence of phenotypic measurement error: Effects, cures, and the role of repeated measurements.

Quantitative genetic analyses require extensive measurements of phenotypic traits, a task that is often not trivial, especially in wild populations. On top of instrumental measurement error, some traits may undergo transient (i.e., nonpersistent) fluctuations that are biologically irrelevant for selection processes. These two sources of variability, which we denote here as measurement error in a broad sense, are possible causes for bias in the estimation of quantitative genetic parameters. We illustrate how in a continuous trait transient effects with a classical measurement error structure may bias estimates of heritability, selection gradients, and the predicted response to selection. We propose strategies to obtain unbiased estimates with the help of repeated measurements taken at an appropriate temporal scale. However, the fact that in quantitative genetic analyses repeated measurements are also used to isolate permanent environmental instead of transient effects requires that the information content of repeated measurements is carefully assessed. To this end, we propose to distinguish "short-term" from "long-term" repeats, where the former capture transient variability and the latter help isolate permanent effects. We show how the inclusion of the corresponding variance components in quantitative genetic models yields unbiased estimates of all quantities of interest, and we illustrate the application of the method to data from a Swiss snow vole population.

Social adversity and epigenetic aging: a multi-cohort study on socioeconomic differences in peripheral blood DNA methylation.

Low socioeconomic status (SES) is associated with earlier onset of age-related chronic conditions and reduced life-expectancy, but the underlying biomolecular mechanisms remain unclear. Evidence of DNA-methylation differences by SES suggests a possible association of SES with epigenetic age acceleration (AA). We investigated the association of SES with AA in more than 5,000 individuals belonging to three independent prospective cohorts from Italy, Australia, and Ireland. Low SES was associated with greater AA (ß = 0.99 years; 95% CI 0.39,1.59; p = 0.002; comparing extreme categories). The results were consistent across different SES indicators. The associations were only partially modulated by the unhealthy lifestyle habits of individuals with lower SES. Individuals who experienced life-course SES improvement had intermediate AA compared to extreme SES categories, suggesting reversibility of the effect and supporting the relative importance of the early childhood social environment. Socioeconomic adversity is associated with accelerated epigenetic aging, implicating biomolecular mechanisms that may link SES to age-related diseases and longevity.

Tea and coffee consumption in relation to DNA methylation in four European cohorts.

Lifestyle factors, such as food choices and exposure to chemicals, can alter DNA methylation and lead to changes in gene activity. Two such exposures with pharmacologically active components are coffee and tea consumption. Both coffee and tea have been suggested to play an important role in modulating disease-risk in humans by suppressing tumour progression, decreasing inflammation and influencing estrogen metabolism. These mechanisms may be mediated by changes in DNA methylation. To investigate if DNA methylation in blood is associated with coffee and tea consumption, we performed a genome-wide DNA methylation study for coffee and tea consumption in four European cohorts (N = 3,096). DNA methylation was measured from whole blood at 421,695 CpG sites distributed throughout the genome and analysed in men and women both separately and together in each cohort. Meta-analyses of the results and additional regional-level analyses were performed. After adjusting for multiple testing, the meta-analysis revealed that two individual CpG-sites, mapping to DNAJC16 and TTC17, were differentially methylated in relation to tea consumption in women. No individual sites were associated with men or with the sex-combined analysis for tea or coffee. The regional analysis revealed that 28 regions were differentially methylated in relation to tea consumption in women. These regions contained genes known to interact with estradiol metabolism and cancer. No significant regions were found in the sex-combined and male-only analysis for either tea or coffee consumption.

DNA methylation changes measured in pre-diagnostic peripheral blood samples are associated with smoking and lung cancer risk.

DNA methylation changes are associated with cigarette smoking. We used the Illumina Infinium HumanMethylation450 array to determine whether methylation in DNA from pre-diagnostic, peripheral blood samples is associated with lung cancer risk. We used a case-control study nested within the EPIC-Italy cohort and a study within the MCCS cohort as discovery sets (a total of 552 case-control pairs). We validated the top signals in 429 case-control pairs from another 3 studies. We identified six CpGs for which hypomethylation was associated with lung cancer risk: cg05575921 in the AHRR gene (p-valuepooled = 4 × 10-17 ), cg03636183 in the F2RL3 gene (p-valuepooled = 2 × 10 - 13 ), cg21566642 and cg05951221 in 2q37.1 (p-valuepooled = 7 × 10-16 and 1 × 10-11 respectively), cg06126421 in 6p21.33 (p-valuepooled = 2 × 10-15 ) and cg23387569 in 12q14.1 (p-valuepooled = 5 × 10-7 ). For cg05951221 and cg23387569 the strength of association was virtually identical in never and current smokers. For all these CpGs except for cg23387569, the methylation levels were different across smoking categories in controls (p-valuesheterogeneity ≤ 1.8 x10 - 7 ), were lowest for current smokers and increased with time since quitting for former smokers. We observed a gain in discrimination between cases and controls measured by the area under the ROC curve of at least 8% (p-values ≥ 0.003) in former smokers by adding methylation at the 6 CpGs into risk prediction models including smoking status and number of pack-years. Our findings provide convincing evidence that smoking and possibly other factors lead to DNA methylation changes measurable in peripheral blood that may improve prediction of lung cancer risk.

Hypomethylation of smoking-related genes is associated with future lung cancer in four prospective cohorts.

DNA hypomethylation in certain genes is associated with tobacco exposure but it is unknown whether these methylation changes translate into increased lung cancer risk. In an epigenome-wide study of DNA from pre-diagnostic blood samples from 132 case-control pairs in the NOWAC cohort, we observe that the most significant associations with lung cancer risk are for cg05575921 in AHRR (OR for 1 s.d.=0.37, 95% CI: 0.31-0.54, P-value=3.3 × 10(-11)) and cg03636183 in F2RL3 (OR for 1 s.d.=0.40, 95% CI: 0.31-0.56, P-value=3.9 × 10(-10)), previously shown to be strongly hypomethylated in smokers. These associations remain significant after adjustment for smoking and are confirmed in additional 664 case-control pairs tightly matched for smoking from the MCCS, NSHDS and EPIC HD cohorts. The replication and mediation analyses suggest that residual confounding is unlikely to explain the observed associations and that hypomethylation of these CpG sites may mediate the effect of tobacco on lung cancer risk.