Implementing Historical Controls in Oncology Trials.

Drug development in oncology has broadened from mainly considering randomized clinical trials to also including single-arm trials tailored for very specific subtypes of cancer. They often use historical controls, and this article discusses benefits and risks of this paradigm and provide various regulatory and statistical considerations. While leveraging the information brought by historical controls could potentially shorten development time and reduce the number of patients enrolled, a careful selection of the past studies, a prespecified statistical analysis accounting for the heterogeneity between studies, and early engagement with regulators will be key to success. Although both the European Medicines Agency and the U.S. Food and Drug Administration have already approved medicines based on nonrandomized experiments, the evidentiary package can be perceived as less comprehensive than randomized experiments. Use of historical controls, therefore, is better suited for cases of high unmet clinical need, where the disease course is well characterized and the primary endpoint is objective. IMPLICATIONS FOR PRACTICE: Incorporating historical data in single-arm oncology trials has the potential to accelerate drug development and to reduce the number of patients enrolled, compared with standard randomized controlled clinical trials. Given the lack of blinding and randomization, such an approach is better suited for cases of high unmet clinical need and/or difficult experimental situations, in which the trajectory of the disease is well characterized and the endpoint can be measured objectively. Careful pre-specification and selection of the historical data, matching of the patient characteristics with the concurrent trial data, and innovative statistical methodologies accounting for between-study variation will be needed. Early engagement with regulators (e.g., via Scientific Advice) is highly recommended.

Prediction of treatment effect perception in cosmetics using machine learning.

Perception of treatment effect (TE) in cosmetics is multifaceted and influenced by multiple parameters that need to be considered simultaneously when evaluating TE. Here we provide a global approach to predicting TE perception using Random Forest (RF) classifier. Data from three randomized double-blind clinical studies with a total of 50 subjects were used. Different products were applied to each facial cheek of subjects at each visit, and post-application photographs were taken. Nine primary endpoints relating to skin pores were assessed by a specific image analysis algorithm. Twenty judges evaluated the relative pore visibility in all possible pairs of cheek photographs. RF was used to construct a prediction model for TE perception based on the primary endpoints and judge's evaluation. Intra-study product ranking was done using the Bradley-Terry model on mean judges' predicted preference. RF demonstrated overall good accuracy in predicting TE perception. Applying RF technique not only addresses issues of multiplicity, nonlinearity and interactions between multiple criteria but also focuses decision-making on one discrete parameter thereby simplifying interpretability and allowing for more consumer-centered claim substantiation in clinical trials.Abbreviations: RF: Random Forest classifier; FDA: The US Food and Drug Agency; ID: Identifier; MCID: Minimal clinical important difference; Param: Parameter; PGIC: Patients' Global Impression of Change; TE: Treatment effect; TRT: Treatment.

Relationship of oxidative stress to visceral adiposity in youth and role played by vitamin D.

BACKGROUND: Visceral adipose tissue (VAT) accumulation is a major cardiometabolic risk factor, associated with increased inflammation. Oxidative stress (OS) is also associated with inflammation and cardiometabolic issues, yet mainly through general obesity. Both OS and obesity were linked to vitamin D deficiency. OBJECTIVES: To investigate whether OS increase is associated with VAT accumulation in youth, and whether in the presence of VAT accumulation, a higher vitamin D status is associated with lower OS. METHODS: One hundred and fifty-eight youth with overweight/obesity, 7 to 17 years old, were recruited (Pediatric Clinic, Luxembourg). We assessed visceral and subcutaneous abdominal adipose tissues by magnetic resonance imaging, OS by DNA/RNA oxidative damage with ELISA and vitamin D by high-performance liquid chromatography. RESULTS: VAT was the body fat compartment the most strongly associated with OS (RPearson : 0.298; P < 10-4 ). The general linear (GLM) models assessing the relationship between OS, VAT and vitamin D concentrations showed that "Log10 OS = (0.003 × VAT) + 3.911 (R2adjusted : 0.083; P-value < 10-4 )"; "Log10 OS = (0.003 × VAT) - (0.156 × log10 vitamin D) + 4.110 (R2adjusted : 0.101; P-value < 10-4 )". After back-transformation of the log-values into normal values, the GLM showed that, for a person with an average value of VAT (40.7 cm2 ), a 10 cm2 increase in VAT would increase OS by approx. 771.833 pg/mL, after age, gender, Tanner stage and physical activity adjustment. An approximate increase of 9 ng/mL of vitamin D would counterbalance this negative effect of increased VAT. CONCLUSION: Dietary strategies improving vitamin D status should be investigated to tackle VAT and OS increase.

Proinflammatory T Cell Status Associated with Early Life Adversity.

Early life adversity (ELA) has been associated with an increased risk for diseases in which the immune system plays a critical role. The ELA immune phenotype is characterized by inflammation, impaired cellular immunity, and immunosenescence. However, data on cell-specific immune effects are largely absent. Additionally, stress systems and health behaviors are altered in ELA, which may contribute to the generation of the ELA immune phenotype. The present investigation tested cell-specific immune differences in relationship to the ELA immune phenotype, altered stress parameters, and health behaviors in individuals with ELA (n = 42) and those without a history of ELA (control, n = 73). Relative number and activation status (CD25, CD69, HLA-DR, CD11a, CD11b) of monocytes, NK cells, B cells, T cells, and their main subsets were assessed by flow cytometry. ELA was associated with significantly reduced numbers of CD69+CD8+ T cells (p = 0.022), increased numbers of HLA-DR+ CD4 and HLA-DR+ CD8 T cells (p < 0.001), as well as increased numbers of CD25+CD8+ T cells (p = 0.036). ELA also showed a trend toward higher numbers of CCR4+CXCR3-CCR6+ CD4 T cells. Taken together, our data suggest an elevated state of immune activation in ELA, in which particularly T cells are affected. Although several aspects of the ELA immune phenotype were related to increased activation markers, neither stress nor health-risk behaviors explained the observed group differences. Thus, the state of immune activation in ELA does not seem to be secondary to alterations in the stress system or health-risk behaviors, but rather a primary effect of early life programming on immune cells.

Random main effects of treatment: A case study with a network meta-analysis.

If the number of treatments in a network meta-analysis is large, it may be possible and useful to model the main effect of treatment as random, that is to say as random realizations from a normal distribution of possible treatment effects. This then constitutes a third sort of random effect that may be considered in connection with such analyses. The first and most common models treatment-by-trial interaction as being random and the second, rather rarer, models the main effects of trial as being random and thus permits the recovery of intertrial information. Taking the example of a network meta-analysis of 44 similar treatments in 10 trials, we illustrate how a hierarchical approach to modeling a random main effect of treatment can be used to produce shrunk (toward the overall mean) estimates of effects for individual treatments. As a related problem, we also consider the issue of using a random-effect model for the within-trial variances from trial to trial. We provide a number of possible graphical representations of the results and discuss the advantages and disadvantages of such an approach.

Comparative analysis of the association between 35 frailty scores and cardiovascular events, cancer, and total mortality in an elderly general population in England: An observational study.

BACKGROUND: Frail elderly people experience elevated mortality. However, no consensus exists on the definition of frailty, and many frailty scores have been developed. The main aim of this study was to compare the association between 35 frailty scores and incident cardiovascular disease (CVD), incident cancer, and all-cause mortality. Also, we aimed to assess whether frailty scores added predictive value to basic and adjusted models for these outcomes. METHODS AND FINDINGS: Through a structured literature search, we identified 35 frailty scores that could be calculated at wave 2 of the English Longitudinal Study of Ageing (ELSA), an observational cohort study. We analysed data from 5,294 participants, 44.9% men, aged 60 years and over. We studied the association between each of the scores and the incidence of CVD, cancer, and all-cause mortality during a 7-year follow-up using Cox proportional hazard models at progressive levels of adjustment. We also examined the added predictive performance of each score on top of basic models using Harrell's C statistic. Using age of the participant as a timescale, in sex-adjusted models, hazard ratios (HRs) (95% confidence intervals) for all-cause mortality ranged from 2.4 (95% CI: 1.7-3.3) to 26.2 (95% CI: 15.4-44.5). In further adjusted models including smoking status and alcohol consumption, HR ranged from 2.3 (95% CI: 1.6-3.1) to 20.2 (95% CI: 11.8-34.5). In fully adjusted models including lifestyle and comorbidity, HR ranged from 0.9 (95% CI: 0.5-1.7) to 8.4 (95% CI: 4.9-14.4). HRs for CVD and cancer incidence in sex-adjusted models ranged from 1.2 (95% CI: 0.5-3.2) to 16.5 (95% CI: 7.8-35.0) and from 0.7 (95% CI: 0.4-1.2) to 2.4 (95% CI: 1.0-5.7), respectively. In sex- and age-adjusted models, all frailty scores showed significant added predictive performance for all-cause mortality, increasing the C statistic by up to 3%. None of the scores significantly improved basic prediction models for CVD or cancer. A source of bias could be the differences in mortality follow-up time compared to CVD/cancer, because the existence of informative censoring cannot be excluded. CONCLUSION: There is high variability in the strength of the association between frailty scores and 7-year all-cause mortality, incident CVD, and cancer. With regard to all-cause mortality, some scores give a modest improvement to the predictive ability. Our results show that certain scores clearly outperform others with regard to three important health outcomes in later life. Finally, we think that despite their limitations, the use of frailty scores to identify the elderly population at risk is still a useful measure, and the choice of a frailty score should balance feasibility with performance.

Identification of beta-arrestin-1 as a diagnostic biomarker in lung cancer.

BACKGROUND: Distinguishing lung adenocarcinoma (ADC) from squamous cell carcinoma (SCC) has a tremendous therapeutic implication. Sometimes, the commonly used immunohistochemistry (IHC) markers fail to discriminate between them, urging for the identification of new diagnostic biomarkers. METHODS: We performed IHC on tissue microarrays from two cohorts of lung cancer patients to analyse the expression of beta-arrestin-1, beta-arrestin-2 and clinically used diagnostic markers in ADC and SCC samples. Logistic regression models were applied for tumour subtype prediction. Parallel reaction monitoring (PRM)-based mass spectrometry was used to quantify beta-arrestin-1 in plasma from cancer patients and healthy donors. RESULTS: Beta-arrestin-1 expression was significantly higher in ADC versus SCC samples. Beta-arrestin-1 displayed high sensitivity, specificity and negative predictive value. Its usefulness in an IHC panel was also shown. Plasma beta-arrestin-1 levels were considerably higher in lung cancer patients than in healthy donors and were higher in patients who later experienced a progressive disease than in patients showing complete/partial response following EGFR inhibitor therapy. CONCLUSIONS: Our data identify beta-arrestin-1 as a diagnostic marker to differentiate ADC from SCC and indicate its potential as a plasma biomarker for non-invasive diagnosis of lung cancer. Its utility to predict response to EGFR inhibitors is yet to be confirmed.

Agreement Between 35 Published Frailty Scores in the General Population.

In elderly populations, frailty is associated with higher mortality risk. Although many frailty scores (FS) have been proposed, no single score is considered the gold standard. We aimed to evaluate the agreement between a wide range of FS in the English Longitudinal Study of Ageing (ELSA). Through a literature search, we identified 35 FS that could be calculated in ELSA wave 2 (2004-2005). We examined agreement between each frailty score and the mean of 35 FS, using a modified Bland-Altman model and Cohen's kappa (κ). Missing data were imputed. Data from 5,377 participants (ages ≥60 years) were analyzed (44.7% men, 55.3% women). FS showed widely differing degrees of agreement with the mean of all scores and between each pair of scores. Frailty classification also showed a very wide range of agreement (Cohen's κ = 0.10-0.83). Agreement was highest among "accumulation of deficits"-type FS, while accuracy was highest for multidimensional FS. There is marked heterogeneity in the degree to which various FS estimate frailty and in the identification of particular individuals as frail. Different FS are based on different concepts of frailty, and most pairs cannot be assumed to be interchangeable. Research results based on different FS cannot be compared or pooled.

Stability-based validation of dietary patterns obtained by cluster analysis.

BACKGROUND: Cluster analysis is a data-driven method used to create clusters of individuals sharing similar dietary habits. However, this method requires specific choices from the user which have an influence on the results. Therefore, there is a need of an objective methodology helping researchers in their decisions during cluster analysis. The objective of this study was to use such a methodology based on stability of clustering solutions to select the most appropriate clustering method and number of clusters for describing dietary patterns in the NESCAV study (Nutrition, Environment and Cardiovascular Health), a large population-based cross-sectional study in the Greater Region (N = 2298). METHODS: Clustering solutions were obtained with K-means, K-medians and Ward's method and a number of clusters varying from 2 to 6. Their stability was assessed with three indices: adjusted Rand index, Cramer's V and misclassification rate. RESULTS: The most stable solution was obtained with K-means method and a number of clusters equal to 3. The "Convenient" cluster characterized by the consumption of convenient foods was the most prevalent with 46% of the population having this dietary behaviour. In addition, a "Prudent" and a "Non-Prudent" patterns associated respectively with healthy and non-healthy dietary habits were adopted by 25% and 29% of the population. The "Convenient" and "Non-Prudent" clusters were associated with higher cardiovascular risk whereas the "Prudent" pattern was associated with a decreased cardiovascular risk. Associations with others factors showed that the choice of a specific dietary pattern is part of a wider lifestyle profile. CONCLUSION: This study is of interest for both researchers and public health professionals. From a methodological standpoint, we showed that using stability of clustering solutions could help researchers in their choices. From a public health perspective, this study showed the need of targeted health promotion campaigns describing the benefits of healthy dietary patterns.

[Users of regional dementia care networks in Germany : First results of the evaluation study DemNet-D]. / Nutzer und Nutzerinnen regionaler Demenznetzwerke in Deutschland : Erste Ergebnisse der Evaluationsstudie DemNet-D.

BACKGROUND: In Germany a growing number of community-based support services for people with dementia (PwD) and their caregivers are organized in dementia care networks (DCN), which provide a single point of entry to social facilities and offer personal care and support. OBJECTIVE: The aim of this study was to describe the health, functional and social characteristics of PwDs enrolled in DCNs throughout Germany because no data are currently available on this aspect. MATERIAL AND METHODS: As part of the multi-center, multi-professional 12-month follow-up study DemNet-D, data on functional and psychological health, sociodemographic and dementia-specific factors and social inclusion were collected in standardized interviews with PwDs living at home. RESULTS: A total of 560 PwDs with an average age of 80 years were enrolled in the study. Of the participants approximately 50 % had Alzheimer's dementia and more than 75 % demonstrated at least a challenging form of behavior. More than half of the participants lived together with a partner or relative. Instrumental activities of daily living (IADLs) were very limited; nevertheless, one in five PwDs showed no long-term care-dependency level. The participants reported having a relatively low feeling of loneliness and a high feeling of social inclusion, depending on the severity of dementia. CONCLUSION: This is one of the very first studies generating data on PwDs who receive domiciliary care within DCNs in Germany. The results suggest that the regional DCNs make a successful contribution to overcoming the interface problem and can, therefore, contribute to a more stable care situation and better social integration of PwDs.

Analysing breast cancer survivors' acceptance profiles for using an electronic pillbox connected to a smartphone application using Seintinelles, a French community-based research tool.

Introduction: Up to 50% of breast cancer (BC) survivors discontinue their adjuvant endocrine therapy (AET) before the recommended 5 years, raising the issue of medication non-adherence. eHealth technologies have the potential to support patients to enhance their medication adherence and may offer an effective way to complement the healthcare. In order for eHealth technologies to be successfully implemented into the healthcare system, end-users need to be willing and accepting to use these eHealth technologies. Aim: This study aims to evaluate the current usability of eHealth technologiesin and to identify differences in BC SURVIVORS BC survivors accepting a medication adherence enhancing eHealth technology to support their AET to BC survivors that do not accept such a medication adherence enhancing eHealth technology. Methods: This study was conducted in 2020 including volunteering BC survivors belonging to the Seintinelles Association. Eligible participants were women, diagnosed with BC within the last 10 years, and been exposed to, an AET. Univariable and multivariable logistic regression analyses were performed to investigate medication adherence enhancing eHealth technology acceptance profiles among BC survivors. The dependent variable was defined as acceptance of an electronic pillbox connected to a smartphone application (hereafter: medication adherence enhancing eHealth technology). Results: Overall, 23% of the participants already use a connected device or health application on a regular basis. The mean age of the participants was 52.7 (SD 10.4) years. In total, 67% of 1268 BC survivors who participated in the survey declared that they would accept a medication adherence enhancing eHealth technology to improve their AET. BC survivors accepting a medication adherence enhancing eHealth technology for their AET, are younger (OR = 0.97, 95% CI [0.95; 0.98]), do take medication for other diseases (OR = 0.31, 95% CI [0.13; 0.68]), already use a medication adherence enhancing eHealth technology or technique (OR = 1.74, 95% CI [1.06; 2.94]) and are willing to possess or currently possess one or more connected devices or health applications (OR = 2.89, 95% CI [2.01; 4.19]). Conclusion: Understanding acceptance profiles of BC survivors is fundamental for conceiving an effective eHealth technology enhancing AET among BC survivors. Hence, such profiling will foster the development of personalized medication adherence enhancing eHealth technology.

Intermittent Scanning Glucose Monitoring or Predicted Low Suspend Pump Treatment: Does It Impact Time in Glucose Target and Treatment Preference? The QUEST Randomized Crossover Study.

Objective: To compare glycemic control and treatment preference in children with type 1 diabetes (T1D) using sensor augmented pump (SAP) with predictive low glucose suspend (SmartGuard®) or pump with independent intermittent scanning continuous glucose monitoring (iscCGM, Freestyle libre ®). Methods: In this open label, cross-over study, children 6 to 14 years of age, treated with insulin pump for at least 6 months, were randomized to insulin pump and iscCGM (A) or SAP with SmartGuard® (B) for 5 weeks followed by 5 additional weeks. The difference in percentages of time in glucose target (TIT), (3.9 - 8.0 mmol/l), <3 mmol/l, > 8 and 10 mmol/l, were analyzed using linear mixed models during the final week of each arm and were measured by blinded CGM (IPro2®). Results: 31 children (15 girls) finished the study. With sensor compliance > 60%, no difference in TIT was found, TIT: A 37.86%; 95% CI [33.21; 42.51]; B 37.20%; 95% CI [32.59; 41.82]; < 3 mmol/l A 2.27% 95% CI [0.71; 3.84] B 1.42% 95% CI [-0.13; 2.97]; > 8 mmol/l A 0.60% 95% CI [0.56, 0.67]; B 0.63% [0.56; 0.70]. One year after the study all participants were on CGM compared to 80.7% prior to the study, with a shift of 13/25 participants from iscCGM to SAP. Conclusions: In this study, no significant difference in glycemic control was found whether treated with SAP (SmartGuard®) or pump with iscCGM. The decision of all families to continue with CGM after the study suggests a positive impact, with preference for SmartGuard®. Clinical Trial Registration: [clinicaltrials.gov], identifier NCT03103867.

Systematic review and network meta-analysis of the efficacy of existing treatments for patients with recurrent glioblastoma.

BACKGROUND: Despite advances in the treatment of cancers over the last years, treatment options for patients with recurrent glioblastoma (rGBM) remain limited with poor outcomes. Many regimens have been investigated in clinical trials; however, there is a lack of knowledge on comparative effectiveness. The aim of this systematic review is to provide an overview of existing treatment strategies and to estimate the relative efficacy of these regimens in terms of progression-free survival (PFS) and overall survival (OS). METHODS: We conducted a systematic review to identify randomized controlled trials (RCTs) investigating any treatment regimen in adult patients suffering from rGBM. Connected studies reporting at least one of our primary outcomes were included in a Bayesian network meta-analysis (NMA) estimating relative treatment effects. RESULTS: Forty RCTs fulfilled our inclusion criteria evaluating the efficacy of 38 drugs as mono- or combination therapy. Median OS ranged from 2.9 to 18.3 months; median PFS ranged from 0.7 to 6 months. We performed an NMA including 24 treatments that were connected within a large evidence network. Our NMA indicated improvement in PFS with most bevacizumab (BV)-based regimens compared to other regimens. We did not find any differences in OS between treatments. CONCLUSION: This systematic review provides a comprehensive overview of existing treatment options for rGBM. The NMA provides relative effects for many of these treatment regimens, which have not been directly compared in RCTs. Overall, outcomes for patients with rGBM remain poor across all treatment options, highlighting the need for innovative treatment options.

Multi-Scale Multivariate Models for Small Area Health Survey Data: A Chilean Example.

BACKGROUND: We propose a general approach to the analysis of multivariate health outcome data where geo-coding at different spatial scales is available. We propose multiscale joint models which address the links between individual outcomes and also allow for correlation between areas. The models are highly novel in that they exploit survey data to provide multiscale estimates of the prevalences in small areas for a range of disease outcomes. Results The models incorporate both disease specific, and common disease spatially structured components. The multiple scales envisaged is where individual survey data is used to model regional prevalences or risks at an aggregate scale. This approach involves the use of survey weights as predictors within our Bayesian multivariate models. Missingness has to be addressed within these models and we use predictive inference which exploits the correlation between diseases to provide estimates of missing prevalances. The Case study we examine is from the National Health Survey of Chile where geocoding to Province level is available. In that survey, diabetes, Hypertension, obesity and elevated low-density cholesterol (LDL) are available but differential missingness requires that aggregation of estimates and also the assumption of smoothed sampling weights at the aggregate level. Conclusions: The methodology proposed is highly novel and flexibly handles multiple disease outcomes at individual and aggregated levels (i.e., multiscale joint models). The missingness mechanism adopted provides realistic estimates for inclusion in the aggregate model at Provincia level. The spatial structure of four diseases within Provincias has marked spatial differentiation, with diabetes and hypertension strongly clustered in central Provincias and obesity and LDL more clustered in the southern areas.

Clustered allocation as a way of understanding historical controls: Components of variation and regulatory considerations.

There has been increasing interest in recent years in the possibility of increasing the efficiency of clinical trials by using historical controls. There has been a general recognition that in replacing concurrent by historical controls, the potential for bias is serious and requires some down-weighting to the apparent amount of historical information available. However, such approaches have generally assumed that what is required is some modification to the standard inferential model offered by the parallel group trial. In our opinion, the correct starting point that requires modification is a trial in which treatments are allocated to clusters. This immediately shows that the amount of information available is governed not just by the number of historical patients but also by the number of centres and of historical studies. Furthermore, once one accepts that external patients may be used as controls, this raises the issue as to which patients should be used. Thus, abandoning concurrent control has implications for many aspects of design and analysis of trials, including (a) identification, pre-specification and agreement on a suitable historical dataset; (b) an agreed, enforceable and checkable plan for recruiting the experimental arm; (c) a finalised analysis plan prior to beginning the trial and (d) use of a hierarchical model with sufficient complexity. We discuss these issues and suggest approaches to design and analysis making extensive reference to the partially randomised Therapeutic Arthritis Research and Gastrointestinal Event Trial study. We also compare some Bayesian and frequentist approaches and provide some important regulatory considerations. We conclude that effective use of historical data will require considerable circumspection and discipline.

Identifying hotspots of cardiometabolic outcomes based on a Bayesian approach: The example of Chile.

BACKGROUND: There is a need to identify priority zones for cardiometabolic prevention. Disease mapping in countries with high heterogeneity in the geographic distribution of the population is challenging. Our goal was to map the cardiometabolic health and identify hotspots of disease using data from a national health survey. METHODS: Using Chile as a case study, we applied a Bayesian hierarchical modelling. We performed a cross-sectional analysis of the 2009-2010 Chilean Health Survey. Outcomes were diabetes (all types), obesity, hypertension, and high LDL cholesterol. To estimate prevalence, we used individual and aggregated data by province. We identified hotspots defined as prevalence in provinces significantly greater than the national prevalence. Models were adjusted for age, sex, their interaction, and sampling weight. We imputed missing data. We applied a joint outcome modelling approach to capture the association between the four outcomes. RESULTS: We analysed data from 4,780 participants (mean age (SD) 46 (19) years; 60% women). The national prevalence (percentage (95% credible intervals) for diabetes, obesity, hypertension and high LDL cholesterol were 10.9 (4.5, 19.2), 30.0 (17.7, 45.3), 36.4 (16.4, 57.6), and 13.7 (3.4, 32.2) respectively. Prevalence of diabetes was lower in the far south. Prevalence of obesity and hypertension increased from north to far south. Prevalence of high LDL cholesterol was higher in the north and south. A hotspot for diabetes was located in the centre. Hotspots for obesity were mainly situated in the south and far south, for hypertension in the centre, south and far south and for high LDL cholesterol in the far south. CONCLUSIONS: The distribution of cardiometabolic risk factors in Chile has a characteristic pattern with a general trend to a north-south gradient. Our approach is reproducible and demonstrates that the Bayesian approach enables the accurate identification of hotspots and mapping of disease, allowing the identification of areas for cardiometabolic prevention.

Identification of a Blood-Based Protein Biomarker Panel for Lung Cancer Detection.

Lung cancer is the deadliest cancer worldwide, mainly due to its advanced stage at the time of diagnosis. A non-invasive method for its early detection remains mandatory to improve patients' survival. Plasma levels of 351 proteins were quantified by Liquid Chromatography-Parallel Reaction Monitoring (LC-PRM)-based mass spectrometry in 128 lung cancer patients and 93 healthy donors. Bootstrap sampling and least absolute shrinkage and selection operator (LASSO) penalization were used to find the best protein combination for outcome prediction. The PanelomiX platform was used to select the optimal biomarker thresholds. The panel was validated in 48 patients and 49 healthy volunteers. A 6-protein panel clearly distinguished lung cancer from healthy individuals. The panel displayed excellent performance: area under the receiver operating characteristic curve (AUC) = 0.999, positive predictive value (PPV) = 0.992, negative predictive value (NPV) = 0.989, specificity = 0.989 and sensitivity = 0.992. The panel detected lung cancer independently of the disease stage. The 6-protein panel and other sub-combinations displayed excellent results in the validation dataset. In conclusion, we identified a blood-based 6-protein panel as a diagnostic tool in lung cancer. Used as a routine test for high- and average-risk individuals, it may complement currently adopted techniques in lung cancer screening.

Prospective Association Among Diabetes Diagnosis, HbA1c, Glycemia, and Frailty Trajectories in an Elderly Population.

OBJECTIVE: Frailty is a dynamic state of vulnerability in the elderly. We examined whether individuals with overt diabetes or higher levels of HbA1c or fasting plasma glucose (FG) experience different frailty trajectories with aging. RESEARCH DESIGN AND METHODS: Diabetes, HbA1c, and FG were assessed at baseline, and frailty status was evaluated with a 36-item frailty index every 2 years during a 10-year follow-up among participants from the English Longitudinal Study of Ageing (ELSA). Mixed-effects models with age as time scale were used to assess whether age trajectories of frailty differed as a function of diabetes, HbA1c, and FG. RESULTS: Among 5,377 participants (median age [interquartile range] 70 [65, 77] years, 45% men), 35% were frail at baseline. In a model adjusted for sex, participants with baseline diabetes had an increased frailty index over aging compared with those without diabetes. Similar findings were observed with higher levels of HbA1c, while FG was not associated with frailty. In a model additionally adjusted for income, social class, smoking, alcohol, and hemoglobin, only diabetes was associated with an increased frailty index. Among nonfrail participants at baseline, both diabetes and HbA1c level were associated with a higher increased frailty index over time. CONCLUSIONS: People with diabetes or higher HbA1c levels at baseline had a higher frailty level throughout later life. Nonfrail participants with diabetes or higher HbA1c also experienced more rapid deterioration of frailty level with aging. This observation could reflect a role of diabetes complications in frailty trajectories or earlier shared determinants that contribute to diabetes and frailty risk in later life.

"To survey or to register" is that the question for estimating population incidence of injuries?

BACKGROUND: Measuring the true incidence of injury or medically attended injury is challenging. Population surveys, despite problems with recall and selection bias, remain the only source of information for injury incidence calculation in many countries. Emergency department (ED) registry based data provide an alternative source.The aim of this study is to compare the yearly incidence of hospital treated Home and Leisure Injuries (HLI), and Road Traffic Injuries (RTI) estimated by survey-based and register-based methods and combine information from both sources in to a comprehensive injury burden pyramide. METHODS: Data from Luxemburg's European Health Examination Survey (EHES-LUX), European Health Interview Survey (EHIS) and ED surveillance system Injury Data Base (IDB) collected in 2013, were used. EHES-LUX data on 1529 residents 25-64 years old, were collected between February 2013-January 2015. EHIS data on 4004 other residents aged 15+ years old, were collected between February and December 2014. Participants reported last year's injuries at home, leisure and traffic and treatment received. Two-sided exact binomial tests were used to compare incidences from registry with the incidences of each survey by age group and prevention domain. Data from surveys and register were combined to build an RTI and HLI burden pyramide for the 25-64 years old. This project was part of the European Union project BRIDGE-Health (BRidging Information and Data Generation for Evidence-based Health Policy and Research). RESULTS: Among 25-64 years old the incidence of hospital treated injuries per thousand population was 60.1 (95% CI: 59.2-60.9) according to IDB, 62.1 (95% CI: 50.6-75.4) according to EHES-LUX and 53.2 (95% CI: 45.0-62.4) according to EHIS. The incidence of hospital admissions was 3.7 (95% CI: 3.5-4.0) per thousand population from IDB-Luxembourg, 12.4 (95% CI: 7.5-19.3) from EHES-LUX and 18.0 (95% CI: 13.3-23.8) from EHIS. For 15+ years-old incidence of hospital treated HLI was 62.8 (95% CI: 62.1-63.5) per thousand population according to IDB whereas the corresponding EHIS estimate was lower at 46.9 (95% CI: 40.4-54.0). About half of HLI and RTI of the 25-64 years old were treated in hospital. CONCLUSION: The overall incidence estimate of hospital treated injuries from both methods does not differ for the 25-64 years old. Surveys overestimate the number of hospital admissions, probably due to memory bias. For people aged 15+ years, the survey estimate is lower than the register estimate for hospital treated HLI injuries, probably due to selection and recall biases. ED based registry data is to be preferred as single source for estimating the incidence of hospital treated injuries in all age groups.