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Pooled analyses integrate data from multiple studies and achieve a larger sample size for enhanced statistical power. When heterogeneity exists in variables' effects on the outcome across studies, the simple pooling strategy fails to present a fair and complete picture of the effects of heterogeneous variables. Thus, it is important to investigate the homogeneous and heterogeneous structure of variables in pooled studies. In this article, we consider the pooled cohort studies with time-to-event outcomes and propose a penalized Cox partial likelihood approach with adaptively weighted composite penalties on variables' homogeneous and heterogeneous effects. We show that our method can characterize the variables as having heterogeneous, homogeneous, or null effects, and estimate non-zero effects. The results are readily extended to high-dimensional applications where the number of parameters is larger than the sample size. The proposed selection and estimation procedure can be implemented using the iterative shooting algorithm. We conduct extensive numerical studies to evaluate the performance of our proposed method and demonstrate it using a pooled analysis of gene expression in patients with ovarian cancer.
Kidney diseases still pose one of the biggest challenges for global health, and their heterogeneity and often high comorbidity load seriously hinders the unraveling of their underlying pathomechanisms and the delivery of optimal patient care. Metabolomics, the quantitative study of small organic compounds, called metabolites, in a biological specimen, is gaining more and more importance in nephrology research. Conducting a metabolomics study in human kidney disease cohorts, however, requires thorough knowledge about the key workflow steps: study planning, sample collection, metabolomics data acquisition and preprocessing, statistical/bioinformatics data analysis, and results interpretation within a biomedical context. This review provides a guide for future metabolomics studies in human kidney disease cohorts. We will offer an overview of important a priori considerations for metabolomics cohort studies, available analytical as well as statistical/bioinformatics data analysis techniques, and subsequent interpretation of metabolic findings. We will further point out potential research questions for metabolomics studies in the context of kidney diseases and summarize the main results and data availability of important studies already conducted in this field.
Exposure to different air pollutants has been linked to type 2 diabetes mellitus, but the evidence for the association between air pollutants and gestational diabetes mellitus (GDM) has not been systematically evaluated. We systematically retrieved relevant studies from PubMed, Embase, and the Web of Science, and performed stratified analyses and regression analyses. Thirteen studies were analyzed, comprising 1 547 154 individuals from nine retrospective studies, three prospective studies, and one case-control study. Increased exposure to particulate matter ≤2.5 µm in diameter (PM2.5) was not associated with the increased risk of GDM (adjusted OR 1.03, 95% CI 0.99 to 1.06). However, subgroup analysis showed positive correlation of PM2.5 exposure in the second trimester with an increased risk of GDM (combined OR 1.07, 95% CI 1.00 to 1.13). Among pollutants other than PM2.5, significant association between GDM and nitrogen dioxide (NO2) (OR 1.05, 95% CI 1.01 to 1.10), nitrogen oxide (NOx) (OR 1.03, 95% CI 1.01 to 1.05), and sulfur dioxide (SO2) (OR 1.09, 95% CI 1.03 to 1.15) was noted. There was no significant association between exposure to black carbon or ozone or carbon monoxide or particulate matter ≤10 µm in diameter and GDM. Thus, systematic review of existing evidence demonstrated association of exposure to NO2, NOx, and SO2, and the second trimester exposure of PM2.5 with the increased risk of GDM. Caution may be exercised while deriving conclusions from existing evidence base because of the limited number and the observational nature of studies.
Good data curation is integral to cohort studies, but it is not always done to a level necessary to ensure the longevity of the data a study holds. In this opinion paper, we introduce the concept of data curation debt-the data curation equivalent to the software engineering principle of technical debt. Using the context of UK cohort studies, we define data curation debt-describing examples and their potential impact. We highlight that accruing this debt can make it more difficult to use the data in the future. Additionally, the long-running nature of cohort studies means that interest is accrued on this debt and compounded over time-increasing the impact a debt could have on a study and its stakeholders. Primary causes of data curation debt are discussed across three categories: longevity of hardware, software and data formats; funding; and skills shortages. Based on cross-domain best practice, strategies to reduce the debt and preventive measures are proposed-with importance given to the recognition and transparent reporting of data curation debt. Describing the debt in this way, we encapsulate a multi-faceted issue in simple terms understandable by all cohort study stakeholders. Data curation debt is not only confined to the UK, but is an issue the international community must be aware of and address. This paper aims to stimulate a discussion between cohort studies and their stakeholders on how to address the issue of data curation debt. If data curation debt is left unchecked it could become impossible to use highly valued cohort study data, and ultimately represents an existential risk to studies themselves.
Background: Consensus is lacking with regard to whether hearing loss is an independent risk factor for dementia. We therefore conducted a meta-analysis to clarify the relationship of hearing loss and dementia. Methods: Prospective cohort studies investigating the association between hearing loss and the incidence of dementia in a community-derived population were included by searching electronic databases that included PubMed, Embase, and Cochrane's Library. A random-effects model was adopted to combine the results. Results: Fourteen cohorts including 726,900 participants were analyzed. It was shown that hearing loss was independently associated with dementia [adjusted hazard ratio (HR): 1.59, 95% confidence interval (CI): 1.37 to 1.86, p < 0.001; I 2 = 86%]. Sensitivity analysis sequentially excluding any of the individual studies included showed similar results. Subgroup analysis according to the diagnostic methods for hearing loss, validation strategy for dementia, follow-up duration, and adjustment of apolipoprotein E genotype also showed consistent results (p-values for subgroup differences all > 0.05). Meta-analysis with five studies showed that hearing loss was also connected to higher risk of Alzheimer's disease (adjusted HR: 2.24, 95% CI: 1.32 to 3.79, p = 0.003; I 2 = 2%). Conclusions: Hearing loss may increase the risk of dementia in the adult population. Whether effective treatment for hearing loss could reduce the incidence of dementia should be explored in the future.
As a comprehensive analysis of all metabolites in a biological system, metabolomics is being widely applied in various clinical/health areas for disease prediction, diagnosis, and prognosis. However, challenges remain in dealing with the metabolomic complexity, massive data, metabolite identification, intra- and inter-individual variation, and reproducibility, which largely limit its widespread implementation. This study provided a comprehensive workflow for clinical metabolomics, including sample collection and preparation, mass spectrometry (MS) data acquisition, and data processing and analysis. Sample collection from multiple clinical sites was strictly carried out with standardized operation procedures (SOP). During data acquisition, three types of quality control (QC) samples were set for respective MS platforms (GC-MS, LC-MS polar, and LC-MS lipid) to assess the MS performance, facilitate metabolite identification, and eliminate contamination. Compounds annotation and identification were implemented with commercial software and in-house-developed PAppLineTM and UlibMS library. The batch effects were removed using a deep learning model method (NormAE). Potential biomarkers identification was performed with tree-based modeling algorithms including random forest, AdaBoost, and XGBoost. The modeling performance was evaluated using the F1 score based on a 10-times repeated trial for each. Finally, a sub-cohort case study validated the reliability of the entire workflow.
Difference in life expectancy between males and females has been suggested to rest on sex difference in physiological dysregulation. But allostatic load, a physiological index, has not been carefully examined for an extended period beyond middle age. We aim to draw longitudinal trajectories of allostatic load in a national sample of older Americans and Britons; also to examine sex-based trajectories and factors behind their differences.
Recent calls to action highlight the need to address gaps in our understanding of survivorship for those living with advanced gynecological cancer to support optimal care. To ensure future research fills these knowledge gaps, we need to understand the breadth of existing survivorship research in this patient group, including the outcomes assessed, the populations included and the duration and retention in follow-up.
Hypertension has been associated with cognitive dysfunction in the general population and patients with Alzheimer's disease (AD). However, there are contradictory data regarding the potential association between hypertension and diagnosis of Parkinson's disease (PD), the second most common neurodegenerative disorder after AD. The purpose of this meta-analysis is to synthesize data from cohort studies to explore the potential association between preexisting hypertension and subsequent PD diagnosis.
Participant retention strategies that minimise attrition in longitudinal cohort studies have evolved considerably in recent years. This study aimed to assess, via systematic review and meta-analysis, the effectiveness of both traditional strategies and contemporary innovations for retention adopted by longitudinal cohort studies in the past decade.
Few studies have reported the relationship between retinal artery occlusion (RAO) and plasma homocysteine (Hcy) levels. Our goal was to evaluate the association between the plasma Hcy level and the risk of RAO disease. Several databases were searched for all published studies that involved Hcy and RAO. Six studies evaluated hyperhomocysteinemia (hHcy) in retinal artery occlusion patients and controls; the incidence of hHcy in patients with RAO was higher than the control and the pooled odds ratio (OR) was 6.64 (95% confidence interval (CI): 3.42, 12.89). Subgroup analyses showed that the ORs were 4.77 (95% CI: 2.69, 8.46) in Western countries, 22.19 (95% CI: 2.46, 200.37) in Asian countries, 9.70 (95% CI: 4.43, 21.20) in the age matched group, 11.41 (95% CI: 3.32, 39.18) in the sex matched group, 9.70 (95% CI: 4.37, 21.53) in the healthy control group, and 6.82 (95% CI: 4.19, 11.10) in the sample size >30. The mean plasma Hcy level from 5 case-control studies was higher than controls, and the weighted mean difference (WMD) was 6.54 (95% CI: 2.79, 10.29). Retinal artery occlusion is associated with elevated plasma Hcy levels. Our study results suggest that hHcy is probably an independent risk factor for RAO.
Background and Objectives:BRCA1 and BRCA2 are genes located in different chromosomes that are disproportionately associated with hereditary breast and ovarian cancer syndrome. Their association with other cancers remains to be explored. Materials and Methods: We systematically reviewed cohort studies to explore the association of BRCA 1 and BRCA2 with various cancers except lung cancer. We searched PubMed, Medline (EBSCOhost) and relevant articles published up to 10 May 2021. The odds ratio, standardised morbidity rate and cancer-specific standardised incidence ratio were pooled together as relative risk (RR) estimates. Results: Twelve studies were included for analysis. BRCA mutation increased pancreatic and uterine cancers by around 3-5- and 1.5-fold, respectively. BRCA mutation did not increase brain cancer; colorectal cancer; prostate, bladder and kidney cancer; cervical cancer; or malignant melanoma. BRCA2 increased gastric cancer with RR = 2.15 (1.98-2.33). Conclusion: The meta-analysis results can provide clinicians and relevant families with information regarding increased specific cancer risk in BRCA1 and BRCA2 mutation carriers.
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