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Journal of Clinical Medicine May 2022Gestational Diabetes Mellitus (GDM) is the most common metabolic complication during pregnancy and is associated with serious maternal and fetal complications such as...
Gestational Diabetes Mellitus (GDM) is the most common metabolic complication during pregnancy and is associated with serious maternal and fetal complications such as pre-eclampsia and stillbirth. Further, women with GDM have approximately 10 times higher risk of diabetes later in life. Children born to mothers with GDM also face a higher risk of childhood obesity and diabetes later in life. Early prediction/diagnosis of GDM leads to early interventions such as diet and lifestyle, which could mitigate the maternal and fetal complications associated with GDM. However, no biomarkers identified to date have been proven to be effective in the prediction/diagnosis of GDM. Proteomic approaches based on mass spectrometry have been applied in various fields of biomedical research to identify novel biomarkers. Although a number of proteomic studies in GDM now exist, a lack of a comprehensive and up-to-date meta-analysis makes it difficult for researchers to interpret the data in the existing literature. Thus, we undertook a systematic review and meta-analysis on proteomic studies and GDM. We searched MEDLINE, EMBASE, Web of Science and Scopus from inception to January 2022. We searched Medline, Embase, CINHAL and the Cochrane Library, which were searched from inception to February 2021. We included cohort, case-control and observational studies reporting original data investigating the development of GDM compared to a control group. Two independent reviewers selected eligible studies for meta-analysis. Data collection and analyses were performed by two independent reviewers. The PROSPERO registration number is CRD42020185951. Of 120 articles retrieved, 24 studies met the eligibility criteria, comparing a total of 1779 pregnant women (904 GDM and 875 controls). A total of 262 GDM candidate biomarkers (CBs) were identified, with 49 CBs reported in at least two studies. We found 22 highly replicable CBs that were significantly different (nine CBs were upregulated and 12 CBs downregulated) between women with GDM and controls across various proteomic platforms, sample types, blood fractions and time of blood collection and continents. We performed further analyses on blood (plasma/serum) CBs in early pregnancy (first and/or early second trimester) and included studies with more than nine samples (nine studies in total). We found that 11 CBs were significantly upregulated, and 13 CBs significantly downregulated in women with GDM compared to controls. Subsequent pathway analysis using Database for Annotation, Visualization and Integrated Discovery (DAVID) bioinformatics resources found that these CBs were most strongly linked to pathways related to complement and coagulation cascades. Our findings provide important insights and form a strong foundation for future validation studies to establish reliable biomarkers for GDM.
PubMed: 35628864
DOI: 10.3390/jcm11102737 -
The British Journal of Surgery Jan 2024Many survivors of a first primary cancer (FPCs) are at risk of developing a second primary cancer (SPC), with effects on patient prognosis. Primary cancers have... (Meta-Analysis)
Meta-Analysis
BACKGROUND
Many survivors of a first primary cancer (FPCs) are at risk of developing a second primary cancer (SPC), with effects on patient prognosis. Primary cancers have different frequencies of specific SPC development and the development of SPCs may be closely related to the FPC. The aim of this study was to explore possible correlations between SPCs and FPCs.
METHODS
Relevant literature on SPCs was retrospectively searched and screened from four databases, namely, PubMed, EMBASE, Web of Science, and PMC. Data on the number of patients with SPC in 28 different organ sites were also collected from The Surveillance, Epidemiology, and End Results (SEER) 8 Registry and NHANES database.
RESULTS
A total of 9 617 643 patients with an FPC and 677 430 patients with an SPC were included in the meta-analysis. Patients with a first primary gynaecological cancer and thyroid cancer frequently developed a second primary breast cancer and colorectal cancer. Moreover, those with a first primary head and neck cancer, anal cancer and oesophageal cancer developed a second primary lung cancer more frequently. A second primary lung cancer and prostate cancer was also common among patients with first primary bladder cancer and penile cancer. Patients with second primary bladder cancer accounted for 56% of first primary ureteral cancer patients with SPCs.
CONCLUSIONS
This study recommends close clinical follow-up, monitoring and appropriate interventions in patients with relevant FPCs for better screening and early diagnosis of SPCs.
Topics: Humans; Incidence; Lung Neoplasms; Neoplasms, Second Primary; Nutrition Surveys; Prostatic Neoplasms; Retrospective Studies; Risk Factors; Urinary Bladder Neoplasms
PubMed: 38055899
DOI: 10.1093/bjs/znad377 -
La Clinica Terapeutica 2023Cancer, a potentially fatal condition, is one of the leading causes of death worldwide. Among males aged 20 to 35, the most common cancer in healthy individuals is... (Review)
Review
BACKGROUND
Cancer, a potentially fatal condition, is one of the leading causes of death worldwide. Among males aged 20 to 35, the most common cancer in healthy individuals is testicular cancer, accounting for 1% to 2% of all cancers in men.
METHODS
Throughout this review, we have employed a targeted research approach, carefully handpicking the most representative and relevant articles on the subject. Our methodology involved a systematic review of the scientific literature to ensure a comprehensive and accurate overview of the available sources.
RESULTS
The onset and spread of testicular cancer are significantly influenced by genetic changes, including mutations in oncogenes, tu-mor suppressor genes, and DNA repair genes. As a result of identifying these specific genetic mutations in cancers, targeted medications have been developed to disrupt the signaling pathways affected by these genetic changes. To improve the diagnosis and treatment of this disease, it is crucial to understand its natural and clinical histories.
CONCLUSIONS
In order to comprehend cancer better and to discover new biomarkers and therapeutic targets, oncologists are increasingly employing omics methods, such as genomics, transcriptomics, proteomics, and metabolomics. Targeted medications that focus on specific genetic pathways and mutations hold promise for advancing the diagnosis and management of this disease.
Topics: Humans; Male; Testicular Neoplasms; Precision Medicine; Genomics; Proteomics
PubMed: 37994745
DOI: 10.7417/CT.2023.2468 -
Metabolic Syndrome and Related Disorders Dec 2022This study aims to systematically evaluate the association between metabolic syndrome (MS) and pulmonary function through meta-analysis. Electronic databases,... (Meta-Analysis)
Meta-Analysis
This study aims to systematically evaluate the association between metabolic syndrome (MS) and pulmonary function through meta-analysis. Electronic databases, including PubMed, Embase, Web of Science, and Cochrane Library, were systematically searched to obtain articles associated with MS and lung function published before December 31, 2021. According to the including and excluding criteria, certain studies were obtained and data were extracted. The Newcastle Ottawa Scale was used to evaluate the quality of the studies. A pooled standardized mean difference (SMD) was calculated by means of random-effects meta-analysis. Different effect models were used according to the heterogeneity. Meta-regression and sensitivity analyses were performed to examine the possible sources of heterogeneity. The Begg's funnel plot and Egger's test were used to evaluate publication bias. Analyses were performed using Stata MP, version14.0 (StataCorp LP, College Station, TX, USA). A total of 15 studies, involving 10,285 cases of MS and 25,416 cases of control, were included in this meta-analysis on the relationship between MS and forced vital capacity (FVC). The pooled SMD for FVC was -0.247 (95% CI = -0.327 to -0.2167, < 0.001) using random effect model, indicating the decrease of FVC in the patients with MS. In the same studies, the pooled SMD for forced expiratory volume in 1 sec (FEV) was -0.205 (95% CI = -0.3278 to -0.133, < 0.001), indicating the decrease of FEV also existed in the MS cases. A total of 13 studies, involving 8167 cases of MS and 19,788 cases of control, were included in this meta-analysis on the relationship between MS and FEV/FVC. The pooled SMD for FEV/FVC was 0.011 (95% CI = -0.072 to 0.093, = 0.798) using random effect model, indicating that there was no significant difference between the patients with MS and the control. After introducing the diastolic blood pressure and glycemia into the regression model of the relationship between MS and FVC, the variance of the studies (tau2) decreased from 0.0190 to 0.006694 and 0.007205, which could explain 66.70% and 78.04% of the sources of heterogeneity, and the values were 0.038 and 0.023. The results suggested that hypertension (diastolic pressure) and hyperglycemia were the factors linked to the heterogeneity among the included studies on both FVC and FEV. The Begg's funnel plot and Egger's test both showed no evidence of publication bias. Our results show that FVC and FEV decrease in MS patients, while FEV/FVC has no significant difference compared with the control group. It indicates that the patients with MS have restrictive ventilatory functional disturbance. Meta-regression analysis suggests that hypertension (diastolic pressure) and hyperglycemia are the factors linked to the heterogeneity among the included studies on both FVC and FEV.
Topics: Humans; Metabolic Syndrome; Lung; Forced Expiratory Volume; Hypertension; Hyperglycemia
PubMed: 36125502
DOI: 10.1089/met.2022.0045 -
Journal of Nephrology Apr 2024Glomerulonephritis inherently leads to the development of chronic kidney disease. It is the second most common diagnosis in patients requiring renal replacement therapy... (Review)
Review
BACKGROUND
Glomerulonephritis inherently leads to the development of chronic kidney disease. It is the second most common diagnosis in patients requiring renal replacement therapy in the United Kingdom. Metabolomics and proteomics can characterise, identify and quantify an individual's protein and metabolite make-up. These techniques have been optimised and can be performed on samples including kidney tissue, blood and urine. Utilising omic techniques in nephrology can uncover disease pathophysiology and transform the diagnostics and treatment options for glomerulonephritis.
OBJECTIVES
To evaluate the utility of metabolomics and proteomics using mass spectrometry and nuclear magnetic resonance in glomerulonephritis.
METHODS
The systematic review was registered on PROSPERO (CRD42023442092). Standard and extensive Cochrane search methods were used. The latest search date was March 2023. Participants were of any age with a histological diagnosis of glomerulonephritis. Descriptive analysis was performed, and data presented in tabular form. An area under the curve or p-value was presented for potential biomarkers discovered.
RESULTS
Twenty-seven studies were included (metabolomics (n = 9)), and (proteomics (n = 18)) with 1818 participants. The samples analysed were urine (n = 19) blood (n = 4) and biopsy (n = 6). The typical outcome themes were potential biomarkers, disease phenotype, risk of progression and treatment response.
CONCLUSION
This review shows the potential of metabolomic and proteomic analysis to discover new disease biomarkers that may influence diagnostics and disease management. Further larger-scale research is required to establish the validity of the study outcomes, including the several proposed biomarkers.
PubMed: 38689160
DOI: 10.1007/s40620-024-01923-w -
The Journal of Maternal-fetal &... Jul 2022Pre-eclampsia (PE) is a serious pregnancy status characterized by high blood pressure. Although visfatin is usually associated with PE. Observational studies evaluating... (Meta-Analysis)
Meta-Analysis
OBJECTIVE
Pre-eclampsia (PE) is a serious pregnancy status characterized by high blood pressure. Although visfatin is usually associated with PE. Observational studies evaluating the relationship between circulating visfatin and pre-eclampsia have reported inconsistent results. We conducted this systematic review and meta-analysis to summarize published data on the association between visfatin and pre-eclampsia.
METHODS
Electronic databases PubMed, ISI web of science, EMBASE, Scopus and the Cochrane library were comprehensively searched for selection of eligible studies until January 5, 2020. A random-effects model and the generic inverse variance method were used for quantitative data synthesis. The assessment of study quality was performed using the e Newcastle-Ottawa scale and the Agency for Healthcare Research and Quality. Sensitivity analyses and prespecified subgroup were conducted to evaluate potential heterogeneity. Random-effects meta-regression was conducted to assess the impact of potential confounders on the estimated effect sizes. The protocol for this study was registered in PROSPERO (No. CRD42018105861) in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA).
RESULTS
Thirteen studies comprising a total of 536 subjects were included in this meta-analysis. We observed that the pre-eclampsia risk is associated with a statistically significant elevation of visfatin level [SMD (1.33 µg/l) (95% CI 0.37, 2.2) = .007]. No significant publication bias was observed in the meta-analysis. Subgroup and sensitivity analyses indicated that the pooled effects size were affected by systolic blood pressure [SMD (1.82 µg/l) 95% CI (0.94, 2.7), < .05], gestational age [SMD (2.01 µg/l) 95% CI (0.57, 3.4), = .006], body mass index [SMD (1.6 µg/l) 95% CI (0.37, 3), < .05] and pregnancy trimesters[SMD (2.3 µg/l) 95% CI (0.95, 3.7), = .001]. Random-effects meta-regression showed a significant association of visfatin level with potential confounders including systolic blood pressure, gestational age and birth weight at delivery of pre-eclampsia patients.
CONCLUSIONS
Collectively, our data revealed that the increase of visfatin level can be associated with the risk of pre-eclampsia. However, further studies on pre-eclampsia populations are warranted for corroboration of our findings.
Topics: Body Mass Index; Female; Humans; Nicotinamide Phosphoribosyltransferase; Pre-Eclampsia; Pregnancy; Pregnancy Trimesters
PubMed: 32635792
DOI: 10.1080/14767058.2020.1789581 -
Heliyon Jun 2020Quantitative proteomic workflow based on mass spectrometry (MS) is recently developed by the researchers to screen for biomarkers in periodontal diseases comprising... (Review)
Review
Quantitative proteomic workflow based on mass spectrometry (MS) is recently developed by the researchers to screen for biomarkers in periodontal diseases comprising periodontitis. Periodontitis is known for chronic inflammatory disease characterized by progressive destruction of the tooth-supporting apparatus, yet has a lack of clear pathobiology based on a discrepancy between specified categories and diagnostic vagueness. The objective of this review was to outlined the accessible information related to proteomics studies on periodontitis. The Preferred Reporting Items for Systematical Reviews and Meta-Analysis (PRISMA) statement guides to acquaint proteomic analysis on periodontal diseases was applied. Three databases were used in this study, such as Pubmed, ScienceDirect and Biomed Central from 2009 up to November 2019. Proteomics analysis platforms that used in the studies were outlined. Upregulated and downregulated proteins findings data were found, in which could be suitable as candidate biomarkers for this disease.
PubMed: 32529063
DOI: 10.1016/j.heliyon.2020.e04022 -
Statistics in Medicine Feb 2023This review condenses the knowledge on variable selection methods implemented in R and appropriate for datasets with grouped features. The focus is on regularized... (Review)
Review
This review condenses the knowledge on variable selection methods implemented in R and appropriate for datasets with grouped features. The focus is on regularized regressions identified through a systematic review of the literature, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. A total of 14 methods are discussed, most of which use penalty terms to perform group variable selection. Depending on how the methods account for the group structure, they can be classified into knowledge and data-driven approaches. The first encompass group-level and bi-level selection methods, while two-step approaches and collinearity-tolerant methods constitute the second category. The identified methods are briefly explained and their performance compared in a simulation study. This comparison demonstrated that group-level selection methods, such as the group minimax concave penalty, are superior to other methods in selecting relevant variable groups but are inferior in identifying important individual variables in scenarios where not all variables in the groups are predictive. This can be better achieved by bi-level selection methods such as group bridge. Two-step and collinearity-tolerant approaches such as elastic net and ordered homogeneity pursuit least absolute shrinkage and selection operator are inferior to knowledge-driven methods but provide results without requiring prior knowledge. Possible applications in proteomics are considered, leading to suggestions on which method to use depending on existing prior knowledge and research question.
Topics: Humans; Computer Simulation
PubMed: 36546512
DOI: 10.1002/sim.9620 -
Frontiers in Endocrinology 2023Polycystic Ovarian Syndrome (PCOS) is the most common endocrinopathy in women of reproductive age and remains widely underdiagnosed leading to significant morbidity....
INTRODUCTION
Polycystic Ovarian Syndrome (PCOS) is the most common endocrinopathy in women of reproductive age and remains widely underdiagnosed leading to significant morbidity. Artificial intelligence (AI) and machine learning (ML) hold promise in improving diagnostics. Thus, we performed a systematic review of literature to identify the utility of AI/ML in the diagnosis or classification of PCOS.
METHODS
We applied a search strategy using the following databases MEDLINE, Embase, the Cochrane Central Register of Controlled Trials, the Web of Science, and the IEEE Xplore Digital Library using relevant keywords. Eligible studies were identified, and results were extracted for their synthesis from inception until January 1, 2022.
RESULTS
135 studies were screened and ultimately, 31 studies were included in this study. Data sources used by the AI/ML interventions included clinical data, electronic health records, and genetic and proteomic data. Ten studies (32%) employed standardized criteria (NIH, Rotterdam, or Revised International PCOS classification), while 17 (55%) used clinical information with/without imaging. The most common AI techniques employed were support vector machine (42% studies), K-nearest neighbor (26%), and regression models (23%) were the commonest AI/ML. Receiver operating curves (ROC) were employed to compare AI/ML with clinical diagnosis. Area under the ROC ranged from 73% to 100% (n=7 studies), diagnostic accuracy from 89% to 100% (n=4 studies), sensitivity from 41% to 100% (n=10 studies), specificity from 75% to 100% (n=10 studies), positive predictive value (PPV) from 68% to 95% (n=4 studies), and negative predictive value (NPV) from 94% to 99% (n=2 studies).
CONCLUSION
Artificial intelligence and machine learning provide a high diagnostic and classification performance in detecting PCOS, thereby providing an avenue for early diagnosis of this disorder. However, AI-based studies should use standardized PCOS diagnostic criteria to enhance the clinical applicability of AI/ML in PCOS and improve adherence to methodological and reporting guidelines for maximum diagnostic utility.
SYSTEMATIC REVIEW REGISTRATION
https://www.crd.york.ac.uk/prospero/, identifier CRD42022295287.
Topics: Female; Humans; Artificial Intelligence; Polycystic Ovary Syndrome; Proteomics; Machine Learning; Cluster Analysis
PubMed: 37790605
DOI: 10.3389/fendo.2023.1106625 -
Journal of Diabetes and Metabolic... Jun 2022Due to growing concerns about the obesity pandemic as a worldwide phenomenon, a global effort has been made for managing it and associated disorders. Accordingly,... (Review)
Review
PURPOSE
Due to growing concerns about the obesity pandemic as a worldwide phenomenon, a global effort has been made for managing it and associated disorders. Accordingly, metabolomics as a promising field of "OMICS" is presented for investigating different molecular pathways in obesity and related disorders through the evaluation of specific metabolites in both animal and human subjects. Herein, the aim of the present study as the first systematic review is to evaluate all available studies about different mechanisms and their biomarkers discovery using metabolomics approaches.
METHOD
The study was designed according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Using a comprehensive search strategy we searched in databases including; Web of Science, PubMed, and Scopus using specific keywords. Based on predefined inclusion/exclusion criteria study selection has been conducted considering the type of studies, participant, and outcome measures. Quality assessment was done using CASP (Critical Appraisal Skills Programme) checklist followed by data extraction according to a predefined data extraction sheet.
RESULTS
Among the articles that resulted from electronic search, a total of 74 articles met our inclusion criteria. The most prevalent studied metabolites were amino acids and lipid derivatives and both targeted and non-targeted approaches were applied for metabolomics studies.
CONCLUSION
This systematic review summarized a wide range of studies regardless of the age, history, language, and type of the study. Further studies are needed to compare the application of emerging methods in the treatment of obesity and related disorders.
SUPPLEMENTARY INFORMATION
The online version contains supplementary material available at 10.1007/s40200-021-00917-w.
PubMed: 35673462
DOI: 10.1007/s40200-021-00917-w