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Scientific Reports Jun 2024Type II diabetes mellitus (T2DM) is a rising global health burden due to its rapidly increasing prevalence worldwide, and can result in serious complications. Therefore,...
Type II diabetes mellitus (T2DM) is a rising global health burden due to its rapidly increasing prevalence worldwide, and can result in serious complications. Therefore, it is of utmost importance to identify individuals at risk as early as possible to avoid long-term T2DM complications. In this study, we developed an interpretable machine learning model leveraging baseline levels of biomarkers of oxidative stress (OS), inflammation, and mitochondrial dysfunction (MD) for identifying individuals at risk of developing T2DM. In particular, Isolation Forest (iForest) was applied as an anomaly detection algorithm to address class imbalance. iForest was trained on the control group data to detect cases of high risk for T2DM development as outliers. Two iForest models were trained and evaluated through ten-fold cross-validation, the first on traditional biomarkers (BMI, blood glucose levels (BGL) and triglycerides) alone and the second including the additional aforementioned biomarkers. The second model outperformed the first across all evaluation metrics, particularly for F1 score and recall, which were increased from 0.61 ± 0.05 to 0.81 ± 0.05 and 0.57 ± 0.06 to 0.81 ± 0.08, respectively. The feature importance scores identified a novel combination of biomarkers, including interleukin-10 (IL-10), 8-isoprostane, humanin (HN), and oxidized glutathione (GSSG), which were revealed to be more influential than the traditional biomarkers in the outcome prediction. These results reveal a promising method for simultaneously predicting and understanding the risk of T2DM development and suggest possible pharmacological intervention to address inflammation and OS early in disease progression.
Topics: Diabetes Mellitus, Type 2; Humans; Biomarkers; Machine Learning; Oxidative Stress; Male; Female; Middle Aged; Risk Assessment; Risk Factors; Blood Glucose; Inflammation; Algorithms
PubMed: 38909127
DOI: 10.1038/s41598-024-65044-x -
Public Health Jun 2024To describe the trends in the nature of general practices in Scotland between 2014/15 and 2023.
OBJECTIVES
To describe the trends in the nature of general practices in Scotland between 2014/15 and 2023.
STUDY DESIGN
Descriptive ecological study.
METHODS
We obtained data from Public Health Scotland and used general practitioner (GP) practice codes, practice names, and the General Medical Council (GMC) numbers of their listed GPs to describe trends in practice characteristics and to identify individual practices that were likely to be operating as a single entity.
RESULTS
Defining practice entities is difficult because different GP practice codes are often retained when GPs are performing across multiple practices. If GP practice codes alone are used, the median practice list size increased from 5094 to 5881, and the mean from 5588 to 6289, between 2013/14 and 2020/21. There was one outlier practice that grew to have over 45,000 patients registered by 2020/21. However, this underestimates the extent of this new mega-practice phenomenon. Using the GMC numbers of GPs listed as performers to identify where the same GPs are working across multiple GP practice codes, we identified a series of mega-practices that span across health board areas and which have experienced a dramatic increase in their list size (with the two largest having list sizes of over 101,000 and 77,000 patients, respectively).
CONCLUSIONS
Further research is needed to better understand: how mega-practices provide services and whether this differs from other practices; where financial rewards accumulate within mega-practices; differences in staffing between mega-practices and other models; and the impacts mega-practices have on the quality and continuity of care and on health and inequality outcomes.
PubMed: 38908308
DOI: 10.1016/j.puhe.2024.05.026 -
European Journal of Vascular and... Jun 2024Complex abdominal aortic aneurysms (cAAA) pose a clinical challenge. The aim of this study was to assess the 30 day mortality and morbidity for open aneurysm repair...
OBJECTIVE
Complex abdominal aortic aneurysms (cAAA) pose a clinical challenge. The aim of this study was to assess the 30 day mortality and morbidity for open aneurysm repair (OAR) and fenestrated/branched endovascular aortic repair (F/BEVAR), and the effect of hospital volume in patients with asymptomatic cAAA in Switzerland.
METHODS
Retrospective, cohort study using data from Switzerland's national registry for vascular surgery, Swissvasc, including patients treated from 1 January 2019 to 31 December 2022. All patients with asymptomatic, true, non-infected cAAA were identified. Primary outcome was 30 day mortality and morbidity reported using the Clavien-Dindo classification. Outcomes were compared between OAR and F/BEVAR after propensity score weighting.
RESULTS
Of the 461 patients identified, 333 underwent OAR and 128 underwent F/BEVAR for cAAA. At 30 days, overall mortality rate was 3.3% after OAR and 3.1% after F/BEVAR (p = .76). Propensity scores weighted analysis indicated similar morbidity rates for both approaches: F/BEVAR (OR 0.69, 95% CI 0.45 - 1.05, p = .055); intestinal ischaemia (1.8% after OAR, 3.1% after F/BEVAR, p = .47) and renal failure requiring dialysis (1.5% after OAR, 5.5% after F/BEVAR, p = .024) were associated with highest morbidity and mortality. Treatment specific complications with high morbidity were abdominal compartment syndrome and lower limb compartment syndrome following F/BEVAR. Overall treatment volume was low for most of the hospitals treating cAAA in Switzerland; outliers with increased mortality were identified among low volume hospitals.
CONCLUSION
Comparable 30 day mortality and morbidity rates were found between OAR and F/BEVAR for cAAA in Switzerland; lack of centralisation was also highlighted. Organ specific complications driving mortality were renal failure, intestinal ischaemia, and limb ischaemia, specifically after F/BEVAR. Treatment in specialised high volume centres, alongside efforts to reduce peri procedural kidney injury and mesenteric ischaemia, offers potential to lower morbidity and mortality in elective cAAA treatment.
PubMed: 38906370
DOI: 10.1016/j.ejvs.2024.06.022 -
Computers in Biology and Medicine Jun 2024Traumatic bone marrow lesions (BML) are frequently identified on knee MRI scans in patients following an acute full-thickness, complete ACL tear. BMLs coincide with...
INTRODUCTION
Traumatic bone marrow lesions (BML) are frequently identified on knee MRI scans in patients following an acute full-thickness, complete ACL tear. BMLs coincide with regions of elevated localized bone loss, and studies suggest these may act as a precursor to the development of post-traumatic osteoarthritis. This study addresses the labour-intensive manual assessment of BMLs by using a 3D U-Net for automated identification and segmentation from MRI scans.
METHODS
A multi-task learning approach was used to segment both bone and BML from T2 fat-suppressed (FS) fast spin echo (FSE) MRI sequences for BML assessment. Training and testing utilized datasets from individuals with complete ACL tears, employing a five-fold cross-validation approach and pre-processing involved image intensity normalization and data augmentation. A post-processing algorithm was developed to improve segmentation and remove outliers. Training and testing datasets were acquired from different studies with similar imaging protocol to assess the model's performance robustness across different populations and acquisition conditions.
RESULTS
The 3D U-Net model exhibited effectiveness in semantic segmentation, while post-processing enhanced segmentation accuracy and precision through morphological operations. The trained model with post-processing achieved a Dice similarity coefficient (DSC) of 0.75 ± 0.08 (mean ± std) and a precision of 0.87 ± 0.07 for BML segmentation on testing data. Additionally, the trained model with post-processing achieved a DSC of 0.93 ± 0.02 and a precision of 0.92 ± 0.02 for bone segmentation on testing data. This demonstrates the approach's high accuracy for capturing true positives and effectively minimizing false positives in the identification and segmentation of bone structures.
CONCLUSION
Automated segmentation methods are a valuable tool for clinicians and researchers, streamlining the assessment of BMLs and allowing for longitudinal assessments. This study presents a model with promising clinical efficacy and provides a quantitative approach for bone-related pathology research and diagnostics.
PubMed: 38905892
DOI: 10.1016/j.compbiomed.2024.108791 -
Medicine Jun 2024Maintaining a balanced bile acids (BAs) metabolism is essential for lipid and cholesterol metabolism, as well as fat intake and absorption. The development of obesity...
Maintaining a balanced bile acids (BAs) metabolism is essential for lipid and cholesterol metabolism, as well as fat intake and absorption. The development of obesity may be intricately linked to BAs and their conjugated compounds. Our study aims to assess how BAs influence the obesity indicators by Mendelian randomization (MR) analysis. Instrumental variables of 5 BAs were obtained from public genome-wide association study databases, and 8 genome-wide association studies related to obesity indicators were used as outcomes. Causal inference analysis utilized inverse-variance weighted (IVW), weighted median, and MR-Egger methods. Sensitivity analysis involved MR-PRESSO and leave-one-out techniques to detect pleiotropy and outliers. Horizontal pleiotropy and heterogeneity were assessed using the MR-Egger intercept and Cochran Q statistic, respectively. The IVW analysis revealed an odds ratio of 0.94 (95% confidence interval: 0.88, 1.00; P = .05) for the association between glycolithocholate (GLCA) and obesity, indicating a marginal negative causal association. Consistent direction of the estimates obtained from the weighted median and MR-Egger methods was observed in the analysis of the association between GLCA and obesity. Furthermore, the IVW analysis demonstrated a suggestive association between GLCA and trunk fat percentage, with a beta value of -0.014 (95% confidence interval: -0.027, -0.0004; P = .04). Our findings suggest a potential negative causal relationship between GLCA and both obesity and trunk fat percentage, although no association survived corrections for multiple comparisons. These results indicate a trend towards a possible association between BAs and obesity, emphasizing the need for future studies.
Topics: Mendelian Randomization Analysis; Humans; Obesity; Genome-Wide Association Study; Bile Acids and Salts; Causality
PubMed: 38905395
DOI: 10.1097/MD.0000000000038610 -
Frontiers in Genetics 2024Previous studies have shown that Alzheimer's disease (AD) can cause myocardial damage. However, whether there is a causal association between AD and non-ischemic...
The causal effect of Alzheimer's disease and family history of Alzheimer's disease on non-ischemic cardiomyopathy and left ventricular structure and function: a Mendelian randomization study.
BACKGROUND
Previous studies have shown that Alzheimer's disease (AD) can cause myocardial damage. However, whether there is a causal association between AD and non-ischemic cardiomyopathy (NICM) remains unclear. Using a comprehensive two-sample Mendelian randomization (MR) method, we aimed to determine whether AD and family history of AD (FHAD) affect left ventricular (LV) structure and function and lead to NICM, including hypertrophic cardiomyopathy (HCM) and dilated cardiomyopathy (DCM).
METHODS
The summary statistics for exposures [AD, paternal history of AD (PH-AD), and maternal history of AD (MH-AD)] and outcomes (NICM, HCM, DCM, and LV traits) were obtained from the large European genome-wide association studies. The causal effects were estimated using inverse variance weighted, MR-Egger, and weighted median methods. Sensitivity analyses were conducted, including Cochran's Q test, MR-Egger intercept test, MR pleiotropy residual sum and outlier, MR Steiger test, leave-one-out analysis, and the funnel plot.
RESULTS
Genetically predicted AD was associated with a lower risk of NICM [odds ratio (OR) 0.9306, 95% confidence interval (CI) 0.8825-0.9813, = 0.0078], DCM (OR 0.8666, 95% CI 0.7752-0.9689, = 0.0119), and LV remodeling index (OR 0.9969, 95% CI 0.9940-0.9998, = 0.0337). Moreover, genetically predicted PH-AD was associated with a decreased risk of NICM (OR 0.8924, 95% CI 0.8332-0.9557, = 0.0011). MH-AD was also strongly associated with a decreased risk of NICM (OR 0.8958, 95% CI 0.8449-0.9498, = 0.0002). Different methods of sensitivity analysis demonstrated the robustness of the results.
CONCLUSION
Our study revealed that AD and FHAD were associated with a decreased risk of NICM, providing a new genetic perspective on the pathogenesis of NICM.
PubMed: 38903751
DOI: 10.3389/fgene.2024.1379865 -
BMC Women's Health Jun 2024Previous observational studies have indicated an inverse correlation between circulating sex hormone binding globulin (SHBG) levels and the incidence of polycystic ovary...
BACKGROUND
Previous observational studies have indicated an inverse correlation between circulating sex hormone binding globulin (SHBG) levels and the incidence of polycystic ovary syndrome (PCOS). Nevertheless, conventional observational studies may be susceptible to bias. Consequently, we conducted a two-sample Mendelian randomization (MR) investigation to delve deeper into the connection between SHBG levels and the risk of PCOS.
METHODS
We employed single-nucleotide polymorphisms (SNPs) linked to serum SHBG levels as instrumental variables (IVs). Genetic associations with PCOS were derived from a meta-analysis of GWAS data. Our primary analytical approach relied on the inverse-variance weighted (IVW) method, complemented by alternative MR techniques, including simple-median, weighted-median, MR-Egger regression, and MR Pleiotropy RESidual Sum and Outlier (MR-PRESSO) testing. Additionally, sensitivity analyses were conducted to assess the robustness of the association.
RESULTS
We utilized 289 SNPs associated with serum SHBG levels, achieving genome-wide significance, as instrumental variables (IVs). Our MR analyses revealed that genetically predicted elevated circulating SHBG concentrations were linked to a reduced risk of PCOS (odds ratio (OR) = 0.56, 95% confidence interval (CI): 0.39-0.78, P = 8.30 × 10) using the IVW method. MR-Egger regression did not detect any directional pleiotropic effects (P intercept = 0.626). Sensitivity analyses, employing alternative MR methods and IV sets, consistently reaffirmed our results, underscoring the robustness of our findings.
CONCLUSIONS
Through a genetic epidemiological approach, we have substantiated prior observational literature, indicating a potential causal inverse relationship between serum SHBG concentrations and PCOS risk. Nevertheless, further research is needed to elucidate the underlying mechanism of SHBG in the development of PCOS.
Topics: Humans; Sex Hormone-Binding Globulin; Polycystic Ovary Syndrome; Female; Polymorphism, Single Nucleotide; Mendelian Randomization Analysis; Genome-Wide Association Study; Genetic Predisposition to Disease; Risk Factors
PubMed: 38902677
DOI: 10.1186/s12905-024-03144-6 -
Computers in Biology and Medicine Jun 2024The Instantaneous Signal Loss Simulation (InSiL) model is a promising alternative to the classical mono-exponential fitting of the Modified Look-Locker...
The Instantaneous Signal Loss Simulation (InSiL) model is a promising alternative to the classical mono-exponential fitting of the Modified Look-Locker Inversion-recovery (MOLLI) sequence in cardiac T mapping applications, which achieves better accuracy and is less sensitive to heart rate (HR) variations. Classical non-linear least squares (NLLS) estimation methods require some parameters of the model to be fixed a priori in order to give reliable T estimations and avoid outliers. This introduces further bias in the estimation, reducing the advantages provided by the InSiL model. In this paper, a novel Bayesian estimation method using a hierarchical model is proposed to fit the parameters of the InSiL model. The hierarchical Bayesian modeling has a shrinkage effect that works as a regularizer for the estimated values, by pulling spurious estimated values toward the group-mean, hence reducing greatly the number of outliers. Simulations, physical phantoms, and in-vivo human cardiac data have been used to show that this approach estimates accurately all the InSiL parameters, and achieve high precision estimation of the T compared to the classical MOLLI model and NLLS InSiL estimation.
PubMed: 38897148
DOI: 10.1016/j.compbiomed.2024.108753 -
Planta Jun 2024By studying Cistus albidus shrubs in their natural habitat, we show that biological outliers can help us to understand the causes and consequences of maximum...
By studying Cistus albidus shrubs in their natural habitat, we show that biological outliers can help us to understand the causes and consequences of maximum photochemical efficiency decreases in plants, thus reinforcing the importance of integrating these often-neglected data into scientific practice. Outliers are individuals with exceptional traits that are often excluded of data analysis. However, this may result in very important mistakes not accurately capturing the true trajectory of the population, thereby limiting our understanding of a given biological process. Here, we studied the role of biological outliers in understanding the causes and consequences of maximum photochemical efficiency decreases in plants, using the semi-deciduous shrub C. albidus growing in a Mediterranean-type ecosystem. We assessed interindividual variability in winter, spring and summer maximum PSII photochemical efficiency in a population of C. albidus growing under Mediterranean conditions. A strong correlation was observed between maximum PSII photochemical efficiency (F/F ratio) and leaf water desiccation. While decreases in maximum PSII photochemical efficiency did not result in any damage at the organ level during winter, reductions in the F/F ratio were associated to leaf mortality during summer. However, all plants could recover after rainfalls, thus maximum PSII photochemical efficiency decreases did not result in an increased mortality at the organism level, despite extreme water deficit and temperatures exceeding 40ºC during the summer. We conclude that, once methodological outliers are excluded, not only biological outliers must not be excluded from data analysis, but focusing on them is crucial to understand the causes and consequences of maximum PSII photochemical efficiency decreases in plants.
Topics: Photosystem II Protein Complex; Seasons; Plant Leaves; Cistus; Photosynthesis; Ecosystem; Water; Temperature; Chlorophyll
PubMed: 38896307
DOI: 10.1007/s00425-024-04466-3 -
Skin Research and Technology : Official... Jun 2024Research has previously established connections between the intestinal microbiome and the progression of some cancers. However, there is a noticeable gap in the...
OBJECTIVE
Research has previously established connections between the intestinal microbiome and the progression of some cancers. However, there is a noticeable gap in the literature in regard to using Mendelian randomisation (MR) to delve into potential causal relationships between the gut microbiota (GM) and basal cell carcinoma (BCC). Therefore, the purpose of our study was to use MR to explore the causal relationship between four kinds of GM (Bacteroides, Streptococcus, Proteobacteria and Lachnospiraceae) and BCC.
METHODS
We used genome-wide association study (GWAS) data and MR to explore the causal relationship between four kinds of GM and BCC. This study primarily employed the random effect inverse variance weighted (IVW) model for analysis, as complemented by additional methods including the simple mode, weighted median, weighted mode and MR‒Egger methods. We used heterogeneity and horizontal multiplicity to judge the reliability of each analysis. MR-PRESSO was mainly used to detect and correct outliers.
RESULTS
The random-effects IVW results showed that Bacteroides (OR = 0.936, 95% CI = 0.787-1.113, p = 0.455), Streptococcus (OR = 0.974, 95% CI = 0.875-1.083, p = 0.629), Proteobacteria (OR = 1.113, 95% CI = 0.977-1.267, p = 0.106) and Lachnospiraceae (OR = 1.027, 95% CI = 0.899-1.173, p = 0.688) had no genetic causal relationship with BCC. All analyses revealed no horizontal pleiotropy, heterogeneity or outliers.
CONCLUSION
We found that Bacteroides, Streptococcus, Proteobacteria and Lachnospiraceae do not increase the incidence of BCC at the genetic level, which provides new insight for the study of GM and BCC.
Topics: Humans; Mendelian Randomization Analysis; Carcinoma, Basal Cell; Gastrointestinal Microbiome; Skin Neoplasms; Genome-Wide Association Study; Streptococcus; Proteobacteria; Bacteroides; Genetic Predisposition to Disease; Polymorphism, Single Nucleotide
PubMed: 38895789
DOI: 10.1111/srt.13804