scholarly journals Using Bayesian Networks to identify musculoskeletal symptoms influencing the risk of developing Psoriatic Arthritis in people with psoriasis

Rheumatology ◽  
2021 ◽  
Author(s):  
Amelia Green ◽  
William Tillett ◽  
Neil McHugh ◽  
Theresa Smith ◽  
Keyword(s):  
Bayesian Networks ◽  
Expert Knowledge ◽  
Smoking Status ◽  
Characteristic Curve ◽  
Predictive Ability ◽  
Musculoskeletal Symptoms ◽  
Musculoskeletal Symptom ◽  
The Uk ◽  
Primary Groups ◽  
Incident Cases

Abstract Objectives To explore Bayesian networks (BNs) in understanding the relationships between musculoskeletal symptoms and the development of PsA in people with psoriasis. Methods Incident cases of psoriasis were identified between 1998 and 2015 from the UK Clinical Research Practice Datalink. Musculoskeletal symptoms identified by medcodes were concatenated into primary groups, each made up of several sub-groups. Baseline demographics for gender, age, body mass index (BMI), psoriasis severity, alcohol use and smoking status were also extracted. Several BN structures were composed using a combination of expert knowledge and data-oriented modelling using: 1) primary musculoskeletal symptom groups, 2) musculoskeletal symptom sub-groups and 3) demographic variables. Predictive ability of the networks using the receiver operating characteristic curve (AUC) was calculated. Results Over one million musculoskeletal symptoms were extracted for the 90,189 incident cases of psoriasis identified, of which 1409 developed PsA. The BN analysis yielded direct relationships between gender, BMI, arthralgia, finger pain, fatigue, hand pain, hip pain, knee pain, swelling, back pain, myalgia and PsA. The best BN, achieved by using the more site-specific musculoskeletal symptom sub-groups, was 76% accurate in predicting the development of PsA in a test set and had an AUC of 0.73 (95% confidence interval (CI): 0.70-0.75). Conclusion The presented BN model may be a useful method to identify clusters of symptoms that predict the development of PsA with reasonable accuracy. Using a BN approach, we have shown that there are several symptoms which are predecessors of PsA, including fatigue, specific types of pain, and swelling.

Ethnic Differences in Self-Poisoning Across South London

Crisis ◽  
2014 ◽  
Vol 35 (4) ◽  
pp. 268-272
Author(s):  
Sean Cross ◽  
Dinesh Bhugra ◽  
Paul I. Dargan ◽  
David M. Wood ◽  
Shaun L. Greene ◽  
...  
Keyword(s):  
Black Women ◽  
Data Collection ◽  
White Women ◽  
Population Based ◽  
Care Services ◽  
Self Harm ◽  
Managing Risk ◽  
The Uk ◽  
Incident Cases ◽  
Minority Ethnic

Background: Self-poisoning (overdose) is the commonest form of self-harm cases presenting to acute secondary care services in the UK, where there has been limited investigation of self-harm in black and minority ethnic communities. London has the UK’s most ethnically diverse areas but presents challenges in resident-based data collection due to the large number of hospitals. Aims: To investigate the rates and characteristics of self-poisoning presentations in two central London boroughs. Method: All incident cases of self-poisoning presentations of residents of Lambeth and Southwark were identified over a 12-month period through comprehensive acute and mental health trust data collection systems at multiple hospitals. Analysis was done using STATA 12.1. Results: A rate of 121.4/100,000 was recorded across a population of more than half a million residents. Women exceeded men in all measured ethnic groups. Black women presented 1.5 times more than white women. Gender ratios within ethnicities were marked. Among those aged younger than 24 years, black women were almost 7 times more likely to present than black men were. Conclusion: Self-poisoning is the commonest form of self-harm presentation to UK hospitals but population-based rates are rare. These results have implications for formulating and managing risk in clinical services for both minority ethnic women and men.

scholarly journals MicroRNAs-1299, -126-3p and -30e-3p as Potential Diagnostic Biomarkers for Prediabetes

Diagnostics ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 949
Author(s):  
Cecil J. Weale ◽  
Don M. Matshazi ◽  
Saarah F. G. Davids ◽  
Shanel Raghubeer ◽  
Rajiv T. Erasmus ◽  
...  
Keyword(s):  
Receiver Operating Characteristic ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Predictive Ability ◽  
Roc Curves ◽  
Tolerance Test ◽  
Cross Sectional Study ◽  
Oral Glucose ◽  
Cross Sectional ◽  
Receiver Operating

This cross-sectional study investigated the association of miR-1299, -126-3p and -30e-3p with and their diagnostic capability for dysglycaemia in 1273 (men, n = 345) South Africans, aged >20 years. Glycaemic status was assessed by oral glucose tolerance test (OGTT). Whole blood microRNA (miRNA) expressions were assessed using TaqMan-based reverse transcription quantitative-PCR (RT-qPCR). Receiver operating characteristic (ROC) curves assessed the ability of each miRNA to discriminate dysglycaemia, while multivariable logistic regression analyses linked expression with dysglycaemia. In all, 207 (16.2%) and 94 (7.4%) participants had prediabetes and type 2 diabetes mellitus (T2DM), respectively. All three miRNAs were significantly highly expressed in individuals with prediabetes compared to normotolerant patients, p < 0.001. miR-30e-3p and miR-126-3p were also significantly more expressed in T2DM versus normotolerant patients, p < 0.001. In multivariable logistic regressions, the three miRNAs were consistently and continuously associated with prediabetes, while only miR-126-3p was associated with T2DM. The ROC analysis indicated all three miRNAs had a significant overall predictive ability to diagnose prediabetes, diabetes and the combination of both (dysglycaemia), with the area under the receiver operating characteristic curve (AUC) being significantly higher for miR-126-3p in prediabetes. For prediabetes diagnosis, miR-126-3p (AUC = 0.760) outperformed HbA1c (AUC = 0.695), p = 0.042. These results suggest that miR-1299, -126-3p and -30e-3p are associated with prediabetes, and measuring miR-126-3p could potentially contribute to diabetes risk screening strategies.

scholarly journals Evaluation of factors that predict the success rate of trial of labor after the cesarean section

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Yang Mi ◽  
Pengfei Qu ◽  
Na Guo ◽  
Ruimiao Bai ◽  
Jiayi Gao ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Cesarean Section ◽  
Receiver Operating Characteristic Curve ◽  
Success Rate ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Predictive Ability ◽  
Training Set ◽  
History Of ◽  
Operating Characteristic Curve

Abstract Background For most women who have had a previous cesarean section, vaginal birth after cesarean section (VBAC) is a reasonable and safe choice, but which will increase the risk of adverse outcomes such as uterine rupture. In order to reduce the risk, we evaluated the factors that may affect VBAC and and established a model for predicting the success rate of trial of the labor after cesarean section (TOLAC). Methods All patients who gave birth at Northwest Women’s and Children’s Hospital from January 2016 to December 2018, had a history of cesarean section and voluntarily chose the TOLAC were recruited. Among them, 80% of the population was randomly assigned to the training set, while the remaining 20% were assigned to the external validation set. In the training set, univariate and multivariate logistic regression models were used to identify indicators related to successful TOLAC. A nomogram was constructed based on the results of multiple logistic regression analysis, and the selected variables included in the nomogram were used to predict the probability of successfully obtaining TOLAC. The area under the receiver operating characteristic curve was used to judge the predictive ability of the model. Results A total of 778 pregnant women were included in this study. Among them, 595 (76.48%) successfully underwent TOLAC, whereas 183 (23.52%) failed and switched to cesarean section. In multi-factor logistic regression, parity = 1, pre-pregnancy BMI < 24 kg/m2, cervical score ≥ 5, a history of previous vaginal delivery and neonatal birthweight < 3300 g were associated with the success of TOLAC. The area under the receiver operating characteristic curve in the prediction and validation models was 0.815 (95% CI: 0.762–0.854) and 0.730 (95% CI: 0.652–0.808), respectively, indicating that the nomogram prediction model had medium discriminative power. Conclusion The TOLAC was useful to reducing the cesarean section rate. Being primiparous, not overweight or obese, having a cervical score ≥ 5, a history of previous vaginal delivery or neonatal birthweight < 3300 g were protective indicators. In this study, the validated model had an approving predictive ability.

scholarly journals Associations between smoking and blood-group, and the risk of dyslipidaemia amongst French women

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
C. J. MacDonald ◽  
A. L. Madika ◽  
G. Severi ◽  
A. Fournier ◽  
M. C. Boutron-Ruault
Keyword(s):  
Blood Group ◽  
Smoking Status ◽  
Effect Modification ◽  
Blood Groups ◽  
Cross Sectional ◽  
Increased Risk ◽  
Cardio Vascular Disease ◽  
Never Smokers ◽  
Cardio Vascular ◽  
Incident Cases

AbstractDyslipidaemia is a major risk factor for cardio-vascular disease, as it promotes atherosclerosis. While cross-sectional studies have identified higher serum cholesterol amongst individuals with the A blood group, there is less evidence from prospective studies whether this translates into a higher risk of dyslipidaemia that requires treatment, nor if this genetic factor interacts with smoking status. This study aimed to prospectively determine potential associations between smoking, ABO blood groups, and risk of incident dyslipidaemia requiring treatment, and to assess associations over strata of blood ABO group. We assessed associations between blood ABO group, smoking and dyslipidaemia in 74,206 women participating in the E3N cohort. We included women who did not have cardiovascular disease at baseline. Logistic regression was used to determine associations between ABO group, smoking and prevalent dyslipidaemia at baseline. Cox proportional hazard models were then used to determine if blood ABO group and smoking were associated with the risk of incident dyslipidaemia, amongst women free of dyslipidaemia at baseline. At baseline 28,281 women with prevalent dyslipidaemia were identified. Compared to the O-blood group, the non-O blood group was associated higher odds of with prevalent dyslipidaemia (ORnon-O = 1.09 [1.06: 1.13]). Amongst the women free of dyslipidaemia at baseline, 6041 incident cases of treated dyslipidaemia were identified during 454,951 person-years of follow-up. The non-O blood groups were associated with an increased risk of dyslipidaemia when compared to the O-group (HRnon-O = 1.16 [1.11: 1.22]), specifically the A blood-group (HRA = 1.18 [1.12: 1.25]). Current smokers were associated with an increased risk of incident dyslipidaemia (HR smokers = 1.27 [1.16: 1.37]), compared to never-smokers. No evidence for effect modification between smoking and ABO blood group was observed (p-effect modification = 0.45), although the highest risk was observed among AB blood group women who smoked (HR = 1.76 [1.22: 2.55]). In conclusion, the non-O blood groups, specifically the A group were associated with an increased risk of dyslipidaemia. Current smokers were associated with a 30% increased risk of dyslipidaemia. These results could aid in personalised approaches to the prevention of cardiovascular risk-factors.

scholarly journals Machine Learning Prediction of Biomarkers from SNPs and of Disease Risk from Biomarkers in the UK Biobank

Genes ◽  
2021 ◽  
Vol 12 (7) ◽  
pp. 991
Author(s):  
Erik Widen ◽  
Timothy G. Raben ◽  
Louis Lello ◽  
Stephen D. H. Hsu
Keyword(s):  
Disease Risk ◽  
Smoking Status ◽  
Glycated Haemoglobin ◽  
European Ancestry ◽  
Risk Scores ◽  
Common Disease ◽  
Atherosclerotic Cardiovascular Disease ◽  
Uk Biobank ◽  
Lipoprotein A ◽  
The Uk

We use UK Biobank data to train predictors for 65 blood and urine markers such as HDL, LDL, lipoprotein A, glycated haemoglobin, etc. from SNP genotype. For example, our Polygenic Score (PGS) predictor correlates ∼0.76 with lipoprotein A level, which is highly heritable and an independent risk factor for heart disease. This may be the most accurate genomic prediction of a quantitative trait that has yet been produced (specifically, for European ancestry groups). We also train predictors of common disease risk using blood and urine biomarkers alone (no DNA information); we call these predictors biomarker risk scores, BMRS. Individuals who are at high risk (e.g., odds ratio of >5× population average) can be identified for conditions such as coronary artery disease (AUC∼0.75), diabetes (AUC∼0.95), hypertension, liver and kidney problems, and cancer using biomarkers alone. Our atherosclerotic cardiovascular disease (ASCVD) predictor uses ∼10 biomarkers and performs in UKB evaluation as well as or better than the American College of Cardiology ASCVD Risk Estimator, which uses quite different inputs (age, diagnostic history, BMI, smoking status, statin usage, etc.). We compare polygenic risk scores (risk conditional on genotype: PRS) for common diseases to the risk predictors which result from the concatenation of learned functions BMRS and PGS, i.e., applying the BMRS predictors to the PGS output.

scholarly journals A microRNA panel compared to environmental and polygenic scores for colorectal cancer risk prediction

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Janhavi R. Raut ◽  
Ben Schöttker ◽  
Bernd Holleczek ◽  
Feng Guo ◽  
Megha Bhardwaj ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Risk Prediction ◽  
Risk Score ◽  
Characteristic Curve ◽  
Predictive Ability ◽  
Population Based ◽  
Polygenic Risk Score ◽  
Nucleotide Polymorphisms ◽  
Polygenic Scores

AbstractCirculating microRNAs (miRNAs) could improve colorectal cancer (CRC) risk prediction. Here, we derive a blood-based miRNA panel and evaluate its ability to predict CRC occurrence in a population-based cohort of adults aged 50–75 years. Forty-one miRNAs are preselected from independent studies and measured by quantitative-real-time-polymerase-chain-reaction in serum collected at baseline of 198 participants who develop CRC during 14 years of follow-up and 178 randomly selected controls. A 7-miRNA score is derived by logistic regression. Its predictive ability, quantified by the optimism-corrected area-under-the-receiver-operating-characteristic-curve (AUC) using .632+ bootstrap is 0.794. Predictive ability is compared to that of an environmental risk score (ERS) based on known risk factors and a polygenic risk score (PRS) based on 140 previously identified single-nucleotide-polymorphisms. In participants with all scores available, optimism-corrected-AUC is 0.802 for the 7-miRNA score, while AUC (95% CI) is 0.557 (0.498–0.616) for the ERS and 0.622 (0.564–0.681) for the PRS.

scholarly journals Simple cardiovascular risk stratification using anthropometric measures instead of serum cholesterol. The MORGAM Prospective Cohort Project

2020 ◽  
Vol 41 (Supplement_2) ◽  
Author(s):  
A Kristensen ◽  
V Rosberg ◽  
J Vishram-Nielsen ◽  
M Pareek ◽  
A Linneberg ◽  
...  
Keyword(s):  
Cardiovascular Risk ◽  
Risk Stratification ◽  
Total Cholesterol ◽  
Serum Cholesterol ◽  
Cardiovascular Events ◽  
Smoking Status ◽  
Predictive Ability ◽  
Serum Total Cholesterol ◽  
Anthropometric Measures ◽  
Blood Pressures

Abstract Background Body composition predicts cardiovascular outcomes, but it is uncertain whether anthropometric measures can replace the more expensive serum total cholesterol for cardiovascular risk stratification in low resource settings. Purpose The purpose of the study was to compare the additive prognostic ability of serum total cholesterol with that of body mass index (BMI), waist/hip ratio (WHR), and estimated fat mass (EFM, calculated using a validated prediction equation), individually and combined. Methods We used data from the MORGAM (MONICA, Risk, Genetics, Archiving, and Monograph) Prospective Cohort Project, an international pooling of cardiovascular cohorts, to determine the relationship between anthropometric measures, serum cholesterol, and cardiovascular events, using multivariable Cox proportional-hazards regression analysis. We further investigated the ability of these measures to enhance prognostication beyond a simpler prediction model, consisting of age, sex, smoking status, systolic blood pressures, and country, using comparison of area under the receiver operating characteristics curve (AUCROC) derived from binary logistic regression models. The primary endpoint was major adverse cardiovascular events (MACE), defined as a composite of death from coronary heart disease, myocardial infarction, or stroke. Results The study population consisted of 52,188 apparently healthy subjects (56.3% men) aged 47±12 years ranging from 20 to 84, derived from 37 European cohorts, with baseline between 1982–2002 all followed for 10 years during which MACE occurred in 2465 (4.7%) subjects. All anthropometric measures (BMI: hazard ratio (HR) 1.04 [95% confidence interval (CI): 1.03–1.05] per kg/m2; WHR: HR 7.5 [4.0–14.0] per unit; EFM: HR 1.02 [1.01–1.02] per kg) as well as serum total cholesterol (HR 1.20 [1.16–1.24] per mmol/l) were significantly associated with MACE (P&lt;0.001 for all), independently of age, sex, smoking status, systolic blood pressures, and country. The addition of serum cholesterol significantly improved the predictive ability of the simple model (AUCROC 0.818 vs. 0.814, P&lt;0.001), as did the combination of WHR, BMI, and EFM (AUCROC 0.817 vs. 0.814, P=0.004). When assessed individually, BMI (AUCROC 0.816 vs. 0.814, P=0.004) and WHR (AUCROC 0.815 vs. 0.814, P=0.02) improved model performance, while EFM narrowly missed significance (AUCROC 0.815 vs. 0.814, P=0.06). There was no significant difference in the predictive ability of a model including serum cholesterol versus that including all three anthropometric measures (AUCROC 0.818 vs. 0.817, P=0.13). The figure shows the pertinent areas under the ROC curve in predicting MACE. Conclusion In this large population-based cohort study, the addition of a combination of anthropometric measures, i.e. BMI, WHR, and EFM, raised the predictive ability of a simple prognostic model comparable to that obtained by the addition of serum total cholesterol. Figure 1 Funding Acknowledgement Type of funding source: None

scholarly journals A Positive Association of Overactivated Immunity with Metabolic Syndrome Risk and Mitigation of Its Association by a Plant-Based Diet and Physical Activity in a Large Cohort Study

Nutrients ◽  
2021 ◽  
Vol 13 (7) ◽  
pp. 2308
Author(s):  
Sunmin Park ◽  
Ting Zhang
Keyword(s):  
Physical Activity ◽  
Metabolic Syndrome ◽  
Cohort Study ◽  
Large Scale ◽  
Smoking Status ◽  
Characteristic Curve ◽  
Daily Intake ◽  
Positive Association ◽  
Cell Counts ◽  
High Hba1c

The association between immunity and metabolic syndrome (MetS) has been studied, but its interaction with lifestyles remains unclear. We studied their association and interactions with lifestyles in 40,768 adults aged over 40 years from a large-scale, hospital-based cohort study collected during 2010–2013. White blood cell counts (WBC) and serum C-reactive protein concentrations (CRP) were used as indexes of immune status. The participants were categorized into four groups by the cutoff points of 6.2 × 109/L WBC(L-WBC) and <0.5 mg/dL CRP(L-CRP): L-WBC+L-CRP(n = 25,604), H-WBC+L-CRP(n = 13,880), L-WBC+H-CRP(n = 464), and H-WBC+H-CRP(n = 820). The participants in the H-WBC+L-CRP were younger and had higher numbers of males than the L-WBC+L-CRP. MetS risk was higher by 1.75- and 1.86-fold in the H-WBC+L-CRP and H-WBC+H-CRP, respectively, than the L-WBC+L-CRP. MetS components, including plasma glucose and triglyceride concentrations, and SBP were elevated in H-WBC+L-CRP and H-WBC+H-CRP compared with L-WBC+L-CR+P. The risk of hyperglycemia and high HbA1c was the highest in the H-WBC+H-CRP among all groups. Areas of WBC counts and serum CRP concentrations were 0.637 and 0.672, respectively, in the receiver operating characteristic curve. Daily intake of energy, carbohydrate, protein, and fat was not significantly different in the groups based on WBC counts and CRP. However, a plant-based diet (PBD), physical activity, and non-smoking were related to lowering WBC counts and CRP, but a Western-style diet was linked to elevating CRP. A high PBD intake and smoking status interacted with immunity to influence MetS risk: a low PBD and current smoking were associated with a higher MetS risk in the H-WBC+H-CRP. In conclusion, overactivated immunity determined by CRP and WBC was associated with MetS risk. Behavior modification with PBD and physical activity might be related to immunity regulation.

scholarly journals Risk score to stratify miscarriage risk levels in preconception women

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Xin Hui Choo ◽  
Chee Wai Ku ◽  
Yin Bun Cheung ◽  
Keith M. Godfrey ◽  
Yap-Seng Chong ◽  
...  
Keyword(s):  
Risk Factors ◽  
Risk Score ◽  
Smoking Status ◽  
Characteristic Curve ◽  
Lifestyle Changes ◽  
Risk Scores ◽  
Risk Level ◽  
Multivariable Logistic Regression Model ◽  
Spontaneous Miscarriage ◽  
Risk Levels

AbstractSpontaneous miscarriage is one of the most common complications of pregnancy. Even though some risk factors are well documented, there is a paucity of risk scoring tools during preconception. In the S-PRESTO cohort study, Asian women attempting to conceive, aged 18-45 years, were recruited. Multivariable logistic regression model coefficients were used to determine risk estimates for age, ethnicity, history of pregnancy loss, body mass index, smoking status, alcohol intake and dietary supplement intake; from these we derived a risk score ranging from 0 to 17. Miscarriage before 16 weeks of gestation, determined clinically or via ultrasound. Among 465 included women, 59 had miscarriages and 406 had pregnancy ≥ 16 weeks of gestation. Higher rates of miscarriage were observed at higher risk scores (5.3% at score ≤ 3, 17.0% at score 4–6, 40.0% at score 7–8 and 46.2% at score ≥ 9). Women with scores ≤ 3 were defined as low-risk level (< 10% miscarriage); scores 4–6 as intermediate-risk level (10% to < 40% miscarriage); scores ≥ 7 as high-risk level (≥ 40% miscarriage). The risk score yielded an area under the receiver-operating-characteristic curve of 0.74 (95% confidence interval 0.67, 0.81; p < 0.001). This novel scoring tool allows women to self-evaluate their miscarriage risk level, which facilitates lifestyle changes to optimize modifiable risk factors in the preconception period and reduces risk of spontaneous miscarriage.

ANOMALY DETECTION IN VESSEL TRACKING – A BAYESIAN NETWORKS (BNs) APPROACH

2021 ◽  
Vol 157 (A3) ◽  
Author(s):  
D Handayani ◽  
W Sediono ◽  
A Shah
Keyword(s):  
Machine Learning ◽  
Bayesian Networks ◽  
Expert Knowledge ◽  
Machine Learning Techniques ◽  
Automatic Identification ◽  
Identification System ◽  
International Convention ◽  
Learning Techniques ◽  
Reporting Data ◽  
Vessel Anomaly

The paper describes the supervised method approach to identifying vessel anomaly behaviour. The vessel anomaly behaviour is determined by learning from self-reporting maritime systems based on the Automatic Identification System (AIS). The AIS is a real world vessel reporting data system, which has been recently made compulsory by the International Convention for the Safety of Life and Sea (SOLAS) for vessels over 300 gross tons and most commercial vessels such as cargo ships, passenger vessels, tankers, etc. In this paper, we describe the use of Bayesian networks (BNs) approach to identify the behaviour of the vessel of interest. The BNs is a machine learning technique based on probabilistic theory that represents a set of random variables and their conditional independencies via directed acyclic graph (DAG). Previous studies showed that the BNs have important advantages compared to other machine learning techniques. Among them are that expert knowledge can be included in the BNs model, and that humans can understand and interpret the BNs model more readily. This work proves that the BNs technique is applicable to the identification of vessel anomaly behaviour.

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