A Bayesian Model to Analyze the Association of Rheumatoid Arthritis With Risk Factors and Their Interactions

Rheumatoid arthritis (RA) is a chronic autoimmune disorder that commonly manifests as destructive joint inflammation but also affects multiple other organ systems. The pathogenesis of RA is complex where a variety of factors including comorbidities, demographic, and socioeconomic variables are known to associate with RA and influence the progress of the disease. In this work, we used a Bayesian logistic regression model to quantitatively assess how these factors influence the risk of RA, individually and through their interactions. Using cross-sectional data from the National Health and Nutrition Examination Survey (NHANES), a set of 11 well-known RA risk factors such as age, gender, ethnicity, body mass index (BMI), and depression were selected to predict RA. We considered up to third-order interactions between the risk factors and implemented factor analysis of mixed data (FAMD) to account for both the continuous and categorical natures of these variables. The model was further optimized over the area under the receiver operating characteristic curve (AUC) using a genetic algorithm (GA) with the optimal predictive model having a smoothed AUC of 0.826 (95% CI: 0.801–0.850) on a validation dataset and 0.805 (95% CI: 0.781–0.829) on a holdout test dataset. Apart from corroborating the influence of individual risk factors on RA, our model identified a strong association of RA with multiple second- and third-order interactions, many of which involve age or BMI as one of the factors. This observation suggests a potential role of risk-factor interactions in RA disease mechanism. Furthermore, our findings on the contribution of RA risk factors and their interactions to disease prediction could be useful in developing strategies for early diagnosis of RA.

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A Bayesian Model for Prediction of Rheumatoid Arthritis from Risk Factors

10.1101/2020.07.09.20150326 ◽

2020 ◽

Author(s):

Leon Lufkin ◽

Marko Budišić ◽

Sumona Mondal ◽

Shantanu Sur

Keyword(s):

Rheumatoid Arthritis ◽

Risk Factors ◽

Predictive Model ◽

Characteristic Curve ◽

Strong Association ◽

Autoimmune Disorder ◽

Mixed Data ◽

Validation Dataset ◽

Third Order ◽

Organ Systems

Rheumatoid arthritis (RA) is a chronic autoimmune disorder that typically manifests as destructive joint inflammation but also affects multiple other organ systems. The pathogenesis of RA is complex where a variety of factors including comorbidities, demographic, and socioeconomic variables are known to influence the incidence and progress of the disease. In this work, we aimed to predict RA from a set of 11 well-known risk factors and their interactions using Bayesian logistic regression. We considered up to third-order interactions between the risk factors and implemented factor analysis of mixed data (FAMD) to account for both the continuous and categorical natures of these variables. The predictive model was further optimized over the area under the receiver operating characteristic curve (AUC) using a genetic algorithm (GA). We use data from the National Health and Nutrition Examination Survey (NHANES). Our optimal predictive model has a smoothed AUC of 0.826 (95% CI: 0.801 −0.850) on a validation dataset and 0.805 (95% CI: 0.781 −0.829) on a holdout test dataset. Our model identified multiple second- and third-order interactions that demonstrate a strong association with RA, implying the potential role of risk factor interactions in the disease mechanism. Interestingly, we find that the inclusion of higher-order interactions in the model only marginally improves overall predictive ability. Our findings on the contribution of RA risk factors and their interaction on disease prediction could be useful in developing strategies for early diagnosis of RA, thus opening potential avenues for improved patient outcomes and reduced healthcare burden to society.

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Individual risk factors of metabolic syndrome in adult patients with GH deficiency: a cross-sectional case-control study

Endocrine Abstracts ◽

10.1530/endoabs.35.p939 ◽

2014 ◽

Author(s):

Ivayla Uzunova ◽

Georgi Kirilov ◽

Sabina Zacharieva ◽

Alexander Shinkov ◽

Anna-Maria Borissova ◽

...

Keyword(s):

Risk Factors ◽

Metabolic Syndrome ◽

Case Control Study ◽

Gh Deficiency ◽

Case Control ◽

Adult Patients ◽

Individual Risk ◽

Cross Sectional ◽

Individual Risk Factors ◽

Control Study

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CARDIORESPIRATORY FITNESS AND CARDIOMETABOLIC RISK FACTORS AMONG UNIVERSITY PROFESSORS

Revista Brasileira de Medicina do Esporte ◽

10.1590/1517-869220182402171631 ◽

2018 ◽

Vol 24 (2) ◽

pp. 102-106

Author(s):

Jaqueline de Oliveira Santana ◽

Juliana Vaz de Melo Mambrini ◽

Sérgio Viana Peixoto

Keyword(s):

Risk Factors ◽

Cardiorespiratory Fitness ◽

Cardiometabolic Risk ◽

Strong Association ◽

Protein Body ◽

Hdl Cholesterol ◽

Work Capacity ◽

Cardiometabolic Risk Factors ◽

Level Of Evidence ◽

Cross Sectional

ABSTRACT Introduction: Cardiorespiratory fitness (CF) is associated with mortality and the development of cardiovascular disease, in addition to being related to work capacity. Objectives: This study aimed to verify the demographic, cardiometabolic and behavioral factors associated with CF in a representative sample of professors from a public university in Minas Gerais, Brazil. Methods: This is a cross-sectional study which evaluated, in addition to the CF, age, sex, glycemia, triglycerides, LDL and HDL cholesterol, C-reactive protein, body mass index (BMI), waist circumference, and physical activity (PA). The association between CF and cardiometabolic risk factors was estimated by logistic regression to obtain the odds ratios and respective confidence intervals (95%). Results: After adjustment, it was observed that professors with lower levels of CF were older, female, had higher BMI and a greater chance of being physically inactive. Conclusion: In general, the results show that the probability of low CF increases with the increase in BMI, in addition to the strong association with PA practice, which is a major focus of intervention measures aimed at improving workers health and their work capacity. Level of Evidence III; Case control study.

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Development and validation of a clinical and genetic model for predicting risk of severe COVID-19

10.1101/2021.03.09.21253237 ◽

2021 ◽

Author(s):

Gillian S. Dite ◽

Nicholas M. Murphy ◽

Richard Allman

Keyword(s):

Logistic Regression ◽

Genetic Model ◽

Characteristic Curve ◽

Accurate Prediction ◽

Training Dataset ◽

Validation Dataset ◽

Individual Risk ◽

Prototype Model ◽

List Type ◽

New Model

SummaryClinical and genetic risk factors for severe COVID-19 are often considered independently and without knowledge of the magnitudes of their effects on risk. Using SARS-CoV-2 positive participants from the UK Biobank, we developed and validated a clinical and genetic model to predict risk of severe COVID-19. We used multivariable logistic regression on a 70% training dataset and used the remaining 30% for validation. We also validated a previously published prototype model. In the validation dataset, our new model was associated with severe COVID-19 (odds ratio per quintile of risk=1.77, 95% confidence interval [CI]=1.64, 1.90) and had excellent discrimination (area under the receiver operating characteristic curve=0.732, 95% CI=0.708, 0.756). We assessed calibration using logistic regression of the log odds of the risk score, and the new model showed no evidence of over- or under-estimation of risk (α=−0.08; 95% CI=−0.21, 0.05) and no evidence or over- or under-dispersion of risk (β=0.90, 95% CI=0.80, 1.00). Accurate prediction of individual risk is possible and will be important in regions where vaccines are not widely available or where people refuse or are disqualified from vaccination, especially given uncertainty about the extent of infection transmission among vaccinated people and the emergence of SARS-CoV-2 variants of concern.Key resultsAccurate prediction of the risk of severe COVID-19 can inform public heath interventions and empower individuals to make informed choices about their day-to-day activities.Age and sex alone do not accurately predict risk of severe COVID-19.Our clinical and genetic model to predict risk of severe COVID-19 performs extremely well in terms of discrimination and calibration.

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Dusty trades and associated rheumatoid arthritis in a population-based study in the coal mining counties of Appalachia

Occupational and Environmental Medicine ◽

10.1136/oemed-2021-107899 ◽

2022 ◽

pp. oemed-2021-107899

Author(s):

Gabriela Schmajuk ◽

Laura Trupin ◽

Edward H Yelin ◽

Paul D Blanc

Keyword(s):

Rheumatoid Arthritis ◽

Coal Mining ◽

Smoking Status ◽

Strong Association ◽

Population Based ◽

Cross Sectional ◽

Silica Exposure ◽

Random Digit Dial ◽

Ergonomic Exposure ◽

Ergonomic Exposures

ObjectivesWe previously showed increased coal mining-associated risk of rheumatoid arthritis (RA). Using additional survey data, we sought to delineate this risk further.MethodsWe used data from two cross-sectional, random-digit-dial, population-based surveys (males;≥50 years) in selected counties in the Appalachian region of the inland, mid-Atlantic USA with elevated pneumoconiosis mortality. Surveys ascertained age, smoking, coal mining and non-coal silica exposure jobs. In a subset, we surveyed ergonomic exposures, scored by intensity. We queried diagnosis of RA, corticosteroid use, and, in a subset, use of disease modifying antirheumatic drugs (DMARDs). Multivariable logistic regression modelled RA risk (defined by glucocorticoid or DMARDs use) associated with coal mining employment, other silica exposure, smoking status, and age and ergonomic exposures.ResultsWe analysed data for 2981 survey respondents (mean age 66.6 years; 15% current, 44% ex-smokers). The prevalence of glucocorticoid-treated and DMARD-treated RA was 11% and 4%, respectively. Glucocorticoid-treated RA was associated with coal mining (OR 3.5; 95% CI 2.5 to 4.9) and non-coal mining silica exposure (OR 3.2; 95% CI 2.4 to 4.4). For DMARD-treated RA, the odds associated with coal mining and other silica remained elevated: OR 2.3 (95% CI 1.18, 4.5) and OR 2.7 (95% CI 1.51, 5.0), respectively. In the same model, the highest intensity ergonomic exposure also was associated with increased odds of RA (OR 4.3; 95% CI 1.96 to 9.6).ConclusionsWe observed a strong association between coal mining and other silica-exposing dusty trades and RA. Clinicians and insurers should consider occupational histories in the aetiology of RA.

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Seroprevalence Study of Anti-HEV IgG among Different Adult Populations in Corsica, France, 2019

Microorganisms ◽

10.3390/microorganisms7100460 ◽

2019 ◽

Vol 7 (10) ◽

pp. 460 ◽

Cited By ~ 1

Author(s):

Capai ◽

Masse ◽

Gallian ◽

Souty ◽

Isnard ◽

...

Keyword(s):

Risk Factors ◽

General Practice ◽

Blood Donors ◽

Adult Population ◽

Strong Association ◽

Hepatitis E ◽

Cross Sectional Study ◽

Antibody Detection ◽

P Value ◽

Cross Sectional

Hepatitis E virus (HEV) is a major cause of acute hepatitis worldwide. In France, hyperendemic areas including Corsica have an anti-HEV Immunoglobulin G (IgG) prevalence higher than 50%. The aim of this study was to determine the seroprevalence of anti-HEV IgG in three adult populations in Corsica and the risk factors associated with antibody detection. Between 2017 and 2019, a total of 930 individuals, including 467 blood donors, 393 students or university staff members and 70 patients from general practice, were tested for the presence of anti-HEV IgG using the Wantai HEV IgG enzyme immunoassay kit and filled a questionnaire. The association between seropositivity and potential risk factors was tested with univariate and multivariate analyses. Out of the 930 samples, 52.3% (486/930) were seropositive—54.4% (254/467) among blood donors, 47.6% (187/393) among university students and 64.3% (45/70) among patients of general practice. Three main risk factors were identified: (i) skinning and butchering (Adjusted Odds Ratio aOR = 2.76, 95% confidence interval [95% CI] [1.51–5.37]; p-value < 10−3), (ii) consumption of a local pork live raw sausage (fittonu) (aOR = 1.95 95% CI [1.45–2.64]; p-value = 10−5), and (iii) increasing age (p-value = 0.003). Seropositivity rates between the different populations were homogeneous after age stratification. This cross-sectional study indicates a high anti-HEV IgG seroprevalence in the Corsican adult population, not significantly different between women and men and increasing with age. This serosurvey also showed homogeneity regarding the exposure to HEV among three different types of populations. Finally, we confirmed the endemicity of Corsica with respect to HEV and identified a strong association between consumption of figatellu/fittonu and the practice of skinning and butchering with the detection of anti-HEV IgG.

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Malnutrition and related risk factors in older adults from different health-care settings: an enable study

Public Health Nutrition ◽

10.1017/s1368980019002271 ◽

2019 ◽

Vol 23 (3) ◽

pp. 446-456 ◽

Cited By ~ 2

Author(s):

Eva Kiesswetter ◽

Miriam G Colombo ◽

Christa Meisinger ◽

Annette Peters ◽

Barbara Thorand ◽

...

Keyword(s):

Risk Factors ◽

Older Adults ◽

Older Age ◽

Dietary Behaviour ◽

Individual Risk ◽

Mobility Limitations ◽

Cross Sectional ◽

Living Situation ◽

Poor Appetite ◽

Individual Risk Factors

AbstractObjective:The origin of malnutrition in older age is multifactorial and risk factors may vary according to health and living situation. The present study aimed to identify setting-specific risk profiles of malnutrition in older adults and to investigate the association of the number of individual risk factors with malnutrition.Design:Data of four cross-sectional studies were harmonized and uniformly analysed. Malnutrition was defined as BMI < 20 kg/m2 and/or weight loss of >3 kg in the previous 3–6 months. Associations between factors of six domains (demographics, health, mental function, physical function, dietary intake-related problems, dietary behaviour), the number of individual risk factors and malnutrition were analysed using logistic regression.Setting:Community (CD), geriatric day hospital (GDH), home care (HC), nursing home (NH).Participants:CD older adults (n 1073), GDH patients (n 180), HC receivers (n 335) and NH residents (n 197), all ≥65 years.Results:Malnutrition prevalence was lower in CD (11 %) than in the other settings (16–19 %). In the CD sample, poor appetite, difficulties with eating, respiratory and gastrointestinal diseases were associated with malnutrition; in GDH patients, poor appetite and respiratory diseases; in HC receivers, younger age, poor appetite and nausea; and in NH residents, older age and mobility limitations. In all settings the likelihood of malnutrition increased with the number of potential individual risk factors.Conclusions:The study indicates a varying relevance of certain risk factors of malnutrition in different settings. However, the relationship of the number of individual risk factors with malnutrition in all settings implies comprehensive approaches to identify persons at risk of malnutrition early.

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Arterial hypertension in the elderly accompanied in primary care: profile and associated factors

Escola Anna Nery ◽

10.1590/2177-9465-ean-2018-0322 ◽

2019 ◽

Vol 23 (2) ◽

Author(s):

Breno de Sousa Santana ◽

Bárbara Soares Rodrigues ◽

Marina Morato Stival ◽

Cris Renata Grou Volpe

Keyword(s):

Risk Factors ◽

Blood Pressure ◽

Strong Association ◽

Federal District ◽

The Elderly ◽

Pressure Control ◽

Sociodemographic Factors ◽

Cross Sectional Study ◽

Cross Sectional ◽

Elderly Hypertensive

Abstract Objective: to analyze blood pressure control in elderly hypertensives accompanied by a Basic Health Unit of the Federal District, determining the sociodemographic profile and the associated risk factors. Method: this is a cross-sectional study with 133 elderly hypertensive patients. Variables related to sociodemographic factors, life habits, clinical factors and adherence to drug therapy were evaluated through the application of semistructured instruments and validated scales. Results with p <0.05 were considered significant. Results: the elderly presented controlled blood pressure (56.4%) predominantly. The majority of the participants were female and women also had a higher rate of uncontrolled blood pressure (86.2%). Older age was associated with higher blood pressure values (p = 0.031). Alcoholism (p = 0.020) and mean body mass index of 33.0 (p <0.000) were factors associated with hypertension adherence to therapy had more controlled values of systolic and diastolic blood pressure. Conclusion and implications for the practice: it was verified that there is a strong association between the risk factors discussed and the lack of control of the blood pressure of elderly hypertensive, especially with regard to advanced age, alcoholism, obesity and overweight, and it is necessary to reorient the planning and strategies of promotion of health and prevention of diseases directed at the elderly hypertensive in the scope of primary health care.

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Individual and Environmental Factors Associated with Recurrent Falls in Elderly Patients Hospitalized after Falls

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph17072441 ◽

2020 ◽

Vol 17 (7) ◽

pp. 2441

Author(s):

Hai Minh Vu ◽

Long Hoang Nguyen ◽

Huong Lan Thi Nguyen ◽

Giang Thu Vu ◽

Cuong Tat Nguyen ◽

...

Keyword(s):

Risk Factors ◽

Environmental Factors ◽

Elderly Patients ◽

Environmental Conditions ◽

Social Consequences ◽

Individual Risk ◽

Cross Sectional ◽

Factors Associated ◽

Recurrent Falls ◽

The Individual

Falls and recurrent falls cause great health and social consequences in older people. However, these problems are poorly understood in Vietnam. A cross-sectional study was performed at seven hospitals in Thai Binh province, Vietnam, to investigate the individual and environmental factors associated with recurrent falls among elderly patients hospitalized due to fall injuries in Vietnam. A history of recurrent falls within the last 12 months, sociodemographic, health, and clinical characteristics, as well as environmental conditions, were obtained via self-reported interviews. Multivariate logistic and Poisson regression models were used to identify associated factors. Overall, the mean fall episodes in the last 12 months were 1.8 (Standard deviation—SD = 1.2) episodes, and the 12-month prevalence of recurrent falls was 40.5%. The individual risk factors included not receiving fall prevention guidelines, walking with devices, loss of sensation in hand or foot, and using pain relief medications. The environmental risk factors comprised having too-high stairs and not having dry, clean, and nonslippery bathrooms. This study highlights a significantly high 12-month prevalence of recurrent falls in older patients hospitalized after falls in Vietnam. Moreover, regular assessments of functional disabilities and hazardous environmental conditions, as well as the provision of prevention programs, have potential to prevent falls and recurrent falls.

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