Dataset Information

A Predictive Model Assessing Genetic Susceptibility Risk at Workplace.

ABSTRACT: (1) Background: The study of susceptibility biomarkers in the immigrant workforce integrated into the social tissue of European host countries is always a challenge, due to high individual heterogeneity and the admixing of different ethnicities in the same workplace. These workers having distinct cultural backgrounds, beliefs, diets, and habits, as well as a poor knowledge of the foreign language, may feel reluctant to donate their biological specimens for the biomonitoring research studies. (2) Methods: A model predicting ethnicity-specific susceptibility based on principal component analysis has been conceived, using the genotype frequency of the investigated populations available in publicly accessible databases. (3) Results: Correlations among ethnicities and between ethnic and polymorphic genes have been found, and low/high-risk profiles have been identified as valuable susceptibility biomarkers. (4) Conclusions: In the absence of workers' consent or access to blood genotyping, ethnicity represents a good indicator of the subject's genotype. This model, associating ethnicity-specific genotype frequency with the susceptibility biomarkers involved in the metabolism of toxicants, may replace genotyping, ensuring the necessary safety and health conditions of workers assigned to hazardous jobs.

SUBMITTER: Chiarella P

PROVIDER: S-EPMC6603935 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature

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A Predictive Model Assessing Genetic Susceptibility Risk at Workplace.

Chiarella Pieranna P Capone Pasquale P Carbonari Damiano D Sisto Renata R

International journal of environmental research and public health 20190605 11

(1) Background: The study of susceptibility biomarkers in the immigrant workforce integrated into the social tissue of European host countries is always a challenge, due to high individual heterogeneity and the admixing of different ethnicities in the same workplace. These workers having distinct cultural backgrounds, beliefs, diets, and habits, as well as a poor knowledge of the foreign language, may feel reluctant to donate their biological specimens for the biomonitoring research studies. (2) ...[more]

PMID: 31195756

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Project description:BackgroundSarcopenia is linked to an unfavorable prognosis in individuals with rheumatoid arthritis (RA). Early identification and treatment of sarcopenia are clinically significant. This study aimed to create and validate a nomogram for predicting sarcopenia risk in RA patients, providing clinicians with a reliable tool for the early identification of high-risk patients.MethodsPatients with RA diagnosed between August 2022 and January 2024 were included and randomized into training and validation sets in a 7:3 ratio. Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis and multifactorial logistic regression analysis were used to screen the risk variables for RA-associated muscle loss and to create an RA sarcopenia risk score. The predictive performance and clinical utility of the risk model were evaluated by plotting the receiver operating characteristic curve and calculating the area under the curve (AUC), along with the calibration curve and clinical decision curve (DCA).ResultsA total of 480 patients with RA were included in the study (90% female, with the largest number in the 45-59 age group, about 50%). In this study, four variables (body mass index, disease duration, hemoglobin, and grip strength) were included to construct a nomogram for predicting RA sarcopenia. The training and validation set AUCs were 0.915 (95% CI: 0.8795-0.9498) and 0.907 (95% CI: 0.8552-0.9597), respectively, proving that the predictive model was well discriminated. The calibration curve showed that the predicted values of the model were basically in line with the actual values, demonstrating good calibration. The DCA indicated that almost the entire range of patients with RA can benefit from this novel prediction model, suggesting good clinical utility.ConclusionThis study developed and validated a nomogram prediction model to predict the risk of sarcopenia in RA patients. The model can assist clinicians in enhancing their ability to screen for RA sarcopenia, assess patient prognosis, make early decisions, and improve the quality of life for RA patients.

Project description:Background & aimsRisk for colorectal cancer (CRC) can be greatly reduced through screening. To aid in the development of screening strategies, we refined models designed to determine risk of CRC by incorporating information from common genetic susceptibility loci.MethodsBy using data collected from more than 12,000 participants in 6 studies performed from 1990 through 2011 in the United States and Germany, we developed risk determination models based on sex, age, family history, genetic risk score (number of risk alleles carried at 27 validated common CRC susceptibility loci), and history of endoscopic examinations. The model was validated using data collected from approximately 1800 participants in the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial, conducted from 1993 through 2001 in the United States.ResultsWe identified a CRC genetic risk score that independently predicted which patients in the training set would develop CRC. Compared with determination of risk based only on family history, adding the genetic risk score increased the discriminatory accuracy from 0.51 to 0.59 (P = .0028) for men and from 0.52 to 0.56 (P = .14) for women. We calculated age- and sex-specific 10-year CRC absolute risk estimates based on the number of risk alleles, family history, and history of endoscopic examinations. A model that included a genetic risk score better determined the recommended starting age for screening in subjects with and without family histories of CRC. The starting age for high-risk men (family history of CRC and genetic risk score, 90%) was 42 years, and for low-risk men (no family history of CRC and genetic risk score, 10%) was 52 years. For men with no family history and a high genetic risk score (90%), the starting age would be 47 years; this is an intermediate value that is 5 years earlier than it would be for men with a genetic risk score of 10%. Similar trends were observed in women.ConclusionsBy incorporating information on CRC risk alleles, we created a model to determine the risk for CRC more accurately. This model might be used to develop screening and prevention strategies.

Project description:BackgroundAcute kidney injury (AKI) is a serious post-surgery complication; however, few preoperative risk models for AKI have been developed for hypertensive patients undergoing general surgery. Thus, in this study involving a large Chinese cohort, we developed and validated a risk model for surgery-related AKI using preoperative risk factors.Methods and findingsThis retrospective cohort study included 24,451 hypertensive patients aged ≥18 years who underwent general surgery between 2007 and 2015. The endpoints for AKI classification utilized by the KDIGO (Kidney Disease: Improving Global Outcomes) system were assessed. The most discriminative predictor was selected using Fisher scores and was subsequently used to construct a stepwise multivariate logistic regression model, whose performance was evaluated via comparisons with models used in other published works using the net reclassification index (NRI) and integrated discrimination improvement (IDI) index.ResultsSurgery-related AKI developed in 1994 hospitalized patients (8.2%). The predictors identified by our Xiang-ya Model were age, gender, eGFR, NLR, pulmonary infection, prothrombin time, thrombin time, hemoglobin, uric acid, serum potassium, serum albumin, total cholesterol, and aspartate amino transferase. The area under the receiver-operating characteristic curve (AUC) for the validation set and cross validation set were 0.87 (95% CI 0.86-0.89) and (0.89; 95% CI 0.88-0.90), respectively, and was therefore similar to the AUC for the training set (0.89; 95% CI 0.88-0.90). The optimal cutoff value was 0.09. Our model outperformed that developed by Kate et al., which exhibited an NRI of 31.38% (95% CI 25.7%-37.1%) and an IDI of 8% (95% CI 5.52%-10.50%) for patients who underwent cardiac surgery (n = 2101).Conclusions/significanceWe developed an AKI risk model based on preoperative risk factors and biomarkers that demonstrated good performance when predicting events in a large cohort of hypertensive patients who underwent general surgery.

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A Predictive Model Assessing Genetic Susceptibility Risk at Workplace.

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A Predictive Model Assessing Genetic Susceptibility Risk at Workplace.

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