Dataset Information

Development and evaluation of a simple and effective prediction approach for identifying those at high risk of dyslipidemia in rural adult residents.

ABSTRACT:

Background

Dyslipidemia is an extremely prevalent but preventable risk factor for cardiovascular disease. However, many dyslipidemia patients remain undetected in resource limited settings. The study was performed to develop and evaluate a simple and effective prediction approach without biochemical parameters to identify those at high risk of dyslipidemia in rural adult population.

Methods

Demographic, dietary and lifestyle, and anthropometric data were collected by a cross-sectional survey from 8,914 participants living in rural areas aged 35-78 years. There were 6,686 participants randomly selected into a training group for constructing the artificial neural network (ANN) and logistic regression (LR) prediction models. The remaining 2,228 participants were assigned to a validation group for performance comparisons of ANN and LR models. The predictors of dyslipidemia risk were identified from the training group using multivariate logistic regression analysis. Predictive performance was evaluated by receiver operating characteristic (ROC) curve.

Results

Some risk factors were significantly associated with dyslipidemia, including age, gender, educational level, smoking, high-fat diet, vegetable and fruit intake, family history, physical activity, and central obesity. For the ANN model, the sensitivity, specificity, positive and negative likelihood ratio, positive and negative predictive values were 90.41%, 76.66%, 3.87, 0.13, 76.33%, and 90.58%, respectively, while LR model were only 57.37%, 70.91%, 1.97, 0.60, 62.09%, and 66.73%, respectively. The area under the ROC cure (AUC) value of the ANN model was 0.86±0.01, showing more accurate overall performance than traditional LR model (AUC = 0.68±0.01, P<0.001).

Conclusion

The ANN model is a simple and effective prediction approach to identify those at high risk of dyslipidemia, and it can be used to screen undiagnosed dyslipidemia patients in rural adult population. Further work is planned to confirm these results by incorporating multi-center and longer follow-up data.

SUBMITTER: Wang CJ

PROVIDER: S-EPMC3429495 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Development and evaluation of a simple and effective prediction approach for identifying those at high risk of dyslipidemia in rural adult residents.

Wang Chong-Jian CJ Li Yu-Qian YQ Wang Ling L Li Lin-Lin LL Guo Yi-Rui YR Zhang Ling-Yun LY Zhang Mei-Xi MX Bie Rong-Hai RH

PloS one 20120828 8

<h4>Background</h4>Dyslipidemia is an extremely prevalent but preventable risk factor for cardiovascular disease. However, many dyslipidemia patients remain undetected in resource limited settings. The study was performed to develop and evaluate a simple and effective prediction approach without biochemical parameters to identify those at high risk of dyslipidemia in rural adult population.<h4>Methods</h4>Demographic, dietary and lifestyle, and anthropometric data were collected by a cross-secti ...[more]

PMID: 22952780

Similar Datasets

Project description:IntroductionDyslipidemia is a major risk factor for cardiovascular diseases (CVD). The prevalence of dyslipidemia is not known among Ethiopian adults. The prevalence is expected to rise due to the socio-economic development accompanied by lifestyle changes. This study was conducted to estimate the prevalence of dyslipidemia and associated risk factors among adult residents of Mekelle City.MethodsA community-based cross-sectional study was conducted among 321 randomly selected subjects. Data were collected on sociodemographic, anthropometric, lifestyle, and clinical characteristics of the participants using the WHO STEPS survey instrument. Data were analyzed using SPSS software version 24.0. Student's t-test and Pearson's Chi-square test were used to assessing the interrelationship between each factor and outcome variables. Bivariate and multivariable logistic regression analysis were used to identify risk factors associated with dyslipidemia. All statistical significance was considered at p ≤0.05.ResultsThe prevalence of dyslipidemia in this study was 66.7%. The prevalence of high low-density lipoprotein cholesterol (LDL-C), elevated triglyceride, elevated total cholesterol, and low high-density lipoprotein cholesterol (HDL-C) was 49.5%, 40.2%, 30.8%, and 16.5%, respectively. Being above 64 years (aOR: 2.196, 95% CI: 1.183-4.078) and 40-64 years old (aOR: 2.196, 95% CI: 1.183-4.078), overweight (aOR: 2.50, 95% CI: 1.314-4.756) and obesity (aOR: 15.489, 95% CI: 3.525-68.070), walking <150 minutes per week (aOR: 1.722, 95% CI: 1.004-2.953), raised fasting blood glucose (FBG) (aOR: 4.804, 95% CI: 1.925-11.988), and medium socio-economic status (aOR: 2.017, 95% CI: 1.044-3.899) were identified as significant predictors of dyslipidemia.ConclusionsThe finding of this study indicated that the prevalence of dyslipidemia is unacceptably high among adult residents of Mekelle City, which underlines an urgent need for early detection and public health interventions through the integrated involvement of public, governmental, and non-governmental organizations.

Project description:ObjectiveHigher selenium level has been hypothesized to have the potential to reduce the risk of cardiovascular diseases including dyslipidemia. However, results from previous studies are inconsistent. This study aims to determine the association between selenium level and dyslipidemia in elderly Chinese with relatively low selenium status.MethodsA cross-sectional study of 1859 participants aged 65 or older from four rural counties in China was conducted. Serum total cholesterol (TC), triglycerides (TG), high density lipoprotein-cholesterol (HDLC) and low-density lipoprotein-cholesterol (LDLC), nail selenium concentration and APOE genotype were measured in all subjects. The four types of dyslipidemia were defined as >5.17 mmol/L for High-TC, >1.69 mmol/L for High-TG, >3.36 mmol/L for High-LDLC, and <1.04 mmol/L for Low-HDLC according to Chinese Guidelines on Prevention and Treatment of Dyslipidemia in Adults. Logistic models adjusting for age, gender, APOE genotype, body mass index, alcohol consumption, smoking, physical activity, medication use for cardiovascular diseases were used to examine the relationship between selenium levels and the risk of dyslipidemia.ResultsMean nail selenium concentration was 0.465 μg/gin this sample. Rates for High-TC, High-LDLC, High-TG, Low-HDLC were 18.13%, 13.23%, 12.21% and 32.76% respectively. Results from logistic models indicated that higher selenium levels were significantly associated with higher risk of High-TC, High-LDLC and lower risk of Low-HDLC adjusting for covariates (p < 0.0001). Compared with the lowest selenium quartile group, participants in selenium quartile groups 2, 3 and 4 had significantly higher rates of High-TC, High-LDLC, High-TG, and lower rate of Low-HDLC adjusting for covariates. No significant association was observed between selenium level and the risk of High-TG. APOEε4 carriers had higher rates of High-TC and High-LDLC. There was no interaction between selenium level and APOE with the rates of dyslipidemia.ConclusionsOur results suggest long-term selenium exposure level may be associated with the risk of dyslipidemia in elderly population. Future studies are needed to examine the underlying mechanism of the association.

Project description:Bacterial communities in water, soil, and humans play an essential role in environmental ecology and human health. PCR-based amplicon analysis, such as 16S rRNA sequencing, is a fundamental tool for quantifying and studying microbial composition, dynamics, and interactions. However, given the complexity of microbial communities, a substantial number of samples becomes necessary for analyses that parse the factors that determine microbial composition. A common bottleneck in performing these kinds of experiments is genomic DNA (gDNA) extraction, which is time-consuming, expensive, and often biased based on the types of species present. Direct PCR method is a potentially simpler and more accurate alternative to gDNA extraction methods that do not require the intervening purification step. In this study, we evaluated three variations of direct PCR methods using diverse heterogeneous bacterial cultures, including both Gram-positive and Gram-negative species, ZymoBIOMICS microbial community standards, and groundwater. By comparing direct PCR methods with DNeasy Blood and Tissue Kits for microbial isolates and DNeasy PowerSoil Kits for microbial communities, we found that a specific variant of the direct PCR method exhibits an overall efficiency comparable to that of the conventional DNeasy PowerSoil protocol in the circumstances we tested. We also found that the method showed higher efficiency for extracting gDNA from the Gram-negative strains compared to DNeasy Blood and Tissue protocol. This direct PCR method is 1,600 times less expensive ($0.34 for 96 samples) and 10 times simpler (15 min hands-on time for 96 samples) than the DNeasy PowerSoil protocol. The direct PCR method can also be fully automated and is compatible with small-volume samples, thereby permitting scaling of samples and replicates needed to support high-throughput large-scale bacterial community analysis. IMPORTANCE Understanding bacterial interactions and assembly in complex microbial communities using 16S rRNA sequencing normally requires a large experimental load. However, the current DNA extraction methods, including cell disruption and genomic DNA purification, are normally biased, costly, time-consuming, labor-intensive, and not amenable to miniaturization by droplets or 1,536-well plates due to the significant DNA loss during the purification step for tiny-volume and low-cell-density samples. A direct PCR method could potentially solve these problems. In this study, we developed a direct PCR method which exhibits similar efficiency as the widely used method, the DNeasy PowerSoil protocol, while being 1,600 times less expensive and 10 times faster to execute. This simple, cost-effective, and automation-friendly direct-PCR-based 16S rRNA sequencing method allows us to study the dynamics, microbial interaction, and assembly of various microbial communities in a high-throughput fashion.

Dataset Information

Development and evaluation of a simple and effective prediction approach for identifying those at high risk of dyslipidemia in rural adult residents.

Background

Methods

Results

Conclusion

Publications

Development and evaluation of a simple and effective prediction approach for identifying those at high risk of dyslipidemia in rural adult residents.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets