Utilization of N-glycosylation profiles as risk stratification biomarkers for suboptimal health status and metabolic syndrome in a Ghanaian population

2019 ◽  
Vol 13 (15) ◽  
pp. 1273-1287 ◽  
Author(s):  
Eric Adua ◽  
Elham Memarian ◽  
Alyce Russell ◽  
Irena Trbojević-Akmačić ◽  
Ivan Gudelj ◽  
...  
Keyword(s):  
Metabolic Syndrome ◽  
Health Status ◽  
Logistic Regression Model ◽  
Area Under The Curve ◽  
Information Criterion ◽  
Ultra Performance Liquid Chromatography ◽  
Clinical Assessments ◽  
Suboptimal Health Status ◽  
Case Status ◽  
Suboptimal Health

Aim: The study sought to apply N-glycosylation profiles to understand the interplay between suboptimal health status (SHS) and metabolic syndrome (MetS). Materials & methods: In this study, 262 Ghanaians were recruited from May to July 2016. After completing a health survey, plasma samples were collected for clinical assessments while ultra performance liquid chromatography was used to measure plasma N-glycans. Results: Four glycan peaks were found to predict case status (MetS and SHS) using a step-wise Akaike’s information criterion logistic regression model selection. This model yielded an area under the curve of MetS: 83.1% (95% CI: 78.0–88.1%) and SHS: 67.1% (60.6–73.7%). Conclusion: Our results show that SHS is a significant, albeit modest, risk factor for MetS and N-glycan complexity was associated with MetS.

scholarly journals Screening the Influence of Biomarkers for Metabolic Syndrome in Occupational Population Based on the Lasso Algorithm

2021 ◽  
Vol 9 ◽  
Author(s):  
Qiao-Ying Xie ◽  
Ming-Wei Wang ◽  
Zu-Ying Hu ◽  
Cheng-Jian Cao ◽  
Cong Wang ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Metabolic Syndrome ◽  
Logistic Regression ◽  
Prediction Model ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Area Under The Curve ◽  
Assessment Model ◽  
Risk Assessment Model ◽  
Lasso Regression

Aim: Metabolic syndrome (MS) screening is essential for the early detection of the occupational population. This study aimed to screen out biomarkers related to MS and establish a risk assessment and prediction model for the routine physical examination of an occupational population.Methods: The least absolute shrinkage and selection operator (Lasso) regression algorithm of machine learning was used to screen biomarkers related to MS. Then, the accuracy of the logistic regression model was further verified based on the Lasso regression algorithm. The areas under the receiving operating characteristic curves were used to evaluate the selection accuracy of biomarkers in identifying MS subjects with risk. The screened biomarkers were used to establish a logistic regression model and calculate the odds ratio (OR) of the corresponding biomarkers. A nomogram risk prediction model was established based on the selected biomarkers, and the consistency index (C-index) and calibration curve were derived.Results: A total of 2,844 occupational workers were included, and 10 biomarkers related to MS were screened. The number of non-MS cases was 2,189 and that of MS was 655. The area under the curve (AUC) value for non-Lasso and Lasso logistic regression was 0.652 and 0.907, respectively. The established risk assessment model revealed that the main risk biomarkers were absolute basophil count (OR: 3.38, CI:1.05–6.85), platelet packed volume (OR: 2.63, CI:2.31–3.79), leukocyte count (OR: 2.01, CI:1.79–2.19), red blood cell count (OR: 1.99, CI:1.80–2.71), and alanine aminotransferase level (OR: 1.53, CI:1.12–1.98). Furthermore, favorable results with C-indexes (0.840) and calibration curves closer to ideal curves indicated the accurate predictive ability of this nomogram.Conclusions: The risk assessment model based on the Lasso logistic regression algorithm helped identify MS with high accuracy in physically examining an occupational population.

scholarly journals Correlation between vascular endothelial function parameters with indicators of suboptimal health status and the factors of cardiovascular risk

2016 ◽  
Vol 7 (3-4) ◽  
pp. 86-90
Author(s):  
E. Yu Marutina ◽  
V. I Kupaev ◽  
P. A Lebedev ◽  
O. Yu Borisov
Keyword(s):  
Risk Factors ◽  
Cardiovascular Risk ◽  
Health Status ◽  
Endothelial Function ◽  
Screening Methods ◽  
Vascular Endothelial ◽  
Vascular Endothelial Function ◽  
Work Activity ◽  
Suboptimal Health Status ◽  
Suboptimal Health

The problem of prevention of chronic non-communicable diseases continues to be relevant. It is a promising non-invasive integration of new screening methods to assess the patient's health system. The goal was to establish the relationship of vascular endothelial function parameters with indicators of suboptimal health status and the factors of cardiovascular risk. Materials and methods. A total of 327 residents of Samara, who had no history of disease and did not receive treatment in the last 3 months. We used a questionnaire diagnostic screening suboptimal health status SHSQ-25, the risk factors of cardiovascular diseases, endothelin-1, human blood index of endothelial function was determined by computer photopletismography. Results and discussion. Suboptimal health status is associated with the prevalence and severity of cardiovascular risk factors, smoking, overweight, total cholesterol, glucose, blood endothelin, vascular endothelium reactivity, indicating that their dominant influence on the quality of life in a population of healthy individuals. Vascular reactivity non-invasively evaluated in terms of endothelial function in the sample with ischaemia of the upper limb by computer photopletismography reflects systemic vascular endothelial function as a negative associated with endothelin blood and the main factors of cardiovascular risk: age, male gender, body mass index, the nature of work activity, blood pressure value.

scholarly journals Suboptimal Health Status of High School Students in Guangzhou, Shanxi, and Tibet, China: A Cross-Sectional Study

2020 ◽  
Author(s):  
Gehendra Mahara ◽  
Jiazhi Liang ◽  
Zhirong Zhang ◽  
Qi Ge ◽  
Jinxin Zhang
Keyword(s):  
Risk Factors ◽  
High School ◽  
High School Students ◽  
Health Status ◽  
Protective Factors ◽  
Associated Factors ◽  
Control Measures ◽  
School Students ◽  
Suboptimal Health Status ◽  
Suboptimal Health

Suboptimal health status (SHS) is a state between health and disease, has several associated factors, although, its underlying mechanism is still unclear. This study aimed to investigate the status of SHS and its associated factors of high school students in three areas of China (Shanxi, Guangzhou, and Tibet). A multidimensional sub-health questionnaire of adolescent (MSQA) is used to evaluate SHS. Among 1461 respondents, females proportion 56.47% was higher than males 43.53% where SHS was higher in Shanxi followed by Tibet and then Guangzhou. The rural area, grade, lack of sleep, home visit in a week, lack of exercise, a heavy burden of study, smoking, drinking, and fewer friends were the risk factors of SHS, while, families living status, seeking help and extroversion were the protective factors. SHS is significantly associated with different influencing factors. For comprehensive prevention and control measures, reduce the risk factors and enhance the protective factors.

scholarly journals sensitivity estimates suboptimal HealtH status using a QuestionnaiRe sHsQ-25

2014 ◽  
Vol 7 (2) ◽  
pp. 18-21
Author(s):  
Vitalii i. KupaeV ◽  
◽  
eKaterin Yu. Marutina ◽  
Oleg Yu. BOrisOV ◽  
◽  
...  
Keyword(s):  
Health Status ◽  
Suboptimal Health Status ◽  
Suboptimal Health

scholarly journals Establishment of Metabolic Syndrome Prediction Model for Occupational Population based on the Lasso Regression Algorithm

2020 ◽  
Author(s):  
Qiao-Ying Xie ◽  
Ming-Wei Wang ◽  
Zu-Ying Hu ◽  
Yan-Ming Chu ◽  
Cheng-Jian Cao ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Metabolic Syndrome ◽  
Logistic Regression ◽  
Prediction Model ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Area Under The Curve ◽  
Assessment Model ◽  
Risk Assessment Model ◽  
Lasso Regression

Abstract Background: Metabolic syndrome (MS) screening is important for the early detection of occupational population. This study aimed to screen out biomarkers related to MS and establish a risk assessment and prediction model for the routine physical examination of an occupational population.Methods: The least absolute shrinkage and selection operator (Lasso) regression algorithm of machine learning was used to screen biomarkers related to MS. Then, the accuracy of the logistic regression model was further verified based on the Lasso regression algorithm. Finally, the screened biomarkers were used to establish a logistic regression model and calculate the odds ratio (OR) of the corresponding biomarkers. Results: A total of 2844 occupational workers were included, and 10 biomarkers related to MS were screened. The area under the curve (AUC) value for non-Lasso and Lasso regression was 0.652 and 0.907, respectively. The established risk assessment model revealed that the main risk factors were basophil absolute count (OR: 3.38), platelet packed volume (OR: 2.63), leukocyte count (OR: 2.01), red blood cell count (OR: 1.99), and alanine aminotransferase level (OR: 1.53). Conclusion: The risk assessment model based on the Lasso regression algorithm helped identify Metabolic syndrome with high accuracy in physically examining an occupational population.

scholarly journals Construct Validity of the Suboptimal Health Status Questionnaire-25 in a Ghanaian Population

2020 ◽  
Author(s):  
Eric Adua ◽  
Ebenezer Afrifa-Yamoah ◽  
Kwasi Frimpong ◽  
Esther Adama ◽  
Shantha P. Karthigesu ◽  
...  
Keyword(s):  
Health Status ◽  
Construct Validity ◽  
Chronic Diseases ◽  
Domain Structure ◽  
Discriminant Validity ◽  
Domain Model ◽  
Fit Indices ◽  
Health Status Questionnaire ◽  
Suboptimal Health Status ◽  
Suboptimal Health

Abstract Background: The Suboptimal Health Status Questionnaire-25 (SHS-Q-25) developed to measure suboptimal health status has been used worldwide, but its construct validity has only been tested in the Chinese population. In this article, we investigate aspects of the construct validity of the SHS-Q-25 to determine the interactions between SHS subscales in a Ghanaian population. Methods: The study involved healthy Ghanaian participants (n = 263; aged 20-80 years; 63% female), who responded to the SHSQ-25. In an exploratory factor and parallel analysis, the study extracted a new domain structure and compared to the established five-domain structure of SHSQ-25. A confirmatory factor analysis (CFA) was conducted and the fit of the model further discussed. Invariance analysis was carried out to establish the consistency of the instrument across multi-groups. Results: The extracted domains were reliable with Cronbach’s of 0.861, 0.821 and 0.853 respectively, for fatigue, immune-cardiovascular and cognitive, confirming the construct validity of the SHSQ-25 instrument. The CFA revealed that the model fit indices were excellent but the fit indices for the three-domain model were statistically superior to the five-domain model. There were, however, issues of insufficient discriminant validity as some average variance extracts (AVE) were smaller than the corresponding maximum shared variance (MSV). The three-domain model was invariant for all constrained aspects of the structural model across age, which is an important risk factor for most chronic diseases. Conclusion: The validity tests provide evidence to endorse the credibility of the tool and suggest that the SHS-Q25 can measure SHS in a Ghanaian population. It can be recommended as a screening tool to early detect possible for chronic diseases especially in developing countries where the access to facilities is diminished.

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