Factors associated with erythrocyte count and hemoglobin concentration in men and women with type 2 diabetes

2019 ◽  
Vol 55 (3) ◽  
pp. 199-208
Author(s):  
Olga Martyna Koper-Lenkiewicz ◽  
Joanna Kamińska ◽  
Ewelina Wilińska ◽  
Anna Milewska ◽  
Sylwia Lewoniewska ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Linear Regression ◽  
Linear Regression Model ◽  
Cardiac Arrhythmias ◽  
Calcium Antagonists ◽  
Hemoglobin Concentration ◽  
Multivariate Linear Regression ◽  
Diabetic Patients ◽  
Multivariate Linear Regression Model

Introduction: The risk of developing anemia in diabetic patients is nearly 3-times higher than in non-diabetic patients. Aim: The aim was to assess factors that may affect both the erythrocyte count (RBC) and hemoglobin concentration (HGB) in type 2 diabetes patients. Material and methods: In type 2 diabetes patients (N = 80) and in control subjects with normal carbohydrate metabolism (N = 40) RBC and HGB were determined in whole blood collected in the EDTA-K3 tube. Results: The degree of metabolic compensation of diabetes, measured by the percentage of HbA1c, did not significantly affect RBC and HGB. In diabetic women, unlike men, there was no relationship between RBC and HGB and kidney function, measured with eGFR. Angiotensin-converting-enzyme inhibitors and angiotensin receptor blockers did not significantly affect RBC and HGB in these patients. Multivariate linear regression analysis showed that in type 2 diabetes women variables that affect the RBC included: systolic BP, arrhythmias, taking fibrates. In the multivariate linear regression model variables that influence the HGB in type 2 diabetes women included systolic BP and fibrate treatment. In type 2 diabetes men a statistically significant model of multivariate linear regression for RBC was not obtained. In the multivariate linear regression model the variables that influence the HGB in type 2 diabetes men included: white blood cell count, age, obesity, and taking statins. Conclusions: In women with type 2 diabetes, the RBC is influenced by other factors (systolic BP, cardiac arrhythmias, fibrates and calcium antagonists) than in men (obesity and the use of oral antidiabetic agents). In type 2 diabetes women also other factors influence HGB (systolic BP, cardiac arrhythmias presence, fibrates, calcium antagonists and ACE inhibitors) than in type 2 diabetes men (hypertension, obesity, leukocyte count, age, proton pump inhibitors and statins medication).

scholarly journals Short period PM2.5 prediction based on multivariate linear regression model

PLoS ONE ◽  
2018 ◽  
Vol 13 (7) ◽  
pp. e0201011 ◽  
Author(s):  
Rui Zhao ◽  
Xinxin Gu ◽  
Bing Xue ◽  
Jianqiang Zhang ◽  
Wanxia Ren
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Multivariate Linear Regression ◽  
Multivariate Linear Regression Model ◽  
Short Period

scholarly journals Impact of Climate Factors in Modeling and Predicting the Transmission of Meningitis in Africa: The Case of Burkina Faso

2020 ◽  
Author(s):  
Haoua Tall ◽  
Issaka Yaméogo ◽  
Ryan Novak ◽  
Lionel L Ouedraogo ◽  
Ousmane Ouedraogo ◽  
...  
Keyword(s):  
Relative Humidity ◽  
Linear Regression ◽  
Regression Model ◽  
Burkina Faso ◽  
Linear Model ◽  
Linear Regression Model ◽  
Multivariate Linear Regression ◽  
Multivariate Linear Regression Model ◽  
Climate Factors ◽  
Multivariate Linear Model

Abstract Background: Meningitis is a major cause of morbidity in the world. Previous studies showed that climate factors influence the occurrence of meningitis. A multiple linear regression model was developed to forecast meningitis cases in Burkina Faso using climate factors. However, the multivariate linear regression model based on times series data may produce fallacious results given the autocorrelation of errors. Aims: The aim of the study is to develop a model to quantify the effect of climate factors on meningitis cases, and then predict the expected weekly incidences of meningitis for each district. Data and methods: The weekly cases of meningitis come from the Ministry of Health and covers the period 2005-2017. Climate data were collected daily in 10 meteorological stations from 2005 to 2017 and were provided by the national meteorological Agency of Burkina Faso. An ARIMAX and a multivariate linear regression model were estimated separately for each district. Results: The multivariate linear model is inappropriate to model the number of meningitis cases due to autocorrelation of errors. With the ARIMAX Model, Temperature is significantly associated with an increase of meningitis cases in 3 of 10 districts, while relative humidity is significantly associated with a decrease of meningitis cases in 3 of the 10 districts. The effect of wind speed and precipitation is not significant at the 5% level in all 10 districts. The prediction of meningitis cases with 8 test observations provides an average absolute error ranging from 0.99 in Boromo and Bogandé to 7.22 in the district of Ouagadougou. Conclusion: The ARIMAX model is more appropriate than the multivariate linear model to analyze the dynamics of meningitis cases. Climatic factors such as temperature and relative humidity have a significant influence on the occurrence of meningitis in Burkina Faso; the temperature influences it positively and the relative humidity influences it negatively.

Impact of Climate Factors in Modeling and Predicting the Transmission of Meningitis in Africa: The Case of Burkina Faso

2020 ◽  
Author(s):  
Haoua Tall ◽  
Issaka Yaméogo ◽  
Ryan Novak ◽  
Lionel L Ouedraogo ◽  
Ousmane Ouedraogo ◽  
...  
Keyword(s):  
Relative Humidity ◽  
Linear Regression ◽  
Regression Model ◽  
Burkina Faso ◽  
Linear Model ◽  
Linear Regression Model ◽  
Multivariate Linear Regression ◽  
Multivariate Linear Regression Model ◽  
Climate Factors ◽  
Multivariate Linear Model

Abstract Background Meningitis is a major cause of morbidity in the world. Previous studies showed that climate factors influence the occurrence of meningitis. A multiple linear regression model was developed to forecast meningitis cases in Burkina Faso using climate factors. However, the multivariate linear regression model based on times series data may produce fallacious results given the autocorrelation of errors.Aims The aim of the study is to develop a model to quantify the effect of climate factors on meningitis cases, and then predict the expected weekly incidences of meningitis for each district.Data and methods The weekly cases of meningitis come from the Ministry of Health and covers the period 2005-2017. Climate data were collected daily in 10 meteorological stations from 2005 to 2017 and were provided by the national meteorological Agency of Burkina Faso. An ARIMAX and a multivariate linear regression model were estimated separately for each district.Results The multivariate linear model is inappropriate to model the number of meningitis cases due to autocorrelation of errors. With the ARIMAX Model, Temperature is significantly associated with an increase of meningitis cases in 3 of 10 districts, while relative humidity is significantly associated with a decrease of meningitis cases in 3 of the 10 districts. The effect of wind speed and precipitation is not significant at the 5% level in all 10 districts. The prediction of meningitis cases with 8 test observations provides an average absolute error ranging from 0.99 in Boromo and Bogandé to 7.22 in the district of Ouagadougou.Conclusion The ARIMAX model is more appropriate than the multivariate linear model to analyze the dynamics of meningitis cases. Climatic factors such as temperature and relative humidity have a significant influence on the occurrence of meningitis in Burkina Faso; the temperature influences it positively and the relative humidity influences it negatively.

scholarly journals Baseline Triglyceride Level Affected the Efficacy of Vildagliptin in Treating Type 2 Diabetes: A Post Hoc Analysis of the VISION Study

2019 ◽  
Vol 2019 ◽  
pp. 1-5
Author(s):  
Lingli Zhou ◽  
Xiaoling Cai ◽  
Yingying Luo ◽  
Fang Zhang ◽  
Linong Ji
Keyword(s):  
Type 2 Diabetes ◽  
Linear Regression ◽  
Linear Regression Analysis ◽  
Multivariate Linear Regression ◽  
Chinese Patients ◽  
High Dose ◽  
Multivariate Linear Regression Analysis ◽  
Post Hoc Analysis ◽  
Post Hoc

Identifying factors that may impact vildagliptin’s efficacy could contribute to individualized treatment for patients with type 2 diabetes. In the current study, we aimed to assess the correlation between patient baseline triglyceride (TG) and efficacy of vildagliptin in Chinese patients with type 2 diabetes in a post hoc analysis of the VISION study. TG-based subgroup analysis was performed to evaluate baseline TG’s impact on the decrease of glycated hemoglobin (HbA1c) in patients receiving vildagliptin plus low-dose metformin (VLDM) vs. high-dose metformin (HDM). Additionally, multivariate linear regression was performed to assess the association between baseline TG and HbA1c reduction at weeks 12 and 24 for patients receiving VLDM vs. HDM. For patients receiving VLDM, baseline TG≤2.03 mmol/L was associated with significantly greater HbA1c reduction vs. TG>2.03 mmol/L at week 12, but not at week 24. Additionally, multivariate linear regression analysis revealed a significant independent association and an association short of statistical significance between patient baseline TG and the HbA1c-reducing efficacy of VLDM at weeks 12 (P<0.001) and 24 (P=0.082), respectively, while such association was absent for HDM. Collectively, baseline TG was an independent predictive factor for the efficacy of a dipeptidyl peptidase-IV in treating type 2 diabetes during its initial use.

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