scholarly journals PROGNOSIS OF FEATURES OF COURSE OF TYPE 2 DIABETES MELLITUS IN PATIENTS WITH NON-PSYCHOTIC MENTAL DISORDERS

2014 ◽  
Vol 13 (1) ◽  
pp. 31-38 ◽  
Author(s):  
N. P. Garganeyeva ◽  
M. F. Belokrylova
Keyword(s):  
Diabetes Mellitus ◽  
Type 2 Diabetes ◽  
Type 2 Diabetes Mellitus ◽  
Logistic Regression ◽  
Psychosocial Stress ◽  
Psychotic Disorders ◽  
Regression Method ◽  
Logistic Regression Method ◽  
Prognostic Importance

The logistic regression method is justified prognostic importance of psychosocial factors, along with other factors (predictors), determining the formation of psychosomatic relations and peculiarities of the course of type 2 diabetes mellitus in 203 patients with neurotic, affective and organic non-psychotic disorders, to develop a comprehensive programme for secondary prevention and rehabilitation. Group of high risk of development of DM is constituted by patients with experience of psychosocial stress, whose heightened level of glycaemia is associated with depressive and anxiety disorders.

scholarly journals ANALYSIS OF CORRELATION BETWEEN BGLAP RS1800247 POLYMORPHISM AND DEVELOPMENT OF TYPE 2 DIABETES MELLITUS IN SMOKERS AND NON-SMOKING INDIVIDUALS

2019 ◽  
Vol 19 (3) ◽  
pp. 87-91
Author(s):  
Ya.D. Chumachenko ◽  
A.A. Roshchupkin ◽  
V.Yu. Harbuzova
Keyword(s):  
Diabetes Mellitus ◽  
Type 2 Diabetes ◽  
Type 2 Diabetes Mellitus ◽  
Polymorphic Locus ◽  
Regression Method ◽  
Diabetic Patients ◽  
Metabolism Regulation ◽  
Logistic Regression Method ◽  
Body Index

It is known that the skeleton tissue performs systemic energy metabolism regulation through the release decarboxylated fracture of osteocalcin. The aim of this study was to analyze the correlation between rs1800247 polymorphic variant of BGLAP gene and the development of type 2 diabetes mellitus in Ukrainian smokers and non-smoking individuals. There was no significant differences in alleles and genotypes distributions in both smoking subgroup (χ2 = 0,062; Р = 0,803 and χ2 = 0,222; Р = 0,895 respectively) and non-smoking (χ2 = 1,382; Р = 0,24 and χ2 = 3,314; Р = 0,191 respectively) groups. Using logistic regression method it was established the lack of association between rs1800247-polymorphic locus of BGLAP and type 2 diabetes mellitus in crude models and after the adjustment for age, sex, mass body index and the presence of arterial hypertension (Р ˃ 0,05). No statistically significant differences were found out between clinical and laboratory values among diabetic patients stratified by genotype in one-way analysis of variance (Р ˃0,05). It suggests the conclusion that rs1800247-polymorphism is not associated with type 2 diabetes mellitus development in both smokers and  non-smoking individuals.

scholarly journals Decreased serum C1Q/TNF-related protein 4 concentrations are associated with type 2 diabetes mellitus

2021 ◽  
Vol 12 ◽  
pp. 204201882110598
Author(s):  
Zheng Liu ◽  
Jinhua Lu ◽  
Daiyi Zhang ◽  
Lijuan Niu ◽  
Bimin Shi
Keyword(s):  
Diabetes Mellitus ◽  
Type 2 Diabetes ◽  
Type 2 Diabetes Mellitus ◽  
Logistic Regression ◽  
Stepwise Logistic Regression ◽  
Newly Diagnosed ◽  
Spearman Correlation ◽  
Related Protein ◽  
Multivariate Stepwise Logistic Regression

Objective: To detect serum C1Q/TNF-related protein 4 (CTRP4) concentrations in patients with newly diagnosed type 2 diabetes mellitus (T2DM) and evaluate the correlation between CTRP4 and other variables in T2DM. Method: Sixty-five patients with newly diagnosed T2DM and eighty-nine healthy volunteers were enrolled in this study. Anthropometric and biochemical data of the study participants was collected, and serum CTRP4 concentrations were detected by enzyme-linked immunosorbent assay (ELISA) kit. The correlation between serum CTRP4 and other indexes was analyzed by Spearman correlation analysis. Trend χ2 test and binary multivariate stepwise logistic regression were performed to assess the correlation between CTRP4 and the risk of T2DM. Results: Serum CTRP4 concentrations in the T2DM group were significantly lower than those in the control group ( P < .01). Spearman correlation analysis showed that CTRP4 concentrations were negatively correlated with BMI, hs-CRP, HOMA-IR, FBG and TG ( r = - 0.430, - 0.453, - 0.371, - 0.361, - 0.506, P < .05), and positively correlated with HDL-c ( r = 0.303, P < .05). Trend χ2 test indicated that with the increase of CTRP4 levels in the population, the risk of T2DM presented a general downward trend ( P < .01). Binary multivariate stepwise logistic regression suggested that serum CTRP4 was an independent impact factor for T2DM and high serum CTRP4 levels were related to the decreased risk of T2DM (P < .05). Conclusions: Serum CTRP4 concentrations decrease in patients with newly diagnosed T2DM. Serum CTRP4 levels are negatively associated with the risk of T2DM.

scholarly journals The frailty syndrome in older adults with type 2 diabetes mellitus and associated factors

2020 ◽  
Vol 23 (1) ◽  
Author(s):  
Bartolomeu Fagundes de Lima Filho ◽  
Antônia Gilvanete Duarte Gama ◽  
Vanessa da Nóbrega Dias ◽  
Eliza Mikaele Tavares da Silva ◽  
Fabricia Azevedo da Costa Cavalcanti ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Type 2 Diabetes ◽  
Older Adults ◽  
Type 2 Diabetes Mellitus ◽  
Logistic Regression ◽  
Logistic Regression Model ◽  
Lower Limbs ◽  
Frailty Syndrome ◽  
Frail Phenotype

Abstract Objective: To compare clinical-functional factors among groups in relation to the frailty syndrome (pre-frail and frail) phenotype profile in older adults with type 2 diabetes mellitus (DM 2). Methods: A descriptive, analytical, cross-sectional study with a quantitative approach was performed. A total of 113 diabetic older adults of both sexes were evaluated in terms of their personal, socio-demographic, clinical-functional, mental, cognitive and fragility phenotype data. The Chi-square test and a logistic regression model were used. Results: The mean age was 68.66±6.62 years, and the sample was mostly female (61.9%), illiterate or with an incomplete primary education (60.2%), pre-frail (52.2%), sedentary (79.6%), and had been diagnosed with DM2 for more than 5 years (58.3%). There was a significant association between “pre-frail and frail” individuals and schooling (p=0.004), social participation (p=0.004), a subjective perception of vision (p=0.004), glycated hemoglobin (p=0.036), limb pain (p=0.012), depressive symptoms (p=0.002) and mobility (p=0.004). The logistic regression model showed an accuracy of 93.6% and the significant variables were education (p=0.039), pain in the lower limbs (p=0.025) and risk of falls (p=0.033). Conclusion: among all the factors related to the “pre-frail” and “frail” phenotype, schooling, pain in the lower limbs and mobility were most related to the worsening of the syndrome and its progress.

scholarly journals Use and effectiveness of dapagliflozin in patients with type 2 diabetes mellitus: a multicenter retrospective study in Taiwan

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e9998
Author(s):  
Jung-Fu Chen ◽  
Yun-Shing Peng ◽  
Chung-Sen Chen ◽  
Chin-Hsiao Tseng ◽  
Pei-Chi Chen ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Blood Pressure ◽  
Type 2 Diabetes ◽  
Weight Loss ◽  
Type 2 Diabetes Mellitus ◽  
Logistic Regression ◽  
Body Weight ◽  
Retrospective Study ◽  
Multivariate Logistic Regression

Aims/Introduction To investigate the clinical outcomes of patients with type 2 diabetes mellitus (T2DM) who initiated dapagliflozin in real-world practice in Taiwan. Materials and Methods In this multicenter retrospective study, adult patients with T2DM who initiated dapagliflozin after May 1st 2016 either as add-on or switch therapy were included. Changes in clinical and laboratory parameters were evaluated at 3 and 6 months. Baseline factors associated with dapagliflozin response in glycated hemoglobin (HbA1c) were analyzed by univariate and multivariate logistic regression. Results A total of 1,960 patients were eligible. At 6 months, significant changes were observed: HbA1c by −0.73% (95% confidence interval [CI] −0.80, −0.67), body weight was -1.61 kg (95% CI −1.79, −1.42), and systolic/diastolic blood pressure by −3.6/−1.4 mmHg. Add-on dapagliflozin showed significantly greater HbA1c reduction (−0.82%) than switched therapy (−0.66%) (p = 0.002). The proportion of patients achieving HbA1c <7% target increased from 6% at baseline to 19% at Month 6. Almost 80% of patients experienced at least 1% reduction in HbA1c, and 65% of patients showed both weight loss and reduction in HbA1c. Around 37% of patients had at least 3% weight loss. Multivariate logistic regression analysis indicated patients with higher baseline HbA1c and those who initiated dapagliflozin as add-on therapy were associated with a greater reduction in HbA1c. Conclusions In this real-world study with the highest patient number of Chinese population to date, the use of dapagliflozin was associated with significant improvement in glycemic control, body weight, and blood pressure in patients with T2DM. Initiating dapagliflozin as add-on therapy showed better glycemic control than as switch therapy.

scholarly journals Associations between total cereals, whole grain, carbohydrate intakes and the risk of type 2 diabetes mellitus

2020 ◽  
Vol 79 (OCE2) ◽  
Author(s):  
Yan-Bin Ye ◽  
Wei Lu ◽  
Shu-Yu Zhuo ◽  
Yu-Ming Chen ◽  
Yan-Bing Li
Keyword(s):  
Diabetes Mellitus ◽  
Type 2 Diabetes ◽  
Type 2 Diabetes Mellitus ◽  
Logistic Regression ◽  
Conditional Logistic Regression ◽  
Whole Grains ◽  
Carbohydrate Intake ◽  
Moderate Amount ◽  
Study Participants

AbstractIntroductionThe associations between grains and carbohydrate intake and type 2 diabetes mellitus are controversial. This study aimed to evaluate the relationship between grains, carbohydrate intakes and the risk of type 2 diabetes mellitus in China.Materials and MethodsThis was a 1:2 (sex/age) matched case-control study, participants were adults. Cases were diabetics diagnosed within 3 months and the controls were without disorder of glucose metabolism. Face-to-face interviews were conducted to collect information on their socio-demographic characteristics, lifestyle factors, and dietary intakes using structured questionnaires. Grains were divided into whole, refined and common grain, and the carbohydrate intake was also calculated. The study participants were divided into quartiles (Q1 (lowest), Q2, Q3, and Q4) by food and nutrients intakes separately. Multivariable conditional logistic regression was used to explore the association of foods and nutrients with type 2 diabetes mellitus after adjusting for potential confounders. Trend test were performed by treating quartiles variables as continuous variables.Results and DiscussionOur study enrolled 384 type 2 diabetes mellitus patients (males 162, females 222) and 768 controls (males 324, females 444). Multivariable conditional logistic regression analysis(Ver. 21.0; PSS Inc.,Chicago,IL,USA) showed that moderate amount intake of total cereals was inversely associated with type 2 diabetes mellitus. The adjusted OR of the second quartile (Q2, 223g/d) and the third quartile (Q3, 255g/d) were 0.60(95%CI:0.38–0.93) and 0.51(95%CI:0.33–0.79), respectively, compared with the lowest quartile (Q1, 165g/d), but this inverse association was not found in the highest quartile (Q4, 307g/d) and the OR was 0.74(95%CI:0.47–1.15). There was significant negative association between whole grains intake and type 2 diabetes mellitus with the OR of the highest intake 0.48(95%CI:0.31–0.77) compared with the lowest intake(Ptrend = 0.001).No association was found between refined grains intake intake and type 2 diabetes mellitus, and neither did common grain intake. Higher carbohydrate intake may have a beneficial effect on type 2 diabetes mellitus. The best effect was found in the second quartile intake (Q2, 264g/d), with an adjusted OR of 0.56 (95%CI:0.37–0.84) compared with the lowest quartile intake (Q1, 220g/d).The OR of Q3 (285g/d) and Q4 (334g/d) were 0.69 (95%CI:0.48–1.00) and 0.66 (95CI:0.44–1.00) respectively(Ptrend p = 0.017).ConclusionModerate amount of total cereals intake may benefit to type 2 diabetes mellitus, however, much lower and higher intake can increase the risk. Higher intake of whole grains was associated with a lower risk of type 2 diabetes mellitus. Carbohydrate intake was negative associated with type 2 diabetes mellitus.

The performance of artificial neural network, logistic regression, and random forest models to predict breast cancer in patients with type 2 diabetes mellitus (Preprint)

2019 ◽  
Author(s):  
Chia-Hung Kao
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Diabetes Mellitus ◽  
Type 2 Diabetes ◽  
Artificial Neural Network ◽  
Type 2 Diabetes Mellitus ◽  
Logistic Regression ◽  
Breast Cancer Risk ◽  
Cancer Risk

BACKGROUND Breast cancer incidence may be higher among patients with type 2 diabetes mellitus (T2DM) compared with the general population. This study evaluated the performance of three models for predicting breast cancer risk in patients with T2DM. OBJECTIVE This study evaluated the performance of three models for predicting breast cancer risk in patients with T2DM. METHODS In total, 1,267,867 patients with newly diagnosed T2DM between 2000 and 2012 were identified from Taiwan National Health Insurance Research Database. By employing their data, we created prediction models for detecting an increased risk of subsequent breast cancer development in T2DM patients. The available potential risk factors for breast cancer were also collected for adjustment in the analyses. The Synthetic Minority Oversampling Technique (SMOTE) was used to augment data points in the minority class. Each data point was randomly allocated to the training and test sets at a ratio of approximate 39:1. The performance of artificial neural network (ANN), logistic regression (LR), and random forest (RF) models were determined using the recall, precision, F1 score, and area under receiver operating characteristic curve (AUC). RESULTS The AUCs of all three models were significantly higher than the area of 0.5 for the null hypothesis (0.959, 0.865, and 0.834 for RF, ANN, and LR models, respectively). The RF model has the largest AUC among all models; moreover, it had the highest values in all other metrics. CONCLUSIONS Although all three models could accurately predict high breast cancer risk in patients with T2DM in Taiwan, the RF model demonstrated the best performance. CLINICALTRIAL This is not a chinical trial.

The Study of Medication Understanding and Medication Adherence in Chinese Patients with Type 2 Diabetes Mellitus

2020 ◽  
Vol 103 (10) ◽  
pp. 1004-1010
Keyword(s):  
Diabetes Mellitus ◽  
Type 2 Diabetes ◽  
Type 2 Diabetes Mellitus ◽  
Logistic Regression ◽  
High Education ◽  
Medication Adherence ◽  
Chinese Patients ◽  
Multiple Logistic Regression ◽  
Low Education

Background: Medication adherence (MA) is a key component to prevent complications of diabetes mellitus. Patients with better medication understanding (MU) showed higher MA. While there were numerous studies of MA in Chinese patients, there has not been any MU study. Objective: To assess the associations of MA and MU in Chinese patients with type 2 diabetes mellitus (T2D). To translate and validate the Chinese version of the Medication Understanding Questionnaire (C-MUQ). Materials and Methods: A cross-sectional study was conducted in 384 Chinese T2D patients. The MA was assessed using the proportion of days covered (PDC), and the MU was assessed using the C-MUQ. The multiple logistic regression was applied to simultaneously assess the association of MA (good or poor MA) with MU and other contributing factors including the sociodemographic characteristics and clinical conditions of patients. Results: The average PDC of study patients was 89.78% (SD 14.75%), and 77.08% of the patients were classified into the good MA group (PDC is 80% or greater). Based on multiple logistic regression as good MA as dependent variable, factors significantly associated with good MA were not long durations of diabetes, not using traditional Chinese medicine (TCM) products, and the interaction effect of high education level and good MU. The adjusted odd ratio (AOR) were 0.28 (95% CI 0.10 to 0.79) for 25 years or more of diabetes durations, 0.33 (95% CI 0.12 to 0.89) for using TCM products, 3.28 (95% CI 1.94 to 8.62) for high education with poor MU, 4.09 (95% CI 1.46 to 7.36) for low education with good MU, and 5.32 (95% CI 2.49 to 11.34) for high education with good MU, respectively. Conclusion: To achieve good MA, the Chinese health professionals should concern about improving MU, especially in the low education patients. Keywords: Medication understanding, Medication adherence, Type 2 diabetes

scholarly journals Prediction of type 2 diabetes mellitus onset using simple logistic regression models

2020 ◽  
Author(s):  
Yochai Edlitz ◽  
Eran Segal
Keyword(s):  
Diabetes Mellitus ◽  
Type 2 Diabetes ◽  
Type 2 Diabetes Mellitus ◽  
Logistic Regression ◽  
Risk Score ◽  
Diabetes Risk ◽  
Blood Tests ◽  
Glutamyl Transferase ◽  
Diabetes Risk Score

Diabetes mellitus has a world death rate of 1.6 million (2016) of which Type 2 diabetes mellitus (T2DM) accounts for ~90% of all cases. Early detection of T2D high-risk patients can reduce the incidence of the disease through a change in lifestyle, diet, or medication. Since lower socio-demographics layers are more susceptible to T2D and might have limited resources for laboratory testing, there is a need for accurate prediction models based on non-laboratory parameters. Here, we analysed data of 44,879 non-diabetic, UK-Biobank participants at the ages 40-65 within a time frame of 7.3±2.3 years. We devise a non-laboratory prediction model for T2DM onset probability using sex, age, weight, height, waist, hips-circumferences, Waist-Hips Ratio (WHR) and Body-Mass Index (BMI). This model achieved an Area Under the Receiver Operating Curve (auROC) of 0.82 (0.79-0.84 95% CI) and an odds ratio (OR) between the top and lowest prevalence deciles of x42 (33-49). The logistic regression top predictive parameters are WHR with OR of 0.67 (0.49-0.88 95%CI) followed by BMI with OR of 0.53 (0.26-0.79). We further analyse the contribution of laboratory-based parameters and devise a blood-test model based on only five blood tests. In this model, we are using age, sex, Glycated Hemoglobin (HbA1c%), reticulocyte count, Gamma Glutamyl-Transferase, Triglycerides, and HDL cholesterol to predict T2D onset more accurately. This model achieves an auROC of 0.89 (0.87-0.92) and a deciles' OR of x59 (27-75). We also analysed a model that included genotyping data and other environmental factors and found that it did not provide further benefit over the five-blood-tests model. Our models outperform the current state of the art, non-laboratory, Finnish Diabetes Risk Score and the German Diabetes Risk Score, trained on our data, achieving auROC of 0.74 (0.7-0.77) and 0.63 (0.59-0.67), respectively.

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