scholarly journals Prognostic Value of Exercise Capacity in Incidence Diabetes: A Country With High Prevalence of Diabetes

2021 ◽  
Author(s):  
Abdelrahman A. Jamiel ◽  
Husam Ardah ◽  
Amjad M. Ahmed ◽  
Mouaz H. Al-Mallah
Keyword(s):  
Risk Factors ◽  
Exercise Capacity ◽  
Predictive Value ◽  
Prediction Models ◽  
Cox Regression ◽  
Preventive Measure ◽  
Incident Diabetes ◽  
Diabetic Patients ◽  
Prediction Ability ◽  
Conventional Risk Factors

Abstract Background: Diabetes Mellitus (DM) is a fast-growing health problem that imposes an enormous economic burden. Several studies demonstrated the association between physical inactivity and predicting the incidence of diabetes. However, these prediction models have limited validation locally. Therefore, we aim to explore the predictive value of exercise capacity in the incidence of diabetes within a high diabetes prevalence population.Methodology: A retrospective cohort study including consecutive patients free of diabetes who underwent clinically indicated treadmill stress testing. Diabetic patients at baseline or patients younger than 18 years of age were excluded. Incident diabetes was defined as an established clinical diagnosis post-exercise testing date. The predictive value of exercise capacity was examined using Harrell’s c-index, net reclassification index (NRI), and integrated discrimination index (IDI).Results: A total of 8,722 participants (mean age 46±12 years, 66.3% were men) were free of diabetes at baseline. Over a median follow-up period of 5.24 (2.17-8.78) years, there were 2,280 (≈26%) new cases of diabetes. In a multivariate model adjusted for conventional risk factors, we found a 12% reduction in the risk of incident diabetes for each one greater MET achieved (HR, 0.9; 95% CI, 0.88–0.92; P<0.001). Using Cox regression, exercise capacity improved the prediction ability beyond the conventional risk factors (AUC=0.62 to 0.66 and c-index= 0.62 to 0.68).Conclusion: Exercise capacity improved the overall predictability of incidence diabetes. Patients with reduced exercise capacity are at high risk for developing incidence diabetes. Improvement of both physical activity and functional capacity represents a preventive measure for the general population.

scholarly journals Prognostic Value of Exercise Capacity In Incidence Diabetes: A Country With High Prevalence of Diabetes

2022 ◽  
Author(s):  
Abdelrahman A. Jamiel ◽  
Husam Ardah ◽  
Amjad M. Ahmed ◽  
Mouaz H. Al-Mallah
Keyword(s):  
Risk Factors ◽  
Exercise Capacity ◽  
Predictive Value ◽  
Prediction Models ◽  
Cox Regression ◽  
Preventive Measure ◽  
Incident Diabetes ◽  
Diabetic Patients ◽  
Prediction Ability ◽  
Conventional Risk Factors

Abstract Background: Diabetes Mellitus (DM) is a fast-growing health problem that imposes an enormous economic burden. Several studies demonstrated the association between physical inactivity and predicting the incidence of diabetes. However, these prediction models have limited validation locally. Therefore, we aim to explore the predictive value of exercise capacity in the incidence of diabetes within a high diabetes prevalence population.Methodology: A retrospective cohort study including consecutive patients free of diabetes who underwent clinically indicated treadmill stress testing. Diabetic patients at baseline or patients younger than 18 years of age were excluded. Incident diabetes was defined as an established clinical diagnosis post-exercise testing date. The predictive value of exercise capacity was examined using Harrell’s c-index, net reclassification index (NRI), and integrated discrimination index (IDI).Results: A total of 8,722 participants (mean age 46±12 years, 66.3% were men) were free of diabetes at baseline. Over a median follow-up period of 5.24 (2.17-8.78) years, there were 2,280 (≈26%) new cases of diabetes. In a multivariate model adjusted for conventional risk factors, we found a 12% reduction in the risk of incident diabetes for each one greater MET achieved (HR, 0.9; 95% CI, 0.88–0.92; P<0.001). Using Cox regression, exercise capacity improved the prediction ability beyond the conventional risk factors (AUC=0.62 to 0.66 and c-index= 0.62 to 0.68).Conclusion: Exercise capacity improved the overall predictability of incidence diabetes. Patients with reduced exercise capacity are at high risk for developing incidence diabetes. Improvement of both physical activity and functional capacity represents a preventive measure for the general population.

scholarly journals Fermented pork fat (Sa-um) and lifestyle risk factors as potential indicators for type 2 diabetes among the Mizo population, Northeast India

2021 ◽  
Vol 40 (1) ◽  
Author(s):  
Freda Lalrohlui ◽  
Souvik Ghatak ◽  
John Zohmingthanga ◽  
Vanlal Hruaii ◽  
Nachimuthu Senthil Kumar
Keyword(s):  
Diabetes Mellitus ◽  
Risk Factors ◽  
Type 2 Diabetes ◽  
Type 2 Diabetes Mellitus ◽  
Cox Regression ◽  
Control Group ◽  
Diabetic Patients ◽  
Lifestyle Habits ◽  
Pork Fat

AbstractOver the last few decades, Mizoram has shown an increase in cases of type 2 diabetes mellitus; however, no in-depth scientific records are available to understand the occurrence of the disease. In this study, 500 patients and 500 healthy controls were recruited to understand the possible influence of their dietary and lifestyle habits in relation with type 2 diabetes mellitus. A multivariate analysis using Cox regression was carried out to find the influence of dietary and lifestyle factors, and an unpaired t test was performed to find the difference in the levels of biochemical tests. Out of 500 diabetic patients, 261 (52.3%) were males and 239 (47.7%) were females, and among the control group, 238 (47.7%) were males and 262 (52.3%) were females. Fermented pork fat, Sa-um (odds ratio (OR) 18.98), was observed to be a potential risk factor along with tuibur (OR 0.1243) for both males and females. Creatinine level was found to be differentially regulated between the male and female diabetic patients. This is the first report of fermented pork fat and tobacco (in a water form) to be the risk factors for diabetes. The unique traditional foods like Sa-um and local lifestyle habits like tuibur of the Mizo population may trigger the risk for the prevalence of the disease, and this may serve as a model to study other populations with similar traditional practices.

scholarly journals Nomogram for Predicting COVID-19 Disease Progression Based on Single-Center Data: Observational Study and Model Development (Preprint)

2020 ◽  
Author(s):  
Tao Fan ◽  
Bo Hao ◽  
Shuo Yang ◽  
Bo Shen ◽  
Zhixin Huang ◽  
...  
Keyword(s):  
Risk Factors ◽  
Regression Analysis ◽  
Clinical Characteristics ◽  
Cox Regression ◽  
Disease Onset ◽  
Lasso Regression ◽  
Prediction Ability ◽  
Calibration Curves ◽  
Independent Risk Factors ◽  
Critical Patients

BACKGROUND In late December 2019, a pneumonia caused by SARS-CoV-2 was first reported in Wuhan and spread worldwide rapidly. Currently, no specific medicine is available to treat infection with COVID-19. OBJECTIVE The aims of this study were to summarize the epidemiological and clinical characteristics of 175 patients with SARS-CoV-2 infection who were hospitalized in Renmin Hospital of Wuhan University from January 1 to January 31, 2020, and to establish a tool to identify potential critical patients with COVID-19 and help clinical physicians prevent progression of this disease. METHODS In this retrospective study, clinical characteristics of 175 confirmed COVID-19 cases were collected and analyzed. Univariate analysis and least absolute shrinkage and selection operator (LASSO) regression were used to select variables. Multivariate analysis was applied to identify independent risk factors in COVID-19 progression. We established a nomogram to evaluate the probability of progression of the condition of a patient with COVID-19 to severe within three weeks of disease onset. The nomogram was verified using calibration curves and receiver operating characteristic curves. RESULTS A total of 18 variables were considered to be risk factors after the univariate regression analysis of the laboratory parameters (<i>P</i>&lt;.05), and LASSO regression analysis screened out 10 risk factors for further study. The six independent risk factors revealed by multivariate Cox regression were age (OR 1.035, 95% CI 1.017-1.054; <i>P</i>&lt;.001), CK level (OR 1.002, 95% CI 1.0003-1.0039; <i>P</i>=.02), CD4 count (OR 0.995, 95% CI 0.992-0.998; <i>P</i>=.002), CD8 % (OR 1.007, 95% CI 1.004-1.012, <i>P</i>&lt;.001), CD8 count (OR 0.881, 95% CI 0.835-0.931; <i>P</i>&lt;.001), and C3 count (OR 6.93, 95% CI 1.945-24.691; <i>P</i>=.003). The areas under the curve of the prediction model for 0.5-week, 1-week, 2-week and 3-week nonsevere probability were 0.721, 0.742, 0.87, and 0.832, respectively. The calibration curves showed that the model had good prediction ability within three weeks of disease onset. CONCLUSIONS This study presents a predictive nomogram of critical patients with COVID-19 based on LASSO and Cox regression analysis. Clinical use of the nomogram may enable timely detection of potential critical patients with COVID-19 and instruct clinicians to administer early intervention to these patients to prevent the disease from worsening.

Abstract 16055: Lipoprotein Subclasses, Size, and Statin-Associated Incident Diabetes: An Analysis From the JUPITER Trial

Circulation ◽  
2014 ◽  
Vol 130 (suppl_2) ◽  
Author(s):  
Sagar Dugani ◽  
Akintunde O Akinkuolie ◽  
Robert J Glynn ◽  
Paul M Ridker ◽  
Samia Mora
Keyword(s):  
Risk Factors ◽  
Cox Regression ◽  
Incident Diabetes ◽  
P Value ◽  
Random Allocation ◽  
Lipoprotein Subclass ◽  
Lipoprotein Subclasses ◽  
Ldl Particles ◽  
Ldl Size ◽  
Increased Risk

Statins reduce CVD events, LDL cholesterol (LDL-C) and triglycerides, with an increased risk of diabetes. The underlying predictors of statin-associated diabetes are unclear. We evaluated lipoprotein subclass and size changes in response to rosuvastatin to identify predictors of diabetes on statin therapy Among 11,918 non-diabetic participants in JUPITER (NCT00239681), lipoprotein subclasses and size were quantified by NMR spectroscopy (LipoScience, NC) prior to and 1 year after randomization to placebo or rosuvastatin (total 370 incident diabetes). Cox regression models were adjusted for diabetes risk factors Compared to baseline, rosuvastatin lowered LDL-C and particles by lowering cholesterol-enriched large LDL (58%) and IDL (46%), with less relative lowering of cholesterol-poor small LDL (22%), resulting in smaller LDL size (1.5%). Rosuvastatin lowered (15%-20%) triglycerides, VLDL triglycerides, and VLDL particles by lowering large (15%), medium (7%), and small (27%) particles, and increasing VLDL size (3%) (all p<0.0001). Among statin-allocated individuals, after adjusting for typical risk factors, incident diabetes was inversely associated with baseline levels of LDL-C, HDL-C, large LDL particles, and LDL size, and positively associated with baseline triglycerides, non-HDL-C, ApoB, LDL particles, VLDL particles, VLDL triglycerides and size (Table). Similar associations were seen in on-treatment rosuvastatin and placebo groups In JUPITER, random allocation to rosuvastatin altered the lipoprotein subclass profile in a manner associated with the development of diabetes Adjusted Hazard Ratios (95% CI) and Risk of Incident Diabetes with Rosuvastatin Baseline parameters HR per 1-SD p value LDL-C .86 (0.76-0.98) .02 HDL-C .69 (0.54-0.87) .002 Triglycerides 1.62 (1.41-1.86) <.0001 Non-HDL-C 1.20 (1.04-1.39) .01 ApoB 1.35 (1.18-1.55) <.0001 Total LDL* 1.32 (1.15-1.51) <.0001 Large LDL* .79 (0.71-0.87) <.0001 Small LDL* 1.71 (1.40-2.08) <.0001 IDL* .97 (0.85-1.11) .69 LDL size .66 (0.58-0.75) <.0001 Total VLDL* 1.16 (1.00-1.34) .046 Large VLDL* 1.78 (1.51-2.10) <.0001 Medium VLDL* 1.35 (1.15-1.58) .0002 Small VLDL* .93 (0.82-1.06) .30 VLDL size 1.58 (1.39-1.80) <.0001 VLDL triglycerides 1.51 (1.31-1.73) <.0001 *particles

scholarly journals Diabetic foot ulcers: Retrospective comparative analysis from Sicily between two eras

PLoS ONE ◽  
2021 ◽  
Vol 16 (12) ◽  
pp. e0259405
Author(s):  
Valentina Guarnotta ◽  
Stefano Radellini ◽  
Enrica Vigneri ◽  
Achille Cernigliaro ◽  
Felicia Pantò ◽  
...  
Keyword(s):  
Risk Factors ◽  
Diabetic Foot ◽  
Cox Regression ◽  
Age Of Onset ◽  
Age At Onset ◽  
Foot Ulcer ◽  
Diabetic Foot Ulcer ◽  
Diabetic Patients ◽  
Type 2 Diabetic Patients ◽  
Risk Of Death

Aim The aim of this study was to analyze changes in the incidence, management and mortality of DFU in Sicilian Type 2 diabetic patients hospitalized between two eras, i.e. 2008–2013 and 2014–2019. Methods We compared the two eras, era1: 2008–13, era2: 2014–19. In era 1, n = 149, and in era 2, n = 181 patients were retrospectively enrolled. Results In the population hospitalized for DFU in 2008–2013, 59.1% of males and 40.9% of females died, whilst in 2014–2019 65.9% of males and 34.1% of females died. Moderate chronic kidney disease (CKD) was significantly higher in patients that had died than in ones that were alive (33% vs. 43%, p < 0.001), just as CKD was severe (14.5% vs. 4%, p < 0.001). Considering all together the risk factors associated with mortality, at Cox regression multivariate analysis only moderate-severe CKD (OR 1.61, 95% CI 1.07–2.42, p 0.021), age of onset greater than 69 years (OR 2.01, 95% CI 1.37–2.95, p <0.001) and eGFR less than 92 ml/min (OR 2.84, 95% CI 1.51–5.34, p 0.001) were independently associated with risk of death. Conclusions Patients with DFU have high mortality and reduced life expectancy. Age at onset of diabetic foot ulcer, eGFR values and CKD are the principal risk factors for mortality.

scholarly journals Which Hemostatic Markers Add to the Predictive Value of Conventional Risk Factors for Coronary Heart Disease and Ischemic Stroke?

Circulation ◽  
2005 ◽  
Vol 112 (20) ◽  
pp. 3080-3087 ◽  
Author(s):  
Ann Smith ◽  
Chris Patterson ◽  
John Yarnell ◽  
Ann Rumley ◽  
Yoav Ben-Shlomo ◽  
...  
Keyword(s):  
Risk Factors ◽  
Coronary Heart Disease ◽  
Heart Disease ◽  
Ischemic Stroke ◽  
Predictive Value ◽  
Hemostatic Markers ◽  
Conventional Risk Factors

scholarly journals Nomograms Predicting the Occurrence of Sepsis in Patients following Major Hepatobiliary and Pancreatic Surgery

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Haoyun Zhang ◽  
Fanyu Meng ◽  
Shichun Lu
Keyword(s):  
Risk Factors ◽  
Predictive Value ◽  
Pancreatic Surgery ◽  
Clinical Characteristics ◽  
Prediction Models ◽  
Validation Cohort ◽  
Postoperative Sepsis ◽  
Training Cohort ◽  
Hepatobiliary And Pancreatic Surgery ◽  
Independent Risk Factors

Purpose. Sepsis is a severe complication in patients following major hepatobiliary and pancreatic surgery. The purpose of this study was to develop and validate a nomogram based on inflammation biomarkers and clinical characteristics. Methods. Patients who underwent major hepatobiliary and pancreatic surgery between June 2015 and April 2017 were retrospectively collected. Multivariate logistic regression was used to identify the independent risk factors associated with postoperative sepsis. A training cohort of 522 patients in an earlier period was used to develop the prediction models, and a validation cohort of 136 patients thereafter was used to validate the nomograms. Results. Sepsis developed in 55 of 522 patients of the training cohort and 19 of 136 patients in the validation cohort, respectively. In the training cohort, one nomogram based on clinical characteristics was developed. The clinical independent risk factors for postoperative sepsis include perioperative blood transfusion, diabetes, operative time, direct bilirubin, and BMI. Another nomogram was based on both clinical characteristics and inflammation biomarkers. Multivariate regression analyses showed that previous clinical risk factors, PCT, and CRP were independent risk factors for postoperative sepsis. The last nomogram showed a good C-index of 0.844 (95% CI, 0.787-0.900) compared with the previous one of 0.777 (95% CI, 0.713-0.840). Patients with a total score more than 109 in the second model are at high risk. The positive predictive value and negative predictive value of the second nomogram were 27% and 97%, respectively. Conclusion. The nomogram achieved good performances for predicting postoperative sepsis in patients by combining clinical and inflammation risk factors. This model can provide the early risk estimation of sepsis for patients following major hepatobiliary and pancreatic surgery.

scholarly journals Constructing Prediction Models for Freezing of Gait by Nomogram and Machine Learning: A Longitudinal Study

2021 ◽  
Vol 12 ◽  
Author(s):  
Kun Xu ◽  
Xiao-xia Zhou ◽  
Run-cheng He ◽  
Zhou Zhou ◽  
Zhen-hua Liu ◽  
...  
Keyword(s):  
Risk Factors ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Longitudinal Study ◽  
Prediction Models ◽  
Cox Regression ◽  
Freezing Of Gait ◽  
Training Set ◽  
Test Set ◽  
Clinical Measurements

Objectives: Although risk factors for freezing of gait (FOG) have been reported, there are still few prediction models based on cohorts that predict FOG. This 1-year longitudinal study was aimed to identify the clinical measurements closely linked with FOG in Chinese patients with Parkinson's disease (PD) and construct prediction models based on those clinical measurements using Cox regression and machine learning.Methods: The study enrolled 967 PD patients without FOG in the Hoehn and Yahr (H&amp;Y) stage 1–3 at baseline. The development of FOG during follow-up was the end-point. Neurologists trained in movement disorders collected information from the patients on a PD medication regimen and their clinical characteristics. The cohort was assessed on the same clinical scales, and the baseline characteristics were recorded and compared. After the patients were divided into the training set and test set by the stratified random sampling method, prediction models were constructed using Cox regression and random forests (RF).Results: At the end of the study, 26.4% (255/967) of the patients suffered from FOG. Patients with FOG had significantly longer disease duration, greater age at baseline and H&amp;Y stage, lower proportion in Tremor Dominant (TD) subtype, a higher proportion in wearing-off, levodopa equivalent daily dosage (LEDD), usage of L-Dopa and catechol-O-methyltransferase (COMT) inhibitors, a higher score in scales of Unified Parkinson's Disease Rate Scale (UPDRS), 39-item Parkinson's Disease Questionnaire (PDQ-39), Non-Motor Symptoms Scale (NMSS), Hamilton Depression Rating Scale (HDRS)-17, Parkinson's Fatigue Scale (PFS), rapid eye movement sleep behavior disorder questionnaire-Hong Kong (RBDQ-HK), Epworth Sleepiness Scale (ESS), and a lower score in scales of Parkinson's Disease Sleep Scale (PDSS) (P &lt; 0.05). The risk factors associated with FOG included PD onset not being under the age of 50 years, a lower degree of tremor symptom, impaired activities of daily living (ADL), UPDRS item 30 posture instability, unexplained weight loss, and a higher degree of fatigue. The concordance index (C-index) was 0.68 for the training set (for internal validation) and 0.71 for the test set (for external validation) of the nomogram prediction model, which showed a good predictive ability for patients in different survival times. The RF model also performed well, the C-index was 0.74 for the test set, and the AUC was 0.74.Conclusions: The study found some new risk factors associated with the FOG including a lower degree of tremor symptom, unexplained weight loss, and a higher degree of fatigue through a longitudinal study, and constructed relatively acceptable prediction models.

scholarly journals Nomogram for Predicting COVID-19 Disease Progression Based on Single-Center Data: Observational Study and Model Development

10.2196/19588 ◽  
2020 ◽  
Vol 8 (9) ◽  
pp. e19588
Author(s):  
Tao Fan ◽  
Bo Hao ◽  
Shuo Yang ◽  
Bo Shen ◽  
Zhixin Huang ◽  
...  
Keyword(s):  
Risk Factors ◽  
Regression Analysis ◽  
Clinical Characteristics ◽  
Cox Regression ◽  
Disease Onset ◽  
Lasso Regression ◽  
Prediction Ability ◽  
Calibration Curves ◽  
Independent Risk Factors ◽  
Critical Patients

Background In late December 2019, a pneumonia caused by SARS-CoV-2 was first reported in Wuhan and spread worldwide rapidly. Currently, no specific medicine is available to treat infection with COVID-19. Objective The aims of this study were to summarize the epidemiological and clinical characteristics of 175 patients with SARS-CoV-2 infection who were hospitalized in Renmin Hospital of Wuhan University from January 1 to January 31, 2020, and to establish a tool to identify potential critical patients with COVID-19 and help clinical physicians prevent progression of this disease. Methods In this retrospective study, clinical characteristics of 175 confirmed COVID-19 cases were collected and analyzed. Univariate analysis and least absolute shrinkage and selection operator (LASSO) regression were used to select variables. Multivariate analysis was applied to identify independent risk factors in COVID-19 progression. We established a nomogram to evaluate the probability of progression of the condition of a patient with COVID-19 to severe within three weeks of disease onset. The nomogram was verified using calibration curves and receiver operating characteristic curves. Results A total of 18 variables were considered to be risk factors after the univariate regression analysis of the laboratory parameters (P<.05), and LASSO regression analysis screened out 10 risk factors for further study. The six independent risk factors revealed by multivariate Cox regression were age (OR 1.035, 95% CI 1.017-1.054; P<.001), CK level (OR 1.002, 95% CI 1.0003-1.0039; P=.02), CD4 count (OR 0.995, 95% CI 0.992-0.998; P=.002), CD8 % (OR 1.007, 95% CI 1.004-1.012, P<.001), CD8 count (OR 0.881, 95% CI 0.835-0.931; P<.001), and C3 count (OR 6.93, 95% CI 1.945-24.691; P=.003). The areas under the curve of the prediction model for 0.5-week, 1-week, 2-week and 3-week nonsevere probability were 0.721, 0.742, 0.87, and 0.832, respectively. The calibration curves showed that the model had good prediction ability within three weeks of disease onset. Conclusions This study presents a predictive nomogram of critical patients with COVID-19 based on LASSO and Cox regression analysis. Clinical use of the nomogram may enable timely detection of potential critical patients with COVID-19 and instruct clinicians to administer early intervention to these patients to prevent the disease from worsening.

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