scholarly journals The Role of Glycemic Index and Glycemic Load in the Development of Real-Time Postprandial Glycemic Response Prediction Models for Patients with Gestational Diabetes

Nutrients ◽  
2020 ◽  
Vol 12 (2) ◽  
pp. 302 ◽  
Author(s):  
Evgenii Pustozerov ◽  
Aleksandra Tkachuk ◽  
Elena Vasukova ◽  
Aleksandra Dronova ◽  
Ekaterina Shilova ◽  
...  
Keyword(s):  
Linear Regression ◽  
Gestational Diabetes ◽  
Pregnant Women ◽  
Glycemic Index ◽  
Prediction Models ◽  
Pearson Correlation ◽  
Glycemic Load ◽  
Response Prediction ◽  
Mobile App ◽  
Glycemic Response

The incorporation of glycemic index (GI) and glycemic load (GL) is a promising way to improve the accuracy of postprandial glycemic response (PPGR) prediction for personalized treatment of gestational diabetes (GDM). Our aim was to assess the prediction accuracy for PPGR prediction models with and without GI data in women with GDM and healthy pregnant women. The GI values were sourced from University of Sydney’s database and assigned to a food database used in the mobile app DiaCompanion. Weekly continuous glucose monitoring (CGM) data for 124 pregnant women (90 GDM and 34 control) were analyzed together with records of 1489 food intakes. Pearson correlation (R) was used to quantify the accuracy of predicted PPGRs from the model relative to those obtained from CGM. The final model for incremental area under glucose curve (iAUC120) prediction chosen by stepwise multiple linear regression had an R of 0.705 when GI/GL was included among input variables and an R of 0.700 when GI/GL was not included. In linear regression with coefficients acquired using regularization methods, which was tested on the data of new patients, R was 0.584 for both models (with and without inclusion of GI/GL). In conclusion, the incorporation of GI and GL only slightly improved the accuracy of PPGR prediction models when used in remote monitoring.

scholarly journals Factors associated with dietary glycemic index and glycemic load in pregnant women and risk for gestational diabetes mellitus

2019 ◽  
Vol 71 (4) ◽  
pp. 516-524 ◽  
Author(s):  
Farah Yasmin Hasbullah ◽  
Barakatun Nisak Mohd Yusof ◽  
Zalilah Mohd Shariff ◽  
Zulida Rejali ◽  
Heng Yaw Yong ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Gestational Diabetes ◽  
Gestational Diabetes Mellitus ◽  
Pregnant Women ◽  
Glycemic Index ◽  
Glycemic Load ◽  
Factors Associated ◽  
Dietary Glycemic Index

Glycemic Index and Glycemic Load of the Diet in a Sample of Pregnant Women with Type 2 Diabetes Mellitus in Northwest Brazil

Diabetes ◽  
2018 ◽  
Vol 67 (Supplement 1) ◽  
pp. 54-LB
Author(s):  
CRISTINA FACANHA ◽  
TATIANA U. PASSOS ◽  
LIVIANE C. MARANHÃO ◽  
FRANCIELLE C. COPPOLA ◽  
JULIANA D. MARTINS ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Type 2 Diabetes ◽  
Type 2 Diabetes Mellitus ◽  
Pregnant Women ◽  
Glycemic Index ◽  
Glycemic Load

scholarly journals Value added Indian flat breads with Ashwagandha and its glycemic response among normal healthy subjects

2018 ◽  
Author(s):  
Neelam Chaturvedi, Nishtha Raj and Ayush Borah
Keyword(s):  
Diabetes Mellitus ◽  
Glycemic Index ◽  
Healthy Subjects ◽  
Functional Foods ◽  
Glycemic Load ◽  
Value Added ◽  
Glycemic Response ◽  
Carbohydrate Quality ◽  
Improve Glucose Tolerance ◽  
Low Glycemic Index

The glycemic index (GI) provides an indication of carbohydrate quality whereas glycemic load (GL) provides carbohydrates quantity in a food and the insulin demand. Diet with low glycemic index and glycemic load have been shown to improve glucose tolerance on normal healthy subjects so there is a need for a more diversified range of foods with a low glycemic response. The objective of present work was to formulate ashwagandha based food products by utilizing their root powder as an ingredient and their glycemic responses on normal healthy subjects. The products (Chappati, Naan and Thepla) were developed by incorporation of 2%, 4%, 6% and 8% aswagandha root. The result showed that the products with 2% root powder were most acceptable by semi trained panels. Further, study was conducted on randomly selected 30 healthy subjects were fed most acceptable test recipe i.e. thepla and their glycemic response was anticipated. GI and GL values were 37.30 and 11.36 found to be lower 2% root incorporated in thepla while comparing with standard thepla. The data demonstrated that the test thepla belongs to low glycemic index and medium glycemic load. Thus, the inclusion of ashwagandha powder as a constituent can be used to achieve a wider range of low glycemic functional foods possessing sensory attributes that could be valuable for managing the diabetes mellitus.

scholarly journals Total Phenolic Content, Antioxidant Activity and Glycemic Values of Non-Meat Burger Patties

2020 ◽  
pp. 1-9
Author(s):  
Dzul Fadly ◽  
Sulvi Purwayantie ◽  
Andi Imam Arundhana
Keyword(s):  
Antioxidant Activity ◽  
Glycemic Index ◽  
Total Phenolic Content ◽  
Phenolic Content ◽  
Communicable Disease ◽  
Glycemic Load ◽  
Meat Products ◽  
Total Phenolic ◽  
Glycemic Response ◽  
Dpph Method

Food choices with high antioxidant and low glycemic values may benefit the body's health. High total phenolic content will influence the antioxidant activity that works as a body shield from free radicals. On the other hand, higher glycemic values will increase the risk of non-communicable disease, specifically diabetes mellitus. It will be thoughtful to know the antioxidant activity and glycemic values of the food that consumed. Such in the case of non-meat products, including patty burger, which intended for meat patty burger substitution. This study aims to analyze the values of total phenolic content, antioxidant activity, glycemic response, glycemic index, and glycemic load of the non-meat burger patty. This is an experimental study with a completely randomized design. The total phenolic content was determined by Folin-ciocalteu method. Antioxidant activity was determined by DPPH method. The glycemic values were determined by an incremental area under the curve (iAUC) method. The values of total phenolic content and antioxidant activity of non-meat patty burger was in line. More phenolic content results in a higher antioxidant activity.  Non-meat patty burger has a lower glycemic response compared to a reference food. Its glycemic index is high. However, it has a low glycemic load. Non-meat burger patty has phenolic substances result in antioxidant activity, while its consumption with the right serving size may contribute a low glycemic effect and protect blood glucose stability.

scholarly journals Gestational Diabetes as a Predictor of Sexual Dysfunction in Pregnant Women

2021 ◽  
Vol 31 (2) ◽  
pp. 114-123
Author(s):  
Ommolbanin Zare ◽  
◽  
Masoumeh Simbar ◽  
Giti Ozgoli ◽  
Adeleh Bahar ◽  
...  
Keyword(s):  
Quality Of Life ◽  
Sexual Dysfunction ◽  
Linear Regression ◽  
Gestational Diabetes ◽  
Sexual Function ◽  
Pregnant Women ◽  
Sampling Method ◽  
Low Risk ◽  
Sexual Distress

Introduction: Pregnancy is associated with changes in sexual function and perhaps many more sexual problems when accompanied by particular disorders such as gestational diabetes. Objective: The present study was conducted to investigate factors associated with sexual functions in women with gestational diabetes. Materials and Methods: The present analytical, cross-sectional study was conducted on 300 women with gestational diabetes (150) and low-risk pregnant women (150) attending clinics affiliated to Mazandaran University of Medical Sciences in the north of Iran in 2019. A multistage cluster random sampling method was used, and samples were selected by convenience sampling method. The study data were collected using a demographic and obstetrics questionnaire, female sexual distress scale-revised, prenatal distress questionnaire, world health organization quality of life questionnaire, depression, anxiety, stress questionnaire, and a female sexual function index. Data analysis was done by descriptive statistics indicators, the Chi-square test, t-test, and multivariate linear regression. Results: The frequency of sexual dysfunction was 87.3% in women with gestational diabetes and 34.67% in low-risk pregnant women. Compared to women with low-risk-pregnancy, women with gestational diabetes reported lower sexual function scores (P=0.001). Women with gestational diabetes experience lower quality of life (P<0.05) than low-risk pregnant women. Besides, women with gestational diabetes experience higher levels of stress (P=0.001), more prenatal concerns (P=0.014), and higher sexual distress (P<0.05). The linear regression test showed that gestational diabetes in pregnant women predicts a significant reduction in sexual desire (β=-0.599; P= 0.001). Conclusion: Gestational diabetes predicts a significant reduction in sexual function during pregnancy due to the physical and psychological effects of gestational diabetes. Thus, it is recommended that pregnant women with gestational diabetes should be trained and counseled about gestational diabetes control and sexual function.

scholarly journals Glycemic Index, Glycemic Load, and Glycemic Response Are Not the Same

Diabetes Care ◽  
2005 ◽  
Vol 28 (7) ◽  
pp. 1839-1840 ◽  
Author(s):  
A. W. Barclay ◽  
J. C. Brand-Miller ◽  
T. M.S. Wolever
Keyword(s):  
Glycemic Index ◽  
Glycemic Load ◽  
Glycemic Response

scholarly journals Development and Evaluation of a Mobile Personalized Blood Glucose Prediction System for Patients With Gestational Diabetes Mellitus (Preprint)

2017 ◽  
Author(s):  
Evgenii Pustozerov ◽  
Polina Popova ◽  
Aleksandra Tkachuk ◽  
Yana Bolotko ◽  
Zafar Yuldashev ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Blood Glucose ◽  
Gestational Diabetes ◽  
Gestational Diabetes Mellitus ◽  
Recommender System ◽  
Prediction Models ◽  
Mobile App ◽  
Prediction System ◽  
Processing Software ◽  
Diabetes Patients

BACKGROUND Personalized blood glucose (BG) prediction for diabetes patients is an important goal that is pursued by many researchers worldwide. Despite many proposals, only a few projects are dedicated to the development of complete recommender system infrastructures that incorporate BG prediction algorithms for diabetes patients. The development and implementation of such a system aided by mobile technology is of particular interest to patients with gestational diabetes mellitus (GDM), especially considering the significant importance of quickly achieving adequate BG control for these patients in a short period (ie, during pregnancy) and a typically higher acceptance rate for mobile health (mHealth) solutions for short- to midterm usage. OBJECTIVE This study was conducted with the objective of developing infrastructure comprising data processing algorithms, BG prediction models, and an appropriate mobile app for patients’ electronic record management to guide BG prediction-based personalized recommendations for patients with GDM. METHODS A mobile app for electronic diary management was developed along with data exchange and continuous BG signal processing software. Both components were coupled to obtain the necessary data for use in the personalized BG prediction system. Necessary data on meals, BG measurements, and other events were collected via the implemented mobile app and continuous glucose monitoring (CGM) system processing software. These data were used to tune and evaluate the BG prediction model, which included an algorithm for dynamic coefficients tuning. In the clinical study, 62 participants (GDM: n=49; control: n=13) took part in a 1-week monitoring trial during which they used the mobile app to track their meals and self-measurements of BG and CGM system for continuous BG monitoring. The data on 909 food intakes and corresponding postprandial BG curves as well as the set of patients’ characteristics (eg, glycated hemoglobin, body mass index [BMI], age, and lifestyle parameters) were selected as inputs for the BG prediction models. RESULTS The prediction results by the models for BG levels 1 hour after food intake were root mean square error=0.87 mmol/L, mean absolute error=0.69 mmol/L, and mean absolute percentage error=12.8%, which correspond to an adequate prediction accuracy for BG control decisions. CONCLUSIONS The mobile app for the collection and processing of relevant data, appropriate software for CGM system signals processing, and BG prediction models were developed for a recommender system. The developed system may help improve BG control in patients with GDM; this will be the subject of evaluation in a subsequent study.

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