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Introduction: Type 2 diabetes is a common non-communicable disease, especially in developing
countries like India, posing a huge economic burden on the family and nation as a whole. It is a chronic
metabolic disorder in which prevalence has been increasing steadily all over the world. In studies of many
chronic medical conditions, the health status...
Context in source publication
Context 1
... characteristics of important demographic and clinical factors are summarized in Table 1. Table 2 shows that the observed transitions between states (rows to column) during the follow up visits. There are 5 transitions from normal to diabetic state and 14 transitions from pre-diabetic to diabetic. ...Citations
... The Markov model demonstrated that fasting blood sugar trends and factors influencing type 2 diabetes progression and regression, estimating transition probabilities and mean sojourn times to reveal valuable insights into disease dynamics [4]. The Markov employed in predicting the reliability of haemoglobin A1c (HbA1c) as a biomarker for type 2 diabetes and assessed its diagnostic and prognostic significance in a three-state Markov model framework [5]. The HMMs effectively validate the Framingham Diabetes Risk Scoring Model (FDRSM), successfully identifying individuals at increased risk of diabetes within an 8-year period [6]. ...
This research focuses on applying Markov modeling to the progression of type 2 diabetes mellitus. The study involves constructing a transition probability matrix that represents various stages in the advancement of type 2 diabetes. By utilising this matrix, the probability distributions for the consecutive occurrences of the same state, looking one and two days ahead. Furthermore, the investigation formulates explicit mathematical expressions for different statistical measures, utilizing Pear-son's coefficients. The developed model behaviour is examined through numerical examples and sensitivity analysis. The primary objective of this study is to create user-friendly tools, and developing appropriate software based on the derived mathematical formulations. The application of these findings can significantly enhance the management of type 2 diabetes in healthcare settings, potentially extending to decision support systems.
... Controlling glycemic response is important to improve clinical outcomes and quality of life especially in patients with metabolic disorders (Lakkakula, Khare, Verma, & Pattnaik, 2019;Livesey, Taylor, Hulshof, & Howlett, 2008;Ramzan et al., 2019). Failure to achieve glycemic response goals is associated with serious consequences and substantial costs (Goel, Grover, Sharma, & Bae, 2018;Livesey et al., 2008). ...
The aim of this study was to perform a systematic review and meta‐analysis of randomized clinical trials (RCTs) to examine the effect of grapes/grape products supplementation on glycemic indices in adults. Our systematic search to find relevant RCTs was performed up to February 2020 using PubMed, Scopus, ISI Web of Science, Cochrane Library, and Google Scholar. Based on the heterogeneity between included studies, a random effects or a fixed model was applied in the meta‐analysis, and results were expressed as weighted mean differences (WMD) with 95% confidence intervals (CI). Twenty‐nine clinical trials (1,297 participants) fulfilled the eligibility criteria of the present meta‐analysis. Overall, the grapes/grape products supplementation significantly reduced homeostatic model assessment of insulin resistance (HOMA‐IR) (WMD: −0.54, 95% CI: −0.91, −0.17, p = . 004) but did not affect fasting insulin levels (WMD: −0.90 μIU/ml, 95% CI: −1.04, 2.84, p = .362) and hemoglobin A1C (Hb1Ac) percentage (WMD: 0.00%, 95% CI: −0.10, 0.11, p = . 916) in the main analyses. In addition, changes to fasting blood glucose (FBG) levels were in favor of the control group (WMD: 1.19 mg/dl, 95% CI: 0.05, 2.34, p = .041). We found that giving grapes/grape products to adults might have beneficial effects on the HOMA‐IR. Further, large‐scale RCTs with longer duration are required to confirm these results.
... Different interventions to tackle high levels of the glycemic index have been proposed to prevent or manage the metabolic disorders. 37 Thus, various strategies such as dietary modification and introduction of new food policies have been developed the past years. 38,39 However, there is one food with pharmacological properties that has attracted the attention of the scientific society; the artichoke and its products. ...
Objectives
Cynara scolymus L. (common artichoke) and its products have been considered as potential phytotherapeutic agents for various conditions, such as cardiovascular, hepatic and gastric diseases, among others. Until now, the effects of artichoke and artichoke products administration on glycemic indices have not been sufficiently appraised. The present study evaluated the effects of artichoke and artichoke products administration on the glycemic indices.
Methods
Clinical trials were identified in the Cochrane Library, PubMed, Embase and Scopus databases; to infinity until 15 March 2020. Weighted mean differences (WMD) were pooled using a random-effects model. Heterogeneity, sensitivity analysis and publication bias were reported using standard methods.
Results
Pooled analysis of nine Randomized controlled trials (RCTs), demonstrated that the administration of artichoke and artichoke products led to a significant reduced fasting blood sugar (FBS) (WMD: -5.28 mg/dl, 95% CI: -8.95, -1.61; p = 0.005). However, other glycemic indeces including fasting insulin (WMD: -0.45 μIU/dL, 95% CI: -1.14, 0.25; p = 0.20), HOMA-IR (MD: -0.25, 95% CI: -0.57, 0.07; p = 0.12) or Hemoglobin A1c (HbA1c) (WMD: -0.09, 95% CI: -0.20, 0.02; p = 0.09) did not alter after the administration of artichoke and artichoke products. A subgroup analysis comparing the kind of intervention, revealed that just the supplementation of artichoke and artichoke products, in a noco-supplementation form, was efficacy for the reduction of Homeostatic model assessment of insulin resistance (HOMA-IR) (WMD: -0.52, 95% CI: -0.85, -0.19; p = 0.002)
Conclusions
The supplementation of artichoke and artichoke products can significantly reduce the FBS concentrations in humans. Moreover, these outcomes suggested that just the supplementation of artichoke and artichoke products is more effective in the reduction of HOMA-IR levels than the co-supplementation form. However, additional clinical trials with longer study periods are necessitated to obtain a robust conclusion for producing new guidelines as part of a healthy diet.
... Sadeghifar et al. (2016) projected new cases of congenital hypothyroidism using HMM. Goel et al. (2018) applied Multistate Markov Model for predicting the progression of type 2 diabetic patients. Grover et al. (2019) used HMM to estimate the misclassification probabilities of chronic kidney disease. ...
Diabetes is a common non-communicable disease affecting substantial proportion of adult population. This is true, especially in developing countries like India thereby posing a huge economic burden not only on the patient’s family but also on the nation as a whole. In this paper, we have employed a hidden Markov model to estimate the transition probabilities between three states of diabetes and applied it to real life data. A total of 184 Type 2 diabetic patients were included in this study. These patients are classified in different states on the basis of their available baseline value of Hemoglobin A1c (HbA1c). A HMM fits well to the data by capturing the misclassified states, and shows that the patients who had HbA1c ≥ 6.5% have minimum chance of recovery and substantially higher risk of complications. All the statistical analysis has been performed using the “Hidden Markov” package in R software.
Number trials have evaluated the effect of almond intake on glycemic control in adults; however, the results remain equivocal. Therefore, the present meta-analysis aims to examine the effectiveness of almond intake on glycemic parameters. Online databases including PubMed, Scopus, ISI web of science, Embase, and Cochrane Library were searched up to August 2021 for trials that examined the effect of almond intake on glycemic control parameters including fasting blood sugar (FBS), insulin, HOMA-IR, and HbA1C. Treatment effects were expressed as mean difference (MD) and the standard deviation (SD) of outcomes. To estimate the overall effect of almond intake, we used the random-effects model. In total, 24 studies with 31 arms were included in our analysis. The meta-analysis revealed that almond intake did not significantly change the concentrations of FBS, HbA1c, insulin levels, and HOMA-IR. In conclusion, there is currently no convincing evidence that almonds have a clear beneficial effect on glycemic control. Future studies are needed before any confirmed conclusion could be drowned.
