Multi-step ahead predictive model for blood glucose concentrations of type-1 diabetic patients.
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ABSTRACT:
SUBMITTER: Zaidi SMA
PROVIDER: S-EPMC8692478 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Epidemiological time series forecasting plays an important role in health public systems, due to its ability to allow managers to develop strategic planning to avoid possible epidemics. In this paper, a hybrid learning framework is developed to forecast multi-step-ahead (one, two and three-month-ahead) meningitis cases in four states of Brazil. First, the proposed approach applies an ensemble empirical mode decomposition (EEMD) to decompose the data into intrinsic mode functions and residual components. Then, each component is used as the input of five different forecasting models, and, from there, forecasted results are obtained. Finally, all combinations of models and components are developed, and for each case, the forecasted results are weighted integrated (WI) to formulate a heterogeneous ensemble forecaster for the monthly meningitis cases. In the final stage, a multi-objective optimization (MOO) using the Non-Dominated Sorting Genetic Algorithm - version II is employed to find a set of candidates' weights, and then the Technique for Order of Preference by similarity to Ideal Solution (TOPSIS) is applied to choose the adequate set of weights. Next, the most adequate model is the one with the best generalization capacity out-of-sample in terms of performance criteria including mean absolute error (MAE), relative root mean squared error (RRMSE) and symmetric mean absolute percentage error (sMAPE). By using MOO, the intention is to enhance the performance of the forecasting models by improving simultaneously their accuracy and stability measures. To access the model's performance, comparisons based on metrics are conducted with: (i) EEMD, heterogeneous ensemble integrated by direct strategy, or simple sum; (ii) EEMD, homogeneous ensemble of components WI; (iii) models without signal decomposition. At this stage, MAE, RRMSE, sMAPE criteria and Diebold-Mariano statistical test are adopted. In all twelve scenarios, the proposed framework was able to perform more accurate and stable forecasts, which showed, on 89.17% of the cases, that the errors of the proposed approach are statistically lower than other approaches. These results showed that combining EEMD, heterogeneous ensemble and WI with weights obtained by optimization can develop precise and stable forecasts. The modelling developed in this paper is promising and can be used by managers to support decision making.
| S-EPMC7507988 | biostudies-literature
Project description:BackgroundLactulose is approved for the symptomatic treatment of constipation, a gastrointestinal (GI) complication common in individuals with diabetes. Lactulose products contain carbohydrate impurities (e.g., lactose, fructose, galactose), which occur during the lactulose manufacturing process. These impurities may affect the blood glucose levels of individuals with type 2 diabetes mellitus (T2DM) using lactulose for the treatment of mild constipation. A previous study in healthy subjects revealed no increase in blood glucose levels after oral lactulose intake. However, it is still unclear whether the intake of lactulose increases blood glucose levels in individuals with diabetes.AimTo evaluate the blood glucose profile after oral lactulose intake in mildly constipated, non-insulin-dependent subjects with T2DM in an outpatient setting.MethodsThis prospective, double-blind, randomized, controlled, single-center trial was conducted at the Clinical Research Center at the Medical University of Graz, Austria, in 24 adult Caucasian mildly constipated, non-insulin-dependent subjects with T2DM. Eligible subjects were randomized and assigned to one of six treatment sequences, each consisting of four treatments stratified by sex using an incomplete block design. Subjects received a single dose of 20 g or 30 g lactulose (crystal and liquid formulation), water as negative control or 30 g glucose as positive control. Capillary blood glucose concentrations were measured over a period of 180 min post dose. The primary endpoint was the baseline-corrected area under the curve of blood glucose concentrations over the complete assessment period [AUCbaseline_c (0-180 min)]. Quantitative comparisons were performed for both lactulose doses and formulations vs water for the equal lactulose dose vs glucose, as well as for liquid lactulose vs crystal lactulose. Safety parameters included GI tolerability, which was assessed at 180 min and 24 h post dose, and adverse events occurring up to 24 h post dose.ResultsIn 24 randomized and analyzed subjects blood glucose concentration-time curves after intake of 20 g and 30 g lactulose were almost identical to those after water intake for both lactulose formulations despite the different amounts of carbohydrate impurities (≤ 3.0% for crystals and approx. 30% for liquid). The primary endpoint [AUCbaseline_c (0-180 min)] was not significantly different between lactulose and water regardless of lactulose dose and formulation. Also with regard to all secondary endpoints lactulose formulations showed comparable results to water with one exception concerning maximum glucose level. A minor increase in maximum blood glucose was observed after the 30 g dose, liquid lactulose, in comparison to water with a mean treatment difference of 0.63 mmol/L (95% confidence intervals: 0.19, 1.07). Intake of 30 g glucose significantly increased all blood glucose endpoints vs 30 g liquid and crystal lactulose, respectively (all P < 0.0001). No differences in blood glucose response were observed between the different lactulose formulations. As expected, lactulose increased the number of bowel movements and was generally well tolerated. Subjects experienced only mild to moderate GI symptoms due to the laxative action of lactulose.ConclusionBlood glucose AUCbaseline_c (0-180 min) levels in mildly constipated, non-insulin dependent subjects with T2DM are not affected by the carbohydrate impurities contained in 20 g and 30 g crystal or liquid lactulose formulations.
| S-EPMC8192256 | biostudies-literature
Project description:BackgroundResearchers today are generating unprecedented amounts of biological data. One trend in current biological research is integrated analysis with multi-platform data. Effective integration of multi-platform data into the solution of a single or multi-task classification problem; however, is critical and challenging. In this study, we proposed HetEnc, a novel deep learning-based approach, for information domain separation.ResultsHetEnc includes both an unsupervised feature representation module and a supervised neural network module to handle multi-platform gene expression datasets. It first constructs three different encoding networks to represent the original gene expression data using high-level abstracted features. A six-layer fully-connected feed-forward neural network is then trained using these abstracted features for each targeted endpoint. We applied HetEnc to the SEQC neuroblastoma dataset to demonstrate that it outperforms other machine learning approaches. Although we used multi-platform data in feature abstraction and model training, HetEnc does not need multi-platform data for prediction, enabling a broader application of the trained model by reducing the cost of gene expression profiling for new samples to a single platform. Thus, HetEnc provides a new solution to integrated gene expression analysis, accelerating modern biological research.
| S-EPMC6686264 | biostudies-literature
Project description:State-of-the-art multivariate forecasting methods are restricted to low dimensional tasks, linear dependencies and short horizons. The technological advances (notably the Big data revolution) are instead shifting the focus to problems characterized by a large number of variables, non-linear dependencies and long forecasting horizons. In the last few years, the majority of the best performing techniques for multivariate forecasting have been based on deep-learning models. However, such models are characterized by high requirements in terms of data availability and computational resources and suffer from a lack of interpretability. To cope with the limitations of these methods, we propose an extension to the DFML framework, a hybrid forecasting technique inspired by the Dynamic Factor Model (DFM) approach, a successful forecasting methodology in econometrics. This extension improves the capabilities of the DFM approach, by implementing and assessing both linear and non-linear factor estimation techniques as well as model-driven and data-driven factor forecasting techniques. We assess several method integrations within the DFML, and we show that the proposed technique provides competitive results both in terms of forecasting accuracy and computational efficiency on multiple very large-scale (>102 variables and > 103 samples) real forecasting tasks.
| S-EPMC8460934 | biostudies-literature
Project description:IntroductionIn times of short health care budgets, reimbursement for self-monitoring of blood glucose (SMBG) in diabetes patients without insulin treatment is subject to debate. The Structured Testing Program (STeP) trial found a positive correlation of test frequency and improved hemoglobin A1c (HbA1c) levels in poorly controlled type 2 diabetes patients not treated with insulin.MethodsA structured literature search for other clinical studies reporting on SMBG frequency was performed.ResultsThere is scarce evidence: three trials, including STeP, noted a significant and relevant correlation between testing frequency and improved HbA1c levels (FA effect), whereas two studies did not. The comparability between the identified studies is problematic.ConclusionFuture research should consider correlations between testing frequency and level of glycemic control. More emphasis should be placed on a structured approach to use SMBG and to address adherence to testing and therapy.
| S-EPMC3440172 | biostudies-literature
Project description:Aims/introductionTo prevent diabetic complications, strict glucose control and frequent monitoring of blood glucose levels with invasive methods are necessary. We considered the monitoring of tear glucose levels might be a possible method for non-invasive glucose monitoring. To develop tear glucose monitoring for clinical application, we investigated the precise correlation between the blood and tear glucose concentrations.Materials and methodsA total of 10 participants and 20 participants with diabetes were admitted, and blood and tear samples were collected. Before statistical analysis, we eliminated tear samples contaminated with blood. We observed the daily blood and tear glucose dynamics, and carried out a random intercept model analysis to examine the association between the blood and tear glucose concentrations.ResultsTear occult blood tests showed that the tear glucose concentrations and their variation increased in both participants with and without diabetes as contamination of blood increased. In both participants with and without diabetes, fluctuations of the plasma glucose concentrations were observed depending on the timing of collection of the samples, and the dynamics of the tear glucose concentrations paralleled those of the plasma glucose concentrations. The random intercept model analysis showed a significant association between the plasma and tear glucose concentrations in participants with diabetes (P < 0.001). This association still existed even after adjusting for the glycated hemoglobin levels and the prandial state (P < 0.001).ConclusionsIt is important to eliminate the tear samples contaminated with blood. Tear glucose monitoring might be a reliable and non-invasive substitute method for monitoring the blood glucose concentrations for diabetes patients, irrespective of glycated hemoglobin levels and timing of sample collection.
| S-EPMC7858102 | biostudies-literature
Project description:Background and aims: Blood glucose prediction (BGP) has increasingly been adopted for personalized monitoring of blood glucose levels in diabetic patients, providing valuable support for physicians in diagnosis and treatment planning. Despite the remarkable success achieved, applying BGP in multi-patient scenarios remains problematic, largely due to the inherent heterogeneity and uncertain nature of continuous glucose monitoring (CGM) data obtained from diverse patient profiles. Methodology: This study proposes the first graph-based Heterogeneous Temporal Representation (HETER) network for multi-patient Blood Glucose Prediction (BGP). Specifically, HETER employs a flexible subsequence repetition method (SSR) to align the heterogeneous input samples, in contrast to the traditional padding or truncation methods. Then, the relationships between multiple samples are constructed as a graph and learned by HETER to capture global temporal characteristics. Moreover, to address the limitations of conventional graph neural networks in capturing local temporal dependencies and providing linear representations, HETER incorporates both a temporally-enhanced mechanism and a linear residual fusion into its architecture. Results: Comprehensive experiments were conducted to validate the proposed method using real-world data from 112 patients in two hospitals, comparing it with five well-known baseline methods. The experimental results verify the robustness and accuracy of the proposed HETER, which achieves the maximal improvement of 31.42%, 27.18%, and 34.85% in terms of MAE, MAPE, and RMSE, respectively, over the second-best comparable method. Discussions: HETER integrates global and local temporal information from multi-patient samples to alleviate the impact of heterogeneity and uncertainty. This method can also be extended to other clinical tasks, thereby facilitating efficient and accurate capture of crucial pattern information in structured medical data.
| S-EPMC10393041 | biostudies-literature
Project description:BackgroundAlthough increasing evidence suggests that hyperglycemia following acute stroke adversely affects clinical outcome, whether the association between glycaemia and functional outcome varies between stroke patients with\without pre-diagnosed diabetes remains controversial. We aimed to investigate the relationship between the fasting blood glucose (FBG) and the 6-month functional outcome in a subgroup of SMART cohort and further to assess whether this association varied based on the status of pre-diagnosed diabetes.MethodsData of 2862 patients with acute ischemic stroke (629 with pre-diagnosed diabetics) enrolled from SMART cohort were analyzed. Functional outcome at 6-month post-stroke was measured by modified Rankin Scale (mRS) and categorized as favorable (mRS:0-2) or poor (mRS:3-5). Binary logistic regression model, adjusting for age, gender, educational level, history of hypertension and stroke, baseline NIHSS and treatment group, was used in the whole cohort to evaluate the association between admission FBG and functional outcome. Stratified logistic regression analyses were further performed based on the presence/absence of pre-diabetes history.ResultsIn the whole cohort, multivariable logistical regression showed that poor functional outcome was associated with elevated FBG (OR1.21 (95%CI 1.07-1.37), p = 0.002), older age (OR1.64 (95% CI1.38-1.94), p<0.001), higher NIHSS (OR2.90 (95%CI 2.52-3.33), p<0.001) and hypertension (OR1.42 (95%CI 1.13-1.98), p = 0.04). Stratified logistical regression analysis showed that the association between FBG and functional outcome remained significant only in patients without pre-diagnosed diabetes (OR1.26 (95%CI 1.03-1.55), p = 0.023), but not in those with premorbid diagnosis of diabetes (p = 0.885).ConclusionThe present results demonstrate a significant association between elevated FBG after stroke and poor functional outcome in patients without pre-diagnosed diabetes, but not in diabetics. This finding confirms the importance of glycemic control during acute phase of ischemic stroke especially in patients without pre-diagnosed diabetes. Further investigation for developing optimal strategies to control blood glucose level in hyperglycemic setting is therefore of great importance.Trial registrationClinicalTrials.gov NCT00664846.
| S-EPMC4975495 | biostudies-literature
Project description:BackgroundDue to China's rapid urbanization, many farmers have relocated to urban resettlement regions. There is limited research on the glycemic control of Type 2 diabetes mellitus (T2DM) farmers in these areas. This study examined their blood glucose control and its determinants.MethodsThis study took place from March 2021 to January 2022 in a resettlement community in Yiwu, Zhejiang Province, China. In the first phase, a quantitative survey of 181 T2DM farmers was conducted using a questionnaire to gather demographic data, blood glucose control status, disease cognition levels, and treatment compliance. Inclusion criteria were migrant workers with ≥ 3 months of residence and local household registration and T2DM patients who met the diagnostic criteria of the Chinese guidelines. In the second phase, qualitative research involved face-to-face, semi-structured interviews with 15 patients with varying blood glucose control levels to analyze their experiences.ResultsThe blood glucose control rate in this particular group was 27.62%, and the average disease cognition score was 2.5 ± 0.75. Many patients (67.96%) had inadequate treatment compliance, specifically in monitoring compliance (4.45 ± 1.92) and regular review compliance (3.58 ± 1.74).ConclusionThere is a need for tailored programs to improve glycemic control among resettled farmers. Enhancing disease awareness and treatment compliance through targeted education and support is crucial. Further studies are needed to evaluate different treatment regimens' impact on glycemic control.Clinical trial numberNot applicable.
| S-EPMC11360284 | biostudies-literature
Project description:Genome-wide association studies (GWAS) have successfully identified genetic loci associated with glycemic traits. However, characterizing the functional significance of these loci has proven challenging. We sought to gain insights into the regulation of fasting insulin and fasting glucose through the use of gene expression microarray data from peripheral blood samples of participants without diabetes in the Framingham Heart Study (FHS) (n = 5,056), the Rotterdam Study (RS) (n = 723), and the InCHIANTI Study (Invecchiare in Chianti) (n = 595). Using a false discovery rate q <0.05, we identified three transcripts associated with fasting glucose and 433 transcripts associated with fasting insulin levels after adjusting for age, sex, technical covariates, and complete blood cell counts. Among the findings, circulating IGF2BP2 transcript levels were positively associated with fasting insulin in both the FHS and RS. Using 1000 Genomes-imputed genotype data, we identified 47,587 cis-expression quantitative trait loci (eQTL) and 6,695 trans-eQTL associated with the 433 significant insulin-associated transcripts. Of note, we identified a trans-eQTL (rs592423), where the A allele was associated with higher IGF2BP2 levels and with fasting insulin in an independent genetic meta-analysis comprised of 50,823 individuals. We conclude that integration of genomic and transcriptomic data implicate circulating IGF2BP2 mRNA levels associated with glucose and insulin homeostasis.
| S-EPMC5127245 | biostudies-literature