Role perceptions and experiences of adult children in remote glucose management for older parents with type 2 diabetes mellitus: a qualitative study.

BACKGROUND: With the advent of the smart phone era, managing blood glucose at home through apps will become more common for older individuals with diabetes. Adult children play important roles in glucose management of older parents. Few studies have explored how adult children really feel about engaging in the glucose management of their older parents with type 2 diabetes mellitus (T2DM) through mobile apps. This study provides insights into the role perceptions and experiences of adult children of older parents with T2DM participating in glucose management through mobile apps. METHODS: In this qualitative study, 16 adult children of older parents with T2DM, who had used mobile apps to manage blood glucose for 6 months, were recruited through purposive sampling. Semi-structured, in-depth, face-to-face interviews to explore their role perceptions and experiences in remotely managing their older parents' blood glucose were conducted. The Consolidated Criteria for Reporting Qualitative Research (COREQ) were followed to ensure rigor in the study. The data collected were analyzed by applying Colaizzi's seven-step qualitative analysis method. RESULTS: Six themes and eight sub-themes were identified in this study. Adult children's perceived roles in glucose management of older parents with T2DM through mobile apps could be categorized into four themes: health decision-maker, remote supervisor, health educator and emotional supporter. The experiences of participation could be categorized into two themes: facilitators to participation and barriers to participation. CONCLUSION: Some barriers existed for adult children of older parents with T2DM participating in glucose management through mobile apps; however, the findings of this study were generally positive. It was beneficial and feasible for adult children to co-manage the blood glucose of older parents. Co-managing blood glucose levels in older parents with T2DM can enhance both adherence rates and confidence in managing blood glucose effectively.

Retrospective, Longitudinal, One-Group Study on the Implementation of Continuous Glucose Monitoring To Improve Quality of Care for Patients With Type I or II Diabetes Mellitus in an Internal Medicine Residency Continuity Community Clinic.

In this three-year retrospective study, data from 51 patients with type 1 or type 2 diabetes mellitus (DM), receiving a minimum of 3-4 insulin injections per day and self-monitoring their blood glucose (SMBG) four times a day, were derived from our internal medicine residency primary care clinic. The patients were equipped with a continuous glucose monitoring (CGM) device that shared 24-hour glucose data with the clinic. They were assigned to members of our CGM team, which included internal medicine or transitional year medical residents who functioned under the supervision of a board-certified endocrinologist. The residents, in consultation with our endocrinologist, assessed the patients' glucose management data and adjusted their treatment regimens biweekly by calling the patients, and monthly by seeing the patients in the clinic. Significant results from the study include a reduction in HbA1c from 9.9% to 7.6%, an average blood glucose decrement from 242 mg/dL to 169 mg/dL, a reduction in the incidence of mild hypoglycemia from below 70 mg/dL to 54 mg/dL, from 4.68% to 0.76% per day, and a more pronounced hypoglycemia with glucose less than 54 mg/dL from 3.1% per day to 0.2% per day. We observed a significant increase in the time in the range of the blood glucose from 33% to 67% per day. Furthermore, 9.5% of the patients in this study eventually discontinued their daily insulin injections and continued treatment with oral diabetic medications with or without the use of injectable GLP-1 receptors once a week. Our study affirms that CGM devices significantly improve glycemic control compared to SMBG, supporting its efficacy in optimizing glycemic control in real-world clinical practice. The results imply that this can be accomplished in internal medicine residency clinics and not exclusively in specialized endocrine clinics. As far as we know, this is the first study of its kind in a residency clinic in the USA. This study confirms the benefits of widening the application of CGM in DM, along with the challenges that must be overcome to realize the evidence-based benefits of this technology. CGM needs to become a part of routine monitoring for type 1 and type 2 DM.

Learning control-ready forecasters for Blood Glucose Management.

Type 1 diabetes (T1D) presents a significant health challenge, requiring patients to actively manage their blood glucose (BG) levels through regular bolus insulin administration. Automated control solutions based on machine learning (ML) models could reduce the need for manual patient intervention. However, the accuracy of current models falls short of what is needed. This is due in part to the fact that these models are often trained on data collected using a basal bolus (BB) strategy, which results in substantial entanglement between bolus insulin and carbohydrate intake. Under standard training approaches, this entanglement can lead to inaccurate forecasts in a control setting, ultimately resulting in poor BG management. To address this, we propose a novel algorithm for training BG forecasters that disentangles the effects of insulin and carbohydrates. By exploiting correction bolus values and leveraging the monotonic effect of insulin on BG, our method accurately captures the independent effects of insulin and carbohydrates on BG. Using an FDA-approved simulator, we evaluated our approach on 10 individuals across 30 days of data. Our approach achieved on average higher time in range compared to standard approaches (81.1% [95% confidence interval (CI) 80.3,81.9] vs 53.6% [95%CI 52.7,54.6], p<0.001), indicating that our approach is able to reliably maintain healthy BG levels in simulated individuals, while baseline approaches are not. Utilizing proxy metrics, our approach also demonstrates potential for improved control on three real world datasets, paving the way for advancements in ML-based BG management.

The potential of glucose management indicator for the estimation of glucose disposal rate in people with type 1 diabetes.

BACKGROUND AND AIMS: Insulin resistance is a growing feature in type 1 diabetes (T1D). It can be quantified by calculating the estimated glucose disposal rate (eGDR) with the Epstein's formula, which includes laboratory-measured glycated hemoglobin (HbA1c). We aimed the current research to assess the agreement between the conventional eGDR formula and an alternative one (eGDR-GMI) incorporating the glucose management indicator (GMI) derived from continuous glucose monitoring (CGM). We also explored the relationship between eGDR-GMI, cardiovascular risk factors, and the prevalence of diabetes-related complications. METHODS AND RESULTS: We designed a cross-sectional study that included adults with T1D. eGDR-GMI and eGDR (mg/kg/min) were calculated using GMI or HbA1c, waist circumference, and hypertensive state. Clinical data were collected from electronic medical records. The analyses encompassed 158 participants with a mean age of 39 ± 13 years. The Bland-Altman analysis showed a good agreement between eGDR-GMI and eGDR. When we divided participants in eGDR-GMI tertiles we found a higher prevalence of diabetes-related complications and a less favorable metabolic profile in the lowest eGDR-GMI tertile. The relative risk of retinopathy, nephropathy, and neuropathy significantly increased by approximately 1 unit with each decrease in eGDR-GMI, regardless of age, sex, disease duration, lipids, and smoking habit. CONCLUSIONS: eGDR-GMI represents a valid and robust alternative to the eGDR to assess insulin resistance in T1D. Low eGDR-GMI is associated with diabetes complications and a less favorable metabolic profile. Incorporating the eGDR-GMI into clinical practice can enhance the characterization of T1D people and allow for a more personalized treatment approach.

Hyperglycemic Patterns in Acute Stroke Patients.

Background and objective Hyperglycemia following a stroke can independently aggravate the ischemic area. Ensuring adequate glucose management can help avoid complications and minimize mortality and disability in these patients. This study aimed to investigate hyperglycemic patterns in acute stroke patients. Materials and methods We conducted a non-interventional prospective observational study involving acute stroke patients by employing continuous glucose monitoring (CGM) for 72 hours after the onset of stroke symptoms. Admission glucose, patients' total mean glucose (TMG), and time in range (TIR) (70-140 mg/dl) were correlated with the hyperglycemic patterns elicited by the CGM system software. Data were analyzed using SPSS Statistics 26.0 (IBM Corp., Armonk, NY) with descriptive statistics, the Kruskal-Wallis test, and χ2 test. Results Our cohort comprised 105 diabetic and non-diabetic stroke patients. The hyperglycaemic patterns that we observed were as follows: (i) hyperglycemia from 23:00 to 10:00, (ii) 06.00 to 10.00, (iii) at night and after meals, iv) no pattern, v) unspecified patterns. Patients with nocturnal and morning hyperglycemia had admission glucose of 183 mg/dl, mean 72-hour glucose of 156 mg/dl, and TIR of 37%. Patients who did not develop a hyperglycemic pattern either had admission glucose of 131 mg/dl and TIR of 89% or had high admission glucose (197 mg/dl) and a short TIR (14%). Conventional pre-meal capillary glucose tests do not appear to detect these patients' hyperglycemic tendencies. Conclusions These results may indicate the necessity for more intensive measurements during the night or dawn in this patient population. Admission glucose could be considered a predictor of hyperglycemic patterns and contribute to the patient's care plan.

Differences Between Glycated Hemoglobin and Glucose Management Indicator in Real-Time and Intermittent Scanning Continuous Glucose Monitoring in Adults With Type 1 Diabetes.

BACKGROUND: This study demonstrates the difference between glucose management indicator (GMI) and glycated hemoglobin (HbA1c) according to sensor mean glucose and HbA1c status using 2 continuous glucose monitoring (CGM) sensors in people with type 1 diabetes. METHODS: A total of 275 subjects (117 Dexcom G6 [G6] and 158 FreeStyle Libre 1 [FL]) with type 1 diabetes was included. The G6 and FL sensors were used, respectively, over 90 days to analyze 682 and 515 glycemic profiles that coincide with HbA1c. RESULTS: The mean HbA1c was 6.6% in Dexcom G6 and 7.2% in FL profiles. In G6 profiles, GMI was significantly higher than HbA1c irrespective of mean glucose (all P < .001, mean difference: 0.58% ± 0.49%). The GMI was significantly higher than the given HbA1c when HbA1c was below 8.0% (all P < .001). The discordance was the highest at 0.9% for lower HbA1c values (5.0%-5.9%). The proportion of discordance >0.5% improved from 60.1% to 30.9% when using the revised GMI equation in G6 profiles. In FL profile, the overall mean difference between GMI and HbA1c was 0 (P = .966). The GMI was significantly lower by 0.9% than HbA1c of 9.0% to 9.9% and higher by 0.5% in HbA1c of 5.0% to 5.9% (all P < .001). CONCLUSIONS: The GMI is overestimated in G6 users, particularly those with well-controlled diabetes, but the GMI and HbA1c discordance improved with a revised equation from the observed data. The FL profile showed greater discordance for lower HbA1c levels or higher HbA1c levels.

Glucose evaluation and management in the ICU (GEM-ICU): Protocol for a bi-centre cohort study.

INTRODUCTION: Hyperglycaemia is common in intensive care unit (ICU) patients. Glycaemic monitoring and effective glycaemic control with insulin are crucial in the ICU to improve patient outcomes. However, glycaemic control and insulin use vary between ICU patients and hypo- and hyperglycaemia occurs. Therefore, we aim to provide contemporary data on glycaemic control and management, and associated outcomes, in adult ICU patients. We hypothesise that the occurrence of hypoglycaemia in acutely admitted ICU patients is lower than that of hyperglycaemia. METHODS: We will conduct a bi-centre cohort study of 300 acutely admitted adult ICU patients. Routine data will be collected retrospectively at baseline (ICU admission) and daily during ICU stay up to a maximum of 30 days. The primary outcome will be the number of patients with hypoglycaemia during their ICU stay. Secondary outcomes will be occurrence of severe hypoglycaemia, occurrence of hyperglycaemia, time below blood glucose target range, time above target range, all-cause mortality at Day 30, number of days alive without life support at Day 30 and number of days alive and out of hospital at Day 30. Process outcomes include the number of in-ICU days, glucose measurements (number of measurements and method) and use of insulin (including route of administration and dosage). All statistical analyses will be descriptive. CONCLUSIONS: This cohort study will provide a contemporary overview of glucose evaluation and management practices in adult ICU patients and, thus, highlight potential areas for improvement through future clinical trials in this area.

Continuous glucose monitoring with FreeStyle Libre PRO sensor in patients with type 2 diabetes and end-stage renal failure on haemoDIALysis (FSLPRO-DIAL pilot study).

AIMS: For end-stage renal disease (ESRD) patients with diabetes on haemodialysis, diabetes control is difficult to achieve. Hypoglycaemia is a major problem in these frailty subjects. Continuous glucose monitoring (CGM) devices appear therefore to be a good tool to help patients monitor their glycaemic control and to help practitioners optimize treatment. We aimed to compare the laboratory value of Hba1c with the sensor-estimated value of Hba1c (= glucose management indicator, GMI) in ESRD patients with type 2 diabetes (T2D) (with or without insulin treatment) on haemodialysis. Secondly, we aimed to identify CGM-derived monitoring parameters [time in range, time in hypo/hyperglycaemia, glycaemic variability (coefficient of variation, CV)] to identify patients at risk of frequent hypo- or hyperglycaemia. METHODS: The FSLPRO-DIAL pilot study (NCT04641650) was a prospective monocentric cohort study including 29 subjects with T2D who achieve the protocol. Inclusion criteria were: age ≥ 18 years, haemodialysis duration for at least 3 months, type 2 diabetes with no change in treatment for at least 3 months. Demographic data and blood sample were collected at the day of inclusion. Freestyle Libre pro IQ sensor (blinded CGM) was inserted for 14 days. After this period, all CGMs data were collected and analysed. RESULTS: Data were available for 27 patients. Mean age was 73 ± 10, mean BMI 27.2 kg/m2, mean duration of diabetes 16.9 years and mean dialysis duration 2.9 years. Twenty-four subjects were treated with insulin. Mean HbA1c was 6.6% (SD 1.2), and mean GMI was 6.7% (SD 0.9) (no significant difference, p = 0.3). Twelve subjects (44.4%) had a discordance between HbA1c and GMI of < 0.5%, 11 (40.8%) had a discordance between 0.5 and 1%, and only 4 (14.8%) had a discordance of > 1%. Mean time in range (70-180 mg/dl) was 71.9%, mean time below range (< 70 mg/dl) was 5.6%, and mean time above range (> 180 mg/dl) was 22.1%. Mean CV was 31.8%. For 13 out of 27 patients, we reduced antidiabetic treatment by stopping treatments or reducing insulin doses. CONCLUSION: In this pilot study, there was no global significant difference between HbA1c and GMI in this particular cohort with very well-controlled diabetes. However, the use of the sensor enabled us to identify an excessive time in hypoglycemia in this fragile population and to adapt their treatment.

HbA1c overestimates the glucose management indicator: a pilot study in patients with diabetes, chronic kidney disease not on dialysis, and anemia using isCGM.

Introduction: There are multiple mechanisms by which HbA1c values can be altered in chronic kidney disease (CKD), which limits its usefulness as a strategy to assess glycemic control in this population. Methods: Concordance and agreement study between two diagnostic tests: HbA1c and glucose management indicator (GMI) measured by intermittently scanned continuous glucose monitoring (isCGM), based in a prospective cohort of patients with diabetes, CKD (glomerular filtration rate between 15 and 60 ml/min/1.73 m²), and anemia. The isCGM was performed for 3 months, and the GMI was compared with the HbA1c levels taken at the end of isCGM. Agreement was evaluated using Bland-Altman graph analysis and Lin's concordance correlation coefficient (CCC). The concordance of the measures with good glycemic control (<7%) was also evaluated. Results: A total of 74 patients were enrolled (median age 68.5 years, 51.3% female, 64.9% with CKD stage 3, hemoglobin 11.1 ± 1.2 g/l). The Bland-Altman analysis shows a mean difference between GMI and HbA1c of 0.757 ± 0.687% (95% limits of agreement: -0.590 and 2.105). Difference was greater as the values of GMI and HbA1c increased. The agreement was poor [CCC 0.477; 95% confidence interval (CI): 0.360-0.594], as well as the concordance of values with good glycemic control according to GMI versus HbA1c (67.5% versus 29.7%, p < 0.001) (Kappa 0.2430; 95% CI: 0.16-0.32). Conclusion: The HbA1c overestimates the GMI values with highly variable ranges of difference, which prevents a precise correction factor. isCGM probably is a safer option for monitoring and decision-making in this population, especially in patients treated with insulin where the risk of hypoglycemia is greater.

The Discrepancy Between Hemoglobin A1c and Glucose Management Indicators in 26 Patients Treated With Continuous Glucose Monitoring in an Internal Medicine Residency Clinic.

We conducted a retrospective observational cohort study between 2020 and 2023 in 26 patients with type 1 and type 2 diabetes mellitus (DM) who were using 3-4 injections per day of insulin and were monitored by continuous glucose monitoring (CGM). The goal of this retrospective observational cohort study is to compare these two metrics in an internal medicine community primary care residency clinic. We used CGM devices, Dexcom G6 and G7, and Freestyle Libre 3. The goal was to compare the patient's hemoglobin A1c (HbA1c) taken during their clinic visit by phlebotomy as a marker for diabetic control with an estimated HbA1c glucose management indicator (GMI) derived from the 30-day CGM readings. HbA1c is derived from the blood, while the GMI value is derived from the interstitial fluid. Both parameters were taken within 30 days of each other. GMI was taken in the last 30 days. We excluded patients with known anemia, chronic kidney disease, polycythemia, cirrhosis of the liver, or metabolic dysfunction associated with steatohepatitis (MASH) because disease states can affect the measured HbA1c. Also, pregnant and African American patients were excluded. We concluded the measured HbA1c was 0.34% (4 mmol/mol) higher than the CGM-derived GMI. The relationship between factors that affect glycemic control was discussed in the article, as well as the future utilization of them in improving diabetic control and management. As the use of CGM continues to grow, addressing differences between laboratory-measured HbA1c and CGM-derived GMI is critical.

Diabetic management and infection risk in total ankle arthroplasty.

BACKGROUND: As total ankle arthroplasty (TAA) increases in popularity nationwide for the management of end-stage arthritis, it is essential to understand ways to mitigate the risk of infection. Diabetes increases the risk of infection due to compromised immunity and impaired wound-healing mechanisms. However, there is limited research on how diabetic management, inclusive of medications and glucose control, may impact infection risks post-TAA. This study aims to demonstrate the impact of diabetic management on the occurrence of periprosthetic joint infection (PJI) following TAA. METHODS: This was a retrospective study of patients who underwent a TAA at a single academic institution from March 2002 to May 2022. Patients with diabetes who developed an intraarticular infection following TAA were propensity score matched (1:3) to diabetic patients who did not. Data collection included demographics, implant types, diabetic medications, and preoperative hemoglobin A1c. PJI was diagnosed based on Musculoskeletal Infection Society (MSIS) criteria. Statistical analyses assessed differences in medication use, glucose control, and infection rates between groups. RESULTS: Of the 1863 patients who underwent TAA, 177 patients had a diagnosis of diabetes. The infection rate in patients with diabetes (2.8%) was higher than the total cohort rate (0.8%). Five patients with diabetes developed a PJI at an average of 2.2 months postoperatively. This cohort (n = 5) was compared to propensity score-matched controls (n = 15). There was no significant difference in diabetic medication use. Patients who developed PJI had higher rates of uncontrolled diabetes (60.0% vs. 6.7%) and average A1c levels (7.02% vs. 6.29%) compared to controls. CONCLUSION: Our findings suggest that the elevated risk of PJI observed in individuals with diabetes subsequent to TAA may be attributed not solely to the presence of diabetes, but to inadequate glycemic control. Effectively managing blood glucose levels is imperative for achieving favorable outcomes following TAA. LEVEL OF EVIDENCE: III.

Dysregulated inflammatory response to urogynecologic meshes in women with diabetes and its implications.

BACKGROUND: Diabetes is an independent risk factor for mesh complications in women undergoing mesh-augmented surgical repairs of stress urinary incontinence and/or pelvic organ prolapse. The underlying mechanism remains unclear. OBJECTIVE: This study aimed to define the diabetes-associated alterations in the host inflammatory response to mesh and correlate them with perioperative glucose management. STUDY DESIGN: Deidentified demographics and medical records of patients who underwent mesh removal and participated in a mesh biorepository study were reviewed (n=200). In patients with diagnosed diabetes (n=25), blood glucose management before initial mesh implantation and before and after mesh removal was assessed by blood glucose and hemoglobin A1c levels. Age- and body mass index-matched tissue samples excised from patients with and without diabetes were examined. Transcriptomic profiles of immune cell markers, immune mediators, key inflammatory regulators, cell senescence, and epigenetic enzymes were determined by multiplex transcriptomic assays (NanoString). Ratios of apoptotic cells to CD68+ macrophages were examined with immunofluorescence. Protein profiles of 12 molecules involved in apoptotic cell clearance were examined with a multiplex protein assay (Luminex). RESULTS: Demographic and clinical characteristics, including duration between mesh implantation and removal, reason for removal, and type of mesh, etc., were comparable between patients with and without diabetes, except for 11.6% higher body mass index in the former (P=.005). In patients with diabetes, suboptimal management of blood glucose following mesh implantation was observed, with 59% of the patients having loosely or poorly controlled glucose before and after the mesh removal. Ongoing chronic inflammatory response was observed in the excised mesh-tissue complexes in both groups, whereas markers for M2 macrophages (Mrc1 [mannose receptor C-type 1]) and helper T cells (Cd4 [CD4 molecule]) were increasingly expressed in the diabetic vs nondiabetic group (P=.023 and .047, respectively). Furthermore, the gene expressions of proinflammatory Ccl24 (C-C motif chemokine ligand 24) and Ccl13 (C-C motif chemokine ligand 13) were upregulated by 1.5- and 1.8-fold (P=.035 and .027, respectively), whereas that of Il1a (interleukin 1 alpha) was paradoxically downregulated by 2.2-fold (P=.037) in the diabetic vs nondiabetic group. Interestingly, strong positive correlations were found between the expression of Ccl13, Setdb2 (SET domain bifurcated histone lysine methyltransferase 2), and M2 macrophage markers, and between the expression of Il1a, Fosl1 (activator protein-1 transcription factor subunit), and dendritic cell markers, suggesting the involvement of macrophages and dendritic cells in the diabetes-dysregulated proinflammatory response. Supportively, apoptotic cell clearance, which is an important function of macrophages, appeared to be impaired in the diabetic group, with a significantly increased protein level of CALR (calreticulin), an "eat-me" signal on the surface of apoptotic cells (P=.031), along with an increase of AXL (AXL receptor tyrosine kinase) (P=.030), which mediates apoptotic cell clearance. CONCLUSION: Diabetes was associated with altered long-term inflammatory response in complicated mesh implantation, particularly involving innate immune cell dysfunction. Suboptimal blood glycemic control following mesh implantation may contribute to this immune dysregulation, necessitating further mechanistic studies.

Role of clinical pharmacists in multidisciplinary collaborative management of blood glucose in COVID-19 patients with hyperglycemia.

BACKGROUND: During the ongoing global pandemic of COVID-19, the association between hyperglycemia and COVID-19 infection has emerged as a notable concern. Therefore, finding effective methods to manage hyperglycemia in patients with COVID-19 is crucial. OBJECTIVE: To introduce the clinical pharmacists participating in multidisciplinary collaborative whole hospital blood glucose management mode, and to explore its effect on blood glucose control in patients with coronavirus disease 2019 infection and complicated with hyperglycemia. METHODS: Patients with COVID-19 treated at Nanjing Drum Tower Hospital from December 2022 to January 2023 were assigned to routine diagnosis and treatment group and whole hospital blood glucose management group according to the blood glucose management plan received by patients. The groups were compared in regards to their adherence to management advice, blood glucose levels, fluctuation, inflammation-related indicators, medical service-related indicators, and incidence of hypoglycemia and adverse events. RESULTS: After 5 days of glucose management, both groups showed a decrease in fasting and postprandial blood glucose. Postprandial blood glucose in the whole hospital glucose management group was significantly lower than the routine group (P < 0.05). The whole hospital glucose management group showed a significant increase in compliance rate, improved inflammation-related indicators, and higher detection rates for hemoglobin and islet function (P < 0.05). Implementation rates for medical orders and treatment plans were also higher in the whole hospital group (P < 0.05). There was no significant difference in incidence of adverse events. CONCLUSIONS: Multidisciplinary blood glucose management is highly recommended for patients with COVID-19 who have hyperglycemia due to its effectiveness, standardization, safety, and improvement of inflammation indicators.

Multicriteria Decision-Making in Diabetes Management and Decision Support: Systematic Review.

BACKGROUND: Diabetes mellitus prevalence is increasing among adults and children around the world. Diabetes care is complex; examining the diet, type of medication, diabetes recognition, and willingness to use self-management tools are just a few of the challenges faced by diabetes clinicians who should make decisions about them. Making the appropriate decisions will reduce the cost of treatment, decrease the mortality rate of diabetes, and improve the life quality of patients with diabetes. Effective decision-making is within the realm of multicriteria decision-making (MCDM) techniques. OBJECTIVE: The central objective of this study is to evaluate the effectiveness and applicability of MCDM methods and then introduce a novel categorization framework for their use in this field. METHODS: The literature search was focused on publications from 2003 to 2023. Finally, by applying the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) method, 63 articles were selected and examined. RESULTS: The findings reveal that the use of MCDM methods in diabetes research can be categorized into 6 distinct groups: the selection of diabetes medications (19 publications), diabetes diagnosis (12 publications), meal recommendations (8 publications), diabetes management (14 publications), diabetes complication (7 publications), and estimation of diabetes prevalence (3 publications). CONCLUSIONS: Our review showed a significant portion of the MCDM literature on diabetes. The research highlights the benefits of using MCDM techniques, which are practical and effective for a variety of diabetes challenges.

Effect of Self-Care Activities and Behaviors on Glycemic Control in Patients With Diabetes at a Tertiary Care Hospital in Perambalur, South India: A Cross-Sectional Study.

Background The complications of type 2 diabetes mellitus (T2DM) continue to cause significant morbidity and mortality, resulting in a substantial economic burden on both individual patients and society. The adoption of self-care practices leads to enhanced glycemic control, decreased complications, and an elevated quality of life. This study aimed to examine self-care activities and their association with glycemic control among individuals with diabetes. Materials and methods A cross-sectional study was conducted, involving 150 previously diagnosed T2DM patients who visited the tertiary care hospital in Perambalur, Tamilnadu, India, from March 2023 to May 2023. The collection of data involved conducting a semi-structured interview using the diabetes self-management questionnaire (DSMQ) over an eight-week period. Following the input of the data into MS Excel (Microsoft® Corp., Redmond, WA), SPSS Statistics version 26.0 (IBM Corp. Released 2019. IBM SPSS Statistics for Windows, Version 26.0. Armonk, NY: IBM Corp) was used for data analysis. Results The mean age of the patients was 58.35±11.97 years, and 54.7% (n=82) of them were male. Nearly 65% of diabetic patients (n=98) were on orally administered antihyperglycemic agents. Fifty-nine percent (n=89) of the patients were observed to possess self-care behaviors that met the criteria for adequacy, as the DSMQ scores were dichotomized into "adequate" (≥6) and "inadequate" (<6) categories. We observed that 65% (n=98) of the patients had uncontrolled T2DM, characterized by an HbA1C level above 7.5%. Out of the four subscales of self-care behaviors assessed in this study, "glucose management" scored the highest (5.27±1.30), followed by "dietary control" (5.09±1.53), "healthcare use" (4.86±1.50), and "physical activity" (3.27±1.42). The proportion of diabetic patients who had adequate self-management (55%, n=49) had better glycemic control compared to diabetic patients who had inadequate self-management (4.91%, n=3), and this difference in proportion was statistically significant by the chi-square test (p-value 0.001). Similarly, a statistically significant association was noted between glycemic control and the subscales of DSMQ, namely glucose management, dietary control, physical activity, and healthcare utilization. Conclusion The findings in this study indicate that a noticeable proportion of T2DM patients practice inadequate self-care behaviors. According to the DSMQ, diabetic patients with adequate self-management had better glycemic control than diabetic patients with inadequate self-management. According to this research, patients with good glycemic control also tend to exercise better self-care management and show a greater concern for their illness.

Sensor-Based Glucose Metrics during Different Diet Compositions in Type 1 Diabetes-A Randomized One-Week Crossover Trial.

By reducing carbohydrate intake, people with type 1 diabetes may reduce fluctuations in blood glucose, but the evidence in this area is sparse. The aim of this study was to investigate glucose metrics during a one-week low-carbohydrate-high-fat (HF) and a low-carbohydrate-high-protein (HP) diet compared with an isocaloric high-carbohydrate (HC) diet. In a randomized, three-period cross-over study, twelve adults with insulin-pump-treated type 1 diabetes followed an HC (energy provided by carbohydrate: 48%, fat: 33%, protein: 19%), HF (19%, 62%, 19%), and an HP (19%, 57%, 24%) diet for one week. Glucose values were obtained during intervention periods using a Dexcom G6 continuous glucose monitoring system. Participant characteristics were: 33% females, median (range) age 50 (22-70) years, diabetes duration 25 (11-52) years, HbA1c 7.3 (5.5-8.3)% (57 (37-67) mmol/mol), and BMI 27.3 (21.3-35.9) kg/m2. Glycemic variability was lower with HF (30.5 ± 6.2%) and HP (30.0 ± 5.5%) compared with HC (34.5 ± 4.1%) (PHF-HC = 0.009, PHP-HC = 0.003). There was no difference between groups in mean glucose (HF: 8.7 ± 1.1, HP: 8.2 ± 1.0, HC: 8.7 ± 1.0 mmol/L, POverall = 0.08). Time > 10.0 mmol/L was lower with HP (22.3 ± 11.8%) compared with HF (29.4 ± 12.1%) and HC (29.5 ± 13.4%) (PHF-HP = 0.037, PHC-HP = 0.037). In conclusion, a one-week HF and, specifically, an HP diet improved glucose metrics compared with an isocaloric HC diet.

Primary care diabetes assessment when HbA1c and other measures of glycemia disagree.

AIMS: Although diabetes management decisions in primary care are typically based largely on HbA1c, mismatches between HbA1c and other measures of glycemia that are increasingly more available present challenges to optimal management. This study aimed to assess a systematic approach to identify the frequency of mismatches of potential clinical significance amongst various measures of glycemia in a primary care setting. METHODS: Following screening to exclude conditions known to affect HbA1c interpretation, HbA1c, and fructosamine were obtained and repeated after ∼90 days on 53 adults with prediabetes or type 2 diabetes. A subset of 13 participants with repeat labs wore continuous glucose monitoring (CGM) for 10 days. RESULTS: As expected, HbA1c and fructosamine only modestly correlated (initial R2 = 0.768/repeat R2 = 0.655). The HbA1c/fructosamine mismatch frequency of ± 0.5% (using the following regression HbA1c = 0.015 *fructosamine + 2.994 calculated from the initial sample) was 27.0%. Of the 13 participants with CGM data, HbA1c and CGM-based Glucose Management Indicator correlated at R2 = 0.786 with a mismatch frequency of ± 0.5% at 46.2% compared to a HbA1c/fructosamine mismatch frequency of ± 0.5% at 30.8%. CONCLUSIONS: HbA1c is frequently mismatched with fructosamine and CGM data. As each of the measures has strengths and weaknesses, the utilization of multiple different measures of glycemia may be informative for diabetes assessment in the clinical setting.

Effectiveness of Blood Glucose Time in Range to Reduce Risk of Blood Glucose Extrusion and Improve Blood Glucose Metrics in Type 1 Diabetic Patients.

BACKGROUND: With evolving diabetes technology, continuous glucose monitoring (CGM) and time in range have been advanced as critical measurements to assess complications. They have shown improvement in A1C levels and decreased episodes of blood glucose extrusion. AIMS: This study aimed to assess the awareness and utilization of blood glucose time in range and its effectiveness in reducing the risk of blood glucose extrusion and improving blood glucose metrics among patients with type 1 diabetes mellitus. METHODS: A retrospective study included 342 patients who met the inclusion criteria and were using the CGM, aiming for a TIR of 70% daily. Glycemic control was followed using TIR data, blood glucose extrusion frequency (including hyperglycemia and hypoglycemia events), active sensor time, average blood glucose, and glucose management indicator (GMI) levels. RESULTS: A total of 342 individuals participated in this study, the majority of whom were below 18 years of age (62.3%). The hypoglycemic frequency was significantly increased compared to the baseline, and most participants experienced hypoglycemia events (p = 0.0001). The incidences increased over time, with 90.9% and 93% having hypoglycemia at 60 and 90 days (p = 0.0001), respectively. The active scan and sensor time were not followed, which led to the blood glucose target not being achieved, with no improvement throughout the study. Consequently, no improvement occurred in glycemic control. CONCLUSION: CGM technology has been promising and proven effective in improving glycemic. However, our study did not show these benefits as expected, which could be explained by the underutilization and improper use of the CGM.

Machine Learning Models for Blood Glucose Level Prediction in Patients With Diabetes Mellitus: Systematic Review and Network Meta-Analysis.

BACKGROUND: Machine learning (ML) models provide more choices to patients with diabetes mellitus (DM) to more properly manage blood glucose (BG) levels. However, because of numerous types of ML algorithms, choosing an appropriate model is vitally important. OBJECTIVE: In a systematic review and network meta-analysis, this study aimed to comprehensively assess the performance of ML models in predicting BG levels. In addition, we assessed ML models used to detect and predict adverse BG (hypoglycemia) events by calculating pooled estimates of sensitivity and specificity. METHODS: PubMed, Embase, Web of Science, and Institute of Electrical and Electronics Engineers Explore databases were systematically searched for studies on predicting BG levels and predicting or detecting adverse BG events using ML models, from inception to November 2022. Studies that assessed the performance of different ML models in predicting or detecting BG levels or adverse BG events of patients with DM were included. Studies with no derivation or performance metrics of ML models were excluded. The Quality Assessment of Diagnostic Accuracy Studies tool was applied to assess the quality of included studies. Primary outcomes were the relative ranking of ML models for predicting BG levels in different prediction horizons (PHs) and pooled estimates of the sensitivity and specificity of ML models in detecting or predicting adverse BG events. RESULTS: In total, 46 eligible studies were included for meta-analysis. Regarding ML models for predicting BG levels, the means of the absolute root mean square error (RMSE) in a PH of 15, 30, 45, and 60 minutes were 18.88 (SD 19.71), 21.40 (SD 12.56), 21.27 (SD 5.17), and 30.01 (SD 7.23) mg/dL, respectively. The neural network model (NNM) showed the highest relative performance in different PHs. Furthermore, the pooled estimates of the positive likelihood ratio and the negative likelihood ratio of ML models were 8.3 (95% CI 5.7-12.0) and 0.31 (95% CI 0.22-0.44), respectively, for predicting hypoglycemia and 2.4 (95% CI 1.6-3.7) and 0.37 (95% CI 0.29-0.46), respectively, for detecting hypoglycemia. CONCLUSIONS: Statistically significant high heterogeneity was detected in all subgroups, with different sources of heterogeneity. For predicting precise BG levels, the RMSE increases with a rise in the PH, and the NNM shows the highest relative performance among all the ML models. Meanwhile, current ML models have sufficient ability to predict adverse BG events, while their ability to detect adverse BG events needs to be enhanced. TRIAL REGISTRATION: PROSPERO CRD42022375250; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=375250.