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BACKGROUND: New onset diabetes (NOD) in people 50 years or older may indicate underlying pancreatic ductal adenocarcinoma (PDAC). The cumulative incidence of PDAC among people with NOD remains uncertain on a population-based level. METHODS: This was a nationwide population-based retrospective cohort study based on the Danish national health registries. We investigated the 3-year cumulative incidence of PDAC in people 50 years or older with NOD. We further characterised people with pancreatic cancer-related diabetes (PCRD) in relation to demographic and clinical characteristics, including trajectories of routine biochemical parameters, using people with type 2 diabetes (T2D) as a comparator group. RESULTS: During a 21-year observation period, we identified 353,970 people with NOD. Among them, 2105 people were subsequently diagnosed with pancreatic cancer within 3 years (0.59%, 95% CI [0.57-0.62%]). People with PCRD were older than people with T2D at diabetes diagnosis (median age 70.9 vs. 66.0 years (P < 0.001) and had a higher burden of comorbidities (P = 0.007) and more prescriptions of medications used to treat cardiovascular diseases (all P < 0.001). Distinct trajectories of HbA1c and plasma triglycerides were observed in PCRD vs. T2D, with group differences observed for up to three years prior to NOD diagnosis for HbA1c and up to two years for plasma triglyceride levels. CONCLUSIONS: The 3-year cumulative incidence of PDAC is approximately 0.6% among people 50 years or older with NOD in a nationwide population-based setting. Compared to T2D, people with PCRD are characterised by distinct demographic and clinical profiles, including distinctive trajectories of plasma HbA1c and triglyceride levels.
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Carcinoma Ductal Pancreático , Diabetes Mellitus Tipo 2 , Neoplasias Pancreáticas , Humanos , Idoso , Diabetes Mellitus Tipo 2/complicações , Diabetes Mellitus Tipo 2/epidemiologia , Estudos Retrospectivos , Estudos de Coortes , Hemoglobinas Glicadas , Neoplasias Pancreáticas/patologia , Carcinoma Ductal Pancreático/complicações , Carcinoma Ductal Pancreático/epidemiologia , Carcinoma Ductal Pancreático/diagnóstico , Dinamarca/epidemiologia , Neoplasias PancreáticasRESUMO
AIMS: A diabetic foot ulcer (DFU) is a severe condition associated with morbidity and mortality. Population-based studies are rare and limited by access to reliable data. Without this data, efforts in primary prevention cannot be evaluated. Therefore, we examined the incidence and changes over time for the first DFU in people with diabetes. We also examined hospitalization and all-cause mortality and their changes over time. METHODS: From the UK primary care CPRD GOLD database (2007-2017), we identified 129,624 people with diabetes by a prescription for insulin or a non-insulin anti-diabetic drug. DFUs were identified using Read codes and expressed as incidence rates (IRs). Changes over time were described using Poisson and logistic regression and expressed as incidence rate ratios (IRRs) and odds ratios (ORs) respectively. RESULTS: The mean IR of first registered DFUs was 2.5 [95% CI: 2.1-2.9] per 1000 person-years for people with type 2 diabetes and 1.6 [1.3-1.9] per 1000 person-years for people with type 1. The IRs declined for people with type 2 diabetes (IRR per year: 0.97 [0.96-0.99]), while no changes were observed for people with type 1 diabetes (IRR per year: 0.96 [0.89-1.04]). Average hospitalization and 1-year mortality risk for people with type 2 diabetes were 8.2% [SD: 4.7] and 11.7% [SD: 2.2] respectively. Both declined over time (OR: 0.89 [0.84, 0.94] and 0.94 [0.89, 0.99]). CONCLUSION: The decline in all IRs, hospitalizations and mortality in people with type 2 diabetes suggests that prevention and care of the first DFU has improved for this group in primary care in the UK.
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Diabetes Mellitus Tipo 2 , Pé Diabético , Diabetes Mellitus Tipo 2/complicações , Diabetes Mellitus Tipo 2/epidemiologia , Pé Diabético/complicações , Pé Diabético/epidemiologia , Pé Diabético/terapia , Hospitalização , Humanos , Incidência , Fatores de RiscoRESUMO
PURPOSE OF REVIEW: To investigate the effect of newer long-acting insulins on the risk of hypoglycemic episodes and fractures in people with diabetes. RECENT FINDINGS: Hypoglycemic episodes are the critical limiting factor in glycemic management due to a deteriorating effect on quality of life. Hypoglycemia may in severe cases lead to unconsciousness and thus fractures. Newer long-acting insulins may result in more stable blood glucose levels, less hypoglycemic episodes, and reduced risk of fractures. Use of insulin increases risk of hypoglycemic episodes, and hypoglycemic episodes increase risk of fractures plausible due to falls. Newer ultra-long-acting insulins reduce risk of hypoglycemic episodes compared to older alternatives, and they are thus promising for reducing fracture risk. However, more studies are needed to determine whether these new insulins reduce risk of fractures.
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Diabetes Mellitus/tratamento farmacológico , Fraturas Ósseas/induzido quimicamente , Hipoglicemia/induzido quimicamente , Insulina de Ação Prolongada/uso terapêutico , Humanos , Hipoglicemiantes/uso terapêuticoRESUMO
AIMS: Real-world evidence of the safety of insulin degludec compared with insulin glargine U100 is sparse. This study sought to investigate the risk of major cardiovascular events, severe hypoglycaemia, and all-cause mortality after initiation of degludec or glargine U100 in the population of Denmark. MATERIALS AND METHODS: All Danish people with diabetes initiating treatment on degludec (n=5159) or glargine (n=4041) in 2016 to 2017 were included in the study. The effect of insulin treatment on the endpoints of major cardiovascular events, severe hypoglycaemia, and all-cause mortality was analysed with Cox proportional hazard models. The models were adjusted for age, sex, diabetes duration, diabetes type, highest completed education, and annual income. The model of severe hypoglycaemia was also adjusted for severe hypoglycaemia prior to baseline. The model of mortality was also adjusted for history of alcohol abuse, use of antidepressants, use of opioids, and use of anxiolytics. Lastly, the models of major cardiovascular events and mortality were also adjusted for Charlson comorbidity index. RESULTS: Use of degludec resulted in an almost twofold decrease in risk of death (hazard rate [HR]: 0.54, 95% CI: 0.44-0.65) compared with use of glargine. No statistically significant risk changes were found for major cardiovascular events (HR: 0.86, 95% CI: 0.62-1.19) and severe hypoglycaemia (HR: 1.13, 95% CI: 0.66-1.93). The proportion of cause of death due to malignant neoplasm of pancreas was almost doubled for glargine compared with degludec. CONCLUSIONS: These results indicate that insulin degludec has a safer profile with respect to all-cause mortality as compared with insulin glargine U100.
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Doenças Cardiovasculares/mortalidade , Diabetes Mellitus Tipo 2/tratamento farmacológico , Hipoglicemia/mortalidade , Hipoglicemiantes/administração & dosagem , Insulina Glargina/administração & dosagem , Insulina de Ação Prolongada/administração & dosagem , Idoso , Biomarcadores/análise , Glicemia/análise , Doenças Cardiovasculares/epidemiologia , Doenças Cardiovasculares/prevenção & controle , Estudos de Coortes , Dinamarca/epidemiologia , Feminino , Seguimentos , Hemoglobinas Glicadas/análise , Humanos , Hipoglicemia/epidemiologia , Hipoglicemia/prevenção & controle , Incidência , Masculino , Prognóstico , Fatores de Risco , Taxa de SobrevidaRESUMO
AIMS: To evaluate the risk of severe hypoglycaemia for patients with Type 1 diabetes (T1D) when exposed to insulin regimens including human insulin only or insulin analogues. METHODS: A total of 19 896 patients with T1D were extracted from the Danish National Patient Register. Of these, 6379 T1D patients experiencing 1 of more severe hypoglycaemic episodes (total of 17 242 episodes) were matched 1:1 with T1D patients without severe hypoglycaemia. A logistic regression model with last insulin regimen used as exposure was constructed to analyse the effect on severe hypoglycaemia. RESULTS: People on a basal-bolus regimen with insulin analogues had a reduced risk of severe hypoglycaemia of 39% (odds ratio: 0.61, 95% confidence interval: 0.54-0.68) compared to patients on a basal-bolus human insulin only regimen. Furthermore, patients on a premixed regimen containing an insulin analogue had a 58% (odds ratio: 0.42, 95% confidence interval: 0.36-0.49) reduced risk of severe hypoglycaemia compared to patients on premixed human insulin only. CONCLUSION: This study indicates that use of a basal-bolus insulin regimen with an insulin analogue is safer with respect to severe hypoglycaemia in patients with T1D than the use of a basal-bolus human insulin only regimen.
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Diabetes Mellitus Tipo 1 , Diabetes Mellitus Tipo 2 , Hipoglicemia , Glicemia , Estudos de Casos e Controles , Dinamarca/epidemiologia , Diabetes Mellitus Tipo 1/tratamento farmacológico , Feminino , Humanos , Hipoglicemia/induzido quimicamente , Hipoglicemia/epidemiologia , Hipoglicemiantes/efeitos adversos , Insulina/efeitos adversos , MasculinoRESUMO
BACKGROUND AND OBJECTIVES: Predicting glucose levels in individuals with diabetes offers potential improvements in glucose control. However, not all patients exhibit predictable glucose dynamics, which may lead to ineffective treatment strategies. We sought to investigate the efficacy of a 7-day blinded screening test in identifying diabetes patients suitable for glucose forecasting. METHODS: Participants with type 1 diabetes (T1D) were stratified into high and low initial error groups based on screening results (eligible and non-eligible). Long-term glucose predictions (30/60 min lead time) were evaluated among 334 individuals who underwent continuous glucose monitoring (CGM) over a total of 64,460,560 min. RESULTS: A strong correlation was observed between screening accuracy and long-term mean absolute relative difference (MARD) (0.661-0.736; p < 0.001), suggesting significant predictability between screening and long-term errors. Group analysis revealed a notable reduction in predictions falling within zone D of the Clark Error Grid by a factor of three and in zone C by a factor of two. CONCLUSIONS: The identification of eligible patients for glucose prediction through screening represents a practical and effective strategy. Implementation of this approach could lead to a decrease in adverse glucose predictions.
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Glicemia , Diabetes Mellitus Tipo 1 , Humanos , Glicemia/análise , Automonitorização da Glicemia/métodos , Monitoramento Contínuo da Glicose , Diabetes Mellitus Tipo 1/diagnóstico , Diabetes Mellitus Tipo 1/terapia , PrevisõesRESUMO
OBJECTIVE: To develop a machine-learning model that can predict the risk of pancreatic ductal adenocarcinoma (PDAC) in people with new-onset diabetes (NOD). METHODS: From a population-based sample of individuals with NOD aged >50 years, patients with pancreatic cancer-related diabetes (PCRD), defined as NOD followed by a PDAC diagnosis within 3 years, were included (n = 716). These PCRD patients were randomly matched in a 1:1 ratio with individuals having NOD. Data from Danish national health registries were used to develop a random forest model to distinguish PCRD from Type 2 diabetes. The model was based on age, gender, and parameters derived from feature engineering on trajectories of routine biochemical variables. Model performance was evaluated using receiver operating characteristic curves (ROC) and relative risk scores. RESULTS: The most discriminative model included 20 features and achieved a ROC-AUC of 0.78 (CI:0.75-0.83). Compared to the general NOD population, the relative risk for PCRD was 20-fold increase for the 1 % of patients predicted by the model to have the highest cancer risk (3-year cancer risk of 12 % and sensitivity of 20 %). Age was the most discriminative single feature, followed by the rate of change in haemoglobin A1c and the latest plasma triglyceride level. When the prediction model was restricted to patients with PDAC diagnosed six months after diabetes diagnosis, the ROC-AUC was 0.74 (CI:0.69-0.79). CONCLUSION: In a population-based setting, a machine-learning model utilising information on age, sex and trajectories of routine biochemical variables demonstrated good discriminative ability between PCRD and Type 2 diabetes.
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Diabetes Mellitus Tipo 2 , Neoplasias Pancreáticas , Humanos , Pessoa de Meia-Idade , Diabetes Mellitus Tipo 2/complicações , Diabetes Mellitus Tipo 2/diagnóstico , Diabetes Mellitus Tipo 2/epidemiologia , Aprendizado de Máquina , Neoplasias Pancreáticas/diagnóstico , Neoplasias Pancreáticas/epidemiologia , Fatores de Risco , Curva ROC , Masculino , FemininoRESUMO
BACKGROUND: Comorbidities such as cardiovascular disease (CVD) and diabetic kidney disease (DKD) are major burdens of type 1 diabetes (T1D). Predicting people at high risk of developing comorbidities would enable early intervention. This study aimed to develop models incorporating socioeconomic status (SES) to predict CVD, DKD, and mortality in adults with T1D to improve early identification of comorbidities. METHODS: Nationwide Danish registry data were used. Logistic regression models were developed to predict the development of CVD, DKD, and mortality within five years of T1D diagnosis. Features included age, sex, personal income, and education. Performance was evaluated by five-fold cross-validation with area under the receiver operating characteristic curve (AUROC) and the precision-recall area under the curve (PR-AUC). The importance of SES was assessed from feature importance plots. RESULTS: Of the 6572 included adults (≥21 years) with T1D, 379 (6%) developed CVD, 668 (10%) developed DKD, and 921 (14%) died within the five-year follow-up. The AUROC (±SD) was 0.79 (±0.03) for CVD, 0.61 (±0.03) for DKD, and 0.87 (±0.01) for mortality. The PR-AUC was 0.18 (±0.01), 0.15 (±0.03), and 0.49 (±0.02), respectively. Based on feature importance plots, SES was the most important feature in the DKD model but had minimal impact on models for CVD and mortality. CONCLUSIONS: The developed models showed good performance for predicting CVD and mortality, suggesting they could help in the early identification of these outcomes in individuals with T1D. The importance of SES in individual prediction within diabetes remains uncertain.
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The adherence to oral antidiabetic drugs (OADs) among people with type 2 diabetes (T2D) is suboptimal. However, new OADs have been marketed within the last 10 years. As these new drugs differ in mechanism of action, treatment complexity, and side effects, they may influence adherence. Thus, the aim of this study was to assess the adherence to newer second-line OADs, defined as drugs marketed in 2012-2022, among people with T2D. A systematic review was performed in CINAHL, Cochrane Trials, Embase, PubMed, PsycINFO, and Scopus. Articles were included if they were original research of adherence to newer second-line OADs and reported objective adherence quantification. The quality of the articles was assessed using JBI's critical appraisal tools. The overall findings were reported according to the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines and summarized in a narrative synthesis. All seven included articles were European retrospective cohort studies investigating alogliptin, canagliflozin, dapagliflozin, empagliflozin, and unspecified types of SGLT2i. Treatment discontinuation and medication possession ratio (MPR) were the most frequently reported adherence quantification measures. Within the first 12 months of treatment, 29%-44% of subjects on SGLT2i discontinued the treatment. In terms of MPR, 61.7%-94.9% of subjects on either alogliptin, canagliflozin, dapagliflozin, empagliflozin or an unspecified SGLT2i were adherent. The two investigated adherence quantification measures, treatment discontinuation and MPR, suggest that adherence to the newer second-line OADs may be better than that of older OADs. However, a study directly comparing older and newer OADs should be done to verify this.
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Compostos Benzidrílicos , Diabetes Mellitus Tipo 2 , Glucosídeos , Adesão à Medicação , Humanos , Canagliflozina , Diabetes Mellitus Tipo 2/tratamento farmacológico , Hipoglicemiantes/uso terapêutico , Estudos RetrospectivosRESUMO
Healthy lifestyle behaviors are essential in the treatment of type 2 diabetes, and meal registration is therefore important. Manual meal registration is cumbersome and could be automated using continuous glucose monitoring (CGM). If such an algorithm is based on patient-reported meals, potential errors might be induced. Thus, the aim was to investigate potential errors in patient-reported mealtimes and the effect on automatic meal detection. Two healthcare professionals (HCPs) reported the mealtimes of the 18 included patients based on the patients' CGM data to assess the agreement between HCP- and patient-reported mealtimes. A developed meal detection algorithm based on detecting the post-prandial glucose response using cross-correlation was used to assess the impact of errors in patient-reported meals. The results showed poor disagreement between HCP- and patient-reported meals and that the meal detection algorithm had a moderately better performance on the HCP-reported meals. Therefore, the possibility of errors in patient-reported mealtimes should be considered in the development of meal detection algorithms. However, more research is needed to confirm the results of this study.
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Automonitorização da Glicemia , Diabetes Mellitus Tipo 2 , Refeições , Humanos , Masculino , Algoritmos , Feminino , Pessoa de Meia-Idade , Autorrelato , Comportamento AlimentarRESUMO
Objective: This study aims to investigate the continuum of glucose control from normoglycemia to dysglycemia (HbA1c ≥ 5.7%/39 mmol/mol) using metrics derived from continuous glucose monitoring (CGM). In addition, we aim to develop a machine learning-based classification model to classify dysglycemia based on observed patterns. Methods: Data from five distinct studies, each featuring at least two days of CGM, were pooled. Participants included individuals classified as healthy, with prediabetes, or with type 2 diabetes mellitus (T2DM). Various CGM indices were extracted and compared across groups. The data set was split 70/30 for training and testing two classification models (XGBoost/Logistic Regression) to differentiate between prediabetes or dysglycemia and the healthy group. Results: The analysis included 836 participants (healthy: n = 282; prediabetes: n = 133; T2DM: n = 432). Across all CGM indices, a progressive shift was observed from the healthy group to those with diabetes (P < 0.001). Statistically significant differences (P < 0.01) were noted in mean glucose, time below range, time above 140 mg/dl, mobility, multiscale complexity index, and glycemic risk index when transitioning from health to prediabetes. The XGBoost models achieved the highest receiver operating characteristic area under the curve values on the test data set ranging from 0.91 [confidence interval (CI): 0.87-0.95] (prediabetes identification) to 0.97 [CI: 0.95-0.98] (dysglycemia identification). Conclusion: Our findings demonstrate a gradual deterioration of glucose homeostasis and increased glycemic variability across the spectrum from normo- to dysglycemia, as evidenced by CGM metrics. The performance of CGM-based indices in classifying healthy individuals and those with prediabetes and diabetes is promising.
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BACKGROUND: There has been an increasing interest in the use of digital health lifestyle interventions for people with prediabetes, as these interventions may offer a scalable approach to preventing type 2 diabetes. Previous systematic reviews on digital health lifestyle interventions for people with prediabetes had limitations, such as a narrow focus on certain types of interventions, a lack of statistical pooling, and no broader subgroup analysis of intervention characteristics. The identified limitations observed in previous systematic reviews substantiate the necessity of conducting a comprehensive review to address these gaps within the field. This will enable a comprehensive understanding of the effectiveness of digital health lifestyle interventions for people with prediabetes. OBJECTIVE: The objective of this systematic review, meta-analysis, and meta-regression is to systematically investigate the effectiveness of digital health lifestyle interventions on prediabetes-related outcomes in comparison with any comparator without a digital component among adults with prediabetes. METHODS: This systematic review will include randomized controlled trials that investigate the effectiveness of digital health lifestyle interventions on adults (aged 18 years or older) with prediabetes and compare the digital interventions with nondigital interventions. The primary outcome will be change in body weight (kg). Secondary outcomes include, among others, change in glycemic status, markers of cardiometabolic health, feasibility outcomes, and incidence of type 2 diabetes. Embase, PubMed, CINAHL, and CENTRAL (Cochrane Central Register of Controlled Trials) will be systematically searched. The data items to be extracted include study characteristics, participant characteristics, intervention characteristics, and relevant outcomes. To estimate the overall effect size, a meta-analysis will be conducted using the mean difference. Additionally, if feasible, meta-regression on study, intervention, and participant characteristics will be performed. The Cochrane risk of bias tool will be applied to assess study quality, and the GRADE (Grading of Recommendations Assessment, Development and Evaluation) approach will be used to assess the certainty of evidence. RESULTS: The results are projected to yield an overall estimate of the effectiveness of digital health lifestyle interventions on adults with prediabetes and elucidate the characteristics that contribute to their effectiveness. CONCLUSIONS: The insights gained from this study may help clarify the potential of digital health lifestyle interventions for people with prediabetes and guide the decision-making regarding future intervention components. TRIAL REGISTRATION: PROSPERO CRD42023426919; http://tinyurl.com/d3enrw9j. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/50340.
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BACKGROUND AND OBJECTIVE: It is not clear how the short-term continuous glucose monitoring (CGM) sampling time could influence the bias in estimating long-term glycemic control. A large bias could, in the worst case, lead to incorrect classification of patients achieving glycemic targets, nonoptimal treatment, and false conclusions about the effect of new treatments. This study sought to investigate the relation between sampling time and bias in the estimates. METHODS: We included a total of 329 type 1 patients (age 14-86 years) with long-term CGM (90 days) data from three studies. The analysis calculated the bias from estimating long-term glycemic control based on short-term sampling. Time in range (TIR), time above range (TAR), time below range (TBR), correlation, and glycemic target classification accuracy were assessed. RESULTS: A sampling time of ten days is associated with a high bias of 10% to 47%, which can be reduced to 4.9% to 26.4% if a sampling time of 30 days is used (P < .001). Correct classification of patients archiving glycemic targets can also be improved from 81.5% to 91.9 to 90% to 95.2%. CONCLUSIONS: Our results suggest that the proposed 10-14 day CGM sampling time may be associated with a high correlation with three-month CGM. However, these estimates are subject to large intersubject bias, which is clinically relevant. Clinicians and researchers should consider using assessments of longer durations of CGM data if possible, especially when assessing time in hypoglycemia or while testing a new treatment.
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Diabetes Mellitus Tipo 1 , Hipoglicemia , Humanos , Adolescente , Adulto Jovem , Adulto , Pessoa de Meia-Idade , Idoso , Idoso de 80 Anos ou mais , Glicemia , Hemoglobinas Glicadas , Automonitorização da Glicemia/métodosRESUMO
AIMS: This systematic review aims to identify current methods used for the assessment of insulin adherence in adults with insulin-treated type 2 diabetes. The primary goal is to offer recommendations for clinical practice to improve quantification of adherence. METHODS: The review was conducted in accordance with PRISMA 2020 and registered at PROSPERO (CRD42022334134). PubMed, Embase, CINAHL, and PsycINFO were searched on 15 November 2022 and included three blocks: Type 2 diabetes, insulin, and adherence. We considered primary full-text studies describing an assessment method and a threshold for assessment of insulin adherence in adults with insulin-treated type 2 diabetes. RESULTS: A final sample of 50 studies were included. Identified methods fell into four categories: self-report, pharmacy claims, inulin count, and data from an insulin pen device. Commonly reported methods included: The Morisky Medication Adherence Scale, the (adjusted) Medication Possession Ratio, and the Proportions of Days Covered. A threshold of <80% was used to define non-adherence in nearly half of the studies. Yet, several thresholds were reported. CONCLUSIONS: Most available methods for assessing insulin adherence in adults with insulin-treated type 2 diabetes are severely limited in providing in-depth insights into timing, dosing size, injection patterns, and adherence behavior. However, recognizing diverse types of non-adherence is crucial, as they denote unique behavioral entities requiring targeted intervention. Employing insulin injection data (e.g., from a smart insulin pen cap) to underlie an assessment method is a potential new approach to objectively assess insulin timing and dosing adherence in adults with insulin-treated type 2 diabetes.
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Diabetes Mellitus Tipo 2 , Adulto , Humanos , Diabetes Mellitus Tipo 2/tratamento farmacológico , Insulina/uso terapêutico , Adesão à Medicação , Injeções , EmpregoRESUMO
AIMS: For people with type 2 diabetes treated with basal insulin, suboptimal glycemic control due to clinical inertia is a common issue. Determining the optimal basal insulin dose can be difficult, as it varies between individuals. Thus, insulin titration can be slow and cautious which may lead to treatment fatigue and non-adherence. A model that predicts changes in fasting blood glucose (FBG) after adjusting basal insulin dose may lead to more optimal titration, reducing some of these challenges. OBJECTIVE: To predict the change in FBG following adjustment of basal insulin in people with type 2 diabetes using a machine learning framework. METHODS: A multiple linear regression model was developed based on 786 adults with type 2 diabetes. Data were divided into training (80%) and testing (20%) sets using a ranking approach. Forward feature selection and fivefold cross-validation were used to select features. RESULTS: Participants had a mean age of approximately 59 years, a mean duration of diabetes of 12 years, and a mean HbA1c at screening of 65 mmol/mol (8.1%). Chosen features were FBG at week 2, basal insulin dose adjustment from week 2 to 7, trial site, hemoglobin level, and alkaline phosphatase level. The model achieved a relative absolute error of 0.67, a Pearson correlation coefficient of 0.74, and a coefficient of determination of 0.55. CONCLUSIONS: A model using FBG, insulin doses, and blood samples can predict a five-week change in FBG after adjusting the basal insulin dose in people with type 2 diabetes. Implementation of such a model can potentially help optimize titration and improve glycemic control.
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BACKGROUND: Hypoglycemia is common in insulin-treated type 2 diabetes (T2D) patients, which can lead to decreased quality of life or premature death. Deep learning models offer promise of accurate predictions, but data scarcity poses a challenge. This study aims to develop a deep learning model utilizing transfer learning to predict hypoglycemia. METHODS: Continuous glucose monitoring (CGM) data from 226 patients with type 1 diabetes (T1D) and 180 patients with T2D were utilized. Data were structured into one-hour samples and labeled as hypoglycemia or not depending on whether three consecutive CGM values were below 3.9 [mmol/L] (70 mg/dL) one hour after the sample. A convolutional neural network (CNN) was pre-trained with the T1D data set and subsequently fitted using a T2D data set, all while being optimized toward maximizing the area under the receiver operating characteristics curve (AUC) value, and it was externally validated on a separate T2D data set. RESULTS: The developed model was externally validated with 334 711 one-hour CGM samples, of which 15 695 (4.69%) were labeled as hypoglycemic. The model achieved an AUC of 0.941 and a positive predictive value of 40.49% at a specificity of 95% and a sensitivity of 69.16%. CONCLUSIONS: The transfer learned CNN model showed promising performance in predicting hypoglycemic episodes and with slightly better results than a non-transfer learned CNN model.
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Background: Multidisciplinary Teams (MDTs) has been suggested as an intervention to overcome some of the complexities experienced by people with diabetes and comorbidities in terms of diagnosis and treatment. However, evidence concerning MDTs within the diabetes field remains sparse. Objective: This review aims to identify and map available evidence on key characteristics of MDTs in the context of diagnosis and treatment in people with diabetes and comorbidities. Methods: This review followed the PRISMA-ScR guidelines. Databases PubMed, EMBASE, and CINAHL were systematically searched for studies assessing any type of MDT within the context of diagnosis and treatment in adult people (≥ 18 years) with diabetes and comorbidities/complications. Data extraction included details on study characteristics, MDT interventions, digital health solutions, and key findings. Results: Overall, 19 studies were included. Generally, the MDTs were characterized by high heterogeneity. Four overall components characterized the MDTs: Both medical specialists and healthcare professionals (HCPs) of different team sizes were represented; interventions spanned elements of medication, assessment, nutrition, education, self-monitoring, and treatment adjustment; digital health solutions were integrated in 58% of the studies; MDTs were carried out in both primary and secondary healthcare settings with varying frequencies. Generally, the effectiveness of the MDTs was positive across different outcomes. Conclusions: MDTs are characterized by high diversity in their outline yet seem to be effective and cost-effective in the context of diagnosis and treatment of people with diabetes and comorbidities. Future research should investigate the cross-sectorial collaboration to reduce care fragmentation and enhance care coordination.
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BACKGROUND: While health care providers (HCPs) are generally aware of the challenges concerning insulin adherence in adults with insulin-treated type 2 diabetes (T2D), data guiding identification of insulin nonadherence and understanding of injection patterns have been limited. Hence, the aim of this study was to examine detailed injection data and provide methods for assessing different aspects of basal insulin adherence. METHOD: Basal insulin data recorded by a connected insulin pen and prescribed doses were collected from 103 insulin-treated patients (aged ≥18 years) with T2D from an ongoing clinical trial (NCT04981808). We categorized the data and analyzed distributions of correct doses, increased doses, reduced doses, and missed doses to quantify adherence. We developed a three-step model evaluating three aspects of adherence (overall adherence, adherence distribution, and dose deviation) offering HCPs a comprehensive assessment approach. RESULTS: We used data from a connected insulin pen to exemplify the use of the three-step model to evaluate overall, adherence, adherence distribution, and dose deviation using patient cases. CONCLUSION: The methodology provides HCPs with detailed access to previously limited clinical data on insulin administration, making it possible to identify specific nonadherence behavior which will guide patient-HCP discussions and potentially provide valuable insights for tailoring the most appropriate forms of support.
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INTRODUCTION/AIM: People with type 1 diabetes (T1D) and type 2 diabetes (T2D) have an increased risk of fractures due to skeletal fragility. We aimed to compare areal bone mineral density (aBMD), volumetric BMD (vBMD), cortical and trabecular measures, and bone strength parameters in participants with diabetes vs. controls. METHODS: In a cross-sectional study, we included adult participants with T1D (n = 111, MA = 52.9 years), T2D (n = 106, MA = 62.1 years) and controls (n = 328, MA = 57.7 years). The study comprised of DXA scans and HR-pQCT scans, biochemistry, handgrip strength (HGS), Timed Up and GO (TUG), vibration perception threshold (VPT), questionnaires, medical histories, alcohol use, and previous fractures. Group comparisons were performed after adjustment for sex, age, BMI, diabetes duration, HbA1c, alcohol, smoking, previous fractures, postmenopausal, HGS, TUG, and VPT. RESULTS: We found decreased aBMD in participants with T1D at the femoral neck (p = 0.028), whereas T2D had significantly higher aBMD at peripheral sites (legs, arms, p < 0.01) vs. controls. In T1D we found higher vBMD (p < 0.001), cortical vBMD (p < 0.001), cortical area (p = 0.002) and thickness (p < 0.001), lower cortical porosity(p = 0.008), higher stiffness (p = 0.002) and failure load (p = 0.003) at radius and higher vBMD (p = 0.003), cortical vBMD(p < 0.001), bone stiffness (p = 0.023) and failure load(p = 0.044) at the tibia than controls. In T2D we found higher vBMD (p < 0.001), cortical vBMD (p < 0.001), trabecular vBMD (p < 0.001), cortical area (p < 0.001) and thickness (p < 0.001), trabecular number (p = 0.024), lower separation (p = 0.010), higher stiffness (p < 0.001) and failure load (p < 0.001) at the radius and higher total vBMD (p < 0.001), cortical vBMD (p < 0.011), trabecular vBMD (p = 0.001), cortical area (p = 0.002) and thickness (p = 0.021), lower trabecular separation (p = 0.039), higher stiffness (p < 0.001) and failure load (p = 0.034) at tibia compared with controls. CONCLUSION: aBMD measures were as expected lower in T1D and higher in T2D than controls. Favorable bone microarchitecture and strength parameters were seen at the tibia and radius for T1D and T2D.
Assuntos
Diabetes Mellitus Tipo 1 , Diabetes Mellitus Tipo 2 , Fraturas Ósseas , Adulto , Humanos , Estudos Transversais , Diabetes Mellitus Tipo 2/complicações , Força da Mão , Densidade Óssea , Absorciometria de Fóton , Fraturas Ósseas/diagnóstico por imagem , Rádio (Anatomia)/diagnóstico por imagem , Tíbia/diagnóstico por imagem , Colo do FêmurRESUMO
BACKGROUND: Chronic obstructive pulmonary disease (COPD) affects millions of people worldwide. A complication of COPD is exacerbations that result in increased utilization of healthcare services, readmissions to the hospital, and a decline in health-related quality of life. Home telehealth has been shown both to improve health-related quality of life and to reduce admission rates. Using clinical data from a home telemonitoring group, this study sought to investigate the clinical impact of telemonitoring. SUBJECTS AND METHODS: Fifty-seven subjects with COPD were included in a 4-month telemonitoring project. Differences between the clinical parameters during the first and last months of participation in the project were tested for significance, and the levels for the first month versus the difference were tested for correlation. RESULTS: Significant declines were observed in prescriptions for antibiotics and steroids (p=0.03), clinical consultations (p=0.05), mean systolic blood pressure (p<0.001), standard deviation of systolic blood pressure (p=0.03), and mean diastolic blood pressure (p=0.02). No significant differences were observed for mean of oxygen saturation (p=0.77), standard deviation of oxygen saturation (p=0.36), mean of forced expiratory volume in 1 s (p=0.17), mean of forced vital capacity (p=0.29), mean of pulse rate (p=0.78), standard deviation of pulse rate (p=0.57), and standard deviation of diastolic blood pressure (p=0.27). CONCLUSIONS: The results suggest that telemonitoring improves the condition of the patient by lowering the blood pressure, the number of prescribed antibiotics and steroids, and the number of clinical consultations.