Diagnosis Of Diabetes Research Articles

Diabetes, life course and childhood socioeconomic conditions: an empirical assessment for Mexico

BackgroundDemographic and epidemiological dynamics characterized by lower fertility rates and longer life expectancy, as well as higher prevalence of non-communicable diseases such as diabetes, represent important challenges for policy makers around the World. We investigate the risk factors that influence the diagnosis of diabetes in the Mexican population aged 50 years and over, including childhood poverty.ResultsThis work employs a probabilistic regression model with information from the Mexican Health and Aging Study (MHAS) of 2012 and 2018. Our results are consistent with the existing literature and should raise strong concerns. The findings suggest that risk factors that favor the diagnosis of diabetes in adulthood are: age, family antecedents of diabetes, obesity, and socioeconomic conditions during both adulthood and childhood.ConclusionsPoverty conditions before the age 10, with inter-temporal poverty implications, are associated with a higher probability of being diagnosed with diabetes when older and pose extraordinary policy challenges.

Development of a clinical calculator to aid the identification of MODY in pediatric patients at the time of diabetes diagnosis

Maturity Onset Diabetes of the Young (MODY) is a young-onset, monogenic form of diabetes without needing insulin treatment. Diagnostic testing is expensive. To aid decisions on who to test, we aimed to develop a MODY probability calculator for paediatric cases at the time of diabetes diagnosis, when the existing “MODY calculator” cannot be used. Firth logistic regression models were developed on data from 3541 paediatric patients from the Swedish ‘Better Diabetes Diagnosis’ (BDD) population study (n = 46 (1.3%) MODY (HNF1A, HNF4A, GCK)). Model performance was compared to using islet autoantibody testing. HbA1c, parent with diabetes, and absence of polyuria were significant independent predictors of MODY. The model showed excellent discrimination (c-statistic = 0.963) and calibrated well (Brier score = 0.01). MODY probability > 1.3% (ie. above background prevalence) had similar performance to being negative for all 3 antibodies (positive predictive value (PPV) = 10% v 11% respectively i.e. ~ 1 in 10 positive test rate). Probability > 1.3% and negative for 3 islet autoantibodies narrowed down to 4% of the cohort, and detected 96% of MODY cases (PPV = 31%). This MODY calculator for paediatric patients at time of diabetes diagnosis will help target genetic testing to those most likely to benefit, to get the right diagnosis.

Living well with diabetes in Alaska

ABSTRACT Many people with diabetes mellitus experience minimal or no complications. Our objective was to determine the proportion of Alaska Native people who experienced four major complications or mortality and to identify factors that may be associated with these outcomes. We used records in a diabetes registry and clinical and demographic variables in our analyses. We used logistic regression and Cox Proportional Hazards models to evaluate associations of these parameters with death and complications that occurred prior to 2013. The study included 591 Alaska Native people with non-type 1 diabetes mellitus, diagnosed between 1986 and 1992. Over 60% of people in this study remained free of four major diabetes-related complications for the remainder of life or throughout the approximately 20-year study period. Lower BMI, higher age at diagnosis of diabetes, and use of at least one diabetes medication were associated with death and a composite of four complications. A majority of Alaska Native people with DM had none of four major complications over a 20-year period. Lower BMI and use of diabetes medications were associated with higher hazard for some deleterious outcomes. This suggests that goals in care of elders should be carefully individualised. In addition, we discuss several programme factors that we believe contributed to favourable outcomes.

Ethnic disparities in quality of diabetes care in Scotland: A national cohort study.

The aim of this study is to compare quality of diabetes care in people with type 2 diabetes by ethnicity, in Scotland. Using a linked national diabetes registry, we included 162,122 people newly diagnosed with type 2 diabetes between 2009 and 2018. We compared receipt of nine guideline indicated processes of care in the first-year post-diabetes diagnosis using logistic regression, comparing eight ethnicity groups to the White group. We compared annual receipt of HbA1c and eye screening during the entire follow-up using generalised linear mixed effects. All analyses adjusted for confounders. Receipt of diabetes care was lower in other ethnic groups compared to White people in the first-year post-diagnosis. Differences were most pronounced for people in the: African, Caribbean or Black; Indian; and other ethnicity groups for almost all processes of care. For example, compared to White people, odds of HbA1c monitoring were: 44% lower in African, Caribbean or Black people (OR 0.56 [95% CI 0.48, 0.66]); 47% lower in Indian people (OR 0.53 [95% CI 0.47, 0.61]); and 50% lower in people in the other ethnicity group (OR 0.50 [95% CI 0.46, 0.58]). Odds of receipt of eye screening were 30%-40% lower in most ethnic groups compared to the White group. During median 5 year follow-up, differences in HbA1c monitoring and eye screening largely persisted, but attenuated slightly for the former. There are marked ethnic disparities in routine diabetes care in Scotland in the short- and medium-term following diabetes diagnosis. Further investigation is needed to establish and effectively address the underlying reasons.

Stacking with Recursive Feature Elimination-Isolation Forest for classification of diabetes mellitus.

Diabetes Mellitus is one of the oldest diseases known to humankind, dating back to ancient Egypt. The disease is a chronic metabolic disorder that heavily burdens healthcare providers worldwide due to the steady increment of patients yearly. Worryingly, diabetes affects not only the aging population but also children. It is prevalent to control this problem, as diabetes can lead to many health complications. As evolution happens, humankind starts integrating computer technology with the healthcare system. The utilization of artificial intelligence assists healthcare to be more efficient in diagnosing diabetes patients, better healthcare delivery, and more patient eccentric. Among the advanced data mining techniques in artificial intelligence, stacking is among the most prominent methods applied in the diabetes domain. Hence, this study opts to investigate the potential of stacking ensembles. The aim of this study is to reduce the high complexity inherent in stacking, as this problem contributes to longer training time and reduces the outliers in the diabetes data to improve the classification performance. In addressing this concern, a novel machine learning method called the Stacking Recursive Feature Elimination-Isolation Forest was introduced for diabetes prediction. The application of stacking with Recursive Feature Elimination is to design an efficient model for diabetes diagnosis while using fewer features as resources. This method also incorporates the utilization of Isolation Forest as an outlier removal method. The study uses accuracy, precision, recall, F1 measure, training time, and standard deviation metrics to identify the classification performances. The proposed method acquired an accuracy of 79.077% for PIMA Indians Diabetes and 97.446% for the Diabetes Prediction dataset, outperforming many existing methods and demonstrating effectiveness in the diabetes domain.

Attention-Oriented CNN Method for Type 2 Diabetes Prediction

Diabetes is caused by insulin deficiency or impaired biological action, and long-term hyperglycemia leads to a variety of tissue damage and dysfunction. Therefore, the early prediction of diabetes and timely intervention and treatment are crucial. This paper proposes a robust framework for the prediction and diagnosis of type 2 diabetes (T2DM) to aid in diabetes applications in clinical diagnosis. The data-preprocessing stage includes steps such as outlier removal, missing value filling, data standardization, and assigning class weights to ensure the quality and consistency of the data, thereby improving the performance and stability of the model. This experiment used the National Health and Nutrition Examination Survey (NHANES) dataset and the publicly available PIMA Indian dataset (PID). For T2DM classification, we designed a convolutional neural network (CNN) and proposed a novel attention-oriented convolutional neural network (SECNN) through the channel attention mechanism. To optimize the hyperparameters of the model, we used grid search and K-fold cross-validation methods. In addition, we also comparatively analyzed various machine learning (ML) models such as support vector machine (SVM), logistic regression (LR), decision tree (DT), random forest (RF), and artificial neural network (ANN). Finally, we evaluated the performance of the model using performance evaluation metrics such as precision, recall, F1-Score, accuracy, and AUC. Experimental results show that the SECNN model has an accuracy of 94.12% on the NHANES dataset and an accuracy of 89.47% on the PIMA Indian dataset. SECNN models and CNN models show significant improvements in diabetes prediction performance compared to traditional ML models. The comparative analysis of the SECNN model and the CNN model has significantly improved performance, further verifying the advantages of introducing the channel attention mechanism. The robust diabetes prediction framework proposed in this article establishes an effective foundation for diabetes diagnosis and prediction, and has a positive impact on the development of health management and medical industries.

Association between age at diagnosis and all-cause mortality in type 2 diabetes: the Renal Insufficiency and Cardiovascular Events (RIACE) Italian Multicenter Study.

It is unclear whether type 2 diabetes diagnosed in young adulthood is associated with increased severity than that occurring later in life beyond longer lifetime exposure to hyperglycemia. This study aimed at assessing the independent association of age at type 2 diabetes diagnosis with all-cause mortality. This prospective cohort study enrolled 15,773 Caucasian patients with type 2 diabetes in 19 Italian centers in 2006-2008. Cardiometabolic risk profile and presence of complications and comorbidities were assessed at baseline and participants were stratified by quartiles of age at diabetes diagnosis. All-cause mortality was verified on 31 October 2015. Valid information on vital status was retrieved for 15,656 participants (99.3%). Patients in the lowest quartile had the longest diabetes duration, the worst glycemic control and the highest prevalence of insulin treatment, obesity, atherogenic dyslipidemia, and smoking habits. All complications were inversely associated with age at diabetes diagnosis after adjustment for age and sex, but not after further adjustment for diabetes duration. Percentages of death, Kaplan-Meier estimates, and unadjusted hazard ratios and mortality rates increased from the lowest to the highest quartile. In contrast, when adjusting for age and sex, participants falling in the lowest quartile, showed the highest mortality risk [hazard ratio 1.321 (95% confidence interval 1.196-1.460), P < 0.0001]. However, differences among quartiles disappeared after adjustment for diabetes duration, complications/comorbidities, or other cardiovascular risk factors. Type 2 diabetes onset in young adulthood is associated with increased mortality that is mainly driven by longer diabetes duration favoring the development of complications. ClinicalTrials.gov, NCT00715481, retrospectively registered 15 July, 2008.

Codevelopment of a model of care for adults living with cystic fibrosis-related diabetes.

Cystic fibrosis (CF) related diabetes affects up to half of all adults with CF and is associated with higher morbidity and mortality. Our aim is to codevelop an ideal model of care that integrates diabetes technology and better meets the needs of adults living with the condition to improve attendance, engagement, service satisfaction, and clinical outcomes. Using qualitative research methods, we evaluated disease perceptions, barriers, and enablers to optimal CF-related diabetes management and service delivery. Integration of continuous glucose monitoring (CGM) was also explored. An initial broad purposive consumer survey was followed by focus groups with end-users. Grounded theory approach was utilized with major problem areas identified then explored, coded, and grouped into requisites for an "ideal model of care"for adults living with CF-related diabetes. Two key themes emerged (i) an ideal model of care consisted of a dual-specialty service co-led by endocrinology and CF physicians and supported by diabetes educator and CF dietitian with a goal to provide consistent and personalized diabetes management and (ii) CGM was acceptable for use in adults with CF-related diabetes with many perceived benefits and should be integrated into the model of care. Barriers to optimizing glycemic control included diet, finger-prick testing, reduced access to CGM, and pulmonary exacerbations. End-user feedback on CGM was overwhelmingly positive with regard to operability. CGM was also identified as a tool that could be used to engage, educate, and empower adults living with CF-related diabetes and facilitate constructive and personalized clinical decision-making by healthcare providers. For adults living with CF, a diagnosis of diabetes is associated with increased treatment burden. Our findings suggest an "ideal model of care"for CF-related diabetes would be co-led by endocrinology services integrated within a pre-existing CF service, incorporating CGM.

Measuring the effectiveness of hybrid diabetes care over 90 days through continuous data monitoring in type 2 diabetic patients

BackgroundDiabetes Mellitus, a global health challenge, affects 537 million individuals. Traditional management relies on periodic clinic visits, but technological advancements, including remote monitoring, offer transformative changes. Telemedicine enhances access, convenience, adherence, and glycemic control. Challenges include trust-building and limitations in face-to-face interactions. Integrating remote monitoring with in-person healthcare creates a hybrid approach. This study evaluates the impact on Type 2 Diabetes patients over 3 months.MethodsA retrospective case-control observational study. Inclusion criteria involved previous Type 2 Diabetes diagnosis and a minimum 3-month GluCare model period with two physical visits. Patients in the case group had in-clinic visits, bi-weekly app engagement, and monthly body weight readings. Control group had in-clinic visits only. Outcomes measured included HbA1c, lipid profile, CV risk, eGFR, urine Albumin/Creatinine Ratio, Uric Acid, and CRP.ResultsCase group showed significant HbA1c improvements (-2.19%), especially in higher baseline levels. Weight, BMI, LDL, total cholesterol, and CVD risk also improved. Controls showed smaller improvements. Higher digital interactions correlated with better outcomes. Patients with ≥11 interactions showed significant reductions in HbA1c (-2.38%) and weight (-6.00 kg).ConclusionThe GluCare.Health hybrid model demonstrates promising outcomes in Type 2 diabetes management. The integration of in-clinic consultations with continuous remote monitoring leads to substantial improvements in glycemic control and clinical parameters. The study highlights the importance of patient engagement in achieving positive outcomes, with higher digital interactions associated with greater reductions in HbA1c and weight. The hybrid approach proves more effective than digital-only interventions, emphasizing the need for comprehensive, end-to-end solutions in diabetes care.

Eight-year nationwide study of the bidirectional association between type 2 diabetes and depression in nearly 8 million German outpatients

IntroductionResearch linking type 2 diabetes and depression mostly relied on hospital-based diagnoses or prescription data, overlooking many outpatient diagnoses. We aimed to quantify the risks of depression in individuals newly...

Sensors constructed from carbon nanomaterials and their applications

Diabetes is a growing disease, with patient morbidity and mortality increasing every year. Since diabetes is associated with short- and long-term health problems, it has become extremely important to detect and effectively treat diabetes. Many research institutes and healthcare organizations are leaning towards the use of cost-effective biosensors to monitor the body's blood glucose standards and provide accurate health diagnosis. Biosensors help in accurate diabetes diagnosis and monitoring for effective treatment and management. Based on the recent advances in nanotechnology, a variety of glucose biosensors made of carbon nanomaterials have been developed, and such sensors are a key factor for diabetes diagnosis and treatment, with better performance and sensitivity for better detection of the disease and tracking of the response to treatment. Thus, this research illustrates the significant advances in nanotechnology-based biosensors for medical applications and the potential of carbon nanomaterials for good applications in medical testing.

Influencing factors of hypertension and diabetes care cascade: a qualitative study

Objective: Employing the cascade care model, this qualitative study explores determinants influencing the cascading care stages of hypertension and diabetes by interviewing various stakeholders. Methods: In July 2023, purposive sampling was employed to recruit participants from Gongyi and Wugang cities in Henan Province, and Linqu County in Weifang City, Shandong Province. Semi-structured in-depth interviews were conducted with representatives of policymakers, healthcare institution managers, providers, and patients with hypertension and diabetes.And thematic analysis was performed using both inductive and deductive approaches. Results: A total of 82 individuals were interviewed, with an age range of (53.8±12.0) years, among which 48 (58.5%) were male; including 5 policymakers, 10 institutional managers, 20 healthcare providers, and 47 patients with hypertension and diabetes. The study identified both barriers and facilitating factors at the patient, healthcare provider, and system levels across various stages: awareness, screening, diagnosis, treatment, long-term management, and control of hypertension and diabetes. Conclusion: By delineating and analyzing the barriers and facilitators at each stage of hypertension and diabetes care, this study lays the groundwork for the development of effective, feasible, and sustainable implementation pathways, with significant implications for the enhanced management of hypertension and diabetes in China.

Glucose at gestational diabetes diagnosis predicts neonatal hypoglycaemia, large-for-gestational age and post-partum abnormal maternal glucose homeostasis

Glucose at gestational diabetes diagnosis predicts neonatal hypoglycaemia, large-for-gestational age and post-partum abnormal maternal glucose homeostasis

Predicting the risk of pancreatic cancer in adults with new-onset diabetes: development and internal-external validation of a clinical risk prediction model.

The National Institute for Health and Care Excellence (NICE) recommends that people aged 60+ years with newly diagnosed diabetes and weight loss undergo abdominal imaging to assess for pancreatic cancer. More nuanced stratification could lead to enrichment of these referral pathways. Population-based cohort study of adults aged 30-85 years at type 2 diabetes diagnosis (2010-2021) using the QResearch primary care database in England linked to secondary care data, the national cancer registry and mortality registers. Clinical prediction models were developed to estimate risks of pancreatic cancer diagnosis within 2 years and evaluated using internal-external cross-validation. Seven hundred and sixty-seven of 253,766 individuals were diagnosed with pancreatic cancer within 2 years. Models included age, sex, BMI, prior venous thromboembolism, digoxin prescription, HbA1c, ALT, creatinine, haemoglobin, platelet count; and the presence of abdominal pain, weight loss, jaundice, heartburn, indigestion or nausea (previous 6 months). The Cox model had the highest discrimination (Harrell's C-index 0.802 (95% CI: 0.797-0.817)), the highest clinical utility, and was well calibrated. The model's highest 1% of predicted risks captured 12.51% of pancreatic cancer cases. NICE guidance had 3.95% sensitivity. A new prediction model could have clinical utility in identifying individuals with recent onset diabetes suitable for fast-track abdominal imaging.

The impact of lifestyle, measured with the HLPCQ questionnaire on the prevalence of metabolic syndrome in Poland: a multicenter study

Metabolic syndrome is one of the most common health problems for people around the world. The aim of our study was to assess the prevalence of metabolic syndrome among adults without prior diagnosis of cardiovascular disease, diabetes, and chronic kidney disease. We also plan to assess the influence of certain lifestyle components on prevalence of metabolic syndrome. The study involved cardiovascularly healthy patients undergoing lab tests, measurements, and the HLPCQ questionnaire (The Healthy Lifestyle and Personal Control Questionnaire). The data were used to diagnose metabolic syndrome. Out of 1044 patients from 10 primary care facilities, 23.3% met the metabolic syndrome criteria, showing a strong link with increased blood pressure, cholesterol, and fasting glucose. Lower scores in the Organized physical exercise subscale of the HLPCQ questionnaire were noted in those with metabolic syndrome. Comparing the subscale of HLPCQ questionnaire, the lower results in Organized physical exercise subscale were found among the participants with metabolic syndrome, both male and females. Metabolic syndrome, a significant risk factor for cardiovascular disease, should be screened for actively, even in apparently healthy populations. Results obtained in our study from analysis of HLPCQ show that screening for metabolic syndrome should be preceded by prevention based on regular physical activity and proper eating habits.

Enhanced Diabetic Retinopathy Diagnosis: A Comparative Analysis of models with an Ensemble classifier and Deep Q Learning

- A novel method that combines the strengths of different classifiers such as Naive Bayes, Multi-Layer Perceptron (MLP), and Support Vector Machine (SVM) is introduced in this paper. This method tackles the urgent need for cutting-edge diagnostic methods in the field of ophthalmology, mainly for the identification of diabetic retinopathy (DR). The approach is ensemble-based. Classical methods in retinal analysis of images often fail as they are static and are unable to adjust to the unique details that each distinct image presents. This constraint results in less accurate and precise diagnostic results, highlighting the urgent need for more adaptable and dynamic methods. The suggested model differs significantly from previous methods. Through the use of an ensemble approach, it capitalizes on the distinct advantages of each classifier: the MLP process's sophisticated feature extraction skill, Naive Bayes' probabilistic analysis, and SVM's non-linear pattern recognition capacity. By combining these techniques, the inherent drawbacks of utilizing a single strategy are addressed, guaranteeing a more thorough examination of retinal samples and images. The core of this idea is the system using Deep Q Learning (DQL) for adaptive classifier selection. Using learned Q Values for various contexts, this reinforcement learning technique selects the best classifier adaptively for each unique retinal image, hence optimizing the ensemble. This approach not only advances diagnosis accuracy and precision but also guarantees ongoing learning and adaptation to keep up with changing data patterns and advances in imaging technology. Extensive experiments on the IDRiD &amp; EyePACS Dataset show the effectiveness of this model with a 5.5% increase in overall accuracy with other performance metrics, the results show a significant improvement over the current method. They represent a significant advancement in the timely and accurate identification of diabetic retinopathy, which will ultimately benefit patients and lessen the strain on healthcare systems. Thus, this work represents a major step forward in patient care as well as a technological advance, opening the door to more efficient supervision and treatment of retinal illnesses.

Reducing non-communicable diseases among Palestinian populations in Gaza: A participatory comparative and cost-effectiveness modeling assessment.

We sought to assess the effectiveness and cost-effectiveness of potential new public health and healthcare NCD risk reduction efforts among Palestinians in Gaza. We created a microsimulation model using: (i) a cross-sectional household survey of NCD risk factors among 4,576 Palestinian adults aged ≥40 years old in Gaza; (ii) a modified Delphi process among local public health experts to identify potentially feasible new interventions; and (iii) reviews of intervention cost and effectiveness, modified to the Gazan and refugee contexts. The survey revealed 28.6% tobacco smoking, a 40.4% prevalence of hypertension diagnosis (with a 95.6% medication treatment rate), a 25.6% prevalence of diabetes diagnosis (with 95.3% on treatment), a 21.9% prevalence of dyslipidemia (with 79.6% on a statin), and a 9.8% prevalence of asthma or chronic obstructive pulmonary disease (without known treatment). A calibrated model estimated a loss of 9,516 DALYs per 10,000 population over the 10-year policy horizon. The interventions having an incremental cost-effectiveness ratio (ICER) less than three times the GDP per capita of Palestine per DALY averted (<$10,992 per DALY averted)(<$10,992 per DALY averted) included bans on tobacco smoking in indoor and public places [$34 per incremental DALY averted (95% CI: $17, $50)], treatment of asthma using low dose inhaled beclometasone and short-acting beta-agonists [$140 per DALY averted (95% CI: $77, $207)], treatment of breast cancer stages I and II [$730 per DALY averted (95% CI: $372, $1,100)], implementing a mass media campaign for healthier nutrition [$737 per DALY averted (95% CI: $403, $1,100)], treatment of colorectal cancer stages I and II [$7,657 per DALY averted (95% CI: $3,721, $11,639)], and (screening with mammography [$17,054 per DALY averted (95% CI: $8,693, $25,359)]). Despite high levels of NCD risk factors among Palestinians in Gaza, we estimated that several interventions would be expected to reduce the loss of DALYs within common cost-effectiveness thresholds.

Metabolomics Signature in Prediabetes and Diabetes: Insights From Tandem Mass Spectrometry Analysis.

This study investigates the metabolic differences between normal, prediabetic and diabetic patients with good and poor glycaemic control (GGC and PGC). In this study, 1102 individuals were included, and 50 metabolites were analysed using tandem mass spectrometry. The diabetes diagnosis and treatment standards of the American Diabetes Association (ADA) were used to classify patients. The nearest neighbour method was used to match controls and cases in each group on the basis of age, sex and BMI. Factor analysis was used to reduce the number of variables and find influential underlying factors. Finally, Pearson's correlation coefficient was used to check the correlation between both glucose and HbAc1 as independent factors with binary classes. Amino acids such as glycine, serine and proline, and acylcarnitines (AcylCs) such as C16 and C18 showed significant differences between the prediabetes and normal groups. Additionally, several metabolites, including C0, C5, C8 and C16, showed significant differences between the diabetes and normal groups. Moreover, the study found that several metabolites significantly differed between the GGC and PGC diabetes groups, such as C2, C6, C10, C16 and C18. The correlation analysis revealed that glucose and HbA1c levels significantly correlated with several metabolites, including glycine, serine and C16, in both the prediabetes and diabetes groups. Additionally, the correlation analysis showed that HbA1c significantly correlated with several metabolites, such as C2, C5 and C18, in the controlled and uncontrolled diabetes groups. These findings could help identify new biomarkers or underlying markers for the early detection and management of diabetes.

Insomnia severity among caregivers of children with type 1 diabetes: A mixed method study

AimThis research was conducted to assess the severity of insomnia experienced by caregivers of children with type 1 diabetes. MethodIn this study, a mixed-method descriptive sequential pattern design was employed, incorporating both qualitative and quantitative research methods. In the study, 152 caregivers were surveyed for quantitative data on survey form and insomnia severity. Additionally, 9 mothers underwent semi-structured interviews for qualitative insights. ResultsOf caregivers, 13.8% were identified to experience clinical insomnia. There were statistically significant differences in insomnia severity index scores in terms of experiencing sleep problems after the child's type 1 diabetes diagnosis, caregivers having adequate sleep duration at night, how they felt on waking in the morning, difficulty managing their child's diabetes the next day due to lack of sleep, difficulty getting to sleep at night, sleeping more than one hour during the day, lack of sleep due to caring and treatment, lack of sleep due to not meeting the desired targets for blood glucose values and receiving support for type 1 diabetes management due to lack of sleep (p < 0.05). Three themes were identified as a result of qualitative analysis: Sleep status of caregivers, effect of sleep problems on daily life and solving sleep problems. ConclusionsCaregivers experience insomnia during the process of type 1 diabetes management and this situation affects their daytime care duties. Practical implicationsResearch shows caregivers of children with type 1 diabetes often face insomnia, highlighting the need for solutions.

COVID-19 infection and incident diabetes in American Indian and Alaska Native people: a retrospective cohort study

Evidence suggests an increased risk of new-onset diabetes following COVID-19 infection. American Indian/Alaska Native (AI/AN) people were disparately impacted by the COVID-19 pandemic and historically have had higher diabetes incidence than other racial/ethnic groups in the US. We measured the association between COVID-19 infection and incident diabetes in AI/AN people. We conducted a retrospective cohort study using de-identified patient data from the Indian Health Service's (IHS) National Patient Information Reporting System. We estimated age-adjusted diabetes incidence rates, incidence rate ratios, and adjusted hazard ratios among three cohorts spanning pre-pandemic (1/1/2018-2/28/2020) and pandemic (3/1/2020-12/31/2021) timeframes: 1) pre-pandemic cohort (1,503,085 individuals); 2) no-COVID-19 pandemic cohort (1,344,339 individuals); and 3) COVID-19 cohort (176,483 individuals). The COVID-19 cohort had an increased hazard of diabetes compared to the no-COVID-19 group (adjusted hazard ratio (aHR)=1.56; 95% CI: 1.50-1.62) and the pre-pandemic group (aHR=1.27; 95% CI: 1.22-1.32). The association between COVID-19 infection and new-onset diabetes was stronger in those with severe COVID-19 illness. A sensitivity analysis comparing the COVID-19 cohort to members of other cohorts that had acute upper respiratory infections showed an attenuated but higher risk of new-onset diabetes in those with COVID-19. AI/AN people diagnosed with COVID-19 had an elevated risk of a new diabetes diagnosis when compared to the no-COVID-19 group and the pre-pandemic group. The increased diabetes risk in the COVID-19 group remained in a sensitivity analysis that limited the comparator groups to individuals with an AURI diagnosis. US National Institute of Diabetes and Digestive and Kidney Diseases.