Diabetes-related Factors Research Articles

Diabetes-related patients’ profile and strategy in improving their knowledge and adherence: A mixed method study

Background: Effective education and information during diabetes patients’ treatment to enhance their knowledge could impact their medication adherence to improve therapeutic outcomes. This study aims to explore diabetes-related patient profiles, including knowledge, adherence, and medication nonadherence factors among diabetic patients. Also, it explores the implemented pharmacists' strategies to raise knowledge and adherence. Methods: This study had two phases. The first phase focused on diabetic patients, assessing their profiles, knowledge about diabetes, medication adherence, and factors contributing to nonadherence. The second phase examined pharmacists' strategies to improve patients' knowledge and adherence. Using a mixed-methods design, participants were recruited from primary healthcare in Yogyakarta. Medication adherence was measured with a four-item Morisky, Green, and Levine Medication Adherence Scale (MGLS), while knowledge was assessed using a 10-item adapted questionnaire from the Diabetes Knowledge Questionnaire (DKQ-24). Results: This study enrolled 104 patients and 24 pharmacists. Most patients (40.4%) had low knowledge of disease and medication related to diabetes. A quarter of the participants (26%) exhibited poor adherence. Almost all of the pharmacists have developed strategies to enhance patients’ knowledge and adherence (95.8%), providing drug information services and medication control cards. Conclusion: Improving patient knowledge, addressing non-adherence, and standardising pharmacists' strategies are necessary, alongside developing a comprehensive service model to enhance therapeutic outcomes.

Data-driven diabetes management: a statistical assessment information system leveraging big data

ABSTRACT Amidst the escalating global burden of diabetes and its multifaceted complications, there arises an urgent need for robust healthcare information systems tailored to address the complexities of diabetic patient management. This paper introduces a novel statistical assessment healthcare information system engineered to meticulously analyze diabetes-related data by harnessing the power of big data technologies. Diabetes, affecting millions worldwide, exerts substantial strain on healthcare infrastructures, necessitating innovative solutions to optimize patient outcomes and treatment efficacy. Leveraging advancements in data processing and analytics alongside vast medical datasets, this study pioneers a statistical assessment model explicitly crafted for diabetes analysis within a big data paradigm. The model’s efficacy is rigorously evaluated using key performance metrics such as accuracy and F-measure, within the Hadoop framework. Remarkably, the results demonstrate a significant enhancement in performance compared to conventional methodologies, affirming the model’s potential to revolutionize the accuracy and efficiency of diabetes analysis within a big data milieu. Moving forward, further research avenues include refining the model’s algorithms to accommodate diverse data sources and expanding its applicability to encompass a broader spectrum of diabetes-related factors.

Investigating the prevalence and associated factors of depression, anxiety, and loneliness among people with type-2 diabetes in Bangladesh: a community-based study

Diabetes mellitus is a major worldwide health concern. Diabetes has been associated with a number of adverse mental health conditions including depression, anxiety, and loneliness that can negatively impact diabetes outcomes. This study aimed to investigate factors associated with depression, anxiety, and loneliness in people, in the community, suffering with diabetes in Bangladesh. A cross-sectional study was conducted with 600 people with type-2 diabetes (54.83% females; mean age: 52.70 ± 11.56 years) between July and September 2022. Purposive sampling method was used to recruit the participants. A validated semi-structured questionnaire was used to collect demographic and other data. Depression, anxiety, and loneliness were measured using the PHQ-9, GAD-7, and UCLA Loneliness scale, respectively. Bivariate and multivariable linear regression analyses were conducted to ascertain factors that were significantly associated with these mental health conditions. The prevalence of depression, anxiety, and loneliness was 31.17%, 21.83%, and 28.00%, respectively. A lack of formal education, and not taking part in physical activities were significantly associated with all three mental health states. Duration of diabetes and being on medication for high cholesterol were also associated with depression and anxiety. Older age and being widowed were significantly associated with loneliness. This study found that depression, anxiety, and loneliness are prevalent among Bangladeshi people with diabetes, with certain sociodemographic and diabetes-related factors associated with increased risk. The findings emphasize the need for targeted interventions to people within the communities, at grassroot levels in order to improve reduce health inequality, and improve the mental health of people living with diabetes.

Podiatry risk in diabetic pilgrims: a prospective study in Tunisia

Background Each year, over 340,000 diabetic patients across the world travel to Saudi Arabia for the Hajj pilgrimage. As they carried out important rituals, several complications might be encountered, particularly foot lesions. We’ve been therefore interested, through this study, in the assessment of podiatric risk in diabetic pilgrims. Methods A prospective longitudinal study was conducted in the region of Sousse, Tunisia. The evaluation of diabetic patients preparing for the pilgrimage was conducted in two phases, before and after the pilgrimage. Results Forty-three diabetic patients planning to perform pilgrimage were included in the study. Post-pilgrimage clinical exams revealed hyperkeratosis (96.8%) and superficial fungal infections (61.3%) as the most common podiatric lesions. Foot ulcers attributed to trauma from walking were found in 7% of patients. Our data showed a significant association between foot ulcer risk and lower total cholesterol (p=0.02), HDL cholesterol (p=0.02), LDL cholesterol (p=0.01), as well as higher triglyceride levels (p=0.04) only in univariate analysis. Other diabetes-related risk factors did not show significant correlations. Conclusion Given the increased risk of foot lesions in diabetic pilgrims, a thorough assessment and tailored management based on individual risk levels are essential before, during, and after the pilgrimage.

Tracing links between micronutrients and type 2 diabetes risk: the singular role of selenium.

Type 2 diabetes (T2D) is a growing global health concern. While micronutrients are crucial for physiological functions and metabolic balance, their precise links to T2D are not fully understood. We investigated the causal relationships between 15 key micronutrients and T2D risk using both univariate and multivariate Mendelian randomization (MR) methods. Our analysis leveraged data from a large prospective cohort genome-wide association study (GWAS) on these micronutrients and T2D. We employed MR techniques such as inverse variance weighting (IVW), MR Egger, weighted median, and simple models. Multivariate analysis adjusted for diabetes-related factors like body mass index (BMI) and hypertension to assess the independent effects of micronutrients, particularly selenium, on T2D risk. Selenium intake was associated with an increased risk of T2D, with an odds ratio (OR) of 1.045, a 95% confidence interval (CI) ranging from 1.009 to 1.082, and a P-value of 0.015. This association was consistent in multivariate analyses, suggesting an independent effect of selenium on T2D risk after adjusting for confounders. Our study presents novel evidence of a positive correlation between selenium intake and T2D risk, underscoring the importance of micronutrients in diabetes prevention and treatment strategies. Further research is necessary to confirm these findings and to clarify the specific biological mechanisms through which selenium influences diabetes risk.

Predictors of fracture in middle-aged and older adults with type 2 diabetes and overweight or obesity.

Persons with type 2 diabetes have increased fracture risk that existing fracture risk assessment tools underestimate. Identify fracture predictors in persons with type 2 diabetes and overweight or obesity, considering traditional and diabetes-related risk factors. Secondary analysis of the Look AHEAD: Action for Health in Diabetes randomized clinical trial, with randomization from 2001-2004 and fracture follow-up until 2015. Multicenter U.S. study. Men and women 45-75 years old with type 2 diabetes and body mass index≥25 kg/m2. Potential fracture predictors ascertained at randomization included traditional and diabetes-related risk factors (diabetes duration, diabetic neuropathy, antidiabetic medication use, hemoglobin A1c, and renal function). Total hip bone mineral density (BMD) was measured in a subcohort. All incident clinical fractures, ascertained by self-report and centrally adjudicated with medical records review. Over a median 12.2 years follow-up, 649 of the 4,703 participants experienced at least one clinical fracture. Thiazolidinedione use [hazard ratio (HR):1.22, 95% confidence interval (CI):1.02-1.46] and insulin use [HR:1.34, 95% CI:1.08-1.66] were significant diabetes-related predictors of all clinical fractures. When measured in a subcohort (n=1,285), total hip BMD was the strongest modifiable predictor of all clinical fractures [Per 1 standard deviation (SD)=0.1 g/cm2 increase, HR:0.47, 95% CI:0.39-0.58]. Thiazolidinedione and insulin use predict clinical fracture in middle-aged and older persons with type 2 diabetes and overweight or obesity. Evaluating BMD is advisable if these medications are prescribed. Fracture risk prediction tools may consider including thiazolidinedione and insulin use to refine prediction in this population.

Depressive Symptoms, Perceived Stress, and Associated Socio-Demographic and Diabetes-Related Factors in People With Type 2 Diabetes in Nepal.

The aim of this study was to assess the prevalence and associated factors of depressive symptoms and perceived stress among people with Type 2 diabetes mellitus (T2DM) in Nepal. Using a cross-sectional design, we collected data from 481 participants with T2DM in Kavrepalanchok and Nuwakot districts of Nepal. Depressive symptoms and perceived stress were assessed using Patient Health Questionnaire (PHQ-9) and Perceived Stress Scale, respectively. Associated independent variables were examined using binary logistic regression analyses. Of 481 participants, 123 (25.8%) had depressive symptoms (PHQ-9 score ≥5) and 156 (32.4%) experienced perceived stress. Low monthly income (<USD 215.52) and low diabetes medication adherence were significantly associated with both depressive symptoms and perceived stress. Mandatory screening and timely treatment for mental health conditions and assessment of medication adherence should be incorporated in the routine primary care of T2DM patients. A person-centred approach to T2DM management, considering the socio-economic determinants of the disease, should be prioritized by the healthcare system. Future studies should identify integrated care for the prevention and management of mental health comorbidities.

Update: the role of epigenetics in the metabolic memory of diabetic complications.

Diabetes, a chronic disease characterized by hyperglycemia, is associated with significantly accelerated complications, including diabetic kidney disease (DKD), which increases morbidity and mortality. Hyperglycemia and other diabetes-related environmental factors such as overnutrition, sedentary lifestyles, and hyperlipidemia can induce epigenetic changes. Working alone or with genetic factors, these epigenetic changes that occur without alterations in the underlying DNA sequence, can alter the expression of pathophysiological genes and impair functions of associated target cells/organs, leading to diabetic complications like DKD. Notably, some hyperglycemia-induced epigenetic changes persist in target cells/tissues even after glucose normalization, leading to sustained complications despite glycemic control, so-called metabolic memory. Emerging evidence from in vitro and in vivo animal models and clinical trials with subjects with diabetes identified clear associations between metabolic memory and epigenetic changes including DNA methylation, histone modifications, chromatin structure, and noncoding RNAs at key loci. Targeting such persistent epigenetic changes and/or molecules regulated by them can serve as valuable opportunities to attenuate, or erase metabolic memory, which is crucial to prevent complication progression. Here, we review these cell/tissue-specific epigenetic changes identified to-date as related to diabetic complications, especially DKD, and the current status on targeting epigenetics to tackle metabolic memory. We also discuss limitations in current studies, including the need for more (epi)genome-wide studies, integrative analysis using multiple epigenetic marks and Omics datasets, and mechanistic evaluation of metabolic memory. Considering the tremendous technological advances in epigenomics, genetics, sequencing, and availability of genomic datasets from clinical cohorts, this field is likely to see considerable progress in the upcoming years.

Incidence of Various Tuberculous Lesions in Indian Patients with Diabetes Mellitus: A Cross-Sectional Study

Background: Globally, TB is a major health issue, especially in low- and middle-income nations. Diabetes mellitus (DM) increases TB risk and complicates treatment. Due to the greatest worldwide TB burden and rising DM incidence, India faces a double public health problem. This study investigated the incidence and types of tuberculous lesions in Indian patients with diabetes mellitus. Methods: A total of 250 individuals with a history of DM and respiratory symptoms were included. Data were collected through patient interviews, clinical examinations, and diagnostic tests. The association between diabetes-related factors and the incidence of tuberculous lesions was analyzed. Results: Out of 250 patients, 70 (28%) were diagnosed with tuberculous lesions. Pulmonary tuberculosis was the most common type (16%), followed by extrapulmonary tuberculosis (8%) and disseminated tuberculosis (4%). The duration of diabetes and higher HbA1c levels were considerably correlated with an increased risk of tuberculous lesions (p&lt;0.05). Patients commonly presented with cough with sputum (85.7%), weight loss (78.6%), and loss of appetite (71.4%). Sputum examination for Acid-Fast Bacilli (AFB) was positive in 64.3% of cases, and chest X-rays showed lesions in 71.4% of patients. Conclusion: The study highlights a significant incidence of tuberculous lesions among diabetic patients, with pulmonary tuberculosis being the predominant type. Poor glycemic control and longer duration of diabetes are critical risk factors. These findings emphasize the need for regular TB screening and proactive management in diabetic patients to prevent complications and improve outcomes. Recommendation: Healthcare providers should integrate routine TB screening into the care protocols for diabetic patients, particularly those with poor glycemic control and long-standing diabetes. Public health strategies should focus on the dual burden of TB and DM to enhance early detection and treatment, thereby improving patient outcomes. Keywords: Tuberculosis, Diabetes Mellitus, Incidence, Glycemic Control

Exploring Machine Learning Models for Predicting Diabetic Retinopathy: A Comprehensive Comparative Study of Logistic Regression an Advanced Technique

This research provides a comprehensive examination of machine learning models for predicting diabetes-related ocular diseases, with a focus on Logistic Regression versus more advanced approaches. A large dataset encompassing a variety of diabetes-related lifestyle and health factors is used in the study to extensively train and analyze multiple models in order to demonstrate their predictive utility. The thorough evaluation results illuminated the subtle differences in performance between Logistic Regression and other advanced algorithms, offering insightful information about the pros and cons of each in terms of predicting the risk of diabetic retinopathy and other complications relating to the eyes. The findings reveal crucial themes for additional research and advancement in the realm of predictive modeling for diabetic eye disorders, in the process of verifying that logistic regression works well in specific situations.

Assessment of depression in children and adolescents with Type 1 diabetes mellitus: Impact and intervention strategies.

Depression is a common comorbidity in children and adolescents with type 1 diabetes mellitus (T1DM), yet its prevalence, impact, and intervention strategies remain underexplored. This study aims to assess the prevalence of depression among children and adolescents with T1DM, investigate its impact on health outcomes, and explore potential intervention strategies. A convenient sampling method was employed to recruit 229 participants aged 6 to 18 years from a single center. Data collection involved validated assessments, demographic surveys, and diabetes-related factor examinations during routine clinic visits. The patient health questionnaire-9 was utilized to evaluate the severity of depressive symptoms. Associations between depression and sociodemographic variables, diabetes management factors, and health behaviors were analyzed using chi-squared tests and logistic regression analysis. The prevalence of depression among participants was 43.23%. Older age, lower parental education levels, lower household income, smoking, and comorbidities were identified as significant risk factors for depression. Associations were found between depression and diabetes management factors, including glycemic control and frequency of glucose monitoring. Depression is highly prevalent among children and adolescents with T1DM and is associated with sociodemographic factors, health behaviors, and diabetes management. Integrated approaches to care that address both physical and mental health aspects are crucial for improving outcomes in this population.

Fear of hypoglycemia in parents of children with type 1 diabetes trained for intranasal glucagon use

ObjectiveTo investigate fear of hypoglycemia (FoH) in parents of children with type 1 diabetes (T1D) before and after undergoing training to learn intranasal (IN) glucagon administration. MethodIn this pre-test/post-test uncontrolled study 364 caregivers of patients with T1D (6–18 years) completed questionnaires measuring sociodemographic characteristics, diabetes-related factors (e.g., type of insulin therapy, glycemic control), and parents' trait anxiety. Parents' FoH was assessed at baseline (T0, training) and after nine months (T1). Two repeated-measure mixed analyses of covariance (ANCOVA) compared the FoH at T0 and at T1 and analyzed the moderating roles of anxiety proneness and type of insulin therapy, as well as of anxiety proneness and use of sensor. Age, T1D duration, HbA1c values, and SES were included as covariates. ResultsParental FoH at T1 (M = 1.72; SE = 0.06/M = 1.57; SE = 0.09) was significantly lower than parental FoH at T0 (M = 1.89; SE = 0.06/M = 1.77; SE = 0.09). The group with high trait-anxiety had a higher level of FoH (M = 2.05; SE = 0.08/M = 1.89; SE = 0.12) than the group with low trait-anxiety (M = 1.57; SE = 0.08/M = 1.46; SE = 0.09) at both time points. SES was negatively associated with FoH at T0 (t = −2.87; p = .004/t = −2.87; p = .005). No other significant effects were found. ConclusionsTraining and educating parents on IN glucagon use can help them effectively manage hypoglycemic episodes and alleviate the fear that generally accompany such events.

Glucagon-like peptide-1 receptor agonists and capsule endoscopy in patients with diabetes: a matched cohort study

Background and AimsVideo capsule endoscopy (VCE) is valuable for assessing conditions like gastrointestinal bleeding, anemia, and inflammatory bowel disease. Glucagon-like peptide-1 receptor agonists (GLP-1 RA) are prescribed for diabetes and weight loss, with their pharmacologic effects including delaying gastric emptying. This study investigates the impact of GLP-1 RA usage on VCE outcomes in patients with diabetes. MethodsThis retrospective cohort study involves patients with diabetes undergoing VCE while on GLP-1 RA, matched 1:1 ratio with controls based on demographics and diabetes related factors, who are not on GLP-RA. The primary outcome is gastric transit time in VCE studies, with secondary outcome being incomplete small bowel evaluation and the small bowel transit time. ResultsIn the 68 GLP-1 RA patient cohort, five (7%) experienced VCE failure to pass through the stomach, while all controls passed successfully (p=0.06). GLP-1 RA patients had longer gastric transit time (99.3 ± 134.2 minutes) compared to controls (25.3 ± 31.6 minutes, p <0.001). Multivariate analysis revealed GLP-1 RA usage was associated with increased gastric transit time by 74.5 minutes (CI: 33.8-115.2, p <0.001) compared to controls, after adjusting on relevant factors. Sixteen GLP-1 RA patients (23.5%) experienced incomplete passage of the VCE through the small intestine, a significantly higher rate compared to three patients in the control group (4.4%) (p<0.01). ConclusionsGLP-1 RA usage is associated with prolonged gastric transit time and a higher rate of incomplete small bowel evaluation during VCE. Future studies may be crucial for evaluating strategies to mitigate these effects.

Supervised Machine Learning-Based Models for Predicting Raised Blood Sugar.

Raised blood sugar (hyperglycemia) is considered a strong indicator of prediabetes or diabetes mellitus. Diabetes mellitus is one of the most common non-communicable diseases (NCDs) affecting the adult population. Recently, the prevalence of diabetes has been increasing at a faster rate, especially in developing countries. The primary concern associated with diabetes is the potential for serious health complications to occur if it is not diagnosed early. Therefore, timely detection and screening of diabetes is considered a crucial factor in treating and controlling the disease. Population screening for raised blood sugar aims to identify individuals at risk before symptoms appear, enabling timely intervention and potentially improved health outcomes. However, implementing large-scale screening programs can be expensive, requiring testing, follow-up, and management resources, potentially straining healthcare systems. Given the above facts, this paper presents supervised machine-learning models to detect and predict raised blood sugar. The proposed raised blood sugar models utilize diabetes-related risk factors including age, body mass index (BMI), eating habits, physical activity, prevalence of other diseases, and fasting blood sugar obtained from the dataset of the STEPwise approach to NCD risk factor study collected from adults in the Palestinian community. The diabetes risk factor obtained from the STEPS dataset was used as input for building the prediction model that was trained using various types of supervised learning classification algorithms including random forest, decision tree, Adaboost, XGBoost, bagging decision trees, and multi-layer perceptron (MLP). Based on the experimental results, the raised blood sugar models demonstrated optimal performance when implemented with a random forest classifier, yielding an accuracy of 98.4%. Followed by the bagging decision trees, XGBoost, MLP, AdaBoost, and decision tree with an accuracy of 97.4%, 96.4%, 96.3%, 95.2%, and 94.8%, respectively.

Discovery and validation of plasma, saliva and multi-fluid plasma-saliva metabolomic scores predicting insulin resistance and diabetes progression or regression among Puerto Rican adults.

Many studies have examined the relationship between plasma metabolites and type 2 diabetes progression, but few have explored saliva and multi-fluid metabolites. We used LC/MS to measure plasma (n=1051) and saliva (n=635) metabolites among Puerto Rican adults from the San Juan Overweight Adults Longitudinal Study. We used elastic net regression to identify plasma, saliva and multi-fluid plasma-saliva metabolomic scores predicting baseline HOMA-IR in a training set (n=509) and validated these scores in a testing set (n=340). We used multivariable Cox proportional hazards models to estimate HRs for the association of baseline metabolomic scores predicting insulin resistance with incident type 2 diabetes (n=54) and prediabetes (characterised by impaired glucose tolerance, impaired fasting glucose and/or high HbA1c) (n=130) at 3 years, along with regression from prediabetes to normoglycaemia (n=122), adjusting for traditional diabetes-related risk factors. Plasma, saliva and multi-fluid plasma-saliva metabolomic scores predicting insulin resistance included highly weighted metabolites from fructose, tyrosine, lipid and amino acid metabolism. Each SD increase in the plasma (HR 1.99 [95% CI 1.18, 3.38]; p=0.01) and multi-fluid (1.80 [1.06, 3.07]; p=0.03) metabolomic scores was associated with higher risk of type 2 diabetes. The saliva metabolomic score was associated with incident prediabetes (1.48 [1.17, 1.86]; p=0.001). All three metabolomic scores were significantly associated with lower likelihood of regressing from prediabetes to normoglycaemia in models adjusting for adiposity (HRs 0.72 for plasma, 0.78 for saliva and 0.72 for multi-fluid), but associations were attenuated when adjusting for lipid and glycaemic measures. The plasma metabolomic score predicting insulin resistance was more strongly associated with incident type 2 diabetes than the saliva metabolomic score. Only the saliva metabolomic score was associated with incident prediabetes.

Diabetes-related risk factors and survival among individuals with type 2 diabetes and breast, lung, colorectal, or prostate cancer

Premature death in diabetes is increasingly caused by cancer. The objectives were to estimate the excess mortality when individuals with type 2 diabetes(T2D) were diagnosed with cancer, and to examine the impact of modifiable diabetes-related risk factors. This longitudinal nationwide cohort study included individuals with T2D registered in the Swedish National Diabetes Register between 1998–2019. Poisson models were used to estimate mortality as a function of time-updated risk-factors, adjusted for sex, age, diabetes duration, marital status, country of birth, BMI, blood pressure, lipids, albuminuria, smoking, and physical activity. We included 690,539 individuals with T2D and during 4,787,326 person-years of follow-up 179,627 individuals died. Overall, the all-cause mortality rate ratio was 3.75 [95%confidence interval(CI):3.69–3.81] for individuals with T2D and cancer compared to those remaining free of cancer. The most marked risk factors associated to mortality among individuals with T2D and cancer were low physical activity, 1.59 (1.57–1.61) and smoking, 2.15 (2.08–2.22), whereas HbA1c, lipids, hypertension, and BMI had no/weak associations with survival. In a future with more patients with comorbid T2D and cancer diagnoses, these results suggest that smoking and physical activity might be the two most salient modifiable risk factors for mortality in people with type 2 diabetes and cancer.

Sensorineural hearing loss among type 2 diabetic patients and its association with peripheral neuropathy: a cross-sectional study from a lower middle-income country

IntroductionDespite potential links between diabetes and sensorineural hearing loss (SNHL), routine hearing assessments for diabetic patients are not standard practice. Our study aimed to investigate the prevalence of SNHL and...

Association between social distancing and incident microvascular events among individuals with diabetes mellitus: a population-based cohort study

IntroductionSocial isolation and loneliness have been rising social determinants of cardiometabolic health.ObjectivesTo investigate the associations of social isolation and loneliness with diabetic microvascular complications (DMC) among individuals with type 2 diabetes mellitus (T2DM) and assess the extent to which intermediate risk factors explained the associations.MethodsData for individuals with T2DM (n=24,297, 62.2% male; mean age=60.0 years) were taken from the UK Biobank. Social isolation and loneliness were assessed using self-reported questionnaires. DMC, mainly including diabetic kidney disease, diabetic retinopathy, and diabetic neuropathy, were identified by linking hospital records and death registries.ResultsIn the multivariate-adjusted model, social isolation was associated with an increased risk for incidence of any DMC (most vs. least: HR: 1.13; 95% CI: 1.05-1.22), especially diabetic kidney disease and neuropathy; loneliness was also associated with any DMC (yes vs. no: HR: 1.12; 95% CI: 1.02-1.23) and diabetic kidney disease. Social isolation and loneliness ranked similarly in relative strength for predicting DMC as other conventional risk factors, such as smoking, high blood pressure, and physical activity. The association between social isolation and DMC was mainly attributed to health behaviors, while the association between loneliness and DMC was primarily explained by health behaviors, psychological factors, and diabetes-related factors.ConclusionsSocial isolation and loneliness were independently associated with a higher risk for incident DMC among individuals with T2DM, which were largely explained by subsequent unhealthy lifestyles, psychosocial stress, and diabetes-related factors. These findings underscore social isolation and loneliness as novel modifiable risk factors for predicting DMC.AcknowledgementsThis research has been conducted using the UK Biobank Resource under Application Number 58082.Funding SupportThis work was supported by the National Natural Science Foundation of China (grant number 32100880), Guangzhou Municipal Key Discipline in Medicine (2021-2023), Guangzhou High-level Clinical Key Specialty, and Guangzhou Research-oriented Hospital. The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.Disclosure of InterestNone Declared

Prediction model for mild cognitive impairment in patients with type 2 diabetes using the autonomic function test.

Type 2 diabetes mellitus (T2DM) is a risk factor for cognitive impairment, and reduced heart rate variability (HRV) has been correlated with cognitive impairment in elderly individuals. This study investigated risk factors and validated a predictive model for mild cognitive impairment (MCI) in patients with T2DM using an autonomic function test. Patients with T2DM, 50-85years of age, who attended the diabetes clinic at Gyeongsang National University Hospital between March 2018 and December 2019, were included. A total of 201 patients had been screened; we enrolled 124 patients according to the inclusion and exclusion criteria in this study. Cognitive function was assessed using the Montreal Cognitive Assessment-Korean version (MOCA-K); MCI was defined as a total MOCA-K score ≤ 23. Risk factors for MCI in patients with T2DM, including demographic- and diabetes-related factors, and autonomic function test results, were analyzed. Based on multivariate logistic regression, a nomogram was developed as a prediction model for MCI. Thirty-nine of 124 patients were diagnosed with MCI. Age, education, and decreased cardiovagal function were associated with a high risk for MCI, with cardiovagal function exerting the greatest influence. However, diabetes-related factors, such as glycemic control, duration of diabetes, or medications, were not associated with the risk for MCI. The nomogram demonstrated excellent discrimination (area under the curve, 0.832) and was well calibrated. Approximately one-third of patients had MCI; as such, carefully evaluating cognitive function in elderly T2DM patients with reduced HRV is important to prevent progression to dementia.

Abstract P140: Using Group Model-Building to Understand and Address Type 2 Diabetes in Native Hawaiian Communities

Introduction: Native Hawaiians, the indigenous people of Hawai‘i, have a prevalence of type 2 diabetes more than twice that of non-Hispanic Whites. Risk factors and social determinants of health for type 2 diabetes are multilevel and interrelated including individual-level factors, household-level, community-level, and societal-level. To address this complex issue effectively, it is crucial to understand the structure of how these factors interrelate and result in prevalence of type 2 diabetes in Native Hawaiian communities. Group Model-Building (GMB) is a participatory method rooted in systems theory, which provides a conceptual framework to understand such complex systems. It engages stakeholders in a structured process that leverages local knowledge to create contextually relevant causal loop diagrams (CLDs).Purpose: To demonstrate the value of GMB in constructing a CLD that captures the relationships and feedback mechanisms underlying type 2 diabetes in a Hawaiian homestead region and identify potential targeted interventions and policies to address type 2 diabetes prevalence, morbidity and mortality. Methods: Prior to the GMB workshops, we identified and engaged key homestead stakeholders, aquired necessary materials, selected a meeting location, create and train a facilitation team, and create the GMB agenda. The workshops were held over two evenings and included structured activities: Hopes and Fears, Graphs over Time, Causal Loop Diagrams, and Action Ideas. Results: Hopes and fears themes included: decreasing prevalence, healing, health education, community programs, decreasing quality of life, death, and the impact on the family spectrum. Graphs over time depicted increases in behavioral risk factors for type 2 diabetes, increases in type 2 diabetes-related morbidity, changing access to care, and changing quality of life. Participants developed and revised a CLD illustrating balancing and reinforcing feedback structures. Finally, participants developed 13 intervention/program ideas to address type 2 diabetes including health-related policies, community programs, and addressing social determinants of health (eg., access to care and healthy food, employment services). Conclusion: GMB was seen as a valuable tool for visualizing the connections and interdependencies between factors which community understands intuitively but hasn’t seen represented on paper. The process revealed the extent to which community programs impact community members, and highlighted collaborations to foster to sustain these effects. The constraints of physical space limiting the programs they can offer were identified. GMB is a promising method for collaboration, knowledge sharing, and co-creating a comprehensive understanding of diabetes-related factors in Native Hawaiian communities.