Addressing Information Biases Within Electronic Health Record Data to Improve the Examination of Epidemiologic Associations With Diabetes Prevalence Among Young Adults: Cross-Sectional Study.

Background: Electronic health records (EHRs) are increasingly used for epidemiologic research to advance public health practice. However, key variables are susceptible to missing data or misclassification within EHRs, including demographic information or disease status, which could affect the estimation of disease prevalence or risk factor associations. Objective: In this paper, we applied methods from the literature on missing data and causal inference to assess whether we could mitigate information biases when estimating measures of association between potential risk factors and diabetes among a patient population of New York City young adults. Methods: We estimated the odds ratio (OR) for diabetes by race or ethnicity and asthma status using EHR data from NYU Langone Health. Methods from the missing data and causal inference literature were then applied to assess the ability to control for misclassification of health outcomes in the EHR data. We compared EHR-based associations with associations observed from 2 national health surveys, the Behavioral Risk Factor Surveillance System (BRFSS) and the National Health and Nutrition Examination Survey, representing traditional public health surveillance systems. Results: Observed EHR-based associations between race or ethnicity and diabetes were comparable to health survey-based estimates, but the association between asthma and diabetes was significantly overestimated (OREHR 3.01, 95% CI 2.86-3.18 vs ORBRFSS 1.23, 95% CI 1.09-1.40). Missing data and causal inference methods reduced information biases in these estimates, yielding relative differences from traditional estimates below 50% (ORMissingData 1.79, 95% CI 1.67-1.92 and ORCausal 1.42, 95% CI 1.34-1.51). Conclusions: Findings suggest that without bias adjustment, EHR analyses may yield biased measures of association, driven in part by subgroup differences in health care use. However, applying missing data or causal inference frameworks can help control for and, importantly, characterize residual information biases in these estimates.

Risk and determinants of sarcopenia in people with diabetes: a case-control study from Qatar Biobank cohort.

BACKGROUND: Diabetes is associated with impairments in muscle mass and quality increasing the risk of sarcopenia. Thus, this study aimed to investigate the odds of sarcopenia and its associated risk factors among Qatari adults (> 18 years), while exploring the modulating effects of health and lifestyle factors. METHODS: Using a case-control design, data from 767 participants (481 cases with diabetes and 286 controls without diabetes) was collected from Qatar Biobank (QBB). Sociodemographic, lifestyle factors including dietary intake, anthropometric and biochemical measures were analyzed. Handgrip strength, Dual X-ray absorptiometry (DXA), and Bio-impedance were used to assess muscle strength, muscle mass and muscle quality, respectively. The risk of sarcopenia was estimated using the European consensus on definition and diagnosis of sarcopenia. RESULTS: Cases with diabetes were older (55 vs. 36 years; P < 0.001), had higher BMI (31.6 vs. 28.3 kg/m2; P < 0.001), lower cardiorespiratory fitness (50.0% "Moderate" fitness for cases, 62.9% "High" fitness for controls), and consumed less total (59.0 vs. 64.0; P = 0.004) and animal protein (39.0 vs. 42.0; P = 0.001), compared to controls based on a computed score. Participants with diabetes also had lower appendicular lean mass/BMI, handgrip strength, and higher probability of sarcopenia/probable sarcopenia (P < 0.005). Adjusted multiple logistic regression revealed that elevated cardiorespiratory fitness (ß = 0.299, 95%CI:0.12-0.74) and blood triglycerides (ß = 1.475, 95% CI: 1.024-2.124), as well as being a female (ß = 0.086, 95%CI: 0.026-0.288) and having higher BMI (ß = 0.908, 95%CI: 0.852-0.967) and ALM/BMI (ß = 0.000, 95% CI: 0.000-0.007) are independent predictors (p < 0.05) of sarcopenia risk. CONCLUSIONS: This study highlights the intricate relationship between diabetes and sarcopenia, revealing modifiable risk factors. Individuals with diabetes were found to have a higher likelihood of sarcopenia, which was associated with lower fitness levels and higher blood triglycerides. Protective factors against sarcopenia included being female and having higher BMI and ALM/BMI ratios.

Knowledge of diabetes mellitus complication prevention among patients in the central region of Ghana.

Diabetes Mellitus (DM) is a serious worldwide health issue that has put pressure on the global economy. The study examined the knowledge of complication prevention among patients living with Diabetes Mellitus (DM) in Ghana. The research was a quantitative study which involved the use of descriptive cross-sectional survey design. In all, 301 DM patients were included in the study. Data were processed using the Statistical Package for the Social Sciences (SPSS) version 25. The study recorded 66.12% (n = 199) good knowledge level. Therefore, educational intervention programme should be organized by the health professionals at Twifo-Atti Morkwa hospital so every DM patient would have optimum knowledge about the disease that would ensure prevention of the DM complications.

Interactive effect of diabetes mellitus and subclinical MRI markers of cerebrovascular disease on cognitive decline and incident dementia: a memory-clinic study.

BACKGROUND: Cognitive impairment is an increasingly recognized comorbidity of diabetes, yet the mechanisms underlying this association remain poorly understood. This knowledge gap has contributed to conflicting findings regarding the impact of diabetes on long-term cognitive outcomes in older adults. The presence of cerebrovascular disease (CeVD) may potentially modify this relationship. However, interactive effect between diabetes and subclinical MRI markers of CeVD on cognitive trajectories and incident dementia remains unexplored. METHODS: A total of 654 participants underwent brain MRI at baseline, from whom 614 with at least one follow-up were selected for longitudinal analysis. Cognitive tests were performed annually up to 5 years. CeVD markers of interest were lacunes, white matter hyperintensities (WMHs), cerebral microbleeds (CMBs), cortical microinfarcts (CMIs), intracranial stenosis (ICS), and cortical infarcts. Blood-based Alzheimer biomarkers, including p-tau181 and p-tau181/Aß42 ratio, were used as indicators of Alzheimer pathology. RESULTS: At baseline, diabetes was associated with lower cognitive performance and higher burden of CeVD, but not p-tau181 or p-tau181/Aß42 ratio. Longitudinally, we found an interactive effect of diabetes and WMHs, rather than an independent effect of diabetes, on cognitive decline and dementia risk. Subgroup analyses showed association of diabetes with cognitive outcomes was stronger in participants with high WMHs load but non-significant in those with low WMHs load. Moreover, these associations remained unchanged after adjusting for blood-based Alzheimer biomarkers. CONCLUSIONS: The effect of diabetes on cognitive decline is contingent upon the presence of WMHs and independent of Alzheimer's pathology. This finding raises the possibility of utilizing WMHs as an imaging biomarker to identify diabetic subgroup at greater risk of developing cognitive impairment. Furthermore, therapeutic interventions targeting WMHs may prevent cognitive deterioration in older adults with diabetes.

Prevalence and influencing factors of malnutrition in diabetic patients: A systematic review and meta-analysis.

The prevalence of malnutrition in diabetic patients and its influencing factors remain poorly described. We aim to investigate the prevalence of malnutrition and the influencing factors in diabetic patients through meta-analysis. Utilizing search terms, such as diabetes, malnutrition, and prevalence, we systematically searched eight databases, including Embase, PubMed, Web of Science, The Cochrane Library, China Knowledge Resource Integrated Database (CNKI), Wanfang Database, Chinese Biomedical Database (CBM), and VIP Database, from inception to May 4, 2023. The search aimed to identify studies related to the prevalence of malnutrition and its influencing factors in adult patients with diabetes. Cohort studies, case-control studies, and cross-sectional studies that met the inclusion criteria were included in the analysis. Stata 16.0 software was used for meta-analysis. Quality of the evidence was assessed using Grading of Recommendations, Assessment, Development and Evaluation (GRADE). The study protocol is registered with Prospective Register of Systematic Reviews (PROSPERO), CRD42023443649. A total of 46 studies were included, involving 18 062 patients with ages ranging from 18 to 95 years. The overall malnutrition prevalence was 33% (95% confidence interval [CI]: 0.25-0.40), compared with an at-risk prevalence of 44% (95% CI: 0.34-0.54). Sixteen factors associated with malnutrition in diabetic patients were identified. This meta-analysis provides insights into the prevalence of malnutrition and its risk factors in diabetic patients. Regular nutritional screening for patients with risk factors is essential for early detection and intervention.

Health Care Usage During the COVID-19 Pandemic and the Adoption of Telemedicine: Retrospective Study of Chronic Disease Cohorts.

BACKGROUND: The COVID-19 pandemic accelerated telehealth adoption across disease cohorts of patients. For many patients, routine medical care was no longer an option, and others chose not to visit medical offices in order to minimize COVID-19 exposure. In this study, we take a comprehensive multidisease approach in studying the impact of the COVID-19 pandemic on health care usage and the adoption of telemedicine through the first 12 months of the COVID-19 pandemic. OBJECTIVE: We studied the impact of the COVID-19 pandemic on in-person health care usage and telehealth adoption across chronic diseases to understand differences in telehealth adoption across disease cohorts and patient demographics (such as the Social Vulnerability Index [SVI]). METHODS: We conducted a retrospective cohort study of 6 different disease cohorts (anxiety: n=67,578; depression: n=45,570; diabetes: n=81,885; kidney failure: n=29,284; heart failure: n=21,152; and cancer: n=35,460). We used summary statistics to characterize changes in usage and regression analysis to study how patient characteristics relate to in-person health care and telehealth adoption and usage during the first 12 months of the pandemic. RESULTS: We observed a reduction in in-person health care usage across disease cohorts (ranging from 10% to 24%). For most diseases we study, telehealth appointments offset the reduction in in-person visits. Furthermore, for anxiety and depression, the increase in telehealth usage exceeds the reduction in in-person visits (by up to 5%). We observed that younger patients and men have higher telehealth usage after accounting for other covariates. Patients from higher SVI areas are less likely to use telehealth; however, if they do, they have a higher number of telehealth visits, after accounting for other covariates. CONCLUSIONS: The COVID-19 pandemic affected health care usage across diseases, and the role of telehealth in replacing in-person visits varies by disease cohort. Understanding these differences can inform current practices and provides opportunities to further guide modalities of in-person and telehealth visits. Critically, further study is needed to understand barriers to telehealth service usage for patients in higher SVI areas. A better understanding of the role of social determinants of health may lead to more support for patients and help individual health care providers improve access to care for patients with chronic conditions.

[Identification process of time-related bias in pharmacoepidemiologic research based on a scoping review].

Objective: To summarize the characteristics of pharmacoepidemiologic research involving diabetes patients, which were published in recent years, in terms of study design and analysis, and develop an identification process for time-related biases in pharmacoepidemiologic research. Methods: PubMed, Embase, CNKI and Wanfang were used for a systematical literature retrieval of relevant study papers published between January 1,2012 and September 26, 2022. Literature screening and data extraction were performed independently by two reviewers. Based on the mechanisms of different time-related biases and the characteristics of included study papers in terms of study design and analysis methods, an identification process for all types of time-related biases was developed. Results: A total of 281 study papers were included, of which 58 (20.64%) specifically mentioned certain time-related biases considered in the study. Based on the scoping review results, key points to identify time-related biases were summarized, involving data source, study design, control selection, comparator drugs, matching the duration of diabetes, identification of the washout period, identification of the induction/latency period, identification of the initiation of follow-up, identification of time window, statistical analysis methods, sensitivity analysis, and other design and analytical elements, in the identification process for time-related biases in pharmacoepidemiologic research. Conclusions: Time-related biases are common in pharmacoepidemiologic research and might significantly impact the study results. Based on scoping review results, this study further developed an identification process for time-related biases in pharmacoepidemiologic research, which will help researchers identify and avoid time-related biases and improve the reliability of related evidence in pharmacoepidemiologic research.

Effect of PM2.5 on burden of mortality from non-communicable diseases in northern Thailand.

Background: Particulate pollution, especially PM2.5from biomass burning, affects public and human health in northern Thailand during the dry season. Therefore, PM2.5exposure increases non-communicable disease incidence and mortality. This study examined the relationship between PM2.5and NCD mortality, including heart disease, hypertension, chronic lung disease, stroke, and diabetes, in northern Thailand during 2017-2021. Methods: The analysis utilized accurate PM2.5data from the MERRA2 reanalysis, along with ground-based PM2.5measurements from the Pollution Control Department and mortality data from the Division of Non-Communicable Disease, Thailand. The cross-correlation and spearman coefficient were utilized for the time-lag, and direction of the relationship between PM2.5and mortality from NCDs, respectively. The Hazard Quotient (HQ) was used to quantify the health risk of PM2.5to people in northern Thailand. Results: High PM2.5 risk was observed in March, with peak PM2.5concentration reaching 100 µg/m3, with maximum HQ values of 1.78 ± 0.13 to 4.25 ± 0.35 and 1.45 ± 0.11 to 3.46 ± 0.29 for males and females, respectively. Hypertension significantly correlated with PM2.5levels, followed by chronic lung disease and diabetes. The cross-correlation analysis showed a strong relationship between hypertansion mortality and PM2.5at a two-year time lag in Chiang Mai (0.73) (CI [-0.43-0.98], p-value of 0.0270) and a modest relationship with chronic lung disease at Lampang (0.33) (a four-year time lag). The results from spearman correlation analysis showed that PM2.5concentrations were associated with diabetes mortality in Chiang Mai, with a coefficient of 0.9 (CI [0.09-0.99], p-value of 0.03704). Lampang and Phayao had significant associations between PM2.5 and heart disease, with coefficients of 0.97 (CI [0.66-0.99], p-value of 0.0048) and 0.90 (CI [0.09-0.99], p-value of 0.0374), respectively, whereas Phrae had a high coefficient of 0.99 on stroke.

The association of triglyceride-glucose index and combined obesity indicators with chest pain and risk of cardiovascular disease in American population with pre-diabetes or diabetes.

Aim: To investigate the correlation of the triglyceride-glucose (TyG) index and its combined obesity indicators with chest pain and cardiovascular disease (CVD) in the pre-diabetes and diabetes population. Methods: This cross-sectional investigation encompassed 6488 participants with diabetes and pre-diabetes who participated in the National Health and Nutrition Examination Survey (NHANES) between 2007 and 2016. The association of the TyG and combined obesity index with chest pain and CVD was investigated using weighted logistic regression models and restricted cubic spline (RCS) analysis. The receiver operating characteristic (ROC) curve analysis was performed to compare different indicators. Results: In multivariate logistic regression fully adjusted for confounding variables, our analyses revealed significant associations between TyG, TyG-BMI, TyG-WC, and TyG-WHtR and chest pain, with adjusted ORs (95% CI) of 1.21 (1.05, 1.39), 1.06 (1.01, 1.11), 1.08 (1.04, 1.14), and 1.27 (1.08, 1.48), respectively. For total-CVD, the adjusted ORs (95% CI) were 1.32 (1.08, 1.61), 1.10 (1.03, 1.17), 1.13 (1.06, 1.19), and 1.63 (1.35, 1.97), respectively, among which TyG, TyG-WC, and TyG-WHtR present curvilinear associations in RCS analysis (all P-nonlinear < 0.05). Furthermore, the ROC curve showed that TyG-WC had the most robust predictive efficacy for total-CVD, coronary heart disease (CHD), and myocardial infarction (MI), while TyG-WHtR had the best predictive ability for angina and heart failure. Conclusion: There are significant associations of TyG and its related indicators with chest pain and total-CVD among the pathoglycemia population. TyG-WC and TyG-WHtR demonstrated superior predictive capability for the incidence of cardiovascular events.

Association between diabetes mellitus and ossification of the spinal ligament: a systematic review and meta-analysis.

PURPOSE: Ossification of the spinal ligament (OSL) is a spinal disorder characterized by abnormal bone formation in the spinal ligaments. Although clinical studies suggested that diabetes mellitus (DM) was associated with OSL, no consistent conclusion was drawn about the relationship between DM and the onset of OSL. METHODS: Studies with data on DM and OSL were retrieved by searching PubMed, Embase, Web of Science, and Cochrane Library from inception to August 23, 2023. Pooled estimates of odds ratios (ORs) with 95% confidence interval (95% CI) were calculated using random-effects models. Statistical analyses were performed by R 4.2.2 software. RESULTS: A total of 17 studies with 70,945 participants were included. The quantitative findings demonstrated that a higher risk of DM was related to the onset of OSL (OR = 2.19, 95% CI: 1.27-3.79, p = 0.008). Subgroup analysis showed a higher rate of DM in OSL patients from Japan (OR = 3.29, 95% CI: 1.51-7.17, [Formula: see text] = 0.009) than from other regions. Moreover, patients with OSL had a higher rate of DM in age < = 60 group (OR = 3.46, 95% CI: 1.14-10.50, p = 0.035) than age > 60 group (OR = 2.26, 95% CI: 1.07-4.79, p = 0.036). CONCLUSION: DM is significantly associated with an increased risk of developing OSL, especially in Japanese and people under 60 years old. Further studies with more participants were warranted to confirm the findings and provide new insights into the prevention and treatment of OSL.

Predictive Modelling of Diabetes Risk in Population Groups Defined by Socioeconomic and Lifestyle Factors in Canada: A Cross-Sectional Study.

Objectives: This study modelled diabetes risk for population groups in Canada defined by socioeconomic and lifestyle characteristics and investigated inequities in diabetes risk using a validated population risk prediction algorithm. Methods: We defined population groups, informed by determinants of health frameworks. We applied the Diabetes Population Risk Tool (DPoRT) to 2017/2018 Canadian Community Health Survey data to predict 10-year diabetes risk and cases across population groups. We modelled a preventive intervention scenario to estimate reductions in diabetes for population groups and impacts on the inequity in diabetes risk across income and education. Results: The population group with at least one lifestyle and at least one socioeconomic/structural risk factor had the highest estimated 10-year diabetes risk and number of new cases. When an intervention with a 5% relative risk reduction was modelled for this population group, diabetes risk decreased by 0.5% (females) and 0.7% (males) and the inequity in diabetes risk across income and education levels was reduced. Conclusion: Preventative interventions that address socioeconomic and structural risk factors have potential to reduce inequities in diabetes risk and overall diabetes burden.

Predictors and clinical outcomes of slow flow phenomenon in diabetic patients with chronic coronary syndrome.

BACKGROUND: Coronary slow flow (CSF) is characterized by late distal coronary perfusion of coronary arteries at the time of angiography despite the vessels appearing normal. The importance of CSF is still debatable. Therefore, this study aimed to investigate CSF's predictors and clinical outcomes in diabetic patients with chronic coronary syndrome (CCS). PATIENT AND METHODS: This retrospective study included 250 diabetic patients diagnosed with chronic stable angina and referred for coronary angiography (CAG), showing normal coronaries with CSF (Group I) and 240 diabetic patients with normal coronaries and normal flow (Group II). The patients in both groups were followed up for one year to evaluate clinical outcomes. RESULTS: The incidence of major adverse cardiac events (MACE) was higher in Group I than in Group II, but the difference was not statistically significant except when the composite endpoints of STEMI, NSTEMI, and unstable angina were combined under the term ACS. The independent predictors of CSF, as detected by multivariate regression analysis, were body mass index (BMI) (OR = 0.694, 95% CI = 0.295-0.842, P = 0.010), blood glucose during catheterization (OR = 0.647, 95% CI = 0.298-0.874, P = 0.008), serum triglycerides (OR = 0.574, 95% CI = 0.289-0.746, P = 0.010), and the neutrophil/lymphocyte ratio (NLR) (OR = 0.618, 95% CI = 0.479-0.892, P = 0.001). CONCLUSION: Serum triglyceride levels, BMI, NLR, and high blood glucose levels at the time of catheterization were independent predictors of CSF in diabetic patients. MACE levels were higher in diabetic patients with CSF.

Triglyceride-glucose index as a predictor of cardiac adverse events in acute coronary syndrome patients undergoing percutaneous coronary intervention: role of diabetes.

BACKGROUND: Triglyceride-glucose index (TyG), a surrogate marker of insulin resistance (IR), could be a potential prognostic marker in patients with acute coronary syndromes (ACS). We evaluated the effect of the TyG index on major adverse cardiac and cerebrovascular events (MACCE) in patients with ACS undergoing percutaneous coronary intervention (PCI). METHODS: This registry-based cohort study was conducted at Tehran Heart Center from 2015 to 2021 and the median follow-up duration was 378 days. The primary outcome was MACCE and the secondary outcomes were MACCE components: all-cause mortality, myocardial infarction, stroke, target vessel revascularization, target lesion revascularization, and coronary artery bypass grafting. For comparison among TyG quartiles (Q), the log-rank test was used. Unadjusted and adjusted hazard ratios (HRs) and their 95% confidence intervals (CIs) were used to describe the association between TyG quartiles and MACCE. A subgroup of euglycemic patients was also evaluated. RESULTS: A total of 13,542 patients were included. Patients in the fourth TyG quartile (Q4) were younger, had higher mean BMI, and higher prevalence of hypertension, diabetes, and dyslipidemia. The adjusted Cox model showed that a 1-unit increment of the TyG index was associated with a significantly higher risk of MACCE (aHR 1.18, 95% CI 1.08 to 1.30, p < 0.001). Among TyG quartiles, there was a higher MACCE incidence in Q4 compared to Q1 (aHR 1.29, 95% CI 1.08 to 1.53, p = 0.005). In the euglycemic subgroup of the population, there was no significant association between MACCE incidence and a 1-unit increase in TyG or among TyG quartiles. CONCLUSION: Based on our findings, while higher TyG levels and quartiles were associated with higher rates of MACCE in ACS, there was no such effect in the euglycemic population. If confirmed in future studies, these results can be beneficial for clinicians to risk stratify these patients with an easy-to-use index and determine clinical plans based on their risk.

Low estimated glomerular filtration rate and proteinuria among adult diabetic patients in a tertiary hospital in Eastern Uganda - a cross-sectional study.

BACKGROUND: Chronic kidney disease (CKD) is one of the most common complications of diabetes mellitus (DM). Diabetes mellitus contributes to about 66% of CKD cases globally. CKD results in increased morbidity and mortality and advanced stages often require kidney replacement therapy that is unaffordable for the majority of the patients. Developing countries have scanty data regarding CKD burden in diabetic patients. OBJECTIVES: This study aimed at determining the prevalence of low estimated glomerular filtration rate (eGFR) and proteinuria and associated clinical and socio-demographic factors among adult diabetic patients attending the diabetic clinic of Mbale Regional Referral Hospital (MRRH). METHODS: A cross-sectional study was conducted at the adult diabetic clinic of MRRH in Eastern Uganda. A total of 374 adult diabetic patients were enrolled. A urine sample for urine albumin creatinine ratio (UACR) determination and a venous blood sample for measurement of serum creatinine were obtained from each participant. The eGFR was determined using the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation and CKD was staged according to the Kidney Disease Improving Global Outcomes (KDIGO) classification. RESULTS: A total of 318 (85%) participants had an eGFR of ≤ 60 mL/min/1.73m2, UACR of ≥ 30g/g, or both. Only 6.1% were aware. Age, duration of DM, hypertension, and dyslipidemia were associated with low eGFR and proteinuria. CONCLUSION: There is a high prevalence of low eGFR and proteinuria among DM patients, 85% of the participants had these markers of CKD and the majority of them were undiagnosed. Over half of the DM patients had an eGFR consistent with advanced CKD. Strengthening routine screening for CKD biomarkers and equipping DM clinics with more diagnostic resources is recommended.

Adaptive measures to deal with the next pandemic caused by climate change in at-risk groups.

BACKGROUND: The intensity of the perceived stress during the pandemic is a very basic variable for the risk analysis and proper disaster response. The present study was conducted with the aim of determining the relationship between perceived stress and social support during covid-19 in diabetic patients in order to design a suitable plan for a possible pandemic. METHODS: This cross-sectional study was conducted in 2021 on 212 diabetics in Hormozgan province / southern Iran. Data were collected online using Whatsapp using social support and perceived stress questionnaires specific to COVID-19. Data were analyzed by SPSS 22 software using Pearson correlation coefficient tests. RESULTS: The mean and standard deviation (SD) of the stress score was 18.46 ± 4.41. Mean ± SD of social support dimensions were emotional support 30.76 ± 5.96, information support 21.63 ± 4.56, instrumental support 32.48 ± 6.68, and evaluative support 23.53 ± 4.83. There was a significant correlation between emotional support (r =-0.377, P < 0.001) and instrumental support (r =-0.280, P < 0.001) with perceived stress. CONCLUSION: The inverse relationship between emotional and instrumental support and perceived stress in diabetic patients during the Covid-19 pandemic suggests that health promotion interventions focus on increasing these two forms of social support in order to reduce stress during disasters. Especially when there are warnings about the release of microbial agents from melting polar ice and the possibility of the next epidemic.

The effect of diabetes in the multifaceted relationship between education and cognitive function.

BACKGROUND: Education has been shown to be positively associated with cognitive performance. However, the pathways via lifestyle-related disease through which education is related to cognitive performance have not been sufficiently explored. Diabetes is an important lifestyle-related disease with increasing prevalence worldwide. Low education is associated with an increased risk of developing diabetes, while diabetes may also lead to a deterioration in cognitive performance. This study aims to explore if the associations between education and cognitive function is mediated by the diabetes status among older adults. METHODS: The data utilized in this study were derived from the first two waves of the Dutch Lifelines Cohort Study (2006-2015). The analyzed sample included 26,131 individuals aged 50 years or above at baseline. The baseline assessment included measurements of educational attainment (exposure) and the potential mediator diabetes. The outcome of cognitive function was assessed using age-standardized reaction times from the psychomotor function and attention tasks, as measured by the Cogstate Brief Battery. The Cogstate Brief Battery was only conducted at the follow-up assessment, not at the baseline assessment. Faster reaction times correspond to higher cognitive performance. The study employed linear and logistic regression models, in addition to a causal mediation approach which estimated the average causal mediation effect (ACME). RESULTS: Higher education was associated with a lower risk of diabetes (b= -0.1976, 95%CI= -0.3354; -0.0597) compared to low or middle education as well as with faster reaction times (b= -0.2023, 95%CI= -0.2246; -0.1798), implying better cognitive function. Diabetes was associated with slower reaction times (b = 0.0617, 95%CI = 0.0162; 0.1072). Most importantly, the mediation approach identified a significant indirect effect of education on cognitive function via the diabetes status (ACME= -0.00061, 95%CI= -0.00142; -0.00011). DISCUSSION: The findings emphasize the potentially importance of diabetes in explaining the role of education in promoting healthy cognitive function and mitigating the risk of cognitive decline. Early detection and treatment of diabetes may be particularly beneficial for individuals with low or middle levels of education in order to maintain good levels of cognitive function.

Gender-based differences in the association of self-reported sleep duration with cardiovascular disease and diabetes.

OBJECTIVE: Insufficient or prolonged sleep each day may contribute to the onset of cardiovascular disease and diabetes, and there may be some variability between genders; however, current research evidence is limited. We aimed to investigate the effects of gender on self-reported sleep duration and the prevalence of cardiovascular disease and diabetes. RESEARCH DESIGN AND METHODS: This study is a population-based, cross-sectional analysis. Data from a nationally representative sample of US adults obtained from the National Health and Nutrition Examination Survey (NHANES) (2005-2020), and 13,002 participants, including 6,774men and 6,228women, were obtained by excluding the missing values for each variable self-reported sleep duration data obtained by using a habitual baseline questionnaire. Logistic regression models investigated the associations between gender-specific self-reported sleep duration, CVDs, and diabetes events. RESULT: In all participants, respectively, compared with sleep 7-8 h/day, the multivariable-adjusted odds ratios significantly associated with < 7 h /day and > 8 h /day were (1.43[1.15, 1.78]) and (1.34[1.01, 1.76]) for CHF, (1.62[1.28, 2.06]) for Angina, (1.42[1.17, 1.71]) for heart attack, (1.38[1.13, 1.70]) and (1.54[1.20, 1.97]) for Stroke, (1.21[1.09, 1.35]) and (1.28[1.11, 1.48]) for diabetes. In men, CHF (1.67[1.21, 2.14]), Angina (1.66[1.18, 2.15]), Stroke (1.55[1.13,1.97]), and diabetes (1.15[1.00, 1.32]) were significantly associated with < 7 h /day, and stroke (1.73[1.16, 2.32]) and diabetes (1.32[1.06, 1.52]) were significantly associated with > 8 h /day. In women, angina(1.83[1.16, 2.50]), heart attack(1.63[1.11, 2.15]), and diabetes (1.32[1.11, 1.54]) were significantly associated with < 7 h /day, while diabetes (1.31[1.03, 1.59]) was significantly associated with > 8 h /day. CONCLUSION: Self-reported long and short sleep duration was independently associated with partial CVDs and diabetes risk. However, sleep duration and gender did not have multiplicative or additive interactions with the onset of diabetes and CVDs.

Evidence of a bi-directional relationship between heart failure and diabetes: a strategy for the detection of glucose abnormalities and diabetes prevention in patients with heart failure.

Prevalence of heart failure (HF) and diabetes are markedly increasing globally. In a population of HF patients, approximately 40% have diabetes which is associated with a more severe HF, poorer cardiovascular outcomes and higher hospitalization rates for HF than HF patients without diabetes. Similar trends were shown in HF patients with prediabetes. In addition, the association between HF and renal function decline was demonstrated in patients with or without diabetes. However, the exact prevalence of dysglycemia in HF patients requires further investigation aiming to clarify the most accurate test to detect dysglycemia in this population. The relationship between HF and diabetes is complex and probably bidirectional. In one way, patients with diabetes have a more than two-fold risk of developing incident HF with reduced or preserved ejection fraction than those without diabetes. In the other way, patients with HF, when compared with those without HF, show an increased risk for the onset of diabetes due to several mechanisms including insulin resistance (IR), which makes HF emerging as a precursor for diabetes development. This article provides epidemiological evidence of undetected dysglycemia (prediabetes or diabetes) in HF patients and reviews the pathophysiological mechanisms which favor the development of IR and the risks associated with these disorders in HF patients. This review also offers a discussion of various strategies for the prevention of diabetes in HF patients, based first on fasting plasma glucose and HbA1c measurement and if normal on an oral glucose tolerance test as diagnostic tools for prediabetes and unknown diabetes that should be performed more extensively in those patients. It discusses the implementation of diabetes prevention measures and well-structured management programs for HF patients who are generally overweight or obese, as well as current pharmacotherapeutic options for prediabetes, including sodium-glucose cotransporter 2 inhibitors which are among the pillars of HF treatment and which recently showed a benefit in the reduction of incident diabetes in HF patients. Thus, there is an urgent need of routine screening for dysglycemia in all HF patients, which should contribute to reduce the incidence of diabetes and to treat earlier diabetes when already present.

Glucose-lowering medicines use before and after entry into long-term care facilities.

AIM: To examine changes in the use of glucose-lowering medicine (GLM) 12 months before and 12 months after long-term care facility (LTCF) entry among people with diabetes. MATERIALS AND METHODS: A national retrospective cohort study was conducted using linked health and aged care data from the Registry of Senior Australians National Historical Cohort. Residents of LTCFs with diabetes aged 65 years or older from 2015 to 2019 were included. Prevalence of GLM use and the number of defined daily doses (DDDs) dispensed per 1000 resident-days were estimated quarterly (91-day) using Poisson regression models, or negative binomial regression when overdispersion was present. RESULTS: Among the 50 993 residents studied (median age 84 years), the prevalence of GLM use was 58.4% (95% confidence interval [CI] 58.0%-58.8%) in the 9-12 months pre-LTCF entry and 56.3% (95% CI 55.9%-56.8%) in the 9-12 months post-entry. The number of DDDs/1000 resident-days increased from 1015.2 (95% CI 1002.3-1028.1) to 1253.8 (95% CI 1168.4-1339.3) during the same period. GLM use in the 3 months pre-entry was 56.8% (95% CI 56.4%-57.2%) compared with 61.7% (95% CI 61.3%-62.1%) in the 3 months post-entry, with the increased use driven mainly by insulin. No marked changes in the number of GLMs dispensed or GLM type were observed at 9-12 months post-entry compared with 3 months pre-entry. Among 22 792 individuals dispensed a GLM in the 3 months prior to LTCF entry, 50.2% continued the same GLM at 9-12 months post-entry. CONCLUSIONS: GLM use peaked in the first 3 months following LTCF entry, driven mainly by insulin, hence, residents may benefit from close monitoring of diabetes treatment during this period.

Generating real-world evidence on diabetes technology using the CareLink Personal data management system.

AIM: To establish trust in real-world evidence (RWE) derived from CareLink Personal (CP), Medtronic's data management system for MiniMed system users, we show that this database and its analyses strictly adhere to the principles of RWE. METHODS: The methodology is applicable to all MiniMed iterations. We described every step from raw data to predefined outcomes. In addition, we showed CP's fitness-for-research by the below metrics (using last year's MiniMed 780G system data as a case study): representative population, relevant endpoints, appropriate granularity, high data completeness, high data representativity and consistency in results. RESULTS: The process from raw data to outcomes has been validated, and metrics/logics adhere to established definitions. Over 95% of users have a CP account; with 96% providing consent, this allows the use of >91% of the census population. There is no rationale for an over-representation of a specific phenotype among users not included. CP includes >50 endpoints, including 'International Consensus on Time in Range' based metrics. Data are recorded at 5-min intervals (maximum 288 per day), and on average there were 263 data points per person per day. Ninety-nine per cent of uploads were automated. For the last year, only 1 in 6 users had a data gap >1 day, and 1 in 50 had a gap >1 week. The time in range from in-silico studies was similar to that of real-world studies from different geographies and with ever growing populations. CONCLUSION: RWE from CP adheres to the principles of RWE and can serve as robust evidence on the performance and safety of MiniMed systems.