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INTRODUCTION: Although poor glycemic control is associated with dementia, it is unknown if variability in glycemic control, even in those with optimal glycosylated hemoglobin A1c (HbA1c) levels, increases dementia risk. METHODS: Among 171,964 people with type 2 diabetes, we evaluated the hazard of dementia association with long‐term HbA1c variability using five operationalizations, including standard deviation (SD), adjusting for demographics and comorbidities. RESULTS: The mean baseline age was 61 years (48% women). Greater HbA1c SD was associated with greater dementia hazard (adjusted hazard ratio = 1.15 [95% confidence interval: 1.12, 1.17]). In stratified analyses, higher HbA1c SD quintiles were associated with greater dementia hazard among those with a mean HbA1c < 6% (P = 0.0004) or 6% to 8% (P < 0.0001) but not among those with mean HbA1c ≥ 8% (P = 0.42). DISCUSSION: Greater HbA1c variability is associated with greater dementia risk, even among those with HbA1c concentrations at ideal clinical targets. These findings add to the importance and clinical impact of recommendations to minimize glycemic variability. Highlights: We observed a cohort of 171,964 people with type 2 diabetes (mean age 61 years).This cohort was based in Northern California between 1996 and 2018.We examined the association between glycosylated hemoglobin A1c (HbA1c) variability and dementia risk.Greater HbA1c variability was associated with greater dementia hazard.This was most evident among those with normal–low mean HbA1c concentrations. [ABSTRACT FROM AUTHOR]

Prior studies suggest that only ∼30% of patients with type 1 diabetes use continuous glucose monitoring (CGM), but most studies to date focused on children and young adults seen by endocrinologists or in academic centers. This study examined national trends in CGM utilization among commercially insured children and adults with type 1 diabetes. Overall, CGM utilization was 20.12% in 2010–2013 and 49.78% in 2016–2019, reflecting a 2.5-fold increase in utilization within a period of <10 years. Identifying populations with low CGM use is a necessary first step in developing targeted interventions to increase CGM uptake. [ABSTRACT FROM AUTHOR]

Cerebrovascular disease encompasses a vast array of conditions. The imaging recommendations for stroke-related conditions involving noninflammatory steno-occlusive arterial and venous cerebrovascular disease including carotid stenosis, carotid dissection, intracranial large vessel occlusion, and cerebral venous sinus thrombosis are encompassed by this document. Additional imaging recommendations regarding complications of these conditions including intraparenchymal hemorrhage and completed ischemic strokes are also discussed. The American College of Radiology Appropriateness Criteria are evidence-based guidelines for specific clinical conditions that are reviewed annually by a multidisciplinary expert panel. The guideline development and revision process support the systematic analysis of the medical literature from peer reviewed journals. Established methodology principles such as Grading of Recommendations Assessment, Development, and Evaluation or GRADE are adapted to evaluate the evidence. The RAND/UCLA Appropriateness Method User Manual provides the methodology to determine the appropriateness of imaging and treatment procedures for specific clinical scenarios. In those instances where peer reviewed literature is lacking or equivocal, experts may be the primary evidentiary source available to formulate a recommendation. [ABSTRACT FROM AUTHOR]

Preventable hospitalizations are common and costly events that burden patients and our health care system. While research suggests that these events are strongly linked to ambulatory care access, emerging evidence suggests they may also be sensitive to a patient's social, environmental, and economic conditions. This study examines the association between variations in social vulnerability and preventable hospitalization rates. We conducted a cross-sectional analysis of county-level preventable hospitalization rates for 33 states linked with data from the 2020 Social Vulnerability Index (SVI). Preventable hospitalizations were 40% higher in the most vulnerable counties compared with the least vulnerable. Adjusted regression results confirm the strong relationship between social vulnerability and preventable hospitalizations. Our results suggest wide variation in community-level preventable hospitalization rates, with robust evidence that variation is strongly related to a community's social vulnerability. The human toll, societal cost, and preventability of these hospitalizations make understanding and mitigating these inequities a national priority. [ABSTRACT FROM AUTHOR]

The authors reflect on the determinants of health and well-being among community-dwelling older people living in rural areas in the U.S. and the use of the social determinants of health (SDH) framework to determine said determinants. Also cited are the vulnerabilities faced by the said aging population like poor health status and low socioeconomic status, and the key role played by rural caregivers (CG) in caring for the said people.

In the United States, diabetes is the seventh leading cause of death and continues to rise in prevalence, with type 2 diabetes accounting for 90–95% of all cases. Rates of diabetes in Kentucky, and, in particular, the Appalachian region, are among the highest in the nation and are increasing faster than the national average. Despite this disproportionate burden, barriers to clinical appointment attendance have not been fully explored in this population. This article examines the association among perceived barriers to clinical attendance, glycemic control, and diabetes self-care as part of an ongoing study. We used a 25-item checklist developed using the Chronic Care Model to assess participants' barriers to clinic attendance. Glycemic control was assessed via A1C measurement. Diabetes self-care was assessed using the Summary of Diabetes Self-Care Activities measure. At the time of analysis, 123 of the 356 participants (34.6%) did not report any barriers to clinic attendance. For the remainder, the major reported barriers included forgetting appointments, inability to afford medicines or other treatment, and placing faith above medical care. The average A1C was 7.7%, and the average diabetes self-care summary score was 17.1 out of 35 points (with higher values indicating better self-care). Missing clinic appointments is associated with lower health outcomes, especially in vulnerable populations. This study can help educate clinic staff on perceived barriers to type 2 diabetes management among people with diabetes in Appalachia. [ABSTRACT FROM AUTHOR]

Background: Type 2 diabetes is associated with a two‐fold increase in dementia risk. Although glycemic variability is becoming increasingly recognised as contributing to end‐organ damage, less is known about its contribution to dementia risk. We aimed to study the association between HbA1c variability and dementia risk in a large, diverse sample of people in Northern California. Method: Over 209,000 members of a fully integrated health care delivery system with type 2 diabetes aged ≥ 50 years were followed from 1996 to 2018 to obtain repeated measures of HbA1c, dementia diagnoses, and comorbidities. Variability was measured using standard deviation (SD), coefficient of variation, and average real variability. Cox proportional hazards models estimated the association between HbA1c variability in the first 3 years of cohort entry and incident dementia at least 2 years after the end of the exposure period adjusting for age (as timescale) and baseline health conditions. Result: There were a total of 209,703 people in the cohort (mean baseline age 61.0 years (SD = 9.2), 47.5% women) (Table1). Approximately 49.6% of the sample were non‐Latino White. The mean number of HbA1c values available for each participant was 5.8 (SD = 2.3). The mean HbA1c for each participant in the first 3 years was 7.4% (SD = 1.33). Greater HbA1c variability was associated with greater hazard of dementia when measured using SD (aHR = 1.12 [95%CI: 1.10, 1.14]), coefficient of variation (aHR = 2.17 [95%CI: 1.85, 2.55]) (Figure1), or average real variability (aHR = 1.06 [95%CI: 1.06, 1.07]). When stratified by mean HbA1c, we found that ‐ relative to the lowest quintile of variation ‐ increasing variability of SD of HbA1c was associated with greater hazard of dementia in HbA1c categories of <6% (p = 0.0004) and 6 ‐8% (p<0.0001) but not in those with a mean HbA1c ≥8% (p = 0.42). Conclusion: Greater HbA1c variability is associated with greater dementia hazard in those with good glycemic control. The absence of association in less well controlled (HbA1c ≥8%) patients may stem from greater variability including lower risk periods of optimal glycemic control. These results support current clinical recommendations to minimize glycemic variability and extend known benefits to include lowering of dementia risk. [ABSTRACT FROM AUTHOR]

This retrospective study examined changes in medication orders as a risk factor for future acute hypoglycemic events. The investigators identified factors associated with acute hypoglycemic events resulting in emergency department visits or inpatient admissions. Non-Hispanic Black race, chronic kidney disease, insulin at baseline, and nonprivate insurance were associated with higher risk of an acute hypoglycemic event, whereas age, sex, and A1C were not. After adjustment for other risk factors, changes in insulin orders after A1C measurement were associated with a 1.5 times higher risk of an acute hypoglycemia (adjusted hazard ratio 1.48, 95% CI 1.08–2.03). These results further understanding of risk factors and clinical processes relevant to predicting and preventing acute hypoglycemia. [ABSTRACT FROM AUTHOR]

Objective: In children with type 1 diabetes (T1D), severe hypoglycemia (SH) is associated with poorer cognition, but the association of SH with cognitive function in late life is unknown. Given the increasing life expectancy in people with T1D, understanding the role of SH in brain health is crucial.Research Design and Methods: We examined the association between SH and cognitive function in 718 older adults with T1D from the Study of Longevity in Diabetes (SOLID). Subjects self-reported recent SH (previous 12 months) and lifetime history of SH resulting in inpatient/emergency department utilization. Global and domain-specific cognition (language, executive function, episodic memory, and simple attention) were assessed. The associations of SH with cognitive function and impaired cognition were evaluated via linear and logistic regression models, respectively.Results: Thirty-two percent of participants (mean age 67.2 years) reported recent SH and 50% reported lifetime SH. Compared with those with no SH, subjects with a recent SH history had significantly lower global cognition scores. Domain-specific analyses revealed significantly lower scores on language, executive function, and episodic memory with recent SH exposure and significantly lower executive function with lifetime SH exposure. Recent SH was associated with impaired global cognition (odds ratio [OR] 3.22, 95% CI 1.30, 7.94) and cognitive impairment on the language domain (OR 3.15, 95% CI 1.19, 8.29).Conclusions: Among older adults with T1D, recent SH and lifetime SH were associated with worse cognition. Recent SH was associated with impaired global cognition. These findings suggest a deleterious role of SH on the brain health of older patients with T1D and highlight the importance of SH prevention. [ABSTRACT FROM AUTHOR]

Objective: Individuals with type 1 diabetes have experienced an increase in life expectancy, yet it is unknown what level of glycemic control is ideal for maintaining late-life brain health. We investigated the association of long-term glycemic control with dementia in older individuals with type 1 diabetes.Research Design and Methods: We followed 3,433 members of a health care system with type 1 diabetes, aged ≥50 years, from 1996 to 2015. Repeated measurements of hemoglobin A1c (HbA1c), dementia diagnoses, and comorbidities were ascertained from health records. Cox proportional hazards models were fit to evaluate the association of time-varying glycemic exposure with dementia, with adjustment for age, sex, race/ethnicity, baseline health conditions, and frequency of HbA1c measurement.Results: Over a mean follow-up of 6.3 years, 155 individuals (4.5%) were diagnosed with dementia. Patients with ≥50% of HbA1c measurements at 8-8.9% (64-74 mmol/mol) and ≥9% (≥75 mmol/mol) had 65% and 79% higher risk of dementia, respectively, compared with those with <50% of measurements exposed (HbA1c 8-8.9% adjusted hazard ratio [aHR] 1.65 [95% CI 1.06, 2.57] and HbA1c ≥9% aHR 1.79 [95% CI 1.11, 2.90]). By contrast, patients with ≥50% of HbA1c measurements at 6-6.9% (42-52 mmol/mol) and 7-7.9% (53-63 mmol/mol) had a 45% lower risk of dementia (HbA1c 6-6.9% aHR 0.55 [95% CI 0.34, 0.88] and HbA1c 7-7.9% aHR 0.55 [95% CI 0.37, 0.82]).Conclusions: Among older patients with type 1 diabetes, those with majority exposure to HbA1c 8-8.9% and ≥9% had increased dementia risk, while those with majority exposure to HbA1c 6-6.9% and 7-7.9% had reduced risk. Currently recommended glycemic targets for older patients with type 1 diabetes are consistent with healthy brain aging. [ABSTRACT FROM AUTHOR]

Statistical models to predict incident diabetes are often based on limited variables. Here we pursued two main goals: 1) investigate the relative performance of a machine learning method such as Random Forests (RF) for detecting incident diabetes in a high-dimensional setting defined by a large set of observational data, and 2) uncover potential predictors of diabetes. The Jackson Heart Study collected data at baseline and in two follow-up visits from 5,301 African Americans. We excluded those with baseline diabetes and no follow-up, leaving 3,633 individuals for analyses. Over a mean 8-year follow-up, 584 participants developed diabetes. The full RF model evaluated 93 variables including demographic, anthropometric, blood biomarker, medical history, and echocardiogram data. We also used RF metrics of variable importance to rank variables according to their contribution to diabetes prediction. We implemented other models based on logistic regression and RF where features were preselected. The RF full model performance was similar (AUC = 0.82) to those more parsimonious models. The top-ranked variables according to RF included hemoglobin A1C, fasting plasma glucose, waist circumference, adiponectin, c-reactive protein, triglycerides, leptin, left ventricular mass, high-density lipoprotein cholesterol, and aldosterone. This work shows the potential of RF for incident diabetes prediction while dealing with high-dimensional data. [ABSTRACT FROM AUTHOR]

Objective: In 2010, the American Diabetes Association (ADA) added hemoglobin A1c (A1C) to the guidelines for diagnosing type 2 diabetes. However, existing models for predicting diabetes risk were developed prior to the widespread adoption of A1C. Thus, it remains unknown how well existing diabetes risk prediction models predict incident diabetes defined according to the ADA 2010 guidelines. Accordingly, we examined the performance of an existing diabetes prediction model applied to a cohort of African American (AA) and white adults from the Coronary Artery Risk Development Study in Young Adults (CARDIA).Research Design and Methods: We evaluated the performance of the Atherosclerosis Risk in Communities (ARIC) diabetes risk prediction model among 2,456 participants in CARDIA free of diabetes at the 2005-2006 exam and followed for 5 years. We evaluated model discrimination, calibration, and integrated discrimination improvement with incident diabetes defined by ADA 2010 guidelines before and after adding baseline A1C to the prediction model.Results: In the overall cohort, re-estimating the ARIC model in the CARDIA cohort resulted in good discrimination for the prediction of 5-year diabetes risk (area under the curve [AUC] 0.841). Adding baseline A1C as a predictor improved discrimination (AUC 0.841 vs. 0.863, P = 0.03). In race-stratified analyses, model discrimination was significantly higher in whites than AA (AUC AA 0.816 vs. whites 0.902; P = 0.008).Conclusions: Addition of A1C to the ARIC diabetes risk prediction model improved performance overall and in racial subgroups. However, for all models examined, discrimination was better in whites than AA. Additional studies are needed to further improve diabetes risk prediction among AA. [ABSTRACT FROM AUTHOR]

Background: Depression is a major risk factor for cognitive impairment among people with type 2 diabetes (T2D) and the general population. Depression is 3 times more prevalent among people with type 1 diabetes (T1D) than in the general population and interferes with self‐management. Understanding the association between depression and cognitive function in older adults with T1D is crucial. Method: Using baseline data from the Study of Longevity in Diabetes (SOLID), we examined the association between depressive symptoms and cognition in older adults with T1D (n=799), T2D (n=240), and without diabetes (n=256 matched on age, sex, race/ethnicity, and education). Based on recommended guidelines, a score of >5 on the 15‐item Geriatric Depression Scale indicated presence of depressive symptoms. Cognitive function was evaluated at baseline. Factor analyses identified the following cognitive domains: executive function (Trail Making A and B, Digit Symbol Substitution, and Stroop Interference Test), language (List sorting, MINT, Phonemic and Category fluency), simple attention (Diamond and TMX cancellation), verbal episodic memory (Word List Learning), and visual episodic memory (Benson). All cognitive scores were standardized (mean=0; SD=1). Linear regression models evaluated the association between depressive symptomatology and cognitive function adjusting for age at interview, sex, race/ethnicity, and education. Result: Subjects (mean age of 67.9 years and 50.9% were female) with diabetes had a greater prevalence of depressive symptoms than those without diabetes (T1D: 9.8%; T2D: 9.2%; no diabetes: 2.7%; chi‐square p‐value=0.002). In T1D, depression was associated with poorer performance on the language, executive function, and verbal episodic memory domains (Table 1). In T2D, depression was associated with poorer performance on the executive function domain. There was no observed association between depression and cognitive function among those without diabetes. Conclusion: Among older individuals with T1D, depressive symptoms were associated with lower scores on a range of cognitive domains. By contrast, depressive symptoms were only associated with executive function in T2D and were not associated with any cognitive domains in those without diabetes. Our findings underscore the importance of mental health as a key component of healthy aging in older adults with T1D and T2D. [ABSTRACT FROM AUTHOR]

Background: Diabetic ketoacidosis (DKA) is a life-threatening complication of diabetes. In the U.S., DKA hospitalizations are increasing. We estimated DKA hospitalization rates in Kentucky across key subgroups. Methods: Using the statewide Kentucky Inpatient Claims Files, we examined rates of DKA hospitalization in Kentucky for adults aged ≥18 in 2008-2018. In each year, the denominator was all diabetes-related hospitalizations (any position; ICD-9 250; ICD-10 E10, E11, E13). The numerator was all DKA-related hospitalizations (any position; ICD-9 250.1; ICD-10 E10.1, E11.1, E13.1). Results: In 2008-2018, rates of DKA hospitalizations in Kentucky increased by 100% (from 2.2% in 2008 to 4.4% in 2018; Fig A). In certain subgroups, rates were markedly higher. In the subset of Medicaid-insured patients DKA rates surpassed 10% of all diabetes-related hospitalizations (Fig B). Compared to patients covered by commercial insurance or Medicare, Medicaid-insured patients show dramatic increase following Medicaid expansion (2014). In non-Hispanic (NH) blacks, rates reached 6% by 2018 (Fig C). Rates for urban and rural populations were mostly parallel but diverged slightly following Medicaid expansion (Fig D). Conclusions: Consistent with previous studies, DKA rates were higher in NH black, Medicaid-insured, and urban patients. Rates increased in all subgroups. The trends for Medicaid enrollees may show the effect of the 2014 Medicaid expansion. Disclosure: M.E. Lacy: None. J. Benitez: None. J. Fowlkes: None. M. Sohn: None. [ABSTRACT FROM AUTHOR]

Introduction: Existing models to predict incident diabetes mellitus (DM) perform better in Whites than African Americans. In models that incorporate hemoglobin A1c (A1C) as a predictor of DM, the difference in model performance by race is more pronounced. In a recent study, we found that A1C was systematically underestimating glycemia in African Americans with sickle cell trait (SCT). Hypothesis: Given the poorer performance of DM prediction models in African Americans than Whites and the impact of SCT on the A1C-glycemia association, we hypothesized that incorporating sickle cell trait into DM prediction models would improve the ability of the model to predict future risk of DM. Methods: We pooled data collected from 2000-2012 on 3,122 African Americans (8.6% with SCT) from the Jackson Heart Study (JHS; n=2,065; mean age=54.71 years) and CARDIA (n=1,057; mean age=44.53). Over 5 years of follow-up in CARDIA and 10 years of follow-up in JHS, 85 CARDIA participants (8.1%) and 342 JHS participants (16.6%) developed DM. Using generalized estimating equations to account for correlation of repeated measures, we compared the discriminative ability and net reclassification improvement (NRI) resulting from the addition of SCT for a series of prediction models. Results: Overall, the addition of SCT to prediction models did not result in significant improvement in the discriminative ability. However, by the NRI index, the addition of SCT to measures of glycemia and to a fuller risk prediction model did improve prediction of DM. In the full model, adding SCT*A1C as a predictor resulted in 2% of events being reclassified as higher risk and 45% of non-events being reclassified as lower risk. Conclusion: Our results suggest that incorporating SCT into DM prediction for African Americans may result in modest improvement in model performance. [ABSTRACT FROM AUTHOR]

Abstract: Long interspersed nuclear elements (LINEs or L1 elements) are targeted for epigenetic silencing during early embryonic development and remain inactive in most cells and tissues. Here we show that E2F–Rb family complexes participate in L1 elements epigenetic regulation via nucleosomal histone modifications and recruitment of histone deacetylases (HDACs) HDAC1 and HDAC2. Our experiments demonstrated that (i) Rb and E2F interact with human and mouse L1 elements, (ii) L1 elements are deficient in both heterochromatin-associated histone marks H3 tri methyl K9 and H4 tri methyl K20 in Rb family triple knock out (Rb, p107, and p130) fibroblasts (TKO), (iii) L1 promoter exhibits increased histone H3 acetylation in the absence of HDAC1 and HDAC2 recruitment, (iv) L1 expression in TKO fibroblasts is upregulated compared to wild type counterparts, (v) L1 expression increases in the presence of the HDAC inhibitor TSA. On the basis of these findings we propose a model in which L1 sequences throughout the genome serve as centers for heterochromatin formation in an Rb family-dependent manner. As such, Rb proteins and L1 elements may play key roles in heterochromatin formation beyond pericentromeric chromosomal regions. These findings describe a novel mechanism of L1 reactivation in mammalian cells mediated by failure of corepressor protein recruitment by Rb, loss of histone epigenetic marks, heterochromatin formation, and increased histone H3 acetylation. [Copyright &y& Elsevier]

Introduction: Although poor glycemic control is associated with dementia, it is unknown if variability in glycemic control, even in those with optimal glycosylated hemoglobin A 1c (HbA 1c ) levels, increases dementia risk., Methods: Among 171,964 people with type 2 diabetes, we evaluated the hazard of dementia association with long-term HbA 1c variability using five operationalizations, including standard deviation (SD), adjusting for demographics and comorbidities., Results: The mean baseline age was 61 years (48% women). Greater HbA 1c SD was associated with greater dementia hazard (adjusted hazard ratio = 1.15 [95% confidence interval: 1.12, 1.17]). In stratified analyses, higher HbA 1c SD quintiles were associated with greater dementia hazard among those with a mean HbA 1c  < 6% (P = 0.0004) or 6% to 8% (P < 0.0001) but not among those with mean HbA 1c ≥ 8% (P = 0.42)., Discussion: Greater HbA 1c variability is associated with greater dementia risk, even among those with HbA 1c concentrations at ideal clinical targets. These findings add to the importance and clinical impact of recommendations to minimize glycemic variability., Highlights: We observed a cohort of 171,964 people with type 2 diabetes (mean age 61 years). This cohort was based in Northern California between 1996 and 2018. We examined the association between glycosylated hemoglobin A 1c (HbA 1c ) variability and dementia risk. Greater HbA 1c variability was associated with greater dementia hazard. This was most evident among those with normal-low mean HbA 1c concentrations., (© 2024 The Author(s). Alzheimer's & Dementia published by Wiley Periodicals LLC on behalf of Alzheimer's Association.)