BMJ Open Diabetes Research & Care最新文献

Introduction: Severe hypoglycemia (SH) in older adults (OAs) with type 1 diabetes is associated with profound morbidity and mortality, yet its etiology can be complex and multifactorial. Enhanced tools to identify OAs who are at high risk for SH are needed. This study used machine learning to identify characteristics that distinguish those with and without recent SH, selecting from a range of demographic and clinical, behavioral and lifestyle, and neurocognitive characteristics, along with continuous glucose monitoring (CGM) measures.

Research design and methods: Data from a case-control study involving OAs recruited from the T1D Exchange Clinical Network were analyzed. The random forest machine learning algorithm was used to elucidate the characteristics associated with case versus control status and their relative importance. Models with successively rich characteristic sets were examined to systematically incorporate each domain of possible risk characteristics.

Results: Data from 191 OAs with type 1 diabetes (47.1% female, 92.1% non-Hispanic white) were analyzed. Across models, hypoglycemia unawareness was the top characteristic associated with SH history. For the model with the richest input data, the most important characteristics, in descending order, were hypoglycemia unawareness, hypoglycemia fear, coefficient of variation from CGM, % time blood glucose below 70 mg/dL, and trail making test B score.

Conclusions: Machine learning may augment risk stratification for OAs by identifying key characteristics associated with SH. Prospective studies are needed to identify the predictive performance of these risk characteristics.

Type 2 diabetes mellitus (T2DM) is characterized by persistent hyperglycemia which is further associated with hyperactivity of the hypothalamic-pituitary-adrenal (HPA) axis. Several studies have shown that HPA axis hyperactivity is heightened in the chronic hyperglycemic state with severe hyperglycemic events more likely to result in a depressive disorder. The HPA axis is also regulated by the immune system. Upon stress, under homeostatic conditions, the immune system is activated via the sympatho-adrenal-medullary axis resulting in an immune response which secretes proinflammatory cytokines. These cytokines aid in the activation of the HPA axis during stress. However, in T2DM, where there is persistent hyperglycemia, the immune system is dysregulated resulting in the elevated concentrations of these cytokines. The HPA axis, already activated by the hyperglycemia, is further activated by the cytokines which all contribute to a diagnosis of depression in patients with T2DM. However, the onset of T2DM is often preceded by pre-diabetes, a reversible state of moderate hyperglycemia and insulin resistance. Complications often seen in T2DM have been reported to begin in the pre-diabetic state. While the current management strategies have been shown to ameliorate the moderate hyperglycemic state and decrease the risk of developing T2DM, research is necessary for clinical studies to profile these direct effects of moderate hyperglycemia in pre-diabetes on the HPA axis and the indirect effects moderate hyperglycemia may have on the HPA axis by investigating the components of the immune system that play a role in regulating this pathway.

Introduction: To examine the role of telehealth in diabetes care and management during versus pre-COVID-19 pandemic.

Research design and methods: We included adults (≥18 years) with prevalent diabetes as of January 1, 2018, and continuously enrolled at Kaiser Permanente Georgia through December 31, 2021 (n=22,854). We defined pre (2018-2019) and during COVID-19 (2020-2021) periods. Logistic generalized estimating equations (GEEs) assessed the within-subject change in adherence to seven annual routine care processes (blood pressure (BP), hemoglobin A1C (HbA1c), cholesterol, creatinine, urine-albumin-creatinine ratio (UACR), eye and foot examinations) pre versus during COVID-19 among telehealth users (ie, more than one telehealth visit per year per period) and non-telehealth users. Linear GEE compared mean laboratory measurements pre versus during COVID-19 by telehealth use.

Results: The proportion of telehealth users increased from 38.7% (2018-2019) to 91.5% (2020-2021). During (vs pre) the pandemic, adherence to all care processes declined in telehealth (range: 1.6% for foot examinations to 12.4% for BP) and non-telehealth users (range: 1.9% for foot examinations to 40.7% for BP). In telehealth users, average HbA1c (mean difference: 0.4% (95% CI 0.2% to 0.6%), systolic BP (1.62 mm Hg (1.44 to 1.81)), and creatinine (0.03 mg/dL (0.02 to 0.04)), worsened during (vs pre) COVID-19, while low density lipoprotein (LDL) cholesterol improved (-9.08 mg/dL (-9.77 to -8.39)). For UACR, odds of elevated risk of kidney disease increased by 48% (OR 1.48 (1.36-1.62)). Patterns were similar in non-telehealth users.

Conclusions: Telehealth use increased during the pandemic and alleviated some of the observed declines in routine diabetes care and management.

Introduction: Type 2 diabetes is preventable in subjects with impaired glucose tolerance based on 2-hour plasma glucose (2hPG) during 75 g oral glucose tolerance test (OGTT). We incorporated routine biochemistry to improve the performance of a non-invasive diabetes risk score to identify individuals with abnormal glucose tolerance (AGT) defined by 2hPG≥7.8 mmol/L during OGTT.

Research design and methods: We used baseline data of 1938 individuals from the community-based "Better Health for Better Hong Kong - Hong Kong Family Diabetes Study (BHBHK-HKFDS) Cohort" recruited in 1998-2003. We incorporated routine biochemistry in a validated non-invasive diabetes risk score, and evaluated its performance using area under receiver operating characteristics (AUROC) with internal and external validation.

Results: The AUROC of the original non-invasive risk score to predict AGT was 0.698 (95% CI, 0.662 to 0.733). Following additional inclusion of fasting plasma glucose, serum potassium, creatinine, and urea, the AUROC increased to 0.778 (95% CI, 0.744 to 0.809, p<0.001). Net reclassification improved by 31.9% (p<0.001) overall, by 30.8% among people with AGT and 1.1% among people without AGT. The extended model showed good calibration (χ²=11.315, p=0.1845) and performance on external validation using an independent data set (AUROC=0.722, 95% CI, 0.680 to 0.764).

Conclusions: The extended risk score incorporating clinical and routine biochemistry can be integrated into an electronic health records system to select high-risk subjects for evaluation of AGT using OGTT for prevention of diabetes.

Introduction: Understanding how race may influence the association between A1c and glycemia can improve diabetes screening. We sought to determine whether, for a given A1c level, glucose levels during an oral glucose tolerance test (OGTT) differed by race.

Research design and methods: From data collected at 22 US clinical sites, we conducted a cross-sectional study of concurrently measured A1c and OGTT and observational longitudinal follow-up of the subset with high-risk pre-diabetes. Numerical integration methods were used to calculate area under the glycemic curve (AUC_glu) during OGTT and least squares regression model to estimate A1c for a given AUC_glu by race, controlling for potential confounders.

Results: 1016 black, 2658 white, and 193 Asian persons at risk of diabetes were included in cross-sectional analysis. Of these, 2154 with high-risk pre-diabetes were followed for 2.5 years. For a given A1c level, AUC_glu was lower in black versus white participants. After adjustment for potential confounders, A1c levels for a given AUC_glu quintile were 0.15-0.20 and 0.02-0.19 percentage points higher in black and Asian compared with white participants, respectively (p<0.05). In longitudinal analyses, black participants were more likely to be diagnosed with diabetes by A1c than white participants (28% vs 10%, respectively; p<0.01). Black and Asian participants were less likely to be diagnosed by fasting glucose than white participants (16% vs 15% vs 37%, respectively; p<0.05). Black participants with A1c levels in the lower-level quintiles had greater increase in A1c over time compared with white participants.

Conclusions: Use of additional testing beyond A1c to screen for diabetes may better stratify diabetes risk in the diverse US population.

Introduction: There is conflicting evidence whether lower extremity arterial calcification coincides with coronary arterial calcification (CAC). The aims of this study were to investigate the associations between (1) femoral and crural calcification with CAC, and (2) femoral and crural calcification pattern with CAC.

Research design and methods: This cross-sectional study included 405 individuals (74% men, 62.6±10.9 years) from the ARTEMIS cohort study at high risk of cardiovascular disease (CVD) who underwent a CT scan of the femoral, crural and coronary arteries. High CVD risk was defined as history/presence of cerebrovascular disease, coronary artery disease, abdominal aortic aneurysm, renal artery stenosis, peripheral artery disease or CVD risk factors: diabetes mellitus type 2, hypertension, hyperlipidemia. Calcification score within each arterial bed was expressed in Agatston units. Dominant calcification patterns (intimal, medial, absent/indistinguishable) were determined via a CT-guided histologically validated scoring algorithm. Multivariable-adjusted multinomial logistic regression analyses were used. Replication was performed in an independent population of individuals with diabetes mellitus type 2 (Early-HFpEF cohort study).

Results: Every 100-point increase in femoral and crural calcification score was associated with 1.23 (95% CI=1.09 to 1.37, p<0.001) and 1.28 (95% CI=1.11 to 1.47, p=0.001) times higher odds of having CAC within tertile 3 (high) versus tertile 1 (low), respectively. The association appeared stronger for crural versus femoral arteries. Moreover, the presence of femoral intimal (OR=10.81, 95% CI=4.23 to 27.62, p<0.001), femoral medial (OR=10.37, 95% CI=3.92 to 27.38, p<0.001) and crural intimal (OR=6.70, 95% CI=2.73 to 16.43, p<0.001) calcification patterns were associated with higher odds of having CAC within tertile 3 versus tertile 1, independently from concomitant calcification score. This association appeared stronger for intimal versus medial calcification patterns. The replication analysis yielded similar results.

Conclusions: Higher femoral and crural calcification scores were associated with higher CAC. Moreover, the presence of femoral intimal, femoral medial and crural intimal calcification patterns was associated with increased CAC. It appears that arterial calcification is a systemic process which occurs simultaneously in various arterial beds.

Introduction: Understanding the role of social determinants of health as predictors of mortality in adults with diabetes may help improve health outcomes in this high-risk population. Using population-based, nationally representative data, this study investigated the cumulative effect of unfavorable social determinants on all-cause mortality in adults with diabetes.

Research design and methods: We used data from the 2013-2018 National Health Interview Survey, linked to the National Death Index through 2019, for mortality ascertainment. A total of 47 individual social determinants of health were used to categorize participants in quartiles denoting increasing levels of social disadvantage. Poisson regression was used to report age-adjusted mortality rates across increasing social burden. Multivariable Cox proportional hazards models were used to assess the association between cumulative social disadvantage and all-cause mortality in adults with diabetes, adjusting for traditional risk factors.

Results: The final sample comprised 182 445 adults, of whom 20 079 had diabetes. In the diabetes population, mortality rate increased from 1052.7 per 100 000 person-years in the first quartile (Q1) to 2073.1 in the fourth quartile (Q4). In multivariable models, individuals in Q4 experienced up to twofold higher mortality risk relative to those in Q1. This effect was observed similarly across gender and racial/ethnic subgroups, although with a relatively stronger association for non-Hispanic white participants compared with non-Hispanic black and Hispanic subpopulations.

Conclusions: Cumulative social disadvantage in individuals with diabetes is associated with over twofold higher risk of mortality, independent of established risk factors. Our findings call for action to screen for unfavorable social determinants and design novel interventions to mitigate the risk of mortality in this high-risk population.

Introduction: Type 2 diabetes mellitus is a risk factor for severe COVID-19 infection and is associated with increased risk of complications. The present study aimed to investigate effectiveness and persistence of different COVID vaccines in persons with or without diabetes during the Delta wave in Hungary.

Research design and methods: Data sources were the national COVID-19 registry data from the National Public Health Center and the National Health Insurance Fund on the total Hungarian population. The adjusted incidence rate ratios and corresponding 95% CIs were derived from a mixed-effect negative binomial regression model.

Results: A population of 672 240 cases with type 2 diabetes and a control group of 2 974 102 non-diabetic persons free from chronic diseases participated. Unvaccinated elderly persons with diabetes had 2.68 (95% CI 2.47 to 2.91) times higher COVID-19-related mortality rate as the 'healthy' controls. Primary immunization effectively equalized the risk of COVID-19 mortality between the two groups. Vaccine effectiveness declined over time, but the booster restored the effectiveness against mortality to over 90%. The adjusted vaccine effectiveness of the primary Pfizer-BioNTech against infection in the 14-120 days of postvaccination period was 71.6 (95% CI 66.3 to 76.1)% in patients aged 65-100 years with type 2 diabetes and 64.52 (95% CI 59.2 to 69.2)% in the controls. Overall, the effectiveness tended to be higher in individuals with diabetes than in controls. The booster vaccines could restore vaccine effectiveness to over 80% concerning risk of infection (eg, patients with diabetes aged 65-100 years: 89.1 (88.1-89.9)% with Pfizer-on-Pfizer, controls 65-100 years old: 86.9 (85.8-88.0)% with Pfizer-on-Pfizer, or patients with diabetes aged 65-100 years: 88.3 (87.2-89.2)% with Pfizer-on-Sinopharm, controls 65-100 years old: 87.8 (86.8-88.7)% with Pfizer-on-Sinopharm).

Conclusions: Our data suggest that people with type 2 diabetes may have even higher health gain when getting vaccinated as compared with non-diabetic persons, eliminating the marked, COVID-19-related excess risk of this population. Boosters could restore protection.