<b>Residential Address Amplifies Health Disparities and Risk of Infection in Individuals with Diabetic Foot Ulcers</b>

Objective: To determine the association between social determinants of health (SDOH) and a diagnosis of diabetic foot ulcer (DFU) infection. Research Design and Methods: Targeted interrogation of electronic health record data using novel search engines to analyze individuals with a DFU infection during a five-year period (2013-2017). We extracted geolocated neighborhood data and SDOH characteristics from the National Neighborhood Data Archive and used univariate and multiple logistic regression to evaluate associations with outcomes in the diabetes population. Results: Among 4.3 million individuals overall and 144,564 persons with diabetes seen between 2013-2017, 8351 developed DFU, of which 2252 were complicated by a DFU infection. Sex interactions occurred as men who experienced a DFU infection more frequently identified as having non-married status than their female counterparts. Other SDOH occurred at higher rates in the DFU infection population and included greater neighborhood disadvantaged index score, increased poverty, increased rates of non-marriage, decreased access to physician/allied health professionals (all p<0.01). In multiple logistic regression, those persons who developed DFU infection came from neighborhoods with greater Hispanic and/or foreign-born concentrations (OR 1.11, p=0.015). Conclusion: We found significant differences in neighborhood characteristics driving a higher risk for DFU infection compared with persons with diabetes overall, including increased risk for individuals with Hispanic and/or foreign-born immigration status. These data strongly support the need to incorporate SDOH, particularly ethnic and immigration status into triage algorithms for DFU risk stratification to prevent severe diabetic foot complications and move beyond biologic-only determinants of health.</p

Body Mass Index and Mortality in the General Population and in Subjects with Chronic Disease in Korea: A Nationwide Cohort Study (2002-2010)

<div><p>Background</p><p>The association between body mass index (BMI) and mortality is not conclusive, especially in East Asian populations. Furthermore, the association has been neither supported by recent data, nor assessed after controlling for weight changes.</p><p>Methods</p><p>We evaluated the relationship between BMI and all-cause or cause-specific mortality, using prospective cohort data by the National Health Insurance Service in Korea, which consisted of more than one million subjects. A total of 153,484 Korean adults over 30 years of age without pre-existing cardiovascular disease or cancer at baseline were followed-up until 2010 (mean follow-up period = 7.91 ± 0.59 years). Study subjects repeatedly measured body weight 3.99 times, on average.</p><p>Results</p><p>During follow-up, 3,937 total deaths occurred; 557 deaths from cardiovascular disease, and 1,224 from cancer. In multiple-adjusted analyses, U-shaped associations were found between BMI and mortality from any cause, cardiovascular disease, and cancer after adjustment for age, sex, smoking status, alcohol consumption, physical activity, socioeconomic status, and weight change. Subjects with a BMI < 23 kg/m² and ≥ 30 kg/m² had higher risks of all-cause and cause-specific mortality compared with the reference group (BMI 23–24.9 kg/m²). The lowest risk of all-cause mortality was observed in subjects with a BMI of 25–26.4 kg/m² (adjusted hazard ratio [HR] 0.86; 95% CI 0.77 to 0.97). In subgroup analyses, including the elderly and those with chronic diseases (diabetes mellitus, hypertension, and chronic kidney disease), subjects with a BMI of 25–29.9 kg/m² (moderate obesity) had a lower risk of mortality compared with the reference. However, this association has been attenuated in younger individuals, in those with higher socioeconomic status, and those without chronic diseases.</p><p>Conclusion</p><p>Moderate obesity was associated more strongly with a lower risk of mortality than with normal, underweight, and overweight groups in the general population of South Korea. This obesity paradox was prominent in not only the elderly but also individuals with chronic disease.</p></div

Association between body mass index category and all-cause mortality.

<p>In the multivariable adjusted model, data was adjusted for age, sex, smoking status, alcohol intake, physical activity, socioeconomic status, and body weight change. In the analyses of stratified subgroups, the variable used in stratification was excluded.</p><p>Association between body mass index category and all-cause mortality.</p

Association between body mass index and all-cause mortality according to disease status.

<p>The association between body mass index (BMI) and mortality was presented separately by those presenting with and without prevalent diabetes mellitus (DM) (A), hypertension (HTN) (B), and chronic kidney disease (CKD) (C). All analyses were adjusted for age, sex, smoking status, alcohol intake, physical activity, socioeconomic status, and body weight change.</p

Baseline characteristics according to body mass index category.

<p>Data are presented by mean ± standard deviation (SD) or %.</p><p>Baseline characteristics according to body mass index category.</p

A Glycemia Risk Index (GRI) of Hypoglycemia and Hyperglycemia for Continuous Glucose Monitoring Validated by Clinician Ratings.

BackgroundA composite metric for the quality of glycemia from continuous glucose monitor (CGM) tracings could be useful for assisting with basic clinical interpretation of CGM data.MethodsWe assembled a data set of 14-day CGM tracings from 225 insulin-treated adults with diabetes. Using a balanced incomplete block design, 330 clinicians who were highly experienced with CGM analysis and interpretation ranked the CGM tracings from best to worst quality of glycemia. We used principal component analysis and multiple regressions to develop a model to predict the clinician ranking based on seven standard metrics in an Ambulatory Glucose Profile: very low-glucose and low-glucose hypoglycemia; very high-glucose and high-glucose hyperglycemia; time in range; mean glucose; and coefficient of variation.ResultsThe analysis showed that clinician rankings depend on two components, one related to hypoglycemia that gives more weight to very low-glucose than to low-glucose and the other related to hyperglycemia that likewise gives greater weight to very high-glucose than to high-glucose. These two components should be calculated and displayed separately, but they can also be combined into a single Glycemia Risk Index (GRI) that corresponds closely to the clinician rankings of the overall quality of glycemia (r = 0.95). The GRI can be displayed graphically on a GRI Grid with the hypoglycemia component on the horizontal axis and the hyperglycemia component on the vertical axis. Diagonal lines divide the graph into five zones (quintiles) corresponding to the best (0th to 20th percentile) to worst (81st to 100th percentile) overall quality of glycemia. The GRI Grid enables users to track sequential changes within an individual over time and compare groups of individuals.ConclusionThe GRI is a single-number summary of the quality of glycemia. Its hypoglycemia and hyperglycemia components provide actionable scores and a graphical display (the GRI Grid) that can be used by clinicians and researchers to determine the glycemic effects of prescribed and investigational treatments