Search

A healthy diet can help attain hemoglobin HbA1c (A1C) , blood pressure (BP) , and cholesterol goals (ABC goals) in people with diabetes. We examined the association of diet quality with achievement of ABC goals (defined as A1C Disclosure K.R. Siegel: None. K.M. Bullard: None. D.B. Rolka: None.

Lifestyle modification –healthy diet and physical activity– can help prevent or delay type 2 diabetes in adults with prediabetes (preDM) . However, 84% of US adults with preDM are unaware they have it. Whether preDM status awareness is associated with leisure-time physical activity (LTPA) levels and dietary intake is unknown. Using data from US adults without DM ≥18 y from the National Health and Nutrition Examination Surveys 2013-2018, we classified preDM as ‘aware’ (self-report of preDM and 5.7%≤A1c0.05) . Mean LTPA levels did not significantly vary across the 3 groups (p=0.22) . Overall, preDM awareness is associated with lower added sugars, red meat, and total overall calories intake, suggesting that efforts to increase awareness may lead to healthier eating among those with preDM. Disclosure K.R.Siegel: None. M.E.Pavkov: None. S.R.Benoit: None. G.Imperatore: None. K.M.Bullard: None.

The ADA recommends that people with diabetes (PWD) consume a high-quality diet to achieve diabetes treatment goals, however, food insecurity may prevent consumption of a high-quality diet. Cross-sectional, national data from 2,351 participants aged ≥20 years with self-reported diabetes that completed the National Health and Nutrition Examination Surveys between 2013-2018 were analyzed. Diet quality was assessed using quartiles of the 2015 Healthy Eating Index. Adjusted odds ratios (aOR, 95% CI) were calculated from logistic regression models to determine the association between individual food insecurity/diet quality and A1c, blood pressure (BP) , and cholesterol control (ABCs) while controlling for sociodemographics, health care utilization, smoking, diabetes, BP, and cholesterol medication use, and BMI. Overall, 17.6% of PWD were food insecure/low diet quality; 14.2% were food insecure/high diet quality; 33.1% were food secure/low diet quality; and 35.2% were food secure/high diet quality. PWD who were food insecure/low diet quality were younger, non-Hispanic black race or Hispanic ethnicity, and uninsured vs. those who were food secure/high diet quality (all p Disclosure S.Casagrande: None. K.M.Bullard: None. K.R.Siegel: None. J.M.Lawrence: None. Funding National Institutes of Diabetes and Digestive and Kidney Diseases (GS-10F-0381L)

In 2021, the US Preventive Services Task Force (USPSTF) expanded the recommended age range for prediabetes and diabetes screening among those with overweight/obesity to 35-70 years. Using 2017 to pre-pandemic March 2020 National Health and Nutrition Examination Survey data, we applied these screening criteria to assess their performance in a nationally representative sample. We included 3243 non-pregnant adults aged ≥18 years without diagnosed diabetes. Screening eligibility was determined based on participants' age and measured body mass index (BMI) [≥23kg/m2 for non-Hispanic (NH) Asian adults and ≥25kg/m2 for other ethnic groups]. Dysglycemia was defined based on A1c ≥5.7% or fasting plasma glucose ≥100mg/dL. We compared participant characteristics by USPSTF eligibility. We also assessed performance [sensitivity, specificity, positive predictive value (PPV) , and negative predictive value (NPV) ] of the screening criteria for detecting dysglycemia overall and by ethnicity. During 2017-2020, 44.4% of US adults aged 35-70 years were eligible for screening, representing 95.9 million US adults. Relative to adults who were ineligible, those who were eligible had a higher burden of diabetes risk factors, as well as greater proportions of some racial ethnic groups, higher proportion of those with less than a high school education, and more likely to be living in poverty. The performance characteristics of the USPSTF screening criteria were: sensitivity 59.4% (95% CI: 56.4-62.4) ; specificity 68.6% (65.1-71.9) ; PPV 62.4% (59.2-65.6) ; and NPV 65.8% (61.9-69.5) . The sensitivity by ethnicity was as follows: NH White adults 59.1% (55.4-62.7) ; NH Black adults 63.6% (57.8-68.9) ; Hispanic adults 62.0% (55.0-68.5) ; and NH Asian adults 57.3% (46.6-67.3) . Screening based on the 2021 USPSTF criteria will detect approximately 60% of US adults with dysglycemia, which was similar across ethnic groups. Disclosure M.J.O'brien: None. K.M.Bullard: None. Y.Zhang: n/a. S.Bailey: Consultant; Lundbeck, Luto, Pfizer Inc., Sanofi, Research Support; Eli Lilly and Company, Gordon and Betty Moore Foundation, Lundbeck, Merck & Co., Inc., Pfizer Inc., Retirement Research Foundation for Aging. S.Khan: None. R.T.Ackermann: Consultant; UnitedHealth Group. M.K.Ali: Advisory Panel; Bayer AG, Research Support; Merck & Co., Inc. S.R.Benoit: None. G.Imperatore: None. C.S.Holliday: None.

To reduce the risk of gum disease, the American Diabetes Association recommends that people with diabetes brush twice and floss once daily and see the dentist twice a year. But only about two-thirds of all US adults seek dental care within a year. Regular dental checkups are more important for those with diabetes. Thus, we studied the current oral health status (visiting a dentist or a dental clinic in the past year and the number with permanent tooth loss) in US adults aged ≥ 18 years with and without diagnosed diabetes using data from the 2020 Behavioral Risk Factor Surveillance System. Both diabetes and oral health-related information were self-reported. We computed overall and state-specific age-adjusted estimates by diabetes status. In 2020, 64.4% (95% CI 64.0%-64.8%) of US adults had a dental visit within the past year. Annual dental checkups were more common among women, older people, non-Hispanic White persons, and those having higher education, better income, and health insurance. The proportion of US adults with diabetes seeing a dentist in the past year was significantly lower compared to those without diabetes (56.7% [95% CI 54.8%-58.5%] vs. 65.4% [95% CI 65.0%-65.8%]) . Both men (56.3% [95% CI 53.6%-58.9%] vs. 62.4% [95% CI 61.9%-63.0%]) and women (57.1% [95% CI 54.6%-59.6%] vs. 68.2% [95% CI 67.7%-68.8%]) with diabetes were also significantly less likely to get dental care than their counterparts. The frequency of annual dental visits among adults with diabetes also varied by state; Texas had the lowest percentage (39.8% [95% CI 32.5%-47.6%]) while Alaska had the highest (77.1% [95% CI 68.8%-83.7%]) . Adults with diabetes (34.1% [95% CI 32.4%-35.9%]) were also more likely to have a history of one or more permanent teeth removed due to tooth decay or gum disease than those without diabetes (26.5% [95% CI 26.1%-26.8%]) . Without having regular dental checkup and adequate oral hygiene, adults with diabetes are at increased risk for gum disease. These findings highlight a gap in preventive oral care among adults with diabetes. Disclosure P. Cho: None. I.A. Hora: None. K.M. Bullard: None.

To identify prediabetes/diabetes, the US Preventive Services Task Force (USPSTF) has recommended glucose testing in 40-70 year olds (2015) and now 35-70 year olds (2021) with overweight/obesity. Since 2002, the American Diabetes Association (ADA) has recommended prediabetes/diabetes screening based on a larger set of diabetes risk factors. We analyzed cross-sectional National Health and Nutrition Examination Survey data from 6,0non-pregnant US adults aged ≥18 years without diagnosed diabetes. For USPSTF 2015 and 2021, and ADA guidelines, we described which adults were eligible and ineligible for glucose testing, and proportions reporting receiving testing. We assessed sensitivity and specificity in identifying prediabetes (fasting plasma glucose [FPG] 100-125mg/dL or A1c 5.7-6.4% or both) or undiagnosed diabetes (FPG≥126mg/dL or A1c≥6.5% or both) . During 2013-2018, an estimated 76.7 million, 90.4 million, and 157.7 million US adults met eligibility for glucose testing by the USPSTF 2015, 2021, and ADA guidelines, respectively. Eligibility and proportions receiving testing varied by guideline (Figure 1) . Across definitions of prediabetes/diabetes, ADA recommendations were most sensitive (range: 91.0-99.1%) and least specific (24.1-35.3%) ; 2015 (sensitivity: 51.9-59.4%; specificity: 63.2-74.5%) and 2021 USPSTF (sensitivity: 57.5-66.6%; specificity: 56.6-67.1%) guideline sensitivity and specificity were comparable. Disclosure M.K.Ali: Advisory Panel; Bayer AG, Research Support; Merck & Co., Inc. G.Imperatore: None. S.R.Benoit: None. C.S.Holliday: None. M.J.O'brien: None. J.B.Echouffo tcheugui: None. K.M.Bullard: None. Funding MKA was partially supported by the Georgia Center for Diabetes Translation Research funded by the National Institute of Diabetes and Digestive and Kidney Diseases (P30DK111024)

Type 1 (T1D) and type 2 (T2D) diabetes are distinct diseases, but most federal surveys used to monitor diabetes prevalence have not included questions on diabetes type. To design and validate survey questions that will distinguish between T1D and T2D in adults, we developed a survey sample in whom we first determined a "gold standard" for diabetes type from review of electronic health record (EHR) data. From >50,000 adult patients in the UNC Health Care System with diabetes, we assembled a sample of 2479 patients using a "strawman" definition based on diabetes-related diagnostic codes, laboratory results, and drugs. We stratified the sample using age, sex, race/ethnicity and crude diabetes type, oversampling T1D. For visits between 10/2014 and 9/2017, we collected clinical and demographic data including body mass index, diagnosis codes, medications, family history, and laboratory tests. We also abstracted chart notes, using trained personnel, to find additional information including diabetes duration and historical treatments. We created two models to determine diabetes type. The first was a decision tree, in which higher nodes sorted straightforward cases; lower nodes included more specific factors for those not categorized in higher nodes. The second was a weighted model, in which factors drawn from clinical or epidemiologic literature weighed weakly, moderately, or strongly in favor of or against each type of diabetes. The two models were 89% congruent for T1D and 96% congruent for T2D. An endocrinologist reviewed and determined type for the 10% of patients for which results of the two models were not congruent or for which neither was able to determine type. Overall, 70% of our sample has T2D, 27% has T1D, and 3% has indeterminate or other diabetes. We will use this sample to validate survey questions to determine type of diabetes. Such "gold standard" determinations may support validation of new and improved methods for measuring diabetes type among the adult population. Disclosure M. Kirkman: Research Support; Self; Bayer AG, Novo Nordisk A/S, Theracos, Inc. J.G. Nooney: None. S.R. Benoit: None. K.A. Bergamo: None. K.M. Bullard: None. J.R. Campione: None. R. Mardon: None. E. Pfaff: None. D.B. Rolka: None. S. Saydah: None. Funding Centers for Disease Control and Prevention

Background: A weight-based adiposity indicator (body mass index; BMI, kg/m 2 ) is often reported for adults. Indicators based on sagittal abdominal diameter (SAD) or waist circumference have also identified cardiometabolic risk. Aim: Compare SAD/height ratio (SADHtR) or waist/height ratio (WHtR) with BMI for identifying risks in a representative sample of non-elderly adults without diagnosed diabetes. Outcome dysfunctions were Dysglycemia (glycated hemoglobin ≥5.7%), HyperNonHDLc (non-HDL-cholesterol ≥160 mg/dL or taking cholesterol meds), Hypertension (SBP ≥140 or DBP ≥90 or taking blood-pressure meds) and HyperALT (alanine transaminase ≥75 th %ile [sex-specific p75]). Methods: Non-pregnant adults (ages 20-64 y; N=3,071) in the 2011-2012 US National Health and Nutrition Examination Survey provided conventional anthropometry and supine SAD (by sliding - beam caliper). Sample weighting permitted estimation of population characteristics, including odds ratios (ORs) associated with each adiposity indicator (logistic regression models adjusted for age, sex and ancestry). For each dysfunction, we compared the ORs for 3 indicators after rescaling them to the indicator’s sex-specific, interquartile range. Results: The population distributions (mean; p25, p75) of indicators among men were: SADHtR (0.129; 0.112, 0.144), WHtR (0.564; 0.505, 0.613), and BMI (28.2; 24.2, 31.0). Among women they were: SADHtR (0.131; 0.112, 0.148), WHtR (0.580; 0.510, 0.636), and BMI (28.3; 23.4, 31.7). Dysfunction prevalence ranged from 21.9% (Dysglycemia in women) to 42.4% (HyperNonHDLc in men). To identify HyperNonHDLc, Hypertension and HyperALT (but not Dysglycemia), the ORs were highest for SADHtR and lowest for BMI. When SADHtR entered models simultaneously with BMI, the ORs associated with BMI no longer contributed to identification of HyperNonHDLc, Hypertension, or HyperALT (Figure). Conclusions: Among US adults, the SADHtR provides low-cost estimation of cardiometabolic risk independently of BMI.