Type 1 Diabetes Cohort Research Articles

8395 Exploring the Relationship of Dietary Advanced Glycation End-products (AGEs) with Glycemic Control among Adults with Type 1 Diabetes (T1D)

Abstract Disclosure: C.E. Nevarez: None. A.B. Murthy: None. G. Jung: None. B. Gray: None. E. Cagliero: None. V. Aroda: None. E.W. Yu: None. Introduction: Accumulation of advanced glycation end products (AGEs) are thought to play a crucial role in the pathophysiology of diabetic complications, yet the contributions of dietary AGE (dAGE) intake to systemic AGE levels and glycemic control have not been established. Utilizing a newly developed methodology for evaluating dAGEs, our objective was to test whether dAGE impact systemic AGEs and glycemic outcomes in adults with longstanding Type 1 Diabetes (T1D). Methods: We examined adults aged 50+ years participating in the T1D BEACON cohort (n=97 T1D, n=53 non-diabetic controls). We developed a novel algorithm to quantify dietary AGE intake by associating items from the 2014 Block Food Frequency Questionnaire with their AGE content (as determined by ELISA from a previously published dataset; Urribarri et al., 2010). Daily dAGE intake was normalized using the nutrient density method (Willett et al., 1997). Additional measurements included autofluorescent skin AGEs (AGE Reader, DiagnOptics Technologies), diabetic history, hemoglobin A1C and 14-day continuous glucose monitoring to assess percentage time in glucose target range (70-180 mg/dL). We compared dAGEs between T1D and controls and evaluated associations of dAGEs with outcomes using independent t-tests, Pearson’s correlations, and multivariate linear regression with adjustments for age, sex, and body mass index (BMI). Results: Mean age and sex distribution was similar in the T1D and control groups (63.54 ± 6.7 yrs, 50.7% female). BMI was higher in the T1D group (27.9 ± 5.0 vs. 26.0 ± 4.9 kg/m^2, p = 0.031). The mean ± SD duration of diabetes in the T1D cohort was 46.1 ± 9.8 yrs, with A1c of 7.1 ± 1.0%. Utilizing our novel algorithm, the mean daily normalized dAGE intake was higher in the T1D than control groups (8.8 ± 2.3 kU/kcal vs. 7.9 ± 2.5 kU/kcal, p = 0.046) although this did not remain significant after multivariate adjustment (adj p = 0.123). Skin AGEs were higher in the T1D group (3.0 vs 2.5 AU, adj p<0.001). Within the full cohort, dAGE intake was significantly correlated with skin AGEs (r = 0.32, adj. p = 0.003), which was driven primarily by association within the T1D group (r = 0.32, adj p = 0.002). Additionally, dAGE intake was associated with higher A1C values within the full cohort (r = 0.27, adj. p = 0.019), and there was a trend that higher dAGE correlated with decreased time in target glucose range (r = -0.22, adj p = 0.062). Conclusion: Our analyses have revealed novel associations between dAGE intake, skin AGEs, and glycemic outcomes among adults with type 1 diabetes, underscoring the contribution of dietary patterns to systemic AGEs and physiological outcomes within diabetic populations. Future investigations will be needed to validate this novel dAGE assessment in external cohorts and to evaluate the potential impact of dietary AGE intake upon diabetic complications. Presentation: 6/1/2024

5120 Contribution Of Proximal Femur Geometry And Bone Density Distribution To Hip Fragility In Adults ≥ 50yrs With Longstanding Type 1 Diabetes

Abstract Disclosure: F. Johannesdottir: None. C. D’Mello: None. A.B. Murthy: None. B. Zahedi: None. K. Engelke: None. M.L. Bouxsein: None. E.W. Yu: Grant Recipient; Self; Amgen Inc. Introduction: Adults with type 1 diabetes (T1D) have a 6-fold increased risk of hip fracture, which is explained only in part by lower areal bone mineral density (aBMD) by DXA. We hypothesize that femoral geometry and regional bone deficits at the proximal femur contribute to higher hip fracture risk in older adults with T1D. Further, we determine the influence of age at T1D diagnosis on femoral bone density and structure. Methods: We enrolled 97 individuals with T1D and 53 non-diabetic controls (CON) as part of the T1D Bone Health Connection (BEACON) Study, an ongoing prospective study of adults aged ≥50 years. We obtained computed tomography (CT) scans of the proximal femur (in-plane pixel size 0.98x0.98 mm, 1.25 mm slice thickness) to measure total (Tt), trabecular (Tb) and cortical (Ct) volumetric BMD (vBMD); and volume (Vol) and Ct thickness (Th) in several regions, including total hip, femoral neck (FN), trochanter (TR) and intertrochanter (IT) (Medical Image Analysis Framework, University of Erlangen, Germany). We also assessed total hip and FN aBMD by DXA. We employed ANCOVA models to compare bone outcomes between T1D and controls with adjustment for age, height, and weight, stratified by sex. When comparing bone outcomes within T1D, we adjusted for age, height, weight, and sex. Differences are reported as % difference between the least-square means for the groups. Results: The average age of participants was 64±7 yrs, and similar between T1D and CON (p=0.21). Male T1D were shorter than male CON (173 cm vs 176 cm, p=0.05), but with similar weight (p=0.73). Female T1D were heavier than female CON (71 kg vs 64 kg, p=0.03). The T1D cohort included 50 men and 47 women with mean diabetes duration of 46±10 yrs, mean A1c 7.1±1.0%, and median age at onset 17 yrs (IQR:10-25). Among T1D, 31 (52% female) were diagnosed ≤ age 12. Among men, total hip and FN aBMD did not differ by group, but T1D had smaller FN Tt.Vol (9%) and FN width (8%) (p<0.05 for all). In contrast, there were no differences between T1D and CON in the female cohort, neither by DXA nor CT (p≥0.13). Of note, T1D men appeared to have more poorly controlled diabetes than women (29% of men vs 11% women had A1c ≥8%, p=0.03). Within T1D, those who were diagnosed ≤ age 12 had lower Tb.vBMD (16-18%) at the total hip, TR and IT regions (p<0.05) and tended to have lower Tb.vBMD (16%, p=0.06) at the neck compared to those diagnosed at a later age, but no differences in DXA aBMD (p>0.31). Conclusion: In sum, older men, but not women, with T1D showed some deficits in bone structure at the proximal femur. Adults with T1D who were diagnosed ≤ age 12 had trabecular bone deficits within the proximal femur. Additional studies are needed to identify mechanisms underlying the high hip fracture risk in T1D. Presentation: 6/1/2024

6870 Cardiac Imaging And Metabolomics Studies Suggest Differential Risk And Protective Biomarkers For Cardiovascular Disease In Renoprotected, Long-Duration Type 1 Diabetes

Abstract Disclosure: M. Yu: None. S. Jangolla: None. N. Ziemniak: None. E. Viebranz: None. T. Chokshi: None. K. Park: None. I. Wu: None. H. Shah: None. G.L. King: None. Cardiovascular disease (CVD) is a major cause of mortality in people with Type 1 diabetes (T1D), especially in those with nephropathy (DN). However, in long-duration T1D, a substantial proportion of individuals without DN still exhibit excessive CVD risks. The Joslin 50-Year “Medalist” Study (n=1043), composed of people with T1D≥50 years, are ideal for studying CVD in a renoprotected T1D cohort, as only 13% have DN, and yet 40% have CVD, despite excellent control of lipids and blood pressure. Stratifying the Medalists by DN revealed that exercise was cardioprotective in the group with DN (OR 0.60, p=0.04); while age at T1D diagnosis (OR 1.03, p=0.01), HbA1c (OR 1.25, p=0.01), C-reactive protein (OR 1.14, p=0.01), and interleukin-6 (OR 1.13, p=0.01) all increased CVD risk in Medalists without DN. A subset of Medalists also underwent coronary artery calcification scans (CAC; n=153) and cardiac magnetic resonance imaging of left ventricular (LV) structure and function (CMR; n=111). The majority of these Medalists (97.8%) had detectable CAC>0; furthermore, the median CAC score of 1000 was higher at any cutoff compared to similar age-matched T1D and Type 2 diabetes (T2D) populations with higher DN prevalence. In contrast, mean CMR values in this Medalist subset were comparable to other cohorts. Continuous glucose monitoring (CGM) metrics for hyperglycemia (mean glucose, β=0.002, p=0.03; glucose management indicator, β=0.09, p=0.03; and time above range>180 mg/dL, β=0.003, p=0.02), but not glucose variability or hypoglycemia, were associated with LV remodeling in the Medalists, even after adjusting for eGFR. Likewise, plasma levels of advanced glycation end-products (AGEs) N-carboxyethyl-lysine (CEL), N-carboxymethyl-lysine (CML), and methylglyoxal-derived hydroimidazolone (MGH1) were associated with CAC (β=16.22, p=0.05; β=15.21, p=0.01; and β=10.18, p=0.01, respectively) and LV remodeling (β=0.001, p=0.09; β=0.001, p=0.05; and β=0.001, p=0.04, respectively) in the Medalists. The association of CEL with CVD replicated in a separate Medalist subset (n=311) with self-reported CVD data (OR 1.26, p=0.01). Plasma global metabolomics studies (n=140) revealed that amino acid activation pathways, with lower levels of branched chain amino acids, were associated with CAC (FDR=0.002); while amino acid metabolism pathways were associated with less favorable LV volumes (FDR=0.03). In this comprehensive study of aging T1D people with no substantial DN, hyperlipidemia, or hypertension, cardiac imaging and metabolomics studies suggest disproportionate advanced CAC, as compared to subclinical LV remodeling or dysfunction, and highlight the importance of targeting hyperglycemia for CVD risk reduction. Plasma metabolites associated with CVD in T1D are different from those in T2D, and may provide novel information on potential therapeutic targets for CVD in T1D. Presentation: 6/1/2024

Receipt of Diabetes Specialty Care and Management Services by Older Adults With Diabetes in the U.S., 2015-2019: An Analysis of Medicare Fee-for-Service Claims.

We characterized the receipt of diabetes specialty care and management services among older adults with diabetes. Using a 20% random sample of fee-for-service Medicare beneficiaries aged ≥65 years, we analyzed cohorts of type 1 diabetes (T1D) or type 2 diabetes (T2D) with history of severe hypoglycemia (HoH), and all other T2D annually from 2015 to 2019. Outcomes were receipt of office-based endocrinology care, diabetes education, outpatient diabetes health services, excluding those provided in primary care, and any of the aforementioned services. In the T1D cohort, receipt of endocrinology care and any service increased from 25.9% and 29.2% in 2015 to 32.7% and 37.4% in 2019, respectively. In the T2D with HoH cohort, receipt of endocrinology care and any service was 13.9% and 16.4% in 2015, with minimal increases. Age, race/ethnicity, residential setting, and income were associated with receiving care. These findings suggest that many older adults may not receive specialty diabetes care and underscore health disparities.

Fracture patterns and associated risk factors in pediatric and early adulthood type 1 diabetes: Findings from a nationwide retrospective cohort study

PurposePeople with pediatric and early adulthood type 1 diabetes (T1D) might have a higher fracture risk at several sites compared to the general population. Therefore, we assessed the hazard ratios (HR) of various fracture sites and determined the risk factors associated with fractures among people with newly diagnosed childhood and adolescence T1D. MethodsAll people from the UK Clinical Practice Research Datalink GOLD (1987–2017), below 20 years of age with a T1D diagnosis code (n = 3100) and a new insulin prescription, were included and matched 1:1 by sex, age, and practice to a control without diabetes. Cox regression was used to estimate HRs of any, major osteoporotic fractures (MOFs) and peripheral fractures (lower-arm and lower-legs) for people with T1D compared to controls. The analyses were adjusted for sex, age, diabetic complications, medication (glucocorticoids, anti-depressants, anxiolytics, bone medication, anti-convulsive), Charlson-comorbidity-index (CCI), hypoglycemia, falls and alcohol. T1D was further stratified by diabetes duration, presence of diabetic microvascular complications (retinopathy, nephropathy, and neuropathy) and boys versus girls. ResultsThe crude HRs for any fracture (HR: 1.30, CI95%: 1.11–1.51), lower-arm (HR: 1.22, CI95%: 1.00–1.48), and lower-leg fractures (HR: 1.54, CI95%: 1.11–2.13) were statistically significant increase in T1D compared to controls, but the effect disappeared in the adjusted analyses. For MOFs, no significant differences were seen. Risk factors in the T1D cohort were few, but the most predominantly one was a previous fracture (any fracture: HR: 2.00, CI95%: 1.70–2.36; MOFs: HR: 1.89, CI95%: 1.44–2.48, lower- arm fractures: HR: 2.08, CI95%: 1.53–2.82 and lower-leg fractures: HR: 2.08, CI95%: 1.34–3.25). Others were a previous fall (any fracture: HR: 1.54, CI95%: 1.20–1.97), hypoglycemia (Any fracture: HR: 1.46, CI95%: 1.21–1.77 and lower-leg fractures: HR: 2.34, CI95%: 1.47–3.75), and anxiolytic medication (Any fracture: HR: 1.52, CI95%: 1.10–2.11). Whereas girls had a lower risk compared to boys (Any fracture: HR: 0.78, CI95%: 0.67–0.90 and lower-arm fractures; HR: 0.51, CI95%: 0.38–0.68). The risk of any fracture in T1D did not increase with longer diabetes duration compared to controls (0–4 years: HR: 1.20, CI95%: 1.00–1.44; 5–9 years: HR: 1.17, CI95%: 0.91–1.50; <10 years: HR: 0.83, CI95%: 0.54–1.27). Similar patterns were observed for other fracture sites. Furthermore, one complication compared to none in T1D correlated with a higher fracture risk (1 complication: HR: 1.42, CI95%: 1.04–1.95). ConclusionThe overall fracture risk was not increased in pediatric and early adulthood T1D; instead, it was associated with familiar risk factors and specific diabetes-related ones.

Tracking DNA-based antigen-specific T cell receptors during progression to type 1 diabetes.

T cells targeting self-proteins are important mediators in autoimmune diseases. T cells express unique cell-surface receptors (TCRs) that recognize peptides presented by major histocompatibility molecules. TCRs have been identified from blood and pancreatic islets of individuals with type 1 diabetes (T1D). Here, we tracked ~1700 known antigen-specific TCR sequences, islet antigen or viral reactive, in bulk TCRβ sequencing from longitudinal blood DNA samples in at-risk cases who progressed to T1D, age/sex/human leukocyte antigen-matched controls, and a new-onset T1D cohort. Shared and frequent antigen-specific TCRβ sequences were identified in all three cohorts, and viral sequences were present across all ages. Islet sequences had different patterns of accumulation based upon antigen specificity in the at-risk cases. Furthermore, 73 islet-antigen TCRβ sequences were present in higher frequencies and numbers in T1D samples relative to controls. The total number of these disease-associated TCRβ sequences inversely correlated with age at clinical diagnosis, indicating the potential to use disease-relevant TCR sequences as biomarkers in autoimmune disorders.

Fracture patterns in adult onset type 1 diabetes and associated risk factors – A nationwide cohort study

ObjectiveThis study aimed to determine the hazard ratios (HR) for various fracture sites and identify associated risk factors in a cohort of relatively healthy adult people with newly diagnosed type 1 diabetes (T1D). MethodsThe study utilized data from the UK Clinical Practice Research Datalink GOLD (1987–2017). Participants included people aged 20 and above with a T1D diagnosis code (n = 3281) and a new prescription for insulin. Controls without diabetes were matched based on sex, year of birth, and practice. Cox regression analysis was conducted to estimate HRs for any fracture, major osteoporotic fractures (MOFs), and peripheral fractures (lower-arm and lower-leg) in people with T1D compared to controls. Risk factors for T1D were examined and included sex, age, diabetic complications, medication usage, Charlson comorbidity index (CCI), hypoglycemia, previous fractures, falls, and alcohol consumption. Furthermore, T1D was stratified by duration of disease and presence of microvascular complications. ResultsThe proportion of any fracture was higher in T1D (10.8 %) than controls (7.3). Fully adjusted HRs for any fracture (HR: 1.43, CI95%: 1.17–1.74), MOFs (HR: 1.46, CI95%: 1.04–2.05), and lower-leg fractures (HR: 1.37, CI95%: 1.01–1.85) were statistically significantly increased in people with T1D compared to controls. The primary risk factor across all fracture sites in T1D was a previous fracture. Additional risk factors at different sites included previous falls (HR: 1.64, CI95%: 1.17–2.31), antidepressant use (HR: 1.34, CI95%: 1.02–1.76), and anxiolytic use (HR: 1.54, CI95%: 1.08–2.29) for any fracture; being female (HR: 1.65, CI95%: 1.14–2.38) for MOFs; the presence of retinopathy (HR: 1.47, CI95%: 1.02–2.11) and previous falls (HR: 2.04, CI95%: 1.16–3.59) for lower-arm and lower-leg fractures, respectively. Lipid-lowering medication use decreased the risk of MOFs (HR: 0.66, CI95%: 0.44–0.99).Stratification of T1D by disease duration showed that the relative risk of any fracture in T1D did not increase with longer diabetes duration (0–4 years: HR: 1.52, CI95%: 1.23–1.87; 5–9 years: HR: 1.30, CI95%: 0.99–1.71; <10 years: HR: 1.07, CI95%: 0.74–1.55). Similar patterns were observed for other fracture sites. Moreover, the occurrence of microvascular complications in T1D was linked to a heightened risk of fractures in comparison to controls. However, when considering the T1D cohort independently, the association was not statistically significant. ConclusionIn a cohort of relatively healthy and newly diagnosed people with T1D HRs for any fracture, MOFs, and lower-leg fractures compared to controls were increased. A previous fracture was the most consistent risk factor for a subsequent fracture, whereas retinopathy was the only diabetes related one. We postulate a potential initial fracture risk, succeeded by a subsequent risk reduction, which might potentially increase in later years due to the accumulation of complications and other factors.

THU336 Correlation Between Dietary Advanced Glycation End-products (Ages) And Skin Ages In Adults With Longstanding T1D

Abstract Disclosure: A.B. Murthy: None. B.E. Gray: None. M. Cote: None. B. Zahedi: None. E.W. Yu: Grant Recipient; Self; Amgen Inc. Background: Advanced glycation end products (AGEs) are involved in the pathophysiology of diabetic complications, and are thought to accrue with aging, hyperglycemia, and through diet. Despite the postulated impact of dietary intake on circulating AGE levels, there is currently no validated methodology for dietary AGE (dAGE) assessment. Thus, we sought to develop a crosswalk algorithm to calculate AGE intake from the commonly utilized Block Food Frequency Questionnaire (FFQ), and to validate our algorithm by correlating dAGE to skin AGE values within a cohort of adults with Type 1 Diabetes (T1D). Methods: We performed preliminary analysis of 50 study participants from the ongoing Type 1 Diabetes Bone Health Connection (T1D BEACON) study evaluating postmenopausal women and men older than 50 years with T1D. Dietary AGE intake was quantified using a novel algorithm we developed to associate items from the 2014 Block FFQ with their AGE content as determined by ELISA in a published data set (Urribarri, et al. 2014). Mean daily dAGE intake (kU/day) was correlated with autoflourescent skin AGE values (AGE Reader, DiagnOptics Technologies) using multivariate linear regression, after adjusting for age and gender. Diabetic characteristics and complications were assessed through questionnaires, physical exams (mTCNS, DPNCheck), chart review, and urine/serum tests. Results: The average age of the T1D cohort was 62.7 ± 6.8 years, 50% were female, the average duration of diabetes was 45 ± 9.4 years, with average A1c of 7.2 ± 1.1. Regarding diabetic complications, 70% had retinopathy, 30% had microalbuminuria (urine albumin-creatinine ratio &amp;gt; 30 mcg/mg), and 33% had neuropathy (mTCNS score &amp;gt; 5). Using our novel algorithm, the mean daily dAGE intake was calculated as 13178 ± 4455 kU/day. Mean daily dAGE intake was significantly higher for men (15279 ± 6337 kU/day) compared to women (11628 ± 3699 kU/day) (p = 0.016). Importantly, dAGEs were significantly correlated with skin AGEs (r = 0.409, p = 0.004), an association that persisted after adjusting for age and gender (p = 0.005). Additionally, dAGEs were correlated with current A1c values (r=0.37, p = 0.009, adj. p = 0.042) and with BMI (r = 0.32, p = 0.024, adj. p = 0.020), even after adjusting for age and gender. No associations were found between dAGEs and retinopathy, microalbuminuria, or neuropathy. Additional analysis of the full planned cohort is ongoing, along with assessments of serum AGE levels. Conclusion: We have developed and validated a novel dietary AGE algorithm that quantifies dAGE intake using the Block FFQ and is correlated with skin AGEs and glycemic control among adults with T1D. If further validated in larger cohorts, we hope this dAGE tool will facilitate future investigation of the impact of dietary AGEs on diabetic complications. Presentation: Thursday, June 15, 2023

Association between sleep variability and time in range of glucose levels in patients with type 1 diabetes: Cross-sectional study

ObjectiveSleep and circadian disturbances emerge as novel factors influencing glycemic control in type 1 diabetes (T1D). We aimed to explore the associations among sleep, behavioral circadian parameters, self-care, and glycemic parameters in T1D. MethodsSeventy-six non–shift-working adult T1D patients participated. Blinded 7-day continuous glucose monitoring (CGM) and hemoglobin A1C (A1C) were collected. Percentages of time-in-range (glucose levels 70-180 mg/dL) and glycemic variability (measured by the coefficient of variation [%CV]) were calculated from CGM. Sleep (duration and efficiency) was recorded using 7-day actigraphy. Variability (standard deviation) of midsleep time was used to represent sleep variability. Nonparametric behavioral circadian variables were derived from actigraphy activity recordings. Self-care was measured by diabetes self-management questionnaire-revised. Multiple regression analyses were performed to identify independent predictors of glycemic parameters. ResultsMedian (interquartile range) age was 34.0 (27.2, 43.1) years, 48 (63.2%) were female, and median (interquartile range) A1C was 6.8% (6.2, 7.4). Sleep duration, efficiency, and nonparametric behavioral circadian variables were not associated with glycemic parameters. After adjusting for age, sex, insulin delivery mode/CGM use, and ethnicity, each hour increase in sleep variability was associated with 9.64% less time-in-range (B = −9.64, 95% confidence interval [−16.29, −2.99], p ≤ .001). A higher diabetes self-management questionnaire score was an independent predictor of lower A1C (B = −0.18, 95% confidence interval [−0.32, −0.04]). ConclusionGreater sleep timing variability is independently associated with less time spent in the desirable glucose range in this T1D cohort. Reducing sleep timing variability could potentially lead to improved metabolic control and should be explored in future research. Data availability statementData are available upon a reasonable request to the corresponding author.

Human immune phenotyping reveals accelerated aging in type 1 diabetes.

The proportions and phenotypes of immune cell subsets in peripheral blood undergo continual and dramatic remodeling throughout the human life span, which complicates efforts to identify disease-associated immune signatures in type 1 diabetes (T1D). We conducted cross-sectional flow cytometric immune profiling on peripheral blood from 826 individuals (stage 3 T1D, their first-degree relatives, those with ≥2 islet autoantibodies, and autoantibody-negative unaffected controls). We constructed an immune age predictive model in unaffected participants and observed accelerated immune aging in T1D. We used generalized additive models for location, shape, and scale to obtain age-corrected data for flow cytometry and complete blood count readouts, which can be visualized in our interactive portal (ImmScape); 46 parameters were significantly associated with age only, 25 with T1D only, and 23 with both age and T1D. Phenotypes associated with accelerated immunological aging in T1D included increased CXCR3+ and programmed cell death 1-positive (PD-1+) frequencies in naive and memory T cell subsets, despite reduced PD-1 expression levels on memory T cells. Phenotypes associated with T1D after age correction were predictive of T1D status. Our findings demonstrate advanced immune aging in T1D and highlight disease-associated phenotypes for biomarker monitoring and therapeutic interventions.

Type 1 Diabetes Genetic Risk Score Differentiates Subgroups of Ketosis-Prone Diabetes.

To determine whether genetic risk for type 1 diabetes (T1D) differentiates the four Aβ subgroups of ketosis-prone diabetes (KPD), where A+ and A- define the presence or absence of islet autoantibodies and β+ and β- define the presence or absence of β-cell function. We compared T1D genetic risk scores (GRS) of patients with KPD across subgroups, race/ethnicity, β-cell function, and glycemia. Among 426 patients with KPD (54% Hispanic, 31% African American, 11% White), rank order of GRS was A+β- > A+β+ = A-β- > A-β+. GRS of A+β- KPD was lower than that of a T1D cohort, and GRS of A-β+ KPD was higher than that of a type 2 diabetes cohort. GRS was lowest among African American patients, with a similar distribution across KPD subgroups. T1D genetic risk delineates etiologic differences among KPD subgroups. Patients with A+β- KPD have the highest and those with A-β+ KPD the lowest GRS.

Comparison of coronary artery calcium score and cardiovascular risk-stratification by European Society of Cardiology Guidelines and Steno Type 1 Risk Engine in statin-naïve adults with type 1 diabetes

BackgroundCoronary artery calcium (CAC) is a marker of atherosclerotic cardiovascular disease (CVD). However, for patients with type 1 diabetes (T1D), its relationship with T1D-specific cardiovascular (CV) risk-stratification tools is unknown. AimsAssess prevalence of CAC and evaluate relationship between CAC and T1D-specific CV risk-stratification methods in T1D. MethodsCross-sectional study of adults with T1D age 20–60 years, statin-naïve and no history of CVD. Data was obtained from electronic medical records and by interview. Presence of CAC was assessed using non-contrast cardiac computed tomography and quantified by Agatston Units (AU). CV risk-stratification was assessed using the 2019 European Society of Cardiology (ESC) Guidelines and the Steno T1 Risk Engine (ST1RE). Results85 patients were included with mean age 35.4 ± 10.3 years, HbA1c 8.3 ± 1.5 % and T1D duration 17.0 ± 10.1 years. 67 patients (78.9 %) had a CAC score of 0 AU, 17 (20.0 %) >0–100 AU, and one (1.2 %) >100 AU. Duration of T1D (p = 0.009), body mass index (p = 0.029), neuropathy (p = 0.016) and low-density lipoprotein cholesterol levels (p = 0.016) were independently associated with a positive CAC score on multivariate analysis. Positive predictive value for a positive CAC score was 85.7 % for the ST1RE high risk category and 31.3 % for the 2019 ESC Guidelines very high risk category. ConclusionsOne-fifth of this T1D cohort had a positive CAC score. The ST1RE was superior in identifying positive CAC compared to the 2019 ESC Guidelines. Further studies are required to elucidate the role of CAC in personalising CV risk-stratification and statin initiation in T1D.

Comparative mortality and its determinants in community-based people with type 1 diabetes: the Fremantle Diabetes Study Phase I

IntroductionThe aim of this study was to compare mortality in community-based Australians with type 1 diabetes (T1D), without diabetes, or with type 2 diabetes (T2D).Research design and methodsThe longitudinal observational...

1454-P: Food Insecurity in People with Type 1 Diabetes and Glycemic Outcomes

Background: This multi-center study aims to explore relationships between individuals with type 1 diabetes (T1D) who are at risk for food insecurity and glycemic outcomes. Methods: Electronic health record data from T1D Exchange Quality Improvement Collaborative (T1DX-QI) categorized completed Hunger Vital Signs (HVS) questionnaires into at risk and no risk for food insecurity. At-risk individuals were defined as selecting “often true” or “sometimes true” to screening questions, whereas primary outcome was recent A1c levels. Demographic information and diabetes related covariates were assessed for potential relationships. Results: In the total T1D cohort (N=4453), 3.8% were at risk for food insecurity. Stratifying by age, 6% of youth (&amp;lt;18 years) and 5% of adults (&amp;gt;18 years) were at risk. Statistically significant differences were observed among food insecurity risk and insurance type. Of those at risk, 54% had public insurance compared with 7% private. Individuals at risk had a higher mean HbA1c when compared with individuals not at risk (9.4% vs 8.5% respectively, p&amp;lt;.001). The odds of an individual having an HbA1c &amp;gt;7% were higher in the group at risk relative to the group not at risk OR1.4 (95% CI 0.9, 2.1). Conclusion: Individuals in this population who were at risk for food insecurity also had elevated A1c levels. Interventions are needed to identify effective ways of improving food insecurity among people with T1D. Disclosure E.L.Ospelt: None. V.Raman: None. B.Seyoum: None. O.Ebekozien: Advisory Panel; Medtronic, Research Support; Eli Lilly and Company, Dexcom, Inc. N.Noor: None. J.Haw: None. S.Hsieh: None. R.S.Weinstock: Consultant; Jaeb Center for Health Research, Other Relationship; Wolters Kluwer Health, Research Support; Insulet Corporation, Medtronic, Eli Lilly and Company, Novo Nordisk, Boehringer Ingelheim Inc., Hemsley Charitable Trust, National Institute of Diabetes and Digestive and Kidney Diseases, Tandem Diabetes Care, Inc., Kowa Pharmaceuticals America, Inc. D.W.Hansen: Research Support; 3Boehringer Ingelheim Canada Ltd./Ltée, Lilly, Insulet Corporation, T1D Exchange, Medtronic. K.Cossen: None. K.Fantasia: None. I.Guttmann-bauman: None. Funding The Leona M. and Harry B. Helmsley Charitable Trust

159-OR: Greater Sleep Variability Is Associated with Less Time in Range of Glucose Levels in Patients with Type 1 Diabetes

Background: Sleep and circadian disturbances emerge as novel factors influencing glycemic control in type 1 diabetes (T1D). We aimed to explore the associations between sleep, behavioral circadian parameters, self-care, and glycemic parameters in T1D. Methods: Baseline data of 76 non-shift working adult T1D patients participating in a randomized study to optimize sleep health (NCT04506151) were analyzed. Glycemic assessments were derived from blinded 7-day continuous glucose monitoring (CGM) and A1C. Percentages of time-in-range (TIR of glucose levels between 70-180 mg/dL) and %CV were calculated from CGM. Sleep was recorded using 7-day actigraphy, yielding sleep duration and efficiency. Variability (SD) of mid-sleep time (MST) was used to represent sleep variability. Non-parametric behavioral circadian variables were derived from actigraphy activity recordings. Self-care was measured by the Diabetes Self-Management Questionnaire (DSMQ-R). Multiple regression analyses were performed to identify independent predictors of glycemic parameters. Results: Mean (SD) age was 36.3 (11) years, 48 (63.2%) female, and median (IQR) A1C 6.8% (6.2, 7.4). Sleep duration, efficiency, and non-parametric behavioral circadian variables were not associated with glycemic parameters. After adjusting for age, sex, insulin delivery mode/CGM use, and ethnicity, each hour increase in SD of MST was associated with 9.64 % less TIR (B= -9.64, 95% (CI) [-16.29, -2.99], p=&amp;lt;0.001). Higher DSMQ score was an independent predictor of lower A1C (B = -0.18, 95% CI -0.32, -0.04). Conclusion: Greater sleep variability is independently associated with less time spent in desirable glucose range in this modern T1D cohort, with clinically important effect sizes. Sleep optimization to reduce variable sleep schedule could potentially lead to improved metabolic control and should be explored in future research. Disclosure P.Martyn-nemeth: None. L.T.Quinn: None. M.Clark withington: None. A.H.Saleh: None. B.Loiacono: None. D.Mihailescu: None. S.Reutrakul: Speaker's Bureau; Eli Lilly and Company. G.Abu irsheed: None. M.Park: None. A.D.Steffen: None. L.Burke: None. S.Pratuangtham: None. K.Baron: Other Relationship; National Sleep Foundation, Research Support; Google. J.Duffecy: Consultant; Chorus Health, K Health, Stock/Shareholder; Healent Health, My Alloy. R.Perez: None. Funding National Institute of Diabetes and Digestive and Kidney Diseases (R01DK121726)

Insulin resistance is more frequent in type 1 diabetes patients with long disease duration.

To investigate the clinical status of insulin resistance (IR) and its correlation with disease duration in patients with type 1 diabetes (T1D). Cross-sectional data from a T1D cohort were obtained (n=923). IR-related metabolic disorders including hypertension, obesity, and dyslipidemia were used as outcome variables to explore the cut-off point for estimated glucose disposal rate (eGDR) by restricted cubic spline (RCS) curve. Regression models were used for multivariate analysis of the clinical factors associated with IR. The correlation between the status of IR and diabetes duration was depicted with the RCS curve. IR-related metabolic disorders were observed in 39.4% of patients, with 9.1% meeting the criteria for metabolic syndrome. Specifically, patients with ≥10years of T1D were more likely to have IR-related metabolic disorders (54.7% vs. 36.9%, p<0.05). The presence of IR, defined as an eGDR ≤9.0mg/kg/min, was observed in 42.2% of patients. Patients with IR had a longer diabetes duration (3.5 vs. 2.7, years, p=0.003) and higher insulin dose (0.5 vs. 0.4, units per kg per day, p<0.001). Moreover, the presence of IR showed a gradual increase during 10years' disease duration and further analysis showed that diabetes duration ≥10years was a key element behind the development of IR and IR-related metabolic disorders. The status of IR is common in T1D patients, especially in those with ≥10years of disease duration. Therapies targeting balancing glycaemic control and IR are needed to decrease the future risk of cardiovascular diseases in T1D. ClinicalTrials.gov NCT03610984 (cohort study of patients with type 1 diabetes).

Human pancreatic capillaries and nerve fibers persist in type 1 diabetes despite beta cell loss.

The autonomic nervous system regulates pancreatic function. Islet capillaries are essential for the extension of axonal projections into islets, and both of these structures are important for appropriate islet hormone secretion. Because beta cells provide important paracrine cues for islet glucagon secretion and neurovascular development, we postulated that beta cell loss in type 1 diabetes (T1D) would lead to a decline in intraislet capillaries and reduction of islet innervation, possibly contributing to abnormal glucagon secretion. To define morphological characteristics of capillaries and nerve fibers in islets and acinar tissue compartments, we analyzed neurovascular assembly across the largest cohort of T1D and normal individuals studied thus far. Because innervation has been studied extensively in rodent models of T1D, we also compared the neurovascular architecture between mouse and human pancreas and assembled transcriptomic profiles of molecules guiding islet angiogenesis and neuronal development. We found striking interspecies differences in islet neurovascular assembly but relatively modest differences at transcriptome level, suggesting that posttranscriptional regulation may be involved in this process. To determine whether islet neurovascular arrangement is altered after beta cell loss in T1D, we compared pancreatic tissues from non-diabetic, recent-onset T1D (<10-yr duration), and longstanding T1D (>10-yr duration) donors. Recent-onset T1D showed greater islet and acinar capillary density compared to non-diabetic and longstanding T1D donors. Both recent-onset and longstanding T1D had greater islet nerve fiber density compared to non-diabetic donors. We did not detect changes in sympathetic axons in either T1D cohort. Additionally, nerve fibers overlapped with extracellular matrix (ECM), supporting its role in the formation and function of axonal processes. These results indicate that pancreatic capillaries and nerve fibers persist in T1D despite beta cell loss, suggesting that alpha cell secretory changes may be decoupled from neurovascular components.NEW & NOTEWORTHY Defining the neurovascular architecture in the pancreas of individuals with type 1 diabetes (T1D) is crucial to understanding the mechanisms of dysregulated glucagon secretion. In the largest T1D cohort of biobanked tissues analyzed to date, we found that pancreatic capillaries and nerve fibers persist in human T1D despite beta cell loss, suggesting that alpha cell secretory changes may be decoupled from neurovascular components. Because innervation has been studied extensively in rodent T1D models, our studies also provide the first rigorous direct comparisons of neurovascular assembly in mouse and human, indicating dramatic interspecies differences.

Glycemic Outcomes and Feature Set Engagement Among Real-Time Continuous Glucose Monitoring Users With Type 1 or Non-Insulin-Treated Type 2 Diabetes: Retrospective Analysis of Real-World Data.

The benefits of real-time continuous glucose monitoring (RT-CGM) are well established for patients with type 1 diabetes (T1D) and patients with insulin-treated type 2 diabetes (T2D). However, the usage and effectiveness of RT-CGM in the context of non-insulin-treated T2D has not been well studied. We aimed to assess glycemic metrics and rates of RT-CGM feature utilization in users with T1D and non-insulin-treated T2D. We retrospectively analyzed data from 33,685 US-based users of an RT-CGM system (Dexcom G6; Dexcom, Inc) who self-identified as having either T1D (n=26,706) or T2D and not using insulin (n=6979). Data included glucose concentrations, alarm settings, feature usage, and event logs. The T1D cohort had lower proportions of glucose values in the 70 mg/dl to 180 mg/dl range than the T2D cohort (52.1% vs 70.8%, respectively), with more values indicating hypoglycemia or hyperglycemia and higher glycemic variability. Discretionary alarms were enabled by a large majority in both cohorts. The data sharing feature was used by 38.7% (10,327/26,706) of those with T1D and 10.4% (727/6979) of those with T2D, and the mean number of followers was higher in the T1D cohort. Large proportions of patients with T1D or T2D enabled and customized their glucose alerts. Retrospective analysis features were used by the majority in both cohorts (T1D: 15,783/26,706, 59.1%; T2D: 3751/6979, 53.8%). Similar to patients with T1D, patients with non-insulin-treated T2D used RT-CGM system features, suggesting beneficial, routine engagement with data by patients and others involved in their care. Motivated patients with diabetes could benefit from RT-CGM coverage.

The metabolic syndrome is frequent in children and adolescents with type 1 diabetes compared to healthy controls

There is a rise in overweight and obesity among children and adolescents with type 1 diabetes (T1D) in parallel with the rise in the metabolic syndrome (MetS) among children and adolescents. The aim of the study was to describe the prevalence and characteristics of MetS in children and adolescents with T1D compared to their healthy counterparts. The study includes two Danish cohorts; (i) the Copenhagen cross sectional cohort 2016 of 277 children and adolescents with T1D that attend the pediatric outpatient clinic at a large hospital in greater Copenhagen and (ii) the CHAMPS-study DK which is a population-based cohort study of Danish children and adolescents (control cohort). Participants were categorized to have MetS if at least two of the following criteria were met: (i) systolic and/or diastolic blood pressure ≥ 90th percentile, (ii) waist circumference ≥90th percentile, and (iii) triglyceride ≥90th percentile and/or HDL ≤10th percentile. The prevalence of children with Mets in the T1D cohort was higher than in the control cohort (p=0.002). Moreover, participants with T1D had MetS at a lower level of BMI (p < 0.001) and waist circumference (p < 0.001) than participants with MetS from the control cohort (z-scores=0.90 and 1.51). Participants with MetS were younger than the other T1D participants (median 12.8 [9.9,14.8] vs. median 14.6 [11.2,16.9] years, p=0.006). Children and adolescents with T1D have an increased risk of MetS compared to healthy controls and clinicians and caretakers should consider early prevention and health promotion strategies.

99-OR: HbA1c Variability Predicts Both Coronary Atherosclerosis and CVD Events in Type 1 Diabetes

Introduction: Interventions that decrease mean glucose have reduced rates of type 1 diabetes (T1D) vascular complications, but the difference in cardiovascular disease (CVD) risk between people with T1D and the general population endures. This suggests that factors beyond hemoglobin A1c (HbA1c) normalization may drive T1D CVD outcomes. HbA1c variability is an established predictor of T1D microvascular disease, but thus far only the FinnDiane Study has implicated it as a risk factor for T1D CVD events. No studies have examined the relationship of HbA1c variability to both CVD risk factors and events in a prospective T1D cohort. Methods: We used multivariable logistic regression to analyze the Coronary Artery Calcification in Type 1 Diabetes (CACTI) T1D cohort (N=597) . Primary outcomes were square root-transformed coronary artery calcium (CAC) volume and CVD events. CVD events were defined as myocardial infarction, coronary artery bypass grafting, angioplasty, or cardiovascular death. HbA1c variability was defined as a 1-standard deviation increment in HbA1c and calculated as standard deviation of all HbA1c values for each subject. Results: After adjustment for age, sex, and T1D duration, HbA1c variability predicted both CVD events (HR: 1.562, 95% CI: 1.052-2.318, P=0.0269) and CAC volume (rpartial= 0.3346, P&amp;lt;0.001) , while mean HbA1c predicted CVD events (HR: 1.399, 95% CI: 1.079-1.816, P=0.0114) but not CAC volume (rpartial= 0.0934, P=0.2886) . Further investigation revealed that the relationship between HbA1c variability and CVD events was strongest in those with good glycemic control (HR: 6.330 at mean HbA1c of 6.0%, HR: 3.920 at mean HbA1c of 7.0%, HR: 2.428 at mean HbA1c of 8.0%, HR: 1.5at mean HbA1c of 9.0%, and HR: 0.931 at mean HbA1c of 10.0%) . Conclusion: HbA1c variability may be a risk factor for both coronary atherosclerosis and CVD events in T1D, and the appropriate care strategy for this population may include maintaining an HbA1c value over time that is both at-target and stable. Disclosure W.B.Horton: None. J.K.Snell-bergeon: Stock/Shareholder; GlaxoSmithKline plc. Funding National Institutes of Health (R01DK116731) National Institutes of Health (R01HL113029)