bluuebunny/crossref_metadata_2025_split huggingface dataset:
Medicare Diabetes Prevention Program Final Rules May Limit Beneficiary Access and Widen Health Disparities
Authors: RITCHIE NATALIE , GRITZ MARK 
Published: June 2018

Introduction: The Centers for Medicare and Medicaid Services recently issued final rules for the Medicare Diabetes Prevention Program (MDPP), offering an unprecedented opportunity to provide lifestyle intervention to the estimated 48.3% of seniors with prediabetes. The MDPP is based on the widely disseminated National Diabetes Prevention Program (NDPP), which has lesser but still beneficial risk-reduction outcomes among racial/ethnic minority participants. We assess the extent to which the MDPP’s pay-for-performance reimbursement model may cover costs of service delivery to diverse Medicare beneficiaries.
Methods: Denver Health Medical Center is a safety net healthcare system that provided the yearlong NDPP to 232 diverse Medicare beneficiaries between 2013-2017, primarily through grant funding. Based on their NDPP attendance and weight loss outcomes, we compared projected performance-based-payments (e.g., $160 for ?5% weight loss) to service costs.
Results: Participating Medicare beneficiaries were 40.6% Hispanic, 31.6% non-Hispanic black, and 26.9% non-Hispanic white. Only 4.7% of beneficiaries achieved all attendance and weight loss outcomes associated with the maximum $470 payment for services in months 1-12. Overall beneficiary performance would result in an average reimbursement of $138.52 (SD=142.58). Program delivery costs were $800.64 per-participant, leaving an average gap of $662.12 per beneficiary served.
Discussion: Numerous MDPP suppliers are needed to reach Medicare beneficiaries with prediabetes, yet insufficient reimbursement is a likely deterrent. Health disparities may also widen as suppliers serving diverse populations will likely receive especially low payments, threatening their MDPP delivery. Higher payments appear needed and are supported by strong return-on-investment findings. A risk-adjusted model that accounts for demographic differences in outcomes is also needed to promote health equity.

Disclosure
N. Ritchie: None. M. Gritz: None.
Individuals with Single Islet Autoantibody Positivity (SA+) at Type 1 Diabetes (T1D) Diagnosis Have Distinct Characteristics
Authors: REDONDO MARIA J. , SOSENKO JAY , LIBMAN INGRID , MCVEAN JENNIFER J. , ATKINSON MARK A. , BECKER DOROTHY J. , GEYER SUSAN ,   THE TYPE 1 DIABETES TRIALNET STUDY GROUP
Published: June 2018

Multiple autoantibody positivity (MA+) usually precedes clinical T1D. However, a subset of patients who develop T1D have SA+. We hypothesized that individuals who at T1D diagnosis express SA+ compared with MA+ have different demographic, metabolic, immunologic and genetic characteristics. We studied 620 TrialNet participants who developed clinical T1D (median age=12.3 years [range=1.4-58.6], 47.5% male, 88% white). Using multivariable modeling and adjusting for potential confounders, we investigated differences between participants with SA+ vs. MA+ (of autoantibodies to GAD65 [GADA], insulin [mIAA], IA2, ZnT8 and ICA) at T1D diagnosis. We found 95 participants who had SA+ (70% GADA+) and 525 participants with MA+. In univariate analyses, individuals with SA+ vs. MA+ were older at T1D diagnosis (median 16.4 vs. 11.7 years, p&lt;0.0001) and had higher fasting C-peptide (1.87 vs. 1.47 nmol/L, p=0.007), higher 30-0 minute C-peptide difference (1.42 vs. 1.18, p=0.009) and lower Index60 (composite measure of fasting C-peptide, 60-min glucose and 60-min C-peptide) (2.31 vs. 2.68, p=0.0005). Gender, race, ethnicity, BMI-Z-score, presence of HLA DR3-DQ2 and/or DR4-DQ8, fasting glucose, AUC glucose and AUC C-peptide were not significantly different. By multivariable analyses, SA+ at onset corresponded with older age (OR=1.05, p&lt;0.0001), lower Index60 (OR=0.77, p=0.008), and lower likelihood of mIAA+ (OR=0.27, p&lt;0.0001) and GADA+ (OR=0.25, p&lt;0.0001); gender, HLA, BMI-Z-score and other autoantibody types were not significantly different. Independent factors of being SA+ differed between DR3/DR4-DQ8 heterozygous participants (older age and not having mIAA+ or GADA+) (p&lt;0.01) and those not DR3/DR4-DQ8 (lower Index60 and not having GADA+) (p&lt;0.01).
In conclusion, the number of islet autoantibodies at T1D diagnosis is associated with distinct characteristics, which may suggest heterogeneous pathogenesis with relevance for T1D prevention and treatment.

Disclosure
M.J. Redondo: None. J. Sosenko: None. I. Libman: Consultant; Self; Novo Nordisk A/S. J.J. McVean: Speaker's Bureau; Self; Medtronic MiniMed, Inc. M.A. Atkinson: Other Relationship; Self; Patent Issued. D.J. Becker: None. S. Geyer: None.
A Comparison of HbA1c and Index 60 Thresholds for Identifying Autoantibody Positive Individuals at High Risk for Type 1 Diabetes (T1D)
Authors: JACOBSEN LAURA M. , ISMAIL HEBA M. , CLEMENTS MARK A. , SCHATZ DESMOND , SOSENKO JAY 
Published: June 2018

Current ADA standards include HbA1c values 5.7%-6.4% as an indicator of prediabetes. However, there is little information with regard to HbA1c as a specific predictor of T1D. We thus compared HbA1c with Index 60, a T1D marker, for identifying individuals at high risk for T1D. Index 60 was derived from a proportional hazards regression model for T1D that included log fasting C-peptide, 60-minute glucose, and 60-minute C-peptide. Diabetes Prevention Trial-type 1 participants with Index 60&lt;2.00 at baseline were followed for the first (incident) OGTT exceeding an Index 60≥2.00 threshold (n=85). Similarly, those with HbA1c values &lt;6.0% at baseline were followed for the first (incident) HbA1c ≥6.0% (n=77). Those with both incident Index 60≥2.00 and incident HbA1c ≥6.0 were excluded. At the incident OGTTs, those with HbA1c ≥6.0 were older (19.2±12.2 years vs. 12.7±7.2 years; p&lt;0.001). The cumulative incidence for T1D was higher (log rank p&lt;0.001) after Index 60≥2.00 than after HbA1c ≥6.0 (3- and 4-year risks for HbA1c ≥6.0: 0.41 and 0.45; 28/77 diagnosed vs. 3- and 4-year risks for Index 60≥2.00: 0.78 and 0.92; 60/85 diagnosed). The hazard ratio for Index 60≥2.00 vs. HbA1c ≥6.0 (with age adjustment) was significant [HR: 1.96 (1.20, 3.16); p=0.006]. Intervals from incident Index 60≥2.00 and HbA1c ≥6.0 to diagnosis were 1.2±0.9 (0.1-3.8) years and 1.0±1.0 (0-4.7) years, respectively (p=0.50). The 59 individuals who had OGTTs on the date of incident HbA1c ≥6.0 had higher 30-0 minute C-peptide (with age adjustment) than those with incident Index 60≥2.00 (2.8±1.9 ng/ml vs. 1.6±1.0 ng/ml; p&lt;0.001).
In conclusion, Index 60≥2.00 was superior to HbA1c ≥6.0 at identifying individuals at high risk for T1D. Moreover, those with incident HbA1c ≥6.0 were much older with higher C-peptide levels, suggesting heterogeneity. These findings add support for including C-peptide together with glucose in developing predictors and pre-diagnostic endpoints that are specific for T1D.

Disclosure
L.M. Jacobsen: None. H.M. Ismail: None. M.A. Clements: Speaker's Bureau; Self; Medtronic. Advisory Panel; Self; Glooko, Inc.. D. Schatz: None. J. Sosenko: None.
Burden of Cardiovascular Risk Factors and Disease in Type 1 Diabetes—Results of the Australian National Diabetes Audit (ANDA)
Authors: PEASE ANTHONY J. , EARNEST ARUL , NANAYAKKARA NATALIE , RANASINHA SANJEEVA , FLACK JEFF R. , DAVIS WENDY A. , KILMARTIN GLORIA M. , WISCHER NATALIE , ZOUNGAS SOPHIA 
Published: June 2018

Aims: To evaluate burden of cardiovascular risk factors and associations with cardiovascular disease (CVD) among adults with type 1 diabetes attending Australian diabetes centers.
Methods: De-identified, prospectively collected data from patients with type 1 diabetes aged ≥18 years included in ANDA-2015 were analysed. Cardiovascular risk factors (age, sex, diabetes duration, HbA1c, BP, high density lipoprotein-cholesterol (HDL-C), BMI, smoking status and albuminuria) and associations with CVD inclusive of stroke, myocardial infarction, coronary artery bypass graft/angioplasty and peripheral vascular disease were assessed. Likelihood ratio test and restricted cubic splines assessed for interactions and non-linear associations. Discriminatory ability of multivariate models was assessed with area under the receiver operating characteristic curves (AUC).
Results: Data from 1,169 patients were analysed. Median (IQR) age and diabetes duration were 37.0 (24.9-52.0) and 16.0 (8.0-27.0) years respectively. Cardiovascular risk factors were prevalent including hypertension (22%), dyslipidemia (89%), overweight or obesity (56%), ever smoking (39%), albuminuria (31%), and an HbA1c &gt;7.0% (53mmol/mol) (81%). Older age, longer diabetes duration, smoking and antihypertensive use were positively associated, while higher HDL-C was negatively associated with CVD (all p&lt;0.05). Association with CVD and diabetes duration remained constant until 20 years when a linear increase was noted. The multivariate model demonstrated good discriminatory ability for CVD (AUC 0.88; 95% CI 0.85-0.92).
Conclusions: Cardiovascular risk factors were prevalent and strongly associated with CVD among adults with type 1 diabetes. Given the approximate J-shaped association between diabetes duration and CVD, the impact of cardiovascular risk stratification and management before and after 20 years duration will need to be further assessed.

Disclosure
A.J. Pease: None. A. Earnest: None. N. Nanayakkara: None. S. Ranasinha: None. J.R. Flack: None. W.A. Davis: Speaker's Bureau; Self; Eli Lilly and Company. Speaker's Bureau; Spouse/Partner; Novo Nordisk A/S. Advisory Panel; Spouse/Partner; Novo Nordisk A/S. Research Support; Spouse/Partner; Novo Nordisk A/S. Other Relationship; Spouse/Partner; Novo Nordisk A/S. Speaker's Bureau; Spouse/Partner; Merck Sharp & Dohme Corp.. Advisory Panel; Spouse/Partner; Merck Sharp & Dohme Corp.. Speaker's Bureau; Spouse/Partner; AstraZeneca. Advisory Panel; Spouse/Partner; AstraZeneca. Speaker's Bureau; Spouse/Partner; Boehringer Ingelheim GmbH, Pfizer Inc.. G.M. Kilmartin: None. N. Wischer: None. S. Zoungas: Advisory Panel; Self; AstraZeneca.
Sclerostin Resistance Protects Bone Mass and Retards the Onset of Metabolic Abnormalities in a Mouse Model of Type 1 Diabetes
Authors: LEANZA GIULIA , STROLLO ROCKY , POZZILLI PAOLO , CIVITELLI ROBERTO , NAPOLI NICOLA 
Published: June 2018

Type 1 diabetes (T1D) is characterized by bone fragility and increased risk of fracture. In humans and preclinical models of T1D, serum markers of bone formation are decreased suggesting reduced bone turnover rates. In a preliminary study, we found high circulating levels of the Wnt inhibitor sclerostin (Scl) in T1D patients. To test the potential role of Scl and Wnt in T1D bone disease, we introduced the Scl-resistant Lrp5A214V mutation, associated with high bone mass (HBM), in mice carrying the Ins2Akita mutation (Akita), which causes hypoinsulinemia and hyperglycemia at age 4-5 week. Both Akita and Akita/HBM mutants developed diabetes (non-fasting blood glucose &gt;300 mg/dl), albeit with different onset timing. At 6 weeks, only 30% of Akita/HBM mice had developed hyperglycemia, compared to 90% of Akita mice (n=10); and at 8 weeks only 40% of Akita/HBM were hyperglycemic vs. 90% in the Akita group. Only at 12 weeks was the majority of Akita/HBM mice hyperglycemic, as the Akita group. Furthermore, Akita mice exhibited a significant impairment in glucose tolerance in an intraperitoneal glucose tolerance test relative to Akita/HBM mice at 6 and 8 weeks (p&lt;0.for difference in areas under the curve; n=3-6). Thus, Wnt signaling hyperactivation in Scl resistant mice may slow the onset of T1D, although it does not prevent it. No significant differences in weight and nose-tail length were found in Akita/HBM mutants and controls. Importantly, bone mass was 18% higher in Akita/HBM relative to Akita littermates at 12 weeks (p&lt;0.001; n=3-6), a difference that persisted up until at least 18 weeks of age, despite prolonged hyperglycemia. Bone mass in compound mutants was not different relative to HBM mice. Thus, the metabolic changes caused by T1D do not alter the consequence of Scl resistance and Wnt hyperactivation on bone.
In summary, even though we have not yet measured bone strength, Wnt stimulation protects bone mass and may retard the onset of metabolic abnormalities in T1D.

Disclosure
G. Leanza: None. R. Strollo: None. P. Pozzilli: Research Support; Self; Sanofi. Speaker's Bureau; Self; Eli Lilly and Company. Research Support; Self; Merck Sharp & Dohme Corp.. R. Civitelli: None. N. Napoli: None.
Functional Evaluation of RNLS, a Gene Harboring Risk Variants for Type 1 Diabetes in European and African Ancestry Subjects
Authors: ONENGUT-GUMUSCU SUNA , VOGLER NOAH , FAIDAS MARIA , PICKIN REBECCA R. , GERSZ ELAINE , RICH STEPHEN S. 
Published: June 2018

Genetic studies of type 1 diabetes (T1D) have identified more than 40 risk loci, yet the causative genes in each locus is widely unknown. Here, we investigated a candidate gene in the 10q23.31 risk locus—FAD-dependent amine oxidase (RNLS). Variants in RNLS gene are associated with T1D risk in both European and African ancestry populations, indicating common genetic contributions to T1D. Currently, our understanding of the function of RNLS is very limited and primarily within the context of a few human diseases, most notably hypertension, kidney disease, cardiovascular disease, and cancer. In previous studies a positive RNLS-STAT3 feedback loop has been suggested. With consideration to these reports, and the importance of JAK-STAT signalling in immune-mediated diseases we evaluated if RNLS functions in activation of STAT3. An IFN-β time course was conducted to understand if overexpression of RNLS affects STAT3 levels and phosphorylation levels of STAT3. While we observed no difference in steady-state levels of STAT3 in cells overexpressing RNLS, we did observe increased levels of phospho-STAT3 upon IFN-β stimulation (p: 0.0051). In addition, to understand the function of RNLS in the immune system, we used RNAseq to compare transcriptomes of Jurkat cells expressing endogenous RNLS vs. ones overexpressing RNLS. RNAseq analysis identified 390 genes that were differentially expressed, by more than two-fold, of which 74.1 (289/390) of genes were up-regulated and 25.9 (101/390) were down-regulated in cells overexpressing RNLS. Tissue specific gene network analysis revealed that genes that were up-regulated mainly functioned in pathways related to chromosome segregation and cell division while genes that were down-regulated by RNLS were involved in IFN and NF-kappa B signaling pathways. Overall our results established a role for RNLS in cell-cycle related pathways and immune signaling pathways, providing new mechanistic insights of the role of RNLS in T1D.

Disclosure
S. Onengut-Gumuscu: None. N. Vogler: None. M. Faidas: None. R.R. Pickin: None. E. Gersz: None. S.S. Rich: None.
Mendelian Randomization Suggests Causal Influence of Glycemic Traits on DNA Methylation
Authors: DICORPO DANIEL A. , LENT SAMANTHA , GUAN WEIHUA , HIVERT MARIE-FRANCE , PANKOW JAMES S. ,   CHARGE EPIGENETICS WORKING GROUP,   CHARGE DIABETES WORKING GROUP
Published: June 2018

Type 2 diabetes (T2D) is caused by interactions between genetic predisposition and environmental exposure. DNA methylation, a key epigenetic mark, is one measure of the molecular impact of environmental factors on DNA. We tested for associations between glycemic traits and peripheral blood cell DNA methylation in 473,669 CpG sites across the genome in individuals from multiple ancestries, and tested for causal relationships at these loci.
We conducted an epigenome-wide fixed effects meta-analysis for 3 glycemic traits-fasting glucose (FG), log fasting insulin (FI), and HbA1c—in 13,543 nondiabetic individuals of African (N=3,746), European (N=9,254), and Hispanic (N=543) ancestry across 16 cohorts. All analyses were adjusted for age, sex, smoking status, cell types, BMI, and technical covariates. We used Mendelian randomization (MR) analyses to determine whether glycemic traits causally influence methylation at the CpG sites identified in the meta-analysis. We performed MR analyses in Framingham Heart Study individuals (N=6,472), using separate genetic risk scores (GRS) as instruments for each of the 3 glycemic traits.
In the epigenome-wide meta-analysis of all ancestries, there were 43 CpG sites associated with FG, 89 with FI, and 54 with HbA1c, using a Bonferroni threshold of P&lt;1.1×10-7. In addition to detecting many novel candidate loci for each trait, we found associations with all 3 glycemic traits at previously reported T2D methylated loci SREBF1, LINC00649, and ABCG1. In MR analyses, we showed that a higher GRS for FI was associated with greater methylation at cg17058475 in CPT1A (P=4×10-4) and higher GRS for HbA1c with greater methylation at cg05779219 near THBD (P=4×10-4).
We identified many novel candidate methylation loci associated with glycemic traits and 2 CpG sites where MR analysis indicated that methylation is causally influenced by glycemic traits. These findings support the hypothesis that variation in glycemia influences peripheral blood DNA methylation.

Disclosure
D.A. DiCorpo: None. S. Lent: None. W. Guan: None. M. Hivert: None. J.S. Pankow: None.
Targeted Metabolomics Identifies Elevated Serotonin Levels in Carriers of a TCF7L2 Diabetes Risk Allele
Authors: LEIHERER ANDREAS , MUENDLEIN AXEL , GEIGER KATHRIN , SAELY CHRISTOPH H. , BRANDTNER EVA-MARIA , EBNER JANINE , LARCHER BARBARA , MADER ARTHUR , FRAUNBERGER PETER , DREXEL HEINZ 
Published: June 2018

The transcription factor 7-like 2 (TCF7L2) is part of the Wnt signaling pathway. Its polymorphism rs7903146 is associated with risk for metabolic diseases, primarily diabetes, whereas the molecular mechanisms explaining how TCF7L2 impacts metabolism have remained unsolved. We evaluated the metabolic profile of a total of 394 angiographied patients with respect to their rs7903146 genotype (C/T) using targeted metabolomics in a discovery (n=154) and a validation (n=240) study. We identified serotonin as the top ranked metabolite to be increased in carriers of the diabetes risk allele (T) in both studies. For all 394 patients fold change was 44.6% (p=2.e-5), and in multivariate logistic regression analyses, serotonin concentration was significantly associated with the rs7903146 genotype even after full adjustment including diabetes (stand. adj. OR 2.69[1.23-5.87]; p=0.013). In a larger cohort of 1660 similar patients, we found that risky allele carriers had a significantly higher fasting glucose (116 vs. 109 mg/dl, p=0.007), post-challenge glucose (147 vs. 135 mg/dl, p=0.036), HbA1c (6.2 vs. 6.0 %, p&lt;0.001), and prevalence of T2DM (30.8 vs. 25.8 %, p=0.022) than patients without the risky allele. Apart from rs7903146, analyzing the whole gene, we found that 9 out of 19 unlinked SNPs in TCF7L2 were significantly associated with serotonin as well.
In conclusion, this study identifies a significant association between elevated serotonin concentrations and the diabetes risk allele of the TCF7L2 rs7903146 polymorphism. Recently, Wnt-signalling has been suggested to be involved in serotonin expression and serotonin has been shown to regulate glucose homeostasis, to increase the risk of diabetes, and to be elevated in diabetic subjects. Together, these new findings suggest that serotonin may be, at least in part, involved in the impact of Wnt/TCF7L2-signalling on metabolic homeostasis and diabetes.

Disclosure
A. Leiherer: None. A. Muendlein: None. K. Geiger: None. C.H. Saely: None. E. Brandtner: None. J. Ebner: None. B. Larcher: None. A. Mader: None. P. Fraunberger: None. H. Drexel: None.
Quantitative Trait Loci G×E and G×G for Glycemic Traits—Response to Metformin and Placebo in the Diabetes Prevention Program (DPP)
Authors: MAXWELL TAYLOR J. , JABLONSKI KATHLEEN A. , FRANKS PAUL W. , KAHN STEVEN E. , KNOWLER WILLIAM C. , MATHER KIEREN J. , FLOREZ JOSE C. ,   DPP RESEARCH GROUP
Published: June 2018

The complex genetic architecture of type 2 diabetes (T2D) includes gene-by-environment (G×E) and gene-by-gene (G×G) interactions. To identify genetic markers for G×E and G×G, we screened individual markers for patterns indicative of interactions (rQTL and vQTL). rQTL exist when the correlation between multiple glycemic traits varies by genotype and vQTL occur when the variance of a glycemic trait varies by genotype (potentially flagging G×G and G×E interactions). In the metformin and placebo arms of the DPP (n=1,762) we screened 280,965 exomic and intergenic SNPs with minor allele frequency &gt;0.01, for rQTL and vQTL patterns in year one changes from baseline in glycemic traits (insulinogenic index [IGI], insulin sensitivity index [ISI], fasting glucose and fasting insulin). Covariates included baseline age and BMI, sex, and ancestry. Significant (P&lt;1.8×10-7) vQTL and rQTL generated a priori hypotheses for G×G screens and individual G×E tests for a SNP × metformin treatment interaction. All P values were obtained via parametric bootstrapping. Several unique vQTL and rQTL were identified, leading to 6 nominally significant metformin treatment × SNP interactions (4 IGI, 1 insulin, 1 glucose) and 12 G×G interactions (all IGI) that exceeded experiment-wide significance (P&lt;4.1×10-9). Of these loci, some are directly associated with incident diabetes, others act as rQTL to modify a glycemic trait’s risk relationship with diabetes (2 diabetes/glucose, 2 diabetes/insulin, 1 diabetes/IGI). rs3197999, an ISI/Insulin rQTL, is a possibly gene damaging missense mutation in MST1, a gene affecting β-cell apoptosis and insulin secretion that in DPP and other studies is associated with diabetes and other glycemic traits. This rQTL may also link MST1 with insulin sensitivity where ISI and insulin responses differentially vary by genotype. This study demonstrates evidence for context-dependent effects (G×G and G×E) and complexity of these T2D-related traits.

Disclosure
T.J. Maxwell: None. K.A. Jablonski: None. P.W. Franks: Research Support; Self; Eli Lilly and Company, Sanofi, Novo Nordisk A/S, Servier, Boehringer Ingelheim GmbH. Consultant; Self; Zoe Ltd. S.E. Kahn: Advisory Panel; Self; Boehringer Ingelheim GmbH, Elcelyx Therapeutics, Inc., Eli Lilly and Company, Intarcia Therapeutics, Inc., Janssen Research & Development, Merck & Co., Inc., Novo Nordisk A/S. W.C. Knowler: None. K.J. Mather: Research Support; Self; Merck & Co., Inc., Sanofi-Aventis, Novo Nordisk A/S, Abbott. Advisory Panel; Self; Merck & Co., Inc. J.C. Florez: Consultant; Self; Intarcia Therapeutics, Inc.. Consultant; Spouse/Partner; Santen. D. Research Group: None.