bluuebunny/crossref_metadata_2025_split huggingface dataset:
Trajectories of Health-Related Quality of Life (HR-QoL) in Older Adults with Diabetes
Authors: PARK SUNHEE , LEE TAEWHA 
Published: June 2018

Background: Health-related quality of life (HR-QoL)—a major outcome of diabetes management interventions—is a general self-rated concept regarding one’s biological functions, symptoms, and functional status. However, there is limited information on the longitudinal changes in HR-QoL of older adults over time.
Objective: To identify different patterns of HR-QoL changes in longitudinal data and reveal potential predictors of these trajectories among older adults with diabetes
Methods: Subjects were older adults over 65 years with diabetes (n = 440), who participated in the Korea Health Panel survey for five consecutive years (2009-2013). HR-QoL longitudinal data were measured using Euro-QoL 5-Dimension Questionnaire. Growth mixture modeling was used to estimate distinct patterns, and binary and logistic regressions were used to determine factors affecting different trajectories of HR-QoL patterns using STATA 14.0.
Results: Four distinct trajectories of HR-QoL were found to be the best based on the Bayesian information criterion. High decrease HR-QoL indicates older adults with diabetes (10.0%) whose HR-QoL remained stable in the high score and began decreasing four years post-estimation. High stable HR-QoL indicates constantly high HR-QoL of older adults with diabetes (52.8%). Medium stable HRQoL indicates constantly medium HR-QoL of older adults with diabetes (36.0%). Medium increase indicates (1.3% of subjects) an HR-QoL trajectory pattern, which decreased from a medium level and increased three years post-estimation. Distinct trajectories of HR-QoL significantly associated with decrease vs. stable were age, having a spouse, impaired vision, subjective health status, and medication compliance.
Conclusion: It is suggested that although most participants had stable HR-QoL, comprehensive assessment and individualized management programs are needed to improve HR-QoL of older adults with diabetes.

Disclosure
S. Park: None. T. Lee: None.
Targeting Ketohexokinase (KHK) with a Novel Antisense Oligonucleotide (ASO) Decreases De Novo Lipogenesis and Improves Insulin-Mediated Whole Body Glucose Metabolism
Authors: LIU DONGQING , STERPKA JOHN A. , VATNER DANIEL F. , BELL MELANIE , MURRAY SUE , BHANOT SANJAY , CLINE GARY , SAMUEL VARMAN 
Published: June 2018

Increased sucrose consumption is associated with rising rates of obesity and related conditions [e.g., insulin resistance, type 2 diabetes and nonalcoholic fatty liver disease (NAFLD)]. Ketohexokinase (KHK) catalyzes the first step of fructose metabolism and is highly expressed in liver, kidney and brain, though found in many tissues. Essential fructosuria (hepatic KHK deficiency) is a benign condition, suggesting that KHK is a viable drug target. Inhibition of hepatic KHK would block metabolism of fructose (50% of dietary sucrose) and improve numerous facets of metabolism. ASO’s potently decrease target expression in liver and WAT and we hypothesized that a novel KHK ASO would prevent NAFLD and insulin resistance in normal rats fed a low fat (12% calories from fat), sucrose containing diet (17% calories from fructose). KHK ASO decreased hepatic KHK mRNA and protein expression by 96 and 90%, respectively. KHK ASO decreased WAT mRNA expression by 65%. KHK ASO did not alter body mass. KHK ASO decreased fasting plasma glucose (102±2 vs. 94±2 mg/dL, P=0.01), fasting plasma triglyceride 60% (52±7 vs. 21±2 mg/dL P&lt;0.01) and liver triglyceride by 30% (8.2±0.4 vs. 5.6±0.3 mg TG/g liver, P&lt;0.001) but not plasma insulin or non-esterified fatty acids. Insulin sensitivity was assessed by a 4 mU kg-1 min-1 hyperinsulinemic-euglycemic clamp. Surprisingly, there were no differences in rates of EGP or suppression of EGP. Instead there was a 25% improvement in insulin stimulated whole body glucose metabolism (36.6±0.8 vs. 45.6±2.2, P&lt;0.01). We attribute this to a 40% decrease in hepatic DNL (53±4 vs. 32±4%, P&lt;0.01) and VLDL secretion rate (11.0± 0.8 vs. 5.5± 0.5 mg/dL-min P&lt;0.01) independent of changes in SREBP1 or ChREBP mRNA expression. Thus, decreased DNL and VLDL export prevents fructose induced peripheral insulin resistance.
Conclusion: KHK ASO prevents NAFLD, decreases plasma triglyceride and improves insulin sensitivity.

Disclosure
D. Liu: None. J.A. Sterpka: None. D.F. Vatner: None. M. Bell: Employee; Self; Ionis Pharmaceuticals, Inc.. Employee; Spouse/Partner; Ionis Pharmaceuticals, Inc. S. Murray: Employee; Self; Ionis Pharmaceuticals, Inc. S. Bhanot: Employee; Self; Ionis Pharmaceuticals, Inc.. G. Cline: None. V. Samuel: None.
Point-of-Care HbA1c—A Case for Diabetes Screening and Diagnosis
Authors: SZABLOWSKI CARRIE J. , SUSCHA EMILY , DAVIS KADDY , XIE CHARLES Z. , MOSKOWITZ KEITH , ANDERSON JAMES H. , MECHLEY AMY 
Published: June 2018

Background: Hemoglobin A1C (A1C) is routinely used to screen, diagnose, and monitor diabetes. A1C is the desired tool to assess glycemic status as it reflects 90 days of glycemic control and does not require fasting. Point-of-care (POC) A1C offers rapid turnaround enabling direct patient engagement. POC A1C tests are indicated for monitoring, but not screening or diagnosis, due to perceived inaccuracy and imprecision. To counter this impression, we evaluated the performance of two POC A1C analyzers relative to the clinical laboratory (CL).
Method: Blood from 48 subjects was collected with A1C values from 4.8-12.0% A1C. Capillary blood was analyzed on the PTS Diagnostics A1CNow®+ system and Siemens DCA Vantage® while venous blood was tested at external labs on the Tosoh G8 and Roche Cobas® c513 as CL comparators. For reference, venous blood was analyzed on the Roche Cobas Integra® 400 plus. Regression analysis was used to evaluate accuracy and paired differences to measure bias. Clinical risk was calculated using A1C category cut points of &lt; 5.7, 5.7-6.4, and ≥ 6.5% A1C. Chi-squared test was used to assess differences between methods. Precision was performed on the POC systems using 10 replicates at approx. 4.5, 7, and 10% A1C.
Results: Average paired biases were identical for the POC and CL analyzers (-0.04% A1C). Slopes were 0.97, 0.99, and 0.97 for the A1CNow+, DCA, and CL analyzers (p = 0.71) and intercepts were 0.15, 0.04, and 0.12 (p = 0.99). Regression values were 0.987, 0.996, and 0.998, respectively. Clinical risk agreement was 94% for A1CNow+, 100% for the DCA, and 98% for CL analyzers (p = 0.17). Pooled precision was 3.3% CV for A1CNow+ and 1.7% CV for DCA.
Conclusion: The A1CNow+ and DCA were shown to be as accurate as the clinical lab in measuring A1C values. Risk analysis showed no statistical differences between the CL and POC analyzers in classifying diabetes. The A1CNow+ and DCA are valuable tools in evaluating the diabetic state and provide physicians with real-time information to better care for patients.

Disclosure
C.J. Szablowski: Employee; Self; PTS Diagnostics. E. Suscha: Employee; Self; PTS Diagnostics. K. Davis: Employee; Self; PTS Diagnostics. C.Z. Xie: Employee; Self; PTS Diagnostics. K. Moskowitz: Employee; Self; PTS Diagnostics. Stock/Shareholder; Self; PTS Diagnostics. J.H. Anderson: Employee; Self; PTS Diagnostics. Board Member; Self; GENEREX Biotechnology. A. Mechley: Consultant; Self; PTS Diagnostics.
Estimation of Population Insulin Sensitivity (SI) from Sparsely Sampled Oral Glucose Tolerance Test (OGTT) Data
Authors: STEFANOVSKI DARKO , UMPIERREZ GUILLERMO E. , VELLANKI PRIYATHAMA 
Published: June 2018

The oral glucose tolerance test (OGTT) minimal model analysis by Cobelli et al. obtains estimates of insulin sensitivity (SI) based on mathematical models that are highly concordant with the euglycemic clamp, the gold standard for estimation SI. This model requires a minimum of 7 blood samples during the 2-hour OGTT. However, in many OGTT studies, the number of samples is below the number that permits the estimation of SI. One way to address (overcome) this issue is by pooling sparsely sampled data (&lt;7 samples) from a specific cohort to obtain population estimates of parameters of SI. Here we present a novel methodology of estimating population SI using sparsely-sampled OGTT protocol. A cohort of 6 African American subjects (3 Female/ 3 Male) without a known history of T2D underwent 75-gm OGTTs with 6 samples performed at 0, 15, 30, 60, 90 and 120 minutes. The population SI was estimated concurrently fitting the data from all subjects. We obtained population estimates of SI using from 3 to 6 samples per subject. SI estimates for each subject were also calculated. Sparse-sampling population estimated SI varied from SI=4.74±.75 10-4.(mu/l)-1.min-1 for 4 samples to SI= 6.40±2.40 10-4.(mu/l)-1.min-1 for 3 samples. The individual estimates of SI had a mean of SI= 4.72±2.43 10-4.(mu/l)-1.min-1. SI obtained with the sparse-sampling methodology did not differ from the mean individual estimates of SI. We conclude that Sparse-sampling estimation of SI (&lt;7 samples) is a useful tool for estimation of population parameters of SI where there are not enough observations per subject to estimate individual insulin sensitivity. This methodology will provide better phenotyping of insulin sensitivity for studies where previously more crude measures were used.

Disclosure
D. Stefanovski: None. G.E. Umpierrez: Research Support; Self; Sanofi US, Merck & Co., Inc., Novo Nordisk Inc., AstraZeneca. Advisory Panel; Self; Sanofi, Intarcia Therapeutics, Inc. P. Vellanki: Consultant; Self; Boehringer Ingelheim Pharmaceuticals, Inc.. Research Support; Self; Boehringer Ingelheim Pharmaceuticals, Inc., AstraZeneca.
Maturity-Onset Diabetes of the Young (MODY) in a Racially Diverse Pediatric Population
Authors: RELAN SHILPI , HESSEL FRANCES C. , TIMMONS KELLY D. , SONABEND RONA , REDONDO MARIA J. 
Published: June 2018

Current practice guidelines recommend considering a diagnosis of MODY in patients with a strong family history but without typical features of type 2 diabetes (T2D) (i.e., obesity, insulin resistance, high-risk ethnicity) or type 1 diabetes (T1D) (i.e., islet autoantibodies).
We aimed to test if these criteria apply to a racially diverse pediatric patient population.
Using EPIC Population Health, we retrospectively reviewed electronic medical records (IRB H-41512) of youth diagnosed with MODY between 2013-2017 in a large academic pediatric hospital in Southwestern U.S.
We found that, out of 46 patients with MODY, 50% (n=23) had MODY 2, 17.3% (n=8) MODY 3, 17.3% (n=8) MODY 5, 8.6% (n=4) MODY 1, 2.1% (n=1) MODY 4, and 8.6% of patients had a clinical diagnosis of MODY due to inability to perform molecular testing. The mean (SD, range) age of diabetes diagnosis was 10.1 years (4.4, 2-17); 52% of patients were females; 50% were non-Hispanic white, 32.5% Hispanic, 6.5% African American, 6.6% Lebanese, 2.2% Asian and 2.2% others/unknown. Notably, 15.2% (n=7, all with a molecular diagnosis of MODY) had positive islet antibodies, 8.6% (n=4) Hashimoto’s thyroiditis and 2.1% (n=1) vitiligo. Obesity/overweight was present in 23.8%, 15.2% had hypertension / prehypertension and 21.7% had dyslipidemia. Diabetes was absent in parents and siblings of 26% (n=12) of patients, although in 5 patients, the mother had gestational diabetes. Persistent microalbuminuria was found in 2 youth with MODY 2 and diabetic neuropathy in 1 patient with MODY 2. The mean (SD, range) time between the diagnoses of diabetes and MODY was 14.3 months (22.3, 1-120).
In conclusion, youth with MODY often were of ethnic minorities, lacked a family history of diabetes and had characteristics typical of T1D or T2D, suggesting that the current guidelines to consider a diagnosis of MODY may not apply to a racially diverse U.S. pediatric population. Furthermore, in contrast to previous studies, we identified microvascular complications in youth with MODY 2.

Disclosure
S. Relan: None. F.C. Hessel: None. K.D. Timmons: None. R. Sonabend: None. M.J. Redondo: None.
The Cuban Prospective Study-Diabetes and Cause-Specific Mortality in Cuba
Authors: CHARLES SHAQUILLE J.C. 
Published: June 2018

Introduction: Large-scale prospective studies of diabetes and cause-specific mortality have been conducted mainly in high-income countries, where there is good access to healthcare. However, there is limited evidence on the effect of diabetes on mortality in low- and middle-income countries, such as Cuba.
Methods: From 1996-2002, we recruited 146,556 participants (aged ≥30) from the general population in Cuba into a prospective cohort study. We recorded the presence or absence of previously diagnosed diabetes, and monitored for deaths through December 31st, 2015. We estimated adjusted hazard ratios (HR) for cause-specific mortality at ages 30-84 years among participants who had diabetes at recruitment vs. those who did not have diabetes at recruitment; participants with prior chronic disease other than diabetes were excluded from the analyses.
Results: At recruitment, the overall prevalence of diabetes was 4% in men and 7% in women, and the mean age was 53 (SD13) years. Previously diagnosed diabetes was associated with about twice the risk of all-cause mortality (HR 1.89 [95% CI 1.79-1.99]; mean age at death 70 [SD12]). The association was stronger at younger ages, but did not differ by sex: HR 2.68 (2.29-3.14) at 30-59 years, 2.20 (2.03-2.38) at 60-74 years, and 1.56 (1.44-1.68) at 75-84 years. Diabetes was strongly associated with mortality from renal, cardiovascular and respiratory diseases, but not with cancer. The excess mortality associated with diabetes accounted for ∼10% of all premature deaths (&lt;75 years) in Cuba for 2015; mostly from cardiovascular disease.
Conclusion: Despite limited resources in Cuba, the association of diabetes with all-cause mortality was similar to high-income countries and less extreme than in many other middle-income countries.

Disclosure
S.J.C. Charles: None.
Liver Fibrosis as Assessed by the FIB-4 Index in Patients with Type 2 Diabetes (T2DM)
Authors: FILOZOF CLAUDIA M. , JONES STEPHEN , GOLDSTEIN BARRY J. 
Published: June 2018

Data about liver fibrosis in T2DM are scarce. The FIB-4 index is a non-invasive panel to evaluate underlying hepatic fibrosis. FIB-4 values &lt;1.45 and ≥3.25 were reported to have a negative predictive value (NPV) of ∼90% and PPV of∼65% for advanced fibrosis. FIB-4 values ≥ 2.67 were reported to be associated with 16X increase in clinical outcomes in NAFLD. FIB-4 range ≥1.45 &lt; 2.67 is suggested to be an indeterminate area. We assessed FIB-4 values and their changes over 4 years in 334,076 patients (48% males) with T2DM. A total of 75.2%, 22.3%, 1.3% and 1.2% of subjects had FIB-4≥ 1.45 (G1), ≥1.45 &lt;2.67 (G2), ≥ 2.67&lt;3.25 (G3) and ≥3.25 (G4). Subjects with low FIB-4 were younger, had lower ALT/AST and higher platelet counts (Table). Almost 50% of the patients with FIB-4≥ 2.67 had ALT/AST within reference ranges (RR). Mean plasma TG and HbA1C were similar among groups. Among G1 patients, 83% stayed G1, 16.3% progressed to G2 and 0.7% had FIB-4 ≥ 2.67 after 4 years. A total of 10% and 27% of patients initially in G2 and G3 progressed to FIB-4 associated with higher risk of liver outcomes.
Conclusion: Only 75% of the patients had FIB-4 suggesting absence of advanced liver fibrosis, ∼ 50% of subjects at risk of liver outcomes had ALT/AST within RR. FIB-4 is a simple, inexpensive method that may help identify patients at risk of liver outcomes. Strategies to slow the growth of liver complications and therapeutic options are necessary to mitigate disease burden.

Disclosure
C.M. Filozof: Employee; Self; Covance Inc.. S. Jones: None. B.J. Goldstein: Employee; Self; Covance Inc..
Using Claims and Consumer Wearable Devices Data to Quantify Influenza-Related Outcomes among Type 2 Diabetes Patients—A Large Population Study
Authors: SAMSON SANDRINE I. , LEE WEI-NCHIH , QUISEL THOMAS , FOSCHINI LUCA , LISKA JAN , MILLS HENRY G. , HOLLINGSWORTH ROSALIND C. , GREENBERG MICHAEL E. , BEAL ANNE C. 
Published: June 2018

Influenza causes substantial morbidity in people with diabetes; annual vaccination is recommended. We conducted a retrospective study of influenza-related outcomes (IRO) among fully insured type 2 diabetics from a large U.S. payer over a 1 year period covering the 2016-17 influenza season. We used bivariate analysis (54,656 diabetics, mean age 54.8 years, s.d. = 10.2) to compare IRO’s inferred from claims data against IRO’s for 113,016 age and gender matched nondiabetics. Diabetics had more influenza events (per ICD-9 and ICD-10 codes, 1.96% vs. 1.37%, p&lt;0.001), and were prescribed more influenza antivirals per 100 ppl (27.1 vs. 22, p&lt;0.001). Within 2-weeks before and 4-weeks after a medical claim for influenza, diabetics with influenza had more observed hyperglycemic events than in a comparable non-influenza period in the same year (3.81% vs. 2.18%, p&lt;0.001), with substantial increases in pneumonia, sepsis and coronary disease (5.5% vs. 0.7%, 5.5% vs. 3.5%, 1.6% vs. 0.3%, p&lt;0.001), and more outpatient antibiotic use per 100 people (54.5 vs. 16.9, p&lt;0.001). 8.2% of type 2 diabetics (vs. 9.1% controls, p&lt;0.001) used commercially available activity and sleep trackers, sharing data through an insurer provided wellness platform. Activity trackers showed that diabetics slept fewer hours compared to controls (6.48 vs. 6.69 hours, p&lt;0.001), and had statistically significant changes in sleep and walking habits in the days around an influenza event as compared to baseline (about 2% more of the night spent restless and 10k fewer steps). This is the first population scale study to use medical claims linked with activity tracking data to quantify the behavioral and clinical effects of influenza in type 2 diabetes. These data highlight the impact of influenza on glycemic control and the daily lives of the Diabetic population, and reinforce the need for annual influenza vaccine, as recommended by the WHO and other global public health bodies.

Disclosure
S.I. Samson: Employee; Self; Sanofi Pasteur. W. Lee: None. T. Quisel: None. L. Foschini: None. J. Liska: Employee; Self; Sanofi. H.G. Mills: Employee; Self; Sanofi. R.C. Hollingsworth: Employee; Self; Sanofi Pasteur. M.E. Greenberg: Stock/Shareholder; Self; Merck & Co., Inc.. Employee; Self; Sanofi. Stock/Shareholder; Self; Sanofi, GlaxoSmithKline plc., Pfizer Inc. A.C. Beal: Employee; Self; Sanofi. Board Member; Self; AcademyHealth.
Efficacy of A1C in the Presence of HbC Trait Needs Evaluation
Authors: BRIKER SARA M. , KABAKAMBIRA JEAN DAMASCENE , BAKER RAFEAL L. , DUBOSE CHRISTOPHER , MABUNDO LILIAN , CHUNG STEPHANIE T. , SACKS DAVID B. , SUMNER ANNE E. 
Published: June 2018

Hemoglobin C (HbC) trait is a Hb variant found in 15% of West Africans. Similar to HbS, HbC occurs because of a single nucleotide substitution on the beta-globin chain of hemoglobin. Many studies have evaluated whether HbS trait influences the diagnostic efficacy of A1C. Data on the effect of HbC trait on A1C is scant. Our goal was to determine by Hb-type the sensitivity and specificity of A1C as a diagnostic test for the detection of abnormal glucose tolerance (abnl-GT) in 369 blacks born in Africa and currently living in the Washington, DC area (age 38±10y (mean±SD), BMI 27.5±4.5 kg/m2). OGTT and Hb electrophoreses were performed. Abnl-GT was defined as fasting glucose≥100 mg/dL or 2h glucose≥140 mg/dL. A1C ≥5.7% was considered diagnostic for abnl-GT. The frequencies of normal Hb-type, HbS trait and HbC trait were: 82%, 14% and 4%, respectively. The prevalence of abnl-GT in each group was: 38%, 42%, and 50% (P=0.59), respectively. Sensitivity and specificity of A1C by group are provided in the Table. No one in the HbC trait group with abnl-GT was detected by A1C. Hence, sensitivity was 0%. Furthermore, specificity of A1C in the HbC trait group was 100% as no one with normal glucose tolerance had A1C≥5.7%. The number of individuals with HbC trait was small but our findings suggest the need for greater investigation into the diagnostic efficacy of A1C in the presence of HbC trait.

Disclosure
S.M. Briker: None. J. Kabakambira: None. R.L. Baker: None. C. DuBose: None. L. Mabundo: None. S.T. Chung: None. D.B. Sacks: Other Relationship; Self; Sebia, Trinity Biotech. A.E. Sumner: None.
SGLT2 Inhibitors Suppress NLRP3 Inflammasome Activity via Changes in Ketones and Insulin in Type 2 Diabetes and Cardiovascular Diseases
Authors: LEE YONG-HO , KIM SO RA , BAE JAEHYUN , LEE BYUNG-WAN , KANG EUN SEOK , AHN CHUL WOO , CHA BONG-SOO 
Published: June 2018

SGLT2 inhibitors significantly reduce cardiovascular events in humans with T2D; however, the underlying mechanism remains unclear. Activation of NLRP3 inflammasome and subsequent IL-1β release induces atherosclerosis and heart failure. Recently, it was revealed that ketone bodies, i.e., β-hydroxybutyrate (BHB) suppresses activation of NLRP3 inflammasome in macrophages. As SGLT2 inhibitors cause increases in serum BHB by pharmacologic profile, we assessed the effect of SGLT2 inhibitor on NLRP3 inflammasome activity. In a randomized, active-controlled study, a total of 61 patients with T2D and high cardiovascular risk (mean age and HbA1C were 64.4 years and 7.32%, respectively) received SGLT2 inhibitor or sulfonylurea for 30 days. NLRP3 inflammasome activation was analyzed in macrophages and the serum levels of glucose, BHB, and insulin from baseline to the end of treatment were tested. While SGLT2 inhibitor’s glucose-lowering capacity was similar to sulfonylurea, it significantly decreased IL-1β secretion compared to baseline (2,394 ± 236 to 1,748 ± 295 pg/mL, p &lt;0.001), whereas sulfonylurea had no effect on IL-1β secretion (2,273 ± 279 to 2,755 ± 331 pg/mL, p = 0.05) (time × group interaction p &lt;0.001). SGLT2 inhibitor caused a significant increase in fasting serum BHB and decrease in serum insulin, while sulfonylurea had no significant effects on these measurements. We performed ex vivo experiments using human macrophages to investigate whether BHB and insulin could affect NLRP3 inflammasome activity. BHB dose-dependently inhibited IL-1β secretion from macrophages. However, co-treatment with insulin attenuated the inhibitory effect of BHB on NLRP3 inflammasome activation.
In conclusion, SGLT2 inhibitors attenuate NLRP3 inflammasome activation, in part, via increased serum BHB and decreased serum insulin and glucose, which might help to explain the cardioprotective effects of SGLT2 inhibitor (clinicaltrials.gov NCT02964572).

Disclosure
Y. Lee: None. S. Kim: None. J. Bae: None. B. Lee: None. E. Kang: None. C. Ahn: None. B. Cha: None.