bluuebunny/crossref_metadata_2025_split huggingface dataset:
1102-P: Identifying Blood Biomarkers for Type 2 Diabetes Subtyping: A Report from the ORIGIN Trial
Authors: PIGEYRE MARIE , GERSTEIN HERTZEL C. , GROOP LEIF , HESS SIBYLLE , PARE GUILLAUME 
Published: May 2022

Diabetes (DM) can be classified into 5 subtypes characterized by distinct progression in dysglycaemia and complications. Using 5 clinical variables, we categorized 7017 participants from the Outcome Reduction with an Initial Glargine Intervention (ORIGIN) trial into 1/auto-immune DM (n=241) , 2/insulin-deficient DM (n=1594) , 3/insulin-resistant DM (n=914) , 4/obesity-related DM (n=1595) , 5/age-related DM (n=2673) .Yet, whether blood biomarkers are associated with these subtypes is unknown. Forward-selection logistic regression models were used to identify biomarkers that were each independent determinant of one cluster versus the others, among 233 selected cardiometabolic proteins measured at baseline. Models were adjusted for age, sex, ethnicity, C-peptide level, diabetes duration. A total of 13, 2, 7 and biomarkers were independent determinants of DM subtypes 2 to 5 respectively (all P&lt;4.3x10-5) . A combination of 5 biomarkers that were distinctively associated with clusters (fig) , showed a diagnosis performance, through AUC-ROC curves, of 0.71, 0.86, 0.88, 0.82 to respectively distinguish cluster 2 to 5 from the others. No biomarkers other than GAD antibodies were determinants of cluster 1. We identified 5 serum biomarkers, as independent determinants of DM subtypes, that could be used as a diagnosis test for DM subtyping. Although this requires further validation in an independent population.

Disclosure
M.Pigeyre: n/a. H.C.Gerstein: Advisory Panel; Abbott, Eli Lilly and Company, Hanmi Pharm. Co., Ltd., Novo Nordisk, Pfizer Inc., Sanofi, Viatris Inc., Consultant; Kowa Company, Ltd., Other Relationship; DKSH, Eli Lilly and Company, Sanofi, Zuellig Pharma Holdings Pte. Ltd., Research Support; AstraZeneca, Eli Lilly and Company, Merck & Co., Inc., Novo Nordisk, Sanofi. L.Groop: None. S.Hess: Employee; Sanofi. G.Pare: Advisory Panel; Amgen Inc., Bayer AG, Sanofi, Research Support; Bayer AG.

Funding
NCT00069784 Canadian Institute of Health Research Sanofi
1104-P: Prevalence and Clinical Determinants of Nonalcoholic Fatty Liver Disease by Liver Scores in Adults with T1DM
Authors: LUNDHOLM MICHELLE D. , BENA JAMES F. , ZHOU KEREN , TSUSHIMA YUMIKO , KASHYAP SANGEETA 
Published: May 2022

NAFLD is common in diabetes, however the prevalence and clinical features of NAFLD in T1DM are not well defined.
A retrospective, unicenter, cross-sectional analysis was performed of adults with T1DM at our institution from 2015-2018. The hepatic steatosis index (HSI) , Framingham steatosis index (FSI) , FIB-4 index, and AST-to-platelet ratio index (APRI) were calculated and associated with clinical parameters.
We identified 447 patients, 38.6 ± 14.5 yrs, 54% (N=241) female, HgA1c 8.4 ± 1.8 %, BMI 28 ± 5.9 kg/m2. The prevalence of NAFLD by HSI ≥36 was 61% (N=271) and by FSI ≥23 was 52% (N=230) . The presence of advanced fibrosis by APRI &gt;0.7 was 4% (N=18) and FIB-4 &gt;2.67 was 4% (N=8 of 221 included) . Patients with metabolic syndrome (N=103, 23%) were significantly more likely to have NAFLD by HSI (86% vs. 53%, p&lt;0.001) and FSI (93% vs. 39%, p&lt;0.001) . Compared to BMI &lt;30, nearly all patients with BMI ≥30 (N=125, 28%) had NAFLD by HSI (100% vs. 45%, p&lt;0.001) and FSI (98% vs. 34%, p&lt;0.001) . Older age (44 vs. 33 yrs) , HTN, HLD, cardiovascular disease, and higher TG/HDL ratio (median 1.8 vs. 1.1) were associated with FSI ≥23. Older age (41 vs. 30 years) , HTN, HLD, and lower HDL (58 vs. 66 mg/dL) were associated with HSI ≥36. On multivariable analysis, older age, metabolic syndrome features, and BMI were associated with higher HSI and FSI. HgA1c was inversely related to HSI. Twenty of 94 patients (21%) with abdominal imaging reported hepatic steatosis; 8 (2%) had hepatology referral and 6 (1%) had a liver biopsy. Of 447 patients, GLP-1 agonist was prescribed in 5% (N=24) and thiazolidinedione in 4% (N=16) .
Liver scores indicating liver steatosis but not fibrosis is common in adults with T1DM with obesity and/or metabolic syndrome, and is associated with older age, HTN, HLD, and high TG/HDL. NAFLD is overlooked; only 21% of patients had any liver imaging and only 1% had a liver biopsy. Thiazolidinedione and GLP-1 agents are effective at preventing and reversing NAFLD damage but appear underutilized in the T1DM population.

Disclosure
M.D.Lundholm: None. J.F.Bena: None. K.Zhou: None. Y.Tsushima: None. S.Kashyap: Advisory Panel; Fractyl Health, Inc., GI Dynamics, Research Support; Janssen Pharmaceuticals, Inc.
1107-P: Is There a Discrepancy between Interstitial Glucose and Laboratory HbA1c in People Living with HIV?
Authors: DAULTREY HARRIET , LEVETT TOM , WRIGHT JULIET , OLIVER NICK , CHAKERA ALI 
Published: May 2022

Background: Diabetes is reported to be more common in people living with HIV (PLWH) . There is no internationally agreed screening method for diabetes in PLWH due to reports that HbA1c is falsely low in this group. The ADA recommend a fasting glucose to screen for diabetes in PLWH whilst the British HIV Association recommend measuring HbA1c.
Aims: To explore the relationship between HbA1c and interstitial glucose in PLWH. Methods Each participant wore a Dexcom G6 for days and had HbA1c measured. Exclusion criteria included factors affecting HbA1c. HbA1c values were compared to GMI values. Bland-Altman plot method was used to assess the agreement between the measurements.
Results: We included 57 PLWH (27 with T2DM, 6 female) , median HbA1c 44mmol/mol (IQR 38-55) , average sensor glucose 7.3mmol/l (IQR 6.4-9.1) . Median completed CGM readings per participant 99% (IQR 92-99) . Bland-Altman plot method (see figure) demonstrates the bias between the two methods is -3.2mmol/mol (95% CI: -1.0 to -5.6) with 95% limits of agreement -14.2 to 20.6. Six participants had an HbA1c 10mmol/mol lower than GMI and 2 had an HbA1c 10mmol/mol higher.
Conclusion: This is the first study using CGM to explore HbA1c discrepancy in PLWH. It shows the bias between interstitial glucose and HbA1c is small with moderate agreement between the two methods. Our data support a potential role for using HbA1c to assess glycaemia in PLWH.

Disclosure
H. Daultrey: None. N. Oliver: Advisory Panel; Dexcom, Inc., Medtronic, Roche Diabetes Care. Research Support; Dexcom, Inc., Medtronic. A. Chakera: None.

Funding
Dexcom - providing the glucose monitors for the study.
1111-P: Prevalence of Preexisting and Newly Detected Diabetes in Young Indians: Retrospective Analysis of Data from RSSDI Countrywide Defeat Diabetes Community Screening Program
Authors: GUPTA AMIT , SABOO BANSHI D. , PARIKH RAKESH M. , CH VASANTH K. , AGARWAL SANJAY , MAKKAR BRIJ M. , KOVIL RAJIV R. , CHAWLA SR., PURVI M. , NANDITHA ARUN , BHATTACHARYYA SUPRATIK , CHITTAWAR SACHIN 
Published: May 2022

Background: The ADA 2022 standard of care recommends routine screening for T2DM beginning from the age of 35 years, while Indian Council of Medical Research recommends to begin screening above the age of 30 years. Current study was aimed at assessing prevalence of diabetes among Indians below 35 and 30 years of age
Methodology: Data collected from One Nation, One Day, One Million Tests campaign by RSSDI was retrospectively analyzed. A total data of 225955 adults was included in the analysis. 466 subjects (28026 males & 18561 females) were below the age of 35 years while 27924 (16460 males and 11446 females) were below the age of 30 years. Prevalence of preexisting (self-reported) diabetes and newly detected diabetes was calculated in this subgroup
Result: 225955 people screened, 44.3% (100074) were having diabetes. 34.8% (78694) were aware that they have diabetes while 9.4% (21380) were detected with diabetes for the first time during screening. Overall 55.3% (55338) of subjects had family history of diabetes.
Subgroup analysis below 35 years of age showed- 12% (5612) had preexisting diabetes while 5.8% (2713) were newly detected with an overall prevalence 17.8% (8325) .
Among subjects below 30 years of age 8.7% (2433) had preexisting diabetes while 4.6% (1642) were newly detected with diabetes with an overall prevalence of 13.3% (3713) .
Conclusion: The prevalence of T2DM among the young population is on rise. Our study supports the ICMR guidelines for screening of diabetes in the Indian population to begin at the age of 30 years. Higher screening age is likely to miss out a significant number of people. The study has important implications as ADA may also consider screening people of Indian origin at a younger age.Thought should be given to begin screening In Indian population even below the age of 30 years especially in those with a family history of diabetes.

Disclosure
A.Gupta: Speaker's Bureau; Lupin Pharmaceuticals, Inc., Medtronic, Novartis AG, Novo Nordisk, Sanofi. S.Bhattacharyya: None. S.Chittawar: None. B.D.Saboo: None. R.M.Parikh: None. V.K.Ch: None. S.Agarwal: Speaker's Bureau; Novo Nordisk, Roche Diabetes Care, Sanofi, USV Private Limited. B.M.Makkar: None. R.R.Kovil: None. P.M.Chawla: Speaker's Bureau; Biocon, Boehringer Ingelheim International GmbH, Eris Lifesciences Ltd., Novo Nordisk, Sanofi. A.Nanditha: None.
1118-P: Prediction of Clamp-Derived Insulin Sensitivity in African Americans from Clinical Risk Factors
Authors: FOWLER LAUREN A. , PARCHA VIBHU , COUCH CATHARINE , GARVEY W. TIMOTHY , GOWER BARBARA 
Published: May 2022

Surrogate indices of insulin sensitivity based on fasting glucose and insulin values fail to adequately predict insulin sensitivity in African-Americans (AA) , a group with high risk for type 2 diabetes. The objective of this study was to evaluate the individual and combined utility of clinically accessible risk factors (demographic, anthropometric, and metabolic traits) to predict clamp-derived insulin sensitivity in 53 AA adults without diabetes. Insulin sensitivity was measured with the euglycemic clamp (SI-clamp) at an insulin dose of 120 µU/m2/min/kg lean body mass to assess primarily skeletal muscle glucose disposal. Concentrations of lipid/lipoproteins, adipokines, glucose, insulin, C-peptide, proinsulin, and inflammatory markers were obtained from fasting sera. Individual associations between SI-clamp and predictors were evaluated with linear regression, adjusting for age and sex. Multiple regression analyses with backward stepwise selection of all candidate predictors were used to derive prediction models. Body mass, triglycerides (TAG) , glucose, insulin, C-peptide, proinsulin, and TNF-α were inversely associated with SI-clamp, whereas female sex, HDL-cholesterol, adiponectin, and IFN-γ were positively associated (P&lt; 0.05) . For the prediction models, HDL-cholesterol, TAG, C-peptide, adiponectin, leptin, and IL-6 explained the highest proportion of variance in SI-clamp (AIC = 15.8 and adjusted R2 = 54.27%) . A second, more clinically applicable model was also derived using HDL-cholesterol, TAG, C-peptide, and adiponectin (AIC = 18.136 and adjusted R2 = 50.63%) . These results suggest that in AA, fasting levels of lipid/lipoproteins, C-peptide, and adiponectin are stronger predictors of insulin sensitivity than fasting glucose and insulin. Development of a surrogate index for insulin sensitivity with these predictors may aid in accurately identifying AA at increased risk for type 2 diabetes in clinical settings and epidemiological studies.

Disclosure
L.A.Fowler: None. V.Parcha: None. C.Couch: None. W.Garvey: Other Relationship; Boehringer Ingelheim International GmbH, Eli Lilly and Company, Epitomee, JAZZ Pharmaceuticals, Novo Nordisk, Novo Nordisk, Pfizer Inc., Pfizer Inc. B.Gower: None.

Funding
NIH/NIDDK (R01DK096288) NIH/NIDDK (P30DK056336) NIH/NIDDK (P60DK079626) NIH/NIDDK (T32DK062710) NIH/NCATS CTSA (U54TR001368-01)
1122-P: Subgroup Analysis to Evaluate Individuals at Risk for Incident Diabetes
Authors: ITO RYOMA , DAIMON MAKOTO , MIZUSHIRI SATORU , ONO SHOMA , NISHIYA YUKI , TAMURA AYUMI , MURABAYASHI MASAYA , KAMBA AYA 
Published: May 2022

Since type 2 diabetes (T2D) is a heterogenous disease, treatment of T2D appears to be better personalized depending on their underlining conditions. A recent attempt to classify adult-onset DM based on only 6 DM related variables (GAD antibodies, age at diagnosis, BMI, HbA1c, and HOMA2 indices) , showed that DM can be classified into 5 clusters, among them 4 represent T2D (Lancet Diabetes Endocrinol. 2018;6:361) . We here classified non-diabetic population to find risk cluster for incident DM, or to extend strategies for prevention of T2D. The participants were recruited from Iwaki study, a health promotion study of Japanese people. Among 1167 participants in 2014 (baseline) , 868 non-diabetic individuals who attended at least one time in 2015 to 20were included in this prospective study. A hierarchical cluster analysis was performed based on 5 variables (age, BMI, HbA1c, HOMA2-β, and HOMA2-IR) . The hazard ratios (HRs) for incident T2D of clusters observed were examined using Cox proportional hazards model. Traits (mean) of 5 clusters observed were as follows: age (clusters; 1: 60.1, 2: 58.7, 3: 63.6, 4: 36.1, 5: 48.3) , BMI (1: 20.8, 2: 24.2, 3: 22.9, 4: 20.7, 5: 27.7) , HbA1c (1: 5.54, 2: 5.67, 3: 6.01, 4: 5.41, 5: 5.73) , HOMA2-β (1: 82.8, 2: 101.8, 3: 78.5, 4: 101.4, 5: 143.1) , and HOMA2-IR (1: 0.53, 2: 0.79, 3: 0.73, 4: 0.57, 5: 1.16) . The HR for incident T2D was significantly higher in cluster 3 than in clusters 1, 2, and 4, even after adjustment for age and sex (HR (p) : 5.00 (0.005) , 4.90 (0.005) , and 10.4 (0.048) , respectively) . Accordingly, cluster 3 had significantly higher age and HbA1c than other clusters, lower HOMA2-β than clusters 2, 4, and 5, and lower HOMA2-IR and BMI than clusters 2 and 5.
In summary, cluster analysis of a general non-diabetic population revealed a risk cluster for incident T2D, and, thus, individuals with characteristics representing the cluster (less insulin resistant but decreased insulin secretion, older age, and poor metabolic control) should be a target for prevention of incident T2D.

Disclosure
R.Ito: None. M.Daimon: None. S.Mizushiri: None. S.Ono: None. Y.Nishiya: None. A.Tamura: None. M.Murabayashi: None. A.Kamba: None.
1125-P: Obstructive Sleep Apnea and Insomnia Are Highly Prevalent in Monogenic Diabetes
Authors: AROSEMENA MARILYN A. , WROBLEWSKI KRISTEN , TASALI ESRA , PHILIPSON LOUIS H. 
Published: May 2022

Background: Sleep disorders are highly common in type 1 and type 2 diabetes with important cardiometabolic implications. Maturity Onset Diabetes of the Young (MODY) has unique features compared to type 1 or type 2 diabetes, such that patients are generally lean and not using insulin.
Objective: To evaluate primary sleep disorders in patients with MODY. Methods: Participants were recruited from the Monogenic Diabetes Registry at the University of Chicago. The presence and severity of obstructive sleep apnea (OSA) was objectively assessed by a validated home OSA screening device (WatchPAT) . Insomnia was assessed by a validated survey, the Insomnia Severity Index.
Results: Demographic characteristics are shown in Table 1. The majority of the patients were white lean women, not using insulin with no diabetic complications. Among all patients (n=24) , OSA was found in 58% (64% had mild OSA, 22% had moderate OSA, 14% had severe OSA) . Among lean patients (n=15) , 53% had mild-to-moderate OSA and 47% had no OSA. Based on the Insomnia Severity Index score, 71% of patients had some degree of insomnia (17% clinical insomnia, 54% subthreshold insomnia) and 29% no insomnia.
Conclusion: OSA and insomnia were highly prevalent in MODY patients despite their lack of most traditional risk factors for sleep disorders. Given their unique characteristics, further research should investigate the pathophysiology of sleep disorders in this population.

Disclosure
M.A.Arosemena: None. K.Wroblewski: None. E.Tasali: None. L.H.Philipson: Advisory Panel; Nevro Corp., Research Support; Dompé, Novo Nordisk, provention BIo.
1126-P: A New Prostate-Specific Antigen Threshold for Screening Prostate Cancer among Diabetic Patients
Authors: LIU CHUNYAN , CHEN YI , LIU JIASHU , YIN JIAJING , ZHA XIAOJUAN , SU BIN , LI HONG 
Published: May 2022

Background: The level of prostate-specific antigen (PSA) was reported to be reduced in type 2 diabetes mellitus (T2DM) . Possible low PSA levels in T2DM patients reduce the sensitivity of prostatic cancer (PCa) screening. To improve the effectiveness of early PCa screening inT2DM, it is necessary to establish a new PSA threshold for T2DM patients.
Methods: We retrospectively analyzed the clinical data of 2151 patients who underwent prostate biopsy for the first time admitted to Shanghai Tenth People′s Hospital from January 20to March 2020. The distribution of PSA was estimated by fitting a probability function. Machine learning methods was used to find a new threshold for PCa screening in T2DM.
Results: This study found that the PSA level in T2DM was lower than the regular patients. The new PCa screening threshold for T2DM was PSA=3.27 ng/mL. Meanwhile, the possible influence of age and body mass index (BMI) on the threshold were excluded. Compared with 4ng/mL, the sensitivity and specificity of the new threshold in T2DM patients for PCa screening were 94.53% (vs.91.54%) and 41.36% (vs.46.78%) , area under the curve (AUC) 0.83 (vs.0.83, P&gt;0.5) .
Conclusion: The serum PSA levels in T2DM patients reduced. The new PCa screening threshold in T2DM based on machine learning is 3.27ng/mL. Lowering the PSA screening threshold for T2DM has a better early warning effect on PCa, which is conducive to improving the early diagnosis rate of PCa in T2DM patients.

Disclosure
C.Liu: None. Y.Chen: None. J.Liu: None. J.Yin: None. X.Zha: None. B.Su: None. H.Li: None.
1130-P: A Prospective Cohort Study on the Predictive Value of Controlled Attenuation Parameter Measured by FibroScan to the Transition of Metabolic Status
Authors: LI JIANGHONG , HUANG ZHIMIN , DENG WANPING , ZHANG ZHI , LI YANBING 
Published: May 2022

Aims: Our previous study validated that controlled attenuation parameter (CAP) was a sensitive surrogate marker for evaluating metabolic derangement. However, prospective studies are needed to assess its usefulness for transition of metabolic status over time. In this study, predictive effect of CAP and its change over time was investigated by following up of medical staff volunteered for MAFLD study over 3 years.
Subjects and Methods: Volunteers were recruited from our hospital to undergo FibroScan examination in 2018 and followed up in 2021. Anthropometric data, CAP and hematological indices for metabolic profiles and insulin resistance were collected. Metabolic syndrome (MetS) components were scored. CAP≥238 dB/m denoted hepatic steatosis. Number of MetS component increased by 1 or more was denoted ‘progression’ , whereas decreased by 1 or more denoted ‘regression’ of metabolic status.
Results: A total of 922 subjects volunteered for FibroScan examination in 2018, and 466 of them were followed in 2021. At baseline, the median age was 49.0 years, BMI was 23.3kg/m2, waist circumference was 80cm. CAP were 240 (216, 275) dB/m, LSM were 3.9 (3.4, 4.7) Kpa, 242 (51.9%) subjects were with CAP≥238 dB/m. During the follow up, 124 (26.6%) subjects had progression, while 81 (17.3%) had regression of metabolic status. Logistic regression showed that CAP at baseline [1.0 (1.001, 1.011) , P = 0.019], change in waist circumference [1.1 (1.047, 1.165) , P = 0.000] and change in CAP [1.0 (1.004, 1.0150) , P = 0.001] were independent predictors for progression. In contrast, CAP at baseline [1.012 (1.006, 1.018) , P= 0.000], change in waist circumference [0.9 (0.857, 0.967) , P = 0.002] and change in LSM [0.813 (0.667, 0.990) , P = 0.040] were significant in predicting regression of metabolic status.
Conclusions: CAP at baseline, changes in CAP and waist circumference are useful surrogate markers for predicting transition of metabolic status over time.

Disclosure
J.Li: None. Z.Huang: None. W.Deng: None. Z.Zhang: None. Y.Li: None.

Funding
National Natural Science Foundation of China (82070918)
1135-P: Concomitant Use of Sulfonylureas and Beta-Blockers and the Risk of Severe Hypoglycemia: A Population-Based Cohort Study
Authors: DIMAKOS JENNY , CUI YING , PLATT ROBERT W. , RENOUX CHRISTEL , FILION KRISTIAN B. , DOUROS ANTONIOS 
Published: May 2022

Background: Beta-blockers may interact with sulfonylureas (SU) and increase their hypoglycemic risk. Our study assessed the potential association between concomitant use of SU and beta-blockers and the risk of severe hypoglycemia.
Methods: We used the UK's Clinical Practice Research Datalink linked to hospitalization and vital statistics data of patients with type 2 diabetes initiating SU between 1998 and 2020, excluding those with beta-blocker use in the past 6 months. Time-dependent Cox models estimated hazard ratios (HRs) with 95% confidence intervals (CIs) of severe hypoglycemia associated with current concomitant use of SU and beta-blockers compared to current SU use alone, adjusted for baseline confounders. To address potential residual confounding, we repeated the analysis with current concomitant use of SU and thiazide diuretics as reference group. We also compared current concomitant use of SU and non-cardioselective (propranolol, carvedilol, sotalol, labetalol) vs. cardioselective beta-blockers (acebutolol, atenolol, bisoprolol, metoprolol, nebivolol, esmolol) to explore the role of beta-blocker cardioselectivity in this association.
Results: Our cohort included 252,869 SU initiators. The crude incidence rate of severe hypoglycemia was 7.8 per 1000/year. Concomitant use of SU and beta-blockers was associated with an increased risk of severe hypoglycemia compared to SU use alone (HR, 1.53; 95% CI, 1.42-1.65) . Changing the reference group led to consistent findings (HR, 1.69; 95% CI, 1.42-2.01) . There was no difference in the risk when comparing concomitant use of SU and non-cardioselective beta-blockers to concomitant use of SU and cardioselective beta-blockers (HR, 0.95; 95% CI, 0.74-1.24) .
Conclusion: Our large cohort study showed an increased risk of severe hypoglycemia associated with concomitant use of SU and beta-blockers compared to SU use alone. This association did not vary with beta-blocker cardioselectivity.

Disclosure
J.Dimakos: None. Y.Cui: None. R.W.Platt: Consultant; Amgen Inc., Biogen, Merck & Co., Inc., Nant Pharma, Pfizer Inc. C.Renoux: None. K.B.Filion: None. A.Douros: None.

Funding
Canadian Institutes of Health Research (PJT-165882)