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OBJECTIVE Most people with type 2 diabetes (T2DM) and nonalcoholic steatohepatitis (NASH) or advanced fibrosis remain undiagnosed, resulting in missed opportunities for early intervention. This multicenter prospective study aimed at assessing the yield of using routinely available data for identifying these patients. RESEARCH DESIGN AND METHODS A total of 713 outpatients with T2DM, screened in four diabetology clinics for nonalcoholic fatty liver disease (NAFLD) according to American Diabetes Association criteria, were referred to hepatologists for further work-up (FIB-4 and vibration controlled transient elastography (VCTE)). A liver biopsy was proposed when ALT levels were persistently >20 IU/L in females or >30 IU/L in males, in the absence of other liver disease. RESULTS Liver biopsies were performed in 360 patients and considered adequate for reading after central review in 330 (median age 59 years, male 63%, BMI 32 kg/m2, HbA1C 7.5 %). Prevalence of NASH, advanced fibrosis, and cirrhosis were 58%, 38%, and 10%, respectively. Liver lesions were independently associated with the components of metabolic syndrome, but not with the micro- and macrovascular complications of T2DM. Models based on routinely available data with or without VCTE had good accuracy to predict advanced fibrosis (AUROC 0.84 and 0.77, correctly classified 59% and 45%, respectively) and NASH (AUROC 0.82 and 0.81, 44% and 42%, respectively). CONCLUSIONS Despite the use of a low ALT threshold, prevalence of NASH (58%) or advanced fibrosis (38%) was high. Routinely available data had a high yield in identifying T2DM patients with advanced fibrosis and/or NASH requiring further liver assessment.

A significant number of studies invoked diabetes as a risk factor for virus infections, but the issue remains controversial. We aimed to examine whether non-autoimmune diabetes mellitus enhances the risk of virus infections compared with the risk in healthy individuals without non-autoimmune diabetes mellitus. In this systematic review and meta-analysis, we assessed case-control and cohort studies on the association between non-autoimmune diabetes and viruses. We searched PubMed, Embase, Cochrane Database of Systematic Reviews, Cochrane Central Register of Controlled Trials, and Web of Science with no language restriction, to identify articles published until February 15, 2021. The main outcome assessment was the risk of virus infection in individuals with non-autoimmune diabetes. We used a random-effects model to pool individual studies and assessed heterogeneity (I2) using the χ2 test on Cochrane’s Q statistic. This study is registered with PROSPERO, number CRD42019134142. Out of 3136 articles identified, we included 68 articles (90 studies, as the number of virus and or diabetes phenotype varied between included articles). The summary OR between non-autoimmune diabetes and virus infections risk were, 10.8(95% CI: 10.3–11.4; 1-study) for SARS-CoV-2; 3.6(95%CI: 2.7–4.9, I2 = 91.7%; 43-studies) for HCV; 2.7(95% CI: 1.3–5.4, I2 = 89.9%, 8-studies;) for HHV8; 2.1(95% CI: 1.7–2.5; 1-study) for H1N1 virus; 1.6(95% CI: 1.2–2.13, I2 = 98.3%, 27-studies) for HBV; 1.5(95% CI: 1.1–2.0; 1-study) for HSV1; 3.5(95% CI: 0.6–18.3 , I2 = 83.9%, 5-studies) for CMV; 2.9(95% CI: 1–8.7, 1-study) for TTV; 2.6(95% CI: 0.7–9.1, 1-study) for Parvovirus B19; 0.7(95% CI: 0.3–1.5 , 1-study) for coxsackie B virus; and 0.2(95% CI: 0–6.2; 1-study) for HGV. Our findings suggest that, non-autoimmune diabetes is associated with increased susceptibility to viruses especially SARS-CoV-2, HCV, HHV8, H1N1 virus, HBV and HSV1. Thus, these viruses deserve more attention from diabetes health-care providers, researchers, policy makers, and stakeholders for improved detection, overall proper management, and efficient control of viruses in people with non-autoimmune diabetes.

Sağır, Özlem (Balikesir Author), Introduction More than 300 million surgical procedures are performed each year. Acute kidney injury (AKI) is a common complication after major surgery and is associated with adverse short-term and long-term outcomes. However, there is a large variation in the incidence of reported AKI rates. The establishment of an accurate epidemiology of surgery-associated AKI is important for healthcare policy, quality initiatives, clinical trials, as well as for improving guidelines. The objective of the Epidemiology of Surgery-associated Acute Kidney Injury (EPIS-AKI) trial is to prospectively evaluate the epidemiology of AKI after major surgery using the latest Kidney Disease: Improving Global Outcomes (KDIGO) consensus definition of AKI. Methods and analysis EPIS-AKI is an international prospective, observational, multicentre cohort study including 10 000 patients undergoing major surgery who are subsequently admitted to the ICU or a similar high dependency unit. The primary endpoint is the incidence of AKI within 72 hours after surgery according to the KDIGO criteria. Secondary endpoints include use of renal replacement therapy (RRT), mortality during ICU and hospital stay, length of ICU and hospital stay and major adverse kidney events (combined endpoint consisting of persistent renal dysfunction, RRT and mortality) at day 90. Further, we will evaluate preoperative and intraoperative risk factors affecting the incidence of postoperative AKI. In an add-on analysis, we will assess urinary biomarkers for early detection of AKI. Ethics and dissemination EPIS-AKI has been approved by the leading Ethics Committee of the Medical Council North Rhine-Westphalia, of the Westphalian Wilhelms-University Münster and the corresponding Ethics Committee at each participating site. Results will be disseminated widely and published in peer-reviewed journals, presented at conferences and used to design further AKI-related trials. Trial registration number NCT04165369. ©

Viruses have been considered potential triggers for the development of diabetes. This study assessed insulin secretion and insulin sensitivity in human herpesvirus 8 (HHV8)-infected and uninfected sub-Saharan African people with diabetes.In all, 173 people with non-autoimmune diabetes were enrolled consecutively: 124 with type 2 diabetes mellitus (T2DM) and 49 with ketosis-prone diabetes (KPD) admitted in hyperglycemic crisis. Those with KPD were further subdivided into those with new-onset ketotic-phase KPD (n = 34) or non-ketotic phase KPD (n = 15). All participants were screened for HHV8-specific antibodies and genomic DNA. Blood samples were collected for analysis of fasting glucose, HbA1c, lipid profile, and C-peptide, with insulin resistance and secretion estimated by homeostasis model assessment.Among the 173 diabetic participants, 88 (50.9%) were positive for HHV8 antibodies (Ac-HHV8+), including 15 (8.7%) positive for HHV8 DNA (DNA-HHV8+). The seroprevalence of HHV8 was similar between T2DM (55.6%) and KPD (61.2%) subjects. Of those with and without ketotic-phase KPD, 35.3% and 46.7% were Ac-HHV8+, respectively. Body mass index was significantly in lower DNA-HHV8+ than DNA-HHV8- subjects. Low-density lipoprotein and total cholesterol were significantly higher, but C-peptide and homeostatic model assessment of β-cell function (HOMA-β) were significantly lower in DNA-HHV8+ than DNA-HHV8- participants. After excluding DNA-HHV8+ participants, triglyceride concentrations were significantly higher in Ac-HHV8+ (n = 73) than Ac-HHV8- (n = 85) subjects. In contrast, HOMA-β was significantly higher among Ac-HHV8+ than Ac-HHV8- participants.In the present study, HHV8 DNA positivity was associated with low insulin secretion in this sub-Saharan African diabetes population.

Despite a well-recognized clinical benefit of the 2nd generation tyrosine kinase inhibitor nilotinib in patients with imatinib-resistant/-intolerant or newly diagnosed chronic myeloid leukemia, recent evidence suggests that nilotinib has a propensity to increase the risk of occlusive arterial events, especially in patients with pre-existing cardiovascular risk factors. Given the key role of lipids in cardiovascular diseases, we studied the plasma lipid profile and global cardiovascular risk prior to and during nilotinib therapy in a series of 27 patients in the setting of an observationalsingle-center study. Data from a minimum 1-year follow-up showed that nilotinib significantly increased total, low and highdensity lipoprotein cholesterol within 3 months. Consequently, the proportion of patients with non-optimal low density lipoprotein cholesterol increased from 48.1% to 88.9% by 12 months, leading to cholesterol-lowering drug intervention in 22.2% of patients. The proportion of patients with low levels of high density lipoprotein cholesterol decreased from 40.7% to 7.4% by 12 months. In contrast, a significant decrease in triglycerides was observed. Global cardiovascular risk worsened in 11.1% of patients due to diabetes or occlusive arterial events. Whether hypercholesterolemia was the main driver of occlusive arterial events was uncertain: a longer follow-up is necessary to ask whether nilotinib-induced hypercholesterolemia increaseslong-term risk of atherosclerotic diseases. Nevertheless, given keyatherogenicproperties of low density lipoprotein cholesterol, we conclude that when prescribing nilotinib, commitment to detect lipid disordersat baseline and during follow-up is mandatory given their frequency, requirement for lifestyle or drug intervention and potential for long-term cardiovascular complications.

Diabetes mellitus (DM) is associated with several musculoskeletal disorders. The incidence of DM and the life expectancy of the diabetic patient have both increased, resulting in the increased prevalence and clinical importance of musculoskeletal alterations in diabetic subjects. The exact pathophysiology of most of these musculoskeletal disorders remains obscure. Connective tissue disorders, neuropathy, vasculopathy or combinations of these problems, may underlie the increased incidence of musculoskeletal disorders in DM. The development of musculoskeletal disorders is dependent on age and on the duration of DM; however, it has been difficult to show a direct correlation with the metabolic control of DM. Most of these disorders can be diagnosed clinically, but some radiological examination may help, especially in differential diagnosis. No specific treatment is available, and treatments used in the general population are also recommended for diabetic subjects. Infectious complications affecting the musculoskeletal system are common in DM subjects, and these, possibly life-threatening, complications should be systematically discussed.

The hypothalamus has a major role in the control of food intake. However, neurotracing studies have shown that the hypothalamus receives input from several other regions of the brain that are likely to modulate its activity. Of particular interest to the understanding of human eating behavior is the possible involvement of the cortex. Using positron emission tomography (PET), we generated functional brain maps of the neuroanatomical correlates of hunger (after a 36-h fast) and satiation (after oral administration of a liquid formula meal) in lean and obese subjects. Results in lean individuals indicate that the neuroanatomical correlates of hunger form a complex network of brain regions including the hypothalamus, thalamus, and several limbic/paralimbic areas such as the insula, hippocampal/parahippocampal formation, and the orbitofrontal cortex. Satiation was associated with preferentially increased neuronal activity in the prefrontal cortex. Our studies also indicate that the brain responses to hunger/satiation in the hypothalamus, limbic/paralimbic areas (commonly associated with the regulation of emotion), and prefrontal cortex (thought to be involved in the inhibition of inappropriate response tendencies) might be different in obese and lean individuals. In conclusion, neuroimaging of the human brain is proving to be an important tool for understanding the complexity of brain involvement in the regulation of eating behavior. PET studies might help to unravel the neuropathophysiology underlying human obesity.

Background Nilotinib is approved for use in pts with CML after failure of imatinib and in newly diagnosed CP-CML. In the ENESTnd trial, 1st line nilotinib was associated with a higher frequency of hypercholesterolemia than imatinib. Moreover, several studies reported a nilotinib-associated risk of artery occlusive disease (AOD). In ENESTnd, pre-existing treatment for dyslipidemia did not preclude pts from enrolment. To avoid this pitfall and to determine which cholesterol fractions are affected and whether triglycerides (TG) are modified, we prospectively studied the plasma lipid profile of nilotinib-treated pts without drug treatment for dyslipidemia. Methods Pts (n=25) in our center with CP-CML either newly diagnosed or after failure of imatinib were proposed 1st or 2nd line nilotinib provided that neither very high risk for fatal cardiovascular disease (CVD) nor nilotinib-resistant ABL kinase domain mutation were present at baseline. Pts with drug treatment for dyslipidemia at baseline were excluded from this study. Total cholesterol (TC), low-density lipoprotein-cholesterol (LDL-C), high-density lipoprotein-cholesterol (HDL-C) and TG were measured prior to the start of nilotinib and at 3, 6, 9 and 12 months. Fasting glucose (FG) and glycosylated hemoglobin (HbA1C) were also measured. Results are shown as medians (min-max). ESC/EAS 2012 guidelines were used to determine CVD risk categories and for the management of dyslipidemias. Results Median age at nilotinib initiation was 52 years (19-69), 13 were males (52%). Sokal risk group was low in 11 pts (44%), intermediate in 11 (44%) and high in 3 (12%). Nilotinib was given upfront in 20 pts (80%) and after 1st line imatinib in 5 pts (20%). Median time from diagnosis was 0.8 month (0.4-4.3) in the former and 15.7 months (2.5-47.9) in the latter. Initial nilotinib dosing regimen was 300mg BID in 22 pts (88%) and 400mg BID in 3 pts (12%). Baseline CVD risk factors included age ≥ 45 years in men and ≥ 55 years in women in 33 pts (52%), active smoking or ceased during the previous year in 6 pts (24%), body mass index (BMI) ≥ 25 kg/m2in 14 pts (56%), hypertension in 1 pt (4%), type 2 diabetes mellitus (DM) in 1 pt (4%). Two pts were discovered pre DM. The CVD risk was low in 12 pts (48%), moderate in 11 pts (44%) and high in 2 pts (8%). At baseline, no pt required drug intervention by lipid lowering agents based on LDL-C, 11 pts (44%) had low HDL-C (≤0.4g/L) and 7 (28%) had high TG (≥1.5g/L). Median FG and HBA1C were 0.88g/L (0.63-1.26) and 5.5% (4.7-6.5), respectively. All pts were given lifestyle medical advice. At M3, TC, LDL-C and HDL-C had increased by a mean of 26.9%, 31.8% and 36.3%, respectively. Median TC at baseline and M3 was 1.84g/L (1.17-2.74) and 2.27g/L (1.08-3.62) (p Three pts (12%) required statins during the first year of nilotinib therapy: 2 pts with a moderate CVD risk at baseline and in whom LDL-C rose above 1.9g/L and 1 pt with a moderate CVD risk at baseline who developed peripheral artery disease (PAD) at M12, leading to nilotinib discontinuation. By M12, the CVD risk changed from low to high in 1 pt due do to the onset of DM and from moderate to very high in the pt with PAD. Conclusions Nilotinib is associated with a significant and early increase in TC, LDL-C and HDL-C. In contrast, a decrease in TG occurs but this may be due to lifestyle medical advice given to our patients. All patients considered for nilotinib therapy should have an evaluation of blood lipids at baseline and early after the start of therapy. Requirement for lifestyle modifications and lipid lowering treatment by statins should be determined at baseline and throughout therapy according to TC, LDL-C and CVD risk category. The role of nilotinib-induced high LDL-C in the onset of AOD needs to be investigated. Disclosures: Rea: Novartis: Honoraria; BMS: Honoraria; Teva: Honoraria; Ariad: Honoraria; Pfizer: Honoraria. Gautier:Novartis: Honoraria. Messas:Novartis: Honoraria. Dombret:Novartis: Research Funding.

Abstract 1686 Background: ENESTnd established the superior efficacy of frontline NIL vs IM in pts with CML-CP. An association between NIL treatment and hyperglycemia has been previously reported. NIL-mediated increases in fasting plasma glucose levels (FGLs) have been shown to be reversible, with levels returning to normal on treatment discontinuation; in contrast, IM has been shown to decrease FGLs in pts with diabetes (Breccia M, Alimena G. Leuk Res. 2009;33:871–875). Here, we report the incidence of hyperglycemia and related glycemic outcomes with NIL and IM by 3 y in ENESTnd. Methods: Pts were randomized to receive NIL 300 mg twice daily (BID; n = 282), NIL 400 mg BID (n = 281), or IM 400 mg once daily (QD; n = 283). This analysis included randomized pts who had ≥1 dose of study drug and no drug treatment for diabetes mellitus /prediabetes at baseline (BL; 264, 262, and 267 pts in the NIL 300 mg BID, NIL 400 mg BID, and IM arms, respectively); pts with treated diabetes at BL (15, 15, and 13 pts, respectively) were excluded. Hyperglycemia was defined as FGL ≥1.1 g/L, prediabetes as FGL ≥1.1 g/L to < 1.26 g/L or glycated hemoglobin (HbA1C) ≥5.7% to < 6.5%, and diabetes as FGL ≥1.26 g/L or HbA1C ≥6.5%. These criteria represent a combination of World Health Organization and American Diabetes Association guidelines. Results: At BL, median age and mean body mass index (BMI) were similar across the 3 arms (Table). In the NIL 300 mg BID, NIL 400 mg BID, and IM arms, respectively, 34.9%, 35.5%, and 37.8% of pts had untreated prediabetes at BL, and 6.1%, 6.1%, and 3.4% of pts had untreated diabetes at BL. Median time on treatment in the 3 arms was similar (≈ 3 y). Cumulative incidence of hyperglycemia by 3 y was higher with NIL vs IM (Table). By 3 y, grade 3/4 hyperglycemia was reported in 6 (2.3%), 7 (2.7%), and 0 pts on NIL 300 mg BID, NIL 400 mg BID, and IM, respectively; no pt discontinued due to hyperglycemia. Median time to hyperglycemia was longer with NIL 300 mg BID than with NIL 400 mg BID. From BL to 3 y, there were minimal changes in mean BMI and mean HbA1C in all 3 arms; greater increases in mean fasting insulin were observed with NIL vs IM. Excluding pts with diabetes at BL, 20.1%, 22.8%, and 8.9% of pts developed diabetes by 3 y, and 48.8%, 51.6%, and 50.0% developed prediabetes by 3 y (NIL 300 mg BID, NIL 400 mg BID, and IM, respectively). Relatively few pts required antidiabetic medication by 3 y. Based on a multivariate analysis, prediabetes at BL was a risk factor for development of hyperglycemia in all 3 treatment arms (P =.0252.0004, and.0296 [NIL 300 mg BID, NIL 400 mg BID, and IM arms, respectively]). Age > 60 y at BL was a risk factor for NIL 300 mg BID (P =.0183) and IM (P =.0010) but was not a risk factor for NIL 400 mg BID (P =.6001). Dyslipidemia (P =.0455) at BL was identified as an additional risk factor for development of hyperglycemia on IM in the multivariate analysis. Conclusions: In ENESTnd, the incidence of hyperglycemia by 3 y was higher with NIL vs IM. However, most events were low grade, HbA1C levels remained stable over time, and few pts required antidiabetic medication. The underlying mechanism of action driving NIL-mediated hyperglycemia is unknown, but the increases in fasting insulin observed suggest NIL therapy is associated with insulin resistance. NIL-treated pts with CML-CP and BL risk factors for diabetes should be monitored closely. Disclosures: Delphine: Novartis: Honoraria; BMS: Honoraria; Teva: Honoraria. Gautier:Novartis: Honoraria, Membership on an entity's Board of Directors or advisory committees. Breccia:Novartis: Honoraria; BMS: Honoraria; Celgene: Honoraria. Saglio:Novartis: Consultancy, Speakers Bureau; BMS: Consultancy, Speakers Bureau; Pfizer: Consultancy. Hughes:Novartis: Consultancy, Honoraria, Research Funding; BMS: Consultancy, Honoraria, Research Funding; Ariad: Consultancy, Honoraria; CSL: Research Funding. Kantarjian:Novartis Pharmaceuticals Corp: Consultancy, Research Funding; BMS: Research Funding; Pfizer: Research Funding. Kemp:Novartis Pharmaceuticals Corp: Employment. Gao:Novartis: Employment. Piccolo:Novartis Pharma AG: Employment. Larson:Novartis: Consultancy, Honoraria, Research Funding; BMS: Consultancy, Research Funding; Pfizer: Consultancy; Ariad: Consultancy, Research Funding. Hochhaus:BMS: Consultancy, Honoraria, Research Funding; Pfizer: Consultancy, Honoraria, Research Funding; Novartis: Consultancy, Honoraria, Research Funding; Ariad: Consultancy, Honoraria, Research Funding.

Introduction The extent to which postprandial glucagon reductions contribute to lowering of postprandial glucose in patients with type 2 diabetes mellitus (T2DM) is currently unknown. The aim of this analysis was to determine whether a reduction in postprandial glucagon following treatment with the glucagon-like peptide-1 receptor agonist lixisenatide correlates with a reduction in postprandial glucose and glycated hemoglobin (HbA1c) in patients with T2DM. Methods A post hoc analysis was performed on pooled data from the modified intent-to-treat populations of two lixisenatide Phase 3 trials: GetGoal-M (lixisenatide versus placebo as add-on to metformin) and GetGoal-S (lixisenatide versus placebo as add-on to sulfonylurea [SU] ± metformin). Glucagon levels were assessed 2 h after a standardized meal test performed at baseline and Week 24 and were examined for correlation with changes in 2-h postprandial glucose and HbA1c. Results Lixisenatide reduced 2-h postprandial glucagon at Week 24 compared with placebo (P