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Introduction: The decision to withhold or withdraw life-sustaining therapy (WLST) is common after acute stroke. Factors that may influence the decision are not well determined. We aimed to investigate factors associated with WLST in hospitalized acute stroke patients. Methods: Patients with acute ischemic stroke (AIS), intracerebral hemorrhage (ICH), and subarachnoid hemorrhage (SAH) were included across 152 Florida hospitals participating in the prospective Florida Stroke Registry from 2008-2021. Importance plots were performed to generate the predictive factors associated with WLST. AUC-ROC curves were generated for the performance of logistic regression (LR) and random forest (RF) models. We used 75/15/15 for training/testing/validation. Results: Among 309,393 AIS patients, 47,485 ICH patients, and 16,694 SAH patients; 9%, 28%, and 19% subsequently had WLST during hospitalization. Patients who had WLST were older (77 vs. 69 years), more women (57% vs. 49%), more White (76% vs. 67%), greater stroke severity at presentation NIHSS ≥ 5 (29% vs.19%), more likely to be treated in comprehensive stroke centers (52% vs. 44%), more likely to have Medicare insurance (53% vs. 44%), less likely to be uninsured (8% vs. 13%), more likely to undergo surgical treatments (1.2% vs 0.3%), and more likely to have impaired level of consciousness (38% vs. 12%). The most predictive factors associated with the decision to WLST in AIS were age, stroke severity, state region, insurance status, stroke center type, race, and level of consciousness (RF AUC of .93 and LR AUC of .85). The most predictive factors in ICH were age, impaired level of consciousness, state region, race, insurance status, stroke center type, and ambulation status at baseline (RF AUC of .76 and LR AUC of .71). Most predictive factors in SAH were age, impaired level of consciousness, state region, insurance status, race, and stroke center type (RF AUC of .82 and LR AUC of .72). Conclusion: Among acute hospitalized stroke patients; age, level of consciousness, state region, race, insurance status, ambulation status at baseline, and stroke center type could contribute to the decision to WLST.

ADA guidelines recommend screening for significant fibrosis in patients with type 2 diabetes and steatosis or elevated ALT. In contrast, there are limited or inconsistent data about the prevalence of NAFLD and liver fibrosis in patients with type 1 diabetes (T1D) . We screened patients with T1D unaware of having NAFLD at a tertiary diabetes center and excluded those with secondary causes of fatty liver or heavy alcohol consumption. We utilized liver stiffness measurement (LSM) by transient elastography for fibrosis screening and controlled attenuation parameter (CAP) for steatosis screening. Referral to hepatology unit was done based on a priori criteria suggestive of clinically significant fibrosis (≥F2) . We screened 285 patients with T1D (age 48±16 yrs; 55% females; 93% non-Hispanic whites; BMI 27±4.9 kg/m2; diabetes duration 28±15 yrs; A1c 7.1±1.1%; total insulin dose 48±27 units/day; AST 23±9 IU/L; ALT 20±12 IU/L) . The prevalence of NAFLD (CAP≥274 dB/m) was 29.3% (95%CI 24-35) and fibrosis (LSM≥7 kPa) 11% (95%CI 7-15) . Among those with NAFLD (n=76) , 13% (95%CI 5-20) had ≥F2 fibrosis (LSM ≥ 8.2 kPa) suggesting nonalcoholic steatohepatitis. Among patients with NAFLD and those with fibrosis respectively, 72% and 59% had ALT≤30 IU/L (men) or ≤IU/L (women) . After multivariate adjustment, larger waist circumference (>102 cm [men]; >88 cm [women]) and BMI >30 kg/m2 were associated with increased risk of steatosis (OR 4.13, 95%CI 1.61-10.6, p=0.014; 2.4, 1.3-4.5, p=0.007, respectively) . Continuous glucose monitoring parameters (e.g., %TIR) were not associated with steatosis or fibrosis. In conclusion, NAFLD and liver fibrosis are common in patients with T1D. ALT alone is inadequate for screening. Central obesity, consistent with insulin resistance, independently predicts NAFLD. Future guidelines should consider screening for steatosis and significant fibrosis in patients with T1D and high BMI or large waistline even without elevated ALT. Disclosure S.Tomah: None. H.Gardner: None. R.Mccarragher: None. A.A.Kaushik: None. N.A.Ziemniak: None. O.Hamdy: Advisory Panel; L-Nutra Inc., Nestlé, Consultant; Abbott, Sanofi, Research Support; Eli Lilly and Company, Gilead Sciences, Inc., National Dairy Council, Novo Nordisk, Stock/Shareholder; Healthimation. T.Salah: None. M.Al-badri: None. S.E.Dhaver: None. M.P.Curry: Consultant; Gilead Sciences, Inc., Mallinckrodt, Sonic Incytes, Research Support; Gilead Sciences, Inc., Mallinckrodt, Sonic Incytes. Z.Jiang: Advisory Panel; Olix Pharmaceutical, Research Support; Gilead Sciences, Inc., Pfizer Inc. M.Lai: None. O.A.Elenani: None. K.Kibaa: None. Funding Investigator-initiated grant from Gilead Sciences (IN-US-989-5675)

Almond milk is often used in lieu of dairy milk with the perception of being a healthier substitute, however, its effect on postprandial glycemia compared to 2% milk is not understood. In this study, we compared the 4-hour postprandial effects of almond milk versus carbohydrate- or caloric-matched 2% milk, each served with a similar amount of oatmeal in patients with T2D. In this cross-over, three-way, open-label study, 22 overweight or obese patients with T2D consumed ½ cup (40 g) of oatmeal served with 360 mL almond milk (ALM) or carbohydrate-matched (246 ml) 2% milk (CarbM) or calorie-matched (182 ml) 2% milk, on separate days, in random order. The primary outcome was glucose incremental area under the curve (iAUC 0-240) while changes in other analytes were secondary outcomes. Blood samples for glucose, free fatty acids (FFA) , gastrointestinal (GI) satiety and hunger hormones were drawn at baseline, 15, 30, 60, 90, 120, 180 and 240 minutes. Incremental AUCs were compared between groups using Wilcoxon signed rank test. P-values for analytes other than glucose were adjusted with the false discovery rate (FDR) . P In conclusion, compared to almond milk used with oatmeal, carbohydrate- or caloric-matched 2% milk has similar effects on postprandial glucose, FFA, triglycerides and GI hormones for up to 4 hrs, indicating no additional benefits of almond milk over 2% milk. On the contrary, carbohydrate-matched 2% milk increased insulin and glucagon secretion compared to almond milk. The long-term significance of this increase in insulin release needs to be further elaborated in a long-term study. Disclosure J.Mitri: Consultant; dairy management, Lnutra. S.Tomah: None. J.Dreyfuss: None. M.Al-badri: None. S.E.Dhaver: None. H.Gardner: None. M.Tasabehji: None. O.Hamdy: Advisory Panel; L-Nutra Inc., Nestlé, Consultant; Abbott, Sanofi, Research Support; Eli Lilly and Company, Gilead Sciences, Inc., National Dairy Council, Novo Nordisk, Stock/Shareholder; Healthimation.

To explore Australian sheep and beef producer vulnerability to an emergency animal disease outbreak, Bayesian Network models have been developed, with the ultimate goal of creating risk management tool for outbreak preparedness. These models were developed using multiple stakeholder elicitation including modelling experts, epidemiologists and on-farm stakeholders, including on-farm/survey data. An evaluation of the model's predictive capacity was conducted, using independent, blinded on-farm vulnerability assessments. Nine properties were visited, four each with sheep and beef enterprises, and one mixed enterprise. There were some discrepancies between the model predictions and on-farm assessment in the beef enterprises, with greater disparity with the sheep properties. Discrepancies between the model predictions and on-farm assessments have created opportunities for examination of the data collection process for the model development, the model itself and the on-farm assessment process. Bayesian Network approaches that allow for the inclusion of both continuous and discrete variables may improve the usefulness of these models, avoiding the loss of nuanced data by the need for discretisation of continuous variables, as will the inclusion of input from on-farm stakeholders in model development. Future work includes more data collection to improve the sensitivity of the model predictions, and a deeper, systemic exploration of the factors that may impact Australian producers' vulnerability to an emergency animal disease outbreak.

Studies have demonstrated differences in gut microbial characteristics in diseases compared to healthy states. Knowledge of how diet alters the gut microbiome is limited by the lack of dietary interventional studies reporting on gut microbial characterization. We explored whether dairy foods differing in fat content alter the gut microbiota of patients with type 2 diabetes (T2D). We randomized 111 subjects with T2D into 3 groups: high-fat (HF) and low-fat (LF) dairy groups (≥ 3 servings/d of HF and LF dairy, respectively), and a control group (CON) maintained baseline dairy intake. Subjects were evaluated at baseline, 12 wk, and 24 wk. Next generation sequencing was performed using an Illumina NextSeq (2x150 bp, high output mode) in 32 subjects (16 in HF, 5 in LF, and 11 in CON). At 24 wk, featurewise testing revealed a higher Faecalibacterium prausnitzii abundance (MaAsLin2 coefficient = 0.0746, FDR-adjusted p = 0.0034; MaAsLin2 coefficient = 0.0691, FDR-adjusted p = 0.0195; respectively) and a higher Bifidobacterium pseudocatenulatum abundance (MaAsLin2 coefficient = 0.0022, FDR-adjusted p = 0.0673; MaAsLin2 coefficient = 0.0022, FDR-adjusted p = 0.1135; respectively) in LF compared to HF and CON in univariable models. Functional analyses revealed 11 significant associations including a lower abundance of UDP-glucose-derived O-antigen building blocks biosynthesis pathway (MaAsLin2 coefficient = –0.0006, FDR-adjusted p = 0.1584) related to lipopolysaccharides (LPS) production in HF compared to CON in a multivariable model and a lower L-methionine biosynthesis III pathway abundance (MaAsLin2 coefficient = –0.0015, FDR-adjusted p = 0.1975) in LF compared to HF in a univariable model. In conclusion, consumption of ≥ 3 servings/d of LF and HF dairy changed the gut microbiota of patients with T2D compared to CON. Further analysis will reveal whether a decrease in LPS biosynthesis-related pathway in HF influences inflammation. Disclosure S. Tomah: Stock/Shareholder; Self; Amarin Corporation plc. O. Hamdy: Advisory Panel; Self; Nestlé, Consultant; Self; Abbott, AstraZeneca, Sanofi, Research Support; Self; Eli Lilly and Company, Gilead Sciences, Inc., National Dairy Council, Novo Nordisk, Stock/Shareholder; Self; Healthimation, LLC. A. Kostic: Other Relationship; Self; DeepBiome Therapeutics, Inc., FitBiomics, Inc. J. Mitri: Consultant; Self; L-Nutra. J. E. Wilkinson: None. C. Li: None. M. Tasabehji: None. A. H. Eldib: None. M. Al-badri: None. H. Gardner: None. Funding National Dairy Council; National Institute of Diabetes and Digestive and Kidney Diseases

Intensive lifestyle intervention (ILI) is essential for diabetes management. The Weight Achievement and Intensive Treatment (Why WAIT) program is a 12-week multidisciplinary weight management program that has been implemented in real-world clinical practice and has shown long-term maintenance of weight reduction for 5 years. During COVID-19 pandemic, the program went virtual using telemedicine and mobile-applications (Healthimation and Good Measures™). We evaluated the outcomes of virtual program (VP) in comparison to in-person physical program (PP). Sixteen subjects were enrolled in VP and were compared to 22 subjects who completed the last 2 PPs. Body weight decreased by -16.49±8 lbs. (-7 % ± 0.3%) from baseline in VP (p In conclusion, virtual multidisciplinary ILI is as effective as the in-person physical program in improving body weight, A1C, BP, lipid profile and in reducing the number of anti-hyperglycemic medications. These results suggest that scaling the Why WAIT program in a virtual format to a larger population of patients with diabetes and obesity is potentially as successful as the costly in-person program. Disclosure M. Al-badri: None. J. M. Beaton: None. C. L. Davis: None. J. M. Rizzotto: None. O. Hamdy: Advisory Panel; Self; Nestlé, Consultant; Self; Abbott, AstraZeneca, Sanofi, Research Support; Self; Eli Lilly and Company, Gilead Sciences, Inc., National Dairy Council, Novo Nordisk, Stock/Shareholder; Self; Healthimation, LLC. C. L. Kilroy: None. J. I. Shahar: None. S. Tomah: Stock/Shareholder; Self; Amarin Corporation plc. H. Gardner: None. M. Sin: None. J. L. Votta: None. A. Phillips-stoll: None. A. M. Price: None.

We previously reported that, in patients with diabetes, intensive lifestyle intervention (ILI) in real-world clinical practice could lead to significant weight loss that was attainable for up to 10 yrs and had favorable impact on glycemic control and lipid profile. These improvements were predicted by achieving ≥7% weight loss at 1 yr. In this secondary analysis, we investigated whether initial weight loss at 1 yr is associated with incidence of nephropathy over 10 yrs. This analysis included 128 patients with diabetes enrolled in a 12-wk ILI (age 53.7±10.1 yrs; 66.4% females; duration of diabetes 9.6±9.4 yrs; body mass index [BMI] 38.3±5.3 kg/m2; and 9.3% had type 1 diabetes). At 1 yr, we divided participants into 2 groups: group A (n=61, 47.7%) maintained Disclosure S. Tomah: Stock/Shareholder; Self; Amarin Corporation plc. H. Zhang: None. H. Gardner: None. M. Al-badri: None. O. Hamdy: Advisory Panel; Self; Nestlé, Consultant; Self; Abbott, AstraZeneca, Sanofi, Research Support; Self; Eli Lilly and Company, Gilead Sciences, Inc., National Dairy Council, Novo Nordisk, Stock/Shareholder; Self; Healthimation, LLC.

The MiniMed™ 670G (670G) is the first FDA approved hybrid closed-loop insulin delivery system for patients with type 1 diabetes (T1D). In Auto Mode, the integrated continuous glucose monitor (CGM) captures glucose values every five minutes and, via a built-in algorithm, the 670G system automatically adjusts basal insulin delivery targeting a glucose value of 120 mg/dL. Impact of the use of 670G system on patient reported outcomes in people with T1D are limited. Data over one year of use in real world-clinical practice were collected. We evaluated 53 patients with T1D (mean age 46.8±11.8 years, 62% females, body weight 82.5±17.6 kg, BMI 28.7±5.5, HbA1c 7.6±0.8%) who started 670G at the Joslin Clinic between December 2017 and December 2019. Data were collected at baseline and after 12 months. We assessed SROs by administering surveys: 36 Item Short-Form Health Survey (SF 36), Pittsburgh Sleep Quality Index (PSQI), and hypoglycemia fear survey/worry scale (HFS_W). We also measured HbA1c, % time-in-range at baseline and 12 months, and % time in auto mode (TiAM) for the two weeks preceding the final study visit. At 12 months, A1C decreased by 0.3±0.1% from baseline (p Disclosure A.H. Eldib: None. S. Tomah: Stock/Shareholder; Self; Amarin Corporation. S.E. Dhaver: None. H. Gardner: None. M. Tasabehji: None. M. Albadri: None. A. Atakov-Castillo: None. E. Toschi: None. O. Hamdy: Consultant; Self; Abbott, AstraZeneca, Merck & Co., Inc., Sanofi-Aventis. Research Support; Self; Novo Nordisk A/S. Stock/Shareholder; Self; Healthimation, LLC.

Continuous glucose monitoring has been shown to be an effective tool in management of type 1 and type 2 diabetes. Non-invasive techniques may provide a convenient alternative to current invasive methods but their precision is lacking. We evaluated the accuracy of “LabPatch”, a non-invasive continuous glucose monitoring system that uses micropipette technology to sip interstitial fluid in comparison to YSI® (laboratory reference glucose analyzer) and 2 glucose meters (Onetouch Verio® and Freestyle Lite®) in 30 insulin-treated patients with diabetes at multiple points over 6 hours. LabPatch glucose readings significantly correlated with those of YSI (R-square=0.23, pA. LabPatch had an absolute relative difference (ARD) of 35.5% compared with YSI indicating wide variation in glucose readings. For glucose values between 70-180 mg/dL, LabPatch readings were within +/-15% of YSI values with a lower one-sided 95 % confidence bound of 75.25%. Estimated average glucose value (eAGV) were the closest between LabPatch and YSI than between either of the 2 glucose meters and YSI. We conclude that first-generation LabPatch Precision technology has potential to identify glucose trends in situations that require close monitoring of diabetes over shorter intervals such as gestational diabetes, pre-pregnancy planning and patients preparing for elective surgery. Additionally, LabPatch has the potential to be used for diabetes screening due to its non-invasive nature. Larger sample size and more precision of manufacturing technology may make it a valid non-invasive and wireless glucose monitor. Disclosure S. Tomah: Stock/Shareholder; Self; Amarin Corporation. A.H. Eldib: None. M. Tasabehji: None. V. Bhatia: None. H. Gardner: None. O. Hamdy: Consultant; Self; Abbott, AstraZeneca, Merck & Co., Inc., Sanofi-Aventis. Research Support; Self; Novo Nordisk A/S. Stock/Shareholder; Self; Healthimation, LLC. Funding Cambridge Medical Technologies, LLC

While the wide-spectrum antimicrobial properties and stability of silver nanomaterials have been copiously utilized in many medical and consumer products, we found that Methicillin Resistant

The speed with which this rash spread and the fact that the patient's skin sloughed off when pressure was applied made the diagnosis clear.

Dairy products are integral parts of healthy diets, however, their association with cardiometabolic (CM) health among patients with type 2 diabetes (T2D) undergoing weight management is debated. We examined the relationship between dairy consumption and CM biomarkers in 45 subjects with T2D and obesity (mean age 56 &plusmn, 9 yrs, 40% female) enrolled in a 12-week intensive multidisciplinary weight management (IMWM) program. After the IMWM program (intervention phase), subjects were followed for 12 weeks (maintenance phase). We stratified subjects based on initial average dairy consumption into infrequent (IFR), less-frequent (LFR), and frequent (FR) consumers. Outcomes were assessed at baseline, 12, and 24 weeks. There were no differences between tertiles at baseline except for higher total energy intake among FR compared with IFR. HbA1c changes showed no association with dairy consumption at 12 or 24 weeks. FR Females achieved greater weight loss at 12 weeks compared with IFR peers (&minus, 4.5 kg, 95%CI: &minus, 5.5, &minus, 3.5). There was a trend towards lower HDL-C with increasing dairy consumption during the intervention phase. In subjects with T2D and overweight or obesity, dairy consumption during weight management is not associated with HbA1c changes but with lower HDL-C and with higher magnitude of weight loss among females.

T2D is a potentially preventable disease that has been ranked the seventh leading cause of mortality in the United States. There is strong evidence demonstrating that preventing type 2 diabetes is, in many cases, attainable through lifestyle intervention. Unfortunately, prediabetes is mostly overlooked and awareness with diabetes prevention tools is lacking among primary care physicians. Nationally, efforts were not successful in reversing this epidemic even with an array of diabetes medications. Among the most effective medications for T2D are glucagon-like peptide-1 receptor agonists (GLP-1 RAs), which have been shown to reduce both A1C and body weight. Dulaglutide, liraglutide and injectable semaglutide also reduced cardiovascular events and cardiovascular mortality in patients with established cardiovascular disease or multiple cardiovascular risk factors. In this review, we will examine the first FDA approved oral GLP-1 RA; semaglutide. Moreover, this review will discuss the potential impact oral semaglutide may have on glycemic control, weight loss and cardiovascular comorbidities. It also examines the factors that may impact patient compliance, including cost, side effects and clinical issues. Finally, it deliberates the optimism surrounding the development of oral semaglutide in the treatment of diabetes as well as related conditions, such as obesity and non-alcoholic fatty liver disease (NAFLD).