Baked Italian Oysters – Southern Diabetic Delicious

By electricdiet / December 18, 2020


Holiday Spectacular Diabetic Side Dish

Can you believe it is already that time of year?! Holiday season may look a little different this year but it is definitely in full gear! But for many, especially people with diabetes, it can be a challenging time to stick to your healthy meal goals. However, you shouldn’t have to choose between good food and your health. Holly’s passion has always been to create healthy recipes that never sacrifice flavor. Baked Italian Oysters from Holly Clegg’s cookbook, Guy’s Guide to Eating Well taste like a splurge but you would never know they are actually good for you – even diabetic friendly!

Baked Italian Oysters

Baked Italian Oysters
Cassanova was rumored to have eaten over 50 oysters to boost his libido – worth a shot! This rich oyster dish with Italian flavor has New Orleans roots.

    Servings10-12 servings
    Prep Time15 minutes
    Cook Time25-30 minutes

    Ingredients

    • 2pints


      oystersdrained

    • 1/3cup


      olive oil

    • 1teaspoon


      minced garlic

    • 1/3cup


      chopped parsley

    • 1


      bunch green onionschopped

    • 2cups


      Italian breadcrumbs

    • 1/3cup


      grated Parmesan cheese

    • 1/4cup


      lemon juice

    • 1


      bunch green onionschopped

    • 2cups


      Italian breadcrumbs

    • 1/3cup


      grated Parmesan cheese

    • 1/4cup


      lemon juice

    Instructions
    1. Preheat oven to 400 ̊F. Coat shallow oblong 2-quart baking dish with nonstick cooking spray.

    2. Place drained oysters on in prepared baking dish.

    3. In bowl, combine remaining ingredients, spread evenly over oysters. Bake 25–30 minutes or until oysters are done and topping is browned.

    Recipe Notes

    Calories 193, Calories from Fat 42%, Fat 9 g, Saturated Fat 2 g, Cholesterol 40 mg, Sodium 405 mg, Carbohydrates 19 g, Dietary Fiber 2 g, Total Sugars 2 g, Protein 9 g, Diabetic Exchanges: 1 ½ starch, 1 lean meat, 1 fat

    Nutritional Nugget: Oysters are high in the mineral zinc, which helps produce testosterone.

    Serving Suggestion: Serve oysters with Barbecue Shrimp (page ?) and Angel Hair Pasta (page ?) and you’ll feel like you’re on a trip to New Orleans.

    Stock Your Kitchen for this Recipe

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    New Orleans Favorite Baked Italian Oysters

    Make Baked Italian Oysters your choice if you are looking for that dish to dress your holiday meal with a little extra flair. All the savory flavors of a classic New Orleans favorite in one easy recipe. Even if you don’t like raw oysters, you are sure to love this combination of savory flavors – Parmesan cheese, red pepper, and garlic baked atop oysters in this outstanding Baked Italian Oysters dish from Holly Clegg’s cookbook, Guy’s Guide to Eating Well.

    Your Holiday Needs Holly’s 12 Ideas For Christmas Foodies Downloadable Only $1.99!

    The holidays are here and you need Holly’s 12 Ideas for Christmas Foodies. From evening appetizers to teacher gifts, even – what to cook Christmas morning, these festive favorite recipes are Holly’s go-to dishes that will get you through all of the parties and last-minute family get-togethers this December.  No need to stress with what to make this holiday season – let Holly do it for you with her December favorites!

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    The post Baked Italian Oysters – Southern Diabetic Delicious appeared first on The Healthy Cooking Blog.



    Sell Unused Diabetic Strips Today!

    COVID-19 and Diabetes: A Collision and Collusion of Two Diseases

    By electricdiet / December 16, 2020


    Introduction

    The coronavirus disease 2019 (COVID-19) pandemic has infected >22.7 million and killed >795,000 people worldwide, as of 21 August 2020 (1). COVID-19 infection is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), a single-stranded RNA β-coronavirus (2). Patients with diabetes are highly susceptible to adverse outcomes and complications of COVID-19 infection (3). The COVID-19 pandemic is superimposing on the preexisting diabetes pandemic to create large and significantly vulnerable populations of patients with COVID-19 and diabetes. Other comorbid conditions frequent in patients with type 2 diabetes, e.g., cardiovascular disease (CVD) and obesity, also predispose COVID-19 patients to adverse clinical outcomes (4,5).

    SARS-CoV-2 pathophysiology remains incompletely understood, but evidence suggests it triggers hyperinflammation in certain patients (6) and that tissue tropism is exhibited (7), pathologies shared with chronic inflammation and multitissue damage in diabetes (8). COVID-19 infection disrupts glucose regulation, rendering glycemic control difficult and necessitating particularly careful management in patients with diabetes (9). Moreover, early indicators and comparison with the previous severe acute respiratory syndrome coronavirus (SARS-CoV) outbreak (10) suggest that survivors may face sequelae, which will require long-term care. Currently, the U.S. and some other countries are experiencing surges in COVID-19 cases (1). This article will review the current state of knowledge of COVID-19 and diabetes to address nine critical questions, some of which remain unanswered (Fig. 1).

    Figure 1
    Figure 1

    Outstanding questions on diabetes in the context of COVID-19.

    Review Methodology

    We initially performed our literature search on PubMed without any filters on publication date and completed it by 10 July 2020. The search keywords varied by section. For the diabetes and comorbidities section, we searched “COVID-19” or “SARS-CoV-2” with “clinical characteristics,” “clinical cohort,” “clinical,” or “cohort,” and prioritized clinical, high-quality medical studies. We did not generally include meta-analyses and excluded preprints, since we had sufficient peer-reviewed material. To the best of our ability, we selected studies that appeared to report different patient cohorts, considering some cohorts may have been duplicated without reporting it (11). However, we may have included studies from the same cohort if the study focus was different. We focused on China, U.S., and Europe as the early epicenters. We also repeated the search with the keyword “diabetes,” “acute kidney injury,” or “acute cardiac injury.” We read all abstracts to select relevant manuscripts, which we searched for the term “diabetes” and all relevant information. During the revision process, we updated the review with relevant literature (same criteria) published up until 18 August. For the pediatric section, we searched “COVID-19” or “SARS-CoV-2” and “diabetes” with “pediatric,” “childhood,” “children,” “youth,” or “adolescent.” For the pregnancy section, we searched “COVID-19” or “SARS-CoV-2” and “diabetes” with “pregnant,” “pregnancy,” or “gestational.” For the race section, we searched “COVID-19” or “SARS-CoV-2” and “race,” “black,” “African American,” “Hispanic,” or “Asian” and prioritized high-quality clinical studies. We also performed a subsearch using “diabetes.”

    Diabetes and COVID-19

    General COVID-19 Patient Cohorts

    Although the COVID-19 pandemic evolved quickly, there were clear early warning signs that comorbidities, including diabetes, predisposed patients to adverse outcomes (Table 1). The first reports that emerged from Wuhan, China, documented that diabetes raised the risk of dangerous infection-induced adverse outcomes and complications, leading to acute respiratory distress syndrome (ARDS), intensive care unit (ICU) admission, mechanical ventilation use, and greater risk of death (12,13). In univariate logistic regression analysis, diabetes had an odds ratio (OR) of 2.85 for in-hospital death (13). At the national level, several China studies found association of diabetes with severe disease (ICU, mechanical ventilation) (14) and death (14,15).

    Table 1

    Overview of adult COVID-19 clinical cohorts

    These findings are replicated in the U.S., where diabetes is one of the three most common comorbid conditions nationwide, with total comorbidity prevalence as high as 78% among ICU COVID-19 admissions (n = 457 total) (16). In New York City (NYC), patients with diabetes were more likely to need mechanical ventilation or ICU admission (17,18). In a different NYC cohort, the diabetes univariate hazard ratio (HR) for in-hospital mortality was 1.65, which did not persist in multivariate analysis after adjustment for age, sex, and seven additional parameters (5). In Detroit (n = 463), diabetes was more frequent in hospitalized versus discharged and ICU versus non-ICU patients but was not a risk in multivariate analysis (19). Diabetes was an independent risk for hospital admission (OR 2.24, with full adjustment for patient characteristics and comorbidities) but not for critical disease or death in a large NYC cohort (n = 5,279) (20).

    In other countries, a German study (n = 50) found no differences in diabetes frequency in ARDS versus non-ARDS patients (21), though these outcomes contrast with those of another study in China (22). An observational U.K. study (n = 1,157) found that diabetes had an age- and sex-adjusted HR of 1.42 for critical care and could be integrated into a 12-point prognostic risk score (critical care admission, death) (23), similar to another 10-variable risk score (24). Collectively, these general cohort studies suggest that patients with diabetes have a higher likelihood of adverse outcomes, although other mitigating risk factors likely exist, contributing to the varying conclusions.

    Cohorts of Patients With COVID-19 and Diabetes

    Several reports have focused specifically on cohorts of patients with diabetes. The multicenter French Coronavirus SARS-CoV-2 and Diabetes Outcomes (CORONADO) study (n = 1,317 participants with diabetes, 88.5% of whom had type 2 diabetes) observed that diabetes type and glycated hemoglobin (HbA1c) level did not affect the primary outcome in univariate analysis, i.e., tracheal intubation for mechanical ventilation and/or death within 7 days of admission (25). Another large study, led by the National Health Service (NHS) England, also focused on both type 1 (n = 364) and type 2 (n = 7,434) diabetes–associated COVID-19 deaths and determined multivariate ORs of 2.86 and 1.80, respectively, with adjustment for age, sex, ethnicity, deprivation, CVD, and cerebrovascular disease, though they could not adjust for other frequent comorbidities, hypertension, chronic kidney disease (CKD), and BMI, due to data set limitations (26). Notably, most studies have not differentiated diabetes type; CORONADO found no differences between type 1 and type 2 diabetes in COVID-19 outcomes, but there were only 39 patients with type 1 diabetes. In contrast, the NHS England study might suggest that patients with type 1 diabetes are at greater risk, though this remains to be validated by additional studies (Fig. 1).

    A study from China with 258 COVID-19 patients, of whom 63 had diabetes, reported diabetes had a multivariate HR of 3.64 for death, with adjustment for age, comorbidities, and inflammatory markers (27). Guo et al. (28) accounted for comorbidities by comparing mortality in patients without diabetes (0%) versus with diabetes (16.5%) without comorbidities; however, they failed to consider age, which significantly differed between groups. In a study of COVID-19 patients with type 2 diabetes, diabetes led to a higher all-cause mortality of 7.8% (vs. 2.7%), with HR 1.49, with adjustment for age, sex, and infection severity (3). These studies of cohorts with diabetes confirm the concept that persons with diabetes who contract COVID-19 disease have poorer outcomes.

    Glycemic Control and Elevated Fasting Blood Glucose

    Well-controlled blood glucose has emerged as an important outcome parameter and conferred lower mortality (HR 0.14) in a propensity score–matching model that accounted for age, sex, comorbidities, and several additional parameters (3). This finding agrees with other studies that identified diabetes and/or uncontrolled or variable hyperglycemia at admission (29,30), ICU admission (31), or during in-hospital stay (32) as a severe disease or mortality risk. In the large U.K. OpenSAFELY study of 10,926 COVID-19 deaths in comparison with a database of 17,278,392 adults, greater mortality occurred with poorer glycemic control (stratified by HbA1c) (4). Patients with diabetes with HbA1c <7.5% had a fully adjusted HR of 1.31 for death, whereas HR was 1.95 with HbA1c ≥7.5%. These findings were mirrored by the NHS England study in both patients with type 1 diabetes (HbA1c ≥10.0%, HR 2.23) and patients with type 2 diabetes (HbA1c 7.5–8.9%, HR 1.22; HbA1c 9.0–9.9%, HR 1.36; and HbA1c ≥10.0%, HR 1.61) (33).

    COVID-19 can also induce hyperglycemia in patients without diabetes, secondary to infection, which increases the risk of critical disease (34,35). Finally, prediabetes, characterized by elevated fasting blood glucose or impaired insulin sensitivity, has been mostly overlooked in COVID-19 studies but could nevertheless pose a threat to clinical outcomes (Fig. 1). In a U.S. study of 184 patients, most had diabetes (62.0%) or prediabetes (23.9%), and stratifying patients solely by elevated fasting blood glucose or HbA1c increased the risk of intubation (36). A China study also found that elevated fasting blood glucose (>7.54 mmol cutoff) independently predicted mortality (HR 1.19) (27).

    Overall, there is a consensus from clinical studies and meta-analyses (36 and reviewed in 37) that diabetes is a risk factor for serious COVID-19 infection and mortality, though this dependency may be less significant by multivariate analysis in some studies. Varying study results are likely due to the fact that many, but not all, patients with diabetes suffer from additional comorbidities, such as obesity, hypertension, and CVD, which are independent risk factors (Fig. 1).

    Comorbidities and COVID-19

    Comorbidities in General COVID-19 Patient Cohorts

    Obesity (19,20,25,3941), CKD (19,20), CVD (5,20), and hypertension (20) persist as risk factors for hospitalization or serious COVID-19 disease in multivariate analysis in some studies, after adjustment for various clinical variables (Table 1 and Fig. 1), and in meta-analyses (37). In a French cohort (n = 124), obesity (BMI ≥35 kg/m2), but not diabetes, was a strong predictor for mechanical ventilation use, with multivariate OR 7.36, after adjustment for age, sex, diabetes, and hypertension (39). The OpenSAFELY study reported that mortality risk increased with BMI, with HR 1.40 for class II obesity (BMI 35–39.9 kg/m2) and HR 1.92 for class III obesity (BMI ≥40 kg/m2) (4). This was similar to a NYC study, where BMI proportionately increased hospitalization risk (20). In a China cohort (n = 150), obesity was an independent predictor of serious infection (multivariate OR 3.0) and obese patients were likelier to have diabetes versus other age- and sex-matched COVID-19 patients, underscoring the frequent occurrence of comorbidities in patients with diabetes (41). Surprisingly, obesity with BMI ≥40 kg/m2 was not a risk for in-hospital mortality in a NYC cohort (5).

    There are fewer reports on comorbid dyslipidemia. The most comprehensive analysis leveraged data from the UK Biobank as a control population (n = 428,494) versus hospitalized COVID-19 patients (n = 900) (40). Diabetes, HbA1c, CVD, hypertension, BMI, and waist-hip-ratio (WHR) were higher and cholesterol and HDL cholesterol lower in COVID-19 patients. Log(HbA1c), BMI, and WHR (OR > 1) and total cholesterol (OR < 1) remained significant in multivariate analysis in a subset of 340,966 UK Biobank registrants vs. 640 COVID-19 hospitalized patients. Finally, LDL did not vary significantly between patients with diabetes with poorly or well-controlled glucose (3) and was protective from ARDS (HR 0.63) but not death (22).

    Comorbidities in Cohorts of Patients With COVID-19 and Diabetes

    Patients with diabetes frequently suffer from comorbidities, e.g., obesity, dyslipidemia, hypertension, CVD, and CKD (42), which would predispose them to poorer COVID-19 outcomes. In mostly CORONADO participants with type 2 diabetes, obesity by BMI positively predicted the study primary outcome, with OR 1.28 (i.e., tracheal intubation and/or death within 7 days of admission) (25). Dyslipidemia, although present in 51.0% of patients, did not significantly increase risk of the composite primary outcome (25). In a second NHS England study, those who died from COVID-19 (type 1 diabetes, n = 464; type 2 diabetes, n = 10,525) were compared with individuals with diabetes registered to a practice (type 1, n = 264,390; type 2, n = 2,874,020) to identify mortality risk factors (33). Type 1 diabetes shared the same risks as type 2 diabetes for COVID-19 mortality, with preexisting CVD, CKD, and obesity identified as independent factors. One study, with COVID-19 patients with diabetes (n = 153) age and sex matched to 153 COVID-19 patients without diabetes reported that CVD and hypertension were independent risk factors for mortality risks among all patients (43). These studies support the idea that comorbidities in patients with diabetes, independent of diabetes itself, increase adverse COVID-19 disease outcomes.

    Cumulative Comorbidities Effect

    Furthermore, COVID-19 patients with more than one comorbidity may be especially vulnerable. In NYC, COVID-19 patients were far likelier to have two or more comorbidities, constituting 88% of hospital admissions versus admissions of patients with only one comorbidity (6.3%) or no comorbidities (6.1%) (17). In a nationwide study in China (n = 1,590), the HR was 1.79 for one comorbidity and as high as 2.59 for two or more comorbidities after adjustment for age and smoking status (44). When the data from this cohort were used to develop a scoring system to predict serious clinical trajectories from admission status, the number of comorbidities (OR 1.60) emerged as 1 of 10 variables (24). The Charlson Comorbidity Index, a score based on the presence of comorbidities from a list that includes diabetes and kidney and cardiac diseases, had a multivariate OR of 1.05 for hospitalization but an HR of only 0.99 for in-hospital death (45).

    Overall, in assessment of risk for a COVID-19 patient with diabetes at admission, overall comorbidities, including degree of glucose control (assessed by HbA1c [36,40]), fasting blood glucose (36), obesity (19,25,39,40), and the number of additional comorbid conditions, will be important clinical parameters to consider (Fig. 1).

    Pediatric Diabetes and Comorbidities in COVID-19

    Fortunately, there is agreement to date that most pediatric COVID-19 patients present with asymptotic or mild disease (46). Nevertheless, some children suffer from more serious COVID-19 infection, requiring hospitalization and even pediatric ICU (PICU) (Table 2). The reasons for serious illness remain incompletely understood; however, drawing a parallel to adults, the presence of comorbidities, which are less frequent in young patients, may be one reason fewer children are vulnerable to COVID-19 but why some still fall critically ill. Given the recent rise in type 2 diabetes and obesity in youth, there could be a significant number of children at risk. Unfortunately, the few studies that have examined diabetes and other comorbidities in children with COVID-19 are relatively small, making it hard to draw conclusions.

    Table 2

    Overview of pediatric and pregnancy COVID-19 clinical cohorts

    A cross-sectional study of 48 pediatric patients (0–21 years old), admitted to PICUs across the U.S. and Canada, found 83% had significant comorbidities: 15% were obese, 8% had diabetes, and 6% had congenital heart disease (47). A children’s hospital in NYC (n = 67, aged 1 month–21 years) admitted 13 patients to PICU, noting the presence of both diabetes (3 of 13) and obesity (3 of 13) but not to significance; however, the cohort was small (48). Another study (n = 50, aged 6 days–21 years) at a different NYC children’s tertiary care center found significantly more obesity in severe (67%) versus nonsevere (20%) COVID-19, but not diabetes, possibly due to the small number of patients with diabetes (n = 3) (49). Obesity is a recurrent theme and was relatively prevalent in other pediatric studies also (50,51).

    The cumulative evidence from pediatric studies suggests that comorbidities may be a predisposing factor for serious COVID-19 infection in children, particularly obesity. The impact of diabetes remains unclear due to relatively low study participant numbers (Fig. 1).

    Pregnancy, Diabetes, and Comorbidities in COVID-19

    Pregnancy is a vulnerable period, particularly since gestational diabetes mellitus may develop; yet, few studies have examined pregnant women admitted for COVID-19 infection (Table 2). A French cohort of 54 pregnant women with suspected or confirmed COVID-19 included four patients with gestational diabetes mellitus and two with gestational hypertension, which were too few to analyze for a potential link to infection severity (52). However, prepregnancy overweight or obese BMI were relatively prevalent, which the authors concluded could be a risk factor for COVID-19 disease. Another small study (n = 46), in the U.S., also found a high prevalence of elevated prepregnancy BMI (28.6%, overweight, and 35.7%, obese) (53). Moreover, 15% of pregnant patients developed severe infection, of whom 80% were overweight or obese. A U.K. study of 427 pregnant women with confirmed COVID-19 drew similar observations, finding that 35% of patients were overweight and 34% were obese (54). The diabetes prevalence was 3%, whereas it was 12% for gestational diabetes mellitus, but no analysis of disease severity was performed.

    The largest study to date was in 617 pregnant French women (55). Preexisting diabetes was present in 2.3% of the total population and raised the chance of severe disease, with a risk ratio (RR) of 3.8. In contrast, gestational diabetes mellitus, at 11.5% prevalence, did not affect outcomes for infection severity. The investigators did not discuss reasons for the difference in risk from preexisting diabetes versus gestational diabetes mellitus, but it raises the question of whether gestational diabetes mellitus interacts distinctly with COVID-19 pathophysiology (Fig. 1). Diabetes complications, for instance, from preexisting diabetes, could be a factor for serious infection, which draws parallels to studies of general populations with diabetes (25). The study also found that BMI has an RR of 1.9, hypertension an RR of 2.4, and gestational hypertension or preeclampsia an RR of 2.4 for severe COVID-19, though the latter two did not reach significance.

    Collectively, the data from pregnancy cohorts echo findings from adult studies, with diabetes, obesity, and comorbidities likely predisposing to poorer outcomes. However, it is possible that gestational diabetes mellitus may not be a factor, though larger studies are needed for us to definitively conclude this.

    Race, Diabetes, and Comorbidities in COVID-19

    Race disparities are an emergent theme during the COVID-19 pandemic (Table 3). The precise reasons to date remain unclear, though the prevalence of comorbidities, including obesity, (56) and socioeconomic factors (57) have been suggested. Of the U.S. population, 18% are Hispanic, 13% Black, and 0.7% American Indian or Alaska Native; yet, these groups have disproportionately constituted 33%, 22%, and 1.3%, respectively, of adult U.S. COVID-19 cases (58) and are also highly represented in hospitalized pediatric patients (50).

    Table 3

    Overview of COVID-19 clinical cohorts with investigation of susceptibility by race and ethnicity

    Several observational studies have taken a more detailed look to understand these racial disparities. In Detroit cohorts, Black race did not increase risk of severe infection (19,59); however, diabetes or comorbidities prevalence by race was not examined (19). These findings partly agree with those of a Georgia study (n = 297), which found that although hospitalizations among Black patients (83.2%) were disproportionate to numbers among other races, indicating greater disease severity, Black patients did not have higher mechanical ventilation use or mortality (60). This study also reported the prevalence of comorbidities, which did not differ significantly for diabetes in Black versus other races but did differ for hypertension and mean BMI. A larger Louisiana cohort (n = 3,481) similarly concluded that Black race was a hospitalization risk but not an independent in-hospital mortality risk (45). Although the investigators found diabetes, hypertension, and CKD prevalence to be higher in Black versus White patients, they did not perform an analysis for disease severity. A California study (n = 1,052) analyzed hospitalization risk for Black, Asian, and Hispanic race relative to White, but only Black race had an OR 2.7, after adjustment for sex, age, comorbidities, and socioeconomic factors (57). U.K. studies have also noted greater susceptibility of Black patients, and other race minorities, to COVID-19 disease (61) and hospitalization (40), after adjustment for several cardiometabolic and socioeconomic factors. Strikingly, a NYC study found that Black race was protective for critical illness and death, whereas Hispanic race was a risk for hospitalization (20).

    Importantly, some studies have reported increased mortality risk for Black race and other minorities. Analysis of NYC demographics and COVID-19 deaths (n = 4,260) revealed that Hispanic (22.8%) and Black (19.8%) patients had the highest age-adjusted mortality per 100,000, which corresponded to the highest obesity rates: 25.7% and 35.4%, respectively (56). However, the study did not adjust for other important variables. Lacking complete U.S. nationwide disaggregated data by race, Millett et al. (62) analyzed county-level demographics and COVID-19 deaths. Counties with a greater proportion of Black residents (i.e., above national average, ≥13%) had more COVID-19 cases (rate ratio 1.24) and deaths (rate ratio 1.18), after adjustment for county-level traits, e.g., age, comorbidities, poverty, and pandemic duration. Diabetes prevalence was also higher (13.9% vs. 11.1%) in counties with high (≥13%) and low (<13%) proportion of Black residents but did not correlate with COVID-19 cases (rate ratio 0.97) or deaths (nonsignificant rate ratio 1.01), after adjustment for demographics, comorbidities, and socioeconomic factors. Thus, diabetes, or other cardiometabolic effects, may not be solely attributable to COVID-19 risk in Black patients. Finally, large population-based studies, OpenSAFELY and NHS England, found higher mortality risk for Asian and Black races, after adjustment for age, sex, comorbidities, and socioeconomic status (4,26,33).

    Overall, Black, Hispanic, and possibly other races may be risk factors for serious COVID-19 infection or death, but the factors driving this disparity are presently unclear (Fig. 1).

    COVID-19 and Diabetes Pathology: Collision and Collusion

    Given the relatively short time that has elapsed since the SARS-CoV-2 pandemic broke out, its pathophysiology remains incompletely understood. However, like its predecessors SARS-CoV and Middle East respiratory syndrome coronavirus (MERS-CoV), SARS-CoV-2 gains cellular entry by leveraging the ACE2 receptor, a master regulator of the renin-angiotensin system. The major viral spike glycoprotein (S1) binds to ACE2 (63), while proximal serine proteases, like the transmembrane serine protease 2, cleave the virus spike protein and ACE2, promoting viral internalization (64). Infection induces cell death, which triggers inflammatory cytokine production and inflammatory immune cell recruitment (65). SARS-CoV-2 also infects circulating immune cells, stimulating lymphocyte apoptosis and inflammatory cytokine secretion, known as “cytokine storm” (6). High circulating cytokine levels contribute to SARS-CoV-2–driven multiorgan failure and disrupted endocrine signaling and hyperglycemia surges (66). Widespread multitissue ACE2 expression, e.g., lung, heart, kidney, and nerve (67), leads to tropism, as validated by viral detection within multiple tissues (7,68). Tropism potentially constitutes another pathway to multiorgan damage in COVID-19 patients, e.g., acute cardiac injury (ACI) and acute kidney injury (AKI) (13,14).

    Although the inflammatory, hyperglycemic, and tissue damage response is intensely acute in COVID-19 infection, it is mirrored by diabetes pathology (Fig. 2), which is characterized by chronic, low-grade inflammation, impaired glycemic control, and slowly progressive multitissue injury, e.g., diabetic microvascular (CKD, neuropathy, brain) and macrovascular (CVD) complications (8,69). Although the underlying reasons for the susceptibility of patients with diabetes to COVID-19 remain unclear, commonalities in pathology suggest that acute COVID-19–induced adverse reactions may superimpose on preexisting inflammation, glucose variability, and multitissue injury in patients with diabetes to aggravate outcomes (Fig. 1).

    Figure 2
    Figure 2

    Illustration of parallels in acute COVID-19 pathology versus chronic diabetes pathology. COVID-19 infection induces acute inflammatory cytokine storm, hyperglycemic surges, and acute organ damage. Diabetes is characterized by chronic, low-grade inflammation, glucose variability, and slowly progressing tissue damage in microvascular (CKD, neuropathy, brain) and macrovascular (CVD) complications. Additional shared detrimental mechanisms include hypercoagulation, endothelial dysfunction, and fibrosis. Drawn in part with BioRender.

    Do Preexisting Diabetes Complications Predispose Patients to Acute COVID-19–Induced Organ Damage?

    Few studies have stratified COVID-19 patients by diabetes status to examine the possibility that preexisting micro- and macrovascular complications render patients susceptible to acute organ injury (Fig. 1). CORONADO (n = 1,317) demonstrated that preexisting microvascular (OR 2.14) and macrovascular (OR 2.54) complications independently associated with 7-day mortality (25), suggesting that the presence of diabetes complications may set patients on poorer clinical trajectories. In a NYC study of 5,449 severe COVID-19 patients, of whom 1,993 developed AKI, diabetes was a risk for renal damage, with 41.6% developing AKI vs. 28.0% who did not (70). Diabetes also correlated with progressive damage in AKI stage 1 (39.7%), stage 2 (43.2%), and stage 3 (43.5%) by Kidney Disease: Improving Global Outcomes (KDIGO) criteria. After adjustment for age, sex, and race, diabetes had an OR of 1.76 for AKI. However, the study did not state whether AKI correlated with preexisting CKD, since baseline CKD data were not available, although associations with preexisting CKD and AKI have been noted in meta-analysis (71).

    Although diabetes was not an independent risk for COVID-19 death in a cohort of 153 patients with diabetes compared with age- and sex-matched individuals without diabetes, patients with diabetes were more likely to have preexisting CVD and be admitted to ICUs and experience acute complications (ACI, AKI, ARDS) (43). Nonsurvivor patients with diabetes had higher blood glucose levels and a greater chance of ACI or AKI, in addition to an altered inflammatory and immune system profile (see Are Patients With Diabetes Predisposed to Acute COVID-19–Induced Inflammatory Response?). Within a cohort with diabetes (n = 952), patients with well-controlled glucose were also less likely to suffer from hypertension and CVD. They were also at lowered risk of AKI (HR 0.12) and ACI (HR 0.24), after adjustment for comorbidities (3), indicating that even if preexisting microvascular complications contribute to acute organ injury, additional factors, such as glucose control or inflammation, may also participate.

    Additional Aspects of COVID-19 Tropism Relevant to Diabetes

    One particular aspect of COVID-19 tropism meriting close attention from a diabetes perspective is the possibility of increasing the incidence of β-islet damage–induced type 1 diabetes. Drawing parallels, SARS-CoV may have been responsible for acute type 1 diabetes onset by leveraging β-islet ACE2 expression to induce loss of islets (72). It is possible that COVID-19 might also trigger acute-onset type 1 diabetes in individuals predisposed to autoimmunity (73). Indeed, the multicenter regional data from North West London just reported an 80% increase in new-onset type 1 diabetes cases and diabetic ketoacidosis in children up to the age of 16 years during the COVID-19 pandemic peak (74). Moreover, COVID-19 tropism through ACE2 expression in adipose tissue may underlie the link to obesity as a serious infection risk, since adipose tissue could potentially serve as a reservoir of viral shedding (75).

    Are Patients With Diabetes Predisposed to Acute COVID-19–Induced Inflammatory Response?

    Although the full cytokine storm profile in COVID-19 is not fully characterized yet, hyperinflammation predicts serious disease (Fig. 1). Lymphopenia along with elevation in white blood cells (WBC), neutrophils, C-reactive protein (CRP), erythrocyte sedimentation (ESR), ferritin, IL-6, and procalcitonin (PCT) associates with poorer COVID-19 clinical course, defined as serious infection, ARDS, ICU admission, or death, in studies in multiple countries (Table 1). COVID-19 patients experience, in parallel to inflammation, elevated AST, brain natriuretic peptide, hypersensitive troponin I (hs-TnI), creatine kinase (muscle and brain type), lactate dehydrogenase (LDH), and creatinine (Cr), indicative of tissue damage. Clotting homeostasis is similarly compromised, e.g., with elevated d-dimer with longer thrombin or prothrombin time, which also correlate with clinical progression. A meta-analysis found higher AST (>40 units/L), Cr (≥133 µmol/L), d-dimer (>0.5 mg/L), hs-TnI (>28 pg/mL), LDH (>245 units/L), and PCT (>0.5 ng/mL) and lower WBC (<4 × 109 per L) defines an OR >1 for critical illness (76).

    Diabetes is also characterized by chronic, low-grade inflammation, which is also a prominent feature of its complications, diabetic CKD, CVD, and neuropathy (8,77,78). Several proinflammatory molecules from the COVID-19 cytokine storm cascade are shared with type 2 diabetes pathophysiology, such as CRP, IL-6 (77), and PCT (79). The underlying chronic inflammatory state in diabetes may be “locked and loaded” for virus-induced damage, promoting a vicious cycle of cytokine release and hyperglycemic surges, leading to more widespread multiorgan damage, including injury to tissues already weakened by preexisting diabetes complications.

    Worryingly for patients with diabetes, and as an added layer of risk, they are more prone to cytokine storm, which predicts poorer outcomes (Table 1). Admission CRP (OR 1.93) and AST (OR 2.23) independently predicted 7-day mortality in the CORONADO COVID-19 patients with diabetes (25). In Chinese cohorts, patients with diabetes had a more inflammatory profile than patients without diabetes (3,27). More favorable inflammatory and tissue biomarker profiles were also evident in patients with type 2 diabetes with well-controlled versus poorly controlled blood glucose (3,30). Another study found differences in numerous inflammation and organ damage biomarkers in nonsurviving versus surviving patients with diabetes, which also correlated with glucose and HbA1c levels (43). Moreover, elevated inflammation and organ damage biomarkers were present in COVID-19 patients with diabetes and hyperglycemia secondary versus without diabetes and with normoglycemia (34).

    One inflammatory biomarker, with deep roots in diabetes pathophysiology, not widely investigated in COVID-19, is soluble urokinase-type plasminogen activator receptor (suPAR). In Greek (n = 57) and U.S. (n = 21) COVID-19 cohorts, we found that admission suPAR predicted severe respiratory failure (80). suPAR correlates with diabetes risk (81) and reflects the underlying chronic inflammatory process of its micro- (82) and macrovascular complications (83).

    The reasons for the susceptibility of patients with diabetes to COVID-19 are multifaceted and reflect the complex pathophysiology of both diabetes and COVID-19 infection. Diabetes and its comorbidities, inflammation, glucose variability, and other factors, may “collide and collude” to disproportionally set COVID-19 patients with diabetes on poorer clinical trajectories (Fig. 2).

    Diabetes and COVID-19 Sequelae

    It is becoming clear that COVID-19 survivors suffer from persistent symptoms (84) and may also face a lifetime of sequelae, which draws parallels to SARS-CoV and MERS-CoV (10,85). Although the pandemic has not yet lasted long enough to measure long-term outcomes, the evidence to date suggests a significant burden of possibly irreversible new complications. For instance, COVID-19, like SARS-CoV and MERS-CoV, may aggravate preexisting CVD or even induce new cardiac pathology (86), including in patients with type 2 diabetes (87). COVID-19 patients with preexisting CKD are likelier to suffer AKI (71). COVID-19 also elicits neurological manifestations (88) and cognitive impairment (89), which exhibit shared pathology with diabetes through cytokine storm, hypercoagulability, and endothelial dysfunction. Since patients with diabetes have a high burden of preexisting comorbidities that share pathology with COVID-19–induced damage, it is possible that COVID-19 survivors with diabetes may be particularly at risk for long-term sequelae, although this remains to be determined (Fig. 1). Moreover, the COVID-19 pandemic has seen significant racial health disparities (57). Indeed, SARS-CoV outbreak survivors have reported psychological and financial hardship, even years later (10,90). Thus, COVID-19 could possibly amplify socioeconomic disparities.

    Conclusions: A Collision and Collusion of Two Diseases

    COVID-19 has collided with diabetes, creating especially susceptible populations of patients with both COVID-19 and diabetes. Vulnerabilities may be further amplified by comorbid medical conditions, racial and ethnic disparities, and access to medical care. Thus, in addition to parallels in pathology, the two diseases also reflect their distinct and shared scope of socioeconomic burdens. As our understanding of COVID-19 increases through the lens of diabetes, identifying prognostic factors could help stratify individuals with diabetes most at risk. Moreover, as more evidence comes to light, improvements in short- and long-term care for patients with and without diabetes will develop while we all await a vaccine.

    Article Information

    Acknowledgments. The authors thank Bhumsoo Kim, University of Michigan, for preliminary literature searches; Evan Reynolds, University of Michigan, for biostatistics discussions; and Lalita Subramanian, University of Michigan, for editorial assistance.

    Funding. This work was supported by the National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health (NIH) (R01 DK107956 to E.L.F. and R.P.-B.; R24 DK082841 to E.L.F., S.P., and M.K.; P30 DK081943 to S.P. and M.K.; and U01 DK119083 to R.P.-B.); the National Heart, Lung, and Blood Institute, NIH (R01 HL15338401); JDRF (5-COE-2019-861-S-B to E.L.F., S.P., M.K., and R.P.-B.); the Frankel Cardiovascular Center (U-M G024231 to S.S.H.); University of Michigan NIH-funded programs Michigan Center for Contextual Factors in Alzheimer’s Disease (MCCFAD) (P30-AG059300 to S.S.H.) and Michigan Institute for Clinical & Health Research (MICHR) (UL1-TR002240 to S.S.H.); the Michigan Economic Development Corporation (CASE-244578 to S.S.H.); and the NeuroNetwork for Emerging Therapies, A. Alfred Taubman Medical Research Institute, and Robert and Katherine Jacobs Environmental Health Initiative (all to E.L.F.).

    Duality of Interest. S.S.H. is a scientific advisory board member for Trisaq and receives consulting fees. No other potential conflicts of interest relevant to this article were reported.



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    Low-Carb Cauliflower Rice – Diabetic Foodie

    By electricdiet / December 14, 2020


    Whether you’re watching your carbs or just don’t want to wait an hour for rice to cook, this low-carb cauliflower rice is a delicious and much healthier option!

    Low-carb cauliflower rice in black bowl

    For many years, I used to eat brown rice as a healthier alternative to white rice. It has a lower glycemic index plus more fiber and nutrients.

    But brown rice still has more carbs than I like to eat. I would cook a measly ⅓ cup to stay within my carb limits, and it took nearly an hour to make

    That’s why I prefer this low-carb cauliflower rice! Cauliflower is a nutritional powerhouse compared to any kind of rice, plus the whole thing only takes 25-30 minutes to make.

    Not to mention, I can actually enjoy a whole cup as my serving.

    So whenever you’re craving rice to go with your stir fry, burrito bowl, or just as a simple side, give this quick and easy recipe a try!

    How to make low-carb cauliflower rice

    This simple dish comes together in just a few steps.

    Step 1: Place a single layer of cauliflower in a food processor fitted with the steel blade.

    Step 2: Pulse until the cauliflower is a little larger than the size of rice, then transfer into a microwave-safe bowl.

    Step 3: Repeat with the remaining cauliflower.

    Step 4: Cover the bowl and microwave the cauliflower at 100% for 5 minutes.

    Step 5: Spread the cauliflower onto a clean dish towel and let it sit until it’s cool enough to handle, about 10 minutes.

    Step 6: Bring the edges of the towel together to form a pouch. While holding the cauliflower rice over a bowl, twist and squeeze to remove as much liquid from the cauliflower as possible.

    Step 7: Heat the oil over medium heat in a large skillet or wok. Once hot, add the onions and sauté, stirring constantly, until translucent and soft, about 5 minutes.

    Step 8: Add the garlic, ginger, and salt, then cook for an additional minute, stirring constantly.

    Step 9: Add the cauliflower and cook, stirring often, until heated through, about 5 minutes.

    I like to season mine with white pepper to taste before serving.

    Adding flavor to your rice

    This recipe uses flavors and spices that would go well with an Asian-inspired dish. I love it with my chicken cashew stir-fry or low-carb General Tso’s chicken!

    But if you want to serve your rice with another kind of cuisine, I recommend adjusting the spices.

    For example, if you’re making a Mexican-inspired dish, try using olive oil instead of coconut oil, skipping the ginger, and adding cumin or chili powder. It would be perfect for a burrito bowl!

    For an Indian-inspired dish, you might want to substitute turmeric or curry powder for the ginger. Try it with my Laziz Tikka Masala.

    Have some fun with it! You can pick any spices plus a healthy fat that will work with your main course.

    Storage

    This recipe is for 4 servings. If you have any leftovers, they can be stored covered in the refrigerator for 3-4 days.

    Want to prep some of this recipe in advance? You can chop the cauliflower ahead of time, then store it covered in the refrigerator.

    When you’re ready to eat, start by cooking the cauliflower in the microwave and then simply follow the rest of the recipe as usual!

    Other low-carb side dishes

    Trying to find side dishes to round out your main course while still keeping your carbs low? Good news: there are so many delicious options! Here are a few of my favorite recipes for low-carb sides:

    When you’ve tried this dish, please don’t forget to let me know how you liked it and rate the recipe in the comments below!

    Recipe Card

    Cauliflower rice in a black bowl

    Low-carb Cauliflower Rice

    Whether you’re watching your carbs or just don’t want to wait an hour for rice to cook, this low-carb cauliflower rice is a delicious and much healthier option!

    Prep Time:15 minutes

    Cook Time:10 minutes

    Total Time:25 minutes

    Author:Shelby Kinnaird

    Servings:4

    Instructions

    • Place a single layer of cauliflower in a food processor fitted with the steel blade.

    • Pulse until the cauliflower is a little larger than the size of rice, then transfer into a microwave-safe bowl.

    • Repeat with the remaining cauliflower.

    • Cover the bowl and microwave the cauliflower at 100% for 5 minutes.

    • Spread the cauliflower onto a clean dish towel and let it sit until it’s cool enough to handle, about 10 minutes.

    • Bring the edges of the towel together to form a pouch. While holding the cauliflower rice over a bowl, twist and squeeze to remove as much liquid from the cauliflower as possible.

    • Heat the oil over medium heat in a large skillet or wok. Once hot, add the onions and sauté, stirring constantly, until translucent and soft, about 5 minutes.

    • Add the garlic, ginger, and salt, then cook for an additional minute, stirring constantly.

    • Add the cauliflower and cook, stirring often, until heated through, about 5 minutes.

    Recipe Notes

    This recipe is for 4 servings.
    The spices in this recipe were intended to go with an Asian-inspired dish. Feel free to use different spices and seasonings to fit your main course.
    Leftovers can be stored covered in the refrigerator for 3-4 days.

    Nutrition Info Per Serving

    Nutrition Facts

    Low-carb Cauliflower Rice

    Amount Per Serving

    Calories 76
    Calories from Fat 36

    % Daily Value*

    Fat 4g6%

    Saturated Fat 3g19%

    Trans Fat 0g

    Polyunsaturated Fat 0g

    Monounsaturated Fat 0g

    Cholesterol 0mg0%

    Sodium 71mg3%

    Potassium 25mg1%

    Carbohydrates 9g3%

    Fiber 4g17%

    Sugar 4g4%

    Protein 3g6%

    Net carbs 5g

    * Percent Daily Values are based on a 2000 calorie diet.

    Course: Side Dishes

    Cuisine: American

    Diet: Diabetic

    Keyword: easy side dish recipes, low-carb cauliflower rice



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    Butternut Squash Twice Baked Potatoes

    By electricdiet / December 12, 2020





    Butternut Squash Twice Baked Potatoes – My Bizzy Kitchen


























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    Skinny Peppermint Mocha | Diabetes Strong

    By electricdiet / December 10, 2020


    Want to add a little holiday cheer to your morning coffee? This rich, delicious, skinny peppermint mocha is easy to make, low in carbs, and full of festive flavor!

    Latte in a white coffee mug topped with whipped cream

    Does anyone else feel like coffee shops are in charge of the seasons these days?

    We all know that fall starts when Dunkin Donuts or Starbucks release their pumpkin spice latte. And a few months later, they offer drinks like peppermint mocha and eggnog lattes to kick off the holiday season.

    One morning, I found myself craving something festive, but didn’t feel like leaving the house. That’s when I decided to whip up this delicious skinny peppermint mocha.

    I may never buy the coffee-shop version again! The creamy, chocolate flavor is so rich and decadent, and the kick of peppermint really brings it all together.

    Plus, this homemade recipe is so much better for you. For comparison, a grande peppermint mocha from Starbucks made with 2% milk and whipped cream on top has a whopping 63 grams of carbs and 54 grams of sugar!

    This skinny version, on the other hand, has 5.3 grams of carbs and 3 grams of fiber per serving. That’s only 2.3 net carbs! And there’s only 1 gram of sugar.

    So whether you’re in the holiday spirit or feel like celebrating Christmas in July, this decadent yet healthy peppermint mocha latte is just what you need.

    How to make a skinny peppermint mocha

    This simple recipe is so luxurious yet easy to make. Just heat the ingredients, blend until frothy, and enjoy!

    Ingredients for peppermint mocha in separate ramekins, as seen from above

    Step 1: Add the coffee and half the milk to a saucepan over medium heat.

    Step 2: Heat for about 5 minutes, then add the cocoa powder, peppermint extract, vanilla extract, and stevia. Mix well.

    Ingredients simmering in a saucepan

    Step 3: Transfer the mixture to a blender. Add the other half of the milk.

    Mixture for beverage in a blender

    Step 4: Blend until frothy.

    Step 5: Pour into mugs and serve immediately.

    Peppermint mocha latte in a white coffee mug

    You can enjoy your peppermint mocha as-is, or feel free to finish with your favorite toppings!

    Toppings for your drink

    Don’t get me wrong, this coffee creation is absolutely delicious all on its own. I would drink it straight out of the blender!

    But sometimes, a few toppings really add to the festivity for me. If you feel the same way, then have some fun with your peppermint mocha.

    Personally, I like to add a little whipped cream, a sprinkle of cocoa powder, and some sugar-free mini marshmallows to mine. It’s like hot cocoa meets coffee with a peppermint twist, and it is amazing!

    Skinny peppermint mocha latte topped with whipped cream on a wooden serving board

    Storage

    This drink is best served immediately. You want it to be hot and frothy!

    If you find yourself with any extra, you can store it in the refrigerator in an airtight container for up to 3 days. When you’re ready to enjoy, I recommend reheating gently on the stove, then re-blending until frothy.

    Other tasty ways to start the day

    Whether you’re looking for caffeine, something yummy, or a combination of the two, there are so many options for starting your day the right way! Jumpstart your morning with one of these delicious recipes:

    For even more inspiration, check out this roundup I created of low-carb smoothies for diabetics!

    When you’ve tried this coffee, please don’t forget to let me know how you liked it and rate the recipe in the comments below!

    Recipe Card

    Skinny Peppermint Mocha

    Want to add a little holiday cheer to your morning coffee? This rich, delicious, skinny peppermint mocha is easy to make, low in carbs, and full of festive flavor!

    Prep Time:10 minutes

    Total Time:10 minutes

    Servings:2

    Skinny peppermint mocha latte topped with whipped cream on a wooden serving board

    Instructions

    • Add the coffee and half of the milk to a saucepan over medium heat.

    • Heat for about 5 minutes, then add the cocoa powder, peppermint extract, vanilla extract, and stevia. Mix well.

    • Transfer the mixture to a blender. Add the other half of the milk.

    • Blend until frothy.

    • Pour into mugs and serve immediately.

    Recipe Notes

    This recipe is for 2 servings of peppermint mocha.
    This drink is best enjoyed immediately.
    Leftovers can be stored in an airtight container in the refrigerator for up to 3 days. I recommend reheating gently on the stove, then blending until frothy.

    Nutrition Info Per Serving

    Nutrition Facts

    Skinny Peppermint Mocha

    Amount Per Serving (1 serving)

    Calories 66
    Calories from Fat 29

    % Daily Value*

    Fat 3.2g5%

    Saturated Fat 0.7g4%

    Trans Fat 0g

    Polyunsaturated Fat 1.4g

    Monounsaturated Fat 0.7g

    Cholesterol 2mg1%

    Sodium 45mg2%

    Potassium 505.4mg14%

    Carbohydrates 5.3g2%

    Fiber 3g12%

    Sugar 1g1%

    Protein 4.8g10%

    Net carbs 2.3g

    * Percent Daily Values are based on a 2000 calorie diet.

    Course: Breakfast, Drinks

    Keyword: coffee, gluten-free, keto, latte, low carb, peppermint mocha



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    Cranberry White Chocolate Bars Top Best Cookie Swap Cookies for Christmas

    By electricdiet / December 8, 2020


    Best Cookie Swap Cookies For Christmas: Cranberry White Chocolate Bars!

    Seasonal ingredients are the best and Holly’s Cranberry White Chocolate Bars make the best Christmas cookies. If you are invited to a holiday party or need some new cookie swap ideas look no further? You probably don’t have an extra minute in the day so here is the holiday cookie solution with Holly’s best cookie swap cookies. They are also called Magic Cranberry Bar cookies and that’s because they disappear off the plate!  With dried cranberries, white chocolate chips and pecans, it doesn’t get much better.  Except, they take only about 5 minutes to make!  These festive cranberry white chocolate recipe is from Holly’s fun cookbook, Too Hot in the Kitchen with trendy, simple healthy easy recipes. Love to make homemade Christmas gifts.

    Cranberry White Chocolate Bars
    Holiday ingredients for easy festive cranberry bar cookies. Tart cranberries, sweet white chocolate, the spice of ginger and nuts pack this delicious dessert with wholesome vitamins and minerals – perfect to indulge in while staying fit this holiday season. And, this recipe is also diabetic-friendly!! I like bar cookies as they are made in one pan and you are done! In fact, I think I make so many pans of this recipe during the holiday season because they are the perfect holiday bars!

      Servings48 servings

      Ingredients

      • 1 1/2cups


        gingersnap crumbs

      • 6tablespoons


        buttermelted

      • 1teaspoon


        vanilla extract

      • 1/2cup


        dried cranberries or craisins

      • 1/3cup


        white chocolate chips

      • 1/3cup


        chopped pecans

      • 2/3(14-ounce) can


        fat-free sweetened condensed milk

      Instructions
      1. Preheat oven 350° F. Coat 13x9x2-inch pan with nonstick cooking spray.


      2. In prepared pan, mix gingersnaps, butter, and vanilla; press into pan.


      3. Sprinkle cranberries, white chocolate chips, and pecans evenly over gingersnap crust. Drizzle sweetened condensed milk over top. Bake 15-20 minutes or until bubbly and light brown.

      Recipe Notes

      Per Serving: Calories 57 Calories from fat 42% Fat 3g Saturated Fat 1g Cholesterol 4mg Sodium 34mg Carbohydrate 8g Dietary Fiber 0g Sugars 6g Protein 1g Dietary Exchanges: 1/2 other carbohydrate, 1/2 fat

      Simple To Make with Holiday Ingredients for Best Cranberry White Chocolate Cookies Recipe

      gingersnap crust for cranberry white chocolate bars-my favorite cranberry cookies

      Start with gingersnaps which are easy to find this time of year. Crush them in the food processor but you can do it however you want. These gingerbread snaps form your crust.

      Next step is to combine the ginger snap crumbs with butter and then sprinkle with the cranberries, white chocolate, and pecans. Then, drizzle the fat-free sweetened condensed milk on top and you’re ready to bake.

      Layer ingredients in the pan and drizzle with sweetened condensed milk.  Pop in the oven and that is it!

      Best Cookie Swap Cookies Recipe Also Makes Perfect Holiday Homemade Gifts

      Turn to this favorite cranberry white chocolate bar recipe for friends and family this time of year. For a quick and delicious gift, just cut the cranberry cookie bars into squares, wrap with plastic wrap and tie with a holiday ribbon. From teachers to coaches, neighbors to doctors, give the delicious gift of nutrition this holiday season! If you have had Hello Dollies, then these cranberry bar cookies are the holiday version with cranberries, white chocolate, pecans and gingersnaps.

      Too Hot in the Kitchen Has So Many Simple Sassy Recipes

      Holly has lot of cookbooks but honestly, people who have Too Hot in the Kitchen cookbook say it is their favorite cookbook. Probably because the recipes are a little more trendy and the chapters are just so great! From Easy Entertaining to Quickies!

      These fabulous Cranberry White Chocolate Bars are from the Easy Entertaining Chapter. The flavor and ingredients are the essence of this time of year.  You can literally find all kinds of simple entertaining recipes in this chapter and you probably already have the ingredients in your pantry.

      Excited To Find Reduced Sugar Craisins for Cranberry White Chocolate Bars

      These Ocean Spray reduced sugar craisins (dried cranberries) are fabulous!! Best all, you cannot taste any difference so they were just as tasty but better for you.  In all of Holly’s recipes that call for dried cranberries, use the reduced sugar craisins.  Why not? You should be able to find them in any grocery store.  They still provide 25% of your daily recommended fruit needs and are an excellent source of fiber. You’ll love these cranberry bar cookies with these craisins and besides, this is a diabetic cranberry cookie!  Amazing, simple to make, festive and diabetic make them the overwhelming best cookie swap cookie recipe.

      Freeze Fresh Cranberries when in Season – You Can Always Substitute Dried Cranberries

      Buy fresh cranberries when in season and freeze in freezable plastic bag for one year to have fresh cranberries year round.  If a recipe calls for fresh cranberries, dried cranberries may be used.  Two top seasonal recipes taking advantage of fresh cranberries are the simple Cranberry Lemon Bundt cake and Cranberry Orange Muffins . Both make great gifts or to keep around your house during the holiday season.

      Your Holiday Needs Holly’s 12 Ideas For Christmas Foodies Downloadable Only $1.99!

      The holidays are here and you need Holly’s 12 Ideas for Christmas Foodies. From evening appetizers to teacher gifts, even – what to cook Christmas morning, these festive favorite recipes are Holly’s go-to dishes that will get you through all of the parties and last-minute family get-togethers this December.  No need to stress with what to make this holiday season – let Holly do it for you with her December favorites!

      The Best Kitchen Gadgets List!

      Have you started making you holiday to-do list but it has you wondering what to give for a gift? Look no further than Holly’s Christmas wish list of favorite and 12 top unique kitchen gadgets!

      From an inexpensive mini spatula perfect for bar cookies to my pricey coffee maker which truly makes the best coffee, the research is done for you. LOVE the silicon bakeware and kitchen tools. Once you use them, you will understand why.

      Another Favorite Bar Cookie For Best Cookie Swap Cookies

      White Chocolate Recipes Make Sensational Seasonal Holiday Recipes

      Who doesn’t like a dessert that is made with white chocolate?  Hard to beat a white chocolate dessert! If you like this cranberry white chocolate holiday treats, wait until you try Holly’s fabulous White Chocolate Cheesecake from Gulf Coast Favorites cookbook or Chocolate Truffles with White Chocolate.

      Favorite Mini Spatula Perfect For Bar Cookies

      Favorite mini spatula because it is the perfect size for bar cookies.  Holly’s Blonde Brownies made with Holiday M&M’s are another great Christmas bar cookie.  Perfect for the spatula!  Holly discovered this amazing little kitchen tool while doing The 700 Club on her Cancer cookbook. In the make up room, someone was selling Pampered Chef so she wanted to see what everyone was buying. She bought this miniature spatula and it’s the perfect size to get bar cookies.

      Get All Holly’s Healthy Easy Cookbooks

      The post Cranberry White Chocolate Bars Top Best Cookie Swap Cookies for Christmas appeared first on The Healthy Cooking Blog.



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      In Vivo Reporter Assays Uncover Changes in Enhancer Activity Caused by Type 2 Diabetes–Associated Single Nucleotide Polymorphisms

      By electricdiet / December 5, 2020


      Abstract

      Many single nucleotide polymorphisms (SNPs) associated with type 2 diabetes overlap with putative endocrine pancreatic enhancers, suggesting that these SNPs modulate enhancer activity and, consequently, gene expression. We performed in vivo mosaic transgenesis assays in zebrafish to quantitatively test the enhancer activity of type 2 diabetes–associated loci. Six out of 10 tested sequences are endocrine pancreatic enhancers. The risk variant of two sequences decreased enhancer activity, while in another two incremented it. One of the latter (rs13266634) locates in an SLC30A8 exon, encoding a tryptophan-to-arginine substitution that decreases SLC30A8 function, which is the canonical explanation for type 2 diabetes risk association. However, other type 2 diabetes–associated SNPs that truncate SLC30A8 confer protection from this disease, contradicting this explanation. Here, we clarify this incongruence, showing that rs13266634 boosts the activity of an overlapping enhancer and suggesting an SLC30A8 gain of function as the cause for the increased risk for the disease. We further dissected the functionality of this enhancer, finding a single nucleotide mutation sufficient to impair its activity. Overall, this work assesses in vivo the importance of disease-associated SNPs in the activity of endocrine pancreatic enhancers, including a poorly explored case where a coding SNP modulates the activity of an enhancer.

      Introduction

      Type 2 diabetes affects >300 million people, causing severe complications and premature death (1), yet the underlying molecular mechanisms are largely unknown. This disease is highly complex, multifactorial, and partially characterized by endocrine pancreatic dysfunction, leading to insufficient insulin production (1). Genome-wide association studies (GWASs) have identified several single nucleotide polymorphisms (SNPs) associated with an increased risk of type 2 diabetes (24). Part of these variants are located in noncoding sequences with epigenetic marks associated to enhancer activity known to regulate the expression of their target genes by interacting with their promoters (5) and some overlap with transcription factor (TF) binding sites (TFBSs) required for proper endocrine pancreatic function (610). In this way, type 2 diabetes–associated SNPs may ultimately translate into transcriptional changes of the target genes (610). Methods to predict enhancers include profiling chromatin accessibility (11) and histone modifications (e.g., H3K27ac, H3K4me1) (12). The majority of the enhancer testing assays are performed in vitro in specific cell lines, missing cellular diversity and physiological contexts. To overcome these limitations, animal models have been used (13,14). The zebrafish has been successfully used for the study of pancreas development and function (15), having an endocrine compartment with the same cell types (α-, β-, δ-, and ε-cells) and functions as in mammal pancreas (16,17). Additionally, orthologous TFs operate in the zebrafish pancreas during early development, some of which are also important for adult pancreas maintenance. As in humans, Pdx1 plays a crucial role in zebrafish pancreas development and β-cell maturation and function (18), and Nkx6.1 is required for the identity of endocrine pancreatic progenitors (19,20).

      Some coding mutations are associated with the development of type 2 diabetes, while others might confer a protective effect (2,21). Interestingly, SLC30A8, a zinc transporter–encoding gene, shows contradicting results. The coding SNP rs13266634 located in the SLC30A8 gene is associated with an increased risk for type 2 diabetes (3) because of a tryptophan-to-arginine switch at protein position 325, causing reduced zinc transport activity (22,23). Zinc is essential for insulin packaging and maturation and secretion in β-cells (24); thus, the decrease of SLC30A8 activity is the simplest explanation for the increased risk for the disease. Surprisingly, recently identified protein-truncating SNPs in SLC30A8 have been associated with a protective effect (2,25) by enhanced insulin secretion (26). Further work is needed to clarify the type 2 diabetes association with SLC30A8 SNPs. An unexplored explanation for these apparently contradicting results could be that the coding SNP rs13266634 exerts a specific impact on adjacent or overlapping cis-regulatory sequences.

      In this work, we investigated the impact that SNPs located in putative enhancer regions have in enhancer activity, using an in vivo approach. Ten sequences that overlap with type 2 diabetes–associated loci and with marks for enhancer activity were tested, one overlapping with an exon of SLC30A8 (seq132wt). To test sequences for enhancer activity in the endocrine pancreas, we performed in vivo mosaic transgenesis assays in zebrafish embryos. We show that this strategy is sensitive, has low noise, and can be quantitative to address enhancer activity. Using this method, we observed that 6 out of 10 tested sequences are endocrine pancreatic enhancers, including the SLC30A8 exon-containing sequence. In addition, two sequences were found to be pancreatic progenitor enhancers. We also found that the type 2 diabetes–associated SNP (henceforth referred to as risk allele) decreased the enhancer activity of two enhancers, while the risk allele of two sequences resulted in a gain of enhancer activity. Interestingly, the SLC30A8 coding risk allele (seq132risk) showed an increase in enhancer activity, demonstrating that coding SNPs have the potential to modulate the target gene activity at both the transcriptional and the protein level. To better understand how SNPs can affect enhancer activity, we focused on the seq132 enhancer. We divided seq132wt into different fragments, observing that all are necessary for a robust pancreatic enhancer activity. We also show that common SNPs in seq132 modulate its pancreatic enhancer activity and that a single nucleotide mutation ablates completely the endocrine pancreatic enhancer activity of seq132. Additionally, we observed a chromatin interaction between seq132 and the promoter of Slc30a8 gene in murine cells and noted that targeting transcriptional modulators to seq132 using CRISPR affects the transcription of Slc30a8, strongly suggesting that the seq132 enhancer belongs to the regulatory landscape of Slc30a8. Overall, in this work, we use an in vivo system to validate enhancers that overlap with type 2 diabetes–associated SNPs, showing several cases where nucleotide variations result in complex changes in enhancer activity, including a classical and poorly understood coding SNP.

      Research Design and Methods

      Zebrafish Husbandry and Embryo Culture

      Zebrafish (Danio rerio) were handled according to European animal welfare regulations and standard protocols. Embryos were cultured at 28°C in Petri dishes containing E3 medium supplemented with 1-phenyl-2-thiourea to delay pigmentation formation (27).

      Putative Enhancer Selection

      Putative enhancer sequences were selected on the basis of GWAS data that uncovered 163 SNPs associated with type 2 diabetes or glycemic traits (P < 5e-8), considering all variants in high linkage disequilibrium (1000 Genomes Project Utah residents with ancestry from northern and western Europe r2 > 0.8), with lead GWAS SNPs being the exception for rs735949 (P < 3.70e-6) (6,2830). Risk alleles associated with seq132, seq117, and seq790 are part of the previously described GWAS credible set SNPs (31). Sequences were analyzed using the Islet Regulome Browser (32), which identifies active enhancers by the presence of H3K4me1, H3K27ac, and H2A.Z epigenetic marks in adult human endocrine pancreatic samples. The analysis was further refined by the presence of PDX1, MAFB, NKX6.1, FOXA2, and NKX2.2 binding obtained by chromatin immunoprecipitation sequencing (ChIP-seq) profiles (6,28), resulting in a list of 10 putative enhancer sequences, each overlapping with one SNP associated with type 2 diabetes (Supplementary Table 1).

      In Vivo Mosaic Transgenesis Assays

      Zebrafish transgenesis was performed using the Tol2 transposon system (33). One-cell embryos from the Tg(sst:mCherry) zebrafish reporter line were microinjected with 3 nL containing 25 ng/μL Tol2 transposase mRNA and 25 ng/μL phenol/chloroform-purified reporter vector. Injections were performed at least two times. Ins:GFP reporter was built by isolating the insulin promoter from the ins-CFP-NTR vector (SacI and BamHI) (34), cloning it in a pEM-MCS vector (35) and recombining it to a Tol2-based transposon containing a Gateway cassette and a GFP reporter gene.

      Human sequences were PCR amplified from human genomic DNA using specific primers (Supplementary Table 2), cloned in TOPO vector (pCR8/GW/TOPO TA Cloning KIT; Invitrogen) and recombined in vitro to the Z48 transgenesis vector (36) by the Gateway system. Risk SNPs from seq58, seq68, seq73, seq219, and seq460 were inserted by site-directed mutagenesis using specific primers containing the risk variant (Supplementary Table 2). Injected embryos showing expression of GFP in the midbrain were selected for immunohistochemistry at 48 hours post fertilization (hpf).

      sst:mCherry Reporter Line

      To identify the zebrafish endocrine pancreatic domain, we developed an in vivo reporter line that drives expression of mCherry in δ-cells. Primers for the somatostatin (sst) promoter amplification were designed (Supplementary Table 2) and the amplified fragment cloned in a Tol2 transposon containing mCherry as reporter gene. The vector was microinjected in one-cell embryos using the Tol2 transposon system. Embryos were selected for sst:mCherry-positive cells and raised until adulthood and a stable transgenic line was isolated.

      Immunohistochemistry

      The 48-hpf microinjected embryos were dechorionated and fixed in 4% formaldehyde in PBS (PBS1×) overnight at 4°C and then washed in PBS-T (0.5% Triton X-100 in PBS1×) with 1% Triton X-100 in PBS1× (2 h) and 5% BSA in PBS-T (0.1%). Embryos were incubated with anti-Nkx6.1 (1:75) (F55A12; Developmental Studies Hybridoma Bank) and anti-insulin (1:50) (ab210560; Abcam) diluted in 5% BSA-PBS-T followed by washing. Embryos were then incubated with anti-mouse Alexa Fluor 647 (1:800), anti-rabbit Alexa Fluor 647 (1:800) (Thermo Fisher Scientific), and DAPI (1:1,000) (Invitrogen) diluted in 5% BSA-PBS-T. Embryos were washed and stored in 50% glycerol in PBS1×. Microscopy slides were prepared using 50% glycerol in PBS1×. Confocal imaging was performed using a Leica SP5II confocal microscope.

      Assessment of Enhancer Activity

      Embryos were analyzed, using confocal microscopy, for the presence of GFP-positive cells in the endocrine pancreatic domain (sst:mCherry reporter domain) or in the endocrine progenitor domain (anti-Nkx6.1). One embryo was considered positive if at least one GFP-positive cell was detected within the endocrine pancreatic domain or progenitor domain. Quantifications are presented as percentages of positive embryos to ensure the quantification of different transposon integrations.

      Prediction of TFBSs Affected by Type 2 Diabetes Risk Variants

      The wild-type (wt) and risk variant sequences were analyzed using 719 specific position weight matrices for vertebrate TFs using JASPAR software (37). TFs were ranked by position-specific score matrix. The relative score is a threshold score between 0 and 1 and is calculated by (score − minimum score) / (maximum score − minimum score), meaning 1 is the highest affinity and 0 is no affinity of binding (37). TFs that showed differential binding affinity in wt and risk variants were selected and filtered by presence of H3K4me3 at their promoters (6,32).

      ChIP on Plasmid

      Seq119wt and risk were cloned into a pLVX lentiviral backbone (#125839; Addgene) as KpnI-ApaI (Anza) fragments. Lentiviral particles were produced in HEK-293 cells (packaging plasmids psPAX2, #12260, and pCMV-VSV-G, #8454; Addgene) and used to infect MIN6 cells (a gift from Lorenzo Pasquali) according to standard procedures. Infected cells were selected with puromycin (1 μg/mL; Sigma-Aldrich) for 12 days, starting 48 h after infection. Three to 10 million cells were used for ChIP (12) with 4 μg of Nkx6.1 antibody (F5510; Developmental Studies Hybridoma Bank) and magnetic Dynabeads (Thermo Fisher Scientific). Eluted chromatin was purified with a MiniElute Kit (QIAGEN). Immunoprecipitated DNA was dissolved in water and further analyzed by real-time PCR (iTaq Universal SYBR Green Supermix, CFX 384; Bio-Rad).

      Real-time Quantitative Expression Analysis

      MIN6 cells were harvested for RNA extraction with TRIzol (Ambion) and treated with DNase (Thermo Fisher Scientific). Five hundred nanograms to 1 μg of DNA-free RNA was retrotranscribed with iScript cDNA Synthesis Kit (Bio-Rad). cDNA was used for quantitative PCR (iTaq Universal SYBR Green Supermix, CFX 384). Slc30a8 expression was calculated by the ΔCt method to actb housekeeping mRNA.

      4C Sequencing

      The 4C sequencing (4C-seq) was performed on 10 million MIN6 cells using sequential DpnII and Csp6I as previously described (38), with minor modifications. The 4C template was purified using an Amicon Ultra-15 Centrifugal Filter Unit (Millipore). Two libraries were independently prepared with the Expand Long Template PCR System (Roche) using specific primers (Supplementary Table 2). Libraries were purified with QIAquick PCR Purification Kit (QIAGEN) followed by the Agencourt AMPure XP reagent (Beckman Coulter). Libraries were sequenced on an Ion S5 XL System (Ion 540 Chip, Ion Torrent; Thermo Fisher Scientific). Previously described processing (39,40) was used with a custom Perl script. More than 3.5 million reads were aligned to the mouse genome (mm10) using Bowtie2 (default parameters, global alignment mode) (41). Reads within fragments flanked by restriction sites of the same enzyme (checked with bedtools) or fragments <40 base pairs (bp) were filtered out. Mapped reads were then converted to reads-per-first-enzyme-fragment-end units and smoothed using a 30-fragment mean running window algorithm.

      CRISPR Inactivation and CRISPR Activation Targeting

      Twenty-nucleotide single guide RNAs (sgRNAs) targeting the murine Slc30a8 enhancer with high predicted cleavage were selected from the UCSC Genome Browser CRISPR target track and cloned into the lentiviral backbone for enhancer inactivation (CRISPRi) [in Lenti-(BB)-hPGK-KRAB-dCas9-2A-BlastR, #118155; Addgene] or activation (CRISPRa) (in lentiSAMv2, #75112; Addgene) as previously described (42). Lentiviral particles were produced in HEK-293 cells (packaging plasmids: pRSV-rev, #12253; pMDLg/pRRE, #12251; and pMD2G, #12259; Addgene) and used to infect MIN6 cells according to standard procedures. Infected cells were selected by blasticidin (8 μg/mL) (Sigma-Aldrich) for 12 days, starting 48 h after infection.

      Statistical Analysis

      Statistical analyses were performed by using the χ2 test with Fisher correction and unpaired t test, applying a significance level of P ≤ 0.05. For real-time expression experiments, statistical analysis was performed with the Mann-Whitney test.

      Data and Resource Availability

      All data generated or analyzed during this study are included in the published article (and its Supplementary Material), with the exception of 4C-seq sequencing data. The 4C-seq data sets have been deposited in the European Nucleotide Archive at EMBL-EBI under accession number PRJEB39688 (https://www.ebi.ac.uk/ena/browser/view/PRJEB39688). The sst:mCherry reporter line generated during the current study is available from the corresponding author upon reasonable request.

      Results

      Identification of Endocrine Pancreatic Enhancers In Vivo by Mosaic Transgenesis in Zebrafish

      In vivo enhancer reporter assays can be performed in zebrafish either by generating stable transgenic lines, a time-consuming approach, or by mosaic transgenesis (43) on the basis of the analysis of many independent integration events. To test sequences for endocrine pancreatic enhancer activity, we used a Tol2 transposon (44) containing a minimal promoter, a GFP reporter gene, and a midbrain-specific enhancer (Z48) acting as an internal control of transgenesis (36) (Fig. 1A). As an endocrine marker, we developed an in vivo reporter line that drives expression of mCherry in δ-cells (sst:mCherry). To validate sst:mCherry as an endocrine pancreatic reporter, we generated double-positive embryos for the sst:mCherry and insulin (ins:GFP) reporter transgenes (Fig. 1B). At 48 hpf, all GFP-positive cells (ins:GFP) were located within the sst:mCherry expression pattern (Fig. 1C), indicating that the sst:mCherry reporter line can be used to define the endocrine pancreatic domain. Next, to understand whether the mosaic strategy to identify endocrine pancreatic regulatory elements was sensitive enough, we mobilized a Tol2 transposon containing the insulin promoter upstream of GFP. The mosaic analysis of 48-hpf–injected embryos, using confocal microscopy, revealed that 69% (n = 23) showed GFP expression in the endocrine pancreatic domain (Fig. 1D and Supplementary Fig. 1). Random integrations of the Z48 transgenesis vector can generate noise as a result of the influence of regulatory elements located in the genomic landscapes of each integration, termed position effect (43). To test whether the Z48 transgenesis vector was prone to position effect, we mobilized this vector without a sequence to test (negative control [NC]). These injected embryos did not show expression of GFP in the endocrine pancreas (0%, n = 43) at 48 hpf. In total, these results show that the use of mosaic transgenic embryos is sensitive enough to identify endocrine pancreatic regulatory sequences and that the associated noise as a result of the position effect is very low (Fig. 1D and Supplementary Fig. 1). Because endocrine pancreatic enhancers might also be active in pancreatic progenitor cells, we asked whether mosaic transgenesis assays could also be applied for these cell types. To test this hypothesis, we used an anti-Nkx6.1 antibody to define the endocrine pancreatic progenitor domain (Fig. 1E), and we mobilized the Z48 vector containing a known progenitor enhancer from the human SOX9 locus (36). Twenty-seven percent (n = 11) of embryos showed expression of GFP within the pancreatic progenitor domain labeled by Nkx6.1, contrasting with the NC, for which GFP was not detected (0%, n = 23) (Fig. 1F and Supplementary Fig. 1).

      Figure 1
      Figure 1

      A: Schematic representation of the Z48 vector (top) and a representative image of a Z48-injected embryo (bottom) showing GFP expression in the midbrain, mediated by the Z48 enhancer (blue), at 48 hpf. The expression of GFP in the midbrain functions as an internal control of transgenesis. Scale bar = 200 μm. B: Representative images of sst:mCherry (top) and ins:GFP (bottom) reporter lines at 48 hpf, showing mCherry and GFP expression in δ- and β-cells, respectively. Scale bars = 50 μm. C: Confocal images showing the endocrine pancreatic domain (dashed line), defined by the cross of the sst:mCherry and ins:GFP reporter lines. The 48-hpf embryos were counterstained with the nuclear marker DAPI. Scale bars = 10 μm. D: Percentage of embryos showing GFP-positive cells within the endocrine domain when injected with a vector containing GFP as reporter gene under the control of the insulin promoter (ins:GFP) (69%, n = 23) or with the Z48 vector without an endocrine enhancer (NC). E: Confocal images from 48-hpf embryos stained with anti-Nkx6.1 antibody (purple) to define the progenitor pancreatic domain (dashed line) in the sst:mCherry reporter line and the nuclear marker DAPI. Scale bars = 10 μm. F: Graph representing the percentage of embryos with GFP expression in progenitor cells when injected with a pancreatic progenitor enhancer (SOX9_PPE) (27%, n = 11) or the NC (0%, n = 43). *P < 0.05, by χ2 test.

      Identification of Endocrine Pancreatic Enhancers Overlapping With Type 2 Diabetes–Associated SNPs

      We selected 10 sequences (Supplementary Table 1) that overlap with SNPs previously associated with type 2 diabetes that are enriched for enhancer marks (H3K4me1, H3K27ac, and H2A.Z) and for TFBSs of endocrine pancreas TFs (FOXA2, NKX2.2, NKX6.1, MAFB, and PDX1) (6) (Fig. 2A and Supplementary Fig. 2AH). Nine of these sequences are noncoding while one, seq132, partially overlaps with a coding exon of the SLC30A8 gene. The respective sequences that do not contain the type 2 diabetes–associated variant (wt alleles) were cloned in Z48 transgenesis vector, and in vivo enhancer assays were performed by mosaic transgenesis in zebrafish embryos (36). Out of the 10 tested sequences, 6 showed a consistent expression of GFP in the endocrine pancreatic domain, therefore being endocrine pancreatic enhancers (seq58wt, seq68wt, seq73wt, seq132wt, seq219wt, and seq460wt) (Fig. 2B and D and Supplementary Figs. 35). Stable transgenic lines were generated for three of these sequences (seq132wt, seq460wt, and seq58wt) to confirm their endocrine pancreatic enhancer activity (Supplementary Fig. 6). Interestingly, for at least three of the tested sequences (seq58, seq68, and seq73), the GFP signal was also detected adjacent to the endocrine pancreatic domain (Supplementary Fig. 7), suggesting that these sequences may be pancreatic progenitor enhancers. To address this hypothesis, we labeled embryos injected with reporters of seq58, seq68, and seq73 with anti-Nkx6.1 antibody, showing that seq68 and seq73 drive GFP expression in the Nkx6.1-positive progenitor domain (45% [n = 13] and 25% [n = 12], respectively) (Fig. 2C and E), being therefore identified as pancreatic progenitor enhancers. To further characterize the identified enhancers, we determined in which endocrine pancreatic cell types they drive expression. For that, we injected the Z48 transgenesis vector containing the respective enhancers in a gcga:mCherry reporter line (α-cells), counterstaining these embryos with anti-insulin to label β-cells. We found that the majority of tested enhancers were able to drive expression in β-cells and that most of them were able to drive expression in more than one cell type (Supplementary Fig. 8).

      Figure 2
      Figure 2

      A: Genomic landscapes of the putative enhancer seq58. Tracks represent H3K27ac, H3K4me1, histone variant H2A.Z, and TF binding (PDX1, NKX2.2, FOXA2, and NKX6.1) from ChIP-seq data of human endocrine pancreatic samples. Human ZFAND3 is the nearest gene to the putative enhancer (blue). The location of the type 2 diabetes–associated SNP (rs58692659) is represented as a vertical black line. B: In vivo reporter assay for endocrine pancreatic enhancers. Top panels show a representative confocal image a sst:mCherry zebrafish embryo injected with the Z48 enhancer reporter vector containing the seq73wt sequence, showing GFP-positive cells within the endocrine pancreatic domain (dashed line), contrasting that absent in embryos injected with NC (bottom). Scale bars = 10 μm. C: In vivo reporter assay for pancreatic progenitor enhancers. Confocal analysis of seq68 reporter assay shows colocalization of GFP-positive cells with Nkx6.1 progenitor marker, contrasting with NC for the pancreatic progenitor domain (dashed line) that did not show GFP-positive cells. All vectors were injected in the sst:mCherry reporter line and embryos analyzed at 48 hpf and stained with DAPI. Scale bars = 10 μm. D: Graph representing the percentage of embryos with GFP expression in endocrine pancreatic domain at 48 hpf for each sequence analyzed: seq58wt (36%, n = 56), seq68wt (13%, n = 47), seq73wt (28%, n = 47), seq132wt (23%, n = 34), seq219wt (24%, n = 38), seq460wt (27%, n = 36), seq72wt (0%, n = 27), seq117wt (0%, n = 21), seq119wt (4%, n = 27), seq790wt (0%, n = 20), and NC (0%, n = 43). E: Graph representing the number of embryos with GFP expression in pancreatic progenitor domain, defined by Nkx6.1 staining at 48 hpf, for each sequence analyzed: seq58wt (0%, n = 12), seq68wt (46%, n = 13), seq73wt (25%, n = 12), and NC (0%, n = 43). *P < 0.05, by χ2 test.

      SNPs Associated With Increased Risk of Type 2 Diabetes Modulate Enhancer Activity

      To address the possible impact that type 2 diabetes–associated SNPs have in overlapping enhancers, we tested the corresponding variants (risk alleles), performing enhancer assays for endocrine pancreas. Of the six previously identified endocrine pancreatic enhancers, two (seq58risk and seq219risk) showed a decreased enhancer activity for the respective risk allele and two an increase (seq68 and seq132) compared with the wt allele (Fig. 3A and B and Supplementary Figs. 9 and 10). Strikingly, for seq132, the risk allele is in a coding exon of SLC30A8 (seq132risk 56%, n = 36; seq132wt 23%, n = 34) (Fig. 3B). For one sequence, the risk allele was able to drive GFP expression in the endocrine pancreas above the established threshold, while the wt allele did not (seq119risk 14%, n = 28; seq119wt 4%, n = 27) (Fig. 3B). Overall, these results demonstrate that type 2 diabetes–associated SNPs have the potential to modulate the activity of enhancers in a sequence-specific manner.

      Figure 3
      Figure 3

      A: Representative confocal images for seq219wt, seq219risk, and NC. Seq219wt showed GFP expression in endocrine pancreatic domain (dashed line), defined by the sst:mCherry reporter line. The 48-hpf embryos were stained with DAPI. Scale bars = 10 µm. B: Graph showing the total percentage of positive embryos in wt and risk alleles for each of the 10 sequences analyzed: seq58wt/risk (36%, n = 56; 12%, n = 43, respectively), seq68wt/risk (13%, n = 47; 50%, n = 32), seq73wt/risk (28%, n = 47; 22%, n = 36), seq132wt/risk (23%, n = 34; 56%, n = 36), seq219wt/risk (24%, n = 38; 6%, n = 35), seq119wt/risk (4%, n = 27; 14%, n = 28), seq72wt/risk (0%, n = 27; 6%, n = 30), seq117wt/risk (0%, n = 21; 0%, n = 8), seq460wt/risk (27%, n = 36; 27%, n = 30), and seq790wt/risk (0%, n = 20; 0%, n = 14). Six sequences showed differential enhancer activity between wt and risk allele. *P < 0.05, by χ2 test.

      Differential binding of TFs to wt and risk alleles could explain the observed differential enhancer activity. To test this hypothesis, we generated a stable mouse MIN6 β-cell line containing human wt and risk sequences of the seq119 enhancer. Seq119 risk showed both an increased predicted affinity to Nkx6.1 binding (Supplementary Fig. 11A) and increased enhancer activity (Fig. 3B). Performing ChIP-PCR, we demonstrated that Nkx6.1 binds with higher affinity to the seq119 risk variant (Supplementary Fig. 11B). Additionally, we predicted bioinformatically TFBSs for wt and risk alleles of each sequence (Supplementary Table 3). Sequences were then clustered in two groups: sequences that had shown differential enhancer activity between wt and risk alleles (seq58, seq68, seq119, seq132, and seq219) and sequences that did not (seq72, seq73, seq117, seq460, and seq790). Although both groups showed a similar number of predicted TFBSs in the wt allele, the differential activity enhancers group showed a higher number of predicted differential binding between wt and risk alleles (Supplementary Fig. 11C). These results suggest that differential binding of TFs might control the regulatory output of wt and risk variants.

      The Enhancer Seq132mm Belongs to the Slc30a8 Regulatory Landscape

      Among the detected enhancers, we found that seq132, a sequence that partially overlaps with an exon of SLC30A8, is an endocrine pancreatic enhancer. Additionally, we showed that the type 2 diabetes–associated risk allele (seq132risk), which encodes a tryptophan-to-arginine substitution causing a decrease in the function of SLC30A8 (24), has increased enhancer activity compared with the wt allele (seq132wt). To determine whether seq132 belongs to the regulatory landscape of SLC30A8, we used the MIN6 cell line to detect chromatin interaction points, since enhancers contact the promotor of the genes that they control. First, we performed enhancer assays in zebrafish, demonstrating that the mouse orthologous sequence (seq132mm) of the seq132 human enhancer is also an endocrine pancreas enhancer (Fig. 4A). Then, using 4C-seq (38), we observed the existence of an interaction between the Slc30a8 promoter and seq132mm (Fig. 4B and Supplementary Fig. 12). To further validate that seq132mm belongs to the regulatory landscape of Slc30a8, we targeted seq132mm using the CRISPR/Cas9 system with a dCas9 fused to a transcriptional activation domain (CRISPRa) and another to a repressor domain (CRISPRi), observing a significant increase and decrease of Slc30a8 expression levels, respectively (Fig. 4C). These results strongly suggest that seq132mm belongs to the regulatory landscape of Slc30a8, and because of the remarkable conservation in the activity and sequence of this enhancer, we propose that this regulatory mechanism is conserved in humans.

      Figure 4
      Figure 4

      A: Representative confocal images for the mouse seq132 (seq132mm enhancer chr15:52334298 + 52335281). Seq132mm enhancer showed GFP-positive cells within the endocrine pancreatic domain (dashed line) defined by the sst:mCherry reporter line. The 48-hpf embryos were stained with DAPI. Scale bars = 10 μm. Representative graph showing the total percentage of positive embryos for the sequence analyzed (13%, n = 35). *P < 0.05, by χ2 test. B: Genomic landscape of the mouse Slc30a8 gene (blue), showing 4C-seq profiles (black), with view point in Slc30a8 promoter (pink asterisk) in the Min6 cell line; zoom-out (top) and zoom-in (bottom) of the Slc30a8 gene and seq132mm enhancer. The targets line represents the regions where the interaction is significant. C: sgRNAs targeting murine Slc30a8 enhancer in CRISPRa or CRISPR assay in Min6 cells. Slc30a8 expression was calculated relative to the β-actin housekeeping gene by quantitative PCR. Dot blots represent the 10–90% quantile of six biological replicates. *P < 0.05, **P < 0.01. ctrl, control.

      The SLC30A8 Seq132 Enhancer Is Divided Into Different Functional Domains

      Next, we wanted to understand whether seq132 is divided into different functional domains. For that, we divided seq132wt into four fragments (Fig. 5A) and performed enhancer assays for each (Fig. 5B). Fragments seq132wt1 (872 bp) and seq132wt2 (967 bp) showed a milder endocrine pancreatic enhancer activity than the seq132wt total fragment (seq132wt 23%, n = 34; seq132wt1 9.8%, n = 41; seq132wt2 6.5%, n = 31). We also tested another fragment, seq132wt3 (899 bp), that contains seq132wt1 and extends to the end of the coding sequence of SLC30A8. Seq132wt3 was able to drive GFP expression in endocrine pancreatic cells (10%, n = 30), as was the remaining fragment seq132wt4 (788 bp), although with a decreased efficiency (4%, n = 23) (Fig. 5C). From these results, we conclude that seq132 has several functional domains spread through this sequence, and the sum of these parts is necessary for this enhancer to be fully functional. These results also suggest that other SNPs could potentially affect the output of this enhancer. To test this, we have performed enhancer assays with seq132risk, which contains three other common SNPs with no known association to type 2 diabetes (seq132risk#: rs2466296, rs2466295, and rs2466294) (Fig. 6A). Interestingly, seq132risk# was a less active enhancer than seq132risk, showing an activity similar to seq132wt (Fig. 6B and C) and demonstrating that the impact of disease risk alleles in the activity of enhancers might be modulated by other adjacent polymorphisms.

      Figure 5
      Figure 5

      A: Schematic representation of the four analyzed fragments derived from seq132wt: seq132wt1, seq132wt2, seq132wt3, and seq132wt4. The gene SLC30A08 is represented in blue. The wt allele is discriminated in black boxes. B: Representative confocal images for the total sequence, seq132wt, and the four analyzed fragments showing GFP-positive cells in the endocrine pancreatic domain (dashed line) defined by the sst:mCherry reporter line. The 48-hpf embryos were stained with DAPI. Scale bars = 10 µm. C: Graph representing the total percentages of positive embryos for seq132wt (23%, n = 34), seq132wt1 (9.8%, n = 41), seq132wt2 (6.5%, n = 31), seq132wt3 (10%, n = 30), and seq132wt4 (4%, n = 23). *P < 0.05, by χ2 test.

      Figure 6
      Figure 6

      A: Schematic representation of the three different analyzed versions of seq132: seq132wt, seq132risk, and seq132risk#. The gene SLC30A08 is represented in blue. The symbol # represents common variants with no association with type 2 diabetes. The wt and risk alleles are represented in black boxes. B: Representative confocal images for the seq132risk#, showing GFP expression in the endocrine pancreatic domain (dashed line) defined by the sst:mCherry reporter line. The 48-hpf embryos were stained with DAPI. Scale bars = 10 μm. C: Graph representing the total percentages of positive embryos for the seq132wt (23%, n = 34), seq132risk (56%, n = 36), and seq132risk# (20%, n = 20). *P < 0.05, by χ2 test.

      A Single Nucleotide Mutation Impairs Seq132 Enhancer

      Focusing on seq132, we wanted to further determine whether a single nucleotide mutation could lead to the complete ablation of the activity of this enhancer. Previous results have shown that Pdx1, an important TF required for proper pancreatic function, controls the activity of one endocrine pancreatic enhancer located in the second intron of the mouse Slc30a8 gene (45). On the basis of this, we hypothesized that seq132 could also be controlled by PDX1 binding. After performing TFBS analysis (Supplementary Table 3), we found that within seq132, there is a high score–predicted binding site for PDX1 (JASPAR score 0.9654) (Fig. 7A). To test whether this binding site is required for enhancer activity, we performed transgenesis assays using seq132wt containing a mutation in the predicted binding site of PDX1 (seq132wtPDX1) (Fig. 7B and C). This is an adenine-to-guanine substitution in the PDX1 consensus binding site, resulting in a predicted ablation of the binding of PDX1 (PDX1 predicted binding score: seq132wt 0.9654; seq132wtPDX1 0). Comparing endocrine pancreatic enhancer activity between seq132wt and seq132wtPDX1, we found that the seq132wtPDX1 sequence is unable to drive GFP expression in the endocrine pancreas (0%, n = 20) (Fig. 7D). Because we previously observed that seq132risk had increased endocrine pancreatic enhancer activity, we explored the possibility of the risk SNP rescuing the loss of function observed for the seq132wtPDX1 sequence. We observed that seq132riskPDX1 also showed no endocrine pancreatic enhancer activity (0%, n = 23); thus, the risk SNP is not sufficient to rescue the loss of the PDX1 binding site (Fig. 7D).

      Figure 7
      Figure 7

      A: PDX1 binding site prediction by JASPAR software. The human SLC30A8 is represented in blue. The vertical black line represents the single nucleotide mutation that overlaps with a PDX1 putative binding site. B: Schematic representation of the analyzed sequences: seq132wt, seq132wtPDX1, seq132risk, and seq132riskPDX1. The wt and risk alleles are discriminated in black boxes and the mutation in red boxes. The green boxes represent the binding site for PDX1. C: Representative confocal images for the analyzed sequence containing the risk variant in the absence (top) of the mutation in the putative binding site for PDX1, showing GFP expression in endocrine pancreatic domain (dashed line) defined by the sst:mCherry reporter line. The sequence containing the wt allele and the mutation (bottom) was not able to drive GFP expression. The 48-hpf embryos were stained with DAPI. Scale bars = 10 μm. D: Graph representing the total percentages of positive embryos for the different sequences represented in panel B: seq132wt (23%, n = 34), seq132wtPDX1 (0%, n = 21), seq132risk (56%, n = 36), and seq132riskPDX1 (0%, n = 20). *P < 0.05, by χ2 test.

      Discussion

      In this work, we demonstrate the feasibility to perform enhancer assays using mosaic transgenesis in zebrafish. The zebrafish pancreas, as its mammal counterpart, is composed mainly by α-, β-, and δ-cells that secrete the hormones glucagon, insulin, and somatostatin, respectively. The malfunction of these cell types can contribute to type 2 diabetes development. Therefore, enhancer assays evaluating type 2 diabetes–associated alleles should consider all these cell types. In the current work, we used an sst:mCherry reporter construct to determine the zebrafish endocrine pancreatic domain in vivo, making available the inherent cellular complexity of a fully functional pancreas to define the activity of enhancers. In contrast, most of in vitro assays are limited to only one endocrine pancreatic cell type, in many cases not fully functional (46). In vivo assays have, however, their own limitations. This is particularly important when studying SNPs that might modulate the activity of enhancers and, therefore, transcriptional levels rather than binary activation or inactivation states. In this work, we demonstrate that a mosaic transgenesis method is sensitive enough and has low levels of noise, making it possible to assay quantitatively the activity of enhancers. In the current study, variation of enhancer activity comprises at least three parameters predicted to have an impact on the number of embryos with GFP expression in the endocrine pancreas: 1) transcriptional output, affecting the amount of GFP expression per cell and its detection potential defined by the GFP detection threshold; 2) expression domain, defined by the potential of GFP expression in different endocrine pancreatic cell types; and 3) robustness, defined by the stability of GFP expression. Although the present assay does not discriminate these three sources of differential enhancer activity, the introduction of further improvements can offer this possibility. Expression domain can be discriminated if specific endocrine pancreatic cell markers are used, as shown for wt alleles (Supplementary Fig. 7), and differences in transcriptional output can be observed if an internal control is used and GFP expression per cell is quantified.

      Using mosaic transgenesis in zebrafish, we validated 6 out of 10 tested sequences as human endocrine pancreatic enhancers, two of which are also pancreatic progenitor enhancers, underlining the accuracy of enhancers prediction. Although species-specific regulatory outputs cannot be completely excluded, genetic networks and TFs that operate in enhancers are usually highly conserved between distantly related vertebrates as zebrafish and human, making interspecies enhancers assays reliable.

      Exploring the impact of the risk-associated SNPs in enhancer activity, we found that for seq58 and seq219, the risk allele dramatically decreased the enhancer activity while for seq68, seq119, and seq132 the opposite result was observed, suggesting that disease-associated SNPs have the potential to be translated into a loss or gain of function of target genes (47). For one case, seq119, we further demonstrated the increased binding affinity of Nkx6.1 in the risk allele, suggesting that the enhancer activity outputs might be explained by the differential binding of TFs to wt and risk alleles, consistent with previous works (48,49).

      Seq132 overlaps with exon 8 of SLC30A8. Strikingly, the risk allele rs13266634 (4), which results in an amino acid substitution impairing the function of SLC30A8 (22), showed a significantly higher level of enhancer activity than the wt sequence. These results demonstrate that coding SNPs have the potential to modulate the activity of overlapping enhancers, a mechanism poorly explored but compatible with described overlapping exon/enhancer functions (50). SLC30A8 encodes a zinc transporter, an ion essential for insulin maturation and secretion in β-cells (22,23). Therefore, the rs13266634-associated increased risk for type 2 diabetes is commonly attributed to the decrease of the zinc transporter activity. In opposition to this hypothesis, the loss of function of SLC30A8 was shown to enhance insulin secretion (26), and 12 identified truncating SNPs in the SLC30A8 gene have been associated with a protective effect against type 2 diabetes (2,25). This incongruence could be explained if the rs13266634 association with type 2 diabetes results, not from the reduced SLC30A8 zinc transporter activity, but from an increase of its transcription caused by a more active enhancer.

      Further exploring the seq132 enhancer, we observed that the increase of its activity when containing the risk allele can be reverted by the presence of three other common SNPs, highlighting how combinatorial variations in single nucleotides can alter enhancer activity. Furthermore, a mutation that disrupts the predicted binding site of PDX1 in seq132 results in the complete ablation of the enhancer. This suggests that the loss of the binding of PDX1 might coincide with the gain of a transcriptional repressor, since this mutation ablates the activity of nonoverlapping seq132 subfragments that have mild autonomous enhancer activity (Fig. 8). Overall, in this work, we open new avenues on the understanding of the code embedded in noncoding regulatory sequences and show the complexity of effects that one single nucleotide variation might have in the activity of enhancers and its possible impact on human disease.

      Figure 8
      Figure 8

      Theoretical model of the functional domains of seq132. Schematic representation from the three possible sequence versions and the respective associated enhancer activity level: high (A), low (B), and no enhancer activity (C). The gene SLC30A08 is represented in blue. The symbol # represents common variants with no association with type 2 diabetes. The wt and risk alleles are represented in black boxes, the putative repressor in red, and the binding site for PDX1 in green. The dashed line shows interdependence.

      Article Information

      Acknowledgments. The authors thank Lorenzo Pasquali (Josep Carreras Leukaemia Research Institute, Barcelona, Spain) for helpful suggestions and critical reading of the manuscript. The authors acknowledge the contribution of Joana Teixeira (i3S–Instituto de Investigação e Inovação em Saúde, Universidade do Porto, and IBMC–Instituto de Biologia Celular e Molecular, Porto, Portugal) for the ChIP-seq data plotting, Silvia Naranjo (CABD – Centro Andaluz de Biología del Desarrollo, Universidad Pablo de Olavide, Seville, Spain) for the sst:mCherry vector, Ana Maia (i3S–Instituto de Investigação e Inovação em Saúde, Universidade do Porto, and IBMC–Instituto de Biologia Celular e Molecular, Porto, Portugal) for the ins:GFP construct, the support of i3S Scientific Platform Advanced Light Microscopy, members of the national infrastructure Portuguese Platform of BioImaging (supported by POCI010145FEDER022122), and the assistance of the Genomics i3S Scientific Platform (supported by POCI-01-0145-FEDER-022184).

      Funding. This study was supported by the H2020 European Research Council under the European Union’s Horizon 2020 research and innovation programme (grant agreement no. ERC-2015-StG-680156-ZPR), the Fundação para a Ciência e a Tecnologia (FCT) (IF/00654/2013), and the European Regional Development Fund (Norte-01-0145-FEDER-000029). A.E., M.D., and F.J.F. are PhD fellows from FCT (grants SFRH/BD/147762/2019 to A.E., SFRH/BD/135957/2018 to M.D., and PD/BD/105745/2014 to F.J.F.). J.B. acknowledges FCT for a scientific stimulus grant (CEECIND/03482/2018).

      Duality of Interest. No potential conflicts of interest relevant to this article were reported.

      Author Contributions. A.E. carried out the experiments. A.E., M.D., and J.B. wrote the manuscript. A.E. and J.B. conceived, designed, and analyzed the data. C.P. performed the ChIP-seq experiment and CRISPRa and CRISPRi assays. F.J.F. performed the 4C-seq assay. M.D. contributed to the development of the sst:mCherry reporter line. M.G. performed the bioinformatic analysis of the 4C-seq experiment. J.B. designed and supervised the study. All authors revised the manuscript. J.B. is the guarantor of this work and, as such, had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

      • Received October 17, 2019.
      • Accepted September 2, 2020.



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      Black Bean and Sausage Stew

      By electricdiet / December 3, 2020





      Black Bean and Sausage Stew – My Bizzy Kitchen

























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      Low Carb Stuffed Mushrooms | Diabetes Strong

      By electricdiet / December 1, 2020


      These low carb stuffed mushrooms are easy to make and packed with melted cheesy goodness! They’re perfect any time you’re looking for a crowd-pleasing appetizer.

      low carb stuffed mushrooms garnished with fresh chives on a baking tray

      I love finding new ways to enjoy vegetables. Oftentimes, it’s also a new way to enjoy cheese!

      These low carb stuffed mushrooms are a great example. Tender baked mushrooms are filled with a creamy mixture of onions, garlic, cream cheese, and mozzarella, then baked to rich and melty perfection.

      Cheese? Check. Vegetables? Check. Easy to throw together in about 10 minutes? Check!

      So the next time you’re looking for an appetizer that will please a crowd, these low carb and meatless stuffed mushrooms are a great option. Who can resist all that melty goodness?

      How to make low carb stuffed mushrooms

      This recipe only takes about 10 minutes to prep and 20 minutes to bake, so you can be ready to serve in about half an hour!

      Ingredients for recipe separated into different ramekins, as seen from above

      Step 1: Preheat your oven to 350 F (180 C).

      Step 2: Remove the thick stems from the mushrooms, then chop the stems roughly.

      Step 3: Place the mushrooms caps into a baking dish.

      Step 4: Heat a large frying pan over high heat. Add the olive oil. Once the oil is hot, add the onions.

      Step 5: Once the onions are translucent (about 5 minutes), add the chopped mushroom stems and cook for another 3 minutes.

      Step 6: Add the garlic and cook until fragrant, about 1 minute.

      Cooked onions and mushroom stems in a frying pan

      Step 7: In a large bowl, mix together the cooked stems and onions, cream cheese, mozzarella, salt, and pepper.

      Cheese mixture combined in a large glass bowl with a spoon

      Step 8: Stuff the mushrooms caps with the mixture.

      Mushroom caps filled with the cheese mixture in the baking tray

      Step 9: Place the mushrooms in the oven and bake for 20 minutes or until the cheese is melted and golden.

      I recommend giving the mushrooms a few minutes to cool. They’re so tempting fresh out of the oven, but the cheese mixture is very hot, and you don’t want to burn your mouth!

      Finally, garnish with fresh chives before serving.

      Overhead view of baked mushrooms stuffed with cheese on a baking tray next to a ramekin of fresh chives

      What kind of mushrooms are best for this recipe?

      I like to use larger brown mushrooms for this recipe. Six of them definitely fill my baking dish!

      So when you’re at the grocery store, look for any good-sized brown mushrooms. You don’t want them to be bite-sized, but you don’t want the biggest portobello mushrooms either. Something right in the middle is perfect!

      Storage

      These mushrooms are best enjoyed immediately. That way, the cheese mixture will be hot and bubbly and the mushrooms will be tender!

      If you do have leftovers, you can store them covered in the refrigerator. I recommend eating within 3-4 days.

      Mushrooms stuffed with cheese and garnished with chives, as seen from above

      Other low-carb meatless recipes

      I love diving into low-carb meals that are packed with healthy veggies! Especially when they involve lots of cheese. If you feel the same way, then here are a few more meatless recipes that I know you’ll enjoy:

      You can also read this roundup I created of 9 Low-Carb Vegan Recipes for even more low-carb plant-based recipe ideas.

      When you’ve made this dish, please don’t forget to let me know how you liked it and rate the recipe in the comments below!

      Recipe Card

      Low Carb Stuffed Mushrooms

      These low carb stuffed mushrooms are easy to make and packed with melted cheesy goodness! They’re perfect any time you’re looking for a crowd-pleasing appetizer.

      Prep Time:10 minutes

      Cook Time:20 minutes

      Total Time:30 minutes

      Servings:6

      Low carb stuffed mushrooms garnished with chives on a baking tray

      Instructions

      • Preheat your oven to 350 F (180 C).

      • Remove the thick stems from the mushrooms, then chop the stems roughly.

      • Place the mushrooms caps into a baking dish.

      • Heat a large frying pan over high heat. Add the olive oil. Once the oil is hot, add the onions.

      • Once the onions are translucent (about 5 minutes), add the chopped mushroom stems and cook for another 3 minutes.

      • Add the garlic and cook until fragrant, about 1 minute.

      • In a large bowl, mix together the cooked stems and onions, cream cheese, mozzarella, salt, and pepper.

      • Stuff the mushrooms caps with the mixture.

      • Place the mushrooms in the oven and bake for 20 minutes or until the cheese is melted and golden.

      Recipe Notes

      This recipe is for 6 servings of stuffed mushrooms.
      You can use any mid-sized brown mushroom for this recipe.
      These mushrooms are best served immediately. If you have any leftovers, you can store them covered in the refrigerator for 3-4 days.

      Nutrition Info Per Serving

      Nutrition Facts

      Low Carb Stuffed Mushrooms

      Amount Per Serving (1 stuffed mushroom)

      Calories 250
      Calories from Fat 195

      % Daily Value*

      Fat 21.7g33%

      Saturated Fat 11.7g59%

      Trans Fat 0g

      Polyunsaturated Fat 1g

      Monounsaturated Fat 5.4g

      Cholesterol 61.3mg20%

      Sodium 411.3mg17%

      Potassium 66mg2%

      Carbohydrates 5.2g2%

      Fiber 1g4%

      Sugar 3.1g3%

      Protein 8.8g18%

      Net carbs 4.2g

      * Percent Daily Values are based on a 2000 calorie diet.

      Course: Appetizer, Side Dish

      Cuisine: American

      Keyword: low carb, Mushroom



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      Shrimp Remoulade Sauce – How To Make Healthy and Simple Remoulade Recipe

      By electricdiet / November 29, 2020


      Shrimp Remoulade Sauce is an all-time favorite!  However, if you order it out at restaurants it is usually high in fat with lots of mayo. To satisfy your cravings, Holly created a simple remoulade sauce recipe that’s healthy.  This Shrimp Remoulade recipe in Guy’s Guide To Eating Well cookbook is actually from the Obesity & Diabetes Chapter!  How about that! A simple remoulade sauce that you can enjoy eating without any guilt! So, this fantastic shrimp remoulade salad you can whip up in seconds and you’ll have a satisfying and impressive wonderful meal.

      simple remoulade sauce

      Shrimp Remoulade Sauce
      Show off your culinary skills with this dimple remoulade sauce recipe.  Serve on a bed of lettuce for a light lunch or fabulous first course. A healthy diabetic remoulade sauce that’s simple and also gluten free!

        Servings8 (1/4 cup) servings

        Ingredients

        • 1pound


          medium peeled shrimpseasoned and cooked

        • 2tablespoons


          light mayonnaise

        • 2tablespoons


          Creole or grainy mustard

        • 1tablespoon


          ketchup

        • 1tablespoon


          lemon juice



        • Dash hot sauce

        • 1/4cup


          chopped green onions

        • 2tablespoons


          finely chopped red onion

        • 2tablespoons


          chopped fresh parsley

        Instructions
        1. Place shrimp in bowl.  In another small bowl, mix together the remaining ingredients and toss with shrimp. Refrigerate until serving.

        Recipe Notes

        Per Serving:  Calories 74, Calories from fat (%) 22, Fat (g) 2, Saturated Fat (g) 0, Cholesterol (mg) 112, Sodium (mg) 243, Carbohydrate (g) 1, Dietary Fiber (g) 0, Sugars (g) 1, Protein (g) 12, Diabetic Exchanges: 2 very lean meat

        Guy’s Guide To Eating Well Includes Obesity & Diabetes Chapter!

        You might be surprised to find this Louisiana Shrimp Remoulade recipe in this man’s cookbook plus in the Obesity & Diabetes Chapter!  First, you won’t find an easier remoulade sauce recipe so anyone can whip it up.  This simple remoulade sauce is made with everyday ingredients that you probably already have at home. This book is about simplicity and good food! You’ll find a “D” to highlight diabetic recipes like this simple remoulade sauce recipe. It is easy to eat healthy with Holly Clegg’s recipes!

        You’ll find lots of your favorite easy Cajun recipes and southern recipes throughout the book. Cook for your man or get the man in the kitchen for preventive health with these healthy easy recipes!

        Don’t Turn Up Your Nose To Entree Salads When You Taste This Shrimp Remoulade Salad

        shrimp remoulade salad with simple remoulade recipe

        You can use the shrimp remoulade sauce as a dip and serve the shrimp around it. Or, you can serve in martini glasses for an appetizer. Holly’s favorite way to enjoy this recipe is a Shrimp Remoulade salad. Add all your favorite fresh salad ingredients to enjoy this wonderful, delicious sauce for the ultimate Shrimp Remoulade Salad that will fill you up and not out! Super-satisfying and simple to create and include whatever fresh vegetables you like.

        Get Creative With Shrimp Remoulade Recipe

        easy shrimp remoulade sauce recipe for crawfish remoulade

        Try Holly’s Easy Blackened Fish Recipe with 6 Ingredient Blackened Seasoning and serve it with Shrimp remoulade sauce. Talk about a great combination! Spicy blackened shrimp complements the light tasty remoulade sauce.

        Also, who doesn’t like Louisiana crawfish recipes? Holly created a Crawfish Remoulade. Perfect for leftover crawfish tails during crawfish season. An easy and fabulous pairing!

        Get Fabulous Easily With Simple Remoulade Sauce In Unbreakable Martini Glasses

        How fun is it to use martini glasses to serve a shrimp remoulade salad for fun appetizers!?! Keep it casual with unbreakable, good quality martini glasses. Fill the glasses with mixed greens and top with this Shrimp Remoulade recipe or turn into Crawfish Remoulade for the EASIEST but most impressive appetizer!

        Of course, these are dishwasher safe.  Start collecting and make your gathering a smashing success!

        12 Ideas For Christmas Foodies: Buy Now

        Christmas recipes

         

        Team Holly is excited to share with you Holly Clegg’s 12 Ideas for Christmas Foodies. From evening appetizers to teacher gifts, even – what to cook Christmas morning, these festive favorite recipes will be your go-to dishes that will get you through all of the parties and last-minute family get-togethers this December.  No need to stress with what to make this holiday season! Your Holiday Needs 12 Ideas For Christmas Foodies Downloadable Only $1.99!

        Get All of Holly’s Healthy Easy Cookbooks

        The post Shrimp Remoulade Sauce – How To Make Healthy and Simple Remoulade Recipe appeared first on The Healthy Cooking Blog.



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