Honey Sriracha Chicken Recipe: 5 Ingredient Best Grilled Chicken Recipe

By electricdiet / May 11, 2020

Honey Sriracha Chicken Thighs Best Grilled Chicken Recipe

Kick it up a notch with Holly’s Honey Sriracha Chicken thighs recipe.  You will love the flavor Sriracha gives to any food, especially when mixed with honey, lime, and cilantro. This Sriracha chicken recipe from Guy’s Guide To Eating Well: A Man’s Cookbook For Health and Wellness is a healthy chicken recipe with only 5 ingredients and takes your meal to another level. You’ll agree this best grilled chicken recipe is a winning chicken dinner because it’s quick to make and so flavorful with just a few ingredients. Honey Sriracha chicken recipe is a crowd favorite, especially if you’re having a BBQ!

Honey Sriracha Chicken Thighs
Stay home and turn to the grill for your favorite buffalo wing flavor with Sriracha sauce, honey and lime juice. Talk about a quick and best grilled chicken recipe with a little kick of heat. Make extra of this Honey Sriracha Chicken so you have leftovers.  Delish! Diabetic-friendly, Gluten-free

    Prep Time5 minutes + marinate time
    Cook Time15-20 minutes


    • 1/2cup

      plus 2 tablespoons sriracha saucedivided

    • 2tablespoons


    • 6

      bonelessskinless chicken thighs (about 2 pounds)

    • 3tablespoons

      lime juice

    • 3tablespoons

      chopped fresh cilantro

    1. In small bowl, mix together 2 tablespoons sriracha sauce and Place chicken in resealable plastic bag. Add remaining sriracha sauce and lime juice to chicken. Coat chicken in bag and let stand 15 minutes.

    2. Spray grill grates with nonstick cooking spray and preheat to medium. Place chicken on grill and sear on medium high on both sides. Continue cooking on indirect heat around 15-20 minutes or until chicken done (170°F.) turning several times.

    3. Transfer chicken to platter, brush with reserved sriracha-honey sauce mixture, cover, let stand 5 minutes. Sprinkle with cilantro.

    Recipe Notes

    Nutritional info per serving: Calories 216, Calories from Fat 27%, Fat 6, Saturated Fat 2g, Cholesterol 144mg, Sodium 435mg, Carbohydrates 9g, Dietary Fiber 0g, Total Sugars 9g, Protein 29g, Dietary Exchanges: ½ other carbohydrate, 4 lean meat

    Terrific Tip: Sriracha is a type of hot sauce made from chili peppers, vinegar, garlic, sugar and salt.

    Grilled shrimp recipe tops grilled pizza recipes

    Holly’s Healthy Chicken Recipe Tops Best Grilled Chicken Recipe

    In Guy’s Guide to Eating Well cookbook, there’s an entire chapter focused on Outdoor Cooking: Grilling and Hunting. When writing this cookbook, Holly wanted to make sure she included foods that men actually WANT to eat.

    The Grilling and Hunting Chapter is filled with recipes for men who like to grill. There’s also recipes to take advantage of the game meat men acquire through the year! If hunting isn’t your thing, no worries as you’ll find tons of fish, chicken, and shrimp recipes to grill up! You also must try the Grilled Shrimp Margherita Pizza recipe!

    What To Serve With This Best Grilled Chicken Recipe

    Grilling is a great way to entertain. Next time you have a party, take advantage of the beautiful outdoors and grill for your guests. Serve the chicken with everyone’s favorite Southwestern Sweet Potato Salad. It perfectly complements the Honey Sriracha Chicken Thighs.  Summer grilling is fun for casual gatherings.

    Sriracha Chicken Recipe Also Diabetic Chicken Recipe

    Also, this Honey Sriracha Chicken recipe is diabetic-friendly plus it’s guaranteed to wow every one. Remember there’s no special diabetic diet but it’s the healthiest way to eat.  What’s great is Holly gives you delicious healthy easy recipes and they are diabetic! How about an easy 5 ingredient easy chicken recipe that should even please almost everyone’s dietary needs as it is a gluten-free dairy-free recipe.

    Chicken Thighs In Honey Sriracha Sauce

    sriracha chicken recipe

    Who doesn’t enjoy Buffalo Wings?  Chicken Thighs in Honey Sriracha Sauce provide your favorite buffalo wing flavor at home straight of the grill!

    Not sure where to get Sriracha Sauce?  You can get it in the grocery or or you can order it easily.

    Get All Holly’s Healthy Easy Cookbooks

    The post Honey Sriracha Chicken Recipe: 5 Ingredient Best Grilled Chicken Recipe appeared first on The Healthy Cooking Blog.

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    A Variation on the Theme: SGLT2 Inhibition and Glucagon Secretion in Human Islets

    By electricdiet / May 9, 2020

    Sodium-glucose cotransporter 2 (SGLT2), encoded by SLC5A2, is primarily responsible for glucose resorption from the proximal tubule of the kidney. Selective inhibitors of SGLT2 (or SGLT2i), such as the gliflozins, are widely used for the treatment of type 2 diabetes (T2D), since they decrease blood glucose levels by increasing excretion of the sugar in the urine (1). These glucose-lowering effects might however be partially offset by the action of SGLT2i to increase circulating glucagon levels (2). Whether SGLT2i influence glucagon secretion via direct (3) or indirect/paracrine (4,5) mechanisms remains keenly debated. A number of studies have shown contrasting effects of SGLT2i on pancreatic α-cells, with increases (3,69), decreases (10), or no change (5,10) in glucagon secretion depending on the species, preparation, glucose concentration, and gliflozin used. Moreover, discordant results have been reported by different investigators regarding expression of SGLT2 and Slc5a1/SLC5A1 in mouse, rat, and human islets, as well as sorted cell populations (3,5,8,10).

    In this issue of Diabetes, Saponaro et al. (11) shed further light on the complex issue of SGLT2i and glucagon secretion. Using a large number of donors, the authors show that responses of isolated human islets to SGLT2i are highly heterogeneous, with large variation in basal and secreted glucagon levels as well as responsiveness to treatment. This apparent heterogeneity was also reflected at the level of SGLT2/SLC5A2 expression, shown using Western blotting with antibodies validated according to established guidelines, or interrogation of bulk islet data from 207 donors deposited in the Translational Human Pancreatic Islet Genotype Tissue-Expression Resource (TIGER) RNA-seq database. While SGLT2 was found to be strongly colocalized with α-cells, high variability in the number of glucagon-positive/SGLT2i-positive cells, as well as strength of colocalization, was observed between donors and even within different islets of the same donor. As such, the authors conclude that future studies assessing SGLT2i in human islets should take into account the appreciable heterogeneity in SGLT2 expression and glucagon responses. The proposed mechanisms by which SGLT2i influence α-cell function are shown in Fig. 1.

    Figure 1
    Figure 1

    Schematic showing effects of SGLT2i on α-cell function. SGLT2i have been proposed to influence glucagon release through direct, paracrine, and indirect effects. Direct: Binding of SGLT2i might alter intracellular glucose and Na+ concentration, leading to changes in α-cell metabolism and membrane potential. Glucagon is decreased through poorly defined and complex mechanisms involving α-cell repolarization. Paracrine: Insulin binds to the insulin receptor on δ-cells to increase SGLT1/2 activity, leading to Ca2+ release from intracellular stores and stimulation of somatostatin release, which tonically inhibits glucagon secretion. SGLT2i block this effect by binding to either SGLT1 or SGLT2 on the δ-cell membrane, decreasing somatostatin secretion and releasing α-cells from tonic inhibition (but note that Saponaro et al. [11] did not detect presence of SGLT2 in δ-cells, unlike what has been reported by others [4]). Indirect: SGLT2i stimulate glycosuria, which lowers blood glucose levels. α-Cells respond to hypoglycemia by releasing glucagon, which increases endogenous glucose production. Heterogeneous: SGLT2/SLC5A2 expression is highly variable between donors and even islets of the same individuals. Some individuals/islets respond to SGLT2i, whereas others are less responsive, unresponsive, or even inhibited. If studies are underpowered, and depending on the samples examined (i.e., responsive, nonresponsive, or inhibited), effects of SGLT2i are likely to be reported as either: 1) positive, 2) negative, or 3) absent. EGP, endogenous glucose production; KATP channel, ATP-sensitive potassium channel; SST, somatostatin; Veh, vehicle. Adapted from Servier Medical Art under a CC BY3.0 license (https://creativecommons.org/licenses/by/3.0/).

    These findings corroborate earlier studies showing that SGLT2i induce glucagon secretion from isolated human islets (3,6), possibly via direct effects on SGLT2 expressed in α-cells. Moreover, the studies further suggest that heterogeneity observed between human islet preparations might contribute to some of the discrepancies previously reported in the literature. Without a large number of donors, an experiment is unlikely to be adequately powered to reliably detect differences in SLC5A2 or glucagon secretion, giving rise to conflicting results depending on whether the samples received respond positively, negatively, or not at all to SGLT2i.

    The study by Saponaro et al. raises a number of interesting questions and avenues of future exploration concerning SGLT2i and glucagon secretion. What are the stimulus-secretion coupling mechanisms by which SGLT2i affects α-cells? Based on its mode of action (inhibiting a sodium-glucose cotransporter), SGLT2i can be expected to repolarize α-cells by dual mechanisms. The reduction of glucose uptake and metabolism will lower the cytoplasmic ATP/ADP ratio and increase KATP channel activity (12). However, SGLT2i will also remove the depolarizing effect of Na+ influx down its electrochemical gradient (9). How these effects influence glucagon secretion is difficult to predict given the complex relationship between membrane potential and secretion in α-cells (12).

    It is also important to elucidate why SGLT2 protein expression is so variable between donors. Does this relate to SLC5A2 transcript or mRNA abundance in the same donor, or is SGLT2 regulated at the posttranscriptional/translational level in islets? To elucidate whether this heterogeneity is an α-cell–intrinsic trait or reflects changes in other cell types, further analyses of sorted populations should be performed as new data sets become available. Suggesting that the methodology used might also influence data, SLC5A2 is readily detected in purified fetal and adult human α-cells and at levels comparable to those of GLUT1 and GLUT3 (SLC2A1 and SLC2A3) (13) but appears virtually absent from single-cell RNA-seq data sets (5).

    The localization of SGLT2 also poses a conundrum. Given its role to transport glucose and Na+ across the membrane, SGLT2 would be expected to be present on the cell surface. However, despite extensive antibody validation, SGLT2 appears to be localized primarily within the cytoplasm of human α-cells. While the authors show that SGLT2 might translocate to the cytoplasm depending on glucose concentration, it should be noted that G-protein–coupled receptors such as GLP1R only appear in the cytosol upon prolonged stimulation with orthosteric ligand (14). Novel chemical probes against SGLT2 might be helpful in clarifying whether SGLT2 compartmentalization represents a real phenomenon or, alternatively, reflects the fixation protocol and antibody used.

    It is also assumed that SGLT2i are highly selective for SGLT2. However, off-target effects cannot be completely excluded. Indeed, the SGLT2i canagliflozin has been reported to activate AMP kinase by inhibiting mitochondrial function (15). The finding that SGLT2i decrease cardiovascular mortality risk in patients with T2D (16) and heart failure (17) might be attributed to such off-target effects, since SGLT2/SLC5A2 is expressed at low levels in cardiomyocytes (18), being ∼1% of that found in in the kidney (9).

    Lastly, what is the relative contribution of direct (i.e., α-cell–centric) and indirect (i.e., via somatostatin or hypoglycemia) SGLT2i actions on glucagon secretion in vivo in humans? As for in vitro experiments, studies have shown opposing effects of SGLT2i in volunteers: under isoglycemic/euglycemic hyperinsulinemic conditions, glucagon has been reported to either increase (19) or remain unchanged (2,20). To this end, preclinical mouse studies using Cre-Lox, or high-fidelity reporter approaches (21), might help to untangle some of the complexity of SGLT2i action in the α-cell on a more homogenous background.

    In summary, Saponaro et al. provide new insight into SGLT2i action in the islet, by showing the presence of considerable heterogeneity between donors in terms of SGLT2 expression and ligand responsiveness. Going forward, it will be important not only to account for this heterogeneity but also for researchers to work together, using well-validated reagents, standardized protocols, and adequately powered experiments, to see whether or not these findings will impact treatment of patients with SGLT2i.

    Article Information

    Funding. D.J.H. was supported by the Medical Research Council (MR/N00275X/1 and MR/S025618/1) and Diabetes UK (17/0005681) project grants. This project has received funding from the European Research Council under the European Union’s Horizon 2020 research and innovation programme (Starting Grant 715884 to D.J.H.). Work in Oxford was supported by the Wellcome Trust, Diabetes UK, and the European Foundation for the Study of Diabetes.

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

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    T2D Healthline: Find Your Tribe

    By electricdiet / May 7, 2020

    By using the T2D Healthline app for a few weeks now, I’ve made new friends (many from my own state of Virginia), learned a lot, and hopefully made suggestions that helped other people. I’ve participated in (and led) Live Chats, read articles about living with Type 2 diabetes, and felt totally comfortable asking “dumb” questions. I’ve found my tribe.

    T2D Healthline - Find Your Tribe

    This is a sponsored post on behalf of Healthline.



    What’s my favorite part of the T2D Healthline app? It’s the Groups feature (that I mentioned in my intro to the app). In particular, I love the Live Chats that happen within the groups.

    So far I’ve hosted a chat in the Exercise & Fitness group about getting more physical activity while we’re all staying home. I’ve also participated in chats about topics ranging from low carb and keto diets to diabetes burnout. On May 12, 2020, I’ll be hosting a Live Chat about becoming an advocate for your own healthcare (in the Healthcare group). On May 24, 2020, I’ll be hosting another on the stigma of insulin therapy (in the Medications and Treatments group). Please join us!

    Find Your Tribe - T2D Healthline live chats


    Why Finding Your Tribe is Important

    You may think you don’t need anyone beyond your health care team to help you manage your diabetes. I once thought that too. Your doctor and diabetes care and education specialists can certainly teach you a lot and I’m grateful to the wonderful providers I’ve had over the years. But ever since I’ve discovered peer support (exactly what is offered through this app), managing everything has become more, well, fun.

    Who else can relate to what you live with every day? How great is it to have a safe place to ask “has anyone ever experienced …?” Who can offer tried-and-true tips for staying in a positive frame of mind when you are living with a chronic condition? Where else can you vent about a stigmatizing comment? Who else can tell you about the side effects they experienced when they started a new medication? Other people who live with Type 2, that’s who. Your peers. Your tribe. They just get it.

    Let’s Get Connected

    T2D Healthline appReady to find your tribe? Download the free T2D Healthline app here. Once you’ve joined the community, look me up! You should be able to find me by searching the Members area for “Shelby” or in one of the online Live Chats that are held Sunday through Thursday evenings. I look forward to welcoming you to the tribe!

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    Turkey Burger Sliders – My Bizzy Kitchen

    By electricdiet / May 5, 2020

    Mariano’s had turkey breast on sale for $4.99 this week which is an awesome price.  I haven’t had a turkey burger in the longest time and that sounded good to me.

    Have you seen turkey burger recipes that call for bread crumbs and eggs?  That sounds more like a meatloaf to me that masks the turkey instead of highlighting it.

    The secret to my turkey burgers?  Butter in the middle!

    This time I just used a teaspoon of I Can’t Believe It’s Not Butter Light in between the patties. Simple seasoning with Dak’s steakhouse seasoning, salt and pepper.


    Turkey Burger Sliders

    The secret to a juicy turkey burger is butter in the middle!  Simple spread with I Can’t Believe It’s Not Butter Light and pinky swear you won’t have another dry turkey burger.



    • 6 ounces ground turkey breast
    • 2 teaspoons I Can’t Believe It’s Not Butter
    • 1 teaspoon Dak’s steakhouse seasoning
    • salt and pepper
    • 2 tablespoons cheddar cheese
    • 1 tablespoon real bacon bits
    • 2 pretzel bun slider rolls


    Divide the burger patties into 1.5 ounce portions.  Spread 1 teaspoon of the butter on one patty, then put the other 1.5 ounce patty on top, crimping together to bring the burger together, so you will have two three ounce burgers.

    Heat skillet over medium heat.  Season with the steak seasoning, salt and pepper on both sides.  Cook burger for 4 minutes, flip.  At the 3 minute mark after the flip, add the cheese and bacon.  I cooked with a lid on, so my burger reached 165 in 8 minutes total.

    Serve on a pretzel bun with your favorite toppings.  I kept mine simple with romaine lettuce and mustard.


    On the purple and blue plans on WW, each burger is 4 WW points.  On green, you have to count the turkey, so each burger is 5 WW points.

    I thought I would eat both, but one was filling!

    I got the pretzel buns at Mariano’s – a six pack was only $2.99 and each one while 3 points is only 110 calories.  So good!

    Are you a fan of turkey burgers?  If not, try this method – promise you’ll love it! 

    Happy Tuesday my friends – make it a great day!


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    Easy Coconut Flour Cookies (Low-Carb) Recipe

    By electricdiet / May 3, 2020

    These soft and fluffy coconut flour cookies are ready in 30 minutes and are the perfect low-carb treat to satisfy your sweet tooth!

    stack of coconut flour cookies on wooden board

    Coconut flour is a great option for delicious low-carb baking It’s high in fiber and protein compared to other flour options and has a great taste of its own, which means you don’t have to add a lot of sugar or sweetener.

    You can easily modify this recipe to make different types of cookies. You can add some sugar-free chocolate chips to make chocolate chip cookies like I have done here or you can add nuts and a pinch of cinnamon and nutmeg to add some festive flavor.

    How to make coconut flour cookies?

    Step 1: Preheat your oven to 350 F (180°C) and line a cookie sheet with baking or parchment paper. Set aside. Measure out all the ingredients.

    ingredients for the cookies laid out

    Step 2: In a large mixing bowl, add the softened butter and granulated stevia. Cream together with a hand mixer or stand mixer for 2 – 3 minutes until the butter and sweetener are light and fluffy. Scrape down the sides of the bowl with a spatula where necessary. 

    creamed butter and sugar

    Step 3: Add the coconut flour, baking powder, salt, eggs, and vanilla extract to the bowl. Keep the chocolate chips aside if using. Combine the mixture well. It will be quite thick so make sure to get everything mixed together well.

    Step 4: Take 2 Tablespoons of the cookie dough and roll into a ball. Place onto the cookie sheet and press down with your palm into a cookie shape. If using chocolate chips, add a few to the top of the cookies and press down slightly. 

    cookies on baking tray

    Step 5: Bake for 10-15 minutes, or until slightly golden brown around the edges.

    Step 6: Remove from the oven and allow to cool down to room temperature. If you move them too soon they may break, so be careful! 

    cookies cooling on a wire rack

    Storing your cookies

    These cookies taste even better the next day! But only if they’re stored properly. After allowing the cookies to cool completely down to room temperature, they should be stored in an airtight container.

    Storing them in an airtight container keeps the coconut flour cookies fresh and crisp for up to a week.

    cooking cooling on a wire rack

    Can you substitute coconut flour for regular flour?

    Because coconut flour has a very unique consistency, you really can’t substitute regular flour, almond flour, or anything else in this recipe. The balance between the dry and wet ingredients won’t be right if you try.

    But if you want a cookie made completely without flour, you can try these awesome low-carb peanut butter cookies instead!

    Other healthy low-carb cookie recipes

    If you liked this recipe, here are some other easy low-carb cookie recipes you might enjoy: 

    You can also read this roundup I created of 10 diabetic cookie recipes for even more great low-carb cookie recipe ideas.

    When you’ve made these coconut flour cookies, please don’t forget to let me know how you liked them and rate the recipe in the comments below!

    Recipe Card

    Easy Coconut Flour Cookies

    These soft and fluffy coconut flour cookies are ready in 30 minutes and are the perfect low-carb treat to satisfy your sweet tooth!

    Prep Time:5 minutes

    Cook Time:15 minutes

    Total Time:20 minutes

    Servings:20 cookies

    coconut flour cookies square


    • 8 ounces unsalted butter (softened)
    • 10 Tbsp. granulated stevia
    • 4.5 ounces coconut flour
    • 1 Tsp. baking powder
    • 1 Tsp. salt
    • 4 medium eggs
    • 2 Tsp. vanilla extract
    • 4 Tbsp. sugar-free chocolate chips (optional)


    • Preheat your oven to 350 F (180°C) and line a cookie sheet with baking or parchment paper. Set aside.

    • In a large mixing bowl, add the softened butter and granulated stevia. Cream together with a hand mixer or stand mixer for 2 – 3 minutes until the butter and sweetener are light and fluffy. Scrape down the sides of the bowl with a spatula where necessary.

    • Add the coconut flour, baking powder, salt, eggs and vanilla extract to the bowl. Keep the chocolate chips aside if using. Combine the mixture well. It will be quite thick so make sure to get everything mixed together well.

    • Take 2 tablespoons of the cookie dough and roll into a ball. Place onto the cookie sheet and press down with your palm into a cookie shape. If using chocolate chips, add a few to the top of the cookies and press down slightly.

    • Bake for 15 minutes, or until slightly golden brown around the edges.

    • Remove from the oven and allow to cool down to room temperature. If you move them too soon they may break so be careful!

    Recipe Notes

    This recipe makes 20 cookies.  You can store the cookies in an airtight container for up to a week.

    Nutrition Info Per Serving

    Nutrition Facts

    Easy Coconut Flour Cookies

    Amount Per Serving

    Calories 132 Calories from Fat 106

    % Daily Value*

    Fat 11.8g18%

    Saturated Fat 7.5g38%

    Trans Fat 0g

    Polyunsaturated Fat 0.3g

    Monounsaturated Fat 2.4g

    Cholesterol 24.2mg8%

    Sodium 40mg2%

    Potassium 3.4mg0%

    Carbohydrates 3.9g1%

    Fiber 2.2g9%

    Sugar 0.5g1%

    Protein 2.2g4%

    Net carbs 1.7g

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

    Course: Dessert, Snack

    Cuisine: American

    Keyword: diabetic cookies, low-carb cookies

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    Family Favorite Homemade Dinner Smothered Chicken Breasts Recipe

    By electricdiet / May 1, 2020

    Family Will Love Smothered Chicken Breasts Recipe 

    Holly’s Smothered Chicken breasts recipe with an easy homemade gravy from KITCHEN 101: Secrets to Cooking Confidence hit the spot. The whole family will clean their plates after eating this smothered chicken recipe. A satisfying home cooked meal! Of course, who doesn’t like a home style chicken recipe with healthy homemade gravy?

    Smothered Chicken
    Effortless flavorsome Smothered Chicken breasts recipe in a rich brown gravy, and served over brown rice is the ultimate comfort food that just happens to be nutritious! When you are done, leave the skillet on simmer until you are ready to serve, because you can’t overcook — the chicken just gets more tender!

      Servings6 servings


      • 1


      • 1teaspoon

        minced garlic

      • 1/2cup


      • 2pounds

        boneless, skinless chicken breasts

      • 1/4cup

        all-purpose flour dissolved in 1/2water

      • 1cup

        fat-free chicken broth

      • 1

        bunch green onionschopped

      • 2tablespoons

        chopped parsley

      • salt and pepper to taste

      1. In large nonstick skillet coated with nonstick skillet coated with nonstick cooking spray, sauté onion and garlic until tender and browned, 5–7 minutes.

      2. Add 1/2 cup water and continue cooking 5 minutes. Add chicken. Bring to boil, reduce heat, cover and cook 20-30 minutes until chicken is tender.

      3. In small cup mix together flour and water and add to skillet, stirring until smooth and thickens.

      4. Gradually add broth, cooking until chicken is done and gravy is bubbling, another 15-20 minutes.
Stir in green onions, parsley, and season to taste.

      Recipe Notes

      Per Serving: Calories 216, Calories from Fat 18%, Fat 4g, Saturated Fat 1g, Cholesterol 97mg, Sodium 249mg, Carbohydrates 9g, Dietary Fiber 2g, Total Sugars 3g, Protein 33g, Dietary Exchanges: 1/2 starch, 1 vegetable, 4 lean meat Terrific Tip: If you’re only using a small amount of parsley, dried parsley is fine.

      healthy college recipes

      Simmer Smothered Chicken Recipe Until Ready to Serve

      Best of all, you can’t overcook it! Often after school activities require some family members to trickle to the dinner table later than others, or maybe pushed back a bit. No problem with this dish as it can simmer until you are ready to eat – it only gets more tender and delicious!

      KITCHEN 101 includes recipes with around 10 ingredients or less to help you with eating healthy. So easy to soak up the gravy when you pair with the perfect Pull Apart Bread recipe.

      Smothered Chicken Recipe Also Easy Diabetic Recipe

      Not only will your family love this Smothered Chicken breasts recipe, but you will also feel good about giving them a healthy – even diabetic friendly – home cooked meal, all in a matter of minutes! If you are trying to eat healthier KITCHEN 101 highlights diabetic recipes in the book with a “D.” It is the healthiest way to eat and wait until you try this diabetic chicken recipe!

      Using Wooden Spoons When Cooking

      An old fashioned wooden spoon still works the best for stirring this smothered chicken and other dishes with gravies.

      This wooden spoon with the corner edge makes it easy to stir the entire pot!  There are so many wooden spoons and you can see which you like but Oxo makes good quality ones.

      You’ll Never Be Bored Again With Healthy Chicken Recipes

      No need to be bored with cooking Holly’s delicious healthy chicken recipes! Visit the healthy food blog for another easy healthy chicken recipe like the super-satisfying Chicken and Dumplings.  There are so many easy recipes and simple healthy chicken recipes for quick family dinners.

      Get All of Holly’s Healthy Easy Cookbooks

      The post Family Favorite Homemade Dinner Smothered Chicken Breasts Recipe appeared first on The Healthy Cooking Blog.

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      Interplay of Placental DNA Methylation and Maternal Insulin Sensitivity in Pregnancy

      By electricdiet / April 29, 2020


      The placenta participates in maternal insulin sensitivity changes during pregnancy; however, mechanisms remain unclear. We investigated associations between maternal insulin sensitivity and placental DNA methylation markers across the genome. We analyzed data from 430 mother-offspring dyads in the Gen3G cohort. All women underwent 75-g oral glucose tolerance tests at ∼26 weeks of gestation; we used glucose and insulin measures to estimate insulin sensitivity (Matsuda index). At delivery, we collected samples from placenta (fetal side) and measured DNA methylation using Illumina EPIC arrays. Using linear regression models to quantify associations at 720,077 cytosine-guanine dinucleotides (CpGs), with adjustment for maternal age, gravidity, smoking, BMI, child sex, and gestational age at delivery, we identified 188 CpG sites where placental DNA methylation was associated with Matsuda index (P < 6.94 × 10−8). Among genes annotated to these 188 CpGs, we found enrichment in targets for miRNAs, in histone modifications, and in parent-of-origin DNA methylation including the H19/MIR675 locus (paternally imprinted). We identified 12 known placenta imprinted genes, including KCNQ1. Mendelian randomization analyses revealed five loci where placenta DNA methylation may causally influence maternal insulin sensitivity, including the maternally imprinted gene DLGAP2. Our results suggest that placental DNA methylation is fundamentally linked to the regulation of maternal insulin sensitivity in pregnancy.


      Insulin sensitivity decreases drastically in the 2nd half of pregnancy to levels that are similar to those in individuals with early type 2 diabetes (T2D) (1). It is hypothesized that this dramatic decrease in insulin sensitivity is meant to help provide nutrients from maternal sources to the growing fetus. The placenta likely plays a role in this physiologic adaptation, but the exact mechanisms remain unclear.

      The placenta is a unique organ of fetal origin that lies at the maternal and fetal interface with primary roles to optimize fetal growth, protect the fetus against infections, and produce key hormones to maintain pregnancy; these hormones profoundly influence maternal physiology. In its role of nutrient transfer, the placenta responds to both fetal demands and maternal availability of nutrients and further adapts to regulate resources allocation. Yet, the “maternal-fetal conflict” theory posits that the mother and the fetus “disagree” on an optimal level of resource allocation from the mother to the fetus to allow pregnancy to its term and a healthy baby (2). During early embryogenesis, the trophectoderm develops to form the placenta with a distinctive epigenetic process (3). The placenta demethylation process in early development is characterized by a great number of genomic regions remaining imprinted from their parent of origin. Some investigators have proposed that the maintenance of the parental origin of imprinted regions in placenta contributes to the “maternal-fetal conflict” where genes that are expressed from paternal alleles act to shunt more nutrients to the fetus while expression driven by maternal alleles helps the mother maintain her resources (2,4). Thus, it is plausible that specific DNA methylation patterns in the placenta may influence the decline in maternal insulin sensitivity that leads to the transfer of glucose and other fuels to the fetus.

      Based on these hypotheses, we investigated associations between maternal insulin sensitivity estimated in the 2nd trimester and genome-wide DNA methylation in placenta collected at birth in a large prospective pregnancy cohort. We had initially hypothesized that maternal insulin sensitivity could lead to changes in placental DNA methylation (given temporality of our measures) but also tested the possibility that placenta DNA methylation may influence maternal insulin sensitivity. To untangle whether placental DNA methylation is influencing maternal insulin sensitivity or vice versa, we tested potential causality using a bidirectional Mendelian randomization (MR) framework. Additionally, we conducted pathway analyses to deepen our understanding of our findings.

      Research Design and Methods

      Description of Participants

      This study is based on mother-child pairs in the Genetics of Glucose regulation in Gestation and Growth (Gen3G) prospective cohort. We have previously published details of recruitment and phenotypic measurements during pregnancy (5). In brief, we recruited women in the 1st trimester (V1: 5–14 weeks), inviting all women presenting for their routine prenatal laboratories at Centre Hospitalier Universitaire de Sherbrooke (CHUS). We excluded women with diabetes prior to pregnancy (known or discovered at 1st trimester) and nonsingleton pregnancies. In addition to collecting blood samples, our trained research staff collected demographics, medical history, and completed standardized anthropometric measurements. We calculated BMI using the standard formula (kg/m2).

      We followed participating women in the 2nd trimester (V2: 24–30 weeks) and completed similar measurements. After overnight fasting, women completed a 75-g oral glucose tolerance testing (OGTT) for gestational diabetes mellitus (GDM) screening. We collected plasma samples at baseline (fasting) and at 1 h and 2 h during the OGTT. We measured glucose and insulin at each OGTT time point, which allowed us to derive indices of insulin sensitivity.

      We followed women until delivery and collected delivery and neonatal outcomes in addition to cord blood and placenta samples. At birth, trained research staff collected placenta samples (<30 min postpartum) based on a standardized protocol: 1 cm3 placenta tissue was collected ∼5 cm from the umbilical cord insertion on both sides of the placenta. Prior studies have shown high concordance of placental DNA methylation levels at specific loci across biopsy locations (6). Placenta samples were rapidly put in RNALater (QIAGEN) and stored at 4°C for a ≥24 h and then stored at −80°C. For this study, we selected fetal side placenta samples based on availability of adequate tissue (high-quality DNA extraction) and excluded complications in late pregnancy or delivery (e.g., preeclampsia, chorioamnionitis, stillbirth).

      The CHUS ethics board review committee approved this study; all women provided written consent. For this study, we included only women of European origin (self-reported) who had consented for genetics investigations.


      We measured glucose by the glucose hexokinase method (Roche Diagnostics) immediately after collection and insulin using multiplexed particle-based flow cytometric assays (Human MILLIPLEX map kits, EMD Millipore) from plasma samples previously frozen (−80°C). We estimated insulin sensitivity using the Matsuda index, given by the following equation: 10,000/ (√[(fasting glucose × fasting insulin) × (mean glucose during OGTT × mean insulin during OGTT)]) (7). We selected the Matsuda index over other measures of insulin sensitivity because it has been validated against euglycemic-hyperinsulinemic clamps in pregnant women (7).

      DNA Methylation Measurements

      We purified DNA from 460 placenta samples using the AllPrep DNA/RNA/Protein Mini Kit (QIAGEN). After bisulfite conversion, the Illumina Laboratory (San Diego, CA) performed epigenome-wide DNA methylation measurements using the Infinium MethylationEPIC BeadChip. We imported methylation data into R for preprocessing using minfi. We performed quality control (QC) at the sample level, excluding samples that failed (n = 5) or had mismatched genotype based on paired cord blood data (n = 6) or sex (n = 1). Our final placenta DNA methylation data set included 448 samples. We then excluded women because of missing data (Matsuda or key covariables). Thus, our final data set for this study was composed of 430 mother-child pairs (see Supplementary Fig. 1), which fully overlap with our prior publication of maternal 2-h glucose associations with placenta DNA methylation (8).

      We normalized our data as previously described (8). We applied functional normalization (9) (FunNorm) and Regression on Correlated Probes (10) (RCP) to adjust for technical variability and probe type bias, respectively. Briefly, FunNorm removes technical variability using control probes from the array and RCP corrects type II probe distributions using the distribution of proximal type I probes to increase precision. We removed cytosine-guanine dinucleotide (CpG) probes with single nucleotide polymorphisms (SNPs) at the target site (4,453), single base extension (5,552), or anywhere within the probe (71,054) with a minor allele frequency (MAF) of >5%; probes on sex chromosomes (19,129); and previously identified cross-reactive probes (40,448) (11) to analyze 720,077 high-quality probes. We used the ComBat function from the sva package to adjust for batches. We logit transformed the β-values to M values for statistical analyses, as they have been shown to be more appropriate, meeting statistical model assumptions (12). However, we also reported effect estimated on the β-value scale to ease interpretability, since it approximates the proportion of methylation (from 0 to 1).


      We isolated DNA from maternal buffy coat using the Gentra Puregene Blood Kit (QIAGEN, Mississauga, Ontario, Canada). We performed genotyping on 598 maternal samples using Illumina MEGAEX arrays. We removed 16 samples with a call rate ≤98% or failed sample QC. All remaining samples passed additional QC measures (percent heterozygosity, sex concordance, principal components derived outliers). We removed SNPs that were monomorphic, on sex chromosomes, or had MAF <1% in our sample, Hardy-Weinburg equilibrium P value <1 × 10−8, and insertions/deletions. After the above steps, we had data available on 838,884 SNPs in 582 women. We performed genotyping imputation using ShapeIT v2.r790 phasing Haplotype Reference Consortium (HRC) r1.1 2016 reference panel and Minimac3 software provided by the Michigan Imputation Server, which resulted in a data set containing a total of 39,183,141 autosomal SNPs for the overall population. Before analyses, we excluded all monomorphic SNPs or those with an MAF <0.05.

      Statistical Analyses

      We described participants’ characteristics using median and interquartile range (IQR) for continuous variables and frequency and percentage for categorical variables. We used natural log (ln) transformation to obtain a normal distribution of Matsuda index and used ln values for all analyses. We conducted analyses using R, version 3.5.1.

      In 430 mother-infant pairs, we performed an epigenome-wide association study (EWAS) by fitting robust linear regression models using the rlm function for each CpG site. In CpG-by-CpG models, we included DNA methylation (M values) as the response variable and Matsuda (ln) as the exposure of interest. In model 1, we adjusted for maternal age, gravidity, smoking during pregnancy, maternal BMI (1st trimester), child sex, and gestational age at delivery. To control for genomic inflation, we used ReFACTor (model 2), a reference-free method that adjusts for heterogeneity putative to cell types in EWAS from heterogenous tissues (13). We used the top-10 principal components (PCs) estimated from ReFACTor as proxy for placenta cellular heterogeneity as suggested by the scree plot (Supplementary Fig. 2). We used quantile-quantile plots and histograms for the regression P values to visually inspect genomic inflation (λ) in both models (Supplementary Fig. 3). We corrected for multiple testing using Bonferroni with significant P values <6.94 × 10−8.

      Gene Annotation and Pathway Analyses

      First, we annotated CpGs with the R package IlluminaHumanMethylationEPICmanifest (14). Second, we utilized the R package humarray (15) to find the nearest gene based on base pair distance upstream and downstream. We tested for enrichment in biologic pathways with Enrichr (16,17) Web platform using only one gene per identified CpG: at each CpG, we prioritized gene annotation from UCSC Reference extracted from the IlluminaHumanMethylationEPICmanifest (except in cases of updated gene names) and then used the closest informative gene name from humarray annotation (priority to coding genes). We focused our attention on TargetScan miRNA, 2017; ENCODE histone modifications, 2015; WikiPathways, 2019; BioCarta, 2016; GWAS Catalogue, 2019; and dbGaP-reported databases in Enrichr (16,17).

      MR Analyses

      We conducted MR analyses to untangle direction of effect of associations based on 401 women with complete data from genotyping arrays, placental DNA methylation, and Matsuda index. First, we used MR to test whether placental DNA methylation levels may influence maternal insulin sensitivity. We searched for SNPs in cis (within 500 kb on each side) at each of 188 CpGs identified in our model 1 and tested SNP-to-methylation associations to identify genetic instrumental variables (IVs) in each region. We removed SNPs with an MAF <0.05. We assumed a genetic additive model and chose the effect allele as associated with greater DNA methylation levels. If multiple SNPs present in the determined cis region were associated with DNA methylation levels at the CpG of interest, we used the elastic net procedure with the glmnet (18) package. We looked at models with α from 0.1 to 1 by steps of 0.1. For each model, λ was chosen as the value that gave the minimum mean cross-validated error (λ.min). Finally, the α was chosen as the value that gave the smallest mean square error. When there was more than one SNP remaining in the final model from the elastic net procedure, we built a genetic risk score (GRS) assuming an additive genetic effect and summed the number of risk alleles as a global genetic IV. If there was only one SNP associated with DNA methylation in the designated in cis region, we used additive genetic modeling for that one SNP as genetic IV. We tested associations between the genetic IVs (GRS or individual SNP) and Matsuda index (ln). To compare effect estimates, we used the two-stage least squares (TSLS) that uses the predicted DNA methylation value by its respective genetic IV as the independent variable in the linear regression to predict Matsuda index (19,20). We used the Durbin-Wu-Hausman test to test whether TSLS estimates were significantly different from the observed estimates. We corrected for the number of CpGs tested (n = 131 with genetic IV available) using false discovery rate (FDR).

      Second, we used prior knowledge of SNPs known to influence insulin sensitivity (21). We selected eight SNPs (see Supplementary Table 1) that were previously established as determinants of fasting insulin in large GWAS (with P < 5 × 10−8 in Meta-Analyses of Glucose and Insulin-related traits Consortium [MAGIC] data sets) (22,23) and were also nominally associated (P < 0.05) with Matsuda index in nonpregnant individuals (24). We built a GRS assuming additive genetic effect and summed the number of risk alleles. We tested associations between the insulin sensitivity GRS and placenta DNA methylation (in M values) for the 188 CpGs identified in model 1 and corrected using FDR.

      Data and Resource Availability

      Data sets analyzed during the current study are not publicly available because we did not obtain consent for such public release of epigenetic data from participants. However, data are available from the corresponding author with the appropriate permission from the Gen3G study team upon reasonable request and approval of institutional review boards. Summary statistics of EWAS results for models 1 and 2 are available via https://figshare.com/s/5040ad2ece334944bf34.


      We present characteristics of participants in Table 1. At the beginning of pregnancy, women’s median age was 28 years (IQR 25; 31), median BMI was 23.7 kg/m2 (IQR 21.6; 27.9), one-third were primigravid, and <9% reported smoking. Median Matsuda insulin sensitivity index was estimated at 7.72 (IQR 5.69; 10.67) and appeared normally distributed after natural log transformation.

      Table 1

      Characteristics of Gen3G mother-child pairs included in maternal insulin sensitivity EWAS of placenta

      In model 1, we found that maternal insulin sensitivity was associated with placental DNA methylation at 188 CpGs (P values <6.94 × 10−8; adjustment for maternal age, gravidity, smoking, maternal BMI, child sex, and gestational age at delivery). Adding GDM status as covariate had minimal impact on association estimates at identified CpGs (0.02%–9.9% changes in β-coefficients). These 188 individual CpGs were distributed across the genome (Fig. 1A); in 14 regions, multiple CpGs were in close genomic vicinity and were annotated to the same gene (Supplementary Table 2). Among annotated genes, we identified 12 genes known to be imprinted in the placenta (9 maternally imprinted, SPHKAP, CNTN6, KCNIP4, PODXL, DLGAP2, KCNQ1, DSCAML1, GPC6, and OCA2, and 3 paternally imprinted, H19/MIR675, MCF2L, and LINC01056) (25). We also noted that specific miRNAs were listed at 11 loci (Supplementary Table 2). We examined our findings and found no CpGs overlapping with the list of CpGs that we had previously identified for associations with maternal 2-h glucose in the same cohort (8), despite moderate correlation (r = −0.44) between Matsuda and 2-h glucose. In model 2, adjusting bioinformatically for cell type heterogeneity, we did not find any individual CpGs that reached Bonferroni (Fig. 1B). Examining PCs generated by ReFACTor that reflect the cellular heterogeneity of tissue samples, we observed that PC1, PC2, and PC5 were strongly associated with Matsuda index, suggesting that cell type–specific placental DNA methylation profile is strongly related to maternal insulin sensitivity (Supplementary Table 3).

      Figure 1
      Figure 1

      Manhattan plots representing the results of the epigenome-wide association analyses between maternal insulin sensitivity (Matsuda index, ln transformed) and placenta methylation (in M values). A: Model 1 adjusted for maternal age, gravidity, smoking, maternal BMI, sex, and gestational age at delivery (genomic inflation = 2.884). The horizontal line indicates the Bonferonni level of statistical significance (P values <6.94 × 10−8). B: Model 2 adjusted for maternal age, gravidity, smoking, maternal BMI, sex, and gestational age at delivery and 10 PCs from ReFACTor (genomic inflation = 1.158).

      MR 1: Does Placental Methylation Affect Maternal Insulin Sensitivity?

      We identified genetic IVs in 131 of the 188 CpGs identified in model 1 (Supplementary Table 4). Specific GRS built with selected cis-SNPs captured respective CpG methylation levels with r2 ranging from 1% to 32%. We found 28 GRS capturing methylation at their respective CpG that were nominally associated (P < 0.05) with Matsuda index: 5 of these were statistically significant at FDR <0.05. These five GRS represented methylation levels at cg01618245 (CHRNA4), cg12673377 (MICALL2/UNCX), cg24475484 (DLGAP2), cg08099672 (ENTPD2), and cg03699074 (BDP1P). In all five cases, higher DNA methylation levels (as represented by GRS) were associated with lower Matsuda index and were in line with the direction of effect detected in our primary observational analyses (Fig. 2). TSLS estimates were also all in the same direction as that of the observed associations and significant (FDR <0.05). In two of the five CpGs (cg03699074 at BDP1P and cg24475484 at DLGAP2), the Durbin-Wu-Hausman test suggested that observed associations between DNA methylation and Matsuda index might be confounded. The fact that MR estimates were larger than the observational estimates suggests that observational estimates were negatively confounded and that “true” causal effects may be larger than the “observed.”

      Figure 2
      Figure 2

      MR supporting direction of effect at five loci where placenta DNA methylation may influence maternal insulin sensitivity: cg01618245 (CHRNA4) (A), cg12673377 (MICALL2/UNCX) (B), cg24475484 (DLGAP2) (C), cg08099672 (ENTPD2) (D), and cg03699074 (BDP1P) (E). In each panel, the a arrow indicates the association between genetic IV representing the fetal placental DNA methylation levels at CpG site (using GRS from maternal genotypes), the b arrow indicates the association with the build genetic IV and Matsuda index, and the c arrow (with β and SE below) indicates the observed (obs) association between methylation levels at the CpG and Matsuda index (reverse of original EWAS, to allow comparison of βs). TSLS estimates, SE, and Durbin-Wu-Hausman test P values are presented under observed estimates for the c arrows. All estimates are unadjusted (no covariates); the adjusted P values for b association results are FDR adjusted for number of tests performed (n = 131 with a genetic IV available).

      MR 2: Does Maternal Insulin Sensitivity Affect Placenta?

      The insulin sensitivity GRS build with eight SNPs captured ∼1.5% of Matsuda index variance (r2 = 0.015). Among our 188 identified CpGs (model 1), we did not identify any CpGs at which the insulin sensitivity GRS was associated with placental DNA methylation levels (Supplementary Table 5).

      Pathways Analyses

      Among transcription pathway databases, we noticed a strong enrichment in the TargetScan miRNA database (Supplementary Table 6): we found 34 miRNA target terms with adjusted P values <0.05 including hsa-miR-3180(-3p), hsa-miR-3196, hsa-miR1538, and hsa-miR-4745-3p at the top of the list. We also observed enrichment in the ENCODE histone modifications database, mainly driven by H3K27me3 in a variety of tissues and cell types (Supplementary Table 7).

      The top term emerging from the GWAS Catalog (Supplementary Table 8) was “DNA methylation (parent-of-origin)” with H19, MIR675, and SEPT5 (P = 3.76 × 10−5; adjusted P = 0.017). Among the top 10 terms from the GWAS Catalog, we also observed “hemoglobin A1c” (driven by TCF7L2, KCNQ1, and PIEZO1) and “fasting insulin” (driven by TCF7L2 and CNTN6), which is the most common proxy of insulin resistance in large GWAS analyses (22) (both adjusted P value >0.05). From dbGaP, we identified 10 terms that reached adjusted P values <0.05 including “body mass index” and “body height” and other cardiovascular traits (triglycerides and blood pressure [Supplementary Table 9]).

      In BioCarta, the top pathway was “role of PPAR-γ coactivators in obesity and thermogenesis,” with MED1 and RXRA leading the emergence of this pathway (Supplementary Table 10). The top pathways emerging from WikiPathways (Supplementary Table 11) were “adipogenesis” (6 of 130 genes: MEF2A, WWTR1, MBNL1, RXRA, GATA4, and PRLR) and “genes targeted by miRNAs in adipocytes,” driven by KCNQ1 and HCN2 (2 of13 genes), yet neither pathway had adjusted P values <0.05.


      Our results suggest that placental DNA methylation is fundamentally linked to maternal insulin sensitivity regulation. Using MR, we identified five loci where placental DNA methylation may be modulating the pregnancy-associated decrease in insulin sensitivity. To our knowledge, this is the 1st study to suggest that placental DNA methylation may causally influence maternal insulin sensitivity. Other identified loci are within known placenta-specific imprinted regions, consistent with the theory of maternal-fetal conflict. In addition, our assessment of cellular heterogeneity showed that the two 1st components of overall placenta DNA methylation profile are strongly associated with maternal insulin sensitivity. On one hand, it is possible that insulin sensitivity influences cell repertoire in the placenta as well as cell lineage commitment early during development. This cellular model has been termed “polycreodism,” or systematic variability in cell fate, which is relevant during embryonic development (26). Our MR 2 analyses did not support this direction, but our IV for insulin sensitivity was limited (r2 = 0.015). On the other hand, placental DNA methylation at delivery might also reflect DNA methylation stability across gestation at some loci. Despite the well-known global increase in placenta DNA methylation throughout gestation, Novakovic et al. (27) showed that substantial changes in methylation (β ≥ 0.2) were observed in 954 CpG sites between the 1st and 3rd trimesters and in only 157 CpG sites between the 2nd and 3rd trimesters (out of >26,000 CpG sites). Moreover, Schroeder et al. (28) demonstrated that partially methylated domains are common in placenta (37% of placental genome) and stable across gestation. Furthermore, by definition, imprinted loci remain stably methylated during fecundation and throughout in utero development (4).

      miRNAs are suspected to have key roles in placenta development and function (29). Many of our findings implicated miRNAs as a potential link between placental DNA methylation and maternal insulin sensitivity. First, among the 188 identified CpGs, 11 were annotated to an miRNA as one of the closest genes. Second, TargetScan miRNA showed that our list of genes was greatly enriched for targets of multiple human miRNAs. In addition, we identified CpGs near genomic imprinted regions that contain miRNAs known to play an important role in placenta, e.g., at the H19/MIR675 and DIO3OS loci. DIO3OS is located near the placenta-specific miRNA cluster on Chr14q32 known as C14MC in the imprinted Dlk1-Dio3 domain. DIO3 is paternally imprinted during fetal development, suggesting that DIO3OS is a noncoding gene that may have a role in maintaining monoallelic maternal expression of DIO3 (30). The locus H19/MIR675 is paternally imprinted and thus maternally expressed (29). miR-675 is a highly conserved miRNA, located in the 1st exon of H19, and is specifically expressed by the placenta, with expression rising as the gestation advances (31). A putative role of miR-675 is to limit placental growth, likely via reducing the expression of IGF1R (main receptor of IGF2 key placental growth factor) (31).

      It is notable that many imprinted genes are predominantly or solely expressed in the placenta (29). The different parental origin of DNA methylation patterns led to the maternal-fetal conflict hypothesis whereby paternal expression should favor fetal growth by deriving more maternal resources, while the maternally expressed genes should act to conserve maternal resources. We identified three loci at which the annotated gene is a known paternally imprinted (maternally expressed) gene in the placenta (including H19/MIR675) and nine loci at which the annotated gene is maternally imprinted (paternally expressed) in placental tissue (25) including KCNQ1. Our MR investigations suggested that methylation levels at cg24073146 in KCNQ1 could causally influence maternal insulin sensitivity, yet our MR estimate at this locus was nonsignificant after accounting for multiple testing. Loss of maternal-specific methylation of KCNQ1 causes Beckwith-Wiedemann syndrome, characterized by prenatal overgrowth and hypoglycemia in infancy (32). During normal fetal development, fetal pancreas shows monoallelic expression of KCNQ1, suggesting an important role of imprinting at this locus during pancreatic development, while adult pancreas shows biallelic expression (33). Genetic variants at KCNQ1 are well-established T2D risk variants with evidence of parent-of-origin effect where the transmission of maternal allele shows a very strong association for risk of T2D in comparison with paternal transmission (34).

      Another identified CpG (cg24475484) is located within a known placenta-specific maternally imprinted region annotated to DLGAP2. DLGAP2 is biallelically expressed in the brain, but only paternally expressed in the testis (35), and was differentially methylated in spermatozoal DNA of infertility studies (36). Our MR analyses supported that methylation at cg24475484 (DLGAP2) causally influences maternal insulin sensitivity (FDR <0.05), in line with the maternal-fetal conflict hypothesis where the paternally expressed gene would reduce maternal insulin sensitivity to drive more nutrients toward the fetus.

      Based on our MR analyses, we found that placenta DNA methylation levels at some identified CpGs may be causal in influencing maternal insulin sensitivity: four loci passed FDR significance threshold in addition to cg24475484 (DLGAP2). These CpGs were not located near genes with known placental function; this highlights the importance of agnostic approaches to discover new biologic candidates. Some of these loci deserve further functional studies in placenta and/or other relevant tissues. For example, cg08099672 is located near ENTPD2 expressed by the placenta, in addition to ovaries and testis, nervous system, and islets of Langerhans (37). The placenta expresses MICALL2 (located at ∼80 kb of cg12673377), and the protein is also highly detected in pancreas, adrenal, and stomach tissues (38). One intriguing finding is cg01618245, located near CHRNA4, which encodes for cholinergic receptor, nicotinic, α4, associated with epilepsy and nicotine addiction (39). It is noteworthy that two miRNAs (MIR3674 and MIR596) are located nearby cg24475484 at DLGAP2 (<150 kb apart) and the MIR4326 is located ∼72 kb from cg01618245 at CHRNA4, again implicating placental miRNAs as potential biologic mediators of pregnancy-associated changes in maternal insulin sensitivity.

      Our pathway analyses highlighted many genes and pathways involved in adipose tissue regulation. From WikiPathways, “adipogenesis” (40) emerged from genes such as MEF2A, WWTR1, MBNL1, RXRA, and GATA4 and, notably, PRLR, which encodes for the prolactin receptor. MED1 and RXRAMEF2A) also highlighted the role of “PPAR-γ coactivators in obesity and thermogenesis” (BioCarta) and “energy metabolism” (WikiPathways), with PPARGC1A central to this pathway (41). We also identified two probes annotated to PRDM16, a key regulator of brown adipose tissue differentiation. Our group previously demonstrated in candidate gene studies that maternal hyperglycemia is associated with placental DNA methylation at PRDM16 and PPARGC1A (42); our current analyses using an agnostic approach validate our previous findings. On the other hand, none of the 188 identified CpGs in the current analyses overlapped with loci identified in our prior EWAS of maternal 2-h glucose and placenta DNA methylation (8). The absence of overlapping findings may be due to different biological phenomena or indicate that we would need a larger sample size to observe associations between placenta DNA methylation and both of these two moderately correlated glycemic traits.

      Strengths and Limitations

      Among our strengths, we have investigated a large number of placenta samples using the most comprehensive DNA methylation array, covering >720,000 CpGs across the genome, in a prospective cohort of pregnant women with well-characterized phenotypes, including a validated measure of insulin sensitivity during pregnancy. We were able to account for many potential confounders and applied an MR approach to test potential causality. Despite our attempts at untangling direction of effect, we found only a handful of CpGs from which MR supported causality. It is likely that the MR 2 analyses were limited in power given that our IV captured only 1.5% of the variance in Matsuda, resulting in a weak IV—one of the major limitations of our MR analyses in this direction. After adjustment for heterogeneity using ReFACTor, none of the CpGs reached P values <6.94 × 10−8, so it is possible that our signals from model 1 reflect placental cell-specific DNA methylation. Top ReFACToR PCs, reflecting cell type heterogeneity, were associated with insulin sensitivity; this might suggest that early DNA methylation programming might be driving a distinctive repertoire of cells in the placenta, also known as polycreodism (26). We feel this is highly biologically relevant and that future studies should investigate whether specific cells are responsible for the signals that we found and potential causal biological effects on maternal insulin sensitivity. Finally, our cohort is composed of women of European descent and thus findings may not be generalizable to other ethnicities.


      In summary, our findings support a placental DNA methylation signature fundamentally linked to maternal insulin sensitivity. We identified CpGs at which our MR investigations supported that placental DNA methylation has a causal influence on maternal insulin sensitivity. The enrichment in miRNA targets and identification of specific miRNAs add to recent literature implicating miRNAs in placenta biology, either as paracrine or endocrine actors. Stimulation of insulin responsive cells (adipocytes, hepatocytes, myocytes) or trophoblasts by exposure to identified miRNAs could reveal potential functions. Finally, the identification of both maternally and paternally imprinted genes is in line with the maternal-fetal conflict hypothesis and yet also suggests that imprinted genes from both parents regulate maternal insulin sensitivity during pregnancy.

      Article Information

      Funding. This work was supported by American Diabetes Association Pathways Accelerator award 1-15-ACE-26 (to M.-F.H), Fonds de recherche du Québec en santé (FRSQ) 20697 (to M.-F.H), Canadian Institutes of Health Research MOP 115071 (to M.-F.H), and Diabète Québec grants (to P.P. and L.B.). L.B. is a senior research scholar from FRSQ. L.B., M.-F.H. and P.P. are members of the FRSQ-funded Centre de Recherche du Centre Hospitalier Universitaire de Sherbrooke. C.E.P. is supported by career development awards from the National Institute of Diabetes and Digestive and Kidney Diseases (K23DK113218) and the Robert Wood Johnson Foundation’s Harold Amos Medical Faculty Development Program (74256).

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

      Author Contributions. M.-F.H. conceived the original study design and analyses plan, supervised the analyses, interpreted the results, and wrote the manuscript. M.D. contributed to sample and data collection. A.C. and C.A. carried out analyses, contributed to interpretation of data, and reviewed and critically edited the manuscript. M.D., C.E.P., P.M.C., P.P., and L.B. reviewed and critically edited the manuscript. All authors approved the final version. M.-F.H. 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.

      Prior Presentation. Parts of this study were presented in abstract form at the 78th Scientific Sessions of the American Diabetes Association, Orlando, FL, 22–26 June 2018.

      • Received August 10, 2019.
      • Accepted December 24, 2019.

      Sell Unused Diabetic Strips Today!

      Black Bean Dip – My Bizzy Kitchen

      By electricdiet / April 27, 2020

      Happy Monday!   I hope you are doing well.  I am at about Day 41 of working from home.  It’s been interesting!   

      First off, if I haven’t mentioned it before, the commute is awesome.  Literally 15 steps from my bed to my dining room table to get to work.  I love being able to sleep in and wake up naturally.  Usually around 7 and I don’t have to clock in until 9, so it’s a leisurely morning sometimes just catching up on my phone, sometimes I’ll start to make something.

      Hannah and Jacob are both working from home too – Hannah starts at 5 a.m. and Jacob at 7:30 a.m.  They both have intermittent conference calls throughout the day, so I can’t get too crazy in the kitchen while they are working.

      I thought I’d be making SO MANY new recipes, but I have to tell you, I am fine with a couple every week.  I am far behind on posting recipes here that I’ve already posted on Instagram, and one of them is this super simple, yet delicious black bean dip.  It literally was my dinner one night last week.

      When Hannah and I last cleaned up my pantry, I had approximately 27 cans of different kinds of beans.  If you see me in the grocery store reaching for a can of black beans, I give you full permission to smack that can out of my hand and say “no!”

      I puffy heart black beans.  And cheese.  And I made this a bit spicy so it’s the trifecta of goodness.  The key to this recipe is to “toast” the seasonings after the garlic has sauteed for a few minutes.  It just gives it a richer flavor in my opinion.


      Black Bean Dip

      This black bean dip is great as a dip, a filling for vegetarian tacos, or as chili mac over pasta – the possibilities are endless!

      • Author: Biz
      • Prep Time: 5
      • Cook Time: 10
      • Total Time: 15 minutes
      • Yield: you decide!
      • Category: appetizer
      • Method: oven



      • 1 tablespoon minced garlic
      • 1/2 can tomato paste
      • 2/3 cup water
      • 1 tablespoon taco seasoning (I used Dak’s Taco Knight)
      • 1/2 teaspoon chili powder
      • 1/2 teaspoon crushed red pepper
      • 1 can black beans, drained
      • 1/4 teaspoon salt
      • 1 ounce cheese (I used quesadilla cheese)
        • chopped cilantro for garnish


      In a small oven safe skillet, spray with avocado oil spray and add the garlic, cook for 3 minutes. Add in the taco seasoning, chili powder, salt and crushed red pepper and cook, stirring constantly for one minute. Stir in the tomato paste and cook an additional minute. Stir in the water and black beans and bake at 450 for 10 minutes. Top with cheese and cook an additional 5 minutes until melty. Garnish with chopped cilantro.

      This dip is simple and complex at the same time.  I literally ate this three days in a row, once as chips and dip, once in taquitos and once scrambled with eggs for brunch.  

      I have a discount code for Dak’s seasonings – they are a salt free seasoning company and I love all their stuff – I love that I can control the amount of salt I put in a dish, and if you happen to have someone who is monitoring their salt intake – this would be perfect – click on this link and use Bizzy10 to get 10% off your order.

      To make the tortilla chips – I cut Mission Brand Thin Corn Tortillas into quarters. I deep fried them for 2 minutes, flipping once, and added lime zest salt on top as soon as they come out of the fryer.  The salt is just 1 teaspoon salt to the zest of one lime.  It makes these chips!

      Hope you are doing well.  Until next time – hugs!  Biz


      Sell Unused Diabetic Strips Today!

      Low-Carb Cheesy Cauliflower Fritters Recipe

      By electricdiet / April 25, 2020

      These low-carb cauliflower fritters are the perfect way to start your day! Cheesy, flavorful and good for you too, while being low-carb and keto-friendly.

      cauliflower fritters on wooden board

      Breakfast should be a quick and easy meal, that gets you going for the day. Not only are these fritters healthy, but they taste amazing and are full of protein to keep you fuller for longer.

      They are ready to eat in less than 45 minutes and can easily be made ahead and stored. Breakfast for the next day is as easy as reheating them, adding a dollop of sour cream and serving.

      How to make cauliflower fritters

      ingredients laid out on a wooden board

      Step 1: Cut the cauliflower head into large florets. Add cauliflower florets to a food processor and process for a few seconds until riced and no large chunks remain.

      riced cauliflower in a food processor

      Step 2: In a mixing bowl, beat the eggs together with a fork. Add the riced cauliflower, almond flour, cheeses, garlic powder, salt, and pepper. Mix everything together very well, making sure the egg is completely mixed through the mixture. Set the mixture aside for 10 minutes. 

      all ingredients mixed together in a bowl with a wooden spoon

      Step 3: While the mixture is sitting, heat a large non-stick pan over medium-high heat. Once hot, add the olive oil. Scoop out a 1/4 cup of the cauliflower fritter mixture and place it into the pan. Use the back of a spatula or bottom of the 1/4 cup measurement to flatten out the fritter mixture until it is about 1/2 an inch thick. You can fit around 2 – 3 fritters into the pan at once. 

      Step 4: Fry on each side for about 5 – 7 minutes before flipping. Be very careful not to flip too early as the fritters are quite fragile while cooking in the beginning. 

      fritters frying in a black frying pan

      Step 5: Once fully cooked, remove and place on a wire rack to allow any excess oil to drip off. The fritters will also become less fragile and more crispy as they cool. 

      Step 6: Garnish your fritters with a spoonful of sour cream and some freshly chopped green onions.

      fritters cooling on a wire rack before serving

      Making them your own

      These cauliflower fritters are perfect just as they are but you can easily make some small changes to the recipe to make them more your own.

      Instead of garnishing with the green onion, why not add it to the mixture itself? You can add almost any greens you like, but keep the amount to about 1/4 of a cup.

      You can use any cheese you like for these low-carb fritters. The parmesan cheese in this recipe is mainly there for flavor so you could omit that if desired. However, the cheddar cheese is quite important as it helps give the fritters structure when it melts. If you change out the cheddar for another cheese, make sure it melts well.

      Storing and reheating your fritters

      You can make these fritters ahead of time and store them, making breakfast for the next day super easy. Store the fritters for up to 3 days in an airtight container in the refrigerator.

      To reheat the fritters, bake them for 5 – 10 minutes at 350 F until heated through.

      More low-carb cauliflower recipes to try

      This recipe is one of many recipes using cauliflower you can find on this website! It’s such a versatile vegetable and can be used in many creative and tasty ways. Here are some of my other favorite cauliflower recipes that you can try out!

      If you like low-carb cauliflower recipes, you can also check out my roundup of low-carb cauliflower recipes for more inspiration. 

      When you’ve tried these low-carb cauliflower fritters, please don’t forget to let me know how you liked it and rate the recipe in the comments below!

      Recipe Card

      Low-carb Cheesy Cauliflower Fritters

      These low-carb cauliflower fritters are the perfect way to start your day! Cheesy, flavorful and good for you too, while being low-carb and keto-friendly.

      Prep Time:15 minutes

      Cook Time:25 minutes

      Total Time:40 minutes


      cauliflower fritters square


      • 1 large cauliflower (roughly 21 ounces)
      • 3 large eggs
      • 1/4 cup almond flour
      • 1/2 cup white cheddar cheese (grated)
      • 2 Tbsp. parmesan cheese (grated)
      • 1 Tsp. garlic powder
      • 1/2 Tsp. salt
      • 1/2 Tsp. pepper
      • 1 Tbsp. olive oil
      • 4 Tbsp. sour cream to garnish
      • chopped green onions to garnish


      • Cut the cauliflower head into large florets. Add cauliflower florets to a food processor and process for a few seconds until riced and no large chunks remain.

      • In a mixing bowl, beat the eggs together with a fork. Add the riced cauliflower, almond flour, cheeses, garlic powder, salt, and pepper. Mix everything together very well, making sure the egg is completely mixed through the mixture. Set the mixture aside for 10 minutes.

      • While the mixture is sitting, heat a large non-stick pan over medium-high heat. Once hot, add the olive oil. Scoop out a 1/4 cup of the cauliflower fritter mixture and place it into the pan. Use the back of a spatula or bottom of the 1/4 cup measurement to flatten out the fritter mixture until it is about 1/2 an inch thick. You can fit around 2 – 3 fritters into the pan at once.

      • Fry on each side for about 5 – 7 minutes before flipping. Be very careful not to flip too early as the fritters are quite fragile while cooking in the beginning.

      • Once fully cooked, remove and place on a wire rack to allow any excess oil to drip off. The fritters will also become less fragile and more crispy as they cool.

      • Garnish your fritters with a spoonful of sour cream and some freshly chopped green onions and enjoy!

      Recipe Notes

      This recipe makes 12 – 14 fritters.  You can store the fritters for up to 3 days in an airtight container in the refrigerator. To reheat the fritters, bake them for 5 – 10 minutes at 350 F until heated through.

      Nutrition Info Per Serving

      Nutrition Facts

      Low-carb Cheesy Cauliflower Fritters

      Amount Per Serving

      Calories 86 Calories from Fat 56

      % Daily Value*

      Fat 6.2g10%

      Saturated Fat 2.6g13%

      Trans Fat 0g

      Polyunsaturated Fat 0.4g

      Monounsaturated Fat 1.4g

      Cholesterol 52.4mg17%

      Sodium 72.4mg3%

      Potassium 240.8mg7%

      Carbohydrates 3.8g1%

      Fiber 1.3g5%

      Sugar 1.4g2%

      Protein 4.7g9%

      Net carbs 2.5g

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

      Course: Breakfast

      Cuisine: American

      Keyword: Cauliflower Fritters

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      Easy Fish Tacos Recipe with Southwestern Cole Slaw for Healthy Fish Tacos

      By electricdiet / April 23, 2020

      Easy Fish Tacos Recipe Makes Quick Fish Dinner!

      Don’t have time to cook and looking for a delicious meatless meal?  Holly’s easy Fish Tacos recipe from her easiest cookbook, KITCHEN 101 and this simple recipe is for healthy fish tacos.  If you LOVE fish tacos these Tilapia Fish tacos are simple to make at home.  A quick fish dinner for the family to eat healthy! Also, this is a diabetic fish taco recipe.  Pair it with the best coleslaw recipe, below, to serve with the easy fish tacos or pick up your favorite condiments!

      easy shrimp tacos, BBQ Shrimp Tacos

      Try Different Seafood Tacos That Are Good For You

      If some of your favorite ingredients are seafood, keep various types – salmon, shrimp, tilapia and even crawfish in the freezer for quick, go-to meals.

      Try this great BBQ Shrimp Taco recipe from KITCHEN 101 that’s a favorite for healthy easy recipes. If you like BBQ Shrimp and tacos, you this recipe will quickly be a favorite!


      Mouth-Watering Healthy Fish Tacos Recipe Easy and Tasty

      When you want a quick seafood dinner, what better time to spotlight this mouth-watering easy fish tacos recipe, Fish Tacos with Southwestern Cole Slaw from KITCHEN 101: Secrets to Cooking Confidence. With only 140-200 calories and very little fat and sodium in 4 ounces –  fish is always a lean heart-healthy choice and you will be amazed that this is a diabetic easy fish tacos recipe. Jarred jalapeno may be used, adjusted to preference, or left out.

      Easy Fish Tacos Recipe Southwestern Cole Slaw = Best Fish Taco Recipe

      The southwestern cole slaw cannot be missed.  Buy cole slaw in a bag and just mix in the canned corn, jalapenos and fresh tomatoes and green onions.  Most of the saturated fat in cole slaw is in the dressing so you will be amazed how great the combination of Greek yogurt and a touch of mayonnaise are the perfect mix and so much better for you!  Greek yogurt really makes a difference and is a rich substitute for sour cream.  You will love this cole slaw recipe served separately as well.  Enjoy this simple but sensational fish taco recipe!

      Fish Tacos with Southwestern Cole Slaw
      Spicy fish and fabulous cool cole slaw — easy and perfect pairing.

        Servings6 tacos
        Prep Time10 minutes
        Cook Time4-6 minutes


        • 1 1/2pounds

          tilapia filetsor fish of choice

        • 1/2teaspoon

          chili powder

        • salt and pepper to taste

        • 6

          flour tortillasor corn tortillas

        • Southwestern Cole slawrecipe follows

        1. Preheat broiler. Season fish with chili powder and season to taste. Broil 4-6 minutes per 1/2–inch thickness or until fish flakes with fork. Fish may be grilled or pan sautéed also.

        2. Fill each tortilla with fish and Southwestern Cole Slaw (see recipe).

        3. Warm tortillas according to package directions or heat in microwave about 30 seconds covered with damp paper towel. Serve with cole slaw or condiments of choice

        Recipe Notes

        Nutritional info per serving: Calories 209, Calories from Fat 19%, Fat 4g, Saturated Fat 2g, Cholesterol 57mg, Sodium 353mg, Carbohydrates 16g, Dietary Fiber 1g, Total Sugars 1g, Protein 26g, Dietary Exchanges: 1 starch, 3 lean meat

        Southwestern Cole Slaw
        Serve extra slaw with burgers.

          Servings12 (1/2-cup) servings
          Prep Time10 minutes


          • 110-ounce

            bag angel hair or classic cole slaw,about 5 cups

          • 1cup

            chopped green onions

          • 2-3tablespoons

            chopped jalapeñosfound in jar

          • 1/2cup

            chopped tomatoes

          • 111-ounce

            can Mexican style corn,drained

          • 3/4cup

            nonfat plain Greek yogurt

          • 1tablespoon

            light mayonnaise

          • 2tablespoons

            seasoned rice vinegar

          • 1

            avocadochopped and drizzled with lime juice

          1. In large bowl, combine cole slaw, green onions, jalapeños, tomatoes, and corn.

          2. In small bowl, mix yogurt, mayonnaise, and vinegar. Toss with slaw. Season to taste and fold in avocados.

          Recipe Notes

          Nutritional info per serving: Calories 76, Calories from Fat 35%, Fat 3g, Saturated Fat 0g, Cholesterol 0mg, Sodium 173mg, Carbohydrates 10g, Dietary Fiber 3g, Total Sugars 4g, Protein 3g, Dietary Exchanges: 1/2 starch, 1/2 fat

          Tilapia Fish Tacos Rank Most Popular Healthy Fish Tacos 

          GET KITCHEN 101 if you are looking for mainstream easy diabetic recipes because this book highlights diabetic recipes with a “D.” You will love this diabetic fresh fish recipe for healthy fish tacos. So simple! You’ll enjoy these tilipia fish tacos if you’re a novice in the kitchen or just too busy to cook.

          You can use any fish but tilapia fish tacos are popular. What’s great about Holly’s recipe is this is for diabetic fish tacos which is truly just a healthy way to eat. You can also use reduced sodium taco seasoning.

          Love A Taco Holder For Your Table and Fun For Kids

          Have you ever used a taco holder? They really are a useful serving tool as tacos always fall over. You will love the fact that you can fill them and they stay upright. There’s different kinds of taco holders but the kid’s taco holders are really fun to have. Who doesn’t want a dinosaur to hold your tacos?

          2 Pack - Stylish Stainless Steel Taco Holder Stand2 Pack – Stylish Stainless Steel Taco Holder StandKidsFunwares TriceraTACO Taco HolderKid Funwares TriceraTACO Taco HolderFred TACO TRUCK Taco HolderFred TACO TRUCK Taco Holder

          Get All Holly’s Healthy Easy Cookbooks

          The post Easy Fish Tacos Recipe with Southwestern Cole Slaw for Healthy Fish Tacos appeared first on The Healthy Cooking Blog.

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