Easy Chicken and Dumplings Recipe with Bisquick – Two Shortcuts + Healthy

By electricdiet / January 23, 2020


Easy Chicken and Dumplings Recipe With Favorite Two Shortcuts!

Tastes like grandma cooked it! This go-to easy Chicken and Dumplings recipe is the ultimate comfort food. Healthy chicken and dumplings recipe is from the Rapid Rotisserie Chicken Chapter in KITCHEN 101. Make this wonderful, scrumptious creamy Bisquick chicken and dumplings recipe in minutes instead of hours. You can find many homemade chicken and dumplings recipes but this simple Chicken and Dumplings Bisquick Recipe with Rotisserie chicken is such a quick chicken and dumplings recipe!

Classic Favorite Gets Makeover for Healthy Chicken and Dumplings

This classic is always a family favorite, but now try this simple version.  Kids love chicken and gravy so they clean their plates.  Actually, this recipe makes a quick last minute dinner effortlessly with all my shortcuts. KITCHEN 101, Holly’s easiest cookbook, is perfect for busy cooks or new cooks who want to cook healthy and eat healthy!

Easy Chicken and Dumplings Recipe from Rapid Rotisserie Chicken Chapter in KITCHEN 101

Easy Chicken and Dumplings
With rotisserie chicken, canned broth and drop Bisquick dumplings, my healthy Chicken and Dumplings recipe becomes an effortless one-dish meal! After making my Chicken and Dumplings Bisquick recipe, you will find yourself making this quick delicious dinner meal all the time. Featured On Reader’s Digest Article: 11 Healthy Makeovers of Your Favorite Family Recipes

    Servings8 (1 cup) servings

    Ingredients

    • 1


      onionchopped

    • 1 1/2cups


      baby carrots

    • 1/2teaspoon


      minced garlic

    • 1/4cup


      all-purpose flour

    • 6cups


      fat-free chicken brothdivided

    • 1/2teaspoon


      dried thyme leaves

    • 2cups


      chopped skinless rotisserie chicken breast

    • 2cups


      biscuit baking mix

    • 2/3cup


      skim milk

    Instructions
    1. In large nonstick pot coated with nonstick cooking spray, sauté onion, carrots, and garlic over medium heat until tender.


    2. In small cup, stir flour and 1/3 cup  broth, mixing until smooth. Gradually add flour mixture and remaining broth to pot; bring to boil. Add thyme and chicken.


    3. In bowl, stir together biscuit baking mix and milk. Drop the mixture by spoonfuls into boiling broth.


    4. Return to boil, reduce heat, and cook, covered, carefully stirring occasionally, 15-20 minutes or until dumplings are done. Season to taste. If soup is too thick, add more broth.

    Recipe Notes

    Per Serving: Calories 218, Calories from Fat 23%, Fat 6g, Saturated Fat 1g, Cholesterol 32mg, Sodium 1207mg, Carbohydrates 28g, Dietary Fiber 2g, Total Sugars 4g, Protein 15g, Dietary Exchanges: 1 1/2 starch, 1 vegetable, 1 1/2 lean meat

    Terrific Tip: Another short-cut for dumplings: cut flaky biscuits into fourths and drop into boiling broth or you can even use flour tortillas cut into fourths. You can slice carrots — but I find baby carrots a time-saver.

    chicken and dumplings Bisquick

    Healthy Chicken And Dumplings Recipe Featured On Reader’s Digest Article: 11 Healthy Makeovers of Your Favorite Family Recipes

    This recipe featured in this Reader’s Digest article for healthy makeovers.  Who doesn’t like this comfort food recipe? If you grew up in the south, this might be your favorite comfort food recipe! Bisquick Chicken and Dumplings recipe makes the dumplings process so simple and remember, this is also a great Rotisserie chicken recipe!

    Best of all, this is a healthy chicken and dumplings recipe from KITCHEN 101 cookbook. Doesn’t get much better than that! After making Holly’s Chicken and Dumplings Bisquick recipe, you’ll make this quick delicious dinner meal all the time. Featured On Reader’s Digest Article: 11 Healthy Makeovers of Your Favorite Family Recipes.

    Bisquick Chicken and Dumplings Recipe Is Healthy Chicken and Dumplings Recipe

    Who  has time to cook?  To the rescue- Holly’s Chicken and Dumplings Bisquick recipe for one of her top healthy easy recipes. There’s a Rotisserie Chicken Chapter in KITCHEN 101.  Look for containers of Rotisserie chicken packaged in the deli section. Another step saver option for this delicious healthy chicken and dumplings recipe.

    KITCHEN 101 is a time saver cookbook and includes your favorite comfort food healthier! Rotisserie chicken is the secret in Quick Chicken Lasagna and turns lasagna into a quick recipe. This cookbook simplifies cooking and perfect for the busy person or new cook.

     Kitchen Scissors Are Kitchen Necessity for Bisquick Chicken and Dumplings

    Why are kitchen scissors a kitchen necessity? Kitchen scissors are a top must have gadget because they help you cook faster and more efficiently.

    Kitchen scissors are the EASIEST way to trim and cut chicken into pieces. Whether you’re using raw, cooked or rotisserie chicken, these scissors really speed up the chopping!

    There’s so many uses from cutting pizza to all meats. You’ll grab these scissors all the time and these inexpensive scissors work great. 

    easy chicken and dumplings

    Make Bisquick Chicken And Dumplings Recipe Diabetic Chicken One-Dish Meal

    Turn this simple recipe into an easy diabetic recipe and use reduced sodium broth for diabetic chicken and dumplings recipe!  No guilt eating because it is a healthy chicken and dumplings recipe. If you used to make a recipe that takes a lot longer, why cook that way anymore?!  KITCHEN 101 simplifies even Chicken and Dumplings with 10 ingredients and shortcuts.  Best of all this is a simple healthy chicken and dumplings recipe!

    More Easy Recipes in This 15 Fast & Fabulous Back To School Dinners E-book Only $1.99!

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    For more family favorite go-to easy, healthy homemade dinners during the back-to-school rush, check out my Back-To-School Downloadable Ebook. With family friendly recipes including shopping lists, serving suggestions, plus tips and hints – make the hustle and bustle extra easy to get that dinner on the table fast!

    Download it now for only $1.99 and you will have all you need to easily feed your family a delicious, healthy homemade meal in minutes!

    Do You Know About Silicon Pot Holders?

    4PCS Multipurpose Silicone Pot Holder Non Slip Heat Resistant4PCS Multipurpose Silicone Pot Holder Non Slip Heat Resistant

    Once you use these silicon pot holders, they will be your one and only kitchen pot holders for several reasons. They are easy to use and best of all, they never get dirty. Cloth pot holders end up so filthy so these colorful clean heat resistant pot holders are inexpensive and the best!  Also, love the fact they double as a handy trivet!

    Check out Holly’s favorite Silicon Gadget List you will love these options.

     

     

    If You Liked Easy Chicken and Dumplings Bisquick Recipe

    There are lots of recipes on the healthy food blog that start with Rotisserie chicken.  You’ll love Holly’s simple Pulled Chicken! There is also the best chicken salad recipe. You’ll get addicted to this time saver because it also adds so much flavor to whatever you are making. Remember to always remove the skin to make healthy chicken recipes.  Like in chicken and dumplings Bisquick recipe, Bisquick is a time saver.  I also have some great easy Bisquick recipes like Simple Baked Chicken.

    Easy Cookbooks To Start Cooking Healthy Easy Recipes

    Just like this healthy chicken and dumplings recipe, you’ll find all your favorite recipes in Holly’s cookbooks.  You don’t have to change what you eat but just how you prepare the recipe.  

    The post Easy Chicken and Dumplings Recipe with Bisquick – Two Shortcuts + Healthy appeared first on The Healthy Cooking Blog.



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    The cGAS-cGAMP-STING Pathway: A Molecular Link Between Immunity and Metabolism

    By electricdiet / January 21, 2020


    Introduction

    For maintenance of normal physiological function, a living organism needs to obtain nutrients from the environment and convert it into energy through its metabolic system. Meanwhile, the organism has to protect itself from attacks of potential pathogenic invaders. Evolutionarily, it is not surprising that the functions of the immune and metabolic systems are closely linked and coordinated. It is well known that an effective immune response is highly energy dependent in order to activate innate immunity and promote adaptive immunity in response to various environmental insults. Under conditions of energy insufficiency such as famine, prolonged and intensive physical activities, and overloaded neuronal and cardiovascular functions, the immune responses in an organism may be sacrificed, leading to increased infections and other immune-related defects (1). On the other hand, overnutrition may lead to overactivated immunity, resulting in inflammation and related metabolic diseases such as insulin resistance and type 2 diabetes (2). To maintain homeostasis, an organism thus codevelops the immune and metabolic systems to adapt to environmental changes. Although metabolic cells and tissue-resident immune cells exert distinct functions, numerous studies have already demonstrated that these cells undergo intensive and dynamic cross talks to coordinate their action in preserving homeostasis. Emerging evidence accumulated over the past several years also reveals the presence of distinct immune signatures and signaling pathways in key metabolic cells and vice versa, further signifying an integral link between immune activity and metabolic function.

    Activation of the cGAS-cGAMP-STING Pathway in Immunity

    Over the past several years, a key DNA immune response pathway, the cGAMP synthase (cGAS [also known as Mb21d1])–cGAMP–stimulator of interferon genes (STING [also called TMEM173, MITA, MPYS, and ERIS]) pathway, has been discovered in immune cells. The cGAS-cGAMP-STING pathway was originally identified as a signaling cascade that is activated by double-stranded DNA (dsDNA) during pathogen infections. cGAS senses viral and bacterial dsDNA aberrantly localized in the cytosol independent of its sequence context (35), and binding dsDNA promotes cGAS oligomerization and activation (3,6,7). In addition to playing a critical role in antiviral immune response, cGAS has also been shown to be involved in some other important biological processes such as macular degeneration (8), cellular senescence (911), myocardial infarction–related inflammation (12), and macrophage transformation (13). Activated cGAS catalyzes the formation of 2′3′-cGAMP, a cyclic dinucleotide (CDN) composed of adenosine and guanosine linked via two phosphodiester linkages. Apart from cGAMP, STING is also activated by CDNs such as cyclic di-AMP or cyclic di-GMP from bacteria (14).CDNs and 2′3′-cGAMP bind to the endoplasmic reticulum (ER)-localized STING, which promotes STING dimerization and translocation from the ER to perinuclear punctuate structures (14,15). During the trafficking process, STING recruits and activates TANK binding kinase 1 (TBK1), stimulating phosphorylation and nuclear translocation of the transcription factor interferon regulatory factor 3 (IRF3), and to a lesser extent nuclear factor-κB (NF-κB), which can also be activated by IκB kinase (IKK) (16,17), leading to the production of type 1 interferons (IFNs) and many other inflammatory cytokines (18) (Fig. 1). By binding to the IFN-α/β receptor on the cell membrane of target cells, IFNs promote the expression of proteins involved in inhibiting viral replication and thus enhances the protective defenses of the immune system (7,19). In addition to initiation of IFN signaling in cells in which it is produced by cGAS, there is some evidence showing that 2′3′-cGAMP is able to promote downstream signaling in neighboring cells via distinct mechanisms such as gap junction–, membrane fusion–, or viral particle–mediated transfer (2022), thus mediating the cross talk between immune cells and their targeting cells. Intriguingly, activation of the cGAS-cGAMP-STING pathway could also be detected in nonimmune cells such as mouse embryonic fibroblasts and adipocytes (23,24), suggesting that activation of this pathway may have broader roles in addition to immune defense functions.

    Figure 1
    Figure 1

    Activation and regulation of the cGAS-cGAMP-STING pathway in cells. The cGAS is activated by viral and bacterial DNA as well as mtDNA and phagocytosed DNA aberrantly localized in the cytosol. Activated cGAS uses ATP and GTP as substrates to catalyze the formation of the second messenger, cGAMP, which binds to STING localized on the ER membrane. The binding of cGAMP to STING promotes STING translocation to the Golgi apparatus. During the translocation, STING recruits and activates TBK1, which in turn catalyzes the phosphorylation and nuclear translocation of IRF3, and to a lesser extent NF-κB, which can also be activated by IKK, leading to increased synthesis of IFN and other inflammatory genes.

    Activation of the cGAS-cGAMP-STING Pathway by Self-DNA

    In a healthy cell, host DNA normally resides in the nucleus or mitochondria. However, under certain pathophysiologic conditions such as DNA instability and/or mitochondrial stress, genomic DNA and/or mtDNA may be released into the cytoplasm, where it serves as a danger-associated molecular pattern to trigger immune responses. West et al. (23) found that, via heterozygosity of the mitochondrial transcription factor A (TFAM), disruption of mtDNA stability promoted mtDNA release into the cytosol, where it activated the cGAS-cGAMP-STING pathway and increased IFN gene expression. mtDNA-mediated activation of the cGAS-cGAMP-STING pathway is also observed by Bax/Bak-induced permeabilization of mitochondrial outer membrane (25,26). Besides mtDNA, recent studies show that the cGAS-cGAMP-STING pathway could also be activated by genomic DNA such as ruptured micronuclei and double-strand broken DNA of the primary nucleus caused by genomic instability and/or DNA damage (19,2729). In addition to genomic DNA and mtDNA, phagocytic DNA inadequately digested in the lysosomes has been shown to activate the cGAS-cGAMP-STING pathway (7,19,30,31) (Fig. 1). These results demonstrate that self-DNA is an important source of sterile inflammatory response induction that has been widely studied in many of the autoimmune disease and cancers. These findings raise an interesting question as to whether self-DNA–induced sterile inflammation is associated with metabolic disorders.

    Activation of the cGAS-cGAMP-STING Pathway in Obesity-Induced Inflammation and Metabolic Diseases

    Obesity is associated with various metabolic diseases such as type 2 diabetes, nonalcoholic fatty liver disease (NAFLD), cardiovascular disease, and many types of cancer. Numerous studies have shown that chronic sterile inflammation in adipose tissue plays a key role in mediating obesity-induced insulin resistance and its associated metabolic diseases (32,33). However, the precise mechanisms by which obesity causes inflammation remain to be fully elucidated.

    During the past several years, evidence has accumulated to suggest an important role of the cGAS-cGAMP-STING pathway in regulating inflammation and energy homeostasis. The expression levels and/or activities of components in this signaling cascade, including cGAS, STING, and TBK1, are significantly upregulated under obesity conditions in mice (24,34,35). Activation of the cGAS-cGAMP-STING pathway in mouse adipose tissue could be triggered by high-fat diet (HFD)–induced mtDNA release, leading to an increase in chronic sterile inflammatory response (24). HFD-induced obesity and activation of the cGAS-cGAMP-STING pathway are prevented by adipose tissue–specific overexpression of disulfide bond A oxidoreductase-like protein (DsbA-L), a chaperone-like and mitochondrial localized protein whose expression in adipose tissue is greatly suppressed by obesity (36). Alternatively, knockout of DsbA-L in adipose tissue impaired mitochondrial function, increased mtDNA release, and activated the cGAS-cGAMP-STING pathway, leading to increased inflammation and exacerbated obesity-induced insulin resistance (24) (Fig. 2). These findings reveal that activation of the cGAS-cGAMP-STING pathway may mediate obesity-induced inflammation and metabolic dysfunction, beyond its well-characterized roles in innate immune surveillance. Consistent with this view, global knockout of the cGAS-cGAMP-STING downstream target TBK1 (37) or IRF3 (38), or pharmacological inhibition of IκB kinase ε (IKKε) and TBK1 by amlexanox, reduced body weight, enhanced insulin sensitivity, and improved glucose tolerance in obese mice and in a subset of patients with type 2 diabetes (34,39). However, it should be noted that while fat-specific knockout of TBK1 increased energy expenditure and attenuated HFD-induced obesity, it also exaggerated adipose tissue inflammation and insulin resistance, suggesting that TBK1 may have a feedback role in regulating obesity-induced inflammation (35) (Fig. 2). Indeed, activation of TBK1 has been found to reduce NF-κB activity and inflammation by promoting phosphorylation-dependent degradation of NF-κB–inducing kinase (NIK), an upstream kinase of IKKs (35). TBK1 has also been shown to attenuate cGAS-cGAMP-STING–mediated response by promoting STING ubiquitination and degradation (40) (Fig. 2). These findings explain the bidirectional roles of TBK1 in regulating inflammation (35). Nevertheless, it remains to be determined what role cGAS-cGAMP-STING signaling, which activates TBK1, may play in inflammation, insulin resistance, and energy expenditure in metabolic cells.

    Figure 2
    Figure 2

    Activation of the cGAS-cGAMP-STING pathway mediates obesity-induced inflammation and metabolic disorders. Obesity reduces the expression levels of disulfide bond A oxidoreductase-like protein (DsbA-L) in adipose tissue, leading to mitochondrial stress and subsequent mtDNA release into the cytosol. Aberrant localization of mtDNA in the cytosol activates the cGAS-cGAMP-STING pathway, leading to enhanced inflammatory gene expression and insulin resistance. Phosphorylated and activated TBK1 exerts a feedback inhibitory role by promoting STING ubiquitination and degradation or stimulating phosphorylation-dependent degradation of NF-κB–inducing kinase (NIK), thus attenuating cGAS-cGAMP-STING–mediated inflammatory response.

    The Potential Role of the cGAS-cGAMP-STING Pathway in NAFLD

    In addition to mediating obesity-induced insulin resistance in adipose tissue, activation of the cGAS-cGAMP-STING pathway has also been implicated in other metabolic diseases including NAFLD. NAFLD is characterized by hepatic steatosis, which contributes to the development of nonalcoholic steatohepatitis (NASH), a potentially progressive liver disease that may lead to cirrhosis and hepatocellular carcinoma. There is some evidence suggesting that the innate immune response contributes to NAFLD and NASH (4144). However, the underlying mechanisms by which the innate immune response promotes NALFD remain elusive. Luo et al. (42) and Yu et al. (45) recently independently found that activation of the liver cGAS-cGAMP-STING signaling pathway may mediate overnutrition-induced NAFLD and/or NASH. Indeed, STING levels were higher in liver tissues from NAFLD human patients compared with those without NAFLD. In addition, the mRNA levels of cGAS and STING are elevated in NASH mouse livers (42,46). Furthermore, the phosphorylation states of TBK1 and IRF3, two downstream targets of the cGAS-cGAMP-STING pathway, were significantly higher in livers of mice fed an HFD, which is coupled with NAFLD (42). Consistent with these findings, cGAS-cGAMP-STING–dependent activation of TBK1 in hepatocytes promotes the formation of insoluble p62/sequestosome 1 (SQSTM1) aggregates, a critical marker of NASH (47). On the other hand, STING deficiency attenuates steatosis, fibrosis, and inflammation in livers of mice fed with either methionine- and choline-deficient diet or HFD (42,45). Interestingly, both systemic or myeloid cell–specific knockout of STING increased resistance to HFD-induced or methionine- and choline-deficient diet–induced hepatic steatosis, inflammation, and/or fibrosis in mice (42,45). In addition, transplantation of bone marrow cells from control mice to STING knockout mice restored HFD-induced severity of steatosis and inflammation (42), suggesting that the improved metabolic phenotypes in the STING knockout mice were due to STING deficiency in liver-resident macrophages rather than in hepatocytes. These results are consistent with the finding that STING is not present in human and murine hepatocytes but is expressed at high abundance in hepatic nonparenchymal cells (48). Indeed, there is some evidence showing that hepatocytes do not express STING (45,49) and that by facilitating hypoxia-induced autophagy in hepatocytes, cGAS protects the liver from ischemia-reperfusion injury via a STING-independent mechanism. The lack of STING in human hepatocytes also explains why hepatitis virus has adapted to specifically replicate in hepatocytes (48). It is well known that selective pressures in evolution promote the development of an effective immune surveillance system to ensure survival in the face of pathogen invasion. While at early stages infection initiates various biochemical processes such as glucose release from stored glycogen, glycogenolysis, and gluconeogenesis, the glucose synthesis ability of the infected body may be greatly impaired at later stages of overwhelming infection, leading to hypoglycemia (50). Because hypoglycemia is detrimental to an organism, in the frame of evolution there is no host survival advantage for chronic pathogen infection. Therefore, a successful immune response is often short-lived, resulting in the termination of the pathogen-induced response quickly to ensure an organism’s survival. However, the lack of STING in hepatocytes results in type 1 IFN deficiency in response to hepatitis virus infection, which facilitates hepatitis viruses to escape from immune detection and causes not only acute but also chronic inflammation in the liver, leading to consequent hepatitis, cirrhosis, and hepatocellular carcinoma (48), which is often accompanied with metabolic dysfunction such as insulin resistance (51,52). Activation of the cGAS-STING pathway by overexpression of STING specifically in hepatocytes significantly suppressed the replication of hepatitis virus in vivo (48,53). Nevertheless, there are some reports showing that STING is present in hepatocytes and that knocking down either STING or IRF3 in hepatocytes alleviated lipid accumulation, hepatic inflammation, and apoptosis (5456). These findings raise a possibility that some of the NAFLD phenotypes observed in the whole-body STING knockout mice may result from STING deficiency in the hepatocytes of the mice. Further studies are needed to clarify this discrepancy.

    The Cross Talk Between the cGAS-cGAMP-STING and the mTORC1 Signaling Pathways

    The mechanistic target of rapamycin complex 1 (mTORC1) is a nutrient sensor that integrates energy, hormonal, metabolic, and nutritional inputs to regulate cellular metabolism, growth, and survival. Activation of the mTORC1 signaling pathway promotes anabolic processes such as protein, nucleotide, fatty acid, and lipid biosynthesis while inhibiting catabolic processes such as lipolysis and autophagy (57). Hasan et al. (58) recently found that chronic activation of the cGAS-cGAMP-STING signaling pathway is associated with reduced mTORC1 signaling in metabolically relevant tissues such as liver, fat, and skeletal muscle of the three-prime repair exonuclease 1 knockout (Trex1−/−) mice, concurrently with increased inflammation and altered metabolic phenotypes such as reduced adiposity and increased energy expenditure. Interestingly, STING deficiency in the Trex1−/− mice rescued both inflammatory and metabolic phenotypes, but IRF3 deficiency only rescued inflammation, suggesting that a component downstream of STING but upstream of IRF3 in the cGAS-cGAMP-STING pathway may play a role in regulating metabolism. Consistent with this view, they found that TBK1, the downstream target of STING, directly inhibited mTORC1 signaling by interacting with the mTORC1 complex (58) (Fig. 3). This result is in agreement with the findings of others that TBK1 inhibits mTORC1 activity in prostate cancer cells (59,60) and in an experimental autoimmune encephalomyelitis model (60). Nevertheless, one study reported that TBK1 may activate, rather than inhibit, mTORC1 through site-specific phosphorylation of mTOR at Ser2159 in response to epidermal growth factor but not insulin treatment (61). On the other hand, there is some evidence showing that mTOR may inhibit the cGAS-cGAMP-STING antiviral pathway. Meade et al. (62) recently identified a cytoplasmically replicating poxviruses–encoded protein, F17, that binds and sequesters Raptor and Rictor. The binding of F17 to Raptor promotes mTORC1-mediated suppression of STING activity and IRF3 translocation to the nucleus. The binding of F17 to Rictor, on the other hand, facilitates mTORC2-mediated cGAS degradation. By disrupting the mTORC1-mTORC2 cross talk, F17 inhibits cGAS-cGAMP-STING signaling and thus retains the benefits of mTOR-mediated stimulation of viral protein synthesis. Likewise, the mTOR downstream effector ribosomal protein S6 kinase 1 (S6K1) has been found to interact with STING in a cGAS-cGAMP–dependent manner, and the interaction promotes TBK1-mediated phosphorylation of STING and recruitment of IRF3 for antiviral immune responses (63). Of note, the interaction of S6K1 with STING is mediated by the kinase domain but not the kinase function of S6K1, suggesting a mTORC1-independent regulation of S6K1 on STING signaling (Fig. 3). Taken together, all these findings indicate a complex cross talk between the cGAS-cGAMP-STING and the mTORC1 signaling pathways. Further investigations will be needed to clarify these controversies and to elucidate the molecular details as well as the metabolic consequences of the cross talk between the cGAS-cGAMP-STING and the mTORC1 signaling pathways.

    Figure 3
    Figure 3

    The interplay between the cGAS-cGAMP-STING pathway with mTORC1 signaling. Knockout of three-prime repair exonuclease 1 (Trex1), which degrades DNA in the cytosol, leads to the activation of the cGAS-cGAMP-STING pathway and suppression of the mTORC1 activity in mice. The cGAS-cGAMP-STING pathway may inhibit mTORC1 activity through a TBK1-dependent mechanism. Conversely, the cGAS-cGAMP-STING pathway may be inhibited by mTORC1-dependent suppression of STING activity and IRF3 translocation or by mTORC2-mediated cGAS degradation. Of note, the kinase domain but not the kinase function of ribosomal protein S6 kinase 1 (S6K1) is essential for S6K to interact with STING, which facilitates TBK1-mediated phosphorylation of STING and recruitment of IRF3 for antiviral immune responses.

    The cGAS-cGAMP-STING Pathway and Autophagy/Mitophagy

    In addition to cross talking to mTORC1, the cGAS-cGAMP-STING pathway has also been found to interact with the autophagy machinery in innate immune responses (Fig. 4). Autophagy exerts its quality control function by sequestering damaged organelles and protein aggregates and invading intracellular pathogens in the cytoplasm for lysosomal-mediated degradation (64). This programmed survival pathway also acts as a recycling system to maintain essential protein biosynthesis during stress conditions, such as nutrient insufficiency, growth factor depletion, and pathogen invasion (65). Thus, autophagy is important for the maintenance of the metabolic homeostasis of a cell, and its dysregulation might contribute to the development of metabolic disorders (6668).

    Figure 4
    Figure 4

    The cross talk between the cGAS-cGAMP-STING pathway and autophagy/mitophagy. Autophagy is initiated with the activation of ULK1 complex. ULK1-induced activation of beclin-1 complex favors the nucleation of autophagosome precursors and promotes autophagy. Mitophagy is a selective form of autophagy. Activation of the cGAS-cGAMP-STING pathway may stimulate autophagy via a cGAS/beclin-1 interaction–dependent mechanism. The cGAS-cGAMP-STING pathway also promotes autophagy/mitophagy by TBK1-dependent phosphorylation and activation of receptors OPTN and p62 (SQSTM). Alternatively, activation of the cGAS-cGAMP-STING pathway promotes an autophagy-dependent negative feedback regulation by 1) the interaction of cGAS with beclin-1, which in turn inhibits cGAS activity; 2) cGAMP-induced activation of ULK1, which promotes STING degradation by phosphorylation at Ser366; and 3) TBK1-p62/SQSTM1–dependent ubiquitination and degradation of STING. PINK/Parkin-induced mitophagy also restrains cGAS-cGAMP-STING signaling and innate immunity by mitophagy-mediated mtDNA clearance.

    Autophagy is initiated with the activation of the ULK1 complex, which is inhibited by mTORC1 and promoted by nutrient deprivation and AMPK activation (6769) (Fig. 4). Autophagy is also regulated by a multiprotein complex comprising beclin-1, a mammalian ortholog of the yeast autophagy-related gene 6 (Atg6), vacuolar protein sorting 34 (VPS34), and autophagy/beclin-1 regulator 1 (AMBRA1), which favors the nucleation of autophagosome precursors (67,70). Activation of the cGAS-cGAMP-STING pathway has been found to prompt ubiquitin-mediated autophagy that delivers bacteria to autophagosomes for degradation (71). Similarly, cGAS protects hepatocytes from ischemia-reperfusion injury–induced apoptosis in vivo and in vitro through an induction of autophagy in mouse hepatocytes (49). How cGAS stimulates autophagy in hepatocytes is currently unknown but appears to be mediated by a STING-independent mechanism (49). Interestingly, cGAS has been found to competitively bind beclin-1 to dissociate the negative autophagy factor rubicon from the beclin-1–phosphatidylinositol 3-kinase class III (PI3KC3) autophagy complex, leading to PI3KC3 activation and subsequent autophagy induction (72,73). This finding provides a possible explanation for the finding that cGAS promotes autophagy via a STING-independent mechanism. It is interesting to note that in addition to stimulating autophagy, the interaction between cGAS and beclin-1 negatively regulates cGAS enzyme activity in immune cells such as RAW264.7 and L929 cells, thus promoting cytosolic DNA degradation and preventing overactivation of the cGAS-cGAMP-STING pathway–mediated IFN responses and persistent immune stimulation (72). Activation of the cGAS-cGAMP-STING pathway also promotes an autophagy-dependent negative feedback regulation of STING, which is mediated by cGAMP-induced dephosphorylation of AMPK and activation of ULK1 that phosphorylates and promotes autophagosome-dependent degradation of STING (74) or by TBK1-p62/SQSTM1–dependent ubiquitination and degradation of STING (40), providing a mechanism to prevent the persistent transcription of innate immune genes (Fig. 4). However, a very recent study showed that upon binding cGAMP, STING translocated from the endoplasmic reticulum to the endoplasmic reticulum-Golgi intermediate compartment, which served as a membrane source for LC3 lipidation, leading to autophagosome formation and autophagy (75). This study reveals that the STING-induced activation of autophagy is mediated by a mechanism that is dependent on the Trp-Asp (W-D) repeat domain phosphoinositide-interacting protein (WIPI2) and autophagy-related gene 5 (ATG5), but independent of TBK1, ULK, or VPS34-beclin kinase complexes. Interestingly, p62/SQSTM1 has been shown to interact with mTOR and Raptor, which is critical for mTOR recruitment to lysosomes and for amino acid signaling–induced activation of S6K1 and 4EBP1 (76). However, it remains unknown whether the cGAS-cGAMP-STING signaling–induced and TBK1-mediated phosphorylation of p62/SQSTM1 plays a role in regulating mTORC1 signaling and function.

    Mitophagy is a selective form of autophagy that mitigates inflammation by removing damaged mitochondria from cells (77). Mitochondrial damage induces mitophagy by promoting the accumulation of the ubiquitin kinase PINK1 on the outer membrane of the damaged mitochondria, which in turn phosphorylates Parkin at Ser65, leading to the activation of this E3 ubiquitin ligase and subsequent degradation of ubiquitinated substrates (78). A recent study showed that Parkin and PINK deficiency promoted mtDNA release and activation of the cGAS-cGAMP-STING pathway in mouse heart tissue, leading to a strong inflammatory phenotype (77). These results support a role of PINK/Parkin-mediated mitophagy in restraining cGAS-cGAMP-STING signaling and innate immunity. By quantitative proteomics analysis, Richter et al. (79) recently found that the cGAS-cGAMP-STING downstream target TBK1 phosphorylated several mitophagy receptors such as optineurin (OPTN) and p62 (SQSTM) at their autophagy-relevant sites, which creates a signal loop amplifying mitophagy (Fig. 4). However, while these findings reveal a link between the cGAS-cGAMP-STING pathway and autophagy/mitophagy, the detailed biochemical mechanism underlying the link remains largely elusive, especially in metabolic tissues. Seeking answers to these questions would be an attractive subject for further investigation.

    The cGAS-cGAMP-STING Signaling and Apoptosis

    Apoptosis is a programmed cell death process that provides a mechanism to maintain organismal homeostasis (80). Apoptosis is regulated by prodeath proteins such as Bak and Bax and prosurvival proteins such as BCL-2 and BCL-XL. Bak/Bax activation promotes mitochondrial outer-membrane permeabilization and the release of apoptotic proteins such as cytochrome c to the cytosol, leading to further activation of the downstream pathway of intrinsic apoptosis through initiator and executioner caspases cascade (81) (Fig. 5). Activation of caspases, which is a hallmark of apoptosis, has been found to not only promote cell death but also prevent dying cells from triggering a host immune response (82). However, while apoptosis has long been known as an immunologically silent form of cell death, the molecular basis underlying the suppression of immune responses remains unknown. Several recent studies suggest that caspase-mediated inhibition of the cGAS-cGAMP-STING signaling pathway may contribute to the silencing of immune process in apoptotic cells. In agreement with this, TBK1 phosphorylation and IFNα-stimulated gene expression are increased in caspase-9 knockout or caspase-3/-7 double knockout mice and cells (25). Constitutive activation of the type I IFN response was also observed in caspase-9–deficient mouse embryonic fibroblasts (26). In addition, inhibition of caspases led to increased phosphorylation of TBK1 and IRF3 in Bax/Bak-sufficient cells but not in Bax/Bak knockout cells. Furthermore, the caspase deficiency–induced IFNβ response was prevented by knocking out cGAS or STING (25). These findings suggest that apoptosis may suppress immune responses by inhibiting the cGAS-cGAMP-STING pathway. However, the precise biochemical mechanism(s) by which caspases negatively regulate the cGAS-cGAMP-STING pathway remains unclear but could result from multiple redundant processes such as attenuated gene expression, cleavage and inactivation of a component or components of the type I IFN production pathway, and caspase-mediated degradation of mtDNA, thereby disrupting its interaction with cGAS, thus preventing the activation of the cGAS-cGAMP-STING signaling pathway and its downstream IFN action (26). Consistent with this, Wang et al. (83) found that cGAS could be cleaved by several inflammatory caspases including caspase-1, which can be activated by mtDNA release–induced formation of inflammasome, or by caspase-4, -5, and -11. Alternatively, activation of the cGAS-cGAMP-STING pathway may promote apoptosis via both transcriptional and nontranscriptional mechanisms (84,85). It has been shown that STING activation in T cells induces apoptosis through an IRF3- and p53-mediated transcriptional proapoptotic program (86). A proapoptotic but transcription-independent role of STING is observed in hepatocytes that is mediated by the association of IRF3 with the proapoptotic molecule Bax/Bak, which contributes to alcoholic liver disease (55). The proapoptotic role of STING was also found in other cells such as B cells and endothelial cells (87,88). Collectively, these findings demonstrate a complicated cross talk between cGAS-cGAMP-STING signaling and apoptosis. Given the importance of apoptosis in regulating metabolic homeostasis, it would be of great interest to determine the functional roles of the cross talk between cGAS-cGAMP-STING signaling and apoptosis in metabolic tissues.

    Figure 5
    Figure 5

    The association of cGAS-cGAMP-STING signaling with apoptosis. The activation of prodeath proteins such as Bax/Bak promotes mitochondrial outer-membrane permeabilization and the release of cytochrome c to the cytosol, leading to activation of intrinsic apoptosis through initiator and executioner caspases cascade. Defects in apoptosis by knocking out caspase-9 or caspase-3/-7 lead to constitutive activation of the cGAS-cGAMP-STING pathway; however, the precise mechanisms by which caspase-induced apoptosis inhibits innate immune remain unknown. Inflammatory caspases including caspase-1, which can be activated by mtDNA release–induced formation of inflammasome, or caspase-4, -5, and -11 cleave cGAS, thus preventing the activation of immune response. Alternatively, activation of the cGAS-cGAMP-STING pathway promotes apoptosis via both transcriptional and nontranscriptional programs in a TBK1-IRF3–dependent manner.

    Concluding Remarks

    Inflammation is now clearly recognized as a major risk factor for obesity-induced metabolic disorders. Suppressing inflammatory pathways, therefore, holds promise for developing effective therapeutic treatment of obesity-related diseases. However, identification of pharmacological targets to suppress inflammation is usually a challenge, requiring better understanding of the mechanisms underlying obesity-induced inflammation. The identification of the cGAS-cGAMP-STING pathway as a key player in mediating obesity-induced chronic low-grade inflammation has pointed out an exciting new direction to elucidate the mechanism underlying obesity-induced metabolic diseases and to develop potential therapeutic strategies to improve metabolic homeostasis. However, a number of important questions remain to be answered.

    First, while the pivotal roles of the cGAS-cGAMP-STING pathway in immune defense against various microbial pathogens have been extensively studied (35), its function in nonimmune cells remains largely unexplored. The findings that the cGAS-cGAMP-STING pathway components such as cGAS, STING, and TBK1 are highly expressed in adipocytes and that the expression levels of these molecules are stimulated under obesity conditions (24) raise a possibility that activation of this pathway may play an important role in metabolic diseases. However, it should be acknowledged that HFD feeding activates the cGAS-cGAMP-STING pathway not only in adipocytes but also in macrophages (24,42), the predominant proinflammatory immune cell type in obese adipose tissue (89), suggesting that activation of the cGAS-cGAMP-STING pathway in adipose tissue–resident macrophages may make a significant contribution to the deteriorated metabolic phenotypes of the obese mice. Further studies will be warranted to dissect the relative contribution of the cGAS-cGAMP-STING pathway in metabolic relevant cells, such as such hepatocytes and adipocytes, and tissue-resident immune cells and the potential cross talk between these cells triggered by cGAS-cGAMP-STING pathway activation.

    It is interesting to note that the function of cGAS and STING is regulated by mTOR complexes and vice versa (63,74) (Fig. 3), suggesting that activation of the cGAS-cGAMP-STING pathway may be modulated by environmental inputs such as cellular nutrient status and/or growth factors’ stimulation. However, the underlying mechanism by which DNA-induced activation of the cGAS-cGAMP-STING pathway is moderated by environmental changes remains unexplored. In addition, given that mTORC1 plays a pivotal role in regulating cell metabolism and energy homeostasis, it is possible that activation of the cGAS-cGAMP-STING pathway may mediate some of the multifaceted roles of mTORC1 signaling pathway. Interestingly, activation of TBK1 has been shown to inhibit mTORC1 activity (58), suggesting a potential negative regulation of mTORC1 signaling by activation of the cGAS-cGAMP-STING pathway. Further studies will be needed to elucidate the precise mechanism underlying the cross talk between these two signaling pathways and the physiological roles of the interaction. In addition to the mTORC1 signaling pathway, available evidence suggests that the cGAS-cGAMP-STING pathway also cross talks to both autophagy and apoptosis (Figs. 4 and 5). Autophagy and apoptosis regulate the turnover of cellular organelles and cells within organisms, and their interaction is highly context dependent and in most of cases mutually inhibitory (90). The identification of the cGAS-cGAMP-STING pathway linking to both autophagy and apoptosis suggests an important new layer of regulation of these distinct mechanisms for the control of cell homeostasis. However, an integrated understanding of the interplay between the cGAS-cGAMP-STING and these cellular pathways remains largely unclear. It is also unknown when and which inputs to the network are dominant and how this depends on the physiological or pathophysiological context. Answers to these questions will likely require both deeper biochemical and physiological studies of the interaction both in vitro and with tissue-specific transgenic and/or knockout mouse models that enable more specific perturbation and monitoring of these pathways in vivo.

    Lastly, it remains to be established whether and how targeting the cGAS-cGAMP-STING pathway is a suitable strategy to treat metabolic diseases. Given that the cGAS-cGAMP-STING pathway is activated by mitochondrial stress under obesity conditions, it is tempting to speculate that at least a part of the effects of increased inflammation may be due to activation of the cGAS-cGAMP-STING pathway. In fact, much progress has been made on the development of small-molecule inhibitors targeting components in the cGAS-cGAMP-STING pathway over the past several years. As of now, several cGAS inhibitors have been reported. By in silico screening of drug libraries using mouse cGAS/DNA target (PDB 4LEZ), An et al. (91) recently identified hydroxychloroquine, quinacrine, and 9-amino-6-chloro-2-methoxyacridine as potential cGAS inhibitors. These molecules do not bind to the active site of cGAS but are instead found to localize to the minor groove of DNA between the cGAS/DNA interface (91). RU.521 and RU.365, however, are found to inhibit cGAS by binding at the cGAS active site (92). Wang et al. (93) showed that suramin, a drug used clinically for the treatment of African sleeping sickness (94), binds to the DNA binding site of cGAS and inhibits cGAS activity by disrupting dsDNA/cGAS binding. In addition to targeting cGAS, several small-molecule inhibitors of STING have also been reported. By a cell-based chemical screen, Haag et al. (95) recently identified two nitrofuran derivatives—C-178 and C-176—that strongly reduced STING-mediated, but not RIG-I– or TBK1-mediated, IFNβ reporter activity. Excitingly, they found that these derivatives reduce STING-mediated inflammatory cytokine production in both human and mouse cells and attenuate pathological features of autoinflammatory disease in mice (95). Very recently, Dai et al. (96) reported that aspirin, a nonsteroidal anti-inflammatory drug, can directly bind and enforce the acetylation of cGAS, leading to a robust inhibition of cGAS enzymatic activity and self-DNA–induced immune response in both Aicardi-Goutières syndrome patient cells and a mouse model of Aicardi-Goutières syndrome. These results provide proof of concept that targeting the cGAS-cGAMP-STING pathway may be efficacious in the treatment of inflammatory diseases, which opens new avenues for developing novel therapies for inflammation-related metabolic diseases. Future work on the molecular details of the regulation and network of the cGAS-cGAMP-STING pathway and its tissue-specific function should enable the rational targeting of this signaling to unravel the full therapeutic potential of this extraordinary pathway in metabolism and relevant biological functions.



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    Banh Mi Chicken Burger Lettuce Wraps

    By electricdiet / January 19, 2020


    I love banh mi sandwiches primarily due to all of their pickled vegetable goodness. (I’m a huge vinegar fan, as you know.) Unfortunately, most of them have way too little meat and vegetables for all of the bread they include. These Banh Mi Chicken Burger Lettuce Wraps give you all of the flavors, but no bun at all.

    Banh Mi Chicken Burger Lettuce Wraps

    In Vietnamese, bánh mi technically means bread, but it also refers to a sandwich made with meat (usually pork), pickled carrots and daikon radish, cucumber, cilantro, and spicy mayonnaise. It’s typically eaten as street food but can also serve as a meal.

    How to make Banh Mi Chicken Burger Lettuce Wraps

    Here I’ve substituted ground chicken for the pork and mixed in a little bit of ginger, garlic, soy sauce, and lime with the meat before grilling. The carrots, cucumbers, and Daikon radish (or onion) get a quick soak in brine to pickle them. The burgers, veggies, plus spicy mayo and jalapeños (if you dare) come together in a low carb lettuce wrap. No bread required.

    Like chicken burgers and lettuce wraps?

    If you like the chicken burger concept, check out my recipe for Buffalo Chicken Burgers. If you’re a fan of lettuce wraps, try Hoisin Chicken Lettuce Wraps. You can cook the chicken in an Instant Pot® or buy a rotisserie chicken for a super-quick meal.

    Banh Mi Chicken Burger Lettuce Wraps

    Enjoy the pickled flavors of a Banh Mi sandwich minus the bread

    Author: Adapted from kikkomanusa.com

    Prep Time: 10 minutes

    Cook Time: 10 minutes

    Freezer Time: 10 mins

    Total Time: 30 minutes

    Course:

    Main Course

    Cuisine:

    Asian

    Keyword:

    banh mi, banh mi chicken burger, chicken banh mi, chicken burger, low-carb banh mi

    Servings: 4

    Banh Mi Chicken Burger Lettuce Wrap

    Ingredients

    FOR THE PICKLED VEGETABLES

    • 1/3
      cup
      rice vinegar
    • 1
      tablespoon
      sugar
    • 1
      teaspoon
      kosher salt
    • 1/2
      English cucumber
      cut into matchsticks
    • 2
      medium carrots
      cut into matchsticks
    • 1/2
      Daikon radish
      cut into matchsticks (optional)

    FOR THE BURGERS

    • 1
      pound
      ground chicken or turkey
    • 1
      scallion
      thinly sliced (about 2 tablespoons)
    • 1
      teaspoon
      freshly grated ginger
    • 1/2
      teaspoon
      minced garlic
    • 1
      tablespoon
      soy sauce or tamari
    • juice from 1/2 lime
    • 1
      tablespoon
      light brown sugar

    FOR SERVING

    • lettuce leaves
    • sriracha mayonnaise
      optional
    • thinly sliced jalapeños
      optional

    Instructions

    MAKE THE PICKLED VEGETABLES

    1. In a medium bowl, stir together the vinegar, sugar, and salt. Add the cucumber, carrot, and radish sticks. Let it sit at room temperature for at least 10 minutes (while you make the burgers).

    2. When ready to serve, drain the vegetables.

    MAKE THE BURGERS

    1. In a medium bowl, combine the ground chicken, scallions, ginger, garlic, soy sauce, lime juice, and brown sugar. Shape into 4 patties and place on a cookie sheet or plate that will fit in your freezer. Freeze for 10 minutes.

    2. Grill burgers for about 5 minutes per side or until they are cooked to an internal temperature of 165°F. If you don’t have a grill, you can use the broiler.

    SERVE

    1. Spread some sriracha mayonnaise, if using, on 4 large lettuce leaves. Top each with a burger.

    2. Add the drained pickled vegetables and the jalapeños, if using.

    Recipe Notes

    If you can’t find daikon radish, you can use another type of radish or some thinly sliced onion.

    Nutrition information does not include optional ingredients.

    Nutrition facts per serving (1 burger)

    Calories: 242kcal

    Fat: 11g

    Saturated fat: 3g

    Cholesterol: 85mg

    Sodium: 561mg

    Potassium: 63mg

    Carbohydrates: 11g

    Fiber: 2g

    Sugar: 11g

    Protein: 23g

    Vitamin A: 57%

    Vitamin C: 12%

    Calcium: 7%

    Iron: 12%



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    Marzetti Avocado Ranch Taco Salad

    By electricdiet / January 17, 2020


    Hi guys!  I’ve missed you!  But promise, things are working behind the scenes to give you the best blog experience.  Guess what?  You’ll actually be able to find my recipes!  I hope to have it up and running with the new look in the next week or so.  Thank you for being patient!

    I have still been busy in my kitchen though.  Some things will never change 😀

    I recently found Marzetti Simply Dressed Salad Dressings in the refrigerated section and I have a new favorite salad dressing.  Most of my salads are zero points, so it’s totally worth the 2-3 points for 2 tablespoons of their dressings.  

    Print

    Marzetti Avocado Ranch Taco Salad

    A quick and easy taco salad using pantry staples and Marzetti Simply Dressed Salad Dressing.  


    Scale

    Ingredients

    • 4 ounces chicken breast
    • 1 tablespoon taco seasoning
    • avocado oil spray
    • 2 cups romaine lettuce, chopped
    • 1/2 cup canned corn, drained
    • 1/2 cup canned black beans, drained
    • 1/4 cup chopped radish
    • 1/3 cup sliced grape tomatoes
    • 2 tablespoons Marzett Avocado Ranch Dressing

    Instructions

    Heat a non-stick skillet over medium heat with avocado spray.

    Season the chicken with the taco seasoning, and cook for 6-8 minutes, flipping half way through, until it reads 165 degrees.

    While the chicken rests, plate the salad – romaine, corn, black beans, radish and cherry tomato.

    Slice the chicken after it’s rested 10 minutes, and top on salad.  Drizzle with Marzetti Avocado Ranch Dressing.  That’s it!

    Notes

    Most salads no matter which plan you are on are zero points.

    On team purple and team blue on WW, this salad is only 3 points – you only have to count the dressing.

    On team green, it’s will be 6 points – you just have to count the chicken. 😀

    This salad is so good and can easily be on your weekly lunch or dinner rotation, because you can meal prep the chicken ahead of time as well as the veggies.

    You can find this dressing in the produce section – they have lots of flavors to choose from and range from 2-3 points for 2 tablespoons.  Totally worth it because this dressing is delicious and will actually make you crave salad – pinky swear!

    I hope you had a great weekend!  I am still going strong with my #dryjanuary – even with having people over on Saturday!  We had a “vision 2020 party” and I’ll post my vision board tomorrow.

    Happy Monday!  Make it a great day!





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    Type 1 Diabetes When You Are Sick with a Cold, Flu, or Stomach Virus

    By electricdiet / January 15, 2020


    Getting “real people” sick with type 1 diabetes inevitably comes with a variety of extra concerns because a severe virus, cold, or infection can have a big impact on your blood sugar and your insulin needs.

    In this article, we’ll discuss the need-to-know details of managing type 1 diabetes and your overall wellbeing when you come down with a cold, the flu, or a stomach virus.

    Type 1 diabetes when you are sick

    During a cold

    The most likely effect a cold will have on your diabetes will be insulin resistance, which means you may need to temporarily increase your background/basal insulin dose. 

    The increase may be anywhere from 10 to 25 percent of your usual total background/basal insulin needs, suggests Gary Scheiner, CDE from  Integrated Diabetes.

    This is an important decision to make with your healthcare team — which means calling them when you realize your illness is affecting your diabetes.

    You may find you also need more rapid-acting insulin with meals.

    If you notice that your blood sugars are stubbornly high, talk to your healthcare team about making an adjustment in your insulin doses.

    Of course, if your cold has a severe impact on your appetite, and you’re eating significantly less than usual, you may find you actually need to decrease your basal/background insulin. 

    Regarding medications, try to use sugar-free cough drops and confirm with the pharmacist that any other cold medications are sugar-free.

    During the flu

    At the very beginning of the flu, you can ask your doctor for a prescription for a newly approved medication called Xofluza, designed to help prevent the flu from fully establishing especially in people for whom the flu can be very dangerous — like people with diabetes!

    For a more traditional bout of the flu — which involves a fever, full-body aches, and exhaustion — it’s important to work with your healthcare team because fighting a severe virus like the flu can lead to ketoacidosis.

    If you don’t address those ketones early on, it can escalate into full-blown diabetic ketoacidosis which can be life-threatening and land you in the hospital.

    • Take acetaminophen or ibuprofen to manage your fever, as directed by your doctor.
    • Drink plenty of water — all day long! 
    • Check your temperature — anything over 101 degrees should warrant a call to your doctor.
    • Check your blood sugar often — anything persistently over 240 mg/dL should warrant a call to your doctor.
    • Sip on watered-down Gatorade. It’s important that your body gets some carbohydrates along with adequate insulin, especially if you aren’t eating very much food.
    • Make sure to eat! If you don’t eat or drink some carbohydrates, you will develop starvation ketones on top of illness-induced ketones. This can escalate and become dangerous.
    • Test your ketones morning and night with urine strips.
    • If you test for “small” ketones (light pink on the strip): eat some carbohydrates, take your insulin as prescribed, and drink plenty of fluids!
    • If you test for “moderate” to “large” ketones, contact your healthcare team. They may advise you to take a small bolus of insulin to help eliminate the ketones (even if your blood sugar isn’t high).
    • If you test for “large” ketones and begin feeling nauseous, visit your nearest emergency or urgent care center.
    • If your blood sugars become too stubborn to manage at home, visit your nearest emergency or urgent care center. 

    During a stomach virus (with vomiting)

    While vomiting once or twice from a rotten Deviled Egg isn’t usually a big deal, a stomach virus for a person with type 1 diabetes can become life-threatening if it isn’t handled quickly and properly.

    If you begin vomiting and cannot keep down fluids or glucose tabs, you could experience severe low blood sugar while the fast or rapid-acting insulin you recently dosed for your last meal is still active in your bloodstream without any food to absorb. 

    If you begin vomiting and cannot keep fluids down, but you do not have excessive insulin on board, the next obstacle you will face is eventual diabetic ketoacidosis due to severe dehydration along with starvation ketones from your inability to eat.

    In either situation, when you are repeatedly vomiting and unable to drink water to rehydrate, you need to visit the nearest emergency room to receive intravenous fluids: a combination of lactated ringers (saline, electrolytes, etc.), and possibly glucose or insulin depending on the state of your blood sugar when you arrive.

    • Gatorade (with or without sugar)
    • Apple juice, ginger ale, etc.
    • Pedialyte

    Buy these things now — find the full suggestion list from the Centers for Disease Control and Prevention (CDC). Do not wait until you are sick and struggling to manage diabetes on top of a stomach virus. Collect these items now and store them with other medical supplies as part of your sick day management toolkit.

    IMPORTANT: You still need to take your background/basal insulin when you are vomiting — as recommended by the National Institute of Diabetes, Digestive, and Kidney Diseases (NIDDK). As a person with type 1 diabetes, you will need to call your healthcare team to adjust your medications but you still need some insulin in your system at all times like every other day of your life.

    You should have a glucagon kit on-hand for flu & stomach bug season.

    Even if you’ve never needed to use a glucagon kit before, it can be life-saving (and panic-reducing) if you suddenly come down with a severe stomach bug. 

    A glucagon kit contains a hormone that tells your liver to release glycogen (stored glucose) which is then converted into glucose and raises your blood sugar.

    If you start vomiting up the large dinner you just ate and your blood sugar begins to plummet because of the insulin you took for that meal, glucagon can save your life and make it a much calmer trip to the emergency room when you head there for intravenous fluids to rehydrate after puking.

    Tip: If you’re conscious and using a glucagon kit on yourself, you don’t likely need the entire full dose of the glucagon kit. Instead, you can use a syringe to withdraw 10 or 20 units to treat your oncoming low blood sugar and then head to the hospital (with a friend to drive, or via ambulance).

    What about anti-vomit medications like Zofran?

    These medications are designed with good intentions, but unless you’re taking the kind that’s inserted anally, it’s most likely going to come right back up when you vomit. If you’re battling a true stomach virus, Zofran won’t be properly digested in order to prevent you from vomiting. 

    Instead, they’re more effective when used with pregnancy-related nausea or sea-sickness.

    Talk to your doctor about insulin dose adjustments

    Above all else, talk to your doctor about making adjustments to your insulin doses during a significant illness. While a runny nose won’t likely change your insulin needs, anything else can cause you to need more or less insulin depending on a variety of variables.

    If you aren’t eating your usual meals, you won’t need to take your usual fast or rapid-acting insulin doses. And you may need to reduce your long-acting/basal insulin dose.

    Even after you’ve recovered, you may need to reduce your background insulin doses if your illness causes a weight-loss of even a few pounds.

    Check your blood sugar often and work closely with your healthcare team to get through the bout of illness safely!

    Suggested next posts:

    If you found this guide to being sick with diabetes useful, please sign up for our newsletter (and get a free chapter from the Fit With Diabetes eBook) using the form below. We send out a weekly newsletter with the latest posts and recipes from Diabetes Strong.



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    Interaction Between Type 2 Diabetes Prevention Strategies and Genetic Determinants of Coronary Artery Disease on Cardiometabolic Risk Factors

    By electricdiet / January 13, 2020


    Abstract

    Coronary artery disease (CAD) is more frequent among individuals with dysglycemia. Preventive interventions for diabetes can improve cardiometabolic risk factors (CRFs), but it is unclear whether the benefits on CRFs are similar for individuals at different genetic risk for CAD. We built a 201-variant polygenic risk score (PRS) for CAD and tested for interaction with diabetes prevention strategies on 1-year changes in CRFs in 2,658 Diabetes Prevention Program (DPP) participants. We also examined whether separate lifestyle behaviors interact with PRS and affect changes in CRFs in each intervention group. Participants in both the lifestyle and metformin interventions had greater improvement in the majority of recognized CRFs compared with placebo (P < 0.001) irrespective of CAD genetic risk (Pinteraction > 0.05). We detected nominal significant interactions between PRS and dietary quality and physical activity on 1-year change in BMI, fasting glucose, triglycerides, and HDL cholesterol in individuals randomized to metformin or placebo, but none of them achieved the multiple-testing correction for significance. This study confirms that diabetes preventive interventions improve CRFs regardless of CAD genetic risk and delivers hypothesis-generating data on the varying benefit of increasing physical activity and improving diet on intermediate cardiovascular risk factors depending on individual CAD genetic risk profile.

    Introduction

    The risk of coronary artery disease (CAD), the leading cause of disability and mortality worldwide (1), is increased by known cardiometabolic risk factors (CFRs), such as obesity, high blood pressure, impaired lipid and glucose metabolism, and systemic inflammation (2,3). These metabolic features are also present in many individuals with type 2 diabetes, which may contribute to the approximate doubling of CAD risk in persons with diabetes (4,5). A number of studies have demonstrated the effectiveness of control of CRFs in reducing the risk of cardiovascular outcomes among patients with type 2 diabetes (610).

    Individual risk of CAD and type 2 diabetes reflects the interplay between lifestyle behaviors acting on a backdrop of genetic predisposition (11,12). Previous studies have shown that preventive interventions for type 2 diabetes—including lifestyle intervention programs, increasing physical activity, dietary modifications, and the administration of metformin—can improve CRFs among individuals with dysglycemia (1315). However, it is unclear whether the benefits of type 2 diabetes preventive interventions on CRFs are similar for individuals at lower or higher genetic risk for CAD.

    In the current study, we leveraged data from the Diabetes Prevention Program (DPP) to examine whether type 2 diabetes prevention strategies, either an intensive lifestyle intervention (ILS) or metformin treatment (MET), modify the association between CAD genetic risk and CRFs in participants at high risk of type 2 diabetes. In addition, we investigated the extent to which separate lifestyle behaviors including physical activity, dietary quality, and body weight loss interact with CAD genetic risk to differently affect CRFs in each DPP intervention group.

    Research Design and Methods

    DPP

    The Diabetes Prevention Program Research Group conducted a multicenter randomized controlled trial in the U.S. that tested the effects of ILS and MET interventions on the incidence of diabetes in glucose-intolerant individuals as previously described in detail (16,17). In brief, a total of 3,234 participants with fasting plasma glucose levels between 5.3 and 6.9 mmol/L, and 2-h plasma glucose levels between 7.8 and 11.0 mmol/L during a standard 75-g oral glucose tolerance test, were randomized to ILS (n = 1,079), MET (850 mg twice daily [n = 1,073]), or placebo (PBO [n = 1,082]). The ILS arm included individual counseling sessions through which participants were encouraged to achieve and maintain a weight reduction of at least 7% of initial body weight through a healthy low-calorie, low-fat diet and to engage in physical activity of moderate intensity, such as brisk walking, for at least 150 min per week. A total of 2,658 participants with available DNA, detailed lifestyle information, and CRF measurements had paired information at both baseline and 1-year follow-up. Each clinical center and the coordinating center obtained institutional review board approval. The 2,658 included in this report provided written informed consent for the main study and subsequent genetic investigations.

    Lifestyle Behaviors

    Specific lifestyle behaviors including changes in physical activity, dietary quality, and weight loss were assessed at baseline and 1 year. Self-reported levels of leisure-time physical activity were assessed at baseline and after 1 year of follow-up with the Modifiable Activity Questionnaire (18). The physical activity level was calculated as the product of the duration and frequency of each activity (in hours per week), weighted by an estimate of the MET of the activity and summed for all activities performed. Usual daily caloric intake during the previous year, including calories from fat, carbohydrate, protein, and other nutrients, was assessed with a modified version of the Block Food Frequency Questionnaire (19). We further characterized overall dietary quality at baseline and after 1 year of follow up using the Alternate Healthy Eating Index-2010 (AHEI-2010) (20). The AHEI-2010 score is based on 11 foods and nutrients emphasizing higher intake of vegetables (excluding potatoes), fruits, whole grains, nuts and legumes, long-chain n-3 fats, and polyunsaturated fatty acids; moderate intake of alcohol; and lower intake of sugar-sweetened drinks and fruit juice, red and processed meats, trans fat, and sodium. Each component is scored from 0 (unhealthiest) to 10 (healthiest) points, with intermediate values scored proportionally. All component scores were summed to obtain a total score ranging from 0 (nonadherence) to 110 (best adherence) points. Body weight change was defined by the difference between baseline and 1-year follow-up.

    Baseline and 1-Year CRF Measurements

    We considered the following well-established risk factors for CAD at baseline and 1-year follow-up: BMI, waist circumference (WC), fasting glucose, LDL cholesterol (LDLc), HDL cholesterol (HDLc), triglycerides (TG), systolic (SBP) and diastolic (DBP) blood pressure, C-reactive protein (CRP), fibrinogen, and tissue plasminogen activator (tPA). Measurements were performed at baseline and at 1-year follow-up (95% of participants completed the 1-year follow-up). We also included diabetes incidence as an intermediate CAD risk factor. Diabetes incidence was ascertained at the end of study follow-up. Anthropometric measures included height, weight, waist circumference, and SBP and DBP using standardized methods. Participants fasted for 12 h the night before blood was drawn from an antecubital vein. Standard blood glucose and lipid measurements (TG, HDLc, calculated LDLc) were performed at the DPP central biochemistry laboratory (Northwest Lipid Metabolism and Diabetes Research Laboratories, University of Washington, Seattle, WA) using enzymatic methods standardized to the Centers for Disease Control and Prevention reference methods (21). Measurements of inflammatory markers, including CRP, fibrinogen, and tPA, were also performed at the DPP central biochemistry laboratory as previously reported (13,22).

    Genotyping and CAD Polygenic Risk Score

    We extracted DNA from peripheral blood leukocytes. Genotyping was done with the HumanCoreExome genotyping array from Illumina at the Genomics Platform. Genotypes were called using Birdsuite (https://www.broadinstitute.org/birdsuite/birdsuite-analysis). A two-stage imputation procedure consisting of prephasing the genotypes into whole chromosome haplotypes followed by imputation itself was conducted. The prephasing was performed using SHAPEIT2 (23). We used 1000 Genomes phase 3 haplotypes as the reference panel (24), and the genotype imputation was done using IMPUTE2 (25). We derived a polygenic risk score (PRS) of 204 variants representative of all the 160 CAD loci that had achieved genome-wide significance for association with CAD in previous association studies published as of December 2017 (26) and used recently to predict the risk of major CAD events among participants with type 2 diabetes at high cardiovascular risk (27) (Supplementary Table 1). For loci with multiple independent variants, the variant with the highest significant association with CAD reported in literature (lead variant) was selected first, followed by any other variant at that locus (independent variant) that was not in linkage disequilibrium (r2 < 0.2) with the lead variant. Three of the 204 CAD risk-increasing variants (rs7797644, rs9365196, and rs9457995) were not available in the DPP genome-wide association study data set (neither were proxies in linkage disequilibrium [r2 > 0.8]). A total of 138 of the 201 CAD risk–increasing variants considered in the study were genotypes and the remaining 63 imputed at high quality (median info score 0.99 [interquartile range 0.97–1.00]).

    For calculation of the CAD PRS, each variant was recoded as 0, 1, or 2 according to the number of risk alleles (CAD increasing alleles) and weighted by its relative effect size (β-coefficient) on CAD obtained by the literature-based estimates. We calculated the CAD PRS by using the following equation: PRS = (β1 × SNP1 + β2 × SNP2 + … + β201 × SNP201) × (201/sum of the β-coefficients), where SNPi is the risk allele number of each single nucleotide polymorphism. The CAD PRS ranges from 0 to 402, with each unit corresponding to one average risk allele and higher scores indicating a higher genetic predisposition to CAD. For participants with missing genetic variants, we adjusted the CAD PRS by the number of missing values. Distribution of the weighted CAD PRS falls into the normal distribution curve (Supplementary Fig. 1).

    Statistical Analysis

    Baseline characteristics that are continuous variables are reported as mean ± SD if normally distributed or as median (25th, 75th percentiles) if not. Categorical variables are presented as frequency. We used generalized linear models to estimate the association of the CAD PRS with CRFs at baseline after adjustment for age at randomization, sex, and the top 10 principal components (PCs) for ancestry. Nonnormally distributed outcomes were log transformed and presented on the ratio scale, exp(β), as the estimated value of CRFs per each 10-unit increase in CAD PRS. In this case, the estimated effect size corresponds with a fractional difference in CRFs. For example, a ratio of 0.9 indicates that the outcome variable changes by a ratio of 0.9 (i.e., 10% lower) per 10-unit higher PRS. Associations were also tested for changes from baseline to 1 year with interaction terms for intervention arms (ILS or MET vs. PBO) after adjustment for age at randomization, sex, PCs, and the respective baseline CRFs. We used Cox proportional hazards models adjusted for the same confounders to investigate the association between CAD PRS and diabetes incidence and the extent to which type 2 diabetes–preventive interventions modified the association between CAD PRS and diabetes incidence. We also tested associations of changes in physical activity, dietary quality, and body weight with 1-year change in CRFs in each treatment group separately. We investigated potential interactions between lifestyle behaviors and PRS on 1-year change in CRFs in each treatment group separately. For statistically significant interactions (P < 0.05), we tested the associations between each 1-SD increase in lifestyle variables and 1-year change in CRFs among individuals classified as at low, intermediate, and high genetic risk on the basis of thirds of the CAD PRS. For each category, we used general linear models after adjustment for age at randomization, sex, the top 10 PCs for ancestry, and the respective baseline CRFs. For rejection of the null hypothesis that type 2 diabetes prevention strategies did not modify the association between CAD genetic risk and CRFs, a two-sided α-level of 0.05 was used to determine statistical significance. SAS, version 9.3, was used for all analyses (SAS Institute, Cary, NC).

    Data and Resource Availability

    Data used in this study are available on request at dppmail{at}bsc.gwu.edu or by accessing the NIDDK Central Repository.

    Results

    To investigate whether type 2 diabetes prevention strategies modify the association between CAD genetic risk and CRFs, we used genetic and clinical data collected from 2,658 participants in the DPP. Participants randomly assigned to PBO, ILS, or MET groups displayed no significant differences in baseline characteristics—exception for a lower HDLc and higher TG in the PBO individuals compared with individuals assigned to MET or ILS (Table 1). There were also no major clinical differences between participants included in this study and all DPP participants (Supplementary Table 2).

    Table 1

    Baseline characteristics of DPP participants according to randomization group among those included in the present analysis

    We first assessed the association between CAD PRS and CRFs before intervention in all three treatment groups combined. At baseline, each 10–risk allele increase in the CAD PRS was associated with higher LDLc (mmol/L) (β = 0.09 [95% CI 0.06, 0.13], P < 0.01) and higher DBP (mmHg) (β = 0.52 [95% CI 0.09, 0.95], P = 0.02) after adjustment for age at randomization, sex, and PCs ancestry markers (Table 2). Adjusted mean values of baseline lipid levels and DBP across CAD PRS quartiles are provided in Supplementary Fig. 2. No additional associations were found between CAD PRS and other baseline CRFs, including glycemic traits, anthropometric measures, and inflammation markers (Table 2).

    Table 2

    Baseline association between the genetic risk score and CAD risk factors

    We next investigated the effect of type 2 diabetes–preventive strategies, CAD PRS, and the interaction between them on 1-year change in CRFs. Participants randomized to ILS had greater improvement in all studied CRFs compared with those in the PBO group (P < 0.01 for all) (Table 3). Individuals randomized to MET displayed a significant improvement in glycemia, anthropometric measures, LDLc, HDLc, CRP, and tPA compared with PBO (Table 3). The PRS did not significantly predict 1-year changes in CRFs when we analyzed the entire study population together or in any of the study arms (P > 0.05 for all) (Table 4). The effect of the interaction between CAD PRS and type 2 diabetes–preventive strategies on CRFs outcomes was not significant (Pinteraction > 0.05 for all) (Table 5).

    Table 3

    Effect of type 2 diabetes–preventive strategies on 1-year change in CAD risk factors by study intervention

    Table 4

    Association between CAD PRS and 1-year change in risk factors by study intervention

    Table 5

    Interaction between CAD PRS and intervention group on 1-year change in CAD risk factors

    We further evaluated associations between changes in physical activity, diet, and body weight and 1-year change in CRFs in each treatment group and the extent to which lifestyle behaviors interacted with CAD PRS to differently affect 1-year change in CRFs. The greatest changes in physical activity, diet, and body weight were observed in ILS compared with PBO (P < 0.01 for all) (Supplementary Table 3). We showed that changes in body weight improved the majority of CRF parameters irrespectively of the intervention arm (P < 0.01 for all except fibrinogen [Supplementary Table 4]) and that changes in physical activity and dietary score associated with improved anthropometrics, blood lipids, and blood pressure measures among participants in the lifestyle intervention arm (Supplementary Tables 5 and 6). We detected nominal significant interactions between PRS and healthy diet and physical activity on 1-year change in BMI, fasting glucose, TG, and HDLc in individuals randomized to MET or PBO, but none of them achieved the multiple-testing correction for significance (Supplementary Table 7). Among these hypothesis-generating interactions, we highlight the association between increasing dietary quality and 1-year changes in BMI among individuals randomized to MET, which was more prominent in participants at high genetic risk (Pinteraction = 0.01). Mean ± SE changes in BMI per 1-SD increase in diet quality score were −0.19 ± 0.10, −0.29 ± 0.10, and −0.52 ± 0.11 kg/m2 among participants at low, intermediate, and high genetic risk, respectively (Supplementary Table 7).

    Discussion

    Our findings in the DPP provide evidence on the interplay between genetic factors and type 2 diabetes–preventive strategies on intermediate CRFs. We show that either an ILS or MET has a beneficial effect on 1-year change in different CRFs. While a genetic risk score (comprised of 201 variants associated with CAD) does not appear to alter the effectiveness of either intervention, we show preliminary evidence that increasing physical activity and adhering to a healthy dietary pattern may have a more prominent effect on BMI, fasting glucose, and TG in people at high genetic risk who were not assigned to an ILS. However, these findings warrant further replication in appropriate randomized clinical studies specially designed to investigate such effects. Taken together, our data suggest that independent of genetic risk, interventions designed to prevent the development of type 2 diabetes in individuals with elevated fasting glucose, impaired glucose tolerance, and overweight/obesity can improve the majority of recognized cardiovascular risk factors and that among individuals not randomized to an ILS, the benefit of increasing physical activity and improving diet may vary depending on individual CAD genetic risk profile.

    There are three important findings. First, a PRS for CAD is associated with baseline lipid levels and DBP but was not associated with other CAD risk factors such as glycemia or inflammatory markers and did not predict 1-year change in these CRFs during preventive interventions for type 2 diabetes. Our findings that CAD PRS has a strong association with lipid levels is well aligned with findings of previous studies (2831), but the positive association with other intermediate risk factors such as DBP or the lack of association with inflammation parameters has not previously been reported as far as we know. While our results need to be interpreted with caution in the context of multiple hypothesis testing, the most recent data on the genetic overlap between CAD and CRFs suggested that 5–10% of CAD loci relate to blood pressure (32). The potential mechanism linking overlapping loci is likely to be via vascular tone regulation and platelet aggregation (28,33), features that have been predominantly linked with SBP rather than DBP and have inflammation as a common underlying factor (2). Our findings, in individuals at high risk of developing type 2 diabetes, where hypertension is a key feature of the metabolic abnormalities present in individuals with type 2 diabetes, highlight the role of DBP in the complex overlap between CRFs and the polygenic architecture of CAD.

    Second, the type 2 diabetes–preventive intervention strategies that we evaluated, MET and ILS, did not interact with CAD genetic risk to differently affect 1-year change in CRFs. In other words, participants were likely to benefit similarly from these interventions despite their genetic susceptibility for CAD. These findings need to be interpreted with caution, since it is possible that highly penetrant single genetic variants could interact with type 2 diabetes prevention strategies with strong effects on specific CRFs. The rationale to use a combined PRS in this study relies on the observation that, similar to type 2 diabetes, for the vast majority of individuals with CAD, genetic susceptibility results from the cumulative effects of numerous variants with modest effects disrupting multiple pathways at the same time (34). Our results are consistent with recent prospective observational studies that have reported that both lifestyle behaviors and genetic predisposition drive CAD risk without evidence of significant interactions (35). In addition, the DPP showed that an intenive lifestyle modification is effective for the prevention of type 2 diabetes regardless of genetic risk based on 34 type 2 diabetes–associated loci (36). Data from Look AHEAD (Action for Health in Diabetes), a long-term randomized clinical trial investigating whether an ILS for weight loss would decrease cardiovascular morbidity and mortality among individuals with type 2 diabetes, showed that a behavioral weight loss treatment did not alter the association between a CAD genetic risk and CAD (37). Findings from the current study in individuals at high risk of type 2 diabetes support the beneficial effect of early type 2 diabetes prevention strategies regardless of CAD genetic susceptibility.

    Third, while our results need to be interpreted in the context of multiple hypothesis testing and lack of replication in independent randomized clinical studies (mainly due to the unavailability of similar resources), we found suggestive evidence that improving dietary quality and increasing physical activity may have a more favorable effect on 1-year changes in BMI, fasting glucose, and TG in people at high genetic risk for CAD than in those at low genetic risk. Thus, while an ILS aimed to achieve and maintain a weight reduction of at least 7% of initial body weight through diet and physical activity can effectively reduce intermediate CRFs regardless of genetic risk, the detection of an interaction of CAD genetic risk with dietary quality and physical activity in participants not assigned to the lifestyle intervention suggests a potential additional benefit in those who are at increased genetic risk for CAD. In these individuals not assigned to the lifestyle intervention, the adoption of at least specific lifestyle behaviors such as improving diet or increasing physical activity may be particularly beneficial. In other words, a comprehensive lifestyle intervention that achieves 7% weight loss is effective across the entire gradient of CAD genetic risk, whereas other combinations (such as MET with healthy lifestyles that have a less dramatic effect on weight loss) benefit most those at highest CAD risk. This hypothesis is supported by the observation that we did not see effects of significant interactions between changes in body weight, as a measure of the overall lifestyle modification, and genetic risk for CAD on specific CRFs. However, further studies are needed to confirm these initial findings.

    Several limitations of our study are worth noting. First, the findings are based on a single randomized clinical trial. We were not able to conduct a replication study due to the lack of available genetic information in other similar clinical intervention studies that included individuals at high risk of developing type 2 diabetes (38,39). Second, we did not directly investigate the association between the CAD PRS and CAD events due to the unavailability of data in the current study; instead, we used CRFs, which provide early insights into the atherosclerosis disease process, as a proxy for CAD events. Third, the significant interactions we observed may be chance observations due to multiple testing or be affected by risk magnification (i.e., because participants at low risk of a clinical outcome cannot have large absolute risk reduction, the risk difference is magnified by having a higher baseline risk). Finally, while the CAD genetic variants included in our PRS were selected on the basis of the most novel discoveries of genetic variants for CAD risk, we cannot rule out the possibility that a PRS constructed from a different set of as-yet-undiscovered CAD risk variants will influence response to type 2 diabetes–preventive interventions.

    In conclusion, our findings in individuals at high risk of type 2 diabetes provide evidence for the beneficial effects of type 2 diabetes–preventive strategies on CRFs regardless of CAD genetic risk profile. Additionally, the effect modification by improving dietary quality and increasing physical activity and diet on the association of CAD genetic risk with cardiovascular risk factors among individuals randomized to MET or PBO illustrates how early CAD-preventive strategies may achieve slightly variable success in individuals with different genetic susceptibility.

    Article Information

    Acknowledgments. The Diabetes Prevention Program Research Group acknowledges the commitment and dedication of the participants of the DPP and the Diabetes Prevention Program Outcomes Study (DPPOS).

    Funding. During the DPP and DPPOS, the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) of the National Institutes of Health provided funding to the clinical centers and the Coordinating Center for the design and conduct of the study and collection, management, analysis, and interpretation of the data (DK-048489). The Southwestern American Indian Centers were supported directly by the NIDDK, including its Intramural Research Program and the Indian Health Service. The General Clinical Research Center Program, National Center for Research Resources, and the Department of Veterans Affairs supported data collection at many of the clinical centers. Funding was also provided by the Eunice Kennedy Shriver National Institute of Child Health and Human Development, the National Institute on Aging, the National Eye Institute, the National Heart, Lung, and Blood Institute, the National Cancer Institute, the Office of Research on Women’s Health, the National Institute on Minority Health and Health Disparities, the Centers for Disease Control and Prevention, the American Diabetes Association, the Swedish Research Council, and the European Commission (CoG-2015_681742_NASCENT and H2020-MSCA IF-2015-703787). Merck KGaA provides medication for DPPOS. DPP/DPPOS have also received donated materials from Bristol-Myers Squibb, Parke-Davis, and LifeScan, Inc. LifeScan, Inc.; Health O Meter; Hoechst Marion Roussel, Inc.; Merck-Medco Managed Care, Inc.; Merck and Co.; Nike Sports Marketing; Slim Fast Foods Co.; and Quaker Oats Co. donated materials, equipment, or medicines for concomitant conditions. McKesson BioServices Corp.; Matthews Media Group, Inc.; and the Henry M. Jackson Foundation provided support services under subcontract with the Coordinating Center.

    The sponsor of this study was represented on the Steering Committee and played a part in study design, how the study was done, and publication. All authors in the writing group had access to all data. The opinions expressed are those of the study group and do not necessarily reflect the views of the funding agencies.

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

    Author Contributions. J.M. conceived the analysis plan, interpreted the data, and wrote the manuscript. K.A.J. conducted statistical analyses, contributed to the interpretation of the results, and edited and reviewed the manuscript before submission. J.M.M. was responsible for imputation and contributed to PRS design. J.M.M., S.E.K., L.C., M.H., L.M.D., M.R.G.A., G.A.W., S.B.R.J., U.N.I., P.W.F., W.C.K., and J.C.F. contributed to the interpretation of the results and edited and reviewed the manuscript before submission. S.E.K., L.M.D., U.N.I., W.C.K., and the Diabetes Prevention Program Research Group conducted the clinical trial and obtained the phenotypic data. J.C.F. 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 77th Scientific Sessions of the American Diabetes Association, San Diego, CA, 9–13 June 2017.

    • Received January 30, 2019.
    • Accepted October 17, 2019.



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    Peach Chia Jam (No Added Sugar)

    By electricdiet / January 11, 2020


    My husband’s natural penchant to chat led us to acquire 28 pounds of fresh, ripe peaches for $10 last week at the farmers’ market. Needless to say, I’ve been looking for peachy ideas and stumbled onto Peach Chia Jam made in the Instant Pot.

    Peach Chia Jam (No Added Sugar)

    I’ve seen a lot of chia jam recipes before, mostly of the strawberry variety. I liked the idea of peach jam thickened with chia seeds because they are so darn good for you. Loaded with omega-3 fatty acids, antioxidants, fiber, calcium, and iron, chia seeds may well improve insulin sensitivity and reduce inflammation. Read these 11 proven benefits of chia seeds.

    We’ve used chia seeds in smoothie bowls, overnight oats, and fruit tarts before, but I don’t think we’d ever made jam. I saw Plant Based Instant Pot’s Vanilla Maple Peach Chia Jam and decided to give it a Diabetic Foodie spin.

    Can I Can or Freeze Chia Jam?

    The recipe below is not formulated for safe canning. After you make it, store it in the refrigerator for up to two weeks or in the freezer for up to six months. Thaw the jam in the refrigerator before using.

    Can I Make Peach Chia Jam without an Instant Pot?

    Certainly! You just need to pay more attention when you cook the fruit on the stovetop. Try Gimme Some Oven’s technique using my ingredients below, adding the lemon juice and extracts along with the chia seeds.

    Serving Suggestions

    Peach Chia Jam can be part of a diabetic-friendly breakfast, dessert, or a snack:

    • Top these Cottage Cheese Pancakes from DiabetesStrong with a tablespoon or two.
    • Add a dollop to plain Greek yogurt or vanilla ice cream.
    • Swirl it into your favorite smoothie.

    Peach Chia Jam (No Added Sugar)

    Use fresh, ripe summer peaches in this jam and you won’t need any sweetener

    Prep Time: 10 minutes

    Cook Time: 7 minutes

    Pressure Up/Down: 25 mins

    Total Time: 42 minutes

    Course:

    Breakfast, Condiments

    Cuisine:

    American

    Keyword:

    peach chia jam, peach jam, sugar-free peach jam

    Servings: 24

    Peach Chia Jam (No Added Sugar)

    Ingredients

    • 4
      cups
      ripe peaches
      peeled and diced
    • 1
      tablespoon
      fresh lemon juice
    • 1
      teaspoon
      vanilla extract
    • 1
      teaspoon
      almond extract
    • 1/4
      cup
      chia seeds

    Instructions

    1. In a 6-quart electric pressure cooker, stir together the peaches, lemon juice, vanilla extract, and almond extract. Close and lock the lid of the pressure cooker and set the valve to sealing. Cook on high pressure for 2 minutes. Allow the pressure to release naturally for 15 minutes, then quick release the remaining pressure. Remove the lid and set it aside.

    2. Use an immersion blender to purée the peach mixture right in the pot. Stir in the chia seeds and hit Sauté. (Use the Sauté/Low setting if your electric pressure cooker has it.) Stir constantly for about 5 minutes or until the jam has reached the consistency you like. Note: splattering will be greatly reduced if you keep stirring.

    3. Cool to room temperature, then transfer the jam to a jar and store in the refrigerator for up to 2 weeks.

    Recipe Notes

    If you don’t have any almond extract, use all vanilla extract (2 teaspoons total).

    This recipe will also work with nectarines.

    Nutrition facts per serving (2 tablespoons)

    Calories: 29kcal

    Fat: 1g

    Polyunsaturated fat: 1g

    Potassium: 93mg

    Carbohydrates: 5g

    Fiber: 1g

    Sugar: 4g

    Protein: 1g

    Vitamin A: 3%

    Vitamin C: 5%

    Calcium: 15%

    Iron: 1%



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    Nyrvana Truffles – My Bizzy Kitchen

    By electricdiet / January 9, 2020


    I’ve been a Nyrvana Partner for a few months and I’ve been loving their product.  They have a chocolate and peanut butter truffle that is out of this world.  I never used to have a sweet tooth – salty food was my jam.  But then I found gummy bears and well, I just can’t stop at a handful.

    I get introduced to a lot of sugar free products being diabetic.  And I have to admit most of them are gross.  Some have weird aftertastes, or just after one bit I have to throw in the trash.

    So I was a bit apprehensive when Nyrvana told me that I could eat one of their truffles and there would be no spike in blood sugar.  Huh!  They have a natural sweetener called (THrē) Natural Sweetener, so each one of these truffles is only 99 calories and 1 net carb.  And they are delicious!

    I can no longer drink coffee after 2 p.m., so eating one of these at 3:00 p.m. has been a game changer.  It gives me a boost of energy because it has “nootropics-like Alpha-GPC, L-Theanine, and B12 that improve brain function and promote happiness.” It’s all-natural, gluten-free, lactose-free, no soy, no additives.

    You can check it our for yourself – you can click on this link to purchase.  Let me know what you think!  This product has become part of my daily routine and I literally look forward to it every day.  And here’s another thing, unlike gummy bears, I can eat on Nyrvana truffle and be complete satisfied!  #winning

    Happy Monday friends – make it a great day!

    Biz

    This is a paid post, however the opinions are solely my own.





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    Workouts for Improved Insulin Sensitivity (Workout Videos)

    By electricdiet / January 7, 2020


    Whether you live with type 1 diabetes, type 2 diabetes, prediabetes or any other type of diabetes, you can benefit from improving your insulin sensitivity.

    The four workout videos in this post can help you do just that.

    Your insulin sensitivity is how effective your body is at using the insulin it produces (if you have insulin production) or you inject. The better your insulin sensitivity is, the less insulin, Metformin, or other diabetes drugs do you need to manage your blood sugars.

    Improving your insulin sensitivity can make your overall diabetes management easier in both the short and the long run, and exercising is one of the most effective ways of improving your insulin sensitivity.

    Workouts for Improved Insulin Sensitivity

    The workouts in this post are resistance training workouts. Resistance training simply means that you put your muscles under tension. This can be done using your own body weight or external resistance such as dumbbells or resistance bands.

    If you’re new to resistance training, I suggest you start with the home bodyweight workout (video 1). When that’s no longer challenging, move on to using resistance bands or weights.

    Instructions

    I will demonstrate each exercise and tell you how many sets and reps to do, typically 3 sets of 10-15 or 12-15 repetitions (reps) for each exercise.

    That means that you’ll do one exercise for 10-15 or 12-15 reps, rest for 30-60 seconds, do 10-15 or 12-15 more reps, rest again, and then do the last set of reps.

    The reason why I’m giving you a range is that you should pick a weight that really challenges you but still allows you to do the target reps. For example, If you can only do 9 reps, the weight is too heavy, and if you can easily bang out 15, it is too light.

    Of course, you probably don’t have dumbbells of every different weight at home, so use whatever you have. If you only have light dumbbells and 15 reps feel too easy, just keep going for as many reps as you can.

    When you do resistance workouts, please remember that you may see an impact on your insulin sensitivity 24-36 hours after your workout, so be diligent about watching your blood sugars. If you aren’t used to resistance workouts, I really recommend you read my post about how resistance training affects your blood sugar before you do this workout.

    And always listen to your body and stop if you feel pain. If you’re not used to exercising and doing a whole workout in one go is too much, just break it up into smaller sessions.

    4 sessions of 5 minutes are just as effective as one session of 20 minutes. 

    The Workouts

    Home (Low Impact) Bodyweight Workout

    Home Resistance band Workout

    Home Dumbbell Workout

    Gym Workout

    If you liked these workout videos, please sign up for our newsletter (and get a free chapter from the Fit With Diabetes eBook) using the form below. We send out a weekly newsletter with the latest posts and recipes from Diabetes Strong.



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    Longitudinal Pattern of First-Phase Insulin Response Is Associated With Genetic Variants Outside the Class II HLA Region in Children With Multiple Autoantibodies

    By electricdiet / January 5, 2020


    Abstract

    A declining first-phase insulin response (FPIR) is associated with positivity for multiple islet autoantibodies, irrespective of class II HLA DR-DQ genotype. We examined the associations of FPIR with genetic variants outside the HLA DR-DQ region in the Finnish Type 1 Diabetes Prediction and Prevention (DIPP) study in children with and without multiple autoantibodies. Association between FPIR and class I alleles A*24 and B*39 and eight single nucleotide polymorphisms outside the HLA region were analyzed in 438 children who had one or more FPIR results available after seroconversion. Hierarchical linear mixed models were used to analyze repeated measurements of FPIR. In children with multiple autoantibodies, the change in FPIR over time was significantly different between those with various PTPN2 (rs45450798), FUT2 (rs601338), CTSH (rs3825932), and IKZF4 (rs1701704) genotypes in at least one of the models. In general, children carrying susceptibility alleles for type 1 diabetes experienced a more rapid decline in insulin secretion compared with children without susceptibility alleles. The presence of the class I HLA A*24 allele was also associated with a steeper decline of FPIR over time in children with multiple autoantibodies. Certain genetic variants outside the class II HLA region may have a significant impact on the longitudinal pattern of FPIR.

    Introduction

    The first-phase insulin response (FPIR), a marker reflecting functional capacity of the β-cells in the pancreas, increases physiologically over time in children and adolescents (1). As a sign of deteriorating β-cell function, a decline in FPIR can, however, be observed several years before clinical type 1 diabetes (T1D) (1).

    The class II HLA DR-DQ region has been shown to affect the appearance of islet-specific autoantibodies. Children with multiple autoantibodies have a high risk of progressing to clinical disease, and the presence of multiple autoantibodies seems to represent a point of no return (2). However, class II HLA does not have any effect on the progression rate from advanced islet autoimmunity to clinical diabetes (3), which in turn is influenced by some class I HLA alleles (4). Genetic variants outside the HLA region also affect the development of islet autoimmunity and/or progression to clinical diabetes (57).

    We recently observed that the association between FPIR and class II HLA DR-DQ is secondary to the presence of multiple autoantibodies (8). The declining pattern of FPIR was associated with multiple autoantibodies irrespective of HLA class II risk group. However, it is possible that other genetic polymorphisms are specifically associated with the evolution of FPIR during progression from autoimmunity to clinical disease.

    Here, we studied the role of two class I HLA alleles and eight selected non-HLA gene polymorphisms in the development of insulin secretory capacity as measured by FPIR in children participating in the Finnish Type 1 Diabetes Prediction and Prevention (DIPP) study. Because the presence of multiple islet autoantibodies is strongly associated with β-cell failure, we analyzed separately children with and without multiple biochemical autoantibodies. The selected HLA class I alleles and non-HLA markers have previously been shown to associate with the progression rate from islet autoimmunity to clinical diabetes (4,5,9,10). However, it is not known how or whether these markers are associated with insulin response. The genetic variants of INS and CTSH genes were selected because of their known role in β-cell function (11,12).

    Research Design and Methods

    The population-based DIPP study was launched in 1994 to screen for diabetes-associated risk by genotyping the major HLA DR-DQ haplotypes at birth (3). The study participants were followed regularly for the appearance of islet autoantibodies at 3–12-month intervals. Children who developed islet autoantibodies (islet cell antibodies and biochemical autoantibodies to insulin, GAD 65, and IA2) underwent an intravenous glucose tolerance test (IVGTT) (1), whereas autoantibodies to zinc transporter 8 were analyzed after IVGTT. β-Cell function was estimated by FPIR and change in FPIR (ΔFPIR) as described previously (8).

    Genotyping Methods

    HLA typing of major DR-DQ haplotypes was performed with a PCR-based lanthanide-labeled hybridization method using time-resolved fluorometry for detection (3). Genotyping using the Sequenom platform (San Diego, CA) of eight single nucleotide polymorphisms (SNPs), including PTPN22 (rs2476601), IFIH1 (rs1990760), INS (rs689), IKZF4 (rs1701704), ERBB3 (rs2292239), CTSH (rs3825932), PTPN2 (rs45450798), and FUT2 (rs6013380), was performed at the University of Eastern Finland (Kuopio, Finland) (5); CTSH (rs3825932) genotyping was performed using the Taqman SNP Genotyping Assay (Thermo Fisher Scientific, Waltham, MA). The assays of class I HLA alleles (B*39, A*24, and B*39:06) were analyzed on the DELFIA platform (4). SNPs in ERBB3 and IKZF4 polymorphisms were highly correlated (Fisher exact test P < 0.0001).

    Autoantibody Analyses

    Autoantibodies to insulin, GAD 65, IA2, and zinc transporter 8 were measured in serum samples by a radiobinding assay (13,14). Islet cell antibodies were measured by classical immunofluorescence method applied to sections of human pancreas, blood group O (15).

    Study Participants

    The 438 study children (268 [61.2%] males) with one or more FPIR results (133 [30.4%] who had progressed to T1D, 35 with a single biochemical, 65 with multiple biochemical autoantibodies who did not progress to T1D during the study period) had been categorized according to the biochemical autoantibody status (none/one or multiple [at least two] biochemical islet autoantibodies) at the time of the first IVGTT. The median age at the first IVGTT, which was performed at least 2 years before diagnosis in progressors, was 4.6 years. Diabetes was diagnosed according to World Health Organization criteria (16).

    Statistical Analyses

    ΔFPIR was calculated in children with and without multiple biochemical autoantibodies. Before data analysis, the response variable FPIR was log-transformed. Age-adjusted hierarchical linear models (8) applied to analyze the repeated measurements of FPIR included autoantibody status (0 or 1 autoantibody) in children without multiple autoantibodies, genotypes (three groups except for class I HLA genotypes, which were categorized into two groups), and interaction terms genotype by time and autoantibody group by time. The period of 0–5 years from the first IVGTT was examined.

    Three types of models (additive, recessive, and dominant) were investigated for the SNP genotypes. In the additive model, all three groups were compared. In the recessive model, children homozygous for the risk allele were compared against those who were not homozygous for the risk allele (two groups). In the dominant model, children carrying the risk allele were compared with those who did not have a risk allele (two groups).

    Statistical analyses were performed with JMP Pro version 11.2 and SAS 9.4 for Windows (SAS Institute, Cary, NC) software. P < 0.05 (two-tailed) was considered statistically significant.

    Ethical Considerations

    This study was conducted according to the guidelines of the Declaration of Helsinki II and was approved by local ethics committees. Written informed consent was obtained from all participants and/or their primary caregivers.

    Data and Resource Availability

    The data sets generated and analyzed during the current study are not publicly available because of privacy regulations. No applicable resources were generated or analyzed during the current study.

    Results

    The median FPIR levels and ΔFPIR over the observation period are shown in children with and without multiple biochemical autoantibodies (Tables 1 and 2). FPIR increased over time in children without multiple autoantibodies (Table 2), whereas it declined in those with multiple autoantibodies (Table 1). When the hierarchical linear mixed models were used in children with multiple autoantibodies, modest associations were observed between the evolution of FPIR and three of the gene regions studied (PTPN2 [rs45450798], FUT2 [rs601338], and CTSH [rs3825932]) in the additive model (P = 0.013, P = 0.020, and P = 0.0042, respectively) (Table 3).

    Table 1

    The median of the first FPIR and ΔFPIR over time according to different genotypes in 195 children with multiple (at least two) biochemical autoantibodies during follow-up

    Table 2

    The median of the first FPIR and ΔFPIR over time according to different genotypes from 243 children with zero or one biochemical autoantibody at the time of the first IVGTT

    Table 3

    FPIR as analyzed by a hierarchical linear mixed model adjusted for age from 195 children with multiple autoantibodies at the time of the first IVGTT

    In general, children carrying susceptibility alleles had a more rapid decline in insulin secretion compared with those who did not carry a susceptibility allele. Children homozygous for the diabetes-associated risk allele in IKZF4 and PTPN2 genes had a steeper decline of FPIR than those who were not homozygous for the risk allele in these genes (recessive model P = 0.026 and P = 0.0035, respectively) (Table 3). Children carrying the T1D-associated risk allele in FUT2 and CTSH genes experienced also a steeper decline of FPIR than those without the risk allele in these genes (dominant model P = 0.0098 and P = 0.0011, respectively) (Table 3). In an analysis where risk scores were calculated on the basis of T1D risk in four SNPs that were significant in the model, there were no clearly additive effects (data not shown).

    The class I HLA A*24 allele was also associated with the evolution of FPIR in children with multiple autoantibodies (P = 0.037) (Table 3) so that the presence of the A*24 allele was associated with a steeper coefficient estimate of FPIR (−0.00037, SE 0.000098, P = 0.0002) (Table 3). In children without multiple autoantibodies, the FPIR increased over time independent on A*24 allele status (Table 2). Furthermore, in children without multiple autoantibodies, ERBB3 (rs2292239) showed a significant association with FPIR in the recessive model (P = 0.0075) (Table 4).

    Table 4

    FPIR as analyzed by a hierarchical linear mixed model adjusted for age and the number of autoantibodies from 243 children without multiple autoantibodies

    Discussion

    In this study, we identified novel associations between FPIR and genetic variants known to affect T1D. In children with multiple autoantibodies, the change in FPIR over time was different between those categorized by their PTPN2 (rs45450798), FUT2 (rs601338), CTSH (rs3825932), and IKZF4 (rs1701704) genotypes. Children carrying disease susceptibility alleles had a more rapid decline in insulin secretion over time compared with those who did not carry the allele associated with susceptibility for T1D.

    Homozygosity for the risk alleles in the IKZF4 and PTPN2 genes was associated with a steeper decline of FPIR compared with nonhomozygosity. IKZF4 encodes for Eos, which is known to play an important role in lymphoid development (17). A decreased tyrosine phosphatase expression associated with the PTPN2 variant has been shown to sensitize β-cells to cytokine-induced apoptosis (18).

    Children with multiple autoantibodies carrying at least one risk allele in the CTSH and FUT2 genes were characterized by a steeper decline of FPIR compared with those who did not carry a risk allele. In recently diagnosed children, however, it was, the CT genotype of CTSH that was associated with the lowest dose of insulin, and the children with the CT genotype were most often in remission 12 months after onset compared with those with other genotypes (11). Interestingly, in healthy adults, the CTSH genotype affected β-cell function in the oral glucose tolerance test but showed no effect on FPIR (11).

    Fructosyltransferase 2 enzyme in the Golgi apparatus is involved in the creation of a precursor of the H antigen, which is needed in the synthesis of A and B antigens found in secretions. Individuals carrying the major allele G are called secretors, and they have a functional FUT2 gene (19). In the current study, we observed a difference between children carrying the AA or AG genotype versus the GG genotype. The mechanisms underlying the association between FUT2 and FPIR are not known but could be related by the observation that the secretor status has been associated with composition of the human microbiome (20), although this is controversial (21).

    IFIH1, PTPN22, and INS did not show any association with FPIR in this study, which could partly be explained by the observation that they all have been found in the DIPP study to have their main effect on the development of islet autoimmunity (5). It is not known whether associations between insulin secretion and various genotypes would be different in children without or before the appearance of islet autoantibodies. In autoantibody-positive children carrying both INS risk alleles but without class II HLA risk, the increase of FPIR was slower than in children who carried one or no INS risk alleles (12). Some effect of these genes could potentially be seen in subgroups; for example, the association of caesarean section with the development of T1D was reported to be affected by the IFIH1 genotype (22).

    Hyperexpression of class I HLA antigens is often seen in pancreatic islets from patients with T1D (23). In this study, the presence of the class I HLA A*24 allele was associated with a steeper decline of FPIR in children with multiple autoantibodies. The presence of the A*24 allele has previously been reported to predict rapid progression to clinical disease in autoantibody-positive relatives of patients with T1D (24).

    The unique possibility to analyze young, genetically predisposed children followed intensively over a relatively long period is a strength of this study. A weakness is the low number of observations within some genotypes, which reduces the statistical power. We did not analyze FPIR and its changes over time in relation to the initiating autoantibody (5,9). Although the overall effect of the genetic markers studied on FPIR is modest, it is conceivable that quite a variation in the β-cell mass exists. A wide range of the estimated β-cell mass was observed in adults, even in subjects with low FPIR and multiple autoantibodies (25).

    In conclusion, our results show that certain genetic variants outside the class II HLA region can have a significant impact on the longitudinal pattern of FPIR. In children with multiple autoantibodies, the diabetes risk alleles were associated with more rapid loss in β-cell secretory capacity. The underlying mechanisms are still unknown.

    Article Information

    Acknowledgments. The authors thank all DIPP personnel and families who have participated in this important study.

    Funding. This study was funded by JDRF; Special Research Funds for Turku, Oulu and Tampere University Hospitals in Finland; Academy of Finland; Turku University Foundation; University of Turku Graduate School Doctoral Programme; Diabetes Research Foundation in Finland; Sigrid Juselius Foundation; Päivikki and Sakari Sohlberg Foundation; Emil Aaltonen Foundation; Alma and KA Snellman Foundation; Kyllikki and Uolevi Lehikoinen Foundation; and Turku Centre of Lifespan Research.

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

    Author Contributions. M.K.K. wrote the first draft. M.K.K., M.-L.M., A.-P.L., J.L., E.L., P.V., A.H., T.H., M.Ki., O.S., M.Kn., R.V., J.I., and J.T. reviewed the manuscript and approved the final version. M.K.K., M.-L.M., A.-P.L., P.V., A.H., T.H., M.Ki., O.S., M.Kn., R.V., J.I., and J.T. acquired the data. M.K.K., A.-P.L., J.L., R.V., J.I., and J.T. interpreted the data. M.K.K. and E.L. analyzed data. M.K.K., O.S., R.V., J.I., and J.T. designed the study. M.K.K. 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 March 29, 2019.
    • Accepted October 2, 2019.



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