White Chicken Chili Recipe and Chicken Gumbo Top Wonderful Winter Soups

By electricdiet / March 19, 2020

Perfect Winter Soup White Chicken Chili Recipe

My easy White Chicken Chili recipe makes a perfect one-pot dinner solution for the cold month of January. This easy chicken chili recipe always pops up high on the menu list, and nothing is more comforting than a fire in the fireplace and a warm, hearty easy chicken chili simmering on the stove. Also, I thoroughly enjoy my gas logs with my remote.  This white chicken chili recipe with a fantastic flavor has only about 10 ingredients, and they feed tons of people because soup recipes double easily. Make it easy on yourself and make it ahead of time, and remember, the longer it sits, the better it gets! Also, it is a diabetic easy chicken chili recipe making it one of my top healthy easy recipes!

Wonderful White Chicken Chili
Love this easy white chicken chili recipe starting with ground chicken (ground turkey may be used). A satisfying warm bowl of chili with minimal preparation and maximum taste and a definite go-to easy chicken chili recipe.

    Servings8 servings


    • 1lb

      ground chicken

    • 1


    • 1tsp


    • 1(16-ounce) can

      white navy beansrinsed and drained

    • 2(14 1/2-ounce) can

      low-sodium fat-free chicken broth

    • 1(4-ounce) can

      green chiliesdiced

    • 2cups

      frozen corn

    • 1tsp

      ground cumin

    • 2tsp

      chili powder

    1. In large nonstick pot, cook chicken, onion, and garlic until chicken is done. Add remaining ingredients and bring to boil. Reduce heat and cook, covered 15 minutes, until heated through.

    Recipe Notes

    Per Serving: Calories 183 kcal, Calories from Fat 11%, Fat 2g, Saturated Fat 0g, Cholesterol 36mg, Sodium 378mg, Carbohydrates 24g, Dietary Fiber 5g, Total Sugars 4g, Protein 18g, Dietary Exchanges: 1 1/2 starch, 2 lean meat

    Easy Chicken Chili From My Arthritis Cookbook With Anti Inflammatory Recipes

    Who doesn’t like a delicious hearty, chunky chili recipe that is also simple to make and good for you!  In my arthritis cookbook, I give you an assortment of everyday recipes to help fight inflammation.  This diabetic white chili recipe packs the most flavor with the least amount of ingredients.  I keep it simple!

    Anyone can easily make my recipes and I’m about making eating healthy easy for you! Eating Well To Fight Arthritis gives you simple healthy recipe options like this white chicken chili recipe. I inspire home cooking.

    Freeze Soups for Another Quick One-Pot Meal on Cold Miserable Day

    Here’s a few freezing tips because when it comes to soups, chilies, and gumbo, remember the longer it sits, the better it gets.

    *Cool and then pour into airtight containers and leave room at the top for expansion.
    *Zip lock freezer bags are great for storage because they stack easily in the freezer.
    *Take out the night before using, thaw in the refrigerator, and reheat.

    chicken and sausage gumbo good as white chicken chili recipe

    Two Favorite Soups As Good As My Easy Chicken Chili Recipe:  Gumbo and Easy Chili

    Gumbo tops the list when we talk about soup in Louisiana and everyone loves my simple Chicken and Sausage Gumbo from my Gulf Coast Favorites cookbook. This easy gumbo recipe comes with my secret tips for the perfect healthy roux trick. My favorite easy chili recipe, my Wonderful White Chili from Eating Well to Fight Arthritis makes the perfect comfort food that pleases a variety of tastes. This chili makes the best, easy crowd pleaser recipe and perfect for entertaining at your home!  If you like this chili recipe, you’ll also enjoy my meat chili for Easy Chili from KITCHEN 101. I have tons of more easy  healthy soup recipes.

    Love these expandable Colanders To Drain Beans and Pasta

    I absolutely love these expandable colanders for several reasons. First, these colanders are easy storage and don’t take up much space. People tell me all the time they just don’t have enough room for all their kitchen gadgets so this is a great solution.

    Then, I like these colanders better than the metal one as they are lighter and easier to use. I highly recommend this colander or any expandable colanders.  Also, this colander comes in two different sizes and that’s always a help when cooking.

    Soups with Chicken: My Easy Chicken Chili and Chicken Gumbo Are Crowd Pleasers

    Doesn’t it seem there’s always extras that show up for dinner? Or, what’s better than to have leftovers for another meal or to pop in the freezer to pull out on a busy night.  I love chili recipes and this simple chicken chili recipe tastes like a long stirred chili, but easy! My friend who doesn’t like to cook just sent me a photo of my white chicken chili recipe and said that she earned bragging rights after serving the meal to her family. I can’t wait for you to try all my soups and chili recipes.  Many are diabetic-friendly but all are delicious!

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    The post White Chicken Chili Recipe and Chicken Gumbo Top Wonderful Winter Soups appeared first on The Healthy Cooking Blog.

    Sell Unused Diabetic Strips Today!

    The Novel Adipokine Gremlin 1 Antagonizes Insulin Action and Is Increased in Type 2 Diabetes and NAFLD/NASH

    By electricdiet / March 17, 2020


    The BMP2/4 antagonist and novel adipokine Gremlin 1 is highly expressed in human adipose cells and increased in hypertrophic obesity. As a secreted antagonist, it inhibits the effect of BMP2/4 on adipose precursor cell commitment/differentiation. We examined mRNA levels of Gremlin 1 in key target tissues for insulin and also measured tissue and serum levels in several carefully phenotyped human cohorts. Gremlin 1 expression was high in adipose tissue, higher in visceral than in subcutaneous tissue, increased in obesity, and further increased in type 2 diabetes (T2D). A similar high expression was seen in liver biopsies, but expression was considerably lower in skeletal muscles. Serum levels were increased in obesity but most prominently in T2D. Transcriptional activation in both adipose tissue and liver as well as serum levels were strongly associated with markers of insulin resistance in vivo (euglycemic clamps and HOMA of insulin resistance), and the presence of nonalcoholic fatty liver disease (NAFLD) and nonalcoholic steatohepatitis (NASH). We also found Gremlin 1 to antagonize insulin signaling and action in human primary adipocytes, skeletal muscle, and liver cells. Thus, Gremlin 1 is a novel secreted insulin antagonist and biomarker as well as a potential therapeutic target in obesity and its complications T2D and NAFLD/NASH.


    Obesity is the major driver of the rising prevalence of insulin resistance/type 2 diabetes (T2D) and related complications, including cardiovascular disease, nonalcoholic fatty liver disease (NAFLD), and its severe form, nonalcoholic steatohepatitis (NASH). Hypertrophic obesity with expanded adipose cells is closely associated with insulin resistance and an inflamed and dysregulated subcutaneous adipose tissue with reduced ability to store excess fat and, instead, promoting ectopic lipid accumulation in other tissues including liver and skeletal muscle (1). An attractive therapeutic approach to treat hypertrophic obesity is to promote browning of white adipose tissue to increase mitochondrial biogenesis and whole-body energy expenditure. However, there is also a need for novel insulin-sensitizing drugs to treat insulin resistance/T2D independent of effects on obesity.

    We and others have earlier demonstrated that increased adipose tissue and circulating levels of BMP4 can counteract obesity by promoting browning of white adipose tissue (1,2). However, both adipose tissue and serum levels of BMP4 are actually increased in obesity in man and in mice (35), while beige/brown adipose cell markers are reduced in obesity.

    An important reason for this is that the endogenous BMP antagonists are also increased. Human adipose tissue expresses several antagonists including Gremlin 1, Noggin, Chordin-like 1, Follistatin, and BAMBI (3) but we found the secreted BMP2/4 antagonist Gremlin 1 to be the major endogenous antagonist inhibiting BMP4-induced precursor cell differentiation and white to beige/brown adipocyte conversion (3). Furthermore, Gremlin 1 is a secreted protein, markedly increased in the adipose tissue in human hypertrophic obesity, while it is actually reduced in obese mice in which Noggin is primarily increased (3).

    The increased levels of these antagonists in obese adipose tissue reduce BMP4 signaling, precursor cell commitment, and subsequent induction of beige/brown adipogenesis (1,3). Consistent with this, we have also demonstrated that BMP4 signaling is markedly reduced in the adipose tissue in obesity despite the increased expression and secretion of BMP4 (1,3). Taken together, these observations suggest that Gremlin 1 is an interesting target in human obesity and that it may also be involved in the development of hypertrophic obesity and the obese phenotype of insulin resistance complications (i.e., T2D and NAFLD/NASH), as a marker of increased ectopic fat accumulation. NAFLD is primary characterized by accumulation of intrahepatic triacylglycerols (TGs) and is present in 75–90% of subjects with T2D (6,7). NAFLD may progress to the severe condition of NASH, characterized by advanced histological remodeling including fibrosis, lobular inflammation, hepatocellular ballooning, and risk of liver cancer.

    In this study, we measured Gremlin 1 serum levels and skeletal muscle, adipose tissue, and liver mRNA in cohorts without and with diabetes and in subjects with NAFLD/NASH. We also assessed the effect of Gremlin 1 on insulin signaling and action in these three major target tissues for insulin. Our results identify Gremlin 1 as a novel biomarker and potential therapeutic target in insulin resistance and associated complications.

    Research Design and Methods

    Study Populations

    All studies were performed in accordance with the Declaration of Helsinki. All subjects gave written informed consent before taking part in the studies.

    Cohort FDR/Control

    In this cohort, 34 nonobese subjects were studied: 17 individuals with at least one known first-degree relative (FDR) with T2D and 17 individuals without known genetic predisposition for T2D defined as no family history (control subjects). The groups were matched for sex (10 females in both groups) and BMI and had similar age (Table 1). Fasting plasma insulin and glucose levels were used to calculate insulin resistance defined as a HOMA of insulin resistance (HOMA-IR) index using the formula: HOMA-IR = (fasting plasma glucose × fasting plasma insulin)/22.5. Local subcutaneous adipose tissue biopsies were obtained from the lower abdominal wall as previously reported (3). Study protocol was approved (S655–03) by the Ethical Committee of the Sahlgrenska Academy, University of Gothenburg.

    Other Cohorts

    Paired samples of subcutaneous, omental visceral adipose tissue, and liver were collected during laparoscopic abdominal surgery as described previously (6). Adipose tissue was immediately frozen in liquid nitrogen and stored at −80°C. The study was approved by the Ethics Committee of the University of Leipzig (approval number 159–12–21052012; Leipzig, Germany). BMI was calculated by weight (kilograms) divided by square of height (meters).

    • Cohort ND/D (Table 1): in a cross-sectional study, we investigated GREMLIN 1 mRNA in paired visceral/omental and abdominal subcutaneous adipose tissue samples (n = 233; BMI >30 kg/m2). Of these, 105 individuals had normal glucose levels, and 128 had T2D.

    • Cohort GIR (Table 1): in 93 individuals (BMI 24–37 kg/m2) with normal glucose tolerance (NGT), adipose tissue GREMLIN 1 mRNA was evaluated in relation to the glucose infusion rate (GIR) in euglycemic-hyperinsulinemic clamps according to previously described procedures (6).

    • Cohort ND/D/NAFLD (Table 1): a cohort of 52 obese individuals with wide range of liver fat content and with (n = 28; BMI 34 ± 5.8 kg/m2) or without T2D (n = 23; BMI 34 ± 5.9 kg/m2) were studied. GREMLIN 1 mRNA was measured both in paired adipose tissue and liver samples. For measurement of metabolic parameters, all baseline blood samples were collected between 8 and 10 a.m. after an overnight fast and analyzed as previously described (6).

    • Cohort Nob/obND/obD (Table 1): serum Gremlin 1 levels were analyzed in 45 individuals with either NGT (n = 30) with a BMI <25 kg/m2 (n = 15) or >30 kg/m2 (n = 15) or with known T2D (n = 15).

    • Two-step bariatric surgery intervention was a cohort of 55 individuals with morbid obesity who underwent a two-step bariatric surgery approach with a sleeve gastrectomy as a first step and, after 12 ± 2 months, a Roux-en-Y gastric bypass surgery as the second step as previously reported (8).

    These different cohorts were also essentially sex neutral, with ∼70% females and 30% males, and are summarized as a flow chart in Supplementary Fig. 1.

    Diagnosis of Diabetes

    The diagnosis of T2D versus normal or impaired glucose tolerance was based on the results of a 75-g oral glucose tolerance test according to the criteria of the American Diabetes Association (9). T2D was defined by 120-min glucose ≥11.1 mmol/L or a repeated fasting plasma glucose ≥7.0 mmol/L.

    Virtually all of the patients with diabetes were treated with metformin and some, as needed, with a dipeptidyl peptidase inhibitor. Only these medications were used for diabetes treatment in these cohorts. Patients with elevated cholesterol and/or blood pressure received regular medication as required.

    Diagnosis of NAFLD/NASH

    In the human cohort for which parallel liver and adipose tissue biopsies were available, NAFLD and NASH have been determined and diagnosed histologically (using hematoxylin and eosin–stained and Masson trichrome–stained slides) following a previous proposal for grading and staging of the histological lesions detected in liver biopsies (10). In accordance, two independent and specialized liver pathologists at the University of Leipzig evaluated the histological lesions—steatosis, ballooning, and intra-acinar and portal inflammation—and summarized those in the score (11).

    Detection of Gremlin 1 in Human Serum

    Sandwich ELISA was used to measure human serum Gremlin 1 levels in the samples. Gremlin 1 was captured using an in-house–developed monoclonal antibody against Gremlin 1 (MedImmune, Gaithersburg, MD) on a 96-well half-area plate. The samples were incubated for 1 to 2 h followed by detection with rabbit polyclonal antibody (catalog number ab157576; Abcam) for 1 h. The rabbit polyclonal was detected using in-house–generated horseradish peroxidase–conjugated anti-rabbit polyclonal antibody. Gremlin 1 levels in the samples were interpolated using the standard curve generated in 50% immune-depleted serum.

    Cell-Based Experiments

    Primary Human Adipocytes

    Primary human adipocytes were isolated as previously described (12). Briefly, subcutaneous adipose tissues, obtained by needle biopsy, were digested with collagenase type II (Sigma-Aldrich, St. Louis, MO) for 60 min at 37°C in shaking water bath. The adipocytes were then filtered through a 250-μm nylon mesh and washed four times, followed by cell size measurement, RNA/protein extractions, and/or additional experimental assays. For insulin signaling and glucose uptake assessments, adipocytes were further incubated in Hank’s medium 199, pH 7.4 (Life Technologies, Carlsbad, CA) containing 4% BSA with or without 6 mmol/L glucose, respectively.

    For glucose uptake, the adipocytes were pretreated with IgG or anti–Gremlin 1 antibody (MedImmune) and/or recombinant Gremlin 1 (200 ng/mL) (R&D Systems, Inc., Minneapolis, MN) for 3 h and stimulated with 10 nmol/L insulin for 15 min before the addition of D-[U-14C] glucose (0.26 mCi/L; final concentration 0.86 μmol/L) (PerkinElmer, Waltham, MA) for additional 45 min. The glucose uptake was immediately stopped by separating the adipocytes from the medium, and incorporated radioactivity was measured in a scintillation counter.

    Primary Skeletal Muscle Cells

    Satellite cells were isolated from five donors with NGT. The cells were grown to >80% confluence in DMEM/F12 containing 10% FBS and antibiotics and further differentiated into multinuclear myotubes in differentiation medium (DMEM, Medium 199, HEPES, zinc sulfate, vitamin B12, FBS, and antibiotics) as described (13). Cells were then starved for 4 h before incubation with recombinant Gremlin 1 (50 ng/mL) and insulin (1–10 nmol/L).

    Human Hepatocytes

    Primary human hepatocytes, HiPS-Hep (Takara Bio Inc., Shiga, Japan), were cultured in hepatocyte medium (Takara Bio) according to the manufacturer’s instructions. Cells were starved 3 h before pretreatment with recombinant Gremlin 1 (50 ng/mL) and insulin (100 nmol/L).

    HepG2 liver cells (ATCC, Manassas, VA) and IHH (human hepatocyte celline) were cultured in DMEM (Lonza, Basel, Switzerland) supplemented with 10% FBS and antibiotics. To study the secretory effects of Gremlin 1, HepG2 cells were transfected with wt.Grem1.myc or trunc.Grem1.myc plasmids (expressing myc-tag fused to the COOH-terminal of human Gremlin 1 with or without the N-terminal signal peptide sequence) constructed in our laboratory. For confocal imaging, the cells were grown on glass chamber slides (Thermo Fisher Scientific) for 72 h. Cells were then washed with PBS, fixed with 4% formaldehyde, permeabilized with 0.1% Triton, blocked by 20% goat serum (1 h), and incubated with anti–myc antibody (Sigma-Aldrich) for 3 h. After washing with PBS and incubation with Alexa 488–probed secondary antibody for 1 h, cells were mounted with Vectashield mounting solution containing DAPI (Vector Laboratories, Inc). Confocal images were then collected with the Leica SP5 confocal microscope.

    To study the effect of protein tyrosine phosphatase 1B (PTP1B) inhibitor (CAS 765317–72–4; Merck Millipore, Danvers, MA), IHHs were starved and pretreated with the inhibitor (10 μmol/L) with or without recombinant Gremlin 1 (200 ng/mL) for 24 h, followed by insulin (10 nmol/L) for 10 min.

    Quantitative Real-time PCR

    mRNA was extracted from cells and tissues followed by cDNA synthesis. The gene expression was then analyzed using the QuantStudio 6 Flex TaqMan system (Applied Biosystems, Foster City, CA). Relative quantification of gene expression was normalized to 18S rRNA or HPRT1. The primers and probes were either designed or ordered commercially as predesigned TaqMan probe kits (Assay On-Demand; Applied Biosystems).


    Western blot analysis were performed as previously described (14). The following primary antibodies were used: Gremlin 1 (MedImmune), pAktS473, AKT (Cell Signaling Technology, Danvers, MA), pY20, IRβ (Santa Cruz Biotechnology, Dallas, TX), and IRS1 (Merck Millipore).

    PTP1B Activity Assay

    PTP1B activity was assessed using the PTP activity assay kit (Millipore). Briefly, IHHs were lysed in lysis buffer lacking sodium orthovanadate. PTP1B was then immonoprecipitated using a PTP1B antibody (Millipore). The measurement of PTP1B activity was carried out using the synthetic Tyrosine Phosphopeptide (TSTEPQpYQPGENL). The phosphate release was measured at OD 650 nmol/L using the malachite green reagent provided with the kit.

    Statistical Analysis

    The experimental data are shown as means ± SD or means ± SEM. Significance is indicated in the figures as P < 0.05, P < 0.01, and P < 0.001. All statistical calculations were performed using IBM SPSS Statistics v20. Pairwise comparisons were performed using the Student t test. For multiple comparisons, one-way ANOVA with Bonferoni post hoc test or Kruskal-Wallis test was used when appropriate. To assess correlation between variables, Pearson or Spearman correlations were used as appropriate. Statistical analysis of the cohorts did not differentiate between females and males because they were all characterized by ∼70% females and 30% males.

    Data and Resource Availability

    The data set and resources generated and analyzed in this study are available from the corresponding author upon reasonable request. The suppliers of antibodies used in this study have been cited above.


    Increased Gremlin 1 in Adipose Tissue, Liver, and Serum in Insulin Resistance and T2D Independent of BMI

    To identify if transcriptional activation of Gremlin 1 is altered in insulin resistance, obesity, T2D, and NAFLD/NASH, we examined tissue mRNA in five different cohorts. In the cohort consisting of sex-, BMI-, and age-matched nonobese control subjects (BMI 24.3 ± 2.4 kg/m2, age 34 ± 9 years) and FDRs (BMI 24.9 ± 2.3 kg/m2, age 38 ± 8 years) of subjects with T2D, GREMLIN 1 mRNA was significantly higher in the subcutaneous adipose tissue of this high-risk FDR subgroup compared with the matched control group (Fig. 1A). In addition, GREMLIN 1 levels were positively correlated with percentage of body fat and insulin resistance measured as HOMA-IR (Fig. 1B and C). FDRs as a group are more insulin resistant than a BMI-matched non-FDR group.

    Figure 1
    Figure 1

    GREMLIN 1 mRNA in adipose tissue and liver and circulating levels of Gremlin 1 are increased in insulin resistance and T2D. A: Differential GREMLIN 1 mRNA in subcutaneous (SC) adipose tissue in individuals with genetic predisposition for T2D (FDR) and matched control subjects in FDR/control cohort. Correlation to body fat percentage (B) and HOMA-IR (C) in the same cohort. D: GREMLIN 1 mRNA in SC and visceral (VIS) adipose tissue in individuals with T2D and with NGT in ND/D cohort. E: GREMLIN 1 mRNA in VIS adipose tissue is inversely correlated to GIR during hyperinsulinemic-euglycemic clamps in cohort GIR. GREMLIN 1 mRNA expression in adipose tissue (F) and liver (G) in individuals with T2D and with NGT in ND/D/NAFLD cohort. Circulating levels of Gremlin 1 in lean NGT, obese NGT, and equally obese subjects with T2D in cohort Nob/NDob/obD (H) and relation to HOMA-IR (I) and HbA1c (J). All graphs display means ± SEM. Statistics were calculated using Mann-Whitney test (A), Kruskal-Wallis one-way analysis (D and H), and ANOVA with Bonferoni post hoc test (F and G). *P < 0.05; **P < 0.01; ***P < 0.001. RQ, relative quantification.

    In the cohorts consisting of subgroups without diabetes and with T2D (ND/D and ND/D/NAFLD cohorts), GREMLIN 1 mRNA was higher in visceral than in subcutaneous adipose tissue in both ND/D subgroups and increased in both tissues in T2D compared with individuals with NGT (Fig. 1D and F). Moreover, GREMLIN 1 mRNA levels in both visceral and subcutaneous adipose tissue were again negatively correlated with insulin sensitivity in individuals without diabetes (cohort GIR), measured by hyperinsulinemic-euglycemic clamps (Fig. 1E in visceral and Supplementary Fig. 2A in subcutaneous adipose tissue). We also found hepatic GREMLIN 1 mRNA to be increased in patients with T2D of the ND/D/NAFLD cohort (Fig. 1G). There was no correlation between age and GREMLIN 1 mRNA levels in either subcutaneous or visceral adipose tissue in the large ND/D cohort of 216 individuals.

    As Gremlin 1 is a secreted protein, we asked if its circulating levels are altered in insulin resistance and T2D. Current ELISAs and commercially available antibodies are not very sensitive, so we used the modified in-house ELISA with a noncommercially available antibody as described (3). We analyzed high-quality serum from the Nob/obND/obD cohorts consisting of nonobese NGT, obese NGT, and equally obese subjects with T2D. Serum Gremlin 1 tended to be higher in the obese than in the nonobese subjects, but it was further significantly increased in equally obese individuals with T2D (Fig. 1H). This observation is consistent with our results of increased adipose tissue and liver GREMLIN 1 mRNA expression in patients with T2D. In addition, circulating levels of Gremlin 1 were positively and significantly correlated with HOMA-IR (Fig. 1I) as well as with glycosylated hemoglobin (HbA1c) (Fig. 1J). Collectively, these results provide evidence that GREMLIN 1 mRNA levels in adipose tissue and liver as well as circulating serum levels are associated with both degree of insulin resistance and obesity and also increased in established T2D irrespective of degree of obesity. However, in contrast to adipose tissue and liver GREMLIN 1, skeletal muscle expression was not increased in biopsies from individuals with T2D compared with individuals without diabetes (data not shown).

    Secreted Gremlin 1 Impairs Insulin Signaling and Action in Adipose, Skeletal Muscle, and Liver Cells but Not Through PTP1B Activation

    Because both adipose tissue and liver GREMLIN 1 mRNA and serum Gremlin 1 levels were strongly associated with degree of insulin resistance and its associated consequences, we assessed the possible effect of Gremlin 1 on insulin signaling in key human target cells.

    We characterized the direct effect of Gremlin 1 protein on insulin signaling in human primary adipocytes, human induced pluripotent stem cell–derived hepatocytes, HepG2, IHHs, and primary human differentiated skeletal muscle cells. Short-term incubations (2–4 h) with recombinant human Gremlin 1 significantly impaired insulin signaling measured as phosphorylation of pS473-AKT in all human cells. We also examined the effect on tyrosine phosphorylation in human adipose cells, and the pY-IRβ subunit was also reduced (Fig. 2AC). We further examined the effect of Gremlin 1 protein on both basal and insulin-stimulated glucose uptake in adipocytes isolated from subcutaneous adipose tissue biopsies of 11 subjects (BMI 22–37 kg/m2). Recombinant Gremlin 1 significantly reduced glucose uptake in response to insulin, and this effect was neutralized by anti–Gremlin 1 antibody (Fig. 2D). In fact, the addition of anti–Gremlin 1 alone significantly improved insulin-stimulated glucose uptake, and this sensitizing effect was related to the initial insulin response (i.e., the lower the incremental insulin response, the larger the positive effect of anti–Gremlin 1 alone on insulin-stimulated glucose uptake) (Fig. 2E). This was further validated by the positive correlation between HOMA-IR, as a marker of donor insulin sensitivity, and the incremental effect of the anti–Gremlin 1 antibody (Supplementary Fig. 3A). These data suggest that Gremlin 1 secretion by adipose cells, which we have also previously demonstrated (3), is directly antagonistic to the effect of insulin and contributing to insulin resistance in the cells. If the effect on glucose uptake is also secondary to the insulin-antagonizing effect or indicates additional effects of Gremlin 1 on GLUT4 protein recycling remains to be studied.

    Figure 2
    Figure 2

    Gremlin 1 inhibits insulin signaling and action. Representative immunoblot analysis with quantifications showing that incubation with recombinant Gremlin 1 (recGREM1) inhibits insulin-induced tyrosine phospho–insulin receptor (pTyr-IR) (A) and serine 473 phospho-AKT (pSer473-AKT) in isolated primary human adipocytes (n = 6) and in primary human differentiated skeletal muscle cells (hSMCs) (n = 4) (B) and human induced pluripotent stem cell (iPS)-derived hepatocytes (n = 3) (C). D: Incubation with recGREM1 decreased insulin-stimulated glucose uptake in isolated primary human adipocytes (n = 11). Presence of anti–Gremlin 1 increased insulin-stimulated glucose uptake and antagonized the effect of recGREM1 compared with adipocytes treated with control IgG (n = 9). E: Correlation between the increase in insulin-stimulated glucose uptake in adipocytes treated with anti–Gremlin 1 antibody and the degree of initial insulin glucose uptake in same cells treated with control IgG. Graphs display means ± SD. Statistics were calculated using the Student t test (AC) and ANOVA with Bonferoni post hoc test (D). *P < 0.05; **P < 0.01.

    To further verify the insulin-antagonistic effect of Gremlin 1 as a secreted molecule, we expressed a nonsecreted truncated and a secreted full-length Gremlin 1 in human HepG2 hepatocytes. We found that only the secreted, and not the nonsecreted, form inhibited insulin signaling (Fig. 3A). This inhibitory effect of secreted Gremlin 1 was again prevented by anti–Gremlin 1 antibody (Supplementary Fig. 2B). Additionally, full-length Gremlin 1 was primarily localized in the cytoplasm prior to its secretion, while the truncated form was only detected in the cell nuclei (Fig. 3B). Nuclear localization of Gremlin 1 has previously been reported by several studies suggesting other functional roles for the nuclear Gremlin 1 (12).

    Figure 3
    Figure 3

    Insulin signaling is inhibited by secreted Gremlin 1 and not a truncated nonsecreted form of Gremlin 1. A: Representative immunoblot analysis showing that insulin-stimulated serine 473 phospho-AKT (pSer473-AKT) is inhibited in HepG2 hepatocytes transfected with wild-type Gremlin 1 (WT.GREM1.myc) but not in cells transfected with a truncated Gremlin 1 that is not secreted (mut.GREM1.myc) (n = 3). B: Cellular localization of WT.GREM1.myc and mut.GREM1.myc in HepG2 hepatocytes stained by Myc antibody (green) and DAPI (blue) and imaged with a confocal microscope. **P < 0.01.

    PTP1B is a well-known inhibitor of insulin signaling and has been a potential therapeutic target for treating T2D (15,16). Considering the reduced tyrosine phosphorylation of the insulin receptor, we asked if Gremlin 1 interferes with PTP1B signaling. IHHs were incubated with a PTP1B inhibitor and/or recombinant Gremlin 1 followed by insulin stimulation. The inhibition of PTP1B by its inhibitor enhanced insulin signaling as expected, and this was seen whether or not Gremlin 1 was present (Fig. 4A). Furthermore, we did not see any direct effect of Gremlin 1 on PTP1B activity (Fig. 4B). Thus, these data do not support that the inhibitory effect of Gremlin 1 on insulin signaling is due to increased PTPB1 activity.

    Figure 4
    Figure 4

    Gremlin 1 and PTP1B activity. A: Representative immunoblot analysis showing that inhibition of PTP1B (PTP1B inhib) increases insulin-stimulated AKT phosphorylation and reverses the effect of recombinant Gremlin 1 (recGREM1) in IHHs (n = 4). B: PTP1B activity in IHHs treated with recGremlin 1, PTP1B inhibitor, or insulin. Graphs display means ± SD. Statistics were calculated using Student t test. *P < 0.05. Tot, total.

    In sum, these data show that secreted and circulating Gremlin 1, probably emanating from the adipose tissue to a large extent in vivo, is insulin antagonistic in all three major human target cells. However, the mechanisms for this inhibitory effect are currently unclear.

    To further validate the adipose tissue as an important source of serum Gremlin 1 levels, we investigated if circulating Gremlin 1 was related to serum adiponectin levels and found a significant negative correlation (R = −0.28; P < 0.01). Negative correlations with adiponectin levels were also seen with adipose tissue GREMLIN 1 mRNA levels in cohort ND/D (subcutaneous tissue, R = −0.23, P < 0.01; visceral tissue, R = −0.35, P < 0.001) and in cohort ND/D/NAFLD (subcutaneous tissue, R = −0.24, P < 0.05; visceral tissue, R = −0.31, P < 0.05).

    The importance of the adipose tissue as a source for Gremlin 1 was further documented in the bariatric surgery investigation cohort of 55 individuals with obesity who underwent the two-step bariatric surgery approach, losing ∼50 kg body weight. GREMLIN 1 mRNA expression, both in subcutaneous and visceral adipose tissues, was significantly reduced (Supplementary Fig. 3B). Thus, the increased Gremlin 1 levels in obesity and T2D are likely to be associated with the increased adipose tissue.

    Increased Adipose Tissue, Liver, and Serum Gremlin 1 Levels Are Associated With Markers of NAFLD/NASH

    As tissue Gremlin 1 expression and function are associated with obesity and insulin resistance/T2D, we next examined if it also was related to other insulin resistance/obesity-linked complications such as NAFLD/NASH. To accomplish this, liver biopsies from 52 obese individuals with or without T2D (cohort ND/D/NAFLD) were carefully characterized using the NAFLD/NASH scoring system as defined by international guidelines (17).

    We found transcriptional activation of GREMLIN 1 in the subcutaneous and visceral adipose tissue and the liver to be positively associated with NAFLD activity scores, including degree of steatosis, ballooning, as well as inflammation and fibrosis scores (Table 2). In addition, they were negatively associated with other markers of insulin sensitivity, including serum adiponectin levels, and positively with liver fat content, circulating free fatty acids, TGs, low HDL cholesterol, and the cytokine interleukin-6 (i.e., key markers of the metabolic syndrome) (Table 2). Of interest, we also observed significantly higher liver GREMLIN 1 mRNA in patients with T2D with biopsy-proven NASH compared with patients with T2D with only NAFLD. This increase was not seen in the NGT individuals (Fig. 5A).

    Table 2

    Levels of GREMLIN 1 in visceral and subcutaneous adipose tissue and liver (cohort ND/D/NAFLD)

    Figure 5
    Figure 5

    Gremlin 1 levels and NAFLD/NASH. A: GREMLIN 1 mRNA in liver biopsies of individuals with NAFLD or NASH and in individuals with or without T2D in cohort ND/D/NAFLD. Circulating levels of Gremlin 1 correlate with CRP (B), ALAT (C), and ASAT (D). Graphs display means ± SEM. Statistics were calculated using Student t test. ***P < 0.001. RQ, relative quantification.

    Consistent with these findings, we found circulating levels of Gremlin 1 (Nob/obND/obD) to be significantly, and positively, correlated with serum levels of C-reactive protein (CRP), alanine aminotransferase (ALAT), and aspartate aminotransferase (ASAT), which are all markers of NALFD/NASH (Fig. 5B–D).

    Taken together, these data show that Gremlin 1 is a secreted and insulin-antagonistic protein, particularly highly expressed in the visceral adipose tissue, increased in both adipose tissue and liver in obesity and T2D, and related to the degree of whole-body insulin resistance and NAFLD/NASH. These novel findings make Gremlin 1 an interesting potential therapeutic target in obesity and insulin resistance, T2D, and NAFLD/NASH.


    BMP4 is a critical regulator of human adipose precursor cell commitment and differentiation (reviewed in Hoffmann et al. [1]), and maintained BMP signaling promotes browning of the differentiated white adipose cells in both murine models and human cells (1,2,5), while brown adipose cells become beige and less oxidative (1,5). Because Gremlin 1 is a secreted protein by human adipose cells and a key endogenous regulator of BMP signaling in these cells (3), we wanted to examine its presence in other metabolic tissues and the relation to obesity and its complications.

    In this study, we investigated in several well-characterized large cohorts if Gremlin 1 serum levels and transcriptional activation in the subcutaneous and visceral adipose tissue, liver, and skeletal muscle also are related to the obesity phenotype and associated complications of T2D and NAFLD/NASH. We show that GREMLIN 1 mRNA levels are particularly high in visceral, compared with subcutaneous, adipose tissue and that insulin sensitivity measured with both euglycemic clamps and the clinical HOMA index showed strong negative correlations between adipose tissue expression in both regions and insulin sensitivity. This is consistent with our previous finding that Gremlin 1 is a secreted protein and markedly increased in subcutaneous adipose tissue with expanded adipose cells (3) (i.e., in hypertrophic obesity), which is related to insulin resistance and other obesity-associated complications (reviewed in Hoffmann et al. [1]). Because we previously found Gremlin 1 to antagonize the early induction of adipogenesis, it is not unexpected that it is also increased in the adipose tissue in hypertrophic, insulin-resistant obesity. Although our current data cannot prove causality in terms of increased adipose tissue Gremlin 1 directly leading to the development of hypertrophic, insulin-resistant obesity, we also cannot exclude it. Apart from our previous experimental studies (3), additional support for this possibility is our current finding that GREMLIN 1 mRNA levels also were increased in lean and fairly young FDR individuals. FDRs are characterized by insulin resistance, an impaired subcutaneous adipogenesis, and development of inappropriately expanded adipose cells (i.e., hypertrophic obesity) (18,19). In addition, recent large studies have demonstrated that individuals with genetic markers of insulin resistance are characterized by reduced subcutaneous adipose tissue, which, even if cell size was not measured, implies an association with impaired adipogenesis (20).

    We have tried to investigate the effects of increased Gremlin 1 serum levels in a murine in vivo model by expressing it in the liver of mature mice with AAV8–Gremlin 1 (R.K.S., J.M. Hoffmann, S.H., S. Heasman, C.C., I. Elias, F. Bosch, J.B., A.H., U.S., unpublished observations). However, mature mouse models are not responsive to increased Gremlin 1 targeting the liver with gene therapy because it accumulated in the liver cells and was apparently not secreted. Also, we did not see any increase in liver inflammation or fibrosis in this model. In addition, intraperitoneal injections were essentially without any effects on phenotype, and Gremlin 1 protein did not antagonize the effect of insulin in murine cells like those that we find in this study in human cells. Thus, mature mice are not good models to characterize effects of Gremlin 1 in vivo.

    Consistent with our current in vivo findings of increased Gremlin 1 levels in insulin resistance, we also find Gremlin 1 protein to directly antagonize insulin signaling in three key target cells for insulin. The inhibitory effect of recombinant and cell-secreted Gremlin 1 and the sensitizing effect of anti–Gremlin 1 on insulin-induced glucose uptake show that Gremlin 1 can attenuate both insulin signaling and insulin-stimulated glucose transport, although these may be partly linked. We also found that the insulin-sensitizing effect of anti–Gremlin 1 is related to degree of cellular insulin responsiveness. Thus, the efficacy of anti–Gremlin 1 treatment is more pronounced in insulin-resistant cells, supporting a direct “tonic” inhibitory effect of Gremlin 1 secreted by the adipose cells. This concept is also supported by our finding of a positive correlation between the magnitude of the insulin-sensitizing effect of anti–Gremlin 1 and HOMA-IR (Supplementary Fig. 3A). GREMLIN 1 mRNA levels were similar in both adipose tissue and liver, but considering the large adipose depot, it is probably a major source of circulating Gremlin 1. This is supported by the reduced GREMLIN 1 levels in the adipose tissue after substantial weight reduction with bariatric surgery. Furthermore, GREMLIN 1 was particularly high in visceral adipose tissue, which is drained by the portal circulation, thus targeting the liver with consequences for insulin resistance and other factors enhancing NAFLD/NASH development. Expanded visceral adipose depot is associated with insulin resistance and exhibits strong associations with future risk of developing cardiometabolic complications (6,21,22).

    We also aimed at identifying mechanisms underlying the insulin-antagonistic effect of Gremlin 1. PTP1B, which is a key phosphatase and inhibitor of insulin signaling, has been extensively studied in vitro and in vivo and is considered as a potential therapeutic target for T2D (2325). Although inhibiting PTP1B nonspecifically reduced the antagonizing effect of Gremlin 1 on insulin signaling, PTP1B activity was not increased in Gremlin 1–treated cells. Thus, our data do not provide any support for PTP1B as a mediator of the reduced insulin signaling by Gremlin 1.

    Gremlin 1 is a member of the DAN family of protein antagonists, primarily inhibiting BMP2 and BMP4, but has also been found to have other non-BMP binding partners such as the Slit protein in monocytes and, unexpectedly, also vascular endothelial growth factor receptor 2 with effects on angiogenesis (26). However, vascular endothelial growth factor receptor 2 as a binding partner could not be confirmed in a recent extensive study (27). It is also unlikely that the inhibitory effects of Gremlin 1 on insulin signaling, which are seen very rapidly (within a few hours), can be accounted for by its inhibitory effects on adipogenesis. This effect of Gremlin 1 is primarily due to inhibiting the early adipogenic commitment effects of BMP4 on the progenitor cells (3). However, direct or indirect effects of cell-endogenous or -exogenous circulating BMP4 on cellular insulin signaling and action cannot be excluded. In our previous study in mice treated with BMP4 gene therapy targeting the liver to increase circulating BMP4 levels, we found increased whole-body insulin sensitivity independent of any change in body weight (1). This has been further examined with similar positive effects on insulin sensitivity in obese mice (28). Thus, we currently favor the concept that Gremlin 1 inhibits insulin signaling and action by antagonizing the positive effects of BMP4, but this needs to be further substantiated.

    In summary, our results identify Gremlin 1 as a prominent adipokine and cell-secreted antagonist of insulin signaling in human adipocytes, skeletal muscle cells, and liver cells. We also found Gremlin 1 serum and tissue levels to be significantly increased in insulin resistance and in individuals with T2D and NAFLD/NASH independent of degree of obesity. Thus, Gremlin 1 is an attractive novel therapeutic target in insulin resistance and associated complications of T2D and NAFLD/NASH.

    Article Information

    Acknowledgments. The authors thank Dr. Ruchi Gupta (MedImmune, Gaithersburg, MD) for developing the serum Gremlin 1 ELISA technique.

    Funding. Financial support for these studies was provided by the Medical Research Council, the Novo Nordisk Foundation, Torsten Söderberg Foundation, Swedish Diabetes Foundation, Swedish Agreement on Medical Education and Research contribution, and MedImmune (Gaithersburg, MD).

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

    Author Contributions. S.H., A.H., M.B., and U.S. designed the studies. S.H., R.K.S., A.H., L.B., and M.B. performed experiments. S.H. and U.S. wrote the paper with input from all authors. All authors have approved the manuscript. U.S. is the guarantor of this work and, as such, had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

    • Received July 16, 2019.
    • Accepted December 8, 2019.

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    Type 2 diabetes: Symptoms, Diagnosis, Causes & Treatment

    By electricdiet / March 13, 2020

    There are approximately 30 million Americans living with type 2 diabetes, and another 84 million with prediabetes, most of whom are completely unaware their blood sugars are high.  

    Over the last 50 years, the Centers for Disease Control & Prevention reports that the number of Americans with diabetes has doubled, pointing to obesity as one of the biggest contributors.

    Let’s take a closer look at the symptoms, diagnosis, causes, and treatment options for type 2 diabetes.

    Everything you need to know about type 2 diabetes

    What is type 2 diabetes?

    Type 2 diabetes is a metabolic disorder characterized by high blood sugar levels that are not the result of an autoimmune disease (type 1 diabetes), or pregnancy (gestational diabetes), MODY (maturity onset diabetes of the young), or LADA (latent autoimmune diabetes in adults).

    For people with type 2 diabetes, there is likely an issue in either their body’s ability to produce normal amounts of insulin or their body’s ability to respond properly to the insulin they do produce.

    This is the difference between “insulin deficiency” and “insulin resistance.”

    Causes: insulin deficiency or insulin resistance?

    “It is now well recognized that 2 factors are involved: impaired [beta-cell] function and insulin resistance,” explains John E. Gerich, MD, in research published by the Mayo Clinic Proceedings

    Insulin resistance is when your body does not respond enough to normal amounts of insulin, which means your body has to produce more and more in an effort to achieve normal blood sugar levels. This can cause gradual weight gain in addition to being the result of weight gain.

    Eventually, the body can’t keep up with the increasing demand for more insulin. This is when blood sugar levels start rising and a diagnosis of prediabetes or type 2 diabetes can be made.

    Insulin deficiency is the result of  “beta-cell dysfunction,” according to the American Diabetes Association’s journal, Diabetes Care

    Beta-cell dysfunction is when the body is struggling to produce healthy beta-cells. Beta-cells are produced in the pancreas and responsible for secreting insulin. Without enough healthy beta-cells, a person cannot produce enough insulin to manage blood sugar levels.

    This gradually worsening beta-cell dysfunction is increased blood sugar levels which leads to further weight gain, making the battle against diabetes a difficult struggle for the person living with it.

    Obesity doesn’t always lead to type 2 diabetes

    There is a significant portion of people who are overweight or obese who do not have elevated blood sugar levels, but the two conditions do often overlap.

    “Excess weight is an established risk factor for type 2 diabetes, yet most obese individuals do not develop type 2 diabetes,” explains research from the Journal of Clinical Endocrinology and Metabolism

    Obesity and type 2 diabetes can both be much more complicated than eating too much and exercising too little.

    Factors that increase your risk

    The CDC lists the following as factors that increase your risk of type 2 diabetes:

    • Have prediabetes
    • Are overweight
    • Are 45 years or older
    • Have a parent, brother, or sister with type 2 diabetes
    • Are physically active less than 3 times a week
    • Have ever had gestational diabetes (diabetes during pregnancy) or given birth to a baby who weighed more than 9 pounds
    • Are African American, Hispanic/Latino American, American Indian, or Alaska Native (some Pacific Islanders and Asian Americans are also at higher risk)

    Symptoms of type 2 diabetes

    The symptoms of type 2 diabetes can be very subtle and easy to ignore for years — until blood sugar levels are high enough to catch your attention. 

    The higher your blood sugar levels rise — or when they spike suddenly after high-carbohydrate meals — the more noticeable these symptoms will be. 

    If you suspect you may be struggling with some of these symptoms, contact your primary healthcare team and ask to have your blood glucose levels and your HbA1c tested. 

    Diagnosing type 2 diabetes

    The diagnosis of type 2 diabetes results from two very simple tests:

    • Testing your blood glucose level
    • Testing your HbA1c

    Here are the blood sugar ranges for a person without diabetes, with prediabetes, and with type 2 diabetes  according to the American Diabetes Association:

    NormalPrediabetesType 2 Diabetes
    Fasting blood sugar70 to 90 mg/dL100 to 125 mg/dL125 mg/dL or higher
    2 hours after a meal90 to 110 mg/dL140 to 200 mg/dL200 mg/dL or higher
    HbA1c (%)Less than 5.75.7 to 6.4 6.5 or higher

    Your doctor can perform both of these tests in the office or you can purchase test kits yourself in most pharmacies. 

    Long-term complications of high blood sugar levels

    Ignoring type 2 diabetes can lead to the development of many complications, all of which result from long-term high blood sugar levels.

    These complications are largely preventable by working with your healthcare team to improve your blood sugars and your overall health.

    High blood sugars are serious and can severely impact your health in the short-term and long-term. Talk to your healthcare team immediately if you believe your blood sugars are consistently running higher than your goal range.

    Treatment options for type 2 diabetes

    Whether your type 2 diabetes is the result of insulin deficiency or insulin resistance, the treatment paths are typically the same, keeping in mind that some patients with type 2 diabetes will likely need support from medications regardless of losing weight and eating a healthy diet.

    Lifestyle changes

    Before or in addition to taking medications, these 6 lifestyle habits can have a tremendous impact on your blood sugars.

    • Improve your diet: focus on healthy non-processed food and be aware of your calorie intake.
    • Exercise daily: aim for 150 minutes per week of physical activity.
    • Lose weight: even losing 5 to 10 pounds makes a difference.
    • Get more sleep and get treatment for sleep apnea, if you have it!
    • Drink less alcohol: limit to 2 to 3 drinks a couple times per week.
    • Quit smoking: the impact of nicotine on insulin resistance is huge!

    Some people can avoid using medication by making changes in their lifestyle habits, but this isn’t true for everyone. 

    These lifestyle changes are what anyone — including those without diabetes — are advised to adopt for optimal health. It’s important to remember that you don’t need to adopt a “perfect” diet 100 percent of the time, or engage in wildly intense exercise for it to all make a difference. 

    Aim for the 90/10 or 80/20 idea. 80 percent of the time, you make smart choices around food. And 20 percent of the time, you have room for less-than-perfect indulgences. The goal is to develop habits you can sustain long-term, and very few of us can sustain perfection day-in and day-out!

    Regardless if these lifestyle habits enable you to prevent or reduce your medications, they will help your overall blood sugar management and improve your overall health!

    Bariatric surgery (weight-loss surgery)

    Bariatric surgery options continue to evolve and improve, and for some this may be a worthwhile option. It’s important to remember that it’s not an easy short-cut. 

    Instead, weight-loss surgery requires certain qualifications to be a candidate, and maintaining weight-loss after surgery only works if you continue to make improvements in your overall lifestyle habits around food, exercise, alcohol, and cigarettes.

    That being said, more and more research is finding that the largest benefit for people of bariatric surgery for people with type 2 diabetes is the “resurfacing” of the duodenum’s mucosal lining in your small intestines. 

    The cells in your intestines responsible for signaling insulin production can be damaged by long-term exposure to high-sugar, high-fat diets and result in severe insulin resistance. By resurfacing the lining here, new healthy cells regrow and are able to properly signal insulin production again.

    Again, it’s not a magic fix, but it offers tremendous potential for the right candidates.


    Today’s pharmaceutical market is flooded with different options for treating type 2 diabetes. They all work in different ways, and depending on your body and how you react, it may take a bit of experimenting with your doctor’s help to determine the most effective medication for you.


    This class includes the #1 most commonly prescribed diabetes drug across the globe — metformin (Glucophage). Taken orally usually twice per day, these drugs lower your blood sugars by reducing your liver’s production of glycogen which is converted into glucose and normally raises blood sugar levels. 

    Metformin, in particular, can also increase the amount of glucose your muscles absorb and make you more sensitive to insulin. 

    The most common side-effect of metformin is diarrhea. It can be significant for many patients, but there are a few steps you can take to reduce this. The first is to always take metformin when you have food in your stomach. The second is to ask your doctor to consider prescribing the “extended-release” version which has shown to be much gentler on the stomach.

    Brands include:

    • Fortamet
    • Glucophage
    • Glumetza
    • Riomet


    One of the first drugs a doctor will likely prescribe to help you lower blood sugars, sulfonylureas help your pancreas produce more insulin.

    Taken orally, sulfonylureas can lead to weight gain, hunger, and mild-to-moderate upset stomach.

    Brands include:

    • DiaBeta
    • Glynase 
    • Micronase Amaryl 
    • Diabinese
    • Glucotrol 
    • Tolinase 
    • Tolbutamide

    Bile Acid Sequestrants (BASs)

    This class of drugs was actually first designed to help lower cholesterol levels, but they also help lower blood sugar levels. While it’s well-understood that BASs lower cholesterol by actually removing LDL cholesterol from the body, it’s not actually clear why it’s effective in lowering blood sugar levels. 

    Taken orally, a unique feature is that BASs are not actually absorbed into the bloodstream which means they are safe for people with liver problems.

    They can result in a little bit of gas or constipation. You may consider taking a gentle laxative along with BASs, like psyllium husk capsules.

    Brands include:

    • Questran
    • Prevalite
    • Colestid
    • Welchol

    Alpha-glucosidase inhibitors

    This class of drugs is taken orally and lowers your blood sugar by actually preventing the breakdown and normal digestion of starches, including bread, potatoes, pasta, and corn. While it doesn’t prevent the breakdown of sugar, it can significantly slow down the rate of digestion which means it will lessen the spike in your blood sugar after eating. 

    These drugs should be taken after you have at least a few bites of food in your stomach to lessen the most common side-effects of gas and diarrhea. 

    Brands include:

    • Precose
    • Glucobay
    • Glyset
    • Volix

    Dopamine-2 Agonists

    Originally designed to treat high cholesterol and taken orally, these drugs have a particularly complex impact on the body’s digestion of fats and dopamine production. The result is increased sensitivity insulin, improved glucose tolerance, and more stable post-meal blood sugar levels.

    Brands include:

    • Clycoset
    • Parlodel
    • Permax
    • Dostinex

    DPP-4 inhibitors

    One of the newer medication options taken orally, DPP-4 inhibitors work to actually block the production of the enzyme DPP-4 in your body. This enzyme destroys a group of digestive hormones called “incretins” which are essential for blood sugar and appetite regulation after eating. 

    They also help your body make better use of a compound already produced in the body called GLP-1. GLP-1 stands for glucagon-like peptide-1 and it plays a major role in blood sugar regulation, appetite, and digestion.

    By taking a DPP-4, your body’s own source of GLP-1 is able to stay in the body longer and lowers blood sugar levels when they’re too high.

    DPP-4 has also proven to lower cholesterol levels.

    Brands include:

    • Nesina
    • Tradjenta
    • Onglyza
    • Januvia

    GLP-1 receptors (or incretins)

    Taken via injection, this class of drugs is generally prescribed only if a patient hasn’t seen improvements in their blood sugar with oral medication options. 

    GLP-1 receptors work to lower your blood sugar levels in a few ways. First, it increases your pancreas’ insulin production in response to rising blood sugar levels. It also slows down the speed of “gastric emptying” which means the glucose from the food digesting in your stomach is going to enter your bloodstream at a slower rate. 

    Brands Include:

    • Byetta
    • Bydureon
    • Victoza
    • Januvia
    • Janumet


    Meglitinides are taken orally and stimulate your pancreas’ natural production of insulin. 

    This class of drugs can cause low blood sugars. Frequent low blood sugars should be discussed with your healthcare team in order to make adjustments in your dosage.

    Brands include:

    SGLT2 Inhibitors

    This class of drugs works by excreting excess glucose through your urine. Taken orally, they cannot be used by patients with kidney issues because the kidneys play a major role in how this drug works. 

    The unique side-effects of this drug include frequent urination, excess thirst, and an increased risk of urinary tract infections or yeast infections. These side-effects are the result of how the drug works. If your kidneys are working to excrete excess glucose, your body will need more water to help make that process possible. That will lead you to urinate more often.

    That excess glucose in the urine can then lead to yeast infections because sugar feeds the growth of yeast.

    Brands include:

    Thiazolidinediones (TZDs)

    This class of drugs, taken orally, works to lower your blood sugar levels in two ways. The first is through helping your body create new fat cells that lower your blood sugar by making better use of the insulin and glucose in your bloodstream. 

    TZDs also reduce the amount of glycogen (eventually converted to glucose) produced by your liver.

    Rezulin is one type of TZD that was removed from the market because it was creating serious liver problems in a very small group of people. The remaining TZDs on the market have not shown signs of creating liver problems. 

    That being said, today’s available TZDs have proven to increase the risk of heart failure in some patients, and possibly the risk of heart attacks. Otherwise, they are known for having few side-effects and are very effective at reducing A1c levels. 

    Brands include:

    • Avandia
    • ACTOS
    • Rezulin (removed from the market)


    For some people with type 2 diabetes, insulin is a necessary and extremely helpful approach to improving your blood sugars. This is especially true for people with severe insulin resistance.

    Insulin is one of the most powerful hormones in the human body, and taking it via injection (with a pen or syringe) comes with a great deal of education and responsibility

    Taking insulin to manage your blood sugar levels can help you prevent the many complications associated with high blood sugar levels, but it can be an overwhelming and scary thing to accept.

    Work with your healthcare to make sure your insulin doses are meeting your body’s current needs, and let them know if you’re struggling to embrace this part of your diabetes treatment plan.

    Suggested next posts:

    If you found this guide to type 2 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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    Cranberry White Chocolate Bars Top Best Cookie Swap Cookies for Christmas

    By electricdiet / March 11, 2020

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

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

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

      Servings48 servings


      • 1 1/2cups

        gingersnap crumbs

      • 6tablespoons


      • 1teaspoon

        vanilla extract

      • 1/2cup

        dried cranberries or craisins

      • 1/3cup

        white chocolate chips

      • 1/3cup

        chopped pecans

      • 2/3(14-ounce) can

        fat-free sweetened condensed milk

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

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

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

      Recipe Notes

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

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

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

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

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

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

      Best Cookie Swap Cookies Recipe Also Makes Perfect Holiday Homemade Gifts

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

      Too Hot in the Kitchen Has So Many Simple Sassy Recipes

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

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

      Excited To Find Reduced Sugar Craisins for Cranberry White Chocolate Bars

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

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

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

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

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

      The Best Kitchen Gadgets List!

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

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

      Another Favorite Bar Cookie For Best Cookie Swap Cookies

      White Chocolate Recipes Make Sensational Seasonal Holiday Recipes

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

      Favorite Mini Spatula Perfect For Bar Cookies

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

      Shop my Cookbooks

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

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      Longitudinal Metabolome-Wide Signals Prior to the Appearance of a First Islet Autoantibody in Children Participating in the TEDDY Study

      By electricdiet / March 9, 2020


      The TEDDY study offers a robust analysis of the metabolome in 417 infants who developed IA, with primarily either IAA only (49%) or GADA only (33%) as the first appearing autoantibodies. The subjects who experienced both IAA and GADA (14%) as the first appearing autoantibodies were not considered in the current analysis. Our aim in the current study was to discover longitudinal metabolic patterns preceding different first appearing IA in the presence of the well-known age effect on metabolic profiles (3,5).

      Our data suggest that IAA-first and GADA-first differ in the way that metabolites and lipids precede seroconversion. The significantly lower abundance of isoleucine and valine prior to seroconversion in IAA-first subjects (Fig. 2) is of interest, as isoleucine and valine are BCAAs widely known to potentiate glucose-stimulated insulin secretion (38,39). On the other hand, proline as a nonessential amino acid produced from glutamate cyclization (40) remained at a reduced level across multiple visits before IA (Fig. 2). In addition to the differentiated trajectory, proline was also found to be negatively associated with the risk of GADA-first in independent time point analysis. Proline biosynthesis involves l-glutamate, which interacts with the metabolic pathway of glutamic acid (41), GAD, and GABA (42), as illustrated in Fig. 3. Another critical metabolite derived from glutamic acid, i.e., α-ketoglutarate (42), also displayed lower concentration in GADA-first case compared with control subjects over time before seroconversion (Fig. 4). These results suggest that an enduring decrease in proline and lower α-ketoglutarate level may imply an abnormal glutamic acid metabolism causally related to the underlying change of GAD enzymatic activity in subjects who later developed GADA-first, with relatively higher glutamic acid levels prior to seroconversion (Fig. 4). The elevated level of glutamic acid may also be an indicator for the emergence of GADA-first.

      Figure 3
      Figure 3

      Metabolic pathways involving trajectory signals and independent time point biomarkers for IA in TEDDY. TCA, tricarboxylic acid.

      Figure 4
      Figure 4

      Mean abundance of preseroconversion GABA, glutamic acid, glutamine, α-ketoglutarate, leucine, and plasmenyl-PC (ether PC) per age point for case and control subjects in GADA-first and IAA-first groups.

      Another major finding in the current study was the association between plasma GABA after birth and the future appearance of IAA-first. This finding was based on both independent time point analysis (Table 2) and enrichment analysis results (Table 4). Why GABA levels would be associated with IAA-first and not GADA-first may be explained by the fact that β-cell GAD65, which produces GABA, may not be affected before GADA onset. The impact of GABA on the pancreatic β-cell function has been thoroughly investigated (43,44). The odds ratio for plasma GABA (Table 2) for IAA-first indicates that higher levels of GABA immediately after birth may be related to β-cell dysfunction, with the appearance of IAA-first possibly related to abnormal insulin synthesis or secretion at early ages. In contrast, we did not find the contribution of GABA to future risk of seroconversion in GADA-first children, providing evidence that GABA has no causal influence on the appearance of GADA-first.

      A third major finding of biomarkers for the risk of IA in TEDDY was DHAA after birth. DHAA identified at 3 months of age did not discriminate between GADA-first and IAA-first but showed statistical significance for both autoantibodies. Elevated DHAA or oxidized vitamin C was found to inhibit insulin secretion in mice (4547), and exposure of isolated mouse pancreatic islets to DHAA or vitamin C reduced the responsiveness of the islets (48) or led to inhibition of insulin secretion from the pancreatic β-cells (47). The results for DHAA in TEDDY samples would seem to be consistent with the existing findings, showing a possible suppressive effect on human pancreatic islet β-cells. Another recent study in TEDDY found immunoassay measurements of plasma vitamin C levels to be associated with lower risk of IAA but not GADA. Further analyses would therefore be required to include immunoassay measurement of DHAA to detail the possible importance of the vitamin C/DHAA ratio and its regulation. Other compounds identified as contributing to IA development in TEDDY were also found to be important metabolic features in existing T1D-related studies, such as amino acids alanine (4) and methionine (5), fatty acids (49), vitamin E (45), sugar alcohols, and unsaturated TGs (5,50). Furthermore, we observed (Table 2) 5-methoxytryptamine at ages 6 months and 9 months contributing to the risk of IAA-first with association altered from positive to negative between 6 and 9 months, prior to and near the age of population-wide IAA-first incidence peak (10). 5-methoxytryptamine is a metabolite of melatonin and serotonin, which have been linked to diabetes and autoimmune disorders in previous studies (51,52).

      The current study in TEDDY represents the largest prospective cohort analysis of metabolomes in children at increased genetic risk for T1D and identifies biomarkers for islet autoimmunity (stage I and II) that precedes the clinical onset of diabetes (stage III) (53). Similar observations were found in the Type 1 Diabetes Prediction and Prevention (DIPP) study (4) reporting that changes in GABA, glutamic acid, glutamine, α-ketoglutarate, leucine, and plasmenyl-PCs (ether PCs) were age dependent and could be associated with the onset of GADA and IAA. Based on the TEDDY longitudinal metabolome profiles, we not only confirmed the DIPP findings using average abundance of these compounds across time points but also separated the age and time-to-seroconversion effects (Fig. 4 and Supplementary Fig. 3). The trend of ether PC between 1 and 2 years of age in TEDDY subjects was similar to the change over time before GADA-first onset observed in DIPP, which was the result of overlapping effects of age and time to seroconversion. On the other hand, the decrease in GABA levels within 1 year before seroconversion observed in DIPP was a pattern determined by time to seroconversion instead of age. Furthermore, our results in metabolite enrichment analysis (Table 4) not only agreed with the reduced level of PC, TGs and plasmenyl- (or ether) phospholipids found in individuals who developed T1D (4) but also revealed lower PE and sugar alcohols during infancy associated with future onset of IA.

      A limitation to the current study is the quarterly time sampling from 3 months of age and onward. The effect of age on metabolites and complex lipids (4,5) would have been better understood with a more frequent blood sampling, especially in relation to IAA as the first appearing autoantibody. IAA-first has been related to prior infectious episodes both in DIPP (54) and TEDDY (55), and at this early age, statistical analyses will have to take both age-related effects and environmental exposures into account to further delineate the mechanisms that trigger an autoimmune response against insulin.

      Current analyses focused on metabolic markers for IA prior to seroconversion and included TEDDY participants who only developed IAA or GADA as the first-appearing autoantibody. It is worthwhile to extend future analyses to participants who experienced multiple autoantibodies either at seroconversion or throughout the follow-up, since the age at development of multiple autoantibodies has been found associated with the risk of progression to T1D (16). Genetics or environmental causes leading to metabolic signals (such as DHAA, GABA, and proline) identified in present analyses were still unknown and should be investigated further jointly with genome-wide SNP data, gut microbiome, and dietary patterns.


      These results from metabolome-wide trajectory, independent time point, and enrichment analyses support the notion that the onset of IA as GADA-first or IAA-first in TEDDY children is heralded by distinct metabolic precursors in plasma after birth. The precursory signals for each autoantibody include DHAA; GABA; amino acids proline, alanine, and methionine; and compounds in BCAA metabolism as well as fatty acids. Unsaturated TGs and PEs at infant age were found to be decreased before appearance of either autoantibody. The distinct metabolic patterns for these autoantibodies support the idea that the causes of each type of initial autoimmunity may be different, and may account for the earlier incidence peak of IAA-first compared with that of GADA-first in TEDDY.


      The members of the TEDDY Study Group are listed below. The numbers listed correspond with the committees as follows: 1Ancillary Studies, 2Diet, 3Genetics, 4Human Subjects/Publicity/Publications, 5Immune Markers, 6Infectious Agents, 7Laboratory Implementation, 8Psychosocial, 9Quality Assurance, 10Steering, 11Study Coordinators, 12Celiac Disease, and 13Clinical Implementation.

      Colorado Clinical Center. Marian Rewers, Principal Investigator (PI),1,4,5,6,9,10 Aaron Barbour, Kimberly Bautista,11 Judith Baxter,8,9,11 Daniel Felipe-Morales, Kimberly Driscoll,8 Brigitte I. Frohnert,2,13 Marisa Stahl,12 Patricia Gesualdo,2,6,11,13 Michelle Hoffman,11,12,13 Rachel Karban,11 Edwin Liu,12 Jill Norris,2,3,11 Stesha Peacock, Hanan Shorrosh, Andrea Steck,3,13 Megan Stern, Erica Villegas,2 and Kathleen Waugh6,7,11: Barbara Davis Center for Childhood Diabetes.

      Finland Clinical Center. Jorma Toppari, PI,¥^1,4,10,13, Olli G. Simell, Annika Adamsson,^11 Suvi Ahonen,*±§ Mari Åkerlund,*±§ Leena Hakola,* Anne Hekkala,µ† Henna Holappa,µ† Heikki Hyöty,*±6 Anni Ikonen,µ† Jorma Ilonen,¥¶3 Sinikka Jäminki,*± Sanna Jokipuu,^ Leena Karlsson,^ Jukka Kero,¥^ Miia Kähönen,µ†11,13 Mikael Knip,*±5 Minna-Liisa Koivikko,µ† Merja Koskinen,*± Mirva Koreasalo,*±§2 Kalle Kurppa,*±12 Jarita Kytölä,*± Tiina Latva-aho,µ† Katri Lindfors,*12 Maria Lönnrot,*±6 Elina Mäntymäki,^ Markus Mattila,* Maija Miettinen,§2 Katja Multasuo,µ† Teija Mykkänen,µ† Tiina Niininen,±*11 Sari Niinistö,±§2 Mia Nyblom,*± Sami Oikarinen,*± Paula Ollikainen,µ† Zhian Othmani,^ Sirpa Pohjola,µ† Petra Rajala,^ Jenna Rautanen,±§ Anne Riikonen,*±§2 Eija Riski,^ Miia Pekkola,*± Minna Romo,^ Satu Ruohonen,^ Satu Simell,¥12 Maija Sjöberg,^ Aino Stenius,µ†11 Päivi Tossavainen,µ† Mari Vähä-Mäkilä,¥ Sini Vainionpää,^ Eeva Varjonen,^11 Riitta Veijola,µ†13 Irene Viinikangas,µ† and Suvi M. Virtanen*±§2: ¥University of Turku; *Tampere University; µUniversity of Oulu; ^Turku University Hospital; Hospital District of Southwest Finland; ±Tampere University Hospital; Oulu University Hospital; §National Institute for Health and Welfare, Finland; and University of Kuopio.

      Georgia/Florida Clinical Center. Jin-Xiong She, PI,,1,3,4,10 Desmond Schatz,*4,5,7,8 Diane Hopkins,11 Leigh Steed,11,12,13 Jennifer Bryant,11 Katherine Silvis,2 Michael Haller,*13 Melissa Gardiner,11 Richard McIndoe, Ashok Sharma, Stephen W. Anderson,^ Laura Jacobsen,*13 John Marks,*11,13 and P.D. Towe*: Center for Biotechnology and Genomic Medicine, Augusta University; *University of Florida; and ^Pediatric Endocrine Associates, Atlanta.

      Germany Clinical Center. Anette G. Ziegler, PI,1,3,4,10 Ezio Bonifacio,*5 Anita Gavrisan, Cigdem Gezginci, Anja Heublein, Verena Hoffmann,2 Sandra Hummel,2 Andrea Keimer,¥2 Annette Knopff,7 Charlotte Koch, Sibylle Koletzko,¶12 Claudia Ramminger,11 Roswith Roth,8 Marlon Scholz, Joanna Stock,8,11,13 Katharina Warncke,13 Lorena Wendel, and Christiane Winkler2,11 from: Forschergruppe Diabetes e.V. and Institute of Diabetes Research, Helmholtz Zentrum München, Forschergruppe Diabetes, and Klinikum rechts der Isar, Technische Universität München; *Center for Regenerative Therapies, TU Dresden; Department of Gastroenterology, Dr. von Hauner Children’s Hospital, Ludwig Maximillians University Munich; and ¥Department of Nutritional Epidemiology, University of Bonn.

      Sweden Clinical Center. Åke Lernmark, PI,1,3,4,5,6,8,9,10 Daniel Agardh,6,12 Carin Andrén Aronsson,2,11,12 Maria Ask, Rasmus Bennet, Corrado Cilio,5,6 Helene Engqvist, Emelie Ericson-Hallström, Annika Fors, Lina Fransson, Thomas Gard, Monika Hansen, Hanna Jisser, Fredrik Johansen, Berglind Jonsdottir,11 Silvija Jovic, Helena Elding Larsson,6,13 Marielle Lindström, Markus Lundgren,13 Marlena Maziarz, Maria Månsson-Martinez, Maria Markan, Jessica Melin,11 Zeliha Mestan, Caroline Nilsson, Karin Ottosson, Kobra Rahmati, Anita Ramelius, Falastin Salami, Anette Sjöberg, Birgitta Sjöberg, Malin Svensson, Carina Törn,3 Anne Wallin, Åsa Wimar13, and Sofie Åberg: Lund University.

      Washington Clinical Center. William A. Hagopian, PI,1,3,4,5,6,7,10,12,13 Michael Killian,6,7,11,12 Claire Cowen Crouch,11,13 Jennifer Skidmore,2 Masumeh Chavoshi, Rachel Hervey, Rachel Lyons, Arlene Meyer, Denise Mulenga,11 Jared Radtke, Matei Romancik, Davey Schmitt, and Sarah Zink: Pacific Northwest Research Institute.

      Pennsylvania Satellite Center. Dorothy Becker, Margaret Franciscus, MaryEllen Dalmagro-Elias Smith,2 Ashi Daftary, Mary Beth Klein, and Chrystal Yates: UPMC Children’s Hospital of Pittsburgh.

      Data Coordinating Center. Jeffrey P. Krischer, PI, 1,4,5,9,10 Sarah Austin-Gonzalez, Maryouri Avendano, Sandra Baethke, Rasheedah Brown,11 Brant Burkhardt,5,6 Martha Butterworth,2 Joanna Clasen, David Cuthbertson, Stephen Dankyi, Christopher Eberhard, Steven Fiske,8 Jennifer Garmeson, Veena Gowda, Kathleen Heyman, Belinda Hsiao, Christina Karges, Francisco Perez Laras, Hye-Seung Lee,1,2,3,12 Qian Li,5,12 Shu Liu, Xiang Liu,2,3,8,13 Kristian Lynch,5,6,8 Colleen Maguire, Jamie Malloy, Cristina McCarthy,11 Aubrie Merrell, Hemang Parikh,3 Ryan Quigley, Cassandra Remedios, Chris Shaffer, Laura Smith,8,11 Susan Smith,11 Noah Sulman, Roy Tamura,1,2,11,12,13 Dena Tewey, Michael Toth, Ulla Uusitalo,2 Kendra Vehik,4,5,6,8,13 Ponni Vijayakandipan, Keith Wood, and Jimin Yang2; past staff, Michael Abbondondolo, Lori Ballard, David Hadley, Wendy McLeod, and Steven Meulemans: University of South Florida.

      Project Scientist. Beena Akolkar1,3,4,5,6,7,9,10: National Institutes of Diabetes and Digestive and Kidney Diseases.

      Other Contributors. Kasia Bourcier5: National Institutes of Allergy and Infectious Diseases. Thomas Briese6: Columbia University. Suzanne Bennett Johnson8,11: Florida State University. Eric Triplett6: University of Florida.

      Autoantibody Reference Laboratories. Liping Yu,^5 Dongmei Miao,^ Polly Bingley,*5 Alistair Williams,* Kyla Chandler,* Olivia Ball,* Ilana Kelland,* and Sian Grace*: ^Barbara Davis Center for Childhood Diabetes and *Bristol Medical School, University of Bristol, U.K.

      HLA Reference Laboratory. William Hagopian,3 Masumeh Chavoshi, Jared Radtke, and Sarah Zink: Pacific Northwest Research Institute, Seattle, WA (previously Henry Erlich,3 Steven J. Mack, and Anna Lisa Fear: Center for Genetics, Children’s Hospital Oakland Research Institute).

      Metabolomics Laboratory. Oliver Fiehn, Bill Wikoff, Brian Defelice, Dmitry Grapov, Tobias Kind, Mine Palazoglu, Luis Valdiviez, Benjamin Wancewicz, Gert Wohlgemuth, and Joyce Wong: West Coast Metabolomics Center.

      SNP Laboratory. Stephen S. Rich,3 Wei-Min Chen,3 Suna Onengut-Gumuscu,3 Emily Farber, Rebecca Roche Pickin, Jonathan Davis, Jordan Davis, Dan Gallo, Jessica Bonnie, and Paul Campolieto: Center for Public Health Genomics, University of Virginia.

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      Safe + Fair Picky Plate

      By electricdiet / March 7, 2020

      I made a batch of my friend Ed’s biscuits, and they have been on repeat all week.  I love a biscuit breakfast sammie!

      Yesterday was a bit different.  I had an 11 call with a potential new campaign, and what was supposed to be a 15 minute call turned into 45 minutes.  I had clocked out for lunch at 11 and was back at my desk at 11:45 – so basically my afternoon felt like FOREVER to get to 5. 

      When Hannah and I cleaned out the freezer over the weekend, we found a bag of lasagna soup – yes!  I used mini spaghetti this time, and Mariano’s giardiniera sausage – so good!

      Every night this week I have given myself a “project.”  Just stuff that needs to get done.  Here’s what happens.  I have a list of like 5-6 things that I need to get done, and when the weekend rolls around, I spend most of my time in the kitchen, and then think “do I really want to spend my weekend doing X, Y and Z?”

      Monday night I cleaned my room.  I know you are thinking “Biz, you are almost 52 years old and you still need to clean your room?”  The answer to that is yes.  😛

      Last night was the “shit bin” for lack of a better word.  This corner of my kitchen used to have a lazy susan, but it broke several years ago.  So it’s just this giant hole.  Part of me last night felt like ripping out all the cabinets, but a clearer head prevailed.

      I took all the muffin tins, cake pans, etc. and put them in the basement.  Shhhh – don’t tell Hannah!  Now I have tupperware with lids on top and ziplock bags and stuff on the bottom.  

      I also found a receipt from September 2019 in there.  #klassy

      I am so excited to announce that Safe + Fair has a new product!  Their amazeballs popcorn quinoa chips are now in single serve, one ounce portions – love!

      A bag of 6 is just $7, and with my 20% discount, you can get an even better deal.  Here is my link to get your discount.

      If you aren’t familiar with Safe + Fair they are a company that provides safe allergy free snacks at a fair price.  I’ve been working with them over a year and I’ve loved all their products.

      Also, it should be noted that a single serve of the olive oil and salt chips is the perfect size to make the best ever air fried popcorn chicken.

      Last night these chips were the inspiration to my picky plate dinner.  I love putting picky plates together – a little of this, a little of that.  I did a quick pan fry of greek shrimp and green beans, then rounded out my plate with hummus, feta cheese, blueberries, strawberries, radish, and these amazing chips.

      The perfect bite:

      Such a delicious dinner.  Next time you think you don’t have anything for dinner, just put random stuff on your plate and call it a picky plate!

      Happy Wednesday friends – make it a great day!

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      GVOKE – a New Option to Treat Severe Hypoglycemia

      By electricdiet / March 5, 2020

      Hypoglycemia or low blood sugar is a common and scary side-effect of insulin diabetes management. Hypoglycemia can not only be uncomfortable and scary but also outright life-threatening.

      If you take insulin to manage your diabetes, you’ll likely experience hypoglycemia at some point. That’s why it’s very important that you know the symptoms of hypoglycemia, and how to handle it when it happens. (And teach your friends how to handle it, too, in case you need help!).

      In this article, I’ll discuss severe hypoglycemia and how it can be treated by using GVOKETM a recently FDA approved glucagon product (approved September 2019).

      This post is sponsored by Xeris Pharmaceuticals, Inc. the manufacturer of GVOKE

      What is severe hypoglycemia

      Hypoglycemia is when your blood sugar levels have fallen low enough that you need to take action to bring them back to your target range. Usually when your blood sugar is less than 70 mg/dL (3.9 mmol/L).

      In people with type 1 and type 2 diabetes, low blood sugar is generally caused by an imbalance of food, activity, and insulin — or other diabetes medications that lower blood sugar. Simply getting one more unit of insulin than you may have needed for a meal can lead to hypoglycemia.

      The symptoms you feel most when your blood sugar is dropping may be a little different than what someone else feels, and you might feel these symptoms even before your blood sugar drops below 70 mg/dl (3.9 mmol/L). On the other hand, some people have what’s called “hypoglycemia unawareness” which means they don’t feel any symptoms at all.

      Severe hypoglycemia is a bit different than the average low blood sugar. Severe hypoglycemia is when your blood sugar is so low that you need help from someone else to treat it and recover.

      You can learn more about the symptoms and causes of hypoglycemia HERE.

      What is glucagon and how does it work?

      In the case of severe hypoglycemia, you should teach your family and friends the dangers of trying to help you by forcing you to eat or drink. This can be dangerous because when your blood sugar is that low, you may not be alert enough to chew or swallow, which means your risk of choking is very high.

      That’s where glucagon comes in — along with a phone-call to 911.

      Glucagon is a hormone produced in your body, but it can also be taken via injection to treat severe hypoglycemia. Glucagon tells your liver to release glycogen — a form of glucose (sugar) stored in your liver. Then your body converts that glycogen into glucose to raise your blood sugar to a safe level.

      According to Jeff Hitchcock, founder and president of Children with Diabetes, many people (particularly parents) are hesitant to use conventional glucagon kits because the complex preparation feels confusing and perhaps overwhelming. It’s important to overcome this fear and learn how to use a glucagon kit because it could save the life of someone suffering from severe hypoglycemia.

      Should you get a glucagon kit?

      Whether you’ve had to use a glucagon kit before or not, it’s something that I would highly recommend for anyone taking insulin to manage their diabetes.

      I’ve lived with diabetes since December 1997 and have never had to use one, but I have always had one. Why? Because when you do actually need a glucagon kit, it could be the only thing that saves your life or prevents you from having a seizure or coma during severe hypoglycemia.

      It’s like wearing a seatbelt. I’ve never been in a car accident but if that happens, I want that seatbelt to protect me.

      I keep my glucagon kit at home and bring it with me whenever I hike or travel. I bring it along because, let’s face it, it won’t be any good to me if I leave it at home in the drawer.

      What is GVOKE

      GVOKE is the first glucagon product approved that is administered via a prefilled syringe (GVOKETM PFS) or auto-injector (GVOKE HypoPenTM). Since it’s prefilled that means it’s always ready to use and significantly reduces the steps to prepare and administer glucagon compared to the glucagon options we’ve had before.

      GVOKE is approved to treat severe hypoglycemia in adults and kids with diabetes ages 2 years and above.

      Since kids will need less glucagon than an adult, GVOKE comes in two pre-measured dosing options – 1 mg for adults ages 12 and older and 0.5 mg option for children under 12 years of age. Kids under 12 who weigh more than 100 lbs should use the 1 mg dose.

      What really sets GVOKE apart is that it is the first-ever liquid glucagon. It is premixed, prefilled, premeasured, and ready to use. And no refrigeration is needed, so you don’t have to worry about keeping it cool.

      Both GVOKE products are currently FDA approved, but at this time only GVOKE PFS is commercially available (we will provide an update when the HypoPen comes to market).

      Since we’re all different, I think this is an important step in order to offer us living with diabetes more options in regard to our diabetes management.

      You should always read the complete safety information on any drug, and since GVOKE is a prescription medicine and should only be used in case of severe hypoglycemia please read the complete safety information at the end of this article and refer to your doctor for administration and usage guidance.

      How to use GVOKE

      You might have used or been trained to use a glucagon kit before but GVOKE is different, so you need to learn the proper way to use GVOKE.

      You should, of course, discuss usage with your doctor and look through the instructions when you receive your first GVOKE prescription, but I find that it’s quite simple. Please note that the GVOKE PFS should remain in the foil pouch until use. So, let’s walk through the steps for administering GVOKE.

      The GVOKE PFS (prefilled syringe) that is currently available in the US has 2 simple steps:

      1) Take cap off.

      2) Pinch the skin and insert needle at 90 degrees. Push plunger to inject.

      Once the cap is taken off, the needle can be inserted on bare skin into someone’s belly fat, outer thigh or upper arm. That does mean that any clothing should be removed before injecting GVOKE.

      It’s as simple as that. And such a simple 2-step injection process really makes me less anxious about someone else being able to give me that injection correctly.

      If I’m passed out from severe hypoglycemia and my husband has to administer glucagon, I really want him to feel very comfortable in the process and how to do it correctly.

      Close-up of a GVOKE syringe

      What to ask your doctor about hypoglycemia

      As with other glucagon products, GVOKE is a prescription medicine so you need to discuss it with your doctor and ask for a prescription.

      Since injecting GVOKE PFS is only a two-step process you might not need extensive training, but general medical guidance is always recommended.

      If your doctor hasn’t heard about GVOKE, you can always send him/her to the GVOKE website. Sometimes we learn about new diabetes medications and products before our medical team. There are a lot of new drugs coming to market all the time so it happens, and in a situation like that, you’ll just have to educate your team.

      How to get GVOKE

      After you have discussed glucagon with your doctor and learned how and when to use it, you’ll need to pick up your prescription at the pharmacy or online home delivery fulfilled by PillPack, an Amazon Company. (If you’d like it delivered to your home, information is available at GvokeGlucagon.com).

      As with most newly approved drugs, it might take a little time before some insurance companies add it to their covered list. If it’s not added your insurance company might deny coverage of GVOKE.

      If you’re denied coverage, it’s recommended that you have your doctor or pharmacist reach out to the insurance company to ask for an exception.

      Some people can qualify for financial assistance using the GVOKE copay card if you have eligible commercial insurance and may pay as little as $25.

      Xeris Pharmaceuticals (the manufacturer of GVOKE) is offering the copay card through the end of 2020. More information on access and savings can be found at GvokeGlucagon.com.

      About GVOKE


      GVOKE is a prescription medicine used to treat very low blood sugar (severe hypoglycemia) in adults and kids with diabetes ages 2 years and above. It is not known if GVOKE is safe and effective in children under 2 years of age.


      Do not use GVOKE if:

      • you have a tumor in the gland on top of your kidneys (adrenal gland), called a pheochromocytoma.
      • you have a tumor in your pancreas, called either an insulinoma or a glucagonoma.
      • you are allergic to glucagon or any other inactive ingredient in GVOKE.


      High blood pressure. GVOKE can cause high blood pressure in certain people with tumors in their adrenal glands.

      Low blood sugar. GVOKE can cause low blood sugar in certain people with tumors in their pancreas.

      Serious skin rash. GVOKE can cause a serious skin rash in certain people with a tumor in their pancreas called a glucagonoma.

      Serious allergic reaction. Call your doctor or get medical help right away if you have a serious allergic reaction including:

      • rash
      • difficulty breathing
      • low blood pressure


      The most common side effects of GVOKE include:

      • nausea
      • vomiting
      • swelling at the injection site
      • headache

      These are not all the possible side effects of GVOKE. For more information, ask your doctor. Call your doctor for medical advice about side effects. You are encouraged to report side effects of prescription drugs to the FDA. Visit www.fda.gov/medwatch, or call 1-800-FDA-1088.


      Before using GVOKE, tell your doctor about all your medical conditions, including if you:

      • have a tumor in your pancreas
      • have not had food or water for a long time (prolonged fasting or starvation) 

      Tell your doctor about all the medicines you take, including prescription and over-the-counter medicines, vitamins, and herbal supplements.

      HOW TO USE

      • Read the detailed Instructions For Use that come with GVOKE.
      • Make sure your caregiver knows where you keep your GVOKE and how to use GVOKE correctly before you need their help.
      • Your doctor will tell you how and when to use GVOKE.
      • GVOKE contains only 1 dose of medicine and cannot be reused.
      • After administering GVOKE, the caregiver should call for emergency medical help right away.
      • If the person does not respond after 15 minutes, another dose may be given.
      • Tell your doctor each time you use GVOKE.
      • Store GVOKE at temperatures between 68°F and 77°F. Do not keep it in the refrigerator or let it freeze.
      • Keep GVOKE in the foil pouch until you are ready to use it.

      Keep GVOKE and all medicines out of the reach of children.

      For more information, call 1-877-937-4737 or go to www.GvokeGlucagon.com.

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      Marinated Shrimp Recipe – Amazing, Quick Shrimp and Artichoke Recipe

      By electricdiet / March 3, 2020

      Go-To Easy Appetizer Marinated Shrimp Recipe for Best Shrimp and Artichoke Dip

      If you are looking for a go-to easy healthy recipe to whip up for your next sports gathering or holiday party, then look no further than this Marinated Shrimp recipe with Artichokes is a favorite shrimp and artichoke dip recipe from Holly’s KITCHEN 101 cookbook. This is the best shrimp and artichoke recipe plus the easiest!! Also, it is a delicious diabetic shrimp appetizer recipe.  Zesty Italian dressing mix adds simple zing of flavor to fresh toss-together ingredients. This healthy easy recipe is a favorite of everyone and truly the best marinated shrimp recipe. Serve with chips, and your guests will be begging for the recipe because it is so good. If you are lucky enough to have leftovers, turn this marinated shrimp appetizer into a delicious salad. There’s tons of easy appetizers on the healthy food blog but there’s also some phenomenal easy shrimp recipes.

      Marinated Shrimp and Artichokes from KITCHEN 101 cookbook

      Marinated Shrimp and Artichokes
      This is my favorite shrimp and artichoke dip recipe that’s always popular.  Use as a dip or enjoy leftovers as a salad. If you like shrimp, try my Coconut Shrimp recipe.

        Servings32 (1/4 cup) servings


        • 1/4cup

          seasoned rice vinegar

        • 3tablespoons

          olive oil

        • 1(0.75 ounce) envelope

          zesty Italian dressing mix

        • 1pound

          peeled medium shrimpcooked

        • 1/2cup

          chopped green onions

        • 1(14-ounce) can

          artichoke heartsquartered and drained

        • 1/3cup

          Kalamata olivessliced or halved

        • 1cup

          grape tomato halves

        • 1/3cup

          crumbled feta cheeseoptional

        1. In small bowl, whisk together vinegar, olive oil, and dressing mix.

        2. In large bowl, combine all remaining ingredients and toss with vinaigrette. Cover and refrigerate 8-24 hours, time permitting.

        Recipe Notes

        Calories 38, Calories from Fat 46%, Fat 2g, Saturated Fat 0g, Cholesterol 25mg, Sodium 226mg, Carbohydrates 2g, Dietary Fiber 0g, Total Sugars 1g, Protein 3g, Dietary Exchanges: 1/2 lean meat

        Marinated Shrimp and Artichoke Recipe Delicious Diabetic Shrimp Appetizer

        What’s great about KITCHEN 101 cookbook is that Holly highlights all the easy diabetic recipes in the cookbook. Not often do you find the best marinated shrimp recipe and it is also a diabetic shrimp appetizer.

        Here’s a great story about this delicious Marinated Shrimp Appetizer and when you watch the video below you will see our little joke.

        Holly’s camera man loved it so much after taping the YouTube segment, he went home to make it for a party.  As always, everyone requested the recipe. You’ll make this healthy shrimp all the time! Just hope there are leftovers for your lunch tomorrow. There are so many great appetizers on the healthy food blog.

        Watch Holly Make Marinated Shrimp Recipe and See What Camera Man Says


        Love Colorful Bowls To Make My Diabetic Shrimp Appetizer-Makes Cooking More Fun

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        Impaired Metabolic Flexibility to High-Fat Overfeeding Predicts Future Weight Gain in Healthy Adults

        By electricdiet / March 1, 2020


        The ability to switch fuels for oxidation in response to changes in macronutrient composition of diet (metabolic flexibility) may be informative of individuals’ susceptibility to weight gain. Seventy-nine healthy, weight-stable participants underwent 24-h assessments of energy expenditure and respiratory quotient (RQ) in a whole-room calorimeter during energy balance (EBL) (50% carbohydrate, 30% fat) and then during 24-h fasting and three 200% overfeeding diets in a crossover design. Metabolic flexibility was defined as the change in 24-h RQ from EBL during fasting and standard overfeeding (STOF) (50% carbohydrate, 30% fat), high-fat overfeeding (HFOF) (60% fat, 20% carbohydrate), and high-carbohydrate overfeeding (HCOF) (75% carbohydrate, 5% fat) diets. Free-living weight change was assessed after 6 and 12 months. Compared with EBL, RQ decreased on average by 9% during fasting and by 4% during HFOF but increased by 4% during STOF and by 8% during HCOF. A smaller decrease in RQ, reflecting a smaller increase in lipid oxidation rate, during HFOF but not during the other diets predicted greater weight gain at both 6 and 12 months. An impaired metabolic flexibility to acute HFOF can identify individuals prone to weight gain, indicating that an individual’s capacity to oxidize dietary fat is a metabolic determinant of weight change.


        Prolonged daily energy intake exceeding energy expenditure (EE) leads to an increase in body weight; however, even in highly controlled settings, some individuals are more prone to gain weight than others during sustained overfeeding (1,2) or, conversely, some individuals are more resistant to weight loss during caloric restriction (3,4) despite comparable dietary conditions among individuals. These results suggest that the individual metabolic response to overfeeding/underfeeding may partly determine the susceptibility to body weight change in conditions of persistent energy imbalance (5). Daily energy balance (EBL), which is the difference between 24-h energy intake and EE, is also reflective of macronutrient balances (i.e., proteins, fats, carbohydrates), which in turn play a major role in body weight regulation (6,7). While carbohydrate and protein balances are usually met over 24 h in conditions of total daily EBL (8), there is a greater variability both in the time needed and in the extent to achieve fat balance, implying that fat balance may represent the strongest contributor to total daily EBL (8). Because positive fat balance leads to fat storage and, ultimately, to increased body fat mass (FM) (9), a reduced fat oxidation that favors positive fat (and thus total) EBL may indicate a greater predisposition to weight gain over time (10).

        The respiratory quotient (RQ) is a measured index of macronutrient preference for oxidation, which in turn influences macronutrient balances. In humans, the association of RQ measured during EBL and future weight change is mixed, with studies showing that a higher RQ is a determinant of future weight gain (1115) and others showing no such association (1618). Oxidation rates measured during EBL and eucaloric feeding with a standard diet may not be as indicative as the individual’s ability to switch fuel for oxidation in response to diets of altered macronutrient proportions (e.g., high-carbohydrate vs. high-fat diets). This fuel switching, or metabolic flexibility (1518), may be more informative of susceptibility to weight change (19,20), particularly in settings of positive EBL (i.e., overfeeding) leading to weight gain. Prior studies assessed metabolic flexibility using the hyperinsulinemic-euglycemic glucose clamp technique (2124), which allows for precise measurement of metabolic flexibility specific to glucose. These studies showed that Rd is a determinant of glucose inflexibility (25) and that greater metabolic flexibility to glucose during the clamp is associated with decreased metabolic flexibility to lipids during fasting (26). Although carefully conducted, studies assessing metabolic flexibility during the glucose clamp may not be physiologically reflective of daily energy intake patterns that include solid diets with a varying mixture of carbohydrates, fats, and proteins. Whether the magnitude of metabolic flexibility to 24-h fasting or overfeeding diets with varying macronutrient proportions in one individual predicts his or her predisposition to weight change is unknown.

        The aim of the current study was to investigate metabolic flexibility to 24-h dietary interventions, including fasting and three normal-protein overfeeding diets (standard, high carbohydrate, and high fat), in healthy participants with normal glucose regulation. We quantified the metabolic flexibility (ΔRQ) as the dietary-related change in 24-h RQ compared with the 24-h RQ as precisely measured during weight stability and EBL. We hypothesized that an impaired metabolic flexibility during these extreme dietary interventions may increase individual predisposition to weight gain over time.

        Research Design and Methods

        Study Participants

        Study volunteers between the ages of 18 and 55 years were recruited from 2008 through 2017 from the Phoenix, Arizona, metropolitan area to participate in an ongoing clinical trial. The primary aim was to assess metabolic responses to acute dietary interventions in relation to free-living weight change. Before admission to the clinical research unit, recruited participants had to be weight stable for 6 months and deemed healthy by medical history, physical examination, and basic laboratory measurements. Once admitted, participants were placed on a standard weight maintenance diet (WMD) calculated from previously derived equations on the basis of sex and weight (27). The WMD consisted of 50% carbohydrates, 30% fat, and 20% protein. After 3 days on this WMD, an oral glucose tolerance test (OGTT) was performed, and only participants with normal glucose regulation on the basis of American Diabetes Association criteria (28) continued in the study (Supplementary Fig. 4). Plasma glucose concentrations were measured by the glucose oxidase method (Beckman Glucose Analyzer 2; Beckman Instruments, Brea, CA).

        The WMD was consumed on all days when 24-h EE was not measured, and volunteers’ physical activity was limited to activities on the unit (watching television, playing pool, etc.). Body composition was measured by DXA scan (Prodigy enCORE 2003, version 7.53.002, software; GE Lunar Corporation, Madison, WI). FM and fat-free mass (FFM) were calculated from the measured body fat percentage (PFAT) and weight as follows: FM = weight × PFAT / 100 and FFM = weight – FM. Body weight was measured daily on a precision scale every morning upon awakening after an overnight fast to ensure that weight was within ±1% of the admission weight and the WMD was adjusted to maintain weight stability throughout the admission stay. The mean coefficient of variation (CV) for body weight during the entire admission was <1% (mean ± SD 0.6 ± 0.3%). All participants were fully informed of the nature of the study and provided written informed consent before participation. The institutional review board of the National Institute of Diabetes and Digestive and Kidney Diseases approved this experimental protocol.

        Measurements of 24-h RQ and EE During Dietary Interventions

        The 24-h EE and RQ were measured inside a whole-room indirect calorimeter (metabolic chamber) as previously described in detail (29,30). Participants entered the metabolic chamber the day after the OGTT and 1 h after consuming breakfast at 0700 h, and subsequent meals were given inside the metabolic chamber at 1100, 1600, and 1900 h. During the 24-h fast, no food was provided after the previous day’s dinner, but participants were permitted to drink water and noncaloric, noncaffeinated beverages. Both O2 consumption and CO2 production during each 24-h EE assessment were measured every minute, averaged and extrapolated to 24 h, and used to calculate the 24-h RQ as an index of fat-to-carbohydrate oxidation (29). Quality control tests of the metabolic chamber were performed monthly during the period of this study by burning instrument-grade propane with average recoveries of predicted O2 consumption and CO2 production equal to 98.8% (CV 3.6%) and 98.3% (CV 3.4%), respectively. Ambient temperature inside the metabolic chamber was set to 24°C, was monitored every minute, and averaged 23.9 ± 1.3°C. Spontaneous physical activity was measured by a radar system inside the metabolic chamber and expressed as a percentage of time when motion was detected. Urine was collected over the 24 h to measure urea nitrogen excretion to calculate the nonprotein RQ and substrate oxidation rates (i.e., lipid [LIPOX], carbohydrate [CARBOX]) and protein oxidation rates as previously described (8,31). Fasting plasma samples were collected before entering the metabolic chamber and frozen at −70°C for later measurements. Nonesterified fatty acids (NEFAs) were measured using the kit from FUJIFILM Wako Diagnostics (Mountain View, CA) at the National Institute of Diabetes and Digestive and Kidney Diseases clinical core laboratory in Bethesda, Maryland. Intra-assay CV was 4.4%, and interassay CV was 5.8%.

        To precisely attain metabolic measurements in conditions of EBL inside the metabolic chamber, two sequential eucaloric assessments of 24-h EE were performed. During the first eucaloric assessment, participants were fed the WMD reduced by 20% to account for reduced movement inside the metabolic chamber. The calculated 24-h EE from this initial eucaloric assessment was used as the prescribed 24-h energy intake for the second session. The 24-h EE and RQ measured in this second eucaloric session were considered the baseline measurements obtained in conditions close to perfect EBL with standard eucaloric feeding (50% carbohydrate, 30% fat, and 20% protein). The subsequent dietary interventions—fasting and three overfeeding diets—were given in random order with 3–4 days on the WMD between each assessment. The value of 24-h EE obtained during EBL was doubled and used as the prescribed 24-h energy intake for the overfeeding sessions (i.e., 200% of energy requirements). All overfeeding diets contained 20% protein but varied in carbohydrate and fat content as follows: 1) standard overfeeding (STOF) with 50% carbohydrate and 30% fat, 2) high-fat overfeeding (HFOF) with 60% fat and 20% carbohydrate, and 3) high-carbohydrate overfeeding (HCOF) with 75% carbohydrate and 5% fat. The metabolic kitchen weighed any remaining food for each overfeeding session in the metabolic chamber to calculate the actual food intake consumed. Only overfeeding sessions where 95% of the food provided was actually eaten were included in the analysis of 24-h RQ.

        Follow-up Visits

        After completion of the inpatient study, participants were discharged from the clinical research unit and invited back after 6 months (median follow-up time 6.6 months, interquartile range 6.0–6.9) and 1 year (median 12.9 months, interquartile range 12.1–13.5) for 1-day outpatient visits to obtain measures of free-living weight change. Participants were not provided with any counseling regarding lifestyle changes.

        Statistical Analysis

        Target sample sizes of 58 and 46 participants with available data at the 6-month and 1-year follow-up, respectively, were calculated before data analyses to provide 80% power (α = 0.05) to detect correlation coefficients ≥0.35 between the changes in 24-h RQ during overfeeding or fasting at baseline and body weight change at follow-up (primary end point). Statistical analyses were performed using SAS 9.2 software (SAS Institute, Cary, NC). Data are presented as mean ± SD or mean with 95% CI. Differences by sex were evaluated by Student unpaired t test, and comparisons between dietary interventions were performed by mixed models accounting for repeated measurements using a compound symmetry covariance structure to estimate the intraclass correlation coefficient (ICC). Paired t tests were used to assess the changes in 24-h RQ from EBL conditions (∆RQ, metabolic flexibility) during the dietary interventions. The Pearson correlation coefficient was used to quantify the relationships between ∆RQ and changes in body weight at each follow-up visit. Multivariable linear models were calculated to assess the effect of ∆RQ on weight change after adjusting for sex, age, and ethnicity. Similar analyses were done for CARBOX and LIPOX. Sensitivity analyses using changes in nonprotein RQ in place of 24-h RQ, as well as accounting for baseline weight using the ANCOVA approach, provided similar results (data not shown).

        Data and Resource Availability

        The data sets analyzed in the current study are available from the corresponding author upon reasonable request.


        The baseline characteristics of the study group are presented in Table 1. The two consecutive eucaloric assessments allowed for precise determination of 24-h RQ in conditions very close to perfect EBL, with an average 24-h deviation of 25 ± 71 kcal/day (range ‒6% to 9%). The CV of 24-h RQ measurements between the two consecutive EBL assessments was 1.6 ± 1.2% with high intraindividual consistency (ICC 0.76; P < 0.001).

        Table 1

        Demographic, anthropometric, and metabolic characteristics of the study group

        The average 24-h RQ during EBL was 0.86, a value very close to the expected food quotient (FQ) of 0.87 (31). Despite being approximately equal to the FQ on average, the 24-h RQ showed a large interindividual variability (SD 0.03), which was unrelated to sex (P = 0.37), age (P = 0.52), ethnicity (P = 0.51), spontaneous physical activity inside the metabolic chamber (P = 0.76), or any measures of body size or adiposity, including BMI (P = 0.66) (Supplementary Fig. 1A), PFAT (P = 0.73) (Supplementary Fig. 1B), FM (P = 0.59), FFM (P = 0.54), waist circumference (P = 0.96), and waist-to-thigh ratio (P = 0.36). Similarly, there were no associations between 24-h RQ and the deviation from 24-h EBL inside the metabolic chamber (P = 0.41) (Supplementary Fig. 1C) or the rate of body weight change during the first days of admission (P = 0.12) (Supplementary Fig. 1D).

        Metabolic Flexibility to 24-h Fasting and Overfeeding Diets

        The time courses of the average 24-h RQ are shown in Fig. 1, while the metabolic measurements during each dietary intervention are reported in Table 2 and Fig. 2. Overall, RQ increased after meal times in the feeding interventions, remained elevated during the day, and decreased in all dietary interventions at night to varying degrees, depending on the diet. The values of 24-h RQ across the dietary interventions showed a strong intraindividual consistency (ICC 0.66, P < 0.001) such that a lower (or higher) 24-h RQ during fasting was associated with lower (or higher) 24-h RQ during feeding and overfeeding, respectively (Fig. 3A and Supplementary Fig. 2). This is graphically shown in Fig. 2A, where “carbohydrate oxidizer” individuals with the top five highest RQ values during EBL showed above-average values for 24-h RQ during all dietary interventions. Similarly, this was the case for “fat oxidizer” individuals with the bottom five lowest RQ values during EBL showing below-average values for 24-h RQ during other diets. Although the macronutrient composition of the dietary interventions was the main determinant of 24-h RQ, explaining approximately two-thirds of its variance (67%; P < 0.001), after accounting for differences among diets, the intraindividual component of 24-h RQ explained an additional one-fifth of its variance (21%; P < 0.001) (Fig. 2B).

        Figure 1
        Figure 1

        Twenty-four-hour time courses of RQ during dietary interventions. The average time course of RQ over 24 h is plotted for each dietary intervention: eucaloric standard diet in EBL (50% carbohydrate and 30% fat), 24-h fasting (FST), HFOF (20% carbohydrate and 60% fat), STOF (50% carbohydrate and 30% fat), and HCOF (75% carbohydrate and 5% fat). The three meals provided inside the metabolic chamber were lunch at 1100 h, dinner at 1600 h, and snack at 1900 h. The total caloric intake of the overfeeding diets was equal to twice the 24-h EE value obtained during EBL.

        Table 2

        Measurements of 24-h RQ and substrate oxidation during each dietary intervention

        Figure 2
        Figure 2

        Measures of 24-h RQ and substrate oxidation rates during dietary interventions. Error bars represent the mean ± SD in each dietary condition. The 24-h RQ (A) is shown during each dietary intervention. Red circles indicate “carbohydrate oxidizers”: the five individuals with the highest 24-h RQ during EBL and standard eucaloric feeding. Blue circles indicate “fat oxidizers”: the five individuals with the lowest 24-h RQ during EBL. These same two groups of individuals are subsequently highlighted during each intervention: 24-h fasting (FST), STOF, HFOF, and HCOF, where the carbohydrate oxidizers remained above the mean 24-h RQ during each intervention and the fat oxidizers remained below the mean 24-h RQ despite being challenged with overfeeding. The determinants of 24-h RQ (B) are shown where two-thirds of the total variance of RQ measurements is explained by diet, one-fifth of RQ is explained by intrinsic factors, and the remaining variance (12%) is explained by other unmeasured factors. The substrate oxidation rates LIPOX (C) and CARBOX (D) are shown during each dietary intervention, where the red dots signify carbohydrate oxidizers during each dietary intervention, and these remained on the lower end during LIPOX and the upper end during CARBOX. Similarly, the fat oxidizers in blue remained on the upper end for LIPOX and were on the lower end of the spectrum during CARBOX.

        Figure 3
        Figure 3

        Metabolic flexibility (ΔRQ) and changes in substrate oxidation rates during dietary interventions. Relationships (±95% CI) between 24-h RQ during each diet and 24-h RQ during EBL (A) and individual changes in 24-h RQ (ΔRQ, metabolic flexibility) from EBL (B) are shown. Individual changes in LIPOX (C) and CARBOX (D) rates also are shown.

        Compared with EBL conditions, the 24-h RQ decreased in all participants during fasting by an average of 8.6% (95% CI ‒9.3 to ‒7.9%; ΔRQ = ‒0.07 ± 0.03; P < 0.001) (Fig. 3A and B) of the 24-h RQ during EBL. Similarly, the average 24-h RQ decreased by 4% (‒5 to ‒3%; ΔRQ = ‒0.03 ± 0.03; P < 0.001) during HFOF, indicating that a greater proportion of lipids were oxidized during these two dietary conditions (Table 2 and Fig. 3C). Conversely, during the overfeeding diets containing a higher carbohydrate content (>50%), 24-h RQ increased on average during STOF by 4% (3.0–4.3%; ΔRQ = 0.03 ± 02; P < 0.001) and increased the highest during HCOF by 8% (7–9%; ΔRQ = 0.07 ± 0.03; P < 0.001), reflecting increased oxidation of carbohydrates during these two diets (Fig. 3D).

        The substrate oxidation rates (LIPOX and CARBOX) during each dietary intervention are shown in Table 2 and Fig. 2C and D. Compared with EBL conditions, LIPOX increased on average by 66% (95% CI 58–74%) during fasting and by 31% (29–69%) during HFOF. Conversely, LIPOX decreased during STOF by 32% (‒39 to ‒25%) and more so during HCOF by 69% (‒79 to ‒59%) (Fig. 3C). Concordant with changes in 24-h RQ, CARBOX increased during STOF by 35% (30–40%) and during HCOF by 72% (64–79%), whereas CARBOX decreased by 19% (‒25 to ‒12%) during HFOF, with the largest decrease (‒55% [‒60 to ‒49%]) observed during 24-h fasting (Fig. 3D).

        A higher fasting NEFA concentration was associated with lower 24-h RQ during EBL (r = −0.35; P = 0.005) (Fig. 4A) and HFOF (r = −0.43; P = 0.001) (Fig. 4B). Similarly, fasting NEFA concentrations were positively associated with LIPOX during both EBL (r = 0.35; P = 0.005) (Fig. 4C) and HFOF (r = 0.44; P = 0.001) (Fig. 4D). There was a weak inverse correlation between fasting NEFA and CARBOX during HFOF (r = −0.30; P = 0.03), whereas there was no association with CARBOX during EBL (P = 0.18).

        Figure 4
        Figure 4

        Relationships between fasting plasma NEFA concentrations and 24-h RQ and LIPOX. Inverse relationships between fasting plasma NEFAs and 24-h RQ during EBL (A) and HFOF (B) and direct relationships between fasting plasma NEFAs and 24-h LIPOX during EBL (C) and HFOF (D) are shown. Relationships were quantified by the Pearson correlation coefficient. Effect size estimates (β-coefficient) were obtained through linear regression analysis.

        Metabolic Flexibility and Future Weight Change

        Follow-up data for free-living weight change after 6 months were available in 58 individuals and after 1 year in 46 individuals (Table 1). Compared with the whole cohort (n = 79), the subgroups with follow-up data did not differ in their baseline characteristics, including 24-h RQ (Supplementary Table 1). On average, participants were weight stable at 6 months (mean weight change 0.8 kg; P = 0.17 vs. 0), despite a large interindividual variability (SD 4.3 kg, range ‒7.0 to 11.2) unrelated to sex (P = 0.80), age (P = 0.14), ethnicity (P = 0.90), or initial body weight (P = 0.32). Similarly, there was a large variability in free-living weight change after 1 year (SD 5.3 kg, range ‒9.3 to 11.0), with participants remaining on average weight stable (mean 0.4 kg; P = 0.63 vs. 0).

        A smaller decrease in 24-h RQ (ΔRQ) during HFOF, but not during the other dietary interventions (all P > 0.15), was associated with weight gain both at 6 months (r = 0.32; P = 0.02; r2 = 10%) (Fig. 5A) and at 1 year (r = 0.39; P = 0.01; r2 = 15%) (Fig. 5C). After adjustment for age, sex, and ethnicity, the ΔRQ during HFOF was an independent determinant of weight change at 6 months (β = 2.1 kg per 0.05 change in 24-h RQ during HFOF; P = 0.02; total r2 = 19%) and at 1 year (β = 2.6 kg; P = 0.02; total r2 = 46%). The change in 24-h EE during HFOF did not predict weight change at any follow-up (both P > 0.30), and the results for ΔRQ during HFOF and weight change were still significant after adjustment for the concomitant change in 24-h EE (data not shown).

        Figure 5
        Figure 5

        Metabolic flexibility (ΔRQ) during HFOF predicts future weight change. The ΔRQ (metabolic flexibility) from EBL during HFOF predicted future weight change at 6 months (A) and 1 year (C); that is, a smaller (or lack of) decrease in RQ during HFOF was associated with greater weight gain. The change in 24-h LIPOX from EBL conditions was inversely associated with weight gain at 6 months (B) and 1 year (D), such that an impaired shift to LIPOX during HFOF was associated with greater future weight gain. The dotted lines denote no changes in 24-h RQ, LIPOX, or body weight at follow-up visits compared with the baseline visit. Relationships were quantified by the Pearson correlation coefficient. Effect size estimates (β-coefficient) were obtained via linear regression analysis.

        When examining the changes in macronutrient oxidation rates, a greater increase in LIPOX during HFOF was associated with more weight loss at 6 months (r = ‒0.36; P = 0.008) (Fig. 5B) and 1 year (r = ‒0.40; P = 0.009) (Fig. 5D). After adjustment for age, sex, and ethnicity, ΔLIPOX during HFOF was still a determinant of weight loss after 6 months (β = ‒1.5 kg per 250 kcal/day increase in LIPOX during HFOF; P = 0.02) and 1 year (β = ‒2.1 kg; P = 0.02). Fat intake and fat balance (fat intake − LIPOX) during 24-h HFOF were not associated with weight change at any follow-up visit (both P > 0.55).

        There were no associations between ΔRQ and 6-month weight change during fasting (P = 0.73) (Supplementary Fig. 3), STOF (P = 0.87), and HCOF (P = 0.29). Similar results were observed at the 1-year follow-up such that there were no associations between ΔRQ and weight change during 24-h fasting (P = 0.91) (Supplementary Fig. 3), STOF (P = 0.17), and HCOF (P = 0.20).


        In the current study, we evaluated metabolic flexibility (ΔRQ), which is defined as the change in 24-h RQ from EBL conditions to extreme dietary interventions, including 24-h fasting and 200% overfeeding with a high-fat or high-carbohydrate content, to assess whether the extent of ΔRQ is a metabolic determinant of free-living weight change. The 24-h RQ measurements obtained during these acute dietary interventions predominantly depended on macronutrient composition, which explained approximately two-thirds of the 24-h RQ variance among diets. However, there was still a strong intraindividual reliance for fuel oxidation observed in each dietary condition, such that individuals relying more on a specific substrate for oxidation (e.g., carbohydrates, lipids) manifested this preference in any dietary conditions. We demonstrated that interindividual variability in ΔRQ, specifically, a reduced metabolic flexibility to HFOF, predicted future weight gain both at 6 months and at 1 year, and this was due to an impairment in the ability to switch to lipid oxidation in a setting of surplus of dietary fats.

        Cross-sectional studies have shown that a higher RQ during EBL or fasting, reflecting a lower fat-to-carbohydrate oxidation, leads to future weight gain (1114,27), although other studies failed to find such association (17,18). In free-living conditions, EBL is likely transient; thus, investigating change in fuel selection during acute over- and underfeeding is important. Concordant with the observations made during EBL that higher RQ, indicative of lower fat oxidation, predicts greater weight gain, we now show that during acute conditions of energy surplus, the metabolic inflexibility to lipids is also a determinant of weight gain. Specifically, individuals who did not decrease their RQ as much in a setting of HFOF, that is, those who were not able to increase their fat oxidation in a setting of dietary fat surplus, gained more weight at follow-up. Concordant with our current results, previous studies reported that in obesity-prone individuals, nighttime RQ is higher after 3 days of overfeeding (32) and that measures of metabolic inflexibility predict long-term weight gain (33). The individual’s ability to be metabolically flexible, which is the capacity to readily adjust substrate oxidation in response to fuel availability (21), may be postulated to be advantageous in the current obesogenic environment where food, specifically energy-dense, high-fat food, is readily available.

        The mechanism by which metabolic inflexibility to fats leads to greater weight gain could be through decreased adipocyte lipolysis and, ultimately, impaired capacity to increase LIPOX. In individuals who are metabolically inflexible to dietary fats, lipolysis may increase to a smaller degree during a high-fat diet (34). We have previously demonstrated that lower rates of in vitro lipolysis are associated with higher 24-h RQ and lower LIPOX during eucaloric feeding, and these individuals with reduced lipolysis gained more weight at follow-up as a result of an increase in FM (35). Supportive of a causal role for lipolysis in determining the degree of metabolic flexibility, we found that higher fasting concentrations of NEFA, a product of fat cell lipolysis (36), and regulators of LIPOX (37), were associated with lower 24-h RQ and greater LIPOX during both eucaloric feeding and HFOF. Concordant with our current findings, lower nocturnal concentrations of plasma NEFAs during HFOF predict weight gain in obesity-prone individuals (33). Altogether, these results strongly point to a key role for lipolysis in obesity and body weight regulation (38,39).

        To obtain accurate measurements of metabolic flexibility during each diet, we used a carefully controlled measurement of 24-h RQ during EBL and eucaloric, standard feeding. Prior to this baseline assessment of 24-h RQ, participants were on an WMD for 5 days, and two metabolic measurements inside the metabolic chamber were used to obtain a baseline RQ value in conditions of almost perfect EBL and weight maintenance. We evaluated the determinants of baseline RQ and found no associations with body size, body composition, deviations from 24-h EBL in the metabolic chamber, or prior fluctuations in body weight, despite these variables being found to be determinants of RQ during EBL in previous studies (11,40). This was likely due to more controlled conditions characterized by sequential 24-h EE assessments that led to more accurate metabolic measurements within a 10% range of expected 24-h EBL (intake − EE) as opposed to a wider range (±30%) that has been previously reported (11). More importantly, we only evaluated individuals with normal glucose regulation (28), therefore eliminating the confounding effect of insulin resistance, which has previously been shown to be a determinant of metabolic inflexibility to glucose (22,25).

        We used extremes of dietary interventions by precisely designing the overfeeding diets to provide twice the individual-specific daily energy needs so that we could maximize the extent of metabolic flexibility for both RQ and substrate oxidation rates. During these 24-h dietary interventions, there was an expected increase in RQ (shift to carbohydrate oxidation) during STOF and HCOF, but we also demonstrated a decrease in RQ (greater lipid oxidation) during fasting and HFOF. The rapid change in RQ in response to 24-h overfeeding observed in the current study is in contrast to a recent study that showed no change in RQ after 3 days of an overfeeding diet with a composition similar to our STOF diet (33), although the degree of overfeeding in the current study (200% of energy needs) was much higher than that (140%) of the previous study. Of note, 3 days of eucaloric feeding with a high dietary fat content (50%)—similar to our HFOF diet (60%)—induced a decrease in 24-h RQ to an average value of 0.83 (41), which is exactly the same value obtained in the current study during HFOF (Table 2). These results strongly support the use of short-term (24-h) but extreme (200% of eucaloric requirements) dietary interventions to obtain valid measures of metabolic flexibility that can be obtained in less extreme but prolonged dietary conditions typical of free-living settings. Importantly, as previously shown in a subset of 14 subjects who underwent repeat assessments of energy metabolism inside a whole-room calorimeter (42), measures of 24-h RQ and EE during fasting, eucaloric feeding, and balanced overfeeding were highly consistent within an individual (CV <5%), indicating high reproducibility of metabolic flexibility during these acute dietary interventions.

        Although diet explained most of the variance in 24-h RQ among diets, we found a participant-specific reliance for macronutrient oxidation during these dietary interventions, which is independent of body habitus and macronutrient proportions in the diet as we have previously shown (43). Thus, the substantial variability in metabolic flexibility to acute overfeeding and fasting also has a strong intraindividual component, which is indicative of the propensity for future weight gain and is independent of body size and the concomitant changes in 24-h EE during these dietary interventions. The extent of metabolic flexibility to change in diets is likely to be genetically determined given the significant heritability of 24-h RQ quantified in family studies of Caucasians (44) and American Indians of southwestern heritage (11).

        Although our dietary interventions to create short-term energy imbalance are not necessarily normal physiological or habitual conditions, we propose that these interventions may constitute an important tool to quantify the propensity for weight gain by acute dietary challenges that can uncover informative metabolic responses. In our carefully controlled setting, we obtained 24-h measures of substrate oxidation in conditions of energy surplus (overfeeding) and energy deficit (fasting) to assess whether these metabolic changes are indicative of the propensity for weight gain to provide insight into the pathogenesis of obesity.

        The major limitation of our study is the lack of formal assessments of free-living energy intake or physical activity in the follow-up period. Yet, participants were recruited to be weight stable for at least 6 months before baseline admission and, on average, were also weight stable at each follow-up visit, suggesting that there were no substantial changes in physical activity or diet in this time period that might have confounded our results. While the strength of the relationship between impaired metabolic flexibility to HFOF and weight gain explained up to ∼15% of the interindividual variance in future weight change, this estimate can be considered a large effect size for a single metabolic parameter given that other metabolic determinants of weight change explain 5–10% of its variance (5).

        In summary, we demonstrated that the 24-h RQ responses to different diets with varying macronutrient content are highly consistent within an individual, such that the individual capacity to oxidize dietary fats is manifested under any dietary regimen, thus indicating that metabolic flexibility is an intrinsic metabolic characteristic of a given individual. Importantly, differences in the degree of metabolic flexibility to HFOF across participants are indicative of the individual propensity for future weight gain. In conclusion, in healthy individuals with normal glucose regulation, we identified a novel metabolic phenotype in which the impaired ability to switch fuels in response to an acute high-fat overload is a determinant of greater weight gain. Specifically, individuals who are more metabolically inflexible to lipids may gain more weight over time than individuals who can readily adjust their macronutrient oxidation to favor lipid oxidation in a setting of fat surplus. Our data indicate that future interventions targeting fuel selection by making individuals more “metabolically flexible” to dietary fats may help to prevent or treat obesity.

        Article Information

        Acknowledgments. The authors thank the study volunteers for their participation and the clinical research staff for their care of the volunteers.

        Funding. This research was supported by the Intramural Research Program of the National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health (DK-069029-11). The work of P.P. was supported by Programma per Giovani Ricercatori “Rita Levi Montalcini” granted by Ministero dell’Istruzione, dell’Università e della Ricerca.

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

        Author Contributions. B.B. contributed to the data collection, analysis, and interpretation; literature review; and drafting of the manuscript. B.B., K.L.V., T.H., T.A., M.W., C.B., J.K., and P.P. read and approved the final manuscript. K.L.V. contributed to the data collection and interpretation. T.H. contributed to the data analysis and interpretation. T.A. and C.B. contributed to the data interpretation. M.W. contributed to the NEFA measurements and data interpretation. J.K. contributed to the study design and data interpretation. P.P. contributed to the study design, data analysis and interpretation, literature review, and drafting of the manuscript. P.P. 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 ObesityWeek 2018, Nashville, TN, 11–15 November 2018.

        • Received July 21, 2019.
        • Accepted November 7, 2019.

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