Myeloid HMG-CoA Reductase Determines Adipose Tissue Inflammation, Insulin Resistance, and Hepatic Steatosis in Diet-Induced Obese Mice

By electricdiet / March 25, 2020


Abstract

Adipose tissue macrophages (ATMs) are involved in the development of insulin resistance in obesity. We have recently shown that myeloid cell–specific reduction of HMG-CoA reductase (Hmgcrm−/m−), which is the rate-limiting enzyme in cholesterol biosynthesis, protects against atherosclerosis by inhibiting macrophage migration in mice. We hypothesized that ATMs are harder to accumulate in Hmgcrm−/m− mice than in control Hmgcrfl/fl mice in the setting of obesity. To test this hypothesis, we fed Hmgcrm−/m− and Hmgcrfl/fl mice a high-fat diet (HFD) for 24 weeks and compared plasma glucose metabolism as well as insulin signaling and histology between the two groups. Myeloid cell–specific reduction of Hmgcr improved glucose tolerance and insulin sensitivity without altering body weight in the HFD-induced obese mice. The improvement was due to a decrease in the number of ATMs. The ATMs were reduced by decreased recruitment of macrophages as a result of their impaired chemotactic activity. These changes were associated with decreased expression of proinflammatory cytokines in adipose tissues. Myeloid cell–specific reduction of Hmgcr also attenuated hepatic steatosis. In conclusion, reducing myeloid HMGCR may be a promising strategy to improve insulin resistance and hepatic steatosis in obesity.

Introduction

One of the characteristics of obesity is chronic low-grade inflammation, which is associated with accumulation of adipose tissue macrophages (ATMs). The ATMs, in turn, can contribute to insulin resistance and type 2 diabetes (1,2). The activation state of ATMs shifts from an alternatively activated state (M2) in lean mice to a classically activated state (M1) in obese mice (3). Adipose tissue inflammation and insulin resistance are ameliorated by inhibiting the accumulation of ATMs through genetic ablation of CD11c+ cells (4) or knockdown of C-C motif chemokine receptor 2 (CCR2) in macrophages (5). These findings indicate that targeting ATMs is a promising strategy to improve insulin resistance.

HMG-CoA reductase (HMGCR) is the rate-limiting enzyme in the mevalonate pathway (6). Inhibitors of HMGCR (statins) are widely used to prevent the occurrence of coronary heart disease and other atherosclerotic diseases, primarily by reducing blood cholesterol levels. The atheroprotective effects of statins have been ascribed not only to cholesterol lowering but also to various effects on vascular endothelial cells, vascular smooth muscle cells, and immune cells, including monocytes and macrophages (7). We recently reported that myeloid cell–specific reduction of Hmgcr protects against atherosclerosis by reducing migration of macrophages to the aorta in hypercholesterolemic mice (8). On the basis of these findings, we hypothesized that myeloid cell–specific reduction of Hmgcr also improves insulin sensitivity by reducing the recruitment of macrophages to adipose tissues in obesity.

Research Design and Methods

Animal studies were performed according to the regulations of the Animal Care Committees of Jichi Medical University. Myeloid cell–specific Hmgcr reduction (Hmgcrm−/m−) mice were generated by crossing Hmgcrfl/fl mice (control) (9) with LysMcre mice (The Jackson Laboratory) (8). All mice used in this study had a C57BL/6J genetic background. Eight-week-old male mice were fed either normal chow diet (NCD) (12% kcal fat, CE-2; CLEA Japan, Inc.) or high-fat diet (HFD) (60% kcal fat from lard, D12492; Research Diets) for 24 weeks. Plasma levels of glucose, insulin, tumor necrosis factor-α (TNF-α), and adiponectin were measured by Glucose CII Test Wako Kit (Wako), Mouse Insulin ELISA Kit (Morinaga), Mouse TNF-α Quantikine HS ELISA Kit (R&D Systems), and Mouse/Rat Adiponectin ELISA Kit (Otsuka Pharmaceuticals), respectively. Plasma lipids and liver triglyceride (TG) content were measured as described previously (8,9).

Protein extracts from the tissues were subjected to SDS-PAGE and visualized with primary antibodies against p-AktSer473 or Akt and chemiluminescence of horseradish peroxidase–conjugated secondary antibody (Cell Signaling Technology) using ECL Prime Western Blotting Detection Reagent (GE Healthcare).

The tissue sections were stained for F4/80 using a rat anti-F4/80 antibody (1:100 dilution; Bio-Rad), Histofine Simple Stain MAX PO (Nichirei), and diaminobenzidine (Sigma-Aldrich). Whole-mount immunostaining was performed as described previously (10) using the rat anti-F4/80 antibody followed by Alexa Fluor 488 goat anti-rat IgG (Thermo Fisher Scientific) or a rabbit anti-Ki67 antibody (Abcam) followed by Alexa Fluor 568 goat anti-rabbit IgG (Thermo Fisher Scientific). Apoptosis was determined by TUNEL staining using an In situ Apoptosis Detection Kit (Takara Bio). F4/80+ cells were sorted using MS Columns (Miltenyi Biotec) after incubation with biotin-conjugated anti-F4/80 antibody (eBioscience) followed by magnetic labeling with Anti-Biotin MicroBeads (Miltenyi Biotec) from the stromal vascular fraction of epididymal white adipose tissue (eWAT).

Chemotaxis of thioglycolate-elicited macrophages (TGEMs) was quantified as described previously (8) with slight modifications. Total quantitative real-time PCR was performed as described previously (8). The primer-probe sets used in the experiments are listed in Supplementary Table 1.

All data are presented as mean ± SD. GraphPad Prism 6 software was used for data analyses. Unpaired Student t test or repeated-measures ANOVA with Bonferroni multiple comparison test was used for comparisons as appropriate. Differences were considered significant at P < 0.05.

Data and Resource Availability

The data and critical resources supporting their reported findings, methods, and conclusions are available from the corresponding author upon reasonable request.

Results

Reduction of Hmgcr in Myeloid Cells Attenuates HFD-Induced Insulin Resistance Without Altering Body Weight

There were no significant differences in the body weight, weights of eWAT, weights of liver, and food intake between the two groups under either the NCD or the HFD condition (Supplementary Fig. 1A–D). Body compositions estimated by CT scan and plasma lipids were not significantly different between the two groups under the HFD condition (Supplementary Fig. 1E–I).

Fasting plasma glucose concentration, insulin concentration, and HOMA of insulin resistance (HOMA-IR) were lower Hmgcrm−/m− mice than in Hmgcrfl/fl mice under the HFD condition (Fig. 1AC). Hmgcrm−/m− mice showed significant improvement in glucose tolerance by glucose tolerance test compared with Hmgcrfl/fl mice under the HFD condition (Fig. 1D). Results of insulin tolerance tests showed that Hmgcrm−/m− mice had significant improvement in insulin sensitivity compared with Hmgcrfl/fl mice under the HFD condition (Fig. 1E). These parameters were not significantly different between the two groups under the NCD condition (Supplementary Fig. 2A–E).

Figure 1
Figure 1

Glucose tolerance and insulin sensitivity in mice fed HFD for 24 weeks. AC: Fasting plasma glucose, insulin concentrations, and HOMA-IR of mice fed HFD (n = 14–15). HOMA-IR was calculated as fasting plasma glucose (mg/dL) × fasting plasma insulin (μIU/mL) divided by 405. D: Excursions of plasma glucose concentrations during glucose tolerance test in mice fed HFD (n = 11–12). After a 16-h fast, a 2.0 g/kg glucose solution was given to the mice orally. E: Excursions of plasma glucose concentrations during insulin tolerance test in mice fed HFD (n = 11–12). After a 4-h fast, 0.75 IU/kg insulin was given to the mice intraperitoneally. FH: Immunoblot analyses for p-AktSer473 and total Akt in the liver, eWAT, and gastrocnemius muscle in mice fed HFD. After a 16-h fast, the mice were injected with insulin (0.75 IU/kg) or saline. Ten minutes later, the mice were sacrificed by cervical dislocation, and the tissues were removed. IK: The ratio of p-AktSer473 to Akt was quantified as fold changes over Hmgcrfl/fl mice injected without insulin (n = 6). *P < 0.05, **P < 0.01 vs. Hmgcrfl/fl mice. AU, arbitrary unit.

To confirm the improvement of systemic insulin sensitivity, we performed immunoblot analysis for p-AktSer473 in the liver, eWAT, and gastrocnemius muscle (Fig. 1FK). Of note, the magnitudes of insulin-stimulated increases in p-AktSer473 levels were significantly higher in Hmgcrm−/m− mice than in Hmgcrfl/fl mice fed HFD. These results indicate that reduction of Hmgcr in myeloid cells attenuates HFD-induced insulin resistance.

Reduction of Hmgcr in Myeloid Cells Attenuates Accumulation of ATMs and HFD-Induced Inflammation in WAT

ATMs typically surround dead adipocytes, known as crown-like structures (CLSs) (11). The number of F4/80+ CLSs was reduced by 77% in Hmgcrm−/m− mice compared with Hmgcrfl/fl mice under the HFD condition (Fig. 2A and B). The mRNA expression levels of marker genes for general macrophages, such as F4/80 and Cd68, and M1, such as Cd11c, Tnf-α, Il-1β, and Mcp-1, were significantly lower in Hmgcrm−/m− mice than in Hmgcrfl/fl mice under the HFD condition (Fig. 2CI). On the other hand, the mRNA expression levels of M2 markers, such as Cd206 and Cd163, were not different between the two groups (Fig. 2J and K).

Figure 2
Figure 2

Immunostaining for ATMs, plasma levels of adiponectin and TNF-α, and mRNA expression in eWAT and ATMs from mice fed either NCD or HFD. A: Representative eWAT sections of staining for F4/80 in mice fed HFD. The sections were counterstained with hematoxylin. Scale bars = 200 μm. Arrowheads indicate CLS. B: Numbers of F4/80+ CLSs per 10 random high-power (HP) fields of sections of eWAT from mice fed HFD at original magnification ×200 (n = 8). CK: Relative mRNA levels of the marker genes for general macrophages (C and D), M1 (EI), and M2 (J and K) in eWAT from mice fed either NCD or HFD (n = 7–8). L: Relative mRNA levels of adiponectin in eWAT from mice fed HFD (n = 8). M: Plasma adiponectin concentrations in mice fed HFD (n = 14). N: Plasma TNF-α concentrations in mice fed HFD (n = 10). O: Relative mRNA levels of genes related to cholesterol metabolism in F4/80+ cells isolated from eWAT of mice fed HFD (n = 8). P: Relative mRNA levels of proinflammatory cytokines in F4/80+ cells isolated from eWAT of mice fed HFD (n = 8). #P < 0.05, ##P < 0.01 vs. NCD-fed Hmgcrfl/fl mice; *P < 0.05, **P < 0.01 vs. HFD-fed Hmgcrfl/fl mice.

To determine how the reduction of myeloid Hmgcr alleviated insulin resistance, we measured mRNA expression levels and plasma concentrations of adiponectin, which is an adipocyte-derived insulin-sensitizing hormone (12), and measured plasma concentrations of TNF-α which antagonizes insulin action (13). Hmgcrm−/m− mice had significantly higher mRNA expression levels and plasma concentrations of adiponectin than Hmgcrfl/fl mice under the HFD condition (Fig. 2L and M). Furthermore, plasma TNF-α concentrations were significantly lower in Hmgcrm−/m− mice under the HFD condition (Fig. 2N). To investigate the inflammatory changes of ATMs, we collected ATMs from eWAT from mice fed an HFD. The mRNA expression levels of genes related to cholesterol metabolism and proinflammatory cytokines in F4/80+ cells were not significantly different between the two groups under the HFD condition except for Hmgcr (Fig. 2O and P). These results indicate that the reduction of Hmgcr in myeloid cells attenuates ATM accumulation and HFD-induced inflammation in WAT.

Reduction of Hmgcr in Myeloid Cells Inhibits HFD-Induced Hepatic Steatosis Without Affecting the Number of Liver Macrophages

Liver TG content reduced significantly by 52% in Hmgcrm−/m− mice compared with Hmgcrfl/fl mice under the HFD condition (Fig. 3A and B). The numbers of F4/80+ macrophages in the liver were not different between the two groups (Fig. 3C and D). In agreement with this, the mRNA expression levels of marker genes for general macrophages, those for M1, and those for M2 were not different between the two groups (Fig. 3EL). These results indicate that the reduction of Hmgcr in myeloid cells inhibits HFD-induced hepatic steatosis without affecting the number of liver macrophages or expression of proinflammatory cytokines in the liver.

Figure 3
Figure 3

Histology, TG content, and mRNA expression in the liver of mice fed either NCD or HFD. A: Formalin-fixed paraffin-embedded sections of the liver were stained with hematoxylin and eosin (H&E). Scale bars = 200 μm. B: Liver TG content (n = 12). C: Representative F4/80 staining of the liver from mice fed HFD. The sections were counterstained with hematoxylin. Scale bars = 200 μm. D: Number of F4/80+ cells in three random high-power (HP) fields of the liver sections from mice fed HFD at original magnification ×200 (n = 5). EL: Relative mRNA levels of the marker genes for general macrophages (E and F), M1 (GK), and M2 (L) in the liver of mice fed either NCD or HFD (n = 5–6). ##P < 0.01 vs. NCD-fed Hmgcrfl/fl mice; *P < 0.05 vs. HFD-fed Hmgcrfl/fl mice.

Reduction of Hmgcr in Myeloid Cells Reduces Chemotaxis of Macrophages Toward MCP-1 but Has Minimal Effects on Local Proliferation and Apoptosis of ATMs

We hypothesized that the attenuation of ATM accumulation in Hmgcrm−/m− mice fed HFD was due to impaired chemotaxis to MCP-1, which is critical for ATM accumulation during obesity (14). The mRNA expression levels of Ccr2, a receptor for MCP-1, was not different between the two groups of F4/80+ cells isolated from eWAT (Supplementary Fig. 3A). On the other hand, chemotaxis of macrophages toward MCP-1 was reduced by 54% in TGEMs from Hmgcrm−/m− mice compared with those from Hmgcrfl/fl mice. The addition of mevalonate or geranylgeranyl pyrophosphate (GGPP) reversed the reduction of chemotaxis toward MCP-1 but not the addition of squalene or farnesyl pyrophosphate (FPP). These results suggest that GGPP plays an important role in macrophage chemotaxis (Fig. 4A and Supplementary Fig. 3B).

Figure 4
Figure 4

Chemotactic activities of TGEMs toward MCP-1, cell proliferation, and apoptosis of ATMs in mice fed HFD. A: TGEM was subjected to a chemotaxis assay for MCP-1 (100 ng/mL). Numbers of migrated cells in three random high-power (HP) fields at original magnification ×200 estimated by fluorescence microscopy (n = 4–5). Mevalonate (1 mmol/L), squalene (1 mmol/L), GGPP (10 μmol/L), and FPP (10 μmol/L) were added according to the indicated conditions. B: Representative immunofluorescent staining for nucleus (DAPI), macrophages (F4/80), and proliferation (Ki67) in eWAT from mice fed HFD. Scale bars = 50 μm. C: Representative immunofluorescent staining for nucleus (DAPI), macrophages (F4/80), and apoptosis (TUNEL) in eWAT from mice fed HFD. Scale bars = 50 μm. D: Percentage of Ki67+ F4/80+ double-positive cells per F4/80+ cells in five random fields at original magnification ×400 (n = 5). E: Percentage of TUNEL+ F4/80+ double-positive cells per F4/80+ cells in five random fields at original magnification ×400 (n = 5). *P < 0.05, **P < 0.01 vs. Hmgcrfl/fl mice.

To evaluate the numbers of proliferating and/or apoptotic ATMs, which are important determinants of the number of ATMs (10,15), we performed whole-mount immunostaining for Ki67 and TUNEL (Fig. 4BE). Neither Ki67+ ATMs nor TUNEL+ ATMs were significantly different between the two groups.

Discussion

In obese mice, adipose tissues are reported to recruit bone marrow–derived macrophages (2). Inhibiting chemotaxis of macrophages, such as knockdown of CCR2, has been reported to alleviate inflammation in adipose tissues and insulin resistance, primarily as a result of the reduction of ATMs (2,5). Consistent with these reports, the current study clearly shows that reduced chemotaxis of macrophages with reduced Hmgcr is associated with a decreased number of ATMs and amelioration of insulin resistance and fatty liver.

How did the reduced supply of GGPP impair chemotaxis in Hmgcrm−/m− TGEMs? It is well known that Rho GTPases play a fundamental role in the control of cell shape and motility (16), and GGPP plays an essential role for the function of these small GTPases, including Rho GTPases, through protein prenylation (7). However, studies using macrophages isolated from knockout mice have shown that Rac1, Rac2, RhoA, RhoB, and RhoC are not essential for macrophage migration, although they do affect cell shape and adhesion (17,18). To further complicate the issue, deficiency of geranylgeranyltransferase type 1 (GGTase 1), which supposedly activate Rho GTPases by geranylgeranylation, in macrophages increases interleukin-1β production by increasing active GTP-bound Rac1, Cdc42, and RhoA in mice (19). Consistently, we found increased expression of proinflammatory cytokines when stimulated with lipopolysaccharide and increased amounts of membrane-bound Rac1, Cdc42, and RhoA in Hmgcrm−/m− TGEMs (8).

Akula et al. (20) have reported the results that would solve the mystery. According to them, protein geranylgeranylation enables Toll-like receptor–induced activation of phosphatidylinositol-3-OH kinase [PI(3)K] by promoting the interaction between the small GTPase Kras and PI(3)K catalytic subunit p110δ. In the absence of geranylgeranylation, compromised PI(3)K activity allows an unchecked toll-like receptor–induced inflammatory response and constitutive activation of pyrin inflammasome. Taken together, it is unlikely that the reduced chemotaxis of Hmgcrm−/m− TGEMs is caused by inactivation of Rho GTPases.

More specifically, G-protein subtypes γ2 and 3, which are essential for intracellular signaling of CCR2 in macrophages, are geranylgeranylated (21). Fluvastatin inhibits the migration of RAW264.7 cells by decreasing PI(3)K-mediated production of phosphatidylinositol (3,4,5)-trisphosphate (22), suggesting that similar defective downstream signaling underlies the impaired chemotaxis of Hmgcrm−/m− TGEMs. It is also possible that decreased supply of GGPP impairs geranylgeranylation of Rab GTPases, which are catalyzed by GGTase 2, also known as Rab GGTase, thereby impairing cell migration (23). Moreover, a decrease in cholesterol in the lipid raft of plasma membrane may inhibit the migratory activity of macrophages, as reported in mice whose fatty acid synthase (Fas) is ablated in myeloid cells (24). Further studies are warranted to clarify the mechanism behind the impaired migratory activity of Hmgcrm−/m− TGEMs.

Why did HFD elicit inflammation in WAT but not in the liver? van der Heijden et al. (25) showed that AT inflammation was present after 24 weeks of HFD, whereas hepatic inflammation was not detected until 40 weeks of HFD, indicating that AT inflammation is established before the development of hepatic inflammation.

In conclusion, reduction of myeloid Hmgcr improves glucose tolerance and insulin sensitivity by decreasing the number of ATMs and inflammation of adipose tissues by reducing the chemotaxis of macrophages to adipose tissues. Moreover, reduction of myeloid Hmgcr attenuates hepatic steatosis. Therefore, reducing myeloid HMGCR may be a promising strategy for improving insulin resistance and hepatic steatosis in obesity.

Article Information

Acknowledgments.

The authors thank Mika Hayashi, Nozomi Takatsuto, and Mihoko Sejimo (Division of Endocrinology and Metabolism, Department of Internal Medicine, Jichi Medical University) for excellent technical support. The authors also thank Biopathology Institute Co., Ltd., for helping with the performance of histological analyses.

Funding. This study was supported by Grants-in-Aid for Scientific Research-KAKENHI and Program for the Strategic Research Foundation at Private Universities (2011-2015) Cooperative Basic and Clinical Research on Circadian Medicine and Non-Communicable Diseases from the Ministry of Education, Culture, Sports, Science, and Technology of Japan.

Duality of Interest. This study was supported by unrestricted grants from Astellas Pharma, Daiichi Sankyo Co, Shionogi Co, Boehriger Ingelheim Japan, Ono Pharma, Mitsubishi Tanabe Pharma, Takeda Pharma Co, Toyama Chemical Co, Teijin, Sumitomo Dainippon Pharma, Sanofi K.K., Novo Nordisk Pharma, MSD K.K., Pfizer Japan, Novartis Pharma, and Eli Lilly Co. No other potential conflicts of interest relevant to this article were reported.

Author Contributions. A.T. and S.T. designed and performed the experiments, maintained the mice, analyzed and interpreted the data, and wrote the manuscript. S.N. designed the experiments, maintained the mice, and contributed to the discussion. D.Y., A.M., T.W., M.I., H.Y., C.E., M.T., and K.E. contributed to the discussion. S.I. designed the experiments and wrote the manuscript. S.I. is the guarantor of this work and, as such, had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

  • Received January 23, 2019.
  • Accepted October 28, 2019.



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Banana Chocolate Chip Scones – My Bizzy Kitchen

By electricdiet / March 23, 2020


One of the best things about working from home is being steps away from my kitchen.  It’s been nice to clock out at 12:30 and instead of meeting my sister for a walk, I walk steps into my kitchen and see what I can come up with!

Hannah doesn’t like bananas unless they are slightly green, or barely able to peel.  Once they start to brown in the slightest way, she’s over it.  And it never fails when I buy a giant bunch of bananas, so does Hannah, then I have ALL THE BANANAS to use up.  Jacob is kind of over me adding bananas everything, but you have to use up what you have.

The secret to scones is to freeze the dough for at least 30 minutes.  I got busy on my computer and these were in the freezer for an hour, but you’ll get clean knife cuts when you do that.

And the peanut butter glaze – #swoon!  It makes these scones.  I used Truvia in the scone batter, so the powdered sugar in the glaze gives just enough sweetness, and then you’ll get a bite of chocolate chip in every other bite.  Love.

This is a shaggy dough.  I made 16 mini scones.  I divided the dough into two circles, and with wet hands, press the dough into a circle and freeze.  Wetting your hands makes the dough not stick to your hands.

Print

Banana Chocolate Chip Scones

These are the perfect sweet treat to start your day or snack on. If you love peanut butter, you’ll love the peanut butter glaze!


Scale

Ingredients

  • 2 2/3 cups self rising flour (regular flour is fine too)
  • 1/3 cup Truvia
  • 1 tablespoon baking powder
  • 1/4 teaspoon salt
  • 1 teaspoon cinnamon
  • 4 tablespoons I Can’t Believe It’s Not Butter
  • 1/2 cup oat milk (any milk works)
  • 1 egg
  • 2 ripe bananas
  • 60 mini Lily’s chocolate chips
  • 1 tablespoon vanilla
  • 2 tablespoons PBFit Sugar Free (powdered peanut butter)

For the glaze:

  • 2 tablespoons PB Fit
  • 2 tablespoons powdered sugar
  • 2 teaspoons oat milk (or any milk) just enough to get to glaze consistency

Instructions

Heat oven to 425.

Mix the flour, Truvia, baking powder, salt, cinnamon in a large bowl.  Mix the butter, oat milk, egg, banana and vanilla extract and whisk just until combined – it’s okay if the bananas are a bit chunky.

Mix the wet ingredients with the dry ingredients.  Don’t freak out on my if you think “the dough is too wet!”  It will come together.  

Divide dough in half and put dough on two separate parchment papers.  Wet your hands and press the dough into a 6-7 inch circle.  Place the parchment paper on a cookie sheet or plate and place in your refrigerator for 30-60 minutes.  It will be so much easier to cut.

Cut each circle into 8 scones.  I baked 8 at a time, so keep the second dough in the freezer until ready to bake.  For a soft scone bake 9 minutes, for a bit of a crust, bake 11 minutes.

Mix the PB2, powdered sugar and oat milk together to make the glaze.  Once the scones are completely cooled, drizzle the glaze over the scones.  I had enough glaze for 15 scones – one lonely scone didn’t get a glaze, but I was too lazy to make more. 😛

Notes

On #teampurple on WW, each scone is 3 points.  If you are on blue or green and make these, can you let me know how many points they are so I can update this?  You’re the best – thanks!

See that clean knife cut?  Don’t skip the freezer part.  Unless you are in a hurry, then you can make drop scones. 

If you make these – let me know!  I’d love to know what you think.

Stay safe everyone – make it a great day – hugs!



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How to use Dexcom CGM Trend Arrows for Insulin Adjustments

By electricdiet / March 21, 2020


If you use a CGM (Continuous Glucose Monitor), you probably already know how much of a game-changer this technology can be when it comes to managing your diabetes.

Having insight into our blood glucose history, where what our blood sugar is right now, and where it’s headed brings peace of mind and makes managing diabetes a lot easier and safer.

But even if you love your CGM, do you utilize the trend arrows for insulin adjustments? If not, you’re truly missing out on the full potential of the technology.

By paying attention to the CGM trend arrows, you’ll be able to make more proactive and informed choices when it comes to adjusting your insulin doses.

The information in this post is specific to the Dexcom CGM system. Other CGM systems use trend arrows as well, but they may have different meanings so please be careful utilizing the information here if you use another system.

What the Dexcom CGM trend arrows tell us

When you open your Dexcom app or look at your receiver, you’ll see your current blood sugar as well as an indication of what your blood sugar will be in 30 minutes.

That indication comes in the form of arrows. On the receiver, the arrows are to the right of your current blood sugar. In the app, it’s shown as a circle with your blood sugar and the arrows around the circle.

Two examples of Dexcom trend arrows

You probably know that an up arrow indicates that your blood sugar is rising and a down arrow that it’s falling, but do you know by how much and how to include it in your insulin adjustments?

Let’s start with what the arrows indicate according to the manufacturer:

Trend ArrowsMeaning
2 arrows straight upBG could increase more than 90 mg/dL (5 mmol/L) in 30 min.
1 arrow straight upBG could increase up to 90 mg/dL (5 mmol/L) in 30 min.
1 arrow slightly upBG could increase 30-60 mg/dL (1.7-3.3 mmol/L) in 30 min.
1 arrow to the sideNo BG increase or decrease of more than 1 mg/dL (0.05 mmol/L) per min.
1 arrow slightly downBG could decrease 30-60 mg/dL (1.7-3.3 mmol/L) in 30 min.
1 arrow straight downBG could decrease up to 90 mg/dL (5 mmol/L) in 30 min.
2 arrows straight downBG could decrease more than 90 mg/dL (5 mmol/L) in 30 min.

Source: Dexcom G6 guide

Real-life example

Let’s say my blood sugar is 120 mg/dL (6.7 mmol/l) and my Dexcom is showing 2 arrows straight up.

In this case, I can expect my blood sugar to potentially increase to above 210 mg/dL (120 + 90) within the next 30 min.

I try to stay below 200 mg/dL so I’d most likely give myself a correction bolus based on this knowledge (as long as I didn’t just eat a meal or already have enough active insulin on board that just hasn’t kicked in yet).

How to calculate your insulin adjustments based on the Dexcom trend arrows

Your CGMs arrows can be incredibly helpful for blood sugar management — but only if you know how to respond to those arrows.

Several medical professionals have come up with approaches for using CGM arrows to help patients dose insulin safely and effectively. Most importantly, they all agree that we should take the rise or fall rate into account when adjusting our insulin doses. What they disagree on, however, is exactly how insulin doses should be adjusted based on those arrows.

The Endocrine Society reviewed four published approaches in 2017 and came up with their own guidelines. Their guidelines were published in the Journal of the Endocrine Society in December 2017 (please note that the guidelines discussed here are for adults only). 

Since I consider the Endocrine Society the highest authority when it comes to this type of recommendation, I’m going to share their recommendation with you.

Although the guidelines were written for Dexcom G5, no changes have been made to the arrows for the newer systems and the recommendations should be valid for Dexcom G6 as well.

The Endocrine Society recommends that you adjust your insulin based on what your blood sugar will be in 30 minutes, rather than what it is right now. But instead of calculating what your future blood sugar will be in 30 minutes, they recommend that you adjust your correction factor (CF).

This correction should be made on top of any correction for your current blood sugar as well as any carbohydrates consumed.

Your CF is how much one unit of rapid-acting insulin (like Humalog or Novolog) will decrease your blood sugar. If you use a pump, you can see your CF in your settings. If you manage your diabetes with injections and don’t know your CF, you can ask your medical team to help you calculate it.

Endocrine Society’s recommended correction based on Dexcom trend arrows

Arrows showingCorrection Factor (CF)Correction dose (IU)
2 arrows straight up<25
25-50
50-75
>75
+4.5
+3.5
+2.5
+1.5
1 arrow straight up<25
25-50
50-75
>75
+3.5
+2.5
+1.5
+1
1 arrow slightly up<25
25-50
50-75
>75
+2.5
+1.5
+1
+0.5
1 arrow to the side <25
25-50
50-75
>75
No adjustment
No adjustment
No adjustment
No adjustment
1 arrow slightly down <25
25-50
50-75
>75
-2.5
-1.5
-1-
0.5
1 arrow straight down <25
25-50
50-75
>75
-3.5
-2.5
-1.5
-1
2 arrows straight down <25
25-50
50-75
>75
-4.5
-3.5
-2.5
-1.5

Source: https://academic.oup.com/jes/article/1/12/1445/4642923

Let’s sum it up with an example:

I’ve just rolled out of bed in the morning, had no lows overnight, and I’m about to sit down for a meal of 20 grams of carbohydrates. My blood sugar is 120 mg/dL (6.8 mmol/l) with two arrows up. This means that I’ll have to do three calculations:

  1. First, I have to calculate the dose for my carbs. Assuming my Insulin to Carb Ratio (ICR) is 10, I’d need 2 IU of insulin to cover my 20 grams of carbs.
  2. Secondly, I’ll have to calculate my correction dose based on my current blood sugar. If my CF is 25 and my target blood sugar is 95 mg/dl (5.3 mmol/l), my correction dose is 1 IU (120-95 = 25 and 25/25 = 1 IU).
  3. Thirdly, I’d have to calculate the correction based on the arrows. Since I have two arrows up and my CF is 25 according to the recommendations, I’ll need a 3.5 IU correction.

Adding all that up my total dose would be 2 IU + 1 IU + 3.5 IU = 6.5 IU. 

Had my blood sugar been lower than my target or had I seen downwards arrows, I would have ended up with a lower recommended dose.

Just to make sure that makes sense, let’s do another example:

I again just rolled out of bed and am about to sit down for a meal of 20 grams of carbohydrates, but now my blood sugar is 120 mg/dL (6.8 mmol/l) with one arrow straight down.

All that changed compared to the last example is the direction of the arrow, which changes calculation #3.

Since I now have one arrow straight down and my CF is 25, according to the recommendations I’ll need a -2.5 IU correction.

Adding all that up, my new total dose would be 2 IU + 1 IU – 2.5 IU = 0.5 IU 

Phew, that’s a lot of math. Check out the next section on how I’d simplify it.

My advice for using the Dexcom trend arrows

The endocrine recommendations make complete sense to me, but it’s a lot of math! They also don’t allow you to take less than 0.5 IU in correction (which can be an issue if you’re very insulin sensitive).

I would rather use a bolus calculator than do all that math every time I need to bolus for a meal or a correction dose. If you have a pump, you most likely have a built-in bolus calculator. If you are on multiple daily injections (MDI) like me, you’ll have to look elsewhere.

In the past, I’ve used an app called RapidCalc for those calculations. I now use InPen, a SmartPen that sends data directly to my phone via Bluetooth and has an associated app that calculates doses and keeps track of IOB.

The InPen app and RapidCalc basically do the same as a bolus calculator, except for actually giving me my dose (please note that the RapidCalc app is not FDA-approved).

After finishing the base calculation in your bolus calculator, you can then manually add in the trend arrow correction based on your CF.

Remember to react based on what you’ll be doing the next 1 to 4 hours, not on what you’re doing when you’re about to bolus.

If you’re planning to exercise or just move more than usual (like go for a walk, shopping, cleaning, gardening, etc.) you might not need to react as aggressively as the guidelines state.

Generally, it’s recommended that you reduce your bolus and potentially also your basal (if you use a pump) before exercise. Because regular rapid insulin is active in the body for up to 4 hours (some even see a tail up to 6 hours after injection), you need to think before you bolus.

You can read my post about adjusting insulin for exercise for a more in-depth explanation.

There are new ultra-fast acting insulins available now and the endocrine guidelines do not work for these. If you use Afrezza or Fiasp, please know that these insulins peak much faster and applying the calculations shown above could potentially be extremely dangerous.

When to NOT use the CGM trend arrows

All the publications advocating for using the trend arrows for making insulin adjustments also state that there are some situations where you should absolutely NOT use them or at least take severe precautions.

If you plan on starting to make adjustments to your insulin dose using trend arrows, please read these precautions first.

4-hour eating window

The endocrine society recommends that the trend arrows are not used during the 4 hours after having a meal with a bolus. Instead, they recommend the following:

  • 2 hours post-meal – Don’t correct high blood sugars to prevent insulin stacking
  • 2-4 hours post-meal, blood sugar 150-250 mg/dL (8.3-13.9 mmol/l) with one or two arrows up – Consider using CF to adjust
  • 2-4 hours post-meal, blood sugar >250 mg/dL (13.9 mmol/l) with one or two arrows up – Confirm with a fingerstick, test for ketones (if >300 mg/dL/16.7 mmol/l), correct using injections, if after 1 hour you still see two arrows up, repeat
  • 2-4 hours post-meal, blood sugar near 150 mg/dL (8.3 mmol/l) with one arrow slightly down – Check again in 30-min
  • 2-4 hours post-meal, blood sugar near 150 mg/dL (8.3 mmol/l) with one or two arrows down – Check again in 15-min
  • 2-4 hours post-meal, blood sugar near 100 mg/dL (5.6 mmol/l) with one arrow slightly down OR one arrow down – Consider eating 15 g of carbs, recheck in 20-min. If >70 mg/dL (3.9 mmol/l) and downward arrows, confirm with a fingerstick and consider another 15 g of fast-acting carbs 
  • 2-4 hours post-meal, blood sugar near 100 mg/dL (5.6 mmol/l) with two arrows down – Follow the instructions above (f) but eat 30 g carbs

Rapidly rising blood sugar

If you see 2 arrows up on your receiver or app before a meal, it’s recommended that you are diligent with your pre-bolus and inject your insulin 15 to 20 min before eating.

Rapidly decreasing blood sugar

If you see 2 down on your receiver or app before a meal, it’s recommended that you wait and inject your insulin when you start eating or if you’re close to 150 mg/dL (8.3 mmol/l) hold off and don’t inject until you see your BGs leveling off.

Fragile / older adults

For fragile or older adults, the endocrine society recommends a less aggressive insulin dose adjustment to limit the risk of hypoglycemia.

For upward rising arrows, they recommend a 50% reduction of the suggested adjustment (e.g. from 1 IU to 0.5 IU) and for downward arrows, they recommend a 50% increase in reduction (e.g. from 1 IU to 2 IU).

Sick day management and medication considerations

Certain medications, both prescription and OTC, can interfere with your CGM readings, and trend arrows should consequently not be used for insulin adjustments. Rather than relying completely on your CGM, accompany your CGM readings with finger sticks. 

Always use common sense before dosing

As with any tool in our diabetes tool kit, I’d encourage you to never just apply the recommendations blindly.

If you have a feeling that the recommended dose might be off, sometimes relying on how well you know your body is the way to go. And although the Dexcom G5 and G6 CGMs are approved for dosing by the FDA, they can sometimes be inaccurate, so I’ll always confirming with a fingerstick before making any large dosing adjustments.



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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

    Ingredients

    • 1lb


      ground chicken

    • 1


      onionchopped

    • 1tsp


      garlicminced

    • 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

    Instructions
    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!

    Shop my Cookbooks

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



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

    By electricdiet / March 17, 2020


    Abstract

    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.

    Introduction

    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).

    Immunoblotting

    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.

    Results

    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.

    Discussion

    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.

    Medication

    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.

    Biguanides

    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

    Sulfonylureas

    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

    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)

    Insulin

    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

      Ingredients

      • 1 1/2cups


        gingersnap crumbs

      • 6tablespoons


        buttermelted

      • 1teaspoon


        vanilla extract

      • 1/2cup


        dried cranberries or craisins

      • 1/3cup


        white chocolate chips

      • 1/3cup


        chopped pecans

      • 2/3(14-ounce) can


        fat-free sweetened condensed milk

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


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


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

      Recipe Notes

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

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

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

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

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

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

      Best Cookie Swap Cookies Recipe Also Makes Perfect Holiday Homemade Gifts

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

      Too Hot in the Kitchen Has So Many Simple Sassy Recipes

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

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

      Excited To Find Reduced Sugar Craisins for Cranberry White Chocolate Bars

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

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

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

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

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

      The Best Kitchen Gadgets List!

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

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

      Another Favorite Bar Cookie For Best Cookie Swap Cookies

      White Chocolate Recipes Make Sensational Seasonal Holiday Recipes

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

      Favorite Mini Spatula Perfect For Bar Cookies

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

      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


      Discussion

      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.

      Conclusion

      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.

      Appendix

      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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