Low-Carb Pound Cake | Diabetes Strong

By electricdiet / January 1, 2021


This rich and buttery low-carb pound cake has the perfect texture and tastes so indulgent! But with only 2.3 net carbs per slice, it’s a Keto-friendly treat you can enjoy any time.

Two slices of pound cake on a white plate next to a fork

Do you know where the dessert “pound cake” gets its name?

Traditionally, this cake was made with exactly one pound of each of the ingredients, which were flour, butter, eggs, and sugar. What a great, simple recipe.

However, if you’re looking for a Keto-friendly treat, you may not be too keen on that much flour and sugar. So why not whip up a wonderful loaf of this low-carb pound cake instead?

Just like the original, this recipe turns out so delicious, buttery, and rich. Plus, it has that classic soft texture that makes it oh-so-indulgent.

And it takes less than 10 ingredients to make! Okay, maybe that’s not QUITE as simple as 4 ingredients… but it doesn’t get much easier than mixing a few ingredients and throwing the loaf in the oven.

Not to mention, the result is SO worth it!

How to make low-carb pound cake

This simple recipe only takes about 15 minutes to prep. Then, all you have to do is throw it in the oven!

Pound cake ingredients in separate ramekins as seen from above

Step 1: Preheat your oven to 350 F (180 C) and line a small loaf tin with parchment paper.

Step 2: Add the butter and erythritol to a large mixing bowl. With an electric mixer, beat on high until light and fluffy.

Pound cake batter mixed with hand beaters in a glass bowl next to a ramekin with 3 eggs

Step 3: Beat in the eggs, two at a time, until well incorporated. Next, add the vanilla extract and beat on slow until mixed in.

Step 4: Add the almond flour, baking powder, salt, psyllium husk powder, and xanthan gum, then mix well. The dough will be quite thick but should not dry out.

Pound cake batter mixed with hand beaters in a glass bowl

Step 5: Spoon the dough into the prepared loaf pan and smooth over the top.

Pound cake batter in the loaf pan, as seen from above

Step 6: Bake for about 50 minutes or until a toothpick inserted into the middle comes out clean. After 30 minutes, if the top is browning too quickly, cover the loaf with foil to prevent burning.

Step 7: Remove from the oven and allow to cool completely.

Once the pound cake loaf has cooled, it’s ready to be sliced and served!

What holds this pound cake together?

When it comes to low-carb baking, texture is everything. You don’t want your baked goods to turn out dry or crumbly… ESPECIALLY not pound cake!

That’s why this recipe calls for both psyllium husk powder and xanthan gum. Together, these ingredients create the right texture and bind the batter together.

Keep in mind that xanthan gum is best added gradually to avoid clumping. I recommend sprinkling it into the batter while the electric mixer is running to evenly distribute it throughout the batter.

Close-up of two slices of pound cake of a white plate with a fork

Storage

This recipe is for 10 slices of pound cake. So unless you’re baking for a crowd or for family over the holidays, you’ll likely have some leftovers!

Simply place the loaf or slices in an airtight container and store in the refrigerator for up to 5 days. When you’re ready to enjoy, you can let the slices come up to room temperature or eat them chilled.

Other low-carb dessert recipes

Just because you’re watching your carb intake doesn’t meant you can’t enjoy the sweeter things in life! Thanks to a few ingredient swaps, it’s easy to make Keto-friendly desserts. Here are a few of my favorite recipes I think you’ll enjoy:

You can also check out my roundup of 10 delicious keto fat bomb recipes for more low-carb inspiration.

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

Recipe Card

Low-Carb Pound Cake

This rich and buttery low-carb pound cake has the perfect texture and tastes so indulgent! But with only 2.3 net carbs per slice, it’s a Keto-friendly treat you can enjoy any time.

Prep Time:15 minutes

Cook Time:50 minutes

Total Time:1 hour 5 minutes

Servings:10

Low-Carb pound cake loaf on a white serving tray with two slices cut

Instructions

  • Preheat your oven to 350 F (180 C) and line a small loaf tin with parchment paper.

  • Add the butter and erythritol to a large mixing bowl. With an electric mixer, beat on high until light and fluffy.

  • Beat in the eggs, two at a time, until well incorporated. Next, add the vanilla extract and beat on slow until mixed in.

  • Add the almond flour, baking powder, salt, psyllium husk powder, and xantahn gum, then mix well. The dough will be quite thick but should not dry out.

  • Spoon the dough into the prepared loaf pan and smooth over the top.

  • Bake for about 50 minutes or until a toothpick inserted into the middle comes out clean. After 30 minutes, if the top is browning too quickly, cover the loaf with foil to prevent burning.

  • Remove from the oven and allow to cool completely.

Recipe Notes

This recipe is for 10 servings. If you cut the loaf into 10 slices, each serving will be one slice.
Leftovers can be stored in an airtight container in the refrigerator for up to 5 days. You can eat them chilled from the refrigerator or let them warm to room temperature.

Nutrition Info Per Serving

Nutrition Facts

Low-Carb Pound Cake

Amount Per Serving (1 slice)

Calories 127
Calories from Fat 95

% Daily Value*

Fat 10.6g16%

Saturated Fat 3.5g18%

Trans Fat 0g

Polyunsaturated Fat 0g

Monounsaturated Fat 0g

Cholesterol 74mg25%

Sodium 410.9mg17%

Potassium 29.6mg1%

Carbohydrates 3.8g1%

Fiber 1.5g6%

Sugar 0.4g0%

Protein 4.7g9%

Net carbs 2.3g

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

Course: Dessert

Cuisine: American

Keyword: gluten-free, keto, keto pound cake, low carb, Low-carb pound cake, pound cake



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A Multifunctional Role of Leucine-Rich α-2-Glycoprotein 1 in Cutaneous Wound Healing Under Normal and Diabetic Conditions

By electricdiet / December 30, 2020


Abstract

Delayed wound healing is commonly associated with diabetes. It may lead to amputation and death if not treated in a timely fashion. Limited treatments are available partially due to the poor understanding of the complex disease pathophysiology. Here, we investigated the role of leucine-rich α-2-glycoprotein 1 (LRG1) in normal and diabetic wound healing. First, our data showed that LRG1 was significantly increased at the inflammation stage of murine wound healing, and bone marrow–derived cells served as a major source of LRG1. LRG1 deletion causes impaired immune cell infiltration, reepithelialization, and angiogenesis. As a consequence, there is a significant delay in wound closure. On the other hand, LRG1 was markedly induced in diabetic wounds in both humans and mice. LRG1-deficient mice were resistant to diabetes-induced delay in wound repair. We further demonstrated that this could be explained by the mitigation of increased neutrophil extracellular traps (NETs) in diabetic wounds. Mechanistically, LRG1 mediates NETosis in an Akt-dependent manner through TGFβ type I receptor kinase ALK5. Taken together, our studies demonstrated that LRG1 derived from bone marrow cells is required for normal wound healing, revealing a physiological role for this glycoprotein, but that excess LRG1 expression in diabetes is pathogenic and contributes to chronic wound formation.

Introduction

Wound healing is a natural reparative response to tissue injury. It proceeds through four continuous and overlapping phases: homeostasis, inflammation, proliferation, and tissue remodeling (1). Failure to progress through these phases in an orderly manner leads to impaired wound healing, which represents one of the common causes of morbidity associated with diabetes, affecting ∼25% of individuals with diabetes (2). These wounds frequently serve as portals of entry for bacterial infection that may lead to sepsis and lower-extremity amputation (3). Staggeringly, patients with lower-extremity amputation have a 5-year mortality rate of up to 50% (4). With the rising prevalence of diabetes, the incidence of wound complications is expected to increase substantially, posing a significant socioeconomic burden (5).

A plethora of factors contributes to delayed wound closure in patients with diabetes, such as excessive neutrophil infiltration and activation, impaired angiogenesis, and defective epithelial cell migration and proliferation (6). These defects lock the wound into a self-perpetuating inflammatory stage (7), which causes further tissue injury by increasing the production of inflammatory cytokines, reactive oxygen species, destructive enzymes, and cytotoxic extracellular traps in a process termed NETosis (8) (where NET is neutrophil extracellular trap). Thus, targeting inflammation serves as an attractive strategy to kick-start the proliferation phase of wound healing and promote repair. A number of anti-inflammatory agents have been developed over the last 20 years (9). Despite effectiveness in promotion of wound closure in rodent models, limited success has been achieved in clinical trials (10). This is likely due to the highly dynamic and complex interactions between different types of cell, extracellular matrix components, and soluble factors present in the wound microenvironment. A better understanding of the molecular mechanisms underlying diabetes-associated healing deficiency will guide the development of more effective therapeutics to treat wounds that do not respond sufficiently to good standard care.

Leucine-rich α-2-glycoprotein 1 (LRG1) is a secreted glycoprotein that was previously reported to regulate pathological neovascularization in the eye by switching the angiostatic TGFβ1-Smad2/3 signaling toward the proangiogenic TGFβ1-Smad1/5/8 signaling in endothelial cells (11). Besides its role in ocular angiogenesis, LRG1 is intimately associated with many inflammatory and autoimmune conditions (1214) and tumor malignancy (1517), which shares fundamental molecular mechanisms with chronic wound healing (18). Recently, elevated serum LRG1 levels were reported in patients with diabetes with peripheral arterial disease (19), a major risk factor for diabetic foot ulcers (DFU) (20). Paradoxically, exogenous LRG1 was reported to accelerate wound healing by promoting keratinocyte migration in animal models (21). Here, we characterized LRG1 expression level and pattern in wound tissue, investigated its contribution to wound healing under normal and diabetic condition using Lrg1-null mice, and explored its mechanism of action.

Research Design and Methods

Human Sample Analysis

This study was approved by Khoo Teck Puat Hospital Ethics Review Board (NHG Domain Specific Review Board). Adults between 21 and 90 years old with type 2 diabetes seen in Diabetes Centre of Khoo Teck Puat Hospital were enrolled into this study. Fasting blood and debrided tissues were collected from patients with ulcers during their podiatry assessment clinic. Fasting blood samples were centrifuged within 1 h after collection and kept at 4°C during this period. Thereafter, they were stored at −80°C in aliquots and used without additional freeze-thaw cycles. Devitalized tissue was obtained from desloughing and debridement performed as part of usual care. These samples were stored in liquid nitrogen until retrieval for assays described below. Control samples were obtained from patients without type 2 diabetes with venous ulcers in the same clinic. Serum level of LRG1 was measured using an ELISA kit (Immuno-Biological Laboratories, Hamburg, Germany) according to the manufacturer’s instructions.

Animals and Induction of Diabetes

C75BL/6J mice were purchased from InVivos (Singapore). Lrg1−/− mice were originally generated by the University of California, Davis, Knockout Mouse Project (KOMP) Repository Collection (https://www.komp.org) and were a generous gift from J. Greenwood and S.E. Moss (UCL Institute of Ophthalmology). Animal experiments were performed in compliance with the guidelines of the Institutional Animal Care and Use Committee (ARF-SBS/NIE-A0268/A19036) of Nanyang Technological University and the Guide for Care and Use of Laboratory Animals published by the National Institutes of Health. Diabetes was induced in 6- to 8-week-old male mice by intraperitoneal injection of 50 mg/kg streptozotocin (STZ) (50 mmol/L sodium citrate buffer, pH 4.5) for five consecutive days as previously described (22). Diabetes was confirmed when fasting blood glucose (FBG) was >200 mg/dL.

Creation of Full-Thickness Cutaneous Wounds

Six full-thickness cutaneous wounds were created on mouse dorsal skin using 4-mm Integra Miltex Standard Biopsy Punches (Thermo Fisher Scientific). Wounds were imaged daily with a digital camera. Wound size was quantified with use of ImageJ (National Institutes of Health). We used 6-mm Integra Miltex Standard Biopsy Punches (Thermo Fisher Scientific) for biopsy collection at different time points following injury. The mouse excisional wound splinting model was employed as previously described (23).

Bone Marrow Transplantation

Six-week old mice were irradiated at two doses of 5.5 Gy irradiation using a BIOBEAM GM γ irradiation device (Gamma-Service Medical, Leipzig, Germany). Bone marrow cells (BMCs) from female mice were harvested and filtrated through a 70-μm cell strainer (Falcon). BMCs (3 × 106) were intravenously injected into the irradiated recipient mice through the tail vein 24 h after the irradiation. Eight weeks after reconstitution, wounds were created at flanks of recipient mice.

Isolation and Flow Cytometry Analysis of Myeloid Cells From Wound Tissue

Wound tissues were digested in Iscove’s modified Dulbecco’s medium (Thermo Fisher Scientific) containing 2% FBS, 2 units/mL DNase I (Roche, Switzerland), and 1 mg/mL collagenase D (Roche) and passed through a 40-mm cell strainer for obtaining single-cell suspension. Red blood cells were lysed using 0.89% NH4Cl lysis buffer and removed by centrifugation. Cell pellet was resuspended and preincubated with anti–Fc receptor antibody (clone 2.4G2) followed by further incubation with anti-mouse BUV737-labeled CD45 (clone 30-F11), allophycocyanin-Cy7–labeled anti-mouse CD11b (clone M1/70), anti-mouse F4/80 (clone BM8), BUV395-labeled anti-mouse Ly6G (clone 1A8), BV605-labeled anti-mouse Ly6c (clone HK1.4), phycoerythrin-Cy7–labeled anti-mouse CD11c (clone N418), and BV421-labeled anti-mouse I-A/I-E clone (M5/114.15.2). Dead cells were labeled with fixable viability stain 510 (BD Biosciences, San Jose, CA). Cells were then fixed and permeabilized before being stained with anti–LRG-1 antibody (cat. no. 13224-1-AP; Proteintech) followed by FITC-labeled donkey anti-rabbit IgG (clone Poly4064). Cells were washed and subjected to analysis on a five-laser flow cytometer (BD LSRFortessa; BD Biosciences). Data were analyzed with FlowJo software (TreeStar, Ashland, OR).

Cells and Cell Culture

Primary mouse and human peripheral blood neutrophils (Institutional Review Board of Nanyang Technological University [IRB-2014-04/27]) were isolated, purified, and cultured as previously described (24,25). Human neutrophil-like cells (dHL-60) were derived from human promyelocytic leukemia cell line (HL-60; ATCC) by incubation with 1% DMSO (Sigma-Aldrich) for 7 days. Human dermal microvascular endothelial cells (HDMECs) (PromoCell), normal human dermal fibroblasts (PromoCell), human keratinocyte line HaCaT (ATCC), and Freestyle 293-F cells (Gibco, Thermo Fisher Scientific) were maintained according to the supplier’s instruction. Cells were treated with 20 μg mL−1 recombinant human LRG1 (rhLRG1), LDN193189 (100 nmol/L) (Sigma-Aldrich), SB431542 (10 μmol/L) (Sigma-Aldrich), and MK-2206 (10 μmol/L) (Selleck Chemicals) and as indicated.

Histology, Immunohistochemistry, and Immunofluorescence

Mice skin tissues were fixed in 4% paraformaldehyde and embedded in paraffin following a standard protocol. Paraffin sections (5 μm thick) were subjected to staining with hematoxylin-eosin or LRG1 antibody (cat. no. 13224-1-AP; Proteintech). For immunofluorescence staining, skin tissues were embedded in O.C.T. Compound (Thermo Fisher Scientific). Cryopreserved skin sections (5 μm thick) were stained with primary antibodies against LRG1 (13224-1-AP; Proteintech), F4/80 (MCA497; Bio-Rad), CD11b (130-113–235; Miltenyi Biotec), CD31 (550274; BD Biosciences), Myeloperoxidase (MPO) (ab9535; Abcam, Cambridge, U.K.), and Ki67 (ab15580; Abcam) followed by staining with Alexa 488 or Alexa 594 secondary antibodies (Thermo fisher Scientific). Images were captured using Leica DM5500 microscope (Leica Microsystems) or Carl Zeiss LSM 710 confocal microscopy (Zeiss, Berlin, Germany), processed using Adobe Photoshop CS6, and analyzed using ImageJ by investigators who were blinded to the identity of the experimental groups.

Quantitative Real-time PCR

Total RNA was extracted and purified with RNAzol RT (cat. no. 888-841-0900; Molecular Research Center) before being reverse transcribed to cDNA with qScript cDNA SuperMix (157031; Quanta Biosciences). PCR was conducted with PrecisionFAST qPCR Master Mix (FASR-LR-SY; Primerdesign Ltd, U.K.) with use of Applied Biosystems StepOnePlus Real-Time PCR System (Life Technologies). The expression levels of respective target genes were normalized to GAPDH, and relative gene expressions were calculated using standard 2−ΔΔCT method. Primers used in this study are listed in Table 1.

Table 1

qRT-PCR primer sequences

SDS-PAGE and Western Blotting

Cells or tissues were lysed on ice in radioimmunoprecipitation assay buffer containing 0.0037 mg/mL protease inhibitor (Roche), 1 mmol/L dithiothreitol (cat. no. D9779; Sigma-Aldrich), 1 mmol/L phenylmethylsulfonyl fluoride (P7626; Sigma-Aldrich), and 100 mmol/L phosphatase inhibitors (P0044 [for cell signaling experiments]; Sigma-Aldrich). Proteins were separated by 10% SDS-PAGE before being transferred onto an Immobilon-PSQ PVDF Membrane (ISEQ-00010; Merck Millipore). Blots were probed with LRG1 antibody (rabbit monoclonal, 13224-1-AP; Proteintech), phosphorylated (phospho-)Smad1/5 antibody (rabbit monoclonal, 9516; Cell Signaling Technology), anti-SMAD1+SMAD5 antibody (mouse monoclonal, ab75273; Abcam), histone H3 (citrulline R2 + R8 + R17) antibody (rabbit polyclonal, ab5103; Abcam), histone H3 antibody (rabbit polyclonal, ab1791; Abcam), phospho-Akt antibody (rabbit monoclonal, 4060; Cell Signaling Technology), Akt antibody (rabbit monoclonal, 9272; Cell Signaling Technology), cyclin D1 antibody (rabbit monoclonal, 2922; Cell Signaling Technology), or GAPDH antibody (rabbit polyclonal, sc-25778; Santa Cruz Biotechnology), followed by horseradish peroxidase–conjugated secondary antibodies (Santa Cruz Biotechnology). Densitometry was performed by use of ImageJ software.

Molecular Biological Methods

Human LRG1 (NM_052972) carrying a 6xHis tag expression vector, pcDNA3.1-LRG1, was generated as previously described (11). rhLRG1 protein was expressed in Freestyle 293 T cells (Invitrogen) and purified as previously described (11). siRNA oligonucleotides (cat. no. L-015179-01; Dharmacon) were used for LRG1 gene knockdown, while control siRNA (D-001810-10-20; Dharmacon) was used as a negative control. Transfection was performed using Lipofectamine 2000 (Invitrogen) for HaCaT cells and RNAiMAX (Invitrogen) for dHL-60 cells according to the manufacturer’s protocol.

Flow Cytometry

Cell-surface CD11b and L-selectin were measured using flow cytometry. dHL-60 cells were treated with rhLRG1 (20 μg/mL) for 1 h, 6 h, or 24 h before being washed with flow buffer (0.1% FBS/PBS). Cells were then incubated with L-selectin antibody (mouse monoclonal, cat. no. sc-390756; Santa Cruz Biotechnology), followed by staining with Alexa Fluor 488 goat anti-mouse (IgG) secondary antibody (Thermo Fisher Scientific) and CD11b-phycoerythrin (130-113-235; Miltenyi Biotec) and fixation in 1% paraformaldehyde. Cell acquisition (10,000 cells per sample) was carried out on BD LSRFortessa X-20 (BD Biosciences) and analyzed with use of FlowJo software (BD).

Neutrophil Adhesion Assay

Lrg1-knockdown dHL-60 cells or dHL-60 cells treated with rhLRG1 (20 μg/mL) cells were labeled with CellTracker Green CMFDA Dye (Invitrogen) before being seeded onto the confluent HDMEC monolayer. In the case of Lrg1-knockdown dHL-60 cells, HDMECs were pretreated with tumor necrosis factor (TNF)α (50 ng/mL; PeproTech). Two hours later, nonadherent neutrophils were subsequently removed by washing with prewarmed PBS. Adherent neutrophils were imaged with the Eclipse Ti-E Inverted Research Microscope (Nikon Instruments, Tokyo, Japan) and quantified by measurement of the fluorescence intensity with Synergy H1 microplate reader (BioTek) at the wavelength of 492 nm/517 nm.

Proliferation Assay

HDMECs were cultured in EGM-2MV media (Lonza, Basel, Switzerland) until 30% confluent and starved in EBM-2 medium (Lonza) containing 0.2% FBS for 16 h before being treated with rhLRG1 (20 μg/mL) for 48 h. Cells were then fixed and stained with Ki67 antibody (rabbit polyclonal antibody, cat. no. ab15580; Abcam) for detection of proliferating cells and DAPI (Thermo Fisher Scientific) for staining cell nuclei. Images were taken with Eclipse Ti-E Inverted Research Microscope and analyzed with ImageJ. Proliferation rate was calculated as the percentage of Ki67+ cells.

Transwell Migration Assay

HDMECs were pretreated with rhLRG1 for 24 h before being seeded onto rat tail collagen I (100 μg/mL; Corning)–coated Transwell Inserts (8 μm) (Corning). EBM2 medium containing 5% FBS served as a chemoattractant. After 5-h incubation, migrated cells were fixed and stained with DAPI (Thermo Fisher Scientific). Images were taken with Eclipse Ti-E Inverted Research Microscope and analyzed with Image J.

Matrigel Tube Formation Assay

Matrigel Growth Factor Reduced Basement Membrane Matrix (Corning) (60 μL) containing vehicle control or rhLRG1 (20 μg/mL) was added to each well of a 96-well plate and incubated for 30 min at 37°C for polymerization. HDMECs in 100 μL EBM-2 medium containing vehicle control or rhLRG1 (20 μg/mL) were seeded onto the polymerized Matrigel gel. HDMEC tube formation was imaged using phase-contrast mode on Eclipse Ti-E Inverted Research Microscope following overnight incubation, and tube formation was analyzed with ImageJ.

Trypan Blue Exclusion Assay

Transfected and rhLRG1 (2 μg/mL)–treated HaCaT cells were trypsinized and stained with Trypan blue before being counted with a hemocytometer under a phase-contrast microscope.

Scratch Wound Healing Assay

Confluent HaCaT cells were starved in DMEM medium containing 0.2% FBS (Thermo Fisher Scientific) for 24 h. A scratch was made to HaCaT cell monolayer with a sterile p200 pipette tip. The cells were washed and subsequently cultured in complete DMEM. Cells were imaged at 0 h and 24 h after scratching with Eclipse Ti-E Inverted Research Microscope 24 h after scratching. Images were analyzed with ImageJ.

SYTOX Green Assay

Primary mouse and human peripheral blood neutrophils were seeded onto each well of a 96-well black polystyrene microplate with a clear flat bottom (Corning). Cells were treated with 5 μmol/L calcium ionophore A23187 or rhLRG1 (100 μg/mL) for 4 h before incubation with the DNA dye SYTOX Green (1 μmol/L) (Invitrogen) for 15 min. NET formation was determined by measurement of the fluorescence intensity with Synergy H1 microplate reader (BioTek) at the wavelength of 504 nm/523 nm.

Induction of NETosis

Primary mouse neutrophils in RPMI-1640 medium containing 25 mmol/L HEPES were seeded onto each well of an eight-well chamber slide. Twenty minutes later, attached cells were incubated with 5 μmol/L calcium ionophore A23187 for 2 h before being fixed and stained with H3Cit antibody (1:100 dilution; Abcam) and DAPI (Thermo Fisher Scientific). Images were taken with Carl Zeiss LSM 710 confocal microscopy and analyzed with ImageJ.

Statistical Analysis

Data are represented as mean ± SEM. Statistical analyses were performed using unpaired, two-tailed Student t test or one-way/two-way ANOVA followed by Tukey/Bonferroni post-test analysis using Prism 5 (GraphPad Software Inc.).

Data and Resource Availability

Complete data sets generated and analyzed during the current study are available from X.W. on request.

Results

LRG1 Is Produced by Wound-Infiltrating Bone Marrow–Derived Cells Following Injury

To address the role of LRG1 in wound healing, we examined the expression of LRG1 in normal C56BL/6 mouse skin tissues by immunohistochemistry and revealed a very weak staining in the dermis (Fig. 1A). Western blot was used to evaluate LRG1 expression in wound tissues at various time points following injury (Fig. 1B). Our data showed that LRG1 was increased as early as 6 h post-injury and reached its highest level 24 h after wounding. LRG1 level then declined gradually and went back to basal level on day 5 following injury. It is worth noting that LRG1 expression in surrounding intact skin tissues remained low throughout the wound healing process (Supplementary Fig. 1).

Figure 1
Figure 1

LRG1 is elevated in cutaneous wounds. A: Immunohistochemical detection of LRG1 (brown) showed low expression of LRG1 in normal mouse skin. Scale bar: 100 μm. B: Representative Western blot (left) and densitometry analysis (right) of wounds harvested at different time points. C: Immunofluorescence staining detecting LRG1 (green), CD11b (red), or DAPI (blue) in day 1 mouse wounds. Scale bars: 120 μm and 20 μm. D: qRT-PCR analysis of day 1 wounds demonstrated reduced Lrg1 expression in irradiated wild-type mice with Lrg1−/− BMC transplantation in comparison with wild-type mice receiving wild-type BMCs. All images are representative; data are represented as mean (95% CI; P) of n ≥ 5 mice per group. Significance was determined by one- or two-way ANOVA followed by Tukey multiple comparisons test. *P < 0.05, ***P < 0.001. WB, Western blot; WT, wild type.

Immunofluorescence staining was used to identify the source of LRG1 during wound healing. We found that Lrg1+ cells were mainly present in the provisional matrix and were colocalized with CD11b+ myeloid cells in day 1 wound tissues (Fig. 1C). Flow cytometry analysis revealed that LRG1 is expressed by Ly6G+/CD11b+ neutrophils, Ly6C+/CD11b+ monocytes, Ly6C/F4/80+/CD11b+ macrophages, and Ly6C/F4/80+/MHC II+/CD11c+/CD11b+ dendritic cells (Supplementary Fig. 2), all of which are bone marrow–derived cells. For confirmation of this observation, allogenic bone marrow transplantation (BMT) study was carried out in irradiated wild-type mice with use of BMCs from Lrg1−/− mice and wild-type littermate controls. Similar to what we observed in unirradiated C56BL/6 mice, quantitative real-time-PCR (qRT-PCR) showed that Lrg1 transcript was significantly higher in day 1 wound tissues of wild-type mice transplanted with wild-type BMCs, whereas Lrg1 was not induced in those that received Lrg1−/− BMCs (Fig. 1D). These data suggest that wound-infiltrating bone marrow–derived cells (BMDCs) serve as a major source of LRG1 during cutaneous wound healing.

LRG1 Is Critical for Timely Wound Closure

For elucidation of the functional role of LRG1 in cutaneous would healing, 4-mm full-thickness wounds were created on the dorsal skin of wild-type and Lrg1−/− mice. Lrg1−/− mice demonstrated a significant delay in wound closure as compared with wild-type controls in both excisional wound model and excisional wound splinting model (Fig. 2A and Supplementary Fig. 3). As LRG1 is markedly induced at the inflammatory phase of wound healing, the number of wound-infiltrating immune cells was analyzed in the wound bed of wild-type and Lrg1−/− mice. Neutrophils are the first inflammatory cells to be recruited to the wound bed (26). After performing their functions, apoptotic neutrophils and tissue debris are cleared by macrophages, which eventually leads to the resolution of inflammation (27). Immunofluorescence staining showed a significant reduction in the number of wound-infiltrating MPO+ neutrophils (Fig. 2B) and F4/80+ macrophages (Fig. 2C) 1 day and 5 days post-injury, respectively. As BMDCs are major LRG1-producing cells, we next performed BMT between Lrg1−/− mice and wild-type controls. Mice subjected to irradiation were previously reported to show delayed wound repair (28). In this study, irradiated Lrg1−/− mice and wild-type control mice were intravenously transplanted with wild-type or Lrg1−/− BMCs, respectively (Fig. 2D). Wild-type mice transplanted with wild-type BMCs and Lrg1−/− mice transplanted with Lrg1−/− BMCs served as controls to exclude the impact of irradiation on wound healing. Consistent with what was observed in unirradiated control mice, wound closure was significantly delayed in Lrg1−/− recipients transplanted with Lrg1−/− BMCs as compared with that in wild-type recipients transplanted with wild-type BMCs. On the other hand, wound closure in wild-type recipients transplanted with Lrg1−/− BMCs was delayed substantially, whereas wild-type BMCs fully rescued the delayed wound healing in Lrg1−/− mice. Together, these data provide compelling evidence that LRG1-producing BMDCs are critical for timely wound closure.

Figure 2
Figure 2

Absence of Lrg1 leads to delayed wound healing. A: Quantification (left) and representative images (right) of wound size in wild-type and Lrg1−/− mice revealed a delayed wound closure in the absence of Lrg1. *P < 0.05. Scale bar: 1 mm. B: Representative immunofluorescence staining of MPO (green) and DAPI (blue) (top) and quantification of the presentation of MOP+ cells (bottom) in day 1 wounds of wild-type and Lrg1−/− mice; 5–10 fields per wound were analyzed. *P < 0.05. Scale bar: 30 μm. C: Representative immunofluorescence staining of F4/80 (green) and DAPI (blue) (top) and quantification of F4/80+ cells (bottom) of day 5 mouse wounds of wild-type and Lrg1−/− mice; 5–10 fields per wound were analyzed. *P < 0.05. Scale bar: 30 μm. D: Quantification (left) and representative images (right) of wound size in irradiated wild-type (WT) mice receiving BMCs from wild-type mice (WT to WT), irradiated Lrg1−/− mice receiving BMCs from Lrg1−/− mice (Lrg1−/− to Lrg1−/−), irradiated Lrg1−/− mice receiving BMCs from wild-type mice (WT to Lrg1−/− ), and irradiated wild-type mice receiving BMCs from Lrg1−/− mice (Lrg1−/− to WT). *P < 0.05: Lrg1−/− to Lrg1−/− vs. WT to WT; #P < 0.05: Lrg1−/− to WT vs. WT to WT; @P < 0.05, @@P < 0.01: WT to Lrg1−/− vs. Lrg1−/− to Lrg1−/−. Scale bar: 1 mm. All images are representative; data are represented as mean (95% CI; P) of n ≥ 6 mice per group. Significance was determined by unpaired, two-tailed Student t test between wild-type and Lrg1−/− or wound size at different time points.

LRG1 Promotes Neutrophil Adhesion via Inducing the Expression of L-Selectin

The ability of neutrophils to adhere to the endothelium is critical for their recruitment to the wound bed (29). To understand LRG1’s role in neutrophil infiltration, we investigated the ability of dHL-60 cells to adhere to a HDMEC monolayer in the presence and absence of rhLRG1. Our study showed that rhLRG1 significantly promoted dHL-60 cell adhesion to the HDMEC monolayer (Fig. 3A). As dHL-60 cells express higher levels of LRG1 compared with other skin cells (Supplementary Fig. 4), we went on investigating the consequence of siRNA-mediated LRG1 knockdown in neutrophil function and demonstrated reduced responsiveness to TNFα-induced adhesion to HDMECs (Fig. 3B). L-selectin, a cell adhesion molecule expressed on neutrophils, serves as a master regulator of neutrophil adhesion (30). We next examined whether LRG1 exerts its function through mediating the expression of L-selectin on neutrophil-like dHL-60 cells. Indeed, immunoblots showed a significant increase in L-selectin expression in dHL-60 cells subjected to 24-h treatment with rhLRG1 (Fig. 3C). Consistent with this, flow cytometry revealed a marked increase of the median of fluorescence intensity in dHL-60 following rhLRG1 treatment (Fig. 3D and Supplementary Fig. 5). However, rhLRG1 did not affect the expression of endothelial adhesion molecules including ICAM-1, VCAM-1, P-selectin, and E-selectin (Supplementary Fig. 6). Together, these data show that LRG1 promotes neutrophil adhesion, at least partially, by regulating the expression of L-selectin on neutrophils.

Figure 3
Figure 3

LRG1 mediates neutrophil adhesion. A: Representative images (left) and quantification (right) of neutrophil adhesion assay demonstrated rhLRG1 induced dH60 cell (labeled with CMFDA dye) adhesion to HDMECs. Scale bar: 200 μm. B: Representative images (left) and quantification (right) of TNFα-induced neutrophil adhesion by use of hHL-60 cells (labeled with CMFDA dye) subjected to siRNA-mediated LRG1 knockdown. Scale bar: 200 μm. C: Representative Western blot (left) and densitometry analysis (right) of L-selectin and GAPDH in rhLRG1-treated dHL-60 cells at different time points. D: Quantification of flow cytometry demonstrated an increase in L-selectinHigh population following rhLRG1treatment. All images are representative; data are presented as the mean (95% CI; P) of n ≥ 3 independent experiments per group. Significance was determined by one- or two-way ANOVA followed by Tukey multiple comparisons test or unpaired, two-tailed Student t test. *P < 0.05, **P < 0.01, ***P < 0.001. WB, Western blot.

LRG1 Promotes Epithelial Cell Proliferation and Epithelial-to-Mesenchymal Transition

Besides inflammation, reepithelialization is essential to prompt wound repair. It is achieved by orchestrated migration and proliferation of epithelial cells adjacent to the wound (31). H-E analysis revealed delayed reepithelialization in day 4 wounds of Lrg1−/− mice (Fig. 4A). Although the denuded surface is completely covered by newly formed epithelium 5 days following injury, the reconstituted epidermis is significantly thinner in Lrg1−/− mice as compared with that in wild-type controls (Fig. 4B). It was previously reported that exogenous LRG1 promotes keratinocyte migration (21). Similarly, we showed that LRG1-overexpressing HaCaT cells migrated much faster as compared with control plasmid transfected cells, whereas the migration ability of the LRG1 siRNA-treated HaCaT was significantly compromised (Fig. 4C). Activation of the partial epithelial-to-mesenchymal transition (EMT) has been reported to drive keratinocyte migration (32). Our study demonstrated that rhLRG1 was able to cause a significant induction of key EMT markers, such as fibronectin (FN1) and N-cadherin (N-Cad) (Fig. 4D). To define LRG1’s role in reepithelialization further, we examined the keratinocyte proliferation as indicated by immunofluorescence staining with Ki67 in day 3 wounds of Lrg1−/− and wild-type control mice. Our study revealed a substantial reduction in the percentage of Ki67+ keratinocytes at the wound edge of Lrg1−/− mice (Fig. 4E). Consistent with this observation, the number of viable LRG1-overexpressing HaCaT cells was significantly higher than control cells, and HaCaT cells subjected to siRNA-mediated LRG1 knockdown showed reduced viability compared with control siRNA-treated HaCaT (Fig. 4F). This observation was supported by a marked increase in cell proliferation marker cyclin D1 in rhLRG1-treated HaCaT (Fig. 4G). Together, these data suggest that LRG1 facilitates reepithelialization by promoting keratinocyte proliferation and migration.

Figure 4
Figure 4

LRG1 regulates reepithelialization during wound healing. A: Representative H-E staining (left) and quantification of reepithelialization (right) of day 4 wounds of wild-type and Lrg1−/− mice. Scale bar: 125 μm. B: Representative H-E staining (left) and quantification of epithelium thickness (right) of day 5 wounds of wild-type and Lrg1−/− mice. Scale bar: 25 μm. C: Representative images (left) and quantification of wound gap (right) in scratch wound healing assay. Scale bar: 100 μm. D: Representative Western blot (left) and densitometry analysis (right) of FN1, N-cad, and GAPDH in rhLRG1-treated HaCaT cells. E: Representative immunofluorescence staining (top) and quantification (bottom) of Ki67 (red) and DAPI (blue) in day 3 wounds. Scale bar: 30 μm. F: Quantification of viable HaCaT cells in Trypan blue exclusion assay (G). Representative Western blot (top) and densitometry analysis (bottom) of cyclin D1 and GAPDH in rhLRG1-treated HaCaT cells. All images are representative, and data are represented as mean (95% CI; P) of n ≥ 5 mice or n ≥ 3 independent experiments per treatment group. Significance was determined by unpaired, two-tailed Student t test. *P < 0.05, **P < 0.01, ***P < 0.001. WB, Western blot.

LRG1 Modulates Dermal Angiogenesis

Our previous study demonstrated an essential role of LRG1 in pathological neovascularization in the eye (11), and angiogenesis is required for the formation of granulation tissue during wound healing (1). To understand LRG1’s role in dermal angiogenesis during wound healing, day 7 wound tissues were subjected to immunofluorescence staining with an endothelial cell (EC)-specific marker, CD31. Although the total vessel area in the distal part of the skin remained unchanged (Supplementary Fig. 7), there was a significant reduction in total vessel area in the wound bed of Lrg1−/− mice (Fig. 5A). In line with the observations in macrovascular human umbilical vein endothelial cells (HUVECs) (11), rhLRG1 was able to induce HDMEC proliferation as visualized by Ki67 staining (Fig. 5B) and the ability of HDMECs to form tube-like structure in Matrigel (Fig. 5C). We also showed increased motility of rhLRG1-treated HDMECs (Fig. 5D). Mechanistically, we found that rhLRG1 significantly stimulated the phosphorylation of proangiogenic Smad1/5 in HDMECs, and blocking of TGFβ type I receptor activin-like kinase 1 (ALK1) and activin-like kinase 5 (ALK5) completely abrogated this activation (Fig. 5E). These data demonstrate an ALK1/5-dependent proangiogenic role of LRG1 in wound healing.

Figure 5
Figure 5

LRG1 modulates wound angiogenesis during wound healing. A: Representative immunofluorescence staining of CD31 (green) and DAPI (blue) (top) and quantification of vessel density (bottom) in day 7 wounds of wild-type and Lrg1−/− mice. Scale bar: 15 μm. B: Representative images of immunofluorescence staining detecting Ki67 (red) and DAPI (blue) (top) and quantification of percentage of Ki67+ cells (bottom) in HDMECs. Scale bar: 50 μm. C: Representative images (top) and quantification (bottom) of Matrigel tube formation. Scale bar: 125 μm. D: Representative images (top) and quantification (bottom) of Transwell migration assay. Scale bar: 100 μm. E: Representative Western blot (left) and densitometry analysis (right) of endothelial TGFβ-Smad1/5 signaling in HDMECs treated with rhLRG1 in the absence and presence of ALK1 inhibitor (LDN193189) or ALK5 inhibitor (SB431542). All images are representative, and data are represented as mean (95% CI; P) of n ≥ 6 mice or n ≥ 3 independent experiments per group. Significance was determined by unpaired, two-tailed Student t test. *P < 0.05, **P < 0.01, ***P < 0.001. WB, Western blot.

LRG1 Is Highly Induced in Diabetic Mice and in Humans With Diabetes

Having established an important role for LRG1 in physiological wound healing, we next examined the association between LRG1 and chronic wound healing in mice and humans with diabetes. ELISA analysis revealed a significantly higher LRG1 level in the serum of DFU patients as compared with that in venous ulcer patients (Fig. 6A). Consistent with this, we showed that LRG1 expression in ulcer tissues of DFU patients was also significantly higher than that from venous ulcer patients (Fig. 6B). To support this observation, we analyzed wound tissues collected from C57BL/6 mice subjected to STZ-induced diabetes for the expression of LRG1. As observed in DFU patients, LRG1 levels were significantly higher in wounds of diabetic mice (Fig. 6C). Consistently, qRT-PCR analysis revealed a sustained high expression of Lrg1 transcript in the wound tissue of diabetic mice throughout the wound healing process (Fig. 6D). Wound closure was significantly impaired in diabetic mice as compared with that in nondiabetic control (Fig. 6E). These data show that in the skin, LRG1 expression is further increased in both humans and mice with diabetes.

Figure 6
Figure 6

Elevated LRG1 expression is observed in diabetic humans and mice. A: ELISA analysis of LRG1 in serum from venous ulcer patients and DFU patients. B: Representative Western blot (top) and densitometry analysis (bottom) of LRG1 and GAPDH in human patients with venous ulcer and DFU. C: Representative Western blot (top) and densitometry analysis (bottom) of LRG1 and GAPDH in normal and diabetic wounds of C57BL/6 mice. D: qRT-PCR analysis of normal and diabetic wounds of C57BL/6 mice. E: Representative images (left) and quantification (right) of wound size revealed a delayed wound closure in C57BL/6 mice with STZ-induced diabetes. Scale bar: 1 mm. All images are representative, and data are represented as mean (95% CI; P) of n ≥ 6 patients or mice per group. Significance was determined by unpaired, two-tailed Student t test. *P < 0.05, **P < 0.01. WB, Western blot.

Deletion of the Lrg1 Gene Was Beneficial to Impaired Wound Healing in Diabetes

As our data thus far have demonstrated an elevated Lrg1 transcript level in wounds of both mice and humans with diabetes, we investigated whether wound closure in mice with STZ-induced diabetes is affected in the absence of Lrg1. Although STZ treatment led to a significant weight loss, there was no difference in body weight between STZ-treated wild-type and Lrg1−/− mice (Supplementary Fig. 8). Unlike what was observed in normoglycemic mice, mice with genetic deletion of Lrg1 were protected from the diabetes-induced delay in would closure (Fig. 7A). Recent studies highlighted the influence of diabetes on NET formation (8). Considering the role of LRG1 in neutrophil functions and its upregulation at the inflammatory phase of wound healing, we next studied whether LRG1 affects NETosis in mice subjected to STZ-induced diabetes. Western blot analysis showed a significant reduction in the expression of a NET marker, H3Cit, in day 3 wounds of diabetic Lrg1−/− mice (Fig. 7B). For confirmation of this observation, bone marrow–derived neutrophils were isolated from wild-type and Lrg1−/− mice and subjected to calcium ionophore–induced NETosis. Consistent with the earlier observation, Lrg1−/− neutrophils were resistant to calcium ionophore–induced expression of H3Cit (Fig. 7C). Similarly, immunofluorescence staining showed that calcium ionophore–induced NETs, as indicated by the presence of H3Cit+ neutrophils, were significantly reduced in Lrg1-deficient neutrophils (Fig. 7D). We also showed that Lrg1-deficient neutrophils formed fewer NETs in comparison with their wild-type counterparts upon calcium ionophore treatments (Fig. 7E). Complementing this observation, SYTOX Green assay showed that LRG1 supplementation significantly induced the formations of NETs in human peripheral blood neutrophils (Fig. 7F). Consistently, immunoblots demonstrated that rhLRG1 significantly induced citrullination of histone H3 in dHL-60 cells (Fig. 7G). Activation of Akt pathway was reported to mediate calcium ionophore–induced NETosis (33). We further showed that LRG1 was able to induce the phosphorylation of Akt in dHL-60 cells and the LRG1-induced expression of H3Cit and Akt phosphorylation were completely abolished in the presence of an allosteric Akt inhibitor, MK2206 (Fig. 7G). LRG1 was previously reported to signal through TGFβ type I receptor activin-like kinase 5 (ALK5) in non-ECs (34). For elucidation of whether LRG1-induced NETosis and Akt activation are dependent on ALK5, ALK5 was inhibited by SB431542, resulting in a complete abrogation of LRG1-induced phosphorylation of Akt and H3Cit (Fig. 7H). Together, our data demonstrate an important role of LRG1 in diabetic wounds and that LRG1 exerts its function through mediating NETosis in a TGFβ/ALK5/Αkt-dependent manner.

Figure 7
Figure 7

LRG1 mediates NETosis. A: Representative images (left) and quantification (right) of wound size in wild-type and Lrg1−/− mice with STZ-induced diabetes. Scale bar: 1 mm. B: Representative Western blot (top) and densitometry analysis (bottom) of H3Cit, histone H3 (H3), and GAPDH in day 3 wounds from wild-type and Lrg1−/− mice with STZ-induced diabetes. C: Representative Western blot (top) and densitometry analysis (bottom) of H3Cit, H3, and GAPDH in calcium ionophore–treated wild-type and Lrg1−/− neutrophils. D: Representative immunofluorescence staining detecting H3Cit (green) and DAPI (blue) (left) and quantification of percentage of H3Cit+ cells (right) in calcium ionophore–treated wild-type and Lrg1−/− neutrophils. Scale bar: 80 μm. E: SYTOX Green assay on calcium ionophore–treated wild-type and Lrg1−/− neutrophils. F: SYTOX Green assay on calcium ionophore–treated dHL-60 cells. G: Representative Western blot (left) and densitometry analysis (right) of H3Cit, H3, AKT, phospho-AKT (pAKT), and GAPDH in rhLRG1- and/or MK2206-treated dHL-60 cells. H: Representative Western blot (left) and densitometry analysis (right) of H3Cit, H3, AKT, phospho-AKT, and GAPDH in rhLRG1 with or without SB431542-treated dHL-60 cells. All images are representative, and data are represented as mean (95% CI; P) of n ≥ 5 mice or n ≥ 3 independent experiments per group. Significance was determined by one- or two-way ANOVA followed by Tukey multiple comparisons test or unpaired, two-tailed Student t test. *P < 0.05, **P < 0.01, ***P < 0.001. CaI, calcium ionophore; WB, Western blot.

Discussion

Impaired wound healing and subsequent formation of foot ulcers is one of the most common complications found in patients with diabetes (2). Considering the important role of inflammation in DFU pathophysiology, multiple anti-inflammatory drugs have been developed but have shown limited success (10). LRG1 is a multifunctional protein that was previously linked to neutrophil activation (35), EMT (16), and angiogenesis (11), all of which are essential for effective wound closure. Here, we investigated the role of LRG1 in both physiological and pathological cutaneous wound healing.

Overwhelming evidence indicated the association between LRG1 and various inflammatory and autoimmune conditions (1214). Infiltrating myeloid cells have been reported to act as the key source of LRG1 in psoriatic skin lesions (13) and remodeling myocardium following infarction (36). In line with these observations, our study revealed that LRG1 is predominantly produced by the wound-infiltrating CD11b+ myeloid cells. Neutrophils are among the first inflammatory cells to reach the wound bed following injury. Both impaired neutrophil function and hyperactive neutrophils have been reported to compromise wound healing (26). Despite being induced during early neutrophil differentiation (37), the role of LRG1 in neutrophil function remains to be elucidated. Our study showed that LRG1 promotes neutrophil adhesion, likely by inducing the expression of neutrophil adhesion molecule, L-selectin. Consistent with this observation, the number of wound-infiltrating neutrophils was significantly reduced in the wound bed of Lrg1−/− mice. We further showed that wild-type recipients transplanted with Lrg1−/− BMCs showed a significant delay in wound closure as compared with wild-type recipients receiving wild-type BMCs. On the other hand, wild-type BMCs were sufficient to rescue the delayed wound closure in Lrg1−/− recipients. These data support the important role of BMDC-derived LRG1 in wound healing.

Reepithelialization plays an indispensable role in wound healing, and it is driven by the proliferation and migration of keratinocytes at the wound edge (31). A previous study using exogenous LRG1 showed that LRG1 does not affect keratinocyte proliferation as demonstrated by EdU+ staining (21). Here, we showed reduced number of proliferating keratinocytes as indicated by Ki67 staining at the wound edge of Lrg1-deficient mice. The discrepancy between the two studies is likely due to the use of different cellular markers for proliferating cell detection. Ki67 is a broad cell proliferation marker that is expressed throughout the active cell cycle (G1, S, G2, and M phases) (38), whereas EdU is only incorporated in nascent DNA during the S phase (39). Therefore, EdU may provide partial information regarding the extent of cell proliferation. Supporting LRG1’s role in keratinocyte proliferation, plasmid-mediated LRG1 overexpression and siRNA-mediated LRG1 knockdown treatment significantly affect the viability of keratinocytes in vitro. We also demonstrated a promoting role of LRG1 in the expression of cell-cycle marker cyclin D1. These results are in line with previous studies that LRG1 increases proliferation of different epithelial-derived cancer cells, such as colorectal cancer cells (40), pancreatic ductal adenocarcinoma (17), non-small-cell lung cancer cells (41), and gastric cancer cells (15). Consistent with the previous report (21), we showed that LRG1 overexpression and knockdown affect keratinocyte migration. To acquire migratory capacity, quiescent epithelial cells undergo phenotypic changes to gain mesenchymal characteristics (42). Our study discovered that rhLRG1 induces the expression of EMT markers, which is consistent with the promoting effect of LRG1 in EMT and colorectal cancer metastasis (16).

The increased metabolic demand of repairing triggers angiogenesis, and failure in forming functional new vessel leads to delayed wound closure (43). Although LRG1 has previously been implicated in ocular (11) and tumor (16) angiogenesis, its role in normal blood vessel formation during wound healing was largely unknown. In this study, we observed reduced blood vessel density in the wound bed of Lrg1−/− mice. We further showed that LRG1 promotes angiogenesis by mediating HDMEC proliferation, migration, and the ability to form tube-like structures. Unlike what was observed in HUVECs (11), both ALK1 and ALK5 are required for LRG1-induced Smad1/5/8 phosphorylation in HDMECs, which is not surprising, as ALK5 kinase activity is necessary for optimal TGFβ/ALK1 action (44).

Our study showed elevated LRG1 levels in serum and wound tissues of human patients with DFU and diabetic mice, which could be explained, at least partially, by the increased infiltration of immune cells, including neutrophils and macrophages, in diabetic wounds (45). While neutrophils are beneficial to normal wound repair, excessive neutrophil infiltration and NET formation are critical culprits in chronic inflammation and delayed wound closure in diabetes (8). Mechanistically, NETosis could be triggered in a NADPH oxidase (NOX)-dependent and -independent manner (33). Our study showed that there was a reduced NETosis in Lrg1−/− mice and Lrg1−/− neutrophils are resistant to calcium ionophore–induced NOX-independent NETosis. Akt is essential for NOX-independent NETosis (33). Furthermore, we demonstrated that LRG1-mediated NET formation is dependent on activation of the Akt pathway through TGFβ type I receptor ALK5, which is in agreement with LRG1-mediated TGFβ signaling in ECs (11), fibroblasts (34), glioma cells (46), and T-helper 17 cells (47). We further demonstrated that Lrg1−/− mice are resistant to diabetes-induced delay in wound closure, especially during the inflammatory phase, which is likely due to its role in NETosis. It is worth highlighting that global Lrg1−/− mice were used in this study, whereas prompt wound healing is achieved by collaborative interactions between multiple types of cells present in the wound microenvironment (1). Our BMT and in vitro experiments provided a direct evidence of BMDC-derived LRG1 on the behavior of other types of skin cells, However, this would not exclude the possible impacts of EC and keratinocyte-derived LRG1 on inflammation, reepithelialization, and angiogenesis during wound closure. To address these questions, we are now generating cell-specific knockout mice.

In conclusion, we define here a complex but critical role of LRG1 in normal and diabetic wound healing. Lrg1 deficiency leads to a significant delay in normal wound healing as a consequence of impaired inflammation, reepithelialization, and angiogenesis. On the other hand, there is a reduced NETosis in diabetic mice with ablation of Lrg1, which protects Lrg1−/− from the diabetes-induced delay in wound healing. Targeting LRG1 may represent an attractive strategy to suppress excessive NETosis, therefore accelerating wound closure in patients with diabetes.

Article Information

Acknowledgments. The authors thank David Becker, Lee Kong Chian School of Medicine, for advice on histology analysis.

Funding. This work was supported by Singapore Biomedical Research Council SPF grant (SIPRAD) to X.W. and W.H. and Singapore National Medical Research Council DYNAMO NMRC/OFLCG/001/2017 and TAPP NMRC/OFLCG/004/2018 to X.W. Singapore Study of Macroangiopathy and Micro-vascular Reactivity in Type 2 Diabetes (SMART2D) is supported by the Singapore Ministry of Health’s National Medical Research Council under its Clinician Scientist Individual Research Grant (CS-IRG) (MOH-000066).

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

Author Contributions. C.L. designed the study, performed experiments, analyzed data, and wrote the manuscript. M.H.Y.T. and S.L.T.P. designed and performed experiments and discussed data. M.L.L. and H.M.T. performed experiments. H.W.H., C.R., S.E.M., J.G., and S.T. contributed to discussion and reviewed and edited the manuscript. W.H. secured funding, conceived the project, contributed to discussion, and reviewed and edited the manuscript. X.W. secured funding, conceived the project, designed the study, and wrote the manuscript. X.W. is the guarantor of this work and, as such, had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Prior Presentation. Parts of this study were presented in the abstract form at the 3rd Singapore International Conference on Skin Research, Singapore, 21 March 2018.

  • Received June 3, 2020.
  • Accepted August 24, 2020.



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Black Pepper Stir Fry Sauce –

By electricdiet / December 28, 2020





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Review: Low-Carb Cauliflower Pizza & Sandwich Thins from Outer Aisle

By electricdiet / December 26, 2020


I really enjoy a slice of pizza or bread, but the carb counting and potential blood sugar headache mean that eating pizza is not always worth it for me.

This is why I love that there are now some absolutely terrific low carb pizza options that make pizza night a little easier to maneuver.

I was super excited when Outer Aisle reached out and asked if I’d be up for trying their Cauliflower Pizza Crust as I have been buying their Cauliflower Sandwich Thins for well over a year now at my local Whole Foods market.

So let me walk you through why I love the Outer Aisle cauliflower products, how my blood sugars react after eating them, and where you can get yours.

Christel holding Outer Aisle pizza crust

Disclaimer: This is a sponsored post that contains affiliate links. All opinions in this post are my own and are based on my own tests of the products.

Why I love Outer Aisle Cauliflower products

What I like about the Outer Aisle products is that they are made with 63% fresh cauliflower, whole cage-free eggs, and Parmesan cheese. It’s as close as it comes to the way I would make pizza crust myself if I wanted to spend the time.

The cauliflower base means that the blood sugar impact is minimal, and the use of eggs and parmesan as binding agents means that the protein amount is higher than the fats.

That is something I appreciate a lot, as high-fat foods mean that the blood sugar impact is slowed down but that the blood sugar increase can “haunt” you for hours after you’ve finished your meal.

Another important thing about these products is that they don’t actually taste like cauliflower. Don’t get me wrong, I like cauliflower, but I don’t want my pizza to taste like a head of cauliflower.

Two Outer Aisle cauliflower pizza crusts in their packaging

How I eat the Outer Aisle products

I came across the Sandwich Thins at my local market when looking for low-carb bread substitutes. What I like about them is that they have only 2 grams of carbs (1 gram of net carb) and 50 calories per Sandwich Thin. They also taste great and the texture kinda reminds me of tortillas.

That means that they are great for scooping up a hot bowl of chili (how I’ve primarily been enjoying them) and are the perfect fit for taco night.

I don’t really eat sandwiches that often, but could also see them being great for that, as well as mini pizzas.

And talking about pizza, the Outer Aisle Pizza Crusts are made from the same base ingredients but are a little thicker and sturdier. They have to be able to hold all of the pizza toppings after all.

A pizza crust has 120 calories and 4 grams of carbs (3 grams of net carbs), so basically nothing.

The Outer Aisle products are really easy to prepare. I bake the Sandwich Thins under the broiler for a few minutes on each side until they get golden brown and firm up a bit.

The pizza I pre-bake at 425º degrees for 8 minutes then add my toppings and bake until the cheese is bubbly. Then I let it sit for a little bit to let the crust firm up. If you have a pizza stone, you probably don’t need to pre-bake it at all.

Cauliflower pizza on table with toppings

How Outer Aisle products impact my blood sugar

Since these products have so few carbs, you might not need any insulin at all. However, I am fairly sensitive to anything I eat so I do need to inject insulin even for a small amount of carbohydrates. I also load my pizzas high, so I have to take insulin for the sauce and cheese as well.

But these products are so gentle on my blood sugars! Let me share my blood sugar experiment from the last time I enjoyed one of the Outer Aisle Pizza crusts.

I was going to one of my girlfriend’s house, and asked her if she wanted me to bring sushi or cauliflower pizza crusts and toppings. She opted for the pizza…which tells you how good this stuff is.

Since I was at her house, and I guess slightly distracted with making the pizzas and catching up, I forgot to pre-bolus (dose 10-15 min before eating) and just took my insulin shot right before we sat down to eat.

We ate at 8 PM and as you can see from the graph below, there was no blood sugar spike after I ate. And that was even without a pre-bolus!

Blood sugar graph

I use MyFitnessPal to track my food and calculate my carb, protein, and fat intake. I bolus not just for the carbs but for protein as well. The pizza had 10 grams of carbs (3 from the pizza, and the rest from the pizza sauce and cheese) and 27 grams of protein, so I took 1 unit of rapid-acting insulin for the carbs and 0.5 units for the protein and that clearly worked out perfectly.

How much insulin you need of course depends on your individual insulin and carb sensitivities but I think it’s safe to say that this product won’t spike blood sugars and it’s low enough in total carbohydrates that it should work for most people living with diabetes, regardless of whether they manage with insulin or not.

Where to get it and pricing

Outer Aisle can be found in many stores across the US (Sprouts, Kroger Family Stores, Albertsons, Whole Foods, Meijer, Wegman’s, etc.). They have a convenient store finder where you can look up stores near you that carry the products.

You can also order it online and get it sent directly to your doorstep.

Prices online are $6.99 for 6 Sandwich Thins and $6.95 for 2 pizza crust (if bought through Instacart).

Some online stores only sell 4 packs at a time, but they freeze really well for up to six months, so you don’t have to worry about them going bad or needing to eat 24 sandwich thins very quickly.

If you would like to order online, you can order from their website here. Use the code DIABETESSTRONG at checkout to take 10% off your order.



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Food for Nausea – Gingerbread Muffins Recipe Make Easy Holiday Snack

By electricdiet / December 24, 2020


When Going Through Cancer Treatment Find Relief with Food for Nausea!

Acute side effects of chemotherapy can cause nausea, vomiting or loss of appetite due to the destruction of rapidly dividing cells lining the gastrointestinal tract when going through chemo. It is important to keep good nutrition for healing, however when not feeling that can be difficult but there is relief with food for nausea.  Holly Clegg’s best-selling Eating Well Through Cancer cookbook, was written to answer the often-asked question, “What can I eat when going through cancer treatment?” This diabetic Gingerbread Muffins recipe is found in the Tummy Troubles chapter because ginger can be soothing.  Also, these Gingerbread Muffins make the best holiday food for everyone.

Gingerbread muffins picmonkey

Gingerbread Muffins
This awesome muffin has all of the flavor of your favorite spiced cookie in a moist anytime snack or breakfast muffin. When you’re not feeling well, this is easy to eat and ginger is food for nausea. Muffins freeze well. These are also a diabetic gingerbread muffins recipe

    Servings20 muffins
    Prep Time15 minutes
    Cook Time25 minutes

    Ingredients

    • 1 1/2cups


      whole-wheat flour

    • 1cup


      all-purpose flour

    • 1teaspoon


      ground ginger

    • 1teaspoon


      ground cinnamon

    • 1/2teaspoon


      ground cloves

    • 1/2cup


      sugar

    • 1/3cup


      canola oil

    • 1cup


      light molasses

    • 2


      eggs

    • 1cup


      boiling water

    • 2teaspoons


      baking soda

    Instructions
    1. Preheat oven 325°F. Coat muffin pans with nonstick cooking spray or line with papers.


    2. In large bowl, combine both flours, ginger, cinnamon, and cloves. Set aside.


    3. In medium bowl, whisk together sugar and oil. Add molasses and eggs whisking until blended. In glass measuring cup combine water and baking soda. Stir to dissolve. Pour in egg mixture and whisk until blended. Add egg mixture to flour mixture, stirring just until combined.


    4. Spoon batter into paper lined tins, filling 1/2-3/4 full. Bake 20-25 minutes or until inserted toothpick comes out clean.

    Recipe Notes

    Per Serving: Calories 161, Calories from Fat 25%, Fat 5g, Saturated Fat 0g, Cholesterol 19mg, Sodium 140mg, Carbohydrates 28g, Dietary Fiber 1g, Total Sugars 14g, Protein 2g, Diabetic Exchanges: 2 starch, 1/2 fat

    Terrific Tip: Keep a few muffins in the freezer to pop out when not feeling well and need a boost. You can always use only all-purpose flour if that’s what you have.

    Nutrition Nugget: Ginger has been shown to help nausea symptoms so these muffins may be just the ticket to feeling better

    Gingerbread Muffins and Recipes For Cancer Patients In Easy Cancer Cookbook

    Eating Well Through Cancer: Easy Recipes & Tips to Guide you Through Treatment and Cancer PreventionEating Well Through Cancer: Easy Recipes & Tips to Guide you Through Treatment and Cancer PreventionEating Well Through Cancer: Easy Recipes & Tips to Guide you Through Treatment and Cancer Prevention

    This cancer cookbook is so helpful for people going through cancer treatment as they experience different side effects such as nausea. Each chapter addresses different symptoms and suggests foods that are best tolerated.

    As you can see from these diabetic Gingerbread Muffins in Eating Well Through Cancer, they really are a recipe the entire family and/or caregiver will also enjoy. All recipes for cancer patients are also for everyday cooking and cancer prevention. Just a practical healthy cookbook to guide you through cancer treatment! Easy diabetic recipes are highlighted in the cookbook just like these diabetic gingerbread muffins.

    When Feeling Queasy – Gingerbread Muffins Can Help With Nausea

    If you experience nausea and vomiting try to drink fluids throughout the day to prevent dehydration, such as sipping water, juices, and other clear, calorie- containing liquids. You may tolerate clear, cool liquids better than very hot or icy fluids. When you have stopped vomiting, try eating easy to digest foods such as crackers, gelatin, and plain toast. Ginger is food for nausea as its an herb recognized to help with nausea and sooth the stomach. Try drinking ginger tea, flat ginger ale, gingersnap cookies, ginger candy, pickled ginger or ginger flavored breads such as these Gingerbread Muffins. Find more recipes when going through cancer treatment on Team Holly’s blog.

    Love These Easy To Use Cupcake Liners and Muffin Molds for Diabetic Gingerbread Muffins

    How cute are these Silicone Cupcake Liners?! Make your muffins and cupcakes fun and bright with these BPA free, FDA approved 100% food grade silicone kitchen utensils reusable cupcake liners.

    These fun colors even make the Gingerbread muffins taste better and will brighten up a cancer patient’s day.

    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!

    Get All of Holly’s Healthy Easy Cookbooks

    The post Food for Nausea – Gingerbread Muffins Recipe Make Easy Holiday Snack appeared first on The Healthy Cooking Blog.



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    VMAT2 Safeguards β-Cells Against Dopamine Cytotoxicity Under High-Fat Diet–Induced Stress

    By electricdiet / December 22, 2020


    Introduction

    Endocrine pancreatic β-cells are highly specialized for making insulin for the maintenance of glucose homeostasis in our bodies. Diabetes is a disease caused by the lack of (type 1) or dysfunction of (type 2) β-cells. Oversupply of nutrients and the subsequent overstimulation of β-cells contribute to insulin secretory failure in type 2 diabetes. Reactive oxygen species (ROS) are produced from mitochondrial respiration with stimulation with glucose and other fuels. In the pancreatic β-cells, glucose metabolism via the tricarboxylic acid cycle is central for triggering insulin secretion. Higher glucose-stimulated insulin secretion (GSIS) activity triggers more elevated levels of ROS production (1,2). In a healthy state, β-cells possess elaborate antioxidant mechanisms to adapt to the cytotoxicity of ROS. However, chronic overnutrition leads to progressive mitochondrial metabolic dysfunction and oxidative stress. Numerous studies have investigated the mechanisms involved in the progression of β-cell failure, in which ROS play an important role.

    There is uptake of monoamines by vesicular monoamine transporter 2 (VMAT2), a protein encoded by the Slc18a2 gene, from the cytoplasm into the vesicles. Cytoplasmic monoamines, namely, dopamine, serotonin, noradrenaline, adrenaline, and histamine, are transported by VMAT2 into cytosolic vesicles, where they are protected from degradation by monoamine oxidase (MAO) and stored for subsequent release (35). Adult β-cells possess the enzymes required to synthesize, interconvert, and catabolize monoamines and to store them in the vesicular granules. Of the two VMAT isoforms that transport monoamines, VMAT2 is the isomer expressed in the pancreas (69). Among the monoamines, dopamine is the most abundant monoamine in β-cells (10,11).

    During GSIS from pancreatic β-cells, dopamine modulates insulin release. Exogeneous dopamine inhibits GSIS in isolated islets through the Drd2 receptor, which is expressed on β-cells (12). Treatment of rat islets with the VMAT2-specific antagonist tetrabenazine (TBZ) significantly enhanced their insulin secretion (13). Dopamine and its precursor L-dopa inhibit GSIS (14). However, disruption of the dopamine D2 receptor results in impairment of insulin secretion and causes glucose intolerance (15). Furthermore, inhibition of MAO activity reduces insulin secretion in response to metabolic stimuli (16), which raises the possibility that dopamine is important for β-cell function. However, it remains unknown how dopamine affects the function of β-cells.

    Previously, we identified TBZ in a screening, searching for small molecular compounds that potentiate the differentiation of embryonic stem (ES) cells into insulin-expressing β-cells (11). We found that treatment with TBZ decreased dopamine content, thereby identifying VMAT2-dopamine signaling as a negative regulator for pancreatic β-cell differentiation. We also identified domperidone, an antagonist for dopamine D2 receptor (Drd2), in another screen as a compound that increases β-cell mass in adult islets (17). We found that the dopamine-Drd2 signal functions as a negative regulator for the maintenance of β-cell mass.

    In the current study, to understand the role of VMAT2 and dopamine signaling in the regulation of β-cell and glucose homeostasis, we generated a pancreatic β-cell–specific Vmat2 mutant mouse line using a rat insulin 2 promotor driving Cre recombinase (RIP-CreER) crossing with a conditional Vmat2 allele, Slc18a2tm1c. We found that VMAT2 plays an important role in protecting β-cells from cytotoxicity of ROS.

    Research Design and Methods

    Ethics Approval

    All studies involving animals were performed following local guidelines and regulations and were approved by the Institutional Committee for Animal Research in Tokyo Institute of Technology and Kumamoto University.

    Monoamine Content Assay

    The monoamine content assay was performed as previously described (11). Isolated islet cells were lysed with lysis buffer containing 1.0% Triton X-100 (Nacalai Tesque, Kyoto, Japan) in 0.1 mol/L PBS (pH 7.2) (Sigma-Aldrich) with protease inhibitor cocktail. Lysates were assayed for dopamine with a dopamine-specific ELISA kit (Labor Diagnostika Nord GmbH & Co. KG, Nordhorn, Germany).

    Generation of Slc18a2tm1a, Slc18a2tm1c, and Slc18a2tm1d Mouse Lines and a Conditional β-Cell–Specific Slc18a2 (Vmat2) Knockout Mouse Line, βVmat2KO

    An ES cell line bearing a targeted mutation at Slc18a2 (encoding VMAT2 protein) (Slc18a2tm1a(EUCOMM)Wtsi; number EPD0242_2_F06) (C57BL/6) was produced for the EUCOMM and EUCOMMTools projects by the Wellcome Trust Sanger Institute. The mutation details (Mouse Genome Informatics [MGI] identifier 4432865) are as follows: The L1L2_Pgk_P cassette was inserted at position 59262507 of chromosome 19 upstream of exon 3 (Build GRCm38). The cassette is composed of an FLP recombinase target (FRT) site followed by En2 SA, IRES and LacZ, SV40 polyA sequences, and a loxP site. This first loxP site is followed by a neomycin resistance (neo) gene under the control of the PGK promoter, SV40 polyA, a second FRT site, and a second loxP site. A third loxP site is inserted downstream of the targeted exon 3 at position 59263523. The exon 3 is thus flanked by loxP sites (Fig. 1). The tm1a allele was initially a Vmat2-nonexpressing form. Slc18a2tm1a/+ mice were generated by injection of the Slc18a2tm1a(EUCOMM)Wtsi ES cells into the perivitelline space of one-cell stage C57BL/6 mouse embryos. We have successfully produced mouse chimeras, 36 males and 15 females, in 200 injections. We then crossed the chimera mice to produce homozygous Slc18a2tm1a/tm1a, but all embryos died at embryonic day 11.5. Heterozygous Slc18a2tm1a/+ mice were alive and fertile. We then created a “conditional ready” Slc18a2tm1c mouse line (floxed) allele by crossing the Slc18a2tm1a/+ mice with a global Flp transgenic mouse strain [Gt(ROSA)26Sortm1(FLP1)Dym/J, stock no. 003946; The Jackson Laboratory, Bar Harbor, ME], so that subsequent cre expression results in a knockout mouse. β-cell–specific Vmat2 mutant mice were produced by crossing the homozygous Slc18a2tm1c/tm1c with RIP-Cre transgenic mice [B6.Cg-Tg(Ins2-cre)25Mgn/J, stock no. 003573; The Jackson Laboratory]. All mice used were maintained on a C57BL/6 background. PCR primers used for genotyping are listed in Supplementary Table 1.

    Figure 1
    Figure 1

    VMAT2 expression in the pancreatic islets and the generation of βVmat2KO mouse. A: Time-dependent VMAT2 expression in the pancreatic islets in response to glucose administration. Immunostaining of the pancreatic islets (left panel) showed the presence of VMAT2 (magenta) expression in insulin-expressing β-cells under high blood glucose (30 min) but not under low glucose (0 or 120 min) conditions, whereas localization of VMAT2 in glucagon-expressing α-cells was constantly observed. Blood glucose (BG) values of mice at 0, 30, and 120 min are shown above. The average intensity of the VMAT2 protein in β-cells or α-cells was plotted (right panel). B: Generation of the Slc18a2-deletion mutant mice. The Slc18a2 locus was inserted with a LacZ gene cassette to make the tm1a allele (Slc18a2tm1a). The tm1c allele (Slc18a2tm1c) was obtained by excising the sequence flanked by two FRT sites. βVmat2KO mice were then obtained by crossing homozygous Slc18a2tm1c with Ins-Cre transgenic mice. Yellow boxes: exons. Lower panel shows genomic PCR results for genotyping of Slc18a2tm1c. Genomic PCR products of the tm1c or WT Slc18a2. C: Immunostaining of control and βVmat2KO mouse islets at 10 weeks of age. VMAT2 expression was observed in control (Slc18a2tm1c) but not the βVmat2KO β-cells, whereas its expression in the α-cells was not affected. At the bottom of the panel, 0 or 30 minutes indicates the time after glucose injection. D: βVMAT2KO islets showed significantly lower dopamine content compared with that of the control islets. E: The effects of TBZ or DMSO treatments on insulin secretion in response to glucose stimulation in the isolated islets. A and C: VMAT2, magenta; INS, green; GCG, yellow; DAPI, blue. βKO, βVmat2KO. Scale bars = 50 μm. A, D, and E: Means ± SD are shown (n = 3). Significant differences vs. control, by one-way repeated-measures ANOVA and Dunnett multiple comparisons test. cont., control; w, weeks.

    SPiDER-βGal Staining of Slc18a2tm1a/+ Mouse Islets

    Slc18a2tm1a mouse genome bearing the LacZ gene was used to report Vmat2 gene expression (Supplementary Fig. 1). LacZ activity in the Slc18a2tm1a/+ pancreas sections was visualized by SPiDER-βGal staining solution (Dojindo Molecular Technologies, Inc., Rockville, MD) (18).

    Measurement of Glucose-Stimulated C-Peptide (Insulin) Secretion by ELISA

    For GSIS assays, mouse islets were preincubated for 30 min in low glucose (5.5 mmol/L) in Krebs-Ringer buffer (133.4 mmol/L NaCl, 4.7 mmol/L KCl, 1.2 mmol/L KH2PO4, 1.2 mmol/L MgSO4, 2.5 mmol/L CaCl2, 5.0 mmol/L NaHCO3, 2.8 mmol/L glucose, 10 mmol/L HEPES [pH 7.4], and 0.2% BSA). Islets were washed twice with PBS and then incubated for 1 h in low glucose (5.5 mmol/L) or high glucose (25.0 mmol/L). Insulin secretion into the buffer and insulin content of the cell lysates were measured using a mouse C-peptide ELISA kit (Shibayagi Co., Ltd., Gunma, Japan) and then normalized with the protein content of the cell lysates.

    High-Fat Diet Feeding

    Male mice were housed in a 12-h light-dark cycle. Feedings were switched from normal diet (ND) (AIN-93M; Oriental Yeast Co., Tokyo, Japan) to high-fat diet (HFD) (60% kcal from fat) (HFD-60; Oriental Yeast Co., Tokyo, Japan) at 6 weeks of age.

    Intraperitoneal Glucose Tolerance Test

    Mice that had been fasted for 16 h were used. Blood glucose levels were measured before (0 min) and at 15, 30, 60, 90, and 120 min after intraperitoneal administration of 25% glucose solution (Wako, Osaka, Japan) at 1.5 g/kg body wt.

    Intraperitoneal Insulin Tolerance Test

    Mice fasted for 6 h were administered with an intraperitoneal injection of insulin solution (0.4 units insulin/kg body wt HUMULIN R [regular human insulin injection]; Eli Lilly, Indianapolis, IN). Glucose levels were monitored.

    Blood Glucose Measurement

    Blood glucose levels were measured with OneTouch Ultra equipped with Life Check Sensor (Gunze, Kyoto, Japan) or blood glucose meter ANTSENSE III (Horiba, Kyoto, Japan).

    Insulin Measurements

    Blood samples were sampled 30 min after glucose intraperitoneal injection (2 mg/kg) and centrifuged to obtain plasma. Plasma insulin was measured using a Mouse Insulin ELISA Kit (Shibayagi Co., Ltd.).

    F-actin Staining and Immunohistochemistry Analysis

    Tissue samples were fixed with 4% formaldehyde, cryoprotected with 30% sucrose, and cut into 10-μm-thick sections. For F-actin staining, Alexa Fluor 555 Phalloidin (cat. no. 8953S, 1:20; Cell Signaling Technology, Tokyo, Japan) was used. For immunohistochemistry, the following antibodies were used: rabbit anti–chromogranin A (ab15160, 1:400; Abcam), mouse anti-glucagon (G2654, 1/1,000; Sigma-Aldrich), guinea pig anti-insulin (A0564, 1/500; Dako), and rabbit anti-VMAT2 (ab81855, 1:500; Abcam). Alexa Fluor 488 donkey anti-guinea pig IgG (706-546-148, 1:1,000; Jackson ImmunoResearch Laboratories, Inc., West Grove, PA), Alexa Fluor 568 donkey anti-rabbit IgG (A10037, 1:1,000; Invitrogen), and Alexa Fluor 647 donkey anti-mouse IgG (Jackson ImmunoResearch Laboratories, Inc.) were used. Tissue sections were counterstained with DAPI (Roche Diagnostics, Basel, Switzerland).

    TUNEL Assay

    The TUNEL assay was performed using the In Situ Cell Death Detection Kit, Fluorescein (cat. no. 11684795910; Roche Applied Science, Mannheim, Germany).

    Caspase-3/7 Detection

    Caspase-3/7–positive cells were stained with use of the CellEvent Caspase-3/7 Green Detection Reagent (Invitrogen Life Technologies Co., Carlsbad, CA).

    RNA Isolation, cDNA Synthesis, and Real-time PCR

    RNA was extracted using the RNeasy Mini Kit (QIAGEN, Hilden, Germany) and then treated with DNase I (QIAGEN). First-strand cDNA was synthesized using a SuperScript VILO cDNA Synthesis Kit (Invitrogen). Real-time PCR analysis was done using THUNDERBIRD SYBR qPCR Mix (Toyobo) with specific primers. All reactions were run on the StepOnePlus Real-Time PCR System (Applied Biosystems). β-Actin was used as an internal control. All primer sequences are listed in Supplementary Table 2.

    Islet Dissociation Culture or Whole Islet Culture

    Mouse islets from 13-week-old mutant or control (Slc18a2tm1c/tm1c) mice were isolated and handpicked as previously described (19). For dissociation culture, islets were incubated with 0.05% trypsin-EDTA (Invitrogen) for 5 min at 37°C, 5% CO2, and pipetted to dissociate into single cells. The dissociated cells were plated in DMEM (glucose concentration: 25 mmol/L) supplemented with 10% FBS, 100 μmol/L nonessential amid acids, 2 mmol/L l-glutamine, 50 units/mL penicillin, 50 μg/mL streptomycin, and 100 μmol/L 2-mercaptoethanol (Sumitomo Bakelite Co. Ltd., Tokyo, Japan). For whole islet culture, isolated islets were used directly for the culture without dissociation.

    ROS Exposure of the Whole Islets

    For testing of the vulnerability of whole islets to ROS, H2O2 was added to the medium and cultured for 6 h and then used for cell count or real-time PCR.

    ROS Staining

    Islet cells were plated at a density of 5,000 cells per well in 384-well polystyrene-coated plates. After 24 h, CellROX Green Reagent (Invitrogen) was added to each well at a concentration of 10 μmol/L and mixed vigorously. The fluorescence of CellROX was measured with a plate reader.

    H2O2 Content Assay

    Isolated islet cells were treated with chemical compounds for 1, 6, or 24 h and lysed with lysis buffer containing 3.0% Triton X-100 (Nacalai Tesque) in 0.1 mol/L PBS (pH 7.2) (Sigma-Aldrich) with protease inhibitor cocktail. Lysates were assayed for H2O2 contents in islet using Amplite Fluorimetric Hydrogen Peroxide Assay Kit (11502; AAT Bioquest, Sunnyvale, CA).

    Chemical Treatments

    The dissociated islet cells were seeded on 384-well plates (Sumitomo Bakelite Co. Ltd.) at a concentration at 2,000 cells/well. Next, 0.1 mmol/L TBZ (T284000; Toronto Research Chemicals) and 0.1 mmol/L pargyline (10007852; Cayman Chemical) were added into the medium on day 3. Both compounds were dissolved in DMSO. Therefore, all tests were performed under 0.1% DMSO (v/v) containing condition.

    Statistical Analyses

    Data were analyzed by one-way ANOVA and Dunnett multiple comparisons test, except the data for Fig. 5E, in which case unpaired Student t tests was used. All data are presented as mean ± SD.

    Data and Resource Availability

    All data generated or analyzed during this study are included here and in Supplementary Material.

    The mutant mouse generated during the current study is deposited in the Mouse Genome Database at the MGI website, The Jackson Laboratory, as Slc18a2<tm1c(EUCOMM)Wtsi> MGI:6386316. The resource will be available from the corresponding author upon reasonable request.

    Results

    β-Cell–Specific Vmat2 Deletion Results in Decreased Dopamine Content and Increased GSIS

    Of the two VMAT isoforms that transport monoamines, VMAT1 and VMAT2, VMAT2 is the isoform that is expressed in the β-cells in healthy adult pancreatic islets (6,9). There is debate regarding the cell types that express VMAT2 in rodents (7,8,20). We hypothesized that VMAT2 expression is regulated in a glucose-dependent manner. Therefore, we examined its expression in the mouse pancreatic islets in response to glucose administration. We administered glucose after fasting; then, we monitored blood glucose levels and harvested the pancreas at 0, 30, 60, 90, and 120 min after glucose administration. Immunohistochemistry revealed that VMAT2 expression in the β-cells was regulated in a glucose-dependent manner: VMAT2 expression was downregulated at low blood glucose levels (107 mg/dL) at 0 min, upregulated when blood glucose reached its highest level (348 mg/dL) at 30 min, and subsequently down-regulated with decreasing blood glucose levels at 60, 90, and 120 min after glucose administration. By contrast, VMAT2 was expressed constantly in α-cells in a blood glucose–independent manner (Fig. 1A). We used a mouse line, Slc18a2tm1a/+, in which a LacZ reporter cassette was inserted into Exon3 of the SLC18a2 gene. We confirmed a similar rapid transient upregulation of Vmat2 expression, by visualizing LacZ activity using SPiDER-βGal (18), at 30 min after exposure to high glucose (Supplementary Fig. 1). The result suggests that Vmat2 expression in response to high glucose is, in part, regulated at the transcription level.

    To study the function of VMAT2 in β-cells, we created a β-cell–specific VMAT2 mutant mouse line, through the crossing of heterozygous Slc18a2tm1a/+ mice with a mouse strain carrying global flp recombinase expression, to produce a “conditional ready” Slc18a2tm1c mouse line (Fig. 1B). We then crossed Slc18a2tm1c/tm1c with RIP-Cre (21) mice to obtain β-cell–specific Vmat2 knockout (βVmat2KO) mice (Fig. 1B). We confirmed that in the βVmat2KO mice, VMAT2 protein expression in β-cells was nonexistent at high blood glucose levels (30 min after glucose administration), while its expression in the α-cells was equivalent to that in wild-type (WT) mice (Fig. 1C).

    In pancreatic β-cells, the most abundant monoamine is dopamine (11). We isolated islets from the control (Slc18a2tm1c/tm1c) and βVmat2KO mice and measured dopamine content using an ELISA. We found that dopamine content was not affected at 5 weeks but was significantly reduced from 10 weeks, in the βVmat2KO islets compared with the control islets at the same age (Fig. 1D).

    We then isolated pancreatic islets from 13-week-old mice and examined GSIS and the effect of TBZ, a VMAT2-specific inhibitor, on βVmat2KO islets. βVmat2KO pancreatic islets showed a significantly higher GSIS activity compared with the control islets. We observed potentiation of GSIS by TBZ at low (5.5 mmol/L) and high (25.0 mmol/L) glucose in control, but not in βVmat2KO, islets (Fig. 1E [also refer to Fig. 3]).

    Our above results confirmed that in the βVmat2KO mice, VMAT2 expression was knocked out specifically in β-cells, which led to a decrease in dopamine content and a higher insulin secretion in response to low or high glucose stimulation.

    Impaired Glucose Tolerance and Insulin Tolerance in HFD-Fed βVmat2KO Mice

    To examine the phenotypic changes between the βVmat2KO and the control mice, we first compared body weight and blood glucose levels under ND conditions; however, no significant differences were found (Fig. 2A and B). We then assayed for glucose tolerance and insulin tolerance and again found no significant differences between the βVmat2KO and control (Slc18a2tm1c/m1c) mice (Fig. 2C and E). It is reported that RIP-Cre mice fed with ND could display glucose intolerance even at 2 months of age in a background of pure C57BL/6 or mixed 129xC57BL/6 background (22), while others reported that no significant glucose intolerance was observed (23). We initially used RIP-Cre+/−; Slc18a2tm1c/+ heterozygous mice as controls and found that neither glucose intolerance nor insulin intolerance was observed under ND-fed conditions (Supplementary Fig. 2). We then used Slc18a2tm1c/tm1c mice as controls for examining the RIP-Cre+/−; Slc18a2tm1c/tm1c homozygous mice in the subsequent experiments.

    Figure 2
    Figure 2

    βVmat2KO mice exhibited impaired glucose and insulin tolerance after prolonged HFD treatment. A and B: Age-dependent body weight (A) or blood glucose (B) of βVmat2KO (βKO) and control (Slc18a2tm1c/tm1c) mice under ND- or HFD-fed conditions was plotted. No significant difference between the βVmat2KO and control mice was observed under ND- or HFD-fed conditions, respectively. Body weight and blood glucose were elevated in the HFD-fed groups. C: Intraperitoneal glucose tolerance tests (IPGTTs) were performed in 8-, 12-, 15-, and 17-week-old βVmat2KO and control mice. The time dependence of blood glucose levels after glucose administration is shown. βVmat2KO but not control mice showed impaired glucose tolerance at 15 weeks and 17 weeks of age. D: AUC of the results at 15 weeks and 17 weeks shown in C reveals that HFD-fed βVmat2KO mice show impaired glucose tolerance compared with that of the ND-fed control mice. E: Insulin tolerance tests (ITTs) were performed with 14- and 16-week-old βVmat2KO and control mice. Plasma glucose levels were presented as % change from glucose level at time 0. F: AUC of the results shown in E reveals that HFD-fed βVmat2KO mice at 14 weeks or 16 weeks of age showed impaired insulin tolerance compared with that of the HFD-fed control mice of the same age (**P < 0.01). G: The plasma insulin levels of 13-, 15-, and 17-week-old mice were calculated. The results of βVmat2KO fed with ND or HFD were compared with those of control mice. The result of the control mouse at 6 weeks is displayed for comparison. Means ± SD are shown. (n = 5–10); significant differences vs. ND-fed control, §P < 0.05 and §§P < 0.01, or significant differences between two values marked by the bars, *P < 0.05 and **P < 0.01, by one-way ANOVA and Dunnett multiple comparisons test. cont., control; w, weeks.

    Since dopamine functions as a negative regulator for β-cell mass (17) and insulin secretion, we hypothesized that impaired VMAT2 function might be critical in conditions of high demand for insulin secretion. We then tested the effects of an HFD on βVmat2KO mice. We started HFD feeding at 5 weeks of age and compared the body weight and blood glucose levels of the mice with those of ND-fed mice. Both control and βVmat2KO mice fed with an HFD showed a rapid increase in body weight and nonfasting blood glucose compared with ND-fed mice (Fig. 2A and B). We then examined glucose tolerance in these mice. There were no significant differences in glucose tolerance in mice <15 weeks old. However, at 15 weeks of age and onward, HFD-fed βVmat2KO mice exhibited impaired glucose tolerance compared with ND-fed or HFD control mice (Fig. 2C). The area under the curve (AUC) of blood glucose concentration following glucose stimulation in 15- and 17-week-old HFD-fed βVmat2KO or control mice was significantly increased compared with that of the ND-fed control mice, with the AUC of the HFD-fed βVmat2KO even higher than that for control mice (Fig. 2D). The results indicate that HFD-fed βVmat2KO mice developed impaired glucose tolerance at increasing ages (>15 weeks). We then assessed the insulin tolerance of the mice at 14 and 16 weeks of age and found that βVmat2KO mice exhibited impaired insulin tolerance compared with ND-fed or HFD control mice (Fig. 2E). The AUC in HFD-fed βVmat2KO mice was significantly increased in 14- and 16-week-old mice (fed with HFD for 8 and 10 weeks, respectively) compared with that of the control mice (Fig. 2F). It was reported that C57BL/6 mice developed insulin resistance after > 11 weeks of HFD feeding (24). Our results show that HFD-fed βVmat2KO mice developed insulin intolerance before the control mice became overtly insulin intolerant. Our results demonstrate that prolonged HFD feeding led to impaired glucose tolerance and deteriorated insulin tolerance in βVmat2KO mice. We then measured plasma insulin levels. Plasma insulin gradually decreased in HFD-fed βVmat2KO but not in control mice. The result suggests that the impaired glucose tolerance observed in the HFD-fed βVmat2KO mice is due to the reduced plasma insulin level.

    Pancreatic Islets of HFD-Fed βVmat2KO Mice Showed an Initial Increase in β-Cell Mass Followed by Impaired GSIS and β-Cell Loss With Increasing Age

    To investigate the underlying molecular mechanism that triggers β-cell dysfunction in HFD-fed βVmat2KO mice, we performed an immunohistochemical analysis of the pancreatic islets harvested from mice at 30 min after glucose administration. A lack of VMAT2 expression was confirmed in the βVmat2KO β-cells but not the control β-cells under high blood glucose conditions, while expression of insulin and glucagon was not affected (Fig. 3A and B). We observed an increase in β-cell mass in young mice at 10 and 13 weeks of age in both βVmat2KO and control HFD-fed mice compared with ND-fed mice (Fig. 3A and B). The enlarged islets were present in control mice fed with an HFD throughout observation (up to 17 weeks of age) (Fig. 3B). In contrast, the β-cell mass decreased in HFD-fed βVmat2KO mice at 15 weeks of age and became much smaller at 17 weeks (Fig. 3A and B). The above results were confirmed by quantitative analysis of β-cell mass and islet mean size (Fig. 3C and D). On the other hand, the Vmat2 expression levels were not significantly different between ND- and HFD-fed control islets at all ages (Fig. 3E).

    Figure 3
    Figure 3

    HFD-fed βVmat2KO islets showed initial β-cell mass increase followed by β-cell loss. A and B: Immunostaining of βVmat2KO (βKO) (A) and control (Slc18a2tm1c/tm1c) (B) islets under ND- or HFD-fed conditions at 13 weeks, 15 weeks, and 17 weeks of age. In βVmat2KO islets, VMAT2 protein expression was not observed in β-cells but remained in α-cells. At 13 weeks of age, β-cell mass in HFD-fed βVmat2KO and control islets increased. At 15 weeks of age, β-cell mass in HFD-fed βVmat2KO but not control mice began to decrease, and β-cell mass further decreased at 17 weeks of age. VMAT2, red; INS, green; GCG, yellow. Scale bars = top panels, 100 μm; middle and lower panels, 50 μm. C and D: Quantitative analysis of the age-dependent changes in β-cell mass (C) and islet mean size (D) in the βVmat2KO and control mice fed with ND or HFD. E: Quantitative analysis of the intensity of anti-VMAT2 antibody staining shown in B. F: Isolated islets from ND- or HFD-fed mice at 6 weeks, 10 weeks, 13 weeks, or 15 weeks old were assayed for insulin secretion in response to glucose stimulation. CF: Means ± SD are shown (n = 3); significant differences between two values marked by the bars, *P < 0.05 and **P < 0.01, by one-way ANOVA and Dunnett multiple comparisons test. cont., control; w, weeks.

    We then isolated pancreatic islets from ND- and HFD-fed βVmat2KO and control mice and performed GSIS analysis in vitro. The islets of ND-fed βVmat2KO mice exhibited significantly elevated levels of insulin secretion compared with the control mice at all ages examined, in agreement with the above results (Fig. 1E). With HFD feeding, islets isolated from control mice showed elevated GSIS activity. In contrast, islets isolated from HFD-fed βVmat2KO mice showed elevated GSIS at 10 weeks of age, but GSIS activity decreased at 13 and 15 weeks of age in spite of the large β-cell mass at 13 weeks, suggesting that β-cell dysfunction occurred (Fig. 3F).

    These results suggest that the pancreatic islets of βVmat2KO mice secrete an elevated level of insulin even under low glucose conditions and respond to high glucose by secreting a higher level of insulin compared with the control mice. βVmat2KO β-cells can meet the metabolic requirements of mice and maintain blood glucose homeostasis under ND conditions. Under HFD conditions, a compensatory increase in β-cell mass occurred in control mice to meet the increased metabolic demands. However, in the βVmat2KO mice, β-cells could not overcome the increased metabolic demands, resulting in β-cell dysfunction and eventual β-cell loss.

    Dedifferentiation of β-Cells Is Accelerated in HFD-Fed βVmat2KO Islets

    β-cell dedifferentiation is known as a mechanism that underlies β-cell dysfunction in type 2 diabetes (25). We found that HFD feeding triggered insulin resistance in βVmat2KO mice. Therefore, we investigated the possibility that dedifferentiation might contribute to β-cell dysfunction by evaluating the expression of chromogranin A (CGA), whose expression is lost upon β-cell failure (26). We detected CGA expression in all ages of β-cells. While CGA expression was reduced in HFD-fed control β-cells at 15 weeks of age, CGA expression almost disappeared in HFD-fed βVmat2KO β-cells (Fig. 4A and B). Since actin remodeling functions during insulin secretion (27), we examined fiber-like F-actin and found a loss of F-actin (Fig. 4C and D) from the islets of HFD-fed βVmat2KO mice but not the islets of control mice at 15 weeks of age. We then examined the expression of other differentiation markers by real-time PCR in islets from ND- or HFD-fed control or βVmat2KO mice. To allow comparison across genotypes, we show expression levels as relative values versus those of ND-fed controls. It was reported that a reduction in the expression of differentiation markers was observed after HFD feeding for 12 weeks or 16 weeks (24). Here, even at early periods when expressions of most β-cell maturation markers were not yet observed in the HFD-fed control islets, decreased expression levels of Ins1, Ins2, Nkx6.1, Pdx1, Glut2, Glucokinase (Gck), and MafA were observed in the HFD-fed βVmat2KO mice. On the other hand, increased expression of MafB and Aldh1a3 was observed in the HFD-fed βVmat2KO and control islets at 13 or 15 weeks of age (HFD feeding for 7–9 weeks) (Fig. 4E), whereas no change in the expression of endocrine progenitor marker Neurog3 was observed (Fig. 4E). Our results revealed that accelerated dedifferentiation seemed to occur in the β-cells of HFD-fed βVmat2KO mice compared with the ND-fed control, which led to β-cell loss and β-cell dysfunction.

    Figure 4
    Figure 4

    Dedifferentiation of β-cells occurs in βVmat2KO mouse islets. AD: Immunostaining of ND- or HFD-fed βVmat2KO (βKO) and control Slc18a2tm1c islets isolated at 10 weeks (upper panels) and 15 weeks (lower panels) of age for CGA (magenta) (A and B) or F-actin (magenta) (C and D) is shown. B and D: Quantitative analyses of CGA (B) or F-actin (D) staining are shown. In 15-week-old βVmat2KO islets, CGA and F-actin staining in β-cells decreased. INS, green; GCG, yellow. Sections were counterstained with DAPI (blue). Scale bars = 50 μm. E: Real-time PCR analyses of age-dependent expression of endocrine maturation markers at 10 weeks, 13 weeks, and 15 weeks of age. The values of the HFD-fed WT or βVmat2KO mice are shown as fold expression vs. ND-fed control or βVmat2KO, respectively. B, D, E: Means ± SD are shown (n = 3); significant differences between βVmat2KO mice and their 10-week-old controls, §P < 0.05 and §§P < 0.01, or between two values marked by the bars, *P < 0.05 and **P < 0.01, by one-way ANOVA and Dunnett multiple comparisons test. Control, white bars; βVmat2KO, gray bars. cont., control; W, weeks.

    We then examined cell proliferation and β-cell death. The expression levels of cell cycle regulator genes, cyclin D1 (CcnD1), cyclin D2 (CcnD2), and proliferating cell nuclear antigen (Pcna), were upregulated in HFD-fed control and βVmat2KO islets from 10 or 13 weeks of age (HFD feeding for 4 or 7 weeks). However, their expression levels decreased at 15 weeks of age in HFD-fed βVmat2KO mice (Fig. 5A). Concurrently, TUNEL-positive cells increased significantly in the HFD-fed βVmat2KO mice (Fig. 5BD).

    Figure 5
    Figure 5

    β-cell–specific Vmat2 deletion increased the expression levels of cell-cycle regulator genes and induced apoptosis under HFD-fed conditions. A: Expression levels of Ccnd1, Ccnd2, and Pcna in the HFD-fed control Slc18a2tm1c or βVmat2KO islets. Values are shown as fold expression vs. in 10-week-old ND-fed control mice. Means ± SD are shown (n = 3); significant differences between βVmat2KO and control, *P < 0.05 and **P < 0.01, by one-way ANOVA and Dunnett multiple comparisons test. BD: TUNEL staining followed by immunostaining of the pancreas tissue sections from HFD-fed control (B) or βVmat2KO (C) mice. C’: High magnification of the box shown in C. TUNEL, magenta; INS, green; DAPI, blue. Scale bars = 50 μm. D: The proportion of TUNEL-positive cells within insulin-expressing β-cells. Scattered plots with individual results together with mean ± SD are presented. Significant differences were analyzed by unpaired two-tailed Student t test and are shown as **P < 0.01. N = 8. βKO, βVmat2KO; cont., control; W, weeks.

    Our results confirmed that HFD triggered the dedifferentiation and adaptive proliferation of β-cells, as previously reported (24,28,29). In HFD-fed βVmat2KO islets, dedifferentiation and cell death in β-cells are accelerated compared with in controls, which seems to attribute to β-cell failure.

    βVmat2KO β-Cells Are Exposed to Elevated ROS Due to Cytoplasmic Dopamine Degradation and Are More Vulnerable to ROS-Induced Cytotoxicity

    We then attempted to reveal the underlying molecular mechanism that triggers the dedifferentiation of β-cells in the islets of βVmat2KO mice. The dopamine content in the islets of βVmat2KO mice decreased with age (Fig. 1D). The reduction in dopamine content was attributed to the MAO-mediated cytoplasmic degradation of dopamine, which contributes to ROS production through H2O2 synthesis during substrate degradation (30).

    We hypothesized that ROS production in the islets of βVmat2KO mice is higher, as VMAT2 depletion leads to increased cytoplasmic dopamine. To test our hypothesis, we used an islet dissociation culture system (Fig. 6AD), in which islets from ND-fed control or βVmat2KO mice were cultured in vitro. We visualized the production of ROS using a fluorogenic probe. We observed a significantly higher ROS intensity in the β-cells of βVmat2KO mice compared with controls (Fig. 6A and B). TBZ treatment increased ROS intensity in control but not βVmat2KO islets, and treatment with pargyline, an MAO inhibitor, reversed the increase in ROS. The number of β-cells in control islets was reduced by TBZ treatment (Fig. 6C). β-cell apoptosis (activated caspase-3/7+ within Ins+ cells) increased in high glucose conditions and under TBZ treatment and was rescued by pargyline treatment (Fig. 6D).

    Figure 6
    Figure 6

    ROS level is significantly higher in βVmat2KO compared with control mouse islets, which confer the vulnerability of βVmat2KO. AD: Islet dissociation culture. Images (A) and quantification (B) of ROS staining in dissociated islets cultured on day 5 under high glucose conditions (25 mmol/L glucose). Compared with control (Slc18a2tm1c/tm1c) islets, βVmat2KO isolated islets showed a higher ROS level, and both were reduced by treatment with 1 μmol/L pargyline, an MAOB inhibitor. Mice at 13 weeks of age were used. ROS, green; INS, magenta; DAPI, blue. C and D: Control or βVmat2KO β-cell numbers treated with chemicals or DMSO were quantified after 5 days of dissociation cultures. The number of β-cells in control islets was reduced by TBZ treatment. D: The proportion of β-cells that underwent apoptosis (caspase-3/7+) increased under high glucose conditions and was reduced by pargyline treatment, but this was reversed by TBZ + pargyline. CASP3, caspase-3/7. EH: Whole islet culture. E: H2O2 generation under low glucose (5.5 mmol/L [left panel]) or high glucose (25.0 mmol/L [middle panels]) conditions in the isolated islets from ND-fed control (left and middle panels) or βVmat2KO (right panel) mice, treated with chemicals. H2O2 was quantified at 1 h, 6 h, and 24 h after high glucose (25.0 mmol/L) stimulation. F: β-cell number in βVmat2KO or control islets after H2O2 treatment. H2O2 decreased βVmat2KO β-cell number compared with untreated control. G: Real-time PCR analyses of Nrf2 expression in whole islet culture treated with 5.5 or 25.0 mmol/L glucose at 10 weeks, 13 weeks, and 15 weeks of age. H: H2O2 contents in whole islets treated with chemicals, with or without cotreatment of 100 μmol/L oltiplaz for 6 h. I: A decrease in the expression of endocrine maturation markers by exposure to H2O2 in βVmat2KO islets compared with control from ND-fed isolated islets at 13 weeks of age. Values are shown as fold expression vs. controls. Means ± SD are shown (n = 3) (BI). Control, white bars; βVmat2KO, gray bars. Significant differences vs. 10-week-old controls, §P < 0.05 and §§P < 0.01, or between two values marked by the bars, *P < 0.05 and **P < 0.01, by one-way ANOVA and Dunnett multiple comparisons test. βKO, βVmat2KO; conc, concentration; cont., control; Parg, pargyline; w, weeks.

    We then used a whole islet culture system and measured the time-dependent generation of H2O2 by glucose stimulation using islets isolated from ND-fed control or βVmat2KO mice (Fig. 6E). In control islets, high glucose stimulation alone showed a slight increase in H2O2 level in whole islet culture. High glucose alone elevated ROS production (Fig. 6E [compare left and middle panels]), which is reported to be toxic in β-cells (31,32). However, in the presence of TBZ, high glucose stimulation induced a rapid and dramatic elevation in H2O2 levels, which decreased with time and returned to basal levels after 24 h and was rescued by the addition of pargyline (Fig. 6E, left and middle panels). In the islets of βVmat2KO mice, the H2O2 level was highest at 1 h after glucose stimulation without TBZ treatment. Pargyline treatment significantly lowered the H2O2 level in the islets of βVmat2KO mice (Fig. 6E, right panel). It has been reported that exogenous H2O2 treatment under basal glucose concentrations induced ROS generation up to a similar level with high glucose stimulation (33). We then examined β-cell number after exposure of the islets to different H2O2 concentrations and found that the islets of βVmat2KO had a significantly lower number of living β-cells than the control mice (Fig. 6F). The results suggested that βVmat2KO islets are more sensitive to ROS.

    It is reported that β-cells possess antioxidant mechanisms, such as the induction of the transcription factor nuclear factor erythroid 2p45-related factor 2 (Nrf2), which regulates the expression of several genes involved in redox metabolism (34). Nrf2 expression significantly increased in response to high glucose stimulation, to a greater extent in βVmat2KO islets compared with the control mice (Fig. 6G). The result suggests that βVmat2KO mice are exposed continuously to high ROS and therefore develop a protective mechanism in response to high glucose stimulation.

    Since ROS are mainly produced during mitochondrial respiration, we then assessed the proportion of MAO-mediated ROS generation, by treating islets with oltipraz, an antioxidant that exerts mitochondrial protective effects in β-cells (35). We found that approximately one-half of the H2O2 generated was reduced by oltipraz treatment in control islets (Fig. 6H). Pargyline treatment decreased ∼92% of the H2O2 produced by TBZ. Oltipraz treatment of the TBZ + pargyline islets further reduced the remaining <8% of the H2O2 triggered by TBZ. Similarly, oltipraz treatment did not reduce H2O2 in βVmat2KO islets. By contrast, pargyline treatment significantly reduced H2O2 in βVmat2KO islets. Therefore, our results suggest that a large proportion of ROS generated upon TBZ treatment or in βVmat2KO islets was derived from MAO-mediated generation of ROS, which plays a vital role in the progression of β-cell failure in these models.

    Real-time PCR analysis of the H2O2-treated islets revealed that the islets of βVmat2KO mice expressed mature markers such as Ins1, Ins2, MafA, Nkx6.1, Gck, and Pdx1 at significantly lower levels and expressed an immature marker, MafB, at significantly higher levels compared with control mice in response to H2O2 (Fig. 6I).

    Therefore, we infer that high glucose triggers dopamine secretion and insulin secretion simultaneously. In the presence of VMAT2, cytoplasmic dopamine is rapidly sequestered and stored. However, in the absence of VMAT2 function, cytoplasmic dopamine cannot be sequestered, and dopamine degradation by MAO results in rapid production of H2O2 upon high glucose stimulation. βVmat2KO β-cells show elevated insulin secretion even under low glucose conditions. β-cells are exposed continuously to ROS. They then develop antioxidative mechanisms to protect themselves. However, being placed under chronic exposure to ROS, βVmat2KO islets are more vulnerable to H2O2 toxicity than those of the controls. Under prolonged HFD feeding, where a high metabolic demand occurs, dedifferentiation, β-cell dysfunction, and β-cell loss are trigged in βVmat2KO β-cells.

    Discussion

    Pancreatic β-cells are susceptible to oxidative stress. Oversupply of nutrients, such as glucose and fatty acids, and overstimulation of β-cells are considered to contribute to β-cell failure in type 2 diabetes. Here, we found that VMAT2 acts to protect β-cells from the toxic effects of oxidative stress triggered by excessive insulin secretion through the compartmentalization of dopamine, which prevents its degradation. VMAT2 protein expression is regulated in a glucose-dependent manner so that β-cells in high glucose conditions show an upregulated VMAT2 expression. It is reported that there are tyrosine hydroxylase activities in the adult rat islets themselves (36). Therefore, β-cells synthesize dopamine themselves and store it in the vesicle via VMAT2 to prevent degradation by MAO. Upon insulin secretion in response to high glucose in normal control β-cells, dopamine is secreted into the extracellular space through exocytosis and acts as negative feedback for insulin secretion through binding to its receptor Drd2, which exists on the β-cell plasma membrane. Extracellular dopamine is cleared by reuptake into the β-cells through dopamine plasma membrane transporter DAT and stored in the vesicle via VMAT2 for subsequent release (Fig. 7). In this way, VMAT2 plays a significant regulatory role in the compartmentalization of dopamine. βVmat2KO β-cells (or control β-cells treated with VMAT2 inhibitor TBZ) cannot uptake dopamine into vesicles; thus, dopamine is subjected to degradation by MAO, leading to a reduced dopamine content and an increased generation of ROS. The decreased dopamine content leads to a reduction in the dopamine negative-feedback loop, which in turn leads to elevated insulin secretion. Under HFD conditions, where excess nutrient stress exists, insulin secretion frequently occurs, increasing β-cell exposure to ROS. In βVmat2KO β-cells, HFD triggers chronic exposure to MAO-derived ROS and leads to increased vulnerability and accelerated β-cell failure. βVmat2KO β-cells show an initial compensation via β-cell growth and increased β-cell mass followed by dedifferentiation and β-cell death, which is a characteristic of the progression of β-cell failure (Fig. 7).

    Figure 7
    Figure 7

    Schematic drawing of the molecular mechanism by which VMAT2 safeguards β-cell function under HFD from dopamine-mediated cytotoxicity. Dopamine is released at insulin secretion following high glucose stimulation and acts as a negative regulator for insulin secretion through dopamine receptor 2 (Drd2). Dopamine is normally taken up through dopamine transporter (DAT) and stored in VMAT2-regulated vesicles. Under ND, βVMAT2KO β-cells exhibit increased insulin release in response to glucose stimulation. Under HFD, where insulin secretion occurs frequently, β-cells are under long-term exposure to ROS and become vulnerable to damages by dopamine cytotoxicity, leading to accelerated dedifferentiation and cell death. Left, control Slc18a2tm1c/tm1c; right, βVMAT2KO β-cells.

    Dedifferentiation Is the Mechanism of β-Cell Failure

    HFD in rodents is a commonly studied model of a compensatory increase in insulin secretion and β-cell mass; β-cells eventually fail, leading to glucose intolerance and insulin intolerance. The HFD model shows an initial increased expression of β-cell functional genes, which is followed by a cessation of gene hyperexpression, endoplasmic reticulum stress, and β-cell functional failure (24). We observed an increase in β-cell mass in the islets of control mice and at early ages in the islets of βVmat2KO mice. However, in βVmat2KO islets, both β-cell mass and insulin secretion were impaired with increasing age. β-cell failure corresponded with a decrease in β-cell mass. Decreased expression of maturation markers, such as Ins1, Ins2, Glut2, Gck, Pdx1, Nkx6.1, and MafA, and increased expression of the dedifferentiation marker MafB occur in islets of βVmat2KO mice, which is in parallel with β-cell failure and precedes the decrease in β-cell mass. Our results agree with previous reports that dedifferentiation is one of the mechanisms of β-cell failure.

    Dopamine Actions in Neuronal Cells

    In neuronal cells, VMAT2 plays an important role in the compartmentation of dopamine to protect the cells from oxidative stress. Improper compartmentation of dopamine contributes to diseases in the neural system such as Parkinson disease. Accumulation of dopamine in the cytosolic space is toxic, inducing neuronal damage and apoptotic cell death (3740). Dopamine can be auto-oxidized to form ROS, including hydroxyl radicals, superoxide, and hydrogen peroxide. Oxidized dopamine can then be converted to highly toxic dopamine quinones and the protein function-altering cysteinyl adduct. Deamination by mitochondrial MAO converts cytosolic dopamine to hydroxyperoxide and a reactive aldehyde intermediate, which can be oxidized to create ROS (41,42). Genetic knockout of Vmat2 is reported to be lethal. Animals with Vmat2 knockouts are hypersensitive to the dopamine agonist apomorphine, and the psychostimulants cocaine and amphetamine, with animals dying a few days after birth (4). Animals with very low VMAT2 levels were reported to survive into adulthood but were more vulnerable to neural damage in the dopaminergic neurons (37,43). On the other hand, increasing dopamine stores by overexpression of VMAT2 attenuated cytosolic dopamine levels and enhanced dopaminergic cell survival by lowering dopamine-dependent oxidative stress (3,44).

    Dopamine as a Negative Regulator for Insulin Secretion in Pancreatic β-Cells

    In pancreatic β-cells, dopamine reportedly functions as a negative regulator for insulin secretion through Drd2. The knockdown of Drd2 in INS-1 cells (a β-cell line) resulted in increased insulin secretion (12). Dopamine treatment decreased insulin secretion in isolated islets (45). We previously reported that dopamine accelerated β-cell dedifferentiation, which could be rescued with the Drd2 antagonist domperidone (17). Long-term VMAT2 deficiency resulted in β-cell failure. This phenotype is in agreement with the previously reported mouse model with Drd2 disruption, which resulted in an impairment in glucose tolerance, diminished β-cell mass, and decreased β-cell replication (15). We interpret this to mean that loss of dopamine-negative signaling enhances insulin secretion. Insulin exocytosis accompanied dopamine exocytosis. Increased insulin secretion increases dopamine release extracellularly. Dopamine into the cytosol is degraded by MAO, which increases the ROS level, thereby contributing to β-cell dysfunction. Islet-specific MAO expression depends on the transcriptional activity of the mature endocrine β-cell marker MAFA. Therefore, mature β-cells develop a mechanism to degrade dopamine and increase the ROS level (16). Although βVmat2KO islets exhibit lower dopamine content compared with the control mice, a certain level of dopamine still exists, since the dopamine-synthesizing enzyme tyrosine hydroxylase is expressed in β-cells (46).

    Glucotoxicity and β-Cell Failure

    Chronic exposure to high glucose causes functional damage to pancreatic β-cells, which is known as glucose toxicity. One mechanism of glucose toxicity is oxidative stress (47) conferred by ROS (1,48). Administration of an antioxidant such as glutathione ameliorates glucotoxicity (49). ROS generation through dopamine degradation seems to play an important role in glucotoxicity. Here, we showed that an MAOB inhibitor, pargyline, reduced the level of ROS in βVmat2KO islets stimulated by glucose.

    Chronic hyperglycemia models are reported to lead to β-cell dysfunction in the long-term. Inducible mouse models selectively expressing gain-of-function KATP channel mutations (Kir6.2-V59M or K185QΔN30) in pancreatic β-cells show β-cell dysfunction due to β-cell dedifferentiation (50,51). Furthermore, loss-of-function mutants of the voltage-dependent K+ (Kv) channel KCNH6 plays a role in the modulation of insulin secretion. In a loss-of-function KCNH6 mutant in humans and mice, hyper–insulin secretion was observed initially, followed by subsequent hypo–insulin secretion as a result of overstimulation of insulin secretion; in the long-term, endoplasmic reticulum stress, apoptosis, loss of β-cell mass, and subsequent decreased insulin secretion were observed (52). These reports suggest that the overstimulation of insulin secretion causes β-cell failure in the long-term, which is in agreement with our results.

    VMAT2 as a Guardian for Maintenance of β-Cell Function

    β-cells are highly heterogeneous (5355), which protects β-cells themselves from overstimulation. Under normal conditions, β-cells secrete dopamine in response to high blood glucose. The secreted dopamine acts through Drd2 on their plasma membrane to negatively regulate insulin secretion. Dopamine is rapidly sequestered into the β-cells by DAT and stored in vesicles by VMAT2, thereby protecting β-cells from dopamine toxicity. However, miscompartmentalization of dopamine might occur with overstimulation of insulin secretion or low VMAT2 expression, increasing ROS accumulation and leading to β-cell failure. Future works on how the dopamine-VMAT2 signaling system works in the heterogenous β-cell population would be necessary to increase the understanding of VMAT2 in the maintenance of β-cell function.

    Article Information

    Acknowledgments. The authors thank the members of the Animal Centers and the Bio Resource Department at the Tokyo Institute of Technology and Kumamoto University for technical assistance. The authors thank Takeshi Nagura (Kumamoto University) for discussions and technical assistance.

    Funding. This work was supported by a grant from the Project for Realization of Regenerative Medicine from Japan Agency for Medical Research and Development (AMED) (grant number 17bm0704004h0101) and Grants-in-Aid from the Ministry of Education, Culture, Sports, Science and Technology, Japan (18H02861 to S.K. and 17K09455 to D.S.). This work was also supported in part by the Takeda Science Foundation and Japan Insulin Dependent Diabetes Mellitus (IDDM) Network.

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

    Author Contributions. D.S. designed the experiments and acquired, analyzed, and interpreted data. F.U., H.T., Y.S., and K.M. acquired and analyzed the data. N.T. performed blastocyst injection of the ES cells and generated the Slc18a2tm1a/+ mouse. N.N. provided technical advice and support for the maintenance of gene knockout mice. K.K. and N.S. discussed the data. S.K. provided conceptual input, discussion, writing, and revision of the manuscript; approved the final version of the manuscript; and obtained funding. D.S. and S.K. are the guarantors of this work and, as such, had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.



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    Join the Fit With Diabetes Challenge

    By electricdiet / December 20, 2020


    In this free 3-week challenge, Christel and a team of top diabetes experts will take you through some of the most important things you need to know to live a healthy life with any type of diabetes.

    Contributors to the Fit With Diabetes Challenge

    How the Fit With Diabetes Challenge works

    The Fit With Diabetes Challenge consists of:

    1. Daily activities or “challenges” that provides accountability and motivates you to work on your diabetes management and overall health
    2. Practical articles covering the most important topics on living a healthy life with diabetes
    3. Webinars with diabetes experts
    4. Healthy and tasty meal plans
    5. On-demand workout programs (you will get free access to the Glucosezone workout app for people with diabetes)
    6. A Facebook peer support group where you can ask questions, share your experiences, and connect with other people living with diabetes

    Expert speakers

    We have a fantastic lineup of expert speakers for this year’s challenge webinars!

    Gary Scheiner headshot

    Gary Scheiner, MS, CDE

    Gary is the Owner of Integrated Diabetes Services and the author of “Think Like A Pancreas”.

    You will get the chance to ask Gary questions live in the webinar “Ask a Diabetes Educator Anything”.

    Toby Smithson headshot

    Toby Smithson, MS, RDN, LD, CDCES, FAND

    Toby is the 2020 Diabetes Educator of the Year. She guides people with diabetes to better practice diabetes self-management at her website DiabetesEveryDay

    She will talk about healthy nutrition and weight loss.

    Bill Polonsky headshot

    Bill Polonsky, Ph.D., CDE, Phycologist

    Bill is Associate Clinical Professor in Psychiatry at the University of California San Diego.

    He will talk about the mental aspects of diabetes and dealing with diabetes burnout.

    Lauren Bongiorno

    Lauren Bongiorno

    Lauren is a Diabetes Health Coach and author.

    She will share the five-phase method she uses to help clients lower their A1C and increase their time in range.

    Paloma (Glitter Glucose)

    Paloma Kemak

    Paloma shares her life with diabetes on her popular Instagram @GlitterGlucose.

    Paloma and Christel will talk about getting started with exercise and managing diabetes when active.

    Mila Clarke Buckley

    Mila Clarke Buckley

    Mila creates delicious recipes on her blog The Hangry Woman.

    Mila and Christel will discuss Mila’s type 2 diabetes misdiagnosis, and how she finally received the correct LADA diagnosis.

    Sponsors and prizes

    The challenge is free because of our amazing sponsors!

    Dario glucose meter system

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    See all your data in one place and get valuable insights with Dario’s powerful app. Record your food, exercise, medications, and mood. 

    Dario’s Diabetes Success Plan includes unlimited test strips and lancets as well as one-on-one coaching. Challenge participants get 80% off and free diabetes socks after signing up!

    You will also have the chance to win prizes from Myabetic and Re:THINK Ice Cream in our weekly giveaways.

    Sign up now!

    If you are ready to join me on this journey to learn more about living a healthy life with diabetes, then sign up for the challenge in the form below.

    Let’s get Fit With Diabetes together in 2021!





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    Baked Italian Oysters – Southern Diabetic Delicious

    By electricdiet / December 18, 2020


    Holiday Spectacular Diabetic Side Dish

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

    Baked Italian Oysters

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

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

      Ingredients

      • 2pints


        oystersdrained

      • 1/3cup


        olive oil

      • 1teaspoon


        minced garlic

      • 1/3cup


        chopped parsley

      • 1


        bunch green onionschopped

      • 2cups


        Italian breadcrumbs

      • 1/3cup


        grated Parmesan cheese

      • 1/4cup


        lemon juice

      • 1


        bunch green onionschopped

      • 2cups


        Italian breadcrumbs

      • 1/3cup


        grated Parmesan cheese

      • 1/4cup


        lemon juice

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

      2. Place drained oysters on in prepared baking dish.

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

      Recipe Notes

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

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

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

      Stock Your Kitchen for this Recipe

      2 Quart Glass Oblong Baking Dish with Plastic Lid - 7 inch x 11 Inch2 Quart Glass Oblong Baking Dish with Plastic Lid – 7 inch x 11 Inch2 Quart Glass Oblong Baking Dish with Plastic Lid - 7 inch x 11 InchProgresso Bread Crumbs Italian Style, 8 ozProgresso Bread Crumbs Italian Style, 8 ozProgresso Bread Crumbs Italian Style, 8 ozOver the Sink Colander Strainer Basket Stainless SteelOver the Sink Colander Strainer Basket Stainless SteelOver the Sink Colander Strainer Basket Stainless Steel

      New Orleans Favorite Baked Italian Oysters

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

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

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

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



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

      By electricdiet / December 16, 2020


      Introduction

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

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

      Figure 1
      Figure 1

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

      Review Methodology

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

      Diabetes and COVID-19

      General COVID-19 Patient Cohorts

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

      Table 1

      Overview of adult COVID-19 clinical cohorts

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

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

      Cohorts of Patients With COVID-19 and Diabetes

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

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

      Glycemic Control and Elevated Fasting Blood Glucose

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

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

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

      Comorbidities and COVID-19

      Comorbidities in General COVID-19 Patient Cohorts

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

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

      Comorbidities in Cohorts of Patients With COVID-19 and Diabetes

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

      Cumulative Comorbidities Effect

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

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

      Pediatric Diabetes and Comorbidities in COVID-19

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

      Table 2

      Overview of pediatric and pregnancy COVID-19 clinical cohorts

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

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

      Pregnancy, Diabetes, and Comorbidities in COVID-19

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

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

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

      Race, Diabetes, and Comorbidities in COVID-19

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

      Table 3

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

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

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

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

      COVID-19 and Diabetes Pathology: Collision and Collusion

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

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

      Figure 2
      Figure 2

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

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

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

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

      Additional Aspects of COVID-19 Tropism Relevant to Diabetes

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

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

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

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

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

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

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

      Diabetes and COVID-19 Sequelae

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

      Conclusions: A Collision and Collusion of Two Diseases

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

      Article Information

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

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

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



      Sell Unused Diabetic Strips Today!

      Low-Carb Cauliflower Rice – Diabetic Foodie

      By electricdiet / December 14, 2020


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

      Low-carb cauliflower rice in black bowl

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

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

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

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

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

      How to make low-carb cauliflower rice

      This simple dish comes together in just a few steps.

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

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

      Step 3: Repeat with the remaining cauliflower.

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

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

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

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

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

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

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

      Adding flavor to your rice

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

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

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

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

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

      Storage

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

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

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

      Other low-carb side dishes

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

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

      Recipe Card

      Cauliflower rice in a black bowl

      Low-carb Cauliflower Rice

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

      Prep Time:15 minutes

      Cook Time:10 minutes

      Total Time:25 minutes

      Author:Shelby Kinnaird

      Servings:4

      Instructions

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

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

      • Repeat with the remaining cauliflower.

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

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

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

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

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

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

      Recipe Notes

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

      Nutrition Info Per Serving

      Nutrition Facts

      Low-carb Cauliflower Rice

      Amount Per Serving

      Calories 76
      Calories from Fat 36

      % Daily Value*

      Fat 4g6%

      Saturated Fat 3g19%

      Trans Fat 0g

      Polyunsaturated Fat 0g

      Monounsaturated Fat 0g

      Cholesterol 0mg0%

      Sodium 71mg3%

      Potassium 25mg1%

      Carbohydrates 9g3%

      Fiber 4g17%

      Sugar 4g4%

      Protein 3g6%

      Net carbs 5g

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

      Course: Side Dishes

      Cuisine: American

      Diet: Diabetic

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



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