DIY Looping: Building Your Own Closed Loop System

By electricdiet / November 21, 2020

If you live with diabetes, you may have heard of the term “DIY Looping” but may not have any idea what it actually is.

DIY Looping is the process by which someone with diabetes “hacks” their existing insulin pump with a single-board computer, such as a RileyLink or Raspberry Pi.

This essentially makes your insulin pump communicate with a continuous glucose monitor (CGM) to make basal insulin adjustments automatically, instead of manually suspending, reducing, or increasing insulin throughout the day.

This is a popular and growing movement and has helped countless people improve not only their blood sugars but their emotional and mental health as well.

In this article, you will learn what DIY looping is, how it works, the pros and cons, and lessons learned from a year of looping myself.

What is ‘looping’?

The term “looping” refers to, “closing the loop” between one’s insulin pump and a continuous glucose monitoring (CGM) system.

Currently, many insulin pumps do not communicate with existing CGM systems, and no tubeless insulin pumps exist that communicate with CGMs.

Your CGM may read that your blood sugar is 60, but you need to manually suspend insulin and/or eat a snack to remedy your hypoglycemic reading.

On a looping system, your CGM would tell your insulin pump that your blood sugar is running low, and your insulin pump would suspend insulin immediately until your blood sugar started to rise again.

It’s important to note that this is NOT FDA approved, so all risk and liability is on the user if they decide to build their own system.

How did this come about?

This movement stemmed from the frustration and disappointment people with diabetes have felt for a long time at the pace that technology (that has the potential to completely change lives) was being developed.

Looping is the brainchild of many brilliant people and families affected by diabetes who started the #WeAreNotWaiting movement nearly a decade ago.

The hashtag was coined in 2013 at the first-ever DiabetesMine D-Data ExChange gathering at Stanford University by Lane Desborough (Chief Engineer at Medtronic) and Howard Look (CEO of Tidepool).

Looping is part of the larger Open Artificial Pancreas System (OpenAPS) movement where advocates in the diabetes community are developing opensource platforms, code, and apps to essentially reserve-engineer existing durable medical equipment (like older insulin pumps) to help people living with diabetes achieve better health outcomes when FDA-approved devices have proven inadequate.

Dana Lewis and Scott Leibrand of Seattle, Washington, were the first couple to develop the OpenAPS, a homemade artificial pancreas, for her own diabetes management.

It is now being used by thousands of people around the world, and many more developers have added onto and built upon the original code, improving the system with each software update. Nate Racklyeft has written a great piece on the history of Loop.

In 2014, diabetes advocate, Anna McCollister-Slipp, told Forbes:

“Everybody seems to think that it’s okay to wait another two or three years for this process to play itself out. In terms of the business or policy cycles, that’s the current trajectory, but for those of us who live with this data dysfunction, two or three years can make the difference between going blind or dying in our sleep. It’s purely an issue of priorities and urgency and despite glowing rhetoric to the contrary – patient needs are nowhere in sight for manufacturers or policymakers.”

The OpenAPS community wanted to change that.

What does the system do?

When you build the system, all of which is free to download with instructions available online, you will see that the DIY loop system immediately starts reading your blood sugars off of your CGM, and transmits that data to an app on your phone (that acts as an insulin pump now).

The system makes adjustments automatically (that you set up with your correction factor, insulin to carbohydrate ratios, etc.), but it doesn’t account for food eaten.

You still need to bolus like normal for carbohydrates, hence the “hybrid” closed-loop system. There is currently no completely closed-loop system that would essentially use artificial intelligence (AI) to predict food or exercise.

Studies have shown that DIY APS systems improve not only time-in-range for people with diabetes, but also vastly improve quality of life.

In a 2016 self-reported outcomes study, 56% of DIY loop users reported a large improvement in sleep quality.

A type 1 parent, Dr. Jason Wittmer, tracked his son’s school nurse visits: before OpenAPS, his 4th-grade son averaged 2.3 visits per day (420 total in a school year); after OpenAPS, his son visited the school nurse 5 times during the entire school year.

Families using DIY loop systems also report less time spent talking about diabetes and improved family communication around diabetes.

They also report improved sleep quality for multiple family members (not only the person living with diabetes) and spending less time thinking about diabetes and doing diabetes-related management tasks, such as treating lows or sitting out of sports due to highs.

How do you build your own system?

This part gets complicated but is doable even for a novice. The OpenAPS community firmly believes in keeping OpenAPS open source, for all to use.

The beauty of this community is everyone’s willingness to help one another to achieve better health outcomes. The community has created and maintains the online instructions to build your own system at a website called LoopDocs with a thorough explanation of the process and FAQ.

To get started, though, this is what you’ll need to build your own DIY loop:

  • A compatible insulin pump and continuous glucose monitor system
  • A RileyLink single-board computer (they sell for about $150)
  • An iPhone
  • A compatible Macbook computer
  • Apple developer account, and download the Xcode app

Since this system is not FDA approved, each user needs to build the code for their own insulin pump, but LoopDocs makes that easy and accessible, with all instructions available online.

You can build the program within 3-4 hours and can start creating your settings by simply transferring your existing insulin pump settings right into the new Loop app on your iPhone. The settings you set yourself are:

  • Target blood glucose
  • Target exercise blood glucose
  • Pre-meal target blood glucose
  • Correction factor
  • Standard basal rates for different times of day
  • Insulin to carbohydrate ratio (can change for different meals)

Once you finish installing Xcode and building the Loop app, you can either opt for an “open” loop mode or “closed” loop mode.

Hand holding RileyLink
This is the RileyLink single board computer in its enclosure. This must be near you and your iPhone at all times to make dosing decisions and bolus. The RileyLink acts as a “bridge” allowing your insulin pump to communicate with your iPhone, where the Loop app is running. 

The open-loop mode merely gives you suggestions for dosing but doesn’t actually override your regular basal insulin pump program, whereas if you “close the loop” the new settings on your Loop app will override your old basal program.

It’s important to remember that your RileyLink hardware needs to be in close proximity to your iPhone at all times. Your iPhone now serves as your new insulin pump and all dosing decisions are made from the app on your phone.

Loop gear on table
All of my loop gear. From left to right: iPhone with the loop app, the RileyLink computer, an omnipod pod, and the Dexcom g6 sensor.
Screenshot of Loop app
This is the loop app, which is on my iPhone. It shows not only your blood sugar and predicted future blood sugar, but also any insulin-on-board (IOB) you have, along with active carbohydrates, and total insulin taken for the day. You make all of your dosing decisions and boluses right in this app on your phone! No need for another insulin pump (or in my case, an insulet PDM).

If your RileyLink is ever out of range for an extended period of time, your basal settings will simply revert back to your original insulin pump settings automatically. You will not be without basal insulin.

Once you do that, you’re done and are now officially looping!

What is the biggest difference between Looping and not?

I’ve been looping for a little over a year now, and I’ve noticed the biggest difference in my overnight blood sugars.

Since this is a hybrid closed-loop system (and not a completely closed-loop that would dose insulin for food and adjust insulin for exercise automatically), the system performs best when there are fewer variables.

I’ve noticed that I require a lot more basal insulin overnight to help combat the dawn phenomenon, which I used to struggle with, but don’t worry about anymore.

Looping has also helped me improve my time-in-range and greatly has decreased the number of hypoglycemic events I have per week (down from 2 to 3 per day to 1 or 2 per month) while achieving my lowest hba1c ever, 5.9%.

Exercise and eating are easier with the loop, too. I can set exercise mode on the app an hour before I begin an exercise, and I rarely go low.

When I am eating a meal, if my carbohydrate count is slightly off, the loop will increase my basal to make up for it, without sacrificing good blood sugar.

Screenshot of graph from Dexcom app
A typical 12-hour graph on my Dexcom continuous glucose monitor (CGM) app. It is much easier to stay in range with an automated system.

The system isn’t perfect

The system has been absolutely mind-blowing, and I am so thankful for all of the smart, kind, and generous people who have shared it with the world, but it’s still not perfect.

Since the loop makes basal adjustments based on your CGM readings, the system is only as strong as your weakest CGM site. Any time I have an error on my CGM, or a site falls out, the loop will disconnect.

This also happens every 10 days when it’s time to change to a new CGM site during the 2 hour warmup period where you do not have blood sugar readings.

Additionally, sometimes CGM readings can be off or completely wrong, in which case your pump is making adjustments on faulty data.

Christine holding iPhone and RileyLInk
Posing with the full hybrid-closed loop system, which includes the RileyLink, iPhone where the Loop app runs, and Dexcom sensor (out of the photo; on my abdomen). I use the Omnipod system for my insulin pump, and I am wearing a pod on my arm.

So thankful for this life-changing technology

In the end, looping has only made my life with diabetes easier. I get to live a more carefree existence, one where I don’t have to micromanage my care 24 hours a day, 7 days a week.

When I’m hiking with my husband in the mountains of Colorado or on vacation at the beach, I trust that my blood sugar is quietly humming along in the background, and I worry about going low and high less often.

It feels great to know that the technology I trust is making dosing decisions based on the amount of insulin I already have “on board”, what my correction factors are, and what time of day it is, and it creates more space in my brain for other, non-diabetes related things, like life, work, family, and fun.

I am anxious for an FDA-approved tubeless hybrid closed-loop insulin system (not to mention a cure!), but until then, I’m a bonafide looper.

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Best Cornbread Dressing Recipe With Sweet Potatoes: Easy Thanksgiving Dish

By electricdiet / November 19, 2020

What is the Best Cornbread Dressing Recipe Using #1 Holiday Ingredient?

OK, so that isn’t exactly a scientific number but sweet potatoes are definitely our number one holiday favorite ingredient.  Holly combined them with the popular Thanksgiving cornbread dressing dish to make the best cornbread dressing recipe ever!  Cornbread dressing and sweet potatoes grace every holiday table. We think healthy sweet potato recipes are the best and this winning combination gives you an amazing southern cornbread dressing recipe.  This combination of cornbread and sweet potatoes creates the best healthy cornbread dressing for a holiday healthy easy recipe. No Thanksgiving table would be complete without it – it will soon be your favorite holiday side dish too. Best of all, this is a diabetic cornbread dressing recipe! Let The Healthy Cooking Blog your #1 resource for an easy, healthy top Thanksgiving recipes and tips!

Delicious and Healthy Cornbread Dressing

The beloved sweet potato, or yam as it’s known in Louisiana, is the sweetest of the sweet potatoes, boasting rich nutrition such as fiber, vitamin A and C making this a diabetic sweet potato recipe. There are lots of easy healthy sweet potato recipes featured on Team Holly’s healthy food blog! What’s great is this diabetic cornbread recipe tops the list for the best healthy cornbread dressing recipes. Just because it is a Thanksgiving cornbread dressing doesn’t mean it can’t be turned into a deliciously healthy cornbread dressing recipe.  I want the only thing stuffed on Thanksgiving to be the turkey! Check out this healthy Thanksgiving menu including the best Sweet Potato Casserole with Praline Topping!

Best of Both Worlds with Healthy Sweet Potato Recipes – Yams and Cornbread

Yam Cornbread Stuffing is the ultimate time saver holiday recipe as it combines yams and dressing into one delectable dish. Can you believe this is diabetic cornbread dressing recipe is diabetic-friendly?! With fresh sweet yams, cornbread, ginger and toasty pecans. For a time-efficient approach, prepare the cornbread and toast the pecans a day ahead (or just buy cornbread), and look for Louisiana yams in your grocery for the sweetest of the sweet potatoes.

Best Cornbread Dressing Recipe Is Healthy Cornbread Dressing Plus EASY!

Yam Cornbread Dressing
Two holiday favorites, Louisiana yams and cornbread, in this scrumptious best cornbread dressing recipe. Hard to believe it is also one of the most delicious healthy cornbread dressing recipes also. Save time and  prepare the cornbread and toast the pecans a day ahead. Or you can even pick up pre-made cornbread.  Keep it simple!

    Servings10 (3/4 cup) servings


    • 2tablespoons

      canola oil

    • 2cups

      peeled chopped Louisiana yamssweet potatoes

    • 1cup

      chopped onion

    • 1cup

      sliced celery

    • 1/4cup

      chopped fresh parsley

    • 1teaspoon

      ground ginger

    • 5cups

      crumbled cooked cornbread

    • 1/4cup

      chopped pecanstoasted

    • 2tablespoons

      fat-free low-sodium chicken or vegetable broth

    1. Preheat oven 375°F. Coat 3-quart baking dish with nonstick cooking spray.

    2. In large nonstick skillet coated with nonstick cooking spray, heat oil over medium heat. Sauté sweet potatoes, onion, and celery 7–10 minutes or until just tender, stirring.

    3. Spoon mixture into large mixing bowl. Stir in parsley and ginger. Add cornbread and pecans and toss gently to coat. Add broth to moisten.

    4. Place stuffing in prepared dish. Bake, uncovered, 35–45 minutes or until heated through.

    Recipe Notes

    Per Serving: Calories 241 Calories from fat 42% Fat 9g Saturated Fat 1g Cholesterol 6mg Sodium 332mg Carbohydrate 36g Dietary Fiber 3g Sugars 12g Protein 4g Dietary Exchanges: 2 1/2 starch, 1 1/2 fat

    Terrific Tidbit: Time saver: Prepare cornbread and toast pecans a day ahead. Sweet potatoes are packed with vitamins and enhance the nutritional value of this recipe.

    Your Favorite Healthy Cajun Recipes Like Southern Cornbread Dressing Recipe

    Who says Louisiana and Cajun food can’t be good for you? Living in Baton Rouge, Holly Clegg wanted to give you all your favorite healthy Cajun recipes so no guilt in eating here. When writing her men’s healthy cookbook, Guy’s Guide To Eating Well, she was so excited to include her favorite diabetic cornbread dressing. 

    There’s a Diabetic-Obesity Chapter in the men’s health cookbook and this Thanksgiving cornbread dressing was a perfect fit!. Guys Guide cookbook includes simple and healthy recipes to make you a healthy star in the kitchen!

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    Thanksgiving recipes


    • Top Ten Time-Saving Thanksgiving Tips!!
    • Shopping List
    • Terrific Tips
    • Serving Suggestions
    • Nutritional Information

    Download it here!  Go-to holiday tips plus shopping lists to organize this busy day along with Holly’s own personal Thanksgiving menu. The only thing you will see missing is stuffing the turkey because her son-in-law is always in charge of making a fried turkey!

    southern cornbread dressing recipe is healthy cornbread dressing for best cornbread dressing recipe and healthy crawfish cornbread dressing

    Another Amazing Cornbread Dressing Recipe – Crawfish Cornbread Wild Rice Dressing

    I know living in Louisiana makes us partial to Louisiana crawfish recipes. However, I know so many of you moved from Louisiana or have tried our beloved crawfish.  Did you know you can have Louisiana crawfish year round because you can freeze crawfish tails. So, if you can’t decide between cornbread or wild rice dressing, you can have them both in this simple and amazing cornbread dressing recipe.

    Do You Have A Good Peeler For All Those Healthy Sweet Potato Recipes?

    A good peeler makes a difference in removing the skin off thee sweet potatoes!  Yes, this inexpensive gadget can be so helpful in the kitchen! All you need is a good peeler and it makes a BIG difference how easy and quickly you can remove the skin.

    Time to replace your peeler now as good gadgets make cooking easier.

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    The post Best Cornbread Dressing Recipe With Sweet Potatoes: Easy Thanksgiving Dish appeared first on The Healthy Cooking Blog.

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    Epigenetic Changes in Islets of Langerhans Preceding the Onset of Diabetes

    By electricdiet / November 17, 2020


    The identification of individuals with a high risk of developing type 2 diabetes (T2D) is fundamental for prevention. Here, we used a translational approach and prediction criteria to identify changes in DNA methylation visible before the development of T2D. Islets of Langerhans were isolated from genetically identical 10-week-old female New Zealand Obese mice, which differ in their degree of hyperglycemia and in liver fat content. The application of a semiexplorative approach identified 497 differentially expressed and methylated genes (P = 6.42e-09, hypergeometric test) enriched in pathways linked to insulin secretion and extracellular matrix-receptor interaction. The comparison of mouse data with DNA methylation levels of incident T2D cases from the prospective European Prospective Investigation of Cancer (EPIC)-Potsdam cohort, revealed 105 genes with altered DNA methylation at 605 cytosine-phosphate-guanine (CpG) sites, which were associated with future T2D. AKAP13, TENM2, CTDSPL, PTPRN2, and PTPRS showed the strongest predictive potential (area under the receiver operating characteristic curve values 0.62–0.73). Among the new candidates identified in blood cells, 655 CpG sites, located in 99 genes, were differentially methylated in islets of humans with T2D. Using correction for multiple testing detected 236 genes with an altered DNA methylation in blood cells and 201 genes in diabetic islets. Thus, the introduced translational approach identified novel putative biomarkers for early pancreatic islet aberrations preceding T2D.


    Alarming incident rates worldwide are projected to increase the current prevalence of type 2 diabetes (T2D) from 422 million to 592 million in 2035 (1). T2D is a progressive, chronic disorder with a long asymptomatic phase, averting detection for many years (2,3). Better disease management might be possible with earlier detection through robust, sensitive, and easily accessible biomarkers of T2D.

    T2D is characterized by chronic hyperglycemia, which is caused by an impaired insulin secretion from pancreatic β-cells and an insulin resistance of target tissues. Aging, a sedentary lifestyle, and obesity contribute to insulin resistance. After long-term exposure to elevated lipid and glucose levels, pancreatic islet function decreases (4,5), which leads to insufficient compensation and a loss of β-cells.

    The involvement of epigenetic mechanisms in T2D development emerged as a promising research area (6). One epigenetic modification is DNA methylation, which mainly occurs at the 5′ carbon of cytosine-phosphate-guanine (CpG) sites (7,8). DNA methylation marks are established during prenatal and early postnatal development and function throughout life to maintain the diverse gene expression patterns of different cell types (9,10) but can also arise later in somatic cells either by random events or under the influence of the environment (11,12). Thus, tissue specificity and flexibility of DNA methylation in response to the environment are two major problems to face in order to identify stable epigenetic biomarkers of disease risk. The aim of our translational study was to identify early epigenetic marks related to T2D by uncovering methylome alterations in pancreatic islets of mice that occur before the onset of severe hyperglycemia and assessing prospective T2D risk information conveyed by congruent differential methylation in human blood.

    Research Design and Methods

    Animals, Diets, and Experimental Design

    A full description of animals and diets was detailed previously (13,14). At 5 weeks of age, female New Zealand Obese (NZO) mice were placed on a high-fat diet (HFD) (20% kcal protein, 20% kcal carbohydrate, 60% kcal fat; D12492; Research Diets) for 5 weeks. Five weeks after switching the diet, mice were killed during midlight cycle with acute exposure to isoflurane (Fig. 2A). Animal studies were approved by the animal welfare committees of the German Institute of Human Nutrition Potsdam-Rehbruecke and local authorities (Landesamt für Umwelt, Gesundheit und Verbraucherschutz, Brandenburg, Germany).

    Pancreatic Islet Isolation

    Islet isolation was performed as described (15). Islets isolated from two to four mice (30–110 islets/mouse) were pooled per sample for RNA sequencing (RNA-seq) and whole-genome bisulfite sequencing (WGBS). The total number of islets used for nucleic acid extraction was 900 and 1,500 for RNA and DNA, respectively. For RNA-seq of diabetes-resistant (DR) mice, four individual islet pools were used that contained islets from 2 mice/pool; for diabetes-prone (DP)mice, five pools were used that contained 2–3 mice/pool. WGBS was performed with five pools per group from 4 animals/pool (Supplementary Table 1). Thus, each sample of islet pools comprised islets from different mice.

    Blood Glucose, Body Weight, Body Composition, and Liver Fat Content

    Body weight and blood glucose were measured from 7:00 to 9:00 a.m. on a weekly basis by using a Contour blood glucose meter (Bayer). At 5, 7, and 10 weeks of age, body composition and liver fat content were analyzed using nuclear magnetic resonance and computed tomography as described (14).

    Plasma Analysis

    Plasma adiponectin and leptin levels were measured by Mouse Adiponectin/Acrp30 (DY1119; R&D Systems) and Mouse/Rat Leptin (MOB00; R&D Systems) ELISA kits, respectively. Plasma triglycerides (T2449, F6428, G7793; Sigma), free fatty acids (91096, 91898, 91696; Wako), cholesterol (10017, HUMAN), ALT (12212, HUMAN), AST (12211, HUMAN), and γ-glutamyl transferase (GGT) (12213, HUMAN) levels were measured according to the manufacturer’s protocol.

    Gene Expression Analysis

    Total RNA was extracted by using miRNeasy Micro Kit (QIAGEN, Hilden, Germany) according to the manufacturer’s protocol, with additional DNase treatment. RNA samples with RNA integrity number ≥8 (Agilent Bioanalyzer) were selected for RNA-seq. Transcriptome sequencing was carried out by GATC biotech (Konstanz, Germany) on an Illumina HiSeq platform. Adapters were trimmed and reads filtered for quality by using the wrapper Trim Galore! v0.4.2 and Cutadapt 1.9.1 with option phred33. FastQC v0.11.5 was used to check sample quality. Alignment of reads to reference genome was performed with HISAT2 v2.1.0, and fragments per kilobase of exon model per million reads mapped values for transcripts was determined by Cufflinks 2.2.1, both with default options for paired reads. We considered only transcripts with fragments per kilobase of exon model per million reads mapped mean values >1/group. Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis was performed by the using DAVID 7 tool (16), with cutoff enrichment score set to >1.7 and enriched P < 0.05. Network analysis was obtained with Ingenuity Pathway Analysis (IPA) (QIAGEN) (Supplementary Table 1).

    WGBS in Pancreatic Islets

    Genomic DNA from NZO islets was isolated using Invisorb Genomic DNA Kit II. One microgram of genomic DNA from each pool was bisulfite converted (Zymo Research Corporation, Irvine, CA), and library preparation and sequencing steps were carried out by GATC. WGBS data in fastq format were generated using an Illumina HiSeq platform for further analyses. Raw data have been quality controlled and processed using Trim Galore! v0.4.2, FastQC v0.11.5, Bismark v0.17.08 (17), and MethPipe v3.4.2 (18) (Supplementary Table 1).

    A reference genome file was generated by combining a GRCm38.68 B6 reference and a GRCm38p4 single nucleotide polymorphism file in order to exchange all B6 with NZO high-quality single nucleotide polymorphisms. Methylation counting was carried out with MethPipe v3.4.2 default options.

    Nonsymmetric CpG sites have been withdrawn, P values have been calculated using log-likelihood ratio test. For the final analysis, CpG sites that fit to the following criteria were used: 1) at least four of five samples with read counts in both groups, 2) average of read counts per group >20, and 3) SD per group >0 in both groups.

    DNA Methylation Analysis in Human Blood Cells

    The study sample is a nested case-control study derived from the prospective European Prospective Investigation into Cancer and Nutrition (EPIC)-Potsdam cohort study (n = 27,548) designed to estimate the association of baseline measurements to incident T2D (19). Baseline recruitment for EPIC-Potsdam was conducted in Potsdam, Germany, and surrounding municipalities between 1994 and 1998. Included participants’ ages ranged from 35 to 64 years. The study was approved by the ethics committee of the Medical Society of the State of Brandenburg, Germany, and participants provided informed consent. Potential incident cases were systematically and continually identified (self-report, biennial questionnaire, death certificates, tumor registries, clinical records linkage) and verified (by last treating physician and study physician). The final sample for analysis comprised 270 case-control pairs matched on age, sex, fasting time before blood draw, time of day, and season at blood sampling. DNA methylation was measured using the Illumina EPIC 850K array (20). Raw DNA methylation data were processed and normalized using the R package meffil (21). Associations of baseline DNA methylation levels within each CpG site with incident T2D were evaluated in conditional logistic regression models accounting for the prospective nested case-control design using z-standardized and winsorized (95%) β-values adjusted for age, waist circumference, smoking, alcohol intake, leisure time, and physical activity as well as for estimated cell composition (22) and unaccounted batch effects (R package smartSVA [23]). T2D risk information on the basis of DNA methylation of a specific gene was summarized through least absolute shrinkage and selection operator (LASSO) regression computed on all available CpG sites annotated to a respective gene (R package clogitL1). β-Values were adjusted (i.e., use of residuals after regressing each CpG site on the adjustment variables) for the aforementioned variables to ensure that prediction of the outcome was independent of those variables. Area under the receiver operating characteristic curve (ROC-AUC) served as the ranking of the conveyed risk information per gene (Supplementary Table 1). For each CpG site, we further tested whether the association with T2D differed by sex by including interaction terms in the pooled models.

    DNA Methylation Data in Human Islets

    Human pancreatic islets were provided by the Nordic Network for Islet Transplantation, Uppsala University, Sweden. Genomic DNA was extracted from 15 participants with T2D and 34 control participants without T2D. Diabetes status was diagnosed before death of participants; control participants with glycated hemoglobin (HbA1c)<6% (42 mmol/mol) were selected (24). DNA methylation analysis was reconsidered for the current study (Supplementary Table 1).

    In Silico Analysis

    Human RNA-seq data were extracted from Gene Expression Omnibus repository GSE50244, and participants without data on HbA1c (55 without T2D and 26 with T2D) were excluded from the statistical analysis. The National Human Genome Research Institute-European Bioinformatics Institute Genome-Wide Association Study (GWAS) Catalog (25) was used to screen for differentially methylated ortholog genes. The islet expression quantitative trait loci data set from the two published studies (26,27) was downloaded and compared with the human orthologs identified in the current study.

    Statistical Analyses

    For animal experiments, Welch t test was performed for the comparison of two groups. All statistical tests were conducted using R 3.5.0 software (23 April 2018). Significance levels were set for P < 0.05, 0.01, and 0.001. After Benjamini-Hochberg correction, we used a threshold of P < 0.1.

    Data and Resource Availability

    All mouse data sets are available in the Gene Expression Omnibus repository (GSE143875) and will be publicly available upon acceptance. All human results are available in the Supplementary Tables. EPIC data sets are not publicly available because of data protection regulations. In accordance with German federal and state data protection regulations, epidemiological data analyses of EPIC-Potsdam may be initiated upon an informal inquiry addressed to the secretariat of the Human Study Center (Office.HSZ{at} Each request will have to pass a formal process of application and review by the respective principal investigator and a scientific board.


    Study Design

    Figure 1 summarizes the strategy of a semiexplorative approach used in the current study to identify early changes in DNA methylation marks related to T2D. We first analyzed the patterns of gene expression by RNA-seq and DNA methylation by WGBS in islets of genetically identical prediabetic mice that are supposed to differ in their later development of hyperglycemia (see below) (Fig. 2). To later translate the findings to human, we focused on genes with conserved CpG sites and further examined them in two different studies: 1) in blood cells of baseline-healthy donors to be related to incident T2D and 2) in islets of donors with and without diabetes. DNA methylation of human samples was measured with different methods: 850K array in blood cells and 450K array in human islets.

    Figure 1
    Figure 1

    Study design. Various steps used in the current study. The diagram includes the cohorts and methods/techniques used in each step.

    Figure 2
    Figure 2

    DP NZO female mice gain more body weight and exhibit higher liver fat content than DR mice. A: Study protocol. After weaning, NZO females were fed with a standard diet. At 5 weeks of age, NZO mice (n = 70) were placed on the HFD. Body weight and blood glucose were measured weekly. Liver density was evaluated by computed tomography (CT) at indicated time points. Higher Hounsfield units (HU) reflect lower hepatic fat content. Mice exhibiting blood glucose levels >16.6 mmol/L for 3 weeks in a row were classified as diabetic. BD: Blood glucose, body weight development, and liver density were measured. EG: Plotted are ROC curves illustrating the predictive capacity of blood glucose concentrations and liver density in week 10 separately or in combination with blood glucose concentration. Values for the AUC as well as number of animals are given within each graph. Number of animals in panels AC are 45 for DP mice and 25 for DR mice. Data are mean ± SEM. Differences between the groups were calculated by two-way ANOVA with Bonferroni correction. **P < 0.01, ***P < 0.001. FPR, false-positive rate; NMR, nuclear magnetic resonance; TPR, true-positive rate.

    Diabetes Prediction in NZO Female Mice

    The NZO mouse is a model of polygenic obesity and shows a T2D-like phenotype (28). The diabetes susceptibility of NZO female mice depends on the diet and heterogeneity. At the age of 5 weeks when fed an HFD (60% fat), ∼64% of the mice displayed a rapid increase in blood glucose starting at the age of 8 weeks, whereas the other mice were protected from severe hyperglycemia and showed only a moderate increase in blood glucose concentration to maximally 11.5 mmol/L by the age of 18 weeks (Fig. 2B). DP prediabetic mice exhibited a slightly higher body weight than diabetes-resistant (DR) mice (Fig. 2C), which was mainly caused by elevated fat mass (Supplementary Fig. 1). Plasma lipids were not different between DP and DR mice. Only the concentration of leptin was slightly lower and that of adiponectin to some extent higher in DR mice, resulting in a significantly lower leptin-to-adiponectin ratio (Table 1). The immunohistochemical costaining of pancreatic sections for insulin, glucagon, and somatostatin revealed that the proportion of β-, α-, and δ-cells were similar between 10-week-old DR and DP mice (Supplementary Fig. 2). In a new cohort of mice, liver fat content was quantified by computed tomography at weeks 5, 7, 8, and 10, showing significant differences at week 10 (Fig. 2D). The ability to predict later development of severe hyperglycemia was evaluated by using data of early blood glucose and liver density alone or in combination. As shown in the ROC-AUC curves of Fig. 2EG, prediction of the T2D-like phenotype was most accurate by combining blood glucose and liver fat content.

    Table 1

    Plasma lipid and cytokine profile of NZO female mice

    Gene Expression Pattern of NZO Islets Before Onset of Severe Hyperglycemia

    To study the impact of altered DNA methylation on the islet transcriptome in DP compared with DR mice, islets were isolated at the age of 10 weeks for genome-wide transcriptome and methylome analyses. Comparative analysis of samples from DR and DP mice identified 3,546 differentially expressed genes, and among these, 3,120 mRNAs displayed a fold change >1.5 (unadjusted P < 0.05) (Fig. 3A). To evaluate the molecular events involved in the transition from mild to severe hyperglycemia in DP mice, KEGG pathway enrichment analysis was performed. Transcripts that are lower abundant in islets of DP mice are linked to lysosome, fatty acid metabolism, tricarboxylic acid cycle, and others (Fig. 3B); those that were higher expressed in DP islets are involved in processes of ribosome, oxidative phosphorylation, proteasome function, and DNA replication (enrichment score >2; P < 10−3) (Fig. 3C). IPA resulted in five significantly enriched networks mainly related to cell death (Supplementary Table 2) and carbohydrate metabolism. The latter links 14 differentially expressed genes to the transcription factor pancreatic and duodenal homeobox 1 (PDX1), which is crucial for islet function (29,30) (Fig. 3D).

    Figure 3

    Transcriptome analysis mirrors the early dysfunction of pancreatic islets in DP mice and shows similarities with humans. A: Volcano plot showing the changes in the whole-genome gene expression profiles. Blue and pink dots indicate up- and downregulated mRNA transcripts, respectively, in DP vs. DR animals. The horizontal dashed line is the negative log10 of the P value threshold, and the vertical dashed lines refer to the log2 of the fold change thresholds between DR and DP (n = 5 DR vs. n = 4 DP; Welch t test unadjusted P < 0.05). B and C: KEGG pathway enrichment analysis of downregulated and upregulated transcripts in islets of DP animals. The white bars refer to the most relevant pathways known to be associated with T2D and islet dysfunction. The gray bars refer to the remaining pathways. The dashed line indicates the threshold used to indicate the most enriched pathways. D: Example of gene expression networks generated by IPA. The network related to carbohydrate metabolism includes 14 differentially expressed genes linked to the transcription factor PDX1. Downregulated genes in DP animals are green, and upregulated genes are red. E: Comparison of differentially expressed mouse islet genes with human data. Venn diagram depicts the strong overlap between mouse islet RNA-seq and the human data set (27). *P < 0.05 by Welch test, unadjusted. DEG, differentially expressed genes; ER, endoplasmic reticulum; TCA, tricarboxylic acid.

    Apart from the alteration in metabolic pathways, RNA-seq analysis revealed changes in the gene expression of 39 chromatin modifiers, 75 transcription factors, 20 RNA-binding proteins, and 11 enzymes involved in the transfer and the maintenance of DNA methylation (unadjusted P < 0.05) (Supplementary Table 3). The differential expression of transcription factors and epigenetic modifiers supports our assumption that epigenetic mechanisms participate in differences in diabetes susceptibility in NZO female mice.

    To clarify to what extent early alterations of gene expression in NZO female mice resemble those detected in human islets of donors with and without diabetes, we compared our transcriptome results with published RNA-seq data (27). Interestingly, 1,374 genes displayed differential expression in islets of both DP prediabetic animals and human donors with diabetes (Fig. 3E). This number of overlapping genes was more frequent than statistically expected by chance (χ2 test P = 0.02), and several gene products are involved in oxidative phosphorylation, ribosome, and cell cycle pathways (Supplementary Table 4). This strong overlap between human and mouse islet expression data further suggests that the NZO mouse is a suitable model to study molecular alterations that precede onset of T2D (28).

    Novel Differentially Methylated Genes in Islets of DP Mice

    WGBS was assessed in islets of DR and DP mice. After quality control, DNA methylation data were obtained for a 21,862,896 CpG sites. There were no differences in the average of β-methylation between DR (76.67%) and DP (76.11%) islets. The degree of DNA methylation throughout the genome was highly intercorrelated, and global DNA methylation displayed the same pattern in islets of DR and DP mice (Supplementary Fig. 3A and B). A total of 37,628 CpG sites exhibited a different degree of methylation between islets of DP and DR mice; 61% were hypermethylated and 39% hypomethylated in DP islets. The majority of DNA methylation changes were observed in intergenic regions (∼80%), and the remaining changes were distributed within promoters (1.3%), gene bodies (12.6%), first introns (5.4%), and first exons and 3′ untranslated regions (UTRs) (0.7%) (Supplementary Fig. 3CF). To further investigate genomic regions with consistent and extended differential methylation, we calculated differentially methylated regions (DMRs), genomic regions with at least two differentially methylated CpG sites consistently hypermethylated or hypomethylated within a maximum width of 1,000 base pairs (bp). We detected 223 DMRs in proximity to 211 genes and 1,107 DMRs in intergenic regions (Supplementary Table 5).

    To translate our findings on changes in DNA methylation to humans, a fully automated method was developed to identify conserved CpG sites in mouse and human. Using pairwise alignment of mouse and human DNA (University of California, Santa Cruz, Genome Browser mm10/hg19), 4,750 CpG sites revealed complete conservation at the specific position, and 8,711 sites presented a CpG in the surrounding 10 bp (Fig. 4A). Among these conserved 13,461 CpG sites, only 1,519 CpG sites were differentially methylated in islets of DR and DP mice. Most of these CpG sites (900) were intergenic (59%), and 41% were located in the vicinity of genes (619 CpG sites) (Supplementary Table 6).

    Figure 4

    Overlap between methylome and transcriptome data sets in pancreatic islets of mice. A: Identification of evolutionary conservation of CpG sites in mice and humans. Schematic diagram summarizes various steps used to generate a fully automated tool, which enables the identification of evolutionary conserved CpG sites in mice and humans. B: The overlap of all differentially expressed transcripts (Welch test unadjusted P < 0.05) and differentially methylated CpG sites within and in the vicinity of genes (log-likelihood P < 0.05) in pancreatic islets of DR vs. DP mice. C: The five most enriched KEGG pathways of the identified 497 differentially methylated and expressed genes. The underlined genes exhibit conserved CpG sites (see Supplementary Tables 5 and 6). D: Scatterplot of differential expression vs. differential DNA methylation in DP vs. DR islets. Genes that are listed more than one time exhibit several differentially methylated CpG sites. E: IPA-based network analysis providing connections among 17 genes related to the transcription factor PDX1. FC, fold change; UCSC, University of California, Santa Cruz.

    To relate DNA methylation and gene expression, we compared all upregulated genes with hypomethylated CpG sites in promoter regions (including 5′ UTR, exon 1, intron 1/2) and hypermethylated CpG sites in gene bodies and vice versa for the downregulated genes (31). All CpG sites not located within a promoter (2 kilobases upstream transcription start site) or within a gene were designated as intergenic and excluded from our analysis. As it is unknown which genes are regulated by intergenic DNA methylation sites and as we later compare data with results obtained from the 450K and 850K arrays (see below), we focused on alterations within or in proximity to genes. On the basis of this, 497 differentially expressed genes exhibited changes in DNA methylation (unadjusted P value), of which 176 exhibited at least one conserved CpG site in the human genome (Fig. 4B), of which 39 genes were located in DMRs (Supplementary Tables 5 and 6). The hypergeometric test demonstrated that the observed intersection of the 497 differentially expressed and methylated genes is not due to randomness (P = 6.42e-09). The top five KEGG pathways of the 497 genes, ranked by fold enrichment, are extracellular matrix (ECM)-receptor interaction, long-term potentiation, insulin secretion, and amphetamine and cocaine addiction. Consideration of the affected genes within these pathways (x-axis of Fig. 4C) indicates an overlap for those enriched in insulin secretion and amphetamine and cocaine addiction. This overlap is even stronger with other pathways, such as pancreas secretion, cAMP, and calcium signaling (Supplementary Table 7). The scatterplot in Fig. 4D illustrates the relationship between gene expression and DNA methylation changes of the 497 identified genes. Atp2b1, for instance, is higher expressed in islets of DP mice and exhibits an elevated DNA methylation within the gene body. Network analysis by IPA revealed 14 differentially expressed and methylated genes that can also be connected to PDX1 and to other key islet transcription factors (e.g., NKX2-2 and MAFB) (Fig. 4E).

    DNA Methylation of Orthologous Genes in Human Blood Associates With Incident T2D

    In a translational approach, we assessed whether DNA methylation of genes identified in mice was related to incident T2D in the EPIC-Potsdam cohort (Fig. 1). Of the 176 genes identified to be differentially expressed in islets of DP and DR mice and that contain at least one conserved CpG site (Fig. 5B), 120 genes with 8,276 annotated CpG sites are covered in the Illumina EPIC 850K array and could thus be analyzed in human samples that were collected a median of 3.8 years before the diabetes diagnosis. In this approach, we contemplated that not only the exact conserved CpG site but also every change in DNA methylation in the ortholog gene could be an epigenetic signature associated with T2D (20) (Table 2). Overall, there was evidence for an association with incident T2D (unadjusted P < 0.05) for 605 CpG sites located in 105 genes (Fig. 5A). We did not observe systematic interaction with sex and, therefore, present results from pooled analyses (data not shown). The most statistically significant associations for single CpG sites were observed for cg25381383 (annotated to MEIS2, odds ratio per Z score 0.33 [95% CI 0.20, 0.55], P = 1.9 × 10−5), cg11995041 (WWOX, 2.36 [1.5, 3.71], 2.2 × 10−4), cg19746591 (TAOK3, 3.18 [1.7, 5.94], 1.3 × 10−4), cg09587151 (ATF7, 0.52 [0.36, 0.74], 1.4 × 10−4), and cg17429772 (PTPRN2, 2.04 [1.38, 3.02], 2.1 × 10−4). In addition, the majority of CpG sites with unadjusted P < 0.001 were located in the gene body (Supplementary Fig. 4 and Supplementary Table 8).

    Figure 5

    DNA methylation associated with T2D risk in human blood samples and visible in islet of participants with diabetes. A: Volcano plot of CpG site associations with incident T2D from conditional logistic regression. Models were adjusted for age, waist circumference, smoking, alcohol intake, leisure time, physical activity, batch effects, and cell composition. Cases and controls were matched for age (±6 months), sex, fasting time, time of day, and season at blood sampling. All CpG sites with unadjusted P ≤ 0.001 are annotated with the respective gene symbols. DNA methylation at CpG site expressed as β-value. Values were winsorized (95%) and z standardized before analysis. Detailed results in Supplementary Table 7. B: Ranking of all genes by risk information conveyed by annotated DNA methylation. Ranking is based on ROC-AUC of a model derived from LASSO logistic regression. ROC-AUC calculated using R package pROC (49), and 95% CIs are based on bootstrapping (2,000 iterations). Detailed results in Supplementary Table 8. Genes marked by red asterisks are differentially methylated and differentially expressed (27) in human islets of participants with T2D. The black asterisks refer to the 19 common genes detected in the semiexploratory and the statistical approach. C: Similar patterns of DNA methylation in islets of DP animals and human donors with diabetes. Selected examples show differences in methylation within the same regulatory elements (top). Examples of differentially methylated genes in islets of NZO mice and human donors (bottom). Mean methylation extracted from WGBS of mouse data and of 450K array data from human donors. Data are mean ± SD. Log-likelihood P < 0.05 in pancreatic islets of DR vs. DP (n = 5 per group) and Student t test unadjusted P < 0.05 in islets of donors with diabetes (n = 15) and control donors without diabetes (n = 34). False discovery rate data are included in Supplementary Table 11. TSS, transcription start site.

    Table 2

    Participant characteristics of prospective matched case-control study in EPIC-Potsdam

    To rank the genes by conveyed T2D risk information of the respectively annotated CpG sites, we derived the best model for each gene on the basis of LASSO regression and calculated the discrimination of the respective model (ROC-AUC). The methylation values were adjusted for the above-mentioned adjustment variables by linear regression (subsequent use of the derived residuals). AKAP13 (ROC-AUC 0.73 [95% CI 0.69, 0.77]), TENM2 (0.70 [0.66, 0.74]), CTDSPL (0.68 [0.64, 0.73]), PTPRN2 (0.68 [0.64, 0.72]), and PTPRS (0.67 [0.62, 0.71]) emerged as the top five genes in this study sample (Fig. 5B and Supplementary Table 9). Of note, the majority of genes associated with diabetes incidence have not been described in GWASs for T2D. All GWASs with 1,503 genes associated with diabetes-related traits, including 403 association signals that were identified in an expanded GWAS discovery performed by Mahajan et al. (32), were considered for this comparison, but only 14 genes overlapped with the 105 differentially methylated genes. The comparison with gene expression quantification loci from islets revealed an overlap of 9 genes with the 2,339 genes detected in the Fadista et al. (27) study and of 7 genes that overlapped with 2,853 genes described in the van de Bunt et al. (26) study (Supplementary Table 10). Thus, the current analysis put a specific focus on novel genes, unknown in previous genetic studies, as a putative contributor in the onset of T2D.

    To evaluate which of the 120 genes detected in blood cells were also affected by DNA methylation changes in pancreatic islets of donors with diabetes versus control donors without diabetes, we reevaluated 450K array methylation data previously published by Dayeh et al. (24). In total, 99 of 120 genes exhibited 655 differentially methylated CpG sites in islets of donors with diabetes compared with control donors (unadjusted P < 0.05) (Supplementary Table 11). After applying multiple testing corrections, 51 CpG sites located in 23 genes (of which 4 are shown in Fig. 5C) were significantly different between donors with and without diabetes (q < 0.05) and showed the same effect in mouse islets. Those encompass AKAP13, CTDSPL, and PTPRN2, which emerged within the list of the top five predictive genes in blood cells. Hence, we demonstrate that the majority of genes highly associated with future diabetes in blood cells exhibit altered DNA methylation levels in islets after diabetes diagnosis. Among the 99 differentially methylated genes in human islets, 34 showed altered mRNA levels in donors with diabetes according to RNA-seq data published by Fadista et al. (27) (marked in red in Fig. 5B).

    Finally, we used more stringent criteria by applying a correction for multiple testing for the initial exploratory transcriptome analysis that was achieved in islets of DP and DR mice and calculated all subsequent analysis again. Benjamini-Hochberg corrections (P < 0.1) resulted in 789 differentially expressed genes (Supplementary Table 11). To observe which of these genes might be affected by DNA methylation, we analyzed those CpG sites within or in proximity that fulfill the following criteria: high sequencing coverage, methylation averages in the range of 10–90%, and methylation differences >10%. By this, we found 479 differentially methylated CpG sites in 274 differentially expressed islet genes. Of these, 253 are covered by the EPIC-850K array, and the majority (236) exhibited an altered DNA methylation on 1,481 CpG sites associated with incident T2D (Supplementary Table 13). Interestingly, 201 of 236 genes exhibited 785 differentially methylated CpG sites in islets of donors with diabetes compared with control donors (unadjusted P < 0.05) (Fig. 6A). After multiple testing, the number of altered CpG sites was reduced to 31 in 23 genes (q < 0.05) (Supplementary Table 14).

    Figure 6
    Figure 6

    Alternative strategy to identify novel candidates affected by DNA methylation and associated with T2D. A: Diagram explaining the steps of this analysis. The overlap of all significantly differentially expressed gene (DEG) transcripts (Benjamini-Hochberg adjusted P < 0.1) and differentially methylated CpG sites within and in the vicinity of genes (log-likelihood P < 0.05) in pancreatic islets of DR vs. DP mice resulted in 274 DEGs and methylated genes. Human orthologs that are covered in the 850K methylation array were investigated in blood cells of participants of the prospective nested case-control study (270 matched pairs) for incident T2D (EPIC-Potsdam) and in islets of donors with diabetes (n = 15) and control donors without diabetes (n = 34) (24). B: Volcano plot of CpG site associations with incident T2D from conditional logistic regression. Models were adjusted for age, waist circumference, smoking, alcohol intake, leisure time, physical activity, batch effects, and cell composition. Cases and controls were matched for age (±6 months), sex, fasting time, time of day, and season at blood sampling. DNA methylation at CpG site expressed as β-value. Values were winsorized (95%) and z standardized before analysis. Detailed results in Supplementary Table 14.

    Finally, the comparison of gene lists generated by the two approaches used in the current study (Fig. 6A) resulted in an overlap of 19 genes detected in blood cells (Fig. 6A and Supplementary Table 14) of which 17 have an ROC-AUC ≥0.58. Sixteen genes appear to be relevant in islets (e.g., CUX2, DACH1, MEIS2; hypergeometric test P = 1 × 10−20) (Supplementary Table 14).


    To our knowledge, this is the first translational study to examine changes in DNA methylation in pancreatic islets before the onset of severe hyperglycemia in mice, showing a significant overlap of DNA methylation marks visible in blood cells of people with a diabetes risk. By integrating methylome and transcriptome analysis, we identified ∼500 differentially expressed and methylated genes of which several were linked to insulin secretion and ECM-receptor pathways. From 176 genes with conserved CpG sites in humans, 120 were detected by the EPIC-850K array, and of those, 105 exhibited an association for at least one differentially methylated CpG site in blood cells and incident T2D diagnosis. Furthermore, 99 genes with strong association with T2D incidence in blood cells exhibited altered DNA methylation profiles in islets of donors with diabetes. Accordingly, >80% of the selected candidates for human translation showed significant results in two independent data sets (blood cells and islets).

    In pancreatic islets, early molecular alterations occur and mediate islet dysfunction before the onset of diabetes (3). The clarification of the pathomechanisms remains challenging because of the difficulty to investigate them in humans. Therefore, an appropriate mouse model that resembles the metabolic syndrome and T2D with impaired β-cell function, such as the NZO mouse, is of particular interest (28). It was used to screen for methylome alterations arising at a very early stage of the disease.

    At 10 weeks of age, in a prediabetic state when blood glucose and liver fat were slightly elevated, the islet gene expression profile reflected the metabolic observations and the expected β-cell failure at later time points. Although DP animals were not diabetic (blood glucose <16.6 mmol/L) at the stage of examination, they exhibited higher blood glucose levels than DR mice, which probably affected the methylome and thereby accelerated diabetes development.

    The methylome analysis of the current study provides the first catalog of epigenetic alterations in pancreatic islets before the development of severe hyperglycemia. It identified ∼500 differentially methylated and expressed genes mainly involved in ECM-receptor interaction and insulin secretion. Several candidates are well known in T2D, such as Insr (insulin receptor), Gcg (glucagon), and others described to be affected by DNA methylation changes in islets of patients with diabetes (e.g., Park2, Adcy5). By silencing or overexpressing Park2 and Socs2 in INS-1 cells, their role in insulin secretion was shown (33,34).

    To translate results to humans, a list of candidate genes that contain conserved CpG sites in the human sequence was generated and analyzed in blood cells of apparently healthy individuals. By this, we uncovered evidence for an association of DNA methylation marks in the selected genes and incident T2D. The most significant association for single CpG sites was observed for the Meis homeobox 2 (MEIS2) gene. Rat experiments demonstrated that Meis2 protein forms a multimeric complex with Pbx1 and modulates the transcriptional activity of Pdx1 (35).

    In an attempt to summarize the risk information per gene through LASSO regression, we identified that nearly all genes showed at least some degree of discriminatory capacity. The top predictive genes even reached relatively high ROC-AUC values of ∼0.7, which is noteworthy given that information of other risk factors was regressed out before analysis. However, overfitting still plays a role in this setting, and the interpretation of the absolute predictive capacity is optimistic and requires replication in other human cohorts. The highest predictive gene was AKAP13, a scaffold protein that plays an essential role in assembling signaling complexes downstream of several G-protein–coupled receptors. Such a downstream protein is the small GTPase RhoA, which is involved in cytoskeleton organization, cell migration, and cell cycle (36). Furthermore, PTPRN2 (receptor-type tyrosine-protein phosphatase N2), one of the top five predictive genes in our study, is required for the accumulation of normal levels of insulin-containing vesicles, preventing their degradation, and plays a role in glucose-stimulated insulin secretion (37). None of our 105 candidates were genome-wide statistically significant in the two largest prospective epigenetic-wide association studies (38,39). In a nested case-control study, Chambers et al. (39) identified an association between differential methylation at five genetic loci (ABCG1, PHOSPHO1, SOCS3, SREBF1, and TXNIP) with T2D incidence among Indian Asians and Europeans. Recently, Cardona et al. (38) confirmed the association of ABCG1, SREBF1, and TXNIP with T2D incidence in a British population (EPIC-Norfolk study) and identified 15 additional CpG sites. Similar to all genome-wide approaches, a conservative threshold for statistical significance to account for multiple testing was used in these studies. Although such corrections are mandatory in fully explorative analyses, associations with lower precision or effect size might be overlooked. To our knowledge, both mentioned publications did not publish full results of all analyzed CpG sites, thereby not allowing a comparison with our results.

    One limitation of our broad screening and translational approach is that the results somehow differ, depending on the type of analysis. We mainly focused on a semiexploratory approach, 1) starting from a selected number of differentially expressed and methylated genes determined in islets of a suitable mouse model, 2) focusing on CpG sites conserved between mouse and human, and 3) considering the direction of expression changes. By this, we detected 105 genes with an altered DNA methylation associated with incident T2D, and most of them (94%) showed an altered methylation in islets of patients with T2D. Correcting the transcriptome data for multiple comparisons resulted in a smaller list of differentially expressed and methylated genes in mouse islets (274) of which the majority (236 genes) revealed an altered DNA methylation in blood cells to be associated with incident T2D diagnosis, of which 85% carried an altered DNA methylation in islets of donors with diabetes (Fig. 6). The number of the genes that overlapped in both analyses is relatively low (19 genes in blood cells and 16 in islets) but with a high degree of significance (hypergeometric test P = 1 × 10−20). Thus, these overlapping genes appear to be the most relevant for the prediction of the disease.

    A second limitation of our study was discarding all CpG sites located in intergenic regions because earlier epigenetic studies have shown that T2D-associated DMRs are located in these regions (33) and that environmental and nutritional effects induce most DNA methylation changes in intergenic regions (4042). Recent discoveries on the three-dimensional structure of the epigenome (43,44) have provided a better understanding on genomic folding and looping. For example, enhancers and superenhancers can be located ∼50–100 kilobases up- or downstream of the transcription starting site. Therefore, it is difficult to link intergenic methylation alterations to a specific differentially expressed gene.

    We and others identified some changes in DNA methylation in tissues to be mirrored in blood cells (20,45,46), whereas other alterations can appear exclusively in one tissue (47). Although, our results clearly showed that several DNA methylation marks are visible in islets and blood cells, it is difficult to speculate which molecular mechanisms are responsible for these observations. One explanation is that these CpG sites (Fig. 5A and B) are established during early development (e.g., metastable epiallele) (48) and remain stably affected, and the second explanation is that the slightly elevated ectopic fat content in liver and elevated blood glucose levels modulate DNA methylation of specific loci in different tissues in a similar manner.

    In summary, we identified novel epigenetic alterations in murine pancreatic islets that occur in the prediabetic state with a mild hyperglycemia before β-cell failure. Alterations in 105 genes were mirrored in blood cells of participants with prediabetes, and 655 CpG sites within 99 genes were also affected in pancreatic islets of participants with diabetes. Thereby, our study provides novel DNA methylation marks as putative biomarkers for T2D.

    Article Information

    Acknowledgments. The authors thank Christine Gumz, Andrea Teichmann, and Anett Helms (German Institute for Human Nutrition Potsdam-Rehbruecke) for excellent technical assistance. Regarding the EPIC-Potsdam Study, the authors thank the Human Study Centre of the German Institute of Human Nutrition Potsdam-Rehbruecke, namely the trustee and data hub for data processing and the biobank for the processing of biological samples, and the head of the Human Study Centre, Manuela Bergmann, for contribution to the study design and leading the underlying processes of data generation. Furthermore, the authors thank all EPIC-Potsdam participants for invaluable contributions to the study.

    Funding. This work was supported by the Federal Ministry of Science, Germany (BMBF:DZD grant 82DZD00302) and the European Union (grant SOC 95201408 05 F02) for the recruitment phase of the EPIC-Potsdam Study, by the German Cancer Aid (grant 70-2488-Ha I) and the European Community (grant SOC 98200769 05 F02) for the follow-up of the EPIC-Potsdam Study, and by a grant from the German Federal Ministry of Education and Research (BMBF) to the German Center for Diabetes Research (DZD) (82DZD00302) and the State of Brandenburg. For human islets study, the work was supported by Novo Nordisk Foundation, Swedish Research Council, Region Skåne, Medical Training and Research Agreement (ALF); ERC-Co Grant (PAINTBOX, No. 725840); European Foundation for the Study of Diabetes; EXODIAB; Swedish Foundation for Strategic Research (IRC15-0067) and Swedish Diabetes Foundation grants (DIA2018-328).

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

    Author Contributions. M.O., S.S., and M.S. performed data acquisition for the animal study. M.O., S.S., F.E., M.J., C.W., T.R., L.Z., and P.G. performed the data analysis. F.E. and M.S. designed and provided data on DNA methylation in blood cells from the EPIC-Potsdam study. T.R. and C.L. provided and analyzed data on human DNA methylation in pancreatic islet samples and critically edited and revised the manuscript. M.O. and A.S. contributed to the study conception and design and wrote the manuscript. All authors read and approved the final manuscript. A.S. is the guarantor of this work and, as such, had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

    • Received March 9, 2020.
    • Accepted August 9, 2020.

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    How Many Types of Diabetes Are There Really?

    By electricdiet / November 13, 2020

    You may have heard of type 1 and type 2 diabetes in the news and on social media, but how many types of diabetes are there really? 

    Turns out, there are many. This article will explain all of the different types of diabetes, how common they are, and how they’re treated. 

    Hands of person measuring blood sugar

    What is diabetes?

    It’s helpful to first define diabetes. According to the International Diabetes Federation, diabetes is, “a chronic disease that occurs when the pancreas is no longer able to make insulin, or when the body cannot make good use of the insulin it produces.” 

    Today, over 463 million people in the world suffer from diabetes, and that number is only slated to increase, especially in low and middle-income countries. 

    Diabetes, whether type 1, 2, or something in between, affects people of every age, color, and gender. 

    What are the general symptoms of diabetes?

    • Extreme thirst 
    • Frequent urination
    • Extreme fatigue 
    • Fruity taste in the mouth
    • Blurred vision 
    • Slow-healing wounds

    What are the most common forms of diabetes? 

    The three main types of diabetes are type 1 diabetes, type 2 diabetes, and gestational diabetes, which are explained below. 

    Type 2 diabetes 

    Type 2 diabetes is the most common type of diabetes in the world. The World Health Organization reported that as of 2015, over 400 million people were affected, which is about 90-95% of cases worldwide. 

    When you have type 2 diabetes, your body becomes resistant to the insulin that it produces. Type 2 diabetes is usually caused by a mix of genetics and environmental factors, such as diet and obesity. 

    The main therapy for type 2 diabetes is maintaining a healthy diet and regular exercise. However, over time people with type 2 diabetes eventually require either insulin or oral diabetes medication. 

    Type 1 diabetes 

    Type 1 diabetes can develop at any age but occurs most frequently in children and adolescents. Type 1 diabetes is an autoimmune disease in which your body produces no insulin. 

    The cause of type 1 diabetes is not completely known, but researchers point to both genetics and an environmental trigger, such as a virus, that causes the immune response to cause the condition. 

    Type 1 diabetes consists of fewer than 5-10% of all worldwide cases of diabetes. All people with type 1 diabetes require daily insulin injections to keep their blood sugars under control.

    There is no cure for type 1 diabetes but several promising research studies for a cure are ongoing.

    Gestational diabetes

    Gestational diabetes is a type of diabetes caused by high blood sugars during pregnancy and can be associated with complications to both mother and child.

    Between 6-9% of all pregnant women develop gestational diabetes, and the numbers have been increasing in recent years. 

    Gestational diabetes is a risk factor for developing type 2 diabetes later in life for both mother and child. Gestational diabetes is usually caused by a mix of genetics and environmental factors. 

    Gestational diabetes is not always treated with insulin; many times mothers can control their blood sugars with a healthy diet and exercise when pregnant, and gestational diabetes usually disappears after giving birth.

    What are the lesser-known types of diabetes? 

    Only about 2% of people have other types of diabetes worldwide. These include different types of monogenic diabetes, cystic fibrosis-related diabetes, and diabetes caused by rare conditions. 

    Certain medications like steroids and antipsychotics can lead to other types of diabetes. 

    People with these types of diabetes can face challenges getting a correct diagnosis, and sometimes wait months or years for answers to their medical questions. 

    Latent Autoimmune Diabetes in Adults (LADA)

    Latent Autoimmune Diabetes in Adults (LADA) is sometimes known as type 1.5 diabetes because the signs and symptoms of it seem to cross between both type 1 and type 2 diabetes. 

    This form of diabetes is almost always diagnosed in adults, between the ages of 30-50, and the symptoms come on much more slowly than type 1 (over the course of several months), but are more aggressive than the symptoms of type 2, with extreme thirst, frequent urination, and rapid weight loss being chief among them. 

    People with LADA often start out on oral diabetes medications such as Metformin and eventually transition over to insulin as their blood sugars elevate higher. 

    People with LADA graduate to insulin much faster than someone with type 2 diabetes ever would. 

    Mature Onset Diabetes of the Young (MODY)

    Mature Onset Diabetes of the Young (MODY) is a rare form of diabetes which is different from both type 1 and type 2 diabetes, but is genetic and runs in families. 

    MODY is caused by a mutation in a single gene. If a parent has this gene mutation, their children have a 50% chance of inheriting the gene for MODY from them.

    If a child inherits the MODY gene, they are likely to develop this form of diabetes before they turn 25, despite any lifestyle changes they may make. 

    MODY is often treated with oral diabetes medications or insulin, and some forms may not require any treatment at all. There are four types of MODY:

    • HNF1-alpha. This gene causes ~70% of MODY cases. It causes diabetes by lowering the amount of insulin made by the pancreas. Diabetes usually develops in adolescence or early twenties, and usually does not need insulin to treat. 
    • HNF4-alpha. This is an extremely rare form of MODY. People who have inherited a change in this gene are likely to have had a high birth weight and needed glucose treatment at birth for low blood sugar. This form of MODY usually progresses to require insulin. 
    • HNF1-beta. People with this type of MODY tend to also suffer from renal cysts, uterine abnormalities, gout, as well as diabetes. This form of diabetes usually requires insulin. 
    • Glucokinase. This gene helps the body to recognize high blood sugars. When this gene is damaged, the body allows the level of blood sugar to continue to rise higher without consequence. Blood sugar levels in people with glucokinase MODY are typically only slightly higher than normal, which can lead to delayed diagnosis. This type of diabetes usually doesn’t require any treatment. 

    Neonatal diabetes

    Neonatal diabetes is when diabetes is diagnosed before 6 months of age. It differs from type 1 diabetes in the sense that it is not an autoimmune disease.

    Neonatal diabetes is caused by a change in the gene that produces insulin. There are two types of neonatal diabetes: transient and permanent. 

    Transient neonatal diabetes usually resolves before a baby turns one. This type accounts for 50–60% of cases. Permanent neonatal diabetes is a chronic condition and accounts for 40–50% of all cases.

    Alström Syndrome 

    Alström Syndrome is a rare, genetically inherited syndrome that has a number of key features, including type 2 diabetes in youth. 

    Children with this syndrome exhibit severe insulin resistance and high blood fat levels, often requiring insulin injections for treatment. 

    Wolfram Syndrome

    Wolfram Syndrome is a rare genetic disorder that is also known as DIDMOAD syndrome (Diabetes Insipidus, Diabetes Mellitus, Optic Atrophy, and Deafness). 

    Everyone with Wolfram Syndrome will develop one of these two types of diabetes at some point: 

    • Diabetes mellitus: People with Wolfram Syndrome who have diabetes mellitus differ from type 1 diabetes because diabetes mellitus is not an autoimmune disease. People with diabetes mellitus do not suffer from diabetes complications such as retinopathy or nephropathy. Although it is treated the same way that type 1 diabetes is: with daily insulin injections, close monitoring of blood glucose levels, and following a healthy diet and exercising regularly. About 50% of people with Wolfram Syndrome develop diabetes mellitus. 
    • Diabetes insipidus: This type of diabetes develops when the body can’t concentrate urine because the posterior pituitary gland isn’t making enough of the hormone vasopressin. Symptoms of diabetes insipidus are extreme thirst and frequent urination. About 50% of people with Wolfram Syndrome develop diabetes insipidus. 

    Steroid-induced diabetes 

    Steroid-Induced Diabetes is a type of diabetes triggered by long-term corticosteroid use (usually 3 months or longer). 

    This form of diabetes is extremely rare and is often found in people who are already at elevated risk for type 2 diabetes. 

    Occasionally this form of diabetes is temporary, but sometimes the condition becomes chronic, with the need to take insulin or oral diabetes medication for life. 

    Type 3c diabetes

    Type 3c diabetes is the result of another condition causing damage to the pancreas. Conditions such as pancreatic cancer, pancreatitis, cystic fibrosis, or even haemochromatosis (excess iron in the blood) can damage the pancreas enough to trigger this rare form of diabetes. 

    People who are in extreme accidents, and suffer trauma to their stomachs may also damage their pancreas and suffer from type 3c diabetes as a result. 

    This requires insulin injections similar to type 1 diabetes. 

    Brittle diabetes 

    Brittle diabetes is a form of type 1 diabetes that is notoriously difficult to control. Often called Labile diabetes, people suffering from brittle diabetes usually experience frequent, extreme swings in blood sugar levels, causing hyperglycemia or hypoglycemia on a daily and even hourly basis. 

    This rare form of diabetes is more common in females than males and tends to occur in young adulthood. 

    It is treated similarly to type 1 diabetes, with a special focus on close blood sugar management, with continuous glucose monitor (CGM) use highly recommended to catch both high and low blood sugars before they become dangerous. 

    Brittle diabetes is often associated with stress, depression, and other mental health issues.

    What are the complications of diabetes?

    While complications can vary between people, common complications, over time, include:

    Pregnant women who have poorly controlled gestational diabetes may face these complications: 

    • Preeclampsia
    • Miscarriage or stillbirth
    • Birth defects
    • Heightened risk of developing type 2 diabetes later in life (both mother and child) 

    How can you prevent diabetes?

    There is no way to prevent most of the forms of diabetes, although you can lower your risk for type 2 diabetes by: 

    • Maintaining a healthy weight
    • Eating a healthy diet
    • Exercising regularly 
    • Avoiding cigarette smoking

    Listen to your body. If you feel that you have been misdiagnosed with a form of diabetes that isn’t fitting your symptoms, or you feel that your needs are not being met, call your doctor immediately and seek a second opinion. 

    There are many lesser-known forms of diabetes that require alternative treatments, and you deserve the best possible care to maintain a healthy and complication-free life! 

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    Easy Chicken and Sausage Gumbo – Secret to Simple Healthy Roux for Gumbo

    By electricdiet / November 11, 2020

    Easy Chicken and Sausage Gumbo Recipe with Healthy Gumbo Roux!

    Easy Chicken and Sausage Gumbo is a Cajun healthy gumbo recipe favorite. If you’re looking for a gumbo recipe healthier, you are in the right place. When you think of Louisiana cuisine, gumbo comes to mind and especially in cold weather. This go-to favorite recipe is in Holly Clegg’s Gulf Coast Favorites cookbookThe secret to a healthy gumbo is in the healthy roux! Diabetic chicken gumbo recipes are hard to find.  Best of all, when you taste this easy gumbo recipe, you would never know this is also a diabetic gumbo recipe!

    No Time Consuming Roux for this Outstanding Healthy Gumbo

    Don’t think this gumbo will have you standing over the stove for hours!  The best part about this healthy roux is you can brown the flour ahead of time.  If you’ve ever made gumbo, the roux is what is time consuming.  With the roux done and if you buy pre-chopped seasoning, you can whip this healthy gumbo up for dinner.

    Easy Chicken and Sausage Gumbo

      Servings14 1-cup servings


      • 1/2cup

        all-purpose flour

      • 1lb

        reduced-fat sausagecut in 1/4-inch pieces

      • 2lb

        boneless, skinless chicken breastscut in pieces

      • 1


      • 1tsp


      • 1

        green bell peppercored and chopped

      • 2

        stalks celerychopped

      • 8cup

        fat-free chicken broth

      • 116-ounce package

        frozen cut okra or fresh cut okra

      • 1tsp

        dried thyme leaves

      • 1/4tsp


      • 1bunch

        green onionschopped

      1. Preheat oven 400ºF.

      2. Place flour on baking sheet and bake 20 minutes.  Stir every 7–10 minutes or until dark nutty brown color. Set aside.

      3. In large nonstick pot coated with nonstick cooking spray, stir-fry sausage over medium heat and cook until crispy brown.  Set aside and remove any excess grease.  Recoat skillet with nonstick cooking spray.

      4. Add chicken and cook, stirring until starting to brown and then add onion, garlic, green pepper, and celery, cooking until tender. Stir in browned flour, stir continuously.

      5. Gradually add chicken broth, okra, thyme, cayenne, and season to taste.  Bring to boil, lower heat and simmer 30 minutes or until chicken is tender. Add sausage and green onions cooking 5 more minutes.

      Recipe Notes

      Per Serving: Calories 160, Calories from fat (%) 11, Fat (g) 2, Saturated Fat (g) 1, Cholesterol (mg) 49, Sodium (mg) 550, Carbohydrate (g) 12, Dietary Fiber (g) 2, Sugars (g) 4, Protein (g) 22, Diabetic Exchanges: 1 carbohydrate, 3 very lean meat

      How To Make Gumbo?

      Who doesn’t associate a Chicken and Sausage gumbo with Louisiana?  But with Holly Clegg’s Gulf Coast Favorites cookbook  you get healthy Cajun recipes and even a healthy gumbo! You’ll find all of your favorite healthy crawfish recipes like most popular Crawfish Etouffee. 

      There are different kinds of gumbo but make yours with a healthy roux. However, if you are not in a region where seafood is plentiful, this chicken sausage gumbo is the answer. This diabetic gumbo boasts the rich traditional flavor of chicken gumbo without all the fat! Yes, you can even enjoy a healthy gumbo.

      Must Have Favorite Louisiana Menus: Four Menus Full of Recipes with Cajun Flair!

      Louisiana menus

      Team Holly is so excited to give you Holly’s absolute favorite Southern recipes in this Louisiana Menus: Four Menus with a Cajun Flair downloadable e-book available for only $1.99! If you thought Southern comfort food isn’t healthy, think again. You get 4 favorite healthy Cajun menus, dinners, brunch and lunch; and best of all, it comes with a SHOPPING LIST so all the work is done for you from your menu to your grocery run!

      Healthier and EASY-TO-MAKE Southern Cajun recipes with the nutritional information.  If you have health concerns, gluten-free and diabetic-recipes are highlighted throughout.  DOWNLOAD this go-to guide of delicious meals.

      Women’s Health Magazine Features Holly’s Gumbo Recipe In Top Healthy and Delicious Soups for Weight Loss!

      This is such a popular gumbo recipe because it’s easy to make wherever you live and also healthy.  It is even a diabetic gumbo recipe making this soup the perfect choice for people who love Cajun recipes.  Chicken and sausage gumbo recipe is even featured in Women’s Heath Magazine! Gulf Coast Favorites cookbook gives you the opportunity to enjoy all your favorite Cajun recipes but made trim and terrific!

      Try Using Brown Rice with Your Healthy Gumbo

      You can leave out the rice of the gumbo or try using brown rice for a nutritional boost.  If you promise not to tell, Team Holly actually serves brown rice with our gumbo and nobody knows!  One cup of white rice is less than 1 gram fiber while 1 cup brown rice has 3 grams of fiber.  That’s an easy nutritious transition.

      Try this rice that comes in the bag.  No more rice intimidation. The boil in a bag works perfectly and gives you smaller portions. Check it out as cooking is about convenience and time! Your family will love rice with all of these great healthy Louisiana recipes!

      Chicken and Sausage Gumbo – Secret In Healthy Roux Plus Diabetic Chicken Gumbo!

      The secret to a good gumbo is the roux and Holly’s secret is to use browned flour for a healthy roux.  Not only is this roux a time saver, but you get that rich nutty flavor without all the fat.  The gumbo tastes great and the roux bakes easily in the oven. You can make this healthy gumbo anywhere. Would you believe this delicious gumbo is also a diabetic chicken gumbo recipe?  Also, gumbo freezes well and did you know you can also freeze extra rice?

      Cook with a Wooden Spoon

      Seems simple and silly to mention but a wooden spoon is where its at when cooking. Don’t get us wrong, heat resistant spatulas are great (we love the colorful ones) but an old fashioned wooden spoon still works the best for stirring Holly’s chicken and gumbo recipe and other soup recipes.

      There’s all sorts of shapes and sizes of wooden spoons. We like this curved spoon to get into the corners of the pot.

      Have You Ever Used Silicon Pot Holders?

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

      Best of all, they double as a trivet to put hot pots on off the stove top or from the oven.

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      Enjoy Louisiana Recipes Wherever You Live

      SHOP Holly Clegg’s cookbooks to enjoy Louisiana recipes wherever you live! Remember, this book includes everyday ingredients to recreate your favorite healthy Cajun recipes easily. BBQ Shrimp recipe is the best New Orleans BBQ Shrimp recipe and the fresh seafood recipes will win you over instantly.  No more dinner decisions when you can flip through this book with 30 minute easy healthy recipes.

      Get All of Holly’s Healthy Easy Cookbooks

      The post Easy Chicken and Sausage Gumbo – Secret to Simple Healthy Roux for Gumbo appeared first on The Healthy Cooking Blog.

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      Noninvasive Monitoring of Glycemia-Induced Regulation of GLP-1R Expression in Murine and Human Islets of Langerhans

      By electricdiet / November 9, 2020


      Glucagon-like peptide 1 receptor (GLP-1R) imaging with radiolabeled exendin has proven to be a powerful tool to quantify β-cell mass (BCM) in vivo. As GLP-1R expression is thought to be influenced by glycemic control, we examined the effect of blood glucose (BG) levels on GLP-1R–mediated exendin uptake in both murine and human islets and its implications for BCM quantification. Periods of hyperglycemia significantly reduced exendin uptake in murine and human islets, which was paralleled by a reduction in GLP-1R expression. Detailed mapping of the tracer uptake and insulin and GLP-1R expression conclusively demonstrated that the observed reduction in tracer uptake directly correlates to GLP-1R expression levels. Importantly, the linear correlation between tracer uptake and β-cell area was maintained in spite of the reduced GLP-1R expression levels. Subsequent normalization of BG levels restored absolute tracer uptake and GLP-1R expression in β-cells and the observed loss in islet volume was halted. This manuscript emphasizes the potency of nuclear imaging techniques to monitor receptor regulation noninvasively. Our findings have significant implications for clinical practice, indicating that BG levels should be near-normalized for at least 3 weeks prior to GLP-1R agonist treatment or quantitative radiolabeled exendin imaging for BCM analysis.


      As a decline in functional β-cell mass (BCM) contributes to diabetes development, current drug development is geared toward compounds that can prevent β-cell death or increase BCM (1). Autopsy studies in patients with both type 1 (2,3) and type 2 (4) diabetes have demonstrated residual BCM, even decades after disease onset, supporting the notion that β-cell–enhancing strategies can be promising in humans suffering from diabetes. While numerous studies report on bioactive molecules that can induce β-cell proliferation or protection in animals (1,5), translation of these compounds to humans is hampered by the inability to measure BCM. As biopsying the human pancreas is associated with an unacceptable complication rate (6), a biomarker that reliably reflects BCM would be imperative to demonstrate the efficacy of candidate compounds in humans (7). Furthermore, such a biomarker would aid decision making early in the drug development process and would allow for patient stratification to identify patients with residual BCM who would be eligible for therapy.

      We have previously developed a noninvasive imaging technology to quantify BCM based on the radiolabeled glucagon-like peptide 1 (GLP-1) analog exendin (8). Preclinical studies demonstrated that pancreatic uptake of exendin linearly correlates with BCM (9) and that both off-target specific and nonspecific binding are negligible in nonhuman primate pancreata (10). The first clinical study revealed marked differences in pancreatic uptake between patients with type 1 diabetes and controls (8). In line with the autopsy studies (2,3), residual exendin uptake was found in two out of five patients, illustrating the potential of this imaging strategy, which is currently considered the most advanced approach to noninvasively quantify BCM (7).

      Nevertheless, there are indications that hyperglycemia downregulates GLP-1 receptor (GLP-1R) expression (11,12) and signaling (12) in β-cells in vitro, possibly affecting GLP-1R–based BCM quantification in individuals with diabetes. Here, we examined the effect of chronic hyperglycemia and subsequent normalization of blood glucose (BG) levels on GLP-1R–mediated exendin uptake in both murine and human islets and its implications for BCM quantification. For this, we used an islet transplantation setup in a chemically induced diabetic mouse model as this allows control over islet-cell mass and glycemic stress exposure.

      Research Design and Methods

      Animal Models and Human Islets

      Animal experiments were approved by the Animal Welfare Committee of the Radboud University (the Netherlands) or the German Center for Diabetes Research (Germany) and carried out in accordance with the local and national guidelines. Female C3H/HeNCrl (Charles River, Sant’Angelo Lodigiano, Italy) and male NOD.CB17-Prkdcscid/J (NOD-Scid) mice (The Jackson Laboratory) were used for syngeneic and allogeneic islet transplantations, respectively. Human islets were obtained from a 37-year-old female organ donor without diabetes (BMI 19.57 kg/m2, HbA1c 4.6%, 90% viability and purity; tebu-bio, Le Perray-en-Yvelines, France).


      Mice were injected with 0.1 μg of 111In-labeled [Lys40(DTPA)]exendin-3 (Peptide Specialty Laboratories, Heidelberg, Germany) (±15 MBq), as previously described (13).

      Syngeneic Islet Transplantation and Imaging

      For the induction of hyperglycemia, C3H/HeNCrl mice were injected i.v. with 100 mg/kg Alloxan (Sigma). BG levels were measured three times a week. After hyperglycemia was confirmed, mice received subcutaneous insulin implants (LinShin, Scarborough, Canada) to normalize BG levels (14). Syngeneic islets were isolated from 8- to 10-week-old mice by collagenase digestion and 200 islets were transplanted in the calf muscle, as previously described (15). Two weeks after transplantation, mice were randomly assigned to one of three experimental groups. The BG levels of the control group were maintained at <200 mg/dL. Insulin pellets were removed from mice assigned to the hyper or restored groups to reinduce hyperglycemia (>350 mg/dL). After 4 weeks of hyperglycemia, the restored group received insulin pellets again. At the end of the experiment, mice were injected with radiolabeled exendin-3 and scanned 1 h after injection on a small animal U-SPECT-II/CT system (MILabs, Utrecht, the Netherlands) with a 1-mm multipinhole ultra-high sensitivity mouse collimator for 50 min. Two hours after injection, mice were sacrificed, and grafts were embedded in paraffin for histology and autoradiography.


      The 4-μm-thick sections from different levels of the islet grafts were exposed to an imaging plate (Fuji Film BAS-SE 2025; Raytest GmbH, Straubenhardt, Germany) for 1 week. Images were visualized with a Typhoon FLA 7000 laser scanner (GE Healthcare Life Sciences). Tracer uptake quantification was done with AIDA Image Analyzer software (Raytest GmbH). To normalize the uptake to the insulin area, sections were stained for insulin (see the immunohistochemistry and morphometric analysis section).

      Human Islet Transplantation Into the Eye, STZ Treatment, and Longitudinal In Vivo Imaging

      Eight-week-old male NOD.CB17-Prkdcscid/J (NOD-Scid) mice were used as transplant recipients. Upon arrival, human islets were cultured overnight in CMRL-1066 (Corning Cellgro, Manassas, VA), supplemented with 2 g/L human serum albumin, 100 units/mL penicillin/streptomycin. Mice were transplanted with 15 islets into the anterior chamber of the eye as previously described (16). Islets were allowed to engraft for 8 weeks as human islets require a prolonged time for proper revascularization and engraftment. Hyperglycemia was induced by injecting 150 mg/kg streptozotocin (STZ) (Sigma-Aldrich, Darmstadt, Germany), and BG levels were monitored. To restore normoglycemia, insulin pellets were implanted after 7 weeks of hyperglycemia.

      Four hours prior to imaging, mice were injected i.v. with 4 nmol of exendin3-Alexa647 in PBS. Mice were intubated and anesthetized by 2% isoflurane in 100% oxygen. To limit pupil dilation and iris movement, 0.4% pilocarpine (Pilomann; Bausch & Lomb) was placed on the cornea. Repetitive in vivo imaging was performed at indicated time points on an upright laser scanning microscope (LSM780 NLO; Carl Zeiss, Ulm, Germany) with a water-dipping objective (W Plan-Apochromat 203/1.0 DIC M27 75 mm; Carl Zeiss) using vidisic eye gel (Bausch & Lomb) as immersion. The total volume of transplanted islets was assessed by detecting 633-nm laser backscatter. Exendin3-Alexa647 uptake was assessed by spectral analysis on an internal array gallium arsenide phosphide (GaAsP) detector at a resolution of 8.7 nm using the lambda unmixing algorithm of ZEN2009 (Zeiss), using combined, 488-, 561-, and 633-nm laser excitation. Reference spectra were acquired for exendin3-Alexa647 after in vivo labeling and for human islet autofluorescence from transplanted human islets without labeling. Islet volume was determined by semiautomatic volume creation, and exendin3-Alexa647 was determined by automatic surface rendering from median filtered Z-stacks with Imaris 8.1 (Bitplane AG, Zurich, Switzerland).

      Immunohistochemistry and Morphometric Analysis

      Paraffin sections of murine grafts were immunostained for insulin (17), or GLP1R. For the latter, pronase (1 mg/mL, 10 min, 37°C) was used as antigen retrieval. After blocking with 3% H2O2 (10 min), sections were immunostained with mouse anti–GLP-1R (Novo Nordisk, Copenhagen, Denmark) (1:50, o/n, 4°C) followed by horseradish peroxidase–conjugated secondary antibody (DAKO, Copenhagen, Denmark) (1:100, 30 min). The staining was developed with DAB (Immunologic BV, Duiven, the Netherlands), and sections were counterstained with hematoxylin. Morphometric analysis was performed with the trainable Weka segmentation plugin in ImageJ/Fiji (

      Islet-containing eyes were fixed in polyformaldehyde and mounted in TissueTek; 10-µm-thick cryosections were prepared and immunostaining was performed with guinea pig anti-insulin (1:200; DAKO) and mouse antiglucagon (1:2,000; Sigma-Aldrich). Secondary antibodies goat anti-guinea pig Alexa Fluor-488 and goat anti-mouse Alexa Fluor-633 (both 1:200; Thermo Fisher, Bremen, Germany) were used, and sections were counterstained with DAPI.

      Statistical Analysis

      Statistical analysis was performed using SPSS version 22 (SPSS). P < 0.05 was considered statistically significant. Results were presented as mean ± SEM. Group comparisons were performed using ANOVA with Tukey post hoc test or Mann-Whitney rank sum test, as indicated. Results obtained from longitudinal in vivo imaging studies were analyzed by linear mixed models (18).

      Data and Resource Availability

      The data sets generated and/or analyzed during the current study are available from the corresponding authors on reasonable request.

      Results and Discussion

      Severe Hyperglycemia Reduces GLP-1R–Mediated Exendin Uptake in Murine Islets

      To analyze the effect of hyperglycemia on GLP-1R–mediated exendin uptake, chemically induced diabetic mice were transplanted with a submarginal islet mass, and BG levels were regulated with insulin implants (Fig. 1A). Hyperglycemia had a substantial effect on exendin uptake, which was evidenced from 111In-exendin SPECT scans (Fig. 1B) and autoradiography images (inserts in Fig. 2A and B). 111In-exendin accumulation within islet grafts significantly decreased when islets were exposed to hyperglycemia (control 1.19 × 10−4 ± 0.34 × 10−4 Bq/m2 vs. hyper 0.37 × 10−4 ± 0.22 × 10−4 Bq/μm2, P < 0.001) (Fig. 1C). This was accompanied by a marked reduction in GLP-1R staining intensity compared with the control (Fig. 2A and C), while the insulin staining intensity was maintained (Fig. 2B and D). The observation that hyperglycemia reduced GLP-1R staining intensity implies that the reduced tracer uptake was caused by a reduction in GLP-1R expression, which is in line with previous in vitro reports (11,12).

      Figure 1
      Figure 1

      GLP-1R–mediated exendin uptake is significantly reduced in murine islets after sustained hyperglycemia, which is restored upon normalization of glycemic levels. A: BG levels of murine syngeneic islet transplants. After hyperglycemia was confirmed, mice received subcutaneous insulin implants to normalize BG levels prior to islet transplantation. Two weeks after transplantation, mice were randomly assigned to one out of three experimental groups (control, hyper, and restored). The BG levels of the control group were maintained within physiological range with insulin pellets (BG <200 mg/dL). Insulin pellets were removed from mice assigned to the hyper or restored group to induce severe hyperglycemia (BG >350 mg/dL). After 4 weeks of hyperglycemia, mice assigned to the restored group received insulin pellets again for a period of 3 weeks. B: 111In-labeled exendin-3 SPECT/CT scans of control, hyper, and restored mice. C: GLP-1R–mediated exendin-3 uptake in islets in the control, hyper, and restored groups. Data are presented as mean ± SEM. ***P <0.001, ****P <0.0001, evaluated by ANOVA with Tuckey post hoc test with n = 6 per group. D: Correlation between exendin-3 uptake and β-cell area in the control, hyper, and restored groups. The slope of the correlation curve was significantly decreased in the hyper group compared with the control group (P = 0.013). Normalization of BG levels completely restored the slope of the correlation curve (P = 0.51).

      Figure 2
      Figure 2

      Anti-insulin and GLP-1R staining of islet grafts in control, hyper, and restored mice. AF: Immunohistochemistry images of islet grafts stained for GLP-1R of control (A), hyper (C), and restored (E) mice and for insulin of control (B), hyper (D), and restored (F) mice. A, C, and E: Inserts depict autoradiography images of grafts with comparable insulin areas. Scale bars = 100 µm.

      Normalization of BG Levels Restores GLP-1R–Mediated Exendin Uptake in Murine Islets

      Since near-normalization of BG levels improves β-cell responsiveness to GLP-1 in patients with type 2 diabetes (19,20), we hypothesized that strict regulation of BG may reinstate GLP-1R expression. Normalization of BG levels restored the observed decrease in tracer uptake (control 1.19 × 10−4 ± 0.34 × 10−4 Bq/μm2 vs. restored 1.41 × 10−4 ± 0.27 × 10−4 Bq/μm2, P = 0.40) (Fig. 1B and C), which was paralleled by normalization of GLP-1R staining intensity (Fig. 2E).

      Linear Correlation Between Exendin Uptake and β-Cell Area Is Preserved After Severe Hyperglycemia

      We have previously shown that exendin uptake linearly correlates with absolute BCM (8,9), indicating the potential of exendin as an imaging biomarker for BCM (7). To examine whether hyperglycemia affects this linear relationship, we correlated the uptake of exendin with the β-cell area. Exendin uptake linearly correlated with the insulin-positive β-cell area (control group, Pearson r = 0.89, P < 0.0001) (Fig. 1D). This linear correlation was maintained after a period of hyperglycemia (hyper group, Pearson r = 0.84, P < 0.0001) (Fig. 1D), though the slope of the correlation curve was significantly decreased compared with the control group (hyper group 0.36 × 10−4 vs. control group 1.29 × 10−4, P = 0.013), reflecting the decrease in absolute uptake. Normalization of BG levels completely reinstated the slope of the correlation curve (restored group 1.47 × 10−4 vs. control group 1.29 × 10−4, P = 0.51) (Fig. 1D).

      Normalization of BG Levels Restores Exendin Uptake in Human Islets

      To assess whether these observations are representative for the human situation, we transplanted human islets into the anterior chamber of the eye of NOD-Scid mice. The advantage of this model is the ability to repetitively image an individual islet at a cellular resolution (21) using fluorescently labeled exendin. After engraftment, mice were imaged and distributed into two groups: one control group and one group receiving a single dose of STZ to induce endogenous β-cell destruction (22) (Fig. 3B). One week after STZ treatment, a significant reduction in islet backscatter signal intensity was observed (Fig. 3A and C), indicative for β-cell exhaustion leading to degranulation (18). In this group, the exendin uptake progressively decreased to 34.45 ± 0.07% of the pretreatment value (Fig. 3D), which was accompanied by a reduction in islet volume (Fig. 3E). Normalization of BG levels (Fig. 3B) resulted in cellular regranulation within a 3-week period and stabilization of the islet volume (Fig. 3E), which is in agreement with previously reported data on human islet recovery in response to transient hyperglycemia (18). Tracer uptake was found to immediately improve after glycemic levels were restored. After 3 weeks of near-normalized BG levels, tracer uptake was 76.81 ± 0.07% of the initial time point (Fig. 3D). This reduction in tracer uptake (23.19%) corresponded to the observed reduction in islet volume (21.64 ± 0.03%) (Fig. 3E), which suggests that the reduction in tracer uptake may be attributed to hyperglycemia-induced loss in BCM. To test this hypothesis, sections of explanted eyes were immunostained for insulin (Fig. 4A), revealing a significantly reduced insulin-positive area in the restored group compared with controls (restored 26.30 ± 0.55% vs. control 40.00 ± 4.26%, P < 0.05) (Fig. 4B). When we compared the tracer uptake in these islets at the last in vivo imaging time-point, a similar reduction in tracer uptake was observed (restored 28.19 ± 0.92% vs. control 36.81 ± 3.63%, P < 0.04) (Fig. 4C). These results indicate that a loss in BCM was responsible for the reduction in tracer uptake.

      Figure 3
      Figure 3

      Exendin uptake in human islets depends on the glycemic status of the recipient. A: Maximum intensity projections of the same human islets in the control and restored group with islet backscatter (gray) and alexa647-labeled exendin-3 (Ex3) (magenta) at indicated time points. B: BG profile of the control and restored groups throughout the experiment. CE: Quantification of backscatter signal in transplanted human islets (C), Ex3+ islet volume (D), and islet volume relative to the initial imaging time point (E). BE: The first dotted line indicates STZ injection, and second dotted line indicates insulin pellet implantation; red background indicates the period of hyperglycemia in the restored group. n = 3 mice (control), 4 mice (restored) with 16 and 22 individual islets, respectively. Data are presented as mean ± SEM, *P <0.05 evaluated by mixed model analysis. Scale bars = 50 µm. wks, weeks.

      Figure 4
      Figure 4

      Insulin area correlates with exendin uptake in human islets. A: Images of explanted islets after termination of the experiment stained for insulin (green, left panel) as well as glucagon (magenta) and DAPI (blue) (merged image, right panel). B and C: Quantification of the insulin (Ins)-positive islet area in tissue sections (B) and the % exendin-3 (Ex3)-positive islet volume in vivo at the last imaging time point (C). Data are presented as mean ± SEM, *P <0.05 evaluated by Mann-Whitney rank sum test with n = 3 mice (control) and 4 mice (restored). Scale bars = 50 µm.

      Taken together, our results highlight a critical role for hyperglycemia in GLP-1R regulation. Although hyperglycemia results in a substantial decrease in exendin uptake in murine and human islets, we have demonstrated that the uptake linearly correlates with BCM and that normalization of BG levels restores exendin uptake. The observed threefold reduction in tracer uptake in murine and human islets under hyperglycemic conditions may be an underestimation, as there are indications that hyperglycemia can increase capillary blood perfusion in islets (23). An important caveat of this study is that we used animal models with prolonged levels of hyperglycemia, whereas patients with diabetes experience fluctuating BG levels, with 2 h-postprandial BG levels of at least 200 mg/dL (2426). Therefore, the reduction in tracer uptake may not be as dramatic in patients as described here. Nevertheless, this outcome emphasizes the potency and clinical relevance of nuclear imaging techniques to monitor receptor regulation noninvasively. Our results indicate that BG levels should be near-normalized for at least 3 weeks prior to GLP-1R agonist treatment or radiolabeled exendin imaging for BCM quantification.

      Article Information

      Acknowledgments. The authors thank the animal technicians for their assistance in animal care and data collection.

      Funding. This project is supported by FP7 Coordination of Non-Community Research Programmes (BetaCure/602812); Paul Langerhans Institute Dresden (PLID) of Helmholtz Zentrum München at the University Clinic Carl Gustav Carus of Technische Universität Dresden; the German Ministry for Education and Research (BMBF) to the German Centre for Diabetes Research (DZD), and Deutsche Forschungsgemeinschaft-Sonderforschungsbereich (DFG-SFB)/Transregio 127; and it has received funding from the IMI 2 joint undertaking (INNODIA/115797 and INNODIA HARVEST 945268). This joint undertaking receives support from the Union’s Horizon 2020 research and innovation programme, European Federation of Pharmaceutical Industries and Associations (EFPIA), JDRF, and The Leona M. and Harry B. Helmsley Charitable Trust.

      Duality of Interest. M.G. declares that he is an inventor and holder of the patent “Invention affecting GLP-1 and exendin” (Philipps-Universität Marburg, June 17, 2009). No other potential conflicts of interest relevant to this article were reported.

      Author Contributions. M.Bu., C.M.C., and W.A.E. contributed to the design and conduct of the study and the analysis and interpretation of the data. M.Bu. and C.M.C. wrote and edited the manuscript. L.C.-J., C.F., D.B., and G.S. provided technical assistance in radiolabeling, tissue sectioning, immunostaining, and imaging. M.Br., S.S., and M.G. contributed to the experimental design, interpretation of the results, and conceptualization of the manuscript. S.S. and M.G. 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.

      Prior Presentation. Parts of this work were given as an oral presentation at the European Association of Nuclear Medicine, Barcelona, Spain, 15–19 October 2016; as a poster presentation at the European Association for the Study of Diabetes, Munich, Germany, 12–16 September 2016; and as a poster presentation at the 2nd Joint Meeting of the European Association for the Study of Diabetes Islet Study Group and Beta Cell Workshop, Dresden, Germany, 7–10 May 2017.

      • Received June 16, 2020.
      • Accepted August 20, 2020.

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      Roasted Broccoli Cheddar Soup – My Bizzy Kitchen

      By electricdiet / November 7, 2020

      Roasted Broccoli Cheddar Soup – My Bizzy Kitchen

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      Beanless Chili (Low Carb) | Diabetes Strong

      By electricdiet / November 5, 2020

      Looking for some low carb comfort food to warm you up on a cold day? This easy beanless chili is packed with flavor and only has 6 net carbs per serving!

      Beanless chili in a white bowl topped with sour cream, shredded cheese, and green onion

      Is there anything quite like a good soup to warm you up on a cold day? From stews to chowders to chilis, there are so many great options!

      Many chili recipes include beans, which can increase the carb count. This beanless chili, on the other hand, only has 6 net carbs per serving and is packed with delicious flavor!

      In place of the beans, this recipe incorporates onion, garlic, bell pepper, green chilis, and plenty of spices for a taste that will keep you coming back again and again.

      Plus, everything comes together in one pot! It’s so easy to make and perfect for anyone following a low carb or keto way of eating.

      How to make beanless chili

      This hearty dish is delightfully simple to make. Just combine the ingredients and then give it a little time for all the flavors to come together!

      Ingredients for chili in separate ramekins, as seen from above

      Step 1: In a large saucepan over medium heat, sauté the onions.

      Step 2: Once the onions are translucent, add the garlic, bell pepper, and green chilis. Cook for another 5 minutes.

      Step 3: Add the ground beef to the pan and cook until browned.

      Cooked ground beef, onion, bell pepper, and green chilis in a white pot with a wooden spoon

      Step 4: Add the diced tomatoes, tomato paste, Worcestershire sauce, chili powder, cumin, Italian seasoning, bay leaf, salt, and pepper.

      All ingredients in the pot, unmixed

      Step 5: Stir until well-combined.

      All ingredients mixed together in a white pot with a wooden spoon

      Step 6: Bring the ingredients to a simmer and cook for 30 minutes to 1 hour. If the mixture appears dry, add a splash of beef broth.

      The longer you cook the chili, the more the flavors will deepen. Once you’re happy with the flavor, serve with your desired toppings and enjoy!

      Chili in a white pot with a wooden spoon

      What to serve with chili

      What’s a hearty bowl of chili without some tasty toppings?

      If you’re watching your carbs, then I recommend sticking to low-carb options like sour cream and shredded cheese. For an extra boost of healthy fats, try adding a little chopped avocado!

      I also like to garnish my chili with some chopped green onion, chives, cilantro, or parsley. They add an extra hint of flavor and a nice pop of green.

      Looking for something to serve on the side? This low carb cornbread is perfect for soaking up any extra chili left at the bottom of the bowl!


      Leftover chili can be stored in an airtight container in the refrigerator. It will stay fresh for up to 5 days.

      I find that the flavors of soups, stews, and chilis usually deepen overnight and are even richer the next day! So I always try to make enough to have some leftovers.

      Two bowls of chili topped with sour cream, shredded cheese, and green onion next to a ramekin of green onion

      Other low carb dinner recipes

      Looking for more Keto-friendly comfort foods? There are so many delicious options! Here are a few of my favorite recipes that I know you’ll love:

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

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

      Recipe Card

      Beanless Chili (Low Carb)

      Looking for some low carb comfort food to warm you up on a cold day? This easy beanless chili is packed with flavor and only has 6 net carbs per serving!

      Prep Time:10 minutes

      Cook Time:1 hour

      Total Time:1 hour 10 minutes


      Beanless chili in a white bowl topped with sour cream, shredded cheese, and green onion


      • In a large saucepan over medium heat, sauté the onions.

      • Once the onions are translucent, add the garlic, bell pepper, and green chilis. Cook for another 5 minutes.

      • Add the ground beef to the pan and cook until browned.

      • Add the diced tomatoes, tomato paste, Worcestershire sauce, chili powder, cumin, Italian seasoning, bay leaf, salt, and pepper.

      • Stir until well-combined.

      • Bring the ingredients to a simmer and cook for 30 minutes to 1 hour. If the mixture appears dry, add a splash of beef broth.

      Recipe Notes

      This recipe is for 8 servings of chili.
      The longer you let the chili simmer, the deeper the flavors will be.
      Leftovers can be stored in an airtight container in the refrigerator for up to 5 days.

      Nutrition Info Per Serving

      Nutrition Facts

      Beanless Chili (Low Carb)

      Amount Per Serving (1 bowl)

      Calories 174
      Calories from Fat 72

      % Daily Value*

      Fat 8g12%

      Saturated Fat 3.5g18%

      Trans Fat 0.4g

      Polyunsaturated Fat 0.2g

      Monounsaturated Fat 0.1g

      Cholesterol 52.5mg18%

      Sodium 457mg19%

      Potassium 328mg9%

      Carbohydrates 8.4g3%

      Fiber 2.5g10%

      Sugar 3.7g4%

      Protein 18.7g37%

      Net carbs 5.9g

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

      Course: Main Course, Soup

      Cuisine: American

      Keyword: Beanless chili, gluten-free, keto, low carb

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      Sizzling Stir-Fry Rice Recipe

      By electricdiet / November 3, 2020

      Fresh Take Out At Home

      Craving stir-fry but don’t want to go out? These days a stir fry rice recipe has become one of America’s most popular meals and is a regular favorite at our house. Quick and healthy, Stir-Fry Rice from Holly Clegg’s Eating Well to Fight Arthritis cookbook is loaded with fresh ingredients and delicious veggies – so you will know you are feeding your family a nutritious meal without the added fat and calories. Try using brown rice for added fiber and nutrition.

      Stir-Fry Rice
      Turn left over rice into this scrumptious Asian specialty.
      Freezer Friendly, Vegetarian, Gluten free, Diabetic

        Servings6 (1-cup) servings


        • 1tablespoon

          canola oil

        • 1cup

          chopped onion

        • 1/2cup

          chopped carrot

        • 1teaspoon

          minced garlic

        • 2

          egg whitesbeaten

        • 3cups

          cooked ricewhite or brown

        • 3tablespoons

          low sodium soy sauce

        • 2teaspoons

          sesame oil

        • 2teaspoon

          teaspoons finely chopped fresh ginger or 1ground ginger

        • 1/2cup

          shelled edamame(if frozen, thaw)

        • 1/2cup

          chopped green onion

        • 1tablespoon

          sesame seedstoasted, optional

        1. In large nonstick skillet, heat oil and sauté onion, carrot, and garlic 5-7 minutes. Add egg whites stirring, until cooked.

        2. Add rice, soy sauce, sesame oil, and ginger cooking and stirring until heated, about 3 minutes. Add edamame and green onions, stirring until well heated. Sprinkle with sesame seeds, if desired.

        Recipe Notes

        Calories 184, Calories from Fat 23%, Fat 5g, Saturated Fat 0g, Cholesterol 0mg, Sodium 227mg, Carbohydrates 29g, Dietary Fiber 2g, Total Sugars 4g, Protein 6g, Dietary Exchanges: 1 ½ starch, 1 vegetable, 1 fat

        Terrific Tip: Be creative and add whatever veggies you have or even leftover, seafood, chicken or meat for a heartier version.

        Nutritional Nugget: Did you know sesame seeds are a great source of calcium? Look for toasted sesame seeds in the spice section of grocery.

        Easy Gluten Free Recipes in Holly’s Healthy Cookbooks

        Holly Clegg’s arthritis cookbook is full of gluten free recipes and focuses on anti inflammatory foods. Her cancer cookbook  focuses on recipes for cancer patients and cancer prevention.  Both easy healthy cookbooks contain “G” a symbol to indicate gluten free recipes throughout the cookbook. This way you have easy reference for gluten free foods.

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        Easy Chicken Lettuce Wraps Recipe

        Quick Stir Fry Menu for a Crowd

        Although authentic stir-fry can be complicated with a long list of fancy ingredients, check out this flavorful recipe that is quicker than take-out! Actually, stir-fries are perfect choices for a quick meal and these Chicken Stir-Fry Lettuce Wraps are fun to eat and serve. These dishes effortlessly serve a crowd and are really good for casual gatherings.

        Get All of Holly’s Healthy Easy Cookbooks

        The post Sizzling Stir-Fry Rice Recipe appeared first on The Healthy Cooking Blog.

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