What To Do if You Miss a Dose of Insulin

By electricdiet / March 28, 2021


For people with insulin-dependent diabetes, the near-constant attention to detail can sometimes be taxing on our mental health.

While perfection is impossible, diabetes demands a lot of precise measuring, counting, and dosing of food, exercise, and especially insulin. 

But we’re human. We are bound to forget to take a dose of insulin from time to time. This article will detail exactly what you should and shouldn’t do if you miss a dose of insulin. 

Insulin pens and vials on a table

What would cause someone to forget to take insulin? 

Literally anything can cause someone to forget to take their insulin: traveling, time-changes, sleepovers, the weather, falling asleep before a scheduled dose, an interruption such as a phone call or text message, spontaneous meals or snacks, or even just being forgetful are common reasons. 

This is perfectly normal and happens to everyone. Do not guilt yourself or feel down if you forget to take your insulin from time to time. It may happen, but these strategies can help. 

What to do if you miss a dose of insulin

Implement these strategies if you’ve missed a dose of insulin to get back to feeling better as soon as possible: 

Make sure you’ve actually missed a dose

There’s nothing worse than being halfway through a dinner out with friends, thinking you’ve forgotten to bolus for your food, and taking twice as much insulin as you need as a result (dessert, anyone?). 

It’s helpful to always announce, or at least tell one other person when you’re bolusing for a meal or taking nighttime long-acting insulin , as two minds are better than one at remembering! Sometimes insulin injections all blur together, so having an extra set of eyes and eyes on your dosing can help. 

This risk is slightly lower if you’re on an insulin pump, as you can simply go into the settings and check your bolus history to see if you in fact have missed a dose. 

Technology such as smartpens that send data to an accompanying app can also help you track both insulin on board and doses already given. You can watch this review of the InPen to learn more. 

If you have indeed missed a dose, proceed, if not, definitely do not double dose! 

Don’t panic 

Missing a single dose of insulin will probably not kill you. The great thing about insulin and diabetes is that you’ll always need more, so if you forgot a dose, it’s not the worst thing in the world. 

The bad thing is that you’re now chasing a high blood sugar, instead of preventing one in the first place.

Self-blame and feelings of guilt have no place here; diabetes is a marathon, not a sprint, and if anyone else is making you feel bad about missing a dose, politely ask them to walk a mile in your shoes. 

Take insulin as soon as possible

As the heading suggests, it’s best to take your insulin as soon as possible, but there are some caveats. 

If you simply forgot to pre-bolus for a meal, and you’re still within a 30-minute window of eating, you should count carbohydrates and bolus for the meal like you would regularly do. 

If you’ve eaten more than a half-hour ago, it’s best to treat the high blood sugar you (most likely) have, instead of trying to count carbohydrates that are now being digested. This helps prevent unnecessary hypoglycemia if you take too much insulin. 

If you’ve missed a dose of your long-acting insulin, it’s best to call your Endocrinologist right away to determine how much of the remaining dose you should take, based on how much time has passed since your typical time of administration, etc., unless it’s within a window of an hour or so of your normal dose. 

If that’s the case, you can most likely proceed as normal (just be cognizant that now you’ll have active insulin for an hour longer, likely affecting the timing of tomorrow’s dose). 

Test for ketones

Ketones are toxic chemicals that build up when your body starts to burn fat for energy instead of glucose. The most common cause of ketones for people with diabetes is lack of insulin. 

Without insulin, glucose builds up in the bloodstream, because it cannot enter the cells. The result is ketones forming in the blood and eventually spilling out into the urine that can be picked up and measured by a ketone strip (found at pharmacies or on Amazon). 

If your blood sugar is over 250 mg/dL and it’s been longer than a half-hour since you finished your meal, test for ketones. This will help inform the amount of insulin you’ll need to treat the high blood sugar. 

If your ketones are moderate or high, call your Endocrinologist for guidance on how much extra insulin you’ll need on top of your normal correction factor (some people require up to 75% more insulin if they have high blood sugars and moderate ketones). 

If your blood sugar and ketones will not come down and dissipate within a few hours, you will need to seek emergency medical care for fluids and an intravenous insulin drip. 

Drink water 

Water helps break up glucose in the body and prevents insulin resistance; this is especially important if you’re dealing with high blood sugar from a missed dose. 

The U.S. National Academies of Sciences, Engineering, and Medicine recommends that appropriate daily fluid intake is about 3.7 liters per day for men and 2.7 liters per day for women (more if you exercise).

Drink up, especially after missing a dose of insulin. 

Test every two hours 

It is crucial to test every two hours after a missed dose (+ correction) to determine if you need more insulin and to stave off the dangers of diabetic ketoacidosis (DKA). 

If you have a continuous glucose monitor (CGM) this will be easier, but it is super important to keep a close watch on your blood sugars for at least 12 hours after a missed dose. 

This can be harder some times throughout the day than others: for instance, you may need to set an alarm to go off every two hours during the night, which isn’t pleasant but is necessary. 

What not to do if you forget to dose insulin 

While it’s important to be action-oriented, here are some things you should never do if you forget to dose insulin: 

  • Blame yourself
  • Call the whole day a wash and let yourself have high blood sugars the rest of the day
  • Go to sleep immediately 
  • Not take a correction dose of insulin at all
  • Not tell anyone about the missed dose 
  • Bolus like normal for the number of carbohydrates you ate, even if you ate several hours before realizing you missed a dose 
  • Take double the amount of insulin for carbohydrates eaten and high blood sugar, without factoring in the time that has passed, carbohydrates already digested, or your blood sugar correction factor (or calling your doctor for help) 

Ways to remember to take insulin

While no one is perfect, here are some tricks and tips to help you remember to take your insulin:

  • Take your long-acting insulin at the same time every day 
  • Build it into your morning routine (first, brush your teeth, then wash your face, then take insulin)  
  • Set an alarm on your phone to remind you to take a long-acting dose or meal boluses 
  • Put a reminder up on a daily schedule to take your insulin
  • Eat the same or similar foods every day, so bolusing for meals is easier 
  • Write down once you’ve given your insulin, so you don’t take it twice! 
  • Ask a partner or friend to remind you to take your insulin before all meals that are eaten together and let them know once you’ve done it 

There is no place for perfection in a life with diabetes, and forgetting to take one’s insulin is bound to happen from time to time. 

It’s best not to blame yourself, correct the mistake, and move on with your day. Tomorrow is a fresh start and a new beginning, and you will undoubtedly have ample opportunities to not forget to take your insulin again next time. 



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Beef Stew in Slow Cooker Classic Easy Healthy Comfort Food

By electricdiet / March 26, 2021


Cozy Classic Comfort Food Beef Stew in Slow Cooker

Beef Stew in Slow Cooker on a cold day is hard to beat! This delicious comfort food from the Fix It Fast, Fix It Slow chapter of Guy’s Guide to Eating Well cookbook is a family favorite. Hearty and satisfying this simple to make meal practically cooks itself in the crock pot! The Beef Stew recipe calls for 10 cups total of 5 different veggies but you can use any combination of your favorite vegetables to make this delicious dish your own. Serve over couscous or rice (brown rice for extra nutrition & fiber) to soak up all of the super sauce!

Beef Stew in Slow Cooker picmonkey

Beef Stew in Slow Cooker
Simple beef stew becomes quick favorite with secret ingredient of barbecue sauce combined with meat and lots of vegetables. I like to serve over couscous or rice to soak up all the super sauce.

    Servings8 (1-cup) servings
    Prep Time15 minutes
    Cook Time5-6 hours

    Ingredients

    • 1 1/2pounds


      beef stew meat

    • 2/3cup


      sweet barbecue sauce

    • 1 1/2teaspoons


      paprika

    • 2cups


      butternut squash chunks

    • 2cups


      peeled sweet potato chunks

    • 2cups


      baby carrots

    • 2cups


      thickly sliced zucchini

    • 2cups


      thickly sliced yellow squash

    • 1/2cup


      water

    • 2cups


      baby carrots

    • 2cups


      thickly sliced zucchini

    • 2cups


      thickly sliced yellow squash

    • 1/2cup


      water

    Instructions
    1. In 3 ½-6-quart slow cooker, add all ingredients. Cook on HIGH 5-6 hours or until meat is tender.

    Recipe Notes

    Calories 231, Calories from Fat 25%, Fat 6 g, Saturated Fat 2 g, Cholesterol 53 mg, Sodium 211 mg, Carbohydrates 24 g, Dietary Fiber 3 g, Total Sugars 13 g, Protein 18 g, Diabetic Exchanges: 1 vegetable, 1 starch, ½ other carbohydrate, 2 ½ lean meat

    Terrific Tip: Use any combination of the 10 cups of vegetables

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    Beef Stew in Slow Cooker is from Holly’s easy men’s cookbook – full of deliciously easy quick fix meals and crock pot cooking recipes. You won’t believe how delicious this diabetic friendly, gluten-free and freezer friendly meal is! It is easier to eat good-for-you foods in simple go-to preparation methods.

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    The post Beef Stew in Slow Cooker Classic Easy Healthy Comfort Food appeared first on The Healthy Cooking Blog.



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    One Week of Bed Rest Leads to Substantial Muscle Atrophy and Induces Whole-Body Insulin Resistance in the Absence of Skeletal Muscle Lipid Accumulation

    By electricdiet / March 24, 2021


    Abstract

    Short (<10 days) periods of muscle disuse, often necessary for recovery from illness or injury, lead to various negative health consequences. The current study investigated mechanisms underlying disuse-induced insulin resistance, taking into account muscle atrophy. Ten healthy, young males (age: 23 ± 1 years; BMI: 23.0 ± 0.9 kg · m−2) were subjected to 1 week of strict bed rest. Prior to and after bed rest, lean body mass (dual-energy X-ray absorptiometry) and quadriceps cross-sectional area (CSA; computed tomography) were assessed, and peak oxygen uptake (VO2peak) and leg strength were determined. Whole-body insulin sensitivity was measured using a hyperinsulinemic-euglycemic clamp. Additionally, muscle biopsies were collected to assess muscle lipid (fraction) content and various markers of mitochondrial and vascular content. Bed rest resulted in 1.4 ± 0.2 kg lean tissue loss and a 3.2 ± 0.9% decline in quadriceps CSA (both P < 0.01). VO2peak and one-repetition maximum declined by 6.4 ± 2.3 (P < 0.05) and 6.9 ± 1.4% (P < 0.01), respectively. Bed rest induced a 29 ± 5% decrease in whole-body insulin sensitivity (P < 0.01). This was accompanied by a decline in muscle oxidative capacity, without alterations in skeletal muscle lipid content or saturation level, markers of oxidative stress, or capillary density. In conclusion, 1 week of bed rest substantially reduces skeletal muscle mass and lowers whole-body insulin sensitivity, without affecting mechanisms implicated in high-fat diet–induced insulin resistance.

    Introduction

    Recovery from injury or illness generally necessitates a period of bed rest, often as a consequence of hospitalization. Prolonged (>10 days) periods of bed rest have been shown to induce substantial changes in body composition and are accompanied by overall metabolic decline (1,2). Though this model of prolonged physical inactivity has taught us much about muscle disuse atrophy, it may be of limited clinical relevance to most patients who are, on average, hospitalized for <7 days (3). Recent data from our laboratory as well as others show that merely a few days of disuse substantially reduces skeletal muscle mass and strength (2,46). As a consequence, it has been suggested that the accumulation of such short (<10 days), successive periods of bed rest or immobilization may largely be responsible for the loss of muscle mass and metabolic decline observed throughout the life span (7,8).

    Impairments in metabolic health following prolonged disuse have been well described and include a decline in glucose tolerance and insulin sensitivity (1,9), a decrease in resting fat oxidation (10), an increase in mitochondrial reactive oxygen species (ROS) production (11), and a decline in basal metabolic rate (12). As the decline in metabolic health predisposes to greater morbidity and mortality of patients (13), it is of major clinical relevance to understand the mechanisms responsible for this decline in metabolic health. Prolonged disuse has been associated with substantial loss of muscle mass and/or gain in fat mass (2). Such changes in body composition lower the body’s capacity for blood glucose disposal and may contribute to the decline in metabolic health. However, changes in body composition can only partly explain the observed metabolic decline, as reduced insulin sensitivity has been observed during bed rest before measurable changes in body composition became apparent (1,2). We hypothesize that the substantial muscle atrophy caused by short-term bed rest will contribute to, but not fully explain, the vast decline in metabolic health.

    One of the key hallmarks of metabolic health is insulin sensitivity. Earlier studies have demonstrated that short periods of bed rest impair glucose tolerance and lower whole-body and/or peripheral insulin sensitivity (1418). The development of insulin resistance under conditions of lipid oversupply (e.g., type 2 diabetes mellitus and [high-fat] overfeeding) has been associated with lipid deposition in skeletal muscle (19) and, more specifically, with an increase in intramuscular lipid intermediates such as diacylglycerols (DAGs), ceramides, and long-chain fatty acyl-CoA, which impair insulin signaling (as reviewed in Ref. 20). Furthermore, both muscle disuse atrophy (21,22) and the development of insulin resistance (23) have also been attributed to a decline in mitochondrial content and/or impairments in skeletal muscle mitochondrial function. Additionally, impairments in micro- and macrovascular function have been linked to peripheral insulin resistance (24). So far, it is unclear which mechanism(s) contribute to the proposed development of insulin resistance during short-term bed rest.

    The objective of the current study was to assess mechanisms that may contribute to the development of insulin resistance during short-term muscle disuse, taking into account the expected muscle atrophy. To achieve this, we subjected healthy, young males to 1 week of strict bed rest and used comprehensive measures of muscle mass and muscle function in combination with detailed metabolic phenotyping (e.g., whole-body insulin sensitivity, substrate metabolism, skeletal muscle lipid content and composition, muscle oxidative capacity, and capillary density) to determine their possible contribution to the development of disuse-induced whole-body insulin resistance. Importantly, this was conducted under energy-balanced conditions to eliminate the contribution of overfeeding to our results. We hypothesized that bed rest–induced insulin resistance is attributed to mechanisms known to induce insulin resistance in chronic metabolic disease (i.e., ectopic lipid deposition, intramuscular accumulation of lipid intermediates, a decline in mitochondrial content and/or impairment in skeletal muscle capillarization). In this study, we demonstrate that short-term bed rest leads to skeletal muscle atrophy, pronounced whole-body insulin resistance, and a decline in skeletal muscle oxidative capacity. Strikingly, these effects do not seem to be mediated via mechanisms involved in obesity-related insulin resistance such as skeletal muscle lipid accumulation, oxidative stress, and micro- and/or macrovascular dysfunction.

    Research Design and Methods

    Subjects

    Ten healthy young men (age 23 ± 1 years) were included in the current study. Subjects’ characteristics are presented in Table 1. Prior to inclusion in the study, subjects filled out a general health questionnaire and completed a routine medical screening to ensure their eligibility to take part in the study. Exclusion criteria were a BMI <18.5 or >30 kg · m−2, a (family) history of thrombosis, type 2 diabetes mellitus (determined by HbA1c values >7.0% [53 mmol·mol−1]), and any back, knee, or shoulder complaints. Furthermore, subjects who had been involved in structured and prolonged resistance-type exercise training during the 6 months prior to the study were also excluded. All subjects were informed on the nature and risks of the experiment before written informed consent was obtained. During the screening visit, a fasting blood sample was taken to assess HbA1c, resting energy expenditure was measured with the use of a ventilated hood, and a one-repetition maximum (1RM) estimation test was performed. The current study was approved by the Medical Ethical Committee of Maastricht University Medical Centre (registration number 14-3-013) in accordance with the Declaration of Helsinki.

    Table 1

    Subjects’ characteristics

    Experimental Outline

    The experimental protocol is depicted in Supplementary Fig. 1. After inclusion into the study, subjects visited the university for a pretesting visit during which the 1RM and peak oxygen uptake (VO2peak) tests were performed. Following this visit, a 7-day period of standardized nutrition was started. On day 6 of the controlled diet, a mixed-meal tolerance test was performed. The day after, on day 7 of the standardized diet and the day prior to bed rest, test day 1 was scheduled. During this day, a muscle biopsy was taken from the m. vastus lateralis, and a hyperinsulinemic-euglycemic clamp and computed tomography (CT) and dual-energy X-ray absorptiometry (DXA) scans were performed. The next morning, subjects arrived at the laboratory to start the bed rest period. The meal tolerance test was repeated on day 6 of bed rest. After exactly 7 days of bed rest, test day 1 was repeated, and subjects were allowed to go home. On the next day, subjects returned to the laboratory to repeat the 1RM and VO2peak tests.

    One Week of Bed Rest

    To mimic the effects of a standard hospitalization procedure, subjects underwent a 7-day period of strict bed rest. On the morning of day 1, subjects reported to the laboratory in the fasted state at 0800. From that moment on, subjects remained in bed. During the day, subjects were permitted to use a pillow and elevation of the bed-back to perform their daily activities. All hygiene and sanitary activities were performed on the bed. Every morning, subjects were woken at 08 00, and lights were turned off at 2300. Participants were monitored continuously by the research team.

    Dietary Intake

    During the screening visit, resting energy expenditure was measured by indirect calorimetry using an open-circuit ventilated hood system (Omnical; Maastricht University, Maastricht, the Netherlands) (25). For 7 days prior to bed rest, subjects were given standardized food to prepare and consume at home. During the bed rest period itself, dietary intake was entirely controlled. During the pre–bed rest period, subjects received all food products and prepared the meals at home. In that week, subjects reported to the laboratory once or twice to allow adjustments of the diet in response to body weight changes (when necessary) to keep body weight stable. During bed rest, energy intake was increased when subjects reported being hungry. Energy requirements were estimated based on indirect calorimetry data, multiplied by an activity factor of 1.55 (prior to bed rest) and 1.35 (during bed rest). Macronutrient composition of the diet was identical before and during the bed rest period (Supplementary Table 1).

    Body Composition

    During test days 1 and 2 (prior to and immediately after bed rest, respectively), anatomical cross-sectional area (CSA) of the quadriceps muscle, hamstrings, and whole thigh were assessed via a single slice CT scan (Philips Brilliance 64; Philips Medical Systems, Best, the Netherlands). While subjects were lying supine, with their legs extended and their feet secured, a 3-mm thick axial image was taken 15 cm proximal to the top of the patella. On test day 1, the precise scanning position was marked with semipermanent ink for replication on test day 2. Next, a single slice CT scan at the level of the upper border of the L3 vertebra was taken to assess total muscle CSA (i.e., all paraspinal and abdominal muscle). For this scan, subjects were lying in a prone position, with their chin resting on both hands. The following scanning characteristics were used: 120 kV, 300 mA, rotation time of 0.75 s, and a field of view of 500 × 500 mm. CT scans were analyzed for the CSA of the whole thigh muscle as well as the quadriceps and hamstring muscles and for total muscle CSA at the level of the L3 vertebra by manual tracing using ImageJ software (version 1.48t; National Institutes of Health, Bethesda, MD) (26). The L3 Skeletal Muscle Index was calculated by dividing the paraspinal muscle area by height squared. Tissue with Hounsfield units between −29 and +150 HU was selected as muscle tissue. The L3 CT scans were also used to determine intramuscular adipose tissue, visceral adipose tissue, and subcutaneous adipose tissue using SliceOmatic software (version 5.0; Tomovision, Montreal, QC, Canada) as described previously (27). Body composition was measured via DXA (Hologic, Discovery A; QDR Series, Bradford, MA). The system’s software package Apex version 2.3 was used to determine whole-body and regional lean mass, fat mass, and bone mineral content.

    Insulin Sensitivity

    On the day prior to bed rest and directly after 1 week of bed rest, a hyperinsulinemic-euglycemic clamp was performed to assess whole-body insulin sensitivity. At the applied level of hyperinsulinemia, hepatic glucose output will be minimal (28,29). Therefore, the presented whole-body insulin sensitivity data presented in this study mainly reflect peripheral insulin sensitivity. Due to the choices for the setup of this experiment, this protocol does not allow assessment of the impact of bed rest on maximal insulin responsiveness. Before the start of the experiment, a Teflon cannula was inserted anterogradely in an antecubital vein of the forearm for the infusion of 20% glucose (Baxter B.V., Utrecht, the Netherlands) and insulin (40 mU · m−2 · min−1; Novorapid, Novo Nordisk Farma, Alphen aan den Rijn, the Netherlands). On the contralateral hand, a second cannula was inserted into a superficial dorsal hand vein. From this catheter, arterialized venous blood was obtained by heating the hand in a hot-box (60°C). A small amount of blood was drawn every 5 min throughout the entire 2.5 h clamp to determine glucose concentration (ABL800 Flex; Radiometer Medical, Brønshøj, Denmark). The amount of glucose infused was altered to maintain euglycemia at 5.0 mmol · L−1. The last 30 min of the clamp were used to calculate the mean glucose infusion rate (GIR).

    At baseline and during the last 30 min of the clamp, fasting and insulin-stimulated energy expenditure and substrate oxidation were assessed by indirect calorimetry using an open-circuit ventilated hood system (Omnical; Maastricht University, Maastricht, the Netherlands) (25). From these data, total fat and carbohydrate oxidation rates and metabolic flexibility were calculated as described before (30). To test glucose tolerance in a practical manner, a meal tolerance test was performed 2 days prior to bed rest and on day 6 of bed rest at 08:30 as part of the standardized diet. Before and after bed rest, subjects received identical test meals which provided 7.6 ± 0.2 kcal · kg body weight−1, 72 ± 1 g carbohydrate (52 ± 0.4 energy percentage [En%]), 19 ± 0.3 g fat (31 ± 0.4 En%), and 24 ± 0.1 g protein (17 ± 0.2 En%). While subjects were in an overnight fasted state, an antecubital vein was cannulated to allow repeated blood sampling. Prior to breakfast, and at t = 30, 60, 90, and 120 min following meal ingestion, a blood sample was collected in a supine position to assess plasma glucose and insulin concentrations. The disposition index (DI), as a measure of β-cell function, was calculated using the following formula: DI = (I120 − I0/G120 − G0) × OGIS.

    Muscle Function Tests

    Eight or 9 days before, and on the day after the 7-day bed rest, an incremental cycle ergometer test was performed with 40-W increments every 3 min to determine peak oxygen uptake (VO2peak). Next, 1RM strength tests on a leg press and leg extension device (Technogym, Rotterdam, the Netherlands) were performed to determine maximal leg strength. The estimations obtained during the screening visit, obtained via the multiple repetitions testing procedure (31), were used to determine 1RM as described previously (32). In short, after warming up, the load was set at 90–95% of the estimated maximum strength and increased after each successful lift until failure. A 2-min resting period was allowed between subsequent attempts. A repetition was deemed valid if the participant was able to complete the entire lift in a controlled manner without assistance. Finally, maximal grip strength was determined using a JAMAR handheld dynamometer (model BK-7498; Fred Sammons, Inc., Burr Ridge, IL). Three consecutive measures were recorded for both hands, and maximal grip strength of both hands was averaged to calculate mean maximal grip strength (33).

    Blood and Muscle Sampling

    During the meal tolerance tests and on each day of bed rest, blood samples were collected in EDTA-containing tubes and directly centrifuged at 1,000 × g for 10 min at 4°C. Aliquots of plasma were snap-frozen in liquid nitrogen and stored at −80°C until further analysis. Additionally, before and after bed rest, a single muscle biopsy was collected from the vastus lateralis muscle. After local anesthesia was induced, a percutaneous needle biopsy was taken ∼15 cm above the patella (34). Any visible nonmuscle tissue was directly removed, and part of the biopsy sample was embedded in Tissue-Tek (4583; Sakura Finetek, Zoeterwoude, the Netherlands) before being frozen in liquid nitrogen-cooled isopentane. All remaining muscle tissue was immediately frozen in liquid nitrogen. Muscle samples were subsequently stored at −80°C until further analyses.

    Plasma Biochemistry

    Plasma glucose and insulin concentrations were analyzed using commercially available kits (GLUC3, reference 05168791 190, Roche; and Immunologic, reference 12017547 122, Roche) (interassay coefficient of variation 4.9% and intra-assay coefficient of variation 1.5%, respectively). Plasma-free fatty acid concentrations were analyzed with an ABX Pentra 400 analyzer (Horiba Diagnostics, Montpellier, France) with test kits purchased from ABX Diagnostics (Montpellier, France).

    Skeletal Muscle Analyses

    Fiber Typing

    Muscle biopsies were stained for muscle fiber typing as described previously (35). The section of the muscle that was mounted and frozen in Tissue-Tek was cut into 5-μm thick cryosections using a cryostat at −20°C. Pre– and post–bed rest samples of each subject were mounted together on uncoated, precleaned glass slides, thereby carefully aligning the samples for cross-sectional fiber analyses. Stainings were performed to analyze muscle fiber-type specific CSA and intramyocellular triglyceride content. To measure fiber type–specific CSA, slides were incubated with primary antibodies directed against myosin heavy chain (MHC)-I (A4.840, dilution 1:25; DSHB) and laminin (polyclonal rabbit antilaminin, L9393, dilution 1:50; Sigma-Aldrich, Zwijndrecht, the Netherlands). After washing, the appropriate secondary antibodies were applied: goat anti-rabbit IgG Alexa Fluor 647 and goat anti-mouse IgM Alexa Fluor 555 (A-21245 and A-21422; dilution 1:400 and 1:500, respectively; Molecular Probes, Invitrogen, Breda, the Netherlands). Myonuclei were stained with DAPI (D1306; 0.238 μmol/L; Molecular Probes). Both primary and secondary antibodies were diluted in 0.1% BSA (A7906; Sigma-Aldrich) in 0.1% Tween 20 (P2287; Sigma-Aldrich) dissolved in PBS. Incubation of antibodies was performed at room temperature. Skeletal muscle tissue was stained as follows. Tissue was fixated in acetone for 5 min, after which the slides were air dried for 15 min and incubated with 3% BSA in 0.1% Tween-PBS for 30 min. Slides were then washed (standard washing protocol: 5 min 0.1% Tween-PBS, 2 × 5 min PBS) and incubated with the first antibodies for 45 min. After washing, slides were incubated with the secondary antibodies, diluted together with DAPI, for 45 min. After a last washing step, cover glasses were mounted by Mowiol (475904-100GM; Calbiochem, Amsterdam, the Netherlands). As a result of the staining procedure, nuclei were stained in blue, MHC-I in red, and laminin in far-red. Images were visualized and automatically captured at ×10 original magnification with a Olympus BX51 fluorescence microscope with customized spinning disk unit (DSU; Olympus, Zoeterwoude, the Netherlands) with a ultra-high sensitivity monochrome electron multiplier CCD camera (1,000 × 1,000 pixels, C9100-02; Hamamatsu Photonics, Hamamatsu City, Japan). Image acquisition was done by Micromanager 1.4 software (36), and images were analyzed with ImageJ (National Institutes of Health). The images were recorded and analyzed by an investigator blinded to subject coding. As a measure of fiber circularity, form factors were calculated by using the following formula: (4pi · CSA) · (perimeter)−2. On average, 176 ± 31 and 212 ± 60 muscle fibers were analyzed in the pre– and post–bed rest samples, respectively.

    Capillary Density

    An immunohistochemical staining for skeletal muscle capillarization (Fig. 6D) was performed as described previously (37). Slides with muscle cryosections of 5 μm were taken from the −80°C freezer and thawed for 30 min at room temperature. After fixation for 5 min with acetone, samples were air dried again for 15 min. Slides were then incubated for 45 min with CD31 (dilution 1:50; M0823; DakoCytomation, Glostrup, Denmark). Slides were then washed (standard washing protocol 3 × 5 min PBS). After that, a 45-min incubation step with goat anti-mouse biotin (BA-2000, dilution 1:200; Vector Laboratories, Burlingame, CA) was started, and a standard wash was performed. Next, slides were incubated with Avidin Texas Red (A2006, dilution 1:400; Vector Laboratories) and antibodies against MHC-I (A4.840, dilution 1:25; DSHB) and laminin (polyclonal rabbit antilaminin, dilution 1:50, L9393; Sigma-Aldrich) for 45 min and washed. In the final incubation step, goat anti-mouse IgM Alexa Fluor 488 and goat anti-rabbit IgG Alexa Fluor 350 (A-21042 and A-11046, dilution 1:200 and 1:133, respectively; Molecular Probes) were applied for 30 min. After washing, slides were mounted with Mowiol. The staining procedure resulted in images with laminin in blue, MHC-I in green, and CD31 in red. Images were automatically captured at ×10 original magnification with a Olympus BX51 fluorescence microscope with customized spinning disk unit (DSU; Olympus) with a ultra-high sensitivity monochrome electron multiplier CCD camera (1,000 × 1,000 pixels, C9100-02; Hamamatsu Photonics). Image acquisition was done by Micromanager 1.4 software (36), and images were analyzed with ImageJ (National Institutes of Health). The images were recorded and analyzed by an investigator blinded to subject coding. In all images, a minimum of 30 fibers were counted per fiber type. The number of capillaries was counted and expressed as capillary-to-fiber ratio and capillary-to-fiber perimeter exchange index (CFPE; number of capillaries per 1,000-μm perimeter).

    Fiber Type–Specific Lipid Content, Lipid Fractions, and Saturation

    An Oil Red O (ORO) staining was performed to analyze muscle fiber type–specific intramyocellular triglyceride content, based on previous work (38). Freshly cut samples were air dried for 30 min and fixated in 3.7% formaldehyde (1040051000; Merck Millipore, Darmstadt, Germany) for 60 min. After rinsing 3 × 30 s with Milli-Q, slides were incubated for 5 min in 0.5% Triton X-100 (108643; Merck Millipore) in PBS. Slides were then washed for 3 × 5 min with PBS and incubated for 45 min with primary antibodies against MHC-I (A4.951, dilution 1:25; DSHB) and laminin (polyclonal rabbit antilaminin, dilution 1:50; L9393; Sigma-Aldrich) in 0.05% Tween-PBS. After washing (1 × 5 min 0.05% Tween-PBS, 2 × 5 min PBS), slides were incubated with the appropriate secondary antibodies: goat anti-mouse IgG1 Alexa Fluor 488 and goat anti-rabbit IgG Alexa Fluor 350 (A-21121 and A-11046; dilution 1:133 and 1:200, respectively; Molecular Probes, Invitrogen). Slides were then washed (1 × 5 min 0.05% Tween-PBS, 2 × 5 min PBS) and rinsed for 30 s with Milli-Q, after which slides were placed in the ORO solution for 30 min. This solution was made by dissolving 250 mg ORO powder (O0625-25G; Sigma-Aldrich) in 50 mL 60% triethyl-phosphate (538728; Sigma-Aldrich). Of this solution, 48 mL was added to 32 mL of Milli-Q, which was then filtered using a paper-folding filter. After incubation with the ORO solution, slides were rinsed with Milli-Q for 3 × 30 s and placed under slow-running cold tap water before being mounted with cover glasses and Mowiol. The staining procedure resulted in images with laminin in blue, MHC-I in green, and ORO in red. Images were semiautomatically captured at ×40 original magnification with using a Nikon E800 fluorescent microscope coupled with a Nikon DS-Fi1c camera (Nikon Instruments, Amsterdam, the Netherlands) using the NIS-Elements BR software package version 4.20.01. Analysis of the images was done using ImageJ software (National Institutes of Health) by an investigator blinded to subject coding. No differences in fiber circularity were observed between pre– and post–bed rest samples. On average, 34 ± 1 muscle fibers were analyzed in both the pre– and post–bed rest samples. A representative image of the ORO staining is displayed in Fig. 3A.

    To determine intramuscular lipid content and the degree of saturation, ∼50 mg wet muscle was used as described elsewhere (39). Total lipid was extracted using chloroform-methanol (1:1 volume for volume) and internal standards and thereafter evaporated under nitrogen at 37°C. The extracted lipids were separated into triacylglycerol, DAG, free fatty acids (FFA), and phospholipid (PL) by thin-layer chromatography and transferred into separate tubes. After incubation with methanol, pentane was added to the samples, which were then vortexed and centrifuged. The pentane extracts (upper phase) were isolated, and the residues were evaporated under nitrogen at 37°C. Finally, the residues were dissolved in iso-octane, and FA concentrations in the fractions were determined using an analytical gas chromatograph (GC-2010 Plus; Shimadzu, Kyoto, Japan). Muscle ceramide content and ceramide fatty acid species were analyzed as described previously (40).

    Enzyme Activities

    For mitochondrial enzyme activities, ∼10 mg of the muscle was immediately homogenized in 100 volume for weight of a 100 mmol/L potassium phosphate buffer and used for the measurements of maximal β-hydroxyacyl-CoA dehydrogenase (β-HAD) and citrate synthase (CS) activities. Total muscle β-HAD activity was measured in TrisHCl buffer (50 mmol/L TrisHCl, 2 mmol/L EDTA, and 250 μmol/L NADH [pH 7]) and 0.04% Triton-X. The reaction was started by adding 100 μmol/L acetoacetyl-CoA, and absorbance was measured at 340 nm over a 2-min period (37°C) (41). The CS activity was assayed spectrophotometrically at 37°C by measuring the disappearance of NADH at 412 nm (42).

    Western Blotting

    Muscle was homogenized as previously described (43), 10 μL of protein was loaded, and standard SDS-PAGE procedures were followed. Antibodies included total and phosphorylated Akt (Cell Signaling Technology, Danvers, MA), oxidative phosphorylation (OXPHOS) (MitoSciences, Eugene, OR), vascular endothelial growth factor (VEGF), hypoxia inducible factor-1α (HIF-1α), endothelial nitric oxide synthase (eNOS), catalase, and superoxide dismutase 2 (SOD2; all Abcam, Cambridge, U.K.), 4-hydroxy-2-nonenal (4-HNE; Alpha Diagnostics, San Antonio, TX), COXIV (Invitrogen), and α-tubulin (Abcam) as a loading control. Protein carbonylation (Oxyblot; Millipore) was determined according to the manufacturer’s instructions. Ponceau staining was used to confirm equal loading for antibodies that required the entire membrane (e.g., 4-HNE and protein carbonylation). All samples for a given protein were detected on the same membrane using chemiluminescence and the FluorChem HD imaging system (Alpha Innotech, Santa Clara, CA).

    Statistics

    All data are expressed as mean ± SEM. Changes over time were analyzed using a paired samples Student t test (before vs. after bed rest) or a one-way ANOVA (for daily measurements) using a Bonferroni post hoc test. Muscle characteristics were analyzed using a repeated-measures ANOVA with time (before vs. after bed rest) and fiber type (type I vs. type II) as within-subjects factors. In case of a significant main effect, paired samples t tests were performed to assess time effects within fiber types. For the ORO analyses, region (subsarcolemmal vs. intramuscular fat) was added as a within-subjects factor. Statistical significance was set at P < 0.05. All data were analyzed using SPSS version 22.0 (SPSS Inc., Chicago, IL).

    Results

    Body Composition

    Figure 1 displays the effect of short-term bed rest on skeletal muscle mass as assessed by DXA (Fig. 1A) and CT (Fig. 1B). After 1 week of bed rest, participants lost 1.4 ± 0.2 kg (range: 0.6 to 2.8 kg) lean tissue mass (Fig. 1A) (P < 0.01), representing a 2.5 ± 0.4% loss of lean tissue mass. Lean tissue was mainly lost from the trunk (1.0 ± 0.2 kg) and legs (0.28 ± 0.12 kg) (Table 2). Fat mass did not change during 1 week of bed rest as participants were fed in energy balance (−0.2 ± 0.1 kg; P > 0.05). A 3.2 ± 0.9% decline in CSA of m. quadriceps femoris was observed following bed rest (from 7,900 ± 315 to 7,664 ± 354 mm2; P < 0.01) (Fig. 1B). CSA of the whole thigh muscle had declined by 2.2 ± 1.0% (P < 0.05). CT scans at the level of the L3 vertebra showed a 1.3 ± 0.4% decline in total muscle CSA (P < 0.01). As a consequence, the L3 Skeletal Muscle Index had declined from 51.9 ± 2.5 to 51.1 ± 2.4 cm2 (P < 0.01). Analyses performed with SliceOmatic revealed no changes in intermuscular adipose tissue (P > 0.05) and visceral adipose tissue (P > 0.05). Subcutaneous adipose tissue declined from 93 ± 28 to 89 ± 27 mm2 (P < 0.01). Following bed rest, nonsignificant declines in type I (from 6,650 ± 725 to 6,218 ± 662 μm2) and type II muscle fiber CSA (from 6,542 ± 746 to 5,982 ± 525 μm2) were observed (P > 0.05) (Supplementary Table 2). No differences in fiber circularity were observed between pre– and post–bed rest samples.

    Figure 1
    Figure 1

    One week of bed rest leads to a substantial decline in muscle mass. A: Whole-body lean mass declined by 1.4 ± 0.2 kg following bed rest. B: CSA of m. quadriceps femoris declined by 3.2 ± 0.9%. Data represent mean ± SEM. *Significantly different from pre–bed rest value (P < 0.05).

    Insulin Sensitivity and Glycemic Control

    GIR during the hyperinsulinemic-euglycemic clamp had declined by 29 ± 5% (range 9–53%; P < 0.01) following 1 week of bed rest (Fig. 2A). Adjustment of GIR for total body weight rather than lean body mass yielded similar results (−29 ± 5%; P < 0.01). Postprandial plasma glucose and insulin concentrations observed during the meal tolerance tests are displayed in Fig. 2C and D. For plasma glucose, the area under the curve (AUC) and incremental area under the curve (iAUC) did not differ between both tests (both P > 0.05). In contrast, plasma insulin concentrations showed a significant increase in AUC (from 4,963 ± 779 to 6,944 ± 513 mU · L−1 · min−1; P < 0.05) and iAUC (from 4,213 ± 773 to 5,736 ± 430 mU · L−1 · min−1; P < 0.05) following bed rest. Fasting plasma glucose concentrations (Fig. 2B) averaged 5.7 ± 0.2 mmol · L−1 prior to bed rest and did not change during the bed rest period (P > 0.05). For plasma insulin concentrations (Fig. 2B), a significant time effect (P < 0.001) was observed such that fasting insulin concentrations had increased from 7.2 ± 1.8 mU · L−1 at baseline to 11.8 ± 1.8 mU·L−1 after 1 week of bed rest. Consequently, the homeostasis model assessment of insulin resistance (HOMA-IR) index increased from 1.9 ± 0.5 to 3.1 ± 0.5 from day 1 to 8 (P < 0.01). The calculated DI was −7,043 ± 11,949 and 16,945 ± 9,972 pre– and post–bed rest, respectively (P > 0.05).

    Figure 2
    Figure 2

    Insulin sensitivity and postprandial glycemic control decline following 1 week of strict bed rest. A: Glucose infusion rates declined by 29 ± 5% following bed rest (P < 0.01). B: Postabsorptive plasma glucose and insulin concentrations on day 1–7 during bed rest. Insulin concentrations increased over time during bed rest (P < 0.001). Postprandial plasma glucose and insulin concentrations in the meal tolerance tests pre– and post–bed rest are depicted in C and D, respectively. For glucose, no changes in iAUC were observed (P > 0.05), whereas iAUC for insulin were increased following bed rest (P < 0.05). FFM, fat-free mass. Data are shown as mean ± SEM. *Significantly different from pre–bed rest value (P < 0.05).

    Energy Expenditure and Whole-Body Substrate Oxidation

    Resting metabolic rate, as measured by indirect calorimetry, tended to decline from 1,694 ± 47 to 1,624 ± 34 kcal · d−1 (−3.8 ± 2.0%; P = 0.070) following bed rest. When corrected for the total lean tissue mass, no such trend was observed (P > 0.05). During both the pre– and post–bed rest clamps, energy expenditure was increased during insulin infusion (time effect; P < 0.01). Stimulation by insulin increased the respiratory quotient from 0.84 ± 0.01 during the baseline period to 0.93 ± 0.01 during exogenous insulin infusion (P < 0.001), without differences between pre– and post–bed rest values. Additionally, carbohydrate oxidation rates were increased during the pre– and post–bed rest clamp (baseline: 0.13 ± 0.01, hyperinsulinemia 0.24 ± 0.01 g · min−1; P < 0.001). Fat oxidation rates decreased from 0.056 ± 0.007 (baseline) to 0.011 ± 0.005 g · min−1 (insulin) during the pre–bed rest clamp and from 0.047 ± 0.004 to 0.014 ± 0.004 g · min−1 during the post–bed rest clamp (effect of insulin, P < 0.001; trend for time × treatment effect; P = 0.065). Total protein content and phosphorylation status of both Akt (Ser473) and Akt (Thr308), measured in fasted biopsies, were not altered following bed rest (P > 0.05).

    Functional Outcomes

    A significant decline in 1RM leg press strength, from 211 ± 16 to 196 ± 45 kg (−7 ± 1%; P < 0.01), was observed following bed rest. Similarly, leg extension strength decreased from 128 ± 7 to 117 ± 7 kg (−8 ± 2%; P < 0.05). Following bed rest, no changes in handgrip strength were observed: grip strength averaged 45 ± 2 kg prior to bed rest and 46 ± 2 kg after the 7-day intervention (P > 0.05). Results from the cycle ergometer test showed a decline in VO2peak from 3,332 ± 200 to 3100 ± 162 mL · min−1, representing a 6.4 ± 2.3% loss in VO2peak following bed rest (P < 0.05) at a maximal workload of 260 ± 16 vs 246 ± 15 W, respectively (P < 0.05).

    Lipid Metabolism

    Plasma FFA concentrations (Supplementary Fig. 2) showed a time effect (P < 0.001) during bed rest. Post hoc analyses revealed that values on day 7 of bed rest were greater than on days 2 through 5 (P < 0.05). At baseline, results from the ORO staining showed a greater lipid area percentage in type I than type II muscle fibers (P < 0.05, Fig. 3B), with smaller droplets in type I versus type II fibers in the subsarcolemmal region (P < 0.05). Following bed rest, no changes in lipid area percentage were observed (P > 0.05). Droplet size (Fig. 3C) changed, such that a significant time × fiber type interaction was found (P < 0.01). Based on this interaction, we showed greater lipid droplets in type I versus type II fibers following bed rest (P < 0.01). Skeletal muscle lipid content of the measured lipid pools did not change with bed rest (all P > 0.05, Fig. 4). In the PL pool, the percentage saturation increased (P < 0.05) (Supplementary Table 3). For the three other pools, the proportion of polyunsaturated fatty acids increased or tended to increase. Although contents of some specific fatty acid species was altered following bedrest, no changes in total contents of any of the measured lipid pools were observed (Supplementary Table 4).

    Figure 3
    Figure 3

    Skeletal muscle lipid contents prior to and following 1 week of bed rest in healthy, young males. Values represent mean ± SEM. A represents an image of the ORO staining, made by immunofluorescence microscopy with a magnification of ×40. I: ORO. II: MHC-I. III: Laminin. IV: Combined image. The lipid area percentage is depicted in B and lipid droplet size in C. *Significantly different from type I fibers (P < 0.05). #Significant difference between type I and II post–bed rest values (P < 0.05).

    Figure 4
    Figure 4

    Skeletal muscle total triacylglycerol (TAG) (A), DAG (C), ceramide (E), PL (G), and FFA (I) content, as well as specific fatty acid species within the different lipid pools. Total content is depicted in the panels on the left, and specific fatty acid species are depicted in panels on the right. Values in B, D, F, H, and J are expressed as relative change from pre–bed rest values (indicated by the dotted line). *Significantly different from pre–bed rest value (P < 0.05).

    Oxidative Capacity

    Fig. 5 depicts results on various parameters of mitochondrial content. CS activity (Fig. 5A) decreased by 8 ± 3% following bed rest (P < 0.05). Activity of β-HAD (Fig. 5B) tended to decrease by 9 ± 6% (P = 0.071). Protein content of the different complexes of the OXPHOS system all decreased or tended to decrease, as depicted in Fig. 5C. Lipid peroxidation, determined by 4-HNE content, did not change following bed rest (Fig. 5D) (P > 0.05). For protein carbonylation (Fig. 5E) content, a trend for a decline was observed (P = 0.075). Both SOD2 (Fig. 5F) and catalase (Fig. 5G) protein expression did not change following 1 week of bed rest (P > 0.05).

    Figure 5
    Figure 5

    Seven days of strict bed rest leads to a decline in mitochondrial function. CS activity (A) decreased (P < 0.05), whereas β-HAD activity tended (P = 0.071) to decrease (B). The protein contents of the different complexes of the oxidative phosphorylation are displayed in C. DG depict protein expression of 4-HNE, protein carbonylation, SOD2 (predicted molecular weight of 27 kDa), and catalase (60 kDa), respectively. OD, optical density. Data represent mean ± SEM. *Significantly different from pre–bed rest (P < 0.05). #Trend for a difference from pre–bed rest value (P < 0.10).

    Vascularization

    Seven days of bed rest did not lead to significant changes in VEGF (−13 ± 10%; P > 0.05) and eNOS (−12 ± 13%; P = 0.086) protein expression (Fig. 6A and C). For HIF-1α protein expression (Fig. 6B), a 35 ± 11% increase was observed following bed rest (P < 0.05). Bed rest did not lead to changes in capillary density or oxidative exchange across the muscle bed, as shown by the capillary-to-fiber ratio (Fig. 6E) and CFPE index (Fig. 6F).

    Figure 6
    Figure 6

    Skeletal muscle capillary content is not altered following short-term bed rest. Values are presented as means ± SEM. No changes in VEGF (A, predicted molecular weight 43 kDa) protein expression were observed. A significant increase in HIF-1α (B, 97 kDa) protein expression was observed following bed rest. Total eNOS (C, 133 kDa) protein expression tended to decline following 1 week of bed rest (P = 0.086). D represents an immunohistochemical image of the CD31 staining, made by microscopy with a magnification of 20×. I: CD31. II: MHC-I. III: Laminin. IV: Combined image. No changes in capillary-to-fiber ratio (E) or CFPE index (F) were observed. OD, optical density. *Significantly different from values prior to bed rest (P < 0.05). #Significantly different from type I fibers (P < 0.05).

    Discussion

    In the current study, we observed that merely 1 week of bed rest strongly reduced muscle mass, strength, and physical performance. Bed rest resulted in the onset of severe whole-body insulin resistance and a strong decline in skeletal muscle oxidative capacity, both of which occurred in the absence of lipid accumulation or a decline in capillary density in skeletal muscle tissue.

    The impact of prolonged bed rest upon skeletal muscle mass and metabolic health has been studied extensively (1,2). Though the model of prolonged disuse is of substantial scientific importance, it may be of more clinical relevance to study short periods of disuse, as patients are typically hospitalized for up to 7 days (3). Recently, we showed that even 5 days of disuse can lead to a ∼4% decline in muscle mass and a concomitant ∼9% decline in muscle strength (5). In keeping with this, in the current study, we report a 3.2% decline in quadriceps CSA following 1 week of bed rest (Fig. 1B) (2). On a whole-body level, this translated to a 1.4 ± 0.2 kg loss of lean tissue (Fig. 1A), which is equivalent to ∼200 g lean tissue loss per day. In comparison, it took a group of healthy, young males 12 weeks of progressive resistance-type exercise training to gain the equivalent amount of lean tissue (1.7 kg) (44). Thus, we can lose as much muscle in 1 week of bed rest as we can gain by 12 weeks of intense resistance-type exercise training. Furthermore, the loss of muscle was accompanied by a substantial ∼8% decline in muscle strength and a ∼6% reduction in VO2peak. These findings clearly demonstrate that even a short period of disuse has severe consequences for muscle mass and physical performance, an effect that is unlikely compensated for during rehabilitation. As a consequence, it has been suggested that successive periods of bed rest or immobilization may be responsible for the progressive decline in muscle mass throughout our lifespan (7,8).

    The loss of skeletal muscle mass and/or strength during hospitalization has been shown to be predictive of morbidity and mortality (13). This may be more related to the impact of disuse on metabolic health than to the decline in muscle mass per se. Therefore, in the current study, we also aimed to assess the impact of short-term disuse on metabolic health. We performed hyperinsulinemic-euglycemic clamps prior to and after 1 week of bed rest to assess whole-body insulin sensitivity and observed a substantial ∼30% decline in glucose disposal (Fig. 2A). Under these conditions, hepatic glucose output is strongly diminished, and skeletal muscle is responsible for ∼85% of glucose disposal (29). This implies that merely 1 week of bed rest can lower insulin sensitivity by as much as 30%. These findings are in line with previous studies, demonstrating similar declines in whole-body and/or peripheral insulin sensitivity following 7–9 days of bed rest (1416,18). This decline in whole-body insulin sensitivity manifested in a greater postprandial insulin response required to maintain normoglycemia following bed rest (Fig. 2C and D), illustrating the impact of physical inactivity on day-to-day metabolic control. Supporting the concept that profound insulin resistance manifested with bed rest, relatively insensitive population markers such as the HOMA-IR index also increased during the intervention. Interestingly, the increase in HOMA-IR over time did not occur until 4 days of bed rest and was entirely attributed to an increase in postabsorptive insulin concentrations (Fig. 2B). Thus, it could be suggested that disuse-induced insulin resistance occurs even more rapidly than 1 week (9). Previous work aiming to elucidate the impact of bed rest on insulin signaling has shown that bed rest induced insulin resistance is accompanied by reductions in the contents and/or activity of key proteins regulating glucose uptake and storage in muscle, such as GLUT4, hexokinase 2, and glycogen synthase (18). However, the decline in insulin sensitivity following bed rest could not be explained by impaired insulin and AMPK signaling, as Akt and AS160 signaling seemed to remain intact following short-term bed rest (45). Consequently, other mechanisms are likely to be responsible for the development of insulin resistance following short-term bed rest.

    Despite substantial muscle atrophy, a ∼3% decline in lean mass likely cannot explain the observed ∼30% decline in whole-body insulin sensitivity. As such, during short-term disuse, other mechanisms must contribute to the development of whole-body insulin resistance. Ectopic lipid deposition has often been suggested to lead to the development of insulin resistance in situations of lipid oversupply (19). Although previous studies have reported increases in intramuscular lipid deposition following prolonged bed rest (1), the impact of short-term disuse on skeletal muscle lipid accumulation has been comparatively underinvestigated (46). In line with our previous findings (46), in the current study, we did not detect a measurable increase in type I or II muscle fiber lipid content (Fig. 3B). We extend on these findings by reporting no increase in subsarcolemmal lipid depots, which have been suggested to more specifically contribute to the development of insulin resistance (47). Of course, it could be speculated that an intracellular increase in specific fatty acid intermediates, such as DAGs, fatty acyl-CoA, ceramides, and/or free fatty acids may be responsible for impairments in insulin receptor function and glucose trafficking (20). Therefore, we also measured muscle lipid content of various lipid fractions (Fig. 4). In line with our fiber type–specific data, we did not observe changes in lipid content of the various lipid fractions, including DAGs, following 1 week bed rest. Whereas we did see changes in some specific DAG species (Supplementary Table 4), these were not the 18:2 species that have been specifically linked to insulin resistance (48). Whereas previous work has been inconclusive about the role for ceramides in the development of insulin resistance (4951), we demonstrate no change in total content and only minor changes in specific fatty acid species within the ceramide pool following bed rest, thereby likely ruling out a mediating role for ceramides in the development of insulin resistance during bed rest. Furthermore, the degree of saturation of specifically the DAG pool has been reported to be increased in insulin-resistant men when compared with control subjects (52). However, we failed to observe any changes in the degree of saturation of the various lipid pools, but actually observed a relative increase in polyunsaturated fatty acids (Supplementary Table 3) in the different lipid pools. This can potentially be explained by a preferential oxidation of saturated fatty acids during disuse, which has been suggested previously (10). Collectively, changes in lipid content and/or lipid composition in skeletal muscle tissue following bed rest are unlikely to explain the observed development of insulin resistance, and therefore other processes must be implicated.

    Mitochondrial dysfunction, and specifically the release of mitochondrial ROS, has been postulated as a key factor in the development of muscle disuse atrophy (23) and insulin resistance (53,54). Indeed, previous disuse studies have demonstrated a decrease in mitochondrial protein content and enzyme activities, the onset of mitochondrial respiratory dysfunction, and an increase in ROS emission in situations of muscle atrophy (11,22,55,56). In keeping with this, we show a tendency for a decline in β-HAD (Fig. 4B) and a significant 8% decline in citrate synthase activity (Fig. 4A), indicative of a decline in mitochondrial content (57). Similarly, protein content of all complexes of OXPHOS (Fig. 4C) decreased with bed rest. Given the lack of a fiber-type shift away from oxidative fibers (Supplementary Table 2) that is normally observed following prolonged bed rest, these changes cannot be explained by differences in fiber-type distribution. Additionally, it has been suggested that short-term bed rest could lead to oxidative stress, which in turn triggers the imbalance between muscle protein synthesis and breakdown (58). However, we did not find increases in either 4-HNE or protein carbonylation, suggesting the absence of overt oxidative damage. These findings are in contrast to a previous report analyzing markers of oxidative damage following a longer period of bed rest (59), suggesting that oxidative damage is a consequence of longer periods of bed rest. Given these data, it was not surprising that no changes in the antioxidants superoxide dismutase 2 (SOD2) and catalase (Fig. 4F and G) were found, as they would usually be increased in the presence of oxidative stress. Previous work by Abadi et al. (22) indicates that muscle oxidative capacity is impaired following short-term disuse. We extend these findings by confirming actual declines in muscle oxidative capacity following bed rest and suggest that, despite not having measured the glutathione/oxidized glutathione ratio to assess short-term redox status, overt oxidative stress does not seem to play a role in the rapid development of insulin resistance during up to 1 week of bed rest. Although time-course studies are clearly warranted to look at instigating factors of muscle atrophy and the rapid development of insulin resistance, our data suggest that impairments in oxidative capacity may (partly) contribute to the observed decline in insulin sensitivity during short-term bed rest.

    As in vivo peripheral insulin sensitivity can also be modulated by changes in macro- and microvascular function (60), we also evaluated the effect of bed rest on various angiogenic markers by measuring the expression of VEGF and eNOS, as well as HIF-1α. These data suggest potential early adaptive responses following 1 week of bed rest, as the expression of eNOS tended to decrease, whereas an increase in HIF-1α was seen (Fig. 5). However, this did not result in actual changes in skeletal muscle capillary density as measured by immunohistochemistry. This is in line with previous work showing no changes in capillary density following bed rest (16,55). Consequently, our data do not provide evidence that a decline in capillary networks contributes to the rapid decline in whole-body insulin sensitivity that was observed following 1 week of bed rest.

    The magnitude of changes that we observed following merely 1 week of bed rest underlines the impact of short-term muscle disuse, as this study demonstrates that 1 week of bed rest can result in a similar amount of muscle mass and strength loss as can be regained within months of intense rehabilitation (35,44). These changes in lean mass and muscle strength were observed despite our participants being in energy balance, suggesting that the impact of bed rest in undernourished individuals will be even greater. Next to the decline in muscle mass and function, the observed loss in metabolic health during disuse is of paramount importance. By means of comparison, the measured decline in insulin sensitivity (i.e., ∼30%) is similar to the difference between a normal glucose-tolerant individual and a patient with type 2 diabetes (52), and is equivalent to a decline that is observed following ∼30–40 years of aging (23,61). As the decline in muscle mass, strength, and peripheral insulin sensitivity have been shown to be good proxy markers for patient outcomes following hospitalization (62), our results emphasize the importance of finding practical and effective interventional strategies that can be applied immediately following the onset of muscle disuse.

    We conclude that short-term muscle disuse leads to substantial declines in muscle mass and function and is associated with the development of peripheral insulin resistance and a decrease in skeletal muscle oxidative capacity. Whereas we are still unclear on the molecular mechanisms responsible, our findings clearly indicate that intramuscular lipid accumulation (implicated in high-fat diet–induced insulin resistance), impairments in mitochondrial function and changes in capillary density in skeletal muscle tissue cannot be held responsible for the rapid onset of insulin resistance during a short period of bed rest. Clearly, early interventions are warranted to prevent or attenuate the negative functional and metabolic consequences of short-term bed rest.

    Article Information

    Acknowledgments. The authors thank the participants in this study for their enthusiasm and dedication. The authors also thank Wendy Sluijsmans and Hasibe Aydeniz (both part of NUTRIM School of Nutrition and Translational Research in Metabolism) for technical expertise during the muscle analyses and Imre Kouw, Irene Fleur Kramer, Kirsten van der Beek, Jorn Trommelen, Jean Nyakayiru, Philippe Pinckaers, Rinske Franssen, Armand Linkens, Kevin Paulussen, Evelien Backx, and Chantal Strijbos (all part of NUTRIM School of Nutrition and Translational Research in Metabolism) for the practical assistance.

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

    Author Contributions. M.L.D. designed the study, organized and performed the experiments, performed the muscle analyses, analyzed the data, interpreted the data, drafted the manuscript, and edited and revised the manuscript. B.T.W. designed the study, organized and performed the experiments, interpreted the data, and edited and revised the manuscript. B.v.d.V. organized and performed the experiments and interpreted the data. T.M.H. and A.C. performed the muscle analyses and interpreted the data. G.P.H. performed the muscle analyses, interpreted the data, and edited and revised the manuscript. G.H.G. designed the study, organized and performed the experiments, interpreted the data, and edited and revised the manuscript. L.J.C.v.L. designed the study, interpreted the data, and edited and revised the manuscript. All authors approved the final version. M.L.D. 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 December 15, 2015.
    • Accepted June 23, 2016.



    Sell Unused Diabetic Strips Today!

    Sage Pork Chops with Apple Slaw

    By electricdiet / March 22, 2021

    Pork chops are an often overlooked low-fat source of protein. And these sage pork chops with apple slaw adds a flavor punch to this great piece of meat.

    Sage pork chop on top of apple slaw on a white plate

    When it’s time to choose a healthy protein source for our dinner or lunch, most people think of poultry or white fish but pork is a real contender when it comes to lean protein.

    Not only does a palm-sized portion of pork contain a whopping 22 grams of protein, it’s also high in vitamins and minerals such as selenium, vitamin B6, vitamin B12, potassium, iron, magnesium, and zinc. 

    But if not cooked properly, pork can become really dry, which is why these sage pork chops with apple slaw rely on fresh herbs, veggies, and fruit to deliver a flavor punch and juicy chops.

    So whether you grew up eating pork or these pork chops are your first attempt at cooking pork, I can guarantee that you’re in for a pleasant surprise and that these chops probably will become a go-to recipe in your household.

    I mean what’s not to love? They’re tasty, filling, low calorie, and easy to prepare.

    How to make sage pork chops with apple slaw

    One of the best ways to see how healthy a recipe really is, is by looking at the raw ingredients. And just take a look at what goes into this recipe!

    The raw ingredients on a marble surface

    Step 1: Combine 1/2 teaspoon sage, garlic, 1/4 teaspoon salt, and some freshly ground pepper in a small bowl. Rub this mixture on both sides of the pork chops and let them sit at room temperature while you chop the vegetables and apples (at least 10 min.)

    Two pork chops rubber with garlic and spices

    Step 2: Thinly slice the onion, apple, and cabbage (don’t bother peeling the apple.) Julienne the carrots. Set aside.

    Step 3: Heat 1 teaspoon olive oil in a large skillet. Add the pork chops and brown on both sides, about 1 to 2 minutes per side. Remove pork chops from the pan.

    Two seared pork chops on a pan

    Step 4: Add the remaining 1 teaspoon olive oil to the same skillet. Add the onion and remaining 1/4 teaspoon sage. Cook, stirring occasionally until the onions are soft and golden brown, about 4 to 5 minutes.

    Step 5: Add the carrots and cook for about 3 minutes. Add the apples and cook for about 3 minutes. Add the cabbage and vinegar and cook for about 3 minutes. Add the broth or water to the pan.

    Pan with all the vegetables

    Step 6: Return the pork chops to the pan and cover them with the slaw mixture. Cover and cook until pork chops are tender, 15 to 20 minutes.

    Step 7: To serve, put slaw on plate first, then top with a pork chop.

    Close-up of the finished dish

    Looking to reduce your sodium intake?

    As mentioned, pork is high in minerals and vitamins and that includes sodium, so if you are watching your sodium intake, you may want to skip the salt and/or use low-sodium chicken stock or water.

    The recipe only calls for 1/4 cup chicken stock so substituting it with water doesn’t impact the taste too significantly.

    Don’t skip the apples

    Since pork dishes can be a little salty, don’t skip the apple slaw as it can cut through that saltiness and it just tastes delicious.

    Most apples will work for this recipe but I’d recommend using Golden Delicious apple, Piñata apples, or Honeycrisp apples since they’re a little sweeter and will stay firm when cooking.

    Pan and plate with porkchops and apple slaw seen from above

    Cooking tips for the perfect tender pork chops and crispy slaw

    If you find that the chops start sticking to your pan, you might have to add a bit more oil.

    I also added the ingredients to the slaw in order of “hardness” as you would with a stir-fry: onions first, then carrots, then apples, then cabbage. I didn’t want the apples to be mushy.

    Fork holding a bite of pork chop and apple slaw

    Storing any leftover chops and slaw

    It’s hard to imaging ending up with leftovers of these delicious sage pork chops but should it happen (maybe you’re making a larger batch as part of your meal prep), you can store the leftover chops for up to 2 days in an airtight container in the fridge.

    When reheating the dish, you can consider heating the pork and apple slaw separately to keep the crunchy texture of the slaw and prevent it from becoming mushy.

    You can also cut up the pork and throw it in a salad, sandwich, or tortilla cold and bring with you for an easy, healthy portable lunch.

    More pork chop recipes to try

    If you’re as much of a fan of pork as I am, you can try some of these other yummy pork recipes.

    When you’ve tried these Sage Pork Chops With Apple Slaw, please don’t forget to let me know how you liked them and rate the recipe in the comments below!

    Recipe Card

    Sage pork chop on top of apple slaw on a white plate

    Sage Pork Chops with Apple Slaw

    Pork chops are an often overlooked low-fat source of protein. And these sage pork chops with apple slaw adds a flavor punch to this great piece of meat.

    Prep Time:15 minutes

    Cook Time:20 minutes

    Total Time:35 minutes

    Author:Diabetic Foodie

    Servings:2

    Instructions

    • Combine 1/2 teaspoon sage, garlic, 1/4 teaspoon salt and some freshly ground pepper in a small bowl. Rub this mixture on both sides of the pork chops and let them sit at room temperature while you chop the vegetables and apples (at least 10 min.)

    • Thinly slice the onion, apple and cabbage. (Don’t bother peeling the apple.) Julienne the carrots. Set aside.

    • Heat 1 teaspoon olive oil in a large skillet. Add the pork chops and brown on both sides, about 1 to 2 minutes per side. Remove pork chops from pan.

    • Add the remaining 1 teaspoon olive oil to the same skillet. Add the onion and remaining 1/4 teaspoon sage. Cook, stirring occasionally, until the onions are soft and golden brown, about 4 to 5 minutes. Add the carrots and cook about 3 minutes. Add the apples and cook about 3 minutes. Add the cabbage and vinegar and cook about 3 minutes. Add the broth or water to the pan.

    • Return the pork chops to the pan and cover them with the slaw mixture. Cover and cook until pork chops are tender, 15 to 20 minutes.

    • To serve, put slaw on plate first, then top with a pork chop.

    Recipe Notes

    If you are watching your sodium intake, you may want to skip the salt and/or use low-sodium chicken stock or water. Most apples will work for this recipe but I’d recommend using Golden Delicious apple, Piñata apples, or Honeycrisp apples. If you find that the chops start sticking to your pan you might have to add a bit more oil. To ensure your slaw is firm stir-fry onions first, then carrots, then apples, then cabbage. Any leftovers can be stored for up to 2 days in an airtight container in the fridge.

    Nutrition Info Per Serving

    Nutrition Facts

    Sage Pork Chops with Apple Slaw

    Amount Per Serving (0 g)

    Calories 275 Calories from Fat 80

    % Daily Value*

    Fat 8.9g14%

    Saturated Fat 2.2g14%

    Trans Fat 0g

    Polyunsaturated Fat 0.5g

    Monounsaturated Fat 3.3g

    Cholesterol 65mg22%

    Sodium 862.7mg38%

    Potassium 337.2mg10%

    Carbohydrates 22.3g7%

    Fiber 6g25%

    Sugar 14.9g17%

    Protein 25g50%

    Vitamin A 0IU0%

    Vitamin C 0mg0%

    Calcium 0mg0%

    Iron 0mg0%

    Net carbs 16.3g

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

    Course: Main Dishes

    Cuisine: American

    Diet: Diabetic

    Keyword: sage pork chops



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    Beef Bolognese – My Bizzy Kitchen

    By electricdiet / March 20, 2021






    Beef Bolognese – My Bizzy Kitchen







































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    Low-Carb Chocolate Cheesecake | Diabetes Strong

    By electricdiet / March 18, 2021


    This decadent low-carb chocolate cheesecake is so indulgently rich, but with only a fraction of the calories and sugar you get from traditional cheesecake!

    Low carb chocolate cheesecake slice on a white plate next to a gold fork, topped with a sliced strawberry

    Whether I’m having guests over for dinner or celebrating a special occasion, one of my go-to choices for dessert is cheesecake. It’s such a beautiful treat!

    And this decadent low carb chocolate cheesecake is my new favorite recipe. It’s so rich and indulgent, you’d never guess it only has a fraction of the calories and sugar you get from traditional cheesecake.

    Plus, because we use a low-carb sweetener, you don’t have to worry about feeling sluggish or experiencing a major crash after eating a slice. You can simply enjoy!

    And whipping up these cheesecake is probably easier than you think. You’ll mix the crust ingredients and prebake, then mix the filling ingredients, add them to the crust, and bake. After that, let your cheesecake chill in the refrigerator and you’re done!

    While this is a great dessert to make for a crowd, I sometimes like to make it just for me and my husband. We can treat ourselves to a slice after dinner all week long.

    How to make low carb chocolate cheesecake

    This amazing dessert comes together in just 10 simple steps.

    Step 1: Line a 9-inch springform pan with parchment paper and grease the sides, then set aside. Preheat your oven to 325°F.

    Step 2: To make the crust, whisk together the almond flour, cocoa powder, sweetener, and shredded coconut. Add the butter and vanilla extract, then mix until crumbly.

    Crust ingredients mixed in a glass bowl with a wooden spoon

    Step 3: Press the mixture into the bottom of the prepared springform pan. Bake for 15 minutes, then remove from the oven to cool.

    Step 4: Turn the oven temperature down to 300°F.

    Step 5: In a large mixing bowl, add the cream cheese and sweetener. Beat with a hand mixer until smooth and slightly fluffy.

    Filling ingredients beaten with an electric mixer in a glass bowl

    Step 6: Add the vanilla extract and one egg, then beat again until the egg is incorporated. Add the rest of the eggs in the same way.

    Step 7: Add the cream, cocoa powder, and melted chocolate, then beat until the mixture is smooth and properly mixed.

    Batter in a glass bowl with a wooden spoon to stir

    Step 8: Pour the filling mixture over the pre-baked crust, then tap on the counter to smooth out the top. Bake for 60 to 70 minutes. The middle should still be slightly jiggly when done.

    Step 9: Remove the cheesecake from the oven and let it cool for 20 minutes before removing it from the springform pan.

    Step 10: Refrigerate the cheesecake for 2½ hours until it is completely set.

    Full cheesecake, un-sliced, topped with two strawberries on a white platter, as seen from above

    Once the cheesecake has chilled, you’re ready to cut into 10 slices and serve! I like to top mine with sliced strawberries.

    Low-carb but not low-calorie

    Cheesecake is definitely not a low-calorie dessert. It’s a treat to enjoy indulging in!

    I like to cut this cheesecake into 10 good-sized slices. That way, each one will be about 420 calories of chocolate decadence with 11.4 net carbs and 6.3 grams of sugar.

    Compare that to a slice of chocolate mousse cheesecake from the Cheesecake Factory, which contains 1,220 calories! It also has 81 net carbs and 69 grams of sugar.

    So really, the nutrition of your cheesecake all depends on where you get it. That’s why I recommend making this homemade version: it’s so indulgently rich, you’d never guess how much healthier it is than traditional cheesecake!

    Two slices of cheesecake on separate plates topped with strawberries, as seen from above

    Storage

    Can’t eat your entire chocolate cheesecake in one sitting? Well good news: it keeps extremely well in the refrigerator!

    Simply store your cheesecake in the refrigerator in an airtight container. It will stay fresh for up to 5 days, so you can enjoy a slice whenever you like.

    Slice of cheesecake on a white plate with a gold fork, topped with strawberry slices

    Other low carb dessert recipes

    I firmly believe a healthy diet includes dessert! By using low-carb ingredients and sweeteners that don’t spike your blood sugar, you can indulge without worrying about feeling sluggish or crashing later.

    The next time you want to satisfy your sweet tooth, I know you’ll love one of these recipes:

    For even more ways to satisfy your sweet tooth, check out this roundup of my favorite decadent low-carb desserts!

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

    Recipe Card

    Low Carb Chocolate Cheesecake

    This decadent low carb chocolate cheesecake is so indulgently rich, but with only a fraction of the calories and sugar you get from traditional cheesecake!

    Prep Time:15 minutes

    Cook Time:1 hour 10 minutes

    Chill Time:2 hours 30 minutes

    Total Time:3 hours 55 minutes

    Servings:10

    Low carb chocolate cheesecake slice on a white plate next to a gold fork, topped with a sliced strawberry

    Instructions

    • Line a 9-inch springform pan with parchment paper and grease the sides, then set aside. Preheat your oven to 325°F.

    • To make the crust, whisk together the almond flour, cocoa powder, sweetener, and shredded coconut. Add the butter and vanilla extract, then mix until crumbly.

    • Press the mixture into the bottom of the prepared springform pan. Bake for 15 minutes, then remove from the oven to cool.

    • Turn the oven temperature down to 300°F.

    • In a large mixing bowl, add the cream cheese and sweetener. Beat with a hand mixer until smooth and slightly fluffy.

    • Add the vanilla extract and one egg, then beat again until the egg is incorporated. Add the rest of the eggs in the same way.

    • Add the cream, cocoa powder, and melted chocolate, then beat until the mixture is smooth and properly mixed.

    • Pour the filling mixture over the pre-baked crust, then tap on the counter to smooth out the top. Bake for 60 to 70 minutes. The middle should still be slightly jiggly when done.

    • Remove the cheesecake from the oven and let it cool for 20 minutes before removing it from the springform pan.

    • Refrigerate the cheesecake for 2½ hours until it is completely set.

    Recipe Notes

    This recipe is for 10 servings. If you cut your cheesecake into 10 slices, each slice will be 1 serving.
    You can substitute Stevia with another low-carb sweetener if you prefer.
    Cheesecake can be stored in an airtight container in the refrigerator for up to 5 days.

    Nutrition Info Per Serving

    Nutrition Facts

    Low Carb Chocolate Cheesecake

    Amount Per Serving (1 slice)

    Calories 420
    Calories from Fat 327

    % Daily Value*

    Fat 36.3g56%

    Saturated Fat 21.3g107%

    Trans Fat 0g

    Polyunsaturated Fat 0.2g

    Monounsaturated Fat 1.3g

    Cholesterol 121.1mg40%

    Sodium 447.2mg19%

    Potassium 216.2mg6%

    Carbohydrates 17g6%

    Fiber 5.6g22%

    Sugar 6.3g7%

    Protein 12.8g26%

    Net carbs 11.4g

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

    Course: Dessert

    Cuisine: American

    Keyword: cheesecake, low carb, Low-carb Cheesecake, sugar-free cheesecake



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    Greek Chicken Burgers – Healthy Easy Diabetic Friendly Meal

    By electricdiet / March 16, 2021


    Fantastic Diabetic Friendly High Protein Greek Chicken Burgers

    Shake up your usual burger routine with these delicious Greek Chicken Burgers from Holly Clegg’s Guy’s Guide to Eating Well cookbook. Fantastic flavor with a few healthy Mediterranean style ingredients makes this hearty satisfying burger not only fantastic tasting but good for you too! This is also a great source of lean protein which helps build and repair muscle. Make them ahead of time and freeze uncooked burgers to pull out on busy nights. For sliders, make 12 miniature patties.

    Greek Chicken Burgers picmonkey 2

    Greek Chicken Burgers
    Get out of your comfort zone and try this amazing chicken burger with Greek flair. Serve with sliced red onion, tomato and cucumber.

      Servings4 burgers
      Prep Time10 minutes
      Cook Time15 minutes

      Ingredients

      • 1pound


        ground chicken

      • 1


        large egg white

      • 1/3cup


        dried bread crumbs

      • 1teaspoon


        minced garlic

      • 2teaspoons


        dried oregano leaves

      • 1/2cup


        coarsely chopped baby spinach leaves

      • 1/4cup


        crumbled reduced-fat feta cheese



      • salt and pepper to taste

      • 2teaspoons


        dried oregano leaves

      • 1/2cup


        coarsely chopped baby spinach leaves

      • 1/4cup


        crumbled reduced-fat feta cheese



      • salt and pepper to taste

      Instructions
      1. Preheat oven 500°F. Line baking sheet with foil.

      2. In large bowl, combine all ingredients and form into four patties. Cook 15 minutes or until done.

      Recipe Notes

      Calories 192 kcal, Calories from Fat 22%, Fat 5 g, Saturated Fat 2 g, Cholesterol 75 mg, Sodium 333 mg, Carbohydrates 8 g, Dietary Fiber 1 g, Total Sugars 1 g, Protein 28 g, Dietary Exchanges: 1/2 starch, 3 lean meat

      Terrific Tip: Make ahead and freeze uncooked burgers to pull out on busy nights. For sliders, make 12 miniature patties.

      Greek Chicken Burgers

      Cookbook Full of Good For You Meals Made Simple & Delicious!

      Greek Chicken Burgers is a diabetic friendly recipe from the GERD chapter in Holly’s easy men’s cookbook. Weight loss is often a successful step in reducing GERD because belly fat worsens reflux symptoms. Hand in hand with obesity, diabetes can also be an underlying cause of GERD. By choosing trim and terrific, high fiber, unprocessed whole foods, lean meat, fruits and vegetables, you will start reducing your weight and in turn reducing your risk for GERD.

      Holly included men’s favorite recipes but made them healthier.  This book is a great resource of information as this chapter gives you the foods to fight inflammation. Plus, this cookbook entices men in the kitchen. Team Holly aims to make all meals deliciously healthy!

      Stock Up Your Kitchen for This Recipe

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      Differentiation of Diabetes by Pathophysiology, Natural History, and Prognosis

      By electricdiet / March 14, 2021


      Abstract

      The American Diabetes Association, JDRF, the European Association for the Study of Diabetes, and the American Association of Clinical Endocrinologists convened a research symposium, “The Differentiation of Diabetes by Pathophysiology, Natural History and Prognosis” on 10–12 October 2015. International experts in genetics, immunology, metabolism, endocrinology, and systems biology discussed genetic and environmental determinants of type 1 and type 2 diabetes risk and progression, as well as complications. The participants debated how to determine appropriate therapeutic approaches based on disease pathophysiology and stage and defined remaining research gaps hindering a personalized medical approach for diabetes to drive the field to address these gaps. The authors recommend a structure for data stratification to define the phenotypes and genotypes of subtypes of diabetes that will facilitate individualized treatment.

      Introduction

      Though therapeutic algorithms for diabetes encourage individualization of approaches (1), they are often broadly applied in treatment and reimbursement decisions, reinforcing the “one-size-fits-all” approach (2). However, if individualized approaches are successful (if they improve morbidity/mortality and are cost-effective), health care systems are persuaded to adopt them. For example, better insights into the pathophysiology of different types of cancer have led to tailored diagnostic tools and therapies, which have dramatically improved outcomes (3). A similar approach should be realized for diabetes.

      Many different paths, driven by various genetic and environmental factors, result in the progressive loss of β-cell mass (4,5) and/or function (6) that manifests clinically as hyperglycemia. Once hyperglycemia occurs, people with all forms of diabetes are at risk for developing the same complications (Fig. 1), though rates of progression may differ. The present challenge is to characterize the many paths to β-cell dysfunction or demise and identify therapeutic approaches that best target each path. By reviewing the current evidence and addressing remaining research gaps, we aim to identify subtypes of diabetes that may be associated with differential rates of progression and differential risks of complications. A personalized approach to intensive therapy to prevent or treat specific complications may help resolve the burden of diabetes complications, particularly in those at highest risk.

      Figure 1
      Figure 1

      Genetic and environmental risk factors impact inflammation, autoimmunity, and metabolic stress. These states affect β-cell mass and/or function such that insulin levels are eventually unable to respond sufficiently to insulin demands, leading to hyperglycemia levels sufficient to diagnose diabetes. In some cases, genetic and environmental risk factors and gene–environment interactions can directly impact β-cell mass and/or function. Regardless of the pathophysiology of diabetes, chronic high blood glucose levels are associated with microvascular and macrovascular complications that increase morbidity and mortality for people with diabetes. This model positions β-cell destruction and/or dysfunction as the necessary common factor to all forms of diabetes.

      Pathophysiology of Diabetes

      Demographics

      Type 1 diabetes and type 2 diabetes differentially impact populations based on age, race, ethnicity, geography, and socioeconomic status.

      Type 1 Diabetes

      Between 2001 and 2009, there was a 21% increase in the number of youth with type 1 diabetes in the U.S. (7). Its prevalence is increasing at a rate of ∼3% per year globally (8). Though diagnosis of type 1 diabetes frequently occurs in childhood, 84% of people living with type 1 diabetes are adults (9). Type 1 diabetes affects males and females equally (10) and decreases life expectancy by an estimated 13 years (11). An estimated 5–15% of adults diagnosed with type 2 diabetes actually have type 1 diabetes or latent autoimmune diabetes of adults (LADA) (12).

      Europoid Caucasians have the highest prevalence of type 1 diabetes among U.S. youth, representing 72% of reported cases. Hispanic Caucasians represent 16%, and non-Hispanic blacks represent 9% (7).

      Incidence and prevalence rates for type 1 diabetes vary dramatically across the globe. At the extremes, China has an incidence of 0.1/100,000 per year and Finland has an incidence of 60/100,000 per year (13). With some exceptions, type 1 diabetes incidence is positively related to geographic distance north of the equator (13). Colder seasons are correlated with diagnosis and progression of type 1 diabetes. Both onset of disease and the appearance of islet autoimmunity appear to be higher in autumn and winter than in spring and summer (14).

      Type 2 Diabetes

      In the U.S., an estimated 95% of the nearly 30 million people living with diabetes have type 2 diabetes. An additional 86 million have prediabetes, putting them at high risk for developing type 2 diabetes (9). Among the demographic associations for type 2 diabetes are older age, race/ethnicity, male sex, and socioeconomic status (9).

      Type 2 diabetes incidence is increasing in youth, especially among the racial and ethnic groups with disproportionately high risk for developing type 2 diabetes and its complications: American Indians, African Americans, Hispanics/Latinos, Asians, and Pacific Islanders (9). Older age is very closely correlated to risk for developing type 2 diabetes. More than one in four Americans over the age of 65 years have diabetes, and more than half in this age-group have prediabetes (9). The prevalence of type 2 diabetes in the U.S. is higher for males (6.9%) than for females (5.9%) (15).

      There is a high degree of variability for prevalence of type 2 diabetes across the globe. East Asia, South Asia, and Australia have more adults with diabetes than any other region (153 million). North America and the Caribbean have the highest prevalence rate, with one in eight affected (8).

      Independent of geography, the risk of developing type 2 diabetes is associated with low socioeconomic status. Low educational level increases risk by 41%, low occupation level by 31%, and low income level by 40% (16).

      Research Gaps

      The assembled experts agreed that research efforts are needed to define causative factors that account for the established correlations among different demographic subsets and the corresponding variable risks for diabetes. Factors associated with race/ethnicity and geography that differentially increase risk for type 1 diabetes and for type 2 diabetes need to be defined. For type 2 diabetes, the drivers of increased risk in individuals of low socioeconomic status also need to be established.

      Genetics

      Both type 1 and type 2 diabetes are polygenic diseases where many common variants, largely with small effect size, contribute to overall disease risk. Disease heritability (h2), defined as sibling-relative risk, is 3 for type 2 diabetes and 15 for type 1 diabetes (17). The lifetime risk of developing type 2 diabetes is ∼40% if one parent has type 2 diabetes and higher if the mother has the disease (18). The risk for type 1 diabetes is ∼5% if a parent has type 1 diabetes and higher if the father has the disease (19). Maturity-onset diabetes of the young (MODY) is a monogenic disease and has a high h2 of ∼50 (20). Mutations in any 1 of 13 different individual genes have been identified to cause MODY (21), and a genetic diagnosis can be critical for selecting the most appropriate therapy. For example, children with mutations in KCJN11 causing MODY should be treated with sulfonylureas rather than insulin.

      Type 1 Diabetes

      The higher type 1 diabetes prevalence observed in relatives implies a genetic risk, and the degree of genetic identity with the proband correlates with risk (2226). Gene variants in one major locus, human leukocyte antigen (HLA) (27), confer 50–60% of the genetic risk by affecting HLA protein binding to antigenic peptides and antigen presentation to T cells (28). Approximately 50 additional genes individually contribute smaller effects (25,29). These contributors include gene variants that modulate immune regulation and tolerance (3033), variants that modify viral responses (34,35), and variants that influence responses to environmental signals and endocrine function (36), as well as some that are expressed in pancreatic β-cells (37). Genetic influences on the triggering of islet autoimmunity and disease progression are being defined in relatives (38,39). Together, these gene variants explain ∼80% of type 1 diabetes heritability. Epigenetic (40), gene expression, and regulatory RNA profiles (36) may vary over time and reflect disease activity, providing a dynamic readout of risk.

      Genetic variants can also identify patients at higher risk, predict rates of C-peptide decline, and predict response to various therapies (41). With a better understanding of inheritance profiles, it may become possible to realize new targets for individualized intervention.

      Type 2 Diabetes

      While a subset of genetic variants are linked to both type 1 and type 2 diabetes (42,43), the two diseases have a largely distinct genetic basis, which could be leveraged toward classification of diabetes (44). Genome-wide association studies have identified more than 130 genetic variants associated with type 2 diabetes, glucose levels, or insulin levels; however, these variants explain less than 15% of disease heritability (4547). There are many possibilities for explaining the majority of type 2 diabetes heritability, including disease heterogeneity, gene–gene interactions, and epigenetics. Most type 2 variants are in noncoding genomic regions. Some variants, such as those in KCNQ1, show strong parent-of-origin effects (48). It is possible that children of mothers carrying KCNQ1 are born with a reduced functional β-cell mass and thereby are less able to increase their insulin secretion when exposed to insulin resistance (49). Another area of particular interest has been the search for rare variants protecting from type 2 diabetes, such as loss-of-function mutations in SLC30A8 (50), which could offer potential new drug targets for type 2 diabetes.

      To date, however, the improvement in predictive value of known genetic variants over that of classic clinical risk factors (BMI, family history, glucose) has proven minimal in type 2 diabetes.

      The rapid development of molecular genetic tools and decreasing costs for next-generation sequencing should make dissection of the black box of genetics of diabetes possible in the near future, but at this point, apart from the profiles that distinguish between type 1 and type 2 diabetes and a limited number of specific variants that identify small subgroups of patients (MODY), genetics has not been successful in further differentiating subclasses of diabetes.

      Research Gaps

      After consideration of the known genetic associations with diabetes risk, consensus developed that the field is not yet at a place where genetics has provided actionable information to guide treatment decisions, with a few notable exceptions, namely in MODY. The experts agreed there is a need to use the increasingly accessible and affordable technologies to further refine our understanding of how genetic variations affect the rate of progression of diabetes and its complications. The expert committee also highlighted the importance of determining categorical phenotypic subtypes of diabetes in order to link specific genetic associations to these phenotypic subtypes. These types of information are necessary to develop the tools to predict response to—and side effects of—therapeutic approaches for diabetes in patient populations.

      Environmental Influences

      Despite the genetic underpinnings of the diseases, the prevalence of both type 1 and type 2 diabetes is increasing globally at a rate that outpaces genetic variation, suggesting that environmental factors also play a key role in both types of diabetes. Common environmental factors are associated with type 1 and type 2 diabetes, including dietary factors, endocrine disruptors and other environmental polluters, and gut microbiome composition. In addition to well-established roles in type 2 diabetes, obesity and insulin resistance may be accelerators of type 1 diabetes. Conversely, islet autoimmunity associated with possible environmental triggers (e.g., diet, infection) may have a role in a subset of people diagnosed with type 2 diabetes.

      Type 1 Diabetes

      Discordance rates in twins, the rise in global incidence, variance in geographic prevalence, and assimilation of local disease incidence rates when individuals migrate from low- to high-incidence countries all support an environmental influence on risk for developing type 1 diabetes. Furthermore, many lines of evidence suggest that environmental factors interact with genetic factors in both the triggering of autoimmunity and the subsequent progression to type 1 diabetes. Supporting this gene–environment interaction is the fact that most subjects with the highest-risk HLA haplotypes do not develop type 1 diabetes.

      The timing of exposure to environmental triggers may also be critical. The variability of age at disease onset complicates the study of environmental exposures, though the early age of onset of islet autoantibodies associated with childhood-onset type 1 diabetes suggests that environmental exposures in the first few years of life may be contributors.

      Among the environmental associations linked to type 1 diabetes are enteroviral and other infections (51,52) and altered intestinal microbiome composition (53). The timing of exposure to foods including cereal (54) and nutrients such as gluten (55) may influence β-cell autoimmunity. Low serum concentrations of vitamin D have been linked to type 1 diabetes. Perinatal risk factors and toxic doses of nitrosamine compounds have been implicated in the genesis of diabetes.

      The effects of any environmental toxin on type 1 diabetes need further exploration. Studies on the environmental contributions to type 1 diabetes have been small and somewhat contradictory, highlighting the need for larger collaborative investigations such as The Environmental Determinants of Diabetes in the Young (TEDDY) (56), which aims to identify infectious agents, dietary factors, and other environmental factors that trigger islet autoimmunity and/or type 1 diabetes.

      Type 2 Diabetes

      Type 2 diabetes develops when β-cells fail to secrete sufficient insulin to keep up with demand, usually in the context of increased insulin resistance. A minority of people diagnosed with type 2 diabetes also have evidence of islet autoimmunity (57,58). Obesity is a major risk factor for type 2 diabetes (59,60) with complex genetic and environmental etiology.

      Insulin resistance develops with ectopic fat deposition in the liver and muscle. Fat may also accumulate in the pancreas and contribute to the decline in β-cell function, islet inflammation, and eventual β-cell death (61). Type 2 diabetes occurs at different levels of BMI/body fat composition in different individuals and at lower BMI for Asians and Asian Americans (62). For susceptible people, there may be a personal “fat threshold” at which ectopic fat accumulation occurs, worsening insulin resistance and resulting in β-cell decompensation.

      Weight loss improves insulin sensitivity in liver and skeletal muscle (63) and may also reduce pancreatic fat accumulation (64). Defects in insulin secretion are at least partially reversible with energy restriction and weight loss in prediabetes and recent-onset type 2 diabetes (65). Unfortunately, it is difficult to reverse long-standing diabetes, even with the large weight loss associated with bariatric surgery (66).

      Both reduced sleep time and increased sleep time are associated with the development of obesity and diabetes. Obstructive sleep apnea reduces sleep time and sleep quality and is associated with type 2 diabetes and metabolic syndrome. The modern “24-hour culture” may reduce sleep time and thereby also contribute to increased risk of type 2 diabetes. And while associations with additional environmental factors exist, there have been no direct causal relationships defined to date.

      Research Gaps

      There is a clear correlation of environmental influences to diabetes risk. Yet, the assembled experts agreed that hypothesis-driven research is needed to define direct causal relationships between specific environmental factors and pathophysiologies leading to diabetes. Research efforts need to address environmental etiologies of type 1 diabetes and determine their relative contribution to onset of autoimmunity and progression to symptomatic disease. Whether there is a direct causal role of the intestinal microbiota in pathogenesis of type 1 and type 2 diabetes and response to therapies needs to be determined. Public health interventions that successfully reduce the levels of consumption of energy-dense foods and/or reduce sedentary time and increase time spent in physical activity need to be evaluated to determine whether they can reduce type 2 diabetes incidence at a population level.

      Natural History and Prognosis

      Regardless of the particular pathophysiology of an individual’s diabetes, the unifying characteristic of the vast majority of diabetes is hyperglycemia resulting from β-cell destruction or dysfunction. There is a continuum of progressive dysglycemia as insulin insufficiency increases over time. Understanding the natural history related to β-cell mass and function is key to staging the diseases and identifying where and how interventions can best be made to prevent or delay disease progression and complications.

      β-Cell Mass and Function

      While type 1 diabetes results from immune-mediated destruction of β-cells and type 2 diabetes is primarily associated with glucose-specific insulin secretory defects, there is growing evidence of significant overlap across the spectrum of diabetes. For example, β-cell mass is also reduced in people with type 2 diabetes (67). In both type 1 and type 2 diabetes, the stress response induced by hyperglycemia may play a role in β-cell apoptosis (68). Changes in β-cell phenotype associated with hyperglycemia may reflect a dedifferentiation of β-cells important to the natural history and staging of diabetes (69). Clearly, an insufficient number or functional decline of β-cells is central to hyperglycemia and the downstream complications of diabetes. Understanding the state of the β-cell is key to defining subtypes of diabetes.

      Type 1 Diabetes

      Abnormal insulin secretion can occur well before the diagnosis of type 1 diabetes (7073), with a gradual decline beginning at least 2 years before diagnosis and accelerating proximal to diagnosis (74,75). A decline in β-cell sensitivity to glucose (76) appears to occur on a similar timeframe. As the early insulin response falters, the later insulin response becomes greater, indicating a possible compensatory mechanism. The accelerated loss of insulin response continues into the early postdiagnostic period (77).

      Insulin secretion decline during the first few years after diagnosis has been described as biphasic, steeper during the first year than during the second year after diagnosis. Data also suggest that the rate of decline is slower in adults (78). The loss of insulin secretion can continue for years after diagnosis until little or no insulin secretion remains. However, low levels of C-peptide are detectable in the majority of patients after 30 years of type 1 diabetes (79).

      Glucose levels are also frequently elevated years before the diagnosis of type 1 diabetes (8082). Even within the normal range, higher glucose levels are predictive of type 1 diabetes (83). There are wide fluctuations of glucose during the progression to type 1 diabetes (84). Metabolic markers of progression, such as the occurrence of dysglycemia, could be utilized to more precisely predict the onset of diabetes in at-risk individuals (41,85). Risk scores that combine dynamic changes in glucose and C-peptide can further enhance prediction (86,87).

      Type 2 Diabetes

      Defective insulin secretion is central to the pathophysiology of type 2 diabetes. To maintain normal glucose levels, insulin secretion varies over a wide range in response to insulin sensitivity. The relationship between insulin secretion and insulin sensitivity is curvilinear and is expressed as the disposition index. People with type 2 diabetes cannot adequately increase insulin secretion to overcome insulin resistance and have a low disposition index (88). Consequently, while absolute insulin levels may be higher in obese subjects with type 2 diabetes who are insulin resistant than they are in lean control subjects who are insulin sensitive, they are lower than appropriate for their degree of insulin resistance. First-phase insulin secretion, especially in response to stimulation by glucose, is markedly impaired or lost (89). Maximal insulin secretion and potentiation by hyperglycemia of insulin responses to nonglucose stimuli are severely reduced (90), and the ratio of proinsulin to insulin (C-peptide) is high in type 2 diabetes (91). Over time, hyperglycemia tends to become more severe and more difficult to treat. This progressive nature of type 2 diabetes is usually due to ongoing deterioration of β-cell function.

      While prediabetes and diabetes are diagnosed by absolute thresholds (92), dysglycemia is a continuum progressing from normal to overt diabetes. Early screening offers a window for treatment that may prevent or delay progression of the disease and its complications (93,94). In prediabetes, impaired glucose tolerance or impaired fasting glucose indicates glucose levels higher than normal but not in the diabetes range (92). Currently, most clinicians do not treat these patients to completely control blood glucose levels. Even after initiation of therapy in frank diabetes, intensification of therapy is often delayed (9597), exposing people to hyperglycemia for years (93).

      Several studies have shown that treatment with lifestyle change or medication can reduce the progression from prediabetes to diabetes (98,99). Furthermore, a clinical benefit of early therapy has been demonstrated (100,101), with reductions in retinopathy and cardiovascular and all-cause mortality (102). This evidence suggests that identifying prediabetes at an early stage and keeping glucose levels close to normal could change the natural history of the disease (93).

      Research Gaps.

      The strong consensus of this group was that the primary defect resulting in hyperglycemia is insufficient β-cell number and/or β-cell function (of various etiologies). From this β-cell–centric view, it is imperative to determine what etiological factors are the basis for abnormal insulin secretion patterns in type 1 diabetes and type 2 diabetes. Biomarkers and imaging tools are needed to assess β-cell mass and loss of functional mass and to monitor progression and response to therapeutic interventions. The point at which β-cell dysfunction becomes irreversible needs to be determined. The molecular basis for the glucose-specific insulin secretory defect and the role of β-cell dedifferentiation in type 1 diabetes and in type 2 diabetes need to be determined. The extent to which insulin resistance contributes to glycemia and the complications of type 1 diabetes remains unknown. Research is needed to determine whether increased β-cell activity, stimulated by insulin resistance, enhances or accelerates the β-cell lesion in type 1 diabetes and in type 2 diabetes and to identify mechanisms by which β-cells can overcome an insulin-resistant environment.

      Autoimmunity

      Circulating autoantibodies against insulin, glutamic acid decarboxylase (GAD), the protein tyrosine phosphatase IA-2, and/or zinc transporter 8 can be detected prior to clinical diagnosis of type 1 diabetes (103). While individuals with single autoantibody positivity frequently revert to negative, reversion is rare in people with multiple autoantibodies (104). Currently, we lack sufficient biomarkers and imaging techniques to monitor autoantibody flare-ups, reversions, and progression to type 1 diabetes. The presence of two or more islet autoantibodies in children with HLA risk genotypes or with relatives who have type 1 diabetes is associated with a 75% risk of developing clinical diabetes within 10 years (105). Risk is incremental with detection of increasing numbers of autoantibodies (105107). A positive test for at least two autoantibodies is now considered a diagnostic stage of type 1 diabetes (Table 1) (41). The presence of islet autoantibodies reflects an underlying immune B- and T-cell response to β-cell antigens. Autoimmune responses to β-cells lead to loss of β-cell mass and function and onset of glucose intolerance, representing the next distinct stage prior to onset of clinical symptoms of diabetes.

      Table 1

      Staging of type 1 diabetes

      Despite the strong prognostic value of autoimmunity in type 1 diabetes, there is no successful strategy to prevent or treat it. HLA confers strong susceptibility for the development of two or more islet autoantibodies (108). For primary prevention of β-cell autoimmunity in children, data suggest there may be a critical period in the first 2 years of life (109111).

      Interestingly, autoantibodies against GAD are present in ∼5% of individuals diagnosed with type 2 diabetes (112). As compared with GAD antibody–negative patients with type 2 diabetes, these patients have lower BMI and residual β-cell function. Further, they carry a genetic profile more similar to that of patients with type 1 diabetes and an earlier requirement for insulin therapy (112), suggesting that autoimmune diabetes in adults may actually be a form of type 1 diabetes that exhibits slow progression associated with later age of onset.

      Research Gaps

      The assembled group agreed that while it is clear that inflammation and autoimmunity lead to β-cell destruction characteristic of type 1 diabetes, much more information is needed to understand the pathophysiology and progression of autoimmunity related to diabetes in order to develop rational approaches to prevent or reverse it. We do not have a clear understanding of whether different antigenic targets, single-antibody positivity, or other contributing factors have variable prognostic, genetic and environmental correlates that can be used to better develop and apply stage-appropriate personalized therapies. The molecular mechanisms by which β-cells die or fail in the presence of β-cell autoimmunity need determination. Biomarkers and imaging tools are needed to define reversion or stable autoimmunity versus active or flaring autoimmunity. Furthermore, inexpensive specific and sensitive assays to identify β-cell autoimmunity are needed, to be deployed on a population-wide level and beyond the confines of specialized laboratories.

      Therapeutics

      Aside from insulin and insulin analogs, therapies for diabetes include those that enhance insulin secretion, those that stimulate insulin action, those that reduce hepatic and endogenous glucose production, and those that impact glycemia through other mechanisms. By better understanding the pathophysiology and natural history of various subtypes of diabetes and applying what we know about the modes of action and pharmacogenomics of existing therapies, we can better apply a personalized approach to diabetes management. There is a growing body of evidence regarding which phenotypic and genotypic subsets of patients with diabetes respond best, or are resistant to, specific therapies (113), including sulfonylureas (114,115), metformin (116,117), thiazolidinediones (118,119), incretin therapies (120), and inhibitors of sodium–glucose cotransporter 2 (SGLT2) (121,122).

      Type 1 Diabetes

      Individuals with type 1 diabetes require intensive therapy, characterized by exogenous insulin administration through multiple daily injections with both fast-acting insulin with meals and basal insulin, or with continuous subcutaneous insulin infusion through pumps. There are no significant generalizable differences in efficacy or safety between the two approaches (123).

      The goal of intensive insulin therapy is to maintain as close to normal glucose concentration as possible while avoiding hypoglycemia. Achieving this goal requires individualization of treatment and targets, which may also change over time within individuals. The American Diabetes Association’s glycemic target for adults is HbA1c <7%. However, consideration of individual circumstances is critical. Pediatric patients are recommended to target <7.5%, whereas adults who are able to do so safely should target <6.5% (92).

      Both long-acting and short-acting insulin analog preparations with more predictable time-action profiles have been developed, allowing patients to achieve more physiological insulin delivery and, therefore, tighter glucose control with fewer side effects. Technologies for self-monitoring blood glucose and continuous glucose monitoring have advanced in recent years and are becoming more widespread. Continuous glucose monitoring allows patients to visualize changes in glucose levels and tailor their treatment in real time (124). The amylin analog pramlintide is approved for use as an adjunct to insulin in patients with type 1 diabetes who have not achieved glycemic goals despite optimized insulin therapy. Pramlintide lowers postprandial glucose (125), thereby improving overall glycemic control, and it has a modest but significant weight loss effect. However, pramlintide added to insulin may increase the risk of hypoglycemia (126,127).

      A number of agents currently approved for the treatment of type 2 diabetes have also been investigated for use in type 1 diabetes, including α-glucosidase inhibitors (128,129), thiazolidinediones (130132), metformin (133), glucagon-like peptide 1 (GLP-1) receptor agonists (134,135), dipeptidyl peptidase 4 (DPP-4) inhibitors (136), and SGLT2 inhibitors (137,138). The benefits of these agents in type 1 diabetes are not well established, and their eventual use in this population will depend on further demonstration of efficacy and safety.

      Type 2 Diabetes

      There are many agents now available to treat hyperglycemia in type 2 diabetes, with varying mechanisms of action and targeting different pathophysiological components of the disease. Many agents are not always able to achieve adequate control unless they are started earlier in disease progression or are used in combinations (metformin, SGLT2 inhibitors, DPP-4 inhibitors, GLP-1 receptor agonists, peroxisome proliferator–activated receptor γ agonists). This limitation in efficacy may be due in part to the fact that these agents are often initiated after β-cell function or mass has deteriorated beyond a critical level or to their limited effects on insulin secretion. Many people with type 2 diabetes ultimately require insulin therapy, which reflects long-standing type 2 diabetes and greatly diminished β-cell function but also likely includes individuals who have slowly progressing autoimmune diabetes with adult onset (LADA) or other ambiguous forms of diabetes.

      Age

      Data from randomized controlled trials in people with type 2 diabetes under the age of 18 years or over the age of 65 years are scarce. Beneficial effects of tight glucose control on complications take years to be realized (139,140). Targets of glucose control should be adapted to life expectancy, frailty, biological age, and social situation rather than just calendar age. HbA1c targets in this population need to be adjusted when using agents that cause side effects such as hypoglycemia. However, overt hyperglycemia needs to be addressed to avoid acute complications of diabetes and a catabolic state (141).

      Comorbidities: Kidney Impairment.

      Kidney impairment is a prevalent complication of diabetes. It is also an independent comorbidity, very often caused by vascular complications in people with type 2 diabetes. Therapeutic choices become more limited because of contraindications (e.g., metformin) or the need for good kidney function for efficacy (e.g., SGLT2 inhibitors), leaving many patients with only insulin therapy (142). Targets for glucose control in the population with kidney impairment may need to be adapted, as kidney impairment also predisposes to hypoglycemia (143). The use of HbA1c is also problematic in people with kidney impairment because of reduced red blood cell survival, use of erythropoietin, modifications of hemoglobin (e.g., carbamylation), and mechanical destruction of red blood cells on dialysis (144).

      Comorbidities: Cardiovascular Complications.

      Cardiovascular complications require a multifactorial approach, including blood pressure and lipid control. Hypoglycemia is linked to arrhythmias and mortality in people with a history of cardiovascular events (145). However, when agents that do not cause hypoglycemia can be used, tight glucose control should be sought. Agents such as DPP-4 inhibitors (146148) and GLP-1 receptor agonists (149) have been shown to be safe in this population. Some agents, such as pioglitazone (150) and metformin (151), may even be cardioprotective. Empagliflozin (152) and liraglutide (153) reduce cardiovascular and all-cause mortality over 2.5–5 years of therapy in patients at high risk of cardiovascular disease. Nephropathy is a recognized risk factor for cardiovascular complications, especially in type 1 diabetes (143).

      Weight

      To avoid comorbidities and complications associated with obesity, weight management should be a priority in all patients, independent of BMI. Weight loss can be achieved by lifestyle intervention, choosing glucose-lowering drugs that promote weight loss, and incorporating obesity pharmacotherapy or bariatric surgery in appropriate patients (154).

      Research Gaps

      While research and development efforts over the past few decades have led to the availability of several new classes of medications and new insulin formulations and delivery methods, we still lack a clear understanding of the ideal approaches to selecting appropriate treatment regimens for particular individuals. With a more in-depth characterization of the pathophysiology and natural history of subtypes of diabetes coupled with the pharmacogenomics of new and existing therapies, we can begin to develop a more personalized approach to diabetes management.

      Several areas can be immediately addressed. This includes performing clinical trials in vulnerable and understudied populations, including the elderly and children, that are critical to validate more precise evidence-based treatments in these populations. Studies examining the appropriate application of immune therapies in combination (sequentially or simultaneously) to target β-cell specific immune response, islet inflammation, and more global defective immunoregulation are critical. For type 2 diabetes, the early use of combinations of glucose-lowering agents needs to be studied. For people with diabetes who are overweight or obese, studies are needed to determine whether weight loss medication and bariatric surgery could be used to support diabetes treatment goals.

      Complications

      Intensive glycemic control can reduce diabetes complications (140,155). In fact, in the decades since these studies were first published, rates of microvascular and macrovascular complications of diabetes and deaths from hyperglycemic crisis have substantially decreased (156). However, complications of diabetes remain the greatest health threat to people living with diabetes. Research efforts to identify clinical variables and biomarkers that indicate the presence or progression of complications may lead to a better understanding of risk and help identify individuals who may benefit from particular therapies to reduce the impact of diabetes.

      Type 1 Diabetes

      The underlying pathophysiology driving an increased risk of cardiovascular complications in type 1 diabetes remains unclear. It is in part related to nephropathy and appears to be distinct from the pathophysiology of cardiovascular complications of type 2 diabetes (157). Intensive treatment of type 1 diabetes with insulin often leads to weight gain. Concurrent with the population-wide rise in incidence of obesity, many people with type 1 diabetes have begun to exhibit features of obesity and metabolic syndrome, likely increasing the development of cardiovascular disease. Current treatment recommendations for management of cardiovascular risk factors predominantly derive from studies on type 2 diabetes or populations that did not discriminate between diabetes type. Risk factors should be monitored and treated in type 1 diabetes to recommended targets, but research is needed to determine distinctions in cardiovascular risk pathophysiology in type 1 diabetes and to identify appropriate therapies to reduce risk.

      Kidney disease predicts cardiovascular disease in people with type 1 diabetes (143) and is associated with development of additional microvascular and macrovascular complications over time. People with type 1 diabetes show signs of premature arterial stiffening that is further exaggerated in those with diabetic nephropathy.

      There is a genetic propensity for diabetic nephropathy that peaks at 10–14 years duration of type 1 diabetes (158). The risk plateaus after 15 years duration, and the incidence of microalbuminuria matches this pattern (FinnDiane Study Group, unpublished observations). The peak incidence of macroalbuminuria and end-stage kidney disease lags 10 to 15 years behind the appearance of microalbuminuria. Progression to end-stage kidney disease is linked to age of onset and duration of diabetes (159). Female sex seems to be protective if age of onset occurs during or after puberty. Similar factors influence risk for and progression of diabetic retinopathy. Intensive glucose control significantly reduces the risk of diabetic peripheral neuropathy and cardiovascular autonomic neuropathy in type 1 diabetes (160).

      Average HbA1c and HbA1c variability are higher in people who progress to diabetic kidney disease (161). Those with more components of metabolic syndrome have more kidney disease and higher HbA1c. A person with type 1 diabetes is much more likely to develop diabetic kidney disease if a sibling with type 1 diabetes has it. The risk of diabetic nephropathy in type 1 diabetes is fourfold higher in children whose mothers have type 1 diabetes than in those without a parent with diabetes (162), indicating a role for epigenetics in the development of kidney disease. Urine metabolites have been identified that highlight potential involvement of mitochondrial dysfunction in diabetic kidney disease (163).

      Type 2 Diabetes

      A large proportion of people with type 2 diabetes also have nonhyperglycemic components of the metabolic syndrome (164), including hypertension, hyperlipidemia, and increased risk for cardiovascular disease. These metabolic features are interrelated and must be considered collectively. Multiple risk factor reduction is critical. Lipoprotein metabolism is often abnormal in diabetic nephropathy, but treatment strategies to avoid cardiovascular disease in this population are unclear. Statins appear to be ineffective at preventing cardiovascular disease in people with end-stage kidney diease (165,166). Once on statins, fibrates may not be beneficial for preventing cardiovascular disease in this population but might have microvascular benefits through anti-inflammatory actions (167). There are reasonably good data indicating that cholesterol absorption is higher in diabetes, suggesting that ezetimibe might have unique effects in diabetes (168,169).

      Cardiovascular disease risk increases substantially when estimated glomerular filtration rate falls below 45 mL/min/1.73 m2. Microalbuminuria is not always due to diabetic nephropathy (170), but it is a marker of inflammation that indicates vascular leakage and increased cardiovascular risk. Albuminuria has been used as a marker of diabetic nephropathy for three decades. Yet, its power is limited. It varies by 25–30% daily in individuals (171174). It is transient and patients can revert to normal albuminuria without treatment.

      Interestingly, the urinary metabolomics signature of diabetic kidney disease is similar in people with type 1 and type 2 diabetes (163). Newly identified biomarkers such as urinary adiponectin and serum tumor necrosis factor-α receptor 1 may be better predictors of nephropathy than albumin excretion rate; however, they require greater evaluation in prospective studies.

      Tight glycemic control is the only strategy known to prevent or delay the development of peripheral neuropathy, and cardiac autonomic neuropathy is perhaps even more important in relation to cardiovascular mortality (175). However, randomized clinical trials to determine appropriate targets are lacking. Outcomes for cardiovascular disease and mortality have been mixed in different studies.

      Research Gaps

      The assembled experts agreed that the means to determine which individuals with diabetes will develop particular complications remain unclear. Research efforts are needed to delineate the mechanisms underpinning the development of complications in type 1 diabetes and type 2 diabetes and identifying the differences between them. For example, the contributions of genetics to development of complications in specific populations need to be determined. The benefits of screening and early treatment to control glucose levels in people with presymptomatic diabetes on the development of complications also needs to be assessed.

      In some cases, the data supporting current treatment recommendations are drawn from populations that are too heterogeneous to be sufficiently representative of subtypes of diabetes. For example, current treatment recommendations for management of cardiovascular complications derive predominantly from data in type 2 diabetes or in populations that did not discriminate between diabetes type. Thus, data to support evidence-based targets to avoid cardiovascular complications in type 1 diabetes are needed.

      There are also some targeted issues that need to be addressed around specific complications to better inform treatment. For example, because of inconclusive associations, trials are needed to determine whether fibrates are able to modify the natural history of retinopathy and, if so, by what mechanisms. Given the limitations of current predictors of kidney disease progression, better biomarkers are needed. Finally, a better understanding of how complications of diabetes affect one another and how they impact treatment approaches is needed. This underlines a need for studies comparing the effectiveness of different strategies for glucose control in subpopulations with comorbidities.

      Conclusions

      Diabetes is currently broadly classified as type 1, type 2, gestational, and a group of “other specific syndromes.” However, increasing evidence suggests that there are populations of individuals within these broad categories that have subtypes of disease with a well-defined etiology that may be clinically characterized (e.g., LADA, MODY). These developments suggest that perhaps, with more focused research in critical areas, we are approaching a point where it would be possible to categorize diabetes in a more precise manner that can inform individual treatment decisions.

      Characterization of disease progression is much more developed for type 1 diabetes than for type 2 diabetes. Studies of first-degree relatives of people with type 1 diabetes suggest that persistent presence of two or more autoantibodies is an almost certain predictor of clinical hyperglycemia and diabetes. The rate of progression depends on the age of antibody onset, the number of antibodies, antibody specificity, and titer. Rising glucose and HbA1c levels substantially precede the clinical onset of diabetes, making diagnosis feasible well before the onset of diabetic ketoacidosis. Three distinct stages of type 1 diabetes can be identified (Table 1) and serve as a framework for future research and regulatory decision-making (41).

      The paths to β-cell demise and dysfunction are less well defined, but deficient β-cell insulin secretion in the face of hyperglycemia appears to be the common denominator. Future classification schemes for diabetes will likely focus on the pathophysiology of the underlying β-cell dysfunction and the stage of disease as indicated by glucose status (normal, impaired, or diabetes).

      Recently, the All New Diabetics in Scania (ANDIS) study reported five distinct subtypes of diabetes on the basis of clustering of clinical, blood-based, and genetic information in newly diagnosed patients in Sweden (176). Importantly, these subtypes of diabetes appear to be differentially linked to risk for particular complications. The researchers confirmed similar groupings and relationships among patients in Finland. This model represents a notable example of an approach that, with additional information, could be refined in more diverse populations to begin developing meaningful classifications based on clinical characteristics, demographics, and novel biomarkers for disease risk, progression, and complications in discreet populations.

      Remaining critical research gaps are currently preventing the realization of true precision medicine for people with diabetes. The authors have outlined some of these key gaps (Supplementary Table 1) and call for the diabetes research community to address these open questions to better understand genetic and molecular mechanisms of diabetes and its complications, define phenotypes and genotypes of subtypes of diabetes, and use this understanding in the development and application of therapies to prevent and treat diabetes and complications.

      Understanding the pathways to loss of β-cell mass and function is key to addressing all forms of diabetes and avoiding complications of diabetes; therefore, the gaps in these topic areas are highlighted as particular priorities among the many critical areas that remain to be investigated. By addressing the noted research gaps, we will be able to further refine models and make meaningful distinctions to stage diabetes.

      Article Information

      Acknowledgments. The authors gratefully acknowledge the Differentiation of Diabetes by Pathophysiology, Natural History and Prognosis research symposium steering committee members and speakers for the excellent presentations, discussions, and contributions to the conference. J.S.S., G.L.B., E.B., R.H.E., L.G., P.-H.G., R.A.I., C.M., J.P.P., A.P., D.A.S., J.M.S., J.P.H.W., and R.E.R. were presenters. Other faculty included Michael Bergman, New York University School of Medicine; Barbara E. Corkey, Boston University School of Medicine; James R. Gavin III, Emory University School of Medicine; Stanley Schwartz, University of Pennsylvania; and Kumar Sharma, University of California at San Diego. The conference was supported in part by an unrestricted educational grant from Novo Nordisk Inc. The sponsor had no influence on the selection of speakers, selection of writing group members, topics or content covered at the conference, or the content of this report. The authors thank Shirley Ash of the American Diabetes Association for assistance with the conference.

      Duality of Interest. J.S.S. reports personal fees from Adocia, AstraZeneca, Boehringer Ingelheim, Dance Biopharm, Debiopharm, DexCom Inc., Genentech, Gilead, Intarcia Therapeutics, Merck, Regeneron, Sanofi, vTv Therapeutics, and Valeritas outside the submitted work. G.L.B. reports personal fees from AstraZeneca, Bayer, Boehringer Ingelheim, GlaxoSmithKline, Janssen, Merck, NxStage, and Sanofi outside the submitted work. G.L.B. is a special government employee of the U.S. Food and Drug Administration and the Centers for Medicare & Medicaid Services. T.D., A.T.M., and R.E.R. are employees of the American Diabetes Association, which received an unrestricted educational grant from Novo Nordisk Inc. to support the research symposium. P.-H.G. reports personal fees from AbbVie, AstraZeneca, Boehringer Ingelheim, Eli Lilly, Genzyme, Janssen, Medscape, MSD, Novartis, Novo Nordisk, and Sanofi outside the submitted work. Y.H. reports grants and personal fees from Amgen, AstraZeneca, Bristol-Myers Squibb, Boehringer Ingelheim, GlaxoSmithKline, Merck, Novo Nordisk, and Sanofi; grants from Esparion, Grifolis, Hamni, Intarcia, and Lexicon; and personal fees from Amarin, Eli Lilly, Eisai, Janssen, Regeneron, and Vivus (all outside the submitted work). R.A.I. reports personal fees from Janssen outside the submitted work. J.P.H.W. reports grants from the Novo Nordisk Research Foundation during the conduct of the study. J.P.H.W. reports grants from AztraZeneca and Novo Nordisk; personal fees from AstraZeneca, Janssen Pharmaceuticals, Orexigen, and Novo Nordisk; and other institutional support from AstraZeneca, Boehringer Ingelheim, Janssen Pharmaceuticals, Orexigen, and Novo Nordisk (all outside the submitted work). No other potential conflicts of interest relevant to this article were reported.

      • Received July 1, 2016.
      • Accepted November 23, 2016.



      Sell Unused Diabetic Strips Today!

      Cauliflower Pizza Crust (Gluten-free) – Diabetic Foodie

      By electricdiet / March 12, 2021


      This cauliflower pizza crust is low-carb, gluten-free, and so easy to make! Just add your favorite toppings and enjoy pizza night guilt-free.

      Cauliflower pizza crust with toppings

      Did you ever think you’d be able to eat pizza as part of a healthy diet?

      Thanks to this cauliflower pizza crust, you can enjoy a hot and cheesy pie without the guilt! In fact, because cauliflower is such a powerhouse of nutrition, you can actually feel good about eating this pizza.

      Cauliflower crust is easy to make and, once baked, sturdy enough to hold all your favorite toppings. Keep it classic with marinara and mozzarella, or get a little funky with some vegetables and goat cheese!

      Whatever you choose, you can guarantee it will be delicious.

      How to make cauliflower pizza crust

      Ready to see how we turn this crunchy vegetable into a tasty pizza crust?

      Step 1: Place the cauliflower in a food processor and pulse until finely chopped.

      Cauliflower in food processor

      Step 2: Scrape the cauliflower into a microwave-safe bowl, cover, and microwave at 100% for 5 minutes.

      Step 3: Transfer the cauliflower onto a clean dish towel and spread it out. Let it sit until cool enough to handle, about 10 minutes.

      Step 4: Preheat the oven to 400°F and line a rimless baking sheet with parchment paper.

      Step 5: Take the dish towel with the cauliflower and bring the edges together to form a pouch. Over a bowl or the sink, twist and squeeze to remove as much liquid as possible.

      Step 6: In a large bowl, combine the cauliflower, mozzarella, Parmesan, oregano, salt, garlic pepper, and egg.

      Step 7: Transfer to the lined baking sheet and form the dough into a circle.

      Step 8: Place in the oven and bake for 20 minutes.

      Your pizza crust is ready! Remove it from the oven, add your toppings, then bake for an additional 7 to 10 minutes until any cheese is melted and everything is heated through.

      Baked cauliflower crust on parchment paper

      Why cauliflower?

      Cauliflower has been popping up in a lot of recipes lately. So why has this vegetable become so popular lately?

      First, it’s a great low-carb and gluten-free ingredient. Second, it’s so versatile! You can use it to make low-carb rice or use it as a thickener like in this creamy cauliflower soup with Brussels sprouts.

      Aside from its versatility, cauliflower is so good for you. It boosts heart and brain health, keeps inflammation in check, and is a great source of vitamins, minerals, antioxidants, and fiber. All from one vegetable!

      And you can get all those benefits just by making a pizza. That sounds like a win-win to me.

      So is this cauliflower crust going to taste exactly like take-out pizza? No, of course not! But it’s incredibly delicious none-the-less, and I guarantee you’ll feel better after eating it.

      Toppings for pizza with cauliflower crust

      Looking for some inspiration for your next pizza night?

      Of course, you can always keep your toppings simple. A little marinara or pizza sauce with shredded mozzarella is a classic for a reason. You can even throw some pepperoni on there if you like.

      Personally, I love making funky veggie pizzas! One of my go-to topping combos is spaghetti squash, sun-dried tomatoes, red peppers, and mozzarella. What a great blend of flavors!

      Or, if I want a little meat, I’ll add chicken-apple sausage, gold beets, and herbed goat cheese. So delicious.

      You could also check out my spinach and artichoke pizza made with a cauliflower crust. I tried a slightly different cooking method for the crust in that recipe, but either way will work just fine!

      Cauliflower pizza with different topping

      Storage

      Cauliflower crusts are a great item to make ahead of time! Once the crust is fully baked, you can store it in the refrigerator for a few days or the freezer for several months. That way, you can easily throw together a pizza any time the craving strikes!

      Be sure to let the crust cool completely to avoid any condensation that could make it soggy. Then, wrap the crust in plastic wrap and place in the fridge or freezer.

      Once you’re ready to enjoy, reheat the crust in the oven before adding your toppings.

      Other healthy comfort food recipes

      We all have those dishes we turn to when we want something delicious and comforting. The good news is that you can often find a healthier version so you can indulge without the guilt! Here are a few of my favorite comfort food recipes that include some healthy modifications:

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

      Recipe Card

      Cauliflower pizza crust

      Cauliflower Pizza Crust (Gluten-free)

      This cauliflower pizza crust is low-carb, gluten-free, and so easy to make! Just add your favorite toppings and enjoy pizza night guilt-free.

      Prep Time:15 minutes

      Cook Time:30 minutes

      Total Time:45 minutes

      Author:Diabetic Foodie

      Servings:4

      Instructions

      • Place the cauliflower in a food processor and pulse until finely chopped.

      • Scrape the cauliflower into a microwave-safe bowl, cover, and microwave at 100% for 5 minutes.

      • Transfer the cauliflower onto a clean dish towel and spread it out. Let it sit until cool enough to handle, about 10 minutes.

      • Preheat the oven to 400°F and line a rimless baking sheet with parchment paper.

      • Take the dish towel with the cauliflower and bring the edges together to form a pouch. Over a bowl or the sink, twist and squeeze to remove as much liquid as possible.

      • In a large bowl, combine the cauliflower, mozzarella, Parmesan, oregano, salt, garlic pepper, and egg.

      • Transfer to the lined baking sheet and form the dough into a circle.

      • Place in the oven and bake for 20 minutes.

      Recipe Notes

      The nutritional information is for the crust only. A serving is 1/4 pizza crust.
      Once you add your toppings, place the pizza back in the oven for 7 to 10 minutes until any cheese is melted and the toppings are heated through.
      To prep your cauliflower crust ahead of time, bake the crust and then allow it to fully cool. Wrap with plastic wrap and store in the refrigerator for up to 5 days or in the freezer for several months. Reheat in the oven before adding your toppings.

      Nutrition Info Per Serving

      Nutrition Facts

      Cauliflower Pizza Crust (Gluten-free)

      Amount Per Serving

      Calories 120
      Calories from Fat 52

      % Daily Value*

      Fat 5.8g9%

      Saturated Fat 2.2g14%

      Trans Fat 0g

      Polyunsaturated Fat 0.3g

      Monounsaturated Fat 0.5g

      Cholesterol 56.6mg19%

      Sodium 308.5mg13%

      Potassium 455.6mg13%

      Carbohydrates 8g3%

      Fiber 3.1g13%

      Sugar 2.8g3%

      Protein 9.8g20%

      Net carbs 4.9g

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

      Course: Main Course

      Cuisine: American

      Diet: Diabetic, Gluten Free

      Keyword: cauliflower crust, cauliflower pizza crust, easy dinner recipes



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