Add all of the ingredients except the ice to a blender.
Blend until smooth.
Add the ice and pulse until the ice is mostly crushed.
Blend until the mixture is smooth and creamy.
Biscuits are the most beloved breakfast food, but have you ever thought of enjoying them for a snack or an appetizer? Holly’s beef biscuit cups from Guy’s Guide To Eating Well cookbook makes the best bite-sized healthy homemade snack for an at-home tailgate party. Everyone absolutely loves this easy ground sirloin recipe. Sort of like Sloppy Joe biscuit cups because they’re filled with a delicious Sloppy Joe like mixture and popped into the oven for just 10 minutes. This simple Pillsbury biscuit recipe adds a savory twist to a classic quick bread.
No hassle here! This is a Pillsbury biscuit recipe, so the only real cooking is the ground beef mixture. The recipe is so quick and easy is makes for the best healthy homemade snack for your at home tailgate party. Any kid (or adult) needing a quick snack will be thrilled coming home from school or practice and having these waiting for them. However, the adults usually grab these first so be sure to add it to your sports watch party. I had to tell my hubby not to eat all the beef biscuit cups!
Nutrition information per serving: Calories 74, Protein 6 g, Carbohydrate 7 g, Fat 2 g, Calories from Fat 28%, Saturated Fat 1 g, Dietary Fiber 0 g, Cholesterol 13 mg, Sodium 146 mg, Diabetic Exchanges: 1 very lean meat, 0.5 starch
This ground sirloin recipe from the Fatigue Chapter is found in Holly Clegg’s easy men’s cookbook, along with many other hearty and healthy recipes for your man. Not only are the recipes in this cookbook geared for men to love, but, they are also geared for men to love cooking!
This men’s cookbook makes the perfect present for any man in your life whether it be your husband, dad, or son!
Can you eat delicious food that is also good for you? Of course! Diabetic friendly meals definitely do not have to be boring and tasteless. This Diabetic Meal Plan & Recipes Downloadable is your easy go-to guide to meal planning diabetic meals the whole family will love. This comprehensive guide includes 13 weekly recipes, from dinners, lunch, snacks and dessert.
These bite size miniature sloppy Joe biscuit cups give you portion control. A diabetic diet is the healthiest way to eat and by making changes in recipe ingredients, you can create these healthy homemade snacks. Who would think these Pillsbury Biscuit Cups would be a diabetic beef recipe? Always use cuts of meat ending in a “loin” or “round” for your leanest cuts of beef. That’s why the recipe calls for ground sirloin. Use reduced-fat cheese and a smaller amount also, cut the biscuits in half. No sacrifice taste here!
There’s more uses for nonstick muffin tins besides cupcakes and muffins! These Beef Biscuit Cups make the ultimate and simple Pillsbury biscuit recipes. You can take any dish whether it is sweet or savory and make a snack version.
You will love the versatility in this recipe because all ages request these Sloppy Joe biscuit cups. The unassuming Sloppy Joe turns out to be a great comfort food and family favorite. Make the filling ahead of time and refrigerate. Then, you cook whenever you are ready to eat these scrumptious ground beef biscuits. Whether you need a quick pick up or you have a house full of kids, you have a winning recipe with the beef biscuit cups. And, they are so simple to make!
When deciding what to make for a last minute recipe on Christmas Eve recipe on TV, Holly knew these Pillsbury Biscuit Cups were just the recipe. Made with everyday ingredients you already probably have and you can whip it up in minutes, not hours. Put these on your short list for that emergency all pleasing pick up snack. Remember, this recipe is a diabetic ground sirloin recipe!
The post Beef Biscuit Cups Recipe For Easy Snack Tastes Like Sloppy Joe Biscuit Cups appeared first on The Healthy Cooking Blog.
Sulfonylureas (SUs) provide an efficacious first-line treatment in patients with hepatocyte nuclear factor 1α (HNF1A) diabetes, but SUs have limitations due to risk of hypoglycemia. Treatment based on the incretin hormones glucose-dependent insulinotropic peptide (GIP) and glucagon-like peptide 1 (GLP-1) is characterized by their glucose-dependent insulinotropic actions without risk of hypoglycemia. The effect of SUs together with GIP or GLP-1, respectively, on insulin and glucagon secretion in patients with HNF1A diabetes is currently unknown. To investigate this, 10 HNF1A mutation carriers and 10 control subjects without diabetes were recruited for a double-blinded, placebo-controlled, crossover study including 6 experimental days in a randomized order involving 2-h euglycemic-hyperglycemic clamps with coadministration of: 1) SU (glimepiride 1 mg) or placebo, combined with 2) infusions of GIP (1.5 pmol/kg/min), GLP-1 (0.5 pmol/kg/min), or saline (NaCl). In HNF1A mutation carriers, we observed: 1) hypoinsulinemia, 2) insulinotropic effects of both GIP and GLP-1, 3) additive to supra-additive effects on insulin secretion when combining SU+GIP and SU+GLP-1, respectively, and 4) increased fasting and arginine-induced glucagon levels compared with control subjects without diabetes. Our study suggests that a combination of SU and incretin-based treatment may be efficacious in patients with HNF1A diabetes via potentiation of glucose-stimulated insulin secretion.
Hepatocyte nuclear factor 1α (HNF1A) diabetes is a monogenic subtype of diabetes, also known as maturity-onset diabetes of the young (MODY) type 3 (MODY3 or HNF1A-MODY). HNF1A mutation carriers are characterized by an impaired insulin response to a glucose stimulus (1). A mutation in the transcription factor HNF1A causes impaired insulin secretion due to decreased expression of proteins involved in insulin gene transcription, glucose uptake (GLUT2), and metabolism (glycolysis and citric acid cycle) in β-cells (2). The disrupted glucose uptake and metabolism result in reduced intracellular levels of ATP, which under normal circumstances plays a vital role in glucose-stimulated insulin secretion. ATP binds to and closes KATP channels, which in turn causes membrane depolarization, initiating a cascade of events that results in secretion of insulin (2,3). Sulfonylureas (SUs) stimulate insulin secretion by enhancing ATP-independent closure of the KATP channel (4,5) and thus bypassing the low level of ATP in the pancreatic β-cells. In mechanistic and clinical studies, HNF1A mutation carriers have been demonstrated to be highly sensitive to SUs due to robust increments in insulin secretion (4,5). Clinically, this translates into a potent glucose-lowering effect when using SUs, which is why they are recommended as first-line treatment of HNF1A-diabetes (6,7). The main limitation of SU treatment in patients with HNF1A diabetes is that treatment intensification with additional glucose-lowering drugs is often needed in the long run to provide glycemic control (8). Additional limitations are problems with recurrent hypoglycemia with SUs in some patients (9,10) and that SUs may also induce body weight gain, as observed in patients with type 2 diabetes (11).
Cross-sectional studies indicate that patients with HNF1A-diabetes suffer from both microvascular and macrovascular complications to the same extent as patients with type 1 and type 2 diabetes (12,13). Thus, investigating add-on treatment to SUs is important to prevent diabetic complications. We have previously shown that HNF1A mutation carriers have impaired insulinotropic effects of the incretin hormones glucagon-like peptide 1 (GLP-1) and glucose-dependent insulinotropic peptide (GIP) compared with control subjects without diabetes (14) but, in contrast, that treatment with pharmacological doses of a GLP-1 receptor agonist in patients with HNF1A diabetes has glucose-lowering properties almost similar to SUs, with an 18-fold lower risk of hypoglycemia (9).
In the current study, we hypothesized that administration of a single dose of an SU (glimepiride) and exogenous infusions of GLP-1 and GIP, respectively, have additive effects on insulin secretion in HNF1A mutation carriers and control subjects without diabetes. The infusion rates of GLP-1 and GIP in this study were chosen to imitate postprandial plasma levels of endogenous GLP-1 and GIP during treatment with dipeptidyl peptidase 4 inhibitor (DPP-4i).
This study was approved by the Scientific Ethical Committee of the Capital Region (protocol number H-16038140) and the Data Protection Agency (HGH-2017–050, I-Suite 05657) and registered at ClinicalTrials.gov (NCT03081676). The study was conducted in accordance with the Declaration of Helsinki, and all participants gave oral and written consent before inclusion.
Ten carriers of mutations in HNF1A (7 treated with glucose-lowering therapies) and 10 control subjects without diabetes were individually matched 1:1 according to age, sex, and BMI (Table 1). HNF1A mutations had previously been established with heterozygous loss-of-function mutations verified by Sanger sequencing (Table 2). Mutations were considered pathogenic if they met at least one of the following criteria: 1) previously published reports on disease-causing effect of the specific mutation, 2) the presence of a truncating mutation, and/or 3) cosegregation of the mutation with diabetes within the family. Participants attended a screening visit after an overnight fast (10 h). Medical history, anthropometric data, and blood samples were obtained. HNF1A mutation carriers were recruited either from the diabetic outpatient clinic at Steno Diabetes Center Copenhagen, Gentofte Hospital, University of Copenhagen, or via letter to an HNF1A mutation registry located at the University of Copenhagen. Inclusion criteria for HNF1A mutation carriers were: 1) pathogenic HNF1A mutation verified by genetic testing, 2) treated with diet, SU monotherapy, or noninsulin treatments with or without SU, 3) aged ≥18 years, and 4) informed consent. Inclusion criteria for control subjects without diabetes were: 1) fasting plasma glucose ≤6 mmol/L, 2) glycated hemoglobin A1c (HbA1c) ≤6.1% (43 mmol/mol), 3) no family history of type 1 or type 2 diabetes, 4) aged ≥18 years, and 5) informed consent. Exclusion criteria in both groups were pregnancy, breastfeeding, and abnormal blood or urine biochemistry (hemoglobin, liver enzymes [alanine and aspartate aminotransferases], plasma creatinine, and urine albumin-to-creatinine ratio). Apart from the antidiabetic drugs (Table 2), none of the participants were treated with drugs suspected to influence the plasma/serum levels of glucose, insulin, C-peptide, glucagon, or incretin hormones.
Synthetic GIP and GLP-1 (Bachem, Bubendorf, Switzerland) were subjected to sterile filtration and microbiological testing and dispensed into vials by the Capital Region Pharmacy (Herlev, Denmark). The peptides were dissolved in sterilized water containing 0.5% human albumin (Statens Serum Institut, Copenhagen, Denmark). All infusions (GIP, GLP-1, and NaCl) had an identical transparent appearance.
Tablets with 1 mg glimepiride (1 mg Amaryl) (Sanofi Denmark A/S, Copenhagen, Denmark) and placebo had identical appearance and were provided by the hospital pharmacy of the Capital Region (Herlev, Denmark). Both glimepiride and placebo were gelatin capsules containing trace amounts of lactose monohydrate, potato starch, talc, and magnesium stearate.
This study was a double-blinded, crossover study with 6 experimental days (separated by a minimum of 4 days) performed in randomized order over a period of at least 3 months. Employees who were not otherwise involved in the study prepared all interventions to ensure blinding of both investigators and participants. Antidiabetic treatments were discontinued prior to each experimental day (repaglinide 24 h, glimepiride 72 h, and metformin/linagliptin/liraglutide 14 days before). After an experimental day, patients recommenced their antidiabetic treatments only if the time interval before the next experimental day was greater than the treatment-specific washout period. Participants were instructed to continue their usual diet (with at least 250 g carbohydrates the day prior to an experimental day) and avoid strenuous exercise and alcohol consumption 24 h before experimental days. After an overnight fast (10 h), the participants rested in a recumbent position, and a cannula was inserted in a cubital vein of each arm, one for infusions and one for collection of arterialized blood samples. Arterialized venous blood was obtained by a modified heated-hand technique by wrapping the forearm and hand with a heating pad (50°C) throughout the experiment (15). A tablet of 1 mg glimepiride or placebo was administered 90 min before the clamp procedures. The two-step glucose clamp consisted of: step 1 at time 0–60 min with a glucose level targeted at the fasting plasma glucose (determined as mean plasma glucose measured at time −105, −100, and −90 min) and step 2 at time 60–125 min at 1.5 × fasting plasma glucose (mimicking postprandial plasma glucose levels). From time 0 to 125 min, GIP (1.5 pmol/kg/min), GLP-1 (1.5 pmol/kg/min), or saline was infused. Infusion of glucose (200 mg/mL) was given from time 0 to 125 min at a rate adjusted according to bedside measurements of plasma glucose, performed every 5th minute. A bolus of 20% glucose was given at time 60 min for 30 s to increase plasma glucose levels to a target of 1.5 × fasting plasma glucose. At the end of the clamp (time 120 min), a bolus of 5 g l-arginine (given as 10% arginine HCl) was infused for 30 s, and from time 120 to 125 min, the rate of the glucose infusion was not changed.
Plasma glucose was measured at time −115, −100, and −90 min and every 5th minute from time 0 to 120. For bedside measurement of plasma glucose, blood was collected into fluoride tubes and centrifuged immediately for 30 s at room temperature and 7,500g. For the analysis of plasma glucagon, GIP, and GLP-1, blood was collected in chilled tubes (on ice) containing EDTA and a specific DPP-4i (valine pyrrolidine, 0.01 mmol/L) (a gift from Novo Nordisk, Måløv, Denmark). For analyses of serum insulin and C-peptide, blood was sampled in plain tubes for coagulation (20 min at room temperature). EDTA tubes and plain tubes were centrifuged for 15 min at 2,900g and 4°C. Plasma samples for glucagon, GIP, and GLP-1 were stored at −20°C and serum samples for insulin and C-peptide at −80°C until analysis.
Plasma glucose was measured bedside by the glucose oxidase method (Model 2900 Series Biochemistry Analyzers; YSI Incorporated, Yellow Springs, OH). Serum insulin and C-peptide concentrations were measured with a two-sided electrochemiluminescence immunoassay (Siemens Healthcare, Ballerup, Denmark). Plasma concentrations of total GIP (16), total GLP-1 (17), and glucagon (18) were measured by radioimmunoassays as described previously. For the GIP, GLP-1, and glucagon assays, plasma samples (EDTA) were extracted with ethanol (70% v/v) to eliminate unspecific interference.
All results in the text and figures are presented as mean ± SEM unless stated otherwise. Differences resulting in P values of <0.05 were considered significant. Area under the curve (AUC) and baseline-subtracted AUC (bsAUC) values were calculated using the trapezoidal rule. Time −105, −100, and −90 min were defined as baseline values for calculations of bsAUC0–60 min, bsAUC60–120 min, and bsAUC0–120 min. Primary end points were differences between interventions in bsAUC0–60 min, bsAUC60–120 min, and bsAUC0–120 min for C-peptide. For calculation of incremental AUC120–125 min (iAUC120–125 min), values were subtracted from the value at time 120 min. Insulin secretion rate (ISR) was calculated based on C-peptide elimination rates and deconvolution as previously described (19,20). To check whether the targeted plasma glucose levels were obtained, the AUC0–120 min for the plasma glucose/fasting plasma glucose ratio was calculated (optimal value: 150 mmol/L/mmol/L/min). Statistical analyses were carried out within each group using linear mixed models with an unrestricted covariance structure and the Kenward-Roger approximation of the df using the algorithm of y = SU × infusion × SU*infusion, in which y is the variant of interest, subject ID is random effect, infusion (GIP, GLP-1, or NaCl) and SU (SU or placebo) are fixed effects, and SU*infusion to test for interaction. To test for differences between groups, we added “group” to the algorithm (y = SU × infusion × group × SU*infusion) and tested the significance level of group. When calculating the total amount of glucose given, we adjusted for fasting plasma glucose levels for HNF1A mutation carriers. To guard against false positives, all comparisons including primary end points were adjusted for multiple testing using the Tukey multiple-comparison test. Extreme outliers were identified according to Tukey fences (21), and extreme outliers are presented explicitly in the results. All analyses were performed in SAS Studio 9.4M5 (SAS Institute, Cary, NC) and graphical presentations in GraphPad Prism 8.0 (GraphPad Software, San Diego, CA).
The data sets generated during and/or analyzed during the current study are available from the corresponding authors on reasonable request.
HNF1A mutation carriers and control subjects without diabetes were well matched according to sex, age, and BMI, and the two groups had similar HOMA of insulin resistance but differed, as expected, on fasting plasma glucose and HbA1c (Table 1). One study day in a control subject without diabetes (SU+GLP-1) and one study day in an HNF1A mutation carrier (SU+placebo) were excluded from the analysis because the randomization sequence had not been followed, resulting in erroneousness infusions (human error). Regarding glucagon, one participant without diabetes qualified as an extreme outlier and was excluded from glucagon data analyses. This participant had extraordinarily high fasting glucagon concentrations (mean 54 pmol/L, range 30–101 pmol/L) compared with other control subjects without diabetes (mean 9.4 pmol/L) but was kept in other analyses because glucagon concentrations were suppressed and insulin/C-peptide response was comparable to other control subjects without diabetes.
Mean baseline concentrations of GIP (HNF1A mutation carriers, 18 ± 3 pmol/L, and control subjects without diabetes, 18 ± 1 pmol/L; P = 0.9167) and GLP-1 (HNF1A mutation carriers, 9 ± 1 pmol/L, and control subjects without diabetes, 11 ± 1 pmol/L; P = 0.2821) did not differ between groups (Fig. 1A–D). GIP peak concentrations were similar on study days with GIP infusion in HNF1A mutation carriers (131 ± 7 pmol/L [placebo+GIP] and 123 ± 10 pmol/L [SU+GIP]; P = 0.2821) and in the group of control subjects without diabetes (124 ± 6 pmol/L [placebo+GIP] and 117 ± 6 pmol/L [SU+GIP]; P = 0.4438). Likewise, GLP-1 peak concentrations were similar on days with GLP-1 infusion in HNF1A mutation carriers (56 ± 6 pmol/L [placebo+GLP-1] and 62 ± 6 pmol/L [SU+GLP-1]; P = 0.2620) and in control subjects without diabetes (60 ± 5 pmol/L [placebo+GLP-1] and 66 ± 3 pmol/L [SU+GLP-1]; P = 0.5830).
Fasting plasma glucose was 3.7 ± 1.1 mmol/L higher in HNF1A mutation carriers compared with control subjects without diabetes (P = 0.0041), with no difference in fasting plasma glucose between experimental days within each group (Table 3). The targeted glucose concentrations (expressed as the AUC0–120 min for plasma glucose/fasting plasma glucose) during the glucose clamp procedure were achieved without differences between groups (P = 0.6135) or between study days within the two groups (Fig. 1E and F and Table 3). The amount of glucose (grams) needed to maintain the plasma glucose concentrations during the experimental days was greatest with the combination of SU+GIP and SU+GLP-1, respectively, in both HNF1A mutation carriers and control subjects without diabetes (Fig. 1G and H and Table 3).
Mean fasting C-peptide concentrations in HNF1A mutation carriers were significantly lower than in control subjects without diabetes (308 ± 16.8 vs. 387 ± 31.7 pmol/L; P = 0.0442), even though their plasma glucose was higher (Table 3). When looking across all indices of insulin secretion (C-peptide, insulin, ISR, C-peptide/glucose, insulin/glucose, and ISR/glucose), the overall trends were the same (Figs. 2 and 3, Table 3, and Supplementary Table 1); below, detailed results for C-peptide are presented. In HNF1A mutation carriers, combinations of SU+GIP and SU+GLP-1, respectively, were significantly more insulinotropic (based on C-peptide bsAUC0–60 min, bsAUC60–120 min, and bsAUC0–120 min) compared with administration of placebo+GIP, placebo+GLP-1, placebo+NaCl, and SU+NaCl (Table 3). In HNF1A mutation carriers, both placebo+GIP and placebo+GLP-1, respectively, compared with placebo+NaCl resulted in significantly greater C-peptide bsAUC0–120 min values. Other analyses (insulin, insulin/glucose, ISR, and ISR/glucose) demonstrated an insignificant insulinotropic trend. SU+NaCl was not significantly more insulinotropic compared with placebo+NaCl (in all insulin secretion parameters). In control subjects without diabetes, SU+GLP-1 was more insulinotropic (C-peptide bsAUC0–120 min) compared with all other interventions, while SU+GIP was the second most insulinotropic intervention (Table 3). In control subjects without diabetes, placebo+GLP-1 and placebo+GIP alone were more insulinotropic (C-peptide bsAUC0–120 min) compared with placebo+NaCl, while SU+NaCl was not significantly different from placebo+NaCl.
We observed a significant interaction between SU (SU or placebo) and infusions (GIP, GLP-1, or NaCl) for C-peptide (bsAUC60–120 min and bsAUC0–120 min) in both HNF1A mutation carriers (P = 0.0190 and P = 0.0294, respectively) and control subjects without diabetes (P = 0.0097 and P = 0.0078, respectively), which is indicative of a supra-additive effect of combining SU and GIP and/or GLP-1, respectively (Table 3). When looking at bsAUC0–60 min for C-peptide, no interaction was observed in HNF1A mutation carriers (P = 0.1617), while an interaction was observed in control subjects without diabetes (P = 0.0233). Regarding C-peptide/glucose, an interaction was present across all time periods (bsAUC0–60 min, bsAUC60–120 min, and bsAUC0–120 min) in both groups (Table 3). The magnitude of the interaction for C-peptide bsAUC0–120 min and C-peptide/glucose is depicted in Fig. 4. In HNF1A mutation carriers, the supra-additive effect on C-peptide was rather small (∼5–10%); however, it was substantially higher when adjusted for glucose concentrations and C-peptide/glucose (∼25–45%).
The arginine-induced maximal secretion test (Table 4 and insets in Fig. 2A and B) displayed a significantly attenuated C-peptide response in HNF1A mutation carriers compared with control subjects without diabetes. In HNF1A mutation carriers, the greatest peak and AUC120–125 min for C-peptide were observed after administration of arginine on experimental days with SU+GIP and SU+GLP-1, while in control subjects without diabetes, SU+GLP-1 was the most potent stimuli. The concentration of C-peptide at the time of the arginine administration was the most important determinant of the C-peptide response given that the difference of the iAUC120–125 min (subtracted the C-peptide level at time 120 min) is small in both groups across all study days.
Fasting glucagon concentrations were higher in HNF1A mutation carriers compared with control subjects without diabetes (11.8 ± 0.5 vs. 9.5 ± 0.8 pmol/L; P = 0.0163) (Table 3 and Fig. 3E and F). Glucagon concentrations decreased from baseline (time −100 min and −90 min) to time 0 min regardless of SU or placebo administration, and the difference between groups was abolished at time 0 min (HNF1A mutation carriers, 8.9 ± 0.9 pmol/L, vs. control subjects without diabetes, 7.4 ± 1.0 pmol/L; P = 0.2777). The glucagon concentrations decreased with increasing glucose concentrations in both HNF1A mutation carriers and control subjects without diabetes. There were no significant differences in bsAUC0–120 min for glucagon between interventions in any of the groups. We observed an insignificant trend toward a greater decrease of glucagon concentrations on days with placebo+GLP-1 and SU+GLP-1, while the smallest decrements in glucagon levels were observed with SU+GIP in both groups (Fig. 3G and H and Table 3).
The arginine-induced glucagon levels were significantly higher in HNF1A mutation carriers compared with control subjects without diabetes evaluated as peak (P = 0.0215), AUC120–125 min (P = 0.0093), and iAUC120–125 min (P = 0.0332) for glucagon (Table 4 and insets in Fig. 3E and F). In both groups, there was no difference between experimental days.
This study investigates the insulinotropic properties of a combination of SU with infusions of either GIP or GLP-1 in HNF1A mutation carriers. The primary finding is that SU combined with GIP or GLP-1 increases C-peptide concentrations in an additive to supra-additive fashion in HNF1A mutation carriers, indicating that a combination of SU and incretin-based therapy may have synergistic effects in the treatment of patients with HNF1A diabetes.
Despite the fact that most patients with HNF1A-diabetes eventually need additional treatment on top of SU, no study has evaluated potential second-line glucose-lowering agents (4,10,14). In the current study, infusion rates of exogenous GIP and GLP-1 were chosen to result in plasma levels seen during treatment with a DPP-4i. A considerable strength of this study is the design, in which we isolate the effects of GIP, GLP-1, and SU on the endocrine pancreas from that of glucose using a two-step glucose clamp. Another strength is the placebo-controlled crossover design, which reduces the intraindividual differences. Considering the rarity of HNF1A mutations, it is also a strength that none of the HNF1A mutation carriers were related. A limitation to our study is the heterogeneity of the HNF1A mutation carriers regarding their diabetes status, fasting plasma glucose, and oral glucose-lowering treatment, which included incretin-based treatment. Our study was powered to detect changes in C-peptide levels but may not be powered adequately to detect changes in glucagon.
We demonstrate a significant insulinotropic effect evaluated as C-peptide bsAUC0–120 min in the current study when using supraphysiological doses of both GIP (1.5 pmol/kg/min) and GLP-1 (0.5 pmol/kg/min). This is in line with a previous study by Vilsbøll et al. (22) that found a significant insulinotropic effect of exogenous infusions of GIP (4 pmol/kg/min) and GLP-1 (1 pmol/kg/min), respectively, compared with saline during a 2-h hyperglycemic clamp (15 mmol/L) in patients with HNF1A diabetes. Together, GIP and GLP-1 are responsible for the incretin effect (i.e., the amplification of insulin secretion with an oral glucose challenge compared with isoglycemic intravenous glucose infusion). Østoft et al. (9,14) described impaired incretin effect in HNF1A mutation carriers and that a GLP-1 receptor agonist has glucose-lowering actions with low risk of hypoglycemia in patients with HNF1A diabetes. Taken together, studies investigating the effect of incretins in HNF1A mutation carriers indicate that a diminished activation of both GIP and GLP-1 receptors contribute to impaired insulin responses and thus hyperglycemia, but that GIP and GLP-1 receptors may constitute viable treatment targets during elevated plasma levels of the peptides seen with incretin-based therapies such as DPP-4i (GIP and GLP-1) and GLP-1 receptor agonist (GLP-1).
Interestingly, during the hyperglycemic part of our clamp study, we found a supra-additive effect on insulin secretion with the combination of SU+GIP and SU+GLP-1 in both HNF1A mutation carriers and control subjects without diabetes. This observation could be explained by a combined effect on the KATP channel by SU, GIP, and GLP-1. SU binds directly to the KATP channel, while GIP and GLP-1 receptor activation increase levels of cAMP, in turn activating protein kinase A, which increases the sensitivity of the KATP channel to ATP (23). The combined actions increase the likelihood of KATP channel closure and depolarization and subsequently increased insulin release. In addition, acute GIP and GLP-1 receptor activation also increase insulin secretion via several other mechanisms than the KATP channel (23), and chronic GLP-1 receptor stimulation has been shown to increase glycolysis and ATP production in β-cells through transcriptional activation and expression of glycolytic genes (24). Whether this is the case in HNF1A mutation carriers is unknown; however, case series have indicated remarkable HbA1c reduction and increase peak insulin levels during an intravenous glucose tolerance test in patients with HNF1A-diabetes when adding a DPP-4i to SU (25). In patients with type 2 diabetes, the glucose-lowering effect of DPP-4i is mainly attributed to the increase of GLP-1 levels because the insulinotropic effect of GIP is severely diminished (26); however, our study indicates that GIP could mediate the glucose-lowering effects of DPP-4i to a greater extent in patients with HNF1A diabetes.
Unexpectedly, we observed a greater mean fasting and arginine-induced glucagon concentration in HNF1A mutation carriers compared with control subjects without diabetes. Our study is the first to indicate increased fasting glucagon concentrations in HNF1A mutation carriers compared with control subjects without diabetes, to our knowledge. In addition, postprandial hyperglucagonemic responses during meal tests have also been observed in HNF1A mutation carriers (9,10,14). The observation of hyperglucagonemia in fasting, postprandial, and after arginine infusion could indicate an altered secretion pattern of glucagon in HNF1A mutation carriers.
To our knowledge, the effect of an HNF1A mutation on α-cell functions has not been investigated in either animals or cell lines. The nature of postprandial hyperglucagonemia could theoretically be due to insufficient glucose sensing in the glucagon-producing α-cells (like that observed in β-cells); however, the normal suppression of glucagon during intravenous glucose infusion in our and other studies contradicts this (14,22). Insulin is also known as an inhibitor of glucagon secretion (27), which is why hypoinsulinemia could disrupt the normal paracrine signaling between β- and α-cells, resulting in increased glucagon concentrations. Finally, an explanation could be that the total α-cell mass or α- to β-cell ratio could be increased. A study of a single pancreatic human islet from a 33-year-old diseased donor with HNF1A diabetes displayed elevated α-cell mass and increased α/β-cell ratio compared with seven control subjects without diabetes (28). Arginine induces maximal glucagon release from α-cells via a mechanism independent of both glucose metabolism and KATP channel (29,30) and is thought to be correlated with total α-cell mass (31). Thus, our study could potentially be in line with an increased α-cell mass; however, our data could just as well indicate increased glucagon secretory capacity of α-cells.
In patients with type 2 diabetes, both exogenous GLP-1 infusions and GLP-1 receptor agonists show glucagonostatic properties alleviating postprandial hyperglucagonemia (32). On the contrary, the hyperglucagonemia of HNF1A mutation carriers does not seem to respond to GLP-1, as we did not see a decrease in glucagon with GLP-1 infusion, nor did 6 weeks of GLP-1 receptor agonism change fasting or postprandial glucagon concentrations (9). The elevated plasma glucagon concentrations in HNF1A mutation carriers most likely add to their state of diabetes, and future studies should investigate the role of glucagon in more detail.
We investigated the insulinotropic properties of SU in combination with incretin hormones in HNF1A mutation carriers and report additive to supra-additive effects on insulin secretion with the combination of a low-dose SU with either GIP or GLP-1. We also report increased fasting and arginine-induced levels of glucagon in HNF1A mutation carriers. Our results and previous work indicate that targeting the GIP and/or GLP-1 receptors in combination with SU therapy may constitute a viable strategy for the management of hyperglycemia in patients with HNF1A diabetes. An ongoing clinical trial (EudraCT no. 2017-000204-15) is investigating the efficacy and safety of combined SU and DPP-4i therapy (33).
Acknowledgments. The authors thank the study participants for commitment and loyalty, Sisse Marie Schmidt and Inass Al Nachar (Steno Diabetes Center Copenhagen, Gentofte Hospital, Hellerup, Denmark) for laboratory assistance, and coworkers in our department for valuable advice and support.
Funding. The clinical study was performed at Steno Diabetes Center Copenhagen (Gentofte Hospital, Hellerup, Denmark) and supported by private foundations: Aase og Ejnar Danielsens Fond, Fonden til Lægevidenskabens Fremme, Aage og Johanne Louis-Hansens Fond, and Axel Muusfeldts Fond. The Novo Nordisk Foundation Center for Basic Metabolic Research is an independent research center at the University of Copenhagen partially funded by an unrestricted donation from the Novo Nordisk Foundation. J.J.H. was supported by the Novo Nordisk Foundation.
Duality of Interest. H.S. has served on scientific advisory panels for Boehringer Ingelheim and Novo Nordisk. J.J.H. has served on advisory boards for Novo Nordisk. F.K.K. has served on scientific advisory panels and/or been part of speakers bureaus for, served as a consultant to, and/or received research support from Amgen, AstraZeneca, Bayer, Boehringer Ingelheim, Carmot Therapeutics, Inc., Eli Lilly and Company, Gubra, Lupin Limited, MedImmune, LLC, Merck Sharp & Dohme/Merck, Mundipharma, Norgine, Novo Nordisk, Sanofi, and Zealand Pharma. T.V. has served on scientific advisory panels and/or speakers bureaus for, served as a consultant to, and/or received research support from Amgen, AstraZeneca, Boehringer Ingelheim, Bristol-Myers Squibb, Eli Lilly and Company, Merck Sharp & Dohme, Mundipharma, Novo Nordisk, Sanofi, and Sun Pharmaceutical Industries Ltd. No other potential conflicts of interest relevant to this article were reported.
Author Contributions. A.S.C., S.H., H.S., T.H., F.K.K., and T.V. designed the study. A.S.C. wrote the study protocol. A.S.C., K.R., and N.L.H. performed the study. J.J.H. generated data. A.S.C. and T.V. performed the data analysis and wrote the manuscript. All authors critically edited the manuscript and approved the final version. A.S.C. and T.V. are the guarantors of this work and, as such, had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.
Prior Presentation. Parts of the results from this study were presented at the Annual Meeting of the Danish Endocrine Society, Nyborg, Denmark, 18–19 January 2019; the 79th Scientific Sessions of the American Diabetes Association, San Francisco, CA, 7–11 June 2019; the 3rd EASD Incretin Study Group of Diabetes, Bochum, Germany, 24–26 January 2019; and the 7th Meeting of the EASD Study Group on Genetics of Diabetes, Prague, Czech Republic, 16–18 May 2019.
This delicious spinach smoothie is made with nut butter, yogurt, and avocado for extra creaminess. It’s a great way to add some leafy greens to your morning!
Are you looking for a healthy way to start your day that can be ready in about 5 minutes or less? This spinach smoothie is just what you need!
It’s a great way to kick start your morning with some leafy greens and healthy fats. The texture is ultra-creamy thanks to the nut butter, Greek yogurt, and avocado, and the taste is lightly sweet with a hint of vanilla.
Basically, it’s the perfect way to start the day. And all you have to do is throw everything in your blender!
Give your morning a boost with this delicious, healthy, and crazy-easy smoothie recipe.
This low-carb smoothie comes together in just 4 simple steps with less than 10 ingredients.
Step 1: Add all of the ingredients except the ice to a blender.
Step 2: Blend until smooth.
Step 3: Add the ice and pulse until the ice is mostly crushed.
Step 4: Blend until the mixture is smooth and creamy.
I like to top mine with some finely-chopped shredded coconut!
There are a few different ways you can make this smoothie based on your tastes and preferences. Feel free to customize it to you!
Don’t have any spinach on hand? Substitute with another leafy green! Kale, chard, or even beet greens would all be delicious in this recipe.
Looking for a vegan-friendly option? Use a vegan brand of Greek yogurt and your favorite kind of nut milk!
Want to add a little tartness? Some lemon juice would give your smoothie a vibrant, bold flavor that would be wonderful on a hot summer’s day.
Have some fun and find the recipe that’s perfect for you!
This recipe is for 2 servings. If you only plan to drink 1 at a time, just store the extra serving in an airtight container in the refrigerator and enjoy it within a day or so.
You can also make your smoothie the night before and store it in the refrigerator so it’s ready to grab-and-go in the morning!
Smoothies are great as a nutritious breakfast or tasty afternoon snack. Honestly, there’s never a bad time to enjoy one! And when you make it at home, you’ll know it isn’t packed with unnecessary sugar or artificial ingredients.
Here are a few of my favorite healthy smoothie recipes I know you’ll love:
When you’ve tried this smoothie, please don’t forget to let me know how you liked it and rate the recipe in the comments below!
This delicious spinach smoothie is made with nut butter, yogurt, and avocado for extra creaminess. It’s a great way to add some leafy greens to your morning!
Add all of the ingredients except the ice to a blender.
Blend until smooth.
Add the ice and pulse until the ice is mostly crushed.
Blend until the mixture is smooth and creamy.
Spinach Smoothie (Low-Carb & Gluten-Free)
Amount Per Serving (250 mL)
Calories 236 Calories from Fat 145
% Daily Value*
Saturated Fat 2.7g17%
Trans Fat 0g
Polyunsaturated Fat 0.7g
Monounsaturated Fat 3.5g
Net carbs 6.6g
* Percent Daily Values are based on a 2000 calorie diet.
This post will help you be able to cook a tomahawk steak at home like a pro, pinky swear.
No Labor Day Weekend would be complete without grilling up some meat.
Mariano’s had Tomahawks steaks on sale for $16 a pound!
A Tomahawk steak is essentially a ribeye.
What makes it so expensive is how it is cut, and you are paying for the bone, which I love to buy because a: it looks cool; and b: my granddogs get the bones 😁
I can’t tell you how much money I have saved being in quarantine and not being able to go to my beloved Chicago restaurants at least twice a week during the summer. So I splurged and it was worth every penny.
Early on in my bbq career I would scour websites and YouTube videos to see how long to cook certain cuts of beef, but there is so much more to know – and mainly – what temperature to cook your beef. Most charts looks something like this (thanks google images!)
Notice for rare beef it says 125. But if you were to cook your beef to 125, by the time it rests, the residual heat will bring it up to 135 which is medium rare. Which is still perfect for beef in my opinion. However if you were to cook your beef to medium rare at 135, by the time it rested you would be at medium at 145. Don’t waste a $44 steak and cook it to medium. You lose all the awesome flavor.
That being said, this is a no judgment bbq zone – if you like your beef well done or medium well or well, that’s your business. 😉
This is a big mistake a lot of people make when grilling. When to SALT is critical. Salt is your best friend in grilling because it brings out amazing flavor. But in order for the salt to work for you, you have to know when to season the meat.
A rule of thumb is this:
You may be thinking to yourself “why does that matter?” Well the first two minutes the salt sits on top of the meat, then goes to the grill and sears to the beef. Nice. You’ll get an awesome salty flavor.
But starting at just 3 minutes in of salting the beef, the salt starts to pull out the moisture of the beef. If you cook your beef after 3 minutes or before 40 minutes, the pulled out moisture will either steam your beef in a cast iron skillet and you won’t get that gorgeous crust, or you’ll put it on the grill and all the juices run out on the coals and your beef won’t be as juicy.
After 40 minutes, the juices will have had time to reabsorb into the meat and you are good to go.
I cannot tell you how many times I brought in raw chicken or beef in from the grill, or overcooked meat because I thought I could tell my look or touching the meat that it was done. 🤷🏻♀️
I have a Cuisinart one (not sponsored – bought at Bed Bath & Beyond with a 20% off coupon) that has dual temperature gauge so I can grill two meats at once, and it also has an app on my phone so I can see what temp I am at inside the house.
This one is great if you just want an instant thermometer.
I am a strong proponent of doing a reverse sear. This basically means that you cook the beef at a slower temperature for longer, and then finish it off under high heat to get a nice sear. This method has never failed me.
Since this is a ribeye and has a bit of fat, I cook it to medium rare. I pulled these steaks off my Pit Barrel Cooker at 115 degrees, then reverse seared them on my Weber grill so that the resting temperature was 125. Once cooled, they came in at 132 degrees.
For chops this size, I rested the beef for 20 minutes before slicing. Whole roasts? I let rest for at least 45 minutes if not an hour. Filet mignon and New York strips? Only 5-10 minutes is needed.
Thank you Hannah for that gorgeous photo of the reverse sear!
Mariano’s also had lobster tails on sale for $4.99 – I cooked them thawed for just 5 minutes in boiling water. My plate:
Have you ever tried cooking a Tomahawk steak? I promise if you follow these easy steps, you’ll be the grill master in your family in no time.
If you have any questions, please leave them in the comment section!
I’ve been using the Dario Glucose Monitoring System for a while now and I’ve been very impressed with the consistent accuracy, size of the device, and how price competitive the solution is.
But there’s so much more to the Dario system than just the accuracy and the price, so let me take you through some of the main benefits that really set the Dario system apart from other offerings on the market.
This post is sponsored by Dario Health, but all opinions are my own and are based on my use of the product.
The FDA-cleared Dario Blood Glucose Monitoring System is the only glucose monitoring system I know where the glucometer, lancing device, and test strips are all combined into one device. That means no more fumbling around after your test strips or lancing device. They are right there, embedded into the same device as your glucometer.
The entire device is smaller than most glucose meters and easily fits in your pocket or purse.
Dario also offers a travel case and a smaller carrying case for when you’re just out and about.
The Dario glucometer is the heart of the system and it’s a completely different design from any other glucometer available. It’s small, only about the size of the tip of my thumb, and works with your smartphone (compatible with iOs and Android).
You simply click it out of the device and plug it into your phone and it automatically links up with the free mobile app so that you’re ready to measure your blood sugar in seconds.
Once the app is open, it will prompt you to insert a test strip and apply a small blood drop. The lancing device can be set to penetrate at different depths to give almost painless finger pricks. That also means that you can adjust the depth depending on what finger you’re measuring from or how much blood flow you have to your fingers (you might need to use the deeper dept when your fingers are cold).
Once you apply the small blood drop (only 0.3µl),your blood sugar will show up on the screen in the app in only 6 seconds. Brilliant, fast, and highly accurate.
When I first started using the Dario Blood Glucose Monitoring System, I was instantly impressed with how consistently accurate the blood sugar readings are. My previous glucometers would often give me dramatically different reading depending on how much blood I managed to put on the strip. That has not been the case with Dario.
You can see exactly how I use the Dario system in this short video
Aside from giving you visual cues on when to insert the test strip and blood drop, the MyDario app offers extensive reporting that can help you manage your diabetes. All blood sugar measurements are automatically recorded in the app and you can easily add tags such as activity, carbs, and medications.
And if you need a little support counting your carbs, the app has a built-in database of 500,000 food items. You can also add a photo of your meals to accompany a blood sugar reading.
The app can be set up to send you personalized reminders, alerts, and guidance or you can simply view trends through detailed weekly reports.
The whole Dario system is of course HIPPA/CCPA compliant so you don’t have to worry about your data privacy.
I’m a complete data geek (I find my blood sugar patterns fascinating) and one of my favorite parts of the system is this app because it allows me to easily track blood sugar fluctuations and trends. And once you see trends, you can start being more proactive about your diabetes care and work on reducing episodes of hyperglycemia or hypoglycemia (high and low blood sugar).
Not everyone is comfortable adjusting their own diabetes care, which is why it’s brilliant that the data stored in the MyDario app can easily be shared with your medical team, caregivers, and family members.
An additional safety and peace of mind functionality is the built-in emergency hypo alert. If enabled, it will share your blood sugar and your GPS location with up to four people by text message. That means that if you measure a low blood sugar and need help, the ones receiving the alert will know what’s going on and where to find you.
Another great feature of the Dario Glucose Monitoring System is that you can get access to the Dario coaches.
The Dario coaches are health and wellness advisers that offer support and motivation to help manage your condition and stay on track. Your coach will teach you how to use the app so that you get the most out of your system and they can review your health management, talk about your challenges, and support your goal setting.
You’ll have access to your coach through the in-app chat or scheduled phone calls
Yes, I like the Dario Glucose Monitoring System but it has to also statistically deliver on its promises of improved care, so I looked up some of the scientific studies that have been presented at different conferences such as ADA, ADCES, DTM, and AATD.
I was pleased to see that in these studies, data shows that using the Dario system can reduce A1c and Hypo and Hyperglycemic events, as well as increase in-range measurements.
Data presented at the American Diabetes Association’s 78th Scientific Sessions on June 25, 2018, showed the results of 3 different studies on people living with type 2 diabetes.
In the largest study (17,156 people), 19% saw a reduction in high blood sugar episodes and an 11% increase in reading within 70-180 mg/dL. The most significant shift in blood sugar management was seen for the users using the system for a month or more
Another study with 225 people living with type 2 diabetes showed that they experienced a decrease in significantly high blood sugar episodes by 58%
With the Dario Glucose Monitoring System, you only pay for what you need so there are different package options. You don’t go through your insurance (although the system is HSA/FSA eligible) but order directly through the Dario app or website.
You can, of course, just order test strips when you need them like I do (100 strips is $59.99), or you can sign up for one of the 3 “diabetes success” plans and get unlimited test strip refills.
What’s really cool is that the price is similar to the co-pay I used to pay for my strips through my insurance. So now I’m getting a better product, shipped directly to my door at no extra costs.
These plans range from $25 to $70 per month. All plans include unlimited test strips, but the Pro and Premium plans also include more hands-on care with access to the Dario coaches and CDEs (Clinical Diabetes Educator).
Because everything is integrated into one system, Dario knows when you’re running low on test strips and will automatically send you more strips directly to your mailbox.
So you never have to run out of test strips again!
Looking for an easy, fresh and delicious way to get in those nutritious veggies? Try roasting as it brings out the explosion of flavor in this delectable vegetarian dish, Roasted Summer Vegetables and Pasta from Gulf Coast Favorites cookbook. You will not believe how simple this gourmet-style meal is to create. Get yourself a sous chef or look for pre-chopped veggies in your supermarket and it’s basically a one-step recipe!
You are eating the rainbow in this hearty veggie dish. Squash alone provides vitamins A, B and C plus fiber, calcium, iron and folate. Grape tomatoes also have vitamins A and C – both powerful antioxidants – vitamin A is needed for eye, bone and skin health while vitamin C promotes immunity and healing. Not to mention, tomatoes are rich in lycopene the valuable antioxidant found to reduce the risk of cancer.
Calories 189 | Calories from fat 29% | Fat 6 g, Saturated Fat 1 g | Cholesterol 2 mg | Sodium 38 mg, Carbohydrate 28 g | Dietary Fiber 2 g | Sugars 5 g | Protein 6 g, Diabetic Exchanges1 1/2 starch | 1 vegetable | 1 fat
While you are at it, choose whole grain pasta over regular pasta for more vitamins, minerals and fiber per serving. They are both interchangeable in recipes as they are prepared the same way; however whole wheat pasta provides 6 grams of fiber per serving versus only 2.5 grams from regular. This will help weight maintenance as fiber helps you feel satisfied and full longer, contributes to a healthy digestive system and may also help lower cholesterol and regulate blood sugar.
These vegetables are in season in the summer, you can find them year round. Make this using your favorite veggies and whatever you find in season. Roasting is always a delicious healthy cooking method!
If you want healthy southern recipes, turn to Gulf Coast Favorites cookbook. All of Holly’s cookbooks have a ‘Pantry Stocking Guide’ to help stock your pantry and help you whip up healthy meals in a hurry! “A well-stocked pantry is like a permanent shopping list!” Then, you can cook any meal in 30 minutes.
The post Easy Vegetarian Gourmet – Roasted Summer Vegetables and Pasta Recipe appeared first on The Healthy Cooking Blog.
The 1918 “Spanish” influenza A (H1N1) pandemic, which caused ∼50 million deaths worldwide, was notable for being fatal to young and healthy subjects aged between 20 and 40 years (1). By contrast, the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which causes coronavirus disease 2019 (COVID-19) and has already killed over 400,000 people worldwide in 4 months, is targeting older subjects with chronic medical comorbidities. This Perspective discusses a paradigm currently unfolding in which old age, chronic metabolic disease (such as obesity, type 2 diabetes, and metabolic syndrome), and male biological sex produce a permissive environment of dysregulated immunometabolism and chronic systemic inflammation that allows SARS-CoV-2 to unleash acute and deadly hyperinflammation. It is intended to highlight research gaps, inspire new research directions, and stimulate funding in this field.
To determine why COVID-19 is especially lethal in those with metabolic disease and particularly impacts older males, we must first understand the pathogenesis of SARS-CoV-2 virally driven hyperinflammation. Our current understanding of the disease is that most of the critically ill and fatal cases did not develop severe clinical manifestations in the early stages. Rather, COVID-19 patients deteriorated suddenly in the later stages of the disease. What appears to be specific to SARS-CoV-2 is the host response that fails to launch a robust interferon-I and -III innate antiviral response to control virus replication (2). Instead, the immune response produces high levels of chemokines to recruit effector inflammatory cells (2). This inappropriate immune response with outpouring of inflammatory chemokines results in lung infiltration and hyperactivation of monocytes and macrophages producing proinflammatory cytokines (such as interleukin-6 [IL-6], IL-8, and IL-1β and tumor necrosis factor-α [TNFα]) and chemokines (such as CCL2, IFNγ-induced protein 10 [IP-10], and CCL3) (2–5). This increased local production of cytokines and chemokines, or “cytokine storm,” attracts more inflammatory neutrophils and monocytes into lung tissue, producing edema and reduced gas exchange in the alveoli, leading to acute respiratory distress syndrome (ARDS) (3,6,7). Ironically, the cytokine storm is a result of well-intentioned but imbalanced efforts by the immune system to protect the host, which results in ARDS and ultimately multiorgan failure. Elevated IL-6 is believed to be central to the development of the cytokine storm (8,9). Notably, in mouse and primate models of severe acute respiratory syndrome coronavirus (SARS-CoV) infection (a closely related coronavirus), the cytokine storm is more severe and deadly in old animals than in young, despite similar virus replication (10,11). In the original series from Wuhan, China, patients developing ARDS exhibited biological features of a cytokine storm (5). Compared with moderate cases, severe cases exhibited higher serum levels of C-reactive protein (CRP), ferritin, and d-dimer as well as markedly higher levels of IL-6, IP-10, CCL2, and TNFα (4,5). As we will argue below, the chronic, low-grade systemic inflammation (e.g., meta-inflammation) with elevated IL-6 that characterizes older male subjects with obesity, type 2 diabetes, or hypertension in the context of metabolic syndrome provides a permissive inflammatory environment that intensifies the rapid development of this cytokine storm.
COVID-19 presents as a severe viral pneumonia with ARDS, but surprisingly, its severity and mortality are not more pronounced in subjects with chronic pulmonary disease or heart disease. Rather, it is especially pronounced in subjects with type 2 diabetes, obesity, and hypertension. Hypertension is not an isolated condition. It is usually part of a metabolic syndrome, which also includes abdominal obesity, fasting hyperglycemia, and dyslipidemia and predisposes to type 2 diabetes (12).
Since the beginning of the SARS-CoV-2 outbreak, diabetes and hypertension were present in ∼25% and 35% of fatal cases in China (13,14) and 36.5% and 48% in Korea (15), respectively. In contrast, cardiac and chronic pulmonary disease were present in only 10% and 9% of fatal cases in Wuhan (13) and 16% and 17.5% in Korea, respectively (15). Diabetes was also present in 35% of fatal cases in Italy (16) and 58% of critically ill cases in Seattle (17). Although obesity rates were not reported in China, the BMI of critically ill patients transferred to intensive care was significantly higher than that of the general group (25.5 kg/m2 vs. 22.0 kg/m2) (18). Similarly, the BMI of individuals in the Seattle series was 33 kg/m2 on average, suggesting that most patients were overweight or obese (17). A retrospective study of 5,700 patients hospitalized for COVID-19 in New York, the epicenter of the outbreak in the U.S., reported that obesity, diabetes, and hypertension were also predominant and present in 42%, 34%, and 57% of cases, respectively (19). In contrast, cardiac (coronary artery disease and heart failure) and chronic respiratory (asthma and chronic obstructive pulmonary disease) disease were present in only 18% and 14%, respectively (19). As of 12 May 2020, the New York State Department of Health reported that diabetes and hypertension were present in 36% and 55% of fatal cases, respectively, compared with only 23% for coronary artery disease and chronic obstructive pulmonary disease combined (20). In Louisiana, the epicenter of death rates per capita in the U.S. in April 2020, diabetes, obesity, and hypertension were present in 35%, 19%, and 57% of fatal cases, respectively (21). In contrast, cardiac and chronic pulmonary disease were present in only 18% and 11%, respectively (21). In a report not yet certified by peer review of over 4,000 patients from New York City, obesity was considered a more severe risk factor for hospitalization than the presence of chronic pulmonary or heart disease (22). In summary, among severe and deadly COVID-19 cases, diabetes, obesity, and metabolic syndrome are generally present in higher proportion than any other comorbidities.
A question then arises: what makes metabolic diseases more fatal systemic environments for COVID-19 outcomes than severe chronic pulmonary or heart disease, in which lung and heart functions are already diminished? One possible explanation is that obesity, type 2 diabetes, and hypertension in the context of metabolic syndrome all share a common hallmark: an increased adiposity associated with chronic, low-grade systemic inflammation or meta-inflammation (23). Meta-inflammation develops following activation of resident macrophages in adipose tissue, promoting the recruitment of M1-polarized macrophages, which display a more proinflammatory phenotype (24), increasing the production of proinflammatory cytokines like TNFα, IL-6, and chemokines, locally and systemically. There are increases in white blood cell counts, acute-phase proteins such as CRP, and plasma levels of coagulation factors (fibrinogen, d-dimers) (25). This adipose inflammation is characterized by a type 1 immune response (Th1) that is usually activated acutely in response to infection. In the case of obesity, this immune response in adipose tissue is chronic and involves effector T cells, B cells, and natural killer (NK) cells that produce cytokines orchestrating the accumulation and activation of proinflammatory M1 macrophages (23). Additionally, obesity and Western diet also alter gut microbiota and increase intestinal permeability (26,27). This is associated with translocation of bacteria and lipopolysaccharide from intestine toward blood and adipose tissue, creating continuous metabolic endotoxemia, which fuels meta-inflammation (26,27). Meta-inflammation via IL-6 and other proinflammatory factors may skew the immune system to enable SARS-CoV-2 to unleash its deadly inflammatory complications, as will be discussed below.
In the past few decades, two other coronavirus outbreaks, the SARS-CoV outbreak of 2003 in Hong Kong and the Middle East respiratory syndrome coronavirus (MERS-CoV) outbreak of 2013 in Saudi Arabia, have caused deadly pneumonias. In both outbreaks, diabetes was associated with increased mortality. Notably, in patients with MERS, diabetes was the strongest factor predicting mortality (reviewed in Drucker ). The impact of diabetes on COVID-19 outcomes has been reviewed based on studies available as of April 2020. Diabetes increases risk of intensive care requirement by two- to threefold, as well as mortality rates, compared with the overall population (29). The mechanisms by which diabetes aggravates COVID-19 outcome are still unclear. Indirect evidence suggests that uncontrolled hyperglycemia could to play a role (29), and improved glycemic control is associated with better outcomes in Chinese patients with COVID-19 and preexisting type 2 diabetes (30). In contrast, in a French multicenter observational study of over 1,300 patients with diabetes and hospitalized for COVID-19, long-term glycemic control (HbA1c) was not associated with disease severity (intubation or mortality at day 7) (31). In contrast, obesity was independently associated with COVID-19 severity. Additionally, systemic inflammation (measured by CRP) was also independently associated with early death (31). Thus, the influence of meta-inflammation in individuals with diabetes could play an important role in COVID-19 severe outcomes. A case series from China reported that hospitalized COVID‐19 patients with diabetes as the only comorbidity exhibited uncontrolled systemic inflammation characterized by elevated leukocyte-to-lymphocyte ratio, CRP, ferritin, and IL-6, as well as a state of hypercoagulability with elevated d-dimer and fibrinogen, compared with those without diabetes (32). Importantly, IL-6 is a strong predictor of disease severity and evolution toward a cytokine storm (8,9). Elevation in ferritin also indicates the activation of the monocyte-macrophage system, which is a crucial part of the inflammatory storm (9,33). This suggests that the meta-inflammation characteristic of patients with type 2 diabetes produces a permissive, dysregulated inflammatory support that facilitates the development of the inflammatory cytokine storm, thus accelerating the deterioration of COVID‐19 patients. Studies are needed to determine the individual contribution of inflammation driven by hyperglycemia, insulin resistance, or obesity in the enhanced mortality of COVID-19 patients with type 2 diabetes.
The influence of obesity in fatal SARS-CoV-2 infection is clearly documented and reinforces the hypothesis that excess adiposity and associated meta-inflammation are central to COVID-19 evolution toward a cytokine storm. In a study involving 3,615 COVID-19 patients in New York City, obese patients aged <60 years with a BMI between 30 and 34 kg/m2 were two times more likely to be admitted to the intensive care unit for ARDS compared with individuals with a BMI <30 kg/m2. Likewise, severely obese patients with a BMI >35 kg/m2 were three times more likely to be admitted to intensive care compared with patients in the same age category with BMI <30 kg/m2 (34). In a Chinese cohort of 383 COVID-19 patients from Shenzhen, those who were overweight (BMI 24.0–27.9 kg/m2) and obese (BMI ≥28 kg/m2) exhibited 1.84-fold and 3.40-fold odds, respectively, of developing severe COVID-19 (respiratory failure) compared with normal-weight patients (BMI 18.5–23.9 kg/m2) after adjustment for age, sex, and multiple comorbidities (30). In another Chinese study involving 150 patients with COVID-19, obesity was associated with a threefold increased risk of disease severity after multiple adjustments. A nearly linear relationship between higher BMI and severe illness was shown with each 1-unit increase in BMI associated with a 12% increase in the risk of severe COVID-19 (35). Further, in a French cohort of 126 patients from Lille, COVID-19 severity (defined by respiratory failure requiring mechanical ventilation) was directly correlated with BMI. This pattern was not observed in 306 obese control subjects with severe acute respiratory disease unrelated to SARS-CoV-2 (36). Taken together, these studies suggest that the increase in adipose tissue mass in obese individuals with SARS-CoV-2 infection specifically aggravates COVID-19 pneumonia compared with other causes of severe acute respiratory disease. The effect of obesity is likely to be correlated to the amount of adipose tissue, independently from reducing lung capacity and ventilation and probably via increasing circulating factors. Consistent with a systemic role of adipose tissue in COVID-19 mortality, and as discussed above, the BMI of critically ill patients in Wuhan was higher than that of control subjects but below the obese range, even for Asian standards (25.5 kg/m2 vs. 22.0 kg/m2) (18), further arguing for a systemic role of adipose tissue. Obese individuals exhibit a state of meta-inflammation, which, as discussed for type 2 diabetes above, may exacerbate SARS-CoV-2–induced immune-inflammatory reaction. Additionally, obese subjects exhibit hyperleptinemia. Elevated leptin levels in obese people may contribute to worsening of ARDS. Leptin has structural similarities with IL-6, and the leptin receptor, OBR, is a member of the class I cytokine receptor family, which includes the receptor for IL-6 (37,38). Leptin acts as a potent inflammatory cytokine and stimulates innate immune responses by promoting the activation of monocytes/macrophages and chemotaxis and activation of neutrophils (38). Leptin also stimulates the production of IL-6 by cultured human airway epithelial cells, thereby exacerbating inflammation. Finally, leptin acts on the leptin receptor in cultured human lung fibroblasts to increase production of proinflammatory cytokines such as IL-6, chemokines involved in airway inflammation (i.e., CCL11/eotaxin, CCL2/MCP-1, CXCL8/IL-8, and CXCL10/IP-10), and the soluble vascular cell adhesion protein 1 (soluble VCAM-1), an important adhesion molecule in the process of recruitment of inflammatory cells (39). Notably, in humans and mice with pneumonia unrelated to COVID-19, hyperleptinemia is associated with rapid progression toward a cytokine storm with ARDS compared with control subjects with pneumonia but without evolution to ARDS (40,41). Thus, hyperleptinemia in severely obese individuals may act as a proinflammatory cytokine in the lung and add to meta-inflammation to facilitate the development of a cytokine storm.
Men with COVID-19 have a uniformly more severe outcome than women. Previous coronavirus outbreaks exhibited the same apparent male predominance. During the first SARS-CoV outbreak, among 1,755 hospitalized patients in Hong Kong, the case fatality rate was 21.9% in men compared with 13.2% in women, with a relative risk of 1.66 (95% CI 1.35, 2.05) for men compared with women (42). During the MERS-CoV outbreak in Saudi Arabia, among 425 reported cases, disease occurrence was higher among men (62% of cases) (43). The case fatality rate was also higher for men (52%) than for women (23%).
Today, in China, Europe, and the U.S., COVID-19 mortality is consistently 1.5- to 2-fold higher in men than in women (13,15,16,19,44,45). The Global Health 50/50 research initiative website at University College London provides real-time sex-disaggregated data on COVID-19 mortality from most countries worldwide (46). It is well documented (although underestimated) that females exhibit heightened innate and adaptive immune response to viral infections compared with males (47), which may help them clear SARS-CoV-2 faster than males. There are multiple biological reasons why females enjoy a more robust immune response to infections than males, including gene dosage in X-linked immune-response genes and the different concentrations of sex steroids between females and males (47). Sex-biased immune responses, however, extend to meta-inflammation. Men exhibit a predominant visceral adipose tissue distribution compared with women, which is associated with a more proinflammatory circulating cytokine profile (48,49). Following ingestion of a high-fat meal, obese men exhibit a significant and prolonged elevation in IL-6 and TNFα (50). As discussed above, activation of myeloid cells (monocytes, macrophages, and neutrophils) is a hallmark of obesity, both in peripheral tissues such as adipose and systemically, which produces meta-inflammation (23,25). In mice, diet-induced obesity elicits a much greater inflammatory response in adipose tissue of males than females, which is only partially reduced by ovariectomy and is therefore only partially mediated by estrogens (51). Notably, when exposed to obesogenic diet, mice from both sexes gain weight, but males develop more meta-inflammation than females, which is related to cell-intrinsic properties of male leukocytes and adipose tissue macrophages (52). Accordingly, cultured peripheral blood mononuclear cells (PBMCs) from men produce more TNFα than PBMCs from women following lipopolysaccharide stimulation (53). The propensity to adipose inflammation in males also relates to androgens’ effects on immune cells (54). Therefore, the higher propensity to meta-inflammation of obese men compared with women is dependent on sex hormones as well as on the cell-autonomous characteristics of male immune cells and may increase their risk of cytokine storm compared with women. In a mouse model of SARS-CoV infection (the coronavirus of 2003), female mice exhibited lower pulmonary inflammatory cell infiltration, producing fewer inflammatory cytokines and chemokines, and resulting in milder pulmonary damage and lower female mortality compared with males (55). Importantly, ovariectomy in female mice resulted in the same mortality rate as in males, suggesting that ovarian sex hormones protect female mice from SARS-CoV inflammatory cytokine storm. Evidence suggests that the inflammatory immune responses in COVID-19 patients might be more elevated in men and associated with worse outcomes than in women (45). In a series of 168 patients hospitalized for severe COVID-19 in Wuhan, blood neutrophil-to-lymphocyte ratio, CRP, and ferritin concentrations were higher in men compared with women as well as in patients who died compared with those who were discharged (not disaggregated by sex) (56). These data suggest that inflammatory immune responses to SARS-CoV-2 are more elevated in men and associated with more lethal outcomes than in women. In the study by Cai and colleagues (30), men who were obese were at 5.66 increased odds of severe COVID-19 outcomes compared with men who were normal weight, even after multiple adjustments. In contrast, obese women exhibited no increased risk of severe disease, although the study lacked statistical power to draw definitive conclusions for women. In future studies, investigators should analyze and report data pertaining to COVID-19 comorbidities disaggregated by sex. Studies are needed to determine the role of biological sex and sex steroids in the immune response to SARS-CoV-2 and the mortality from COVID-19 in relation to various comorbidities.
Other sex-related factors may participate in the male bias in COVID-19 morbidity and mortality. The first deadly coronavirus of 2003 (SARS-CoV) used the angiotensin-converting enzyme 2 (ACE2) as receptor for cell entry, which is also the receptor for SARS-CoV-2 (57). SARS-CoV-2 also requires the transmembrane serine protease TMPRSS2 for S protein priming (57). Notably, ACE2 is located on the X chromosome. Since women have two copies compared with one in men, ACE2 may be regulated differently in men than in women. Additionally, TMPSS2 is a direct androgen receptor target gene and its expression is increased by androgens (58), which could also have an impact on TMPSS2 expression in men. Together, ACE2 and TMPSS2 are likely to be regulated differently in men and women, which may affect virus entry and pathogenicity. Additional studies are needed to address these questions.
COVID-19 morbidity and mortality are higher in older people. Overall, in Asia, Europe, and the U.S., 80% of deaths associated with COVID-19 were observed among adults aged ≥65 years, with the highest percentage of severe outcomes among persons aged ≥80 years (13–16,19,59).
Aging is associated with a progressive decline and dysregulation in immune functions and produces a systemic, chronic, and low-grade proinflammatory response called inflammaging (60). The accumulation of senescent cells during aging is one of the contributors to inflammaging, owing to their acquisition of a proinflammatory senescence-associated secretory phenotype whose goal is to promote the immune-mediated clearance of senescent cells (61). As in the case of meta-inflammation, inflammaging is characterized by increases in systemic proinflammatory cytokine levels, namely IL-1β, IL-6, and TNFα (60). Inflammaging is influenced by changes in body composition, such as decreased lean muscle mass and increase in adiposity (62). Sex steroids are important modulators of immune cells (47). Testosterone and progesterone are generally anti-inflammatory, suppressing immune responses involved in inflammation, whereas estrogens are proinflammatory at low concentrations but anti-inflammatory at high concentrations (47). Sex steroid concentrations decline rapidly in women and more gradually in men after midlife, which may explain why sex differences in inflammaging decrease in older age. Therefore, in young people with a healthy immune system, COVID-19 is usually mild or asymptomatic (63). In old people, however, inflammaging constitutes another permissive environment for the development of a cytokine storm. In fact, in mice, the combination of aging, male sex, and increased adiposity produces a lethal cytokine storm following systemic administration of stimulatory immunotherapy (64).
Minorities are also disproportionally affected by COVID-19 mortality. In New York, Black and Hispanic individuals represent 22% and 29% of the population, respectively, yet represented 28% and 34% of fatalities (21). In contrast, non-Hispanic White people represent 32% of the population but represented only 27% of fatalities (21). In Louisiana, non-Hispanic Black people represented 56% of fatalities compared with 41% for non-Hispanic Whites. One explanation for the higher prevalence of COVID-19–related deaths among minority groups could be that the prevalence of obesity, metabolic syndrome, and type 2 diabetes is disproportionally high in Black and Hispanic individuals compared with Whites (65,66). However, meta-inflammation could also play a role. A study of the transcriptional response of primary macrophages to pathogens found a stronger inflammatory response in individuals of African versus European ancestry, most of which was under genetic control (67). In a cross-sectional study of over 1,000 participants, Black individuals exhibited higher concentrations of inflammatory markers such as IL-6 and fibrinogen compared with White individuals, even after adjusting for socioeconomic status and demographic factors (68). A retrospective study of over 1,300 patients hospitalized for COVID-19 in Louisiana reported that non-Hispanic Black patients were more likely than non-Hispanic White patients to exhibit increased inflammatory biomarkers such as CRP and procalcitonin (69). Further, the Family and Community Health Study, spanning over a 20-year period and including data from over 400 Black Americans, reported that race-related stressors such as discrimination and segregation in juvenile years predicted systemic inflammation in adults (70). Therefore, biological and social factors may also predispose minorities to systemic inflammation and therefore to COVID-19 storm.
Acute inflammation is a fundamental immune response to cope with stresses (60). In young and healthy individuals, this acute and regulated response is necessary and efficient in protecting against infectious diseases, like SARS-CoV-2 infection, which can be asymptomatic. In later life, however, it can be detrimental. Aging and chronic metabolic disease like obesity, type 2 diabetes, and metabolic syndrome are characterized by dysregulated immune function leading to chronic meta-inflammation. Male sex is also characterized by an intrinsic propensity to meta-inflammation compared with female sex. The cumulative effect of old age, male sex, and excess adiposity is likely to produce a state of heightened meta-inflammation that dysregulates and skews the immune system toward a perfect inflammatory cytokine storm (Fig. 1). In agreement with this concept, therapeutic strategies targeting the inflammatory response such as IL-6 blockade (71) or the transplantation of mesenchymal stem cells (72) are showing some promising preliminary results in preventing the cytokine storm.
In conclusion, between the appearance of COVID-19 in December 2019 and the time of writing of this Perspective, the story unfolding is suggestive of a collusion between a new pandemic of SARS-CoV-2 and an existing pandemic of metabolic disease combined with other factors predisposing to meta-inflammation including older age, male sex, and socio-biological factors in minority groups. Funding for research in this area is needed.
This hearty beef stew is the perfect way to warm up on a chilly day. It’s low-carb, full of rich flavor, and so easy to make — everything comes together in one pot!
The moment I feel a chill in the air, I start craving rich and hearty stews. There’s nothing quite like a big bowl of comfort to warm you up.
This low-carb beef stew has to be one of my favorite options. The delicious flavors of the beef and veggies really infuse into the broth as it cooks. Not to mention, everything comes together in one pot!
But can a hearty stew really be low in carbs? Absolutely! The secret is to swap out high-carb, starchy potatoes for low-carb turnips instead.
After simmering in the broth, the turnips will be so tender and delicious, no one will ever guess they aren’t potatoes!
Ready to see how this delicious one-pot stew comes together?
For a super-easy cooking process, I like to start by prepping all of my ingredients. That way, I can just add them as I need them!
Step 1: Heat a cast iron dutch oven over high heat. Add 1 tablespoon of oil.
Step 2: Season the beef with salt and pepper.
Step 3: Add the beef to the pan in a single layer and sear for a few minutes until well browned. Remove the beef from the pan and set aside.
Step 4: Add another tablespoon of oil to the pan, then add the onions, carrots, garlic, and fresh thyme. Mix well and cook until the onions are tender.
Step 5: Add the vinegar. Scrape any brown bits off the bottom of the dutch oven.
Step 6: Add the broth, beef, bay leaves, turnips, and butter. Mix well, then bring everything to a boil.
Step 7: Once the mixture is boiling, reduce to a simmer. Cook for 45 – 60 minutes until the turnips and beef are tender.
Ladle into 4 bowls, garnish with fresh thyme if you like, and enjoy!
At 11 net carbs per serving, this recipe is already pretty low-carb. But if you’re adapting it for a Keto way of eating, there are a few ways to lower the carbs even more.
The first option is to reduce or omit the carrots. A medium carrot has around 4.5 net carbs, and there will be about half a carrot per serving.
If you’re cooking this stew for others who aren’t as concerned about carbs, you could also just give them all the carrots!
Next, consider reducing the amount of turnips. They add a great heartiness to the stew in place of potatoes, so I don’t recommend omitting them entirely.
The turnips add about 20 net carbs to the whole recipe, or 5 net carbs per serving. You could cut the amount in half to reduce the turnips to 2 net carbs per serving.
Feel free to play around with the ingredients to best-suit your way of eating!
Stews are a great meal to make ahead of time and enjoy throughout the week. The flavors will deepen even more overnight!
Any leftover stew can be stored covered in the refrigerator for up to 5 days. To reheat, I recommend placing your serving in a large pot over medium heat and heating the stew slowly until it reaches your desired temperature.
Hearty stews are the perfect way to warm yourself up all fall and winter long! If you’re looking for more delicious and satisfying recipes, here are a few of my favorites that I know you’ll love:
When you’ve tried this stew, please don’t forget to let me know how you liked it and rate the recipe in the comments below!
This hearty beef stew is the perfect way to warm up on a chilly day. It’s low-carb, full of rich flavor, and so easy to make — everything comes together in one pot!
Heat a cast iron dutch oven over high heat. Add 1 tablespoon of oil.
Season the beef with salt and pepper.
Add the beef to the pan in a single layer and sear for a few minutes until well browned. Remove the beef from the pan and set aside.
Add another tablespoon of oil to the pan, then add the onions, carrots, garlic, and fresh thyme. Mix well and cook until the onions are tender.
Add the vinegar. Scrape any brown bits off the bottom of the dutch oven.
Add the broth, beef, bay leaves, turnips, and butter. Mix well, then bring everything to a boil.
Once the mixture is boiling, reduce to a simmer. Cook for 45 – 60 minutes until the turnips and beef are tender.
Beef Stew (Low-Carb)
Amount Per Serving (1 serving)
Calories 414 Calories from Fat 180
% Daily Value*
Saturated Fat 7.1g44%
Trans Fat 0g
Polyunsaturated Fat 2.3g
Monounsaturated Fat 6.2g
Net carbs 11.2g
* Percent Daily Values are based on a 2000 calorie diet.
This spicy tomato soup is delicious. Best part is that it doesn’t taste like pasta sauce which used to happen to me in my early cooking days.
Fun fact about me though is that I hate tomatoes. Sliced on a cutting board with the seed guts spilling out – ew!
But I love tomatoey things. Pasta sauce, pizza sauce and one of my favorite soups – tomato soup! And I love spicy tomato soup.
My late husband HATED tomato soup, and when I didn’t feel well all I wanted was a bowl of Campbell’s tomato soup with saltine crackers. I can remember hearing him almost throw up in his mouth while making it for me – true love!
He told me once that he went on a date and the woman invited him over for an Italian dinner. She literally heated up a can of tomato soup, added Italian seasoning to it, and called it pasta sauce. He was forever scared.
To me, tomato soup is a giant hug in a bowl. My friend gifted me with some of the tomatoes from her garden. Thank you Alison and Cameron!
I used flour to thicken the soup instead of heavy cream.
Absolutely! I used two pounds of fresh tomatoes which was 32 ounces, but a 28 can of whole plum tomatoes would work just as well.
Not all chipotle peppers are created equal. I’ve learned this the hard way. I’ve added way to many to a recipe, and simply put, you can always add more, but you can’t take it back. I originally had two peppers in the soup, but pulled one out and I am glad I did because this was flavorful spicy, not knock your socks off spicy.
When summer’s bounty gives you tomatoes, make this spicy tomato soup!
Spray a stock pot with avocado oil spray. Add the garlic and cook for 2 minutes, just until it starts to get color.
Add 1/3 cup of the broth to the pan, add the carrots and celery and cook 5 minutes. Add in the 1/4 cup flour and cook for one minute.
Add remaining ingredients and simmer for 25 minutes with a lid on.
Using a stick blender, blend to puree.
On all WW plans, each 1 cup serving is 1 smart point.
Calories: 113kcalCarbohydrates: 22gProtein: 6gFat: 1gSaturated Fat: 1gSodium: 1027mgPotassium: 832mgFiber: 5gSugar: 9gVitamin A: 7549IUVitamin C: 35mgCalcium: 61mgIron: 2mg
Since my soup was so low in calories and points, I made a grilled cheese sandwich on the side, using a low carb bread found in the Chicago area that is only 100 calories, 2 points for two slices. I cut up half of the sandwich and made grilled cheese croutons, making my lunch 6 points of deliciousness.
What’s your favorite kind of soup? I think a close second would be this copycat Panera lemon orzo soup.
I could eat soup 365 days a year. 😁
Until next time, Be Kind, Be Fearless, Have Hope.
Testosterone is a steroid hormone. It’s often referred to as the “male sex hormone” as men have significantly higher levels of testosterone than women.
Research has shown that diabetes and high blood sugar levels can affect testosterone levels in both men and women.
Most commonly, diabetes is associated with lower than normal testosterone levels in men but higher than normal testosterone levels in women.
According to the National Institute of Health, testosterone helps regulate how fat is deposited on the body, the development of muscle mass, bone density, metabolism, and maintaining libido (sex drive).
Testosterone in males is also important for the development of masculine features, male genitals, facial hair, and deepening of the voice.
In females, testosterone is associated with maintaining libido after menopause. Women with higher than normal levels of testosterone may experience irregular periods and increased body hair and muscle mass.
High blood sugar levels can affect practically every aspect of how well your body functions — including the production of testosterone.
When your blood sugar levels are consistently higher than normal, the pituitary gland that produces “luteinizing hormone” or “LH” struggles to produce normal amounts. This hormone is what stimulates the production of testosterone in your testicles.
Without enough LH, you won’t produce enough testosterone.
When your body isn’t responding normally to the insulin your pancreas produces, it’s referred to as “insulin resistance.”
To maintain normal blood sugar levels, your pancreas will work harder to produce more insulin. Over time, it’s hard to keep up with the demand. And this then leads to gradually increasing blood sugar levels, too.
You might also notice you’re also gaining fat more easily and lose muscle mass more easily which just adds further to insulin resistance.
Muscle helps manage blood sugar levels and insulin resistance by burning more calories even when you’re resting. The less testosterone you produce, the more muscle you’ll lose and the more calories you’ll store as body fat, which then increases insulin resistance and blood sugar levels.
It’s a complicated system, and everything is affected! When your hormones are struggling, your blood sugar struggles. When your blood sugar struggles, your hormones struggle.
High blood sugar levels not only damage the blood vessels and nerve endings in your penis, but they also limit healthy blood flow. Without decent blood flow, maintain an erection is pretty much impossible and you can develop erectile dysfunction.
When your blood sugar level is high, nearly everything else in your body will struggle to function at full capacity.
It’s not completely clear to science how testosterone affects sex drive, but low testosterone is one of the possible causes of low libido. If testosterone is lowered far enough, virtually all men will experience some decline in sex drive.
Improve your blood sugar. The healthier your blood sugar levels are, the healthier every part of your body will be — including your testosterone production!
Get moving. Exercise of any kind not only improves your blood sugar levels and insulin sensitivity, but it also encourages testosterone production, too. You don’t have to go to the gym to get a good workout. Even walking for 30 minutes a day is a very worthwhile goal if you’re currently not exercising at all. Just get moving.
Improve your diet. If you’re drinking a lot of sodas and eating a lot of highly processed, packaged foods, all of your body’s critical hormone levels are going to struggle. You don’t need to follow an extremely restrictive diet to improve how you eat. Get more vegetables and cook more real, whole food. Keep it simple while still making room for occasional treats so you can enjoy a healthier diet longterm.
Reduce your alcohol intake. Binge drinking, which is defined as the consumption of five drinks of alcohol within 2 hours in men or four drinks in women, once a month or more often has been directly linked to a significantly increased risk of insulin resistance and high blood sugar levels. By drinking less alcohol, you’ll increase your insulin sensitivity which can help increase testosterone production.
Get more sleep. Not enough sleep can have a huge impact on both your testosterone production and your sensitivity to insulin, both of which lead to higher blood sugar levels. Consistently getting 5 hours of sleep instead of 8 hours, for example, lead to a 10 to 15 percent reduction in testosterone production, according to 2011 research.
Quit smoking. Smoking cigarettes has a direct impact on your sexual desire and how long it actually takes for you to become aroused. Every time you smoke a cigarette, your arteries become more narrow, restricting healthy blood flow! Also a contributing problem, every time nicotine is present in your body, you become more insulin resistant which increases blood sugar levels which further decreases your testosterone production!
You don’t have to improve everything all at once. Pick one area to focus on and get started!