Keto Hot Chocolate | Diabetes Strong

By electricdiet / June 17, 2021


Treat yourself to a cup of rich, dreamy, decadent keto hot chocolate! You only need five ingredients plus water and about 10 minutes to whip up this low-carb indulgence.

Two mugs of hot chocolate resting on a wooden coaster, topped with whipped cream and cocoa powder

Whether you’re trying to warm up on a cold winter’s day or just craving something decadently comforting, you have to try this keto hot chocolate! It’s a dreamy combination that’s sure to put a smile on your face.

You’ll also love how easy it is to make. In fact, you only need five ingredients plus water and about 10 minutes to whip up this delicious treat!

How to make keto hot chocolate

Rich, creamy, low-carb indulgence is only four easy steps away. Ready to see how simple it is?

Ingredients for recipe in separate bowls and ramekins, as seen from above

Step 1: Add the water, cocoa powder, stevia, vanilla, and cinnamon (if using) to a medium saucepan over medium heat. Whisk well until the mixture is smooth.

Ingredients combined in a medium saucepan with a whisk, as seen from above

Step 2: Allow the mixture to come to a light simmer, then add the heavy cream and stir.

Heavy cream added to the hot chocolate mixture in the pan, as seen from above

Step 3: Continue heating until the hot chocolate is steaming.

Steam hot chocolate in the saucepan, ready to serve

Step 4: Pour into two mugs.

Hot chocolate served in two clear, plastic mugs

That’s it! Your low-carb, no-sugar-added hot chocolate is ready to enjoy.

I like to top mine with some sugar-free whipped cream. Sometimes, I’ll even add a sprinkle of cocoa powder as well!

Close-up of hot chocolate in a clear plastic mug, topped with whipped cream and cocoa powder

Variations for this recipe

This basic hot chocolate recipe is very easy to modify based on your tastes or preferences. Feel free to get creative with your ingredients or your toppings!

Looking for a vegan or dairy-free version? Just replace the heavy cream with a non-diary milk of your choice! Canned coconut milk will be the best substitute if you want your hot chocolate to be rich and creamy.

If you want something lighter, you could also replace the heavy cream with nut or oat milk. The hot chocolate won’t be quite as thick, so it’s great if you’re looking for something less heavy. And it will make your drink vegan-friendly!

Looking for a pick-me-up? Add a shot of espresso to your mug, or replace some of the water with coffee instead. A keto hot mocha is a great way to start the day.

And, of course, you can always play around with your toppings! Around the holidays, I like to crush up a sugar-free candy cane to sprinkle on top of my hot chocolate. Or, if you have a favorite brand of keto marshmallows, feel free to throw a few in your drink.

Keto hot chocolate in a clear plastic mug, topped with whipped cream and cocoa powder

Storage

This luscious drink is best served right away. If you only want one serving instead of two, I would recommend cutting the recipe in half rather than trying to save the second serving. Otherwise, the ingredients may separate and the sweetener could crystalize.

If you do have extra and don’t want to throw it out, it’s okay to store it in an airtight container in the refrigerator for a few days. I recommend reheating in a saucepan, stirring constantly. Don’t let it come to a boil, otherwise it could curdle the milk.

Two mugs of hot chocolate topped with whipped cream and cocoa powder, as seen from above

Other keto chocolate dessert recipes

Why fight your chocolate cravings when you can indulge them the low-carb, sugar-free way instead? I always say a balanced diet should leave PLENTY of room for chocolate, and the keto diet is no different. Here are a few of my favorite recipes to indulge your sweet tooth while keeping it low carb:

You should also checkout my roundup of the best keto-friendly dessert recipes for even more delicious ideas!

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

Recipe Card

Keto Hot Chocolate

Treat yourself to a cup of rich, dreamy, decadent keto hot chocolate! You only need 5 ingredients plus water and 10 minutes to whip up this low-carb indulgence.

Prep Time:5 minutes

Cook Time:5 minutes

Total Time:10 minutes

Servings:2

Keto hot chocolate in a clear plastic mug, topped with whipped cream and cocoa powder

Instructions

  • Add the water, cocoa powder, stevia, vanilla, and cinnamon (if using) to a medium saucepan over medium heat. Whisk well until the mixture is smooth.

  • Allow the mixture to come to a light simmer, then add the heavy cream and stir.

  • Continue heating until the hot chocolate is steaming.

  • Pour into two mugs.

Recipe Notes

This recipe is for 2 servings of hot chocolate.
Because this drink is best served right away, storing is not recommended.

Nutrition Info Per Serving

Nutrition Facts

Keto Hot Chocolate

Amount Per Serving (1 cup)

Calories 434
Calories from Fat 435

% Daily Value*

Fat 48.3g74%

Saturated Fat 28g140%

Trans Fat 0g

Polyunsaturated Fat 0g

Monounsaturated Fat 0g

Cholesterol 160mg53%

Sodium 40.3mg2%

Potassium 128.7mg4%

Carbohydrates 8.8g3%

Fiber 5.7g23%

Sugar 0.3g0%

Protein 2.6g5%

Net carbs 3.1g

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

Course: Dessert, Drinks

Cuisine: American

Diet: Diabetic, Gluten Free

Keyword: gluten-free, hot chocolate, keto, keto hot chocolate, low carb



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Phenotypic and Genetic Characterization of Lower LDL Cholesterol and Increased Type 2 Diabetes Risk in the UK Biobank

By electricdiet / June 15, 2021


Abstract

Although hyperlipidemia is traditionally considered a risk factor for type 2 diabetes (T2D), evidence has emerged from statin trials and candidate gene investigations suggesting that lower LDL cholesterol (LDL-C) increases T2D risk. We thus sought to more comprehensively examine the phenotypic and genotypic relationships of LDL-C with T2D. Using data from the UK Biobank, we found that levels of circulating LDL-C were negatively associated with T2D prevalence (odds ratio 0.41 [95% CI 0.39, 0.43] per mmol/L unit of LDL-C), despite positive associations of circulating LDL-C with HbA1c and BMI. We then performed the first genome-wide exploration of variants simultaneously associated with lower circulating LDL-C and increased T2D risk, using data on LDL-C from the UK Biobank (n = 431,167) and the Global Lipids Genetics Consortium (n = 188,577), and data on T2D from the Diabetes Genetics Replication and Meta-Analysis consortium (n = 898,130). We identified 31 loci associated with lower circulating LDL-C and increased T2D, capturing several potential mechanisms. Seven of these loci have previously been identified for this dual phenotype, and nine have previously been implicated in nonalcoholic fatty liver disease. These findings extend our current understanding of the higher T2D risk among individuals with low circulating LDL-C and of the underlying mechanisms, including those responsible for the diabetogenic effect of LDL-C–lowering medications.

Introduction

Rates of cardiovascular disease (CVD) and type 2 diabetes (T2D) are among the most pressing health concerns worldwide. These two diseases share many risk factors and tend to co-occur, because there is an excess of CVD among individuals with T2D (1,2). Yet, controversy remains over whether all risk factors exert similar effects on the development of these two conditions. LDL cholesterol (LDL-C) is a class of highly atherogenic particles, and circulating levels of LDL-C are a causal risk factor for CVD across the life span (3). However, several lines of evidence suggest that decreased levels of circulating LDL-C are associated with an increased T2D risk.

Lipid-lowering medications, in particular from the statin drug class, are effective at lowering levels of circulating LDL-C and rates of adverse cardiovascular events (4) but convey an increased T2D risk (odds ratio [OR] 1.09) (5,6) in a dose-dependent manner (7). This increased risk, however, is outweighed at a population level by the cardiovascular event rate reduction. An increased T2D risk has also been reported in observational studies. Individuals with low levels of circulating LDL-C (e.g., <60 mg/dL) exhibit a higher risk of prevalent and incident T2D (8,9), and among individuals with coronary disease, LDL-C and T2D are inversely related (10). In addition, individuals with familial hypercholesterolemia exhibit a decreased risk of T2D as well as lower BMI and triglyceride (TG) levels (11).

Genetic studies have lent further support to inverse phenotypic associations between LDL-C and T2D, with recent studies pointing to genetic loci that harbor variants exerting opposing effects on LDL-C and T2D. These include loci containing the HMGCR (12), APOE (13,14), PCSK9 (12,14,15), NPC1L1 (12,14), PNPLA3 (14), TM6SF2 (14), GCKR (14), and HNF4A (14) genes. Furthermore, Fall et al. (16) and White et al. (17) have both found that genetically predicted higher circulating LDL-C was associated with a lower risk of T2D. Yet, genetic findings show that not all variants have opposing effects on circulating LDL-C levels and T2D risk. LDL-C–lowering variants in ABCG5/G8 and LDLR genes were not shown to alter T2D risk (12), and subsets of LDL-C–lowering alleles pose a stronger risk for T2D than the full gamut (16). Circulating LDL-C levels, like T2D, are reflective of a number of physiological processes. The findings outlined above suggest that there is heterogeneity in T2D outcomes, depending on which pathways are the primary LDL-C–lowering mechanisms, and that genetic studies may give us insights into these pathways. For example, it is not clear whether these associations are driven by changes in circulating levels of LDL-C or by changes in intracellular levels of cholesterol. A better understanding of which genetic loci lower circulating levels of LDL-C and increase T2D risk may yield mechanistic insights that could help develop therapeutic options that lower lipid levels without raising the risk of T2D and help identify individuals at greater risk for T2D with statin use.

Here, we first examined the relationship of directly measured circulating LDL-C levels with prevalent T2D, HbA1c, and BMI. We then sought to identify, for the first time on a genome-wide scale, loci simultaneously associated with lower LDL-C and increased T2D (and vice versa). Upon identifying variants, we sought to generate additional mechanistic insights by testing of associations with seven other traits in the UK Biobank related to T2D, LDL-C, and nonalcoholic fatty liver disease (NAFLD).

Research Design and Methods

UK Biobank

Data from the UK Biobank were used for 1) phenotypic data analysis, which examined the associations of circulating levels of LDL-C and TG with T2D, HbA1c, and BMI, and 2) discovery genome-wide association study (GWAS) for variants that are associated with lower circulating levels of LDL-C and higher T2D. The UK Biobank is a prospective cohort study of ∼500,000 individuals between the ages of 39 and 72 years living throughout the U.K. Participants attended 1 of 21 assessment centers in the U.K. and had their blood drawn for biomarker and genetic analysis and weight and height measured to derive BMI (kg/m2). Directly measured circulating LDL-C, HbA1c, HDL-C, TG, alanine aminotransferase (ALT), and AST were obtained from all UK Biobank participants at the baseline visit between 2006 and 2010 in a nonfasting state. LDL-C was assessed by enzymatic protective selection analysis on a Beckman Coulter AU5800.

To define prevalent T2D case and control subjects, we used criteria previously used by Yaghootkar et al. (18) and Eastwood et al. (19). We first excluded individuals with a missing age of T2D diagnosis, reporting a T2D diagnosis within 1 year of the baseline examination, those self-reporting type 1 diabetes in the verbal interview, and women reporting only gestational diabetes on the touchscreen or verbal interview. Prevalent T2D was defined using the following criteria: 1) self-reported diabetes diagnosed by a doctor during the touchscreen, or self-reported T2D or generic diabetes in verbal interviews; 2) having a nonmissing age of diagnosis and an age of diagnosis >35 years of age (>30 years of age for participants reporting an ethnicity of South Asian or African Caribbean); and 3) not using insulin within 1 year of diagnosis to exclude possible type 1 diabetes case subjects. Control subjects were participants with no self-reported diabetes of any type from the touchscreen or verbal interview, no self-reported insulin use in the touchscreen or verbal interview, those not excluded according to the aforementioned criteria, and those not reporting nonmetformin T2D medication (see the list in Supplementary Table 2).

UK Biobank Genotypes

Genotypes in the UK Biobank were obtained with the Affymetrix UK Biobank Axiom Array (Santa Clara, CA), whereas 10% of participants were genotyped with the Affymetrix UK BiLEVE Axiom Array. Details regarding imputation, principal components analysis, and quality control procedures are described elsewhere (20). The analysis excluded individuals with unusually high heterozygosity, with a high (>5%) missing rate, or with a mismatch between self-reported and genetically inferred sex. Single nucleotide polymorphisms (SNPs) out of Hardy-Weinberg equilibrium (P < 1 × 10−6), with a high missing rate (>1.5%), with a low minor allele frequency (<0.1%), or with a low imputation accuracy (info <0.4) were excluded from analyses. This resulted in the availability of ∼15 million SNPs for analysis.

Diabetes Genetics Replication and Meta-Analysis and Global Lipids Genetics Consortium GWAS Meta-Analysis Summary Statistics

The latest GWAS meta-analysis summary statistics for T2D (unadjusted for BMI) were obtained from the Diabetes Genetics Replication and Meta-Analysis consortium (DIAGRAM), which includes data on up to 898,130 individuals (74,124 case and 824,006 control subjects), including UK Biobank individuals (21). We used the results of our GWAS of circulating LDL-C in UK Biobank, along with the aforementioned DIAGRAM-T2D results, for the discovery of inverse association signals. We then replicated LDL-C associations of our top hits with an independent GWAS meta-analysis of LDL-C from the Global Lipids Genetics Consortium (GLGC) (22) (n = 188,577); this meta-analysis does not include the UK Biobank study. Across the UK Biobank, DIAGRAM, and GLGC summary statistics, we aligned all SNP alleles and their corresponding effects by using the harmonize function in the TwoSampleMR package in R software (23).

Statistical Analyses

To evaluate and plot the prevalence of T2D and of BMI and HbA1c by decile of circulating LDL-C and TG in the UK Biobank, we excluded all participants who self-reported (at baseline) use of cholesterol-lowering medications during the touchscreen survey, or cholesterol-lowering medication during the verbal interview (see Supplementary Table 1 for list of medications). Approximately 91% of individuals taking cholesterol-lowering medications were taking statins. To examine levels of HbA1c by decile of circulating LDL-C, we excluded participants defined as T2D cases (see above). We further excluded individuals with outlier values of HbA1c >4 SDs from the mean. Deciles were calculated using the “quantcut” function in the “gtools v3.5.0” library in R software. Once decile were established, T2D prevalence by LDL-C/TG decile was calculated and plotted with CIs determined by the Clopper-Pearson interval (24). Mean HbA1c and BMI and their distributions are shown in boxplots for each decile of circulating LDL-C. We further examined T2D prevalence by circulating LDL-C decile separately in men and women, and in different age-groups (40–49 years, 50–59 years, and 60–69 years).

To statistically evaluate these phenotypic associations, we performed logistic regression with T2D prevalence as the outcome and linear regression with HbA1c and BMI as outcomes. As mentioned above, all individuals on cholesterol-lowering medication were excluded. To normalize residuals, we transformed circulating LDL-C, TG, HbA1c, and BMI by inverse normalization for all linear regression analyses. For each analysis, we used the same exclusion criteria as those mentioned above and adjusted for “last eating” time (excluding individuals reporting extreme values, >16 h), age, sex, and center. We considered an expanded model with additional covariates: education (college/university degree or not), Townsend Deprivation Index, BMI, hypertension status (self-reported status or hypertension medication), ethnicity (white/European or not), family history of T2D (at least one first-degree family member), smoking status (never, past, current), and alcohol consumption (never or only special occasions, one to three times per month, one to two times per week, three to four times per week, daily/almost daily). In analyzing the association of circulating LDL-C with T2D, we also tested for interactions with sex and age and provided stratified analyses accordingly. Finally, we also examined the association of LDL-C with T2D among only the individuals taking cholesterol-lowering medication.

To address possible ascertainment bias of prevalent T2D case subjects due to exclusion of people taking cholesterol-lowering medication, we performed a sensitivity analysis using propensity score matching to remove bias between the two groups due to observed covariates (25). We used the propensity score to match on the probability of taking cholesterol-lowering medication given the set of baseline characteristics listed in Supplementary Table 3. All covariates were selected based on previous literature or if considered potential significant confounders for cholesterol-lowering medication use or T2D (2628). Matching analyses were performed using R software and the package MatchIt v3.0.2 with 1:1 nearest-neighbor matching and a caliper width equal to 0.1 to achieve balanced covariates between the two groups (29). Standardized mean differences were used to assess covariate balance before and after matching. Standardized mean differences <0.1 were considered adequately balanced to reduce significant differences between the two groups (30). Subsequent analyses were conducted among all individuals, regardless of cholesterol-lowering medication use, using logistic regression for T2D as the outcome and linear regression for HbA1c as the outcome. Model 1 was adjusted for time since eating, age, sex, and center. Model 2 was additionally adjusted for BMI and use of cholesterol-lowering medication. BMI was included as a covariate because the addition of BMI to the unadjusted model in the matched sample changed the regression coefficient for LDL-C by >10%. Although cholesterol medication use did not result in this magnitude of change, we adjusted for cholesterol medication use in model 2 to address any potential confounding. The only covariates considered in these regression analyses were those used in the main analyses stratified by cholesterol-lowering medication (see above and model 2 in Supplementary Tables 6 and 7). To meet the assumptions of linear regression, HbA1c, LDL-C, and BMI were inverse normalized in all linear regression models of HbA1c regressed on LDL-C. We used complete case analysis to address missing data. As a result of greater missingness for HDL-C (n = 35,382), analyses were repeated excluding HDL-C from the matching covariates. However, the regression results were not attenuated, and only results including HDL-C were reported.

For the GWAS of LDL-C in the UK Biobank, the circulating LDL-C level of individuals on cholesterol-lowering medication was corrected by dividing it by a correction factor of 0.63 (31). We also ran a GWAS only on individuals not taking cholesterol-lowering medication. We transformed LDL-C by inverse normalization. We used BOLT-LMM software (32) to perform GWAS on individuals of European descent (n = 431,167) and included “last eating” time (see above), sex, age, age2, center, genotyping chip, and the first 10 principal components as covariates. BOLT-LMM performs a linear mixed model regression that includes a random effect of all SNP genotypes other than the one being tested. We aligned effect sizes across the GWAS summary statistics of each trait to the same effect allele using the harmonize function, as mentioned above. We used metaCCA v1.12.0 (33) to perform a multivariate GWAS with the LDL-C and T2D GWAS summary statistics. Briefly, metaCCA implements a canonical correlation analysis on GWAS summary statistic data in which the phenotype correlation structure was estimated from the univariate GWAS summary statistics. We first selected only those SNPs that exhibited opposite directions of univariate effects for LDL-C and T2D and having a metaCCA P < 5 × 10−8. To further minimize the potential of selecting false-positive loci, we selected among these SNPs only those with a univariate association P < 5 × 10−5 for each of LDL-C and T2D. At this univariate P-value threshold, the prior probability of a given SNP associated with two traits and with discordant direction of effect under the null hypothesis corresponds to 0.00005 × 0.000025 = 1.25 × 10−9 (34). SNPs within <500 kb of each other or in linkage disequilibrium of r2 > 0.05 were clumped together, and the SNP with the lowest metaCCA P value was reported.

For the replication of the 44 discovered loci, we considered both the univariate results for LDL-C from GLGC and multivariate results from metaCCA using the GLGC LDL-C and DIAGRAM T2D. Because of incomplete overlap of SNPs in GLGC with those in the UK Biobank and DIAGRAM and differences in population composition, we examined all SNPs within each locus identified in the discovery stage (i.e., the base pair range at a given locus for which all SNPs satisfied the above univariate and multivariate criteria in the discovery analysis). After further restricting to only variants for which the effect size for (GLGC) LDL-C and T2D exhibited opposite directions of effect, we chose the SNP with the lowest metaCCA P value. A locus was considered to be successfully replicated if this top SNP had a univariate LDL-C P < 5 × 10−3 and a metaCCA P < 5 × 10−5. Among the replicated loci, we tested for colocalization using the DIAGRAM T2D and UK Biobank LDL-C results to determine whether, at a given locus, the two traits are likely to be affected by the same causal variant. Specifically at each of the replicated loci, we used the coloc v3.2-1 package in R software (35) to test for colocalization using all SNPs within 250 kb of the SNP with the lowest metaCCA P value. We used default parameters and priors. We considered that there was evidence for colocalization if the posterior probability for a shared causal variant hypothesis 4 (PP.H4) was >80%.

To test the association of the 31 SNPs (T2D increasing allele) that we identified with a range of other cardiometabolic traits that are known to be related to LDL-C and T2D and are available in the UK Biobank, we used similar methods described above for the circulating LDL-C GWAS. For TG and HDL-C, we excluded individuals reporting cholesterol-lowering medication. For ALT and AST, we excluded 15,138 individuals with medical conditions, other than NAFLD, that could affect liver enzyme levels (36). For HbA1c, we excluded individuals with prevalent T2D (see above). For the waist-to-hip ratio, we additionally adjusted for BMI before inverse normalization and subsequent GWAS. We inverse normalized all traits before the GWAS. We tested the association of each of the 31 SNPs with each of these seven additional phenotypes. We then normalized the effect sizes by dividing the β-coefficients by the corresponding SEs and dividing by the square root of the respective sample size. We used hierarchical clustering to group the identified variants according to their pattern of association with all nine traits, including T2D and circulating LDL-C. Clustering was performed with the hclust v3.6.2 function in R, with the Euclidian metric to calculate distances, and the Ward clustering method (37). Cluster stability was assessed by using the clValid v0.6-6 package in R software, evaluating the hierarchical, k-means, and partitioning around medoids methods, and evaluating 2–10 clusters (38). Finally, using UK Biobank individual-level data, we used a multivariate approach, MultiPhen v2.0.3 (39), which uses ordinal regression to model each SNP as the outcome and includes all traits as covariates (except for T2D) in addition to age and sex. We present only β-coefficients from these models because the P values are nearly all >0.05, possibly due to the inclusion of many correlated phenotypes into each model.

Data and Resource Availability

The data that support the findings of this study are available to researchers, upon application, from the UK Biobank, but restrictions apply to the availability of these data, which were used under license for the current study. Data from the DIAGRAM and GLGC consortia are publically available at their respective websites: https://www.diagram-consortium.org/ and http://lipidgenetics.org/.

Results

Participant Characteristics

In a sample size of 375,783 individuals after exclusion of individuals on lipid-lowering medication, T2D prevalence was 0.8% and was higher in men (1.15%) than in women (0.54%). Individuals with prevalent T2D had lower circulating LDL-C and HDL-C, higher circulating TG, higher HbA1c, and higher BMI (Supplementary Table 4). Among individuals on cholesterol-lowering medication (n = 78,626), T2D prevalence was 18.2%, and individuals with T2D had lower circulating LDL-C and HDL-C, and higher circulating TG, HbA1c, and BMI (Supplementary Table 5).

Association of Circulating LDL-C With T2D

We observed an inverse relationship between circulating LDL-C and T2D prevalence (OR 0.41 [95% CI 0.39, 0.43] per mmol/L unit of LDL-C, P = 1.26 × 10−263). Individuals in the lowest decile of circulating LDL-C exhibited the highest prevalence of T2D, and a consistent decrease in T2D prevalence was observed with increasing circulating LDL-C (Fig. 1). We found a very similar negative association of circulating LDL-C with T2D among only the individuals reporting the use of cholesterol-lowering medication. We found a significant interaction of circulating LDL-C with sex (P = 1.52 × 10−13), whereby the association of circulating LDL-C with T2D prevalence was stronger among men (OR 0.35 [95% CI 0.32, 0.37] per mmol/L unit of LDL-C, P = 7.37 × 10−215) than among women (OR 0.51 [0.47, 0.55] per mmol/L unit of LDL-C, P = 3.63 × 10−62) (Supplementary Table 6 and Supplementary Fig. 1). We also observed a stronger inverse association between circulating LDL-C and T2D prevalence among older individuals (Pinteraction = 3.54 × 10−13) (Supplementary Table 6 and Supplementary Fig. 3). Positive associations were found between circulating LDL-C and both HbA1c (after exclusion of individuals with T2D; β = 0.14, SE = 0.0017, P < 5.0 × 10−300) and BMI (β = 0.16, SE = 0.0016, P < 5.0 × 10−300) (Fig. 1 and Supplementary Table 6). We also observed a positive association between circulating TG and T2D prevalence (OR 1.34 [95% CI 1.31, 1.38], P = 8.03 × 10−109) (Supplementary Table 6 and Supplementary Fig. 4). Among individuals on cholesterol-lowering medications, we found a nearly identical negative association of circulating LDL-C and T2D prevalence but a much weaker positive association with HbA1c, and a negative association with BMI (Supplementary Table 7 and Supplementary Fig. 1). In models including additional covariates, the results remained very similar (Supplementary Tables 6 and 7). Results were also very similar in propensity score–matching analyses. In a total sample size of ∼70,000 individuals, the T2D prevalence was 6.69%, and these analyses showed similar negative associations of circulating LDL-C with T2D (OR 0.51 [95% CI 0.49, 0.54] in model 2) and positive associations of circulating LDL-C with HbA1c (Supplementary Tables 3 and 8).

Figure 1
Figure 1

T2D prevalence, HbA1c, and BMI by circulating LDL-C deciles in the UK Biobank. T2D prevalence is shown as a percentage, with error bars corresponding to the Clopper-Pearson CI. Whisker plots show the median value (horizontal line in box), the 25th and 75th percentile delimited by the box, and the vertical lines extending to the 5th and 95th percentile.

Loci Associated Inversely With LDL-C and T2D

We identified 44 loci associated in opposite directions with circulating LDL-C and T2D using the UK Biobank LDL-C and the DIAGRAM T2D results (Supplementary Table 9). In an analysis in which we used a GWAS of circulating LDL-C excluding individuals on cholesterol-lowering medication, we observed nearly identical results (Supplementary Table 10). Among these 44 loci, 31 replicated with respect to LDL-C association when using the GLGC LDL-C GWAS results instead of UK Biobank (Table 1). Several loci are previously known or suspected to be inversely associated with circulating LDL-C and T2D (HMGCR, APOE, NPC1L1, PNPLA3, TM6SF2, GCKR, and HNF4A). However, most of the loci are novel for this LDL-C–T2D trait. Of these novel loci, 12 have previously been identified for LDL-C in the GLGC GWAS, 14 were previously identified in T2D GWAS, and 14 have not been identified previously with either trait. The loci with the strongest degree of opposing effects include FNDC7-STXBP3, SORT1-PSMA5, HMGCR-POC5, PPP1R3B, and GCKR (Fig. 2). Colocalization analyses suggest that of the 31 loci, GCKR, PPP1R3B, TM6SF2, HNF4A, MICAL3, and PNPLA3 have the same causal variant(s) influencing circulating LDL-C and T2D (PP.H4 > 0.8). Although most SNPs showed colocalization at shared or distinct causal variants, a few loci showed no evidence of colocalization (Supplementary Table 11).

Table 1

List of replicated genetic loci in which all SNPs have P < 5E−5 for both circulating LDL-C and T2D and with opposite directions of effect, for UK Biobank LDL-C and DIAGRAM T2D

Figure 2
Figure 2

Plot of β-coefficients for circulating LDL-C vs. T2D for SNPs with opposite directions of effect on these two traits. β-Coefficients correspond to log-ORs for T2D, and SDs for circulating LDL-C. Shaded area corresponds to the 95% CI for the best fit regression line.

The variants that we have identified can be linked with genes that affect de novo fatty acid synthesis, hepatic lipid uptake, hepatic lipid export, peripheral tissue lipid balance, fatty liver of unknown origin, insulin secretion, and insulin action (Supplementary Table 12). They are associated in distinct patterns across a range of cardiometabolic traits (Fig. 3). At these loci, the T2D-increasing alleles are generally associated with higher HbA1c levels and lower HDL-C levels, although this pattern is not entirely consistent across all 31 SNPs. According to cluster stability evaluation, two clusters were optimally identified by hierarchical clustering (Supplementary Table 13). However, it is difficult to discern any consistent trait association patterns that differentiate the two sets of loci. In Supplementary Fig. 6 we present the trait-specific β-coefficients based on MultiPhen, some of which are substantially different from the univariate results.

Figure 3
Figure 3

Association of T2D-increasing allele at 31 identified SNPs with nine cardiometabolic traits, based on univariate analyses, and hierarchical clustering dendogram based on corresponding standardized effect sizes. WHR, waist-to-hip ratio.

Discussion

We used the largest sample to date to examine the association of circulating LDL-C with T2D prevalence and found that individuals with low circulating LDL-C exhibit a higher prevalence of T2D. Then, in the first genome-wide analysis aimed at identifying variants associated with both lower circulating LDL-C and higher T2D risk, we identified 24 novel loci exerting opposite-direction effects on these traits. Our analyses lend weight to the notion that the association between lower circulating LDL-C and increased T2D risk is driven, at least in part, by a specific group of genetic variants that may be implicated via diverse mechanisms, including hepatic lipid synthesis, export, and uptake, as well as insulin secretion and action. These variants provide insight into the heterogeneous outcomes for different lipid and glucose metabolism pathways.

We found that low circulating LDL-C is associated with greater T2D prevalence, which is consistent with two previous studies examining T2D prevalence (8) and incidence (9). In addition, we found that lower levels of circulating LDL-C are associated with lower HbA1c (among individuals without T2D) and lower BMI. Our finding that lower circulating LDL-C is associated with increased T2D prevalence but lower HbA1c appears counterintuitive. It is important to note that the latter association was performed in a slightly different subset than the first association (i.e., excluding those with T2D). We may be observing a threshold effect, whereby the etiology of “normal” HbA1c variation is somewhat distinct from the etiology of crossing into overt T2D (e.g., 37). It is also possible that our results could be affected by collider bias because individuals on cholesterol-lowering medications are excluded from our main analysis. However, we observed a similar inverse relationship of circulating LDL-C with T2D in the set of people on cholesterol-lowering medication and in a propensity score–matching analysis. We also find that unlike LDL-C, TG levels are positively associated with T2D prevalence. This opposing relationship of circulating LDL-C and TG with T2D prevalence may suggest that LDL particles are being overfilled in individuals with T2D.

Previous research into loci that jointly alter the risk for circulating LDL-C and T2D has focused on the genomic targets of lipid-lowering medications in the hope that these analyses will give specific insights into associated T2D risk. On one hand, our analyses confirmed that variants in HMGCR (41) and NPC1L1 (14) are associated with lower circulating LDL-C and increased T2D risk. On the other hand, our analyses did not identify variants at PCSK9. The lowest T2D P value was 0.003 in this region for a SNP with opposite direction coefficient. However, our analyses identified a fourth target of lipid-lowering medications: variants in the peroxisome proliferator–activated receptor (PPARG) gene, the target of fibrates and thiazolidinediones.

We observed nine variants previously identified as being associated with NAFLD: PNPLA3, GCKR, TM6SF2, PPP1R3B, ERLIN1CWF19L1, REEP3, HNF1A, SLC2A2, and MICAL3 (4244). Furthermore, five of the seven colocalizing loci are among these nine. This enrichment for NAFLD-related genes may reflect increased synthesis and storage of TG and reduced export/secretion of VLDL, leading to reduced circulating LDL-C. Indeed, the LDL-C–decreasing alleles at most of these loci are associated with increased liver enzymes, indicative of hepatic steatosis, with the exception of GCKR and SLC2A2, consistent with a previous finding (45). In turn, lower levels of circulating LDL-C along with increased liver enzymes would be expected to indicate increased NAFLD and T2D. A recent bidirectional Mendelian randomization study provides support for this hypothesized causal effect of NAFLD on T2D (46). Our findings that liver fat may be an important mediator of the effect of cholesterol lowering on T2D is consistent with a report showing that liver fat may help identify statin-taking individuals at risk for T2D (47). Finally, it is noteworthy that the HMGCR variant that lowers circulating LDL-C is not associated with any significant change in liver enzymes, potentially reflecting the lack of an increase in NAFLD incidence seen with statin medications (48).

Our analyses identified a number of variants previously implicated in lipid and glucose metabolism. Sortilin 1 (SORT1) is highly expressed in adipocytes, and the sortilin gene product facilitates the formation and export of VLDL from the liver (49,50). The role of SORT1 in T2D risk is not well understood. Sortilin 1 is required for insulin-dependent glucose uptake (5153), yet Sort1-knockout mice may show reduced glucose and glycolic intermediates in the fasted state (54). This highlights again the potential for heterogeneous paths in T2D risk and the dependence on multiple pathways of lipid and glucose metabolism to explain our findings.

Several loci were also identified that are known to be related to T2D, without known associations with circulating LDL-C. These include THADA, C2CD4A, CENPW, and SLC12A8 (21). In addition, we identified several variants associated with lower circulating LDL-C but increased T2D risk with no known biological pathways linking these loci to either trait. SLC2A2, which encodes GLUT2, has not previously been associated with circulating LDL-C or T2D in the large respective GWAS consortia. However, GLUT2 is key to hepatic glucose uptake after a meal and the associated hepatic de novo lipogenesis (55). In fact, liver-specific GLUT2 knockout decreases liver TG concentrations. Importantly, GLUT2 expression in the β-cell is required for the glucose-stimulated insulin response (56). In turn, a locus that decreases GLUT2 expression would be expected to limit serum insulin, increase HbA1c, and decrease circulating LDL-C.

Our approach is subject to several limitations. We used prevalent T2D in the UK Biobank, which limits inferences related to the direction of causality. As incident T2D cases develop in the UK Biobank, it will be important to examine the association of circulating LDL-C at baseline with incident T2D. The risk of a false-positive finding (i.e., a SNP that is associated with two traits in opposite directions, each with P < 5 × 10−5, and with a genome-wide significant metaCCA P value) is extremely low. However, a limitation of our study is that the replication is limited to a replication of the LDL-C effect estimates of these SNPs. This could lead to an increased risk of false-positive signals with respect to the associations of the SNPs with T2D. It is also difficult to identify the causal gene at identified loci. Although we annotated these loci according to nearby genes and/or previous annotation, the listed and mentioned genes may not necessarily be directly implicated, if at all. Our identification of loci associated with both LDL-C and T2D does not necessarily imply that in each case, the effects of the genetic variant on each trait are linked by a common pathway or mechanism. In other words, it is possible that the way in which a variant causes a lowering of LDL-C could be distinct (different tissue and/or pathway) from the way in which it increases T2D. It is thus possible that some of the variants identified are not having effects on a common pathway. Furthermore, because we are not necessarily identifying a single causal variant, it is possible that within a locus, multiple different variants affect each trait. Results from colocalization do suggest that in many cases, there are different causal variants. However, differences in patterns of linkage disequilibrium between the UK Biobank and DIAGRAM consortium studies could reduce our ability to colocalize causal variants. If there was not a shared pathway, we might expect that the effect size of LDL-C would be directly proportional to the effect on T2D across all LDL-C–lowering alleles. However, there are many variants that are known to be strongly associated with LDL-C that are not identified in this analysis (e.g., LDLR, APOB, ABCG5/8).

In conclusion, our results suggest that low circulating LDL-C may be a risk factor for T2D, although further study is warranted. We have identified a collection of genetic variants that may provide insight into the mechanisms underlying the diabetogenic risk of low circulating LDL-C and of lipid-lowering medications, and the decreased T2D risk among individuals with familial hypercholesterolemia.

Article Information

Acknowledgments. The authors acknowledge the vital contributions of the GLGC and DIAGRAM as well as all organizers and participants of individual participating studies. This research was conducted using the UK Biobank resource under application number 15678. The authors thank the participants and organizers of the UK Biobank.

Funding. The study received support from the National Heart, Lung, and Blood Institute (R01-HL-136528). A.C.W. was funded, in part, by U.S. Department of Agriculture/Agricultural Research Service cooperative agreement no. 58-3092-5-001.

The contents of this publication do not necessarily reflect the views or policies of the U.S. Department of Agriculture, nor does mention of trade names, commercial products, or organizations imply endorsement by the U.S. Government. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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

Author Contributions. Y.C.K. conceived and designed the study. Y.C.K., A.A., M.N., and J.Z. performed data analyses. Y.C.K., M.N., B.J.R., and A.C.W. wrote the manuscript. J.M.O., B.J.R., and A.C.W. contributed to writing of the introduction and discussion. All authors read and edited the full manuscript. Y.C.K. is the guarantor of this work and, as such, had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Prior Presentation. Parts of this study were presented in abstract form at the American Society of Human Genetics 2019 Annual Meeting, Houston, Texas, 15–19 October 2020.

  • Received November 12, 2019.
  • Accepted May 29, 2020.



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Instant Pot French Dip Sandwiches

By electricdiet / June 13, 2021






Instant Pot French Dip Sandwiches – My Bizzy Kitchen




































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Keto No Bake Cookies | Diabetes Strong

By electricdiet / June 11, 2021


These keto no bake cookies made with coconut, sunbutter, cocoa powder, hemp hearts, and sunflower seeds are such an easy and delicious treat. They’re also gluten-free and vegan!

Stack of three keto no bake cookies surrounded by more cookies

Craving some crunchy, chocolatey goodness that’s packed with healthy fats and super easy to make? You’re going to love these keto no bake cookies!

Coconut, sunbutter, cocoa powder, hemp hearts, and sunflower seeds come together for a delicious and satisfying treat — no oven required. These cookies are also low-carb, gluten-free, and totally vegan.

How to make keto no bake cookies

These tasty cookies are ready in just five simple steps!

Ingredients for recipe in separate bowls and ramekins, as seen from above

Step 1: Add the coconut oil, sunbutter, cocoa powder, stevia, and vanilla extract to a small saucepan over low heat.

Coconut oil, sunbutter, cocoa powder, stevia, and vanilla extract added to a small saucepan, as seen from above

Step 2: Stir until the coconut oil and sunbutter are melted and the mixture is smooth. Taste for sweetness and adjust if necessary.

Ingredients stirred in a saucepan with a spatula until melted and smooth

Step 3: Remove from heat, then add the coconut, hemp hearts, and sunflower seeds. Stir until completely coated with the coconut oil mixture.

Adding the hemp hearts and coconut to the coconut oil mixture in a small saucepan and mixing with a rubber spatula until well combined

Step 4: Use a spoon to drop 1-2 tablespoons at a time onto a parchment lined baking sheet. You should have 15 cookies total.

Mixture formed into 12 cookies on a baking tray lined with parchment paper, as seen from above

Step 5: Chill in the refrigerator for at least 1 hour to set.

That’s it! Next time you’re craving a little chocolate with a healthy dose of fats, you’ll have a perfect snack waiting for you.

Stack of cookies on a white plate with more cookies on a baking tray in the background

Variations for this recipe

There are lots of ways to modify these simple cookies. Feel free to play around based on taste, preference, or convenience!

First, this recipe is not very sweet. Think of it like dark chocolate, where you get that wonderful flavor without the sweetness taking over. However, you’re welcome to add more low-carb sweetener if you like. Just taste the recipe as you go and adjust until you find your perfect cookie.

Second, this recipe was written to be vegan-friendly and nut-free. However, if you don’t mind nuts, you could use any other nut butter in place of the sun butter. If you’re not vegan, you can also replace the coconut oil with butter if you prefer.

Don’t love sunflower seeds? Or don’t have any in the pantry right now? Chopped nuts like almonds or walnuts could easily be used instead.

Want even more chocolate? Add some sugar-free chocolate chips! I recommend folding them in after the hemp hearts and the coconut so the mixture has a little time to cool. Otherwise, the chips may melt in the warm cookie batter.

Storage

Your cookies can be stored in an airtight container in the refrigerator for up to 5 days. They’re best served cold, so go ahead and eat them right out of the fridge!

You can also store them in the freezer for 2-3 months. When you’re ready to enjoy, let them thaw in the fridge for at least 4 hours before eating.

Closeup of a cookie with a bite taken out of it

Other low-carb cookie recipes

I love having a variety of keto-friendly cookie recipes to satisfy my sweet tooth! There are so many different flavors to try. If you’re looking for more cookie inspiration, here are a few of my favorite recipes I know you’ll love:

You should also take a peek at this roundup of my favorite 10 diabetic cookie recipes for even more delicious inspiration!

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

Recipe Card

Keto No Bake Cookies

These keto no bake cookies made with coconut, sunbutter, cocoa powder, hemp hearts, and sunflower seeds are such an easy and delicious treat. They’re also gluten-free and vegan!

Prep Time:15 minutes

Chill Time:1 hour

Total Time:1 hour 15 minutes

Servings:15

Stack of three keto no bake cookies surrounded by more cookies

Instructions

  • Add the coconut oil, sunbutter, cocoa powder, stevia, and vanilla extract to a small saucepan over low heat.

  • Stir until the coconut oil and sunbutter are melted and the mixture is smooth. Taste for sweetness and adjust if necessary.

  • Remove from heat, then add the coconut, hemp hearts, and sunflower seeds. Stir until completely coated with the coconut oil mixture.

  • Use a spoon to drop 1-2 tablespoons at a time onto a parchment lined baking sheet. You should have 15 cookies total.

  • Chill in the refrigerator for at least 1 hour to set.

Recipe Notes

This recipe is for 15 servings. If you split the batter into 15 cookies, a serving will be 1 cookie.
If you’re not sensitive to nuts, you can substitute any nut butter for sunbutter and/or replace the sunflower seeds with chopped nuts.
Cookies can be stored in an airtight container in the refrigerator for up to 5 days or in the freezer for 2-3 months.

Nutrition Info Per Serving

Nutrition Facts

Keto No Bake Cookies

Amount Per Serving (1 cookie)

Calories 130
Calories from Fat 104

% Daily Value*

Fat 11.6g18%

Saturated Fat 5.4g27%

Trans Fat 0g

Polyunsaturated Fat 3g

Monounsaturated Fat 1.7g

Cholesterol 0mg0%

Sodium 16mg1%

Potassium 94.4mg3%

Carbohydrates 2.6g1%

Fiber 2.1g8%

Sugar 1g1%

Protein 3.8g8%

Net carbs 0.5g

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

Course: Dessert, Snack

Cuisine: American

Diet: Diabetic, Gluten Free, Vegan

Keyword: diabetic cookies, gluten-free, Keto no bake cookies, low-carb cookies, vegan



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8 Health Tips For Men: How To Improve Good Health And Stay A Healthy Man

By electricdiet / June 9, 2021


8 Health Tips for Men For Preventive Health and To Stay A Healthy Man  

Start with these simple 8 health tips for men to become and stay a healthy man! It’s not always about pumping weights and the latest fad diet! A man can control certain lifestyle aspects for continual wellness and Holly Clegg and Dr. Curtis Chastain’s men’s wellness cookbook, Guy’s Guide To Eating Well  focuses on how to keep men healthy!  By following these men’s health tips, a man can improve his health and hopefully, live a longer, healthier life. Start now by including these simple lifestyle health tips in your daily routine for preventive men’s health. You know Holly is all about healthy, easy delicious recipes, and this oven roasted salmon recipe sums it all up! This Oven Roasted Super Salmon recipe is a mush try!

main dish salads for simple shrimp recipes

8 Health Tips How To Keep Men Healthy Start with Your Heart!

1.  Show Your Heart Some Love: As the #1 cause of death for men in America, that’s enough reason to start on the heart. Keep your heart healthy by cutting out tobacco, keeping your blood pressure in check, and choosing good for you, low saturated fat, high-fiber foods like fruits, vegetables and whole grains.

Southwestern Shrimp Salad Bowl is from the HEART DISEASE: I’M AFRAID OF A BROKEN HEART CHAPTER  giving you the most satisfying shrimp salad for a heart -healthy main dish salad option.

2. Don’t Count Out the Scale: Being overweight and obese increases your risk for arthritis, diabetes, heart disease and GERD. Maintain a healthy weight through diet and exercise to reduce your risk. Guy’s Guide To Eating Well: A Man’s Cookbook for Heath and Wellness focuses on realistic recipes for men’s health. Research shows that eating high quality protein such as lean beef, turkey and chicken can support weight loss with appetite control and satiety. Look for whole unprocessed foods high in fiber, such as fruits, vegetables and whole grains.  These foods also help keep you full without being calorie dense.

Preventive Screenings Are Important Health Tips For Men

3. Screenings Reduce Risk for the Hidden Sniper: Approximately 80% of all cancer deaths in men are caused by only four cancers: lung cancer, colon cancer, skin cancer, and prostate cancer.  Don’t put off screenings – so they will be hidden no longer!

4. Fats Aren’t Created Equal: Evidence shows that fat can increase your risk for heart disease and some cancers. However all fat is not created equal with saturated (fatty beef, poultry with skin, butter, cheese, whole milk) and trans fat (margarine, crackers, fried fast foods, frozen pizza) being more likely to increase your risk. Although all fat should be eaten in moderation  for weight maintenance as they are calorie dense. Try to substitute unhealthy fats for healthy unsaturated fat sources like peanut butter, avocado, nuts, olive oil and fish.

Diabetic Diet is Best Overall Diet – Favorite of Health Tips for Men

5.  Diabetic Diets Aren’t Just For Diabetes: Everyone should be practicing the same well- balanced healthy lifestyle that is required for diabetics. In Guy’s Guide To Eating Well, there is a Diabetes-Obesity Chapter.  More importantly, throughout this book and several of Holly Clegg’s Eating Well cookbooks, diabetic recipes are highlighted with a “D” to show you that a mainstream diet can be diabetic-friendly.  You should eat whole nutritious foods that are moderate sugar, healthy fat and portion control. Choose unprocessed whole foods, and low
 in simple carbohydrates.  Then, you are triggering less insulin while giving your cells nutrients it can use for energy. Fiber, healthy fats, fruits, vegetables and lean protein should be the basis of not just diabetics’ diets but everyone’s for optimum health!

6.  Don’t Let Joint Pain Slow You Down: Arthritis in its most basic definition is inflammation in the joints. Did you know that Arthritis is the 2nd most frequently reported chronic condition with over 100 different types? Everyone can benefit from a plant based whole foods anti-inflammatory diet. Simple changes in your diet can make a healthy man.  

A few specific nutrients are especially important to fight against inflammation. Vitamin C (bell peppers, oranges, spinach) and vitamin A (sweet potato, tomato, carrots) are antioxidant powerhouses that have been shown to reduce inflammation, joint weakness and pain.  

Follow These Men’s Health Tips Before There’s An Issue

7.  Prevent Heartburn before it Starts: Keeping your weight in check and not smoking are two main ways to reduce heart burn risk. Keep a food diary for your personal trigger foods that worsen GERD symptoms. Common trigger foods to avoid include foods high in fat, caffeine, chocolate, onion, citrus, tomato and alcohol.

8.  Eating for Energy: We are not talking about energy drinks and coffee! If you find yourself constantly reaching for these, why not try a different route this time. Consider good nutrition! A balanced diet of complex carbohydrates, protein, and unsaturated fats fuel and help keep your energy levels up throughout the day. Stay hydrated with water and keep nutritious snacks on hand to prevent sluggish blood sugar dips.

Oven Roasted Salmon Recipe Great Recipe for How To Keep Men Healthy

Doesn’t get much better than this Oven Roasted Super Salmon from OBESITY& DIABETES: Why Can’t I Lose Weight? Chapter

This delicious and quick salmon recipe fits in all the 8 Tips For Healthy Men. It’s a delicious diabetic salmon recipe and gluten-free for a perfect choice for how to keep men healthy. With only a few ingredients, the oven roasted salmon doesn’t get any easier or tastier.

Oven Roasted Super Salmon
If you’re trying to include more salmon in your diet, here you go with an unbeatable sweet and spicy rub for a super tasting simple salmon recipe. Diabetic friendly, Gluten-free

    Servings4 serving
    Prep Time5 minutes
    Cook Time15 minutes

    Ingredients

    • 2tablespoons


      light brown sugar

    • 4teaspoons


      chili powder

    • 1teaspoon


      ground cumin

    • 1/4teaspoon


      ground cinnamon



    • salt and pepper to taste

    • 46-ounce


      salmon fillets

    Instructions
    1. Preheat oven 400°F. Coat 11x7x2-inch baking dish coated with nonstick cooking spray.

    2. In small bowl, mix together brown sugar, chili powder, cumin, cinnamon and season to taste. Rub over salmon and place in prepared dish.

    3. Bake 12 – 15 minutes or until fish flakes easily when tested with fork.

    Recipe Notes

    Nutritional information per serving: Calories 257, Calories from Fat 29%, Fat 8g, Saturated Fat 1g, Cholesterol 80mg, Sodium 177mg, Carbohydrates 8g, Dietary Fiber 1g, Total Sugars 7g, Protein 36g, Dietary Exchanges: 1/2 other carbohydrate, 5 lean meat

    Nutritional Nugget: At least two servings of fish (fatty fish preferred) per week is recommended intake by the American Heart Association. Serving suggestions: Try serving with my Quick Caesar Salad (page 86) and Roasted Honey Djion Carrots (page 40).

    How To Keep Men Healthy with Relatable Information and Easy-To-Make Recipes

    How to keep your man healthy? How about keeping your man healthy with quick and delicious recipes! Your entire family will enjoy all these favorites.

    This men’s health cookbook will be a wonderful resource of information plus realistic recipes with ingredients you probably already have.  No worries because the recipes are short and easy to fix.  There’s even a pantry stocking guide.

    Do You Want Menus?  What To Serve With Oven Roasted Salmon

    What to serve with the oven roasted salmon?  You’ll find throughout the book, Holly included serving suggestions so this gives you menu guidance in the kitchen. So, for the oven roasted salmon recipe, she suggests the Quick Caesar Salad and Roasted Lemon Broccoli recipes.

    Men’s Health Tips To Combat  Preventive Men’s Health

    Men's health tips for how to keep men healthy with recipes like oven roasted salmon for healthy man

     

     

     

     

     

     

     

     

    Holly partnered with Dr. Curtis Chastain for this men’s health cookbook, Guy’s Guide to Eating Well. This is your wellness bible packed with simple recipes, reference information, and terrific tips to keep you and those surrounding you healthy. We all cook for different reasons: relaxation, necessity, or just because you have to fire up the grill. But, what about cooking for your health? Health doesn’t discriminate. This book is for men, women, and families. Start with these Top 8 Health Tips For Men for How to Keep Men Healthy this Father’s Day and forever. A healthy man is especially important on this special day!

    Get All Holly’s Healthy Easy Cookbooks

    The post 8 Health Tips For Men: How To Improve Good Health And Stay A Healthy Man appeared first on The Healthy Cooking Blog.



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    Subtypes of Type 2 Diabetes Determined From Clinical Parameters

    By electricdiet / June 7, 2021


    Heterogeneity of Diabetes

    Current Classifications for Diabetes

    Diabetes is a heterogeneous disease with varying manifestation and risk of complications (1). While diabetes is diagnosed on the basis of a single metabolite, glucose, hyperglycemia can arise due to multiple complex etiological processes that can vary between individuals (2). These processes influence the clinical characteristics, progression, drug response, and development of complications. Diabetes is therefore traditionally divided into different types (Fig. 1A). Type 1 diabetes (T1D) and latent autoimmune diabetes of the adult (LADA) both result from autoimmune destruction of β-cells, often, but not always, reflected by presence of pancreatic autoantibodies in the blood that can be used as a diagnostic marker (3). Identification of such antibodies is a strong indicator that the patient will eventually need insulin treatment to maintain glucose homeostasis (4). Rare monogenic diabetes types, such as maturity-onset diabetes of the young (MODY) and neonatal diabetes, account for about 3% of diabetes diagnosed in individuals <30 years of age. Diagnosis requires sequencing of known monogenic diabetes genes, and the consequences for those affected are life changing, since a correct diagnosis has major implications on choice of treatment (5).

    Figure 1
    Figure 1

    Schematic representations of diabetes classification and models of heterogeneity. A: The traditional classification of diabetes where T1D and T2D are viewed as distinct subgroups but with an overlap including intermediate diabetes types such as LADA. Monogenic and secondary diabetes are rare, etiologically distinct subtypes that are not included in the novel subclassification. B: The palette model where the many molecular pathways involved in diabetes pathogenesis are represented as colors and each individual with T2D is represented by the mixed colors of all the pathways affected in that individual. C: The ANDIS model, where colors instead represent clinical parameters, reflecting the underlying mechanisms, that are easy to measure in the clinic. With this approach, individuals are grouped based on their main color into subtypes with different clinical characteristics.

    After exclusion of these and a few other subtypes, such as diabetes secondary to steroid use, cystic fibrosis, and hemochromatosis, the remaining patients, 75–85%, are considered to have type 2 diabetes (T2D). Because autoantibodies are not always measured and genetic diagnostics are often not available, the T2D group may include patients with undiagnosed autoimmune or monogenic diabetes. While the two main types of diabetes have been recognized for thousands of years, the names and definitions have changed and there is still no clear-cut definition that will allow all patients to be classified as either T1D or T2D (2). Some patients show signs of both autoimmune destruction of β-cells and profound insulin resistance with features of the “insulin resistance syndrome.” The large remaining group of true T2D is still highly heterogeneous with respect to clinical characteristics, progression, and risk of complications. Even before diabetes onset, the two prediabetes states, impaired glucose tolerance and impaired fasting glucose, only show partial overlap, suggesting they may result from different pathophysiological mechanisms (6). T2D is thus clearly a multifactorial disease with multiple underlying etiologies that results from the combined effects of numerous genetic and environmental risk factors (7,8).

    Proposed Models of Heterogeneity Within T2D

    The nature and origin of the heterogeneity in T2D has been discussed, contrasting a model where T2D is seen as a mixture of patients with homogeneous phenotypes and distinct mechanisms with a model that assumes that each patient develops diabetes due to a combination of many small defects in different pathways placing patients on a quantitative spectrum of metabolic disturbance (9,10). It is clear that the first alternative is an overly simplistic model, even for the division of patients into T1D and T2D, which has served the clinic well for a long time (11).

    A version of the second alternative was described as the palette model (Fig. 1B), in which each pathway is imagined as a color and each patient given the hue of the combined pathways that are defective in that individual, which for most patients is assumed to be brown (10). As a strategy for personalized medicine, small archetypal groups dominated by one mechanism would be identified to study the mechanism in isolation. While this model fits well with the commonly held view of complex diseases, it does not offer many actionable new avenues for research or clinical implementation, and it does not recognize the easily measurable differences in clinical features between patients.

    Instead, we propose an intermediary model (Fig. 1C), painting with a broader brush and colors that reflect major clinical parameters instead of individual molecular pathways. This model still assumes that diabetes is caused by many overlapping mechanisms, but it postulates that most patients have a predominant color and that dividing patients into shades of red, green, and blue is more useful and informative than thinking of them all as shades of brown, even if some have a muddled or intermediary color. Some mechanisms might play roles, to different extents, in all individuals with diabetes, but it seems reasonable to assume that different pathways dominate, or are even uniquely involved, in leaner patients with insulin deficiency and obese patients with severe insulin resistance and that studying them separately offers advantages similar to studying archetypal groups, with a trade-off of less specificity for larger, more inclusive groups.

    Subclassification of Adult-Onset Diabetes

    ANDIS (All New Diabetics In Scania) is a large diabetes cohort started in 2008, with the purpose of studying diabetes heterogeneity. ANDIS aims to include all newly diagnosed individuals with any type of diabetes in the Scania region in southern Sweden, within 1 year from diagnosis. To date, it includes >20,000 individuals representing >90% of newly diagnosed patients. At registration, two blood measurements are added to standard measurements: glutamate decarboxylase autoantibodies (GADA) and C-peptide.

    In this cohort, we stratified adult individuals into subtypes using a data-driven approach based on the most relevant, easily available clinical variables for individuals with diabetes: GADA, BMI, glycosylated hemoglobin (HbA1c), age at diabetes onset, β-cell function (HOMA2-B), and insulin resistance (HOMA2-IR) estimated from fasting glucose and C-peptide (12). These variables were selected based on clinical experience and current knowledge of T2D, postulating that diabetes develops when insulin secretion fails to meet the demands of insulin resistance (2). Hyperglycemia can thus result from either a deficient insulin secretion, the extreme being T1D, or from severe insulin resistance, with T2D patients seen across this spectrum. Including key measures of the pathogenesis of diabetes, i.e., measures of insulin secretion and action in the definition of diagnosis therefore seems logical.

    We applied two different methods for clustering (12). The first is TwoStep, which determines the optimal number of clusters based on silhouette width, followed by hierarchical clustering. In separate analyses for males and females, the optimal number of clusters was five, which was replicated in the Scania Diabetes Registry (SDR) cohort. One cluster was completely defined by GADA positivity and was therefore referred to as severe autoimmune diabetes (SAID). Using the k-means method, we could identify clusters of similar sizes with the same combination of clinical characteristics in four independent cohorts, including ANDIS and SDR.

    Characteristics of Subtypes

    The five identified clusters had different clinical characteristics, disease progression, and outcomes (Fig. 2). The SAID cluster, defined by presence of GADA, includes antibody-positive individuals traditionally referred to as T1D and LADA and represented 6% of adult individuals in ANDIS. SAID was characterized by relatively early disease onset, low insulin secretion, relatively low BMI, and poor metabolic control (high HbA1c). Measuring additional autoantibodies may increase the prevalence of this subtype, as it has been shown that up to 7% of patients from the GADA-negative clusters have positive islet cell antibodies and/or insulin autoantibodies (13). The second cluster, severe insulin-deficient diabetes (SIDD), comprised 18% of included individuals, with similar characteristics as SAID but without GADA. Patients in the third cluster (15%), severe insulin-resistant diabetes (SIRD), were characterized by very high HOMA2-IR and HOMA2-B and high BMI but low HbA1c. The fourth cluster, mild obesity-related diabetes (MOD) (22%) was also characterized by high BMI but not by insulin resistance. The fifth and largest cluster (39%), mild age-related diabetes (MARD), had late-onset diabetes but otherwise no extreme characteristics. Based on these results, we suggested a new subclassification system for T2D (12,14,15).

    Figure 2
    Figure 2

    Novel diabetes subtype characteristics. Overview of distribution and characteristics of subtypes generated by clustering based on clinical parameters in the Swedish ANDIS cohort.

    Risk of Complications

    The clusters were not only different in their characteristics, but they also had different risks of diabetes-related outcomes (12).

    The SAID and SIDD clusters had very high HbA1c at diagnosis (31% and 25%, respectively, presented with ketoacidosis) and progressed more rapidly to insulin treatment compared with the other clusters. The SIDD group had the highest prevalence of diabetic retinopathy, with 23% showing signs of at least mild retinopathy even when scanned soon after diagnosis.

    The new diabetes clusters were further studied in the German Diabetes Study (GDS), where the low C-peptide secretory capacity of SIDD patients was confirmed using intravenous glucose tolerance tests (13). In GDS, SIDD patients also had higher prevalence of diabetic sensorimotor polyneuropathy and cardiac autonomic neuropathy at diagnosis. In spite of restored glucose homeostasis at the 5-year follow-up, neuronal signaling and nerve function were not restored (13). These results indicate that SIDD patients would benefit from early, intense treatment, frequent monitoring of complications, and sensitive diagnostic methods for early prediction.

    Based on extrapolation from T1D, many clinicians have been misled to think that microangiopathic complications, such as retinopathy, neuropathy, and nephropathy, coincide also in T2D. However, while retinopathy and neuropathy clustered in SIDD individuals, the SIRD subtype had the highest risk of developing diabetic kidney disease (DKD) (12). The SIRD subtype had the lowest mean estimated glomerular filtration rate (eGFR) at diabetes diagnosis and were at increased risk of developing chronic kidney disease (CKD), macroalbuminuria, and end-stage renal disease (ESRD). In the SDR cohort, SIRD patients had two times higher risk of developing CKD and macroalbuminura and almost five times higher risk of ESRD after adjustment for age and sex than MARD patients. Increased DKD in early stages of SIRD was also replicated in GDS (13).

    Dennis et al. (15) looked at risk for CKD stage 3A during the first 4 years after diagnosis in two clinical trials, ADOPT (A Diabetes Outcome Progression Trial) and RECORD (Rosiglitazone Evaluated for Cardiac Outcomes and Regulation of Glycaemia in Diabetes). Despite inclusion criteria that exclude most of the typical SIDD and SIRD individuals (16), they confirmed early signs of kidney disease in SIRD but could not replicate differences in progression after adjustment for initial eGFR (15). In contrast, the increased risk of kidney disease in SIRD remained significant for all CKD stages after adjustment for initial eGFR as well as sex and age in ANDIS (12). In SDR, with an average follow-up of 11 years, the odds ratio for ESRD in SIRD (compared with MARD) was 3.61 (P = 3.9 × 10−5) after adjustment for initial eGFR (12). Even after exclusion of individuals with CKD at diagnosis, the SIRD group had increased risk after adjustment for eGFR and other commonly used variables (Fig. 3).

    Figure 3
    Figure 3

    Adjusted risk of diabetic kidney complications. Cumulative incidence of CKD stage 3A in ANDIS (HRSIRDvsMARD = 1.39, P = 0.018) (A) and ESRD in SDR (HRSIRDvsMARD = 2.53, P = 0.047) (B) in individuals with eGFR >60 at the first measurement after diagnosis, adjusted for sex, age, BMI, HbA1c, and first eGFR. HR, hazard ratio.

    The relationship between insulin resistance and kidney disease is complex. Insulin resistance is a common and early alteration in CKD and almost universal in ESRD (17). In healthy individuals, more than half of the plasma insulin is cleared by the kidney (18). Zaharia et al. (13) used hyperinsulinemic-euglycemic clamp to estimate whole-body insulin resistance showing that whole-body insulin sensitivity was similar to HOMA2-IR estimates, with the lowest M-values in SIRD patients both at diagnosis and after 5 years of follow-up. This shows that SIRD patients are truly insulin resistant and that the increased HOMA2-IR is not merely a result of impaired C-peptide clearance due to reduced kidney function (19).

    SIRD patients also had the highest frequency of nonalcoholic fatty liver disease (NAFLD), as described in ANDIS, defined by two pathological measurements of the liver enzyme ALT and high BMI (12). In line with these findings, individuals assigned to the SIRD cluster in GDS had the highest hepatocellular lipid content (19% compared with <7% for other clusters) and the highest NAFLD fibrosis scores, fatty liver index, and AST-to-platelet ratio index (13).

    Subtypes of Diabetes in Other Populations

    An important question is whether the subclassification can be replicated and used also in other populations. India and China are the most populated countries with the fastest growing economies in the world, and the prevalence of diabetes has tripled in China and doubled in India in less than two decades (20). Both Indian and Chinese populations develop diabetes at a younger age and at lower BMI than Caucasians (21,22). The body composition of South Asians in general is also different compared with the Caucasian population. For a given BMI, the fat percentage is significantly higher in South Asians, accompanied by higher insulin resistance (23).

    An effort to identify the new subtypes of diabetes in the Chinese population was published recently (24). The novel diabetes clustering based on commonly available phenotypes was robustly replicated in 2,316 participants with newly diagnosed diabetes from the China National Diabetes and Metabolic Disorders Study (CNDMDS) and 685 participants from the National Health and Nutrition Examination Survey (NHANES III) from China and the U.S. In the absence of GADA, four clusters could be identified by k-means clustering using age at diagnosis, BMI, HbA1c (or alternatively mean plasma glucose), HOMA2-B, and HOMA2-IR. MARD comprised nearly half the participants in both studies, followed by MOD, whereas SIRD and SIDD were least prevalent. However, SIDD was more prevalent in Chinese than in Caucasian populations. The cluster distribution was similar in other ethnic groups including non-Hispanic White and non-Hispanic Black participants (24). The four clusters recapitulated the cluster characteristics defined in the Swedish population and other follow-up studies based on European populations, suggesting at least some generalizability of the classification in non-European populations.

    Similar efforts are underway in other populations. We have confirmed the generalizability of the classification system in an Indian population diagnosed at <45 years of age (R.B.P. et al., unpublished observations), where we applied the clustering method to 972 T2D patients ∼10 years post-diagnosis. Here we first mapped to cluster centers from the ANDIS cohort and subsequently confirmed the subtypes by applying de novo k-means clustering wherein we obtained a >80% concordance. Using both de novo clustering and a reference is valuable since the de novo clustering allows validation of the stability and reproducibility of clusters in different populations, while using a reference allows comparison between populations. We identified the expected four clusters, albeit with different distributions. Contrary to popular belief, the insulin-deficient SIDD cluster was predominant followed by MOD. Higher degree of nephropathy, retinopathy, and neuropathy was seen in the SIDD cluster compared with the other subtypes (R.B.P. et al., unpublished observations). While these data offer insights into the generalizability of the clustering, further large-scale studies in unselected cohorts are needed before this can be taken into the clinic.

    Important Considerations for the Subclassification

    While the clustering strategy has strong advantages in form of simplicity and utility, it also has limitations. We have shown that clustering is reproducible in unselected European diabetes cohorts using C-peptide and glucose measured at diagnosis and that individuals with these data can be individually classified using ANDIS as reference (12). This does not prove that the clustering method will produce reliable and comparable results in cohorts selected using different inclusion criteria, surrogate variables, or lower-quality data. Using a reference for classification can solve some of these problems but requires large high-quality population-specific cohorts as reference. For European populations, ANDIS can be used as a reference, and a simple online tool allows classification for single individuals as well as full cohorts directly from the clinical parameters already measured (O. Asplund et al., unpublished observations). Importantly, this eliminates the need for scaling full-cohort data that can cause problems in selected cohorts. This tool could easily be expanded to include a selection of reference populations of different ethnicity as well as adaptions for different treatment status and duration of diabetes. However, such reference data from large unselected cohorts is still missing and the validity of the methods in other populations needs to be tested. Once available and tested, such a tool could be made available to clinicians to allow direct classification of patients.

    The importance of correct classification is illustrated by a recent study where Kahkoska et al. (25) attempted a validation of the subtype and assessed their association with diabetes complications in the DEVOTE (A Trial Comparing Cardiovascular Safety of Insulin Degludec Versus Insulin Glargine in Patients With Type 2 Diabetes at High Risk of Cardiovascular Events), LEADER (Liraglutide Effect and Action in Diabetes: Evaluation of Cardiovascular Outcome Results), and SUSTAIN-6 (Trial to Evaluate Cardiovascular and Other Long-term Outcomes With Semaglutide in Subjects With Type 2 Diabetes) cardiovascular outcomes trials. Instead of de novo clustering, they used ANDIS as reference to assign study participants into clusters based on age at diabetes diagnosis, HbA1c, and BMI only. They identified the highest risk for cardiovascular events in the cluster of participants with high HbA1c and low BMI that most closely resembles SIDD but could not replicate the increased risk of kidney disease in cluster B, which most resembles SIRD. This is likely explained by poor performance of the simplified clustering strategy for this subtype. Using the same method in ANDIS, cluster A would include 90% SIDD patients but cluster B only 40% SIRD patients. Given the differences in inclusion criteria, the proportions could be even smaller in the clinical trials. This emphasizes the importance of the HOMA measures for identification of the high-risk SIRD group and the importance of properly validating alternative clustering methods.

    An important question is whether clustering gives the same results at diagnosis and after a longer duration of diabetes, and to what extent individuals will move from one cluster to another. In the DIREVA (DIabetes REgistry VAsa) study from Finland, we clustered both individuals registered within 2 years after diagnosis and individuals with longer duration of diabetes with similar results supporting the robustness of clusters and limited influence of disease duration. One of the few studies addressing this question was GDS, which showed that 5 years after diagnosis 23% could change cluster allocation (13). However, the generalizability of these results is uncertain given that it is a selected cohort and based on only 367 individuals.

    Etiological Differences and Genetics

    Another question concerns etiological differences between clusters. Are they to some extent mechanistically distinct, or do the same mechanisms operate in all subtypes? Or do the clusters perhaps reflect different stages of the natural progression of T2D, with cluster differences depending on how long the individual had undiagnosed diabetes? Or are SIDD patients a group of GADA-negative autoimmune patients? One way to answer this question is using genetic information. More than 400 loci affecting risk of T2D have been identified; many of them are also associated with subphenotypes such as BMI, insulin secretion, and insulin resistance (8). These genetic associations help shed light on what pathways are involved in disease etiology.

    Genetically, SIRD stands out by neither being associated with the well-established T2D locus in TCF7L2 (26,27) nor with a weighed risk score for insulin secretion, which are both strongly associated with SIDD, MOD, and MARD (12), clearly showing that SIRD is a distinct subtype with at least partially different etiology. Another important conclusion from genetics is that only SAID is associated with T1D-associated single nucleotide polymorphisms in the HLA region, giving no indication of involvement of the adaptive immune system in the development of SIDD, and thereby clearly distinguishing it from autoimmune diabetes (12,28). Recently, a genetic variant in PNPLA3 was shown to associate positively with hepatocellular lipid content and the SIRD subtype. Hepatocellular lipid content was higher in the SIRD group compared with the MOD, MARD, and SAID groups and a glucose-tolerant control group. Although the PNPLA3 polymorphism did not directly associate with whole-body insulin sensitivity in SIRD, the G-allele carriers had higher circulating free fatty acid concentrations and greater adipose tissue insulin resistance compared with noncarriers (29).

    A more comprehensive genome-wide analysis of genetic differences between clusters has also been performed, suggesting stronger heritability for the SIDD and MOD groups (D. Mansour-Aly et al., unpublished observations). Thus far, hundreds of genetic loci have been implicated in various diabetes syndromes. Deep phenotyping and a more precise definition can lead to identification of more precise pathways and mechanisms, going beyond what the palette model proposes.

    Clinical Importance

    The fundamental clinical differences between the subtypes suggest that they could benefit from a pathogenetically defined treatment, addressing their main dysfunction (e.g., insulin deficiency in SIDD and insulin resistance in SIRD). Identifying individuals with autoimmune diabetes, in itself a heterogeneous syndrome, at an early stage also has relevance for choice of medication, providing an opportunity to prevent β-cell failure and avoid T2D medication that might even exacerbate the autoimmune process (4).

    The recommendations for management of T2D issued by the American Diabetes Association and the European Association for the Study of Diabetes emphasize the importance of personalization of treatment depending on patient characteristics and comorbidities (30). For patients with established cardiovascular disease or CKD, treatment with glucagon-like peptide 1 receptor agonists (GLP-1RA) or sodium–glucose cotransporter 2 inhibitors (SGLT2i) is recommended, but for other patients, selection between existing treatment options is focused on glucose reduction by stepwise addition of medication on top of metformin, and guided by severity of hyperglycemia, obesity, or cost restrictions. Apart from obesity, the causes and nature of underlying defects are not considered.

    Individuals with SIRD show lower eGFR at diagnosis, indicating that the pathological process starts before diabetes diagnosis. Insulin resistance has long been recognized as a risk factor for DKD and has been shown to contribute to the development of disease through multiple mechanisms (31). Podocyte-specific deletion of the insulin receptor in mice causes albuminuria, together with histological features that recapitulate DKD, even in a normoglycemic environment (32). The fact that the SIRD subtype develops DKD in spite of relatively good metabolic control and low HbA1c levels indicates that treatment of these patients should not aim solely at lowering HbA1c but that improving insulin sensitivity could be beneficial to prevent complications.

    The ADOPT and RECORD trials explored three different treatments in GADA-negative patients (15). The insulin sensitizer rosiglitazone, a thiazolidinedione, showed the strongest effect in reducing HbA1c in the SIRD subgroup (15). Unfortunately, the effect on eGFR was not tested, but it has previously been shown that thiazolidinediones are effective in reducing albuminuria, even when achieving the same HbA1c targets (33). Given the apparent key role of insulin resistance in development of DKD, this effect could be even stronger in SIRD patients.

    Another finding in the ADOPT trial was that age-related (MARD) patients responded best to the insulin secretagogue sulfonylurea (15). In the ANDIS cohort, MARD and SIRD patients had been prescribed similar treatment in spite of considerable differences in risk of complications.

    In the same article, Dennis et al. suggested that simple clinical variables (sex, age, BMI, and HbA1c) perform at least as well as clusters for selecting therapy. However, since there are limitations of the study, e.g., exclusion of the most typical patients from the severe subtypes, the types of medication tested, and the use of HbA1c reduction as the only outcome, this remains to be further tested. It will be interesting to see corresponding studies in clinical trials testing treatments that address the insulin deficiency in SAID and SIDD, such as insulin or GLP-1RA, and effects of insulin sensitizers on complications in SIRD since HOMA measurements are important for the identification of this subtype.

    Another clinical benefit is the possibility to focus resources, including more frequent screening and immediate intensive treatment, to the individuals most likely to develop complications, i.e., the three severe subtypes, whereas the milder forms could perhaps be managed safely by lifestyle interventions and standard care and given lower priority for screenings for microvascular complications.

    Other Clustering Efforts

    There have been a few other efforts to cluster T2D patients using clinical variables. For example, Li et al. (34) used patient electronic medical records, identifying three patient-patient networks with different characteristics, genetic associations, and comorbidities (including everything from micro- and macrovascular complications to allergies and HIV infections). Vaccaro et al. (35) used decision tree–based clustering based on sex, urinary albumin-to-creatinine ratio, and BMI in TOSCA.IT (Thiazolidinediones or Sulfonylureas and Cardiovascular Accidents Intervention Trial). A group including male patients with albumin-to-creatinine ratio >9 mg/g and BMI >28.7 kg/m2 had several conditions associated with insulin resistance including high waist circumference, blood pressure, triglycerides, and HDL cholesterol as well as increased risk of cardiovascular end points, which was better prevented by pioglitazone than sulfonylurea (35).

    Given the robustness of genetics compared with phenotypes, efforts have also been made to identify subtypes using genetics. In one such approach, multivariant-trait association patterns obtained from genome-wide association studies (GWAS) across various traits were leveraged to identify shared disease mechanisms based on the assumption that variants that act along a shared pathway will have similar directional impact on subphenotypes (36). A soft clustering method was applied to group variant-trait associations from publicly available GWAS for 94 known T2D variants and 47 T2D-related traits. Five clusters were obtained, of which two were related to insulin deficiency and three to insulin resistance. Interestingly, seven out of the ten variants associated with SIDD in ANDIS had the strongest weights in the proinsulin/β-cell genetic clusters in the study by Udler et al.; the genetic obesity cluster seemed to correspond to MOD, whereas the genetically obtained insulin resistance cluster shared genetic associations with SIRD, with four variants from Ahlqvist et al. having the strongest weights in the liver/lipid and lipodystrophy clusters in Udler et al. While genetics is certainly more robust over time, clinical application is more problematic. Moreover, it does not reflect the environmental risk factors; heritability is only a minor determinant of risk of T2D. Further, the identified risk loci for T2D only explain a small proportion of the heritability.

    None of these methods have the same reproducibility and utility as the ANDIS-based subtypes.



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    Insulin Resistance and Type 2 Diabetes

    By electricdiet / June 2, 2021


    For well over half a century, the link between insulin resistance and type 2 diabetes has been recognized. Insulin resistance is important. Not only is it the most powerful predictor of future development of type 2 diabetes, it is also a therapeutic target once hyperglycemia is present. In this issue of Diabetes, Morino et al. (1) report a series of studies that provide evidence of a genetic mechanism linking expression of lipoprotein lipase (LPL) to peroxisome proliferator–activated receptor (PPAR)-δ expression and mitochondrial function. This is likely to contribute to the muscle insulin resistance that predisposes to type 2 diabetes.

    Observation of abnormal mitochondrial function in vitro in type 2 diabetes (2) was soon followed by in vivo demonstration of this abnormality in insulin-resistant, first-degree relatives of people with type 2 diabetes (3). Further reports of a modest defect in muscle mitochondrial function in type 2 diabetes were published shortly thereafter (4,5). These studies raised the question of whether type 2 diabetes could be a primary disorder of the mitochondria. However, the study of first-degree relatives tended to be misinterpreted as having shown a major defect in mitochondrial function in type 2 diabetes, although it had studied nondiabetic groups from the opposite ends of the insulin resistance–sensitivity spectrum. Indeed, other studies showed no defect in mitochondrial function in type 2 diabetes (6,7), which led to further confusion. Mitochondrial function was then shown to be acutely modifiable by changing fatty acid availability (8) and that it was affected by ambient blood glucose concentration (9). When ambient blood glucose levels were near normal in diabetes, no defect in mitochondrial function was apparent.

    But if mitochondrial function in well-controlled type 2 diabetes is not abnormal, is a defect in insulin-resistant, first-degree relatives clinically relevant? The answer is provided in Fig. 1, which shows population distributions of insulin sensitivity for normoglycemia, impaired glucose tolerance, and type 2 diabetes. The wide range of insulin sensitivity in the normoglycemic population fully encompasses the range observed in type 2 diabetes. Even though mean insulin sensitivity in diabetes is lower than that of matched control subjects, values are drawn from the same distribution and, with matching for body weight and physical activity, differences will be relatively small. Differences in insulin sensitivity will be particularly evident when making comparisons between groups selected from the extreme ends of the population distribution (Fig. 1). When parameters directly linked to muscle insulin resistance are compared between groups selected in this way, any linked difference will be maximized, making this strategy entirely appropriate to investigate the pathophysiology of muscle insulin resistance.

    FIG. 1.
    FIG. 1.

    Distribution curves of insulin sensitivity as measured by the euglycemic-hyperinsulinemic clamp showing that people with type 2 diabetes sit within the range of the nondiabetic distribution, but toward the lower range. Identification of factors underlying muscle insulin resistance itself can be investigated by comparing groups drawn from the extremes of the total population distribution. Such factors may not be clearly discernible when type 2 diabetic individuals are compared with normoglycemic control subjects matched for weight and physical activity. The data are from previously published population studies of normal glucose tolerance (n = 256), impaired glucose tolerance (n = 119), and type 2 diabetes (n = 194) (20,21).

    Muscle insulin resistance as determined by the euglycemic-hyperinsulinemic clamp is clearly a risk factor for development of type 2 diabetes (10). However, the pathophysiology of hyperglycemia in established diabetes relates to hepatic not muscle insulin resistance. This distinction has been elegantly demonstrated in studies of moderate calorie restriction in type 2 diabetes, which resulted in a fall in liver fat, normalization of hepatic insulin sensitivity, and fasting plasma glucose, but no change in muscle insulin resistance (11). More recent work employing severe calorie restriction confirmed previous findings and also demonstrated a longer-term return of normal insulin secretion as intrapancreatic fat content fell (12). The fact that fasting and postprandial normoglycemia can be restored in type 2 diabetes without change in muscle insulin resistance should not be surprising. Mice totally lacking in skeletal muscle insulin receptors do not develop diabetes (13). People with inactive muscle glycogen synthase are not necessarily hyperglycemic (14), and many normoglycemic individuals maintain normal blood glucose with a degree of muscle insulin resistance identical to that among people who develop type 2 diabetes (Fig. 1). The relevance of muscle insulin resistance for development of type 2 diabetes is more subtle. Over many years and only in the presence of chronic calorie excess, hyperinsulinemia steadily brings about hepatic fat accumulation and hepatic insulin resistance. Onset of hyperglycemia is ultimately determined by failure of nutrient-stimulated insulin secretion (15). This new understanding is described by the twin cycle hypothesis (16). So what actually determines this critical primary insulin resistance in muscle?

    Morino et al. (1) report analyses of mRNA in muscle biopsies to compare expression of genes involved in mitochondrial fatty acid oxidation. Their experiments compare data for subjects at opposite extremes of the insulin resistance spectrum. Findings were confirmed in independent groups selected in the same way and two genes were found to be consistently lower in expression. Using knock down of expression by appropriate inhibitory RNA, Western blotting showed that LPL was the important gene product. In both human rhabdomyosarcoma cells and L6 myocytes, such knock down of LPL induced a decrease in mitochondrial density. The function of LPL is to release fatty acids from triglyceride for direct cellular uptake. The biological relevance of the link between decreased mitochondrial numbers and RNA interference (RNAi) inhibition of LPL was confirmed by observing that the effect was only seen if fat was present in the extracellular media. To test the hypothesis that fatty acid flux into cells regulates mitochondrial biogenesis by a PPAR-dependent process, knock down of PPAR-δ was also shown to decrease mitochondrial density. Furthermore, limitation of fatty acid uptake by directly inhibiting the transmembrane fatty transporter CD36 was shown to achieve the same effect. Overall, these studies suggest that insulin resistance is related to decreased mitochondrial content in muscle due, at least in part, to reductions in LPL expression and consequent decreased PPAR-δ activation.

    This important article establishes a biological mechanism whereby insulin resistance in muscle is causally linked to genetic influences that are measurable in the general population. It focuses on insulin resistance by comparing extremes of the distribution of this characteristic in the normal population. But does insulin resistance cause mitochondrial dysfunction, or vice versa? The former appears more likely on the basis of current evidence. Exercise can reduce insulin resistance and ameliorate mitochondrial dysfunction (17), whereas established mitochondrial dysfunction does not necessarily produce insulin resistance in animal models or in humans (18,19). Understanding the nature of common insulin resistance in muscle and its relationship to type 2 diabetes is long overdue. Future work should determine whether specific therapeutic manipulation can offset the effect of identifiable genetic influences and interrupt the long run-in to type 2 diabetes.

    ACKNOWLEDGMENTS

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

    The author is grateful to Leif Groop of Lund University for permission to use combined data from the Botnia Study and the Malmö Prospective Study in Fig. 1 and to Jasmina Kravic of Lund University for replotting the data.



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    Jalapeno Corn Fritters – My Bizzy Kitchen

    By electricdiet / May 31, 2021






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    Keto Tomato Soup | Diabetes Strong

    By electricdiet / May 29, 2021


    This mouth-watering keto tomato soup is a one-pot recipe that comes together with only a few minutes of prep! Top with feta and fresh basil to enjoy classic comfort food the low-carb way.

    Two bowls of Keto tomato soup topped with feta and garnished with fresh basil, as seen from above

    You’ll be amazed how easy it is to make this keto tomato soup from scratch! Everything comes together in one pot on the stove for a rich, hearty soup with amazing depth of flavor.

    Whether it’s a chilly day outside or you’re simply craving some comfort food, this soup is a year-round favorite. Top with feta and fresh basil for a deliciously cozy meal.

    How to make keto tomato soup

    Once you see how simple it is to make this dish from scratch, you’ll never want to buy the canned stuff again!

    Step 1: In a heavy-bottomed pot, heat the olive oil over medium heat. Once hot, add the onion and sauté for a few minutes.

    Step 2: When the onion is translucent, add the garlic and cook for 1-2 minutes until fragrant.

    Garlic and onion cooking in olive oil in a heavy bottomed pan, as seen from above

    Step 3: Add the Roma tomatoes, tomato paste, oregano, basil, salt, and pepper, then mix well.

    Tomatoes, tomato paste, and seasoning added to the heavy bottomed pan

    Step 4: Cook on medium-high heat for 5 minutes until the tomatoes begin to blister and their skins start to peel off easily.

    Cooking tomatoes starting to blister and peel in the heavy bottom pan

    Step 5: Add the water and mix well. Bring everything to a boil, then reduce the heat to a simmer. Cook for 20 – 25 minutes.

    Step 6: Add the cream and fresh basil.

    Cream and basil added to the base of the soup

    Step 7: Blend with an immersion blender or by carefully transferring the soup to your blender and blending until smooth.

    Blended soup in a heavy bottom pan with a wooden spoon

    How easy was that? Now, you can enjoy low-carb tomato soup without all the unnecessary carbs, fillers, and sodium you get from the canned stuff.

    I recommend garnishing with fresh basil, cracked black pepper, and crumbled feta. Enjoy!

    Two bowls of soup in white bowls, garnished with fresh basil and feta

    Variations for this recipe

    The beauty of this simple soup is that it gives you a great base. From there, you’re welcome to modify however you like.

    Since we use coconut cream, the only dairy in the recipe comes from the optional feta topping. To keep your dish dairy-free and vegan, simply skip the feta. You can top with your favorite vegan cheese if you prefer!

    On the other hand, if you don’t like or don’t have coconut cream, you can easily use heavy whipping cream instead. Just note that this will change the nutritional information slightly, although it shouldn’t alter the net carbs.

    Want to use different toppings? Play around with different cheeses, add a dollop of sour cream, or try some pumpkin seeds for a little crunch. Go ahead and have some fun with it!

    Soup in two white bowls, garnished with feta and fresh basil, next to a ramekin of chopped basil, as seen from above

    What to serve with tomato soup

    Of course, everyone knows that tomato soup and grilled cheese are a classic pairing. If this is what you’re craving, just whip up a grilled cheese with your favorite brand of low-carb bread and dig in.

    Looking for some lighter sides? Keto cheese crackers offer great crunch, and you can even dip them right in the soup. Low-carb cornbread would also be a great way to soak up anything left in the bowl.

    Or, if you’re really craving the comfort, serve your soup alongside some keto cauliflower mac and cheese! It doesn’t get much better than that.

    Storage

    This recipe makes six servings of soup, so unless you’re feeding a crowd, you should have some leftovers to enjoy throughout the week! And what could be better than coming home to ready-made tomato soup?

    Make sure you let your soup cool completely, then transfer to an airtight container. You can store in the refrigerator for up to 5 days.

    Soup in two white bowls, garnished with feta and fresh basil, next to a ramekin of chopped basil, as seen from above

    Other easy keto recipes

    Looking for more low-carb dishes that are simple to make and still bring the flavor? Here are a few of my favorite recipes that I know you’ll love:

    And if you need a way to satisfy your sweet tooth, make sure to check out my roundup of 10 Keto-Friendly Dessert Recipes!

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

    Recipe Card

    Keto Tomato Soup

    This mouth-watering keto tomato soup is a one-pot recipe that comes together with only a few minutes of prep! Top with feta and fresh basil to enjoy classic comfort food the low-carb way.

    Prep Time:10 minutes

    Cook Time:30 minutes

    Total Time:40 minutes

    Servings:6

    Keto tomato soup in a white bowl topped with feta and garnished with fresh basil, as seen from above

    Instructions

    • In a heavy-bottomed pot, heat the olive oil over medium heat. Once hot, add the onion and sauté for a few minutes.

    • When the onion is translucent, add the garlic and cook for 1-2 minutes until fragrant.

    • Add the Roma tomatoes, tomato paste, oregano, basil, salt, and pepper, then mix well.

    • Cook on medium-high heat for 5 minutes until the tomatoes begin to blister and their skins start to peel off easily.

    • Add the water and mix well. Bring everything to a boil, then reduce the heat to a simmer. Cook for 20 – 25 minutes.

    • Add the cream and fresh basil.

    • Blend with an immersion blender or by carefully transferring the soup to your blender and blending until smooth.

    Recipe Notes

    This recipe is for 6 servings of tomato soup.
    For vegan and dairy-free, omit the feta garnish.
    Leftovers can be stored in an airtight container in the refrigerator for up to 5 days.

    Nutrition Info Per Serving

    Nutrition Facts

    Keto Tomato Soup

    Amount Per Serving (1 cup)

    Calories 92
    Calories from Fat 66

    % Daily Value*

    Fat 7.3g11%

    Saturated Fat 2.9g15%

    Trans Fat 0g

    Polyunsaturated Fat 0.6g

    Monounsaturated Fat 3.5g

    Cholesterol 0mg0%

    Sodium 206.6mg9%

    Potassium 291.5mg8%

    Carbohydrates 5.2g2%

    Fiber 2.2g9%

    Sugar 3.5g4%

    Protein 2.3g5%

    Net carbs 3g

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

    Course: Main Course, Soup

    Cuisine: American

    Diet: Diabetic, Gluten Free

    Keyword: dairy-free, gluten-free, Keto Tomato soup, low-carb soup



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    Nicotine and Insulin Resistance: When the Smoke Clears

    By electricdiet / May 27, 2021


    Although cigarette smoking is associated with insulin resistance and an increased risk for type 2 diabetes (1), few studies have examined the metabolic and molecular effects of smoking cessation in humans. Epidemiological data from the Atherosclerosis Risk in Communities study in middle-aged men and women offer several insights into the relationship between smoking cessation and diabetes. This study suggests that individuals who quit smoking are at increased risk of type 2 diabetes and that this risk is highest in the first 2 years after smoking cessation, but that risk declines after this point until no excess risk is observed at 12 years after cessation. The increased risk of type 2 diabetes associated with smoking cessation seems to be partially mediated by weight gain (2,3). In this issue of Diabetes, Bergman et al. (4) examined the metabolic and molecular effects of smoking cessation (for 1–2 weeks) in young, lean, otherwise healthy college students, a population that is increasingly vulnerable to the effects of both cigarette smoking and second-hand smoke exposure. Their results suggest that skeletal muscle insulin resistance in smokers is associated with increased mammalian target of rapamycin (mTOR)/p70S6 K activity and insulin receptor substrate-1 (IRS-1) Ser636 phosphorylation by nicotine, and these effects are reversible with smoking cessation.

    Cigarette smoking increases energy expenditure (Fig. 1), and this effect may be mediated in part by the sympathetic nervous system (5). In addition, smoking enhances lipid mobilization. Hellerstein et al. (6) demonstrated that cigarette smoking acutely increased free fatty acid (FFA) and glycerol fluxes as well as circulating FFA concentrations due to nicotine-induced lipolysis. Cigarette smoking increased delivery of FFA to the liver, increased hepatic reesterification of FFA, and enhanced VLDL secretion, thereby promoting the atherogenic effects of smoking. There were no acute effects of smoking on de novo lipogenesis, whole body fat oxidation, adipocyte reesterification of FFA, or basal hepatic glucose production. Finally, cessation of smoking for a period of only 1 week (while on an isocaloric diet) was not associated with a rebound reduction in fat mobilization, suggesting the absence of a metabolic predisposition to gain weight, assuming there is no increase in caloric intake. However, chronic nicotine withdrawal increases appetite and food intake. Nicotine directly activates the brain melanocortin system in rodents via hypothalamic α3β4-nicotinic acetylcholine receptors, and the weight gain associated with cessation of smoking is due to reduced activity of melanocortin 4 receptors (7,8). More recently, nicotine has been shown to inhibit hypothalamic AMP-activated protein kinase and enhance brown adipose tissue activation, likely via the sympathetic nervous system. On the other hand, nicotine withdrawal restores energy balance by normalizing feeding, thermogenesis, and lipid mobilization via normalization of AMP-activated protein kinase activity (9).

    In rodents, nicotine treatment is associated with enhanced lipolysis, decreased adipose tissue lipoprotein lipase activity, and increased muscle lipoprotein lipase activity (10). Given these findings, Bergman et al. (11) previously examined the effects of smoking on insulin sensitivity and skeletal muscle lipid metabolism. It is noteworthy that there were no differences in skeletal muscle triglyceride (IMTG) or diacylglycerol (DAG) concentration between smokers and nonsmokers in this work despite the presence of nicotine-induced insulin resistance. IMTG fractional synthesis rates were also similar between smokers and nonsmokers. However, Bergman et al. reported increased saturation of both IMTG and DAG in smokers. The only intracellular mediator of nicotine-induced skeletal muscle insulin resistance that the investigators identified in smokers was increased basal IRS-1 Ser636 phosphorylation in skeletal muscle. Interestingly, the effects of smoking on skeletal muscle long-chain fatty acyl-CoA and ceramide, two other lipid metabolites associated with skeletal muscle insulin resistance (12,13), have not been studied.

    As a logical extension of their previous work, Bergman et al. now report the effects of smoking cessation for 1–2 weeks on insulin sensitivity, skeletal muscle insulin signaling, and lipid metabolism in young smokers and compare these results to a control group of healthy nonsmokers. They also explore the role of IRS-1 Ser636 phosphorylation as a mediator of nicotine-induced skeletal muscle insulin resistance. Bergman et al. report that smoking cessation is associated with an improvement in insulin sensitivity in the absence of changes in adiposity or body weight. Consistent with previous studies, the rate of appearance of palmitate was significantly higher in smokers versus nonsmokers and did not change after smoking cessation. As expected, the rate of glucose appearance, whole body fat oxidation, rate of oxidation of palmitate, skeletal muscle IMTG and DAG concentrations as well as IMTG fractional synthesis rates were similar in smokers and nonsmokers and did not change significantly after the intervention. Interestingly, the increase in IMTG and DAG saturation in smokers persisted following smoking cessation. Basal IRS-1 Ser636 phosphorylation was elevated in smokers but decreased significantly following smoking cessation. In cultured L6 myotubes, nicotine exposure significantly increased IRS-1 Ser636 phosphorylation. Since IRS-1 Ser636 phosphorylation is increased by mTOR activation, the investigators proceeded to demonstrate that nicotine acutely increases mTOR activation in cultured myotubes. These effects on IRS-1 Ser636 phosphorylation were blocked by rapamycin. Consistent with these results, nicotine reduced insulin-stimulated glucose uptake in L6 myotubes, and insulin sensitivity was restored by rapamycin.

    This study provides convincing evidence of a direct and partially reversible effect of nicotine on skeletal muscle insulin resistance by increasing IRS-1 Ser636 phosphorylation. However, it should be noted that the improvement in insulin sensitivity following smoking cessation was partial, and the metabolic and molecular mechanisms responsible for the persistence of residual insulin resistance despite smoking cessation need to be further investigated. Although the bulk of evidence from their work does not suggest a key role for intramyocellular lipid as a mediator of nicotine-induced insulin resistance in smokers, measurements of skeletal muscle ceramide and long-chain fatty acyl-CoA content following smoking cessation need to be performed in future studies. In addition, the precise molecular mechanisms by which activation of the nicotinic acetylcholine α1 receptors in the muscle by nicotine results in mTOR activation need to be investigated. Furthermore, measurement of skeletal muscle IRS-1 Ser636 phosphorylation was performed by these investigators in a basal state, and the effect of insulin stimulation on downstream insulin signaling needs to be examined in smokers. Finally, the chronic effects of smoking cessation on insulin sensitivity and β-cell function, as well as adiposity and fat topography, need to be examined in future studies given the association between smoking cessation and weight gain.

    As suggested by the authors, these results are important as they provide the basis for investigating therapeutic agents that oppose skeletal muscle mTOR activation as a strategy for preventing the deleterious metabolic effects of smoking in those who cannot break the habit. Future clinical studies are essential to provide further evidence for the effectiveness of such interventions. However, it must be emphasized that these therapeutic strategies will never replace effective educational programs to prevent smoking among adolescents and young adults.

    FIG. 1.
    FIG. 1.

    Metabolic and molecular effects of smoking (411). Nicotine inhibits hypothalamic AMP-activated protein kinase (AMPK) activity, decreases food intake, and increases thermogenesis. Nicotine enhances lipolysis and increases the delivery of FFA to the liver and skeletal muscle. These effects of nicotine are associated with increased hepatic VLDL secretion and intramyocellular lipid (IMCL) saturation as well as peripheral insulin resistance. Nicotine increases mTOR/p70S6 K activity in cultured L6 myotubes in association with increased IRS-1 Ser 636 phosphorylation and reduced insulin-stimulated glucose uptake, and mTOR inhibitor rapamycin blocks these effects of nicotine. NAchR, nicotinic acetylcholine α1 receptors; P, phosphorylation.

    ACKNOWLEDGMENTS

    This work was supported by a Clinical and Translational Research Grant from the American Diabetes Association to M.B. Additional support was from Diabetes and Endocrinology Research Center (NIH P30 DK-079638).

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



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