Type 1 Diabetes When You Are Sick with a Cold, Flu, or Stomach Virus

By electricdiet / January 15, 2020


Getting “real people” sick with type 1 diabetes inevitably comes with a variety of extra concerns because a severe virus, cold, or infection can have a big impact on your blood sugar and your insulin needs.

In this article, we’ll discuss the need-to-know details of managing type 1 diabetes and your overall wellbeing when you come down with a cold, the flu, or a stomach virus.

Type 1 diabetes when you are sick

During a cold

The most likely effect a cold will have on your diabetes will be insulin resistance, which means you may need to temporarily increase your background/basal insulin dose. 

The increase may be anywhere from 10 to 25 percent of your usual total background/basal insulin needs, suggests Gary Scheiner, CDE from  Integrated Diabetes.

This is an important decision to make with your healthcare team — which means calling them when you realize your illness is affecting your diabetes.

You may find you also need more rapid-acting insulin with meals.

If you notice that your blood sugars are stubbornly high, talk to your healthcare team about making an adjustment in your insulin doses.

Of course, if your cold has a severe impact on your appetite, and you’re eating significantly less than usual, you may find you actually need to decrease your basal/background insulin. 

Regarding medications, try to use sugar-free cough drops and confirm with the pharmacist that any other cold medications are sugar-free.

During the flu

At the very beginning of the flu, you can ask your doctor for a prescription for a newly approved medication called Xofluza, designed to help prevent the flu from fully establishing especially in people for whom the flu can be very dangerous — like people with diabetes!

For a more traditional bout of the flu — which involves a fever, full-body aches, and exhaustion — it’s important to work with your healthcare team because fighting a severe virus like the flu can lead to ketoacidosis.

If you don’t address those ketones early on, it can escalate into full-blown diabetic ketoacidosis which can be life-threatening and land you in the hospital.

  • Take acetaminophen or ibuprofen to manage your fever, as directed by your doctor.
  • Drink plenty of water — all day long! 
  • Check your temperature — anything over 101 degrees should warrant a call to your doctor.
  • Check your blood sugar often — anything persistently over 240 mg/dL should warrant a call to your doctor.
  • Sip on watered-down Gatorade. It’s important that your body gets some carbohydrates along with adequate insulin, especially if you aren’t eating very much food.
  • Make sure to eat! If you don’t eat or drink some carbohydrates, you will develop starvation ketones on top of illness-induced ketones. This can escalate and become dangerous.
  • Test your ketones morning and night with urine strips.
  • If you test for “small” ketones (light pink on the strip): eat some carbohydrates, take your insulin as prescribed, and drink plenty of fluids!
  • If you test for “moderate” to “large” ketones, contact your healthcare team. They may advise you to take a small bolus of insulin to help eliminate the ketones (even if your blood sugar isn’t high).
  • If you test for “large” ketones and begin feeling nauseous, visit your nearest emergency or urgent care center.
  • If your blood sugars become too stubborn to manage at home, visit your nearest emergency or urgent care center. 

During a stomach virus (with vomiting)

While vomiting once or twice from a rotten Deviled Egg isn’t usually a big deal, a stomach virus for a person with type 1 diabetes can become life-threatening if it isn’t handled quickly and properly.

If you begin vomiting and cannot keep down fluids or glucose tabs, you could experience severe low blood sugar while the fast or rapid-acting insulin you recently dosed for your last meal is still active in your bloodstream without any food to absorb. 

If you begin vomiting and cannot keep fluids down, but you do not have excessive insulin on board, the next obstacle you will face is eventual diabetic ketoacidosis due to severe dehydration along with starvation ketones from your inability to eat.

In either situation, when you are repeatedly vomiting and unable to drink water to rehydrate, you need to visit the nearest emergency room to receive intravenous fluids: a combination of lactated ringers (saline, electrolytes, etc.), and possibly glucose or insulin depending on the state of your blood sugar when you arrive.

  • Gatorade (with or without sugar)
  • Apple juice, ginger ale, etc.
  • Pedialyte

Buy these things now — find the full suggestion list from the Centers for Disease Control and Prevention (CDC). Do not wait until you are sick and struggling to manage diabetes on top of a stomach virus. Collect these items now and store them with other medical supplies as part of your sick day management toolkit.

IMPORTANT: You still need to take your background/basal insulin when you are vomiting — as recommended by the National Institute of Diabetes, Digestive, and Kidney Diseases (NIDDK). As a person with type 1 diabetes, you will need to call your healthcare team to adjust your medications but you still need some insulin in your system at all times like every other day of your life.

You should have a glucagon kit on-hand for flu & stomach bug season.

Even if you’ve never needed to use a glucagon kit before, it can be life-saving (and panic-reducing) if you suddenly come down with a severe stomach bug. 

A glucagon kit contains a hormone that tells your liver to release glycogen (stored glucose) which is then converted into glucose and raises your blood sugar.

If you start vomiting up the large dinner you just ate and your blood sugar begins to plummet because of the insulin you took for that meal, glucagon can save your life and make it a much calmer trip to the emergency room when you head there for intravenous fluids to rehydrate after puking.

Tip: If you’re conscious and using a glucagon kit on yourself, you don’t likely need the entire full dose of the glucagon kit. Instead, you can use a syringe to withdraw 10 or 20 units to treat your oncoming low blood sugar and then head to the hospital (with a friend to drive, or via ambulance).

What about anti-vomit medications like Zofran?

These medications are designed with good intentions, but unless you’re taking the kind that’s inserted anally, it’s most likely going to come right back up when you vomit. If you’re battling a true stomach virus, Zofran won’t be properly digested in order to prevent you from vomiting. 

Instead, they’re more effective when used with pregnancy-related nausea or sea-sickness.

Talk to your doctor about insulin dose adjustments

Above all else, talk to your doctor about making adjustments to your insulin doses during a significant illness. While a runny nose won’t likely change your insulin needs, anything else can cause you to need more or less insulin depending on a variety of variables.

If you aren’t eating your usual meals, you won’t need to take your usual fast or rapid-acting insulin doses. And you may need to reduce your long-acting/basal insulin dose.

Even after you’ve recovered, you may need to reduce your background insulin doses if your illness causes a weight-loss of even a few pounds.

Check your blood sugar often and work closely with your healthcare team to get through the bout of illness safely!

Suggested next posts:

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Interaction Between Type 2 Diabetes Prevention Strategies and Genetic Determinants of Coronary Artery Disease on Cardiometabolic Risk Factors

By electricdiet / January 13, 2020


Abstract

Coronary artery disease (CAD) is more frequent among individuals with dysglycemia. Preventive interventions for diabetes can improve cardiometabolic risk factors (CRFs), but it is unclear whether the benefits on CRFs are similar for individuals at different genetic risk for CAD. We built a 201-variant polygenic risk score (PRS) for CAD and tested for interaction with diabetes prevention strategies on 1-year changes in CRFs in 2,658 Diabetes Prevention Program (DPP) participants. We also examined whether separate lifestyle behaviors interact with PRS and affect changes in CRFs in each intervention group. Participants in both the lifestyle and metformin interventions had greater improvement in the majority of recognized CRFs compared with placebo (P < 0.001) irrespective of CAD genetic risk (Pinteraction > 0.05). We detected nominal significant interactions between PRS and dietary quality and physical activity on 1-year change in BMI, fasting glucose, triglycerides, and HDL cholesterol in individuals randomized to metformin or placebo, but none of them achieved the multiple-testing correction for significance. This study confirms that diabetes preventive interventions improve CRFs regardless of CAD genetic risk and delivers hypothesis-generating data on the varying benefit of increasing physical activity and improving diet on intermediate cardiovascular risk factors depending on individual CAD genetic risk profile.

Introduction

The risk of coronary artery disease (CAD), the leading cause of disability and mortality worldwide (1), is increased by known cardiometabolic risk factors (CFRs), such as obesity, high blood pressure, impaired lipid and glucose metabolism, and systemic inflammation (2,3). These metabolic features are also present in many individuals with type 2 diabetes, which may contribute to the approximate doubling of CAD risk in persons with diabetes (4,5). A number of studies have demonstrated the effectiveness of control of CRFs in reducing the risk of cardiovascular outcomes among patients with type 2 diabetes (610).

Individual risk of CAD and type 2 diabetes reflects the interplay between lifestyle behaviors acting on a backdrop of genetic predisposition (11,12). Previous studies have shown that preventive interventions for type 2 diabetes—including lifestyle intervention programs, increasing physical activity, dietary modifications, and the administration of metformin—can improve CRFs among individuals with dysglycemia (1315). However, it is unclear whether the benefits of type 2 diabetes preventive interventions on CRFs are similar for individuals at lower or higher genetic risk for CAD.

In the current study, we leveraged data from the Diabetes Prevention Program (DPP) to examine whether type 2 diabetes prevention strategies, either an intensive lifestyle intervention (ILS) or metformin treatment (MET), modify the association between CAD genetic risk and CRFs in participants at high risk of type 2 diabetes. In addition, we investigated the extent to which separate lifestyle behaviors including physical activity, dietary quality, and body weight loss interact with CAD genetic risk to differently affect CRFs in each DPP intervention group.

Research Design and Methods

DPP

The Diabetes Prevention Program Research Group conducted a multicenter randomized controlled trial in the U.S. that tested the effects of ILS and MET interventions on the incidence of diabetes in glucose-intolerant individuals as previously described in detail (16,17). In brief, a total of 3,234 participants with fasting plasma glucose levels between 5.3 and 6.9 mmol/L, and 2-h plasma glucose levels between 7.8 and 11.0 mmol/L during a standard 75-g oral glucose tolerance test, were randomized to ILS (n = 1,079), MET (850 mg twice daily [n = 1,073]), or placebo (PBO [n = 1,082]). The ILS arm included individual counseling sessions through which participants were encouraged to achieve and maintain a weight reduction of at least 7% of initial body weight through a healthy low-calorie, low-fat diet and to engage in physical activity of moderate intensity, such as brisk walking, for at least 150 min per week. A total of 2,658 participants with available DNA, detailed lifestyle information, and CRF measurements had paired information at both baseline and 1-year follow-up. Each clinical center and the coordinating center obtained institutional review board approval. The 2,658 included in this report provided written informed consent for the main study and subsequent genetic investigations.

Lifestyle Behaviors

Specific lifestyle behaviors including changes in physical activity, dietary quality, and weight loss were assessed at baseline and 1 year. Self-reported levels of leisure-time physical activity were assessed at baseline and after 1 year of follow-up with the Modifiable Activity Questionnaire (18). The physical activity level was calculated as the product of the duration and frequency of each activity (in hours per week), weighted by an estimate of the MET of the activity and summed for all activities performed. Usual daily caloric intake during the previous year, including calories from fat, carbohydrate, protein, and other nutrients, was assessed with a modified version of the Block Food Frequency Questionnaire (19). We further characterized overall dietary quality at baseline and after 1 year of follow up using the Alternate Healthy Eating Index-2010 (AHEI-2010) (20). The AHEI-2010 score is based on 11 foods and nutrients emphasizing higher intake of vegetables (excluding potatoes), fruits, whole grains, nuts and legumes, long-chain n-3 fats, and polyunsaturated fatty acids; moderate intake of alcohol; and lower intake of sugar-sweetened drinks and fruit juice, red and processed meats, trans fat, and sodium. Each component is scored from 0 (unhealthiest) to 10 (healthiest) points, with intermediate values scored proportionally. All component scores were summed to obtain a total score ranging from 0 (nonadherence) to 110 (best adherence) points. Body weight change was defined by the difference between baseline and 1-year follow-up.

Baseline and 1-Year CRF Measurements

We considered the following well-established risk factors for CAD at baseline and 1-year follow-up: BMI, waist circumference (WC), fasting glucose, LDL cholesterol (LDLc), HDL cholesterol (HDLc), triglycerides (TG), systolic (SBP) and diastolic (DBP) blood pressure, C-reactive protein (CRP), fibrinogen, and tissue plasminogen activator (tPA). Measurements were performed at baseline and at 1-year follow-up (95% of participants completed the 1-year follow-up). We also included diabetes incidence as an intermediate CAD risk factor. Diabetes incidence was ascertained at the end of study follow-up. Anthropometric measures included height, weight, waist circumference, and SBP and DBP using standardized methods. Participants fasted for 12 h the night before blood was drawn from an antecubital vein. Standard blood glucose and lipid measurements (TG, HDLc, calculated LDLc) were performed at the DPP central biochemistry laboratory (Northwest Lipid Metabolism and Diabetes Research Laboratories, University of Washington, Seattle, WA) using enzymatic methods standardized to the Centers for Disease Control and Prevention reference methods (21). Measurements of inflammatory markers, including CRP, fibrinogen, and tPA, were also performed at the DPP central biochemistry laboratory as previously reported (13,22).

Genotyping and CAD Polygenic Risk Score

We extracted DNA from peripheral blood leukocytes. Genotyping was done with the HumanCoreExome genotyping array from Illumina at the Genomics Platform. Genotypes were called using Birdsuite (https://www.broadinstitute.org/birdsuite/birdsuite-analysis). A two-stage imputation procedure consisting of prephasing the genotypes into whole chromosome haplotypes followed by imputation itself was conducted. The prephasing was performed using SHAPEIT2 (23). We used 1000 Genomes phase 3 haplotypes as the reference panel (24), and the genotype imputation was done using IMPUTE2 (25). We derived a polygenic risk score (PRS) of 204 variants representative of all the 160 CAD loci that had achieved genome-wide significance for association with CAD in previous association studies published as of December 2017 (26) and used recently to predict the risk of major CAD events among participants with type 2 diabetes at high cardiovascular risk (27) (Supplementary Table 1). For loci with multiple independent variants, the variant with the highest significant association with CAD reported in literature (lead variant) was selected first, followed by any other variant at that locus (independent variant) that was not in linkage disequilibrium (r2 < 0.2) with the lead variant. Three of the 204 CAD risk-increasing variants (rs7797644, rs9365196, and rs9457995) were not available in the DPP genome-wide association study data set (neither were proxies in linkage disequilibrium [r2 > 0.8]). A total of 138 of the 201 CAD risk–increasing variants considered in the study were genotypes and the remaining 63 imputed at high quality (median info score 0.99 [interquartile range 0.97–1.00]).

For calculation of the CAD PRS, each variant was recoded as 0, 1, or 2 according to the number of risk alleles (CAD increasing alleles) and weighted by its relative effect size (β-coefficient) on CAD obtained by the literature-based estimates. We calculated the CAD PRS by using the following equation: PRS = (β1 × SNP1 + β2 × SNP2 + … + β201 × SNP201) × (201/sum of the β-coefficients), where SNPi is the risk allele number of each single nucleotide polymorphism. The CAD PRS ranges from 0 to 402, with each unit corresponding to one average risk allele and higher scores indicating a higher genetic predisposition to CAD. For participants with missing genetic variants, we adjusted the CAD PRS by the number of missing values. Distribution of the weighted CAD PRS falls into the normal distribution curve (Supplementary Fig. 1).

Statistical Analysis

Baseline characteristics that are continuous variables are reported as mean ± SD if normally distributed or as median (25th, 75th percentiles) if not. Categorical variables are presented as frequency. We used generalized linear models to estimate the association of the CAD PRS with CRFs at baseline after adjustment for age at randomization, sex, and the top 10 principal components (PCs) for ancestry. Nonnormally distributed outcomes were log transformed and presented on the ratio scale, exp(β), as the estimated value of CRFs per each 10-unit increase in CAD PRS. In this case, the estimated effect size corresponds with a fractional difference in CRFs. For example, a ratio of 0.9 indicates that the outcome variable changes by a ratio of 0.9 (i.e., 10% lower) per 10-unit higher PRS. Associations were also tested for changes from baseline to 1 year with interaction terms for intervention arms (ILS or MET vs. PBO) after adjustment for age at randomization, sex, PCs, and the respective baseline CRFs. We used Cox proportional hazards models adjusted for the same confounders to investigate the association between CAD PRS and diabetes incidence and the extent to which type 2 diabetes–preventive interventions modified the association between CAD PRS and diabetes incidence. We also tested associations of changes in physical activity, dietary quality, and body weight with 1-year change in CRFs in each treatment group separately. We investigated potential interactions between lifestyle behaviors and PRS on 1-year change in CRFs in each treatment group separately. For statistically significant interactions (P < 0.05), we tested the associations between each 1-SD increase in lifestyle variables and 1-year change in CRFs among individuals classified as at low, intermediate, and high genetic risk on the basis of thirds of the CAD PRS. For each category, we used general linear models after adjustment for age at randomization, sex, the top 10 PCs for ancestry, and the respective baseline CRFs. For rejection of the null hypothesis that type 2 diabetes prevention strategies did not modify the association between CAD genetic risk and CRFs, a two-sided α-level of 0.05 was used to determine statistical significance. SAS, version 9.3, was used for all analyses (SAS Institute, Cary, NC).

Data and Resource Availability

Data used in this study are available on request at dppmail{at}bsc.gwu.edu or by accessing the NIDDK Central Repository.

Results

To investigate whether type 2 diabetes prevention strategies modify the association between CAD genetic risk and CRFs, we used genetic and clinical data collected from 2,658 participants in the DPP. Participants randomly assigned to PBO, ILS, or MET groups displayed no significant differences in baseline characteristics—exception for a lower HDLc and higher TG in the PBO individuals compared with individuals assigned to MET or ILS (Table 1). There were also no major clinical differences between participants included in this study and all DPP participants (Supplementary Table 2).

Table 1

Baseline characteristics of DPP participants according to randomization group among those included in the present analysis

We first assessed the association between CAD PRS and CRFs before intervention in all three treatment groups combined. At baseline, each 10–risk allele increase in the CAD PRS was associated with higher LDLc (mmol/L) (β = 0.09 [95% CI 0.06, 0.13], P < 0.01) and higher DBP (mmHg) (β = 0.52 [95% CI 0.09, 0.95], P = 0.02) after adjustment for age at randomization, sex, and PCs ancestry markers (Table 2). Adjusted mean values of baseline lipid levels and DBP across CAD PRS quartiles are provided in Supplementary Fig. 2. No additional associations were found between CAD PRS and other baseline CRFs, including glycemic traits, anthropometric measures, and inflammation markers (Table 2).

Table 2

Baseline association between the genetic risk score and CAD risk factors

We next investigated the effect of type 2 diabetes–preventive strategies, CAD PRS, and the interaction between them on 1-year change in CRFs. Participants randomized to ILS had greater improvement in all studied CRFs compared with those in the PBO group (P < 0.01 for all) (Table 3). Individuals randomized to MET displayed a significant improvement in glycemia, anthropometric measures, LDLc, HDLc, CRP, and tPA compared with PBO (Table 3). The PRS did not significantly predict 1-year changes in CRFs when we analyzed the entire study population together or in any of the study arms (P > 0.05 for all) (Table 4). The effect of the interaction between CAD PRS and type 2 diabetes–preventive strategies on CRFs outcomes was not significant (Pinteraction > 0.05 for all) (Table 5).

Table 3

Effect of type 2 diabetes–preventive strategies on 1-year change in CAD risk factors by study intervention

Table 4

Association between CAD PRS and 1-year change in risk factors by study intervention

Table 5

Interaction between CAD PRS and intervention group on 1-year change in CAD risk factors

We further evaluated associations between changes in physical activity, diet, and body weight and 1-year change in CRFs in each treatment group and the extent to which lifestyle behaviors interacted with CAD PRS to differently affect 1-year change in CRFs. The greatest changes in physical activity, diet, and body weight were observed in ILS compared with PBO (P < 0.01 for all) (Supplementary Table 3). We showed that changes in body weight improved the majority of CRF parameters irrespectively of the intervention arm (P < 0.01 for all except fibrinogen [Supplementary Table 4]) and that changes in physical activity and dietary score associated with improved anthropometrics, blood lipids, and blood pressure measures among participants in the lifestyle intervention arm (Supplementary Tables 5 and 6). We detected nominal significant interactions between PRS and healthy diet and physical activity on 1-year change in BMI, fasting glucose, TG, and HDLc in individuals randomized to MET or PBO, but none of them achieved the multiple-testing correction for significance (Supplementary Table 7). Among these hypothesis-generating interactions, we highlight the association between increasing dietary quality and 1-year changes in BMI among individuals randomized to MET, which was more prominent in participants at high genetic risk (Pinteraction = 0.01). Mean ± SE changes in BMI per 1-SD increase in diet quality score were −0.19 ± 0.10, −0.29 ± 0.10, and −0.52 ± 0.11 kg/m2 among participants at low, intermediate, and high genetic risk, respectively (Supplementary Table 7).

Discussion

Our findings in the DPP provide evidence on the interplay between genetic factors and type 2 diabetes–preventive strategies on intermediate CRFs. We show that either an ILS or MET has a beneficial effect on 1-year change in different CRFs. While a genetic risk score (comprised of 201 variants associated with CAD) does not appear to alter the effectiveness of either intervention, we show preliminary evidence that increasing physical activity and adhering to a healthy dietary pattern may have a more prominent effect on BMI, fasting glucose, and TG in people at high genetic risk who were not assigned to an ILS. However, these findings warrant further replication in appropriate randomized clinical studies specially designed to investigate such effects. Taken together, our data suggest that independent of genetic risk, interventions designed to prevent the development of type 2 diabetes in individuals with elevated fasting glucose, impaired glucose tolerance, and overweight/obesity can improve the majority of recognized cardiovascular risk factors and that among individuals not randomized to an ILS, the benefit of increasing physical activity and improving diet may vary depending on individual CAD genetic risk profile.

There are three important findings. First, a PRS for CAD is associated with baseline lipid levels and DBP but was not associated with other CAD risk factors such as glycemia or inflammatory markers and did not predict 1-year change in these CRFs during preventive interventions for type 2 diabetes. Our findings that CAD PRS has a strong association with lipid levels is well aligned with findings of previous studies (2831), but the positive association with other intermediate risk factors such as DBP or the lack of association with inflammation parameters has not previously been reported as far as we know. While our results need to be interpreted with caution in the context of multiple hypothesis testing, the most recent data on the genetic overlap between CAD and CRFs suggested that 5–10% of CAD loci relate to blood pressure (32). The potential mechanism linking overlapping loci is likely to be via vascular tone regulation and platelet aggregation (28,33), features that have been predominantly linked with SBP rather than DBP and have inflammation as a common underlying factor (2). Our findings, in individuals at high risk of developing type 2 diabetes, where hypertension is a key feature of the metabolic abnormalities present in individuals with type 2 diabetes, highlight the role of DBP in the complex overlap between CRFs and the polygenic architecture of CAD.

Second, the type 2 diabetes–preventive intervention strategies that we evaluated, MET and ILS, did not interact with CAD genetic risk to differently affect 1-year change in CRFs. In other words, participants were likely to benefit similarly from these interventions despite their genetic susceptibility for CAD. These findings need to be interpreted with caution, since it is possible that highly penetrant single genetic variants could interact with type 2 diabetes prevention strategies with strong effects on specific CRFs. The rationale to use a combined PRS in this study relies on the observation that, similar to type 2 diabetes, for the vast majority of individuals with CAD, genetic susceptibility results from the cumulative effects of numerous variants with modest effects disrupting multiple pathways at the same time (34). Our results are consistent with recent prospective observational studies that have reported that both lifestyle behaviors and genetic predisposition drive CAD risk without evidence of significant interactions (35). In addition, the DPP showed that an intenive lifestyle modification is effective for the prevention of type 2 diabetes regardless of genetic risk based on 34 type 2 diabetes–associated loci (36). Data from Look AHEAD (Action for Health in Diabetes), a long-term randomized clinical trial investigating whether an ILS for weight loss would decrease cardiovascular morbidity and mortality among individuals with type 2 diabetes, showed that a behavioral weight loss treatment did not alter the association between a CAD genetic risk and CAD (37). Findings from the current study in individuals at high risk of type 2 diabetes support the beneficial effect of early type 2 diabetes prevention strategies regardless of CAD genetic susceptibility.

Third, while our results need to be interpreted in the context of multiple hypothesis testing and lack of replication in independent randomized clinical studies (mainly due to the unavailability of similar resources), we found suggestive evidence that improving dietary quality and increasing physical activity may have a more favorable effect on 1-year changes in BMI, fasting glucose, and TG in people at high genetic risk for CAD than in those at low genetic risk. Thus, while an ILS aimed to achieve and maintain a weight reduction of at least 7% of initial body weight through diet and physical activity can effectively reduce intermediate CRFs regardless of genetic risk, the detection of an interaction of CAD genetic risk with dietary quality and physical activity in participants not assigned to the lifestyle intervention suggests a potential additional benefit in those who are at increased genetic risk for CAD. In these individuals not assigned to the lifestyle intervention, the adoption of at least specific lifestyle behaviors such as improving diet or increasing physical activity may be particularly beneficial. In other words, a comprehensive lifestyle intervention that achieves 7% weight loss is effective across the entire gradient of CAD genetic risk, whereas other combinations (such as MET with healthy lifestyles that have a less dramatic effect on weight loss) benefit most those at highest CAD risk. This hypothesis is supported by the observation that we did not see effects of significant interactions between changes in body weight, as a measure of the overall lifestyle modification, and genetic risk for CAD on specific CRFs. However, further studies are needed to confirm these initial findings.

Several limitations of our study are worth noting. First, the findings are based on a single randomized clinical trial. We were not able to conduct a replication study due to the lack of available genetic information in other similar clinical intervention studies that included individuals at high risk of developing type 2 diabetes (38,39). Second, we did not directly investigate the association between the CAD PRS and CAD events due to the unavailability of data in the current study; instead, we used CRFs, which provide early insights into the atherosclerosis disease process, as a proxy for CAD events. Third, the significant interactions we observed may be chance observations due to multiple testing or be affected by risk magnification (i.e., because participants at low risk of a clinical outcome cannot have large absolute risk reduction, the risk difference is magnified by having a higher baseline risk). Finally, while the CAD genetic variants included in our PRS were selected on the basis of the most novel discoveries of genetic variants for CAD risk, we cannot rule out the possibility that a PRS constructed from a different set of as-yet-undiscovered CAD risk variants will influence response to type 2 diabetes–preventive interventions.

In conclusion, our findings in individuals at high risk of type 2 diabetes provide evidence for the beneficial effects of type 2 diabetes–preventive strategies on CRFs regardless of CAD genetic risk profile. Additionally, the effect modification by improving dietary quality and increasing physical activity and diet on the association of CAD genetic risk with cardiovascular risk factors among individuals randomized to MET or PBO illustrates how early CAD-preventive strategies may achieve slightly variable success in individuals with different genetic susceptibility.

Article Information

Acknowledgments. The Diabetes Prevention Program Research Group acknowledges the commitment and dedication of the participants of the DPP and the Diabetes Prevention Program Outcomes Study (DPPOS).

Funding. During the DPP and DPPOS, the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) of the National Institutes of Health provided funding to the clinical centers and the Coordinating Center for the design and conduct of the study and collection, management, analysis, and interpretation of the data (DK-048489). The Southwestern American Indian Centers were supported directly by the NIDDK, including its Intramural Research Program and the Indian Health Service. The General Clinical Research Center Program, National Center for Research Resources, and the Department of Veterans Affairs supported data collection at many of the clinical centers. Funding was also provided by the Eunice Kennedy Shriver National Institute of Child Health and Human Development, the National Institute on Aging, the National Eye Institute, the National Heart, Lung, and Blood Institute, the National Cancer Institute, the Office of Research on Women’s Health, the National Institute on Minority Health and Health Disparities, the Centers for Disease Control and Prevention, the American Diabetes Association, the Swedish Research Council, and the European Commission (CoG-2015_681742_NASCENT and H2020-MSCA IF-2015-703787). Merck KGaA provides medication for DPPOS. DPP/DPPOS have also received donated materials from Bristol-Myers Squibb, Parke-Davis, and LifeScan, Inc. LifeScan, Inc.; Health O Meter; Hoechst Marion Roussel, Inc.; Merck-Medco Managed Care, Inc.; Merck and Co.; Nike Sports Marketing; Slim Fast Foods Co.; and Quaker Oats Co. donated materials, equipment, or medicines for concomitant conditions. McKesson BioServices Corp.; Matthews Media Group, Inc.; and the Henry M. Jackson Foundation provided support services under subcontract with the Coordinating Center.

The sponsor of this study was represented on the Steering Committee and played a part in study design, how the study was done, and publication. All authors in the writing group had access to all data. The opinions expressed are those of the study group and do not necessarily reflect the views of the funding agencies.

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

Author Contributions. J.M. conceived the analysis plan, interpreted the data, and wrote the manuscript. K.A.J. conducted statistical analyses, contributed to the interpretation of the results, and edited and reviewed the manuscript before submission. J.M.M. was responsible for imputation and contributed to PRS design. J.M.M., S.E.K., L.C., M.H., L.M.D., M.R.G.A., G.A.W., S.B.R.J., U.N.I., P.W.F., W.C.K., and J.C.F. contributed to the interpretation of the results and edited and reviewed the manuscript before submission. S.E.K., L.M.D., U.N.I., W.C.K., and the Diabetes Prevention Program Research Group conducted the clinical trial and obtained the phenotypic data. J.C.F. 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 77th Scientific Sessions of the American Diabetes Association, San Diego, CA, 9–13 June 2017.

  • Received January 30, 2019.
  • Accepted October 17, 2019.



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Peach Chia Jam (No Added Sugar)

By electricdiet / January 11, 2020


My husband’s natural penchant to chat led us to acquire 28 pounds of fresh, ripe peaches for $10 last week at the farmers’ market. Needless to say, I’ve been looking for peachy ideas and stumbled onto Peach Chia Jam made in the Instant Pot.

Peach Chia Jam (No Added Sugar)

I’ve seen a lot of chia jam recipes before, mostly of the strawberry variety. I liked the idea of peach jam thickened with chia seeds because they are so darn good for you. Loaded with omega-3 fatty acids, antioxidants, fiber, calcium, and iron, chia seeds may well improve insulin sensitivity and reduce inflammation. Read these 11 proven benefits of chia seeds.

We’ve used chia seeds in smoothie bowls, overnight oats, and fruit tarts before, but I don’t think we’d ever made jam. I saw Plant Based Instant Pot’s Vanilla Maple Peach Chia Jam and decided to give it a Diabetic Foodie spin.

Can I Can or Freeze Chia Jam?

The recipe below is not formulated for safe canning. After you make it, store it in the refrigerator for up to two weeks or in the freezer for up to six months. Thaw the jam in the refrigerator before using.

Can I Make Peach Chia Jam without an Instant Pot?

Certainly! You just need to pay more attention when you cook the fruit on the stovetop. Try Gimme Some Oven’s technique using my ingredients below, adding the lemon juice and extracts along with the chia seeds.

Serving Suggestions

Peach Chia Jam can be part of a diabetic-friendly breakfast, dessert, or a snack:

  • Top these Cottage Cheese Pancakes from DiabetesStrong with a tablespoon or two.
  • Add a dollop to plain Greek yogurt or vanilla ice cream.
  • Swirl it into your favorite smoothie.

Peach Chia Jam (No Added Sugar)

Use fresh, ripe summer peaches in this jam and you won’t need any sweetener

Prep Time: 10 minutes

Cook Time: 7 minutes

Pressure Up/Down: 25 mins

Total Time: 42 minutes

Course:

Breakfast, Condiments

Cuisine:

American

Keyword:

peach chia jam, peach jam, sugar-free peach jam

Servings: 24

Peach Chia Jam (No Added Sugar)

Ingredients

  • 4
    cups
    ripe peaches
    peeled and diced
  • 1
    tablespoon
    fresh lemon juice
  • 1
    teaspoon
    vanilla extract
  • 1
    teaspoon
    almond extract
  • 1/4
    cup
    chia seeds

Instructions

  1. In a 6-quart electric pressure cooker, stir together the peaches, lemon juice, vanilla extract, and almond extract. Close and lock the lid of the pressure cooker and set the valve to sealing. Cook on high pressure for 2 minutes. Allow the pressure to release naturally for 15 minutes, then quick release the remaining pressure. Remove the lid and set it aside.

  2. Use an immersion blender to purée the peach mixture right in the pot. Stir in the chia seeds and hit Sauté. (Use the Sauté/Low setting if your electric pressure cooker has it.) Stir constantly for about 5 minutes or until the jam has reached the consistency you like. Note: splattering will be greatly reduced if you keep stirring.

  3. Cool to room temperature, then transfer the jam to a jar and store in the refrigerator for up to 2 weeks.

Recipe Notes

If you don’t have any almond extract, use all vanilla extract (2 teaspoons total).

This recipe will also work with nectarines.

Nutrition facts per serving (2 tablespoons)

Calories: 29kcal

Fat: 1g

Polyunsaturated fat: 1g

Potassium: 93mg

Carbohydrates: 5g

Fiber: 1g

Sugar: 4g

Protein: 1g

Vitamin A: 3%

Vitamin C: 5%

Calcium: 15%

Iron: 1%



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Nyrvana Truffles – My Bizzy Kitchen

By electricdiet / January 9, 2020


I’ve been a Nyrvana Partner for a few months and I’ve been loving their product.  They have a chocolate and peanut butter truffle that is out of this world.  I never used to have a sweet tooth – salty food was my jam.  But then I found gummy bears and well, I just can’t stop at a handful.

I get introduced to a lot of sugar free products being diabetic.  And I have to admit most of them are gross.  Some have weird aftertastes, or just after one bit I have to throw in the trash.

So I was a bit apprehensive when Nyrvana told me that I could eat one of their truffles and there would be no spike in blood sugar.  Huh!  They have a natural sweetener called (THrē) Natural Sweetener, so each one of these truffles is only 99 calories and 1 net carb.  And they are delicious!

I can no longer drink coffee after 2 p.m., so eating one of these at 3:00 p.m. has been a game changer.  It gives me a boost of energy because it has “nootropics-like Alpha-GPC, L-Theanine, and B12 that improve brain function and promote happiness.” It’s all-natural, gluten-free, lactose-free, no soy, no additives.

You can check it our for yourself – you can click on this link to purchase.  Let me know what you think!  This product has become part of my daily routine and I literally look forward to it every day.  And here’s another thing, unlike gummy bears, I can eat on Nyrvana truffle and be complete satisfied!  #winning

Happy Monday friends – make it a great day!

Biz

This is a paid post, however the opinions are solely my own.





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Workouts for Improved Insulin Sensitivity (Workout Videos)

By electricdiet / January 7, 2020


Whether you live with type 1 diabetes, type 2 diabetes, prediabetes or any other type of diabetes, you can benefit from improving your insulin sensitivity.

The four workout videos in this post can help you do just that.

Your insulin sensitivity is how effective your body is at using the insulin it produces (if you have insulin production) or you inject. The better your insulin sensitivity is, the less insulin, Metformin, or other diabetes drugs do you need to manage your blood sugars.

Improving your insulin sensitivity can make your overall diabetes management easier in both the short and the long run, and exercising is one of the most effective ways of improving your insulin sensitivity.

Workouts for Improved Insulin Sensitivity

The workouts in this post are resistance training workouts. Resistance training simply means that you put your muscles under tension. This can be done using your own body weight or external resistance such as dumbbells or resistance bands.

If you’re new to resistance training, I suggest you start with the home bodyweight workout (video 1). When that’s no longer challenging, move on to using resistance bands or weights.

Instructions

I will demonstrate each exercise and tell you how many sets and reps to do, typically 3 sets of 10-15 or 12-15 repetitions (reps) for each exercise.

That means that you’ll do one exercise for 10-15 or 12-15 reps, rest for 30-60 seconds, do 10-15 or 12-15 more reps, rest again, and then do the last set of reps.

The reason why I’m giving you a range is that you should pick a weight that really challenges you but still allows you to do the target reps. For example, If you can only do 9 reps, the weight is too heavy, and if you can easily bang out 15, it is too light.

Of course, you probably don’t have dumbbells of every different weight at home, so use whatever you have. If you only have light dumbbells and 15 reps feel too easy, just keep going for as many reps as you can.

When you do resistance workouts, please remember that you may see an impact on your insulin sensitivity 24-36 hours after your workout, so be diligent about watching your blood sugars. If you aren’t used to resistance workouts, I really recommend you read my post about how resistance training affects your blood sugar before you do this workout.

And always listen to your body and stop if you feel pain. If you’re not used to exercising and doing a whole workout in one go is too much, just break it up into smaller sessions.

4 sessions of 5 minutes are just as effective as one session of 20 minutes. 

The Workouts

Home (Low Impact) Bodyweight Workout

Home Resistance band Workout

Home Dumbbell Workout

Gym Workout

If you liked these workout videos, please sign up for our newsletter (and get a free chapter from the Fit With Diabetes eBook) using the form below. We send out a weekly newsletter with the latest posts and recipes from Diabetes Strong.



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Longitudinal Pattern of First-Phase Insulin Response Is Associated With Genetic Variants Outside the Class II HLA Region in Children With Multiple Autoantibodies

By electricdiet / January 5, 2020


Abstract

A declining first-phase insulin response (FPIR) is associated with positivity for multiple islet autoantibodies, irrespective of class II HLA DR-DQ genotype. We examined the associations of FPIR with genetic variants outside the HLA DR-DQ region in the Finnish Type 1 Diabetes Prediction and Prevention (DIPP) study in children with and without multiple autoantibodies. Association between FPIR and class I alleles A*24 and B*39 and eight single nucleotide polymorphisms outside the HLA region were analyzed in 438 children who had one or more FPIR results available after seroconversion. Hierarchical linear mixed models were used to analyze repeated measurements of FPIR. In children with multiple autoantibodies, the change in FPIR over time was significantly different between those with various PTPN2 (rs45450798), FUT2 (rs601338), CTSH (rs3825932), and IKZF4 (rs1701704) genotypes in at least one of the models. In general, children carrying susceptibility alleles for type 1 diabetes experienced a more rapid decline in insulin secretion compared with children without susceptibility alleles. The presence of the class I HLA A*24 allele was also associated with a steeper decline of FPIR over time in children with multiple autoantibodies. Certain genetic variants outside the class II HLA region may have a significant impact on the longitudinal pattern of FPIR.

Introduction

The first-phase insulin response (FPIR), a marker reflecting functional capacity of the β-cells in the pancreas, increases physiologically over time in children and adolescents (1). As a sign of deteriorating β-cell function, a decline in FPIR can, however, be observed several years before clinical type 1 diabetes (T1D) (1).

The class II HLA DR-DQ region has been shown to affect the appearance of islet-specific autoantibodies. Children with multiple autoantibodies have a high risk of progressing to clinical disease, and the presence of multiple autoantibodies seems to represent a point of no return (2). However, class II HLA does not have any effect on the progression rate from advanced islet autoimmunity to clinical diabetes (3), which in turn is influenced by some class I HLA alleles (4). Genetic variants outside the HLA region also affect the development of islet autoimmunity and/or progression to clinical diabetes (57).

We recently observed that the association between FPIR and class II HLA DR-DQ is secondary to the presence of multiple autoantibodies (8). The declining pattern of FPIR was associated with multiple autoantibodies irrespective of HLA class II risk group. However, it is possible that other genetic polymorphisms are specifically associated with the evolution of FPIR during progression from autoimmunity to clinical disease.

Here, we studied the role of two class I HLA alleles and eight selected non-HLA gene polymorphisms in the development of insulin secretory capacity as measured by FPIR in children participating in the Finnish Type 1 Diabetes Prediction and Prevention (DIPP) study. Because the presence of multiple islet autoantibodies is strongly associated with β-cell failure, we analyzed separately children with and without multiple biochemical autoantibodies. The selected HLA class I alleles and non-HLA markers have previously been shown to associate with the progression rate from islet autoimmunity to clinical diabetes (4,5,9,10). However, it is not known how or whether these markers are associated with insulin response. The genetic variants of INS and CTSH genes were selected because of their known role in β-cell function (11,12).

Research Design and Methods

The population-based DIPP study was launched in 1994 to screen for diabetes-associated risk by genotyping the major HLA DR-DQ haplotypes at birth (3). The study participants were followed regularly for the appearance of islet autoantibodies at 3–12-month intervals. Children who developed islet autoantibodies (islet cell antibodies and biochemical autoantibodies to insulin, GAD 65, and IA2) underwent an intravenous glucose tolerance test (IVGTT) (1), whereas autoantibodies to zinc transporter 8 were analyzed after IVGTT. β-Cell function was estimated by FPIR and change in FPIR (ΔFPIR) as described previously (8).

Genotyping Methods

HLA typing of major DR-DQ haplotypes was performed with a PCR-based lanthanide-labeled hybridization method using time-resolved fluorometry for detection (3). Genotyping using the Sequenom platform (San Diego, CA) of eight single nucleotide polymorphisms (SNPs), including PTPN22 (rs2476601), IFIH1 (rs1990760), INS (rs689), IKZF4 (rs1701704), ERBB3 (rs2292239), CTSH (rs3825932), PTPN2 (rs45450798), and FUT2 (rs6013380), was performed at the University of Eastern Finland (Kuopio, Finland) (5); CTSH (rs3825932) genotyping was performed using the Taqman SNP Genotyping Assay (Thermo Fisher Scientific, Waltham, MA). The assays of class I HLA alleles (B*39, A*24, and B*39:06) were analyzed on the DELFIA platform (4). SNPs in ERBB3 and IKZF4 polymorphisms were highly correlated (Fisher exact test P < 0.0001).

Autoantibody Analyses

Autoantibodies to insulin, GAD 65, IA2, and zinc transporter 8 were measured in serum samples by a radiobinding assay (13,14). Islet cell antibodies were measured by classical immunofluorescence method applied to sections of human pancreas, blood group O (15).

Study Participants

The 438 study children (268 [61.2%] males) with one or more FPIR results (133 [30.4%] who had progressed to T1D, 35 with a single biochemical, 65 with multiple biochemical autoantibodies who did not progress to T1D during the study period) had been categorized according to the biochemical autoantibody status (none/one or multiple [at least two] biochemical islet autoantibodies) at the time of the first IVGTT. The median age at the first IVGTT, which was performed at least 2 years before diagnosis in progressors, was 4.6 years. Diabetes was diagnosed according to World Health Organization criteria (16).

Statistical Analyses

ΔFPIR was calculated in children with and without multiple biochemical autoantibodies. Before data analysis, the response variable FPIR was log-transformed. Age-adjusted hierarchical linear models (8) applied to analyze the repeated measurements of FPIR included autoantibody status (0 or 1 autoantibody) in children without multiple autoantibodies, genotypes (three groups except for class I HLA genotypes, which were categorized into two groups), and interaction terms genotype by time and autoantibody group by time. The period of 0–5 years from the first IVGTT was examined.

Three types of models (additive, recessive, and dominant) were investigated for the SNP genotypes. In the additive model, all three groups were compared. In the recessive model, children homozygous for the risk allele were compared against those who were not homozygous for the risk allele (two groups). In the dominant model, children carrying the risk allele were compared with those who did not have a risk allele (two groups).

Statistical analyses were performed with JMP Pro version 11.2 and SAS 9.4 for Windows (SAS Institute, Cary, NC) software. P < 0.05 (two-tailed) was considered statistically significant.

Ethical Considerations

This study was conducted according to the guidelines of the Declaration of Helsinki II and was approved by local ethics committees. Written informed consent was obtained from all participants and/or their primary caregivers.

Data and Resource Availability

The data sets generated and analyzed during the current study are not publicly available because of privacy regulations. No applicable resources were generated or analyzed during the current study.

Results

The median FPIR levels and ΔFPIR over the observation period are shown in children with and without multiple biochemical autoantibodies (Tables 1 and 2). FPIR increased over time in children without multiple autoantibodies (Table 2), whereas it declined in those with multiple autoantibodies (Table 1). When the hierarchical linear mixed models were used in children with multiple autoantibodies, modest associations were observed between the evolution of FPIR and three of the gene regions studied (PTPN2 [rs45450798], FUT2 [rs601338], and CTSH [rs3825932]) in the additive model (P = 0.013, P = 0.020, and P = 0.0042, respectively) (Table 3).

Table 1

The median of the first FPIR and ΔFPIR over time according to different genotypes in 195 children with multiple (at least two) biochemical autoantibodies during follow-up

Table 2

The median of the first FPIR and ΔFPIR over time according to different genotypes from 243 children with zero or one biochemical autoantibody at the time of the first IVGTT

Table 3

FPIR as analyzed by a hierarchical linear mixed model adjusted for age from 195 children with multiple autoantibodies at the time of the first IVGTT

In general, children carrying susceptibility alleles had a more rapid decline in insulin secretion compared with those who did not carry a susceptibility allele. Children homozygous for the diabetes-associated risk allele in IKZF4 and PTPN2 genes had a steeper decline of FPIR than those who were not homozygous for the risk allele in these genes (recessive model P = 0.026 and P = 0.0035, respectively) (Table 3). Children carrying the T1D-associated risk allele in FUT2 and CTSH genes experienced also a steeper decline of FPIR than those without the risk allele in these genes (dominant model P = 0.0098 and P = 0.0011, respectively) (Table 3). In an analysis where risk scores were calculated on the basis of T1D risk in four SNPs that were significant in the model, there were no clearly additive effects (data not shown).

The class I HLA A*24 allele was also associated with the evolution of FPIR in children with multiple autoantibodies (P = 0.037) (Table 3) so that the presence of the A*24 allele was associated with a steeper coefficient estimate of FPIR (−0.00037, SE 0.000098, P = 0.0002) (Table 3). In children without multiple autoantibodies, the FPIR increased over time independent on A*24 allele status (Table 2). Furthermore, in children without multiple autoantibodies, ERBB3 (rs2292239) showed a significant association with FPIR in the recessive model (P = 0.0075) (Table 4).

Table 4

FPIR as analyzed by a hierarchical linear mixed model adjusted for age and the number of autoantibodies from 243 children without multiple autoantibodies

Discussion

In this study, we identified novel associations between FPIR and genetic variants known to affect T1D. In children with multiple autoantibodies, the change in FPIR over time was different between those categorized by their PTPN2 (rs45450798), FUT2 (rs601338), CTSH (rs3825932), and IKZF4 (rs1701704) genotypes. Children carrying disease susceptibility alleles had a more rapid decline in insulin secretion over time compared with those who did not carry the allele associated with susceptibility for T1D.

Homozygosity for the risk alleles in the IKZF4 and PTPN2 genes was associated with a steeper decline of FPIR compared with nonhomozygosity. IKZF4 encodes for Eos, which is known to play an important role in lymphoid development (17). A decreased tyrosine phosphatase expression associated with the PTPN2 variant has been shown to sensitize β-cells to cytokine-induced apoptosis (18).

Children with multiple autoantibodies carrying at least one risk allele in the CTSH and FUT2 genes were characterized by a steeper decline of FPIR compared with those who did not carry a risk allele. In recently diagnosed children, however, it was, the CT genotype of CTSH that was associated with the lowest dose of insulin, and the children with the CT genotype were most often in remission 12 months after onset compared with those with other genotypes (11). Interestingly, in healthy adults, the CTSH genotype affected β-cell function in the oral glucose tolerance test but showed no effect on FPIR (11).

Fructosyltransferase 2 enzyme in the Golgi apparatus is involved in the creation of a precursor of the H antigen, which is needed in the synthesis of A and B antigens found in secretions. Individuals carrying the major allele G are called secretors, and they have a functional FUT2 gene (19). In the current study, we observed a difference between children carrying the AA or AG genotype versus the GG genotype. The mechanisms underlying the association between FUT2 and FPIR are not known but could be related by the observation that the secretor status has been associated with composition of the human microbiome (20), although this is controversial (21).

IFIH1, PTPN22, and INS did not show any association with FPIR in this study, which could partly be explained by the observation that they all have been found in the DIPP study to have their main effect on the development of islet autoimmunity (5). It is not known whether associations between insulin secretion and various genotypes would be different in children without or before the appearance of islet autoantibodies. In autoantibody-positive children carrying both INS risk alleles but without class II HLA risk, the increase of FPIR was slower than in children who carried one or no INS risk alleles (12). Some effect of these genes could potentially be seen in subgroups; for example, the association of caesarean section with the development of T1D was reported to be affected by the IFIH1 genotype (22).

Hyperexpression of class I HLA antigens is often seen in pancreatic islets from patients with T1D (23). In this study, the presence of the class I HLA A*24 allele was associated with a steeper decline of FPIR in children with multiple autoantibodies. The presence of the A*24 allele has previously been reported to predict rapid progression to clinical disease in autoantibody-positive relatives of patients with T1D (24).

The unique possibility to analyze young, genetically predisposed children followed intensively over a relatively long period is a strength of this study. A weakness is the low number of observations within some genotypes, which reduces the statistical power. We did not analyze FPIR and its changes over time in relation to the initiating autoantibody (5,9). Although the overall effect of the genetic markers studied on FPIR is modest, it is conceivable that quite a variation in the β-cell mass exists. A wide range of the estimated β-cell mass was observed in adults, even in subjects with low FPIR and multiple autoantibodies (25).

In conclusion, our results show that certain genetic variants outside the class II HLA region can have a significant impact on the longitudinal pattern of FPIR. In children with multiple autoantibodies, the diabetes risk alleles were associated with more rapid loss in β-cell secretory capacity. The underlying mechanisms are still unknown.

Article Information

Acknowledgments. The authors thank all DIPP personnel and families who have participated in this important study.

Funding. This study was funded by JDRF; Special Research Funds for Turku, Oulu and Tampere University Hospitals in Finland; Academy of Finland; Turku University Foundation; University of Turku Graduate School Doctoral Programme; Diabetes Research Foundation in Finland; Sigrid Juselius Foundation; Päivikki and Sakari Sohlberg Foundation; Emil Aaltonen Foundation; Alma and KA Snellman Foundation; Kyllikki and Uolevi Lehikoinen Foundation; and Turku Centre of Lifespan Research.

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

Author Contributions. M.K.K. wrote the first draft. M.K.K., M.-L.M., A.-P.L., J.L., E.L., P.V., A.H., T.H., M.Ki., O.S., M.Kn., R.V., J.I., and J.T. reviewed the manuscript and approved the final version. M.K.K., M.-L.M., A.-P.L., P.V., A.H., T.H., M.Ki., O.S., M.Kn., R.V., J.I., and J.T. acquired the data. M.K.K., A.-P.L., J.L., R.V., J.I., and J.T. interpreted the data. M.K.K. and E.L. analyzed data. M.K.K., O.S., R.V., J.I., and J.T. designed the study. M.K.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.

  • Received March 29, 2019.
  • Accepted October 2, 2019.



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How to Make a Pizza Frittata

By electricdiet / January 3, 2020


Craving pizza? Try making a pizza frittata like Chicken Tikka Masala Frittata. No carby crust in sight!

Pizza Frittata (Chicken Tikka Masala Frittata)

I’d never quite mastered the technique of making a frittata, but many of my friends rave about what a quick dinner they can be. Craving pizza and finding a jar of tikka masala sauce in the refrigerator encouraged me to try again. Hence, this pizza-style Indian frittata recipe was born. It makes a great brunch or a light Sunday night dinner. Try serving it with a spinach salad with mustard vinaigrette or steamed cauliflower and broccoli.

General Pizza Frittata Technique

No extra tikka masala sauce lying around? Use the following technique with any combination of veggies, protein, cheese, and sauce:

  1. Sauté aromatic vegetables like onions, peppers, celery, and garlic in olive oil on the stovetop in an ovenproof skillet.
  2. Add protein like chicken, turkey pepperoni, or chickpeas.
  3. Pour in beaten eggs and cook until set on the bottom, using a spatula to allow raw eggs to run underneath.
  4. Top with cheese and sauce.
  5. Broil until eggs cook through and cheese melts.

Topping Ideas

If the flavors in the Chicken Tikka Masala Frittata don’t appeal to you, try any combination you like. You can go with customary pizza toppings like (turkey) pepperoni and (chicken) sausage or veer off into a Mexican direction. You can see what Cooking Light suggests or try these ideas:

  • Shredded chicken, salsa, and cheddar cheese.
  • Turkey pepperoni, pasta sauce, and mozzarella.
  • Canadian bacon, pineapple tidbits, pasta sauce, and mozzarella.
  • Shredded chicken, salsa verde or Roasted Tomatillo Salsa, and Mexican blend cheese.
  • Chickpeas, tikka masala sauce, and mozzarella (recipe below, substituting chickpeas for chicken).

Please share what toppings you’d like to try on pizza frittata!

Chicken Tikka Masala Frittata

Chicken, cheese, and tikka masala sauce served in a pizza-style frittata

Author: Shelby Kinnaird (Diabetic Foodie)

Prep Time: 10 minutes

Cook Time: 15 minutes

Resting Time: 5 mins

Total Time: 30 minutes

Course:

Breakfast, Brunch, Main Dishes

Cuisine:

Indian

Keyword:

frittata, indian frittata, pizza frittata

Servings: 4

Pizza Frittata (Chicken Tikka Masala Frittata)

Ingredients

  • 6
    large eggs
  • 1/8
    teaspoon
    freshly ground black pepper
  • 1
    tablespoon
    olive oil
  • 1/2
    cup
    diced onion
  • 1/2
    cup
    diced green bell pepper or banana pepper
  • 1
    cup
    cooked shredded chicken breast
  • 3
    ounces
    shredded mozzarella
  • 1/2
    cup
    tikka masala sauce

Instructions

  1. Place rack about 6 inches from heat and preheat broiler.

  2. In a medium bowl, whisk eggs with pepper until well-combined. Set aside.

  3. In an 8-inch ovenproof skillet, heat oil over medium heat. Add onions and peppers and cook, stirring frequently, until the vegetables have softened, 3 to 5 minutes. Stir in the chicken, then pour in the eggs, tilting the pan to distribute the eggs evenly in the pan.

  4. As the eggs begin to set on the bottom, use a spatula to lift the mixture up so the raw eggs flow underneath. When the eggs are about halfway cooked, scatter the mozzarella on top and spoon on the sauce. Remove from heat.

  5. Broil until the top is set, the eggs are cooked, and the cheese has melted, about 5 minutes.

  6. Remove from broiler and let sit for 5 minutes before slicing into quarters and serving.

Recipe Notes

For a vegetarian option, substitute chickpeas for the chicken.

Nutrition facts per serving (1 serving)

Calories: 277kcal

Fat: 17g

Saturated fat: 6g

Polyunsaturated fat: 1g

Monounsaturated fat: 3g

Cholesterol: 352mg

Sodium: 291mg

Potassium: 172mg

Carbohydrates: 7g

Fiber: 1g

Sugar: 3g

Protein: 24g

Vitamin A: 13%

Vitamin C: 57%

Calcium: 13%

Iron: 11%



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Breakfast Casserole Muffins – My Bizzy Kitchen

By electricdiet / January 1, 2020


My friend MaryBeth is the inspiration for these breakfast casserole muffins.  She was our Head Chef at our last Cooking Club and she made the most delicious breakfast casserole.  While delicious, it wasn’t the most WW friendly dish – it was made with Grand’s biscuits, pork sausage, heavy cream, cheddar cheese – full metal jacket!

But she said the only problem with making it for her family was that her daughter Eden is gluten free and can’t eat it when she makes a big casserole, so I decided to make individual breakfast casserole muffins so that she could make them ahead of time and defrost as needed.  

I used Jennie-O turkey breakfast sausage, which is 1 point per ounce and delicious.  It’s got a spicy kick to it which I love.

I made my skinny pizza dough using all purpose gluten free flour from Bob’s Red Mill.  I put one ounce of dough in the bottom of each muffin tin.  Topped with one cracked egg, a tablespoon of the sausage gravy and cheese.

I sprinkled with a bit of paprika and baked them – when you first take them out of the oven they are really puffy, but they will sink just a bit as they cool.  

The muffins are 3 points each if you are on Team Purple or Team Green – add a point for the extra tablespoon of gravy on the side.  If you are on Team Blue they are 5 points because you have to count the egg.

Print

Breakfast Casserole Muffins

Your comfort food breakfast casserole lightened up a bit, gluten free and WW friendly!  3 points for the muffin if you are on Team Green or Team Purple, 5 points for Team Blue.

  • Author: Biz
  • Prep Time: 5
  • Cook Time: 19
  • Total Time: 24 minutes
  • Yield: 10 1x
  • Category: breakfast

Scale

Ingredients

10 ounces gluten free skinny pizza dough
5 ounces turkey breakfast sausage (I used @jennieo)
2 tablespoons gluten free flour
3/4 cup unsweetened almond milk
1 teaspoon crushed red pepper
1/2 teaspoon pepper
10 eggs
1.25 cups reduced fat mozzarella
1 teaspoon paprika
1 tablespoon chopped fresh parsley

Instructions

Heat oven to 375. Spray muffin tin with avocado spray. Place one ounce of dough into the bottom to make 10 muffins.

To make the sausage gravy: cook turkey sausage in a skillet, breaking up the sausage as it cooks. Add in 2 tablespoons gluten free flour and cook for 1 minute. Stir in almond milk and cook a couple minutes until thickened. Stir in the pepper and crushed red pepper, set aside.

Add one scrambled egg to each muffin tin. Add a tablespoon of sausage gravy, then top with 1/8 cup of cheese to each muffin, sprinkle with paprika and bake for 19 minutes.

Let cool for 10 minutes before removing and garnish with a tablespoon of gravy and chopped parsley.

Each muffin is 3 @ww points. These will last in the fridge for a few days. You can freeze on a cookie sheet individually for 30 minutes, then store in a ziplock bag.

Notes

These would be great to make ahead for Thanksgiving weekend, or when you have company over.

So the new WW program rolled out on Monday.  I am using this week to get a handle on it.  I picked the Purple Team, which gives me 300 free foods and 16 points a day, plus my weeklies and activity points.  So far I am loving it!  The freedom of not having to weigh every.single.thing is liberating!

I’ll keep you posted – I will have a full recap next Monday.  Until next time, have a great day!





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How to Bring High Blood Sugar Down Fast

By electricdiet / December 30, 2019


Everyone with diabetes is bound to experience high blood sugars. There are simply too many variables out of our control to prevent high blood sugars from ever happening. 

But the best thing we can do when they do happen is to help them come down as quickly as possible.

Here are four things you can do to bring a high blood sugar level down quickly.

How to Bring High Blood Sugar Down Fast

If you take insulin…

For people with type 1 or type 2 diabetes who take insulin, insulin will always play a major role in how you correct a high blood sugar. There are several things to keep in mind when you use insulin to correct a high blood sugar.

First, check your ketones

In people with type 1 diabetes, high blood sugars can put you at risk of developing ketones. Blood sugar levels over 250 mg/dL with too little insulin can quickly turn into diabetic ketoacidosis (DKA) if it continues to rise.

Test your urine for ketones, and contact your doctor or visit an urgent care facility if you measure with “moderate to large” ketones. When large ketones are present, correcting a high blood sugar with insulin via pump or injection won’t be effective. Instead, you’ll likely need intravenous fluids for a few hours to restabilize. 

If you only have moderate ketones or less, you may be able to correct with insulin at home but you’ll likely need a larger dose than normal. Contact your healthcare team if you test positive for ketones and are unsure of how to safely manage the situation.

Take a correction dose

For those who take fast or rapid-acting insulin — Novolog, Humalog, Apidra, Fiasp, Admelog, Afrezza — you can take a “correction” dose to bring a high blood sugar down.

A “correction factor” is determined with support from your healthcare team. This number tells you how much 1 unit of fast or rapid-acting insulin will lower your blood sugar. For example, a correction factor of 1:50 means that 1 unit of insulin will lower your blood sugar by 50 points.

It’s important to keep in mind how much insulin you may already have on board in your bloodstream before taking an additional dose of insulin. 

Most fast and rapid-acting insulin stays in your system for approximately 3 to 4 hours, which means taking an additional dose of insulin to correct a high could lead to a severe low blood sugar if you already have a significant amount of insulin from your most recent dose still in your bloodstream.

Also, remember to give that correction dose of insulin at least two hours to make an impact on your blood sugar before getting frustrated and taking another injection. You won’t see a significant improvement in your blood sugar until it’s been in your system for at least 2 to 3 hours (unless you use ultra-fast insulins like Afrezza).

Take an “intra-muscular” injection

Insulin was designed to be injected into body fat, but if your blood sugar is high, injecting that “correction dose” of insulin into muscle can help.

When you inject insulin into muscle, it’s absorbed more quickly. This is not something you should do on a daily basis — it will likely leave bruising and again, is not how insulin is supposed to be taken for your everyday insulin needs. But for those severe highs (over 250 mg/dL), it could be a useful option.

Discuss “intra-muscular” injections with your healthcare team before adding them to your diabetes management regimen. 

Consider using Afrezza

Afrezza is a rapid-acting inhaled insulin that some people with type 1 or type 2 diabetes find to be very useful for treating high blood sugar levels.

For a person with type 1, inhaled insulin is never going to replace all of your insulin needs, but many are using it in addition to their normal insulin via syringe or insulin pump because of how quickly it starts working in your bloodstream. 

It’s fairly easy to use but the results and experience of using it can differ from person to person.

Ask your healthcare team for more information about Afrezza. 

Beware of low blood sugars

Remember, above all else, it’s very easy to over-treat a high blood sugar and wind up low. Then you’ll be tempted to binge-eat and wind up high again. This blood sugar roller coaster is exhausting — and dangerous, too.

Frequently finding yourself on the blood sugar roller coaster means your approach to taking insulin and/or how you treat low blood sugars isn’t working and needs some fine-tuning. Work with your healthcare team to reduce and prevent these wild swings to ensure your overall safety and quality of life!

Other things you can do

Even if you don’t take insulin, there are several things you can do to bring a high blood sugar down to a healthier range more quickly. Let’s take a look.

Exercise (even just 10 or 15 minutes)

Exercise can be a very effective method of reducing a high blood sugar. 

If you don’t take insulin, exercise can be a very simple approach to reducing high blood sugar levels. Even just a 15-minute walk can have a big impact on your blood sugar. 

If you do take insulin, it’s important to know that exercising when your blood sugar is above 250 mg/dL and without enough insulin in your system can actually result cause your blood sugar to rise further and put you at risk of developing ketones.

If you already test positive for ketones using a urine strip, you should not try to lower your blood sugar by exercising. This will only increase your ketone level and put more stress on your body.

First, you’ll need a correction dose of insulin but if you plan to exercise, too, you’ll likely be advised by your healthcare team to reduce the correction dose by 50 to 75 percent to prevent a subsequent low blood sugar. 

Drink, drink, drink some water!

Dehydration can cause high blood sugars, which means getting hydrated can help prevent and reduce high blood sugars.

Your blood consists partly of water. When you don’t drink enough water throughout the day, the other things in your blood (like glucose) become more concentrated! And thus, higher blood sugar levels.

You know that uncomfortable thirst you feel when your blood sugars are high? Give in to it. This is how your body helps flush excess sugar out through your urine, and how you replenish the necessary fluid balance in your bloodstream. 

Can oral mediations help a high blood sugar?

While any oral medications you take to help manage your diabetes do improve your blood sugar levels, they are not something you would take an “extra dose” off to correct an occasional high blood sugar level. 

However, if you realize that you forgot to take your dose of a daily medication, this should be part of the process of bringing your high blood sugar down.

Some medications can be taken late, and some medications may need to wait until your next normally scheduled dose. Contact your healthcare team to determine if the diabetes medication you take can be taken late after missing your usual dose.

If you have been skipping this medication altogether — for days or weeks — it’s very likely a significant contributor to the reason your blood sugar is high. These medications are designed to improve your blood sugar levels in a variety of different ways. 

Talk to your healthcare team to better understand the medications you’ve been prescribed and the issues you’re having in taking them as directed. 

When to go to the ER

If you have type 1 diabetes, blood sugar levels over 250 mg/dL accompanied by large ketones and/or symptoms of DKA will likely require a trip to the ER for intravenous fluids.

Let’s take a look at the symptoms of ketosis based on how high your ketone levels are.

Small to Moderate ketones (ketone levels between 10 to 20 mmol/L):

  • Increased thirst
  • Frequent urination
  • Lack of energy
  • Craving sugar

Large ketones / DKA (ketone levels over 20 mmol/L):

  • Severe nausea & vomiting
  • Severe thirst
  • Frequent urination
  • Severe fatigue
  • Blood sugar levels that won’t budge
  • Craving sugar
  • Rotten-fruit smelling breath

If you are puking from a stomach bug, along with high blood sugar levels, you should absolutely go to the ER.

Preventing high blood sugars

Everyone with diabetes experiences high blood sugars sometimes — there are simply too many variables in the human body out of your control to prevent them altogether.

That being said, there are a few guidelines we can all follow to minimize the frequency of high blood sugars:

  • Avoid full-sugar beverages including soda, juice, coffee drinks, iced tea, etc.
  • Choose your carbohydrates carefully — starchy carbs from pasta, candy, bread, desserts, etc. will spike your blood sugar the most
  • Take your medications as prescribed — and contact your healthcare team if you miss a dose to determine if you can take it late
  • Exercise daily — even a 20-minute walk makes a big difference on a daily basis
  • Drink plenty of water to prevent dehydration

And of course, if you’re experiencing high blood sugars are a daily basis and you’re unsure of the cause, talk to your healthcare team about making adjustments in your diabetes management regimen. A slight increase in your medications can have a big impact!

Suggested next posts:

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Crucial Role of the SH2B1 PH Domain for the Control of Energy Balance

By electricdiet / December 28, 2019


Abstract

Disruption of the adaptor protein SH2B1 (SH2-B, PSM) is associated with severe obesity, insulin resistance, and neurobehavioral abnormalities in mice and humans. Here, we identify 15 SH2B1 variants in severely obese children. Four obesity-associated human SH2B1 variants lie in the Pleckstrin homology (PH) domain, suggesting that the PH domain is essential for SH2B1’s function. We generated a mouse model of a human variant in this domain (P322S). P322S/P322S mice exhibited substantial prenatal lethality. Examination of the P322S/+ metabolic phenotype revealed late-onset glucose intolerance. To circumvent P322S/P322S lethality, mice containing a two-amino acid deletion within the SH2B1 PH domain (ΔP317, R318 [ΔPR]) were studied. Mice homozygous for ΔPR were born at the expected Mendelian ratio and exhibited obesity plus insulin resistance and glucose intolerance beyond that attributable to their increased adiposity. These studies demonstrate that the PH domain plays a crucial role in how SH2B1 controls energy balance and glucose homeostasis.

Introduction

Hyperphagia, severe obesity, insulin resistance, and neurobehavioral abnormalities have been reported in individuals with rare coding variants in the gene encoding SH2B1 (SH2-B, PSM) (1,2). Consistently, mice null for Sh2b1 exhibit obesity, impaired glucose homeostasis, and often, aggressive behavior (35). Transgenic expression of the β-isoform of SH2B1 (SH2B1β) in the brain largely corrects the obesity and glucose intolerance of otherwise Sh2b1-null mice (6), suggesting the importance of brain SH2B1 for the control of energy balance and glucose homeostasis.

At the cellular level, SH2B1 is an intracellular adaptor protein that is recruited to phosphorylated tyrosine residues on specific membrane receptor tyrosine kinases (e.g., receptors for brain-derived neurotrophic factor [BDNF], nerve growth factor [NGF], insulin) and cytokine receptor/Janus kinase (JAK) complexes (e.g., leptin receptor/JAK2) and enhances the function of these receptors (713). The exact mechanism(s) by which it does so is unclear, although a variety of mechanisms have been proposed. These include enhanced dimerization causing increased activation of the kinase itself (14), stabilization of the active state of the kinase (15), decreased dephosphorylation or increased complex formation of insulin receptor substrate (IRS) proteins bound to receptors or receptor/JAK2 (16,17), regulation of the actin cytoskeleton (18), and activation of specific pathways, including extracellular signal–regulated kinases (ERKs), Akt, and/or phospholipase Cγ (10,19). Some of these receptors, including the leptin, BDNF, and insulin receptors, play important roles in the central control of energy expenditure and/or glucose homeostasis (20). SH2B1β has been shown to enhance BDNF- and NGF-stimulated neurite outgrowth in PC12 cells (13,21).

The four isoforms of SH2B1 (α, β, γ, δ), which differ only in their COOH termini, share 631 NH2-terminal amino acids. These amino acids possess a dimerization domain, Pleckstrin homology (PH) domain, src-homology 2 (SH2) domain, nuclear localization sequence (NLS), and nuclear export sequence (NES) (2224) (Fig. 1A). The SH2 domain enables SH2B1 recruitment to specific phosphorylated tyrosine residues in activated tyrosine kinases (25). The NLS and NES are essential for SH2B1 to shuttle among the nucleus, the cytosol, and the plasma membrane (22,23). The NLS combined with the dimerization domain enables SH2B1 to associate with the plasma membrane (26). However, the function and importance of the SH2B1 PH domain remains largely unknown. Four human obesity-associated variants lie in the SH2B1 PH domain (Fig. 1A), suggesting the importance of the PH domain in SH2B1 function. The PH domains of some proteins bind inositol phospholipids to mediate membrane localization (27,28). However, 90–95% of all human PH domains do not bind strongly to phosphoinositides and presumably mediate other functions (29). Indeed, the PH domain of SH2B1 neither localizes to the plasma membrane nor is required to localize SH2B1β to the plasma membrane (23,30). Here, we tested the importance of the PH domain of SH2B1 in vivo by generating and studying mice containing human obesity-associated (P322S) or engineered (in-frame deletion of P317 and R318 [ΔPR]) mutations in the SH2B1 PH domain. Our results demonstrate that the SH2B1 PH domain plays multiple crucial roles in vivo, including for the control of energy balance and glucose homeostasis, and in in vitro studies, changes the subcellular distribution of SH2B1β and enhances NGF-stimulated neurite outgrowth in PC12 cells.

Figure 1
Figure 1

Identification of SH2B1 variants and generation of P322S mice. A: Human SH2B1 protein (NP_001139268). Amino acid residues for newly characterized human obesity-associated SH2B1 variants and the previously characterized variant P322S are shown. DD, dimerization domain; P, proline-rich region. B: SH2B1 mutations impair the ability of SH2B1 to enhance neurite outgrowth. PC12 cells transiently coexpressing GFP and either empty pcDNA3.1(+) vector (−), human SH2B1β WT, or SH2B1β mutant were treated with 20 ng/mL rat NGF for 3 days, after which neurite outgrowth was assessed. R555E lacking an intact SH2 domain and the human mutation P322S reported previously were included as positive controls. GFP-positive cells were scored for the presence of neurites two times the length of the cell body (≥400 cells/condition/experiment). The percentage of cells with neurites was determined by dividing the number of GFP-positive cells with neurites by the total number of GFP-positive cells. Data are mean ± SEM (n = 5). Each construct was compared with WT using a two-tailed Student t test. *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001. C: CRISPR/Cas9 schematic for Sh2b1 P322S gene editing. RNA guide sequence, PAM sequence, and cut site for Cas9 are shown for guide 2. The region of the 180-nucleotide oligo donor template used to direct homology-directed repair in the vicinity of the ΔPR deletion and P322 is shown. Mutations in donor template that introduce the C>T mutation to code for P322S and silent mutations to create a diagnostic XbaI site and disrupt guide RNA binding following repair are highlighted in red. aa seq., amino acid sequence. D: Proteins in brain tissue lysates from Sh2b1 WT, P322S/P322S, and KO male mice were immunoblotted with αSH2B1 or αβ-tubulin. Migration of the 100-kDa protein standard and the four isoforms of SH2B1 are shown. IB, immunoblot. E: P322S/P322S mice from intercrosses of heterozygous mice were born at approximately one-half of the expected Mendelian ratio. P < 0.05, χ2 test. n = 179 mice.

Research Design and Methods

Human Studies

The Genetics of Obesity Study (GOOS) is a cohort of >7,000 individuals with severe obesity with age of onset of <10 years (31,32). Severe obesity is defined as a BMI (kg/m2) SD score >3 (U.K. reference population). Whole-exome sequencing and targeted resequencing were performed as in Hendricks et al. (33). All variants were confirmed by Sanger sequencing (1). HOMA of insulin resistance (HOMA-IR) was calculated using the equation HOMA-IR score = [(fasting insulin in μU/mL) × (fasting glucose in mg/dL)] / 405, which estimates steady-state β-cell function and insulin sensitivity (34,35). All human studies were approved by the Cambridge local research ethics committee. Each subject (or parent for those <16 years of age) provided written informed consent; minors provided oral consent.

Animal Care

Animal procedures were approved by the University of Michigan Committee on the Use and Care of Animals in accordance with Association for Assessment and Accreditation of Laboratory Animal Care and National Institutes of Health guidelines. Mice were bred at the University of Michigan and housed in ventilated cages at 23°C on a 12-h light (0600–1800 h)/12-h dark cycle with ad libitum access to food and tap water except as noted. Mice were fed standard chow (20% protein, 9% fat [PicoLab Mouse Diet 20 5058, #0007689]) or, as described in Fig. 2HM and Supplementary Fig. 2FK, a high-fat diet (HFD) (20% protein, 20% carbohydrate, 60% fat [D12492; Research Diets]).

Figure 2
Figure 2

The P322S mutation in SH2B1 leads to impaired glucose homeostasis in male mice challenged with an HFD. AG: Male mice fed standard chow. A: Body weight was assessed at weeks 4–29 (n = 8 WT, 16 P322S/+). B: Food intake was assessed at weeks 5–25 and cumulative food intake graphed (n = 7 WT, 8 P322S/+). C: Body fat mass was determined at week 30. Percent fat mass was determined by dividing fat mass by body weight (n = 9 WT, 16 P322S/+). D: GTT was assessed in 28-week-old mice. After a 4-h fast, mice were injected intraperitoneally with d-glucose (2 mg/kg of body weight). Blood glucose was monitored at indicated times (n = 8 WT, 14 P322S/+). E: ITT was assessed in 29-week-old mice. After a 6-h fast, mice were injected intraperitoneally with insulin (1 IU/kg of body weight). Blood glucose was monitored at indicated times (n = 8 WT, 15 P322S/+). F: At week 30, serum from P322S/+ and WT mice was assayed for leptin (n = 5 WT, 8 P322S/+). G: Thirteen-week-old mice were fasted overnight (1800–0900 h), and insulin levels were determined (n = 5). HM: Male mice fed an HFD. H: Starting at week 6, body weight of mice was assessed weekly (n = 7 WT, 12 P322S/+). I: Food intake in mice was measured during week 27 (n = 7 WT, 12 P322S/+). J: Body fat mass was determined at week 30 (n = 7 WT, 11 P322S/+). K: GTT was assessed as in D at 28 weeks and blood glucose monitored at the times indicated (n = 7 WT, 12 P322S/+). L: ITT was assessed as in C at 29 weeks and blood glucose monitored at the times indicated (n = 6 WT, 10 P322S/+). M: At week 30, mice were fasted overnight, and insulin levels were determined (n = 6 WT, 7 P322S/+). For all comparisons, data are mean ± SEM. *P < 0.05, **P < 0.01. n.s., not significant.

Mouse Models, Genotyping, and Gene Expression

CRISPR/Cas9 genome editing was used to insert the P322S mutation into mice. The reverse complement of the genomic Sh2b1 sequence in C57BL/6J mice (accession number NC_000073, GRC m38) was used to design the reagents for CRISPR. The guides were designed using the website described in Ran et al. (36). The mutations in the donor are summarized in Fig. 1C (details in the Supplementary Data). After testing, each guide/donor combination was injected into C57BL/6J oocytes by the University of Michigan Transgenic Animal Model Core. P322S and ΔPR founders were backcrossed to C57BL/6J mice. The mice were genotyped as described in Truett et al. (37) using primers listed in Supplementary Table 1. The P322S and ΔPR PCR products were digested with XbaI or purified and sequenced. Sh2b1 knockout (KO) mice were obtained from Dr. Liangyou Rui (University of Michigan) and genotyped according to Duan et al. (3). C57BL/6J mice used to invigorate our C57BL/6J colony came from The Jackson Laboratory. Relative levels of Sh2b1 gene expression were determined using RT-PCR (details in the Supplementary Data).

Mouse Body Weight and Food Intake

Mice were individually housed, and body weight and food consumption were assessed weekly.

Mouse Glucose Tolerance Tests, Insulin Tolerance Tests, and Hormone Levels

Mice were fasted 0900–1300 h for glucose tolerance tests (GTTs) or 0800–1400 h for insulin tolerance tests (ITTs). Glucose or human insulin was injected intraperitoneally, and blood was collected from the tail vein. Blood glucose levels were assessed using a Bayer Contour glucometer. For plasma insulin levels, mice were fasted 0800–1400 h, and tail blood was assayed using an Ultra Sensitive Mouse Insulin ELISA kit (#90080; Crystal Chem). Tail blood (0900–1000 h) (Figs. 2F and 4G) or trunk blood after sacrifice (1000–1300 h) (Fig. 6B) from fed mice was tested for leptin using a Mouse Leptin ELISA kit (#90030; Crystal Chem).

Mouse Body Composition, Metabolic Assessment, and Tissue Collection

Body composition was measured at room temperature in the morning or evening (Fig. 6A) using a Minispec LF90 II Bruker Optics nuclear magnetic resonance (NMR) analyzer (University of Michigan Animal Phenotyping Core). To assess metabolic state, mice were single-housed for 3 days and then tested for 72 h using a Comprehensive Lab Animal Monitoring System (Columbus Instruments). O2 consumption (VO2), CO2 production (VCO2), X activity, and Z activity were collected in 20-min bins. The final 24 h of recordings are presented. Mice were sacrificed (1000–1300 h) using decapitation under isoflurane. Trunk blood was collected and the serum stored at −80°C. Tissues were collected, weighed, cryopreserved in liquid nitrogen, and stored at −80°C.

Immunoblotting

Frozen tissues were lysed in L-RIPA lysis buffer (50 mM Tris, 150 mM NaCl, 2 mM EGTA, 0.1% Triton X-100, pH 7.2 containing 1 mM Na3V04, 1 mM PMSF, 10 μg/mL aprotinin, 1 μg/mL leupeptin). Equal amounts of protein were immunoblotted with antibody to SH2B1 (αSH2B1) (sc-136065, RRID:AB_2301871; Santa Cruz Biotechnology) (1:1,000 dilution) or β-tubulin (sc-55529, RRID:AB_2210962; Santa Cruz Biotechnology) (1:1,000 dilution) as described in Joe et al. (19). For immunoprecipitations, tissue lysates containing equal amounts of protein were incubated with αSH2B1 (1:100) and immunoprecipitated and immunoblotted as in Joe et al. PC12 cells (ATCC) were cultured and treated as in Joe et al. Briefly, the cells were grown in PC12 medium A (RPMI medium, 5% FBS, 10% heparan sulfate) in 10-cm dishes coated with rat tail type I collagen (#354236; Corning). Cells were transfected and, 24 h later, incubated overnight in deprivation medium (RPMI medium, 2% heparan sulfate, 1% FBS) before being lysed and immunoblotted with αSH2B1.

Live Cell Imaging

The indicated construct was transiently transfected into 293T cells or PC12 cells. Cells were treated and live cell images captured by confocal microscopy using an Olympus FV500 laser scanning microscope and FluoView version 5.0 software, as in Joe et al. (19).

Neurite Outgrowth

For Fig. 3D, PC12 cells were plated in six-well collagen-coated dishes, transiently transfected as indicated for 24 h, and incubated overnight in deprivation medium. Cells were treated, and neurite outgrowth was determined as in Joe et al. (19). For Fig. 1B, PC12 cells were treated as in Joe et al., with modifications described in the Supplementary Data.

Figure 3
Figure 3

Disruption of the PH domain changes the subcellular localization of SH2B1 and impairs the ability of SH2B1 to enhance NGF-induced neurite outgrowth. A: Proteins in whole-cell lysates from PC12 cells transiently expressing the indicated GFP-SH2B1β were immunoblotted with αSH2B1. Migration of molecular weight standards are on the left. IB, immunoblot. B and C: Live 293T cells and PC12 cells transiently expressing GFP-SH2B1β WT or GFP-SH2B1β ΔPR were imaged by confocal microscopy. D: PC12 cells transiently expressing GFP, GFP-SH2B1β WT, or GFP-SH2B1β ΔPR were treated with 25 ng/mL mouse NGF for 2 days, after which neurite outgrowth was assessed. GFP-positive cells were scored for the presence of neurites more than two times the length of the cell body (total of 300 cells/condition/experiment). The percentage of cells with neurites was determined by dividing the number of GFP-positive cells with neurites by the total number of GFP-positive cells counted. Data are mean ± SEM (n = 3). *P < 0.05.

Structural Modeling and ClustalW Analysis

A structural model for human SH2B1 was created by overlaying the PH domain sequence of human SH2B1 onto the mouse NMR structure of APS (Protein Data Bank ID 1V5M) using the PyMOL Molecular Graphics System version 2.2.3. ClustalW alignments were performed using LaserGene version 14.0.0 (DNASTAR, Madison, WI). Functional homology was defined as residues that match the consensus within 1 distance unit using the PAM250 mutation probability matrix.

Statistics

All nonhuman analyses were carried out using GraphPad Prism software. Body weight, GTTs, and ITTs were analyzed by two-way ANOVA followed by Fisher least significant difference posttest. Food intake was analyzed by linear regression. Significance of the deviation of birth rate from the expected Mendelian ratio was assessed using χ2 test. For other physiological parameters, experimental animals were compared with their wild-type (WT) littermates by two-tailed Student t test. Neurite outgrowth was analyzed by a two-tailed Student t test. For all comparisons, P < 0.05 was considered significant.

Data and Resource Availability

Any raw data sets generated during the current study are available from the corresponding author on reasonable request, with all reagent and analytical details included in the published article (and its Supplementary Data). The mouse models generated and analyzed during the current study are available from the corresponding author upon reasonable request.

Results

Identification and Characterization of 15 Rare Human Variants in SH2B1

Using exome sequencing, targeted resequencing, and Sanger sequencing of 3,000 individuals exhibiting severe obesity before the age of 10 years (33), we identified 15 rare variants in SH2B1 in 16 unrelated individuals (Table 1 and Fig. 1A). Eleven variants are newly identified, while four (R227C, R270W, E299G, V209I) have been previously reported in other obese individuals but not well characterized (4,38,39). Fourteen of these variants are in the first 631 amino acids shared by all isoforms of SH2B1. The mean ± SD BMI SD score of variant carriers was 4.0 ± 0.6. The 15th variant causes the G638R mutation in the COOH-terminal tail unique to the β-isoform of SH2B1. A number of the SH2B1 variant carriers had HOMA-IR (34,35) scores of >1.9, indicating insulin resistance and increased risk of type 2 diabetes (40). Some of the HOMA-IR scores, including those for people carrying three variants in or near the PH domain (G238C, R270Q, and M388V), were particularly high. A spectrum of neurobehavioral abnormalities, including learning difficulties, dyspraxia, hyperactivity/inattention, aggression/emotional lability, anxiety, and autistic traits, were detected in all the individuals for whom behavioral information was available (Table 1). In the neurite outgrowth assay, 7 of these 15 rare variants impaired the ability of SH2B1β to stimulate NGF-induced neurite outgrowth (Fig. 1B), suggesting that many of the variants negatively affect the neuronal function of SH2B1. Because the variants are found in multiple domains in SH2B1 and throughout the SH2B1 sequence, it is not surprising that individuals with different SH2B1 variants have different phenotypes. These newly characterized variants add support to SH2B1 being an important regulator of human body weight, insulin sensitivity, and behavior. Interestingly, four of the human obesity-associated SH2B1 variants lie in the PH domain of SH2B1, suggesting the importance of the PH domain in the ability of SH2B1 to regulate energy balance, glucose metabolism, and behavior.

Table 1

Phenotypes seen in carriers of rare variants in SH2B1

Developmental Lethality in Mice Homozygous for the SH2B1 P322S Mutation

To gain insight into the role of the PH domain of SH2B1 in energy balance and glucose metabolism, we studied the effect of the P322S human obesity-associated SH2B1 PH domain variant in mice. We chose the P322S variant because of its strong association with obesity in the proband family (1), the conservation of P322 across mammals and with the SH2B1 family member SH2B2/APS, its predicted disruptive effect on PH domain function by Provean (RRID:SCR_002182) and PolyPhen (RRID:SCR_013189) analysis, and P322S-dependent deficiencies in SH2B1 function observed in cultured cells (1). We used CRISPR/Cas9-based genome editing to introduce the P322S variant into Sh2b1 in C57BL/6J mice (Fig. 1C). DNA sequencing confirmed germline transmission of the P322S edit (Supplementary Fig. 1A). The P322S mutation neither affects the mRNA levels for any of the Sh2b1 isoforms in the examined tissues (brain, liver, and heart) (Supplementary Fig. 1B and C) nor alters SH2B1 protein levels or isoform selection in brain tissue (Fig. 1D and Supplementary Fig. 1D). However, we found that homozygous (P322S/P322S) mice are born at much less than the expected Mendelian ratio (Fig. 1E), suggesting that the P322S mutation disrupts SH2B1 PH domain function in a manner that interferes with embryo implantation and/or development. Consistent with this, preliminary data from timed pregnancies reveal that at embryonic day 17, the homozygous embryos (P322S/P322S) are also present at less than the expected Mendelian ratio.

Mice Heterozygous for P322S Exhibit Altered Glucose Tolerance, but Not Altered Energy Balance

In addition to the difficulty of producing sufficient P322S/P322S mice for study, the high rate of embryonic lethality in P322S/P322S mice suggested that the surviving P322S/P322S mice might have underlying poor health, which could interfere with the analysis of their metabolic phenotype. For these reasons, and because human obesity is linked with heterozygosity for P322S (1), we studied energy balance and glucose homeostasis in heterozygous (P322S/+) male (Fig. 2) and female (Supplementary Fig. 2) mice. We found no difference in food intake, body weight, or adiposity between WT and P322S/+ mice fed standard chow (9% fat) or an HFD (60% fat). However, in contrast to their WT littermates, 28-week-old HFD-fed P322S/+ male and female mice displayed glucose intolerance in an intraperitoneal GTT. Neither insulin concentrations nor the response to an ITT were altered in the P322S/+ animals compared with littermate controls, however. These findings suggest that the PH domain of SH2B1 is important for SH2B1 function, including for the control of glucose homeostasis, but that the resultant metabolic phenotype is less penetrant in the heterozygous state in mice than it is in humans. We thus sought to study mice homozygous for mutations in the SH2B1 PH domain.

Deletion of P317 and R318 in the PH Domain Alters the Subcellular Localization of SH2B1

Because of the early lethality of P322S/P322S mice, we examined the function of another SH2B1 mutation containing a two-amino acid deletion (ΔPR) within the PH domain of SH2B1 (Fig. 1C and Supplementary Fig. 1E). This mutation arose as a separate line during the generation of the P322S mice.

When transiently expressed as green fluorescent protein (GFP) fusion proteins in PC12 cells, SH2B1β and SH2B1β ΔPR demonstrated similar expression levels (Fig. 3A), suggesting that ΔPR does not destabilize the protein. However, while GFP-SH2B1β localizes primarily to the plasma membrane and cytoplasm in 293T and PC12 cells (as previously shown [22,26]), SH2B1β ΔPR localizes primarily to the nucleus (Fig. 3B and C). The nuclear localization of SH2B1β ΔPR suggests that the ΔPR mutation alters SH2B1 nuclear cycling to favor retention in the nucleus. We predicted that this altered localization would change the cellular function of SH2B1β ΔPR. Indeed, SH2B1β-dependent NGF-stimulated neurite outgrowth in PC12 cells was decreased in cells expressing SH2B1β ΔPR (Fig. 3D). Thus, disruption of the PH domain by the ΔPR mutation alters the subcellular distribution of SH2B1β and impairs the ability of SH2B1β to enhance neurotrophic factor–induced neurite outgrowth.

Obesity, Hyperphagia, and Disrupted Glucose Homeostasis in Mice Homozygous for the SH2B1 ΔPR Mutation

We examined the phenotype of the mice containing the ΔPR mutation with the hope that this mutation might produce a less dramatic reproductive phenotype than that observed with P322S in the homozygous state, allowing us to examine the effects of the ΔPR mutation on energy balance and glucose homeostasis in homozygous mice. As with the P322S mutation, ΔPR did not affect the mRNA levels for any of the Sh2b1 isoforms in the tissues tested (brain or heart) (Fig. 4A). At the protein level, the ΔPR mutation did not alter the relative levels of the different isoforms in brain tissue, although levels of SH2B1 protein were somewhat reduced (Fig. 4B). Importantly, in contrast to P322S/P322S mice, ΔPR/ΔPR homozygous mice were born and survived at the expected Mendelian frequency (Supplementary Fig. 1F), permitting us to examine the effect of this SH2B1 PH domain mutation in the homozygous state.

Figure 4
Figure 4

Disruption of the PH domain in SH2B1 results in obesity. A: mRNA was extracted from brain and heart tissue of WT and ΔPR/ΔPR male mice. The migration of DNA standards (left) and isoform-specific PCR products (right) are shown. bp, base pair. B: Proteins in brain lysates from Sh2b1 WT, ΔPR/ΔPR, and KO male mice were immunoblotted with αSH2B1 and αβ-tubulin. The migration of the 100-kDa protein standard (left) and the four known isoforms of SH2B1 and β-tubulin (right) are shown. IB, immunoblot. C: Body weight was assessed weekly starting at week 4 (males: n = 9 WT, 14 ΔPR/+, 9 ΔPR/ΔPR; females: n = 7 WT, 16 ΔPR/+, 11 ΔPR/ΔPR). D: Representative Sh2b1 WT and ΔPR/ΔPR male mice (6 months). E: Perigonadal fat of representative Sh2b1 WT and ΔPR/ΔPR male littermates (6 months). F and H: Body fat and lean mass was determined at weeks 24–26. Percent fat or lean mass was determined by dividing by body weight (males: n = 5 WT, ΔPR/+, and ΔPR/ΔPR; females: n = 5 WT, 11 ΔPR/+, 8 ΔPR/ΔPR). G: At weeks 24–26, serum from Sh2b1 WT, ΔPR/+, and ΔPR/ΔPR male and female mice was assayed for leptin (males: n = 8 WT, ΔPR/+, and ΔPR/ΔPR; females: n = 7 WT, 6 ΔPR/+, 9 ΔPR/ΔPR). For all comparisons, data are mean ± SEM. *P < 0.05, **P < 0.01, ***P < 0.001. n.s., not significant.

ΔPR/ΔPR mice fed standard chow exhibit significantly increased body weight compared with their WT littermates (Fig. 4C and D). By 20 weeks of age (Fig. 4C), ΔPR/ΔPR male mice were 15 g (>40%) heavier than their WT littermates, while female ΔPR/ΔPR mice were ∼9 g (∼35%) heavier than their WT littermates. It should be noted that we do not believe that the reduced levels of SH2B1 protein in the ΔPR/ΔPR mice can account for the increased obesity detected in ΔPR/ΔPR mice because heterozygote Sh2b1−/+ mice are not obese (5). Body length was not significantly different in preliminary studies (Supplementary Fig. 1G and H). Overall adiposity (Fig. 4E and F) as well as circulating leptin concentrations (Fig. 4G) were increased in ΔPR/ΔPR homozygotes but not lean body mass (Fig. 4H). The heterozygous (ΔPR/+) male and female mice showed no significant increase in adiposity (Fig. 4F). However, ΔPR/+ males had a slight increase in circulating leptin levels (Fig. 4G), suggesting that in males, even a single copy of the ΔPR mutation may be sufficient to produce a minor effect on energy balance.

Increased food intake (assessed at 18–20 weeks) is observed in ΔPR/ΔPR male and female mice compared with their WT and ΔPR/+ littermates (Fig. 5A), while VO2 (at 11–12 weeks of age) (Fig. 5B), respiratory exchange ratio (data not shown), and locomotor activity (data not shown) were not altered. On the basis of these data and the previous finding that Sh2b1 KO mice are obese primarily as a consequence of increased food intake (5), we believe it most likely that the ΔPR mutation caused obesity in the mice primarily as a consequence of increasing food intake rather than decreasing energy expenditure.

Figure 5
Figure 5

The ΔPR mice exhibit increased food intake and reduced glucose tolerance and insulin sensitivity. A: Food intake was measured for weeks 18–20 and cumulative food intake graphed (males: n = 11 WT, 10 ΔPR/+, 7 ΔPR/ΔPR; females: n = 6 WT, 9 ΔPR/+, and ΔPR/ΔPR). Same cohort of mice as in Fig. 4. B: Energy expenditure was assessed at 11–12 weeks using a Comprehensive Lab Animal Monitoring System. VO2 was normalized to lean body mass (LBM) (males: n = 13 WT, 10 ΔPR/+, 11 ΔPR/ΔPR; females: n = 11 WT, 15 ΔPR/+, 13 ΔPR/ΔPR). C: At week 18, mice were fasted for 4 h, and blood glucose was measured (males: n = 8 WT, 12 ΔPR/+, 10 ΔPR/ΔPR; females: n = 7 WT, 14 ΔPR/+, 10 ΔPR/ΔPR). D: GTT was assessed at 18 weeks as in Fig. 2D and blood glucose monitored at times indicated (males: n = 8 WT, 12 ΔPR/+, 10 ΔPR/ΔPR; females: n = 7 WT, 14 ΔPR/+, 10 ΔPR/ΔPR). E: ITT was assessed at 19 weeks as in Fig. 2E and blood glucose monitored at the times indicated (males: n = 9 WT, 14 ΔPR/+, 11 ΔPR/ΔPR; females: n = 6 WT, 13 ΔPR/+, 11 ΔPR/ΔPR). Same cohort of mice as Fig. 4. For all comparisons, data are mean ± SEM. *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001 compared with WT littermates. n.s., not significant.

Glucose Tolerance and Insulin Resistance of ΔPR/ΔPR Mice

We initially examined parameters of glycemic control in ΔPR mice at 18–19 weeks of age. In homozygous ΔPR/ΔPR mice, hyperglycemia at baseline was evident (Fig. 5C) as well as impaired glucose tolerance (male and female mice) and insulin resistance (male mice) in intraperitoneal GTT and ITT, respectively (Fig. 5D and E). Male heterozygous ΔPR/+ mice (like P322S/+ mice) also displayed impaired glucose tolerance (Fig. 5D), although other parameters of glucose homeostasis were not different from WT littermates.

Because the disruption of glucose homeostasis in the aged ΔPR/ΔPR mice presumably resulted (at least in part) from their increased adiposity, we examined glucose homeostasis in young preobese mice to define any adiposity-independent effects of SH2B1 ΔPR on glucose homeostasis. We examined the adiposity of younger ΔPR/ΔPR mice to determine an age at which we might examine glucose homeostasis without it being confounded by increased adiposity. At 11–12 weeks of age, adiposity was already increased in male ΔPR/ΔPR mice but not detectably increased in female ΔPR/ΔPR mice (Fig. 6A). By 7 weeks, leptin levels were increased in male, but not female, ΔPR/ΔPR mice (Fig. 6B). We thus examined glucose homeostasis in ΔPR/ΔPR mice at 8 weeks of age, revealing hyperinsulinemia and glucose intolerance (with unchanged insulin tolerance) in both male and female ΔPR/ΔPR mice (Fig. 6CF). The hyperinsulinemia and glucose intolerance in the presence of unchanged leptin and adiposity in young preobese females suggest that the ΔPR mutation interferes with glucose homeostasis independently of adiposity. Taken together, our results suggest that in obese ΔPR mice, the ΔPR mutation likely interferes with glucose homeostasis both independently of adiposity and secondary to the effects of adiposity on energy balance.

Figure 6
Figure 6

ΔPR female mice exhibit reduced glucose tolerance before the onset of obesity. A: Body fat mass was determined at weeks 11–12. Percent fat mass was determined by dividing the mass by body weight (males: n = 13 WT, 10 ΔPR/+, 11 ΔPR/ΔPR; females: n = 11 WT, 15 ΔPR/+, 13 ΔPR/ΔPR). B: At week 7, serum from WT, ΔPR/+, and ΔPR/ΔPR male mice was assayed for leptin (males: n = 8 WT and ΔPR/ΔPR, 7 ΔPR/+; females: n = 8 WT, 5 ΔPR/+, 12 ΔPR/ΔPR). C: Eight-week-old mice were fasted for 6 h, and insulin levels were determined (males: n = 11 WT, 9 ΔPR/+, 10 ΔPR/ΔPR; females: n = 9 WT, 11 ΔPR/+, 13 ΔPR/ΔPR). D: At week 8, mice were fasted for 4 h, and blood glucose was measured (males: n = 13 WT, 10 ΔPR/+, 11 ΔPR/ΔPR; females: n = 10 WT, 13 ΔPR/+, 12 ΔPR/ΔPR). E: In a separate study, GTT was assessed at 8 weeks as in Fig. 2D and blood glucose monitored at the times indicated (males: n = 13 WT, 10 ΔPR/+, 11 ΔPR/ΔPR; females: n = 10 WT, 13 ΔPR/+, 12 ΔPR/ΔPR). F: ITT was assessed at 9 weeks as in Fig. 2E and blood glucose monitored at the times indicated (males: n = 11 WT, 9 ΔPR/+, 10 ΔPR/ΔPR; females: n = 9 WT, 11 ΔPR/+, 13 ΔPR/ΔPR). Same cohort of mice as Fig. 5B. For all comparisons, data are mean ± SEM. *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001 compared with WT littermates. n.s., not significant.

Discussion

The identification of four human obesity-associated variants in the PH domain-encoding region of SH2B1, the fact that the three individuals with PH domain variants whose behavior has been documented all displayed behavioral abnormalities (1,2, and the present study), and the fact that the three individuals with variants in or near the PH domain had HOMA-IR scores suggesting severe insulin resistance and risk of type 2 diabetes, highlight the importance of the PH domain in SH2B1 function. The lethality of the human obesity-associated P322S mutation in the PH domain of SH2B1 in homozygous P322S/P322S mice demonstrates the importance of this mutation for SH2B1 function in vivo. Similarly, the obesity and diabetes observed in ΔPR/ΔPR mice highlight the importance of the SH2B1 PH domain for SH2B1-mediated metabolic control. The adiposity-independent glucose intolerance of young ΔPR/ΔPR female mice before the onset of obesity as well as of P322S/+ and ΔPR/+ mice also reveals the importance of SH2B1 and its PH domain for the control of glucose homeostasis independent of body weight, as previously suggested from the phenotype of humans bearing mutations in SH2B1 (1,2).

On the basis of the increased food intake of ΔPR/ΔPR mice and the findings in the Sh2b1 KO mice (5), we believe it likely that the increased body weight of the ΔPR/ΔPR mice is due to impaired function of SH2B1 in the hypothalamus. However, SH2B1 is also expressed in the periphery. The increased insulin concentrations with glucose intolerance in young, nonobese female ΔPR/ΔPR mice suggest alterations in tissues that control glucose uptake. However, the presence of glucose intolerance despite the increased insulin levels is consistent with the islets of ΔPR/ΔPR mice having an impaired ability to fully compensate for those alterations (41,42).

While humans heterozygous for P322S exhibit severe obesity, P322S/+ mice display mild glucose intolerance only in aged, HFD-fed animals. Because the region surrounding P322 is conserved between mice and humans (Fig. 7), it is unlikely that the more modest phenotype of P322S/+ mice compared with humans reflects species differences that result in structural changes in the SH2B1 PH domain, per se, but rather that PH domain binding partners may have different tolerances for P322S in mice and humans and/or that human physiology adapts more poorly to the resultant alterations in SH2B1 function. Consistent with the importance of the PH domain for the function of SH2B family members, at least nine point mutations (E208Q/E, A215V, G220V/R, A223V, G229S, D234N, F287S) have been identified in the PH domain of the SH2B1 ortholog SH2B3/Lnk in patients with myeloproliferative neoplasms (4348) (Fig. 7).

Figure 7
Figure 7

ClustalW analysis and modeling of the three-dimensional structure of the PH domain of SH2B1. ClustalW of SH2B1, SH2B2/APS, and SH2B3/Lnk in the region included in the NMR structure of SH2B2/APS. Homologous residues are highlighted in black, and functionally homologous residues are cyan. The PH domain is indicated by the blue line below the sequences. P317, R318 in SH2B1 and the residues in SH2B1 for which variants are associated with obesity are indicated by magenta ovals. The variants in Lnk associated with myeloproliferative neoplasms are indicated by orange ovals. The variants are noted above the ClustalW. Residues within 8 Å of P317 in SH2B1 (P240 in SH2B3/Lnk) are denoted by taupe ovals. Residues within 8 Å of R318 in SH2B1 (K241 in SH2B3/Lnk) in the 3-dimensional structure (Video 1) are denoted by purple ovals. Residues within 8 Å of P322 in SH2B1 (P245 in SH2B3/Lnk) are denoted by green ovals. hum, human; mus, mouse.

Video 1
Video 1

This image is from a video available online at https://bcove.video/2lNxwhM. Modeling of the three-dimensional structure of the PH domain of SH2B1. A model of human SH2B1 was created by overlaying the sequence of the PH domain of human SH2B1 onto the mouse structure of APS. The two PH domain sequences are 75% similar (55.3% identical). The SH2B1 model is missing a single amino acid insertion at residue 296 and the 16-residue insertion at residue 262 which contains the R270W/Q human variant in SH2B1. The amino acid sequence of residues surrounding the P322 site is highly conserved between the two proteins. P322 is shown as yellow sticks. The other human variants in SH2B1 as well as residues P317 and R318 are magenta sticks. The human variants in Lnk are orange sticks. Differences between mouse and human SH2B1 are brown (no sticks). Oxygen atoms are red and nitrogen blue. β-Pleated sheets, α-helices, connecting loops, and hydrogen bonds in the region between the N-terminal end of β-strand 2 and the loop containing P317, R318, and P322 are indicated. The surface of the PH domain is tinted gray. P322 in SH2B1 is within 8 Å of the site of the D234N variant in SH2B3/Lnk. The turn that contains D234N also contains the G229S variant in SH2B3/Lnk and the E299G variant in SH2B1. P317 and R318 in SH2B1 are within 8 Å of the site of G220 R/V and A223V in SH2B3/Lnk. This region is stabilized by π-π stacking between P317 and F309 in SH2B1 (P248 and F240 in Lnk) and a network of hydrogen bonds.

To gain insight into how the SH2B1 P322S mutation or deletion of residues P317 and R318 in SH2B1 might regulate the function of the PH domain in SH2B1, we performed ClustalW analysis of SH2B family members and analyzed a model of SH2B1 that was based on the NMR structure of the PH domain of the SH2B1 family member SH2B2/APS (49). ClustalW analysis of the PH domains of the SH2B family members reveals that the PH domains are highly conserved (Fig. 7). In the model, residues P317, R318, and P322S in SH2B1 are on an exterior surface of the PH domain (Video 1). This surface is presumably a binding interface that interacts with either another region in SH2B1 or another protein. Another human obesity-associated variant in SH2B1 (E299G) as well as five of the human myeloproliferative neoplasm-associated variants in SH2B3/Lnk (G220V/R, A223V, G229S, and D234N) are in proximity to P317, R318, and P322 in SH2B1. In addition, eight of the human variants (E208Q, A215V, G220 V/R, G229S, D234N in SH2B3/Lnk and E299G, P322S in SH2B1) as well as P317 and R318 in SH2B1 are on or in proximity to this putative protein-binding interface (Fig. 7 and Video 1).

The number of human variants in SH2B1 and SH2B3 in this region of the PH domain suggests that small structural changes in this region as a result of mutation or other modification have the potential to produce substantial functional consequences. Because the residues corresponding to P317 and R318 in SH2B1 are on the surface of the PH domain and do not substantially change the direction of the loop, the P317, R318 deletion in SH2B1 would shorten the loop but not severely damage the overall structure. However, the deletion would be expected to diminish stabilization of the turn provided by the predicted π-π stacking between residues P317 and F309 in SH2B1. In addition, the deletion would be expected to alter the shape and electrostatics of the interface surface in the region of P317 and R318 in SH2B1.

Because SH2B1 from humans and mice share 95% sequence identity, with only one conservative difference (S325T) near P322 (Fig. 7), the structures in mouse and human are expected to be nearly identical. Therefore, the more modest phenotypes of P322S/+ mice compared with humans may be due to differences in the affinity of PH domain binding partners. The mouse binding partners may be able to accommodate the P322S mutation better than human binding partners, and/or human physiology adapts more poorly to the resultant alterations in SH2B1 function. Given the different phenotypes produced by the P322S and ΔPR mutations in mice, we postulate that the two mutations alter the structure of the SH2B1 PH domain in different ways to produce distinct changes in cell physiology. That relatively small changes in the PH domain, predicted to have only minor effects on PH domain structure, cause a rather profound effect on SH2B1β localization at the cellular level and energy balance and glucose homeostasis at the whole-animal level provides some of the first real evidence of the importance of the PH domain in SH2B1 function. While SH2B1β has been shown to cycle through the nucleus, it is generally found at the plasma membrane and in the cytoplasm (22,23,26). The accumulation of SH2B1β ΔPR in the nucleus indicates that the ΔPR deletion greatly alters the ratio between nuclear import and nuclear export of SH2B1β. Consistently, the ΔPR mutation as well as many of the other human obesity-associated SH2B1 variants impair the ability of SH2B1β to promote neurotrophic factor–induced neurite outgrowth of PC12 cells. Because neurite outgrowth in PC12 cells shares many properties with the formation of axons and/or dendrites (50), and because the Sh2b1 KO mice have impaired leptin signaling, it will be important in the future to examine the impact of SH2B1 PH domain changes on the structure of neurons that control energy balance.

Article Information

Acknowledgments. The authors thank Drs. Malcolm Low, Miriam Meisler, Lei Yin, Xin Tong, Liangyou Rui, and Stephanie Bielas (University of Michigan) for helpful discussions and Dr. Rui (University of Michigan) for the gift of the Sh2b1 KO strain. The authors acknowledge the Wellcome-MRC Institute of Metabolic Science Translational Research Facility and Imaging Core Facility, both supported by a Wellcome Strategic Award (100574/Z/12/Z); the University of Michigan DNA Sequencing Core for DNA sequencing; and Dr. Thomas Saunders, Galina Gavrilina, and Dr. Wanda Filipiak of the University of Michigan Transgenic Animal Model Core as well as the Michigan Diabetes Research Center Molecular Genetics Core for help making the mouse models. The authors are indebted to the patients and their families for their participation and to the physicians involved in the Genetics of Obesity Study (www.goos.org.uk).

Funding. This work was supported by National Institutes of Health (NIH) grants R01-DK-54222 and R01-DK-107730 (to C.C.-S.). A.F. was supported by predoctoral fellowships from the Horace H. Rackham School of Graduate Studies, University of Michigan (Rackham Merit Fellowship); the Systems and Integrative Biology Training Program (NIH T32-GM-8322); and the Howard Hughes Medical Institute (Gilliam Fellowship for Advanced Study). Mouse body composition measurements were partially supported by the NIH-funded Michigan Diabetes Research Center (P30-DK-020572), Michigan Nutrition Obesity Research Center (P30-DK-089503), and Michigan Mouse Metabolic Phenotyping Center (U2C-DK-110678). Generation of the CRISPR mice was partially supported by the Molecular Genetics Core of the Michigan Diabetes Research Center (P30-DK-020572). Studies in humans were supported by the Wellcome Trust (207462/Z/17/Z to I.S.F. and WT206194 to I.B.); National Institute for Health Research Cambridge Biomedical Research Centre (to I.S.F.); and Bernard Wolfe Health Neuroscience Endowment (to I.S.F.).

The views expressed are those of the authors and not necessarily those of the NHS, National Institute for Health Research, or NIH.

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

Author Contributions. A.F. directed and conducted experiments, analyzed data, and prepared the manuscript. A.F. and L.S.A. designed and generated the mice. A.F., L.S.A., I.S.F, M.G.M., and C.C.-S. developed the concept, designed experiments, and interpreted the data. L.S.A. and J.S. analyzed the model of the PH domain (Fig. 7 and Video 1). L.S.A., I.S.F., M.G.M., and C.C.-S. made revisions to the manuscript. L.K.J.S. and E.M.d.O. characterized the human mutations in cells (Fig. 1B). A.E.M. helped to regenotype the mice. A.E.M., L.C.D., G.C., and Y.H. helped to measure body weight and food intake (Figs. 2A, B, H, and I, 4C, and 5A and Supplementary Fig. 2A, B, F, and G). P.B.V. conducted neurite outgrowth experiments (Fig. 3D) and helped with experiments for Fig. 3A and C. R.M.J. conducted preliminary experiments for Fig. 3B. J.M.C. made the GFP-SH2B1β WT and GFP-SH2B1β ΔPR constructs (Fig. 3). J.M.K., E.H., and I.S.F. performed the clinical studies in mutation carriers (Fig. 1A and Table 1). I.B. and I.S.F. performed the genetic studies (Fig. 1A and Table 1). E.S.C. maintained mouse colonies and helped to genotype mice and collect blood samples (Figs. 4G and 6B). All authors approved the final content. I.S.F. and C.C.-S. are the guarantors of this work and, as such, had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

Prior Presentation. Parts of this work were presented in poster form or as short oral presentations at the 2015 Neurotrophic Factors Gordon Research Conference, Newport, RI, 31 May–5 June 2015; 2016 Keystone Symposium on Molecular and Cellular Biology—Axons: From Cell Biology to Pathology, Santa Fe, NM, 24–27 January 2016; 2017 Keystone Symposium on Molecular and Cellular Biology: Neuronal Control of Appetite, Metabolism and Weight, Copenhagen, Denmark, 9–13 May 2017; 2017 Society for Advancement of Chicanos/Hispanics and Native Americans in Science (SACNAS) National Diversity in STEM Conference, Salt Lake City, UT, 19–21 October 2017; 2018 Molecular and Cellular Neurobiology Gordon Research Conference, Hong Kong, China, 1–6 July 2018; 2018 SACNAS National Diversity in STEM Conference, San Antonio, TX, 11–13 October 2018; Experimental Biology 2018, San Diego, CA, 21–25 April 2018; and 2019 Keystone Symposium on Molecular and Cellular Biology: Functional Neurocircuitry of Feeding Disorders, Banff, AB, Canada, 10–14 February 2019.

  • Received June 21, 2019.
  • Accepted August 12, 2019.



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