The cardiovascular benefits of fibrates have been shown to be heterogeneous and to depend on the presence of atherogenic dyslipidemia. We investigated whether genetic variability in the PPARA gene, coding for the pharmacological target of fibrates (PPAR-α), could be used to improve the selection of patients with type 2 diabetes who may derive cardiovascular benefit from addition of this treatment to statins. We identified a common variant at the PPARA locus (rs6008845, C/T) displaying a study-wide significant influence on the effect of fenofibrate on major cardiovascular events (MACE) among 3,065 self-reported white subjects treated with simvastatin and randomized to fenofibrate or placebo in the ACCORD-Lipid trial. T/T homozygotes (36% of participants) experienced a 51% MACE reduction in response to fenofibrate (hazard ratio 0.49; 95% CI 0.34–0.72), whereas no benefit was observed for other genotypes (Pinteraction = 3.7 × 10−4). The rs6008845-by-fenofibrate interaction on MACE was replicated in African Americans from ACCORD (N = 585, P = 0.02) and in external cohorts (ACCORD-BP, ORIGIN, and TRIUMPH, total N = 3059, P = 0.005). Remarkably, rs6008845 T/T homozygotes experienced a cardiovascular benefit from fibrate even in the absence of atherogenic dyslipidemia. Among these individuals, but not among carriers of other genotypes, fenofibrate treatment was associated with lower circulating levels of CCL11—a proinflammatory and atherogenic chemokine also known as eotaxin (P for rs6008845-by-fenofibrate interaction = 0.003). The GTEx data set revealed regulatory functions of rs6008845 on PPARA expression in many tissues. In summary, we have found a common PPARA regulatory variant that influences the cardiovascular effects of fenofibrate and that could be used to identify patients with type 2 diabetes who would derive benefit from fenofibrate treatment, in addition to those with atherogenic dyslipidemia.
Cardiovascular events due to accelerated atherogenesis are major determinants of morbidity and mortality in patients with type 2 diabetes (1). The causes of increased atherogenesis in type 2 diabetes are complex and include, in addition to exposure to hyperglycemia, the presence of other cardiovascular risk factors that frequently accompany type 2 diabetes such as dyslipidemia and hypertension (2). The current recommendations to prevent major adverse cardiovascular events (MACE) in patients with type 2 diabetes include lifestyle modifications, improvement of glycemic control, treatment of hypertension, and use of cholesterol-lowering therapies (1).
Treatment with fibrates as an additional intervention to further improve cardiovascular outcomes in type 2 diabetes has been studied several times in the last decades (3–7). Fibrates are agonists of peroxisome proliferator–activated receptor-α (PPAR-α)—a transcription factor that functions as a master regulator of lipid homeostasis, cardiac energy metabolism, vascular inflammation, and cell differentiation. PPAR-α activation reduces serum triglycerides, raises plasma HDL cholesterol (HDL-c) levels, and reduces systemic inflammation (8,9). However, despite such beneficial effects, clinical trials of fibrates have shown inconsistent benefit of this treatment in preventing MACE (4,5,10), including among subjects with type 2 diabetes (4,5). At the same time, analyses of these trials have consistently shown that fibrates might have a beneficial effect among subjects with atherogenic dyslipidemia (defined by low HDL-c and high triglycerides levels) (11–14). For these reasons, fibrates are not currently recommended as a standard treatment to prevent MACE in type 2 diabetes but may be considered as second- or third-line treatments in patients with atherogenic dyslipidemia (1,15,16).
The lipid and inflammatory responses to fibrates vary in the population, in part due to genetic factors (17,18). Thus, one can hypothesize that the inconclusive results from clinical trials may be partly due to an underlying genetic heterogeneity in the cardiovascular response to fibrates. A corollary of this hypothesis is that it may be possible to develop genetic tests that can help distinguish individuals who would benefit from fibrates from those who would not. We have tested these postulates in the Action to Control Cardiovascular Risk in Diabetes lipid study (ACCORD-Lipid) (4), the largest randomized clinical trial to date on fibrate treatment as add-on to statin therapy in type 2 diabetes. We specifically directed our attention to PPARA—the gene that codes for the molecule (PPAR-α) through which fibrates are believed to exert their pharmacological effects.
Research Design and Methods
ACCORD-Lipid was part of ACCORD, a clinical trial that tested the effectiveness of intensive versus standard glycemic control in preventing MACE (a composite of nonfatal myocardial infarction, nonfatal stroke, and cardiovascular death) among 10,251 subjects with type 2 diabetes at high risk of atherosclerotic cardiovascular disease (19). The trial had a double, 2 × 2 factorial design, with 4,733 patients additionally randomized to a blood pressure trial (ACCORD-BP) (20) and 5,518 to a lipid trial (ACCORD-Lipid) (4). Subjects were specifically enrolled in ACCORD-Lipid if they had HDL-c <55 mg/dL for women and blacks or <50 mg/dL for all other groups, LDL cholesterol level between 60 and 180 mg/dL, and fasting triglycerides <750 mg/dL without triglyceride-lowering treatment or <400 mg/dL if they were receiving triglyceride-lowering treatment. ACCORD-Lipid investigated whether fenofibrate, given in addition to statins, was more effective than statins alone in preventing MACE. This trial showed a modest, nonsignificant trend toward a benefit of fenofibrate (hazard ratio [HR] 0.92; 95% CI 0.79–1.08) (4). Genetic data were available for 4,414 ACCORD-Lipid participants (80% of the total), who had provided consent for genetic studies. For avoidance of race/ethnicity confounding, genetic analyses were initially restricted to self-reported non-Hispanic whites (n = 3,065) and then extended to self-reported African Americans (n = 585). Other racial/ethnic groups were too sparse to be considered individually.
ACCORD-BP, ORIGIN, and TRIUMPH Cohorts
ACCORD-BP (blood pressure) (20) investigated whether reducing systolic blood pressure to <120 mmHg was more effective than standard treatment (target <140 mmHg) in preventing MACE among 4,733 subjects with type 2 diabetes at high cardiovascular risk. After a median follow-up time of 4.7 years, the study reported lack of significant cardiovascular effect of this treatment.
The Outcome Reduction With Initial Glargine Intervention (ORIGIN Trial) (21) investigated, in a 2 × 2 factorial design, the effect of titrated basal insulin versus standard care and of n-3 fatty acid supplements versus placebo on MACE occurrence among 12,537 subjects with impaired fasting glucose, impaired glucose tolerance, or type 2 diabetes and high cardiovascular risk. After a median follow-up time of 6.2 years, the study reported lack of significant cardiovascular effect of these two treatments.
The Translational Research Investigating Underlying disparities in acute Myocardial infarction Patients’ Health status (TRIUMPH) study is a large, prospective, observational cohort study of 4,340 consecutive patients, (31% with type 2 diabetes), designed to examine the complex interactions between genetic and environmental determinants of post–myocardial infarction outcomes (22).
The current study included 1,407, 1,244, and 408 self-reported white participants from ACCORD-BP, ORIGIN, and TRIUMPH, respectively, who had type 2 diabetes or dysglycemia and were on concomitant statin + fibrate or statin alone therapies before the occurrence of cardiovascular events or before being censored and for whom genetic data were available.
The ACCORD Memory in Diabetes (ACCORD-MIND) study included 2,977 participants from the overall ACCORD trial, and 562 of these participants, with available serum samples, participated in an ancillary biomarker study (23). This study included 133 self-reported white subjects from this ancillary study (24), who were also included in the ACCORD-Lipid trial and for whom genetic data were available.
In this post hoc study, the primary outcome was a three-point MACE (a composite of nonfatal myocardial infarction, nonfatal stroke, and cardiovascular death), defined according to the prespecified primary end point definitions in ACCORD (19) and ORIGIN (21) clinical trials. In TRIUMPH, the primary outcome was mortality after acute myocardial infarction.
Effect of PPARA Single Nucleotide Polymorphism × Fenofibrate Treatment Interaction on MACE Risk
For identification of common variants in or around the candidate gene PPARA that modulated the effect of fenofibrate on the ACCORD primary outcome (MACE), genotype data for 360 genotyped or imputed single nucleotide polymorphisms (SNPs) having minor allele frequencies (MAFs) >5% and spanning the entire PPARA gene plus 40 Kb on either side (GRCh37/hg19 base pair coordinates of chromosome 22: 46,506,499–46,679,653) were extracted from the ACCORD genetic data set. Detailed DNA extraction, genotyping, quality control methods, and imputation can be found in the previously published supplemental material of the article in which this data set was first reported (25). Separate analyses were conducted in the two genotyping subsets that compose the ACCORD genetic data set (ANYSET, including patients who gave consent to genetic studies by any investigator, and ACCSET, including patients who gave consent only to genetic studies by ACCORD investigators), and results were meta-analyzed as previously described (25). When the two subsets were analyzed together, an indicator variable for the genotyping platform was used as covariate. The SNPs were analyzed according to an additive genetic model. The effect of interaction between fenofibrate treatment and each of the 360 SNPs on MACE risk was assessed by means of Cox proportional hazards models, each including the SNP minor allele dosage, fenofibrate assignment (yes/no), and a SNP × fenofibrate interaction term along with assignment to intensive or standard glycemic control group, clinical center network, presence of cardiovascular disease (CVD) at baseline, age, and sex as covariates. The number of independent tests that were conducted by analyzing these 360 SNPs was estimated by means of the simpleM method (26), which considers the correlation, or linkage disequilibrium (LD), among variants. Based on the results of this analysis (81 independent comparisons), the Bonferroni-adjusted threshold for significance was set to P = 6.2 × 10−4 (α = 0.05/81) (Supplementary Fig. 1).
All self-reported white (N = 3,065) and African American (N = 585) participants randomized to fenofibrate or placebo for whom genetic data were available were included in the study. All 360 SNPs were analyzed for their interaction with fenofibrate in whites. SNPs found to have a significant effect in whites were then analyzed in African Americans. A summary estimate of the interaction effect across the two races was obtained by means of a fixed effects meta-analysis using an inverse variance approach.
The number of patients who need to be treated to prevent one additional MACE event over 5 years (number needed to treat) with fenofibrate + statin compared with statin alone treatments was calculated as previously described (27).
Replication of the rs6008845 × Fenofibrate Treatment Interaction
The SNP showing a significant interaction with fenofibrate in ACCORD-Lipid (rs6008845) was further investigated in the ACCORD-BP trial (20) by contrasting 87 participants who were on concomitant fibrate + statin therapy with 1,320 participants who were only on concomitant statin therapy, in ORIGIN (21) by contrasting 82 participants who were on concomitant fibrate + statin therapy with 1,162 participants who were only on concomitant statin therapy, and in TRIUMPH (22) by contrasting 21 participants who were on concomitant fibrate + statin therapy with 387 participants who were only on concomitant statin therapy. The SNP × fenofibrate interaction on the primary outcome was evaluated by Cox proportional hazards models including treatment arms, age, sex, and history of CVD as covariates (in TRIUMPH, since all subjects were in secondary prevention by study design, history of CVD was not included in the analyses). Results were meta-analyzed with a fixed effects inverse variance approach.
Effect of rs6008845 × Fenofibrate Treatment Interaction on Other Clinical Features
The association between rs6008845 and baseline characteristics was evaluated by means of ANCOVA or logistic regression models. Presence of atherogenic dyslipidemia was defined by the same previously used cutoff (4) of having both low HDL-c (≤34 mg/dL, i.e., the first tertile of HDL-c distribution) and high triglyceride levels (≥204 mg/dL, i.e., in the third tertile of triglyceride distribution). The influence of rs6008845 on the effect of fenofibrate (SNP × fenofibrate interaction) on change from baseline to the average on-trial value of plasma lipids was tested by ANCOVA with the baseline biomarker level included as a covariate along with the predictors included in the Cox regression models.
Effect of rs6008845 × Fenofibrate Treatment Interaction on Chemokines Levels
Data on serum levels of seven chemokines (CCL2, CCL3, CCL4, CCL11, CXCL8, CXC10, and CXC3CR1) were available from the ACCORD-MIND ancillary study, in which biomarkers were measured in a subset of ACCORD participants by means of multiplexing kits from Millipore and Luminex using a single lot of reagent and quality control material. Baseline and 12-month levels were log transformed, and the influence of rs6008845 on the effect of fenofibrate (SNP × fenofibrate interaction) on 12-month levels was tested by ANCOVA with the baseline chemokine level included as a covariate along with the same predictors included in the Cox regression models.
Expression Studies and Functional Annotation
The association between rs6008845 and PPARA expression was tested using RNA sequencing data from 44 tissues collected from up to 449 donors as part of the Genotype-Tissue Expression (GTEx) project (release version V6p) (28). In single-tissue expression quantitative trait locus analysis, the SNP effect size (β) was estimated as the slope of the linear regression of normalized expression data versus the three genotype categories coded as 0, 1, and 2 (www.gtexportal.org/home/documentationPage). Trans–expression quantitative trait locus analyses of the influence of rs6008845 on PPAR-α target genes were performed using the same approach. Results across tissues were summarized, as in the GTEx portal, by means of Han and Eskin’s random effects model (RE2) with METASOFT (29). Additional functional annotations were derived using two Web-based tools integrating data from ENCODE, RegulomeDB (https://regulomedb.org/), and the Roadmap Epigenomics project (HaploReg) (https://pubs.broadinstitute.org/mammals/haploreg/haploreg.php). Additional information on the association between the top significant SNPs and other phenotypes of interest was obtained by browsing the GWAS catalog (https://www.ebi.ac.uk/gwas/).
Statistical analyses were performed using SAS, version 9.4 (SAS Institute, Cary, NC). Graphs were edited with GraphPad Prism (version 7.02).
Institutional Review Board Approval
The institutional review board or ethics committee at each ACCORD center approved the ACCORD study protocol prior to data collection.
Data and Resource Availability
The ACCORD database is available upon request from the National Heart, Lung, and Blood Institute Biologic Specimen and Data Repository (https://biolincc.nhlbi.nih.gov/studies/accord/). The ACCORD genetic data are deposited in dbGAP, accession: phs0001411.
PPARA Variant Modulating the Fenofibrate Effect on MACE in Whites
Among 3,065 self-reported white subjects from ACCORD-Lipid (Supplementary Table 1), fenofibrate treatment was associated with a nonsignificant reduction of MACE risk during a median follow-up of 4.7 years (HR 0.82; 95% CI 0.66–1.02). In this population, a total of 360 SNPs in the PPARA gene region were tested for a modulatory influence on the effect of fenofibrate treatment on MACE risk. Results are shown in Fig. 1 as a function of the SNP positions along the genome. Evidence of interaction with fenofibrate meeting study-wide significance (P < 6.2 × 10−4 based on a Bonferroni adjustment for the 81 independent comparisons that were made by analyzing those 360 SNPs) was observed for SNP rs6008845: a T to C substitution placed ∼25 Kb upstream of the PPARA transcription start site (P = 3.7 × 10−4) (Table 1). The interaction was such that the T allele conferred protection among subjects treated with statin + fenofibrate (HR 0.75; 95% CI 0.60–0.95), whereas it was associated with a higher risk of MACE among those randomized to statin alone (HR 1.27; 95% CI 1.01–1.60) (Fig. 2).
Regional plot of the PPARA gene region. Each point represents one SNP. The base pair position on chromosome (chr) 22 (from 46.5 to 46.7 Mb) is on the x-axis, and the negative log transformation of the P value for interaction between each SNP and fenofibrate on the primary outcome is on the y-axis. The significance threshold (P = 6.2 × 10−4) after adjustment for multiple comparisons is indicated by the dashed line. Colors indicate the amount of linkage disequilibrium between plotted SNPs and rs6008845 (specific information of top SNPs can be found in Supplementary Table 2). Plots were generated using LocusZoom v1.1 (Abecasis Laboratory, University of Michigan School of Public Health).
Characteristics of the top SNPs modulating the fenofibrate effectiveness in ACCORD among the self-reported non-Hispanic whites (with Pinteraction <5 × 10−3)
rs6008845 association with primary outcome stratified by fenofibrate treatment in discovery and validation cohorts of subjects with type 2 diabetes and high cardiovascular risk. Note: the association between rs6008845 and the primary outcome is depicted as the effect per each T allele copy.
Transethnic and External Validation of the Gene × Fenofibrate Interaction
The interaction between rs6008845 and fenofibrate treatment was internally validated among 585 self-reported African Americans enrolled in ACCORD-Lipid. Despite the lower frequency of the T allele in this racial group (0.21 vs. 0.60 in whites [Supplementary Fig. 2]), the interaction was in the same direction as in whites, with the T allele being associated with MACE prevention in subjects randomized to fenofibrate + statin (HR 0.31; 95% CI 0.11–0.90) but not among those randomized to statin + placebo (odds ratio 1.37; 95% CI 0.74–2.53, P for rs6008845 × fenofibrate interaction = 0.02 [Fig. 2]). Meta-analyses of results from self-reported whites and African Americans led to a P value for rs6008845 × fenofibrate interaction of 6 × 10−5 (Supplementary Table 2).
A similar synergism between rs6008845 T allele and fenofibrate was observed in an observational setting by analyzing data on concomitant medications among self-reported whites from ACCORD-BP and ORIGIN and from the TRIUMPH cohort (Fig. 2). In a combined analysis of the three cohorts (see baseline clinical characteristics in Supplementary Table 3), the T allele was associated with a significantly lower risk of events among subjects on concomitant fibrate + statin therapy (HR 0.45; 95% CI 0.25–0.79), whereas no association was present among participants on statin alone (HR 1.03; 95% CI 0.89–1.20). The summary P value for interaction was 0.0046. The meta-analysis of results from ACCORD-Lipid (whites and African American), combined with those from observational studies, yielded a P value for interaction of 1 × 10−6 (Fig. 2).
Fenofibrate Effect on MACE According to rs6008845 Genotype and Lipid Profile
The top panel of Fig. 3 shows the interaction described above from a different perspective, that is, as the effect of fenofibrate on MACE risk reduction across genotypes, which is more meaningful from a clinical viewpoint. Among whites from ACCORD-Lipid, T/T homozygotes (approximately one-third of the cohort) experienced a 51% reduction in MACE risk when randomized to fenofibrate (HR 0.49; 95% CI 0.34–0.72), while no beneficial response was observed among heterozygotes (HR 0.98; 95% CI 0.72–1.34) or C/C homozygotes (HR 1.38; 95% CI 0.79–2.48). As shown in the bottom panel of Fig. 3, this interaction was only evident among participants without overt atherogenic dyslipidemia, resulting in a beneficial effect of fenofibrate among T/T homozygotes even in the absence of the combination of both low HDL-c and high triglycerides. Among participants with atherogenic dyslipidemia, the known beneficial effect of fenofibrate on MACE risk reduction was confirmed with no significant modulation by rs6008845 genotypes. Notably, in the group of participants without overt atherogenic dyslipidemia and with rs6008845 T/T genotype, the HR of fenofibrate and the number needed to treat to prevent one MACE over 5 years were similar to those for subjects with atherogenic dyslipidemia, for whom fibrates are currently indicated (Table 2). As shown in Supplementary Table 4, results were similar using an alternative definition of atherogenic dyslipidemia (HDL-c <50.2 mg/dL or 1.3 mmol/L for women and <38.7 mg/dL or 1.0 mmol/L for men combined with triglycerides >203.7 mg/dL or 2.3 mmol/L, regardless of sex ).
Fenofibrate cardiovascular effectiveness according to rs6008845 genotypes. Top panel: among all self-reported white subjects randomized to fenofibrate or placebo in the ACCORD-Lipid study. Middle and bottom panels: in the same population according to absence or presence of atherogenic dyslipidemia at baseline (a few subjects, N = 15, were not included due to missing data on lipid profile at baseline).
Number of subjects needed to be treated with fenofibrate to prevent one MACE in 5 years in different subgroups
rs6008845 Effect on Lipid and Chemokine Response to Fenofibrate
As shown in Table 3, the larger cardiovascular benefit of fenofibrate among T/T homozygotes was not paralleled by differences in clinical characteristics at baseline, the only nominally significant difference being in the age of onset of diabetes. The lipid response to fenofibrate treatment, in terms of increase in HDL-c and decrease in triglycerides and total cholesterol, was also equivalent in the three genotypes (Fig. 4). Consistent with the lack of association with lipid profile in ACCORD, rs6008845 was not in LD with any of the PPARA variants previously reported to be associated with lipid profile at GWAS levels (30). Rather, a recent genome-wide association study reported an association between genetic variants in the PPARA region, including rs6008845, and serum levels of a proinflammatory chemokine (CCL27) (31). Thus, we evaluated the effect of the “rs6008845-by-fenofibrate” interaction on circulating levels of seven chemokines available for 133 subjects from ACCORD-Lipid included in the ACCORD-MIND ancillary study (24). This subset had slightly different clinical characteristics compared with the whole ACCORD-Lipid cohort; in particular, they were characterized by shorter duration of diabetes; lower blood pressure, HbA1c, and LDL-cholesterol levels; and lower prevalence of CVD at baseline (Supplementary Table 5). As shown in Fig. 5 and Supplementary Table 6, we found a significant interaction between rs6008845 and fenofibrate in the circulating levels of CCL11 (also known as eotaxin), in the sense that fenofibrate was associated with lower levels of CCL11 levels (P = 0.01) among T/T homozygotes but not among T/C or C/C subjects. Though not reaching statistical significance, a similar pattern of interaction was observed for CLL3 and CXCL8 (Supplementary Table 7).
Baseline characteristics according to rs6008845 genotype in self-reported whites
Effects of fenofibrate on changes in lipid levels among self-reported whites in ACCORD-Lipid, stratified by rs6008845 genotypes. Error bars represent SEs. One SD is equal to 5.4 mg/dL for HDL-c, 87.1 mg/dL for triglycerides, 35.2 mg/dL for total cholesterol, and 29.8 mg/dL for LDL cholesterol. To convert cholesterol values to millimoles per liter, multiply by 0.02586. To convert the values for triglycerides to millimoles per liter, multiply by 0.01129. Chol, cholesterol.
Fenofibrate effects on chemokine levels according to rs6008845 genotypes among self-reported whites from the ACCORD-MIND biomarker study. Error bars represent SEs.
rs6008845 Regulatory Function
In an analysis of RNA sequencing data from the GTEx project, the rs6008845 T allele was significantly associated with lower PPARA expression in skin (P = 6 × 10−17), whole blood (P = 9 × 10−3), skeletal muscle (P = 2 × 10−2), vagina (P = 3 × 10−3), and esophageal mucosa (P = 4 × 10−4). The association also approached significance in liver (P = 7 × 10−2) despite the smaller sample size. In a meta-analysis across all 44 tissues available in GTEx, rs6008845 was significantly associated with PPARA mRNA levels (P = 3 × 10−21 [Supplementary Fig. 3]), although these results should be interpreted with caution due to the correlation between expression measurements in different tissues obtained from the same donors. Of note, consistent with its influence on PPARA mRNA levels, rs6008845 was also associated in the above tissues with the expression of multiple PPAR-α targets (24 genes yielding P < 0.05 out of 98 tested, binomial P value = 6 × 10−11 [Supplementary Table 8]). The regulatory role of rs6008845 was also supported by data from ENCODE and the Roadmap Epigenomics project (Supplementary Fig. 4 and Supplementary Table 9), indicating that SNP rs6008845 is placed in a DNAse I hypersensitivity cluster in the 5′ flanking region of the PPARA gene where chromatin immunoprecipitation experiments have shown binding of several transcription factors in different cell types (mainly white blood cell derived). Histone modification chromatin immunoprecipitation sequencing peaks confirmed the occurrence of rs6008845 in a regulatory locus in multiple cell lines, as this variant was found to be placed inside epigenetic peaks for histone 3 (H3K4Me1, H3K4Me1, and H3K27Ac—as shown in Supplementary Fig. 5), which suggests an active enhancer region.
Clinical trials assessing the effect of fibrates on cardiovascular risk among patients with type 2 diabetes (4,5) have demonstrated small, if any, cardiovascular benefits of these drugs (10). These studies, however, have shown a highly variable response to these agents (3–7,11,12), suggesting the possibility of designing precision medicine algorithms to identify patients who have a higher probability of deriving cardiovascular benefit from fibrates (32). In this study, we have identified a genetic variant near the gene coding for the pharmacological target of fenofibrate (PPAR-α) that could be used for this purpose. Among homozygotes for the major allele of this variant (approximately one-third of ACCORD-Lipid participants), randomization to fenofibrate + statin rather than statin alone yielded a 50% reduction in MACE—much larger than in the overall study population. Importantly, this benefit was present in the absence of overt atherogenic dyslipidemia—the only condition that today represents an indication for the addition of fenofibrate to statins for cardiovascular prevention (16). If the results of this study were brought to the clinic, they would translate into more than a doubling in the number of patients with type 2 diabetes who would benefit from treatment with fenofibrate as an add-on to statins.
Several aspects of these findings make them especially robust. First, the genetic effect is linked to the gene that codes for the main pharmacological target of fibrates and as such had a very high prior probability of being involved in the modulation of fenofibrate effects. Second, the SNP × fenofibrate interaction was observed in the rigorous setting of a double-blind, randomized, placebo-controlled clinical trial characterized by excellent adherence to the study protocol. Third, the statistical significance of the interaction was robust to adjustment for the number of independent polymorphisms that were tested at the PPARA locus. Fourth, the interaction was validated through transethnic replication in African Americans from ACCORD-Lipid and also by using concomitant medication data from three well-characterized cohorts (ACCORD-BP, ORIGIN, and TRIUMPH), through which we were able to reproduce the same exact exposures as in ACCORD-Lipid (fibrate + statin vs. statin alone)—an essential factor for a meaningful replication of genetic findings (33). The fact that we could observe the same rs6008845-by-fibrate interaction in these cohorts as in ACCORD-Lipid is quite remarkable, considering the different clinical characteristics and settings (i.e., observational and interventional) of study populations.
Another critical element in support of the robustness of our findings is the association observed in multiple tissues between the SNP interacting with fenofibrate and mRNA levels of PPARA and PPAR-α targets. The decrease in PPARA expression associated with the SNP suggests that the latter is functional, providing experimental confirmation of the in silico predictions based on ENCODE and Roadmap Epigenomics project data. The association with the expression of PPAR-α targets indicates that the effect of the rs6008845 on PPARA expression translates into allelic differences in PPAR-α activity that propagate downstream and influence cellular functions. As PPARA expression is reduced in rs6008845 T/T homozygotes, one can speculate that carriers of this genotype derive benefit from fibrate treatment because they start from lower PPAR-α activity, whereas C allele carriers derive no benefit because their PPAR-α activity is already optimal. Consistent with this interpretation was the tendency of the T allele to be associated with increased risk of MACE among subjects treated with statins alone, which was reversed to significant protection from MACE by the addition of fenofibrate.
The PPARA variant does not appear to act by influencing the effect of fenofibrate on circulating lipids—a finding hardly surprising, considering that changes in plasma lipid profile have been shown to explain <25% of the cardiovascular benefit of fibrates (34). Rather, our finding suggests that the variant exerts its modulatory effects by enhancing the ability of fenofibrate to dampen proinflammatory chemokines such as CCL11. The circulating levels of this chemokine were unaffected by fenofibrate in the overall ACCORD study population. However, T/T subjects, i.e., those experiencing the cardiovascular benefit of fenofibrate, had significantly lower levels of CCL11 when treated with fenofibrate. CCL11, also known as eotaxin, is a chemokine expressed in multiple tissues, which, in addition to its chemotactic activity on eosinophils, basophils, and Th2 lymphocytes (35,36), has been consistently identified, with its receptor CCR3, as a player in vascular inflammatory processes (37–39). Moreover, several epidemiological studies have found a significant association between higher CCL11 levels and increased cardiovascular risk (40–42), with randomized clinical trials showing that cardioprotective therapies such as metformin and atorvastatin reduce circulating CCL11 (43,44). There are no reports in the literature, besides the present one, describing a similar effect of fenofibrate in humans. However, such an effect is supported by a study in a mouse model, in which upregulation of PPAR-α activity decreased skin expression of CCL11 and other chemokines (45). Such actions may relate to the inhibitory effect of PPAR-α activation on the nuclear factor-κB pathway (e.g., by inducing the nuclear factor-κB inhibitor IκBα) (45–47), which is known to regulate the expression of CCL11 and its receptor CCR3 (48,49). Altogether, our findings provide support for a complex mechanism of action of fibrates on cardiovascular risk, consistent with the pleiotropic effects that activation of PPAR-α has in multiple cell types relevant to atherogenesis including monocytes/macrophages, smooth muscle cells, endothelial cells, platelets, and fibroblasts (9,50,51).
Some limitations of our study must be acknowledged. First, this was a post hoc analysis that included only 80% of the subjects in the ACCORD-Lipid trial (i.e., those for whom DNA was available). As such, this analysis deviates from an intention-to-treat approach. On the other hand, the lack of differences in baseline clinical characteristics between treatment arms in the subset included in the study, and the external validation in three different cohorts, attenuates the importance of this limitation. While these other cohorts were from observational studies, in which fibrate and statin treatments were not randomized and were based on self-report, the lack of differences in clinical characteristics among rs6008845 genotypes and the fact that results were similar across the three cohorts and consistent with the findings from ACCORD-Lipid provide reassurance about the validity of these data. Second, since ACCORD-Lipid investigated fenofibrate given in combination with a statin and was specifically directed to subjects with type 2 diabetes at high cardiovascular risk (as were the ORIGIN, ACCORD-BP, and TRIUMPH cohorts), caution should be exercised in generalizing these findings to treatment with fenofibrate alone or to subjects having different characteristics. This aspect deserves special attention given the known cross talk between fibrates and statins (52,53). Importantly, although we were able to replicate our findings among African Americans, further evidence should be gathered before extending conclusions to other races or ethnic groups. Third, since only mortality data were available for the TRIUMPH cohort, we could not analyze the effect of the variant on MACE in this study, as was done in the other two cohorts. However, all TRIUMPH participants were enrolled in that study after an acute myocardial infarction; thus, the majority of deaths in this cohort are likely to have had a cardiovascular cause. Fourth, due to the relatively small sample size, our analysis was limited to common polymorphisms (MAF >5%) and we cannot therefore exclude that other, less common variants may exist in the PPARA gene region that also modulate the cardiovascular responsiveness to fenofibrate. Finally, given the small number of participants with chemokine data, the finding of association between fenofibrate treatment and reduced CCL11 levels in T/T homozygotes should be interpreted with caution and should be considered at this point as merely hypothesis generating.
Our findings have promising implications for the treatment of patients with diabetes at high cardiovascular risk. Due to the lack of clear cardiovascular benefit, in 2016 the U.S. Food and Drug Administration removed the indication of the addition of fibrates to statins for cardiovascular prevention (54), and current guidelines do not recommended this treatment for that purpose (1,15). The only exceptions are patients with atherogenic dyslipidemia, i.e., with low HDL-c and high triglycerides, for whom this treatment may be considered due to the consistent evidence of benefit in this small subgroup (12–14). Our findings confirm the established benefit of fenofibrate in the presence of atherogenic dyslipidemia but, importantly, suggest that rs6008845 could be used as a marker to identify an additional group of subjects (i.e., those with T/T genotype) for whom therapy with a fibrate as an add-on to statins could be indicated for cardiovascular disease prevention even in the absence of atherogenic dyslipidemia. The use of this marker would at least double the proportion of patients eligible for fenofibrate therapy, with obvious public health implications. However, before this approach can be brought into clinical practice, our findings will require validation through specifically designed pharmacogenetics clinical trials, in which randomization to fibrate or placebo is stratified by PPARA genotype. In the case of fenofibrate, the low cost and the well-documented safety profile of this drug may facilitate this goal by making pragmatic clinical trials possible. Our findings may also prompt ancillary studies of ongoing cardiovascular clinical trials of new fibrates, such as the PROMINENT (Pemafibrate to Reduce Cardiovascular OutcoMes by Reducing Triglycerides IN patiENts With DiabeTes) clinical trial of pemafibrate (ClinicalTrials.gov: NCT03071692).
In conclusion, we have identified a genetic variant at the PPARA locus that modulates the cardiovascular response to fenofibrate in patients with type 2 diabetes. These findings suggest a precision medicine approach to prescribe fenofibrate optimally, rescuing a drug that would be otherwise dismissed as ineffective and offering a cardioprotective drug to those patients that are most likely to experience a robust benefit from this medication.
Acknowledgments. The authors thank the investigators, staff, and participants of ACCORD for their support and contributions and for giving the authors access to this rich data set. The data used for the gene expression analyses described in this manuscript were obtained from the GTEx Portal on 3 March 2017 (release V6).
Funding. The ACCORD genome-wide association analysis was supported by National Heart, Lung, and Blood Institute (NHLBI), National Institutes of Health (NIH), grants HL110400 (to A.D.) and HL110380 (to J.B.B.) and National Institute of Diabetes and Digestive and Kidney Diseases, NIH, grant DK36836 (Advanced Genomics and Genetics Core of the Diabetes Research Center at the Joslin Diabetes Center). The project described was also supported by the National Center for Advancing Translational Sciences (NCATS), NIH, through grant UL1TR001111 (to J.B.B.). H.N.G. was also supported by NHLBI grant HL110418. M.L.M. was supported by a William Randolph Hearst Fellowship provided by the Hearst Foundation and by a Research Fellowship provided by FONDAZIONE S.I.S.A. S.P. was supported by the Italian Ministry of Health (Ricerca Corrente 2018-2020). V.T. was supported by the Italian Ministry of Health (Ricerca Corrente 2015 and 2016), by the Italian Ministry of University and Research (PRIN 2015), and by Fondazione Roma (“Biomedical Research: Non-Communicable Diseases 2013 grant). H.C.G. is supported by the McMaster-Sanofi Population Health Institute Chair in Diabetes Research and Care. A.M.R. is supported by the Intramural Research Program of the National Institute of Environmental Health Sciences, NIH. S.C., H.C., and P.A.L. efforts were in part supported by NIH grant R01 NR013396 (to S.C.). TRIUMPH was sponsored by the NIH: Washington University School of Medicine Specialized Centers of Clinically Oriented Research (SCCOR) grant P50 HL077113. ACCORD (ClinicalTrials.gov, clinical trial reg. no. NCT00000620) was supported by NHLBI contracts N01-HC-95178, N01-HC-95179, N01-HC-95180, N01-HC-95181, N01-HC-95182, N01-HC-95183, N01-HC-95184, and IAA #Y1-HC-9035 and IAA #Y1-HC-1010. Other components of the NIH, including the National Institute of Diabetes and Digestive and Kidney Diseases, the National Institute on Aging, and the National Eye Institute, contributed funding. The Centers for Disease Control and Prevention funded substudies within ACCORD on cost-effectiveness and health-related quality of life. General Clinical Research Centers and Clinical and Translational Science Awards provided support at many sites. The GTEx project was supported by the Common Fund (https://commonfund.nih.gov/GTEx/index) of the Office of the Director of the NIH and by the National Cancer Institute, National Human Genome Research Institute, NHLBI, National Institute on Drug Abuse, National Institute of Mental Health, and National Institute of Neurological Disorders and Stroke. In the ACCORD study, the following companies provided study medications, equipment, or supplies: Abbott Laboratories (Abbott Park, IL), Amylin Pharmaceutical (San Diego, CA), AstraZeneca (Wilmington, DE), Bayer HealthCare (Tarrytown, NY), Closer Healthcare (Tequesta, FL), GlaxoSmithKline (GSK) (Philadelphia, PA), King Pharmaceuticals (Bristol, TN), Merck & Co. (Whitehouse Station, NJ), Novartis Pharmaceuticals (East Hanover, NJ), Novo Nordisk (Princeton, NJ), Omron Healthcare (Schaumburg, IL), Sanofi U.S. (Bridgewater, NJ), Schering-Plough Corporation (Kenilworth, NJ), and Takeda Pharmaceuticals (Deerfield, IL).
None of these companies had an interest in or bearing on the genome-wide analysis of the ACCORD data. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH or other funders.
Duality of Interest. ORIGIN (ClinicalTrials.gov, clinical trial reg. no. NCT00069784) was funded by Sanofi. M.L.M. received lecture fees from Servier and funding and research grant support from Amryt Pharma (outside of the submitted work). R.J.S. was supported by a Health Senior Scholar award from Alberta Innovates – Health Solutions. G.P. received research funding from Sanofi. E.P. received research funding from GSK and Boehringer Ingelheim (outside the submitted work). J.B.B.’s contracted consulting fees and travel support for contracted activities are paid to the University of North Carolina by Adocia, AstraZeneca, Dance Biopharm, Eli Lilly, MannKind, NovaTarg, Novo Nordisk, Senseonics, vTv Therapeutics, and Zafgen, and J.B.B. receives grant support from Novo Nordisk, Sanofi, Tolerion, and vTv Therapeutics; is a consultant to Cirius Therapeutics, CSL Behring, Mellitus Health, Neurimmune AG, Pendulum Therapeutics, and Stability Health; and holds stock/options in Mellitus Health, Pendulum Therapeutics, PhaseBio, and Stability Health. H.C.G. has received research grant support from Sanofi, Lilly, AstraZeneca, and Merck; honoraria for speaking from Sanofi, Novo Nordisk, AstraZeneca, and Boehringer Ingelheim; and consulting fees from Sanofi, Lilly, AstraZeneca, Merck, Novo Nordisk, Abbot, Amgen, and Boehringer Ingelheim. H.N.G. is a consultant to Kowa and member of the PROMINENT trial steering committee. A.D. received research funding from Sanofi (outside the submitted work). No other potential conflicts of interest relevant to this article were reported.
Author Contributions. M.L.M. designed the study; acquired, analyzed, and interpreted the data; and wrote the manuscript. H.S.S. designed the study; acquired, analyzed, and interpreted the data; and reviewed the manuscript. A.A.M.-R. and H.G. acquired and interpreted data and reviewed the manuscript. J.S., P.A.L., H.C., L.L., and G.P. acquired and analyzed data and reviewed the manuscript. S.P., A.P., M.G.P., D.M.R., E.P., L.M., and V.T. interpreted the data and reviewed the manuscript. R.J.S. and E.Y.C. designed the study and reviewed the manuscript. S.C. and H.C.G. designed the study, acquired and interpreted data, and reviewed the manuscript. S.M.M., J.B.B., and M.J.W. acquired and interpreted data and reviewed the manuscript. P.K. designed the study, interpreted the data, and reviewed the manuscript. H.N.G. designed the study, acquired and interpreted the data, and reviewed the manuscript. J.C.M. designed the study; acquired, analyzed, and interpreted the data; and reviewed the manuscript. A.D. designed the study; acquired, analyzed, and interpreted the data; and wrote the manuscript. A.D. is the guarantor of this work and, as such, had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.
Prior Presentation. Preliminary data of this study were presented at the National Congress of the Italian Society for the Study of Atherosclerosis (SISA), Palermo, Sicily, 19–21 November 2017.
Members of the ACCORD Data Safety Monitoring Board. Antonio M. Gotto. Jr. (chair), Kent Bailey, Dorothy Gohdes, Steven Haffner, Roland Hiss, Kenneth Jamerson, Kerry Lee, David Nathan, James Sowers, and LeRoy Walters.
If you’re like me, you’ve tried lots of health-related apps. Imagine one that has relevant articles, peer support, and diabetes management tips specifically for people with Type 2. Now you know what the new T2D Healthline app is all about.
This is a sponsored post on behalf of Healthline.
Why T2D Healthline?
Living with Type 2 diabetes can be lonely. Even when you seek support online, there’s so much stigma and shame. Comments like “you did this to yourself” and “my child doesn’t have THAT type of diabetes” aren’t helpful and can lead to lowered self-esteem, additional stress, and even depression.
But what if there was a private online community specifically for people living with Type 2? What if you could find support and helpful suggestions from folks just like you? Would you like to participate in a live online chat most nights of the week where you could learn and share?
Then you need T2D Healthline.
Setting Up the App
Setting up the app is easy: just download it and answer a few simple questions to set up your profile. (The app is currently available for iPhone/iPad and Android.)
You may wonder why they ask about things like your treatment plan, lifestyle, and interests. Well, it’s because in a sense T2D Healthline works like a dating app. It tries to match you with others who are taking the same meds and like the same things you do. If you’re uncomfortable participating in the group discussions, you’ll find folks you can chat with privately.
T2D Healthline Features
Within the app, there are five sections:
Home Think of this as your News Feed. Recent posts from the various groups will appear here interspersed with articles about Type 2 and member profiles.
Groups Most discussions are associated with a group. Currently, the available groups are:
Coping with COVID-19
Escape from T2D
Diet and Nutrition
Exercise and Fitness
Medications and Treatments
Women’s (or Men’s) Topics
Monitoring and Lab Work
Members Here you’ll find a list of members with indicators about who is currently online. You can also turn on/off the “matching” feature to find other people with similar interests.
Messages In the Messages area, you can see who you’ve been matched with (if you’ve turned that feature on) and have private conversations. Basically, it’s the area for direct messaging.
Discover The Discover area provides links to diabetes news articles and tips for living well with Type 2.
Let’s Get Connected
Once you download the app and join the T2D Healthline community, look me up! You should be able to find me by searching the Members area for “Shelby.” And please participate in the online chats in the evenings at 8 pm ET / 7 pm CT / 6 pm MT / 5 pm PT. I promise you’ll learn something!
I love potstickers in any form, but having leftover pork makes that an easy decision. My pork belly that I grilled on Easter Sunday was sliced thin and added to . . . Natural Heaven hearts of palm pasta! [This post contains affiliate links and I may get paid a small amount of you buy something or take an action after clicking that link – full disclosures can be found here].
I am loving this product. Think of hearts of palm as the tofu of vegetables. It really doesn’t have a whole lot of flavor, and the texture is a bit crunchy – not the mouth feel that you are looking for in regular pasta. While I don’t mind the texture, I find the best way to use this product is in recipes – and these pork wontons with hearts of palm pasta were delicious!
A delicious way to use Natural Heaven hearts of palm . . . pasta! At zero WW points on any plan you are on, these are a great way to bulk up recipes without adding points.
Yield:7 wontons per serving 1x
7 wonton skins 2 ounces chopped pork 1/3 cup broccoli slaw half a package of Natural Heaven hearts of palm, squeezed dry 1 tablespoon Teriyaki sauce 1 teaspoon hoisin 1 teaspoon chili garlic sauce 1 teaspoon vinegar
Chop the pork, broccoli slaw and hearts of palm together. Put in a bowl and mix with remaining ingredients. Divide mixture between the skins, brush water with your finger around the edges of each wonton and cinch closed. Heat skillet with 1/2 teaspoon peanut or coconut oil, and avocado spray. Cook wontons for 2 minutes then add 1/4 cup water. Put a lid on the pan and cook 3 more minutes. That’s it! I served mine with 1/2 cup white rice and sautéed chopped sugar snap peas.
No matter which @ww plan you are on – each wonton is 1 smart point – 7 points for the whole plate – and I added 3 points for the 1/2 cup of rice. So filling!
I have been doing intermittent fasting for the last two weeks – my first week I lost 1.7 and tomorrow is week 2 weigh in. I know it’s not a “true” fast as everyone has an opinion on the matter on the internet, because I drink a cup of coffee with creamer in it, but what this fasting is doing for me is closing the kitchen for a good portion of the day.
The first two weeks I was working from home I gained 2.7 pounds because the kitchen, well, was RIGHT THERE. Literally steps from my office – er, dining room!
My hours of eating are noon to 8 p.m. I am a night time snacker too, so this has helped in that department immensely – it’s as if there is a virtual “kitchen closed” sign at the stroke of 8:00 p.m. and that’s just the way it is.
I’ll be back for a pretzel chicken nugget tomorrow with a new to me product – I can’t wait to share it with you – you are going to love it!
Carb counting is the golden standard when it comes to maximizing glycemic control while expanding food choices.
Sure, you can get away with a preset insulin dose or arbitrarily guessing how much insulin to take for your usual meals and snacks, but carb counting allows for a much higher level of finesse when you are trying varying amounts of carbs and different foods outside your usual comfort zone.
If you enjoy eating the same foods all day, every day, then not knowing how to carb count isn’t a big deal. But the majority of people (with or without diabetes) enjoy venturing outside the daily same-ole routine, trying new experiences, and living life!
With diabetes, trying a new food can become a daunting event because you are unsure of how it will affect your blood sugar. Learning to carb count can be freeing because it allows stability during times of uncertainty.
In this article, I will cover how to carb count, the available tools and apps that can help you, how to “guestimate” carbs when you are eating out, and how to account for protein and fat before calculating your insulin doses.
How to carb count
Carb counting can seem overwhelming at first, but the basic concept is not difficult and access to modern apps and other tools has made it even easier.
Step 1: Identify the total amount of carbs
The nutrition label is a great tool to easily find not only the total number of carbohydrates in each serving, but also the type of carbohydrates, such as sugar and dietary fiber.
To figure out how many carbohydrates are in a food with a nutrition label, you first need to identify the serving size. The calories and nutrient amounts shown on a label refer to this single serving size.
It’s important to identify the serving size because the amount that fills you up may be substantially larger than the prescribed serving size listed on the label.
To add up the carbs when you have access to a nutrition label, use the following:
Identify the serving size
Look for the total carbohydrates
Multiply the total carbs by the number of servings you will consume
If you don’t have access to a nutrition label (for fresh produce for example), you can use a list of common foods and their carbohydrate content or one of the many available food tracking apps. We will get back to that later in this post.
Step 2: Subtract for fiber and sugar alcohols
Man-made fiber and sugar alcohols are common ingredients found in many protein bars, shakes, and other various “low carb” products.
Simply put, dietary fiber refers to the nutrients in the diet that your gastrointestinal enzymes cannot digest. If you cannot digest a nutrient, the nutrient cannot raise your blood sugar, therefore you do not have to take insulin when you eat that said nutrient (fiber).
You therefore need to subtract fiber from the total carbohydrate count to calculate the total number of carbs that do raise blood sugar. This process is known as calculating the “net carbs” of a food.
The formula for net carbs is to subtract the fiber (and half of any sugar alcohols if present) to get the total net carbs that raise blood sugar and thus require insulin.
The reason sugar alcohols need to be divided by half before being subtracted is due to sugar alcohols being only partially digested. With partial digestion, your blood sugar will still rise, but not to the same degree as regular sugar.
As a rule of thumb, if your food serving contains less than 5 grams of fiber, my advice is not to worry about subtracting. If your food contains sugar alcohol, I recommended dividing that total number by two and subtracting from the total carbohydrates. If the food has a substantial amount of fiber and sugar alcohols, you can use the following formula.
Net carbs = Total carbohydrates – fiber (if greater than 5 grams) – (sugar alcohols / 2)
Note: There are many different opinions on how much fiber to subtract to find the net carbs. Some people only subtract 50% of fibers or even less. You will have to do a little trial and error to find the amount that’s right for you.
How to account for protein and fat
When you eat carbs, they are absorbed and rapidly increase the amount of glucose in your blood.
When you eat protein, you will likely experience a delayed, yet more prolonged increase in blood sugar due to the conversion of amino acids to glucose through gluconeogenesis. Gluconeogenesis is the formation of glucose within the body from substances other than carbohydrates, such as amino acids from proteins, glycerol from fats, or lactate produced by muscle during anaerobic exercise .
Somewhat similar to protein, dietary fat results in a delayed rise in blood glucose due to an impact on cellular responses that causes increased insulin resistance . Consuming fat will also delay the rate of stomach emptying which also adds to the prolonged postprandial (after meal) spike in blood glucose levels .
When combined with carbs, protein and fat typically soften the blood sugar rise, but then prolong the postprandial spike and increase overall insulin resistance (AKA make your correction factor not work as well).
On average, someone with diabetes will likely need to increase their insulin dose with an additional 30% for a meal that is high in protein and an additional 60% for a meal that is high in protein and fat . However, the actual dose needs to be tailored to the amount and method that works best for you.
The takeaway here is that while carbohydrates are the main macro that raises your blood sugar, you need to consider how protein and fat not only also raise your blood sugar, but cause a prolonged spike, and increase insulin resistance anywhere between 3-8 hours after a meal.
The lower the amount of carbs in your diet, the higher the fat and protein amount, the more you will see prolonged postprandial spikes and increased insulin resistance. This isn’t necessarily a bad thing, but a trend you should be aware of so you can make the necessary adjustments for better blood sugar control.
The Diabetic Exchanges (list of common foods)
The Diabetic Exchange List was created to simplify carb counting. A diabetic exchange is the serving size of a starchy food that constitutes about 15 grams of carbohydrates. Below is a list of common diabetic exchanges. To access the full list, click here.
Don’t feel that you need to memorize every diabetic exchange. Print off the list, highlight the foods you like and eat most frequently, then put the list where you will see and can review often.
Diabetic Exchange List for Common Carbohydrate Sources
1 serving equals about 15 grams of carbohydrates
1 slice bread (1 ounce)
1 tortilla (6-inch size)
¼ large bagel (1 ounce)
2 taco shells (5-inch size)
½ hamburger or hotdog bun (1 ounce)
¾ cup ready-to-eat cereal
½ cup oatmeal (cooked)
1 cup broth-based soup
4-6 small crackers
⅓ cup pasta or rice (cooked)
½ cup starchy vegetables (such as peas, corn, potatoes, and winter squash), beans, or legumes (cooked)
¾ ounce pretzels, potato chips, or tortilla chips
3 cups popcorn (popped)
1 small fresh fruit (4 ounces)
½ cup canned fruit
¼ cup dried fruit (2 tablespoons)
17 small grapes (3 ounces)
1 cup melon or berries
2 tablespoons raisins or dried cranberries
½ cup fruit juice
1 cup fat-free or reduced-fat milk
1 cup soy milk
2-inch square cake (unfrosted)
2 small cookies (⅔ ounce)
½ cup ice cream or frozen yogurt
¼ cup sherbet or sorbet
1 tablespoon syrup, jam, jelly, table sugar, or honey
Carb counting apps
There is an ever-growing number of mobile apps that can help you count carbs. Some of them are just food databases while others are more interactive diet tracking systems. Here are four of some of the most popular carb counting apps:
While these apps are generally accurate, some of them accept user-generated info in their databases, which may not always be reliable (e.g. people can enter the nutrient data for their favorite restaurant foods themselves).
Always use common sense when looking at the carb counts and judge if the numbers seem right before dosing your insulin.
Learning the art of guesstimating
In today’s world, we have easy access to the carb counts of our favorite foods using our smartphones. The issue however is when we are eating at a friend’s house or some hole-in-the-wall restaurant.
No worries though, by memorizing your favorite diabetic food exchanges, the next step is to learn a couple measuring tricks so you can guesstimate the total carb amount of your meal.
Guesstimating = Guess + Estimating
The secret to learning how to measure in unknown situations is to use your hand to size up your food. Generally speaking, a regular-sized hand is equivalent to the following . . .
Clenched fist = 1 cup.
Thumb (base to tip) = 1 tbsp
Thumb tip = 1 tsp
Handful = 1 oz
Of course, hand sizes vary so to make this trick work, be sure to compare your fist to an actual measuring cup before using your hand as a measuring device. Learning to size up your food will take practice, and perhaps even a bit of physically comparing your clenched fist to a mound of rice or pasta at dinner.
By knowing your favorite diabetic exchanges and being able to eyeball your portion sizes, the next step is to add everything up to estimate your total carb count.
For example, let’s say you ate the following:
1 cup of rice (or about 1 clenched fist)
3 oz chicken covered in teriyaki sauce
1 cup steamed broccoli
1 fortune cookie
Let’s break it down
⅓ cup of rice = 15 grams [15 x 3 servings (3 servings per cup)] = 45 grams
3 oz chicken = ~0 grams
Teriyaki sauce = ~10 grams (guestimating here)
1 fortune cookie = ~7 grams (you looked it up on your phone)
You would then either enter the total grams into your insulin pump or divide into your carb ratio calculated for you by your provider or diabetes educator.
Count the carbs that matter to YOU
In this article, we have covered tips on counting all carbs, but, you do not need to know the carb counts for every piece of food on the entire planet, just the foods YOU like. If you think about it, we often stick to the same 20 foods or less, which makes carb counting much more feasible.
An easy way to learn the carb counts of your usual foods is to use google docs or word (or you can always go the old-fashioned route with pen and paper) to create the following chart.
Column 1 = Food
Column 2 = Content and Amounts
Column 3 = Carbohydrate Count (grams)
Here is an example of what an entry for a turkey sandwich could look like.
2 slices wheat bread
3 slices of turkey
2 slices lettuce
1 slice of cheese
2 slices tomato
½ tsp mayo
You can add multiple meals and snacks so that you have a detailed reference sheet in place for all your go-to meals. After referring to your chart for a week or so, you’ll quickly become a master of the carbohydrate counts that are most important to you.
BONUS TIP: To be extra helpful with your carb counting experience, take notes on how certain foods affect your blood sugar 2 hours after eating. This can be especially helpful if you eat a lower carb diet with higher amounts of protein and fat.
Carb counting takeaways
Learning how to count carbs can be a bit challenging at first, but once you break down the rules and memorize the serving sizes and carb amounts for the foods that matter most to you, you are well on your way to making carb counting a part of your usual routine.
As you are learning how to carb count, times will be messy. However, the goal here is never perfection, but progression. We do the best we can, we learn from the highs and lows, and we move on a little wiser than before.
Understand that good control and diabetes management goes through ebbs and flows of motivation. Sometimes we are really in a groove and doing a killer job, and sometimes we feel a little burnt out and need a break. As we strive for better control as our baseline, during our periodic deviations we can still stay healthy.
Day by day, we continue to learn about our diabetes so we can live a vibrant and fulfilling life. By taking the time to take care of ourselves, we can do the things we love most and continue to share moments with our loved ones.
What is the Best Cornbread Dressing Recipe Using #1 Holiday Ingredient?
OK, so that isn’t exactly a scientific number but sweet potatoes are definitely the number one holiday ingredient in my family. That’s why I combined them with the popular Thanksgiving cornbread dressing dish to make the best cornbread dressing recipe ever! Cornbread dressing and sweet potatoes grace every holiday table! I create healthy sweet potato recipes and this winning combination gives you an amazing southern cornbread dressing recipe? This combination of cornbread and sweet potatoes creates the best healthy cornbread dressing for a holiday healthy easy recipe! Trust me, no Thanksgiving tablewould be complete without it – it will soon be your favorite holiday side dish too! Best of all, this is a diabetic cornbread dressing recipe! I can be your #1 resource for an easy, healthy top Thanksgiving recipes and tips!
Delicious and Healthy Cornbread Dressing
The beloved sweet potato, or yam as it’s known in Louisiana, is the sweetest of the sweet potatoes, boasting rich nutrition such as fiber, vitamin A and C. making this a diabetic sweet potato recipe. I have lots ofeasy healthy sweet potato recipes featured on my healthy food blog! What’s great is this diabetic cornbread recipes topping the list for the best healthy cornbread dressing recipes and this this recipe is on my holiday table every year! Just because it is a Thanksgiving cornbread dressing doesn’t mean I can’t turn it into a healthy cornbread dressing recipe. I want the only thing stuffed on Thanksgiving to be the turkey!! Check out my healthy Thanksgiving menu including the best Sweet Potato Casserole with Praline Topping!
Best of Both Worlds with My Healthy Sweet Potato Recipes – Yams and Cornbread
Yam Cornbread Stuffing is the ultimate time saver holiday recipe as it combines yams and dressing into one delectable dish. Can you believe this is a diabetic cornbread dressing recipe is diabetic-friendly – with fresh sweet yams, cornbread, ginger and toasty pecan. For a time-efficient approach, prepare the cornbread and toast the pecans a day ahead (or just buy cornbread), and look for Louisiana yams in your grocery for the sweetest of the sweet potatoes.
Best Cornbread Dressing Recipe Is Healthy Cornbread Dressing Plus EASY!
Yam Cornbread Dressing
Two holiday favorites, Louisiana yams and cornbread, in this scrumptious best cornbread dressing recipe. Hard to believe it is also one of the most delicious healthy cornbread dressing recipes also. Save time and prepare the cornbread and toast the pecans a day ahead. Or you can even pick up pre-made cornbread. Keep it simple!
Servings10(3/4 cup) servings
2tablespoons canola oil
2cups peeled chopped Louisiana yamssweet potatoes
1cup chopped onion
1cup sliced celery
1/4cup chopped fresh parsley
1teaspoon ground ginger
5cups crumbled cooked cornbread
1/4cup chopped pecanstoasted
2tablespoons fat-free low-sodium chicken or vegetable broth
Terrific Tidbit: Time saver: Prepare cornbread and toast pecans a day ahead. Sweet potatoes are packed with vitamins and enhance the nutritional value of this recipe.
Your Favorite Healthy Cajun Recipes Like Southern Cornbread Dressing Recipe
Who says Louisiana and Cajun food can’t be good for you? Living in Baton Rouge, I wanted to give you all your favorite healthy Cajun recipes so no guilt in eating here. When I was writing my men’s healthy cookbook, Guy’s Guide To Eating Well, I was so excited to include my favorite diabetic cornbread dressing.
There’s a Diabetic-Obesity Chapter in my men’s health cookbook and this Thanksgiving cornbread dressing was a perfect fit!. My men’s cookbook includes simple and healthy recipes to make you a healthy star in the kitchen!
Download it here!I’ve included some of my go-to holiday tips plus shopping lists to organize this busy day along with my personal Thanksgiving menu. The only thing you will see missing is stuffing the turkey because my son-in-law is always in charge of making a fried turkey!
Another One of My Best Cornbread Dressing Recipe
This has been the best cornbread recipe dressing recipe and I’m also giving you a scrumptious healthy cornbread dressing. Louisiana is known for crawfish so if you like Louisiana crawfish, check out my healthy crawfish recipes! Have you ever had my Crawfish Wild Rice and Cornbread Dressing? It’s also on my crawfish blog!
I know living in Louisiana makes me partial to Louisiana crawfish recipes. However, I know so many of you moved from Louisiana or have tried our beloved crawifsh. Did you know you can have Louisiana crawfish year round because you can freeze crawfish tails. So, if you can’t decide between cornbread or wild rice dressing, you can have them both in this simple and amazing best cornbread dressing recipe.
Do You Have A Good Peeler For All Those Healthy Sweet Potato Recipes?
A good peelermakes a difference in removing the skin off thee sweet potatoes! Yes, this inexpensive gadget can be so helpful in the kitchen! All you need is a good peeler and it makes a BIG difference how easy and quickly you can remove the skin.
Time to replace your peeler now and you will be thanking me I promise. Good gadgets make cooking easier.
Scrumptious Crawfish Cornbread and Wild Rice Dressing- Another of My Best Southern Cornbread Dressing Recipes
I have the secret to making the best seafood cornbread dressing recipe. You’ll think this is the best southern cornbread dressing recipe ever or maybe I should say Cajun cornbread dressing recipe? Regardless, cornbread, wild rice and Louisiana crawfish come together in a dressing I created last year and WOW!!! This was the talk of the town. Yes, you can make this wonderful healthy cornbread dressing without the crawifsh or maybe make half with crawfish and half plain for those with seafood allergies. I bet you didn’t even know crawfish was healthy? If you like to watch how recipes are made, I have this quick video for you to see.
Get All My Healthy Easy Cookbooks
My cookbooks make the best holiday gift…the gift that keeps on giving. You’ll especially love my new men’s cookbook as men are always hard to buy a gift! Keep the man in your life healthy while everyone enjoys the good meals!
Sodium–glucose cotransport 2 inhibitors (SGLT2i) lower plasma glucose but stimulate endogenous glucose production (EGP). The current study examined the effect of dapagliflozin on EGP while clamping plasma glucose, insulin, and glucagon concentrations at their fasting level. Thirty-eight patients with type 2 diabetes received an 8-h measurement of EGP ([3-3H]-glucose) on three occasions. After a 3-h tracer equilibration, subjects received 1) dapagliflozin 10 mg (n = 26) or placebo (n = 12); 2) repeat EGP measurement with the plasma glucose concentration clamped at the fasting level; and 3) repeat EGP measurement with inhibition of insulin and glucagon secretion with somatostatin infusion and replacement of basal plasma insulin and glucagon concentrations. In study 1, the change in EGP (baseline to last hour of EGP measurement) in subjects receiving dapagliflozin was 22% greater (+0.66 ± 0.11 mg/kg/min, P < 0.05) than in subjects receiving placebo, and it was associated with a significant increase in plasma glucagon and a decrease in the plasma insulin concentration compared with placebo. Under glucose clamp conditions (study 2), the change in plasma insulin and glucagon concentrations was comparable in subjects receiving dapagliflozin and placebo, yet the difference in EGP between dapagliflozin and placebo persisted (+0.71 ± 0.13 mg/kg/min, P < 0.01). Under pancreatic clamp conditions (study 3), dapagliflozin produced an initial large decrease in EGP (8% below placebo), followed by a progressive increase in EGP that was 10.6% greater than placebo during the last hour. Collectively, these results indicate that 1) the changes in plasma insulin and glucagon concentration after SGLT2i administration are secondary to the decrease in plasma glucose concentration, and 2) the dapagliflozin-induced increase in EGP cannot be explained by the increase in plasma glucagon or decrease in plasma insulin or glucose concentrations.
Sodium–glucose cotransport 2 inhibitors (SGLT2i) are a novel class of antidiabetic agents that lower the plasma glucose concentration by inhibiting renal glucose reabsorption and producing glucosuria (1,2). We (3,4) and others (5,6) have shown that members of the SGLT2i class stimulate basal endogenous glucose production (EGP) and that the increase in EGP offsets by ∼50% the amount of glucose excreted in the urine (3), thereby blunting the decrease in the plasma glucose concentration and attenuating the clinical efficacy of SGLT2i. Although the increase in EGP can be viewed as a compensatory response to urinary glucose loss to prevent hypoglycemia, in patients with type 2 diabetes mellitus (T2DM), it occurs while the plasma glucose concentration still is well within the hyperglycemic range (3).
We and others have demonstrated (3–6) that the increase in EGP caused by SGLT2i is associated with a small but significant decrease in the plasma insulin concentration and a larger (∼25–35%) increase in the plasma glucagon concentration. Thus, the plasma glucagon-to-insulin ratio increases markedly by ∼50%. Because of the important role of plasma glucagon and insulin concentrations in the regulation of EGP (7), we hypothesized that the decrease in plasma insulin and increase in plasma glucagon concentrations could at least partly explain the increase in EGP caused by SGLT2i. The current study examined this hypothesis by testing the effect SGLT2i on EGP while 1) clamping the plasma glucose concentration at its basal fasting level and 2) clamping the plasma glucagon and insulin concentrations at their fasting level with somatostatin infusion and basal replacement of insulin and glucagon.
Research Design and Methods
Of 52 patients with T2DM who were screened, 47 eligible subjects were enrolled, and 38 subjects completed the study. Supplementary Fig 1 depicts the study profile. Except for diabetes, all subjects were in good general health based on medical history, physical examination, blood chemistry analysis, complete blood count, thyroid function, urinalysis, and electrocardiogram. Patients had stable (±1.5 kg) body weight over the 3 months before the study, and no subject participated in any excessively heavy exercise program. Patients were drug naive (n = 5) or on a stable dose of metformin with (n = 10) or without (n = 23) a sulfonylurea. The distribution of background medication in the two treatment groups is presented in Table 1. The results were similar whether subjects were drug naive or taking metformin with or without a sulfonylurea. All background medications were continued without change throughout the study. Subjects with evidence of proliferative diabetic retinopathy or serum creatinine >1.4 mg/dL (women) or >1.5 mg/dL (men), or with estimated glomerular filtration rate (eGFR) <60 mL/min/1.73 m2 were excluded.
Clinical and anthropometric characteristics of study participants
The University of Texas Health Science Center at San Antonio Institutional Review Board approved the study, and informed written consent was obtained from all subjects before their participation. The study is registered at ClinicalTrials.gov NCT02592421.
All studies were performed at the Texas Diabetes Institute Clinical Research Center at 6:00 a.m. after a 10-h overnight fast. After confirming eligibility, subjects were consecutively randomized to receive dapagliflozin or placebo in 2:1 ratio. Subject stratification was done according to the following parameters: age, BMI, diabetes duration, fasting plasma glucose (FPG), eGFR, and HbA1c. Each subject received three, 8-h measurements of EGP that were performed in random order with a 7- to 10-day interval between studies. Background medications (metformin and/or sulfonylurea) were withheld in the morning of the study day. In study 1, EGP was measured with prime-continuous [3-3H]glucose infusion. In study 2, EGP was measured under conditions when the plasma glucose concentration was clamped, and in study 3, the EGP measurement was performed under pancreatic clamp conditions.
Measurement of EGP
Subjects reported to the Clinical Research Center at 6:00 a.m., after an overnight fast. A catheter was placed into an antecubital vein, and an adjusted prime (40 μCi × FPG/100)-continuous (0.4 μCi/min) infusion of [3-3H]glucose was started and continued until 2:00 p.m. At 8:00 am, a second catheter was inserted retrogradely into a vein on the dorsum of the hand, which was placed in a heated box (70°C) for sampling of arterialized blood. After 2.5 h of tracer equilibration (8:30 a.m.), arterialized blood samples were drawn at −30, −20, −15, −10, −5, and 0 (time zero = drug administration) min. At time zero (9:00 a.m.), subjects received dapagliflozin 10 mg (n = 26) or placebo (n = 12) on separate days within a 1- to 2-week interval. After 9:00 a.m. (time zero), plasma samples were obtained every 10–20 min for 300 min for determination of plasma glucose, insulin, C-peptide, and glucagon concentrations and [3-3H]glucose-specific activity. At 6:00 a.m., subjects voided, and the urine was discarded. Urine was collected from 6:00 a.m. to 9:00 a.m. (baseline period) and from 9:00 a.m. to 2:00 p.m. (drug treatment period). Urinary volume and glucose concentration were measured to determine the urinary glucose excretion (UGE) rate. At 2:00 p.m., subjects received a meal and returned home.
The measurement of EGP was similar to that in study 1, with one difference. After time zero (drug administration), the plasma glucose concentration was measured every 5 min, and a variable infusion of 20% dextrose solution was adjusted to maintain the plasma glucose concentration at the fasting level.
The measurement of EGP was similar to that in study 1, with the following exceptions. Somatostatin infusion (750 µg/h) was started 5 min before the start of the [3-3H]glucose infusion, and the basal plasma insulin and glucagon concentrations were maintained with infusion of insulin (0.1 mU/kg ⋅ min) and glucagon (0.3 ng/kg ⋅ min).
Plasma glucose was measured using the glucose-oxidase method (Analox Reagent Instruments, International Point of Care, Toronto, Ontario, Canada). Plasma insulin (IBL America, Minneapolis, MN) and C-peptide (MP Biomedicals, Santa Ana, CA) were measured with immunoradiometric assays. Plasma glucagon (MilliporeSigma, Burlington, MA) was measured by radioimmunoassay.
Under steady-state postabsorptive conditions, the basal rate of EGP equals the [3-3H]glucose infusion rate (GIR) (disintegrations per minute/min) divided by steady-state plasma titrated glucose-specific activity (disintegrations per minute/mg). Supplementary Fig. 2 depicts [3-3H]glucose-specific activity during the three studies in subjects receiving dapagliflozin and placebo. After drug administration, nonsteady conditions for [3-3H]glucose-specific activity prevail, and the total body Ra is calculated using the Steele equation. The change in EGP during the last hour of the study (i.e., 240–300 min) from baseline was considered the drug effect on EGP and was compared between dapagliflozin and placebo with ANOVA. All values are presented as mean ± SEM. A P value <0.05 was considered statistically significant.
Data and Resource Availability
The data sets generated and analyzed during the current study are available from the corresponding author upon reasonable request.
Table 1 presents the baseline characteristics of subjects receiving dapagliflozin and placebo. Patients were well matched in age, sex, BMI, duration of diabetes, FPG, HbA1c, and eGFR.
In study 1, dapagliflozin increased UGE during the EGP measurement to 2.54 ± 0.20 g/h. In study 2, the increase in UGE was significantly greater (3.74 ± 0.41 g/h) than in study 1 (P = 0.02). In study 3, UGE (1.86 ± 0.26 g/h) was significantly less (P < 0.005) than in study 1.
During EGP measurement in study 1, dapagliflozin produced a significant decrease in the plasma glucose concentration which, during the last hour (i.e., 240–300 min), was 29 ± 4 mg/dL below the FPG concentration, compared with a 17 ± 4 mg/dL decrease in subjects receiving placebo (Fig. 1A and Table 2). Thus, the placebo-subtracted decrease in the plasma glucose concentration brought about by dapagliflozin in study 1 was 12 mg/dL (−7.8%, P = 0.03). The basal rate of EGP at the last hour of the EGP measurement increased by +0.10 ± 0.10 mg/kg/min above the fasting level after dapagliflozin administration (+2.7%) compared with a −0.56 ± 0.11 mg/kg/min decrease (−19.6%) in subjects receiving placebo (Supplementary Fig. 3). Thus, compared with placebo, dapagliflozin caused a +0.66 mg/kg/min increase (+22.3%) (Table 2) in EGP (P < 0.03) (Fig. 2A). Consistent with previous studies (3), compared with placebo, dapagliflozin caused a small but significant increase in the plasma glucagon concentration (Table 3 and Supplementary Fig. 4) and a decrease in the plasma insulin concentration (Table 3 and Supplementary Fig. 5). Thus, the plasma glucagon-to-insulin ratio markedly increased (Table 3 and Supplementary Fig. 6).
The time course of the change in the plasma glucose concentration (PGC) in subjects receiving dapagliflozin or placebo is shown in study 1 (A), study 2 (C), and study 3 (E). The change in PGC from baseline to the last hour of the study is shown in study 1 (B), study 2 (D), and study 3 (F).
Change from baseline to the last hour of the study (240–300 min) in plasma glucose concentration and EGP in study 1, study 2 (glucose clamp), and study 3 (pancreatic clamp)
Percentage change from baseline in total EGP measured with [3-3H]glucose infusion in subjects receiving dapagliflozin or placebo in study 1 (A), study 2 (glucose clamp) (C), and study 3 (pancreatic clamp) (E). The change in EGP from baseline to the last hour of the study is depicted in B (study 1), D (study 2), and F (study 3).
Plasma insulin and glucagon concentrations during measurement of EGP with dapagliflozin, with dapagliflozin plus glucose clamp, and with dapagliflozin plus pancreatic clamp versus placebo
Under glucose clamp conditions (study 2), the plasma glucose concentration remained unchanged during the EGP measurement (Fig. 1C and Table 2) in both the placebo and dapagliflozin groups. Unlike study 1, the change in plasma insulin and glucagon concentrations under glucose clamp conditions was comparable in subjects receiving dapagliflozin and placebo (Table 3 and Supplementary Figs. 4 and 5), as was the plasma glucagon-to-insulin ratio (Table 3 and Supplementary Fig. 6). During the glucose clamp study, placebo-treated subjects experienced a precipitous decrease in EGP. The mean EGP during the last hour of study 2 (i.e., 240–300 min) was −1.28 ± 0.17 mg/kg/min (−46.6% decrease) below the fasting EGP. However, in dapagliflozin-treated subjects, the decrease in EGP was markedly attenuated −0.57 ± 0.12 mg/kg/min (−26.1% decrease). Thus, under glucose clamp conditions, dapagliflozin produced a +0.71 mg/kg/min (+20.4%, P = 0.01) increase in EGP compared with placebo (Fig. 2 and Table 2), even though the plasma insulin and glucagon concentrations were comparable in the dapagliflozin and placebo groups. The GIR during the glucose clamp (study 1) increased over time in both study groups. The mean GIR in subjects receiving dapagliflozin (1.45 ± 0.20 mg/kg/min) was greater compared with placebo (0.93 ± 0.10 mg/kg/min). However, the difference did not reach statistical significance (P = 0.10). The difference between the dapagliflozin and placebo groups was time-dependent (Supplementary Fig. 7A), with statistical significance being reached at 60 min. With time, the difference between the two groups diminished, most likely due to the increase in EGP. At 260 min, the difference was not statistically significant. The mean GIR in subjects receiving dapagliflozin strongly correlated with urinary glucose loss (r = 0.84, P < 0.001) (Supplementary Fig. 7B).
Under pancreatic clamp conditions (study 3), the plasma glucagon concentration remained unchanged (Supplementary Fig. 4) in subjects receiving placebo or dapagliflozin, while plasma insulin concentration remained unchanged in subjects receiving placebo (Supplementary Fig. 3). There was a small decrease in the plasma insulin concentration in subjects receiving dapagliflozin (12 ± 1 to 10 ± 1, P = 0.002) (Table 2 and Supplementary Fig. 5). However, the change from baseline to the last hour of the EGP measurement in plasma insulin concentration in subjects receiving dapagliflozin and placebo was not statistically different. Further, the glucagon-to-insulin ratio was comparable in the two groups (Table 3 and Supplementary Fig. 6B). Dapagliflozin produced a large decrease (−30 ± 4 mg/dL) in the plasma glucose concentration compared with placebo (−7 ± 5 mg/dL). Thus, the placebo-subtracted decrease in the plasma glucose concentration produced by dapagliflozin under pancreatic clamp conditions was −23 mg/dL (−16.3%, P = 0.0008), which was significantly greater than that in study 1 (P < 0.01).
Under pancreatic clamp conditions (study 3), EGP progressively decreased in subjects receiving placebo and, during the last hour, was reduced by −0.48 ± 0.05 mg/dL (−18.5%) below the fasting EGP. In subjects receiving dapagliflozin, EGP precipitously declined (−0.41 mg/kg/min) during the first hour after dapagliflozin administration and at 60 min was −8% below that in placebo. However, EGP progressively increased after 60 min and, from 60 to 300 min, the increase in EGP was 0.22 ± 0.08 mg/kg/min (P < 0.0001). During the last hour of EGP measurement (i.e., 240–300 min), EGP in subjects receiving dapagliflozin was 0.26 mg/kg/min (+10.6%, P = 0.04) (Fig. 2C) higher than in subjects receiving placebo.
Consistent with previous studies (3–6), inhibition of renal SGLT2 transport with dapagliflozin was associated with a significant decrease in the plasma glucose concentration (−12 mg/dL) and a “paradoxical” increase in EGP with a concomitant increase in the plasma glucagon concentration and a decrease in the plasma insulin concentration. The present results demonstrate that prevention of the decrease in plasma glucose concentration blocked the decline in the plasma insulin concentration and inhibited the rise in the plasma glucagon concentration, indicating that the changes in the plasma insulin and glucagon concentrations after SGLT2i administration are secondary to the decrease in the plasma glucose concentration and are not due to direct action of the drug on the pancreatic islets (8). These results are consistent with recent studies (9–11) which demonstrated that prevention of the decrease in plasma glucose concentration blocked the increase in plasma glucagon concentration in patients with T2DM after dapagliflozin administration. Most importantly, despite the absence of any difference in the change in plasma insulin and glucagon concentrations between subjects receiving dapagliflozin and placebo under glucose clamp conditions, dapagliflozin administration still provoked an increase in EGP that was of similar magnitude to that caused by dapagliflozin without the glucose clamp in study 1 (Supplementary Fig. 6). The absolute difference between dapagliflozin-treated and placebo-treated subjects in the change in EGP from baseline to the last hour of EGP measurement (i.e., 240–300 min) was comparable in study 1 (glucose allowed to drop) and study 2 (glucose clamp condition), +0.66 vs. +0.71 mg/kg/min, respectively (Table 2 and Supplementary Fig. 8). This finding provides strong evidence against an important role for the increase in plasma glucagon and decrease in plasma insulin or change in plasma glucose concentration in mediating the acute increase in EGP caused by dapagliflozin. These results are consistent with a recent study in which we demonstrated that blocking the increase in plasma glucagon concentration and preventing the decrease in plasma insulin concentration with coadministration of a glucagon-like peptide 1 receptor agonist (liraglutide) plus SGLT2i (canagliflozin) failed to prevent the increase in EGP caused by canagliflozin (10). Collectively, these observations demonstrate that neither the change in plasma glucose concentration nor the change in pancreatic hormone (glucagon and insulin) concentrations can explain the acute stimulation of EGP by SGLT2i.
During the pancreatic clamp study with somatostatin and basal glucagon/insulin replacement (study 3), the plasma glucagon concentration remained unchanged in both study groups. While there was small decrease in plasma insulin concentration in subjects receiving dapagliflozin, the change from baseline to the last hour of the EGP measurement in plasma insulin concentration was comparable in both groups (Table 2 and Supplementary Fig. 5), as was the plasma glucagon-to-insulin ratio (Supplementary Fig. 6). However, the time pattern of change differed among the two groups. Dapagliflozin produced a large and rapid decline in EGP, such that at 60 min, EGP was significantly lower than in subjects receiving placebo. After 60 min, EGP rose progressively (Fig. 2E) to a level higher than in placebo. It should be emphasized that the increase in EGP from 60 to 300 min in subjects receiving dapagliflozin took place without a change in the plasma glucagon concentration. Further, it is unlikely that the small decrease in plasma insulin concentration is responsible for the progressive increase in EGP after dapagliflozin because the change in the plasma insulin concentration was comparable in dapagliflozin-treated and placebo-treated subjects.
Previous studies in experimental animals have reported a stimulatory action of somatostatin on renal gluconeogenesis (12). Although a similar stimulatory action of somatostatin in humans could have contributed to the increase in EGP in study 3, we believe that this is unlikely because we would have expected a similar increase in EGP in subjects receiving placebo, while in contrast, EGP dropped progressively throughout the study. More likely, the rise in EGP reflects the effect of dapagliflozin as was seen in studies 1 and 2.
It is noteworthy that the placebo-subtracted increase in EGP after dapagliflozin administration was attenuated in study 3 compared with studies 1 and 2 (Supplementary Fig. 8). It is well known that somatostatin markedly decreases splanchnic blood flow (13,14). Because of the important role of substrate supply in the regulation of hepatic gluconeogenesis (15), it is possible that decreased hepatic blood flow could have attenuated the increase in EGP caused by dapagliflozin under conditions of somatostatin infusion. Regardless of the mechanism(s) responsible for the attenuation of the increase in EGP by dapagliflozin, the placebo-subtracted decrease in the plasma glucose concentration in study 3 was significantly greater than in study 1 (23 vs. 12 mg/dL, P < 0.05). This emphasizes the clinical importance of the increase in EGP after SGLT2 administration in attenuating the clinical efficacy of SGLT2 inhibitors in lowering the plasma glucose concentration.
Urinary glucose loss caused by dapagliflozin varied significantly among the three studies. UGE under glucose clamp conditions (study 2) was significantly greater than in study 1. Conversely, under pancreatic clamp conditions (study 3), UGE was significantly less than in study 1. Because UGE is influenced by the amount of filtered glucose (product of the GFR and plasma glucose concentration) (16), the higher UGE under glucose clamp conditions (study 2) can be explained by the higher mean plasma glucose concentration compared with study 1 (136 vs. 116 mg/dL, P < 0.0001). However, the mean plasma glucose concentration from time 0–300 min under pancreatic clamp conditions in study 3 (134 ± 5 mg/dL) was comparable to that in study 2 (134 ± 5 mg/dL, P = ns) and was significantly higher than in study 1 (115 ± 3 mg/dL P > 0.0001). Nonetheless, UGE in study 3 was the smallest. This marked reduction in UGE in study 3 suggests a reduction in GFR by somatostatin secondary to decreased renal blood flow (17), neither of which were measured in the current study.
In summary, clamping the plasma glucose concentration at the fasting level prevented the change in plasma insulin and glucagon concentrations caused by SGLT2i but failed to prevent the dapagliflozin-induced increase in EGP. These results argue against an important role for changes in the plasma insulin and glucagon concentrations in mediating the increase in EGP caused by SGLT2i.
Acknowledgments. The authors thank Khanh Horst for her excellent care of the patients throughout the study and Lorrie Albarado and Deena Murphy for their expert secretarial assistance in preparation of the manuscript.
Funding. This study was funded by National Institutes of Health, National Institute of Diabetes and Digestive and Kidney Diseases grant R01-DK-107680 to R.D.
Duality of Interest. R.D. receives grant support from AstraZeneca, Merck, and Janssen, is a member of the advisory boards of AstraZeneca, Janssen Pharmaceuticals, Intarcia Therapeutics, Boehringer Ingelheim, and Novo Nordisk, and is a member of the speakers bureaus of Novo Nordisk and AstraZeneca. E.C. receives grant support from AstraZeneca and Janssen Pharmaceuticals, is a member of the advisory boards of VeroScience, the Boehringer Ingelheim and Lilly Diabetes Alliance, and Sanofi, and is a member of the speakers bureaus of AstraZeneca, Janssen Pharmaceuticals, and the Boehringer Ingelheim and Lilly Diabetes Alliance. No other potential conflicts of interest relevant to this article were reported.
Author Contributions. M.A., N.L., R.M., C.A., A.M.A., H.A-J., O.L., and J.A. generated the data. C.T., R.D., and E.C. and reviewed and revised the manuscript. M.A.-G. analyzed the data and wrote the manuscript. M.A.-G. 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.
I don’t know about you, but in these times of social distancing, I’ve been cooking a lot of beans and lentils. This White Bean Lentil Soup combines both in one hearty dish.
First, I highly recommend you cook your own dried beans using the technique in Low Sodium No Soak Beans. Use Great Northern Beans. Set aside three cups for this white bean lentil soup and freeze the rest. They will taste so much better than canned beans and you’ll be able to control the sodium. (Canned beans are notoriously high in sodium although rinsing helps.)
Second, customize this recipe and make it your own. You can use chicken stock or vegetable broth. Try thyme or rosemary instead of Herbes de Provence. Use any type of leafy greens you have on hand; Swiss chard, spinach, or baby kale will do. If your greens have fat stems, remove them first, then chop the leaves.
Whenever you have a hunk of Parmesan cheese and get down to the rind, don’t throw it out. Save it to flavor soups like this one.
Try different types of pepper. I like McCormick’s Hot Shot! blend (affiliate link), which is a combination of black and cayenne peppers. I’ve also used lemon pepper, garlic pepper, and an orange pepper blend I picked up in Budapest last summer.
Add different types of beans. Navy or cannellini beans would both be good substitutes for Great Northern.
Make White Bean Lentil Soup in a Slow Cooker
If you don’t have an Instant Pot® (affiliate link), you can make this soup in a slow cooker. Just follow these instructions:
Combine beans, lentils, broth, 1 cup water, onion, carrots, garlic, Herbes de Provence, pepper, salt, bay leaf, and Parmesan rind in a slow cooker. Cover and cook on low until lentils are tender (about 7 hours).
Stir in greens and lemon juice. Cover and cook on low an additional 30 minutes or until greens have wilted. Discard rind and bay leaf.
Divide soup among 8 serving bowls and sprinkle each with about 1 tablespoon of grated Parmesan.
White Bean Lentil Soup (Instant Pot)
A hearty soup for a rainy day
Author:Adapted from Cooking Light
Course: Soups and Stews, Vegetarian
Keyword: bean soup, lentil soup, Tuscan white bean soup
3 cups cooked Great Northern beans drained and rinsed (if using canned)
1 cup uncooked brown lentils rinsed
4 cups vegetable broth
2 cups water
1 cup chopped onion
4 large carrots chopped
2 cloves garlic minced
1/2 teaspoon Herbes de Provence
1/2 teaspoon black and red pepper blend or freshly ground black pepper
1/4 teaspoon kosher salt
1 bay leaf
1 (2-inch) piece Parmesan cheese rind
4 cups leafy greens such as Swiss chard or spinach stems removed and chopped
2 teaspoons fresh lemon juice
1/2 cup Parmesan cheese freshly grated
Combine beans, lentils, broth, water, onion, carrots, garlic, Herbes de Provence, pepper, salt, bay leaf, and rind in an electric pressure cooker (e.g. Instant Pot). Close and lock the lid. Set the valve to Sealing. Cook on High pressure for 15 minutes.
When the cooking is complete, let the pressure release naturally for 10 minutes, then quick release any remaining pressure.
Carefully uncover the pot, then stir in the greens and lemon juice. Remove and discard the rind and bay leaf.
Divide soup among 8 serving bowls and sprinkle each with about 1 tablespoon of grated Parmesan.
Dried beans you’ve cooked yourself taste better. If you have an electric pressure cooker (e.g. Instant Pot), cook the beans ahead of time (see Low Sodium No Soak Beans) and save out 3 cups for this recipe. Freeze or use the rest in other dishes.
In 2013 my late husband and I drove across the country from Chicago to Austin, Texas. It was pretty much the longest trip of my life. Not only did Tony hate my driving, and complain about it that I was going too slow and that I had to keep up with the assholes driving 90+ mph, we thought once we hit the border of Texas “we were almost there!” Nope, still hours to go.
Since Tony did most of the driving, I just looked up what restaurants I wanted to go to – not a shocker! But what I was really interested in was the food trucks! We just started having food trucks in Chicago, but I live in the NW suburbs of Chicago – not one anywhere near me. I had dreams of ordering brisket and pulled pork sandwiches.
Once we got there, we quickly realized that most food trucks didn’t even open until 11 PM when bars started to get busy. By luck we found one lone food truck that I think I screamed “pull over – there’s a food truck!” Only to discover it was a . . . .lobster roll truck. Really, in Texas??!!
But I had to pull the trigger and didn’t realize until well after the tarragon butter was wiped from my mouth an hour later, that we just paid $100 for four sandwiches.
So that’s where my Poor Man’s Lobster Roll was born. I originally wrote this for a Chopping Block blog post in 2019. I switched up the recipe a bit to add some veggies to the roll (I know, not traditional) and did a basil butter over the top. #swoon
A delicious light sandwich that can be made ahead of time. Simply butter toast the buns and you’ve got a delicious meal in minutes. And a lot cheaper than lobster! This filling is for 4 sandwiches.
12 ounces cooked shrimp, chopped in half 1/3 cup non-fat Greek yogurt 1 tablespoon lemon juice zest from 1 lemon 2 teaspoons minced garlic 2 tablespoons chopped fresh parsley 1/2 cup finely chopped cucumber 1/2 cup pan fried sugar snap peas, sliced thin Pinch of salt, pepper, cayenne pepper 4 tablespoons I Can’t Believe Its Not Butter, divided 2 tablespoons fresh chopped basil 4 good hotdog buns (I used Martin’s @potatorolls)
Mix the shrimp through cayenne pepper in a bowl. Melt one tablespoon butter and place the buns face down, until toasted. Divide the mixture between the buns. Melt the remaining butter, toss in the basil and pour over the rolls.
This is not the time to skimp on good buns 😉On #teampurple and #teamblue, each sandwich is 5 points. #teamgreen needs to add the shrimp – so if someone can tell me what that is, I’d appreciate it.
I don’t do mayonnaise so that’s why I subbed in the Greek yogurt. This is light, bright with the lemon juice and zest, and as close as I will get to a lobster roll anytime soon.
Hannah and I had a productive weekend. She has officially released the pantry organization to me – basically she is quitting after this last reorganization and I don’t blame her. I will try to keep it up – pinky swear!
I still have so many pantry items that I know I can meal prep with what I have.
I found this gem while cleaning out the basement – Hannah art from 1998!
And my friend Morgan sent me this belated birthday gift. I joke that on the Weight Watchers plan #teamturquoise – where everything is zero points. It’s a fun plan to be on by the way. I had about 60 people tell me that since being quarantined, this is the team plan they follow!
Our weather was glorious at times this weekend – the dogs are loving all the fresh air – love Roman’s face in this picture!
Cauliflower risotto has all the flavor of traditional risotto but with almost none of the carbs! A creamy, satisfying dish that is perfect for weeknights.
There is nothing like a hearty, creamy dish for dinner after a long day. This low-carb mushroom cauliflower risotto is the perfect dinner any day of the week. It’s can be prepared in under an hour and is packed full of flavor.
Traditional risotto is made with arborio rice which absorbs vegetable broth slowly to make a creamy, rich rice dish. We use cauliflower as a rice replacement in many recipes and it works perfectly here too!
How to make cauliflower risotto
Preparing a low-carb risotto is quite similar to making traditional risotto and couldn’t be easier to make.
Step 1: Clean and finely slice the mushrooms. Finely chop the onion.
Step 2: Heat a large pan over medium heat. Add butter and allow to melt. Once the butter has melted and is hot, add onion and sauté until the onion is translucent (4-5 minutes). Add garlic and cook for another minute.
Step 3: Add lemon juice, thyme, salt, pepper, and sliced mushrooms. Fry for a few minutes until the mushrooms start to release their juices.
Step 4: Add cauliflower and stir to coat with the melted butter. Add the white wine and stir until it has mostly evaporated.
Step 5: Turn the heat down to low. Add the vegetable broth 1/4 cup at a time while stirring. Allow most of the vegetable broth to bubble away before adding the next 1/4 cup.
Step 6: Once the last bit of broth has been added, stir in the cream. Cover the pan and cook for a further 3 – 5 minutes till the cauliflower has steamed through and is tender.
Step 7: Remove from the heat and add the parmesan cheese. Mix well and serve.
Step 8: Garnish with more parmesan cheese if desired.
You can store any leftover risotto in the refrigerator for up to 3 days. Use an airtight container to keep it tasting fresh and delicious.
More low-carb cauliflower recipes to try
This recipe is one of many recipes using cauliflower you can find on this website! It’s such a versatile vegetable and can be used in many creative and tasty ways. Here are some of my other favorite cauliflower recipes that you can try out!
When you’ve tried this low-carb cauliflower risotto, please don’t forget to let me know how you liked it and rate the recipe in the comments below!
Mushroom Cauliflower Risotto
Cauliflower risotto has all the flavor of traditional risotto but with none of the carbs! A creamy, satisfying dish that is perfect for weeknights.
1/2cupwhite onion(chopped fine)
8ounceswhite button mushrooms(sliced)
1/4cupdry white wine
1/2cupgrated parmesan cheese
Clean and finely slice the mushrooms. Finely chop the onion.
Heat a large pan over medium heat. Once hot, add butter and allow to melt. Once the butter has melted and is hot, add onion and sauté until onion is translucent (4-5 minutes). Add garlic and cook for another minute.
Add lemon juice, thyme, salt, pepper and sliced mushrooms. Fry for a few minutes until the mushrooms start to release their juices.
Add cauliflower and stir to coat with the melted butter. Add the white wine and stir until it has mostly evaporated.
Turn the heat down to low. Add the vegetable broth 1/4 cup at a time while stirring. Allow most of the vegetable broth to bubble away before adding the next 1/4 cup.
Once the last bit of broth has been added, stir in the cream.
Cover the pan and cook for a further 3 – 5 minutes till the cauliflower has steamed through and is tender.
Remove from the heat and add the parmesan cheese. Mix well and serve. Garnish with more parmesan cheese if desired.
This recipe makes 4 servings. You can store leftovers in the refrigerator for 4 – 5 days.
Nutrition Info Per Serving
Mushroom Cauliflower Risotto
Amount Per Serving
Calories 391Calories from Fat 301
% Daily Value*
Saturated Fat 21g105%
Polyunsaturated Fat 1.2g
Monounsaturated Fat 8.1g
Net carbs 10g
* Percent Daily Values are based on a 2000 calorie diet.
Course: Main Course
Keyword: Cauliflower Risotto
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Sweet Potato Pancakes Recipe Makes Quick Make- Ahead Breakfast
In the mornings, you need breakfast to start the day but who has the time? This is one of the best pancake recipes ever. You must try my easy Sweet Potato Pancakes recipe from my best selling cancer cookbook, Eating Well Through Cancer. This book contains simple recipes for quick healthy meals. These healthy Sweet Potato Pancakes recipe is also diabetic-friendly. Fast with a shortcut of canned yams and Bisquick, this is my favorite of Bisquick Pancake recipes.Yam Pancakes known as Louisiana Sweet Potato Pancakes have a naturally sweet flavor.
Easy Sweet Potato Pancakes Quickly Won Me Over
I actually was looking up sweet potato pancake recipes in all my cookbooks and I found one in Too Hot in the Kitchen cookbook. Looked absolutely delicious. However, it was with lots of ingredients so I took a quick look in my cancer cookbookto find my recipe for Yam Pancakes. When I saw how simple it was and I had most of the ingredients, I definitely decided on the quick healthy sweet potato pancakes recipe!
Best Make Ahead Breakfast – My Best Pancakes Recipe Freezes Well
This recipe makes a lot of pancakes but that’s a good thing! I freeze them in a zip-lock plastic bag and pull out every morning for breakfast. I heat the pancakes up in the microwave for about 30 seconds-1 minute. To freeze: I flash freeze on a baking sheet by laying them all over the pan. When frozen, I put them in the freezer zip-top bag and this way they don’t stick together. You can pull out however many you want to eat that morning.
Healthy Sweet Potato Pancakes Recipe Simple To Make
By starting with Bisquick, you save so many steps. You’ll love the flavor the sweet potato adds to the pancakes. With a little cinnamon, eggs, oil and vanilla, you whip up the pancake batter quickly in one bowl. I really like the flavor the mashed sweet potatoes add to the pancakes. Louisiana sweet potato pancakes are the best! You will find Bisquick Pancake recipes but this one is also diabetic!
Start the morning off right with this bundle of flavor. Pancakes, sugar, spice, and everything nice – feel free to forget the syrup!
2cups all-purpose baking mix
2teaspoons ground cinnamon
1/2cup mashedcanned sweet potatoes (drained)
112-ounce can evaporated skim milk
1 egg white
1tablespoon canola oil
1teaspoon vanilla extract
In bowl, combine all ingredients just until combined. (batter will be lumpy).
Heat large nonstick skillet or griddle coated with nonstick cooking spray. Spoon 1/4 cup batter onto pan for each pancake. Cook pancakes 1–2 minutes on each side, or until lightly browned.
Terrific Tip: Make a batch of pancakes, freeze and pop in the microwave for a quick breakfast.
Nutritional Nugget: Toss in some dried fruit, pecans or chocolate chips, if desired.
Eating Well Through Cancer Best Easy Healthy Cookbook with Everyday Ingredients
My cookbook Eating Well Through Cancer is divided into chapter to help a person going through cancer treatment to find the foods best tolerated during treatment. However, the book contains healthy, everyday recipes that the entire family will enjoy. Since I have been going through cancer treatment myself, I have used the cancer book as a patient. This new perspective shows me that the bookcontains truly my favorite recipes. These delicious Yamcakes have been my new favorite breakfast.
Cook Easy Sweet Potato Pancakes On Nonstick Griddle Pan
Whether you’re making pancakes or even burgers, a nonstick griddle pan makes a good choice of cookware to have on hand. Honestly, it really makes cooking pancakes like these Yam Cakes or my Oatmeal Pancakes easier and I like this size pan. Once you have a griddle pan, I promise you’ll find all kinds of uses for it. Any brand works but I like a nonstick and about this size because it covers the burner to help cook evenly.
What Makes the Best Pancakes Recipe?
I think flavor, simplicity and health determine the best pancakes recipe. You’ll enjoy how easy these pancakes are to make with the two shortcuts of Bisquick and canned yams. However, if you have leftover baked sweet potatoes, here’s an easy sweet potato recipe for the leftovers. Just like my Oatmeal Pancakes, I try to give you simple healthy breakfast recipe. Both recipes include nutritional information and are diabetic friendly. Start you day with a delicious and healthy breakfast!
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