Excessive Glucocorticoids During Pregnancy Impair Fetal Brown Fat Development and Predispose Offspring to Metabolic Dysfunctions

By electricdiet / August 5, 2020


Abstract

Maternal stress during pregnancy exposes fetuses to hyperglucocorticoids, which increases the risk of metabolic dysfunctions in offspring. Despite being a key tissue for maintaining metabolic health, the impacts of maternal excessive glucocorticoids (GC) on fetal brown adipose tissue (BAT) development and its long-term thermogenesis and energy expenditure remain unexamined. For testing, pregnant mice were administered dexamethasone (DEX), a synthetic GC, in the last trimester of gestation, when BAT development is the most active. DEX offspring had glucose, insulin resistance, and adiposity and also displayed cold sensitivity following cold exposure. In BAT of DEX offspring, Ppargc1a expression was suppressed, together with reduced mitochondrial density, and the brown progenitor cells sorted from offspring BAT demonstrated attenuated brown adipogenic capacity. Increased DNA methylation in Ppargc1a promoter had a fetal origin; elevated DNA methylation was also detected in neonatal BAT and brown progenitors. Mechanistically, fetal GC exposure increased GC receptor/DNMT3b complex in binding to the Ppargc1a promoter, potentially driving its de novo DNA methylation and transcriptional silencing, which impaired fetal BAT development. In summary, maternal GC exposure during pregnancy increases DNA methylation in the Ppargc1a promoter, which epigenetically impairs BAT thermogenesis and energy expenditure, predisposing offspring to metabolic dysfunctions.

Introduction

Stress, including anxiety and depression, has become a serious health concern in women during pregnancy (1,2). National population-based studies reported that 15.2% of pregnant women have anxiety disorder and 37% and 17% of pregnant women have depressive symptoms and severe depressive disorders, respectively (1,2). Glucocorticoids (GC) are major hormones responsive to stress, and the maternal stress during pregnancy significantly increases fetal exposure to GC (3,4). The activity of 11β-hydroxysteroid dehydrogenase, an enzyme limiting maternal GC across the placental barrier, was also reduced by maternal stress (5), further exaggerating excessive exposure of fetuses to GC (6,7).

Programming effects of hyper–intrauterine GC increase susceptibility of offspring to not only neurophysiological and psychological disorders but also the risk of childhood and adult obesity (811). Overweight and obesity, due to excessive accumulation of white adipose tissue, are closely associated with type 2 diabetes, cardiovascular diseases, and several cancers (12,13). Different from white adipose tissue, brown adipose tissue (BAT) dissipates energy through thermogenic activity of uncoupling protein 1 (UCP-1), preventing obesity and type 2 diabetes (1214). Mitochondria are key organelles in maintaining BAT thermogenic functions and energy expenditure, and UCP-1 is anchored in inner mitochondrial membrane (1214). Peroxisome proliferator–activated receptor γ coactivator-1α (Ppargc1a) (protein PGC-1a) is a master regulator of mitochondrial biogenesis (15,16), and its transcriptional inhibition decreases mitochondrial density and UCP-1 protein, contributing to cold sensitivity and obesity (17). Recently, hyper–DNA methylations have been identified in the Ppargc1a promoter in pancreas (18) and skeletal muscle (19) of patients with type 2 diabetes. Adiposity, glucose tolerance, and insulin resistance induce epigenetic modulations, which reduce Ppargc1a expression and mitochondrial biogenesis across tissues (18,19). Currently, little information is available on changes and functional roles of Ppargc1a promoter DNA methylation in BAT function of obese and diabetic mice or humans with obesity and diabetes. Because of the BAT importance in energy expenditure and glucose homeostasis (12,13), revealing the link between offspring BAT phenotypes and DNA methylation of Ppargc1a promoter in responding to maternal hyper-GC during pregnancy is of key interest.

Previous evidence suggests that excessive GC in circulation increase the risks of obesity in adult mice (20). Similarly, patients with Cushing syndrome, due to chronic hypercortisolism, are also characterized with insulin resistance and central obesity (20). Hyper-GC were reported to inhibit BAT thermogenesis, but a recent study reversibly showed that excessive GC activate human BAT thermogenic activity (21). The inconsistency in reports underscores the knowledge deficiency in the direct GC exposure on adult BAT thermogenesis. Moreover, the effects of maternal hyper-GC exposure during pregnancy on fetal BAT development and long-term energy expenditure and thermogenesis in adult offspring remain unexamined. In this study, pregnant mice were administrated with synthetic GC dexamethasone (DEX) during the last trimester; synthetic GC can readily cross the placenta, imitating maternal stress–induced fetal hyper-GC exposure as previously reported (2224). In addition, DEX injection is used for >70% of women at risk of preterm delivering (25). The development of BAT is initiated around mid-gestation and followed by rapid brown adipogenic differentiation in the last trimester (12,13). Targeting this most active stage of brown adipogenesis (1214), we hypothesized that the intrauterine hyper-GC exposure has substantial impacts on fetal BAT development, leading to changes of postnatal developmental trajectory and functions. We further hypothesized that maternal hyper-GC exposure epigenetically inhibits Ppargc1a expression, persistently reducing its expression and BAT energy expenditure and thermogenesis in offspring.

Research Design and Methods

Animal Studies

Wild-type female C57BL/6J mice (The Jackson Laboratory, Bar Harbor, ME) at 4 months of age were mated with wild-type male mice fed a regular diet (10% energy from fat, D12450H; Research Diets, New Brunswick, NJ). Successful mating was determined by the presence of a copulation plug in the vagina and marked as embryonic day 0.5 (E0.5). At E14.5, pregnant mice were randomly separated into two groups intraperitoneally administrated with PBS (as control) or 0.1 mg/kg body wt DEX (D4902; Sigma-Aldrich) daily until E20.5. After birth, the litter size was standardized to six and the maternal mice together with their offspring were housed at room temperature (22°C) until weaning (postnatal day 21 [P21]). After weaning, male offspring in control and DEX groups were further randomly assigned to a chow (10% energy from fat) or obesogenic (high-fat diet [HFD]) (60% energy from fat; D12492) diet until 4 months of age. At 4 months old, a portion of offspring were acclimated to thermoneutrality (30°C) for 4 weeks to examine BAT-independent thermogenic changes due to maternal DEX exposure (2628). At P0, P21, and 4 months, offspring were euthanized by CO2 and brown fat (BAT) was collected for analyses. Due to the small size of neonatal BAT, neonates from the same litter were pooled. Each dam (each pregnancy) was treated as an experimental unit. All animal studies were conducted in a Washington State University animal facility accredited by the Association for Assessment and Accreditation of Laboratory Animal Care. Animal protocol was approved by the Washington State University Institute of Animal Care and Use Committee.

Flow Cytometry Sorting (FACS)

FACS was performed as previously described (14). BAT was digested in 0.2% collagenase type II (285 units/mg) in DMEM-free FBS medium for 30 min at 37°C. Tissues debris was filtered through 40-µm strainers, and the filter solution was centrifuged at 400g for 5 min. After centrifugation, BAT stromal vascular fractions were blocked in 1% BSA for 15 min on ice. Cells were washed by PBS and incubated in conjugated CD45 (phycoerythrin/Cy7; BioLegend) and PDGFRa (allophycocyanin; BioLegend) in the dark at 4°C for 1 h. Cells were washed with PBS and sorted in a SY3200 Cell Sorter (Sony Biotechnology, San Jose, CA). Flow cytometry data were analyzed using FlowJo software (Treestar). Gates were established using fluorescence minus controls.

DNA Methylation Immunoprecipitation

DNA Methylation Immunoprecipitation (MeDIP) analysis was performed as previously described (14). BAT was digested in protease K solution. Total DNA was isolated from solution containing Tris-phenol, chloroform, and isoamyl alcohol. Isolated DNA was dissolved into Tris-EDTA buffer followed by sonication (30% power, 10 s on/off for 5 min) on ice. Average 300–500 base pair DNA fragment size was obtained and verified by electrophoresis in 2% agarose gel. Denatured DNA (2 mg) containing RNase A was incubated with 2 μg antibody against 5-methylcytosine (5mC) (Zymo Research, Irvine, CA) or anti-mouse IgG (Thermo Fisher Scientific) overnight at 4°C. The DNA-antibody solution was further incubated with precleaned protein A magnetic beads (Cell Signaling Technology) at 4°C for 1 h. Washed beads were incubated in digestion buffer to recover precipitated DNA at 65°C for 3 h. Recovered DNA was quantified by quantitative PCR (qPCR). Primers for MeDIP qPCR are listed in Supplementary Table 1.

Bisulfite Pyrosequencing

Genomic DNA was converted by bisulfite using EZ DNA Methylation-Direct Kit (D5021; Zymo Research). Converted genomic DNA was used as a template to amplify the target sequence. GC-purified biotinylated primers were targeted to the Ppargc-1a proximal promoter (19,29), including the following sequence: 5′attttttttttcctctctctctaagcgttacttcactgaggcagagggccccttggagtgacg3′. Pyrosequencing and data analyses were performed by EpigenDx (Hokinton, MA).

Coimmunoprecipitation Assay

Total protein was isolated in lysis buffer as previously described (14). Lysate was precleaned with protein A magnetic beads (Cell Signaling Technology) at 4°C for 1 h. GC receptor (GR) primary antibody (2 μg) (Cell Signaling Technology) was added to lysate containing 500 μg total proteins, and the mixture was gently rotated overnight at 4°C. After that, precleaned protein A magnetic beads were mixed into lysate and rotated for 1 h at room temperature. Samples were followed with 3× cold PBS washing, and proteins on beads were eluted for immunoblotting analyses of GR, DNMT3b (Santa Cruz Biotechnology), DNMT3a (Cell Signaling Technology) and DNMT1 (Cell Signaling Technology).

Chromatin Immunoprecipitation Assay

Chromatin immunoprecipitation assay (ChIP) analyses were performed as previously described (14). Briefly, protein and DNA were cross-linked in 1% formaldehyde followed by addition of 125 mmol/L glycine at room temperature. Grounded tissues were suspended in cold ChIP lysis buffer containing protease inhibitor (Thermo Fisher Scientific). Samples were sonicated to shear DNA fragments to average length 300–500 base pairs. DNA (30 μg) was used as inputs and the same amount of DNA was incubated with 3 μg antibodies against GR (Cell Signaling Technology), DNMT3b (Santa Cruz Biotechnology), or negative control IgG (Thermo Fisher Scientific) at 4°C overnight. Precleaned protein A magnetic beads (40 μL) (Cell Signaling Technology) were further added into the solution and rotated at 4°C for 3 h. Precipitated DNA was recovered from washing beads in low salts, high salts, and LiCl solutions. After washing with elution buffer, recovered supernatant was treated with RNase A (10 mg/mL) and proteinase K (20 mg/mL). Isolated DNA was dissolved into Tris-EDTA buffer and quantified by qPCR. Primers in ChIP analyses are listed in Supplementary Table 1.

qPCR Analysis

TRIzol reagent (Invitrogen) was used to extract total RNA according to the manufacturer’s instruction (14). RNA expression was standardized to 18s rRNA. Primer sequences are listed in Supplementary Table 1.

Western Blot

Western blot was performed to analyze target proteins using UCP-1 (Cell Signaling Technology), IRS1 (Cell Signaling Technology), IRS1-T612 (Thermo Fisher Scientific), AKT (Cell Signaling Technology), AKT-S473 (Cell Signaling Technology), β-tubulin (Cell Signaling Technology), PGC-1a (Proteintech), PRDM16 (Thermo Fisher Scientific), VDAC (Cell Signaling Technology), and Cytochrome c (Cell Signaling Technology) antibodies (14).

Brown Adipogenic Induction

Brown adipogenic induction was conducted as previously described (14). PDGFRa + brown progenitors were isolated from BAT using a manual magnetic cell separation system (Miltenyi Biotec, Bergisch Gladbach, Germany). Isolated brown progenitors were cultured in DMEM with 10% FBS. After confluence, brown adipogenic induction was induced in medium containing 0.1 μg/mL insulin, 0.5 mmol/L isobutylmethylxanthine, 1 μmol/L DEX, 125 μmol/L indomethacin, and 1 nmol/L T3 for 3 days followed by 0.1 μg/mL insulin and 1 nmol/L T3 for 2 days (14).

Transmitting Electronic Microscopy

Transmitting electronic microscopy (TEM) was performed as previously described (14). Imaging was recorded in TEM FEI Technai G2 20 Twin (200 kv LaB6).

Immunostaining and Oil Red O Staining

Immunostaining was performed to detect mitochondria using MitoSpy Green FM (BioLegend) (14). EVOS XL Core Imaging System was used to obtain images. Cellular lipid droplets were stained using 60% of oil red O (14). The absorbance of oil red O was measured at 492 nm in a Synergy H1 microplate reader (BioTek Instruments, Winooski, VT).

Indirect Calorimetry

During the analyses, mice were caged individually in the metabolic chamber with ad libitum water and their respective diets. Comprehensive Lab Animal Monitoring System (CLAMS) (Columbus Instruments) was used for an indirect open-circuit calorimetry measurement. Data were normalized according to ANCOVA energy expenditure guidelines (30,31).

Surface and Core Body Temperature

A FLIR E6 infrared thermal camera (FLIR Systems, Wilsonville, OR) was used to measure surface body temperature (14). After mice were removed from nests, the surface temperature was immediately obtained using a double-blind protocol. During capture of thermal images, animal behavior and scanning distance were controlled to avoid artificial effects. For reduction of variances, rectal body temperature was also measured using a highly precise electronic thermometer (TH-5 Thermalert Clinical Monitoring Thermometer; Physitemp Instruments).

Glucose Tolerance Test

After 6 h fasting with ad libitum water, mice were intraperitoneally injected with 2 g/kg body wt d-glucose. Glucose concentration was measured in blood obtained from tail tip at 0, 15, 30, 60, and 120 min after administration using a Contour glucose monitor (Bayer).

Insulin Resistance Test

After 6 h of fasting, insulin was measured using a mouse insulin ELISA (ALPCO). Insulin resistance was calculated according to the following formula: HOMA of insulin resistance = (fasting blood glucose mg/dL × 0.055) × (fasting insulin μU/mL)/22.5.

DEX Suppression Test

For assessment of hypothalamic-pituitary-adrenal axis activity, offspring at 4 months of age were intraperitoneally administrated with DEX (20 μg/kg) and blood was collected after 2 h. Corticosterone concentration in serum was measured with an ELISA kit (ADI-900; Enzo Life Sciences, Farmingdale, NY) according to the manufacturer’s instructions.

MTT Assay

Cells were incubated with 6 mg/mL 3-[4,5-dimethylthiazol-2-yl]-2,5-diphenyltetrazolium bromide (MTT) (Sigma-Aldrich) for 4 h. Formazan was dissolved in 100 μL DMSO, and the absorbance was measured at 540 nm using a microplate reader.

BrdU Proliferation Assay

Cells were incubated in 1 μmol/L BrdU (Sigma-Aldrich) for 24 h followed by BrdU (Cell Signaling Technology) immunostaining.

Statistical Analyses

Results were presented as means ± SEM. All statistical analyses were performed using SAS, version 9.4 (SAS Institute, Cary, NC). Unpaired two-tail Student t test was applied for two-group comparison. One-way ANOVA was used in comparisons for multiple groups. Each dam (pregnancy) was used as an individual replicate. Metabolic chamber data were analyzed by National Institute of Diabetes and Digestive and Kidney Diseases minimal-change chronic pancreatitis ANCOVA multiple linear regression models (30,31). Significant differences are shown in figures as P < 0.05, P < 0.01, P < 0.001, and P < 0.0001.

Data and Resource Availability

The data sets generated and analyzed during this study are available from the corresponding author upon reasonable request.

Results

Maternal GC Renders Metabolic Dysfunctions and Cold Sensitivity in Offspring

Pregnant mice were intraperitoneally administrated with DEX during the last trimester of pregnancy, and DEX-treated maternal mice displayed similar food intake, body weight, and blood glucose compared with placebo mice at room temperature (22°C) (Supplementary Fig. 1AC). Regardless of feeding with chow or HFD, DEX offspring at 4 months of age had similar calorie intake and body mass (Fig. 1A and Supplementary Fig. 1D) but showed higher glucose tolerance, fasting insulinemia, and reduced insulin sensitivity compared with control offspring (Fig. 1BD and Supplementary Fig. 1E). Metabolically, the use of ANCOVA with total body mass or lean mass as a covariate revealed that DEX offspring robustly exhibited lower O2 consumption, CO2 respiration, and energy expenditure (30,31) (Fig. 1EG and Supplementary Fig. 1FN). The difference of energy expenditure between control and DEX offspring was exaggerated when body fat effects were accounted for (Fig. 1EG and Supplementary Fig. 1JN), showing presence of metabolic dysfunctions in thermogenic fat. Consistently, DEX offspring had lower BAT mass (Fig. 1H) and thermogenic protein UCP-1 content (Fig. 1I and J), indicating blocked thermogenic function. In agreement with the hyperinsulinemia, BAT of DEX offspring had impaired insulin signaling as indicated by the reduced tyrosine phosphorylation of IRS-1 (T612) and serine phosphorylation of AKT (S473) (Fig. 1I, K, and L). Such reduction was further confirmed following insulin stimulation (Supplementary Fig. 2A). Maternal GC exposure also decreased expression of genes involved in fatty acid oxidization and BAT thermogenesis in offspring BAT (Fig. 1M). The reduced energy expenditure and fatty acid oxidation in BAT contributed to lipid accumulation in gonadal and inguinal fat and increased total fat mass in DEX offspring regardless of chow or HFD feeding (Fig. 1N and O and Supplementary Fig. 2B). Moreover, muscle mass tended to be lower in DEX offspring, which was exacerbated by HFD feeding (Supplementary Fig. 2C and D).

Figure 1
Figure 1

Maternal DEX administration decreases BAT energy expenditure, contributing to offspring metabolic dysfunctions and obesity at room temperature (22°C). AG: Data were collected in offspring fed with chow or HFD until 4 months of age. A: Body mass of offspring at 4 months of age (n = 6 per group). After 6 h of fasting, glucose tolerance test (B), insulin in serum (C), and insulin resistance (HOMA of IR [HOMA-IR]) (D) were measured in offspring fed chow or HFD (n = 6 per group). In metabolic analyses, volumes of oxygen consumption (E), CO2 respiration (F), and energy expenditure (G) were measured in offspring fed chow or HFD. Metabolic data were regressed to total body mass (TBM) (n = 6 per group). HM: Intrascapular BAT was collected in offspring at 4 months of age fed with chow or HFD. H: BAT mass (% body mass) in offspring at 4 months of age (n = 6 per group). I: Representative images of immunoblotting measuring UCP-1 expression and total and phosphorylation (p-) of IRS1 (T612) and AKT (S473) in offspring BAT. Quantified protein contents of UCP-1 (J), ratio of phosphorylated IRS1 to total IRS1 (K), and phosphorylated AKT to total AKT (L) in offspring BAT. β-Tubulin was used as a loading control (n = 6 per group). M: Gene expressions in glucose uptake and fatty acid oxidization in offspring BAT. mRNA expression was normalized to 18S rRNA (n = 6 per group). Gonadal (N) and inguinal (O) fat mass in offspring fed chow or HFD at 4 months of age (n = 6 per group). Data are means ± SEM, and each dot represents one replicate (one pregnancy). *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001; unpaired one-way ANOVA (multiple tests) was used in analyses. CON, control; EE, energy expenditure; Rel., relative; WAT, white adipose tissue.

To further test the mediatory roles of BAT thermogenesis in energy metabolic dysfunctions of DEX offspring, we acclimated offspring to thermoneutrality (30°C) to block BAT thermogenic activation (2628). Consistent with previous reports (2628), thermoneutrality blunted BAT activation as indicated by similar UCP-1 protein content between control and DEX offspring (Supplementary Fig. 3A). No significant difference was observed for calorie intake and body mass (Supplementary Fig. 3B and C), but the difference in glucose, insulin sensitivity, and energy expenditure between control and DEX offspring was ameliorated under thermoneutrality (Supplementary Fig. 3DL), showing that BAT thermogenic inactivation mediates metabolic dysfunctions in DEX offspring observed at room temperature.

After offspring were subjected to cold challenge (4°C) for 2 days, the difference of BAT mass became even greater between control and DEX offspring receiving either chow or HFD (Fig. 2A), showing the reduced BAT thermogenic plasticity. DEX offspring also had lower interscapular surface and core body temperatures (Fig. 2BD), which were in agreement with inactivation of brown adipogenic and thermogenic proteins, including PGC-1a, PRDM16, and UCP-1 (Fig. 2E and Supplementary Fig. 4AC). Accordingly, the mitochondrial density was also reduced due to maternal DEX exposure (Fig. 2F). Taken together, these data showed that maternal hyper-GC impaired offspring BAT energy expenditure and thermogenesis, contributing to metabolic dysfunctions and cold intolerance in adult offspring.

Figure 2
Figure 2

Maternal DEX administration decreases offspring BAT thermogenesis and increases cold sensitivity under cold challenge. AD: At 4 months old, offspring fed with chow or HFD were exposed to 4°C for 2 days. A: BAT mass of offspring after cold challenge. B: Thermal imaging of surface temperature at interscapular region of offspring before and after cold challenge. Quantified surface temperature (C) and rectal temperature (D) in offspring fed chow or HFD before and after cold challenge (n = 5 per group). E: After 2 days of cold challenge, BAT of offspring fed chow or HFD was collected for immunoblotting analyses. β-Tubulin was used as a loading control (n = 5 per group). F: Immunohistochemical staining of UCP-1 and mitochondria in BAT of offspring fed chow or HFD after cold challenge. Scale bars, 100 μm. Data are means ± SEM, and each dot represents one replicate (litter). *P < 0.05, **P < 0.01, ***P < 0.001; unpaired one-way ANOVA (multiple tests) was used in analyses. CON, control.

Maternal GC Impairs Mitochondrial Biogenesis in BAT of Adult Offspring

Due to the importance of mitochondria in mediating thermogenesis and energy expenditure (15,16), we further analyzed mitochondrial density and structure in offspring BAT and found that brown adipocytes had larger sizes of lipid droplets but reduced mitochondria number and cristae length and density in DEX offspring fed with both chow and HFD (Fig. 3AE). Consistent with the reduced mitochondria number, BAT of DEX offspring also showed substantial decreases of mtDNA content (Fig. 3F) and mitochondrial biomass indicators VDAC and CYTO-C (Fig. 3G and Supplementary Fig. 4D and E). PGC-1a centrally regulates mitochondrial biogenesis and UCP-1 thermogenesis, which was also profoundly reduced in DEX offspring BAT (Fig. 3G and H and Supplementary Fig. 4F), in alignment with the reduced expression of mitochondrial biogenic nuclear genes (Fig. 3I). Moreover, mitochondrial encoding genes involved in oxidative phosphorylation and respiration chain were also reduced in DEX offspring (Fig. 3I), showing that maternal GC exposure impaired Ppargc1a expression and mitochondrial biogenesis in offspring BAT.

Figure 3
Figure 3

Maternal DEX administration inhibits Ppargc1a expression and mitochondrial biogenesis in BAT of offspring at 4 months old. AE: In TEM imaging, BAT of offspring fed chow or HFD was analyzed (A) followed by quantifications of lipid droplet size (B), mitochondria (Mito) number (C), and mitochondrial cristae length (D) and density (E). (Scale bar, ×3,500 for 2 μm, ×9,600 for 500 nm.) n = 4 per group. F: mtDNA copy number in offspring BAT. Amplification of mitochondrial genes was standardized to 18s rRNA and GAPDH (n = 6 per group). G: Immunoblotting in measuring mitochondrial biomass proteins: CYTO-C, VDAC, and master regulator protein PGC-1a. β-Tubulin was used as a loading control (n = 6 per group). H: mRNA expression of Ppargc1a in BAT of offspring fed chow or HFD. Expression was normalized to 18S rRNA (n = 6). I: Gene expression of Ppargc1a and downstream mitochondrial biogenic genes in nucleus and mitochondria encoded genes for oxidative phosphorylation and respiration chain in offspring BAT. Expression was normalized to 18S rRNA (n = 6 per group). Data are means ± SEM, and each dot represents one replicate (litter). *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001; unpaired one-way ANOVA (multiple tests) was used in analyses. CON, control; Rel., relative.

Maternal GC Suppresses Mitochondrial Biogenesis in Brown Progenitors of Adult Offspring

Because brown progenitors are responsible for BAT plasticity, we further isolated BAT stromal vascular cells and sorted for brown progenitors using well-characterized surface markers Lin CD45/PDGFRa+ by FACS (32,33). BAT of DEX offspring had profoundly reduced brown progenitor density (Fig. 4A and B). Brown progenitors of DEX offspring also had the lower Ppargc1a expression (Fig. 4C and D) and mitochondrial biomass as indicated by CYTO-C, VDAC, and mtDNA contents (Fig. 4C and EG). Immunostaining using mitochondrial tracker robustly displayed lower mitochondrial density (Fig. 4H and I), verifying that the brown progenitors of DEX offspring had reduced capacity of mitochondrial biogenesis; progenitor plasticity is required for maintaining BAT thermogenic activation in responses to cold and other stimuli (3335). In addition, isolated brown progenitors in DEX offspring BAT had reduced NAD(P)H-dependent oxidoreductase enzyme activity in MTT assay (Fig. 4J), and less incorporation of thymidine analog BrdU into nuclear DNA (Fig. 4K and L), suggesting lower proliferative capacity. Following a standard brown adipogenic induction, brown progenitors of DEX offspring also displayed a reduced capacity to differentiate into brown adipocytes based on oil red O staining (Fig. 4M and N). Taken together, our data showed that maternal GC exposure decreased Ppargc1a expression, mitochondrial biogenesis, and brown adipogenesis of progenitors in offspring BAT.

Figure 4
Figure 4

Maternal DEX administration inhibits Ppargc1a expression and mitochondrial biogenesis of brown progenitors in offspring BAT at 4 months of age. A and B: Flow cytometry analyses (FACS) in sorting and measuring the population of brown progenitors in offspring BAT using brown progenitor markers Lin CD45/PDGFRa+ (also named as CD140). Positive population of brown progenitors shown in black ovals (A) and quantified in B (n = 6 per group). CF: Immunoblotting in measuring protein content of PGC-1a (D), CYTO-C (E), and VDAC (F) in sorted brown progenitors in offspring BAT (n = 6 per group). β-Tubulin was used as a loading control. G: mtDNA copy of brown progenitors isolated in offspring BAT. Amplification of mitochondrial genes was standardized to 18S rRNA and GAPDH. n = 6 per group. H and I: Immunostaining (H) and fluorophore intensity (I) of mitochondria (Mito) in sorted brown progenitors using MitoSpy (green). Scale bars, 100 μm. JL: Cell proliferation of brown progenitors was measured using MTT and BrdU assays. Progenitor cells were treated with BrdU for 24 h followed by BrdU and DAPI immunostaining (K); scale bars, 200 μm. Proliferative cells were displayed as BrdU+ cells (L). M and N: Brown progenitors isolated from offspring BAT were induced into brown adipocytes using standard brown adipogenic differentiation protocol in vitro followed by lipid staining (M) and quantification (N) using oil red O (n = 4 per group). Data are means ± SEM, and each dot represents one replicate (litter). *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001; unpaired Student t test with two-tailed distribution was used in analyses. APC, allophycocyanin; A.U., arbitrary units; CON, control; PE, phycoerythrin; Rel., relative.

Maternal GC Increases DNA Methylation in Ppargc1a Promoter, Which Has a Fetal Origin

Ppargc1a is a master regulator of mitochondrial biogenesis, which was inhibited in BAT and brown progenitors in DEX offspring. We hypothesized that such inhibition was due to DNA methylation in the Ppargc1a promoter, which is known to regulate its expression (18,19). Indeed, we noticed the proximal promoter of Ppargc1a harbors CpG and noncanonical CpG methylation sites (Fig. 5A and Supplementary Fig. 5A). To analyze, we used MeDIP analysis and found that 5mC was higher in the Ppargc1a promoter in both BAT and brown progenitors of DEX offspring at 4 months of age compared with control offspring (Fig. 5B and C). Intriguingly, the elevated DNA methylation was also observed in BAT and brown progenitors of DEX offspring at weaning (Supplementary Fig. 5B and C) and further traced back to neonates at P0 (Fig. 5D and E), showing that higher DNA methylation in the Ppargc1a promoter of DEX offspring BAT had a fetal origin. However, DNA hypermethylations were not observed in the promoters of unrelated genes in BAT and brown progenitors of DEX neonates, including Nrf1, Pax3, and Zfp423 (Supplementary Fig. 5D and E), suggesting locus-specific DNA methylation in gene promoters.

Figure 5
Figure 5

Maternal DEX administration increases DNA methylations in the Ppargc1a promoter, which has a fetal origin and inhibits Ppargc1a transcriptional activity in offspring BAT and brown progenitors. A: Diagram shows Ppargc1a promoter and transcriptional starting site (TSS) shores, including a potential binding region (R) of GR (GRBR, R1), proximal tissue specific-DNA methylation region (T-DMR, R2), and intergenic regulated region rich in CpG sites (IR, R3). BE: Relative fold changes of 5mC quantified by MeDIP qPCR in methylated regulatory regions R1, R2, and R3 of the Ppargc1a promoter in BAT and sorted brown progenitors in offspring at 4 months of age (4 M) (B and C) and neonates at P0 (D and E). Mock IgG was used as a negative control. F: Diagram shows bisulfite pyrosequencing covering cytosine sites in the proximal promoter region of Ppargc1a. G: Methylation content of individual cytosine in the sequenced Ppargc1a proximal promoter in BAT and brown progenitors at P0 and 4 months old in offspring. H: Averaged cytosine methylation in CpG and CpW (A and T). n = 6 in each group. Data are means ± SEM, and each dot represents one replicate (litter). *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001; unpaired Student t test with two-tailed distribution was used in analyses.

To identify methylation sites in the Ppargc1a proximal promoter, bisulfite pyrosequencing was performed (19,29) (Fig. 5F). BAT of DEX offspring did not only display higher methylation of CpG sites; intriguingly, hypermethylation was also observed in non-CpG sites (Fig. 5G and H). Similar patterns were also revealed from brown progenitors in DEX offspring (Fig. 5G and H), further highlighting that non-CpG methylation was likely involved in the regulation of Ppargc-1a transcriptional activity.

In alignment with the higher DNA methylation in the Ppargc1a promoter, Ppargc1a expression was also profoundly lower in BAT and brown progenitors of DEX neonates (Fig. 6AC). In agreement, BAT of DEX neonates had elevated GR expression but reduced contents of UCP-1, VDAC, and CYTO-C, as well as mitochondria number and cristae density (Fig. 6BD and Supplementary Fig. 6A and B). Consistent with GR activation in BAT, neonates displayed higher concentration of corticosterone, which was further confirmed in offspring at 4 months of age as assessed by GC suppression test (Supplementary Fig. 6C). Considering the critical role of neonatal BAT in maintaining body temperature, the reduced BAT thermogenesis explained the hypothermia in DEX neonates (Fig. 6E), which correlated with reduced survival of offspring in the first postnatal week (Supplementary Fig. 6D). Brown progenitors isolated from DEX neonates also had lower contents of mitochondrial biomass protein markers, mitochondrial density, and mtDNA (Fig. 6C and F and Supplementary Fig. 6E). Consistently, the population of brown progenitors was also reduced in DEX neonatal BAT (Fig. 6G and H). Furthermore, the brown progenitors had reduced capacity undergoing brown adipogenic differentiation in vitro (Supplementary Fig. 6F), supporting the suppressed BAT mass in DEX neonates (Fig. 6I). Similar data regarding to the Ppargc1a transcriptional inactivation and reduced BAT mitochondrial biogenesis and thermogenesis were also observed in DEX offspring at weaning (Supplementary Fig. 7AH). Collectively, the DNA methylation of Ppargc1a promoter was inversely correlated with transcriptions in both BAT and brown progenitors in offspring (Fig. 6J and K). These data support the notion that the reduced Ppargc1a expression resulted from the abnormal gaining of DNA methylation in the Ppargc1a promoter during fetal BAT development of DEX mothers.

Figure 6
Figure 6

Maternal DEX inhibits Ppargc1a expression and mitochondrial biogenesis in fetal BAT and brown progenitors. AC: After birth (P0), neonatal BAT and brown progenitors isolated from BAT were used for measurement of Ppargc1a mRNA (A) and protein contents of PGC-1a, GR, CYTO-C, VDAC, and UCP-1 (B and C). mRNA expression was standardized to 18S rRNA, and β-actin was used as a loading control in Western blot. D: TEM imaging displayed mitochondrial density in neonatal BAT (scale bar, ×3,500 for 2 μm, ×6,500 for 1 μm). E: Thermal imaging of the surface temperature in the interscapular region of neonates at P0 (n = 6 per group). Measurements were performed immediately after neonates were separated from the nests in order to avoid heat loss at room temperature. F: Immunostaining and intensity of mitochondria (Mito) in brown progenitors sorted from neonatal BAT. MitoSpy (green) was used as a mitochondrial tracker. G and H: FACS for measuring the population of brown progenitors in neonatal BAT using progenitor markers: Lin CD45/PDGFRa+. Positive population of brown progenitors shown in black ovals (G) and quantified in H (n = 6 per group). I: BAT mass (% body mass) in neonates at P0 and offspring at weaning P21 (n = 6 per group). J and K: Correlation between 5mC abundance and mRNA expression of Ppargc1a in offspring BAT (J) and brown progenitors (K). Statistical analyses were assessed by linear and higher-order nonlinear regressions, respectively, and Bayesian information criterion was used for model selection among a finite set of regressions. With use of random Legendre regression analyses, second-degree polynormal regression model identified a higher prediction likelihood R2. Data are means ± SEM, and each dot represents one replicate (litter). *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001; unpaired Student t test with two-tailed distribution was used in analyses. APC, allophycocyanin; A.U., arbitrary units; CON, control; iBAT, intrascapular BAT; Rel., relative.

GR/DNMT3b Complex Potentially Mediates Ppargc1a Silencing During Fetal BAT Development

To identify the underlying mechanisms leading to the locus-specific increase in DNA methylation of Ppargc1a promoter, we analyzed DNA binding elements and identified a potential GR-responsive binding element as previously described (36,37) (Supplementary Fig. 5A). Using GR ChIP analyses, we observed that the maternal DEX exposure increased GR binding to the Ppargc1a promoter in both neonatal BAT and brown progenitors (Fig. 7A). In GR coimmunoprecipitation analyses, DNMT3b, but neither DNMT3a nor DNMT1, physically interacted with GR (Fig. 7B and C). Accordingly, maternal GC exposure enhanced the interaction between GR and DNMT3b (Fig. 7B and C), suggesting that GR might increase the recruitment of DNMT3b into the Ppargc1a promoter for de novo DNA methylation in BAT and brown progenitors. Using DNMT3b ChIP analyses, we confirmed that the DEX administration increased DNMT3b binding to the Ppargc1a promoter in both BAT and brown progenitors (Fig. 7D). Increased GR interaction with DNMT3b was also observed in BAT of DEX offspring at 4 months of age (Supplementary Fig. 7I). Taken together, these data showed that Ppargc1a, as a GR target gene, was subjected to the enhanced DNA methylation in its promoter during fetal BAT development, potentially involving the recruitment of the GR/DNMT3b complex to the Ppargc1a promoter, which persistently suppresses its expression and impairs mitochondrial biogenesis and fetal BAT development, programming postnatal offspring to metabolic dysfunctions.

Figure 7
Figure 7

Maternal DEX increases the formation of GR and DNMT3b complex in binding to the Ppargc1a promoter in BAT and brown progenitors of fetus. A: Relative fold changes of GR abundance binding to Ppargc1a promoter and transcriptional starting site shores, including GR potential binding region (GRBR, region R1), proximal tissue specific-DNA methylation region (T-DMR, R2), and intergenic regulated region rich in CpG sites (IR, R3). Analyses were quantified using GR ChIP qPCR (GR-ChIP) in the Ppargc1a promoter region R1, R2, and R3 in BAT and brown progenitors of neonates at P0. Mock IgG was used as a negative control (n = 6 in each group). B and C: Coimmunoprecipitation of GR in neonatal BAT (B) and sorted brown progenitors (C). GR, DNMT3b, DNMT3a and DNMT1 were immunoprecipitated followed with SDS-PAGE separation and measured in Western blot. IgG was used as a negative control in immunoprecipitation (n = 6). D: Relative fold changes of DNMT3b binding to the Ppargc1a promoter and transcriptional starting site shores, including GR potential binding region (GRBR, R1), proximal tissue specific-DNA methylation region (T-DMR, R2), and intergenic regulated region rich in CpG sites (IR, R3) in BAT and brown progenitors of neonates at P0. Analyses were quantified by DNMT3b ChIP qPCR (DNMT3b-ChIP) in the Ppargc1a transcriptional regulatory regions in BAT and brown progenitors of neonates at P0. Mock IgG was used as a negative control (n = 6 in each group). Data are means ± SEM, and each dot represents one replicate (litter). *P < 0.05, **P < 0.01, ***P < 0.001; unpaired Student t test with two-tailed distribution was used in analyses. CON, control; iBAT, intrascapular BAT; IP, immunoprecipitation; R, region; Rel., relative.

Discussion

The intrauterine environment has profound effects on fetal development and birth outcome, which can program metabolic health of offspring, including obesity and type 2 diabetes (4,811,38). Antenatal stress, depression, and preterm delivery are prevalent in women during pregnancy, leading to fetal exposure to substantial levels of GC in the uterus (6,7). Maternal stress and antenatal GC administration are associated with outcomes of low birth weight (3942), which is also a strong indicator for obesity and type 2 diabetes risks (43), but mechanisms of maternal hyper-GC linking to offspring obesity remain poorly understood. The present findings represent, to our best knowledge, the first report that the maternal GC exposure during pregnancy impaired offspring BAT thermogenesis, which was correlated with glucose and insulin resistance, obesity, and cold sensitivity. The finding highlighted the importance of maintaining maternal psychological health and discretion using exogenous GC during pregnancy in order to protect offspring from metabolic dysfunctions (3). Mechanistically, our study also disclosed that the maternal GC exposure persistently inhibited Ppargc1a expression in BAT and brown progenitors, leading to impaired mitochondrial biogenesis (15,16). We also found that DEX elicited excessive DNA methylation in both canonical and noncanonical CpG sites in the Ppargc1a proximal promoter (19). Furthermore, the DNMT3b and GR complex was identified to mediate the accretion of DNA methylation in the Ppargc1a promoter during fetal BAT development. The discovery provides a potential target to reduce the risks of obesity and metabolic dysfunctions in offspring born from mothers who suffered from hyper-GC conditions.

In this study, we administered pregnant mice with 100 μg/kg DEX in the third trimester, which was in the range of GC concentrations in maternal circulation under stress or during preterm delivering as previously described (44,45). This dose was >10-fold lower than the DEX administration in nonpregnant mice for anti-inflammatory therapy or pathological GC secretion in patients with an adrenal tumor such as in Cushing syndrome (46). The low dose and a relatively short duration of treatment might explain the lack of difference in food intake and body weight of maternal mice between treatments. However, we observed a profound GR activation in both BAT and brown progenitors of DEX neonates, suggesting that the low dose is sufficient to induce corresponding changes in fetuses. Therapeutic DEX administration to mothers with preterm delivery improves lung maturation of fetuses, but it also increases risks of low birth weight (40,41). As for programming effects, under HFD, adult DEX offspring displayed insulin resistance, impaired glucose and fatty acid oxidization, and adiposity (8,14). Notably, DEX offspring fed with a normal calorie diet also displayed metabolic dysfunctions and adiposity, showing the negative effects of maternal hyper-GC on offspring health regardless of postnatal diets. For validation of the mediatory roles of BAT thermogenesis in reducing energy expenditure of DEX offspring at room temperature, adult offspring were also acclimated to thermoneutrality and exposed to acute cold, respectively (2628). Under thermoneutrality, the reduction of energy expenditure in DEX offspring was substantially blunted. Meanwhile, DEX adult offspring, regardless of postweaning diet, displayed severe cold sensitivity and reduced brown thermogenesis under acute cold challenge, demonstrating that maternal hyper-GC exposure significantly impaired offspring BAT thermogenic activity, which mediated reduced energy expenditure and metabolic dysfunctions of their offspring. Of note, physical activity was not directly measured in offspring; thus, the potential effects of physical activity on offspring energy expenditure could not be excluded. On the other hand, we observed lower metabolic rates of DEX offspring during both nighttime (physically active) and daytime (inactive), suggesting physical activity was not a major factor contributing to metabolic dysfunction in DEX offspring.

Through regulating mitochondrial biogenesis, PGC-1a transduces a variety of cellular functions (15,16), facilitating differentiation of progenitors and stem cells (34,35). Mitochondrial biogenesis activates energy expenditure and heat production of brown adipocytes (12). Sufficient energy provided from tricarboxylic cycle is also required for the biosynthetic platform during proliferation and differentiation of progenitors (47). In the absence or limitation of mitochondrial biogenesis, the proliferation and differentiation of progenitors are not sustained, suppressing tissue growth and regeneration (47). Maternal GC exposure reduced Ppargc1a expression in BAT of adult offspring, impairing mitochondrial biogenesis and thermogenesis. The detrimental effects were further observed in the sorted brown progenitors with use of well-characterized markers (32,33), explaining impaired BAT plasticity under cold stimulation. Brown progenitors are essential for BAT plasticity in response to environmental stimuli, including hormones and cold (13,33). Intriguingly, the impaired mitochondrial biogenesis and Ppargc1a transcription were further traced back to neonates. The neonatal offspring born from DEX dams displayed hypothermia and reduced BAT mass and thermogenic activity. The BAT thermogenic inactivation increases postnatal death in responsive to severe cold (12). Collectively, we revealed that maternal hyper-GC impaired fetal BAT development, which had persistent effects in impairing offspring BAT thermogenic function and plasticity.

Epigenetic regulation during fetal development exerts long-term programming impacts on gene expression, which may shape tissue developmental trajectory and lifelong health of offspring (4). We identified that BAT and brown progenitors of DEX offspring had higher DNA methylation in the Ppargc1a promoter. In the meantime, the hypermethylation was also observed in non-CpG sites, supporting a previous report that noncanonical CpG methylation may play a critical role in pathogenic Ppargc1a inactivation in patients with diabetes (19). The DNA methylations in the Ppargc1a promoter durably suppress Ppargc1a expression and mitochondrial biogenesis (18,19). Excessive DNA methylation in the Ppargc1a promoter was also reported in the muscle of patients with diabetes, causing impaired mitochondrial biogenesis and muscle atrophy (19). In this study, we also noticed the hyper–DNA methylation of Ppargc1a promoter in the muscle of DEX offspring (data not shown). As the energy expenditures in muscle and BAT are highly dependent on mitochondrial biogenesis, the Ppargc1a promoter methylation contributes to the pathogenesis of obesity and type 2 diabetes (18,19). Maternal hyper-GC exposure also programmed offspring hypothalamic-pituitary-adrenal axis as indicated by high cortisol in circulation and in response to GR activation, which might contribute to gaining of DNA methylation from elevated interaction between GR and DNMT3b in offspring BAT and brown progenitors (22). A similar interaction was also reported in neurons (48,49). Taken together, results indicate that the GC exposure enhanced the GR binding to the regulatory region of the Ppargc1a promoter, likely serving as a dock for DNMT3b recruitment, locally driving the de novo DNA methylation in the transcriptional regulatory regions of corresponding genes (48,49). These findings deepen our mechanistic understanding of maternal hyper-GC exposure in reducing energy expenditure and increasing susceptibility to metabolic dysfunctions of offspring (19). Of note, in this study, we only examined male offspring. Because the negative effects of excessive GC exposure during pregnancy have been well-documented to predispose offspring to obesity and metabolic dysfunctions in both sexes of mice and humans (811), biological changes identified in male offspring should be applicable to females.

In conclusion, we discovered that the maternal hyper-GC exposure during pregnancy substantially inhibits fetal BAT development, which was associated with persistent elevation of DNA methylation in the Ppargc1a promoter and increased risks of obesity and metabolic dysfunctions in offspring. These findings underscored that the prevalent maternal physiological stress or pharmaceutic GC administration during pregnancy impaired metabolic health of offspring, which may contribute to the obesity epidemics in the modern society.

Article Information

Acknowledgments. The authors thank Franceschi Microscopy & Imaging Center for help with electronic microscopy imaging.

Funding. This work was funded by the National Institutes of Health (NIH R01-HD067449).

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

Author Contributions. Y.-T.C. and M.D. developed the concept, designed experiments, and interpreted the data. Y.-T.C., Y.H., J.S.S., X.-D.L., and J.M.d.A. conducted experiments and collected data. Y.-T.C., Y.H., and Q.-Y.Y. analyzed data. Y.-T.C. and M.D. prepared the manuscript. Y.-T.C., M.-J.Z., and M.D. made revisions to the manuscript. All authors approved the final content. Y.-T.C. and M.D. are the guarantors of this work and, as such, had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

  • Received January 3, 2020.
  • Accepted May 6, 2020.



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White Bean Lentil Soup (Instant Pot)

By electricdiet / August 3, 2020


This white bean lentil soup topped with Parmesan is a cozy, satisfying dish you’ll want to eat again and again. And it takes less than 10 minutes to prep!

White Bean Lentil Soup in a bowl topped with freshly grated Parmesan cheese

Lentils and beans are the building blocks for a nice, hearty recipe. And when you combine them into this white bean lentil soup, the result is a cozy and satisfying dish you’ll want again and again!

Best of all, this soup is so simple to make. Just combine your ingredients in an Instant Pot or slow cooker — that’s it!

It’s a perfect set-it-and-forget-it dish to prep early in the day or a quick dinner to throw together in the evening. With these cozy flavors and satisfying texture, you’ll love curling up with a bowl of this hearty soup!

How to make white bean lentil soup

One of the easiest ways to make this recipe is in an electric pressure cooker like the Instant Pot. Just combine the ingredients and then let the Instant Pot do the work for you!

Step 1: Add the beans, lentils, broth, water, onion, carrots, garlic, Herbes de Provence, pepper, salt, bay leaf, and rind to your Instant Pot.

Step 2: Close and lock the lid, ensuring that the valve is set to “sealing.”

Step 3: Cook on manual High pressure for 15 minutes.

Step 4: When the cooking is complete, let the pressure release naturally for 10 minutes, then quick release any remaining pressure.

Step 5: Once the pressure gauge drops, carefully uncover the pot, then stir in the leafy greens and lemon juice. Remove and discard the rind and bay leaf.

Just like that, your delicious soup is ready to enjoy! Divide among 8 bowls and sprinkle each with about 1 tablespoon of grated Parmesan to serve.

How to make it in a slow cooker

If you don’t have an electric pressure cooker, this recipe can also be made in a slow cooker.

It will take longer to cook this way, but the process is just as easy. Simply combine the ingredients and let the slow cooker work its magic!

Step 1: Combine the beans, lentils, broth, 1 cup of water, onion, carrots, garlic, Herbes de Provence, pepper, salt, bay leaf, and Parmesan rind in a slow cooker.

Step 2: Cover and cook on low until the lentils are tender (about 7 hours).

Step 3: Stir in the leafy greens and lemon juice, then cover and cook on low for an additional 30 minutes or until the greens have wilted.

Step 4: Discard the rind and bay leaf before serving.

No matter which method you choose to cook your soup, the result will be wonderfully delicious and flavorful!

Dried vs. canned beans

You do have the option to use canned beans for this recipe.

However, I highly recommend you cook your own dried beans using the technique in my low sodium no soak beans guide. If you make Great Northern Beans, just set aside three cups for this recipe and then freeze the rest for later use.

Cooking your own beans tastes so much better than canned beans. They won’t be soaked in preservatives, and you’ll be able to control the sodium levels.

If you didn’t know, canned beans are notoriously high in sodium. So if you do decide to use canned, make sure to rinse them well, as this helps a bit.

Recipe variations

This recipe is very easy to customize and make it your own!

For example, you can use chicken stock in place of vegetable broth. Or maybe try using thyme or rosemary instead of Herbes de Provence. You can also use any type of leafy greens you have on hand like Swiss chard, spinach, or baby kale.

I also love trying different types of pepper. I like McCormick’s Hot Shot! blend, 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.

And of course, you can use different types of beans. Navy or cannellini beans would both be good substitutes for Great Northern.

Feel free to have some fun with this soup!

Storage

This recipe is great to make ahead of time and have ready to enjoy throughout the week. You’ll be so happy if you have a bowl waiting for you at the end of a long day!

Leftovers can be stored covered in the refrigerator for 4-5 days. You can also freeze this soup for longer storage.

Other hearty soup recipes

Who doesn’t love trying different delicious and healthy variations of soups? The flavor and texture possibilities are endless! If you’re looking for more inspiration, here are a few of my favorites I know you’ll love:

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

Recipe Card

Bowl of instant pot lentil soup on table

White Bean Lentil Soup (Instant Pot)

This white bean lentil soup topped with Parmesan is a cozy, satisfying dish you’ll want to eat again and again. And it takes less than 10 minutes to prep!

Prep Time:10 minutes

Cook Time:15 minutes

Pressure Up/Down:30 minutes

Total Time:55 minutes

Author:Shelby Kinnaird

Servings:8

Instructions

  • Add the beans, lentils, broth, water, onion, carrots, garlic, Herbes de Provence, pepper, salt, bay leaf, and rind to your Instant Pot.
  • Close and lock the lid, ensuring that the valve is set to “sealing.”

  • Cook on manual High pressure for 15 minutes.

  • When the cooking is complete, let the pressure release naturally for 10 minutes, then quick release any remaining pressure.

  • Once the pressure gauge drops, carefully uncover the pot, then stir in the leafy greens and lemon juice. Remove and discard the rind and bay leaf.

Recipe Notes

This recipe is for 8 servings of soup. To make in the slow cooker instead, combine all ingredients except leafy greens and lemon juice and cook on low for 7 hours. Add greens and lemon juice, cook on low for 30 more minutes, then discard rind and bay leaf before serving. Leftovers can be stored covered in the refrigerator for 4-5 days or in the freezer for several months.

Nutrition Info Per Serving

Nutrition Facts

White Bean Lentil Soup (Instant Pot)

Amount Per Serving (1 cup)

Calories 188 Calories from Fat 38

% Daily Value*

Fat 4.2g6%

Saturated Fat 2g13%

Trans Fat 0g

Polyunsaturated Fat 0.2g

Monounsaturated Fat 0.5g

Cholesterol 9.1mg3%

Sodium 439.1mg19%

Potassium 319.5mg9%

Carbohydrates 25g8%

Fiber 8.5g35%

Sugar 4g4%

Protein 11.3g23%

Vitamin A 6250IU125%

Vitamin C 15.7mg19%

Calcium 150mg15%

Iron 2.2mg12%

Net carbs 16.5g

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

Course: Soups and Stews, Vegetarian

Cuisine: Italian

Diet: Low Fat

Keyword: bean soup, gluten-free, lentil soup, Tuscan white bean soup, vegetarian



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Peach Pie – My Bizzy Kitchen

By electricdiet / August 1, 2020


The app Timehop reminded me last week that it had been six years since I made a peach pie.

In July 2014 my late husband hadn’t been feeling well all year.  It started with a trip to the ER on New Years Eve.  My husband hadn’t been working and we were waiting to see if his disability would be approved, which turned out to be a nine month process.  I had to get a second job to make ends meet, which meant even more time away from my husband, but desperate times called for desperate measures.  Here is my first post in 2014 about his trip to the ER on New Years Day.

He was in and out of the hospital on and off that through May of 2014.  By June though things weren’t getting much better, and we decided to ditch our local doctors and head to Mayo Clinic for more answers.  If anyone in your family is ever sick, Mayo is the place to go.

We got placed on a list to come for a string of appointments, which would begin on a Monday and we had to stay in Rochester until they completed their tests.

In July of 2014, my husband started not liking the smell of food at all.  If I cooked anything at all, it would be on the grill outside and I would eat outside by myself.  So he shocked the shit out of me when we were watching America’s Test Kitchens as they were making peach pie and he looked me dead in the eyes and said “peach pie is my favorite.  Can you make me a peach pie?!”  “FUCK YES!” was what I think I said.

I followed Natasha’s recipe in my 2014 version.  This time I still used her recipe for the filling, but I made my own pie crust.  It’s simple, easy, and yes while it has 8 tablespoons of butter, there are some versions out there with both butter and shortening!

So this weekend I decided to make my second peach pie.  Even better was that peaches were on sale for .49 cents a pound.  You need to blanch the peaches for a minute in boiling water, and the skin peels off really easily.

For the pie crust – simply put the flour to the butter in a food processor and pulse, just until crumbly.  Add in the cold water and pulse again – do not keep your food processor going, otherwise you’ll have a pie crust paste.  Not that I’ve ever done that before, I just read that on the internet – ha!

Divide the dough into two portions, wrap in plastic and chill for at least an hour.  With so much butter, you shouldn’t need too much flour to roll out.  While everyone will tell you to work with the dough quickly while still cold, I put the oven ready pie back in the fridge for 30 minutes before baking.  Cold butter + hot even = flaky pie!

For the top I found an Instagramer called LokoKitchen – she makes the most amazing pies.  She made one called a “spoke” pie and I tried to copy her as best I could.  Simply roll out the top dough and using a sharp knife, cut the pie into thin strips.  She used a ruler – um, mine is a bit more . . . rustic?

She put a small ramekin in the middle and wrapped the strips around that.  Trim the edges, remove the ramekin and put it in the fridge for 30 minutes.  Heat oven to 425.

Right before going into the oven, brush with an egg wash, and sprinkle with coarse sugar.  Bake at 425 for 15 minutes, reduce heat and bake at 375 for 30 minutes, putting foil over the top if it’s browning too fast.

After it comes out of the oven and cools for two hours, toss some blueberries in a drop of grape seed oil and roll in the coarse sugar and put in the middle of the pie.

Print

Peach Pie

This peach pie is by no means WW friendly, but at 324 calories a slice, you can fit it into your weekly budget.  It screams summer.


Scale

Ingredients

For the pie filling: (this part of the recipe is from Natasha’s Kitchen)

  • 3 1/2 cups peaches, sliced
  • 1 tablespoon lemon juice
  • 1/2 cup sugar
  • 3 tablespoons corn starch
  • 1/8 teaspoon salt
  • (I left out the vanilla and nutmeg in her recipe – personal preference)

For the dough:  (enough for the top and bottom, with about 3 ounces dough leftover)

  • 3 cups flour
  • 1 tablespoon sugar
  • 1 teaspoon salt
  • 8 tablespoons butter
  • 10 tablespoons cold water

Egg wash: one egg yolk

Course sugar

 

Instructions

Blanch the peaches in boiling water for 1 minute.  The skins will come off really easily.

In a large bowl, mix all the filling ingredients together and chill until ready to use.

In a food processor, put the flour through butter into the food processor and pulse until the butter is about the size of peas.  Add the cold water and PULSE just until the dough is crumbly – if you process to much the dough will become a paste.

Divide the dough into two sections.  Wrap in plastic and refrigerate.

Roll the dough out, put the rolled out dough in the pie pan.  Fill with peach filling.  

Watch a shit ton of youtube videos and decide which pattern you want to put on your pie.  

Once combined, refrigerate for 30 minutes before baking.  Before going in the oven, brush with egg wash and sprinkle with coarse sugar.

Bake at 425 for 15 minutes, reduce heat to 350 and bake for 30 minutes.   Once cooled, toss 1/3 cup blueberries in a drop of grape seed oil and toss in some more coarse sugar and fill the hole in the middle of the pie.

You need to cool the pie for at least two hours to let the filling set.  It will be hard, but worth the wait!

Notes

I put the recipe into the recipe builder, and each slice is 14 points on all plans, or 324 calories.  Worth every one!

————————————————————————————————-

I had thoughts of recreating the first peach pie that I made, but changed my mind.  I am not the same person I was six years ago.  I’ve grown, my cooking has gotten better and my photography skills are better.  It seemed fitting that I made a pie that suits me today, not in the past.

Of course I wish my husband was here to share a slice of pie with.  Was I sad here and there throughout the day? Of course.  But I found solace and comfort in the kitchen without eating like an asshole or drinking several glasses of wine to push those feelings down.

I don’t live in the past anymore.  I have to let it go and embrace today.  Here.  Now.  Yesterday was a great day.

Be Kind.  Be Fearless.  Have Hope.

Until next time, Love, Biz





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Low-Carb Chicken Cordon Bleu Casserole

By electricdiet / July 30, 2020


This delicious, creamy chicken cordon bleu casserole is so simple to prepare that it makes for the perfect low-carb dinner recipe. You can also prepare the recipe ahead and freeze for added convenience.

chicken cordon bleu casserole

From kitchen to table this recipe is ready in under 30 minutes and will be a smash hit with the entire family.

Because we don’t use breading, the recipe is also low-carb, keto, gluten-free, and definitely diabetes-friendly!

How to make chicken cordon bleu casserole

ingredients for the casserole on a wooden board

Step 1: In a large bowl, stir together melted butter, milk, cream cheese, Dijon mustard, lemon juice, minced garlic, Worcestershire sauce, parmesan cheese, and half of the shredded cheese (1 cup) until smooth. 

cheese mixture in a large glass bowl

Step 2: Fold in the shredded chicken and ham.

chicken and ham added to the cheese mixture

Step 3: Transfer the casserole mixture into a 9×13 in (23×33 cm) casserole dish and add the remaining shredded cheese.

 casserole ready to go in the oven

Step 4: Bake for 10 minutes at 400F, until the cheese is melted, and the casserole is bubbly and golden. Garnish with fresh chives (optional).

cooked casserole from the side with fresh chives

Tips and tricks

It is best to cook the chicken right before you plan on making the Cordon Bleu casserole as the chicken can dry out if left for too long.

You can either roast or grill the chicken, leave it to rest for 10 minutes after cooking, and then chop up for the casserole.

You can easily change up the recipe by adding in some fresh herbs to the dijon sauce or add some crushed pork rinds to the top before baking for a crispy top.

Can the casserole be prepared ahead?

Yes! You can definitely prepare this entire meal a few days before and store in the refrigerator. You can also cover with plastic wrap and freeze for up to 4 weeks. This makes dinner as easy as turning on the oven!

To reheat from frozen, allow the casserole to thaw completely. Then bake as normal and dinner is served.

You might wonder how the creamy sauce holds up after it has been frozen. Since everything was mixed together, the dairy will have stabilized and is fine to freeze without worrying about it separating when reheating.

What to serve with this casserole

The cordon bleu casserole is a very hearty and rich main course so you can complement it with a salad or some simple vegetable side dishes. Steamed or roasted broccoli, cauliflower, zucchini, carrots, or mushrooms work well.

casserole ready to serve

Other healthy low-carb dinner recipes

If you liked this recipe, here are some other easy low-carb recipes you might enjoy: 

You can also read this roundup I created of 10 healthy dinner recipes for diabetics for even more great low-carb dinner recipe ideas.

When you’ve made this low-carb chicken cordon bleu casserole, please don’t forget to let me know how you liked it and rate the recipe in the comments below!

Recipe Card

Low-carb Chicken Cordon Bleu Casserole

This delicious, creamy chicken cordon bleu casserole is so simple to prepare that it makes for the perfect low-carb dinner recipe. You can also prepare the recipe ahead and freeze for added convenience. 

Prep Time:10 minutes

Cook Time:15 minutes

Total Time:25 minutes

Servings:6

Instructions

  • Preheat your oven to 400°F (200°C).

  • In a large bowl, stir together melted butter, milk, cream cheese, Dijon mustard, lemon juice, minced garlic, Worcestershire sauce, parmesan cheese, and half of the shredded cheese (1 cup) until smooth. 

  • Fold in the chopped chicken and ham.

  • Transfer the casserole mixture into a 9×13 inch casserole dish and add the remaining shredded cheese on top.

  • Bake for 10 minutes, until the cheese is melted, and the casserole is bubbly and golden. Garnish with fresh chives and serve.

Recipe Notes

This recipe makes 6 servings.  Can be stored for several days in the refrigerator or frozen for up to four weeks. To reheat from frozen, allow the casserole to thaw completely. Then bake as normal.

Nutrition Info Per Serving

Nutrition Facts

Low-carb Chicken Cordon Bleu Casserole

Amount Per Serving

Calories 568 Calories from Fat 352

% Daily Value*

Fat 39.1g60%

Saturated Fat 22.5g113%

Trans Fat 0g

Polyunsaturated Fat 1g

Monounsaturated Fat 8.1g

Cholesterol 183.2mg61%

Sodium 762.7mg32%

Potassium 74.7mg2%

Carbohydrates 7.5g3%

Fiber 0g0%

Sugar 3.4g4%

Protein 48.6g97%

Net carbs 7.5g

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

Course: Main Course

Cuisine: American

Keyword: chicken casserole, Low carb casserole



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Is Type 2 Diabetes Causally Associated With Cancer Risk? Evidence From a Two-Sample Mendelian Randomization Study

By electricdiet / July 28, 2020


Abstract

We conducted a two-sample Mendelian randomization study to investigate the causal associations of type 2 diabetes mellitus (T2DM) with risk of overall cancer and 22 site-specific cancers. Summary-level data for cancer were extracted from the Breast Cancer Association Consortium and UK Biobank. Genetic predisposition to T2DM was associated with higher odds of pancreatic, kidney, uterine, and cervical cancer and lower odds of esophageal cancer and melanoma but not associated with 16 other site-specific cancers or overall cancer. The odds ratios (ORs) were 1.13 (95% CI 1.04, 1.22), 1.08 (1.00, 1.17), 1.08 (1.01, 1.15), 1.07 (1.01, 1.15), 0.89 (0.81, 0.98), and 0.93 (0.89, 0.97) for pancreatic, kidney, uterine, cervical, and esophageal cancer and melanoma, respectively. The association between T2DM and pancreatic cancer was also observed in a meta-analysis of this and a previous Mendelian randomization study (OR 1.08; 95% CI 1.02, 1.14; P = 0.009). There was limited evidence supporting causal associations between fasting glucose and cancer. Genetically predicted fasting insulin levels were positively associated with cancers of the uterus, kidney, pancreas, and lung. The current study found causal detrimental effects of T2DM on several cancers. We suggest reinforcing the cancer screening in T2DM patients to enable the early detection of cancer.

Introduction

Type 2 diabetes mellitus (T2DM) and cancer are two major global health issues, causing approximately 5.0 and 8.7 million deaths and 143.0 and 208.3 million disability-adjusted life-years in 2015 worldwide, respectively (1,2). Evidence from epidemiological studies indicates that T2DM is a risk factor for overall cancer (3) and several site-specific cancers, such as colorectal (4,5), liver (6), kidney (7,8), uterine (9), and breast cancer (10). A bidirectional relationship has been suggested for T2DM and pancreatic cancer (1115), whereas an inverse association has been observed between T2DM and risk of prostate cancer (16,17). Findings for other site-specific cancers are conflicting (3), and the causality of the observed associations remains unclear due to possible residual confounding and reverse causality in observational studies.

Exploiting genetic variants as proxies for a risk factor, Mendelian randomization (MR) is a method that can strengthen the exposure-outcome association inference by diminishing the likelihood of confounding and eliminating reverse causality in conventional observational studies (18). This method minimizes confounding, since genetic variants are randomly assorted at conception, thereby being unrelated to self-adapted lifestyle and environmental factors. In addition, it overcomes reverse causality, as allelic randomization antedates the disease’s onset.

Given the inconsistent results and potential methodological limitations of previous observational studies of T2DM and cancer risk, we conducted a two-sample MR study to assess the causal associations of liability to T2DM with the risk of overall cancer and 22 site-specific cancers. For pancreatic cancer, a bidirectional MR study was conducted. We additionally explored the causal associations of genetically predicted fasting glucose (FG) and fasting insulin (FI) levels with the same cancer outcomes in secondary analyses. Moreover, we performed meta-analyses of available MR studies of the associations of T2DM, FG, or FI levels with cancer risk.

Research Design and Methods

Data Sources

This two-sample MR study used summary-level genetic data from the DIAbetes Genetics Replication And Meta-analysis (DIAGRAM) consortium (19), Meta-Analyses of Glucose and Insulin-related traits Consortium (MAGIC) (20), Pancreatic Cancer Cohort Consortium (PanScan) and Pancreatic Cancer Case-Control Consortium (PanC4) (21), Breast Cancer Association Consortium (BCAC) (22), and UK Biobank (23) (Supplementary Table 1). Data for breast cancer came from BCAC and UK Biobank and were based on 228,951 European-descent participants (122,977 breast cancer case and 105,974 control subjects) and 367,643 European-descent participants (13,666 breast cancer case and 353,977 control subjects), respectively. The genome-wide association study (GWAS) in BCAC used phase 3 of 1000 Genomes Project as a reference panel in the imputation stage and adjusted for genetic principal components and country. From UK Biobank, we additionally derived genetic association data, adjusted for age, sex, and 10 genetic principal components, for overall cancer and 21 other site-specific cancers among 367,643 unrelated participants. We identified a total of 75,037 cancer cases, and information on incident cancer cases was obtained until 31 March 2017 in UK Biobank. The cancer diagnosis source of included studies is shown in Supplementary Table 2. Most studies defined the cancer cases based on cancer registry or hospital/clinic data. The original GWAS had been approved by corresponding ethics committee, and the current study was approved by the Swedish Ethical Review Authority.

Instrumental Variable Selection

Instrumental variable selection for T2DM and FG and FI levels was based on a meta-analysis of 32 GWAS with 74,124 T2DM case and 824,006 control subjects of European ancestry (known as DIAGRAM consortium) (19) and a genome-wide association meta-analysis of up to 133,010 individuals of European ancestry without diabetes (known as MAGIC) (20), respectively. Instrumental variables for pancreatic cancer were obtained from a GWAS of 9,040 cancer case and 12,496 control subjects of European ancestry from PanScan and PanC4 (21). Single nucleotide polymorphisms (SNPs) that met the locus-wide significance level (P < 10−5) and the genome-wide statistical significance threshold (P < 5 × 10−8) were proposed as instrumental variables for T2DM (n = 403), FG (n = 35), FI (n = 18), and pancreatic cancer (n = 22). Selected SNPs explained approximately 17.4%, 4.8%, and 1.2% variance associated with T2DM, FG, and FI, respectively. Used instrumental variables for T2DM and glycemic traits were validated in previous studies (2426). Previous studies reported that the effects of T2DM- and FI-related genetic variants in the FTO gene were entirely driven by BMI-mediation effects (20,27). Thus, we excluded SNPs in or near the FTO gene region, leaving 399 SNPs as instrumental variables for T2DM, 35 for FG, and 17 for FI. With regard to T2DM, 295 SNPs (variants in FTO excluded) reaching the genome-wide significance level were used in the sensitivity analysis. Detailed information for instrumental variables of T2DM, FG, FI, and pancreatic cancer is presented in Supplementary Table 3 and Supplementary Table 4.

Meta-analysis of MR Studies

The procedure of systematic review and literature selection is shown in Supplementary Fig. 1. A systematic literature search was conducted in two data sets of PubMed and Embase. We identified 165 manuscripts published before 17 October 2019 by use of the following medical subject heading terms and/or text words: “diabetes,” “glucose,” “insulin,” “glycemic,” “cancer,” “carcinoma,” “Mendelian randomization,” “Mendelian randomization,” “instrumental variable causal inference,” “causal inference using instrumental variable,” and “causal inference using genetic variants.” After title, abstract, and full text screening, seven studies were included in this meta-analysis (2834). Details of exclusion criteria are presented in Supplementary Fig. 1. We extracted data of publication (the first author’s name and year of publication), T2DM and related traits (FG and FI), cancer site, number of cancer case and control subjects, number of SNPs used as instrumental variables, variance explained by used SNPs, and risk estimates with their corresponding CIs. Information of included studies is shown in Supplementary Table 5.

Statistical Analysis

The random-effects inverse variance–weighted method was used to assess the associations of genetically predicted T2DM, FG, and FI with overall cancer and 22 site-specific cancers. Cochran I2 statistic was used to measure heterogeneity among instrumental variables. For T2DM, three sensitivity analyses, including the weighted median, MR-Egger, and MR Pleiotropy RESidual Sum and Outlier (MR-PRESSO) methods, were performed for the associations that showed suggestive evidence of associations in the inverse variance–weighted analysis. The weighted median approach provides accurate estimates with the prerequisite that at least half of the instrumental variables are valid (35). The MR-Egger regression detects and adjusts for pleiotropy; however, the derived estimates are imprecise (36). The MR-PRESSO method is able to detect and correct for possible outliers, thereby removing horizontal pleiotropy via outlier removal (37). To minimize the influence from BMI, we used a multivariable MR method with the adjustment of BMI. In the meta-analysis, effect sizes from different MR studies were combined using fixed-effects meta-analysis. Odds ratios (ORs) and CIs of cancer were scaled to 1-unit increase in log odds of liability to T2DM and 1-SD increase in log of genetically predicted FG and FI levels. The SD of FG and FI corresponds to 0.65 mmol/L and 0.60 pmol/L, respectively, based on the Fenland or Ely study (38,39). For pancreatic cancer, we additionally performed a bidirectional MR analysis. Power calculation for the analyses of T2DM was based on a web tool (40), and results are displayed in Supplementary Table 6. All statistical tests were two sided and performed in Stata/SE 15.0 and R 3.6.0 software. We did not use P values strictly to define statistical significance but interpreted the results based on the strengths of the associations (41) as well as the consistency across sensitivity analyses.

Data and Resource Availability

Data for T2DM-associated SNPs can be obtained from the DIAGRAM consortium (https://diagram-consortium.org/index.html). Data for FG- and FI-associated SNPs can be obtained from MAGIC (https://www.magicinvestigators.org/). Summary-level data from BCAC are publicly available (http://bcac.ccge.medschl.cam.ac.uk/). The PanScan and PanC4 genome-wide association data are available through dbGaP (accession numbers phs000206.v5.p3 and phs000648.v1.p1, respectively). UK Biobank data are available through application (https://www.ukbiobank.ac.uk/). Summary-level data for the used SNPs in the current study are available upon a reasonable request to the corresponding author.

Results

We found no MR evidence of association between genetic liability to T2DM and overall cancer in the primary analysis or the sensitivity analyses (Fig. 1). However, there was some evidence of associations of genetic liability to T2DM with higher odds of liver, pancreatic, kidney, uterine, and cervical cancer and lower odds of melanoma and esophageal cancer (Fig. 2). The ORs per 1-unit increase in genetically predicted log odds of T2DM were 1.16 (95% CI 0.99, 1.36; P = 0.059) for liver cancer, 1.13 (95% CI 1.04, 1.22; P = 0.002) for pancreatic cancer, 1.08 (95% CI 1.00, 1.17; P = 0.039) for kidney cancer, 1.08 (95% CI 1.01, 1.15; P = 0.031) for uterine cancer, 1.07 (95% CI 1.01, 1.15; P = 0.031) for cervical cancer, 0.93 (95% CI 0.89, 0.97; P = 0.001) for melanoma, and 0.89 (95% CI 0.81, 0.98; P = 0.018) for esophageal cancer (Fig. 2). Estimates of similar magnitude were observed between genetic liability to T2DM and thyroid cancer (OR 1.08; 95% CI 0.94, 1.24; P = 0.281) and brain cancer (OR 0.92; 95% CI 0.84, 1.02; P = 0.104) (Fig. 2). The findings were consistent between analyses using 399 SNPs and 295 SNPs for T2DM (Supplementary Fig. 2). Results of sensitivity analyses showed same patterns in the analysis of esophageal and pancreatic cancer and melanoma (Fig. 3). We detected significant heterogeneity in the analysis of uterine and liver cancer and melanoma and pleiotropy in the MR-Egger analysis of cervical cancer. After outlier removal, all significant associations obtained from the inverse variance–weighted model remained in the MR-PRESSO analysis. In addition, a suggestive positive association between genetically predicted risk of pancreatic cancer and T2DM was observed in the reverse MR analysis (Supplementary Fig. 3). After adjustment for BMI, the patterns of the associations between genetically predicted log odds of T2DM and cancers remained albeit with wider CIs (Supplementary Fig. 4).

Figure 1
Figure 1

Association between T2DM and overall cancer in UK Biobank with 75,037 cancer patients and 292,606 participants without cancer. Heterogeneity was observed in both analyses. There was no detected pleiotropy in MR-Egger analyses. Two and three outliers were detected and corrected in the MR-PRESSO analysis using 399 SNPs and 295 SNPs for T2DM, respectively. IVW, inverse variance weighted.

Figure 2
Figure 2

Associations between T2DM (399 SNPs) and 22 site-specific cancers in UK Biobank. All estimations were based on the inverse variance weighted method. ER, estrogen receptor; UKBB, UK Biobank.

Figure 3
Figure 3

Sensitivity analyses of the associations between T2DM and certain site-specific cancers in UK Biobank. Heterogeneity was observed in the analysis of uterine, liver, and melanoma cancer. There was detected pleiotropy in MR-Egger analysis of cervical cancer. One and three outliers was detected and corrected in the MR-PRESSO analysis of cervical and liver cancer, respectively. IVW, inverse variance weighted.

In the meta-analysis combining the present MR findings with those of previous MR studies (Supplementary Table 5), an association was observed between genetically predicted log odds of T2DM and pancreatic cancer (OR 1.08; 95% CI 1.02, 1.14; P = 0.009) among a total of 8,374 pancreatic cancer cases. The results of meta-analysis showed no associations of genetically predicted log odds of T2DM with kidney, uterine, or ovarian cancer (Fig. 4).

Figure 4
Figure 4

Meta-analysis of the association of T2DM with certain site-specific cancers. *Effect size in a study by Song et al. (34) was estimated in men and women separately. First author names appear for Cancer site & Study column. UKBB, UK Biobank.

There was limited evidence of associations of genetically predicted FG and FI levels with overall cancer and the 22 site-specific cancers (Supplementary Fig. 5, Supplementary Fig. 6, and Supplementary Fig. 7). However, the precision was low in most analyses and the magnitude of the estimates was relatively strong for some cancer sites. For example, the OR was >1.5 for genetically predicted high FG levels in relation to biliary tract cancer (Supplementary Fig. 6). In addition, for FI levels, the ORs were >1.5 for kidney, uterine, cervical, and stomach cancer and <0.5 for liver cancer (Supplementary Fig. 7).

In the meta-analysis, there was no evidence of association between genetically predicted FG levels and five site-specific cancers (Supplementary Fig. 8). Genetically predicted FI levels showed evidence of positive associations with cancers of the pancreas, kidney, uterus, and lung with combination of the findings from this MR study and previous MR studies (Supplementary Fig. 9).

Discussion

The current study is the first MR study that has systematically investigated the causal associations of genetic liability to T2DM and related traits with overall cancer and 22 site-specific cancers. We found evidence that genetic liability to T2DM was associated with increased risks of pancreatic, kidney, uterine, and cervical cancer and with lower risks of melanoma and esophageal cancer. The positive association between genetic liability to T2DM and pancreatic cancer was further verified in a supplementary meta-analysis of MR studies. There was limited MR evidence supporting causal associations between genetically predicted FG and any cancer but genetically predicted high FI levels increased the risks of pancreatic, kidney, uterine, and lung cancer.

The present MR findings do not support observational studies suggesting an elevated risk of overall cancer among T2DM patients (3). An umbrella meta-analysis of 27 studies found that having T2DM was associated with a 10% higher risk of developing cancer (38,010 cancer cases) and a 16% higher cancer mortality rate (11,386 cancer-caused deaths) (3). In a national register–based cohort study in Australia, the standardized incidence and mortality ratios for all cancers combined were significantly higher (ORs ranging from 1.03 to 1.22) among both men and women with T2DM than in individuals without diabetes (16). However, our findings were in line with a recent individual-level MR study with 10,536 Japanese adults (3,541 cancer cases). Using 29 SNPs as instrumental variables for T2DM, that study found no strong evidence supporting an association between T2DM and overall cancer (42). The discrepancy with our overall cancer findings may be explained by the driver effects of the T2DM-unrelated cancers that contributed a large proportion of cancer cases, including breast cancer (18%), prostate cancer (10%), and colorectal cancer (7%), in the present MR study, or from residual confounding or reverse causation bias in the observational studies.

Findings of the present MR study and the meta-analysis of MR studies showed a consistent causal positive association between T2DM and pancreatic cancer, supporting observational studies. An umbrella meta-analysis of 27 studies obtained a pooled OR of 1.95 when comparing T2DM patients with control subjects based on 52,445 pancreatic cancer cases (3). It has been demonstrated that both new-onset and long-standing T2DM facilitate the development of pancreatic cancer (11,12). Pathophysiologically, this may relate to carcinogenic or cancer-promoting effects of glucose and glycation end products in reactive oxygen species generation, DNA damage, and cell proliferation (15,43,44). It could also be due to the role of diabetes in the metabolic syndrome, which is associated with increased risk of pancreatic cancer (15,45), or due to increased insulin levels (15). Prediabetes is characterized by a long-standing increase in insulin secretion by the β-cells of the pancreas to compensate for insulin resistance that occurs in the early stages of diabetes development. Such an increase in insulin in the pancreatic portal circulation could be carcinogenic or cancer promoting, as insulin has proliferative effects (15). It is therefore notable that we also report a positive association between FI levels and pancreatic cancer. Our findings suggest that the insulin resistance of early diabetes, in combination with hyperglycemia, may increase risk of pancreatic cancer, and importantly, this could be targeted with insulin-sensitizing agents such as metformin, which reduce such risk (46). Nevertheless, a recent MR study did not observe a positive association between diabetes and pancreatic cancer among Japanese adults. This null finding might be caused by inadequate power, since the study only had 129 pancreatic cancer cases (42).

A bidirectional relationship between T2DM and pancreatic cancer has been found in recent years (11,15). Pancreatic cancer can increase diabetes risk through enhanced insulin secretion with consequent insulin resistance or due to destruction of pancreatic tissue with loss of insulin-producing β-cells. Even though several pathological features, such as insulin levels and glucose-dependent insulinotropic polypeptide levels, were different between new-onset T2DM and pancreatic cancer–caused T2DM, inaccurate classification of diabetes was common in clinical practice (15). Thus, the established observational association between T2DM and pancreatic cancer could be the result of reverse causality. The current study using MR design confirmed a causal pathway from T2DM to pancreatic cancer but also found suggestive evidence of an inverse causal pathway from pancreatic cancer to T2DM risk. This could be important clinically, and with further research the development of diabetes or prediabetes could be useful in monitoring cancer progression.

The current study also detected possible positive associations of liability to T2DM with some other site-specific cancers, with the strongest evidence for kidney, uterine, and cervical cancer. A systematic review including nine cohort studies stated that patients with diabetes had a significant increased risk of kidney cancer after adjustment for BMI and cigarette smoking (8). It could relate to increased exposure to carcinogenic or cancer-promoting growth factors or insulin-like products due to reduced excretion or be a consequence of urinary tract infections, which are common due to the relative immunosuppression seen in diabetes (47,48). Similarly, the association of T2DM with uterine cancer is supported by the findings of a meta-analysis of 16 observational studies with multivariate adjustment (49). However, studies concerning the association of T2DM with cervical cancer are limited and conflicting. A retrospective cohort study with 328,994 patients with diabetes and 327,572 participants without diabetes found that newly diagnosed T2DM patients (within 3 months) had significantly increased risk of cervical cancer. However, the risk was not higher among T2DM patients after the initial 3-month period compared with risk in those without T2DM (50). In another study including 397,783 adults, the prevalence of cervical cancer was 30% higher in the group with diabetes compared with counterparts without diabetes with adjustment for age, BMI, ethnicity, lifestyle, and physical activity (51). A nationwide Australian study showed that long-term T2DM was associated with the age-standardized incidence ratio of cervical cancer but not with mortality from cervical cancer (16). Further studies are warranted to verify the causal positive association between T2DM and cervical cancer.

Our finding of an inverse association between T2DM and melanoma is in line with the findings of most but not all observational studies. A nationwide hospital-based study showed that the risk of melanoma for familial T2D patients was lower among 26,641 patients (including 125,126 T2DM patients) who had a T2DM-affected family member compared with all patients in Sweden (52). Another nationwide study in Australia also found a decreased risk of melanoma among 953,382 T2DM case subjects compared with the general Australian population (16). Nonetheless, a study with 4,501,578 veterans admitted to Veterans Affairs hospitals reported that men with diabetes had a higher risk of melanoma (53). With regard to esophageal cancer, previous findings were inconsistent. Three meta-analyses documented a positive association between T2DM and esophageal cancer; however, the results might be less robust due to substantial heterogeneity and potential confounding factors within the included studies (3,54,55). A large-scale cohort study of 4,501,578 black and white U.S. veterans found that male T2DM patients had a decreased risk of esophageal cancer (53). Findings of two studies focusing on T2DM and risk of esophageal cancer in Australian and Asian populations showed no association between diabetes diagnosis and risk of esophageal cancer (16,56). Thus, the role of T2DM in esophageal cancer development and mortality needs more investigation.

Even though most observational studies observed a strong inverse association between T2DM and risk of prostate cancer (17), the current study provides limited evidence supporting such a causal association, which is supported by a previous MR study (25). The possible explanation for the discrepancy is anticancer effect of several drugs used for the management of T2DM, such as metformin and thiazolidinediones (57), in previous observational studies. There was suggestive evidence of a positive association of T2DM with liver cancer in the present MR study, confirming previous observational findings (3), which is likely to occur through driving nonalcoholic fatty liver disease, which can progress to hepatocellular carcinoma.

The detrimental effects of T2DM in relation to certain site-specific cancers may be driven by high insulin levels in response to insulin resistance that occurs in the development of prediabetes. It is therefore notable that we found positive associations of both T2DM and FI with pancreatic, kidney, and uterine cancer, which suggests a possible pathophysiologic mechanism. Meta-analysis of FI was also positively associated with lung cancer risk. Observational studies have proposed that hyperinsulinemia increases the risk of several cancers, such as pancreatic (58), uterine (59), gastric (60), and kidney (61) cancer, but not lung cancer (62), and insulin has multiple potential carcinogenic or cancer-promoting effects (63). Although limited evidence of an association between FG and cancer was found in the current study, except for a possible positive association with biliary tract cancer (64), hyperglycemia might play a role in the onset of certain cancers, especially liver (65) and bladder (64,65) cancer. Inflammation (66), elevated hemoglobin A1c levels (67), and drugs used for the management of T2DM (68) may also mediate the pathway from T2DM to cancer. Detailed mechanisms need further investigations. Further validating our findings or hypothesis, several T2DM medications have been revealed to lower the risk of common cancers, such as lung, colorectal, and breast cancer, in preclinical or clinical settings among patients with diabetes (69,70). Review articles suggested that even though metformin and thiazolidinedione appeared to inhibit the proliferation and growth of certain cancer types in preclinical data, a vast majority of clinical trials have been conducted to assess the usefulness of these medications in cancer prevention and treatment (69,70). Those results will facilitate the assessment of the place of metformin in cancer prevention and therapy and define the target populations.

A major strength of this study is the MR study design, which diminishes confounding and reverses causality potentially biasing the results in observational studies. In addition, we comprehensively assessed the causal associations of T2DM and related traits with overall cancer and 22 site-specific cancers using summary-level data from large genetic consortia. We conducted our study merely among European populations. Thus, the results were less likely to be biased by population stratification, but this confined the transferability of our findings to other populations. A major limitation is that the number of cases was few for several site-specific cancers, causing low precision of the estimates. Thus, it is likely that we have missed weak associations. However, we have performed a systematic review and meta-analysis to combine the data from the previous and present MR studies, thereby expanding the sample size and increasing the accuracy of the estimation as possible. Furthermore, we interpreted results relying on the consistency across three sensitivity analyses and the strengths of the associations but not the significance level (41). Even though there was heterogeneity among instrumental variables in a few analyses, no pleiotropy in the MR-Egger suggested balanced pleiotropy, which is less likely to bias the results (36). We still cannot exclude that there is any direct causal pathway from the T2DM-predisposing genetic variants to cancer. A further limitation is that we examined the liability to T2DM rather than the disease itself. Our results are therefore not fully comparable with those of observational studies where study participants have or do not have a T2DM diagnosis. Even though most of the included studies defined cancer cases based on a reliable source, such as registry and hospital/clinic data, a possible detection bias in T2DM patients may overestimate the association between T2DM and cancer. Nonetheless, considering that we examined the association of T2DM with >20 site-specific cancers, it is less likely that an increased or decreased chance of being diagnosed with a site-specific cancer is caused by the diagnosis of diabetes assuming no causal association between them.

Conclusion

This MR study strengthened the evidence in favor of causal associations of T2DM with increased risks of pancreatic, kidney, uterine, and cervical cancer and decreased risks of esophageal cancer and melanoma. Additionally, there was evidence of a positive association of FI levels with some overlapping cancers, which suggests that insulin resistance in early diabetes may contribute to this risk. This study lent limited support to causal associations of T2DM, FG, and FI with overall cancer risk. We suggest a higher index of suspicion for cancer and reinforcement of cancer screening recommendations among patients with T2DM to enable the early detection of cancer in this group of patients.

Article Information

Acknowledgments. Summary-level data for SNPs associated with T2DM-related traits were extracted from DIAGRAM consortium and MAGIC. Summary-level data for genetic associations with the cancers were contributed by the BCAC, PanScan and PanC4, and UK Biobank. The analyses of UK Biobank data were conducted under application 29202. The authors thank all investigators for sharing these data.

Funding. Funding for this study came from the Swedish Research Council (Vetenskapsrådet) (grant 2019-00977). S.B. is supported by a Sir Henry Dale Fellowship jointly funded by the Wellcome Trust and the Royal Society (grant 204623/Z/16/Z). S.K. is supported by a Cancer Research UK programme grant, the Integrative Cancer Epidemiology Programme (C18281/A19169), and a Junior Research Fellowship from Homerton College, Cambridge, U.K.

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

Author Contributions. S.Y. analyzed and interpreted data and wrote and reviewed the manuscript. S.K., M.V., and P.C. reviewed the manuscript. A.M.M. and S.B. prepared the data and reviewed the manuscript. S.C.L. designed the research, analyzed and interpreted the data, and reviewed the manuscript. All authors read and approved the final manuscript. S.C.L. has confirmed that the manuscript is an honest, accurate, and transparent account of the study being reported and that no important aspects of the study have been omitted. S.C.L. is the guarantor of this work and, as such, had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

  • Received January 22, 2020.
  • Accepted April 25, 2020.



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T2D Healthline: An App for People Living with Type 2

By electricdiet / July 26, 2020


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.

T2D Healthline app

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 app screenshots
Screenshots from the T2D Healthline app: Groups (left) and Discover (right)

 

T2D Healthline Features

Within the app, there are five sections:

  1. 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.
  2. Groups
    Most discussions are associated with a group. Currently, the available groups are:
    • General
    • Daily Life
    • Coping with COVID-19
    • Escape from T2D
    • Diet and Nutrition
    • Complications
    • Exercise and Fitness
    • Medications and Treatments
    • Mental Health
    • Women’s (or Men’s) Topics
    • Monitoring and Lab Work
    • Relationships
    • Healthcare
    • Newly Diagnosed
    • Travel
  3. 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.
  4. 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.
  5. Discover
    The Discover area provides links to diabetes news articles and tips for living well with Type 2.

Let’s Get Connected

T2D Healthline appOnce 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!



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Carrot Potato Mash – My Bizzy Kitchen

By electricdiet / July 24, 2020


I’ve been doing Mariano’s pick up groceries since I am not on Day 11 of no results yet of my COVID test.  The sweet girl who did my shopping called me and said “we don’t have any one pound bag of carrots, can I substitute a pound of baby carrots?

Um, the question to that answer will always be “no thank you.”  Hamsters should be the only ones eating baby carrots – has!  

I told her to get me a bigger bag of bagged carrots if she could.  And when I got home and opened my grocery bag, I was gifted a FIVE POUND BAG of carrots.  So that’s how the carrot mash potatoes came to be – I am going to be making ALL THINGS CARROTS this week.

And as I type this, I may or may not have a batch of carrot cake pancakes waiting to be cooked.  Stay tuned for that.

Here is how I start out recipe developing.  I search existing recipes.  Um, turns out a lot of people went a very sweet route on mashed carrots adding maple syrup, cinnamon and brown sugar.  The carrots are already sweet, and that didn’t appeal to me at all.  I also thought I wanted to add potato to get it the consistency I wanted – like a mashed potato, not baby food carrot puree.  And I added crushed red pepper to balance the sweetness.

I peeled the potato and not the carrots.  The internet will tell you that peeling carrots strips away some of the nutrients.  But you are peeling such a thin layer, you aren’t really missing much by doing that.  I don’t find the carrot skin to have a bitter taste, so it boils down to personal preference.  Also, it’s a lot easier to just leave it on. 😀

Print

Carrot Potato Mash

A delicious side dish to beef, chicken or pork.  This carrot mash is slightly sweet with a hit of spicy from the crushed red pepper.

  • Author: Biz
  • Prep Time: 5
  • Cook Time: 20
  • Total Time: 25 minutes
  • Yield: 4 1x

Scale

Ingredients

  • 9 ounces potato, peeled and cut into cubes
  • 9 ounces carrot, cut into cubes
  • 1 tablespoon I Can’t Believe It’s Not Butter
  • 1/3 cup unsweetened almond milk
  • 1 teaspoon crushed red pepper (adjust to taste)
  • 1/2 teaspoon salt
  • 1/2 teaspoon pepper

Instructions

Put water in a stock pot.  Add potatoes and carrots.  Bring to a boil, cook for 5 minutes, remove from heat, put a lid on and let sit for 15 minutes.  The veggies will be fork tender.

Drain the veggies.  Add the butter, milk and seasonings and using a potato masher or stick blender, blend until smooth.  Add additional salt to taste.

Notes

On team purple each serving is 1 WW point – on team blue and green it is 2 WW points.

Nutrition

  • Serving Size: 3/4 cup
  • Calories: 89
  • Sugar: 3
  • Sodium: 99
  • Fat: 3
  • Saturated Fat: 1
  • Carbohydrates: 15
  • Fiber: 4
  • Protein: 2

The cut of steak I used is called a petite sirloin.  It’s closer to the rump so the internet will tell you that this cut is best for braising or roasting.  But it’s a steak – how do you braise a half pound of beef without cooking it to death?

So I treat it as I would a sirloin steak.  Some tips about cooking beef:  (1) let the steak come to room temperature before cooking.  Never put a cold steak into a hot pan.  (2) salt the beef within two minutes of cooking or after 40 minutes.  Within two minutes the salt still remains on the top and will sear into the meat.  Left longer than that, the salt starts to wick out the moisture in the beef.  After forty minutes though, the juices will have reabsorbed into the meat and you are good to go.  (3) cook with a meat thermometer.  I cooked my beef to 120 before removing it to rest.  That only took 3 minutes on one side and 2 minutes on the other to get to that temperature.

For my chimichurri sauce:  1 cup cilantro, 4 tablespoons grape seed oil, 2 tablespoons red wine vinegar, 4 cloves garlic, 1 teaspoon salt, 1 tablespoon sugar free orange marmalade.  Yes, you read that correctly – the orange marmalade balances the acidity of the cilantro and vinegar in the best way possible.  I suppose you could add honey, but I’ve not tried that.  I just put all the ingredients in a wide mouth mason jar and used a stick blender to blend.  You only need a teaspoon drizzled over the steak – 1 point on all WW plans and only 26 calories of delicious flavor.

 

So while I wish I was at a restaurant being served this meal with a nice glass of cabernet sauvignon, I had to make do and make it myself, and drink seltzer water since I am on Day 20 of #dryjuly.   If I were to have this steak dinner at a restaurant I am sure I would have spent $50 and up depending on where I went.  My dish cost me approximately $4.12.  Um, last time I checked, you can’t get McDonalds for that price.

I hope you try this potato mash.  Oh, and I realized people will ask me about the green beans and mushrooms.  I simply sauteed the mushrooms in avocado oil spray with salt and pepper.  The green beans I cooked in the microwave for 1 minute, then added them to the cast iron skillet when the steak came out to get a bit of char, and added salt and pepper.  That’s it!

This literally was about 20 minutes to make from beginning to end.  I hope you give this a try – tag me on Instagram if you do!

Until next time, Be Kind, Be Fearless, Have Hope – Love, Biz





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Easy Keto Lemon Bars Recipe

By electricdiet / July 22, 2020


These low-carb lemon bars are the perfect balance between sweet and lemony and they will leave you reaching for more! With a coconut flour crust and creamy filling, they are the perfect teatime treat. 

Four keto lemon bar squares on a white plate

If you love lemony, tangy desserts then this recipe is for you. Not only is this one of the best tasting keto desserts you can make but it’s so easy to prepare too. 

The creamy lemon filling and buttery shortcrust melt in your mouth and will leave you wondering how on earth something that tastes so rich and indulgent could possibly be low-carb! 

How to make keto lemon bars

Step 1: Measure out all the ingredients and preheat your oven to 325 ° F (160 ° C).

Step 2: Grease a 9”x 9” (23cm x 23cm) baking pan and line it with a strip of parchment paper (you don’t need to use the parchment strip if your pan has a removable base.)

Ingredients for the lemon bars on wooden board

Step 2: Combine the coconut flour, ground stevia, and salt in the bowl of a food processor by pulsing once or twice.

Step 3: Add the unsalted butter and pulse to combine, until it reaches a sandy consistency.

Preparing the coconut flour crust in a food processor

Step 4: Add the egg and vanilla extract to the food processor. Pulse to combine until the dough is well mixed and forms pea-sized pieces. There should be no dry areas in the dough. 

Step 5: Press the shortbread dough firmly into the prepared pan using the back of a glass or your hands. 

Dough pressed into a square pan

Step 6: Bake for 12-15 minutes, until the outer edges of the crust have just begun to brown. It should not be fully cooked at this stage as it will cook further once the filling has been added. Remove and leave the oven on.

Step 7: Mix together all the ingredients for the filling until completely smooth and pour over the baked crust. 

mixing all the filling ingredients in a large glass bowl

Step 8: Bake for 30 – 35 min. The lemon bars are done cooking once the filling stops moving if shaken slightly. Remove from the oven and allow the lemon bars to cool completely for at least an hour. Cut and serve when desired. 

Baked lemon bars in the pan

Tips for making the perfect lemon bars

Before you start preparing the shortcrust base of the lemon bars, remove the cream cheese from the refrigerator and let it sit out for at least 30 minutes before using it the filling. This makes sure that the cream cheese is easy to beat and won’t leave any lumps in your filling. 

One of the keys to getting perfect lemon bars is to make sure they cool completely before you decide to gobble them up. Let them cool enough that they are completely firm before eating. 

Storing the bars

Once the lemon bars have completely cooled, wrap them gently in plastic wrap so that they don’t dry out. They can be stored like this in the refrigerator for up to 4 days. 

baked lemon bars ready to serve

More healthy low-carb snack recipes

Great tasting keto treats are a great way to keep your blood sugar levels stable and satisfy your sweet tooth! Here are some of my favorite low-carb snacks to keep me going:

You can also check out my roundup of 10 diabetic cookie recipes for more snacking inspiration.

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

Recipe Card

Easy Keto lemon bars

These low-carb lemon bars are the perfect balance between sweet and lemony and they will leave you reaching for more! With a coconut flour crust and creamy filling, they are the perfect teatime treat. 

Prep Time:15 minutes

Cook Time:45 minutes

Total Time:1 hour

Servings:9

Ingredients

For the shortbread crust:

Instructions

For the shortbread crust:

  • Preheat your oven to 325 ° F (160 ° C).

  • Grease a 9”x 9” (23cm x 23cm) baking pan and line it with a strip of parchment paper. You don’t need to use the parchment strip if your pan has a removable base.

  • Combine the coconut flour, ground stevia, and salt in the bowl of a food processor by pulsing once or twice.

  • Add the unsalted butter and pulse to combine until it reaches a sandy, well-mixed consistency.

  • Add the egg and vanilla extract to the bowl of the food processor and pulse to combine until the dough is well mixed comes together in small balls.

  • Add the dough to the prepared pan and press down firmly using your fingers or back of a glass.

  • Bake for 15 minutes, until the outer edges of the crust have just begun to brown. It will not be completely cooked at this point as it will bake further once the filling has been added. Remove and leave the oven on.

For the filling:

  • Mix together all the ingredients for the filling until completely smooth and pour over the baked crust. If the mixture has some lumps of butter or cream cheese, microwave for 1 – 2 minutes until you can mix until completely smooth.

  • Bake for 30 – 35 min. The lemon bars are done cooking if you shake the pan and the middle of the lemon bars shake slightly. Remove from the oven and allow the lemon bars to cool completely for at least an hour. Cut and serve when desired.

Recipe Notes

This recipe makes 9 servings.  The lemon bars can be stored like this in the refrigerator for up to 4 days.

Nutrition Info Per Serving

Nutrition Facts

Easy Keto lemon bars

Amount Per Serving

Calories 322 Calories from Fat 271

% Daily Value*

Fat 30.1g46%

Saturated Fat 18.5g93%

Polyunsaturated Fat 0.7g

Monounsaturated Fat 4.3g

Cholesterol 160mg53%

Sodium 129.6mg5%

Potassium 55.7mg2%

Carbohydrates 8.1g3%

Fiber 4g16%

Sugar 1.6g2%

Protein 5.2g10%

Net carbs 4.1g

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

Course: Dessert

Cuisine: American

Keyword: Keto Lemon Bars, low carb bars



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Cold Avocado Soup Recipe – 5-Ingredient Cold Cucumber Avocado Soup

By electricdiet / July 20, 2020


Cold Avocado Soup Recipe For Amazing Cold Cucumber Avocado Soup

If you like a refreshing starter, wait until you try this simple cold avocado soup recipe.  Two summer favorites, avocado and cucumber, are combined to create an amazing simple cold cucumber avocado soup recipe. This creamy avocado soup will be a favorite and this Avocado Cucumber Soup from Eating Well Through Cancer is wonderful for so many reasons.  An easy recipe for cancer patients, this cold cucumber soup is soothing and satisfying.  However, it isalso perfect for anyone who desires a light refreshing and delicious cold cucumber soup.

Cold Cucumber Avocado Soup
Creamy avocados and cucumbers pair together for the best cold avocado soup ever. This cold cucumber soup has great flavor and makes the best cold soup recipe. Most importantly, the recipes takes minutes to make.

    Servings6 (3/4 cup) servings

    Ingredients

    • 1


      large avocadopeeled, pitted, and halved

    • 2


      cucumberspeeled, seeded, and halved

    • 1cup


      or vegetable broth or low-sodium fat-free chicken

    • 1cup


      fat-free evaporated milk

    • 2tablespoons


      lemon juice



    • salt and pepper to taste

    Instructions
    1. In blender or food processor, blend avocado, cucumbers, broth, evaporated milk, and lemon juice until smooth. Season to taste. Refrigerate, covered, until chilled.


    2. If soup is too thick, gradually add more broth or evaporated milk.

    Recipe Notes

    Per Serving: Calories 99, Calories from fat 46%, Fat 5 g, Saturated Fat 1 g, Cholesterol 2 mg, Sodium 64 mg, Carbohydrate 10 g, Dietary Fiber 3 g, Sugars 7 g, Protein 5 g, Diabetic Exchanges: 1 vegetable, 1/2 fat-free milk, 1 fat

    Terrific Tip: To easily seed cucumbers: cut in half and run a knife or spoon down the center of the cucumber to scrape out the seeds.

    Nutrition Nugget: Avocados contain healthy unsaturated fats that help your body absorb and use vitamins, as well as help to maintain cell membranes.

    You Will Love These Gadgets To Make Cold Avocado Soup Recipe

    Avocado slicer, 3 In 1 Avocado Slicer Avocado CutterAvocado slicer, 3 In 1 Avocado Slicer Avocado CutterAvocado slicer, 3 In 1 Avocado Slicer Avocado CutterMSC International 33005 CLEAR COVER AVOCADO POD, GreenMSC International 33005 CLEAR COVER AVOCADO POD, GreenMSC International 33005 CLEAR COVER AVOCADO POD, GreenCuisinart CSB-75BC Smart Stick 200 Watt 2 Speed Hand BlenderCuisinart CSB-75BC Smart Stick 200 Watt 2 Speed Hand BlenderCuisinart CSB-75BC Smart Stick 200 Watt 2 Speed Hand Blender

    Whip Up 5-Ingredient Cold Avocado Soup Recipe In 5 Minutes

    This cold avocado soup recipe is in Holly’s cancer cookbook, however, it will quickly become a daily favorite.  Serve this recipe for lunch with a sandwich. On a hot day, there is nothing better!  You can even top it with a dollop of yogurt or some salsa for a little zing. Holly has even had parties and served a cup of this chilled avocado soup as an appetizer in a punch cup or demi tasse.  Everyone always really enjoys it.

    What’s great about Eating Well Through Cancer cookbook is it contains healthy easy recipes for everyone.  As you see, this cold cucumber soup recipe is great for cancer patients but it is also refreshing on a hot summer day!

    Delicious Cookbooks To Highlight Diabetic Recipes

    This wonderful 5-ingredient creamy avocado soup is also an easy diabetic recipe!  Remember, eating recipes in Holly’s cookbooks highlighted as simple diabetic recipes means it is the healthiest way to eat.  These cookbooks contain a “D” to highlight diabetic recipes!

    EATING WELL TO FIGHT ARTHRITIS: 200 easy recipes and practical tips to help REDUCE INFLAMMATION and EASE SYMPTOMSEATING WELL TO FIGHT ARTHRITIS: 200 easy recipes and practical tips to help REDUCE INFLAMMATION and EASE SYMPTOMSEATING WELL TO FIGHT ARTHRITIS: 200 easy recipes and practical tips to help REDUCE INFLAMMATION and EASE SYMPTOMSHolly Clegg's trim&TERRIFIC KITCHEN 101: Secrets to Cooking Confidence: Cooking Basics Plus 150 Easy Healthy RecipesHolly Clegg’s trim&TERRIFIC KITCHEN 101: Secrets to Cooking Confidence: Cooking Basics Plus 150 Easy Healthy RecipesHolly Clegg's trim&TERRIFIC KITCHEN 101: Secrets to Cooking Confidence: Cooking Basics Plus 150 Easy Healthy RecipesEating Well Through Cancer: Easy Recipes & Tips to Guide you Through Treatment and Cancer PreventionEating Well Through Cancer: Easy Recipes & Tips to Guide you Through Treatment and Cancer PreventionEating Well Through Cancer: Easy Recipes & Tips to Guide you Through Treatment and Cancer Prevention

    Get All of Holly’s Healthy Easy Cookbooks

    The post Cold Avocado Soup Recipe – 5-Ingredient Cold Cucumber Avocado Soup appeared first on The Healthy Cooking Blog.



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    Blood Flow in the Pancreatic Islet: Not so Isolated Anymore

    By electricdiet / July 18, 2020


    The pancreatic islet is a highly vascularized endocrine mini-organ that depends on blood supply to function efficiently. As blood flows through islet capillaries reaching different endocrine cell types, it significantly impacts nutrient sensing, paracrine communication, and the final hormonal output. Thus, any change in blood flow, either induced physiologically (e.g., nervous input) or as a result of pathological changes (e.g., fibrosis), could affect islet function. It is not a stretch to state that the way the islet vasculature is arranged anatomically and regulated functionally must have consequences for glucose homeostasis.

    Despite its potential impact for islet function, interest in the islet vasculature has been sporadic and is certainly not equal to that professed to the cells it serves. Still, there has been a substantive research effort in this arena. From beautiful scanning electron images of corrosion casts (1) to creative physiological experiments using perfused pancreases (2) and microbeads (3), investigators have employed various approaches to study the microcirculation of the islet. The results of these studies provided structural and functional insight but also raised questions. To settle a debate that had started in the mid-1960s, a group of prominent islet biologists decided to meet in 1996 to review existing notions about islet blood flow and “agreed to disagree” that there were three models (4). In model 1, non–β-cells are perfused before β-cells, allowing other endocrine cells to influence β-cells located downstream. In model 2, β-cells are perfused before the other endocrine cells and thus dominate islet function. In model 3, there is no apparent order of perfusion, but blood flows from the afferent to the efferent pole of the islet. The three flow patterns were confirmed more recently in in vivo studies in mice (5). Incidentally, the hierarchical organization based on blood flow may be possible in the mouse islet with its mantle versus core organization, but this scenario is highly unlikely in the desegregated human islet.

    Their differences notwithstanding, all three models assumed that the islet microcirculation is self-contained, with each islet having its own vascular network comprising feeding arterioles, a glomerulus-like capillary net, and dedicated venous drainage (Fig. 1). This arrangement allows islet blood flow to be regulated independently from that of the exocrine pancreas. This notion is now being challenged by hot-off-the-press findings appearing in this issue of Diabetes. In their study, Dybala et al. (6) used intravital imaging of the exteriorized mouse pancreas to track individual red blood cells moving through islet capillaries in real time. In these technically challenging experiments, the authors followed red blood cells as they exited and entered the islet and found that blood flow is bidirectional and continuously integrated with that of the exocrine pancreas at multiple locations. The idea that islets, in particular smaller islets, could be incorporated into the exocrine capillary system is not completely new (7), but in their study, Dybala et al. moved anatomical guesswork into real-time in vivo physiology to show directly that the islet microcirculation is open and not isolated from that of the surrounding exocrine tissue.

    Figure 1
    Figure 1

    Cartoons depicting three different models of blood flow in the pancreas. Until recently, the microcirculation of the islet was considered to be independent of that of the surrounding exocrine tissues (left). In an article published in this issue of Diabetes, Dybala et al. (6) now show that the circulation of the islet is integrated with that of the exocrine tissue, with blood flowing bidirectionally between both compartments (center). In a more sophisticated model, regulatory elements such as nervous input and vascular gates can be incorporated into this scheme to allow for local control of blood flow (right).

    The authors not only produced astonishingly detailed images of the pancreas vasculature but were further able to measure the basal velocity of individual red blood cells. The average speeds were similar inside and outside the islet, contradicting the prevalent view that islet blood flow is 5–10 times higher than in exocrine tissues. Because in vivo recordings of capillary blood flow are not yet possible in the human pancreas, the authors could only obtain structural data for human islets. The results are in line with previous observations made on corrosion casts that islets in the human pancreas are connected with the exocrine tissue through insulo-acinar portal vessels (1). Blood flow in the human islet can be expected to be integrated with its surroundings, as its vasculature already shows less of the tortuosity typical of the mouse islet vasculature (8,9). It is difficult to tell apart endocrine from exocrine regions based on vascular architecture in human pancreas sections. In addition, human islets do not have distinctive boundaries such as a capsule, which eliminates another barrier for full integration into the pancreatic vascular network.

    The model proposed by Dybala et al. (6) still requires experimental confirmation by peers in the field before it becomes the new canon (Fig. 1). Nevertheless, from a physiological point of view, it makes sense that blood supply to endocrine and exocrine compartments is integrated. Secretion of digestive enzymes and insulin is simultaneously activated when nutrients need to be absorbed (i.e., the fed state), which requires coordinated increases in blood perfusion to both regions. The intimate relationship between islets and acinar tissues is evident in findings showing that diabetes is often associated with abnormal pancreatic exocrine function (10) and that pancreases of individuals at risk for or with type 1 diabetes are smaller (11). If these compartments are integrated through their vasculature, then acinar tissues should be exposed to high concentrations of islet secretory products, such as insulin, and vice versa. Indeed, insulin has been shown to regulate exocrine function by affecting protein biosynthesis and zymogen discharge, in particular of amylase (12). In general, however, there is relatively little interest in understanding how the exocrine and endocrine tissues of the pancreas influence each other. In view of the results shown here, the biology of the endocrine pancreas (studied by endocrinologists) should no longer be studied separately from that of the exocrine pancreas (the focus of gastroenterologists).

    If endocrine and exocrine regions are really this integrated, is there still a chance for blood flow to be regulated selectively? Blood flow in the islet was proposed to be controlled by external gates at the level of the arteriole as well as by internal gates at the level of capillaries (4). These internal gates were described in early in vivo microscopy studies as “bulging endothelial cells” within islet capillaries that could influence the velocity and volume of blood flowing through capillaries (13). These internal gates are now known to be pericytes capable of changing islet capillary diameter and blood flow in response to increased β-cell activity or sympathetic nervous input (14). Thus, by responding to local and neural signals, the islet vasculature can alter blood flow selectively (Fig. 1). It is likely that similar mechanisms exist in acinar regions. Thus, we can imagine a scenario in which the perfusion of endocrine and exocrine compartments is regulated conjointly at the level of the pancreatic lobe while allowing for local control without the need for separate circulations. This should make everyone happy.

    Article Information

    Funding. This work was supported by National Institutes of Health grants K01DK111757 (J.A.), National Institute of Diabetes and Digestive and Kidney Diseases–supported Human Islet Research Network (UC4DK104162, New Investigator Pilot Award to J.A.), R01DK084321 (A.C.), R01DK111538 (A.C.), R01DK113093 (A.C.), U01DK120456 (A.C.), R33ES025673 (A.C.), and R21ES025673 (A.C.), and The Leona M. and Harry B. Helmsley Charitable Trust grants G-2018PG-T1D034 and G-1912-03552 (A.C.).

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



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