Article Cite This: J. Agric. Food Chem. 2018, 66, 8805−8813
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Lentil (Lens culinaris Medikus) Diet Affects the Gut Microbiome and Obesity Markers in Rat Niroshan Siva,† Casey R. Johnson,‡ Vincent Richard,§ Elliot D. Jesch,∥ William Whiteside,∥ Abdullah A. Abood,§ Pushparajah Thavarajah,† Susan Duckett,⊥ and Dil Thavarajah*,†
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Plant and Environmental Sciences, Clemson University, 270 Poole Agricultural Center, Clemson, South Carolina 29634, United States ‡ Mayo Clinic School of Medicine, 200 First Street SW, Rochester, Minnesota 55905, United States § Biological Sciences, Clemson University, Clemson, South Carolina 29634, United States ∥ Food, Nutrition, and Packaging Sciences, Clemson University, Clemson, South Carolina 29634, United States ⊥ Animal and Veterinary Sciences, Clemson University, Clemson, South Carolina 29634, United States ABSTRACT: Lentil, a moderate-energy high-protein pulse crop, provides significant amounts of essential nutrients for healthy living. The objective of this study was to determine if a lentil-based diet affects food and energy intake, body weight, percent body fat, liver weight, and body plasma triacylglycerols (TGs) as well as the composition of fecal microbiota in rats. A total of 36 Sprague−Dawley rats were treated with either a standard diet, a 3.5% high amylose corn starch diet, or a 70.8% red lentil diet for 6 weeks. By week 6, rats fed the lentil diet had significantly lower mean body weight (443 ± 47 g/rat) than those fed the control (511 ± 51 g/rat) or corn (502 ± 38 g/rat) diets. Further, mean percent body fat and TG concentration were lower, and lean body mass was higher in rats fed the lentil diet than those fed the corn diet. Fecal abundance of Actinobacteria and Bacteriodetes were greater in rats fed the lentil or corn starch diets than those fed the control diet. Fecal abundance of Firmicutes, a bacterial phylum comprising multiple pathogenic species, decreased in rats fed the lentil and high-amylose corn starch diets vs the control diet. The lentil-based diet decreased body weight, percent body fat, and plasma triacylglycerols in rats and suppressed intestinal colonization by pathogens. KEYWORDS: lentil, corn, prebiotic carbohydrates, obesity, gut microbiome, rat study
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INTRODUCTION
A healthy intestinal microbiome and a prebiotic-rich, lowcalorie diet are both associated with reduced obesity prevalence.11,12 Prebiotic-rich diets change microbial colonies in the human gut, which leads to increased satiety, regulation of intestinal motility, production of short-chain fatty acids, prevention of diarrhea and constipation, and reduction of pathogen colonization.13−15 Further, consumption of a prebiotic-rich diet may also stimulate the immune system,16 promote mineral absorption (especially iron and selenium), decrease the risk of colon cancer,17,18 and decrease risk factors associated with obesity and metabolic syndrome.13,19 Prebiotics reduce excess circulating glucose, reduce bile salt binding capacity leading to reduced blood cholesterol levels,20 and improve insulin sensitivity.21 American diets are generally low in prebiotic carbohydrates, ranging from 1 to 10 g per day per person.22 The Institute of Medicine has set the acceptable macronutrient distribution range for carbohydrates at 45−65% of total energy intake and the adequate daily intake of total fiber at 38 g for men and 25 g for women.23 Dietary and functional fibers include prebiotic carbohydrates, but no specific official recommendations for the consumption of prebiotics have been made aimed at reducing obesity in the
Lentil (Lens culinaris Medikus) nutritional quality has improved over the past decade due to national breeding programs; for example, a 50-g serving of lentil provides 3.7− 4.5 mg of iron (Fe), 2.2−2.7 mg of zinc (Zn), and 22−34 μg of selenium (Se).1 Lentil is also very low in phytic acid (2.5−4.4 mg g−1) and therefore promotes Fe bioavailability compared to other legumes and cereals.1,2 Lentil has a balanced amino acid profile, which complements cereals and contributes to a diet of superior nutritional value. In addition, lentil is an excellent source of prebiotic carbohydrates, including sugar alcohols (SA), raffinose family oligosaccharides (RFO), fructooligosaccharides (FOS), and resistant starch (RS).3−8 A large portion (40−50%) of the total carbohydrates in lentil consists of low digestible (prebiotic) carbohydrates.7 Lentil provides 13−15 g of prebiotics per 100 g serving, with mean concentrations of RFO, SA, FOS, and RS of 4071 mg, 1423 mg, 62 mg, and 7.5 g 100 g−1 dry matter, respectively.7 Overall, lentil contains significant amounts of protein, is low in fat, and is rich in micronutrients, and is thus considered a suitable whole food to combat obesity-related noncommunicable diseases.9,10 Lentil consumption in the USA is about 10−20% of production, with this crop gaining attention in the healthy whole-food industry and through public TV health/food shows. Lentils are now promoted in the USA as a popular ethnic food in the form of humus, dahl, or whole grain soups. © 2018 American Chemical Society
Received: Revised: Accepted: Published: 8805
June 22, 2018 July 31, 2018 August 1, 2018 August 13, 2018 DOI: 10.1021/acs.jafc.8b03254 J. Agric. Food Chem. 2018, 66, 8805−8813
Article
Journal of Agricultural and Food Chemistry USA.24 However, traditional food approaches are finding wide acceptance among the American population despite little being known about the true nutritional efficacy of many whole foods, including pulses such as lentil. Differences in the relative proportion of gut bacteria are correlated with human intestinal diseases.25,26 The Human Microbiome Project clearly revealed that gut microorganisms are not just passive entities in the host gut but are responsible for a range of biological functions related to human physiology, nutrition, and general well-being of the individual.27 Human gut microbiota include >1014 bacteria and archaea representing nearly 1,100 microbial species. These species belong to 8−55 known phyla with Firmicutes (low-GCC Gram-positive), Bacteroidetes, and Actinobacteria (high-GCC Gram-positive) being the most represented in the human gut.27 The relative proportion of Bacteroidetes is less in obese individuals compared to lean individuals; however, this relative proportion rebounds with weight loss on a prebiotic-rich, low-calorie diet.28 Consumption of nondigestible, fermentable carbohydrates (or prebiotics) is recommended to stimulate the activity of hind gut bacteria 29 and suppress colonization by pathogens.30−32 We hypothesize that a diet rich in lentil might have significant health benefits over time as a result of its superior nutritional nature and the increase in activity of hind gut bacteria. The objective of this study was to determine the ability of a lentil-based diet to combat obesity-related noncommunicable diseases by assessing changes in feed and energy intake, body weight, percent body fat, lean mass, liver weight, and body plasma triacylglycerol (TG) as well as the composition of fecal microbiota in rats over a 6-week period.
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Table 1. Composition of Standard, Corn (3.5% High Amylose Corn Starch), and Lentil (71%) Diets formula
control
casein (g/kg) 200 lentils (g/kg) 0 L-cysteine (g/kg) 3 corn starch (g/kg) 398 maltodextrin (g/kg) 132 sucrose (g/kg) 100 soybean oil (g/kg) 70 cellulose (g/kg) 50 mineral mix, AIN-93G-MX (94046) (g/kg) 35 vitamin mix, AIN-93-VX (94047) (g/kg) 10 choline bitartrate (g/kg) 2.5 TBHQ, antioxidant (g/kg) 0.014 high amylose corn starch (g/kg) 0 Calculated Composition protein, N × 6.25 (%) 18 Gross energy, kcal/kg 3800 total carbohydrate (%) 60 resistant starch (%) 4 fat (%) 7
corn
lentil
200 0 3 362 132 100 70 50 35 10 2.5 0.014 35
0 708 0 0 76 58 61 50 35 10 2.5 0.014 0
18 3700 59 6 7
18 3400 52 3 7
−80 °C until further analysis. Percent body fat and lean body mass were measured using a dual energy X-ray absorptiometry technique (Hologic Discovery A, Hologic Inc., Marlborough, MA, USA). Finally, liver samples were extracted, weighed, and stored at −80 °C until further analysis. Triacylglycerol (TG) Analysis. Blood plasma TG concentration was measured using a colorimetric assay kit (Cayman TG Colorimetric Assay Kit, Cayman Chemicals, Ann Arbor, MI, USA). Aliquots (10 μL) of blood plasma were added to wells in a microwell plate. Standards and blanks were prepared as described in the assay kit. Then, 150 μL of solution containing lipoprotein lipase, glycerol kinase, glycerol phosphate oxidase, peroxidase, 4-aminoantipyrine, Nethyle-N-(3-sulfopropyl)-m-anisidine, and sodium phosphate buffer were added to each well. The microwell plate was shaken for 5 s and then incubated for 15 min at room temperature. Absorbance was measured at 540 nm using a microplate reader (SpectaMax M2 with SoftMax Pro software, Molecular Devices Corporation, Sunnyvale, CA, USA). The TG concentration of the sample (in mg/dL) was determined as the (corrected absorbance minus y-intercept)/slope. Fecal 16S rRNA. Fecal DNA was extracted using a QIAamp DNA stool mini kit (QIAGEN Inc., Germantown, MD, USA). The DNA concentration was checked using Qubit dsDNA HS Assay Kit via a Qubit 3.0 fluorometer (Invitrogen Corporation, Carlsbad, CA, USA) to ensure proper DNA extraction from each sample. Extracted fecal DNA samples were stored at −80 °C until further analysis. Gene specific primers were used to amplify the V4 region of the bacterial 16S rRNA gene as previously described.36 The 16S rRNA V4 primers were as follows: 16S forward, GTGCCAGCMGCCGCGGTAA; 16S reverse, GGACTACHVGGGTWTCTAAT.37 Illumina sequencing libraries were built in a single polymerase chain reaction (PCR) by adding index and flow cell adaptor sequences to the 16S primers.37 Each primer consisted of an Illumina adaptor, an 8-nt index sequence, a 10-nt pad sequence, a 2-nt linker, and the gene specific primer. Index primers and Illumina primers were purchased from IDT (Integrated DNA Technologies Inc., Coralville, IA, USA). Amplicons were generated using PCR (AccuPrime Pf x super mix; Invitrogen) and then quantified using a bioanalyzer (Agilant 2100 Bioanalyzer, Agilent Technologies, Santa Clara, CA, USA). Amplicons were pooled into equimolar concentrations using a SequalPrep plate normalization kit (Invitrogen Corporation, Carlsbad, CA, USA). The final concentration of the library was determined using a previously published protocol.37 Nucleotide diversity of the pooled sample was increased by spiking with 10% phiX DNA.
MATERIALS AND METHODS
Animals and Diet Formulation. All experiments involving rats were performed using protocols approved by the Clemson University Institutional Animal Care and Use Committee (Protocol number: AUP2015-036). Adult (8-week old) male Sprague−Dawley rats (Charles River Laboratories, Wilmington, MA, USA; n = 36) were housed in individual cages with controlled environmental conditions (temperature 25 °C, relative humidity 60%, and 12-h light/dark cycle). Rats were housed for 1 week to adapt to the environment prior to the experiment. Experimental groups (n = 12) were fed a standard diet (control diet), 3.5% high amylose corn starch diet (corn diet), or 70.8% red lentil diet (representative of a traditional pulse diet33) for 6 weeks (Table 1). Diets were formulated as 0.5-in. pellets using a cold press method (Teklad Lab Animal Diets, Indianapolis, USA). The standard diet was formulated based on the AIN-93 M diet according to recommendations from the American Institute of Nutrition.34 The corn starch diet was formulated to assess the effect of RS. Commercial food grade red split lentil was obtained from United Pulse, ND, USA to prepare the lentil diet. Nutritional composition of red split lentil was determined using standard analytical methods used for the annual pulse quality survey in 2010−2013 (Table 2).35 Sample Collection. Feed intake was measured every 3 days for 6 weeks, with feed intake measured by subtracting the weight of remaining feed from the weight of feed provided. At the beginning of every week, body weights were measured using a weighing balance (TS2KS, OHAUS Corporation, Parsippany, NJ, USA). Bedding material was changed each day, with fecal samples collected for bacterial composition analysis at 0, 2, 4, and 6 weeks. Fecal samples were collected separately from individual rats into sterilized conical tubes, and immediately stored at −80 °C until further analysis. At 6 weeks, rats were euthanized using a carbon dioxide closed chamber followed by a bilateral pneumothorax. Blood samples (3 mL) were drawn from individual rats into sterilized tubes containing heparin as an anticoagulant. Tubes were then centrifuged at 1000g for 10 min at 4 °C and the plasma layer transferred into 1.5 mL vials and stored at 8806
DOI: 10.1021/acs.jafc.8b03254 J. Agric. Food Chem. 2018, 66, 8805−8813
Article
Journal of Agricultural and Food Chemistry
Statistical Analysis. The experimental design was a complete randomized design with three diet treatments and 12 replicates per treatment (n = 36). Animal cages were considered experimental units. Diet type, week, and replicates were considered random factors and were included as class variables. Analysis of variance was performed using the General Linear Model procedure (PROC GLM) of SAS version 9.4.38 Fisher’s protected least significant difference (LSD) at P < 0.05 was used to separate means. For the microbial analysis, Python scripts within the software package QIIME version 1.9.039 were used to analyze sequence reads. Taxonomic assignments for assembled reads were obtained using the Greengenes database (greengenes.lbl.gov). Normalized taxon counts were used to calculate beta diversity measures (Bray−Curtis). Sample groups were tested for significant differences in beta diversity using PERMANOVA tests. P values were corrected for multiple testing using the false discovery rate (FDR). The frequency of operational taxonomic units (OTUs) among sample groups was tested for significant difference using a Kruskal−Wallis test. P values were generated using 10,000 permutations and corrected for multiple testing using FDR.
Table 2. Chemical and Nutritional Quality Composition of Red Split Lentil (71%) components moisture (g/100 g) protein (g/100 g) fat (g/100 g) total carbohydrates (g/100 g) sugar alcohols sorbitol (mg/100 g) mannitol (mg/100 g) xylitol (mg/100 g) simple sugars glucose (mg/100 g) fructose (mg/100 g) sucrose (g/100 g) mannose (mg/100 g) raffinose family oligosaccharides raffinose (mg/100 g) stachyose (g/100 g) verbascose (g/100 g) fructo-oligosaccharides kestose (mg/100 g) polysaccharides hemicellulose (g/100 g) cellulose (mg/100 g) soluble starch (g/100 g) resistant starch (g/100 g) amylose (g/100 g) amylopectin (g/100 g) total prebiotic carbohydrates (g/100 g) minerals calcium (mg/100 g) copper (μg/100 g) iron (mg/100 g) potassium (mg/100 g) magnesium (mg/100 g) manganese (mg/100 g) selenium (μg/100 g) zinc (mg/100 g)
concentration 5−6 17−18 0.5−0.6 39−40 455−489 13−18 20−21 15−16 0.1−0.2 1.6−1.7 5−6
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355−558 1.6−1.8 1.2−1.3
RESULTS Food and Energy Intake. Average rat food intake ranged from 23 to 26 g per day regardless of diet treatment (25 ± 3 g per day at P < 0.05). A significant treatment effect on average food intake was observed only for the initial week (P < 0.05), when intake of the lentil diet was significantly lower (P < 0.05) than the control diet (Figure 1). At the beginning of the
265−297 1.1−1.3 410−535 26−29 1−2 11−13 16−18 10−11 21−22 572−604 4.7−4.9 651−671 89−91 1.07−1.14 9−37 2.6−2.8
Liver Tissue Slicing, Staining, and Imaging. Proximal and distal parts of livers were kept in tissue cassettes to process for slicing. Liver tissues were processed in a tissue processor (Tissue-Tek VIP, Sakura Finetek USA Inc., Torrance, CA, USA). The following cycle was used to process liver samples: buffered formalin (10%) for 2 min, then 30 min; ethanol (70%) for 30 min; ethanol (80%) for 30 min; ethanol (95%) for 45 min, then 30 min; ethanol (100%) for 45 min, then 45 min; xylene for 20 min, then 40 min; and paraffin for 30 min × 4. All cycles were performed at 35 °C except for paraffin cycles that were performed at 58 °C. Cassettes with processed liver samples were transferred to a Tissue-Tec tissue embedding console system (Sakura Finetek USA Inc., Torrance, CA, USA). Liver samples were kept at 4 °C until the paraffin blocks solidified. Liver tissue sections (5-μm thickness) were obtained using a Leica RM 2155 rotary microtome (Leica Microsystems, Nussloch, Germany). Tissue sections were transferred to slides and kept in a slide warmer (Premiere Slide Warmer XH-2004, C&A Scientific, Manassas, VA, USA) at 44 °C for 15 min to remove excess water. Slides were then incubated (12−140E Incubator, Quincy Lab Inc., Chicago, IL, USA) at 55 °C to fix the tissue. Finally, dried slides were stained using a hematoxylin and eosin protocol. Liver tissue images were taken using a stereo microscope (M125, Leica Microsystems Inc., Buffalo Grove, IL, USA). Extended focus depth images were taken by focus stacking using Helicon Focus software (HeliconSoft, Kharkiv, Ukraine).
Figure 1. Food and energy intake of rats fed different diets. Columns and bars represent the mean and standard deviation, respectively. Values within each week followed by different letters are significantly different; P < 0.05.
experiment, average energy intake of the lentil diet was correspondingly significantly lower (P < 0.05) than that of the other two diets (Figure 1). Food and energy intakes were similar among treatment groups irrespective of different diets after week 4 (P < 0.05). Body Weight, Body Fat, Lean Mass, and TG Levels. The lentil diet resulted in a significantly lower (P < 0.05) body weight per rat than the other two diets (Figure 2). Specifically, initial body weight (range, 245−291 g/rat; mean, 267 ± 11 g/ rat across all treatments) increased to 383−522 g/rat with the lentil diet (mean, 443 ± 47 g/rat) vs 431−555 g/rat for the corn diet (mean, 502 ± 38 g/rat) and 440−609 g/rat for the control diet (mean, 511 ± 51 g/rat) by week 6. The average growth rate (increase in body weight per week per rat) was significantly lower (P < 0.05) in rats fed the lentil diet (29 ± 7 g/rat/week) vs the corn (39 ± 6 g/rat/week) and control (41 8807
DOI: 10.1021/acs.jafc.8b03254 J. Agric. Food Chem. 2018, 66, 8805−8813
Article
Journal of Agricultural and Food Chemistry
the control diet (66−82%) and lentil diet (75−81%) was significantly greater (P < 0.05) than that in rats fed the corn diet (65−73%; Figure 3b). Rats fed the lentil diet had significantly (P < 0.05) lower blood plasma TG levels (range, 68−150 mg/dL; mean, 109 ± 41 mg/dL) than those fed the corn diet (range, 128−210 mg/ dL; mean, 169 ± 41 mg/dL) and had similar levels to those fed the control diet (range, 98−168 mg/dL; mean, 133 ± 35 mg/ dL) (Figure 3c). Liver weights were similar among the different diets, ranging from 12 to 20 g for rats fed the lentil diet (mean, 16 ± 4 g) to 14−22 g for rats fed the corn diet (mean, 18 ± 4 g) and 14−20 g for rats fed the control diet (mean, 17 ± 3 g; Figure 3d). Fecal Bacteria Abundance. The most abundant rat fecal bacterial phyla were Firmucutes (range, 46−73%; mean, 59 ± 7%) and Bacteriodetes (range, 25−44%; mean, 35 ± 6%), followed by Actinobacteria (range, 1−5%; mean, 3 ± 2%) and Proteobacteria (range, 0−5%; mean, 3 ± 2%) (Figure 4). The
Figure 2. Mean body weight of rats fed different diets. Columns and bars represent the mean and standard deviation, respectively. Values within each week (n = 36) followed by different letters are significantly different; P < 0.05.
± 8 g/rat/week) diets. Rats fed the lentil diet had an average body weight that was 13% less than rats fed the control diet and 2% less than rats fed the corn diet. Total percent body fat varied with diet treatment. Rats fed the control (range, 16−32%; mean, 24 ± 8%) and lentil (range, 17−23%; mean, 20 ± 3%) diets had significantly lower (P < 0.05) percent body fat at week 6 than those fed the corn diet (range, 25−33%; mean, 29 ± 4%) (Figure 3a). Rats fed the lentil diet had 17% less body fat than rats fed the control diet; however, rats fed the corn diet had 21% more body fat than rats fed the control diet. Lean body mass (%) of rats fed
Figure 4. Most abundant bacterial phyla (percentage of total) in rat feces. Columns and bars represent the mean (n = 36) and standard deviation, respectively. Abundance was calculated after eliminating unassigned species.
relative abundance of Actinobacteria and Proteobacteria varied over time in response to the different diets. At 6 weeks, fecal Actinobacteria abundance was similar in rats fed the lentil diet (5%) vs the corn (4%) and control (2%) diets. The abundance of fecal Proteobacteria varied over time. The following fecal bacterial species were identified for each phylum: (1) Firmicutes (8 species: Lachnospiraceae spp., Peptostreptococcus stomatis, Shutterworthia satelles, Clostridiales spp., Lachnoanaerobaculum spp., Oribacterium spp., Enterococcus spp., and Streptococcaceae spp.); (2) Bacteroidetes (2 species: Bacteroides heparinolyticus spp. and Tannerella spp.); (3) Proteobacteria (2 species: Lautropia mirabilis and Aggregatibacter spp.); and (4) Actinobacteria (2 species: Bif idobacterium spp. and Eggerthella lenta) (Figure 5). Bacterial species changed with diet and experimental time. The abundance of Lachnospiraceae spp. at 6 weeks was significantly lower (P < 0.05) in rats fed the lentil diet (8.7%) vs the corn (11.7%) and control (14.6%) diets. Similarly, Peptostreptococcus stomatis abundance was reduced in rats fed the lentil diet (2.9%) vs the corn (3.2%) and control (6.3%) diets. Furthermore, the lentil diet significantly reduced (P < 0.05) the abundance of Streptococcaceae spp. and increased the abundance of Shutterworthia satelles (Figure 5). The abundance
Figure 3. (a) Body fat (%), (b) lean body mass (%), (c) plasma TGs (triacylglycerol; mg/dL), and (d) liver weight (g) at week 6 for rats fed with different diets. Columns and bars represent the mean and standard deviation, respectively. Values within body fat, liver weight, plasma TGs, and lean body mass followed by different letters are significantly different; P < 0.05. 8808
DOI: 10.1021/acs.jafc.8b03254 J. Agric. Food Chem. 2018, 66, 8805−8813
Article
Journal of Agricultural and Food Chemistry
Figure 5. Relative abundance of dominant species of phyla Actinobacteria, Proteobacteria, Bacteroidetes, and Firmicutes in rat fecal samples at week 0 (a) and week 6 (b). *Lachnospiraceae sp. and Streptococcaceae sp. were significantly lower in rats fed with the lentil diet vs the other two diets (P < 0.05). **Shutterworthia satelles was significantly higher in rats fed with lentil diet vs other two diets (P < 0.05).
Figure 6. Light microscopic images of proximal tissues of the rat liver from rats fed a lentil (A), corn (B), or control (C) diet for 6 weeks. The scale bar for all images is 100 μm.
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DISCUSSION Dietary nutrients are metabolized in the human gastrointestinal tract using digestive enzymes. Certain nutrients, however, called “prebiotics” are not utilized by digestive enzymes and are instead used by human gastrointestinal microflora. Lentil is an excellent source of protein, healthy fat, carbohydrates, dietary fiber, micronutrients, and a rich source of prebiotic carbohydrates (10−15 g/100 g).40 The present study demonstrates that rats fed the lentil diet have significantly lower mean body weight than those fed the control or corn diets. Further, rats fed the lentil diet had significantly lower percent body fat and plasma TG levels, and higher lean body mass than those fed the corn diet. Fecal abundance of Actinobacteria and Bacteriodetes were increased in rats fed the lentil diet and the corn diet vs the control diet. Fecal abundance of Firmicutes, a bacterial phylum comprising multiple pathogenic species, decreased in rats fed the lentil
of Bacteroides heparinolyticus spp. decreased over time with the lentil and control diets but increased with the corn diet. The abundance of Tannerella spp. increased by week 6 for all diets, but this increase was the least for the lentil diet and greatest for the control diet. After 6 weeks, the abundance of Bif idobacterium spp. increased more in rats fed the lentil (5.3%) or corn (4.0%) diets than the control diet (1.5%). The abundance of Eggerthella lenta decreased for all diets, but these effects were not significant (P > 0.05). However, this decrease was smaller in rats fed the lentil (4.7%) or corn diets (5.7%) than that in rats fed the control diet (14.2%). Light microscopic images indicated only minor differences in liver cell structure (Figure 6), with rats fed the corn diet showing nonspecific histological changes not observed in rats fed the other two diets. 8809
DOI: 10.1021/acs.jafc.8b03254 J. Agric. Food Chem. 2018, 66, 8805−8813
Article
Journal of Agricultural and Food Chemistry
25% reduction in plasma triacylglycerol in hyperlipidemic men after four months of a legume diet including lentils.54 Furthermore, a lentil and apple-based diet was shown to decrease plasma triacylglycerols in rats.55 Plasma triacylglycerols in our study were the lowest among rats fed the lentil diet (Figure 3), confirming the ability of lentil consumption to lower serum triacylglycerols. Possibly, a lentil diet may modulate the expression of genes responsible for glucose and lipid metabolism pathways.56 Mechanistically, components of the lentil diet are degraded by the hindgut microbiome into short-chain fatty acids (SCFAs),57 which then exert lipidlowering effects at the hepatic or luminal axes.58,59 Changes in microbial populations may also result in direct deconjugation of bile salts, interrupting lipid absorption.58 Lipid-lowering effects of a lentil diet could have several beneficial implications for common human diseases, including coronary heart disease60 and acute pancreatitis.61 The lentil diet in our study was associated with the lowest percent body fat and triacylglycerol levels (Figure 3, a difference that was significant compared to the high-RS corn starch diet. Although more evidence is needed, these observations suggest that a lentil diet preferentially reduces fat accumulation in rats. Liver weight remained constant across diets (Figure 3d), consistent with light microscopy imaging of liver tissues that showed no evidence of macro- or microvesicular steatosis in any of the experimental groups (Figure 6). Fatty liver changes are mostly associated with high-fat62 and high-sugar63 diets, which are not characteristic of the present experimental diets. Since the landmark discovery that obesity is associated with changes within the microbiome,25 other studies have confirmed and even demonstrated the prevention and causation of the development of obesity via interactions with certain gut microbes.64,65 A long-standing debate still exists in the literature regarding the influence of dominant bacterial phyla (Firmicutes and Bacteroidetes) on obesity,25,66−69 reflecting the vast complexity of the microbiome; differences in study populations, environments, and study methods drastically influence patterns of microbial communities. Nevertheless, bacterial groups within phyla share many common traits (e.g., enriched expression of genes involved in carbohydrate metabolism and transportation), and these patterns within phyla provide clues to understanding the relationships between the microbiome and host metabolism.66 Consistent with these discoveries, exploration of predominant microbial populations in rats throughout exposure to experimental diets reveals significant changes in bacterial phyla and species (Figure 4). Patterns among dominant phyla in the feces of rats fed the high-RS corn starch and lentil diets are similar and contrast those of rats fed the control diet. By week 6, concentrations of 16 s rRNA were greater for Bacteroidetes and lower for Firmicutes in the high-RS corn starch and lentil diets compared to the control; these data correlate with the prebiotic content of these diets. Actinobacteria were likewise greater in these groups compared to the control. Bif idobacterium spp. are the most well-known of the Actinobacteria for their beneficial effects;70 increases in their abundance were most pronounced in the lentil diet group (Figure 5), suggesting a greater effect of lentil prebiotics on these species than corn RS. The lentil diet and to a lesser degree the corn RS diet also induced decreases in commensal Lachnospiraceae spp., one species of which has been linked to diabetes in mice.71 Other changes in microbial populations induced by lentil and corn RS in rat diet include decreases in Clostridiales order,
diet vs the control diet. Overall, lentil is a promising diet to improve total body weight, percent body fat, plasma triacylglycerol, and lean body mass as well as to suppress intestinal colonization by pathogens. The concept of prebiotic-rich foods is relatively new to the scientific community; however, diets rich in prebiotics are not. In fact, people have consumed prebiotics in large quantities since prehistoric times up until the Industrial and Green Revolutions. Prebiotic-rich grains, fruits, and vegetables were a large component of ancient Indian diets.33 The yacon, a root rich in fructooligosaccharides, has been a staple among tribes along the Andes Mountains for centuries;41 and inulin-type fructans from agave, sotol, and onion were consumed at a rate of over 100 g/day among prehistoric populations in the Chihuahuan Desert of Mexico.42 Likewise, cultivation of the precursor to modern lentil varieties dates back approximately 11,000 years in the Ancient Near East,43 and since then, lentil has been consumed as a dietary staple by many populations. We formulated the rat diets in this study with this context in mind; i.e., the high proportion of lentil in the experimental diet reflects a traditional diet rich in lentils.33,44 Study results and interpretation are, therefore, meant to reflect the physiological effects of lentil as a dietary staple, not as a supplement. In addition, a diet providing adequate and appropriate nutrition was selected as the control, rather than a Western or obesogenic diet, to allow for better comparison of effects at the level of the lumen and intestinal border rather than due to gross differences in diet quantity or macronutrient density.33 As expected, no differences were seen in feed and energy intake among experimental diets, except for slightly lower intake in the lentil group at the beginning of the study (Figure 1). Hence, a more valid comparison of physiologic and microbiome parameters across groups can be made. Relatively few investigations of the effects of lentil diet on body weight have been performed;45,46 however, strong evidence exists for a relationship between pulse consumption in general and weight loss. A systematic review and metaanalysis of 21 human trials of pulse consumption (including lentil) revealed a significant weight loss effect, even when diets were not calorically restricted.45 Of these, a randomized controlled trial of mixed pulse crops (lentils, chickpeas, peas, and beans) with obese adults revealed significant reductions in body weight, serum lipids, inflammation, and blood pressure.46 Overall, trials have examined the effects of isolated lentil diet (lentil flour) on body weight, but these studies were not specifically designed to investigate body composition changes.45−47 Our study results (Figure 2) align with other reports that indicate a lentil diet induces weight loss and/or restriction of weight gain in animal models, including rat,48,49 pig,50 lamb,51 and rainbow trout.52 These results support the growing body of evidence that suggests that lentil, when consumed as a dietary staple, is an effective treatment for obesity and increased weight.29 Importantly, the lack of weight restriction observed among the high-RS corn starch group indicates that RS supplementation alone is not sufficient to induce significant weight loss (Figure 2). A systematic review and meta-analysis of prebiotic supplement trials in humans support this idea, revealing inconclusive or insignificant effects of prebiotic supplementation on weight loss.53 Significant effects of experimental diets on other body parameters in our study are also consistent with reported human and animal investigations. Jenkins et al. demonstrate 8810
DOI: 10.1021/acs.jafc.8b03254 J. Agric. Food Chem. 2018, 66, 8805−8813
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Journal of Agricultural and Food Chemistry Peptostreptococcus spp., and Lachnoanaerobaculum spp., all of which include species with the potential to cause human disease; however, the mechanisms behind these changes in rats and humans remains unclear. Important limitations of our study include the duration of the experimental diets (6 weeks). Although the intent was to establish a treatment effect, an extended study could offer more information on long-term diet-induced effects on the microbiome and the sustainability of physiologic changes. Furthermore, although a rat model is widely utilized in investigations and extrapolated to human pathophysiology and disease prevention, human trials remain the gold standard. Additionally, older lentil varieties are well-known to contain significant quantities of trypsin inhibitors,72 confounding the availability of nutrients across experimental diets. However, in vivo digestibility of protein isolates from modern Canadian lentil varieties is 91% in rats,73 suggesting a minimal risk of confounding by trypsin inhibitors in our study. Finally, cooked lentil is not suitable for investigations in rats; however, human trials will require cooking, which is known to change the food matrix and prebiotic profile of lentil.40 Growing evidence for the efficacy of a lentil-based diet for the treatment of clinical obesity warrants continued investigation, especially via well-designed human trials. Analysis of microbiome patterns in response to a prolonged lentil diet could also prove useful in further delineating mechanisms of lentil-induced physiologic changes in the host. Other avenues of research include the development of pulse-based meals tailored to Western countries, where consumption of pulses such as lentil is growing but still quite low.74,75 Finally, community-based research programs designed to promote adherence to recommended dietary guidelines (MyPlate.gov), of which pulses play an important role, are strongly encouraged. Overall, lentil remains a candidate whole food choice for population-based interventions of body weight management and merits further scientific investigation.
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University, SC, USA; the American Pulse Association; and the USA Dry Pea and Lentil Council. Notes
The authors declare no competing financial interest.
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ACKNOWLEDGMENTS We thank Dr. Jerry Combs Jr., Cornell University, for his guidance on experimental design and nutritional data interpretation, Dr. Mike Gore, Cornell University, for bacteria composition analysis, bioinformatics, and data interpretation, and Nathan Johnson, Clemson University for reviewing the final manuscript. We also thank Clemson Light Imaging Facility, BioE Research Services, and Godley-Snell Research Center, Clemson University, SC, USA.
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REFERENCES
(1) Thavarajah, D.; Thavarajah, P.; Wejesuriya, A.; Rutzke, M.; Glahn, R. P.; Combs, G. F.; Vandenberg, A. The potential of lentil (Lens culinaris L.) as a whole food for increased selenium, iron, and zinc intake: preliminary results from a 3 year study. Euphytica 2011, 180, 123−128. (2) Thavarajah, P.; Thavarajah, D.; Vandenberg, A. Low phytic acid lentils (Lens culinaris L.): A potential solution for increased micronutrient bioavailability. J. Agric. Food Chem. 2009, 57, 9044− 9049. (3) Campbell, J. M.; Bauer, L. L.; Fahey, G. C.; Hogarth, A. J. C. L.; Wolf, B. W.; Hunter, D. E. Selected fructooligosaccharide (1-kestose, nystose, and 1 F -β-fructofuranosylnystose) composition of foods and feeds. J. Agric. Food Chem. 1997, 45, 3076−3082. (4) Kuo, T. M.; VanMiddlesworth, J. F.; Wolf, W. J. Content of raffinose oligosaccharides and sucrose in various plant seeds. J. Agric. Food Chem. 1988, 36, 32−36. (5) Wang, N.; Hatcher, D. W.; Toews, R.; Gawalko, E. J. Influence of cooking and dehulling on nutritional composition of several varieties of lentils (Lens culinaris). LWT - Food Sci. Technol. 2009, 42, 842− 848. (6) Sajilata, M. G.; Singhal, R. S.; Kulkarni, P. R. Resistant starch? A review. Compr. Rev. Food Sci. Food Saf. 2006, 5, 1−17. (7) Johnson, C. R.; Thavarajah, D.; Combs, G. F.; Thavarajah, P. Lentil (Lens culinaris L.): A prebiotic-rich whole food legume. Food Res. Int. 2013, 51, 107−113. (8) Johnson, C. R.; Thavarajah, D.; Thavarajah, P.; Fenlason, A.; McGee, R.; Kumar, S.; Combs, G. F. A global survey of low-molecular weight carbohydrates in lentils. J. Food Compos. Anal. 2015, 44, 178− 185. (9) Thavarajah, D.; Thavarajah, P.; Johnson, C. R.; Kumar, S. Lentil (Lens culinaris Medikus): A Whole Food Rich in Prebiotic Carbohydrates to Combat Global Obesity. In Grain Legumes; Aakash Kumar, G., Ed.; InTech, 2016; pp 35−53. (10) Siva, N.; Thavarajah, D.; Johnson, C. R.; Duckett, S.; Jesch, E. D.; Thavarajah, P. Can lentil (Lens culinaris Medikus) reduce the risk of obesity? J. Funct. Foods 2017, 38, 706−715. (11) Dumas, M. E.; Barton, R. H.; Toye, A.; Cloarec, O.; Blancher, C.; Rothwell, A.; Fearnside, J.; Tatoud, R.; Blanc, V.; Lindon, J. C.; Mitchell, S. C.; Holmes, E.; McCarthy, M. I.; Scott, J.; Gauguier, D.; Nicholson, J. K. Metabolic profiling reveals a contribution of gut microbiota to fatty liver phenotype in insulin-resistant mice. Proc. Natl. Acad. Sci. U. S. A. 2006, 103, 12511−12516. (12) Wu, G. D.; Chen, J.; Hoffmann, C.; Bittinger, K.; Chen, Y. Y.; Keilbaugh, S. A.; Bewtra, M.; Knights, D.; Walters, W. A.; Knight, R.; Sinha, R.; Gilroy, E.; Gupta, K.; Baldassano, R.; Nessel, L.; Li, H.; Bushman, F. D.; Lewis, J. D. Linking long-term dietary patterns with gut microbial enterotypes. Science 2011, 334, 105−108. (13) de Sousa, V. M. C.; dos Santos, E. F.; Sgarbieri, V. C. The Importance of Prebiotics in Functional Foods and Clinical Practice. Food Nutr. Sci. 2011, 2, 133−144.
AUTHOR INFORMATION
Corresponding Author
*Tel: +18648887638. Fax: +18646564960. E-mail: dthavar@ clemson.edu. ORCID
Dil Thavarajah: 0000-0002-4251-7476 Author Contributions
N.S. conducted the experiment, data analysis, manuscript preparation, and wrote a Master’s thesis based on this work under the supervision of D.T.; C.J. contributed to the experimental design, data interpretation, and wrote sections of this manuscript; V.R. oversaw the bacterial composition analysis; J.E. oversaw the dual energy X-ray absorptiometry analysis; W.W. oversaw the experimental design and feed processing; A.A. provided technical assistance for the microbial analysis; P.T. was the project collaborator and oversaw the analytical chemistry assays and experimental design; S.D. was the project Co-PI, administered the animal study and provided ethics approval; and D.T. was the project PI, with overall responsibility for experimental design, data analysis, data interpretation, NS supervision, and manuscript writing, revisions, and final editing. Funding
Funding support for this project was provided by the University Research Grants Committee (URGC), Clemson 8811
DOI: 10.1021/acs.jafc.8b03254 J. Agric. Food Chem. 2018, 66, 8805−8813
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Journal of Agricultural and Food Chemistry (14) Manning, T. S.; Gibson, G. R. Prebiotics. Best Pract. Res. Clin. Gastroenterol. 2004, 18, 287−298. (15) Scheppach, W.; Luehrs, H.; Menzel, T. Beneficial health effects of low-digestible carbohydrate consumption. Br. J. Nutr. 2001, 85, S23−S30. (16) Lee, Y. K.; Mazmanian, S. K. Has the microbiota played a critical role in the evolution of the adaptive immune system? Science 2010, 330, 1768−1773. (17) Burns, A. J.; Rowland, I. R. Anti-carcinogenicity of probiotics and prebiotics. Curr. Issues Intest. Microbiol. 2000, 1, 13−24. (18) Conlon, M. A.; Kerr, C. A.; McSweeney, C. S.; Dunne, R. A.; Shaw, J. M.; Kang, S.; Bird, A. R.; Morell, M. K.; Lockett, T. J.; Molloy, P. L.; Regina, A.; Toden, S.; Clarke, J. M.; Topping, D. L. Resistant starches protect against colonic DNA damage and alter microbiota and gene expression in rats fed a western diet. J. Nutr. 2012, 142, 832−840. (19) Brugman, S.; Klatter, F. A.; Visser, J.; Bos, N. A.; Elias, D.; Rozing, J. Neonatal oral administration of DiaPep277, combined with hydrolysed casein diet, protects against Type 1 diabetes in BB-DP rats. An experimental study. Diabetologia 2004, 47, 1331−1333. (20) Kaur, N.; Gupta, A. Applications of inulin and oligofructose in health and nutrition. J. Biosci. 2002, 27, 703−714. (21) Johnston, K. L.; Thomas, E. L.; Bell, J. D.; Frost, G. S.; Robertson, M. D. Resistant starch improves insulin sensitivity in metabolic syndrome. Diabetic Med. 2010, 27, 391−397. (22) Van Loo, J.; Coussement, P.; De Leenheer, L.; Hoebregs, H.; Smits, G. On the presence of inulin and oligofructose as natural ingredients in the western diet. Crit. Rev. Food Sci. Nutr. 1995, 35, 525−552. (23) Institute of Medicine. Dietary Reference Intakes for Energy, Carbohydrate, Fiber, Fat, Fatty Acids, Cholesterol, Protein, and Amino Acids (Macronutrients); National Academies Press: Washington, D.C., 2005. (24) Brownawell, A. M.; Caers, W.; Gibson, G. R.; Kendall, C. W. C.; Lewis, K. D.; Ringel, Y.; Slavin, J. L. Prebiotics and the health benefits of fiber: Current regulatory status, future research, and goals. J. Nutr. 2012, 142, 962−974. (25) Ley, R. E.; Turnbaugh, P. J.; Klein, S.; Gordon, J. I. Microbial ecology: Human gut microbes associated with obesity. Nature 2006, 444, 1022−1023. (26) Khan, M. T.; Nieuwdorp, M.; Bäckhed, F. Microbial modulation of insulin sensitivity. Cell Metab. 2014, 20, 753−760. (27) The Human Microbiome Project Consortium. Structure, function and diversity of the healthy human microbiome. Nature 2012, 486, 207−214. (28) Turnbaugh, P. J.; Ley, R. E.; Mahowald, M. A.; Magrini, V.; Mardis, E. R.; Gordon, J. I. An obesity-associated gut microbiome with increased capacity for energy harvest. Nature 2006, 444, 1027− 1031. (29) Jenkins, D. J.; Kendall, C. W.; Vuksan, V. Inulin, oligofructose and intestinal function. J. Nutr. 1999, 129, 1431S−1433S. (30) Hosseini, E.; Grootaert, C.; Verstraete, W.; Van de Wiele, T. Propionate as a health-promoting microbial metabolite in the human gut. Nutr. Rev. 2011, 69, 245−258. (31) Tremaroli, V.; Bäckhed, F. Functional interactions between the gut microbiota and host metabolism. Nature 2012, 489, 242−249. (32) Borgo, F.; Verduci, E.; Riva, A.; Lassandro, C.; Riva, E.; Morace, G.; Borghi, E. Relative abundance in bacterial and fungal gut microbes in obese children: A case control study. Child. Obes. 2017, 13, 78−84. (33) Samanta, A. K.; Kolte, A. P.; Senani, S.; Sridhar, M.; Jayapal, N. Prebiotics in ancient Indian diets. Curr. Sci. 2011, 101, 43−46. (34) Reeves, P. G.; Nielsen, F. H.; Fahey, G. C. AIN-93 purified diets for laboratory rodents: final report of the American Institute of Nutrition ad hoc writing committee on the reformulation of the AIN76A. J. Nutr. 1993, 123, 1939−1951. (35) Thavarajah, D. 2013 U.S. Pulse Quality Survey. http:// northernpulse.com/uploads/resources/944/2013-u-s-pulse-qualitysurvey-final.pdf (accessed May 2, 2018).
(36) Caporaso, J. G.; Lauber, C. L.; Walters, W. A.; Berg-Lyons, D.; Lozupone, C. A.; Turnbaugh, P. J.; Fierer, N.; Knight, R. Global patterns of 16S rRNA diversity at a depth of millions of sequences per sample. Proc. Natl. Acad. Sci. U. S. A. 2011, 108, 4516−4522. (37) Kozich, J. J.; Westcott, S. L.; Baxter, N. T.; Highlander, S. K.; Schloss, P. D. Development of a dual-index sequencing strategy and curation pipeline for analyzing amplicon sequence data on the MiSeq Illumina sequencing platform. Appl. Environ. Microbiol. 2013, 79, 5112−5120. (38) SAS Institute Inc. User’s Guide: Statistics, version 9.4.; SAS Institute: Cary, NC, 2013. (39) Caporaso, J. G.; Kuczynski, J.; Stombaugh, J.; Bittinger, K.; Bushman, F. D.; Costello, E. K.; Fierer, N.; Peña, A. G.; Goodrich, J. K.; Gordon, J. I.; Huttley, G. A.; Kelley, S. T.; Knights, D.; Koenig, J. E.; Ley, R. E.; Lozupone, C. A.; McDonald, D.; Muegge, B. D.; Pirrung, M.; Reeder, J.; Sevinsky, J. R.; Turnbaugh, P. J.; Walters, W. A.; Widmann, J.; Yatsunenko, T.; Zaneveld, J.; Knight, R. QIIME allows analysis of high-throughput community sequencing data. Nat. Methods 2010, 7, 335−336. (40) Johnson, C. R.; Thavarajah, D.; Thavarajah, P.; Payne, S.; Moore, J.; Ohm, J. B. Processing, cooking, and cooling affect prebiotic concentrations in lentil (Lens culinaris Medikus). J. Food Compos. Anal. 2015, 38, 106−111. (41) Castro, A.; Céspedes, G.; Carballo, S.; Bergenståhl, B.; Tornberg, E. Dietary fiber, fructooligosaccharides, and physicochemical properties of homogenized aqueous suspensions of yacon (Smallanthus sonchifolius). Food Res. Int. 2013, 50, 392−400. (42) Leach, J. D.; Sobolik, K. D. High dietary intake of prebiotic inulin-type fructans in the prehistoric Chihuahuan Desert. Br. J. Nutr. 2010, 103, 1558−1561. (43) Weiss, E.; Kislev, M.; Hartmann, A. Autonomous cultivation before domestication. Science 2006, 312, 1608−1610. (44) Flint-Hamilton, K. B. Legumes in Ancient Greece and Rome: Food, Medicine, or Poison? Hesperia 1999, 68, 371−385. (45) Kim, S. J.; de Souza, R. J.; Choo, V. L.; Ha, V.; Cozma, A. I.; Chiavaroli, L.; Mirrahimi, A.; Blanco Mejia, S.; Di Buono, M.; Bernstein, A. M.; Leiter, L. A.; Kris-Etherton, P. M.; Vuksan, V.; Beyene, J.; Kendall, C. W.; Jenkins, D. J.; Sievenpiper, J. L. Effects of dietary pulse consumption on body weight: a systematic review and meta-analysis of randomized controlled trials. Am. J. Clin. Nutr. 2016, 103, 1213−1223. (46) Hermsdorff, H. H. M.; Zulet, M. Á .; Abete, I.; Martínez, J. A. A legume-based hypocaloric diet reduces proinflammatory status and improves metabolic features in overweight/obese subjects. Eur. J. Nutr. 2011, 50, 61−69. (47) Veenstra, J. M.; Duncan, A. M.; Cryne, C. N.; Deschambault, B. R.; Boye, J. I.; Benali, M.; Marcotte, M.; Tosh, S. M.; Farnworth, E. R.; Wright, A. J. Effect of pulse consumption on perceived flatulence and gastrointestinal function in healthy males. Food Res. Int. 2010, 43, 553−559. (48) Cuadrado, C.; Grant, G.; Rubio, L. A.; Muzquiz, M.; Bardocz, S.; Pusztai, A. Nutritional utilization by the rat of diets based on lentil (Lens culinaris) seed meal or its fractions. J. Agric. Food Chem. 2002, 50, 4371−4376. (49) Pirman, T.; Combe, E.; Patureau Mirand, P.; Stekar, J.; Oresnik, A. Differential effects of cooked common bean (Phaseolus vulgaris) and lentil (Lens esculenta puyensis) feeding on protein and nucleic acid contents in intestines, liver and muscles in rats. Ann. Nutr. Metab. 2004, 48, 281−287. (50) Landero, J. L.; Beltranena, E.; Zijlstra, R. T. The effect of feeding lentil on growth performance and diet nutrient digestibility in starter pigs. Anim. Feed Sci. Technol. 2012, 174, 108−112. (51) Surra, J.; Purroy, A.; Muñoz, F.; Treacher, T. Lentils and faba beans in lamb diets. Small Rumin. Res. 1992, 7, 43−49. (52) Tiril, S.; Karayucel, I.; Alagil, F.; Dernekbasi, S. Evaluation of extruded chickpea, common bean and red lentil meals as protein source in diets for juvenile rainbow trout (Oncorhynchus mykiss). J. Anim. 2009, 8, 2079−2086. 8812
DOI: 10.1021/acs.jafc.8b03254 J. Agric. Food Chem. 2018, 66, 8805−8813
Article
Journal of Agricultural and Food Chemistry
A. M.; Meheust, A. Prebiotic effects: metabolic and health benefits. Br. J. Nutr. 2010, 104, S1−S63. (71) Kameyama, K.; Itoh, K.; Mosthaf, L.; Grover, S.; Treacher, D.; Turner, R.; Nielsen, J.; Bäckhed, F. Intestinal colonization by a Lachnospiraceae bacterium contributes to the development of diabetes in obese mice. Microbes Environ. 2014, 29, 427−430. (72) Norton, G. Proteinase Inhibitors. In Toxic Substances in Crop Plants; D’Mello, J. P. F., Duffus, J. H., Duffus, C. M., Eds.; Royal Society of Chemistry, 1991; pp 68−106. (73) Ghumman, A.; Kaur, A.; Singh, N.; Singh, B. Effect of feed moisture and extrusion temperature on protein digestibility and extrusion behaviour of lentil and horsegram. LWT - Food Sci. Technol. 2016, 70, 349−357. (74) Mudryj, A. N.; Yu, N.; Hartman, T. J.; Mitchell, D. C.; Lawrence, F. R.; Aukema, H. M. Pulse consumption in Canadian adults influences nutrient intakes. Br. J. Nutr. 2012, 108, S27−S36. (75) Mitchell, D. C.; Lawrence, F. R.; Hartman, T. J.; Curran, J. M. Consumption of dry beans, peas, and lentils could improve diet quality in the US population. J. Am. Diet. Assoc. 2009, 109, 909−913.
(53) Beserra, B. T. S.; Fernandes, R. V.; do Rosario, A.; Mocellin, M. C.; Kuntz, M. G. F.; Trindade, E. B. S. M. A systematic review and meta-analysis of the prebiotics and synbiotics effects on glycaemia, insulin concentrations and lipid parameters in adult patients with overweight or obesity. Clin. Nutr. 2015, 34, 845−858. (54) Jenkins, D. J.; Wong, G. S.; Patten, R.; Bird, J.; Hall, M.; Buckley, G. C.; McGuire, V.; Reichert, R.; Little, J. A. Leguminous seeds in the dietary management of hyperlipidemia. Am. J. Clin. Nutr. 1983, 38, 567−573. (55) Abdel-Rahim, E.; El-Beltagi, H. Alleviation of hyperlipidemia in hypercholesterolemic rats by lentil seeds and apple as well as parsley in semi-modified diet. Adv. Food Sci. 2011, 33, 2−7. (56) Martínez, R.; Kapravelou, G.; Donaire, A.; Lopez-Chaves, C.; Arrebola, F.; Galisteo, M.; Cantarero, S.; Aranda, P.; Porres, J. M.; López-Jurado, M. Effects of a Combined Intervention with a Lentil Protein Hydrolysate and a Mixed Training Protocol on the Lipid Metabolism and Hepatic Markers of NAFLD in Zucker Rats. Food Funct. 2018, 9, 830−850. (57) Gibson, G. R.; Probert, H. M.; Van Loo, J.; Rastall, R. A.; Roberfroid, M. B. Dietary modulation of the human colonic microbiota: introducing the concept of prebiotics. Nutr. Res. Rev. 2004, 17, 259. (58) Pereira, D. I. A.; Gibson, G. R. Effects of consumption of probiotics and prebiotics on serum lipid levels in humans. Crit. Rev. Biochem. Mol. Biol. 2002, 37, 259−281. (59) Delzenne, N.; Williams, C. Prebiotics and lipid metabolism. Curr. Opin. Lipidol. 2002, 13, 61−67. (60) Hokanson, J. E.; Austin, M. A. Plasma triglyceride level is a risk factor for cardiovascular disease independent of high-density lipoprotein cholesterol level: A metaanalysis of population-based prospective studies. Eur. J. Cardiovasc. Prev. Rehabil. 1996, 3, 213− 219. (61) Lindkvist, B.; Appelros, S.; Regnér, S.; Manjer, J. A prospective cohort study on risk of acute pancreatitis related to serum triglycerides, cholesterol and fasting glucose. Pancreatology 2012, 12, 317−324. (62) Carmiel-Haggai, M.; Cederbaum, A. I.; Nieto, N. A high-fat diet leads to the progression of non-alcoholic fatty liver disease in obese rats. FASEB J. 2005, 19, 136−138. (63) Spruss, A.; Bergheim, I. Dietary fructose and intestinal barrier: potential risk factor in the pathogenesis of nonalcoholic fatty liver disease. J. Nutr. Biochem. 2009, 20, 657−662. (64) Li, F.; Jiang, C.; Krausz, K.; Li, Y.; Albert, I.; Hao, H.; et al. Microbiome remodelling leads to inhibition of intestinal farnesoid X receptor signalling and decreased obesity. Nat. Commun. 2013, 4, 1− 10. (65) Everard, A.; Belzer, C.; Geurts, L.; Ouwerkerk, J. P.; Druart, C.; Bindels, L. B.; Guiot, Y.; Derrien, M.; Muccioli, G. G.; Delzenne, N. M.; de Vos, W. M.; Cani, P. D. Cross-talk between Akkermansia muciniphila and intestinal epithelium controls diet-induced obesity. Proc. Natl. Acad. Sci. U. S. A. 2013, 110, 9066−9071. (66) Turnbaugh, P. J.; Hamady, M.; Yatsunenko, T.; Cantarel, B. L.; Duncan, A.; Ley, R. E.; Sogin, M. L.; Jones, W. J.; Roe, B. A.; Affourtit, J. P.; Egholm, M.; Henrissat, B.; Heath, A. C.; Knight, R.; Gordon, J. I. A core gut microbiome in obese and lean twins. Nature 2009, 457, 480−484. (67) Schwiertz, A.; Taras, D.; Schäfer, K.; Beijer, S.; Bos, N. A.; Donus, C.; Hardt, P. D. Microbiota and SCFA in lean and overweight healthy subjects. Obesity 2010, 18, 190−195. (68) Collado, M. C.; Isolauri, E.; Laitinen, K.; Salminen, S. Distinct composition of gut microbiota during pregnancy in overweight and normal-weight women. Am. J. Clin. Nutr. 2008, 88, 894−899. (69) Finucane, M. M.; Sharpton, T. J.; Laurent, T. J.; Pollard, K. S.; Zafar, N. Taxonomic signature of obesity in the microbiome? Getting to the guts of the matter. PLoS One 2014, 9, No. e84689. (70) Roberfroid, M.; Gibson, G. R.; Hoyles, L.; McCartney, A. L.; Rastall, R.; Rowland, I.; Wolvers, D.; Watzl, B.; Szajewska, H.; Stahl, B.; Guarner, F.; Respondek, F.; Whelan, K.; Coxam, V.; Davicco, M. J.; Léotoing, L.; Wittrant, Y.; Delzenne, N. M.; Cani, P. D.; Neyrinck, 8813
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