Short-chain fatty acids are associated with adiposity and energy and glucose homeostasis among different metabolic phenotypes in the Nutritionists’ Health Study

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It is unknown how SCFA concentrations are associated among the phenotypes metabolically healthy normal weight (MHNW), metabolically unhealthy normal weight (MUNW), metabolically healthy obese/overweight (MHO), and metabolically unhealthy obese/overweight (MUO). We compared plasma and fecal SCFA concentrations among adult women categorized according to the metabolic phenotypes mentioned above and examined associations between SCFA and adiposity and components of energy and glucose homeostasis. Methods: This was a cross-sectional study involving 111 participants. Body composition was assessed by DEXA. Energy and glycemic homeostasis were assessed by the standard mixed-meal tolerance test coupled with indirect calorimetry. SCFAs were quantified by gas chromatography and mass spectrometry. Results: Only plasma propionate was increased in the MHNW phenotype compared to the MHO and MUO phenotypes [p<0.05]. Fecal propionate and butyrate concentrations and plasma propionate concentrations were inversely associated with total and visceral adiposity [p<0.05]. Fecal and plasma SCFA concentrations were associated with reduced glucose, insulin, and HbA1c levels, increased fasting and postprandial GLP-1 levels and more preserved beta-cell function [p<0.05]. Fecal and plasma SCFA concentrations were positively correlated with resting energy expenditure and lipid oxidation rate and inversely correlated with oxidation rate of carbohydrates [p<0.05]. Conclusion: These findings reinforce the concept that fecal and plasma SCFA concentrations are linked to specific components of energy and glucose homeostasis and body adiposity. On the other hand, it was not possible to discriminate the different metabolic phenotypes of adiposity based on the determination of fecal SCFA concentration. short-chain fatty acids obesity metabolic phenotype of obesity adiposity glucose homeostasis energy homeostasis Figures Figure 1 Figure 2 Introduction Obesity is a heterogeneous disease with complex pathophysiology and interconnecting genetic, environmental, and behavioral factors. The association between obesity and the development of cardiometabolic complications is well established [ 1 ]. However, each individual has their own subcutaneous adipose tissue expansion limit [ 2 ], and once this limit is reached, ectopic fat accumulation generates lipotoxicity and cardiometabolic dysfunction. In particular, four metabolic phenotypes are recognized: metabolically healthy normal weight (MHNW), metabolically unhealthy normal weight (MUNW), metabolically healthy obese/overweight (MHO), and metabolically unhealthy obese/overweight (MUO). Over the last two decades, the study of the metabolic phenotypes of obesity has provided an interesting human model for understanding the underlying mechanisms that promote body fat accumulation and the development of obesity-related changes and cardiometabolic diseases [ 3 ]. Gut microbiome composition has been identified as an important factor associated with the development of dysmetabolic phenotypes [ 4 ]. A recent study demonstrated higher plasma concentrations of short-chain fatty acids (SCFAs) of propionate in individuals with the MHO phenotype compared to MUO, while butyrate and acetate did not differ between both phenotypes [ 5 ]. SCFAs are organic acids composed of 1 to 6 carbons, of which acetate (C2:0), propionate (C3:0), and butyrate (C4:0) are the main ones since they account for 90 to 95% of all SCFAs found in the colon. SCFAs are mainly produced by bacterial fermentation of nondigestible carbohydrates [ 6 ] and can be used as an energy source and as trophic factors by intestinal cells. They can also be absorbed and exert crucial physiological effects in peripheral tissues such as adipose tissue, liver, brain, skeletal muscle, and pancreatic beta cells [ 6 , 7 ]. Although studies addressing the timing of postprandial absorption of SCFA from a meal are scarce, it is well documented that the gut lumen is the major site of SCFA production; however, the concentration gradient decreases from the lumen to the peripheral organs, with preferential use of butyrate by the epithelium, propionate by the liver, and acetate in the periphery [ 8 ]. SCFAs and their receptors (G-protein coupled receptor 41/43 - GPR41 and GPR43) play a critical role in energy [ 9 – 12 ] and glucose homeostasis [ 11 , 13 , 14 ]. In mice fed a high-fat diet, supplementation with SCFA prevented the development of insulin resistance and glucose intolerance and promoted the development of adaptive thermogenesis with increased fat oxidation rates [ 11 ]. Oral administration of acetate in rats treated with a high-fat diet promoted a decrease in total body fat and led to an increase in the hepatic expression of proteins related to thermogenesis [ 12 ]. In animals, SCFA supplementation improved metabolic control in type 2 diabetes, promoting the development of beta cells and reducing apoptosis [ 13 ]. Furthermore, serum [ 15 ] and fecal [ 14 ] concentrations of SCFA are positively associated with fasting plasma levels of GLP-1. In humans, supplementation with oral sodium propionate increased lipid oxidation and energy expenditure and reduced body weight, intra-abdominal fat, and hepatic lipid content in healthy individuals compared to the placebo group [ 10 ]. Human studies addressing associations of SCFA between metabolic phenotypes and distribution of adiposity are scarce. Degrees of body adiposity that characterize the abovementioned phenotypes are commonly classified according to body mass index (BMI) and rarely using a more accurate measurement of the central distribution of fatness. To explore the potential associations between SCFA, metabolic health, and adiposity, we compared SCFA concentrations in plasma and feces among adult women, assessed by dual-energy X-ray absorptiometry and categorized according to the four metabolic phenotypes: MHNW, MUNW, MHO, and MUO. Moreover, we examined associations between SCFA and specific components of energy and glucose homeostasis in fasting and postprandial states using a more dynamic physiological test (standard mixed-meal tolerance test) coupled to indirect calorimetry among the participants with different body adiposity compositions and distributions. Finally, we investigated the relationship between fecal and circulating SCFA concentrations. Material & Methods Study design and participants The Nutritionists’ Health Study (NutriHS) was conceived in the School of Public Health of the University of São Paulo, Brazil, aiming to investigate factors associated with health outcomes in nutritionists and undergraduates of the nutrition course [16]. This cross-sectional part of NutriHS was conducted at the University of Campinas, São Paulo, Brazil. Recruitment occurred between 2018 and 2019 in the metropolitan area of Campinas. Only women were included in this NutriHS-UNICAMP arm, since the vast majority of Brazilian nutritionists are female. The eligibility criteria were women aged between 19 and <45 and body mass index (BMI) between 18.5 and 40.0 kg/m². Women who were pregnant or lactating, using medication affecting glucose metabolism and/or body adiposity, who had used probiotics or antibiotics in the last three months and who had diabetes mellitus, heart, kidney, and liver diseases or other severe systemic diseases were excluded. Ethical Approval This study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the Ethics Committee of each institution – University of Campinas (UNICAMP) and University of São Paulo (USP) (ethical approval number 79775817.4.1001.5404). All experiments were performed in accordance with relevant guidelines and regulations. All participants signed an electronic informed consent form, available at e -NutriHS, a specific web-based system developed for the study (www.fsp.usp.br/nutrihs) [17]. In total, 248 women answered the online questionnaires, 127 met the inclusion criteria and attended a clinical visit at our research laboratory, and 111 concluded the whole protocol. Clinical Measurements Body weight, height, and waist circumference - measured at the midpoint between the last rib and the iliac crest - were obtained. Body composition parameters, including total fat and visceral fat mass (in grams), were assessed by GE-Healthcare Lunar iDEXA using the automatic whole-body scan mode. All scans were performed and analyzed by trained researchers following a standard protocol. Instrument quality control was performed daily following the manufacturer’s instructions. Blood pressure was measured in a sitting position using a mercury sphygmomanometer following recent guidelines [18]. Blood samples were obtained after a 12-h overnight fast. Energy and glucose homeostasis assessment The components of energy (resting energy expenditure – REE and substrate oxidation rate) and glucose (insulin resistance, insulin, and GLP-1 secretion) homeostasis were evaluated by the standardized mixed-meal tolerance test associated with indirect calorimetry in a 180-minute protocol, depicted in Figure 1. The standardized meal test was the choice for investigational purposes because it better reproduces real-life conditions among free-living individuals. Indirect calorimetry canopy (VMAX N Encore) was performed according to the method proposed by Fulmer et al. [19]. The volunteers were instructed not to consume alcoholic beverages and not to engage in moderate/vigorous physical activity 48 hours before the test. On the previous night, the volunteers consumed a standardized meal containing 600 calories, 60% carbohydrate, 22% fat, 18% protein, and approximately 8 g of total fiber, since the standardization of the last meal guarantees accuracy in the evaluation of energy expenditure and the oxidation of energy substrates, avoiding any interference in the residual thermogenesis induced by diet [20, 21]. After this meal, the participants remained fasting for 12 hours until the first indirect calorimetry was performed. On the day of the examination, the calorimetry monitor remained on for 30 minutes before the start of the test for stabilization. The equipment was calibrated with a known gas concentration. The room was kept between 22 and 25 °C, with natural lighting and as little noise as possible. The volunteers arrived at the laboratory at 7:00 am and fasted for 12 hours to minimize any residual thermic effect of the diet. In addition, they were instructed to collect all urine produced in these 12 hours in a specific collector. After the initial rest, an evaluation of energy metabolism was performed for 30 minutes to determine the fasting REE and the oxidation rate of energy substrates. Subsequently, a venous catheter was inserted into an antecubital vein for blood collection. Next, the volunteers received a standardized liquid meal containing 524 kcal (Nutren 1.5®) in 340 mL, composed of 75 g of carbohydrates, 19 g of protein, 16.3 g of lipids, and 0 g of fiber, which was consumed in up to 5 minutes [22]. Blood samples were collected in dry tubes at -15, 0 (before food intake), 30, 60, 120, and 180 minutes (after food intake). For GLP-1 analysis, EDTA tubes containing 20 μL of dipeptidyl peptidase-IV inhibitor were used. For SCFA, lithium heparin tubes were used. Glucose and insulin were quantified at all times. GLP-1 was quantified at 0, 30, 60, 120, and 180 minutes. SCFA concentrations were quantified at 0, 60, 120, and 180 min. Indirect calorimetry was performed at the end of the 20 minutes preceding each blood collection. At the end of the test, the participants were instructed to empty their bladders in a specific collector. The total volume of urine was computed and aliquoted, and urinary nitrogen was analyzed as a marker of protein oxidation. Biochemical analysis Biochemical tests were performed to characterize the fasting lipid profile (LDL-cholesterol, HDL-c, triglycerides, and total cholesterol) using colorimetric and enzymatic methods. LDL-c was calculated by the Friedwald equation [23]. Glycated hemoglobin was measured with high-performance liquid chromatography (HPLC). Plasma glucose levels were promptly measured in the fasting state and during the dynamic test using a glucose analyzer (YSI 2700; YSI Life Sciences, Yellow Spring, OH, USA) with a coefficient of variation (CV) of 2%. Plasma insulin levels were analyzed using an automated two-site chemiluminescent immunometric assay (Immulite 1000 System; Siemens Health Diagnostics, USA). The intra-assay and interassay CVs were 5.2–6.4% and 5.9–8.0%, respectively. Total COOH-terminal amidated GLP-1 was measured using ELISA (cat. EGLP-35K Sigma Aldrich) with intra-assay and interassay CVs of 7% to 9% and <1% to 13%, respectively. Urinary nitrogen excretion was calculated by the UV enzymatic method by urinary urea dosage. Fecal and plasma concentrations of SCFA (acetic [C2:0], propionic [C3:0], and butyric [C4:0]) were quantified using the gas chromatography technique coupled to mass spectrometry. For stool samples, the protocol previously described by Fellows et al. [24] was applied, and for plasma samples, we used the protocol described by Wang et al. (25). The butyrate concentration was not detectable in plasma samples, and the concentrations of detectable SCFAs were summed to obtain the total concentration of SCFAs. SCFA analysis Fragments of feces or blood samples were harvested from volunteers under fasting or after meal conditions and used for measurement of SCFA, following a protocol similar to that used by Fellows et al. [24]. The day before the investigation, the subjects collected a fecal sample from their first sterile stool receptacles provided by the research team. The samples were directly stored on dry ice at home. Upon arrival in the laboratory, fecal samples were aliquoted and frozen at −80 °C until further analysis. After this, fecal samples were thawed, weighed, crushed, and homogenized in water. Sodium chloride, citric acid, hydrochloric acid, and butanol were added, and the mix was placed in a vortex and centrifuged at 4 °C. The supernatants were recovered. For blood samples, after 15 minutes of centrifugation of total blood to separate the serum, we followed the method proposed by Wang et al. [25]. Briefly, ethanol (P. A), n-hexane (P. A), and an internal standard (caprylic acid) was added to 200 mL of serum. All samples were vortexed and centrifuged at 13,000 rpm at 4 °C for 8 minutes. Immediately after, samples were transferred to specific vials, and p. H was adjusted to 4.0 using chloride acid (10%). For all samples (feces and blood), a calibration curve with 0.015–0.1 mg/mL SCFA was used in the quantification. Chromatographic analyses were performed using a gas chromatograph-mass spectrometer (model GCMS-QP2010 Ultra; Shimadzu®) and a fused-silica capillary Stabilwax column (Restec Corporation, USA) with dimensions of 30 m × 0.25 mm internal diameter and coated with a 0.25-µm thick layer of polyethylene glycol. Samples (1 µL) were injected at 250 °C using a 25:1 split ratio for feces or splitless (1:1) for blood samples. High-grade pure helium (He) was used as the carrier gas with a constant flow rate of 1.0 mL/min. Mass conditions were as follows: ionization voltage, 70 eV; ion source temperature, 200 °C; full scan mode in the 35–500 mass range with 0.2 s/scan velocity. The runtime for each analysis was 11.95 min. Mathematical calculations Total resting energy expenditure (REE) (kcal/day) and REE adjusted by body weight (kcal/kg body weight) were calculated by the complete equation of Weir [26], considering the last 20 minutes, known as steady-state [27]. At 0, 30, 60, 120, and 180 minutes, fat and carbohydrate oxidation rates were calculated using standard equations for oxygen consumption, carbon dioxide production, and urinary nitrogen excretion [26, 28]. The HOMA-IR homeostasis model assessment of insulin resistance index [29] and PREDIM ( predicted M value ) [30] were calculated as markers of resistance and sensitivity to insulin, respectively. The insulinogenic index was calculated as a marker of insulin secretion considering the formula ΔT30glycemia/ΔT30glycemia. [31]. This index reflects the insulin response to the glucose stimulus related to the first phase of insulin secretion, which includes the prompt response of the beta-cell to the food stimulus. A decreased insulinogenic index is linked to the deterioration of beta-cell function. The calculation of the areas under the curve (AUC) for the dosages of glucose, insulin, GLP-1, SCFA, and the lipid and carbohydrate oxidation rates were performed using the trapezoidal method and represent a complete picture of the postprandial state [32]. Definition of metabolic phenotypes BMI was classified as normal if values ranged between 18.5 and 24.9 kg/m² and as overweight if values were ≥25.0 kg/m² [33]. Metabolic health was defined based on the strict criteria proposed in the BioShare Health Obese Project [34], which consider the absence of any one of the following alterations: systolic blood pressure ≥130 mmHg, diastolic blood pressure ≥85 mmHg, or use of antihypertensive medication; fasting plasma glucose ≥110 mg/dL or nonfasting plasma glucose ≥126 mg/dL or use of antidiabetic agent; HDL-cholesterol <50 mg/dL or triglycerides ≥150 mg/dL or use of lipid-lowering medication; and diagnosis of cardiovascular disease. Based on this definition, participants were classified into four phenotypes: normal-weight metabolically healthy (MHNW) or unhealthy (MUNW), obese/overweight metabolically healthy (MHO), or unhealthy (MUO). Statistical analysis Analyses were performed using IBM SPSS Statistics software. The Kolmogorov–Smirnov test was applied to test data normality. Median values and interquartile ranges are provided. Minimum and maximum values were presented for sample characterization variables to present the range of variation of some parameters. Tertiles of body adiposity measures were obtained for comparative analyses. Mann–Whitney and Kruskal–Wallis tests were applied for comparisons between two or ≥three independent groups, respectively. Spearman correlation was applied to evaluate the correlation between SCFA and variables of food consumption and glycemic and energetic homeostasis. Friedman's analysis of variance of two factors associated with multiple comparisons by pairs per rank was applied to compare the metabolic variables throughout the standard mixed-meal tolerance test. A significant p value was set as <0.05. Results The main characteristics of the study participants are shown in Table 1 . There was a predominance of graduates (60.4%) over undergraduate nutritionists (39.6%). Subgroups classified according to nutritional status or metabolic phenotypes did not differ according to age. Expected differences in anthropometric and biochemical variables were verified stratifying by metabolic phenotype (data not shown). Medians of HOMA-IR differed between lean and overweight participants [0.89 (0.66-1.21) versus 1.37 (0.77-2.01); p=0.034], between healthy and unhealthy participants [0.90 (0.52-1.43) versus 1.17 (0.78-2.02); p=0.036], and concerning the metabolic phenotypes, only between MHNW and MUO [0.89 (0.50-1.04) versus 1.31 (0.83-2.30); p=0.048], respectively. Table 1 – Main clinical and metabolic characteristics of women from the NutriHS, São Paulo, Brazil. Total participants (n = 111) Median (IQ) Range Age (years) 28 (24 - 31) 19 - 42 Body mass index (kg/m²) 25 (21 - 28) 18 - 40 Waist circumference (cm) 79 (72 - 88) 61 - 128 Total body fat (%) 38 (32 - 44) 24 - 55 Visceral adipose tissue (g) 176 (87 - 561) 13 - 1283 Resting energy expenditure (kcal/day) 1168 (1078 - 1250) 872 - 1601 Systolic blood pressure (mmHg) 110 (100 - 110) 80 - 150 Diastolic blood pressure (mmHg) 70 (70 - 80) 60 - 100 Biochemical parameters Glycated hemoglobin (%) 5.1 (4.9 - 5.3) 4.2 - 6.1 Fasting plasma glucose (mg/dL) 84 (81 - 88) 59 - 105 Fasting insulin (mU/L) 5.60 (3.90 - 8.50) 0.20 - 26.90 Total cholesterol (mg/dL) 164 (148 - 190) 95 - 288 LDL-cholesterol (g/dL) 92 (76 - 110) 38 - 137 HDL-cholesterol (mg/dL) 56 (49 - 67) 26 -108 Triglycerides (mg/dL) 75 (60 - 103) 28 - 267 Metabolic phenotype Metabolically healthy normal-weight, n (%) 39 (35) - Metabolically unhealthy normal-weight, n (%) 18 (16) - Metabolically healthy overweight/obese, n (%) 26 (23) - Metabolically unhealthy overweight/obese, n %) 28 (25) - Values are medians or number of participants (interquartile range and percentage in parenthesis). Characterization of plasma SCFA concentrations throughout the standard mixed-meal tolerance test and comparisons between the metabolic phenotypes Acetate concentrations exhibited a postprandial rise at times 60 (p=0.001) and 120 (p=0.016) minutes after eating the standardized meal compared to time zero. There was also an increase in plasma concentrations of total SCFA at time 60 minutes (p=0.003), and no alterations were observed for propionate concentrations ( Figure 2 ). The MHNW group had higher propionate concentrations at fasting than the MHO and MUO groups (p=0.023). On the other hand, the concentration of fecal SCFAs did not differ between the four studied phenotypes ( Table 2 ). When comparisons were performed between the four metabolic phenotypes excluding all participants with overweight for the MHO and MUO groups, the findings remained the same (data not shown). Table 2 – Medians (interquartile range in parenthesis) of fecal short-chain fatty acid concentrations of women categorized according to metabolic phenotypes and anthropometric and adiposity distribution measurements from the NutriHS, São Paulo, Brazil. Acetate (mg/g) p Propionate (mg/g) p Butyrate (mg/g) p Metabolic phenotype MHNW 1.15 (0.92 - 1.78) 0.797 0.51 (0.40 - 0.73) 0.625 0.41 (0.28 - 0.80) 0.834 MUNW 1.15 (0.78 - 2.03) 0.56 (0.27- 1.03) 0.46 (0.13- 1.16) MHO 1.16 (0.63 - 2.39) 0.46 (0.26- 0.74) 0.33 (0.17- 0.86) MUO 1.31 (0.67 - 2.89) 0.50 (0.29- 0.76) 0.39 (0.23- 0.87) Anthropometric and DXA measurements - Body mass index 1st tertile 1.17 (1.00 - 1.78) 0.487 0.61 (0.41 - 0.92) 0.026 0.65 (0.31 - 1.16) 0.047 2nd and 3rd tertiles 1.17 (0.63 - 2.38) 0.47 (0.26 - 0.72) 0.34 (0.20 - 0.81) - Waist circumference 1st tertile 1.15 (0.92 - 1.59) 0.895 0.59 (0.44 - 0.92) 0.017 0.64 (0.33 - 1.16) 0.010 2nd and 3rd tertiles 1.21 (0.64 - 2.59) 0.46 (0.26 - 0.72) 0.32 (0.17 - 0.81) - Percentage of total body fat 1st tertile 1.18 (0.94 - 1.90) 0.391 0.52 (0.42 - 0.73) 0.260 0.52 (0.25 - 1.07) 0.373 2nd and 3rd tertiles 1.17 (0.63 - 2.32) 0.47 (0.26 - 0.82) 0.34 (0.22 - 0.91) - Visceral adipose tissue mass 1st tertile 1.16 (0.92 - 1.63) 0.644 0.62 (0.42 - 0.85) 0.018 0.77 (0.33 - 1.19) 0.023 2nd and 3rd tertiles 1.18 (0.70 - 2.66) 0.46 (0.26 - 0.74) 0.34 (0.19 - 0.69) Values are medians (interquartile range). Mann–Whitney and Kruskal–Wallis tests were used. MHNW = metabolically healthy normal weight; MUNW = metabolically unhealthy normal weight; MHO = metabolically healthy obesity; MUO = metabolically unhealthy obesity. Associations between SCFA and adiposity indicators Table 2 also depicts fecal SCFA concentrations values of participants categorized according to anthropometric and adiposity distribution measurements. Propionate and butyrate concentrations were inversely associated with body adiposity assessed by anthropometric measurements and DXA parameters. In general, as adiposity increases, propionate and butyrate concentrations decrease, reaching significance for measurements of BMI (p=0.026; p=0.047), waist circumference (p=0.017; p=0.010), and visceral adipose tissue compartment (p=0.018; p=0.023), respectively. The same was not observed for acetate (p>0.05). Considering the total fecal concentration of SCFA, mean values did not differ across tertiles for any variables studied (p>0.05 – data not shown). Regarding the concentration of plasma SCFA ( Table 3 ), fasting propionate correlated inversely with BMI (p=0.021), waist circumference (p=0.019), total body fat (p=0.048), and visceral adipose tissue mass (p=0.007). In addition, propionate AUC was inversely correlated with visceral adipose tissue (p=0.046). The concentration of acetate and total SCFA did not correlate with anthropometric and adiposity parameters (p>0.05). Table 3 – Correlations between plasma SCFA concentrations and anthropometric and adiposity distribution measurements in women from the NutriHS, São Paulo, Brazil. Fasting acetate AUC mtt of Acetate Fasting propionate AUC mtt of Propionate r p r p r p r p Body mass index -0.014 0.887 -0.028 0.778 -0.271 0.021 -0.100 0.470 Waist circumference -0.007 0.941 0.001 0.993 -0.295 0.019 -0.096 0.484 Percent of total body fat -0.088 0.382 -0.049 0.625 -0.244 0.048 -0.120 0.385 Visceral adipose tissue mass -0.073 0.465 -0.028 0.781 -0.326 0.007 -0.275 0.046 AUC = area under the curve. The fasting and AUC of butyrate columns does not appear since its concentration was not detectable in plasma samples. Glucose homeostasis The fecal propionate concentration correlated positively with the insulinogenic index (p=0.047) and with the AUC of GLP-1 (p=0.033). Fecal acetate and propionate correlated negatively with the AUC of glucose (p≤ 0.04) (Table 4) . Regarding plasma concentrations ( Table 5 ), the AUC of total SCFA concentration (r=-0.313; p=0.012) and AUC of acetate concentration (r=-0.206; p=0.039) were inversely correlated with glycated hemoglobin. The AUC of acetate concentration was inversely correlated with fasting insulin (r=-0.211; p=0.034), and the AUC of propionate concentration had an inverse correlation with fasting glucose (r=-0.361; p=0.007). However, the AUC of total SCFA (r=0.320; p=0.009) and the AUC of acetate concentration (r=0.262; p=0.008) had a positive correlation with the AUC of glucose. Fasting acetate concentration (r=0.274; p=0.016) and total SCFA concentration (r=0.268; p=0.040) had a positive correlation with the AUC of GLP-1. There were no positive correlations between plasma SCFA concentration and insulin secretion and insulin sensitivity markers. Table 4 – Correlations between fecal short-chain fatty acids and energy and glucose homeostasis in women from the NutriHS, São Paulo, Brazil. Acetate Propionate Butyrate r p r p r p Glucose Homeostasis Glycated hemoglobin 0.026 0.792 -0.136 0.163 -0.034 0.733 Fasting plasma glucose -0.174 0.086 -0.045 0.661 -0.014 0.892 Fasting insulin 0.114 0.262 0.017 0.865 0.043 0.679 Fasting GLP-1 -0.121 0.292 -0.012 0.915 -0.101 0.386 HOMA-IR 0.161 0.113 0.032 0.752 -0.008 0.940 PREDIM -0.067 0.516 0.134 0.192 0.121 0.246 Insulinogenic Index 0.062 0.545 0.219 0.030 0.096 0.357 AUC mtt Plasma glucose -0.209 0.040 -0.210 0.039 -0.106 0.308 AUC mtt Insulin 0.134 0.187 0.024 0.816 0.029 0.777 AUC mtt GLP-1 0.047 0.653 0.221 0.033 0.084 0.430 Energetic Homeostasis Resting Energy Expenditure 0.214 0.039 -0.077 0.454 -0.073 0.486 Resting Energy Expenditure/kg body 0.050 0.625 0.249 0.013 0.230 0.025 AUC mtt Carbohydrate Oxidation 0.019 0.851 -0.137 0.178 -0.121 0.242 AUC mtt Lipid Oxidation 0.151 0.137 0.214 0.036 0.077 0.457 Spearman correlation test. AUC mtt = area under the curve obtained from the standard mixed-meal tolerance test, HOMA-IR = homeostatic model assessment, PREDIM = predicted M value, GLP-1 = glucagon-like peptide-1. Table 5 – Correlations between plasma short-chain fatty acids and energy and glucose homeostasis in the women studied from NutriHS, São Paulo, Brazil. Total SCFA Acetate Propionate Fasting AUC mtt Fasting AUC mtt Fasting AUC mtt r p r p r p r p r p r p Glucose Homeostasis Glycated hemoglobin -0.060 0.554 -0.313 0.012 -0.081 0.420 -0.206 0.039 -0.003 0.984 -0.053 0.707 Fasting plasma glucose -0.069 0.581 -0.014 0.914 0.035 0.728 0.136 0.169 -0.235 0.057 -0.361 0.007 Fasting insulin -0.171 0.169 0.101 0.418 -0.211 0.034 -0.066 0.507 0.001 0.991 0.126 0.361 Fasting GLP-1 0.262 0.039 -0.037 0.766 0.051 0.607 -0.020 0.839 -0.026 0.835 0.048 0.730 HOMA-IR -0.039 0.755 0.226 0.068 -0.079 0.430 0.027 0.789 -0.066 0.601 0.087 0.528 PrediM 0.086 0.488 -0.154 0.214 0.057 0.566 -0.039 0.696 0.103 0.407 -0.061 0.657 Insulinogenic Index 0.019 0.883 0.052 0.685 -0.051 0.610 -0.099 0.320 -0.062 0.621 0.003 0.985 AUC mtt Plasma glucose 0.082 0.512 0.320 0.009 0.110 0.270 0.262 0.008 0.035 0.778 0.099 0.473 AUC mtt Insulin -0.083 0.509 0.083 0.508 -0.056 0.577 -0.031 0.754 -0.171 0.170 0.029 0.833 AUC mtt GLP-1 0.268 0.040 0.014 0.910 0.229 0.022 0.049 0.622 0.049 0.696 0.117 0.397 Energetic Homeostasis REE 0.032 0.802 0.102 0.420 -0.012 0.905 -0.014 0.888 -0.157 0.211 0.006 0.964 REE/kg body -0.031 0.759 -0.020 0.875 -0.044 0.662 -0.029 0.768 0.300 0.019 0.113 0.411 AUC mtt CHO Oxidation 0.005 0.967 0.004 0.974 0.036 0.719 -0.085 0.392 0.158 0.205 0.097 0.479 AUC mtt Lipid Oxidation -0.082 0.510 -0.075 0.551 -0.125 0.209 0.014 0.886 -0.035 0.778 0.047 0.735 Spearman correlation test. SCFA = short-chain fatty acids, AUC mtt = area under the curve obtained from the standard mixed-meal tolerance test, HOMA-IR = homeostatic model assessment, PREDIM = predicted M value, GLP-1 = glucagon-like peptide-1, REE = resting energy expenditure, CHO = carbohydrate. The fasting value and AUC of butyrate columns does not appear since its concentration was not detectable in plasma samples. Energy homeostasis The fecal acetate concentration was positively correlated with REE (p=0.039). The fecal propionate concentration was positively correlated with REE adjusted for body weight (p=0.013) and with the AUC of lipid oxidation (p=0.036). Additionally, fecal butyrate concentration correlated positively with REE adjusted for kg of body weight (p=0.025) ( Table 4 ). Moreover, by correlating the oxidation rates of lipids and carbohydrates at each of the times with the fecal concentrations of SCFA, it was observed that fecal propionate concentration was inversely correlated with the oxidation rate of carbohydrates in fasting (r=-0.214; p=0.035), and propionate concentration (r=0.219, p=0.030; r=0.204, p=0.044) was positively correlated with lipid oxidation at 60 min. and 120 min., respectively. Furthermore, fecal butyrate concentration presented a positive correlation with lipid oxidation at 60 min (r=0.210; p=0.043). For the other oxidation rates, there was no significant correlation (data not shown). Regarding plasma concentrations of SCFA, only fasting propionate concentration had a positive correlation with the REE adjusted for body weight (r=0.300; p=0.019) ( Table 5 ). Associations between fecal and plasma SCFA concentrations In general, the concentrations of fecal SCFAs were not statistically correlated with their respective plasma concentrations (data not shown), except for the concentrations of total SCFAs in feces, which were inversely correlated with postprandial concentrations of total SCFAs (r=-0.262; p=0.043). Discussion Using indirect calorimetry associated with the standard mixed-meal tolerance test and the evaluation of body composition by densitometry, this study investigated the hypothesis that fecal and plasma SCFA concentrations were associated with the different metabolic phenotypes of obesity, adiposity, and specific components of energy and glucose homeostasis. To the best of our knowledge, studies investigating SCFA among the metabolic phenotypes of obesity, in addition to the quantification of plasma SCFA simultaneously with GLP-1 postprandially under physiological conditions, are scarce. The main findings were as follows: 1) fecal SCFA concentration did not differ among the metabolic phenotypes, and only fasting plasma propionate was increased in MHNW compared to MHO and MUO participants; 2) concentrations of fecal propionate and butyrate and plasma propionate were inversely associated with total and visceral adiposity; 3) fecal and plasma SCFA concentrations were associated with favorable glucose homeostasis represented by reduced fasting glucose and insulin and HbA1c levels and accompanied by increased fasting and postprandial GLP-1 and a more preserved beta-cell function regarding the first phase of insulin secretion; and 4) fecal and plasma SCFA concentrations were also associated with favorable energy homeostasis, characterized by a positive correlation with resting energy expenditure and lipid oxidation rate and an inverse correlation with the oxidation rate of carbohydrates. Concerning the comparisons of SCFA among the four metabolic phenotypes, it was expected that the highest fecal concentrations of SCFA would be found in metabolically healthy phenotypes (MHNW and MHO), but this hypothesis was not confirmed. Concentrations of fecal SCFA did not differ among the metabolic phenotypes, and only fasting plasma propionate was increased in the MHNW compared to the MHO and MUO phenotypes, indicating that SCFA were more associated with adiposity than with metabolic health itself. It is speculated that the absence of SCFA differences among the four phenotypes studied may be because the cardiometabolic alterations of metabolically unhealthy participants were not very severe. Recently, Telle-Hansen et al. [5] demonstrated higher serum concentrations of propionate in individuals with the MHO phenotype than in those with the MUO phenotype, while butyrate and acetate did not differ between the groups. The mentioned study evaluated a very small sample of eutrophic and obese people but did not include overweight individuals, as in the present study. Few studies to date have evaluated the distribution of body fat, measured by DXA, with concentrations of SCFA for comparison purposes. The data of this study showed that fecal propionate and butyrate and plasma propionate were inversely associated with total and visceral adiposity. Dugas et al. [35] demonstrated that higher fecal concentrations of SCFA in individuals with normal weight were associated with lower weight gain. On the other hand, previous studies have shown that overweight and obese individuals have higher fecal [36-38] concentrations of SCFA than normal-weight individuals. Studies have addressed the “energy harvesting” hypothesis as an explanation for additional weight gain, whereby SCFA may contribute to extra calories through fermentation in obese individuals [39]. Conversely, Muller et al. [15] found that circulating, but not fecal, SCFAs were associated with BMI. Experimental studies [7, 10, 40] and clinical trials [11, 12] have suggested that oral supplementation with SCFA can be effective in reducing body weight. A favorable association between glucose homeostasis and concentrations of fecal and plasma SCFAs was demonstrated, with an emphasis on positive associations with fasting and postprandial GLP-1. Recently, Muller et al. [15] demonstrated that fasting circulating SCFAs, but not fecal SCFAs, are positively associated with fasting plasma GLP-1 in humans. These findings are corroborated mainly by the results of experimental and clinical studies, which have shown that the administration of SCFA can improve the production and liberation of GLP-1 [10] and glycemia [41-43]. SCFAs are important signaling molecules for at least two G-coupled receptors, GPR41 and GPR43 [44]. These receptors, in addition to binding to SCFA, are also important means of binding for the production and liberation of GLP-1 [45]. The findings regarding GLP-1 may also explain the favorable association found in the present study between SCFA and the insulinogenic index, which ultimately reflects the first phase of insulin secretion. Regarding insulin resistance or sensitivity, in this study, HOMA-IR and PREDIM did not show any correlation with SCFA. These results may have been influenced by the presence of normal glucose tolerance in most of the participants, as well as by low levels of insulin resistance. It is speculated that if the hyperinsulinemic-euglycemic clamp test - the gold standard for the evaluation of insulin sensitivity - had been used, in view of its accuracy and precision, perhaps we would have detected significant associations with SCFA. Regarding energetic homeostasis, the concentrations of all three fecal SCFAs and fasting propionate were positively correlated with resting energy expenditure, in agreement with previous findings in the scientific literature [9, 46]. The concentration of fecal propionate was positively correlated with the lipid oxidation rate in the postprandial state and inversely correlated with the oxidation rate of carbohydrates in fasting. In obese and overweight men, the infusion of sodium acetate into the distal colon led to an increased lipid oxidation rate compared to the control group [47]. In obese rats [11], administration of sodium butyrate was effective in reducing body weight, and this benefit was attributed to increased REE and lipid oxidation, explained by the positive regulation of gene expression involved in the thermogenesis of PPARγ [11, 48]. In normoglycemic men with overweight or obesity [46], the administration of a mixture containing the three main SCFA in the distal colon increased fat oxidation in fasting and REE, measured by indirect calorimetry, which was also correlated with increased plasma acetate concentrations. Vadder et al. [40] pointed out that the explanation for these potential effects is intestinal gluconeogenesis, in which propionate can induce intestinal gluconeogenesis through the brain-intestine axis, with this communication being mediated by the FFA3 receptor present in the portal vein. Finally, increased fat oxidation by SCFA and increased oxidative capacity of the skeletal muscle can improve metabolic flexibility, resulting in possible partial inhibition of intracellular lipolysis in adipocytes, induced mainly by acetate, and causing reduced fat accumulation and an improvement in insulin action in peripheral tissues [46]. Using a liquid standard meal with no fiber content, we observed that concentrations of plasma acetate and total SCFA exhibit a postprandial rise. In rodents, a recent study demonstrated that SCFA exhibit a postprandial rise similar to ingested macronutrients [49]. In previous studies with humans, plasma SCFA concentrations increased 30 minutes after meal tests [50, 51]. The reason for this increase is currently unknown, and we can hypothesize that the increase reflects the metabolism of dietary fiber from previous meals. Another hypothesis would be greater intestinal absorption of SCFA present in the colon lumen after meal intake through enteroendocrine cell signaling or, with the availability of macronutrients in the postprandial period in the portal vein, reduced liver metabolization/oxidation of SCFA may occur as substrates in gluconeogenesis and lipogenesis. In the present study, the correlation between plasma and fecal concentrations of the different SCFA was significant and negative only between the fecal concentration of total SCFA and the postprandial plasma concentration of total SCFA. In the same vein, Vogt and Wolever [52] demonstrated that the rate of absorption and fecal excretion of acetate were inversely correlated. However, Muller et al. [15], when comparing plasma and fecal concentrations of SCFA, detected only a positive association between fecal and serum propionate, suggesting that these associations still deserve further investigation. This study has limitations. Among the potential flaws, we can point out that the study has a cross-sectional design, which did not allow us to point out causality. The selection bias predominantly considered young and educated women, with only small abnormalities classified in the metabolically obese phenotypes. Consequently, our results cannot be extrapolated to the general population. As we applied the more rigorous definition criteria for metabolic health proposed by the “BioSHaRE-EU Healthy Obese Project” [34], in most cases, the participants in the study with a metabolically unhealthy phenotype presented alterations in only one or two parameters. Therefore, a study composed of patients with more deteriorated metabolic health may demonstrate differences in SCFA levels among phenotypes. Nevertheless, the study presents strong points, such as the evaluation of a relatively large cohort of 111 participants under dynamic and physiological conditions, the assessment of body composition through DXA, which is gold standard equipment for the evaluation of body fat distribution, as well as that the sample strictly composed of women of fertile age, considering that hormonal changes common in menopause could affect body adiposity and, consequently, glycemic homeostasis. Moreover, the participants were nutritionists or students of nutrition, which ensures that they are aware of the relevance of the study and the importance of contributing to the research by providing good quality data from the questionnaires, increasing the credibility of the reproducibility of the results. Finally, considering that the main studies investigating the roles of SCFA in energy and glucose metabolism were conducted in animal models, cohort studies and interventions with humans are necessary to investigate if the possible positive effects of SCFA translate into benefits for body weight loss and control, improvement in glucose homeostasis and biochemical parameters associated with cardiometabolic risk. Conclusion These findings reinforce the concept that the concentrations of fecal and plasma SCFAs are linked to specific components of energy and glucose homeostasis and body adiposity. On the other hand, it was not possible to discriminate the different metabolic phenotypes of adiposity based on the determination of fecal SCFA concentration. Considering that the sample of this study was composed of young women, it is believed that the long-term follow-up of these participants could open up new research opportunities and answer whether there is a cause-and-effect relationship of SCFA in the natural history of obesity and type 2 diabetes, providing input to plan interventions with a new therapeutic focus. Declarations Acknowledgments: The authors would like to thank Camila Machado Xavier, Najla Simão Kfouri Crouchan, Vinícius Ferreira dos Santos, Isis Tande da Silva, and Luciana Dias Folchetti for their valuable support during data collection. They would also like to thank the São Paulo Research Foundation (FAPESP) and the National Council for Scientific and Technological Development (CNPq) for financial support, the Obesity and Comorbidities Research Center – OCRC for the additional financial support, and Laboratório de Investigação em Metabolismo e Diabetes (LIMED) and Gastrocentro UNICAMP for the space and support provided. Espaço da Escrita – Pró-Reitoria de Pesquisa - UNICAMP – for the language services provided. Funding Statement: This project received funding from the São Paulo Research Foundation (FAPESP), grant n.2017/10185-9, and three research scholarships n. 2017/24578-2, 2019/05450-0 and 2019/07769-4. Two other research grants were supported by the National Council for Scientific and Technological Development (CNPq). Author contributions: IS recruited participants and contributed to data collection, project administration, laboratory, and statistical analysis, writing and editing the manuscript. FB recruited participants and contributed to data collection and project administration. MGB recruited participants and contributed to data collection and biochemical analysis. RGBONF recruited participants and contributed to data collection. ASH recruited participants and contributed to data collection. COR contributed to the methodology and biochemical analysis. MRA contributed to the methodology and biochemical analysis. TC contributed to the methodology and biochemical analysis. BAP contributed to the study conception. AT contributed to the data analysis and discussion. DEC contributed to methodology conception and implementation and project administration. BG contributed to project conception and administration. SRGF contributed to the study conceptualization and manuscript preparation. ACJV contributed to the study conceptualization, funding acquisition, project administration, data analysis, and manuscript preparation. 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Fecal acetate is inversely related to acetate absorption from the human rectum and distal colon. J. Nutr. 2003, 133, 3145–3148. Cite Share Download PDF Status: Published Journal Publication published 08 Apr, 2023 Read the published version in Endocrine → Version 1 posted Editorial decision: Minor Revisions Needed 01 Mar, 2023 Reviewers agreed at journal 03 Jan, 2023 Reviewers invited by journal 03 Oct, 2022 Editor assigned by journal 13 Sep, 2022 First submitted to journal 09 Sep, 2022 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Vasques","email":"data:image/png;base64,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","orcid":"","institution":"UNICAMP: Universidade Estadual de Campinas","correspondingAuthor":true,"prefix":"","firstName":"Ana","middleName":"Carolina Junqueira","lastName":"Vasques","suffix":""}],"badges":[],"createdAt":"2022-08-23 17:26:02","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-1991138/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-1991138/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s12020-023-03356-0","type":"published","date":"2023-04-08T20:23:56+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":27439444,"identity":"c4d1c1e4-3d0a-44b6-9030-20c7bdb99a5e","added_by":"auto","created_at":"2022-10-06 19:54:44","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":41004,"visible":true,"origin":"","legend":"\u003cp\u003eExperimental protocol of the standardized mixed-meal tolerance test associated with the indirect calorimetry test for the evaluation of energy and glucose homeostasis in participants of NutriHS.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-1991138/v1/434a453b0849729143a6d25c.png"},{"id":27439123,"identity":"e5c35c09-6c10-4ab0-b819-2c74f8260eb0","added_by":"auto","created_at":"2022-10-06 19:49:44","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":512174,"visible":true,"origin":"","legend":"\u003cp\u003ePlasma concentrations of acetate, propionate, and total SCFA in the total sample (black line) and according to metabolic phenotypes (colored lines) during the standard mixed-meal tolerance test, with comparisons including concentrations throughout the test for the total sample and punctual concentrations at fasting and AUCs for each phenotype.\u003c/p\u003e\n\u003cp\u003eAUC = area under the curve; MHNW = metabolically healthy normal weight; MUNW = metabolically unhealthy normal weight; MHO = metabolically healthy obesity; MUO = metabolically unhealthy obesity; SCFA = short-chain fatty acids.\u003c/p\u003e\n\u003cp\u003eValues are medians, and bars are semi-interquartile ranges.\u003c/p\u003e\n\u003cp\u003e* = comparisons of fasting concentrations of SCFA between the four phenotypes. The bars are the comparisons of the total sample values ​​over the dynamic test.\u003c/p\u003e\n\u003cp\u003eThe comparisons between SCFA concentrations for each phenotype throughout the dynamic test were performed using Friedman's analysis of variance of two factors associated with multiple comparisons by pairs per ranks. Comparisons between phenotypes AUC in bar graphs were performed using the Kruskal–Wallis test followed by the post hoc test of Duncan.\u003c/p\u003e","description":"","filename":"Fig2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-1991138/v1/9853ab7b04cb8c5d085ecff0.jpg"},{"id":44724630,"identity":"87c89586-bf51-4083-9147-9f3338b6a1b6","added_by":"auto","created_at":"2023-10-16 20:32:45","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":605077,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1991138/v1/92650a50-7677-4fc5-8d5e-a0b9ba376b3d.pdf"}],"financialInterests":"","formattedTitle":"Short-chain fatty acids are associated with adiposity and energy and glucose homeostasis among different metabolic phenotypes in the Nutritionists’ Health Study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eObesity is a heterogeneous disease with complex pathophysiology and interconnecting genetic, environmental, and behavioral factors. The association between obesity and the development of cardiometabolic complications is well established [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. However, each individual has their own subcutaneous adipose tissue expansion limit [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], and once this limit is reached, ectopic fat accumulation generates lipotoxicity and cardiometabolic dysfunction. In particular, four metabolic phenotypes are recognized: metabolically healthy normal weight (MHNW), metabolically unhealthy normal weight (MUNW), metabolically healthy obese/overweight (MHO), and metabolically unhealthy obese/overweight (MUO). Over the last two decades, the study of the metabolic phenotypes of obesity has provided an interesting human model for understanding the underlying mechanisms that promote body fat accumulation and the development of obesity-related changes and cardiometabolic diseases [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eGut microbiome composition has been identified as an important factor associated with the development of dysmetabolic phenotypes [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. A recent study demonstrated higher plasma concentrations of short-chain fatty acids (SCFAs) of propionate in individuals with the MHO phenotype compared to MUO, while butyrate and acetate did not differ between both phenotypes [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. SCFAs are organic acids composed of 1 to 6 carbons, of which acetate (C2:0), propionate (C3:0), and butyrate (C4:0) are the main ones since they account for 90 to 95% of all SCFAs found in the colon. SCFAs are mainly produced by bacterial fermentation of nondigestible carbohydrates [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] and can be used as an energy source and as trophic factors by intestinal cells. They can also be absorbed and exert crucial physiological effects in peripheral tissues such as adipose tissue, liver, brain, skeletal muscle, and pancreatic beta cells [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Although studies addressing the timing of postprandial absorption of SCFA from a meal are scarce, it is well documented that the gut lumen is the major site of SCFA production; however, the concentration gradient decreases from the lumen to the peripheral organs, with preferential use of butyrate by the epithelium, propionate by the liver, and acetate in the periphery [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSCFAs and their receptors (G-protein coupled receptor 41/43 - GPR41 and GPR43) play a critical role in energy [\u003cspan additionalcitationids=\"CR10 CR11\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] and glucose homeostasis [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. In mice fed a high-fat diet, supplementation with SCFA prevented the development of insulin resistance and glucose intolerance and promoted the development of adaptive thermogenesis with increased fat oxidation rates [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Oral administration of acetate in rats treated with a high-fat diet promoted a decrease in total body fat and led to an increase in the hepatic expression of proteins related to thermogenesis [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. In animals, SCFA supplementation improved metabolic control in type 2 diabetes, promoting the development of beta cells and reducing apoptosis [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Furthermore, serum [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] and fecal [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] concentrations of SCFA are positively associated with fasting plasma levels of GLP-1. In humans, supplementation with oral sodium propionate increased lipid oxidation and energy expenditure and reduced body weight, intra-abdominal fat, and hepatic lipid content in healthy individuals compared to the placebo group [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHuman studies addressing associations of SCFA between metabolic phenotypes and distribution of adiposity are scarce. Degrees of body adiposity that characterize the abovementioned phenotypes are commonly classified according to body mass index (BMI) and rarely using a more accurate measurement of the central distribution of fatness. To explore the potential associations between SCFA, metabolic health, and adiposity, we compared SCFA concentrations in plasma and feces among adult women, assessed by dual-energy X-ray absorptiometry and categorized according to the four metabolic phenotypes: MHNW, MUNW, MHO, and MUO. Moreover, we examined associations between SCFA and specific components of energy and glucose homeostasis in fasting and postprandial states using a more dynamic physiological test (standard mixed-meal tolerance test) coupled to indirect calorimetry among the participants with different body adiposity compositions and distributions. Finally, we investigated the relationship between fecal and circulating SCFA concentrations.\u003c/p\u003e"},{"header":"Material \u0026 Methods","content":"\u003cp\u003e\u003cem\u003eStudy design and participants\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe Nutritionists\u0026rsquo; Health Study (NutriHS) was conceived in the School of Public Health of the University of S\u0026atilde;o Paulo, Brazil, aiming to investigate factors associated with health outcomes in nutritionists and undergraduates of the nutrition course [16]. This cross-sectional part of NutriHS was conducted at the University of Campinas, S\u0026atilde;o Paulo, Brazil. Recruitment occurred between 2018 and 2019 in the metropolitan area of Campinas. Only women were included in this NutriHS-UNICAMP arm, since the vast majority of Brazilian nutritionists are female. The eligibility criteria were women aged between 19 and \u0026lt;45 and body mass index (BMI) between 18.5 and 40.0 kg/m\u0026sup2;. Women who were pregnant or lactating, using medication affecting glucose metabolism and/or body adiposity, who had used probiotics or antibiotics in the last three months and who had diabetes mellitus, heart, kidney, and liver diseases or other severe systemic diseases were excluded.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eEthical Approval\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThis study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the Ethics Committee of each institution \u0026ndash; University of Campinas (UNICAMP) and University of S\u0026atilde;o Paulo (USP) (ethical approval number 79775817.4.1001.5404). All experiments were performed in accordance with relevant guidelines and regulations. All participants signed an electronic informed consent form, available at \u003cem\u003ee\u003c/em\u003e-NutriHS, a specific web-based system developed for the study (www.fsp.usp.br/nutrihs) [17]. In total, 248 women answered the online questionnaires, 127 met the inclusion criteria and attended a clinical visit at our research laboratory, and 111 concluded the whole protocol.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eClinical Measurements\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eBody weight, height, and waist circumference - measured at the midpoint between the last rib and the iliac crest - were obtained. Body composition parameters, including total fat and visceral fat mass (in grams), were assessed by GE-Healthcare Lunar iDEXA using the automatic whole-body scan mode. All scans were performed and analyzed by trained researchers following a standard protocol. Instrument quality control was performed daily following the manufacturer\u0026rsquo;s instructions.\u003c/p\u003e\n\u003cp\u003eBlood pressure was measured in a sitting position using a mercury sphygmomanometer following recent guidelines [18]. Blood samples were obtained after a 12-h overnight fast.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eEnergy and glucose homeostasis assessment\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe components of energy (resting energy expenditure \u0026ndash; REE and substrate oxidation rate) and glucose (insulin resistance, insulin, and GLP-1 secretion) homeostasis were evaluated by the standardized mixed-meal tolerance test associated with indirect calorimetry in a 180-minute protocol, depicted in Figure 1. The standardized meal test was the choice for investigational purposes because it better reproduces real-life conditions among free-living individuals.\u003c/p\u003e\n\u003cp\u003eIndirect calorimetry canopy (VMAX N Encore) was performed according to the method proposed by Fulmer et al. [19]. The volunteers were instructed not to consume alcoholic beverages and not to engage in moderate/vigorous physical activity 48 hours before the test. On the previous night, the volunteers consumed a standardized meal containing 600 calories, 60% carbohydrate, 22% fat, 18% protein, and approximately 8 g of total fiber, since the standardization of the last meal guarantees accuracy in the evaluation of energy expenditure and the oxidation of energy substrates, avoiding any interference in the residual thermogenesis induced by diet [20, 21]. After this meal, the participants remained fasting for 12 hours until the first indirect calorimetry was performed. On the day of the examination, the calorimetry monitor remained on for 30 minutes before the start of the test for stabilization. The equipment was calibrated with a known gas concentration. The room was kept between 22 and 25 \u0026deg;C, with natural lighting and as little noise as possible. The volunteers arrived at the laboratory at 7:00 am and fasted for 12 hours to minimize any residual thermic effect of the diet. In addition, they were instructed to collect all urine produced in these 12 hours in a specific collector. After the initial rest, an evaluation of energy metabolism was performed for 30 minutes to determine the fasting REE and the oxidation rate of energy substrates. Subsequently, a venous catheter was inserted into an antecubital vein for blood collection. Next, the volunteers received a standardized liquid meal containing 524 kcal (Nutren 1.5\u0026reg;) in 340 mL, composed of 75 g of carbohydrates, 19 g of protein, 16.3 g of lipids, and 0 g of fiber, which was consumed in up to 5 minutes [22]. Blood samples were collected in dry tubes at -15, 0 (before food intake), 30, 60, 120, and 180 minutes (after food intake). For GLP-1 analysis, EDTA tubes containing 20 \u0026mu;L of dipeptidyl peptidase-IV inhibitor were used. For SCFA, lithium heparin tubes were used. Glucose and insulin were quantified at all times. GLP-1 was quantified at 0, 30, 60, 120, and 180 minutes. SCFA concentrations were quantified at 0, 60, 120, and 180 min. Indirect calorimetry was performed at the end of the 20 minutes preceding each blood collection. At the end of the test, the participants were instructed to empty their bladders in a specific collector. The total volume of urine was computed and aliquoted, and urinary nitrogen was analyzed as a marker of protein oxidation.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eBiochemical analysis\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eBiochemical tests were performed to characterize the fasting lipid profile (LDL-cholesterol, HDL-c, triglycerides, and total cholesterol) using colorimetric and enzymatic methods. LDL-c was calculated by the Friedwald equation [23]. Glycated hemoglobin was measured with high-performance liquid chromatography (HPLC). Plasma glucose levels were promptly measured in the fasting state and during the dynamic test using a glucose analyzer (YSI 2700; YSI Life Sciences, Yellow Spring, OH, USA) with a coefficient of variation (CV) of 2%. Plasma insulin levels were analyzed using an automated two-site chemiluminescent immunometric assay (Immulite 1000 System; Siemens Health Diagnostics, USA). The intra-assay and interassay CVs were 5.2\u0026ndash;6.4% and 5.9\u0026ndash;8.0%, respectively. Total COOH-terminal amidated GLP-1 was measured using ELISA (cat. EGLP-35K Sigma Aldrich) with intra-assay and interassay CVs of 7% to 9% and \u0026lt;1% to 13%, respectively. Urinary nitrogen excretion was calculated by the UV enzymatic method by urinary urea dosage. Fecal and plasma concentrations of SCFA (acetic [C2:0], propionic [C3:0], and butyric [C4:0]) were quantified using the gas chromatography technique coupled to mass spectrometry. For stool samples, the protocol previously described by Fellows et al. [24] was applied, and for plasma samples, we used the protocol described by Wang et al. (25). The butyrate concentration was not detectable in plasma samples, and the concentrations of detectable SCFAs were summed to obtain the total concentration of SCFAs.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eSCFA analysis\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eFragments of feces or blood samples were harvested from volunteers under fasting or after meal conditions and used for measurement of SCFA, following a protocol similar to that used by Fellows et al. [24]. The day before the investigation, the subjects collected a fecal sample from their first sterile stool receptacles provided by the research team. The samples were directly stored on dry ice at home. Upon arrival in the laboratory, fecal samples were aliquoted and frozen at \u0026minus;80 \u0026deg;C until further analysis. After this, fecal samples were thawed, weighed, crushed, and homogenized in water. Sodium chloride, citric acid, hydrochloric acid, and butanol were added, and the mix was placed in a vortex and centrifuged at 4 \u0026deg;C. The supernatants were recovered. For blood samples, after 15 minutes of centrifugation of total blood to separate the serum, we followed the method proposed by Wang et al. [25]. Briefly, ethanol (P. A), n-hexane (P. A), and an internal standard (caprylic acid) was added to 200 mL of serum. All samples were vortexed and centrifuged at 13,000 rpm at 4 \u0026deg;C for 8 minutes. Immediately after, samples were transferred to specific vials, and p. H was adjusted to 4.0 using chloride acid (10%).\u003c/p\u003e\n\u003cp\u003eFor all samples (feces and blood), a calibration curve with 0.015\u0026ndash;0.1\u0026thinsp;mg/mL SCFA was used in the quantification. Chromatographic analyses were performed using a gas chromatograph-mass spectrometer (model GCMS-QP2010 Ultra; Shimadzu\u0026reg;) and a fused-silica capillary Stabilwax column (Restec Corporation, USA) with dimensions of 30\u0026thinsp;m\u0026thinsp;\u0026times;\u0026thinsp;0.25\u0026thinsp;mm internal diameter and coated with a 0.25-\u0026micro;m thick layer of polyethylene glycol. Samples (1\u0026thinsp;\u0026micro;L) were injected at 250\u0026thinsp;\u0026deg;C using a 25:1 split ratio for feces or splitless (1:1) for blood samples. High-grade pure helium (He) was used as the carrier gas with a constant flow rate of 1.0\u0026thinsp;mL/min. Mass conditions were as follows: ionization voltage, 70\u0026thinsp;eV; ion source temperature, 200\u0026thinsp;\u0026deg;C; full scan mode in the 35\u0026ndash;500 mass range with 0.2\u0026thinsp;s/scan velocity. The runtime for each analysis was 11.95\u0026thinsp;min.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eMathematical calculations\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eTotal resting energy expenditure (REE) (kcal/day) and REE adjusted by body weight (kcal/kg body weight) were calculated by the complete equation of Weir [26], considering the last 20 minutes, known as steady-state [27]. At 0, 30, 60, 120, and 180 minutes, fat and carbohydrate oxidation rates were calculated using standard equations for oxygen consumption, carbon dioxide production, and urinary nitrogen excretion [26, 28].\u003c/p\u003e\n\u003cp\u003eThe HOMA-IR homeostasis model assessment of insulin resistance index [29] and PREDIM (\u003cem\u003epredicted M value\u003c/em\u003e) [30] were calculated as markers of resistance and sensitivity to insulin, respectively. The insulinogenic index was calculated as a marker of insulin secretion considering the formula \u0026Delta;T30glycemia/\u0026Delta;T30glycemia. [31]. This index reflects the insulin response to the glucose stimulus related to the first phase of insulin secretion, which includes the prompt response of the beta-cell to the food stimulus. A decreased insulinogenic index is linked to the deterioration of beta-cell function.\u003c/p\u003e\n\u003cp\u003eThe calculation of the areas under the curve (AUC) for the dosages of glucose, insulin, GLP-1, SCFA, and the lipid and carbohydrate oxidation rates were performed using the trapezoidal method and represent a complete picture of the postprandial state [32].\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eDefinition of metabolic phenotypes\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eBMI was classified as normal if values ranged between 18.5 and 24.9 kg/m\u0026sup2; and as overweight if values were \u0026ge;25.0 kg/m\u0026sup2; [33]. Metabolic health was defined based on the strict criteria proposed in the BioShare Health Obese Project [34], which consider the absence of any one of the following alterations: systolic blood pressure \u0026ge;130 mmHg, diastolic blood pressure \u0026ge;85 mmHg, or use of antihypertensive medication; fasting plasma glucose \u0026ge;110 mg/dL or nonfasting plasma glucose \u0026ge;126 mg/dL or use of antidiabetic agent; HDL-cholesterol \u0026lt;50 mg/dL or triglycerides \u0026ge;150 mg/dL or use of lipid-lowering medication; and diagnosis of cardiovascular disease. Based on this definition, participants were classified into four phenotypes: normal-weight metabolically healthy (MHNW) or unhealthy (MUNW), obese/overweight metabolically healthy (MHO), or unhealthy (MUO).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eStatistical analysis\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAnalyses were performed using IBM SPSS Statistics software. The Kolmogorov\u0026ndash;Smirnov test was applied to test data normality. Median values and interquartile ranges are provided. Minimum and maximum values were presented for sample characterization variables to present the range of variation of some parameters. Tertiles of body adiposity measures were obtained for comparative analyses. Mann\u0026ndash;Whitney and Kruskal\u0026ndash;Wallis tests were applied for comparisons between two or \u0026ge;three independent groups, respectively. Spearman correlation was applied to evaluate the correlation between SCFA and variables of food consumption and glycemic and energetic homeostasis. Friedman\u0026apos;s analysis of variance of two factors associated with multiple comparisons by pairs per rank was applied to compare the metabolic variables throughout the standard mixed-meal tolerance test. A significant p value was set as \u0026lt;0.05.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eThe main characteristics of the study participants are shown in \u003cstrong\u003eTable 1\u003c/strong\u003e. There was a predominance of graduates (60.4%) over undergraduate nutritionists (39.6%). Subgroups classified according to nutritional status or metabolic phenotypes did not differ according to age. Expected differences in anthropometric and biochemical variables were verified stratifying by metabolic phenotype (data not shown). Medians of HOMA-IR differed between lean and overweight participants [0.89 (0.66-1.21) \u003cem\u003eversus\u0026nbsp;\u003c/em\u003e1.37 (0.77-2.01); p=0.034], between healthy and unhealthy participants [0.90 (0.52-1.43) \u003cem\u003eversus\u003c/em\u003e 1.17 (0.78-2.02); p=0.036], and concerning the metabolic phenotypes, only between MHNW and MUO [0.89 (0.50-1.04) \u003cem\u003eversus\u003c/em\u003e 1.31 (0.83-2.30); p=0.048], respectively.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e \u0026ndash; Main clinical and metabolic characteristics of women from the NutriHS, S\u0026atilde;o Paulo, Brazil.\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"55.55555555555556%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" width=\"44.44444444444444%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal participants\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n = 111)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"56.12244897959184%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"24.489795918367346%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMedian (IQ)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"19.387755102040817%\"\u003e\n \u003cp\u003e\u003cstrong\u003eRange\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"56.12244897959184%\"\u003e\n \u003cp\u003eAge (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"24.489795918367346%\"\u003e\n \u003cp\u003e28 (24 - 31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.387755102040817%\"\u003e\n \u003cp\u003e19 - 42\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"56.12244897959184%\"\u003e\n \u003cp\u003eBody mass index (kg/m\u0026sup2;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"24.489795918367346%\"\u003e\n \u003cp\u003e25 (21 - 28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.387755102040817%\"\u003e\n \u003cp\u003e18 - 40\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"56.12244897959184%\"\u003e\n \u003cp\u003eWaist circumference (cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"24.489795918367346%\"\u003e\n \u003cp\u003e79 (72 - 88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.387755102040817%\"\u003e\n \u003cp\u003e61 - 128\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"56.12244897959184%\"\u003e\n \u003cp\u003eTotal body fat (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"24.489795918367346%\"\u003e\n \u003cp\u003e38 (32 - 44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.387755102040817%\"\u003e\n \u003cp\u003e24 - 55\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"56.12244897959184%\"\u003e\n \u003cp\u003eVisceral adipose tissue (g)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"24.489795918367346%\"\u003e\n \u003cp\u003e176 (87 - 561)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.387755102040817%\"\u003e\n \u003cp\u003e13 - 1283\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"56.12244897959184%\"\u003e\n \u003cp\u003eResting energy expenditure (kcal/day)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"24.489795918367346%\"\u003e\n \u003cp\u003e1168 (1078 - 1250)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.387755102040817%\"\u003e\n \u003cp\u003e872 - 1601\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"56.12244897959184%\"\u003e\n \u003cp\u003eSystolic blood pressure (mmHg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"24.489795918367346%\"\u003e\n \u003cp\u003e110 (100 - 110)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.387755102040817%\"\u003e\n \u003cp\u003e80 - 150\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"56.12244897959184%\"\u003e\n \u003cp\u003eDiastolic blood pressure (mmHg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"24.489795918367346%\"\u003e\n \u003cp\u003e70 (70 - 80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.387755102040817%\"\u003e\n \u003cp\u003e60 - 100\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"56.12244897959184%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eBiochemical parameters\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"24.489795918367346%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.387755102040817%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"56.12244897959184%\"\u003e\n \u003cp\u003eGlycated hemoglobin (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"24.489795918367346%\"\u003e\n \u003cp\u003e5.1 (4.9 - 5.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.387755102040817%\"\u003e\n \u003cp\u003e4.2 - 6.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"56.12244897959184%\"\u003e\n \u003cp\u003eFasting plasma glucose (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"24.489795918367346%\"\u003e\n \u003cp\u003e84 (81 - 88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.387755102040817%\"\u003e\n \u003cp\u003e59 - 105\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"56.12244897959184%\"\u003e\n \u003cp\u003eFasting insulin (mU/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"24.489795918367346%\"\u003e\n \u003cp\u003e5.60 (3.90 - 8.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.387755102040817%\"\u003e\n \u003cp\u003e0.20 - 26.90\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"56.12244897959184%\"\u003e\n \u003cp\u003eTotal cholesterol (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"24.489795918367346%\"\u003e\n \u003cp\u003e164 (148 - 190)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.387755102040817%\"\u003e\n \u003cp\u003e95 - 288\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"56.12244897959184%\"\u003e\n \u003cp\u003eLDL-cholesterol (g/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"24.489795918367346%\"\u003e\n \u003cp\u003e92 (76 - 110)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.387755102040817%\"\u003e\n \u003cp\u003e38 - 137\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"56.12244897959184%\"\u003e\n \u003cp\u003eHDL-cholesterol (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"24.489795918367346%\"\u003e\n \u003cp\u003e56 (49 - 67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.387755102040817%\"\u003e\n \u003cp\u003e26 -108\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"56.12244897959184%\"\u003e\n \u003cp\u003eTriglycerides (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"24.489795918367346%\"\u003e\n \u003cp\u003e75 (60 - 103)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.387755102040817%\"\u003e\n \u003cp\u003e28 - 267\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"56.12244897959184%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eMetabolic phenotype\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"24.489795918367346%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.387755102040817%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u003cs\u003e\u0026nbsp;\u003c/s\u003e\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"56.12244897959184%\"\u003e\n \u003cp\u003eMetabolically healthy normal-weight, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"24.489795918367346%\"\u003e\n \u003cp\u003e39 (35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.387755102040817%\"\u003e\n \u003cp\u003e\u003cs\u003e-\u003c/s\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"56.12244897959184%\"\u003e\n \u003cp\u003eMetabolically unhealthy normal-weight, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"24.489795918367346%\"\u003e\n \u003cp\u003e18 (16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.387755102040817%\"\u003e\n \u003cp\u003e\u003cs\u003e-\u003c/s\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"56.12244897959184%\"\u003e\n \u003cp\u003eMetabolically healthy overweight/obese, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"24.489795918367346%\"\u003e\n \u003cp\u003e26 (23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.387755102040817%\"\u003e\n \u003cp\u003e\u003cs\u003e-\u003c/s\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"56.12244897959184%\"\u003e\n \u003cp\u003eMetabolically unhealthy overweight/obese, n %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"24.489795918367346%\"\u003e\n \u003cp\u003e28 (25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.387755102040817%\"\u003e\n \u003cp\u003e\u003cs\u003e-\u003c/s\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eValues are medians or number of participants (interquartile range and percentage in parenthesis).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCharacterization of plasma SCFA concentrations throughout the standard mixed-meal tolerance test and comparisons between the metabolic phenotypes\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAcetate concentrations exhibited a postprandial rise at times 60 (p=0.001) and 120 (p=0.016) minutes after eating the standardized meal compared to time zero. There was also an increase in plasma concentrations of total SCFA at time 60 minutes (p=0.003), and no alterations were observed for propionate concentrations (\u003cstrong\u003eFigure 2\u003c/strong\u003e). The MHNW group had higher propionate concentrations at fasting than the MHO and MUO groups (p=0.023). On the other hand, the concentration of fecal SCFAs did not differ between the four studied phenotypes (\u003cstrong\u003eTable 2\u003c/strong\u003e). When comparisons were performed between the four metabolic phenotypes excluding all participants with overweight for the MHO and MUO groups, the findings remained the same (data not shown).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e \u0026ndash; Medians (interquartile range in parenthesis) of fecal short-chain fatty acid concentrations of women categorized according to metabolic phenotypes and anthropometric and adiposity distribution measurements from the NutriHS, S\u0026atilde;o Paulo, Brazil.\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"36.8421052631579%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.789473684210526%\"\u003e\n \u003cp\u003e\u003cstrong\u003eAcetate\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(mg/g)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.2631578947368425%\"\u003e\n \u003cp\u003e\u003cstrong\u003ep\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.789473684210526%\"\u003e\n \u003cp\u003e\u003cstrong\u003ePropionate\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(mg/g)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.2631578947368425%\"\u003e\n \u003cp\u003e\u003cstrong\u003ep\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.789473684210526%\"\u003e\n \u003cp\u003e\u003cstrong\u003eButyrate\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(mg/g)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.2631578947368425%\"\u003e\n \u003cp\u003e\u003cstrong\u003ep\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"36.8421052631579%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eMetabolic phenotype\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.789473684210526%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.2631578947368425%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.789473684210526%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.2631578947368425%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.789473684210526%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.2631578947368425%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"36.8421052631579%\"\u003e\n \u003cp\u003eMHNW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.789473684210526%\"\u003e\n \u003cp\u003e1.15 (0.92 - 1.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.2631578947368425%\"\u003e\n \u003cp\u003e0.797\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.789473684210526%\"\u003e\n \u003cp\u003e0.51 (0.40 - 0.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.2631578947368425%\"\u003e\n \u003cp\u003e0.625\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.789473684210526%\"\u003e\n \u003cp\u003e0.41 (0.28 - 0.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.2631578947368425%\"\u003e\n \u003cp\u003e0.834\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"36.8421052631579%\"\u003e\n \u003cp\u003eMUNW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.789473684210526%\"\u003e\n \u003cp\u003e1.15 (0.78 - 2.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.2631578947368425%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.789473684210526%\"\u003e\n \u003cp\u003e0.56 (0.27- 1.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.2631578947368425%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.789473684210526%\"\u003e\n \u003cp\u003e0.46 (0.13- 1.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.2631578947368425%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"36.8421052631579%\"\u003e\n \u003cp\u003eMHO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.789473684210526%\"\u003e\n \u003cp\u003e1.16 (0.63 - 2.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.2631578947368425%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.789473684210526%\"\u003e\n \u003cp\u003e0.46 (0.26- 0.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.2631578947368425%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.789473684210526%\"\u003e\n \u003cp\u003e0.33 (0.17- 0.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.2631578947368425%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"36.8421052631579%\"\u003e\n \u003cp\u003eMUO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.789473684210526%\"\u003e\n \u003cp\u003e1.31 (0.67 - 2.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.2631578947368425%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.789473684210526%\"\u003e\n \u003cp\u003e0.50 (0.29- 0.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.2631578947368425%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.789473684210526%\"\u003e\n \u003cp\u003e0.39 (0.23- 0.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.2631578947368425%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"36.8421052631579%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eAnthropometric and DXA measurements\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.789473684210526%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.2631578947368425%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.789473684210526%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.2631578947368425%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.789473684210526%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.2631578947368425%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" width=\"24.210526315789473%\"\u003e\n \u003cp\u003e- Body mass index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.631578947368421%\"\u003e\n \u003cp\u003e1st tertile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.789473684210526%\"\u003e\n \u003cp\u003e1.17 (1.00 - 1.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.2631578947368425%\"\u003e\n \u003cp\u003e0.487\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.789473684210526%\"\u003e\n \u003cp\u003e0.61 (0.41 - 0.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.2631578947368425%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.026\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.789473684210526%\"\u003e\n \u003cp\u003e0.65 (0.31 - 1.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.2631578947368425%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.047\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"16.666666666666668%\"\u003e\n \u003cp\u003e2nd and 3rd tertiles\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.833333333333332%\"\u003e\n \u003cp\u003e1.17 (0.63 - 2.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.944444444444445%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.833333333333332%\"\u003e\n \u003cp\u003e0.47 (0.26 - 0.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.944444444444445%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.833333333333332%\"\u003e\n \u003cp\u003e0.34 (0.20 - 0.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.944444444444445%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"24.210526315789473%\"\u003e\n \u003cp\u003e- Waist circumference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.631578947368421%\"\u003e\n \u003cp\u003e1st tertile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.789473684210526%\"\u003e\n \u003cp\u003e1.15 (0.92 - 1.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.2631578947368425%\"\u003e\n \u003cp\u003e0.895\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.789473684210526%\"\u003e\n \u003cp\u003e0.59 (0.44 - 0.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.2631578947368425%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.017\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.789473684210526%\"\u003e\n \u003cp\u003e0.64 (0.33 - 1.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.2631578947368425%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.010\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"24.210526315789473%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.631578947368421%\"\u003e\n \u003cp\u003e2nd and 3rd tertiles\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.789473684210526%\"\u003e\n \u003cp\u003e1.21 (0.64 - 2.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.2631578947368425%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.789473684210526%\"\u003e\n \u003cp\u003e0.46 (0.26 - 0.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.2631578947368425%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.789473684210526%\"\u003e\n \u003cp\u003e0.32 (0.17 - 0.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.2631578947368425%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"24.210526315789473%\"\u003e\n \u003cp\u003e- Percentage of total body fat\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.631578947368421%\"\u003e\n \u003cp\u003e1st tertile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.789473684210526%\"\u003e\n \u003cp\u003e1.18 (0.94 - 1.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.2631578947368425%\"\u003e\n \u003cp\u003e0.391\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.789473684210526%\"\u003e\n \u003cp\u003e0.52 (0.42 - 0.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.2631578947368425%\"\u003e\n \u003cp\u003e0.260\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.789473684210526%\"\u003e\n \u003cp\u003e0.52 (0.25 - 1.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.2631578947368425%\"\u003e\n \u003cp\u003e0.373\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"24.210526315789473%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.631578947368421%\"\u003e\n \u003cp\u003e2nd and 3rd tertiles\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.789473684210526%\"\u003e\n \u003cp\u003e1.17 (0.63 - 2.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.2631578947368425%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.789473684210526%\"\u003e\n \u003cp\u003e0.47 (0.26 - 0.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.2631578947368425%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.789473684210526%\"\u003e\n \u003cp\u003e0.34 (0.22 - 0.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.2631578947368425%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"24.210526315789473%\"\u003e\n \u003cp\u003e- Visceral adipose tissue mass\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.631578947368421%\"\u003e\n \u003cp\u003e1st tertile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.789473684210526%\"\u003e\n \u003cp\u003e1.16 (0.92 - 1.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.2631578947368425%\"\u003e\n \u003cp\u003e0.644\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.789473684210526%\"\u003e\n \u003cp\u003e0.62 (0.42 - 0.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.2631578947368425%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.018\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.789473684210526%\"\u003e\n \u003cp\u003e0.77 (0.33 - 1.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.2631578947368425%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.023\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"24.210526315789473%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.631578947368421%\"\u003e\n \u003cp\u003e2nd and 3rd tertiles\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.789473684210526%\"\u003e\n \u003cp\u003e1.18 (0.70 - 2.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.2631578947368425%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.789473684210526%\"\u003e\n \u003cp\u003e0.46 (0.26 - 0.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.2631578947368425%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.789473684210526%\"\u003e\n \u003cp\u003e0.34 (0.19 - 0.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.2631578947368425%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eValues are medians (interquartile range). Mann\u0026ndash;Whitney and Kruskal\u0026ndash;Wallis tests were used. MHNW = metabolically healthy normal weight; MUNW = metabolically unhealthy normal weight; MHO = metabolically healthy obesity; MUO = metabolically unhealthy obesity.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAssociations between SCFA and adiposity indicators\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e also depicts fecal SCFA concentrations values of participants categorized according to anthropometric and adiposity distribution measurements. Propionate and butyrate concentrations were inversely associated with body adiposity assessed by anthropometric measurements and DXA parameters. In general, as adiposity increases, propionate and butyrate concentrations decrease, reaching significance for measurements of BMI (p=0.026; p=0.047), waist circumference (p=0.017; p=0.010), and visceral adipose tissue compartment (p=0.018; p=0.023), respectively. The same was not observed for acetate (p\u0026gt;0.05). Considering the total fecal concentration of SCFA, mean values did not differ across tertiles for any variables studied (p\u0026gt;0.05 \u0026ndash; data not shown). Regarding the concentration of plasma SCFA (\u003cstrong\u003eTable 3\u003c/strong\u003e), fasting propionate correlated inversely with BMI (p=0.021), waist circumference (p=0.019), total body fat (p=0.048), and visceral adipose tissue mass (p=0.007). In addition, propionate AUC was inversely correlated with visceral adipose tissue (p=0.046). The concentration of acetate and total SCFA did not correlate with anthropometric and adiposity parameters (p\u0026gt;0.05).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3\u003c/strong\u003e \u0026ndash; Correlations between plasma SCFA concentrations and anthropometric and adiposity distribution measurements in women from the NutriHS, S\u0026atilde;o Paulo, Brazil.\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"25.77319587628866%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"16.49484536082474%\"\u003e\n \u003cp\u003e\u003cstrong\u003eFasting acetate\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"17.52577319587629%\"\u003e\n \u003cp\u003e\u003cstrong\u003eAUC\u003csub\u003emtt\u003c/sub\u003e of Acetate\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"19.587628865979383%\"\u003e\n \u003cp\u003e\u003cstrong\u003eFasting propionate\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"20.61855670103093%\"\u003e\n \u003cp\u003e\u003cstrong\u003eAUC\u003csub\u003emtt\u003c/sub\u003e of Propionate\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.041666666666668%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003er\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003er\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.333333333333334%\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\"\u003e\n \u003cp\u003er\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\"\u003e\n \u003cp\u003er\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.041666666666668%\"\u003e\n \u003cp\u003eBody mass index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003e-0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\"\u003e\n \u003cp\u003e0.887\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e-0.028\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.333333333333334%\"\u003e\n \u003cp\u003e0.778\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.271\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.021\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\"\u003e\n \u003cp\u003e-0.100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e0.470\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.041666666666668%\"\u003e\n \u003cp\u003eWaist circumference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003e-0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\"\u003e\n \u003cp\u003e0.941\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.333333333333334%\"\u003e\n \u003cp\u003e0.993\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.295\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.019\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\"\u003e\n \u003cp\u003e-0.096\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e0.484\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.041666666666668%\"\u003e\n \u003cp\u003ePercent of total body fat\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003e-0.088\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\"\u003e\n \u003cp\u003e0.382\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e-0.049\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.333333333333334%\"\u003e\n \u003cp\u003e0.625\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.244\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.048\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\"\u003e\n \u003cp\u003e-0.120\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e0.385\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.041666666666668%\"\u003e\n \u003cp\u003eVisceral adipose tissue mass\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003e-0.073\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.291666666666667%\"\u003e\n \u003cp\u003e0.465\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e-0.028\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.333333333333334%\"\u003e\n \u003cp\u003e0.781\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.326\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.007\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.275\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.046\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAUC = area under the curve. The fasting and AUC of butyrate columns does not appear since its concentration was not detectable in plasma samples.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eGlucose homeostasis\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe fecal propionate concentration correlated positively with the insulinogenic index (p=0.047) and with the AUC of GLP-1 (p=0.033). Fecal acetate and propionate correlated negatively with the AUC of glucose (p\u0026le; 0.04) \u003cstrong\u003e(Table 4)\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003eRegarding plasma concentrations (\u003cstrong\u003eTable 5\u003c/strong\u003e), the AUC of total SCFA concentration (r=-0.313; p=0.012) and AUC of acetate concentration (r=-0.206; p=0.039) were inversely correlated with glycated hemoglobin. The AUC of acetate concentration was inversely correlated with fasting insulin (r=-0.211; p=0.034), and the AUC of propionate concentration had an inverse correlation with fasting glucose (r=-0.361; p=0.007). However, the AUC of total SCFA (r=0.320; p=0.009) and the AUC of acetate concentration (r=0.262; p=0.008) had a positive correlation with the AUC of glucose. Fasting acetate concentration (r=0.274; p=0.016) and total SCFA concentration (r=0.268; p=0.040) had a positive correlation with the AUC of GLP-1. There were no positive correlations between plasma SCFA concentration and insulin secretion and insulin sensitivity markers.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4\u003c/strong\u003e \u0026ndash; Correlations between fecal short-chain fatty acids and energy and glucose homeostasis in women from the NutriHS, S\u0026atilde;o Paulo, Brazil.\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.42424242424242%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"19.19191919191919%\"\u003e\n \u003cp\u003e\u003cstrong\u003eAcetate\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"19.19191919191919%\"\u003e\n \u003cp\u003e\u003cstrong\u003ePropionate\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"19.19191919191919%\"\u003e\n \u003cp\u003e\u003cstrong\u003eButyrate\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.75%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003er\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003er\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003er\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.75%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eGlucose Homeostasis\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"43.75%\"\u003e\n \u003cp\u003eGlycated hemoglobin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e0.026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e0.792\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e-0.136\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e0.163\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e-0.034\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e0.733\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"43.75%\"\u003e\n \u003cp\u003eFasting plasma glucose\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e-0.174\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e0.086\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e-0.045\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e0.661\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e-0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e0.892\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"43.75%\"\u003e\n \u003cp\u003eFasting insulin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e0.114\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e0.262\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e0.017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e0.865\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e0.043\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e0.679\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.75%\"\u003e\n \u003cp\u003eFasting GLP-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e-0.121\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e0.292\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e-0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e0.915\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e-0.101\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e0.386\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.75%\"\u003e\n \u003cp\u003eHOMA-IR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e0.161\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e0.113\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e0.032\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e0.752\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e-0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e0.940\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.75%\"\u003e\n \u003cp\u003ePREDIM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e-0.067\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e0.516\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e0.134\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e0.192\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e0.121\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e0.246\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.75%\"\u003e\n \u003cp\u003eInsulinogenic Index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e0.062\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e0.545\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.219\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.030\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e0.096\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e0.357\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.75%\"\u003e\n \u003cp\u003eAUC\u003csub\u003emtt\u0026nbsp;\u003c/sub\u003ePlasma glucose\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.209\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.040\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.210\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.039\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e-0.106\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e0.308\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.75%\"\u003e\n \u003cp\u003eAUC\u003csub\u003emtt\u0026nbsp;\u003c/sub\u003eInsulin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e0.134\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e0.187\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e0.024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e0.816\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e0.029\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e0.777\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.75%\"\u003e\n \u003cp\u003e\u0026nbsp;AUC\u003csub\u003emtt\u0026nbsp;\u003c/sub\u003eGLP-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e0.047\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e0.653\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.221\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.033\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e0.084\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e0.430\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.75%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eEnergetic Homeostasis\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.75%\"\u003e\n \u003cp\u003eResting Energy Expenditure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.214\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.039\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e-0.077\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e0.454\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e-0.073\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e0.486\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.75%\"\u003e\n \u003cp\u003eResting Energy Expenditure/kg body\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e0.050\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e0.625\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.249\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.013\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.230\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.025\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.75%\"\u003e\n \u003cp\u003eAUC\u003csub\u003emtt\u003c/sub\u003e Carbohydrate Oxidation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e0.851\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e-0.137\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e0.178\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e-0.121\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e0.242\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.75%\"\u003e\n \u003cp\u003eAUC\u003csub\u003emtt\u003c/sub\u003e Lipid Oxidation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e0.151\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e0.137\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.214\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.036\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e0.077\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e0.457\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eSpearman correlation test. AUC\u003csub\u003emtt\u0026nbsp;\u003c/sub\u003e= area under the curve obtained from the standard mixed-meal tolerance test, HOMA-IR = homeostatic model assessment, PREDIM = predicted\u0026nbsp;\u003cem\u003eM\u003c/em\u003e value, GLP-1 = glucagon-like peptide-1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5\u003c/strong\u003e \u0026ndash; Correlations between plasma short-chain fatty acids and energy and glucose homeostasis in the women studied from NutriHS, S\u0026atilde;o Paulo, Brazil.\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" width=\"20.408163265306122%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" width=\"25.510204081632654%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal SCFA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" width=\"25.510204081632654%\"\u003e\n \u003cp\u003e\u003cstrong\u003eAcetate\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" width=\"28.571428571428573%\"\u003e\n \u003cp\u003e\u003cstrong\u003ePropionate\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" width=\"15.789473684210526%\"\u003e\n \u003cp\u003e\u003cstrong\u003eFasting\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"15.789473684210526%\"\u003e\n \u003cp\u003e\u003cstrong\u003eAUC\u003csub\u003emtt\u003c/sub\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"15.789473684210526%\"\u003e\n \u003cp\u003e\u003cstrong\u003eFasting\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"15.789473684210526%\"\u003e\n \u003cp\u003e\u003cstrong\u003eAUC\u003csub\u003emtt\u003c/sub\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"15.789473684210526%\"\u003e\n \u003cp\u003e\u003cstrong\u003eFasting\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"21.05263157894737%\"\u003e\n \u003cp\u003e\u003cstrong\u003eAUC\u003csub\u003emtt\u003c/sub\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8%\"\u003e\n \u003cp\u003er\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8%\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8%\"\u003e\n \u003cp\u003er\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8%\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8%\"\u003e\n \u003cp\u003er\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8%\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8%\"\u003e\n \u003cp\u003er\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8%\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8%\"\u003e\n \u003cp\u003er\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8%\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8%\"\u003e\n \u003cp\u003er\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12%\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.05263157894737%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eGlucose Homeostasis\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.315789473684211%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.315789473684211%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.315789473684211%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.315789473684211%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"21.05263157894737%\"\u003e\n \u003cp\u003eGlycated hemoglobin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e-0.060\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e0.554\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.315789473684211%\"\u003e\n \u003cp\u003e-0.313\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.315789473684211%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.012\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e-0.081\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e0.420\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.315789473684211%\"\u003e\n \u003cp\u003e-0.206\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.315789473684211%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.039\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e-0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e0.984\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e-0.053\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.707\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"21.05263157894737%\"\u003e\n \u003cp\u003eFasting plasma glucose\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e-0.069\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e0.581\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.315789473684211%\"\u003e\n \u003cp\u003e-0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.315789473684211%\"\u003e\n \u003cp\u003e0.914\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e0.035\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e0.728\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.315789473684211%\"\u003e\n \u003cp\u003e0.136\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.315789473684211%\"\u003e\n \u003cp\u003e0.169\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e-0.235\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e0.057\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e-0.361\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.007\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"21.05263157894737%\"\u003e\n \u003cp\u003eFasting insulin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e-0.171\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e0.169\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.315789473684211%\"\u003e\n \u003cp\u003e0.101\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.315789473684211%\"\u003e\n \u003cp\u003e0.418\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e-0.211\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.034\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.315789473684211%\"\u003e\n \u003cp\u003e-0.066\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.315789473684211%\"\u003e\n \u003cp\u003e0.507\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e0.991\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e0.126\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.361\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.05263157894737%\"\u003e\n \u003cp\u003eFasting GLP-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e0.262\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.039\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.315789473684211%\"\u003e\n \u003cp\u003e-0.037\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.315789473684211%\"\u003e\n \u003cp\u003e0.766\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e0.051\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e0.607\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.315789473684211%\"\u003e\n \u003cp\u003e-0.020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.315789473684211%\"\u003e\n \u003cp\u003e0.839\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e-0.026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e0.835\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e0.048\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.730\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.05263157894737%\"\u003e\n \u003cp\u003eHOMA-IR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e-0.039\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e0.755\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.315789473684211%\"\u003e\n \u003cp\u003e0.226\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.315789473684211%\"\u003e\n \u003cp\u003e0.068\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e-0.079\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e0.430\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.315789473684211%\"\u003e\n \u003cp\u003e0.027\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.315789473684211%\"\u003e\n \u003cp\u003e0.789\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e-0.066\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e0.601\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e0.087\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.528\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.05263157894737%\"\u003e\n \u003cp\u003ePrediM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e0.086\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e0.488\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.315789473684211%\"\u003e\n \u003cp\u003e-0.154\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.315789473684211%\"\u003e\n \u003cp\u003e0.214\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e0.057\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e0.566\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.315789473684211%\"\u003e\n \u003cp\u003e-0.039\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.315789473684211%\"\u003e\n \u003cp\u003e0.696\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e0.103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e0.407\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e-0.061\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.657\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.05263157894737%\"\u003e\n \u003cp\u003eInsulinogenic Index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e0.883\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.315789473684211%\"\u003e\n \u003cp\u003e0.052\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.315789473684211%\"\u003e\n \u003cp\u003e0.685\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e-0.051\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e0.610\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.315789473684211%\"\u003e\n \u003cp\u003e-0.099\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.315789473684211%\"\u003e\n \u003cp\u003e0.320\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e-0.062\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e0.621\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.985\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.05263157894737%\"\u003e\n \u003cp\u003eAUC\u003csub\u003emtt\u0026nbsp;\u003c/sub\u003ePlasma glucose\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e0.082\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e0.512\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e0.320\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.009\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e0.110\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e0.270\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e0.262\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.008\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e0.035\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e0.778\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e0.099\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.473\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.05263157894737%\"\u003e\n \u003cp\u003eAUC\u003csub\u003emtt\u0026nbsp;\u003c/sub\u003eInsulin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e-0.083\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e0.509\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e0.083\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e0.508\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e-0.056\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e0.577\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e-0.031\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e0.754\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e-0.171\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e0.170\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e0.029\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.833\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.05263157894737%\"\u003e\n \u003cp\u003eAUC\u003csub\u003emtt\u0026nbsp;\u003c/sub\u003eGLP-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e0.268\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.040\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e0.910\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e0.229\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.022\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e0.049\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e0.622\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e0.049\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e0.696\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e0.117\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.397\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.05263157894737%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eEnergetic Homeostasis\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.05263157894737%\"\u003e\n \u003cp\u003eREE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e0.032\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e0.802\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e0.102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e0.420\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e-0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e0.905\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e-0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e0.888\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e-0.157\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e0.211\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.964\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.05263157894737%\"\u003e\n \u003cp\u003eREE/kg body\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e-0.031\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e0.759\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e-0.020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e0.875\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e-0.044\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e0.662\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e-0.029\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e0.768\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e0.300\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.019\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e0.113\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.411\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.05263157894737%\"\u003e\n \u003cp\u003eAUC\u003csub\u003emtt\u003c/sub\u003e CHO Oxidation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e0.967\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e0.974\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e0.036\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e0.719\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e-0.085\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e0.392\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e0.158\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e0.205\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e0.097\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.479\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.05263157894737%\"\u003e\n \u003cp\u003eAUC\u003csub\u003emtt\u003c/sub\u003e Lipid Oxidation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e-0.082\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e0.510\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e-0.075\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e0.551\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e-0.125\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e0.209\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e0.886\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e-0.035\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e0.778\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.315789473684211%\"\u003e\n \u003cp\u003e0.047\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\"\u003e\n \u003cp\u003e0.735\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eSpearman correlation test. SCFA = short-chain fatty acids, AUC\u003csub\u003emtt\u0026nbsp;\u003c/sub\u003e= area under the curve obtained from the standard mixed-meal tolerance test, HOMA-IR = homeostatic model assessment, PREDIM = predicted \u003cem\u003eM\u003c/em\u003e value, GLP-1 = glucagon-like peptide-1, REE = resting energy expenditure, CHO = carbohydrate. The fasting value and AUC of butyrate columns does not appear since its concentration was not detectable in plasma samples.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eEnergy homeostasis\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe fecal acetate concentration was positively correlated with REE (p=0.039). The fecal propionate concentration was positively correlated with REE adjusted for body weight (p=0.013) and with the AUC of lipid oxidation (p=0.036). Additionally, fecal butyrate concentration correlated positively with REE adjusted for kg of body weight (p=0.025) (\u003cstrong\u003eTable 4\u003c/strong\u003e). Moreover, by correlating the oxidation rates of lipids and carbohydrates at each of the times with the fecal concentrations of SCFA, it was observed that fecal propionate concentration was inversely correlated with the oxidation rate of carbohydrates in fasting (r=-0.214; p=0.035), and propionate concentration (r=0.219, p=0.030; r=0.204, p=0.044) was positively correlated with lipid oxidation at 60 min. and 120 min., respectively. Furthermore, fecal butyrate concentration presented a positive correlation with lipid oxidation at 60 min (r=0.210; p=0.043). For the other oxidation rates, there was no significant correlation (data not shown). Regarding plasma concentrations of SCFA, only fasting propionate concentration had a positive correlation with the REE adjusted for body weight (r=0.300; p=0.019) (\u003cstrong\u003eTable 5\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAssociations between fecal and plasma SCFA concentrations\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eIn general, the concentrations of fecal SCFAs were not statistically correlated with their respective plasma concentrations (data not shown), except for the concentrations of total SCFAs in feces, which were inversely correlated with postprandial concentrations of total SCFAs (r=-0.262; p=0.043).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eUsing indirect calorimetry associated with the standard mixed-meal tolerance test and the evaluation of body composition by densitometry, this study investigated the hypothesis that fecal and plasma SCFA concentrations were associated with the different metabolic phenotypes of obesity, adiposity, and specific components of energy and glucose homeostasis. To the best of our knowledge, studies investigating SCFA among the metabolic phenotypes of obesity, in addition to the quantification of plasma SCFA simultaneously with GLP-1 postprandially under physiological conditions, are scarce. The main findings were as follows: 1) fecal SCFA concentration did not differ among the metabolic phenotypes, and only fasting plasma propionate was increased in MHNW compared to MHO and MUO participants; 2) concentrations of fecal propionate and butyrate and plasma propionate were inversely associated with total and visceral adiposity; 3) fecal and plasma SCFA concentrations were associated with favorable glucose homeostasis represented by reduced fasting glucose and insulin and HbA1c levels and accompanied by increased fasting and postprandial GLP-1 and a more preserved beta-cell function regarding the first phase of insulin secretion; and 4) fecal and plasma SCFA concentrations were also associated with favorable energy homeostasis, characterized by a positive correlation with resting energy expenditure and lipid oxidation rate and an inverse correlation with the oxidation rate of carbohydrates.\u003c/p\u003e\n\u003cp\u003eConcerning the comparisons of SCFA among the four metabolic phenotypes, it was expected that the highest fecal concentrations of SCFA would be found in metabolically healthy phenotypes (MHNW and MHO), but this hypothesis was not confirmed. Concentrations of fecal SCFA did not differ among the metabolic phenotypes, and only fasting plasma propionate was increased in the MHNW compared to the MHO and MUO phenotypes, indicating that SCFA were more associated with adiposity than with metabolic health itself. It is speculated that the absence of SCFA differences among the four phenotypes studied may be because the cardiometabolic alterations of metabolically unhealthy participants were not very severe. Recently, Telle-Hansen et al. [5] demonstrated higher serum concentrations of propionate in individuals with the MHO phenotype than in those with the MUO phenotype, while butyrate and acetate did not differ between the groups. The mentioned study evaluated a very small sample of eutrophic and obese people but did not include overweight individuals, as in the present study.\u003c/p\u003e\n\u003cp\u003eFew studies to date have evaluated the distribution of body fat, measured by DXA, with concentrations of SCFA for comparison purposes. The data of this study showed that fecal propionate and butyrate and plasma propionate were inversely associated with total and visceral adiposity. Dugas et al. [35] demonstrated that higher fecal concentrations of SCFA in individuals with normal weight were associated with lower weight gain. On the other hand, previous studies have shown that overweight and obese individuals have higher fecal [36-38] concentrations of SCFA than normal-weight individuals. Studies have addressed the \u0026ldquo;energy harvesting\u0026rdquo; hypothesis as an explanation for additional weight gain, whereby SCFA may contribute to extra calories through fermentation in obese individuals [39]. Conversely, Muller et al. [15] found that circulating, but not fecal, SCFAs were associated with BMI. Experimental studies [7, 10, 40] and clinical trials [11, 12] have suggested that oral supplementation with SCFA can be effective in reducing body weight.\u003c/p\u003e\n\u003cp\u003eA favorable association between glucose homeostasis and concentrations of fecal and plasma SCFAs was demonstrated, with an emphasis on positive associations with fasting and postprandial GLP-1. Recently, Muller et al. [15] demonstrated that fasting circulating SCFAs, but not fecal SCFAs, are positively associated with fasting plasma GLP-1 in humans. These findings are corroborated mainly by the results of experimental and clinical studies, which have shown that the administration of SCFA can improve the production and liberation of GLP-1 [10] and glycemia [41-43]. SCFAs are important signaling molecules for at least two G-coupled receptors, GPR41 and GPR43 [44]. These receptors, in addition to binding to SCFA, are also important means of binding for the production and liberation of GLP-1 [45]. The findings regarding GLP-1 may also explain the favorable association found in the present study between SCFA and the insulinogenic index, which ultimately reflects the first phase of insulin secretion. Regarding insulin resistance or sensitivity, in this study, HOMA-IR and PREDIM did not show any correlation with SCFA. These results may have been influenced by the presence of normal glucose tolerance in most of the participants, as well as by low levels of insulin resistance. It is speculated that if the hyperinsulinemic-euglycemic clamp test - the gold standard for the evaluation of insulin sensitivity - had been used, in view of its accuracy and precision, perhaps we would have detected significant associations with SCFA.\u003c/p\u003e\n\u003cp\u003eRegarding energetic homeostasis, the concentrations of all three fecal SCFAs and fasting propionate were positively correlated with resting energy expenditure, in agreement with previous findings in the scientific literature [9, 46]. The concentration of fecal propionate was positively correlated with the lipid oxidation rate in the postprandial state and inversely correlated with the oxidation rate of carbohydrates in fasting. In obese and overweight men, the infusion of sodium acetate into the distal colon led to an increased lipid oxidation rate compared to the control group [47]. In obese rats [11], administration of sodium butyrate was effective in reducing body weight, and this benefit was attributed to increased REE and lipid oxidation, explained by the positive regulation of gene expression involved in the thermogenesis of PPAR\u0026gamma; [11, 48]. In normoglycemic men with overweight or obesity [46], the administration of a mixture containing the three main SCFA in the distal colon increased fat oxidation in fasting and REE, measured by indirect calorimetry, which was also correlated with increased plasma acetate concentrations. Vadder et al. [40] pointed out that the explanation for these potential effects is intestinal gluconeogenesis, in which propionate can induce intestinal gluconeogenesis through the brain-intestine axis, with this communication being mediated by the FFA3 receptor present in the portal vein. Finally, increased fat oxidation by SCFA and increased oxidative capacity of the skeletal muscle can improve metabolic flexibility, resulting in possible partial inhibition of intracellular lipolysis in adipocytes, induced mainly by acetate, and causing reduced fat accumulation and an improvement in insulin action in peripheral tissues [46].\u003c/p\u003e\n\u003cp\u003eUsing a liquid standard meal with no fiber content, we observed that concentrations of plasma acetate and total SCFA exhibit a postprandial rise. In rodents, a recent study demonstrated that SCFA exhibit a postprandial rise similar to ingested macronutrients [49]. In previous studies with humans, plasma SCFA concentrations increased 30 minutes after meal tests [50, 51]. The reason for this increase is currently unknown, and we can hypothesize that the increase reflects the metabolism of dietary fiber from previous meals. Another hypothesis would be greater intestinal absorption of SCFA present in the colon lumen after meal intake through enteroendocrine cell signaling or, with the availability of macronutrients in the postprandial period in the portal vein, reduced liver metabolization/oxidation of SCFA may occur as substrates in gluconeogenesis and lipogenesis.\u003c/p\u003e\n\u003cp\u003eIn the present study, the correlation between plasma and fecal concentrations of the different SCFA was significant and negative only between the fecal concentration of total SCFA and the postprandial plasma concentration of total SCFA. In the same vein, Vogt and Wolever [52] demonstrated that the rate of absorption and fecal excretion of acetate were inversely correlated. However, Muller et al. [15], when comparing plasma and fecal concentrations of SCFA, detected only a positive association between fecal and serum propionate, suggesting that these associations still deserve further investigation. This study has limitations. Among the potential flaws, we can point out that the study has a cross-sectional design, which did not allow us to point out causality. The selection bias predominantly considered young and educated women, with only small abnormalities classified in the metabolically obese phenotypes. Consequently, our results cannot be extrapolated to the general population. As we applied the more rigorous definition criteria for metabolic health proposed by the \u0026ldquo;BioSHaRE-EU Healthy Obese Project\u0026rdquo; [34], in most cases, the participants in the study with a metabolically unhealthy phenotype presented alterations in only one or two parameters. Therefore, a study composed of patients with more deteriorated metabolic health may demonstrate differences in SCFA levels among phenotypes.\u003c/p\u003e\n\u003cp\u003eNevertheless, the study presents strong points, such as the evaluation of a relatively large cohort of 111 participants under dynamic and physiological conditions, the assessment of body composition through DXA, which is gold standard equipment for the evaluation of body fat distribution, as well as that the sample strictly composed of women of fertile age, considering that hormonal changes common in menopause could affect body adiposity and, consequently, glycemic homeostasis. Moreover, the participants were nutritionists or students of nutrition, which ensures that they are aware of the relevance of the study and the importance of contributing to the research by providing good quality data from the questionnaires, increasing the credibility of the reproducibility of the results.\u003c/p\u003e\n\u003cp\u003eFinally, considering that the main studies investigating the roles of SCFA in energy and glucose metabolism were conducted in animal models, cohort studies and interventions with humans are necessary to investigate if the possible positive effects of SCFA translate into benefits for body weight loss and control, improvement in glucose homeostasis and biochemical parameters associated with cardiometabolic risk.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThese findings reinforce the concept that the concentrations of fecal and plasma SCFAs are linked to specific components of energy and glucose homeostasis and body adiposity. On the other hand, it was not possible to discriminate the different metabolic phenotypes of adiposity based on the determination of fecal SCFA concentration. Considering that the sample of this study was composed of young women, it is believed that the long-term follow-up of these participants could open up new research opportunities and answer whether there is a cause-and-effect relationship of SCFA in the natural history of obesity and type 2 diabetes, providing input to plan interventions with a new therapeutic focus.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments:\u0026nbsp;\u003c/strong\u003eThe authors would like to thank Camila Machado Xavier, Najla Sim\u0026atilde;o Kfouri Crouchan, Vin\u0026iacute;cius Ferreira dos Santos, Isis Tande da Silva, and Luciana Dias Folchetti for their valuable support during data collection. They would also like to thank the S\u0026atilde;o Paulo Research Foundation (FAPESP) and the National Council for Scientific and Technological Development (CNPq) for financial support, the Obesity and Comorbidities Research Center \u0026ndash; OCRC for the additional financial support, and Laborat\u0026oacute;rio de Investiga\u0026ccedil;\u0026atilde;o em Metabolismo e Diabetes (LIMED) and Gastrocentro UNICAMP for the space and support provided. Espa\u0026ccedil;o da Escrita \u0026ndash; Pr\u0026oacute;-Reitoria de Pesquisa - UNICAMP \u0026ndash; for the language services provided.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding Statement:\u0026nbsp;\u003c/strong\u003eThis project received funding from the S\u0026atilde;o Paulo Research Foundation (FAPESP), grant n.2017/10185-9, and three research scholarships n. 2017/24578-2, 2019/05450-0 and 2019/07769-4. Two other research grants were supported by the National Council for Scientific and Technological Development (CNPq).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions: IS\u003c/strong\u003e recruited participants and contributed to data collection, project administration, laboratory, and statistical analysis, writing and editing the manuscript. \u003cstrong\u003eFB\u003c/strong\u003e recruited participants and contributed to data collection and project administration. \u003cstrong\u003eMGB\u003c/strong\u003e recruited participants and contributed to data collection and biochemical analysis. \u003cstrong\u003eRGBONF\u0026nbsp;\u003c/strong\u003erecruited participants and contributed to data collection. \u003cstrong\u003eASH\u0026nbsp;\u003c/strong\u003erecruited participants and contributed to data collection. \u003cstrong\u003eCOR\u003c/strong\u003e contributed to the methodology and biochemical analysis. \u003cstrong\u003eMRA\u0026nbsp;\u003c/strong\u003econtributed to the methodology and biochemical analysis. \u003cstrong\u003eTC\u0026nbsp;\u003c/strong\u003econtributed to the methodology and biochemical analysis. \u003cstrong\u003eBAP\u0026nbsp;\u003c/strong\u003econtributed to the study conception. \u003cstrong\u003eAT\u0026nbsp;\u003c/strong\u003econtributed to the data analysis and discussion. \u003cstrong\u003eDEC\u003c/strong\u003e contributed to methodology conception and implementation and project administration.\u003cstrong\u003e\u0026nbsp;BG\u0026nbsp;\u003c/strong\u003econtributed to project conception and administration. \u003cstrong\u003eSRGF\u0026nbsp;\u003c/strong\u003econtributed to the study conceptualization and manuscript preparation. \u003cstrong\u003eACJV\u003c/strong\u003e contributed to the study conceptualization, funding acquisition, project administration, data analysis, and manuscript preparation. All authors contributed to the critical revision and the final approval of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest:\u0026nbsp;\u003c/strong\u003eThe authors declare no conflicts of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eBluher M (2019). Obesity: global epidemiology and pathogenesis. Nat Rev Endocrinol. https://doi.org/10.1038/s41574-019-0176-8\u003c/li\u003e\n \u003cli\u003eCuthbertson DJ, Steele T, Wilding JP, et al (2017). What have human experimental overfeeding studies taught us about adipose tissue expansion and susceptibility to obesity and metabolic complications? Int J Obes (Lond). https://doi.org/10.1038/ijo.2017.4.\u003c/li\u003e\n \u003cli\u003eBl\u0026uuml;her M (2020). Metabolically Healthy Obesity. Endocrine Reviews. https://doi.org/10.1210/endrev/bnaa004.\u003c/li\u003e\n \u003cli\u003e4. Castaner O, Goday A, Park YM, et al (2018). 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Metabolism. https://doi.org/10.1016/0026-0495(88)90110-2.\u003c/li\u003e\n \u003cli\u003eBorges JH, Langer RD, Cirolini VX, P\u0026aacute;scoa MA, Guerra-J\u0026uacute;nior G, Gon\u0026ccedil;alves EM (2016). Minimum Time to Achieve the Steady State and Optimum Abbreviated Period to Estimate the Resting Energy Expenditure by Indirect Calorimetry in Healthy Young Adults. Nutr Clin Pract. https://doi.org/10.1177/0884533615627268.\u003c/li\u003e\n \u003cli\u003eFrayn KN (1983). Calculation of substrate oxidation rates in vivo from gaseous exchange. J Appl Physiol Respir Environ Exerc Physiol. https://doi.org/10.1152/jappl.1983.55.2.628.\u003c/li\u003e\n \u003cli\u003eMatthews DR, Hosker JP, Rudenski AS, Naylor BA, Treacher DF, Turner RC (1985). Homeostasis model assessment: insulin resistance and beta-cell function from fasting plasma glucose and insulin concentrations in man. 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The American Journal of Clinical Nutrition. https://doi.org/10.3945/ajcn.2009.28521.\u003c/li\u003e\n \u003cli\u003eLappi J, Mykk\u0026auml;nen H, Knudsen KEB, et al (2014). Postprandial glucose metabolism and SCFA after consuming wholegrain rye bread and wheat bread enriched with bioprocessed rye bran in individuals with mild gastrointestinal symptoms. Nutrition Journal. https://doi.org/10.1186/1475-2891-13-104.\u003c/li\u003e\n \u003cli\u003eVogt, J.A.; Wolever, T.M. Fecal acetate is inversely related to acetate absorption from the human rectum and distal colon. J. Nutr. 2003, 133, 3145\u0026ndash;3148.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"endocrine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"endo","sideBox":"Learn more about [Endocrine](https://www.springer.com/journal/12020)","snPcode":"12020","submissionUrl":"https://submission.nature.com/new-submission/12020/3","title":"Endocrine","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"short-chain fatty acids, obesity, metabolic phenotype of obesity, adiposity, glucose homeostasis, energy homeostasis","lastPublishedDoi":"10.21203/rs.3.rs-1991138/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-1991138/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePurpose: The gut microbiome is associated with obesity, mainly mediated by bacteria-produced short-chain fatty acids (SCFAs). It is unknown how SCFA concentrations are associated among the phenotypes metabolically healthy normal weight (MHNW), metabolically unhealthy normal weight (MUNW), metabolically healthy obese/overweight (MHO), and\u003cem\u003e \u003c/em\u003emetabolically unhealthy obese/overweight (MUO). We compared plasma and fecal SCFA concentrations among adult women categorized according to the metabolic phenotypes mentioned above and examined associations between SCFA and adiposity and components of energy and glucose homeostasis.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMethods: This was a cross-sectional study involving 111 participants. Body composition was assessed by DEXA. Energy and glycemic homeostasis were assessed by the standard mixed-meal tolerance test coupled with indirect calorimetry. SCFAs were quantified by gas chromatography and mass spectrometry.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eResults: Only plasma propionate was increased in the MHNW phenotype compared to the MHO and MUO phenotypes [p\u0026lt;0.05]. Fecal propionate and butyrate concentrations and plasma propionate concentrations were inversely associated with total and visceral adiposity [p\u0026lt;0.05]. Fecal and plasma SCFA concentrations were associated with reduced glucose, insulin, and HbA1c levels, increased fasting and postprandial GLP-1 levels and more preserved beta-cell function [p\u0026lt;0.05]. Fecal and plasma SCFA concentrations were positively correlated with resting energy expenditure and lipid oxidation rate and inversely correlated with oxidation rate of carbohydrates [p\u0026lt;0.05].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eConclusion: These findings reinforce the concept that fecal and plasma SCFA concentrations are linked to specific components of energy and glucose homeostasis and body adiposity. On the other hand, it was not possible to discriminate the different metabolic phenotypes of adiposity based on the determination of fecal SCFA concentration.\u003c/p\u003e","manuscriptTitle":"Short-chain fatty acids are associated with adiposity and energy and glucose homeostasis among different metabolic phenotypes in the Nutritionists’ Health Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-10-06 19:49:42","doi":"10.21203/rs.3.rs-1991138/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Minor Revisions Needed","date":"2023-03-02T03:12:02+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2023-01-03T08:25:39+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2022-10-03T07:11:37+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2022-09-13T14:36:21+00:00","index":"","fulltext":""},{"type":"submitted","content":"Endocrine","date":"2022-09-09T09:20:31+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"endocrine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"endo","sideBox":"Learn more about [Endocrine](https://www.springer.com/journal/12020)","snPcode":"12020","submissionUrl":"https://submission.nature.com/new-submission/12020/3","title":"Endocrine","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"ece342a0-f646-459b-9d9f-4fef16efee43","owner":[],"postedDate":"October 6th, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2023-10-16T20:29:17+00:00","versionOfRecord":{"articleIdentity":"rs-1991138","link":"https://doi.org/10.1007/s12020-023-03356-0","journal":{"identity":"endocrine","isVorOnly":false,"title":"Endocrine"},"publishedOn":"2023-04-08 20:23:56","publishedOnDateReadable":"April 8th, 2023"},"versionCreatedAt":"2022-10-06 19:49:42","video":"","vorDoi":"10.1007/s12020-023-03356-0","vorDoiUrl":"https://doi.org/10.1007/s12020-023-03356-0","workflowStages":[]},"version":"v1","identity":"rs-1991138","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-1991138","identity":"rs-1991138","version":["v1"]},"buildId":"_2-kVJe1T_tPrBINL-cwx","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}