Study population
In the current matched case-control study, we enrolled 36 healthy women (18 cases and 18 controls), who had been referred to a nutritional laboratory according a public invitation in Tehran. We used a Telegram bot and a total of 1300 participants were signed up among which, 122 individuals were eligible samples. Finally, after assessing their medical history, 85 volunteers were qualified for the study (Figure-1). After a follow-up period of 2-3 months, to ensure no medication and supplementation, as well as no fluctuations in weight and other inclusion criteria were maintained, individuals were referred to the Nutrition Laboratory of the Faculty of Nutrition and Dietetics located at the School of Health of Tehran University of Medical Sciences for further evaluation. With the purpose of random distribution, the participants in the case and control groups were divided into paired blocks in terms of age, BMI, and RMR level. Each sample in the case group (low RMR) was randomly matched with a sample in the control group (normal RMR) with a maximum difference in age ± 2 years and BMI ± 2 units. Volunteers were randomly selected for the study based on the following inclusion criteria (for both cases and controls): 25≤ BMI <40 (obesity and overweight), aged 18-50 years old. To ensure comparable data, we included the following exclusion criteria: use of antibiotics (within the previous 3 months) [79], use of alcohol, smoking, significant infection, history of diabetes, coronary, thyroid diseases or other hormonal disease and cancer. Use of medications or treatments effective on their RMR, use of supplementary vitamins and minerals, being pregnant, lactating or menopausal, daily or irregular intake of probiotics within the previous 2 months, history of digestive diseases, such as inflammatory bowel disease, irritable and constipation. Also those with gastrointestinal surgery, use of dietary supplements for weight loss during the past 6 months [38] were excluded. The inclusion and exclusion criteria of the samples were based on the individual's medical history or their own statements. The study protocol was approved by the Ethics Commission of Tehran University of Medical Sciences (IR.TUMS.VCR.REC.1398.562) and prior to the study, all subjects signed a written informed consent.
Demographic questions
Demographic questions were used to collect data about characteristics such as age, smoking status, education level, lifestyle, marital status, menopause, medical history, taking medication, supplement use etc.
Anthropometric assessment
For each participant, body composition, including weight, BMI, body fat mass (BFM), fat free mass (FFM), body fat percentage (%), waist to hip ratio and waist circumference were measured. All measurements performed by using a multi-frequency bioelectrical impedance analyzer, InBody 770 scanner (Inbody Co., Seoul, Korea). For all participants, a very low, safe electrical signal was sent from four metal electrodes through both hands and feet. The electrical signal passes quickly through water that is present in hydrated muscle tissue but meets resistance when it hits fat tissue. This resistance, known as impedance, can be measured to infer the proportion of fat free mass and fat mass. Measurement was conducted according to the manufacturer’s guidance.
Resting Metabolic Rate measurements
RMR was measured by indirect calorimetry (MetaLyzer®3B, made in Germany). The RMR was assessed in the morning after a requested overnight fast (10-12 hour). It was measured under “resting conditions,” which included no prior severe exercise, and abstinence from alcohol and caffeine. The indirect calorimetry device was calibrated before each assessment. To measure RMR (m-RMR), after 20 minutes of rest, the patients assumed the supine position without movement for 30 min, and the middle 20 min was used for calculation (the first and the last 5 min were ignored). We stratified participants based on measured RMR (m-RMR) and predicted RMR values (p-RMR) [80]. Patients were defined as “hypometabolic” when their measured RMR was less than 85 % of the predicted RMR, based on the Harris and Benedict equation [81], or “normometabolic” when it was within ± 15% of the predicted RMR. During the luteal phase of the menstrual cycle, RMR typically rises and is lower during menstruation. For this purpose, indirect calorimetry performed for all premenopausal women during the follicle process [82-84].
Dietary assessments
Dietary intake was assessed by a semi-quantitative 147-item food frequency questionnaire (FFQ) that has been validated in previous work [85]. Questionnaires were completed in the presence of a trained nutritionist, and participants reported the intake frequency of each food item over the past year. Household measurements and servings were then converted into weight (grams per day). Dietary intakes were analyzed using NUTRITIONIST 4 (First Data Bank, San Bruno, CA) software for estimating energy and nutrient intake.
Physical activity assessment
For evaluating physical activity in the form of metabolic equivalent hours per week (MET-h/wk), the short form of the International Physical Activity Questionnaire (IPAQ) was used, whose Persian language version has been validated by Moghaddam et al. [86]. Scores were calculated according to the frequency and time spent on light, moderate, high, and very high-intensity activities, based on a list of common daily activities.
Blood sampling
Following a 12-hour overnight fasting, 10 cc of venous blood sample was drawn between 7 and 10:30 a.m., and immediately divided. Half of each sample was kept for 30 minutes at room temperature until clotting, then, blood samples were centrifuged at 3000 g for 20 minutes, and decanted into several separate clean micro-tubes and stored in a freezer at −80°C for further analysis.
Biochemical and hormonal assessments
All hormones were determined by using enzyme-linked immunosorbent assay (ELISA) method. Leptin, ghrelin and insulin levels were assessed using a LDN kit (Nordhorn, Germany) with a sensitivity of 0.50 ng/ml, a Crystal Day Christian Day kit with a sensitivity of 0.01 ng/ml, and an IBT kit (infinitum biotech, IBT; Netherland) with a sensitivity of 0.11 μU / ml. Intra- and inter-assay coefficients of variation (CV) reported by the manufacturer for leptin, ghrelin, and insulin were 3.7– 5% and 5.9–5.8%, CV<8% and CV<10%, and 3.7-4.2%, and 3.7-4.2%, respectively.
Fecal sampling and DNA extraction
Participants were asked to collect their stool samples in a conventional laboratory plastic container dedicated to fecal sampling. The samples were moved immediately to the laboratory in ice packs and stored at -80 °C (flash frozen) upon arrival (within 2 h.), before further processing. Extraction of total bacterial DNA from 200 mg of each stool sample has been done using QIAamp The Fast DNA Stool Mini Kit (51604) (Qiagen, Hilden, Germany) was used according to manufacturer’s instructions. The purity and concentration of the extracted DNA were determined by Nanodrop spectrophotometer (Thermo Scientific NanoDrop, USA) [87]. The extracted DNAs were stored at −20 °C until further analysis. By using the nucleotide BLAST in NCBI, the specificity of the primers was evaluated. The specific sequences of primers are shown in Table 1.
q-PCR analyses
The abundance of bacteria was analyzed using Quantitative real-time PCR based on SYBER green method (LightCycler® 96 SW 1.1; Roche, Germany) [35,87,88]. Each 10 μl of q-PCR reaction was composed of SYBR Premix Ex Taq II (RR820L; Takara, Japan), 0.5 μl of each of the specific 16s rRNA primers [35,87-94] (Table 1), and 1 μl of the DNA template. The q-PCR reactions were carried out in duplicate using LightCycler® 8-Tube Strips (clear; Roche). An appropriate annealing temperature was used for designing the amplification program: 1 cycle of 95 °C for 1 min, 40 cycles of denaturation at 95 °C for 5 s, then annealing at 55 °C for 30 s, and extension at 72 °C for 30 s. Finally, melting curve analysis was performed after amplification to confirm the specificity of PCR reactions, followed by 1 cycle at 95 °C for 5 s, 60 °C for1 min, and 95 °C for 1 s.
Table 1. Primers used in this study
Target Bacteria
|
Forward (5´ to 3´)
|
Reverse (5´ to 3´)
|
Reference
|
Prevotellaceae
|
CACCAAGGCGACGATCA
|
GGATAACGCCYGGACCT
|
(89)
|
Faecalibacterium prausnitzii
|
GGAGGAAGAAGGTCTTCGG
|
AATTCCGCCTACCTCTGCACT
|
(88)
|
Bifidobactrium spp
|
TCGCGTCYGGTGTGAAAG
|
CCACATCCAGCRTCCAC
|
(92)
|
lactobacillus spp
|
AGCAGTAGGGAATCTTCCA
|
CACCGCTACACATGGAG
|
(91)
|
Akkermansia muciniphila
|
CAGCACGTGAAGGTGGGGAC
|
CCTTGCGGTTGGCTTCAGAT
|
(87)
|
Bacteroides fragilis
|
CTGAACCAGCCAAGTAGCG
|
CCGCAAACTTTCACAACTGACTTA
|
(93)
|
Firmicutes
|
GGAGYATGTGGTTTAATTCGAAGCA
|
AGCTGACGACAACCATGCAC
|
(94)
|
Bacteroidetes
|
AAACTCAAAKGAATTGACGG
|
GGTAAGGTTCCTCGCGCTAT
|
(35)
|
Escherichia coli
|
CATTGACGTTACCCGCAGAAGAAGC
|
CTCTACGAGACTCAAGCTTGC
|
(90)
|
Standard curve
For calculating the abundance of bacteria in each sample we made a comparison between obtained cycle threshold (CT) values with the standard curves using serial dilutions of DNA obtained from the standard strain Escherichia coli (ATCC 25922), with known DNA concentration. This curve enables measurement of each bacterium's DNA concentration from stool samples. As a semi-log regression line plot of CT value vs. log of DNA concentration, the standard curve is graphically represented.
Statistical analysis
The normality of the data was assessed using Kolmogorov-Smirnov test and was shown to be non-normally distributed. Categorical variables were presented as percentages, and continuous variables presented as median and interquartile range. Wilcoxon test and Chi-square or Fischer exact tests were used for comparison quantitative and qualitative variables, respectively, across the two studied groups. To assess associations between gut microbiota and RMR, linear regression was used. In an adjusted model, body fat percent was adjusted. All statistical analyses were performed using SPSS software (version 23, SPSS Inc., Chicago, IL, USA), whilst P<0.05 was, a priori, considered statistically significant.