Subjects characteristics
A total of 18 Chinese men aged 21–40 years participated in this randomized, cross-over trial. Demographic data, medical and drug history, and data on lifestyle factors were collected using interviewer-administered questionnaires. Body weight was measured in light clothing to the nearest 0.1 kg using an electronic scale (SECA, Vogel & Halke, Germany). Height was measured without shoes to the nearest 0.1 cm using a wall-mounted stadiometer. Body mass index (BMI) was calculated as weight divided by height squared (kg/m2). We used the WHO definition for obesity in Asians [21]. Subjects of two distinct metabolic phenotypes were recruited based on BMI and the Homeostatic Model Assessment-Insulin Resistance (HOMA-IR). This included 9 lean and insulin-sensitive (20 ≤ BMI < 23 kg/m2 and HOMA-IR < 1.2) and 9 obese and insulin-resistant (27.5 ≤ BMI < 35 kg/m2 and HOMA-IR ≥ 2.5) subjects. We excluded subjects with known first degree family history or personal history of diabetes mellitus, previous or current thyroid disorders, history of malignancy, recent change in the body weight (≥5% during the past 3 months), hospitalization or surgery during the past 6 months, intake of any medication during the past 3 months, daily alcohol consumption > 3 units, present or past history of smoking, and high level of physical activity (>5 h per week).
Experimental design and meal tests
During the screening visit, height, weight and waist circumference were measured, and fasting blood was obtained for the determination of plasma glucose, serum insulin, electrolytes, non-esterified fatty acid (NEFA) concentrations, and lipid profile. All subjects had a fasting plasma glucose concentration < 5.6 mmol/l. Eligible participants underwent 3 isocaloric liquid mixed meal tolerance tests (MMTTs) differing in macronutrient composition [high carbohydrate (HC), high fat (HF), or high protein (HP)] in random order with 7 days washout in-between each. HC, HF, and HP meals contained 56.4% carbohydrate, 56.5% fat (with a 1:1:1 ratio of SFA, MUFA, and PUFA), and 51.4% protein, respectively (Additional file 1: Table S1). Following a 10-h overnight fast, baseline venous blood samples were collected and subjects were given a 2 510 kJ (600 kcal) liquid mixed meal to consume over 5 min. Fasting (t = 0 min) and postprandial (30, 60, 90, 120, 180, 240, 300, and 360 min) venous blood samples were collected through an intravenous catheter for the determination of plasma glucose and serum insulin concentrations. Separate venous blood samples were collected in EDTA vacutainers at 0, 120, and 360 min for MNC gene expression and measurement of plasma cytokine concentrations.
MNC RNA isolation, cDNA synthesis and real time reverse transcription-polymerase chain reaction
EDTA-anticoagulated blood samples (9 ml) were layered over 9 ml of Ficoll-paque Plus (GE healthcare, Buckinghamshire, UK) and centrifuged. The MNC layer was harvested and washed with phosphate-buffered saline. Red blood cell lysis was performed according to manufacturer’s instructions (Sigma-Aldrich, St. Louis, MO, USA).
Total RNA from MNC was isolated using the RNeasy Mini Kit (QIAGEN, Netherlands), according to manufacturer’s instructions. Subsequently, 500 ng of total RNA was reverse transcribed using the high capacity cDNA Reverse-Transcription Kit (Applied Biosystems, Waltham, MA, USA), according to manufacturer’s instructions. Real-time reverse transcription-polymerase chain reaction (RT-PCR) was performed using the Vii A 7 Real-Time PCR System (Applied Biosystems). The PCR mix consisted of 2 μL (10 ng) cDNA, 5 μL QuantiFast SYBR Green PCR Master mix (QIAGEN), and 0.1 μL of 100 μmol/L gene-specific primers (AIT Biotech, Singapore). Primer sequences used were chosen based on the sequences available in NCBI nucleotide database and designed using Primer3Plus (http://primer3plus.com/cgi-bin/dev/primer3plus.cgi). The specificity of the PCR products was tested by analysis of the melt curve at the end of the amplification. All samples were run in duplicates and variations in the threshold cycle (CT) between technical replicates were within 10%. All values were normalized to the expression of a housekeeping gene (GAPDH). The expression of GAPDH gene was stable and did not show significant variation across the different time points, meals and phenotypes. The panel of genes examined included IL-6, TNFα, IL-1β, IL-18, IL-8, IL-10, TGFβ, TLR4, MCP-1, and those related to the NFκB complex, i.e., Rel-A (p65 subunit of NFκB), p105 (precursor of p50 subunit of NFκB), and IκB-α and IκB-β (inhibitors of NFκB). We excluded 2 set of samples following the HC meal (1 set from lean subjects and 1 set from obese subjects) due to insufficient or poor quality of RNA.
Biochemical analysis
Serum glucose and triglyceride concentrations were measured by using enzymatic and colorimetric methods, respectively (AU5800, Beckman Coulter Inc., California, USA). Serum insulin was measured by using a chemiluminescence immunoassay (ADVIA Centaur, Siemens Healthcare Diagnostics, Hamburg, Germany). These analyses were carried out in a laboratory accredited by the College of American Pathologists. NEFA was measured at Mayo Medical Laboratories (Rochester, MN, USA), using an enzymatic colorimetric method (Cobas® 6000, Roche Diagnostics, Indianapolis, USA). Plasma IL-6 (cat no. HS600B) concentrations were measured using Quantikine® high-sensitivity enzyme-linked immunosorbent assay (ELISA) kits (R&D Systems, Minneapolis, MN, USA). Plasma TNFα concentration was measured using an ultrasensitive ELISA kit (cat no. 45-TNFHUU-E01, Alpco Diagnostics, Salem, NH, USA). Intra- and inter-assay coefficient of variations for IL-6 and TNFα were largely within 10%.
Statistical analysis
Our primary outcome was fold change in transcription of inflammatory genes regulated by NF-κB in MNC from baseline after each meal, as an indicator of NF-κB transcriptional activity and binding. Initial power analysis was based on the postprandial NF-κB expression, in which a sample size of nine subjects per group per test meal was calculated to provide at least 80% power at 5.0% significance level [22]. All statistical analyses were performed using SPSS version 22.0 for Windows (SPSS Inc., Chicago, IL, USA). All variables except NEFA were normally distributed and for NEFA, log transformed values were tested accordingly. All values are presented as means ± standard errors (SEMs) unless stated otherwise. A P value of <0.05 was considered statistically significant. Student’s t-test was used to compare continuous variables between the two groups at baseline (lean vs. obese).
Fasting insulin (mU/l) × fasting glucose (mmol/l)/22.5 was used to calculate HOMA-IR [23]. Postprandial changes in plasma glucose and insulin concentrations over 6 h were calculated as the incremental area under the curve (iAUC) by using the trapezoid rule. We performed linear mixed modelling to analyse differences among test meals (i.e., HC, HF, or HP) and metabolic phenotypes (i.e., LIS vs. OIR). Test meal and postprandial time were entered as repeated factors in the model, and iAUC (for plasma glucose and serum insulin) or delta values (i.e., changes from baseline; for plasma cytokines and MNC gene expression) were entered as dependent variables; their baseline values were used as covariates in the analyses. If statistical significance was found, post-hoc (Bonferroni) tests were performed to identify differences among test meals and metabolic phenotypes. We also examined if there was any significant interaction between metabolic phenotype and test meal, indicating that differences between lean and obese subjects were test meal-dependent. Pearson’s correlation was performed to examine between and within subjects’ associations between postprandial immunometabolic parameters [24, 25].