4. Outcome measurements

Metabolic data were obtained using standard protocols of the CBUH clinical laboratory. All subjects who visited CTCF2 underwent a safety assessment at the first visit (baseline, week 0) and fourth visit (week 4). Data on demographics, smoking, physical activity, alcohol drinking, medical history, dietary intake, anthropometric and biochemical parameters, and vital signs were obtained from each individual in both groups. Hematology examinations, blood biochemical tests, and urine tests were conducted after subjects had fasted for 12 hours.

Efficacy assessments included glucose (fasting prandial glucose [FPG], during an oral glucose tolerance test [OGTT]), postprandial plasma glucose (PPG), glucose incremental area under the curve (iAUC), insulin (fasting plasma insulin [FPI]), homeostatic model assessment of insulin resistance (HOMA-IR), glycated albumin (GA), and HbA1c. The glucose and insulin iAUCs during OGTTs were determined using the trapezoidal method.

Anthropometric data were obtained for height, weight, and waist and hip circumferences, and body mass index (BMI) in kg/m²was obtained from the height and weight measurements obtained with the GL150 system (G-Tech Co., Uijeongbu, Korea) in light clothing. Waist circumference (WC) was measured using a tape measure parallel to the lower rib and the middle of the pelvis when the subject was standing and breathing comfortably. Body fat, body fat rate, and muscle mass were measured using an Inbody 720 body analyzer (Biospace Co., Seoul, Korea).

Total cholesterol (TC), neutral blood lipid levels, and high-density lipoprotein cholesterol (HDL-C) levels were analyzed with a Hitachi 7600-100 analyzer (Hitachi High Technologies Corporation, Tokyo, Japan), and low-density lipoprotein cholesterol (LDL-C) content was calculated with the Friedewald formula [22]. Lipid metabolic indexes of apolipoprotein A1 and apolipoprotein B along with liver enzyme indexes for gamma-glutamyl transferase, ALT, AST and total bilirubin were analyzed with an ADVIA 2400 chemistry system (SIEMENS, Munich, Germany). Inflammatory indesxs were measeured using a serum high sensitivity C-reactive protein (hs-CRP) latex immunoassay method and erythrocyte sedimentation rate(ESR) for Westergren methods.

Blood pressure was measured with an HBP-9020 (Omron Healthcare Co., Ltd, Kyoto, Japan) analyzer after the subject arrived at the research site and had rested comfortably for at least 10 min. Three measurements of systolic blood pressure (SBP), diastolic blood pressure (DBP), and pulse rate were recorded at intervals of approximately 2 min while the subject was seated, and an average was calculated. A medical team carried out the examination through interviews, ocular inspections, auscultation, percussion, and palpation.

Genetic information was analyzed by selecting eight tag-SNPs related to blood-glucose metabolism that can predict type 2 DM occurrence. Two genes relate to blood-glucose metabolism (CDKN2A and CDKN2B). GLIS3 involves both the growth of beta cells and the production of insulin in the pancreas. Three genes (MTNR1B, DGKB, and SLC30A8) are functionally associated with the secretion of insulin, and three (GCK, GCKR, and G6PC2) were selected and tested for a relationship with cellular or mitochondrial metabolic function [23-28]. Analysis of genetic tests was performed using a peripheral blood sample after obtaining consent from the subjects. Genotype analysis was performed by Therazen Co., Ltd.(Seoul, Republic of Korea) on all 30 subjects according to manufacturer guidelines using the QuantStudio 12K the analysis system (Flex Accefill, Life Technologies, Carisbad, CA, USA) and applying the Taqman assay method.

Polymorphisms were analyzed using the eight gene markers related to blood-glucose metabolism, and the genetic risk was evaluated based on the results. A genetic risk score (GRS) was calculated by assigning 0 points for two homozygous non-risk alleles, 1 point for two heterozygous alleles, and 2 points for two homozygous risk alleles.

To investigate changes in dietary habits, a nutritionist trained in dietary records management explained to subjects how to prepare a dietary diary and collected data after face-to-face interviews when retrieving each subjects’ dietary diary. During the first visit (baseline, week 0) and the third visit (week 4), the dietary diary from the previous day was examined using CAN-Pro 4.0, a computer-aided nutritional analysis program from the Korean Nutrition Society Forum, Seoul, Korea, and average values were calculated. To investigate changes in physical activity, the global physical activity questionnaire was administered on the first visit (baseline, week 0) and the third visit (week 4), and a metabolic equivalent task (MET) value was calculated.

Subject clinical conditions, including adverse reactions, were evaluated and recorded in case report lists. All subjects underwent safety evaluations at baseline (week 0) and after completing the four-week study. Safety assessments included electrocardiograms and laboratory tests. Hematological examinations included counts of white blood cells (WBCs), red blood cells, and basophils, and levels of hemoglobin, hematocrit, platelets, neutrophils, and lymphocytes. Liver and kidney functions, including total bilirubin, total protein, alkaline phosphatase (ALP), ALT, AST, blood urea nitrogen, and creatinine assessments, were measured by staff of the clinical pathology department of our hospital.

All statistical processing used SAS 9.2 (SAS Institute, Cary, NC, USA). All data were presented as a mean ± standard deviation for continuous variables and as a frequency for categorical variables. Per protocol analyses were performed. Categorical variables were compared using the chi-square test (Fisher’s exact test). For average comparisons between the two groups, an independent-sample t-test was used for independent samples, and a paired-sample t-test was used for paired samples. p < 0.05 were considered statistically significant.