Sixty-two women after menopause were recruited from the Physical Examination Centre of the Affiliated Hangzhou First People’s Hospital, Zhejiang University School of Medicine, between January and April 2019. The study was approved by institutional review boards at the Affiliated Hangzhou First People’s Hospital, Zhejiang University School of Medicine.
Exclusion criteria were pre- and peri-menopausal status; acute infection phase; a history of autoimmune, hepatic, renal, malignant tumoral or psychiatric diseases; a history of diabetes or heart stents; and the intake of a female hormone that is known to affect circulating sex steroids within the three months prior to the study.
2.2 Anthropometric evaluation
Participants were invited to the Physical Examination Centre between 8:00 and 10:00 am after overnight fasting. Anthropometric measurements, such as age, weight and height, were collected by trained personnel. BMI was calculated as weight in kilograms divided by height in metres squared. Blood pressure was measured in a resting state.
2.3 Blood sampling and biochemical analysis
Venous blood samples were collected and kept at room temperature for 30 min and then centrifuged at 1500×g and 4 °C for 15 min within 5 h of collection. Biochemical variables such as alanine transaminase (ALT), triglycerides (TG), total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), fasting plasma glucose (FPG), and fasting insulin (FINS) were determined according to standard laboratory methods.
Homeostatic model assessment of insulin resistance (HOMA-IR) indices were calculated as follows: HOMA-IR index = fasting glucose (mmol/dl) × fasting insulin (µmol/L)/22.5.
2.4 Measurement of circulating Gal3-BP by ELISA
Serum Gal-3BP levels were measured with an enzyme immunoassay kit according to standard instructions (Human LAGLS3BP ELISA Kit, RayBio Inc.).
All assays were performed in duplicate, and average data were collected.
2.5 Statistical analysis
Statistical Package for the Social Sciences software (SPSS Inc. version 22.0, Armonk, USA) and GraphPad Prism (version 6.0, CA, USA) were used to analyse the data. The Kolmogorov-Smirnov test was used for normality evaluation. Normally distributed data are expressed as the mean ± SD. Data not normally distributed were expressed as the median (25th -75th IQR). Before performing further analyses, non-normally distributed parameters were log-, square-, or normal-score- transformed. One-way ANOVA was performed for multi-group comparisons. The Kruskal-Wallis test was used for non-normally distributed data. Pearson’s correlation analysis was used to determine the correlation of serum Gal-3BP with demographic and biochemical parameters. Multivariate analysis was performed by logistic regression to observe variables influencing the risk for insulin resistance adjusted for age and BMI. The diagnostic performance of Gal-3BP was analysed by receiver operating characteristic (ROC) curves and the area under the ROC curve (AUC). The Youden index was used to identify the optimal cut-off points. Probability values were 2 tailed, and a p value <0.05 was considered significant.