Participants were recruited from the China Cardiometabolic Disease and Cancer Cohort (4C) Study, a nationwide prospective cohort study investigating the associations of metabolic factors with specific clinical outcomes, including diabetes, cardiovascular disease, cancer, and all-cause mortality [10, 11]. The data presented in this study are based on the subsamples from the Chongming District in Shanghai, China. From May to November 2011, a total of 2,765 postmenopausal women with normal BMI of Chinese origins were enrolled in the study. From June to December 2014, the subjects were invited for follow-up assessments. The cross-sectional survey included 2,492 subjects, and the follow-up investigation included 1,354 individuals without NAFLD at baseline (Figure 1). Subjects with the following conditions were excluded from this study: virus hepatitis, autoimmune hepatitis, drug-induced liver disease, current drinkers, ex-drinkers, presence of tumor, biliary obstructive diseases, thyroid dysfunction, total parenteral nutrition, Wilson’s disease, severe renal insufficiency, significant hematologic disorders, and current treatment with systemic corticosteroids.
The study protocol was approved by the Ethics Committee of Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine. Written informed consent was obtained from all participants.
Clinical diagnosis of NAFLD
Guidelines for the diagnosis of NAFLD proposed by the Asia-Pacific Working Party were used . NAFLD was clinically defined as manifestations of B-mode ultrasonography, after the exclusion of the habit of drinking and the history of specific diseases that could lead to fatty liver. Abdominal ultrasonography was performed by experienced ultrasonographers who were blinded to clinical presentation and laboratory data. Hepatic steatosis was defined as a diffuse increase of fine echoes in the liver parenchyma compared with that in the kidney or spleen parenchyma based on standard criteria.
Anthropometric and biochemical measurements
Neck circumference was measured horizontally at the lower margin of the laryngeal prominence (Adam’s apple), with head erect and eyes facing forward. BMI was calculated as the weight in kilograms divided by the square of the height in meters. Waist circumference was measured at the midpoint between the inferior costal margin and the superior border of the iliac crest on the midaxillary line.
Serum total cholesterol, high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol, triglycerides, alanine aminotransferase, aspartate aminotransferase, and γ-glutamyltranspeptidase were measured on an autoanalyzer (Hitachi 7080; Tokyo, Japan). Venous plasma glucose level was determined by glucose oxidase method (ADVIA-1650 Chemistry System, Bayer, Leverkusen, Germany) and hemoglobin A1c was measured by high-performance liquid chromatography (BIO-RAD, D10, CA). Fasting insulin was measured by RIA (Linco Research, St. Charles, MO). The homeostasis model assessment-insulin resistance (HOMA-IR) was used to assess insulin resistance, which was calculated using the following equation: HOMA-IR = insulin (uU/mL) * glucose (mmol/L)/22.5 . Serum C-reactive protein (CRP) and adiponectin levels were quantified using the enzyme-linked immunosorbent assay (ELISA) kits (DY1707, DY1065; R&D Systems, Minneapolis, MN).
The continuous variables with normal distribution are expressed as means ± SDs. The continuous variables with skewed distribution are shown as medians (interquartile range) and log-transformed to approximate normality before analysis. Categorical variables are reported as frequencies (%). For comparisons between groups, we conducted an independent-samples Student t test for normally distributed variables and a Mann-Whitney U test for variables with highly skewed distributions. The Chi-squared test was performed to compare categorical variables. The correlation coefficients between neck circumference and metabolic parameters were calculated using the Pearson correlation analysis and Partial correlation analysis after adjustment for age, smoking, physical activity, educational attainment, body mass index, and waist circumference. Logistic regression analyses were used to assess the relationship between neck circumference and the prevalence of NAFLD in the cross-sectional survey. Model 1 was adjusted for age, smoking status, physical activity, and educational attainment. Model 2 was further adjusted for BMI and waist circumference. Model 3 was further adjusted for HOMA-IR, CRP, and adiponectin. Model 4 was further adjusted for model 3 variables and fasting glucose, post-loading plasma glucose, systolic blood pressure, diastolic blood pressure. Model 5 was further adjusted for lipid profiles and liver enzymes.
Multivariate Cox regression analyses were run to evaluate the potential association between neck circumference and the incidence of NAFLD. Covariates were selected on the basis of biologic interest, well established risk factors for NAFLD, or associated exposures and outcomes. Variables showing p <0.05 in the univariable regression were entered into the multivariable model. Multivariable adjusted models were used to explore the independent effect of neck circumference on incidence of NAFLD. We also used restricted cubic splines with five knots at percentiles 5%, 35%, 50%, 65%, and 95% of the distribution to flexibly model to assess the association of neck circumference on a continuous scale and the incidence of NAFLD. Hazard ratios (HRs) and 95% confidence intervals (CIs) for the relationship between neck circumference and the incidence of NAFLD were generated with the Cox regression models. The surface fitting based on least square method was performed to assess joint effect of neck circumference with BMI, waist circumference, and HOMA-IR on NAFLD after adjustment for age, smoking status, physical activity, educational attainment, BMI, waist circumference, HOMA-IR, CRP, adiponectin, fasting plasma glucose, post-loading plasma glucose, systolic blood pressure, diastolic blood pressure, lipid profiles, and liver enzymes.
Several risk factors may affect the association between neck circumference and incident NAFLD, particularly age, BMI, waist circumference, CRP, diabetes status, and physical activity. Consequently, we conducted a subgroup analysis to examine whether the relationship between neck circumference and the risk of incident NAFLD was robust in the presence of potential confounders. Subgroups were stratified by age <65 years versus age ≥65 years, BMI <23 kg/m2 versus BMI ≥23 kg/m2, waist circumference <80 cm versus waist circumference ≥80 cm, CRP <3.0 mg/L versus CRP ≥3.0 mg/L, without diabetes versus with diabetes, and low physical activity versus moderate physical activity versus high physical activity.
A 2-tailed p < 0.05 was considered statistically significant. All analyses were performed using R version 4.0.2 and SPSS software version 25.0.