Introduction
It is well known that subjects with normal weight (defined as a body mass index (BMI) of 18.5–24.9 kg/m2) have a lower risk for cardiometabolic diseases and all-cause mortality compared with overweight and obese subjects.1 2 As BMI does not differentiate fat-free mass from adipose tissue, an individual with normal weight may have low, appropriate or excess fat. Normal-weight obesity is characterized by the presence of high body fat despite having a normal BMI and is associated with cardiometabolic morbidity and mortality.3 Epidemiological and experimental data indicate that nutritional or environmental stressors during early development can induce long-term adaptations that increase risk of diabetes and cardiovascular disease.4 Altered body composition characterized by increased fat mass and reduced muscle mass is a common phenotype.5 In addition, current evidence suggests that growth and body weight trajectories in infancy and childhood are useful indicators of later obesity and type 2 diabetes.6 As far as we know, however, studies are missing in people with normal-weight obesity.
Most of triglycerides and cholesterol in the circulation are carried in very low-density lipoprotein (VLDL) and low-density lipoprotein (LDL) particles, respectively, both of which contain one molecule of apolipoprotein B (ApoB). ApoB is a single index that quantitates the atherogenic risk due to the ApoB-containing lipoprotein particles.7 Although triglycerides and LDL cholesterol are both risk factors of cardiovascular disease, a study indicates that the clinical benefit of lowering triglyceride and LDL cholesterol levels may be proportional to the absolute change in ApoB.8 Although many studies reported associations of normal-weight obesity with cardiometabolic abnormalities including high triglyceride and LDL cholesterol,3 studies are limited on the association with ApoB.9 10 Age, sex and race/ethnicities may be related to normal-weight obesity.3 We, therefore, studied whether normal-weight obesity may be associated with body weight trajectories since birth to childhood, current body composition, dietary intake and a broad range of cardiometabolic risks including ApoB in young Japanese women in the present study.
Subjects and methods
We reanalyzed cross-sectionally 251 normal weight (BMI: 18.5–24.9 kg/m2) women, whose age and BMI averaged 20.6 years and 20.6 kg/m2, respectively, among 307 young Japanese women whose details were reported previously,11 from which 56 underweight (BMI <18.5 kg/m2) and overweight (BMI: 25.0–29.9 kg/m2) women were excluded. They were students of Department of Food Sciences and Nutrition, Mukogawa Women’s University and were recruited as volunteers. Among 251 women, 181 and 166 women provided data on weight trajectory and dietary intake, respectively. Women with clinically diagnosed acute or chronic inflammatory diseases, endocrine, cardiovascular, hepatic, renal diseases, hormonal contraception, unusual dietary habits were excluded from the study. This research followed the tenets of the Declaration of Helsinki.
Weight at birth, and height and weight at age 12 and 15 years were obtained either through maternal health check notes or child health notebook records (issued by each municipal office).
After a 12-hours overnight fast, participants underwent blood sampling, measurement of anthropometric indices, blood pressure and body composition as previously described.10–14 Blood pressure was measured using an automated sphygmomanometer (BP-203RV II, Colin, Tokyo, Japan) after participants were seated at least for 5 min. Plasma glucose, serum insulin, triglycerides, cholesterol, high-density lipoprotein (HDL) cholesterol, free fatty acid, aspartate aminotransferase (AST), alanine aminotransferase (ALT), γ-glutamyl transpeptidase (GGT), HbA1c and high-sensitivity C reactive protein (hsCRP) were measured as previously reported.10–14 LDL cholesterol was calculated using the Friedewald’s formula. Adipose tissue-insulin resistance index (AT-IR) and homeostasis model assessment-insulin resistance (HOMA-IR) were calculated as previously reported.14 15
Whole-body dual-energy X-ray absorptiometry (DXA) (Hologic QDR-2000 software version 7.20D, Waltham, Massachusetts, USA) was used to measure lean tissue mass, fat mass and bone mineral mass for arms, legs (lower body), trunk and the total body.12 General adiposity was assessed using BMI, percentage body fat (%BF) and fat mass index (FMI), the last of which was calculated as body fat mass in kg divided by height in meter squared. Waist circumference, percentage trunk fat and the ratio of trunk to leg fat16 were considered as markers of abdominal fat accumulation. Muscle characteristics were evaluated by relative appendicular skeletal muscle mass (ASM) as percentage of body mass (%ASM) and absolute ASM index (ASM/height2 in kg/m2). %ASM is suggested to be a better predictor of insulin resistance and diabetes risk than ASM or ASM index.17
There are no clearly established cut points of %BF for normal-weight obesity. A study employing DXA showed that %BF for a BMI of 18.5 and 25.0 kg/m2 corresponded to 25.0% and 35.0%, respectively, in Japanese women aged 20–39 years.18 Similar results were obtained in our analyses in the entire 307 young Japanese women (data not shown). Accordingly, high %BF, that is, normal-weight obesity, was defined by ≥35.0% (n=24, 9.6%). Because a substantial number of normal-weight women (n=67, 26.7%) had %BF <25.0%, they were considered as having low %BF and used as an internal reference. A %BF of 25.0–34.9% was defined as normal (n=160, 63.7%).
Dietary intake of the previous month was assessed using the self-administered diet history questionnaire.19 This has been widely used throughout Japan, and its validity with respect to commonly studied nutrition factors has been confirmed.
Data were presented as mean±SD unless otherwise stated. Due to deviation from normal distribution, hsCRP were logarithmically transformed for analyses. Differences among three groups were analyzed by analysis of variance and then Bonferroni’s multiple comparison procedure. Stepwise multivariate logistic regression analyses were used to identify most important determinants of normal-weight obesity. Independent variables included were variables that showed significant difference among three groups. A two-tailed p<0.05 was considered statistically significant. All calculations were performed with SPSS system V.23.0 (SPSS Inc).