Linear and non-linear relationships between body fat mass distribution and bone mineral density in adults: The NHANES, 2011-2018.

OBJECTIVE: The relationship between body fat mass and bone mineral density (BMD) remains controversial. This research aimed to explore the linear or non-linear relationship between body fat mass and BMD among adults in the United States. METHODS: This cross-sectional study identified adults aged 18 years or older in the National Health and Nutrition Examination Survey from 2011 to 2018. After adjusting for covariates, linear relationships between body fat mass and BMD in different genders were tested by generalized linear models, and potential non-linear relationships were explored by generalized additive models and piecewise linear regression models. RESULTS: The research included 4691 (57.9% of the total sample) males and 3417 (42.1% the of total sample) females. In both males and females, we found a negative association between android or total body fat mass and lumbar spine BMD and a positive association between appendicular, android, gynoid, or total body fat mass and whole body BMD (all P < 0.05). The relationships between body fat mass in all regions and lumbar spine BMD were U-shaped in males and inverted U-shaped in females (all Pnon-linear < 0.05). Inverted U-shaped relationships existed between body fat mass in all regions and whole body BMD in females (all Pnon-linear < 0.05). CONCLUSIONS: Body fat mass was negatively and linearly associated with lumbar spine BMD, but positively associated with whole body BMD. Body fat mass had a U-shaped relationship with lumbar spine BMD in males and an inverted U-shaped association with lumbar spine and whole body BMD in females.

Body fat mass distribution and interrupter resistance, fractional exhaled nitric oxide, and asthma at school-age.

BACKGROUND: Obesity and asthma often coexist. We hypothesized that detailed body fat distribution measures might be more strongly associated than body mass index (BMI) with childhood asthma. OBJECTIVE: We examined the associations of total body and abdominal fat measures with respiratory resistance (Rint), fractional exhaled nitric oxide (Feno), and risks of wheezing and asthma in school-aged children. METHODS: In a population-based prospective cohort study among 6178 children aged 6 years, we measured BMI, fat mass index, android/gynoid ratio, and preperitoneal and subcutaneous fat mass by physical examinations, dual-energy x-ray absorptiometry, and ultrasound, respectively. We performed Rint and Feno measurements, and assessed physician-diagnosed wheezing and asthma by questionnaires. RESULTS: A higher BMI was associated with a higher Rint (Z score [95% CI], 0.06 [0.01-0.12]) and increased risk of wheezing (odds ratio [95% CI], 1.07 [1.00-1.14], per Z score BMI increase), but not with Feno or asthma. A high fat mass index was associated with a higher Rint (Z score [95% CI], 0.40 [0.13-0.68]). A high android/gynoid fat mass ratio was associated with a lower Feno (Sym% [95% CI], -9.8 [-16.3 to -3.4]), whereas a high preperitoneal fat mass was associated with a higher Feno (Sym% [95% CI], 6.5 [0.1-12.9]). Subcutaneous fat mass was not associated with any respiratory outcome. CONCLUSIONS: Studying detailed body fat distribution measures might provide better insight into the obesity-asthma paradigm.

Genetically predicted body fat mass and distribution with diabetic kidney disease: A two-sample Mendelian randomization study.

The aim of this study is to apply a Mendelian randomization (MR) design to investigate the potential causal associations between the body mass index (BMI), body fat mass such as trunk fat mass and waist circumference (WC), and diabetic kidney disease (DKD). A two-sample MR study was conducted to obtain exposure and outcome data from previously published studies. The instrumental variables for BMI, trunk fat mass, and WC were selected from genome-wide association study datasets based on summary-level statistics. The random-effects inverse-variance weighted (IVW) method was used for the main analyses, and the weighted median and MR-Egger approaches were complementary. In total, three MR methods suggested that genetically predicted BMI, trunk fat mass, and WC were positively associated with DKD. Using IVW, we found evidence of causal relationships between BMI [odds ratio (OR) = 1.99; 95% confidence interval (CI), 1.47-2.69; p = 7.89 × 10-6], trunk fat mass (OR = 1.80; 95% CI, 1.28-2.53; p = 6.84 × 10-4), WC (OR = 2.48; 95% CI, 1.40-4.42; p = 1.93 × 10-3), and DKD. MR-Egger and weighted median regression also showed directionally similar estimates. Both funnel plots and MR-Egger intercepts showed no directional pleiotropic effects involving the aforementioned variables and DKD. Our MR analysis supported the causal effect of BMI, trunk fat mass, and WC on DKD. Individuals can substantially reduce DKD risk by reducing body fat mass and modifying their body fat distribution.

Causal Effects of Overall and Abdominal Obesity on Insulin Resistance and the Risk of Type 2 Diabetes Mellitus: A Two-Sample Mendelian Randomization Study.

Overall and abdominal obesity were significantly associated with insulin resistance and type 2 diabetes mellitus (T2DM) risk in observational studies, though these associations cannot avoid the bias induced by confounding effects and reverse causation. This study aimed to test whether these associations are causal, and it compared the causal effects of overall and abdominal obesity on T2DM risk and glycemic traits by using a two-sample Mendelian randomization (MR) design. Based on summary-level statistics from genome-wide association studies, the instrumental variables for body mass index (BMI), waist-to-hip ratio (WHR), and WHR adjusted for BMI (WHRadjBMI) were extracted, and the horizontal pleiotropy was analyzed using MR-Egger regression and the MR-pleiotropy residual sum and outlier (PRESSO) method. Thereafter, by using the conventional MR method, the inverse-variance weighted method was applied to assess the causal effect of BMI, WHR, and WHRadjBMI on T2DM risk, Homeostatic model assessment of insulin resistance (HOMA-IR), fasting insulin, fasting glucose, and Hemoglobin A1c (HbA1c). A series of sensitivity analyses, including the multivariable MR (diastolic blood pressure, systolic blood pressure, high-density lipoprotein cholesterol, and low-density lipoprotein cholesterol as covariates), MR-Egger regression, weighted median, MR-PRESSO, and leave-one-out method, were conducted to test the robustness of the results from the conventional MR. Despite the existence of horizontal pleiotropy, consistent results were found in the conventional MR results and sensitivity analyses, except for the association between BMI and fasting glucose, and WHRadjBMI and fasting glucose. Each one standard deviation higher BMI was associated with an increased T2DM risk [odds ratio (OR): 2.741; 95% confidence interval (CI): 2.421-3.104], higher HbA1c [1.054; 1.04-1.068], fasting insulin [1.202; 1.173-1.231], and HOMA-IR [1.221; 1.187-1.255], similar to findings for causal effect of WHRadjBMI on T2DM risk [1.993; 1.704-2.33], HbA1c [1.061; 1.042-1.08], fasting insulin [1.102; 1.068-1.136], and HOMA-IR [1.127; 1.088-1.167]. Both BMI (P = 0.546) and WHRadjBMI (P = 0.443) were unassociated with fasting glucose in the multivariable MR analysis. In conclusion, overall and abdominal obesity have causal effects on T2DM risk and insulin resistance but no causal effect on fasting glucose. Individuals can substantially reduce their insulin resistance and T2DM risk through reduction of body fat mass and modification of body fat distribution.