Impact of anthropometric cut-off values in determining the prevalence of metabolic alterations

Investigating the causal associations between five anthropometric indicators and nonalcoholic fatty liver disease: Mendelian randomization study

BACKGROUND

Although the etiology of nonalcoholic fatty liver disease (NAFLD) has not been thoroughly understood, the emerging roles of anthropometric indicators in assessing and predicting the risk of NAFLD have been highlighted by accumulating evidence.

AIM

To evaluate the causal relationships between five anthropometric indicators and NAFLD employing Mendelian randomization (MR) design.

METHODS

The Anthropometric Consortium provided genetic exposure data for five anthropometric indicators, including hip circumference (HC), waist circumference (WC), waist-to-hip ratio (WHR), body mass index (BMI), and body fat percentage (BF). Genetic outcome data for NAFLD were obtained from the United Kingdom Biobank and FinnGen Consortium. Genome-wide significant single nucleotide polymorphisms were chosen as instrumental variables. Univariable MR (UVMR) and multivariable MR (MVMR) designs with analytical approaches, including inverse variance weighted (IVW), MR-Egger, weighted median (WM), and weighted mode methods, were used to assess the causal relationships between anthropometric indicators and NAFLD.

RESULTS

Causal relationships were revealed by UVMR, indicating that a higher risk of NAFLD was associated with a per-unit increase in WC [IVW: odds ratio (OR) = 2.67, 95%CI: 1.42-5.02, P = 2.25 × 10-3], and BF was causally associated with an increased risk of NAFLD (WM: OR = 2.23, 95%CI: 1.07-4.66, P = 0.033). The presence of causal effects of WC on the decreased risk of NAFLD was supported by MVMR after adjusting for BMI and smoking. However, no causal association between BF and NAFLD was observed. In addition, other causal relationships of HC, WHR (BMI adjusted), and BMI with the risk of NAFLD were not retained after FDR correction.

CONCLUSION

This study establishes a causal relationship, indicating that an increase in WC is associated with a higher risk of NAFLD. This demonstrates that a suitable decrease in WC is advantageous for preventing NAFLD.

Discriminatory capacity of obesity indicators as predictors of high liver fat in US adolescents

BACKGROUND

The aims of this study were to assess the potential of 16 anthropometric, body composition and endocrine indexes as predictors of high liver fat and determine the most appropriate cut-off points in US adolescents.

METHODS

A cross-sectional study was conducted on a population of 816 adolescents aged 12-17 years. The FibroScan^® 502V2 device was used to estimate the controlled attenuation parameter (CAP). Body fat percentage, fat mass, trunk fat percentage and trunk fat mass were measured by dual-energy X-ray absorptiometry. Anthropometric data and metabolic parameters were determined. Receiver operating characteristic curves were analysed to estimate the optimal cut-off points that best identify adolescents with high liver fat (CAP ≥90th percentile).

RESULTS

In boys, triponderal mass index (TMI) had the highest area under curve (AUC) value (0.865) and the optimal cut-off score for TMI was 17.47 kg/m³ , which had 81.32 sensitivity and 82.99 specificity. In girls, trunk fat index (TFI) had the highest AUC value (0.826) and its optimal cut-off score in screening for high liver fat was 3.76 kg/m² , which had 74.04 sensitivity and 88.03 specificity. Fat mass index (FMI) index had the second highest AUC values (0.863 in boys 0.812 in girls) in both sex; the cut-off point for the detection of high liver fat was <8.66 kg/m² for girls and <7.45 kg/m² for boys.

CONCLUSION

Assessment of TMI in boys, TFI in girls, and FMI in both sexes are low-cost and easy-to-use parameters that may be useful as early screening tools for possible high liver fat in adolescents.

Sex Differences in the Physiological Network of Healthy Young Subjects

Within human physiology, systemic interactions couple physiological variables to maintain homeostasis. These interactions change according to health status and are modified by factors such as age and sex. For several physiological processes, sex-based distinctions in normal physiology are present and defined in isolation. However, new methodologies are indispensable to analyze system-wide properties and interactions with the objective of exploring differences between sexes. Here we propose a new method to construct complex inferential networks from a normalization using the clinical criteria for health of physiological variables, and the correlations between anthropometric and blood tests biomarkers of 198 healthy young participants (117 women, 81 men, from 18 to 27 years old). Physiological networks of men have less correlations, displayed higher modularity, higher small-world index, but were more vulnerable to directed attacks, whereas networks of women were more resilient. The networks of both men and women displayed sex-specific connections that are consistent with the literature. Additionally, we carried out a time-series study on heart rate variability (HRV) using Physionet's Fantasia database. Autocorrelation of HRV, variance, and Poincare's plots, as a measure of variability, are statistically significant higher in young men and statistically significant different from young women. These differences are attenuated in older men and women, that have similar HRV distributions. The network approach revealed differences in the association of variables related to glucose homeostasis, nitrogen balance, kidney function, and fat depots. The clusters of physiological variables and their roles within the network remained similar regardless of sex. Both methodologies show a higher number of associations between variables in the physiological system of women, implying redundant mechanisms of control and simultaneously showing that these systems display less variability in time than those of men, constituting a more resilient system.

Metabolic Physiological Networks: The Impact of Age

Metabolic homeostasis emerges from the interplay between several feedback systems that regulate the physiological variables related to energy expenditure and energy availability, maintaining them within a certain range. Although it is well known how each individual physiological system functions, there is little research focused on how the integration and adjustment of multiple systems results in the generation of metabolic health. The aim here was to generate an integrative model of metabolism, seen as a physiological network, and study how it changes across the human lifespan. We used data from a transverse, community-based study of an ethnically and educationally diverse sample of 2572 adults. Each participant answered an extensive questionnaire and underwent anthropometric measurements (height, weight, and waist), fasting blood tests (glucose, HbA1c, basal insulin, cholesterol HDL, LDL, triglycerides, uric acid, urea, and creatinine), along with vital signs (axillar temperature, systolic, and diastolic blood pressure). The sample was divided into 6 groups of increasing age, beginning with less than 25 years and increasing by decades up to more than 65 years. In order to model metabolic homeostasis as a network, we used these 15 physiological variables as nodes and modeled the links between them, either as a continuous association of those variables, or as a dichotomic association of their corresponding pathological states. Weight and overweight emerged as the most influential nodes in both types of networks, while high betweenness parameters, such as triglycerides, uric acid and insulin, were shown to act as gatekeepers between the affected physiological systems. As age increases, the loss of metabolic homeostasis is revealed by changes in the network’s topology that reflect changes in the system−wide interactions that, in turn, expose underlying health stages. Hence, specific structural properties of the network, such as weighted transitivity, i.e., the density of triangles in the network, can provide topological indicators of health that assess the whole state of the system. Overall, our findings show the importance of visualizing health as a network of organs and/or systems, and highlight the importance of triglycerides, insulin, uric acid and glucose as key biomarkers in the prevention of the development of metabolic disorders.

Relationships between metabolic markers and obesity measures in two populations that differ in stature-The SAMINOR Study

Background

The relationships between metabolic markers and obesity measures may differ by ethnicity, sex, and height. Questions have been posed whether these relationships differ by ethnicity in the population in Northern Norway, but this has not been explored yet.

Objectives

Investigate the relationships between metabolic markers and obesity measures in Sami and non‐Sami and explore the impact of stature.

Methods

In total, 13 921 men and women aged 30 and 36 to 79 years (22.0% Sami) from a population‐based cross‐sectional survey in Norway, the SAMINOR 1 Survey (2003‐2004, 57.2% attendance), were included. Relationships between triglycerides, high‐density lipoprotein cholesterol, glucose, systolic/diastolic blood pressure (BP), metabolic syndrome and diabetes mellitus as outcomes, and body mass index (BMI), waist circumference (WC), and waist‐to‐height ratio (WHtR), respectively, were modelled using fractional polynomial regression. Appropriate interaction analyses and adjustments were made.

Results

The non‐Sami were approximately 6 cm taller than the Sami. No interactions were found between ethnicity and obesity. At the same levels of WC, BMI, or WHtR, levels of lipids and BP differed marginally between Sami and non‐Sami, but these were eliminated by height adjustment, with one exception: At any given WC, BMI, or WHtR, Sami had approximately 1.4 mmHg (95% CI, −2.1 to −0.7) lower systolic BP than non‐Sami (P values < .001).

Conclusions

Height explained the marginal ethnic differences in metabolic markers at the same level of obesity, except for systolic BP, which was lower in Sami than in non‐Sami at any given BMI, WC, or WHtR.

The influence of phthalates and bisphenol A on the obesity development and glucose metabolism disorders

The prevalence of obesity and type 2 diabetes mellitus epidemics presents a great health problem worldwide. Beside the changes in diet and decreased physical activity, there is growing interest in endocrine disrupting chemicals that may have effects on these conditions. Among them, the role of certain phthalates and bisphenol A is confirmed. We have summarized the existing literature on this issue including cross-sectional, follow up epidemiological studies and in vivo and in vitro studies. Most data support the effects of bisphenol A and some phthalates, such as di-2-ethyl-hexyl phthalate, diethyl phthalate, dibuthyl phthalate, dimethyl phthalate, dibenzyl phthalate, diisononyl phthalate and others on the development obesity and type 2 diabetes mellitus. These endocrine disrupting chemicals interfere with different cell signaling pathways involved in weight and glucose homeostasis. Since the data are rather inconsistent, there is a need for new, well-designed prospective studies.