A Body Shape Index (ABSI) as a Variant of Conicity Index Not Affected by the Obesity Paradox: A Cross-Sectional Study Using Arterial Stiffness Parameter

Relation between waist circumference and the renal hemodynamic in healthy individuals

BACKGROUND AND AIMS

Obesity has been shown to be an independent risk factor for the development of CKD. Little is known about pathways of interaction of visceral fat mass estimated by waist circumference (WC) and metabolic factors with the renal and intraglomerular hemodynamic profile in healthy, non-obese individuals.

METHODS AND RESULTS

The study population of this post-hoc analysis in 80 healthy individuals, who participated in a randomized, controlled clinical trial (www.

CLINICALTRIALS

gov: NCT02783456) was divided into two groups based on median of WC (high WC and low WC group). Renal hemodynamic profiles were analyzed using steady state input clearance (infusion of para-amino-hippuric acid and inulin). Intraglomerular pressure (IGP) and resistances of the afferent (RA) and efferent (RE) arterioles were calculated (Gomez equation). The analysis included healthy, non-smoking individuals, aged 27 ± 9 years with median WC of 84.75 ± 9 cm. Glomerular filtration rate (GFR) (110 ± 15 vs. 127 ± 16 ml/min/m2, p < 0.001), renal plasma flow (RPF) (620 ± 109 vs. 700 ± 104 ml/min, p = 0.001) and IGP (36.7 ± 2.3 vs. 38.5 ± 3.1 mmHg, p = 0.003) were lower in the high WC compared to the low WC group. Patients in the high WC group showed higher renal vascular resistance (RVR) (85 ± 19 vs. 70 ± 12 mmHg/(ml/min), p < 0.001), higher RA (4034 ± 1177 vs. 3069 ± 786 dyn∗s/cm5, p < 0.001) and higher RE (2283 ± 339 vs. 2118 ± 280 dyn∗s/cm5, p = 0.021) compared to the low WC group. Individuals in the high WC group showed higher leptin levels (p = 0.003) and higher HOMA-IR (p = 0.024) compared to the low WC group.

CONCLUSION

Increased WC in healthy young individuals was associated with reduced GFR and RPF likely mediated by increased RVR.

Selecting, optimizing and externally validating a preexisting machine-learning regression algorithm for estimating waist circumference

Obesity, typically defined by the body mass index (BMI), has well known negative health effects. However, the BMI has serious deficiencies in predicting the adverse risks associated to obesity. Waist circumference (WC) is an alternative to define obesity and a better disease predictor according to the literature. However, old databases often lack this information, it is inaccurate (collected via self-report) or it is incomplete. Thus, this study accurately assesses WC using machine learning. The novel approaches are: 1) predictor variables (weight, height, age and sex) likely to appear in most data sets are used. 2) Publicly available data (including non-adults) and algorithms are used. 3) Systematic methods for data cleanup, model selection, hyperparameter optimization and external validation are performed. DATA ARE CLEANED: one variable per column, no special codes, missing values or outliers. Preexisting regression algorithms are gaged by cross-validation, using one data set. The hyperparameters of the best performing algorithm are optimized. The tuned algorithm is externally validated with other data sets by cross-validation. In spite of the limited number of features, the tuned algorithm outperforms prior WC approximations, using the same or similar predictor variables. The tuned algorithm enables using data where WC is not measured, is incomplete or is unreliable. A similar approach would be useful to estimate other variables of interest.

Cumulative Cigarette Consumption is Associated with Cardio-Ankle Vascular Index (CAVI) Mediated by Abdominal Obesity Assessed by A Body Shape Index (ABSI): A Cross-Sectional Study

AIM

To elucidate the mechanism by which cigarette smoking causes vascular damage, we examined the relationship between cumulative cigarette consumption and abdominal obesity, and the possible mediating effect of smoking on arterial stiffness.

METHODS

Cross-sectional data from 19499 never smokers and 5406 current smokers receiving health screening was analyzed. Abdominal obesity was assessed by ABSI, and arterial stiffness by CAVI. High CAVI was defined as CAVI ≥ 9.0.

RESULTS

Current smoker showed higher ABSI than never smokers after propensity score matching. Cumulative cigarette consumption expressed in pack-years correlated with ABSI (Rs: 0.312 in men, 0.252 in women), and was also extracted as an independent factor associated with ABSI by multiple regression analysis. A linear relationship between pack-year and CAVI was observed (Rs: 0.544 in men, 0.423 in women). Pack-year had almost equal discriminatory power in predicting high CAVI in both sexes (C-statistic: 0.774 in men, 0.747 in women), and the best cut-offs of pack-year for high CAVI were 24.5 in men and 14.7 in women. Bivariate logistic regression models revealed that the association between pack-year higher than cut-off and high CAVI was independent of traditional risks. A mediating effect of ABSI (mediation rate: 9.9% in men and 11.2% in women), but not waist circumference (WC), on the association of pack-year with CAVI was observed, after adjusting for traditional risks.

CONCLUSION

Cumulative cigarette smoking in pack-years was independently associated with ABSI. ABSI partially mediates the association between pack-year and CAVI, suggesting that abdominal obesity partially mediates smoking-related vascular dysfunction.

Role of Machine Learning-Based CT Body Composition in Risk Prediction and Prognostication: Current State and Future Directions

CT body composition analysis has been shown to play an important role in predicting health and has the potential to improve patient outcomes if implemented clinically. Recent advances in artificial intelligence and machine learning have led to high speed and accuracy for extracting body composition metrics from CT scans. These may inform preoperative interventions and guide treatment planning. This review aims to discuss the clinical applications of CT body composition in clinical practice, as it moves towards widespread clinical implementation.

Functionally Significant Variants in Genes Associated with Abdominal Obesity: A Review

The high prevalence of obesity and of its associated diseases is a major problem worldwide. Genetic predisposition and the influence of environmental factors contribute to the development of obesity. Changes in the structure and functional activity of genes encoding adipocytokines are involved in the predisposition to weight gain and obesity. In this review, variants in genes associated with adipocyte function are examined, as are variants in genes associated with metabolic aberrations and the accompanying disorders in visceral obesity.