The relationship between depression and relative fat mass (RFM): A population-based study

BACKGROUND

Relative fat mass (RFM) is a novel indicator for measuring body fat. The relationship between RFM and depression was explored using National Health and Nutrition Examination Survey (NHANES) data from 2005 to 2018.

METHODS

A general statistical description of the population included in the study was performed, and logistic analyses were used to explore the association between body mass index (BMI), waist circumference (WC), RFM and depression. Sensitivity analyses and restricted cubic spline (RCS) were also conducted to investigate the association between RFM and depression.

RESULTS

A total of 28,836 participants were included in the study. In multivariate models, all obesity indices were associated with depression (P < 0.001). An increase of 1 SD in BMI, WC, and RFM was associated with a respective increased risk of depression of 2.3 %, 1.0 %, and 3.3 %. Excluding those taking antidepressants, the risk of depression was OR 1.88 (95 % CI: 1.26-2.79) for those with RFM in the highest quartile compared with those in the lowest quartile. After Inverse probability of weighting (IPW), the risk of depression in individuals with RFM in the highest quartile compared with individuals in the lowest quartile was 2.62 (95 % CI: 2.21-3.09). The RCS showed a possible nonlinear relationship between RFM and depression.

CONCLUSIONS

RFM is associated with depression, suggesting that attention to RFM may be helpful for depression research.

Comparison of Several Anthropometric Indices Related to Body Fat in Predicting Cardiorespiratory Fitness in School-Aged Children-A Single-Center Cross-Sectional Study

Body fat (BF) and cardiorespiratory fitness (CRF) are important health markers that ought to be considered in screening exams. The aim of this study was to assess the value of six indicators, i.e., tri-ponderal mass index (TMI), relative fat mass (RFM), waist-BMI ratio, waist-to-height ratio (WHtR), waist-to-hip ratio (WHR) and body mass index (BMI) in predicting CRF in school-aged children. The analysis was based on the data coming from the examination of 190 children participating in school physical education (PE) classes. Their body weight (BW) and height (BH), waist and hip circumference (WC; HC) and percentage of body fat (%BF) were measured; the CRF test was performed with the use of the 20 m shuttle run test (20 mSRT); peak heart rate (HRpeak) was measured; TMI, relative fat mass pediatric (RFMp), waist-BMI ratio, WHtR, BMI and WHR were calculated. Statistical analysis was mainly conducted using regression models. The developed regression models, with respect to the sex and age of the children, revealed RFMp as the strongest CRF indicator (R2 = 51.1%) and WHR as well as waist-BMI ratio as the weakest ones (R2 = 39.2% and R2 = 40.5%, respectively). In predicting CRF in school-aged children, RFMp turned out to be comparable to body fat percentage obtained by means of the bioimpedance analysis (BIA) (R2 = 50.3%), and as such it can be used as a simple screening measure in prophylactic exams of school children. All of these models were statistically significant (p < 0.001).

Lower Prevalence of Body Fat-Defined Obesity at Higher Altitudes in Peruvian Adults

Woolcott, Orison O., Till Seuring, and Oscar A. Castillo. Lower prevalence of body fat-defined obesity at higher altitudes in Peruvian adults. High Alt Med Biol. 24:214-222, 2023. Background: Previous studies have reported a lower prevalence of obesity (defined as a body mass index [BMI] ≥30 kg/m2) in populations from higher altitudes. Since BMI does not distinguish fat mass and fat-free mass, it is unclear whether there is an inverse association between altitude and body fat-defined obesity. Methods: We performed an analysis of cross-sectional data to examine the association between altitude and body fat-defined obesity (as opposed to BMI-defined obesity) using individual-level data from a nationally representative sample of the Peruvian adult population living between 0 and 5,400 m altitude. Body fat-defined obesity was diagnosed using the relative fat mass (RFM), an anthropometric index validated to estimate whole-body fat percentage. RFM cutoffs for obesity diagnosis were ≥40% for women and ≥30% for men. We utilized Poisson regression to estimate the prevalence ratio and confidence intervals (CIs) as the measure of the association, adjusting for age, cigarette use, and diabetes. Results: Analysis comprised 36,727 individuals (median age, 39 years; 50.1% women). In rural areas, for a one-km increase in altitude, the prevalence of body fat-defined obesity decreased by 12% among women (adjusted prevalence ratio: 0.88; 95% CI, 0.86 - 0.90; p < 0.001) and 19% among men (adjusted prevalence ratio: 0.81; 95% CI, 0.77 - 0.86; p < 0.001), on average, when all the other variables were held constant. The inverse association between altitude and obesity was less strong in urban areas than in rural areas but remained significant among women (p = 0.001) and men (p < 0.001). However, the relationship between altitude and obesity in women who live in urban areas appears to be nonlinear. Conclusions: In Peruvian adults, the prevalence of body fat-defined obesity was inversely associated with altitude. Whether this inverse association is explained by altitude per se or confounded by socioeconomic or other environmental factors, or differences in race/ethnicity or lifestyle, warrants further investigation.

Adherence predictor variables in AIDS patients: an empirical study using the data mining-based RFM model

Background

Highly active antiretroviral therapy (ART) is still the only effective method to stop the disease progression in acquired immunodeficiency syndrome (AIDS) patients. However, poor adherence to the therapy makes it ineffective. In this work, we construct an adherence prediction model of AIDS patients using the classical recency, frequency and monetary value (RFM) model in the data mining-based customer relationship management model to obtain adherence predictor variables.

Methods

We cleaned 257,305 diagnostic data elements of AIDS outpatients in Shanghai from August 2009 to December 2019 to obtain 16,440 elements. We tested the RFM and RFm (R: recent consultation month, F: consultation frequency, M/m: total/average medical costs per visit) models, three clustering methods (K-means, Kohonen and two-step clustering) and four decision algorithms (C5.0, the classification and regression tree, Chi-square Automatic Interaction Detector and Quick, Unbiased, Efficient, Statistical Tree) to select the optimal combination. The optimal model and clustering analysis were used to divide the patients into two groups (good and poor adherence), then the optimal decision algorithm was used to construct the prediction model of adherence and obtain its predictor variables.

Results

The results revealed that the RFm model, K-means clustering analysis and C5.0 algorithm were optimal. After three rounds of k-means clustering analysis, the optimal RFm clustering model quality was 0.8, 10,614 elements were obtained, including 9803 and 811 from patients with good or poor adherence, respectively, and five types of patients were identified. The prediction model had an accuracy of 100% with the recent consultation month as an important adherence predictor variable.

Conclusions

This work presented a prediction model for medication adherence in AIDS patients at the designated AIDS center in Shanghai, using the RFm model and the k-means and C5.0 algorithms. The model can be expanded to include patients from other centers in China and worldwide.

Relative fat mass is a better tool to diagnose high adiposity when compared to body mass index in young male adults: A cross-section study

BACKGROUND AND AIM

Relative fat mass (RFM) is a new method to estimate whole-body fat percentage in adults using an anthropometric linear equation. We aimed to assess the association between RFM and body fat (BF), evaluated by dual x-ray absorptiometry (DXA) or bioelectrical impedance (BIA), in young male adults.

METHODS

Eighty-one young males were assessed for BF fat and free fat mass (by BIA and DXA), waist circumference. BMI and RFM were then calculated from data collected from the subjects. The agreement between BMI and RFM or BIA/DXA was assessed by Pearson's Correlation and Kappa index. Univariate and multivariate linear regression were applied.

RESULTS

Analyzing all the participants together, the correlation between RFM and DXA (r_DXA = 0.90) or RFM and BIA (r_BIA = 0.88) were slightly higher than the correlation between BMI and DXA (r_DXA = 0.79) or BMI and BIA (r_BIA: 0.82). When analyzed by BF, low BF (LBF) individuals showed a much higher correlation with RFM (r_DXA = 0.58; r_BIA = 0.73) than BMI (r_DXA = 0.24; r_BIA: 0.46). However, subjects with excess BF (EBF) presented similar correlations when comparing RFM (r_DXA = 0.80; r_BIA = 0.64) and BMI (r_DXA = 0.78; r_BIA = 0.64). In general, RFM presented a higher strength of agreement with DXA and BIA (k_DXA = 0.75; k_BIA = 0.67) than BMI (k_DXA = 0.63; k_BIA = 0.60). Multivariable linear regression also revealed high associations between RFM and DXA or RFM and BIA (r²_DXA = 0.85; r²_BIA = 0.81).

CONCLUSION

Our findings suggest that RFM shows a good correlation and association with BF measured by DXA and BIA in young male adults. Furthermore, RFM seems to be better correlated to BF in LBF individuals when compared to BMI. Therefore, further studies investigating RFM as a tool to assess BF and obesity are motivated.

Management and Outcome of Reduced Fetal Movements-is Ultrasound Necessary?

OBJECTIVE

To review the management and outcome of pregnancies of women presenting to obstetrical triage with decreased fetal movements (DFM).

STUDY DESIGN

A retrospective review of women presenting with DFMs to two large Canadian obstetrical centres with a combined 9490 deliveries per year. The charts were reviewed for compliance with the Canadian guidelines for demographics (age, parity, GA, comorbidities, etc.), pregnancy management (admission vs. discharge, need to deliver), and pregnancy outcomes (mortality, morbidity, GA at delivery, Apgar scores, etc.). Patients who did not comply with the Canadian guidelines (requiring the patient to count six movements within two hours) were not excluded.

RESULTS

The charts of 579 patients who self-reported DFMs between January 2012 and December 2012 were reviewed. The distribution of ages was between 18 and 47 year old. The majority of these patients had no comorbidities (454/579). A significant minority of patients had FM in the triage area (231/579). The Canadian guidelines were interpreted differently in the two centres. In one (level 3), the protocol was to have a biophysical profile (BPP) on all patients prior to discharge, whereas in the other (level 2), only patients with a non-reactive non-stress test (NST) and/or oligohydramnios or intrauterine growth restriction (IUGR) underwent a BPP. All patients had an evaluation by an RN and MD and had a NST on arrival. A combination of NST and BPP was performed on 235/579. The frequency of DFM was 6.1% (level 3 centre: 5.6%, level 2 centre: 7.8%). There were 8 stillbirths on arrival. The 187 patients who had a reactive NST and a normal BPP and were sent home did not have a single stillbirth within 2 weeks. In the level 3 centre, 19 patients were sent home without a BPP and one had a stillbirth within 2 days (5%); in the level 2 hospital, there was only one stillbirth among the NST-only group (0.35%). There were 65 admissions; 46 of them (71%) were delivered, and 50% of them had a Caesarean delivery (baseline around 30%).

CONCLUSIONS

This is the first study looking at the performance of the Canadian guidelines of 2007. We found that the DFM rate was compatible with the literature (6.1% vs. 5%). The frequency of stillbirth on arrival was 1.4% (8/579). Patients discharged after normal NST and BPP did extremely well (no stillbirths), whereas those admitted following DFM had a relatively high Caesarean delivery rate (50%). This study was not designed to address changes in stillbirth rate, but it outlines the patients who experience DFM and their eventual outcomes.

Knowledge discovery from patients' behavior via clustering-classification algorithms based on weighted eRFM and CLV model: An empirical study in public health care services

The rapid growing of information technology (IT) motivates and makes competitive advantages in health care industry. Nowadays, many hospitals try to build a successful customer relationship management (CRM) to recognize target and potential patients, increase patient loyalty and satisfaction and finally maximize their profitability. Many hospitals have large data warehouses containing customer demographic and transactions information. Data mining techniques can be used to analyze this data and discover hidden knowledge of customers. This research develops an extended RFM model, namely RFML (added parameter: Length) based on health care services for a public sector hospital in Iran with the idea that there is contrast between patient and customer loyalty, to estimate customer life time value (CLV) for each patient. We used Two-step and K-means algorithms as clustering methods and Decision tree (CHAID) as classification technique to segment the patients to find out target, potential and loyal customers in order to implement strengthen CRM. Two approaches are used for classification: first, the result of clustering is considered as Decision attribute in classification process and second, the result of segmentation based on CLV value of patients (estimated by RFML) is considered as Decision attribute. Finally the results of CHAID algorithm show the significant hidden rules and identify existing patterns of hospital consumers.