Influence of the different hormonal status changes during their life on fat mass localisation in women: a narrative review

Impact of menstrual cycle or combined oral contraception on elite female cyclists' training responses through a clustering analysis of training sessions

Objectives

(i) To classify training sessions of elite female cyclists according to an intensity index based on a longitudinal follow-up using multiparametric data collected in situ (ii) to measure the effect of estimated menstrual cycle (MC) phases and oral contraceptive pills (OC) phases on the athletes' training responses on each type of training identified.

Method

Thirteen elite French cyclists were followed up over 30 months and 5,190 training sessions were collected and 81 MC/OCs full cycles analyzed. Power sensors and position devices captured training data in situ, which was summarized into 14 external load variables. Principal Component Analysis and K-means clustering were used to identify cycling sessions according to an intensity load index. The clusters were then verified and categorized through the analysis of heart rate and rate of perceived effort. A calendar method was used to estimate 3 phases of the MC: menstruation, mid-cycle phase (MP) and late-cycle phase (LP). Two phases were defined among monophasic OC users: pills' taking/withdrawal.

Results

Four main types of training effort were identified: Intensive, Long, Medium and Light. In the MC group (n = 7; 52 cycles), the intensity index is 8% higher during the mid-cycle (vs. menstrual phase, p = 0.032) in the Intensive effort sessions. No differences were observed in Long, Medium or Light effort, nor between the phases of pills' taking/withdrawal among OC users.

Conclusion

The clustering analyses developed allows a training classification and a robust method to investigate the influence of the MC/OC in situ. A better training response during the mid-cycle when the sessions are the most intense suggest an impact of the MC when the athletes approach their maximal capacity.

Steatosis Quantification on Ultrasound Images by a Deep Learning Algorithm on Patients Undergoing Weight Changes

INTRODUCTION

A deep learning algorithm to quantify steatosis from ultrasound images may change a subjective diagnosis to objective quantification. We evaluate this algorithm in patients with weight changes.

MATERIALS AND METHODS

Patients (N = 101) who experienced weight changes ≥ 5% were selected for the study, using serial ultrasound studies retrospectively collected from 2013 to 2021. After applying our exclusion criteria, 74 patients from 239 studies were included. We classified images into four scanning views and applied the algorithm. Mean values from 3-5 images in each group were used for the results and correlated against weight changes.

RESULTS

Images from the left lobe (G1) in 45 patients, right intercostal view (G2) in 67 patients, and subcostal view (G4) in 46 patients were collected. In a head-to-head comparison, G1 versus G2 or G2 versus G4 views showed identical steatosis scores (R2 > 0.86, p < 0.001). The body weight and steatosis scores were significantly correlated (R2 = 0.62, p < 0.001). Significant differences in steatosis scores between the highest and lowest body weight timepoints were found (p < 0.001). Men showed a higher liver steatosis/BMI ratio than women (p = 0.026).

CONCLUSIONS

The best scanning conditions are 3-5 images from the right intercostal view. The algorithm objectively quantified liver steatosis, which correlated with body weight changes and gender.

Sex-specific relationships between obesity, physical activity, and gray and white matter volume in cognitively unimpaired older adults

Independently, obesity and physical activity (PA) influence cerebral structure in aging, yet their interaction has not been investigated. We examined sex differences in the relationships among PA, obesity, and cerebral structure in aging with 340 participants who completed magnetic resonance imaging (MRI) acquisition to quantify grey matter volume (GMV) and white matter volume (WMV). Height and weight were measured to calculate body mass index (BMI). A PA questionnaire was used to estimate weekly Metabolic Equivalents. The relationships between BMI, PA, and their interaction on GMV Regions of Interest (ROIs) and WMV ROIs were examined. Increased BMI was associated with higher GMV in females, an inverse U relationship was found between PA and GMV in females, and the interaction indicated that regardless of BMI greater PA was associated with enhanced GMV. Males demonstrated an inverse U shape between BMI and GMV, and in males with high PA and had normal weight demonstrated greater GMV than normal weight low PA revealed by the interaction. WMV ROIs had a linear relationship with moderate PA in females, whereas in males, increased BMI was associated with lower WMV as well as a positive relationship with moderate PA and WMV. Males and females have unique relationships among GMV, PA and BMI, suggesting sex-aggregated analyses may lead to biased or non-significant results. These results suggest higher BMI, and PA are associated with increased GMV in females, uniquely different from males, highlighting the importance of sex-disaggregated models. Future work should include other imaging parameters, such as perfusion, to identify if these differences co-occur in the same regions as GMV.

Unique role for lncRNA HOTAIR in defining depot-specific gene expression patterns in human adipose-derived stem cells

In this study, Erdos et al. investigated the role of HOX transcript antisense intergenic RNA (HOTAIR) in adipose tissue biology. Using three different approaches (silencing of HOTAIR in GF human adipose-derived stem cells [GF hASCs], overexpression of HOTAIR in ABD hASCs, and ChIRP-seq) to localize HOTAIR binding in GF hASC chromatin, they found that HOTAIR binds and modulates expression, both positively and negatively, of genes involved in adipose tissue-specific pathways, including adipogenesis, and demonstrate a unique function for HOTAIR in hASC depot-specific regulation of gene expression.

Accumulation of fat above the waist is an important risk factor in developing obesity-related comorbidities independently of BMI or total fat mass. Deciphering the gene regulatory programs of the adipose tissue precursor cells within upper body or abdominal (ABD) and lower body or gluteofemoral (GF) depots is important to understand their differential capacity for lipid accumulation, maturation, and disease risk. Previous studies identified the HOX transcript antisense intergenic RNA (HOTAIR) as a GF-specific lncRNA; however, its role in adipose tissue biology is still unclear. Using three different approaches (silencing of HOTAIR in GF human adipose-derived stem cells [GF hASCs], overexpression of HOTAIR in ABD hASCs, and ChIRP-seq) to localize HOTAIR binding in GF hASC chromatin, we found that HOTAIR binds and modulates expression, both positively and negatively, of genes involved in adipose tissue-specific pathways, including adipogenesis. We further demonstrate a direct interaction between HOTAIR and genes with high RNAPII binding in their gene bodies, especially at their 3′ ends or transcription end sites. Computational analysis suggests HOTAIR binds preferentially to the 3′ ends of genes containing predicted strong RNA–RNA interactions with HOTAIR. Together, these results reveal a unique function for HOTAIR in hASC depot-specific regulation of gene expression.