BRD4 inhibition for the treatment of pathological organ fibrosis [version 1; referees: 2 approved]

Fibrosis is defined as excess deposition of extracellular matrix, resulting in tissue scarring and organ dysfunction. It is estimated that 45% of deaths in the developed world are due to fibrosis-induced organ failure. Despite the well-accepted role of fibrosis in the pathogenesis of numerous diseases, there are only two US Food and Drug Administration–approved anti-fibrotic therapies, both of which are currently restricted to the treatment of pulmonary fibrosis. Thus, organ fibrosis represents a massive unmet medical need. Here, we review recent findings suggesting that an epigenetic regulatory protein, BRD4, is a nodal effector of organ fibrosis, and we highlight the potential of small-molecule BRD4 inhibitors for the treatment of diverse fibrotic diseases

Promiscuous actions of small molecule inhibitors of the protein kinase D-class IIa HDAC axis in striated muscle

AbstractPKD-mediated phosphorylation of class IIa HDACs frees the MEF2 transcription factor to activate genes that govern muscle differentiation and growth. Studies of the regulation and function of this signaling axis have involved MC1568 and Gö-6976, which are small molecule inhibitors of class IIa HDAC and PKD catalytic activity, respectively. We describe unanticipated effects of these compounds. MC1568 failed to inhibit class IIa HDAC catalytic activity in vitro, and exerted divergent effects on skeletal muscle differentiation compared to a bona fide inhibitor of these HDACs. In cardiomyocytes, Gö-6976 triggered calcium signaling and activated stress-inducible kinases. Based on these findings, caution is warranted when employing MC1568 and Gö-6976 as pharmacological tool compounds to assess functions of class IIa HDACs and PKD

Recurrent histone mutations in T-cell acute lymphoblastic leukaemia.

Mutations affecting key modifiable histone type 3 (H3; Supplementary Table 1) residues are frequent oncogenic events in certain solid tumours (Feinberg, et al 2016), and have also recently been implicated in a subset of acute myeloid leukaemia (AML)(Lehnertz, et al 2017). Here, we systematically reviewed the somatic mutations in >20,000 cancer specimens to identify tumours harbouring H3 mutations. In a subset of T-cell acute lymphoblastic leukaemia (T-ALL) we identified non-methionine mutations of the key modifiable H3 residues, lysine (K) 27 and 36

Influence of Acute Water Ingestion on Bioelectrical Impedance Analysis Estimates of Body Composition

Body composition estimation is a significant component of health and fitness assessments. Multi-frequency bioelectrical impedance analysis (MFBIA) uses multiple electrical frequencies that travel through body tissues in order to estimate fluid content and body composition. Prior to body composition assessments, it is common to implement a wet fast (i.e., a fasting period that allows water intake); however, the influence of a wet fast as compared to a dry fast (i.e., disallowing water intake) is relatively unknown. PURPOSE: To determine the effects of acute water consumption on MFBIA body composition estimates. METHODS: A randomized crossover study was conducted in 16 adults (8 F, 8 M; age: 22.0 ± 2.9 y; height: 173.6 ± 9.9 cm; weight: 74.3 ± 21.6 kg; body mass index: 24.6 ± 4.7; body fat % [BF%]: 16.7 ± 8.1%). On two occasions, participants reported to the laboratory after an overnight food and fluid fast. After a baseline MFBIA assessment, participants either consumed 11 mL/kg of bottled water (W condition) or consumed no fluid as the control (CON condition). The 11 ml/kg dose of water corresponded to absolute intakes of 531 to 1360 mL. After the water consumption time point, MFBIA tests were performed every 10 minutes for one hour. Participants stood upright for the entire research visit. MFBIA estimates of body mass (BM), fat mass (FM), fat-free mass (FFM), and BF% were analyzed using 2 x 7 (condition x time) analysis of variance with repeated measures, follow-up pairwise comparisons, and evaluation of the partial eta-squared (ηp2) effect sizes. RESULTS: No variables differed between conditions at baseline. Condition x time interactions were present for all variables (BM: pp2=0.89; FM: p=0.0008, ηp2=0.30; BF%: p=0.005, ηp2=0.23) except FFM (p=0.69, ηp2=0.03). Follow-up testing indicated that BM was ~0.6 kg higher in W as compared to CON at all post-baseline time points (pp2=0.32), regardless of condition. CONCLUSION: Up to one hour after ingestion, acute water intake was exclusively detected as increased FM by MFBIA. This contrasts with the common belief that ingesting water prior to bioimpedance tests would result in inflated FFM and decreased BF%. Since body composition estimates never returned to baseline within the hour after water ingestion, it is not clear how long this effect would persist. These results suggest acute water ingestion can produce an inflation of MFBIA body fat estimates for at least one hour. These results indicate that water intake during fasting periods should be considered as part of pre-assessment standardization

Impact of Fluid Consumption on Estimates of Intracellular, Extracellular, and Total Body Water from Multi-Frequency Bioelectrical Impedance Analysis

Multi-frequency bioelectrical impedance analysis (MFBIA) is able to distinguish between total body water (TBW), extracellular water (ECW) and intracellular water (ICW). Low-frequency currents are thought to primarily pass through ECW, while high-frequency currents pass through all body fluids (i.e., TBW). ICW can then be estimated by subtracting ECW from TBW. As such, MFBIA may have utility for monitoring health conditions resulting in water retention within specific fluid compartments. However, the sensitivity of fluid estimates from MFBIA is not fully established. PURPOSE: To evaluate the effects of acute fluid ingestion on body water estimates produced by a MFBIA analyzer. METHODS: Sixteen adults (8 F, 8 M; age: 22.0 ± 2.9 y; height: 173.6 ± 9.9 cm; weight: 74.3 ± 21.6 kg; body fat %: 16.7 ± 8.1%) participated in a randomized crossover study consisting of two conditions: 1) no fluid ingestion (control; C); and 2) acute ingestion of 11 mL/kg of bottled water (W). In both conditions, participants reported to the laboratory after an overnight food and fluid fast for serial assessments using 8-point standing MFBIA. An initial MFBIA assessment was performed at baseline, followed by a 5-minute period during which water was ingested (W condition) or the participant continued to rest in the lab (C condition). Beginning 10 minutes after this time period, participants were assessed by MFBIA every 10 minutes for one hour. Participants stood upright for the entirety of each research visit. Analysis of variance with repeated measures was used to examine differences in MFBIA estimates of body mass (BM), TBW, ECW, and ICW between conditions and across time. Follow-up pairwise comparisons were performed and partial eta-squared (ηp2) effect sizes were calculated. RESULTS: A group-by-time interaction was present for BM (pp2: 0.89) but not TBW (p=0.74; ηp2: 0.03), ECW (p=0.85; ηp2: 0.02), or ICW (p=0.87; ηp2: 0.05). Follow-up indicated that BM did not differ between conditions at baseline but was ~0.6 ± 0.2 kg higher in the W condition as compared to C at all post-baseline time points (pp2: 0.29 to 0.38). No significant effects were observed for ECW. CONCLUSION: The lack of change in body fluids with acute water ingestion likely indicates that: 1) within one hour, ingested water has not been assimilated into body fluids to the extent that it is detectable by MFBIA; or 2) the quantity of fluid ingestion is below the detection limits of the MFBIA analyzer. In support of the first point, it is likely that bioelectrical currents do not penetrate the gastrointestinal tract, meaning fluids contained therein are unlikely to be detected by MFBIA as fluids

Body Fat Gain Automatically Increases Lean Mass by Changing the Fat-Free Component of Adipose Tissue

Estimating alterations in lean mass in response to various training interventions is a primary concern for many investigations. However, previous reports have suggested that lean mass estimates from weight loss interventions may be significantly altered by attempting to correct for changes in the fat-free component of adipose tissue (FFAT). This component, consisting primarily of water and protein, has been estimated as ~15% of adipose tissue (AT) mass. While a preliminary examination of this correction method has been conducted in the instance of weight loss, it has yet to be investigated after a period of purposeful weight gain and resistance training. PURPOSE: To examine the impact of corrections for FFAT on estimates of lean mass accretion during a period of weight gain and resistance training. METHODS: Twenty-one resistance trained males underwent 6 weeks of supervised training and followed a hypercaloric diet in order to elicit weight gain. Body composition was assessed pre- and post-intervention via dual energy x-ray absorptiometry (DXA). AT was estimated using DXA-derived fat mass (FM) in the equation: AT = FM/0.85. FFAT was then estimated via the equation: FFAT = 0.15 × AT. Lastly, FFAT was subtracted from DXA-derived lean mass (LMDXA) to yield the new corrected lean mass value (cLM). Changes in LMDXA and cLM in response to the training intervention were calculated, and dependent samples T-tests were employed to determine if significant differences were present between changes in LMDXA and cLM. RESULTS: Significant differences (p ≤ 0.001) were noted for estimates of LM gain, with a larger increase observed for LMDXA as compared to cLM (LMDXA :2.42 ± 1.58kg; cLM: 2.14 ± 1.65kg). CONCLUSION: Correcting DXA-derived LM for the fat-free component of adipose tissue reduces the magnitude of LM accretion after a period of weight gain. However, while LM estimates did significantly differ, the small degree to which they differed indicates questionable practical relevance of such corrections in future investigations

Agreement Between 4-Compartment Model and 7-Site Ultrasonography for Tracking Weight Training-Induced Changes in Body Composition

Tracking body composition changes provides valuable information in a variety of contexts, including aging, disease, and lifestyle interventions. The 4-Compartment (4C) model is widely accepted as a criterion molecular-level method for evaluating body composition by integrating data from dual-energy x-ray absorptiometry (DXA), air displacement plethysmography (ADP), and bioimpedance spectroscopy (BIS). Ultrasonography (US) is another method of body composition estimation that evaluates subcutaneous adipose tissue at various body sites. PURPOSE: To evaluate the agreement between body composition changes detected by a molecular-level 4C model and a 7-site skinfold thickness-based US method in response to weight training and a hypercaloric diet. METHODS: Seventeen adult males (age:­­ 22.5 ± 2.4 y, body mass: 72.8 ± 11.6 kg, body fat % [BF%]: 14.0 ± 4.8%) who were moderately resistance-trained completed a 6-week period of supervised resistance training in conjunction with overfeeding via provision of a high-calorie, carbohydrate/protein dietary supplement. At the beginning and end of this period, body composition was evaluated via 4C model, necessitating assessments via DXA, ADP, and BIS. Additionally, body composition was estimated via US by utilizing subcutaneous adipose tissue thicknesses at seven sites on the body as described by Jackson and Pollock. Changes in fat mass (ΔFM) and fat-free mass (ΔFFM) detected by the 4C model and US were compared using paired-samples t-tests, Bland-Altman analysis, equivalence testing, and evaluation of validity metrics. RESULTS: ΔFM and ΔFFM were significantly correlated between methods (ΔFM: r=0.48 [95% confidence interval {CI}: 0.002 to 0.78]; ΔFFM: r=0.87 [95% CI: 0.66 to 0.95]. However, both ΔFM (4C: 0.6 ± 1.2 kg; US: 2.8 ± 2.5 kg) and ΔFFM (4C: 3.3 ± 1.6 kg; US: 1.0 ± 3.4 kg) significantly differed between methods (p \u3c 0.001). The total error for ΔFM and ΔFFM estimates was 3.1 kg (95% CI: 3.0 to 3.2 kg). 4C and US predicted the same direction of change in ΔFFM but not ΔFM, based on equivalence testing with an equivalence interval equal to 4C change. Proportional bias was observed for both ΔFM and ΔFFM. CONCLUSION: Although changes in body composition were correlated between methods, ΔFM and ΔFFM significantly differed between 4C and US. As compared to the 4C, US detected a greater proportion of increased body mass as FM rather than FFM. Overall, the magnitude of differences in body composition changes do not support the interchangeability of 4C and US. Although tracking body composition changes provides valuable information, it is important to take into account that different assessment methods may produce varying results in response to a given intervention

Tracking Resistance Training-Induced Changes in Body Composition via 3-Dimensional Optical Scanning

Tracking changes in body composition is potentially useful for monitoring health status, disease risk, and results of lifestyle interventions. In active individuals, evaluating body composition changes over time may provide useful information regarding the effectiveness of nutrition and exercise programs. PURPOSE: The purpose of this study was to compare changes in body composition estimates obtained from a 4-compartment (4C) model and a 3-dimensional optical (3DO) scanner in resistance-trained males. METHODS: Twenty resistance-trained males underwent assessments via 4C and 3DO before and after 6 weeks of supervised resistance training plus overfeeding with a high-calorie protein/carbohydrate supplement. To generate the 4C model, tests were performed using dual-energy x-ray absorptiometry, air displacement plethysmography, and bioimpedance spectroscopy. Changes in fat mass (ΔFM) and fat-free mass (ΔFFM) detected by 3DO were compared with the reference 4C model using paired-samples t-tests, Bland-Altman analysis, equivalence testing, and evaluation of validity metrics. RESULTS: Both ΔFM (mean ± SD: 4C: 0.6 ± 1.1 kg; 3DO: 1.9 ± 1.9 kg) and ΔFFM (4C: 3.2 ± 1.7 kg; 3DO: 1.9 ± 1.4 kg) differed between methods (p \u3c 0.002). The correlation (r) for ΔFM was 0.49 (95% confidence interval [CI]: 0.06 to 0.77) and was 0.42 (95% CI: -0.03 to 0.73) for ΔFFM. The total error for ΔFM and ΔFFM estimates was 2.1 kg. ΔFFM demonstrated equivalence between methods based on a ± 2 kg (~62% of 4C change) equivalence interval, whereas ΔFM failed to exhibit equivalence even with a 100% equivalence interval. Proportional bias was observed for ΔFM but not ΔFFM. CONCLUSION: Our data indicate that changes in FM and FFM detected by a 3D scanner did not exhibit strong agreement with changes detected by a 4C model. However, within the context of our study, agreement in FFM changes was superior to agreement in FM changes based on the results of equivalence testing and lack of proportional bias in FFM changes. Therefore, depending on the level of accuracy needed, the error in FFM changes observed for the 3D scanner may be potentially acceptable for some applications. Future research should investigate the utility of 3D scanners for monitoring changes in body composition and anthropometric variables in healthy and clinical populations, as well as investigate novel body phenotypes that may be associated with disease risk or health status

The Effect of Body Composition Methodology on Resulting Energy Availability Assessments

Energy availability (EA) is defined as the total daily energy available to an individual after accounting for that expended during exercise and standardized to fat-free mass (FFM). Generally, EA values less than 30 kcal/kg FFM/day are considered “low” and have been associated with deleterious effects on reproductive and hormonal health in females. However, it is unclear whether the method used to estimate FFM influences the resulting EA values to a degree that may affect interpretation and clinical decision-making. PURPOSE: To determine the effect of FFM values derived from various methods of body composition assessment on the resulting range and interpretation of EA values. METHODS: Four EA estimates were generated in 38 healthy females (mean ± SD age: 25.6 ± 6.2 years; height: 163.6 ± 7.4 cm; weight: 64.7 ± 13.8 kg) using different combinations within a reasonable range of lower and higher (25 and 35 kcal/kg bodyweight, respectively) energy intake values and lower and higher (3.5 and 7 kcal/kg bodyweight, respectively) exercise energy expenditure values. Resulting estimates were then standardized to FFM values from air displacement plethysmography (ADP), bioelectrical impedance spectroscopy (BIS), and bioelectrical impedance analysis (BIA) from both a research-grade (multi-frequency) and consumer-grade (dual-frequency) device. Resulting EA values were then compared to those using FFM from dual-energy x-ray absorptiometry (DXA). Each estimate was assigned to one of three EA “zones”: “low” (less than 30 kcal/kg FFM), “reduced” (30-44.9 kcal/kg FFM), or “adequate” (≥45 kcal/kg FFM). Individual EA estimates that were in different zones when compared between two devices were considered discordant. RESULTS: When compared to DXA-derived estimates, EA values were discordant in up to 13-16% of individuals depending on body composition method used. Discordant values were generally more common in the plots assuming higher (35 kcal/kg bodyweight) energy intake values and were most likely to be considered “adequate” using DXA-derived FFM versus “reduced” using alternate methods. CONCLUSION: EA estimates are generally robust to the method of body composition assessment used. However, divergent interpretations may occur in a small minority of individuals in which alternate methods may provide lower EA values than DXA

A Between-sex Comparison of the Validity of Body Fat Percentage Estimates From Four Bioelectrical Impedance Analyzers

Bioelectrical impedance analysis (BIA) devices administer electrical currents through surface electrodes in contact with the hands and/or feet. The measured reactance and resistance of various bodily tissues to these currents are then used to estimate body fat percentage (BFP) and other body composition values of interest based on algorithms derived from validation data. Owing to different patterns of fat distribution between sexes, it is unclear whether the configuration of electrodes (i.e., hand-to-hand, foot-to-foot, or hand-to-foot) may affect the validity of these devices in males versus females. PURPOSE: The purpose of this study was to determine the validity of BFP values across four BIA devices – one consumer-grade foot-to-foot device (RENPHO Smart Bathroom Scale), one consumer-grade hand-to-hand device (Omron HBF-306), one consumer-grade octapolar device (InBody H20N), and one research-grade octapolar device (Seca mBCA 515/514) – against a criterion four-compartment model (4C), and to compare these values between males and females. METHODS: Seventy-four healthy participants (35 males and 39 females) were included in this analysis. Participants abstained from all food, fluid, caffeine, and alcohol for at least 8 hours prior to each visit. Total error (TE) was calculated as the root mean square error between the estimate of each BIA device and that of the 4C model. Standard error of the estimate (SEE) was defined as the residual standard error value from ordinary least squares regression. Constant error (CE) was calculated as the average difference between the estimate of each BIA device and that of the 4C model. RESULTS: Participants had a mean ±SD age of 27.2 ±7.3 years, height of 168.1 ±8.9 cm, weight of 72.2 ±16.7 kg, and 4C BFP of 24.9 ±9.2%. In the entire sample, ranges for validity metrics of interest were as follows: TE: 3.2% (Seca) to 7.2% (RENPHO); SEE: 3.3% (Seca) to 5.7% (RENPHO); CE: -0.02 ±3.4% (InBody) to -3.46 ±4.1% (Omron). Across all devices, both TE and SEE were lower in females, with the largest between-sex differences observed for the InBody and RENPHO. Both octapolar devices (InBody and Seca) exhibited low group-level error in males and females (all CE within ±0.32%). Meanwhile, the RENPHO and Omron devices generally underestimated BFP with a greater degree of underestimation in females (CE of -2.6% and -3.7%, respectively) than males (CE of -0.1% and -3.2%, respectively), particularly for the RENPHO. CONCLUSION: Among the four BIA devices investigated, octapolar devices tended to have higher validity overall. All devices demonstrated lower TE and SEE in females, with the greatest between-sex differences observed in the InBody and RENPHO models. Users should be aware that commercially available hand-to-hand or foot-to-foot BIA devices such as the Omron and RENPHO models used in this study may systematically underestimate BFP compared to a criterion 4C model. In contrast, hand-to-foot octapolar analyzers exhibit strong group-level validity in both sexes