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Effects of n-3 EPA and DHA supplementation on fat free mass and physical performance in elderly. A systematic review and meta-analysis of randomized clinical trial.
Rondanelli, M, Perna, S, Riva, A, Petrangolini, G, Di Paolo, E, Gasparri, C
Mechanisms of ageing and development. 2021;:111476
Abstract
The most studied n-3 polyunsaturated fatty acids (n-3 PUFAs) are eicosapentaenoic acid (EPA; 20:5n-3) and docosahexaenoic acid (DHA; 22:6n-3), and their intake seem to have a positive effect on skeletal muscle. This systematic review and meta-analysis aims to investigate the effect of n-3 EPA and DHA supplementation on fat free mass, and on different indexes of physical performance in the elderly. Eligible studies included RCT studies that investigated EPA and DHA intervention. Random-effects models have been used in order to estimate pooled effect sizes, the mean differences, and 95 % CIs. Findings from 14 studies (n = 2220 participants) lasting from 6 to 144 weeks have been summarized in this article. The meta-analyzed mean differences for random effects showed that daily n-3 EPA + DHA supplementation (from 0.7 g to 3.36 g) decreases the time of Time Up and Go (TUG) test of -0.28 s (CI 95 %-0.43, -0.13;). No statistically significant effects on physical performance indicators, such as 4-meter Walking Test, Chair Rise Test and Handgrip Strength, have been found. The fat free mass follows an improvement trend of +0.30 kg (CI 95 % -0.39, 0.99) but not statistically significant. N-3 EPA + DHA supplementation could be a promising strategy in order to enhance muscle quality and prevent or treat frailty.
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Effect of exercise on hepatic steatosis: Are benefits seen without dietary intervention? A systematic review and meta-analysis.
Baker, CJ, Martinez-Huenchullan, SF, D'Souza, M, Xu, Y, Li, M, Bi, Y, Johnson, NA, Twigg, SM
Journal of diabetes. 2021;(1):63-77
Abstract
BACKGROUND Interventions involving both exercise and dietary modification are effective in reducing steatosis in nonalcoholic fatty liver disease (NAFLD). However, exercise alone may reduce liver fat and is known to have other positive effects on health. The primary aim of this study was to systematically review the effect of exercise alone without dietary intervention on NAFLD and to examine correlations across changes in liver fat and metabolic markers during exercise. METHODS Relevant online databases were searched from earliest records to May 2020 by two researchers. Studies were included where the trial was a randomized controlled trial, participants were adults, exercise intervention was longer than 4 weeks, no dietary intervention occurred, and the effect of the intervention on liver fat was quantified via magnetic resonance imaging/proton magnetic resonance spectroscopy. RESULTS Of 21 597 studies retrieved, 16 were included involving 706 participants. Exercise was found to have a beneficial effect on liver fat without dietary modification (-2.4%, -3.13 to -1.66) (mean, 95% CI). Pearson correlation showed significant relationships between change in liver fat and change in weight (r = 0.67, P = .007), liver enzymes aspartate aminotransferase (r = 0.76, P = .002) and alanine aminotransferase (r = 0.91, P < .001), and cardiorespiratory fitness VO2 peak (peak volume oxygen consumption) (r = -0.88, P = .004). By multivariate regression, change in weight and change in VO2 peak significantly contributed to change in liver fat (R2 = 0.84, P = .01). CONCLUSIONS This systematic review found that exercise without dietary intervention improves liver fat and that clinical markers may be useful proxies for quantifying liver fat changes.
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The effects of rapid growth on body mass index and percent body fat: A meta-analysis.
Chen, Y, Wang, Y, Chen, Z, Xin, Q, Yu, X, Ma, D
Clinical nutrition (Edinburgh, Scotland). 2020;(11):3262-3272
Abstract
BACKGROUND & AIMS Rapid growth in childhood and obesity are highly prevalent in congenital deficiency infants, but the associations between them remain controversial. This meta-analysis was performed to explore the effects of rapid growth on body mass index (BMI) and percent body fat (PBF), and to clarify potential confounders. METHODS A systematic search was performed using electronic databases including EMBASE (1985 to July 2019) and Medline (1966 to July 2019) for English articles. China National Knowledge Infrastructure Chinese citation database (CNKI) and WANFANG database were used to search articles in Chinese. Reference lists were also screened as supplement. All relevant studies that compare BMI or PBF between rapid group and control group were identified. The definition of rapid growth should be clearly specified. Means and standard deviations/95% confidence intervals (CIs) of BMI and PBF should be available. Relevant information was extracted independently by two reviewers. Study quality was reassessed using the Newcastle-Ottawa Scale. Publication bias and heterogeneity were detected. The random effect model was adopted for combined and stratified analysis. RESULTS About the effect of rapid growth on BMI, seventeen researches (4473 participants) involving 49 comparisons were included. Pooled analysis showed rapid group had higher BMI of 0.573 (95% CI, 0.355 to 0.791; P < 0.001). Stratified analyses revealed that catch-up weight gain, follow-up age >6 years old, rapid growth duration >2 years, full-term, comparing rapid growth SGA infants with control SGA infants, and from developed and developing countries, would all lead to higher BMI in rapid groups. About the effect of rapid growth on PBF, eleven researches (4594 participants) involving 37 comparisons were included. Pooled analysis showed rapid group had higher PBF of 2.005 (95% CI, 1.581 to 2.429; P < 0.001). Subgroup analyses suggested that catch-up weight gain, follow-up age ≤6 years old, rapid growth duration >2 years, full-term, comparing rapid growth SGA infants with control AGA infants, and participants from developing countries, would lead to increased PBF in rapid groups. CONCLUSION Rapid growth has a positive correlation with BMI and PBF. However, stratified analyses show that it is catch-up weight gain that lead to higher BMI and PBF, but not catch-up growth. In addition, rapid growth have long-term effect on BMI and short-term effect on PBF. Rapid growth duration longer than 2 years may increase the risk effect of rapid growth on BMI and PBF. But given that rapid growth would induce multiple health outcomes apart from BMI and PBF, the benefits and risks of rapid growth must be carefully considered and weighted.
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Development and validation of a prediction model for fat mass in children and adolescents: meta-analysis using individual participant data.
Hudda, MT, Fewtrell, MS, Haroun, D, Lum, S, Williams, JE, Wells, JCK, Riley, RD, Owen, CG, Cook, DG, Rudnicka, AR, et al
BMJ (Clinical research ed.). 2019;:l4293
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Abstract
OBJECTIVES To develop and validate a prediction model for fat mass in children aged 4-15 years using routinely available risk factors of height, weight, and demographic information without the need for more complex forms of assessment. DESIGN Individual participant data meta-analysis. SETTING Four population based cross sectional studies and a fifth study for external validation, United Kingdom. PARTICIPANTS A pooled derivation dataset (four studies) of 2375 children and an external validation dataset of 176 children with complete data on anthropometric measurements and deuterium dilution assessments of fat mass. MAIN OUTCOME MEASURE Multivariable linear regression analysis, using backwards selection for inclusion of predictor variables and allowing non-linear relations, was used to develop a prediction model for fat-free mass (and subsequently fat mass by subtracting resulting estimates from weight) based on the four studies. Internal validation and then internal-external cross validation were used to examine overfitting and generalisability of the model's predictive performance within the four development studies; external validation followed using the fifth dataset. RESULTS Model derivation was based on a multi-ethnic population of 2375 children (47.8% boys, n=1136) aged 4-15 years. The final model containing predictor variables of height, weight, age, sex, and ethnicity had extremely high predictive ability (optimism adjusted R2: 94.8%, 95% confidence interval 94.4% to 95.2%) with excellent calibration of observed and predicted values. The internal validation showed minimal overfitting and good model generalisability, with excellent calibration and predictive performance. External validation in 176 children aged 11-12 years showed promising generalisability of the model (R2: 90.0%, 95% confidence interval 87.2% to 92.8%) with good calibration of observed and predicted fat mass (slope: 1.02, 95% confidence interval 0.97 to 1.07). The mean difference between observed and predicted fat mass was -1.29 kg (95% confidence interval -1.62 to -0.96 kg). CONCLUSION The developed model accurately predicted levels of fat mass in children aged 4-15 years. The prediction model is based on simple anthropometric measures without the need for more complex forms of assessment and could improve the accuracy of assessments for body fatness in children (compared with those provided by body mass index) for effective surveillance, prevention, and management of clinical and public health obesity.
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Linearity, Bias, and Precision of Hepatic Proton Density Fat Fraction Measurements by Using MR Imaging: A Meta-Analysis.
Yokoo, T, Serai, SD, Pirasteh, A, Bashir, MR, Hamilton, G, Hernando, D, Hu, HH, Hetterich, H, Kühn, JP, Kukuk, GM, et al
Radiology. 2018;(2):486-498
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Purpose To determine the linearity, bias, and precision of hepatic proton density fat fraction (PDFF) measurements by using magnetic resonance (MR) imaging across different field strengths, imager manufacturers, and reconstruction methods. Materials and Methods This meta-analysis was performed in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. A systematic literature search identified studies that evaluated the linearity and/or bias of hepatic PDFF measurements by using MR imaging (hereafter, MR imaging-PDFF) against PDFF measurements by using colocalized MR spectroscopy (hereafter, MR spectroscopy-PDFF) or the precision of MR imaging-PDFF. The quality of each study was evaluated by using the Quality Assessment of Studies of Diagnostic Accuracy 2 tool. De-identified original data sets from the selected studies were pooled. Linearity was evaluated by using linear regression between MR imaging-PDFF and MR spectroscopy-PDFF measurements. Bias, defined as the mean difference between MR imaging-PDFF and MR spectroscopy-PDFF measurements, was evaluated by using Bland-Altman analysis. Precision, defined as the agreement between repeated MR imaging-PDFF measurements, was evaluated by using a linear mixed-effects model, with field strength, imager manufacturer, reconstruction method, and region of interest as random effects. Results Twenty-three studies (1679 participants) were selected for linearity and bias analyses and 11 studies (425 participants) were selected for precision analyses. MR imaging-PDFF was linear with MR spectroscopy-PDFF (R2 = 0.96). Regression slope (0.97; P < .001) and mean Bland-Altman bias (-0.13%; 95% limits of agreement: -3.95%, 3.40%) indicated minimal underestimation by using MR imaging-PDFF. MR imaging-PDFF was precise at the region-of-interest level, with repeatability and reproducibility coefficients of 2.99% and 4.12%, respectively. Field strength, imager manufacturer, and reconstruction method each had minimal effects on reproducibility. Conclusion MR imaging-PDFF has excellent linearity, bias, and precision across different field strengths, imager manufacturers, and reconstruction methods. © RSNA, 2017 Online supplemental material is available for this article. An earlier incorrect version of this article appeared online. This article was corrected on October 2, 2017.