Difference in Abdominal Muscularity at the Umbilicus Level between Young and Middle-aged Men

Tanaka, Noriko; Yamada, Masafumi; Tanaka, Yasuhiro; Fukunaga, Tetsuo; Nishijima, Takeshi; Kanehisa, Hiroaki

doi:10.2114/jpa2.26.527

Cited by 9 publications

(4 citation statements)

References 19 publications

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“…A plausible explanation for the lack of hypertrophy is the training status of the participants. The pre-training value of RA CSA in the training group (8.4 ± 0.8 cm 2 ) was approximately 25% greater than the previously reported value (6.7 ± 1.9 cm 2 ) in 23 young sedentary men [29]. This implies that the RA of the participants in the training group had already hypertrophied to some extent by their competitive experience in track and field, especially in strength-oriented events such as sprint running and jumping.…”

Section: Discussionmentioning

confidence: 51%

Effects of neuromuscular electrical stimulation training on muscle size in collegiate track and field athletes

Wakahara

Shiraogawa

2019

PLoS ONE

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The purpose of this study was to examine the effects of neuromuscular electrical stimulation training for 12 weeks on the abdominal muscle size in trained athletes. Male collegiate track and field athletes participated in the present study and were randomly allocated to either training or control groups. Eleven participants of the training group completed a 60-session training program over a 12-week period (23 min/session, 5 days/week) involving neuromuscular electrical stimulation (mostly 20 Hz) for the abdominal muscles in addition to their usual training for the own events. The participants of the control group (n = 13) continued their usual training. Before and after the intervention period, cross-sectional areas of the rectus abdominis and abdominal oblique muscles (the internal and external obliques and transversus abdominis) and subcutaneous fat thickness were measured with magnetic resonance and ultrasound imaging. There were no significant changes in cross-sectional area of the rectus abdominis or abdominal oblique muscles or in subcutaneous fat thickness in the training or control groups after the intervention period. The change in cross-sectional area of the rectus abdominis in each participant was not significantly correlated with pre-training cross-sectional area and neither was the mean value of fat thickness at pre- and post-training. These results suggest that low-frequency (20 Hz) neuromuscular electrical stimulation training for 12 weeks is ineffective in inducing hypertrophy of the abdominal muscles in trained athletes, even when they have a thin layer of subcutaneous fat.

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Section: Discussionmentioning

confidence: 51%

Effects of neuromuscular electrical stimulation training on muscle size in collegiate track and field athletes

Wakahara

Shiraogawa

2019

PLoS ONE

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“…In terms of pancreas aging, age-related changes visible on MRI images include pancreatic atrophy, fatty degeneration, and lobulation 31 . Finally, aging is also associated with abdominal changes in adipose tissue 32 , 33 , muscles 34 – 36 , and bones 37 .…”

Section: Discussionmentioning

confidence: 99%

Using deep learning to predict abdominal age from liver and pancreas magnetic resonance images

Diai

Collin

et al. 2022

Nat Commun

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With age, the prevalence of diseases such as fatty liver disease, cirrhosis, and type two diabetes increases. Approaches to both predict abdominal age and identify risk factors for accelerated abdominal age may ultimately lead to advances that will delay the onset of these diseases. We build an abdominal age predictor by training convolutional neural networks to predict abdominal age (or “AbdAge”) from 45,552 liver magnetic resonance images [MRIs] and 36,784 pancreas MRIs (R-Squared = 73.3 ± 0.6; mean absolute error = 2.94 ± 0.03 years). Attention maps show that the prediction is driven by both liver and pancreas anatomical features, and surrounding organs and tissue. Abdominal aging is a complex trait, partially heritable (h_g2 = 26.3 ± 1.9%), and associated with 16 genetic loci (e.g. in PLEKHA1 and EFEMP1), biomarkers (e.g body impedance), clinical phenotypes (e.g, chest pain), diseases (e.g. hypertension), environmental (e.g smoking), and socioeconomic (e.g education, income) factors.

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Section: Discussionmentioning

confidence: 99%

“…In terms of pancreas aging, age-related changes visible on MRI images include pancreatic atrophy, fatty degeneration, and lobulation 32 . Finally, aging is also associated with abdominal changes in adipose tissue 33,34 , muscles [35][36][37] and bones 38 . We found that accelerated abdominal aging is heritable (h_g 2 =26.3±1.9%) and associated with seven SNPs in six different genes.…”

Section: Genetic Factors and Heritability Of Accelerated Abdominal Agingmentioning

confidence: 99%

Using deep learning to predict age from liver and pancreas magnetic resonance images allows the identification of genetic and non-genetic factors associated with abdominal aging

Diai

Collin

et al. 2021

Preprint

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With age, abdominal organs and tissues undergo important changes. For example, liver volume declines, fatty replacement increases in the pancreas, and patients become more vulnerable to age-related diseases such as non-alcoholic fatty liver disease, alcoholic liver disease, hepatitis, fibrosis, cirrhosis, type two diabetes, cancer, gallstones and inflammatory pancreatic disease. Detecting early abdominal aging and identifying factors associated with this phenotype could help delay the onset of such diseases. In the following, we built the first abdominal age predictor by training convolutional neural networks to predict age from 45,552 liver magnetic resonance images [MRIs] and 36,784 pancreas MRIs (R-Squared=73.3+-0.6; root mean squared error=3.70+-0.03). Attention maps show that the prediction is driven not only by liver and pancreas anatomical features, but also by surrounding organs and tissue. We defined accelerated abdominal aging as the difference between abdominal age and chronological age, a phenotype which we found to be partially heritable (h_g2=26.3+-1.9%). Accelerated abdominal aging is associated with seven single nucleotide polymorphisms in six genes (e.g PNPT1, involved in RNA metabolic processes). Similarly, it is associated with biomarkers (e.g body impedance), clinical phenotypes (e.g chest pain), diseases (e.g hypertension), environmental (e.g smoking) and socioeconomic (e.g education) variables, suggesting potential therapeutic and lifestyle interventions to slow abdominal aging. Our predictor could be used to assess the efficacy or emerging rejuvenating therapies on the abdomen.

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Difference in Abdominal Muscularity at the Umbilicus Level between Young and Middle-aged Men

Cited by 9 publications

References 19 publications

Effects of neuromuscular electrical stimulation training on muscle size in collegiate track and field athletes

Effects of neuromuscular electrical stimulation training on muscle size in collegiate track and field athletes

Using deep learning to predict abdominal age from liver and pancreas magnetic resonance images

Using deep learning to predict age from liver and pancreas magnetic resonance images allows the identification of genetic and non-genetic factors associated with abdominal aging

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