Very nice review of the signaling networks governing skeletal muscle growth, atrophy, and cachexia

I am pleased to share our new research article (in press version) entitled: Glycolytic metabolism and biomass production from glucose in human skeletal muscle growth. Link in comments. 1/3 #myoblue @JYUBiolPA

Very interesting work by @HeWaHippo et al highlighting importance of muscle mass: "Results of our systematic literature review suggest that the stimulation of skeletal muscle hypertrophy is sufficient to cause anti-obesity and anti-diabetic effects" 💪

🚨AI Hits the Gym 💪 📊Resistance training plans from ChatGPT & Gemini show promise but lack precision — coaches still needed to tailor for muscle growth & strength ✍️Tim Havers et al. 🔓#OpenAccess 🔗 https://t.co/p6LqNbd7MR

Effects of Skeletal #Muscle #Hypertrophy on Fat Mass and Glucose Homeostasis in Humans and Animals: A Narrative Review with Systematic Literature Search @Tim_Havers et al. #openaccess https://t.co/NJCiL7yZt6

@SportsMedicineJ

Gain muscle to lose fat? "This analysis reveals that a 1.9–3.3% increase in global muscle mass in humans is associated with 4.1 ± 5.8% lower fat mass." Great work by @HeWaHippo and co with important health implications.

https://t.co/mwK4QBrMhx

Excited to share that our review — and my first PhD project — is now published! 📄“Effects of Skeletal Muscle Hypertrophy on Fat Mass and Glucose Homeostasis in Humans and Animals” Thanks to Steffen Held, Martin Schönfelder, Stephan Geisler & @HeWaHippo @TUMExerciseBio

Looking forward to hosting on the 26.6. a SMHS "metabolism in action" session with Philipp Zimmer. First, legend Hermann Heck will discuss the vLamax. Second talents Tim Havers and David Walzik will present muscle hypertrophy metabolism and exercise immune metabolism!

@PeterDuking @Fitnessprofess6

Open access: DOI:

4. ️No plan received a perfect score.

3. More input information led to better rated plans.

2. GPT-4's plans were rated higher overall than Google Gemini's.

1. Google Gemini and GPT-4 produced plans of almost statistically similar quality when the same prompt was inserted twice.

12 resistance training experts evaluated the plans based on important aspects of training plan design criteria. Here's what we found:

We are excited to announce that our study is now published! We evaluated muscle hypertrophy training plans generated by Google Gemini and GPT-4 (via Microsoft Copilot) using two levels of information-dense prompts (little vs. detailed input), each inserted twice.

https://t.co/E8iTzPh458

https://t.co/CAbpxkcTuw