Commentary: The Effects of Creatine Supplementation on Cognitive Function in Adults: A Systematic Review and Meta-Analysis

Why It Matters

Methodological flaws can create false‑positive claims about creatine’s cognitive benefits, shaping consumer demand and regulatory decisions.

Key Takeaways

• Xu et al. treated multiple cognitive tests per study as independent
• Double‑counting inflates sample size and statistical power, risking false positives
• EFSA flagged the same error, deeming the evidence insufficient for health claims
• Proper re‑analysis using multilevel models may limit benefits to older adults only

Pulse Analysis

Creatine has become a staple supplement not only for athletes but also for professionals seeking a mental edge. While several small trials suggested modest improvements in memory and executive function, the broader scientific community relies on meta‑analyses to synthesize such findings. When a meta‑analysis aggregates data, the statistical assumptions about independence and variance are critical; any breach can distort the pooled effect size and mislead stakeholders ranging from nutritionists to investors.

Citherlet’s commentary spotlights a pervasive issue: the double‑counting of non‑independent cognitive outcomes. By treating each subtest as a separate observation, the original analysis artificially inflated the effective sample size, a problem echoed in the EFSA’s 2024 opinion that dismissed the health claim. Multilevel modeling or pre‑averaging correlated outcomes can correct this bias, as demonstrated in the re‑analysis of Prokopidis et al.’s work, which left only a marginal benefit for older adults. Such methodological rigor is essential to distinguish genuine neuro‑enhancement from statistical artefacts.

The stakes extend beyond academia. Supplement manufacturers leverage positive meta‑analytic results to market creatine as a cognitive enhancer, influencing consumer spending and potentially prompting regulatory scrutiny. A revised, statistically sound synthesis could recalibrate market expectations, guide future clinical trials, and inform policy on health claims. For investors and industry leaders, understanding the nuance behind these analyses is as vital as the raw performance data, ensuring decisions are grounded in robust evidence rather than inflated statistics.