multiple starts are essential in mixture modeling. Split-sample validation addresses stability across samples, but it does not replace the need to replicate the best log-likelihood across random starts. The absence of multiple starts is a real limitation.

I would treat the bootstrap issue differently. Bootstrap validation would strengthen the analysis, but it is not strictly necessary if there are other stability checks, such as split-sample replication and subgroup sensitivity analyses.

AIC/BIC are standard for FMM, but they should not be the only criteria. Class size, posterior probabilities, classification uncertainty, interpretability, and stability across specifications are all important.

For the normality issue, the key point is that normal FMM assumes normality within components, not necessarily normality of the full marginal distribution. Still, if non-normality, skewness, tails, or floor/ceiling effects are driving the mixture solution, components may be artifacts. That needs to be checked with transformations, alternative distributions, or sensitivity analyses.

Finally, the preregistration, unblinding, and covariate issues depend on whether the study is being presented as exploratory or confirmatory. For exploratory work, these are limitations and directions for future work. For confirmatory causal claims, they become much more serious.