The search for statistically signiﬁcant relationships between molecular markers and outcomes is challenging when dealing with high-dimensional, noisy and collinear multivariate omics data. Permutation procedures allow for the estimation of adjusted signiﬁcance levels without assuming independence among the metabolomic variables. Nevertheless, the complex non-normal structure of the metabolic proﬁles and outcomes may bias the permutation results leading to overly conservative threshold estimates i.e. lower than a Bonferroni or Sidak correction. Within a univariate permutation procedure we employ parametric simulation methods based on the multivariate (log-)Normal distribution to obtain adjusted signiﬁcance levels which are consistent across phenotypes while eﬀectively controlling the type I error rate at the α level. Next, we derive an alternative closed-form expression for the estimation of the number of non-redundant metabolic variates based on the spectral decomposition of their correlation matrix. The eﬃcacy of our methods is tested for diﬀerent model parametrizations and across a wide range of correlation levels of the variates using both synthetic and real data sets. Both the permutation-based formulation and the more practical closed form expression are found to give an eﬀective indication of the number of indipendent metabolic eﬀects exhibited by the system.