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Differences in drug intake levels (high versus low takers) do not necessarily imply distinct drug user types: Insights from a new cluster-based model

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  • Diego M Castaneda
  • Martin O Job

Abstract

Background: A current model categorizes drug takers into high versus low takers (HT and LT) based on their drug intake levels, with the assumption that these groups represent different phenotypes. When several drug doses are considered, the inverted u-shaped dose-response curves (IUDR) of HT are shifted upwards and rightward, relative to that of LT. However, these IUDR ‘shifts’ are not quantitative metrics and may be subjective. Also, differences in intake levels do not necessarily imply distinctions in other variables (such as demand elasticity) that are important for drug user phenotypology. With supporting evidence from a recent report, we hypothesized that, contrary to assumptions in the field, HT and LT do not necessarily represent distinct phenotypes. Methods: Male Sprague Dawley rats (n = 12) self-administered different doses of cocaine, and we obtained IUDR and demand curves per individual. We developed a new model to quantify the variables that defined the structure of the IUDR and we employed behavioral economic principles to obtain variables that defined the demand curve. We conducted principal component analysis/gaussian mixtures model clustering of variables from both IUDR and demand curves, to identify/compare the clusters that were revealed to HT/LT groups that were distinguished via median split. Results: The cluster-based model identified groups more distinct than LT versus HT. LT and HT were composed of mixtures of individuals from these distinct clusters. LT/HT were not very different when several other variables were considered. Conclusions: Differences in drug intake levels (HT versus LT) do not necessarily imply distinct phenotypes.

Suggested Citation

  • Diego M Castaneda & Martin O Job, 2026. "Differences in drug intake levels (high versus low takers) do not necessarily imply distinct drug user types: Insights from a new cluster-based model," PLOS ONE, Public Library of Science, vol. 21(2), pages 1-22, February.
  • Handle: RePEc:plo:pone00:0340165
    DOI: 10.1371/journal.pone.0340165
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