The authors present a classification of DPN based on pain characteristics and underlying mechanisms, aiming to match each phenotype with targeted treatments. This effort reflects a conceptual move toward personalized medicine and is enriched by the use of advanced diagnostic tools—quantitative sensory testing, corneal confocal microscopy, and functional neuroimaging. These technologies, together with detailed sensory descriptors such as burning, paroxysms, or mechanical allodynia, help define clinical subgroups that may benefit from tailored interventions.

While the proposed phenotypic framework is promising and includes several recommended therapeutic classes such as gabapentinoids, serotonin-norepinephrine reuptake inhibitors, and tricyclic antidepressants, it does not explicitly incorporate sodium channel blockers, an evidence-based group endorsed by major guidelines. Their omission narrows the treatment scope and may overlook options with distinct efficacy profiles across phenotypes. Integration with current therapeutic standards could further strengthen clinical applicability. The Standards of Care in Diabetes 2025 from the American Diabetes Association recommend these same drug classes, while discouraging opioids such as tramadol and tapentadol due to limited long-term benefit and safety concerns [2]. This position is reinforced by the American Academy of Neurology’s 2021 guideline, which confirms comparable efficacy among recommended options and strongly advises against opioid use, even in refractory presentations [3].

Incorporating consensus-based guidelines into the phenotypic model would enhance credibility and support safer, scalable implementation. Rather than replacing standard protocols, phenotype-specific considerations may complement guideline-driven treatment by enabling rational adaptation to individual clinical features.

Additionally, the article’s focus on symptom-driven subtypes would be strengthened by discussing response predictors. Neurobiological factors, including dopaminergic connectivity, prefrontal cortical activity, and sodium channel variants like Nav1.7, have emerged as markers of therapeutic response [4,5]. Practical clinical variables such as age, glycemic status, pain duration, and onset may also help tailor decisions more effectively.

A particularly accessible marker is the neutrophil-to-lymphocyte ratio (NLR), which has been found elevated in patients with DPN versus diabetic individuals without neuropathy [4]. While NLR does not predict specific drug outcomes, it may help identify inflammatory profiles relevant to disease progression or multimodal intervention. Including such metrics could facilitate phenotype-based approaches in less specialized care environments.

The temporal dimension of pain also merits consideration. Pregabalin has shown efficacy across all stages of pain duration in pooled analyses of more than 5,700 patients [6], while topical capsaicin appears less effective in chronic, long-standing DPN [7]. These findings suggest that phenotype-based therapy would benefit from aligning with disease chronology to optimize outcomes.

Finally, the proposed diagnostic modalities, though clinically rich, may face constraints outside research settings. For the phenotypic framework to be broadly implemented, it should incorporate pragmatic tools such as bedside neurological assessments, validated questionnaires, and scalable biomarkers. These additions would bridge the gap between theoretical precision and operational feasibility.

In summary, the article offers a valuable conceptual model for personalized treatment in DPN. Its clinical impact would be enhanced by reinforcing alignment with established guidelines, expanding therapeutic scope to include sodium channel blockers, and integrating practical predictors and diagnostic tools. The phenotypic approach should serve as a complementary lens that adds biological context to established protocols and supports individualized care.

DPN remains a multifaceted condition, and its future management must combine diagnostic sophistication with realworld practicality. By bridging phenotypic insight with guideline-informed therapy, predictive markers, and scalable methods, we can advance toward precision medicine that is both intellectually rigorous and clinically actionable.