Prognostic Value of Sudomotor Small-Fiber Function and Large Nerves for Accelerated Renal Decline in Type 2 Diabetes: A Real-World Cohort Study.

AIMS: To evaluate the longitudinal prognostic value of small- and large-fiber peripheral nerve assessments for accelerated renal decline in patients with Type 2 diabetes (T2D).

METHODS

An initial cross-sectional analysis evaluated concurrent neuro-renal associations, while a subsequent longitudinal cohort assessed accelerated renal progression (eGFR loss > 3 mL/min/1.73 m2/year) using paired baseline and follow-up data. Prognostic predictors were selected via LASSO regression. Model utility was assessed using area under the curve (AUC), net reclassification improvement (NRI), and decision curve analysis (DCA).

RESULTS

Cross-sectional sudomotor small-fiber dysfunction accurately mirrored concurrent renal impairment (β = 0.18, 95% CI: 0.10-0.26, p < 0.001). Longitudinally, ESCF demonstrated no independent prognostic value for accelerated renal decline, and its apparent longitudinal association with future eGFR was completely attenuated after adjusting for baseline renal function. LASSO regression retained right peroneal motor amplitude, serum albumin, platelet count, and SGLT2i use as robust prognostic factors. Notably, subgroup analyses revealed this large-nerve predictive capacity was significant primarily in patients with established DKD (p for interaction = 0.006). Incorporating this neurofunctional phenotype into standard clinical models yielded a modest AUC improvement but achieved a clinically relevant continuous NRI of 30.35%, successfully recalibrating risk profiles. DCA confirmed a superior net clinical benefit.

CONCLUSION

A distinct prognostic dissociation exists within the diabetic neuro-renal axis. Sudomotor small-fiber dysfunction primarily reflects established microvascular impairment, whereas large motor nerve degeneration demonstrates independent prognostic value for future accelerated renal decline. Incorporating this neurofunctional phenotype into traditional clinical models yields substantial individualized risk reclassification, thereby potentially aiding in identifying high-risk individuals.