**Background:** Peripheral symmetric sensorimotor polyneuropathy (DSPN) affects up to 50% of diabetes patients and contributes to reduced quality of life. Preventive strategies require early detection of incipient sensory changes and identification of modifiable risk factors. While glycemic control is recommended, it has not shown convincing benefit in type 2 diabetes (T2DM). Insulin resistance (IR), metabolic syndrome (MetS), and advanced glycation end-products (AGEs) may promote neuropathy independently of glycemia. Quantitative sensory testing (QST) can detect early sensory dysfunction, but its utility in preclinical stages is not well established.
**Methods:** This study analyzed data from the Heidelberg Study of Diabetes and its Complications. A total of 225 participants without peripheral neuropathy (PN) based on clinical (NDS <6 or NSS <5 with NDS <3) and electrophysiological criteria (sural nerve conduction velocity ≥38.7 m/s and SNAP ≥2.53 μV) were included: 117 without diabetes and 108 with T2DM. QST was performed on the right foot using 13 parameters standardized for age, sex, and body area. Early peripheral sensory dysfunction (EPSD) was defined as categorization into any of three neuropathic phenotypes (thermal hyperalgesia, mechanical hyperalgesia, or sensory loss) using the Vollert algorithm; absence of EPSD was defined as the healthy phenotype. Cross-sectional analysis compared healthy vs. EPSD groups within each diabetes status. Multivariable backward elimination logistic regression identified independent predictors of EPSD. Longitudinal follow-up (mean 2.64 ± 1.25 years) was available for 196 participants (106 without diabetes, 90 with T2DM). Cox proportional hazard regression models assessed predictors of new PN occurrence (clinical or electrophysiological criteria).
**Key Results:** Among those without diabetes, EPSD was independently associated with male sex (OR 38.15, P = .003), height (OR 1.20, P = .002), higher fat mass (OR 1.10, P = .012), lower lean mass (OR 0.75, P = .002), and higher IR (HOMA-IR OR 2.00, P = .004; McAuley index OR 0.60, P = .005). In T2DM, independent predictors of EPSD were height (OR 1.12, P = .011), lower lean mass (OR 0.92, P = .012), MetS (OR 18.32, P < .001), and skin AGEs (OR 5.66, P = .003). During follow-up, PN occurred in 12 (11.3%) without diabetes and 36 (40.0%) with T2DM (HR 3.32, P < .001). EPSD at baseline was associated with a higher risk of PN (adjusted HR 1.88, P = .049). In multivariable models, log(HOMA-IR) (HR 3.74, P < .001) and McAuley index (HR 0.75, P = .005) were independent predictors of PN, along with age and height. Skin AGEs were also significant in the McAuley model (HR 1.89, P = .042). Among EPSD phenotypes, sensory loss (aHR 4.35, P = .011) and thermal hyperalgesia (aHR 2.20, P = .022) predicted PN, but mechanical hyperalgesia did not (aHR 1.04, P = .94).
**Clinical Implications:** This study demonstrates that QST-based sensory phenotyping can identify very mild sensory deficits (EPSD) in individuals without clinical or electrophysiological neuropathy. These deficits are strongly associated with dysmetabolic factors—particularly insulin resistance, metabolic syndrome, and AGEs—rather than hyperglycemia alone. The findings suggest that insulin resistance, not just diabetes, drives early nerve damage and progression to overt neuropathy. Therefore, interventions targeting insulin resistance (e.g., lifestyle modification, insulin-sensitizing medications) may be more effective for neuropathy prevention than strict glycemic control alone, especially in prediabetes and early T2DM. QST could serve as a screening tool to identify high-risk individuals for early intervention.