This study aimed to investigate circulating biomarkers associated with the risk of developing diabetic peripheral neuropathy (DPN) and nephropathy in type 1 diabetes (T1D). Patients with childhood-onset T1D (n = 49, age 38.3 ± 3.8 yrs.) followed prospectively were evaluated after 30 years of diabetes duration. DPN was defined as an abnormality in nerve conduction tests. Matrix metalloproteinase-9 (MMP-9) and its tissue inhibitor TIMP-1, neutrophil gelatinase-associated lipocalin-2 (NGAL), soluble P-selectin (sP-selectin), estimated GFR (eGFR), micro/macroalbuminuria and routine biochemistry were assessed. For comparison, control subjects were included (n = 30, age 37.9 ± 5.5 yrs.). In all, twenty-five patients (51 %) were diagnosed with DPN, and nine patients (18 %) had nephropathy (five microalbuminuria and four macroalbuminuria). Patients with DPN had higher levels of TIMP-1 (p = 0.036) and sP-selectin (p = 0.005) than controls. Patients with DPN also displayed higher levels of TIMP-1 compared to patients without DPN (p = 0.035). Patients with macroalbuminuria had kidney disease stage 3 with lower eGFR, higher levels of TIMP-1 (p = 0.038), and NGAL (p = 0.002). In all patients, we found only weak negative correlations between eGFR and TIMP-1 (rho = -0.304, p = 0.040) and NGAL (rho = -0.277, p = 0.062, ns), respectively. MMP-9 was higher in patients with microalbuminuria (p = 0.021) compared with normoalbuminuric patients. Our findings indicate that TIMP-1 and MMP-9, as well as sP-selectin and NGAL, are involved in microvascular complications in T1D. Monitoring and targeting these biomarkers may be a potential strategy for treating diabetic nephropathy and neuropathy.

Introduction: To investigate whether circulating chemokines contribute to the development of diabetic peripheral neuropathy (DPN) in patients with type 1 diabetes (T1D).Methods: Fifty‐two patients with childhood‐onset T1D (mean age 28 ± 4 yrs.; diabetes duration 19.5 ± 5.5 yrs.) and 19 control subjects (mean age 26.5 ± 4.5 yrs.) were included in a cross‐sectional analysis of this long‐term longitudinal cohort study. A subgroup of 24 patients was followed prospectively for a further 10 yrs. Plasma levels of Th1‐ (CXCL9, CXCL10 and CXCL11), Th2‐ (CCL17 and CCL22) and Th17‐associated (CXCL8 and CCL20) chemokines were assessed in all subjects. Additionally, the TID patients underwent clinical examination and electroneurography.Results: The frequency of neuropathy was 21% (11/52). Higher levels of CXCL9 levels were found in patients with DPN compared with control subjects (p = .019); by contrast, no difference between patients without DPN and control subjects was seen after adjustment for multiple comparisons. In patients with DPN, CXCL10 correlated negatively with suralis MCV and suralis SNAP (rho −0.966, p < .001 and rho −0.738, p < .001, respectively) and was positively correlated with the vibration perception threshold (rho 0.639, p = .034), while CXCL8 correlated negatively with the cold perception threshold (rho −0.645, p = .032). The frequency of neuropathy increased to 54% (13/24) in the subgroup of 23 TID patients, followed by an additional 10 yrs.Conclusions: Changes in Th1‐ and Th17‐associated chemokines were associated with impaired peripheral sensory nerve function and nerve conduction after long disease duration in childhood‐onset T1D.

Diabetic neuropathy and nephropathy are common complications of type 1 diabetes (T1D). The symptoms are often elusive in the early stages, and available diagnostic methods can be improved using biomarkers. Matrix metalloproteinase 3 (MMP-3) has been identified in the kidneys and is thought to be involved in diabetic nephropathy. Growth differentiation factor 15 (GDF-15) has been suggested to have positive effects in diabetes, but is otherwise associated with adverse effects such as cardiovascular risk, declined kidney function, and neurodegeneration. This study aims to investigate plasma MMP-3 and GDF-15 as systemic biomarkers for diabetic neuropathy and nephropathy in T1D. The study involves patients with childhood-onset T1D (n = 48, age 38 ± 4 years) and a healthy control group (n = 30, age 38 ± 5 years). Neurophysiology tests, evaluations of albuminuria, and measurements of routine biochemical markers were conducted. The neuropathy impairment assessment (NIA) scoring system, where factors such as loss of sensation and weakened reflexes are evaluated, was used to screen for symptoms of neuropathy. MMP-3 and GDF-15 concentrations were determined in heparinized plasma using ELISA kits. In total, 9 patients (19%) had albuminuria, and 25 (52%) had diabetic neuropathy. No significant differences were found in MMP-3 concentrations between the groups. GDF-15 levels were higher in T1D, with median and interquartile range (IQR) of 358 (242) pg/mL in T1D and 295 (59) in controls (p < 0.001). In the merged patient group, a positive correlation was found between MMP-3 and plasma creatinine, a negative correlation was found between MMP-3 and estimated glomerular filtration rate (eGFR; rho = −0.358, p = 0.012), and there was a positive correlation between GDF-15 and NIA (rho = 0.723, p < 0.001) and high-sensitive C-reactive protein (rho = 0.395, p = 0.005). MMP-3 was increased in macroalbuminuria and correlated positively with NIA only in the nine T1D patients with albuminuria (rho = 0.836, p = 0.005). The present study indicates that high MMP-3 is associated with low eGFR, high plasma creatinine, and macroalbuminuria, and that GDF-15 can be a biomarker for diabetic neuropathy in T1D. MMP-3 may be useful as biomarker for neuropathy in T1D with albuminuria.

Aims: We have evaluated long‐term weighted mean HbA_1c (wHbA_1c), HbA_1c variability, diabetes duration, and lipid profiles in relation to the development of diabetic peripheral neuropathy (DPN), nephropathy, and retinopathy in childhood‐onset type 1 diabetes.Materials and Methods: In a longitudinal cohort study, 49 patients (21 women) with childhood‐onset type 1 diabetes were investigated with neurophysiological measurements, blood tests, and clinical examinations after a diabetes duration of 7.7 (±3.3) years (baseline) and followed with repeated examinations for 30.6 (±5.2) years. We calculated wHbA_1c by integrating the area under all HbA_1c values since the diabetes diagnosis. Lipid profiles were analysed in relation to the presence of DPN. Long‐term fluctuations of HbA_1c variability were computed as the standard deviation of all HbA_1c measurements. Data regarding the presence of other diabetes complications were retrieved from medical records.Results: In this follow‐up study, 51% (25/49) of the patients fulfilled electrophysiological criteria for DPN. In nerve conduction studies, there was a deterioration in the amplitudes and conduction velocities for the median, peroneal, and sural nerves over time. Patients with DPN had a longer duration of diabetes, higher wHbA_1c, and increased HbA_1c variability. The lowest wHbA_1c value associated with the development of DPN was 62 mmol/mol (7.8%). The presence of albuminuria and retinopathy was positively correlated with the presence of neuropathy.Conclusions: More than half of the patients had developed DPN after 30 years. None of the patients who developed DPN had a wHbA_1c of less than 62 mmol/mol (7.8%).

We assessed feasibility and effectiveness of AI-based VF screening in CT, integrated with a local FLS. The system identified VFs in 14% of patients, half previously unrecognized or untreated. This suggests that 2-3 patients with VFs were identified daily at our hospital, highlighting the potential clinical impact of AI-assisted detection. To evaluate the feasibility and efficacy of integrating an AI algorithm into the radiology workflow for opportunistic vertebral fracture (VF) screening in CT and align it to a local fracture liaison service (FLS). The AI algorithm was integrated into the radiology workflow and applied to all non-skeletal CT scans covering thorax and/or abdomen from patients aged ≥ 50 years over a four-month period at our hospital (catchment area ~ 250,000). Detected VFs were verified by radiologists and subsequently referred to the FLS for further management. A system was established to enable both technical and clinical monitoring. The AI setup and workflow were considered feasible and robust, and AI showed a high performance. During the study period, 3971 unique patients (mean age 72 ± 11 years; 51% female) underwent 5147 CT scans. The AI algorithm identified VFs in 566 patients (14%, mean age 78 ± 10; 62% women), all of which were confirmed by radiologist. After clinical triage, 49% were considered in need of further osteoporosis evaluation/treatment, the remainder where either terminally ill/died shortly after CT or were considered correctly handled before. AI-based opportunistic screening for VF is feasible and effective in routine clinical practice. Integration of such tools into radiology workflows enhances the detection of at-risk patients and supports timely referral to FLS, potentially reducing the burden of untreated osteoporosis and future fracture risk. In our clinical setting, this meant 2-3 new identified patients every day. These findings support the broader implementation of AI in secondary fracture prevention strategies. ClinicalTrials.gov ID: NCT07100756.

C‐peptide has a complex role in human physiology, but its effects are not fully understood. Studies have shown a protective impact against diabetic complications, but also that C‐peptide levels associate with cardiovascular events. Among the many applications to assess cardiovascular risk, circulating lipids are widely used, and one of the strongest biomarkers is apolipoprotein B. The aim of this investigation was to study the association of C‐peptide with markers of metabolic, inflammatory, or cardiovascular alterations in a limited group of healthy individuals. Body mass index (BMI), lipids, and other plasma markers were studied in 28 consecutive healthy individuals within the age of 30–50 years. The results showed significant positive correlations between C‐peptide and BMI (r = 0.498; p = 0.007); hsCRP (r = 0.530; p = 0.004); triglycerides (r = 0.530; p = 0.005); cholesterol (r = 0.507; p = 0.006), LDL‐cholesterol (r = 0.550; p = 0.002), LDL/HDL ratio (r = 0.460; p = 0.014); apoB (r = 0.622; p < 0.001), apoB/apoA1 ratio (r = 0.563; p = 0.002); and non‐HDL cholesterol (r = 0.566; p = 0.002). According to BMI values, 16 of the 28 individuals were overweight (BMI >25.0 kg/m²). If overweight individuals were excluded, C‐peptide did only correlate with apoB (r = 0.636; p = 0.026). To conclude, C‐peptide within normal levels associate with BMI and atherogenic lipids in healthy individuals, and apoB associate with C‐peptide even at normal weight. These results suggest that C‐peptide can be an early additional cardiovascular risk marker.