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Background: Diabetic sensorimotor polyneuropathy (DSPN) is often asymptomatic and remains undiagnosed. The ability of clinical and anthropometric variables to identify individuals likely to have DSPN might be limited. Here, we aimed to integrate protein biomarkers for reliably predicting present DSPN. Methods: Using the proximity extension assay, we measured 135 neurological and protein biomarkers of inflammation in blood samples of 423 individuals with recent‐onset diabetes from the German Diabetes Study (GDS). DSPN was diagnosed based on the Toronto Consensus Criteria. We constructed (i) a protein‐based prediction model using LASSO logistic regression, (ii) an optimised traditional risk model with age, sex, waist circumference, height and diabetes type and (iii) a model combining both. All models were bootstrapped to assess the robustness, and optimism‐corrected AUCs (95% CI) were reported. Results: DSPN was present in 16% of the study population. LASSO logistic regression selected the neurofilament light chain (NFL) and fibroblast growth factor‐19 (FGF‐19) as the most predictive protein biomarkers for detecting DSPN in individuals with recent‐onset diabetes. The protein‐based model achieved an AUC of 0.66 (0.59, 0.73), while the traditional risk model had an AUC of 0.66 (0.61, 0.74). However, combined features boosted the model performance to an AUC of 0.72 (0.67, 0.79). Conclusion: We developed a prediction model for DSPN in recent‐onset diabetes based on two protein biomarkers and five standard anthropometric, demographic and clinical variables. The model has a fair discrimination performance and might be used to inform the referral of patients for further testing. [ABSTRACT FROM AUTHOR]

Aims: A data‐driven cluster analysis in a cohort of European individuals with type 2 diabetes (T2D) has previously identified four subgroups based on clinical characteristics. In the current study, we performed a comprehensive statistical assessment to (1) replicate the above‐mentioned original clusters; (2) derive de novo T2D subphenotypes in the Kooperative Gesundheitsforschung in der Region Augsburg (KORA) cohort and (3) describe underlying genetic risk and diabetes complications. Methods: We used data from n = 301 individuals with T2D from KORA FF4 study (Southern Germany). Original cluster replication was assessed forcing k = 4 clusters using three different hyperparameter combinations. De novo clusters were derived by open k‐means analysis. Stability of de novo clusters was assessed by assignment congruence over different variable sets and Jaccard indices. Distribution of polygenic risk scores and diabetes complications in the respective clusters were described as an indication of underlying heterogeneity. Results: Original clusters did not replicate well, indicated by substantially different assignment frequencies and cluster characteristics between the original and current sample. De novo clustering using k = 3 clusters and including high sensitivity C‐reactive protein in the variable set showed high stability (all Jaccard indices >0.75). The three de novo clusters (n = 96, n = 172, n = 33, respectively) adequately captured heterogeneity within the sample and showed different distributions of polygenic risk scores and diabetes complications, that is, cluster 1 was characterized by insulin resistance with high neuropathy prevalence, cluster 2 was defined as age‐related diabetes and cluster 3 showed highest risk of genetic and obesity‐related diabetes. Conclusion: T2D subphenotyping based on its sample's own clinical characteristics leads to stable categorization and adequately reflects T2D heterogeneity. [ABSTRACT FROM AUTHOR]

Aims: We recently reported that genetic variability in the TKT gene encoding transketolase, a key enzyme in the pentose phosphate pathway, is associated with measures of diabetic sensorimotor polyneuropathy (DSPN) in recent‐onset diabetes. Here, we aimed to substantiate these findings in a population‐based KORA F4 study. Materials and Methods: In this cross‐sectional study, we assessed seven single nucleotide polymorphisms (SNPs) in the transketolase gene in 952 participants from the KORA F4 study with normal glucose tolerance (NGT; n = 394), prediabetes (n = 411), and type 2 diabetes (n = 147). DSPN was defined by the examination part of the Michigan Neuropathy Screening Instrument (MNSI) using the original MNSI > 2 cut‐off and two alternative versions extended by touch/pressure perception (TPP) (MNSI > 3) and by TPP plus cold perception (MNSI > 4). Results: After adjustment for sex, age, BMI, and HbA1c, in type 2 diabetes participants, four out of seven transketolase SNPs were associated with DSPN for all three MNSI versions (all p ≤ 0.004). The odds ratios of these associations increased with extending the MNSI score, for example, OR (95% CI) for SNP rs62255988 with MNSI > 2: 1.99 (1.16–3.41), MNSI > 3: 2.27 (1.26–4.09), and MNSI > 4: 4.78 (2.22–10.26); SNP rs9284890 with MNSI > 2: 2.43 (1.42–4.16), MNSI > 3: 3.46 (1.82–6.59), and MNSI > 4: 4.75 (2.15–10.51). In contrast, no associations were found between transketolase SNPs and the three MNSI versions in the NGT and prediabetes groups. Conclusions: The link of genetic variation in transketolase enzyme to diabetic polyneuropathy corroborated at the population level strengthens the concept suggesting an important role of pathways metabolising glycolytic intermediates in the evolution of diabetic polyneuropathy. [ABSTRACT FROM AUTHOR]

Aims: The aim of this study was to assess associations between neurological biomarkers and distal sensorimotor polyneuropathy (DSPN). Materials and Methods: Cross‐sectional analyses were based on 1032 participants aged 61–82 years from the population‐based KORA F4 survey, 177 of whom had DSPN at baseline. The prevalence of type 2 diabetes was 20%. Prospective analyses used data from 505 participants without DSPN at baseline, of whom 125 had developed DSPN until the KORA FF4 survey. DSPN was defined based on the examination part of the Michigan Neuropathy Screening Instrument. Serum levels of neurological biomarkers were measured using proximity extension assay technology. Associations between 88 biomarkers and prevalent or incident DSPN were estimated using Poisson regression with robust error variance and are expressed as risk ratios (RR) and 95% CI per 1‐SD increase. Results were adjusted for multiple confounders and multiple testing using the Benjamini–Hochberg procedure. Results: Higher serum levels of CTSC (cathepsin C; RR [95% CI] 1.23 (1.08; 1.39), pB‐H = 0.044) and PDGFRα (platelet‐derived growth factor receptor A; RR [95% CI] 1.21 (1.08; 1.35), pB‐H = 0.044) were associated with prevalent DSPN in the total study sample. CDH3, JAM‐B, LAYN, RGMA and SCARA5 were positively associated with DSPN in the diabetes subgroup, whereas GCP5 was positively associated with DSPN in people without diabetes (all pB‐H for interaction <0.05). None of the biomarkers showed an association with incident DSPN (all pB‐H>0.05). Conclusions: This study identified multiple novel associations between neurological biomarkers and prevalent DSPN, which may be attributable to functions of these proteins in neuroinflammation, neural development and myelination. [ABSTRACT FROM AUTHOR]

Intramyocellular lipid content (IMCL) is elevated in insulin-resistant humans, but it changes over time, and relationships with comorbidities remain unclear. We examined IMCL during the initial course of diabetes and its associations with complications. Participants of the German Diabetes Study (GDS) with recent-onset type 1 (n = 132) or type 2 diabetes (n = 139) and glucose-tolerant control subjects (n = 128) underwent 1H-MRS to measure IMCL and muscle volume, whole-body insulin sensitivity (hyperinsulinemic-euglycemic clamps; M-value), and cycling spiroergometry (VO2max). Subgroups underwent the same measurements after 5 years. At baseline, IMCL was ∼30% higher in type 2 diabetes than in other groups independently of age, sex, BMI, and muscle volume. In type 2 diabetes, the M-value was ∼36% and ∼62% lower compared with type 1 diabetes and control subjects, respectively. After 5 years, the M-value decreased by ∼29% in type 1 and ∼13% in type 2 diabetes, whereas IMCL remained unchanged. The correlation between IMCL and M-value in type 2 diabetes at baseline was modulated by VO2max. IMCL also associated with microalbuminuria, the Framingham risk score for cardiovascular disease, and cardiac autonomic neuropathy. Changes in IMCL within 5 years after diagnosis do not mirror the progression of insulin resistance in type 2 diabetes but associate with early diabetes-related complications. Article Highlights: Intramyocellular lipid content (IMCL) can be elevated in insulin-resistant humans, but its dynamics and association with comorbidities remain unclear. Independently of age, sex, body mass, and skeletal muscle volume, IMCL is higher in recent-onset type 2, but not type 1 diabetes, and remains unchanged within 5 years, despite worsening insulin resistance. A degree of physical fitness modulates the association between IMCL and insulin sensitivity in type 2 diabetes. Whereas higher IMCL associates with lower insulin sensitivity in people with lower physical fitness, there is no association between IMCL and insulin sensitivity in those with higher degree of physical fitness. IMCL associates with progression of microalbuminuria, cardiovascular disease risk, and cardiac autonomic neuropathy. [ABSTRACT FROM AUTHOR]

Aims/hypothesis: Emerging evidence suggests that in addition to hyperglycaemia, dyslipidaemia could represent a contributing pathogenetic factor to diabetic neuropathy, while obesity and insulin resistance play a role in the development of diabetic cardiac autonomic neuropathy (CAN) characterised by reduced heart rate variability (HRV), particularly in type 2 diabetes. We hypothesised that distinct lipid metabolites are associated with diminished HRV in recent-onset type 2 diabetes rather than type 1 diabetes. Methods: We analysed 127 plasma lipid metabolites (11 acylcarnitines, 39 NEFA, 12 sphingomyelins (SMs), 56 phosphatidylcholines and nine lysophosphatidylcholines) using MS in participants from the German Diabetes Study baseline cohort recently diagnosed with type 1 (n = 100) and type 2 diabetes (n = 206). Four time-domain HRV indices (number of normal-to-normal (NN) intervals >50 ms divided by the number of all NN intervals [pNN50]; root mean square of successive differences [RMSSD]; SD of NN intervals [SDNN]; and SD of differences between adjacent NN intervals) and three frequency-domain HRV indices (very-low-frequency [VLF], low-frequency [LF] and high-frequency [HF] power spectrum) were computed from NN intervals recorded during a 3 h hyperinsulinaemic–euglycaemic clamp at baseline and in subsets of participants with type 1 (n = 60) and type 2 diabetes (n = 95) after 5 years. Results: In participants with type 2 diabetes, after Bonferroni correction and rigorous adjustment, SDNN was inversely associated with higher levels of diacyl-phosphatidylcholine (PCaa) C32:0, PCaa C34:1, acyl-alkyl-phosphatidylcholine (PCae) C36:0, SM C16:0 and SM C16:1. SD of differences between NN intervals was inversely associated with PCaa C32:0, PCaa C34:1, PCaa C34:2, PCae C36:0 and SM C16:1, and RMSSD with PCae C36:0. For VLF power, inverse associations were found with PCaa C30:0, PCaa C32:0, PCaa C32:1, PCaa C34:2 and SM C16:1, and for LF power inverse associations were found with PCaa C32:0 and SM C16:1 (r = −0.242 to r = −0.349; p ≤ 0.0005 for all correlations). In contrast, no associations of lipid metabolites with measures of cardiac autonomic function were noted in participants recently diagnosed with type 1 diabetes. After 5 years, HRV declined due to ageing rather than diabetes, whereby prediction analyses for lipid metabolites were hampered. Conclusions/interpretation: Higher plasma levels of specific lipid metabolites are closely linked to cardiac autonomic dysfunction in recent-onset type 2 diabetes but not type 1 diabetes, suggesting a role for perturbed lipid metabolism in the early development of CAN in type 2 diabetes. [ABSTRACT FROM AUTHOR]