Plasma lipid metabolites associate with diabetic polyneuropathy in a cohort with type 2 diabetes

Metabolomic and lipidomic profiling of the spinal cord in type 2 diabetes mellitus rats with painful neuropathy

In this paper we investigated lipid and metabolite changes in diabetic neuropathy, using untargeted lipidomics and metabolomics analyses of the spinal cords from streptozotocin-treated diabetic rats.170 metabolites and 45 lipids were dysregulated in the painful diabetic neuropathy (PDN) phase. Pathway enrichment analysis revealed perturbations in starch and sucrose, tryptophan, pyrimidine, cysteine and methionine, thiamine, tyrosine, and nucleotides. The disturbance of tyrosine, tryptophan, methionine, triacylglycerol, and phosphatidylethanolamine metabolism indicated that pathological mechanisms in the PDN involved energy metabolism, oxidative stress, and neural reparative regeneration. These revelations offered potential biomarkers for PDN and enriched the comprehension of the complex molecular mechanisms characterizing PDN, establishing a solid foundation for subsequent inquiries into neural convalescence and recovery after PDN.

Development and validation of a risk nomogram model for predicting peripheral neuropathy in patients with type 2 diabetes mellitus

Objective

Diabetic peripheral neuropathy frequently occurs and presents severely in individuals suffering from type 2 diabetes mellitus, representing a significant complication. The objective of this research was to develop a risk nomogram for DPN, ensuring its internal validity and evaluating its capacity to predict the condition.

Methods

In this retrospective analysis, Suqian First Hospital's cohort from January 2021 to June 2022 encompassed 397 individuals diagnosed with T2DM. A random number table method was utilized to allocate these patients into two groups for training and validation, following a 7:3 ratio. By applying univariate and multivariable logistic regression, predictive factors were refined to construct the nomogram. The model's prediction accuracy was assessed through metrics like the ROC area, HL test, and an analysis of the calibration curve. DCA further appraised the clinical applicability of the model. Emphasis was also placed on internal validation to confirm the model's dependability and consistency.

Results

Out of 36 evaluated clinicopathological characteristics, a set of four, duration, TBIL, TG, and DPVD, were identified as key variables for constructing the predictive nomogram. The model exhibited robust discriminatory power, evidenced by an AUC of 0.771 (95% CI: 0.714-0.828) in the training cohort and an AUC of 0.754 (95% CI: 0.663-0.845) in the validation group. The congruence of the model's predictions with actual findings was corroborated by the calibration curve. Furthermore, DCA affirmed the clinical value of the model in predicting DPN.

Conclusion

This research introduces an innovative risk nomogram designed for the prediction of diabetic peripheral neuropathy in individuals suffering from type 2 diabetes mellitus. It offers a valuable resource for healthcare professionals to pinpoint those at elevated risk of developing this complication. As a functional instrument, it stands as a viable option for the prognostication of DPN in clinical settings.

Tryptophan metabolism and small fibre neuropathy: a correlation study

Small nerve fibres located in the epidermis sense pain. Dysfunction of these fibres decreases the pain threshold known as small fibre neuropathy. Diabetes mellitus is accompanied by metabolic changes other than glucose, synergistically eliciting small fibre neuropathy. These findings suggest that various metabolic changes may be involved in small fibre neuropathy. Herein, we explored the correlation between pain sensation and changes in plasma metabolites in healthy Japanese subjects. The pain threshold evaluated from the intraepidermal electrical stimulation was used to quantify pain sensation in a total of 1021 individuals in the 2017 Iwaki Health Promotion Project. Participants with a pain threshold evaluated from the intraepidermal electrical stimulation index <0.20 mA were categorized into the pain threshold evaluated from the intraepidermal electrical stimulation index-low group (n = 751); otherwise, they were categorized into the pain threshold evaluated from the intraepidermal electrical stimulation index-high group (n = 270). Metabolome analysis of plasma was conducted using capillary electrophoresis time-of-flight mass spectrometry. The metabolite set enrichment analysis revealed that the metabolism of tryptophan was significantly correlated with the pain threshold evaluated from the intraepidermal electrical stimulation index in all participants (P < 0.05). The normalized level of tryptophan was significantly decreased in participants with a high pain threshold evaluated from the intraepidermal electrical stimulation index. In addition to univariate linear regression analyses, the correlation between tryptophan concentration and the pain threshold evaluated from the intraepidermal electrical stimulation index remained significant after adjustment for multiple factors (β = -0.07615, P < 0.05). These findings indicate that specific metabolic changes are involved in the deterioration of pain thresholds. Here, we show that abnormal tryptophan metabolism is significantly correlated with an elevated pain threshold evaluated from the intraepidermal electrical stimulation index in the Japanese population. This correlation provides insight into the pathology and clinical application of small fibre neuropathy.

Using metabolomics and proteomics to identify the potential urine biomarkers for prediction and diagnosis of gestational diabetes

Gestational diabetes mellitus (GDM) is one of the most common metabolic complications during pregnancy, threatening both maternal and fetal health. Prediction and diagnosis of GDM is not unified. Finding effective biomarkers for GDM is particularly important for achieving early prediction, accurate diagnosis and timely intervention. Urine, due to its accessibility in large quantities, noninvasive collection and easy preparation, has become a good sample for biomarker identification. In recent years, a number of studies using metabolomics and proteomics approaches have identified differential expressed urine metabolites and proteins in GDM patients. In this review, we summarized these potential urine biomarkers for GDM prediction and diagnosis and elucidated their role in development of GDM.

Residual risk of cardiovascular complications in statin-using patients with type 2 diabetes: the Taiwan Diabetes Registry Study

BACKGROUND

The residual risks of atherosclerotic cardiovascular disease in statin-treated patients with diabetes remain unclear. This study was conducted to identify factors associated with these residual risks in patients with no prior vascular event.

METHODS

Data on 683 statin-using patients with type 2 diabetes mellitus (T2DM) from the Taiwan Diabetes Registry were used in this study. Patients aged < 25 or > 65 years at the time of diabetes diagnosis and those with diabetes durations ≥ 20 years were excluded. The United Kingdom Prospective Diabetes Study risk engine (version 2.01; https://www.dtu.ox.ac.uk/riskengine/ ) was used to calculate 10-year residual nonfatal and fatal coronary heart disease (CHD) and stroke risks. Associations of these risks with physical and biochemical variables, including medication use and comorbidity, were examined.

RESULTS

The 10-year risks of nonfatal CHD in oral anti-diabetic drug (OAD), insulin and OAD plus insulin groups were 11.8%, 16.0%, and 16.8%, respectively. The 10-year risks of nonfatal stroke in OAD, insulin and OAD plus insulin groups were 3.0%, 3.4%, and 4.3%, respectively. In the multivariate model, chronic kidney disease (CKD), neuropathy, insulin use, calcium-channel blocker (CCB) use, higher body mass indices (BMI), low-density lipoprotein (LDL), fasting glucose, log-triglyceride (TG), and log-alanine transaminase (ALT) levels were associated with an increased CHD risk. The residual risk of stroke was associated with CKD, neuropathy, CCB use, and lower LDL cholesterol levels, higher BMI and diastolic blood pressure.

CONCLUSION

This study indicated that insulin was probably a residual risk factor of CHD but not stroke, and that there was a possible presence of obesity paradox in patients with T2DM on statin therapy. In addition to lowering TG and normalizing fasting glucose levels, lower LDL cholesterol level is better for reduction of risk of CHD on statin therapy. On the other hand, lower LDL cholesterol level could potentially be related to higher risk of stroke among populations receiving statin therapy. These findings suggest potential therapeutic targets for residual cardiovascular risk reduction in patients with T2DM on statin therapy.

Sweat gland nerve fiber density and association with sudomotor function, symptoms, and risk factors in adolescents with type 1 diabetes

PURPOSE

To quantify sweat gland nerve fiber density in adolescents with diabetes. Additionally, to investigate associations between sudomotor innervation, sweat responses, and possible risk factors for sudomotor neuropathy.

METHODS

Cross-sectional study where 60 adolescents with type 1 diabetes (duration > 5 years) and 23 control subjects were included. Clinical data, quantitative sudomotor axon reflex test, and skin biopsies were obtained. Skin tissue was immunostained and imaged by confocal microscopy. Quantification of the sweat gland volume and three-dimensional reconstruction of the nerve fibers was performed using a design-unbiased technique.

RESULTS

Adolescents with diabetes had a significant reduction of maximum and mean values of nerve fiber length and nerve fiber density in sweat glands compared to controls (p values < 0.05). No association between nerve fiber density and sweat responses was found (p = 0.21). In cases with reduced sweat gland nerve fiber length, nerve fiber density, and volume, the sweat response was reduced or absent. Height, systolic blood pressure, time in hypoglycemia, and total daily and basal/total insulin dose were positively correlated to sweat response, while low-density lipoprotein, and HbA1c were negatively correlated with sweat response (p values < 0.05). Other microvascular complications and high cholesterol levels increased the relative risk for reduced sweat gland nerve fiber density.

CONCLUSION

Our findings of reduced sweat gland innervation in a selected group of adolescents add new knowledge about the structural changes that occur in autonomic nerves due to diabetes. Evaluating both the sweat gland innervation and sweat gland volume was important for understanding the association with sweat responses. Further research is needed to understand its clinical relevance.

Identification of blood metabolic biomarkers associated with diabetic distal symmetric sensorimotor polyneuropathy in patients with type 2 diabetes mellitus

BACKGROUND

Distal symmetric sensorimotor polyneuropathy (DSPN) is a common neurologic complication of type 2 diabetes mellitus (T2DM), but the underlying mechanisms and changes in serum metabolites remain largely undefined. This study aimed to characterize the plasma metabolite profiles of participants with T2DM using targeted metabolomics analysis and identify potential biomarkers for DSPN.

METHODS

A combined liquid chromatography MS/MS and direct flow injection were used to quantify plasma metabolite obtained from 63 participants with T2DM, 81 with DSPN, and 33 nondiabetic control participants. A total of 130 metabolites, including amino acids, biogenic amines, sphingomyelins (SM), phosphatidylcholines, carnitines, and hexose, were analyzed.

RESULTS

A total of 16 plasma metabolites and 3 cholesterol-related laboratory parameters were found to have variable importance in the projection score >1.0 and false discovery rate <5.0% between control, T2DM, and DSPN. Among these variables, five serum metabolites, including phenylalanine (AUC = 0.653), alanine (AUC = 0.630), lysine (AUC = 0.622) tryptophan (AUC = 0.620), and SM C16:0 (AUC = 0.630), are potential biomarkers (all p < .05) in distinguishing T2DM with DSPN from those without (AUC = 0.720).

CONCLUSIONS

In this cross-sectional study, derangement of several metabolites in the plasma was observed in T2DM with and without DSPN, and these metabolites may be potential biomarkers for predicting DSPN. Longitudinal studies are warranted.

Serum neurofilament light chain - A potential biomarker for polyneuropathy in type 2 diabetes?

AIMS

To investigate the relationship between neurofilament light chain (NfL) and the presence and severity of diabetic polyneuropathy (DPN).

METHODS

We performed cross-sectional analysis of data from 178 participants of the ADDITION-Denmark cohort of people with screen-detected type 2 diabetes and 32 healthy controls. Biobank serum samples were analyzed for NfL using single-molecule array. DPN was defined by Toronto criteria for confirmed DPN. Original and axonal nerve conduction study (NCS) sum z-scores were used as indicators of the severity of DPN and peripheral nerve damage.

RESULTS

39 (21.9%) participants had DPN. Serum NfL (s-NfL) was significantly higher in participants with DPN (18.8 ng/L [IQR 14.4; 27.9]) than in participants without DPN (15.4 ng/L [IQR 11.7; 20.1]). There were no unadjusted s-NfL differences between controls (17.6 ng/L [IQR 12.7; 19.8]) and participants with or without DPN. Higher original and axonal NCS sum z-scores were associated with 10% higher s-NfL (10.2 and 12.1% [95% CI's 4.0; 16.8 and 6.6; 17.9] per 1 SD). The AUC of s-NfL for DPN was 0.63 (95% CI 0.52; 0.73).

CONCLUSIONS

S-NfL is unlikely to be a reliable biomarker for the presence of DPN. S-NfL is however associated tothe severity of the nerve damage underlying DPN.

Transcriptomic analysis of diabetic kidney disease and neuropathy in mouse models of type 1 and type 2 diabetes

Diabetic kidney disease (DKD) and diabetic peripheral neuropathy (DPN) are common complications of type 1 (T1D) and type 2 (T2D) diabetes. However, the mechanisms underlying pathogenesis of these complications are unclear. In this study, we optimized a streptozotocin-induced db/+ murine model of T1D and compared it to our established db/db T2D mouse model of the same C57BLKS/J background. Glomeruli and sciatic nerve transcriptomic data from T1D and T2D mice were analyzed by self-organizing map and differential gene expression analysis. Consistent with prior literature, pathways related to immune function and inflammation were dysregulated in both complications in T1D and T2D mice. Gene-level analysis identified a high degree of concordance in shared differentially expressed genes (DEGs) in both complications and across diabetes type when using mice from the same cohort and genetic background. As we have previously shown a low concordance of shared DEGs in DPN when using mice from different cohorts and genetic backgrounds, this suggests that genetic background may influence diabetic complications. Collectively, these findings support the role of inflammation and indicate that genetic background is important in complications of both T1D and T2D.

A systematic review of clinical health conditions predicted by machine learning diagnostic and prognostic models trained or validated using real-world primary health care data

With the advances in technology and data science, machine learning (ML) is being rapidly adopted by the health care sector. However, there is a lack of literature addressing the health conditions targeted by the ML prediction models within primary health care (PHC) to date. To fill this gap in knowledge, we conducted a systematic review following the PRISMA guidelines to identify health conditions targeted by ML in PHC. We searched the Cochrane Library, Web of Science, PubMed, Elsevier, BioRxiv, Association of Computing Machinery (ACM), and IEEE Xplore databases for studies published from January 1990 to January 2022. We included primary studies addressing ML diagnostic or prognostic predictive models that were supplied completely or partially by real-world PHC data. Studies selection, data extraction, and risk of bias assessment using the prediction model study risk of bias assessment tool were performed by two investigators. Health conditions were categorized according to international classification of diseases (ICD-10). Extracted data were analyzed quantitatively. We identified 106 studies investigating 42 health conditions. These studies included 207 ML prediction models supplied by the PHC data of 24.2 million participants from 19 countries. We found that 92.4% of the studies were retrospective and 77.3% of the studies reported diagnostic predictive ML models. A majority (76.4%) of all the studies were for models' development without conducting external validation. Risk of bias assessment revealed that 90.8% of the studies were of high or unclear risk of bias. The most frequently reported health conditions were diabetes mellitus (19.8%) and Alzheimer's disease (11.3%). Our study provides a summary on the presently available ML prediction models within PHC. We draw the attention of digital health policy makers, ML models developer, and health care professionals for more future interdisciplinary research collaboration in this regard.

New perspectives in diabetic neuropathy

Diabetes prevalence continues to climb with the aging population. Type 2 diabetes (T2D), which constitutes most cases, is metabolically acquired. Diabetic peripheral neuropathy (DPN), the most common microvascular complication, is length-dependent damage to peripheral nerves. DPN pathogenesis is complex, but, at its core, it can be viewed as a state of impaired metabolism and bioenergetics failure operating against the backdrop of long peripheral nerve axons supported by glia. This unique peripheral nerve anatomy and the injury consequent to T2D underpins the distal-to-proximal symptomatology of DPN. Earlier work focused on the impact of hyperglycemia on nerve damage and bioenergetics failure, but recent evidence additionally implicates contributions from obesity and dyslipidemia. This review will cover peripheral nerve anatomy, bioenergetics, and glia-axon interactions, building the framework for understanding how hyperglycemia and dyslipidemia induce bioenergetics failure in DPN. DPN and painful DPN still lack disease-modifying therapies, and research on novel mechanism-based approaches is also covered.

Ultra-high performance liquid chromatography coupled to tandem mass spectrometry-based metabolomics study of diabetic distal symmetric polyneuropathy

AIMS/INTRODUCTION

Distal symmetric polyneuropathy (DSPN) is a common complication of type 2 diabetes mellitus, but the underlining mechanisms have not yet been elucidated. The current study was designed to screen the feature metabolites classified as potential biomarkers, and to provide deeper insights into the underlying distinctive metabolic changes during disease progression.

MATERIALS AND METHODS

Plasma metabolite profiles were obtained by the ultra-high liquid chromatography coupled to tandem mass spectrometry method from healthy control participants, patients with type 2 diabetes mellitus and patients with DSPN. Potential biomarkers were selected through comprehensive analysis of statistically significant differences between groups.

RESULTS

Overall, 938 metabolites were identified. Among them, 12 metabolites (dimethylarginine, N6-acetyllysine, N-acetylhistidine, N,N,N-trimethyl-alanylproline betaine, cysteine, 7-methylguanine, N6-carbamoylthreonyladenosine, pseudouridine, 5-methylthioadenosine, N2,N2-dimethylguanosine, aconitate and C-glycosyl tryptophan) were identified as the specific biomarkers. The content of 12 metabolites were significantly higher in the DSPN group compared with the other two groups. Additionally, they showed good performance to discriminate the DSPN state. Correlation analyses showed that the levels of 12 metabolites might be more closely related to the glucose metabolic changes, followed by the levels of lipid metabolism.

CONCLUSIONS

The finding of the 12 signature metabolites might provide a novel perspective for the pathogenesis of DSPN. Future studies are required to test this observation further.

Single-cell RNA-seq uncovers novel metabolic functions of Schwann cells beyond myelination

Schwann cells (SCs) support peripheral nerves under homeostatic conditions, independent of myelination, and contribute to damage in prediabetic peripheral neuropathy (PN). Here, we used single-cell RNA sequencing to characterize the transcriptional profiles and intercellular communication of SCs in the nerve microenvironment using the high-fat diet-fed mouse, which mimics human prediabetes and neuropathy. We identified four major SC clusters, myelinating, nonmyelinating, immature, and repair in healthy and neuropathic nerves, in addition to a distinct cluster of nerve macrophages. Myelinating SCs acquired a unique transcriptional profile, beyond myelination, in response to metabolic stress. Mapping SC intercellular communication identified a shift in communication, centered on immune response and trophic support pathways, which primarily impacted nonmyelinating SCs. Validation analyses revealed that neuropathic SCs become pro-inflammatory and insulin resistant under prediabetic conditions. Overall, our study offers a unique resource for interrogating SC function, communication, and signaling in nerve pathophysiology to help inform SC-specific therapies.

Phenylacetylglutamine as a novel biomarker of type 2 diabetes with distal symmetric polyneuropathy by metabolomics

PURPOSE

Type 2 diabetes mellitus (T2DM) with distal symmetric polyneuropathy (DSPN) is a disease involving the nervous system caused by metabolic disorder, while the metabolic spectrum and key metabolites remain poorly defined.

METHODS

Plasma samples of 30 healthy controls, 30 T2DM patients, and 60 DSPN patients were subjected to nontargeted metabolomics. Potential biomarkers of DSPN were screened based on univariate and multivariate statistical analyses, ROC curve analysis, and logistic regression. Finally, another 22 patients with T2DM who developed DSPN after follow-up were selected for validation of the new biomarker based on target metabolomics.

RESULTS

Compared with the control group and the T2DM group, 6 metabolites showed differences in the DSPN group (P < 0.05; FDR < 0.1; VIP > 1) and a rising step trend was observed. Among them, phenylacetylglutamine (PAG) and sorbitol displayed an excellent discriminatory ability and associated with disease severity. The verification results demonstrated that when T2DM progressed to DSPN, the phenylacetylglutamine content increased significantly (P = 0.004).

CONCLUSION

The discovered and verified endogenous metabolite PAG may be a novel potential biomarker of DSPN and involved in the disease pathogenesis.

Amino acid analysis as a method of discovering biomarkers for diagnosis of diabetes and its complications

Diabetes mellitus (DM) is a severe chronic diseases with a global prevalence of 9%, leading to poor health and high health care costs, and is a direct cause of millions of deaths each year. The rising epidemic of diabetes and its complications, such as retinal and peripheral nerve disease, is a huge burden globally. A better understanding of the molecular pathways involved in the development and progression of diabetes and its complications can facilitate individualized prevention and treatment. High diabetes mellitus incidence rate is caused mainly by lack of non-invasive and reliable methods for early diagnosis, such as plasma biomarkers. The incidence of diabetes and its complications in the world still grows so it is crucial to develop a new, faster, high specificity and more sensitive diagnostic technologies. With the advancement of analytical techniques, metabolomics can identify and quantify multiple biomarkers simultaneously in a high-throughput manner, and effective biomarkers can greatly improve the efficiency of diabetes and its complications. By providing information on potential metabolic pathways, metabolomics can further define the mechanisms underlying the progression of diabetes and its complications, help identify potential therapeutic targets, and improve the prevention and management of T2D and its complications. The application of amino acid metabolomics in epidemiological studies has identified new biomarkers of diabetes mellitus (DM) and its complications, such as branched-chain amino acids, phenylalanine and arginine metabolites. This study focused on the analysis of metabolic amino acid profiling as a method for identifying biomarkers for the detection and screening of diabetes and its complications. The results presented are all from recent studies, and in all cases analyzed, there were significant changes in the amino acid profile of patients in the experimental group compared to the control group. This study demonstrates the potential of amino acid profiles as a detection method for diabetes and its complications.

Peripheral Neuropathy in Diabetes Mellitus: Pathogenetic Mechanisms and Diagnostic Options

Diabetic neuropathy (DN) is one of the main microvascular complications of both type 1 and type 2 diabetes mellitus. Sometimes, this could already be present at the time of diagnosis for type 2 diabetes mellitus (T2DM), while it appears in subjects with type 1 diabetes mellitus (T1DM) almost 10 years after the onset of the disease. The impairment can involve both somatic fibers of the peripheral nervous system, with sensory-motor manifestations, as well as the autonomic system, with neurovegetative multiorgan manifestations through an impairment of sympathetic/parasympathetic conduction. It seems that, both indirectly and directly, the hyperglycemic state and oxygen delivery reduction through the vasa nervorum can determine inflammatory damage, which in turn is responsible for the alteration of the activity of the nerves. The symptoms and signs are therefore various, although symmetrical painful somatic neuropathy at the level of the lower limbs seems the most frequent manifestation. The pathophysiological aspects underlying the onset and progression of DN are not entirely clear. The purpose of this review is to shed light on the most recent discoveries in the pathophysiological and diagnostic fields concerning this complex and frequent complication of diabetes mellitus.

Towards prevention of diabetic peripheral neuropathy: clinical presentation, pathogenesis, and new treatments

Diabetic peripheral neuropathy (DPN) occurs in up to half of individuals with type 1 or type 2 diabetes. DPN results from the distal-to-proximal loss of peripheral nerve function, leading to physical disability and sometimes pain, with the consequent lowering of quality of life. Early diagnosis improves clinical outcomes, but many patients still develop neuropathy. Hyperglycaemia is a risk factor and glycaemic control prevents DPN development in type 1 diabetes. However, glycaemic control has modest or no benefit in individuals with type 2 diabetes, probably because they usually have comorbidities. Among them, the metabolic syndrome is a major risk factor for DPN. The pathophysiology of DPN is complex, but mechanisms converge on a unifying theme of bioenergetic failure in the peripheral nerves due to their unique anatomy. Current clinical management focuses on controlling diabetes, the metabolic syndrome, and pain, but remains suboptimal for most patients. Thus, research is ongoing to improve early diagnosis and prognosis, to identify molecular mechanisms that could lead to therapeutic targets, and to investigate lifestyle interventions to improve clinical outcomes.

Obesity-Associated Neuropathy: Recent Preclinical Studies and Proposed Mechanisms

Significance: The prevalence of metabolic syndrome (MetS) and associated obesity has increased in recent years, affecting millions worldwide. One of the most common complications of obesity is damage to the peripheral nerve system, referred to as neuropathy. The lack of disease-modifying therapy for this complication is largely due to a poor understanding of the complex neurobiology underlying neuropathy. Recent preclinical studies suggest that in addition to glucotoxic events, other mechanisms, including lipid signaling, microbiome, or inflammation, may be viable targets to prevent nerve damage and neuropathic pain in obesity. Recent Advances: Clinical and preclinical studies using diet-induced obesity rodent models have identified novel interventions that improve neuropathy. Notably, mechanistic studies suggest that lipid, calcium signaling, and inflammation are converging pathways. Critical Issues: In this review, we focus on interventions and their mechanisms that are shown to ameliorate neuropathy in MetS obese models, including: (i) inhibition of a sensory neuron population, (ii), modification of dietary components, (iii) activation of nuclear and mitochondrial lipid pathways, (iv) exercise, and (v) modulation of gut microbiome composition and their metabolites. Future Directions: These past years, novel research increased our knowledge about neuropathy in obesity and discovered the involvement of nonglucose signaling. More studies are necessary to uncover the interplay between complex metabolic pathways in the peripheral nerve system of obese individuals. Further mechanistic studies in preclinical models and humans are crucial to create single- or multitarget interventions for this complex disease implying complex metabolic phenotyping. Antioxid. Redox Signal. 37, 597-612.

A Role for Fatty Acids in Peripheral Neuropathy Associated with Type 2 Diabetes and Prediabetes

Significance: As the global prevalence of diabetes rises, diabetic complications are also increasing at an alarming rate. Peripheral neuropathy (PN) is the most prevalent complication of diabetes and prediabetes, and is characterized by progressive sensory loss resulting from nerve damage. While hyperglycemia is the major risk factor for PN in type 1 diabetes (T1D), the metabolic syndrome (MetS) underlies the onset and progression of PN in type 2 diabetes (T2D) and prediabetes. Recent Advances: Recent reports show that dyslipidemia, a MetS component, is strongly associated with PN in T2D and prediabetes. Dyslipidemia is characterized by an abnormal plasma lipid profile with uncontrolled lipid levels, and both clinical and preclinical studies implicate a role for dietary fatty acids (FAs) in PN pathogenesis. Molecular studies further show that saturated and unsaturated FAs differentially regulate the nerve lipid profile and nerve function. Critical Issues: We first review the properties of FAs and the neuroanatomy of the peripheral nervous system (PNS). Second, we discuss clinical and preclinical studies that implicate the involvement of FAs in PN. Third, we summarize the potential effects of FAs on nerve function and lipid metabolism within the peripheral nerves, sensory neurons, and Schwann cells. Future Directions: Future directions will focus on identifying molecular pathways in T2D and prediabetes that are modulated by FAs in PN. Determining pathophysiological mechanisms that underlie the injurious effects of saturated FAs and beneficial properties of unsaturated FAs will provide mechanistic targets for developing new targeted therapies to treat PN associated with T2D and prediabetes. Antioxid. Redox Signal. 37, 560-577.

A High-Fat Diet Disrupts Nerve Lipids and Mitochondrial Function in Murine Models of Neuropathy

As the prevalence of prediabetes and type 2 diabetes (T2D) continues to increase worldwide, accompanying complications are also on the rise. The most prevalent complication, peripheral neuropathy (PN), is a complex process which remains incompletely understood. Dyslipidemia is an emerging risk factor for PN in both prediabetes and T2D, suggesting that excess lipids damage peripheral nerves; however, the precise lipid changes that contribute to PN are unknown. To identify specific lipid changes associated with PN, we conducted an untargeted lipidomics analysis comparing the effect of high-fat diet (HFD) feeding on lipids in the plasma, liver, and peripheral nerve from three strains of mice (BL6, BTBR, and BKS). HFD feeding triggered distinct strain- and tissue-specific lipid changes, which correlated with PN in BL6 mice versus less robust murine models of metabolic dysfunction and PN (BTBR and BKS mice). The BL6 mice showed significant changes in neutral lipids, phospholipids, lysophospholipids, and plasmalogens within the nerve. Sphingomyelin (SM) and lysophosphatidylethanolamine (LPE) were two lipid species that were unique to HFD BL6 sciatic nerve compared to other strains (BTBR and BKS). Plasma and liver lipids were significantly altered in all murine strains fed a HFD independent of PN status, suggesting that nerve-specific lipid changes contribute to PN pathogenesis. Many of the identified lipids affect mitochondrial function and mitochondrial bioenergetics, which were significantly impaired in ex vivo sural nerve and dorsal root ganglion sensory neurons. Collectively, our data show that consuming a HFD dysregulates the nerve lipidome and mitochondrial function, which may contribute to PN in prediabetes.

Serum lipidomic determinants of human diabetic neuropathy in type 2 diabetes

OBJECTIVE

The serum lipidomic profile associated with neuropathy in type 2 diabetes is not well understood. Obesity and dyslipidemia are known neuropathy risk factors, suggesting lipid profiles early during type 2 diabetes may identify individuals who develop neuropathy later in the disease course. This retrospective cohort study examined lipidomic profiles 10 years prior to type 2 diabetic neuropathy assessment.

METHODS

Participants comprised members of the Gila River Indian community with type 2 diabetes (n = 69) with available stored serum samples and neuropathy assessment 10 years later using the combined Michigan Neuropathy Screening Instrument (MNSI) examination and questionnaire scores. A combined MNSI index was calculated from examination and questionnaire scores. Serum lipids (435 species from 18 classes) were quantified by mass spectrometry.

RESULTS

The cohort included 17 males and 52 females with a mean age of 45 years (SD = 9 years). Participants were stratified as with (high MNSI index score > 2.5407) versus without neuropathy (low MNSI index score ≤ 2.5407). Significantly decreased medium-chain acylcarnitines and increased total free fatty acids, independent of chain length and saturation, in serum at baseline associated with incident peripheral neuropathy at follow-up, that is, participants had high MNSI index scores, independent of covariates. Participants with neuropathy also had decreased phosphatidylcholines and increased lysophosphatidylcholines at baseline, independent of chain length and saturation. The abundance of other lipid classes did not differ significantly by neuropathy status.

INTERPRETATION

Abundance differences in circulating acylcarnitines, free fatty acids, phosphatidylcholines, and lysophosphatidylcholines 10 years prior to neuropathy assessment are associated with neuropathy status in type 2 diabetes.

CCDB: A database for exploring inter-chemical correlations in metabolomics and exposomics datasets

Inter-chemical correlations in metabolomics and exposomics datasets provide valuable information for studying relationships among chemicals reported for human specimens. With an increase in the number of compounds for these datasets, a network graph analysis and visualization of the correlation structure is difficult to interpret. We have developed the Chemical Correlation Database (CCDB), as a systematic catalogue of inter-chemical correlation in publicly available metabolomics and exposomics studies. The database has been provided via an online interface to create single compound-centric views. We have demonstrated various applications of the database to explore: 1) the chemicals from a chemical class such as Per- and Polyfluoroalkyl Substances (PFAS), polycyclic aromatic hydrocarbons (PAHs), polychlorinated biphenyls (PCBs), phthalates and tobacco smoke related metabolites; 2) xenobiotic metabolites such as caffeine and acetaminophen; 3) endogenous metabolites (acyl-carnitines); and 4) unannotated peaks for PFAS. The database has a rich collection of 35 human studies, including the National Health and Nutrition Examination Survey (NHANES) and high-quality untargeted metabolomics datasets. CCDB is supported by a simple, interactive and user-friendly web-interface to retrieve and visualize the inter-chemical correlation data. The CCDB has the potential to be a key computational resource in metabolomics and exposomics facilitating the expansion of our understanding about biological and chemical relationships among metabolites and chemical exposures in the human body. The database is available at www.ccdb.idsl.me site.

Sphingolipid metabolism plays a key role in diabetic peripheral neuropathy

INTRODUCTION

As the most common chronic complication of diabetes mellitus (DM), diabetic peripheral neuropathy (DPN) seriously affects the quality of life of DM patients. So, it is of great significance for the diagnosis and treatment of DPN. In recent years, there have been numerous studies on pathogenesis and biomarkers of DM, but there are few studies on the biomarkers of DPN.

OBJECTIVES

This research is intended to identify abnormal metabolic pathways, search for potential biomarkers of DPN, and provide a metabolic basis for the diagnosis and mechanism of DPN.

METHODS

Serum samples from 23 healthy controls (HC), 42 DM patients and 30 DPN patients and urine samples from 42 HC, 40 DM patients, and 30 DPN patients were collected. UPLC-Q-TOF/MS was used to analyze the samples. Potential biomarkers were screened from principal component analysis (PCA) to orthogonal partial least squares discriminant analysis (OPLS-DA) and further evaluated by receiver operating characteristic analysis (ROC). The biomarkers were then enriched and pathway analyzed.

RESULTS

12 potential DPN biomarkers were identified from patient's serum. 11 potential DPN biomarkers were identified from the patient's urine. Among them, the diagnostic ability of gluconic acid, lipoic acid, sphinganine, bilirubin, sphingosine and 4-hydroxybenzoic acid was increased by ROC analysis. Potential biomarkers suggest that the disorder of DPN metabolism may be linked to sphingolipid metabolism.

CONCLUSIONS

This research laid a theoretical foundation for the diagnosis and pathogenesis of DPN.

Plasma Metabolomics and Lipidomics Differentiate Obese Individuals by Peripheral Neuropathy Status

CONTEXT

Peripheral neuropathy (PN) is a frequent prediabetes and type 2 diabetes (T2D) complication. Multiple clinical studies reveal that obesity and dyslipidemia can also drive PN progression, independent of glycemia, suggesting a complex interplay of specific metabolite and/or lipid species may underlie PN.

OBJECTIVE

This work aimed to identify the plasma metabolomics and lipidomics signature that underlies PN in an observational study of a sample of individuals with average class 3 obesity.

METHODS

We performed plasma global metabolomics and targeted lipidomics on obese participants with (n = 44) and without PN (n = 44), matched for glycemic status, vs lean nonneuropathic controls (n = 43). We analyzed data by Wilcoxon, logistic regression, partial least squares-discriminant analysis, and group-lasso to identify differential metabolites and lipids by obesity and PN status. We also conducted subanalysis by prediabetes and T2D status.

RESULTS

Lean vs obese comparisons, regardless of PN status, identified the most significant differences in gamma-glutamyl and branched-chain amino acid metabolism from metabolomics analysis and triacylglycerols from lipidomics. Stratification by PN status within obese individuals identified differences in polyamine, purine biosynthesis, and benzoate metabolism. Lipidomics found diacylglycerols as the most significant subpathway distinguishing obese individuals by PN status, with additional contributions from phosphatidylcholines, sphingomyelins, ceramides, and dihydroceramides. Stratifying the obese group by glycemic status did not affect discrimination by PN status.

CONCLUSION

Obesity may be as strong a PN driver as prediabetes or T2D in a sample of individuals with average class 3 obesity, at least by plasma metabolomics and lipidomics profile. Metabolic and complex lipid pathways can differentiate obese individuals with and without PN, independent of glycemic status.

Metabolomics of Type 1 and Type 2 Diabetes: Insights into Risk Prediction and Mechanisms

PURPOSE OF REVIEW

Metabolomics enables rapid interrogation of widespread metabolic processes making it well suited for studying diabetes. Here, we review the current status of metabolomic investigation in diabetes, highlighting its applications for improving risk prediction and mechanistic understanding.

RECENT FINDINGS

Findings of metabolite associations with type 2 diabetes risk have confirmed experimental observations (e.g., branched-chain amino acids) and also pinpointed novel pathways of diabetes risk (e.g., dimethylguanidino valeric acid). In type 1 diabetes, abnormal metabolite patterns are observed prior to the development of autoantibodies and hyperglycemia. Diabetes complications display specific metabolite signatures that are distinct from the metabolic derangements of diabetes and differ across vascular beds. Lastly, metabolites respond acutely to pharmacologic treatment, providing opportunities to understand inter-individual treatment responses. Metabolomic studies have elucidated biological mechanisms underlying diabetes development, complications, and therapeutic response. While not yet ready for clinical translation, metabolomics is a powerful and promising precision medicine tool.

Lipids and peripheral neuropathy

PURPOSE OF REVIEW

Hyperlipidaemia is associated with the development of neuropathy. Indeed, a mechanistic link between altered lipid metabolism and peripheral nerve dysfunction has been demonstrated in a number of experimental and clinical studies. Furthermore, post hoc analyses of clinical trials of cholesterol and triglyceride-lowering pharmacotherapy have shown reduced rates of progression of diabetic neuropathy. Given, there are currently no FDA approved disease-modifying therapies for diabetic neuropathy, modulation of lipids may represent a key therapeutic target for the treatment of diabetic nerve damage. This review summarizes the current evidence base on the role of hyperlipidaemia and lipid lowering therapy on the development and progression of peripheral neuropathy.

RECENT FINDINGS

A body of literature supports a detrimental effect of dyslipidaemia on nerve fibres resulting in somatic and autonomic neuropathy. The case for an important modulating role of hypertriglyceridemia is stronger than for low-density lipoprotein cholesterol (LDL-C) in relation to peripheral neuropathy. This is reflected in the outcomes of clinical trials with the different therapeutic agents targeting hyperlipidaemia reporting beneficial or neutral effects with statins and fibrates. The potential concern with the association between proprotein convertase subtilisin/kexin type 9 (PCSK9) inhibitor therapy and cognitive decline raised the possibility that extreme LDL-C lowering may result in neurodegeneration. However, studies in murine models and data from small observational studies indicate an association between increased circulating PCSK9 levels and small nerve fibre damage with a protective effect of PCSK9i therapy against small fibre neuropathy. Additionally, weight loss with bariatric surgery leads to an improvement in peripheral neuropathy and regeneration of small nerve fibres measured with corneal confocal microscopy in people with obesity with or without type 2 diabetes. These improvements correlate inversely with changes in triglyceride levels.

SUMMARY

Hyperlipidaemia, particularly hypertriglyceridemia, is associated with the development and progression of neuropathy. Lipid modifying agents may represent a potential therapeutic option for peripheral neuropathy. Post hoc analyses indicate that lipid-lowering therapies may halt the progression of neuropathy or even lead to regeneration of nerve fibres. Well designed randomized controlled trials are needed to establish if intensive targeted lipid lowering therapy as a part of holistic metabolic control leads to nerve fibre regeneration and improvement in neuropathy symptoms.