Exploring the Relationship Between Lymphatic System Damage and Insulin Resistance in T2DM Based on Vascular Periadventitial Tensor Analysis and Triglyceride-Glucose Index

doi:10.21203/rs.3.rs-3603905/v1

Download PDF

Research Article

Exploring the Relationship Between Lymphatic System Damage and Insulin Resistance in T2DM Based on Vascular Periadventitial Tensor Analysis and Triglyceride-Glucose Index

https://doi.org/10.21203/rs.3.rs-3603905/v1

This work is licensed under a CC BY 4.0 License

Version 1

posted

You are reading this latest preprint version

Type 2 diabetes mellitus (T2DM) can lead to dysfunction in the brain's glymphatic system, but the relationship between glymphatic system function and insulin resistance remains unclear. The aim of this study was to investigate the connection between changes in the brain's glymphatic system function in T2DM and the surrogate marker for insulin resistance, the Triglyceride-Glucose Index (TyG). A total of 28 T2DM patients (T2DM group) and 24 healthy volunteers (HC group) were included in this study. We outlined circular regions with a diameter of 5.0 mm on diffusion tensor imaging (DTI) in the projection and association fibers in the body of the lateral ventricle. We compared the differences in DTI-ALPS index and related fiber diffusivity between the two groups. Additionally, TyG index in T2DM patients was calculated based on fasting blood glucose and triglyceride levels. Age was included as a covariate to analyze the correlation between the ALPS index and TyG index in T2DM patients. The results showed that the ALPS index and Dxproj in the T2DM group were lower than those in the HC group, while Dyproj was higher than in the HC group. In the T2DM group, the ALPS index, Dxassoc, and TyG index were all negatively correlated. The decreased ALPS index and fiber diffusivity in T2DM patients may be associated with the TyG index, suggesting a relationship between insulin resistance, lymphatic system damage, and white matter microstructure.

Type 2 Diabetes Mellitus

Diffusion tensor imaging

Lymphatic system

Triglyceride-glucose index

The glymphatic system, a recently spotlighted and extensively researched element of brain metabolism(Shen et al., 2022), shares its operational and physiological characteristics with the peripheral lymphatic system in the human body, from which it derives its name(Nedergaard et al., 2020). In 2012, scientists(Iliff et al., 2012) initially identified the glymphatic system through two-photon microscopy, confirming its crucial role in removing metabolic waste from the brain. Type 2 diabetes mellitus (T2DM) is a prevalent chronic metabolic disorder in clinical practice. Individuals with T2DM, due to pathological changes like oxidative stress(Michailidis et al., 2022) and systemic inflammation(Mussa et al., 2021), are more prone to central nervous system degeneration and damage to brain vascular endothelium, leading to a decline in the glymphatic system's functionality(Zhang et al., 2019).

Insulin resistance stands as the central mechanism among various factors contributing to brain damage in T2DM(Arnold et al., 2018). In individuals with T2DM, insulin resistance can instigate mitochondrial dysfunction and oxidative stress, further aggravating nervous system complications. Inflammatory responses and the proliferation of glial cells caused by oxidative stress can also disrupt the normal glymphatic system function(Tarasoff-Conway et al., 2015). AQP4 (Aquaporin-4) plays a pivotal role in maintaining glymphatic system function through its expression and polarization on the end-feet of perivascular astrocytes. Insulin resistance might potentially reduce the expression of AQP4 protein in the perivascular space by elevating extracellular vesicle miRNA levels, thus affecting brain waste metabolism efficiency(Zhang et al., 2023). Nevertheless, the precise mechanisms through which insulin resistance leads to glymphatic system damage remain unclear, and there is a dearth of adequate clinical and imaging data to support these findings.

In 2017, Taoka introduced the diffusion tensor image analysis along the perivascular space (DTI-ALPS)(Taoka et al., 2017), a non-invasive and real-time assessment method for evaluating glymphatic system changes using diffusion MRI imaging without the need for contrast agents(Hsu et al., 2023). DTI-ALPS involves initially identifying the medullary veins running perpendicular to the lateral ventricular wall. It subsequently computes corresponding indices by measuring the diffusion rates of projecting fibers around the medullary veins in the X, Y, and Z axes, enabling a quantitative evaluation of glymphatic system functionality(Taoka et al., 2017). Previous studies have often employed diffusion tensor imaging (DTI) sequences with b-values of 1000 s/mm² and 2000 s/mm² to measure the ALPS index in the dominant hemisphere of the brain and further assess glymphatic system function in patients. Nevertheless, DTI, due to limitations in its underlying model, is less effective in fiber tracking than diffusion spectrum imaging (DSI) sequences obtained with multiple b-values(Glenn et al., 2016). Despite this, research on evaluating the glymphatic system in T2DM patients using DSI scans with multiple b-values remains limited to date.

Unlike other commonly used insulin resistance indicators in clinical practice, the Triglyceride Glucose (TyG) index has demonstrated its simplicity and reliability(Tao et al., 2022). It has gained increasing attention from researchers in recent years and has become a focal point of investigation(Yan et al., 2022). The hyperinsulinemic-euglycemic clamp procedure is recognized as the most accurate method for assessing insulin resistance. However, its complexity and time-consuming nature limit its practicality in clinical settings. Currently, an alternative and relatively new indicator, the TyG index, has gained prominence. This index is calculated by combining metabolic parameters, specifically blood lipids and blood glucose. It is known for its user-friendliness and has become a valuable tool in clinical and epidemiological research for evaluating insulin resistance.

Hence, in this study, we have utilized a healthy population as the control group. We have employed a multiple b-value Diffusion Spectrum Imaging (DSI) sequence to calculate the diffusion rates and ALPS index of projecting and associating fibers around both brain ventricles. This assessment aims to explore potential changes in glymphatic system function and related fiber diffusion rates in T2DM patients. Subsequently, we have analyzed the relationships between these abnormal indicators and the TyG index. Our objective is to offer a fresh perspective and a scientific basis for the comprehensive investigation of the intricate neuropathological mechanisms underlying brain damage in T2DM.

2.1 Study Design

This investigation took place from August 2021 to August 2022 at the Endocrinology Department of Dalian University Affiliated Zhongshan Hospital. A total of 28 patients with Type 2 Diabetes Mellitus (T2DM) were enrolled during this period, alongside 24 well-matched healthy volunteers. The study obtained ethical approval from the hospital's ethics committee (Approval No: 2020017), and all participants provided informed consent.

2.2 Study Subjects

Inclusion Criteria: 1) T2DM diagnosis following the "Standards of Medical Care in Diabetes" as defined by the American Diabetes Association (ADA) in 2021. 2) Absence of previous significant brain lesions or psychiatric disorders. 3) Minimum education level of 6 years. 4) Right-handed individuals. Exclusion Criteria: 1) Inability to cooperate for MRI examinations. 2) Conventional MRI revealing brain abnormalities such as white matter lesions, cranial trauma, or brain tumors. 3) Coexisting metabolic disorders like hyperthyroidism or Cushing's syndrome.

2.3 Magnetic Resonance Imaging Equipment and Examination Sequences

In this study, a Siemens 3.0T superconducting magnetic resonance scanner (Magnetom Skyra, Germany ) equipped with a 32-channel dedicated head phased-array coil was utilized. The imaging sequences included an initial T2-weighted imaging (T2WI) scan with the following parameters: TR was set at 3610ms, TE was set at 112ms, the number of slices was 20, slice thickness was 5.5mm, the field of view was 230mm×186mm, the matrix size was 384×218, and a scan time of 54 seconds was allowed. Participants who met the inclusion criteria underwent Diffusion Spectrum Imaging (DSI) sequence examinations with the following parameters: TR was set at 5100ms, TE was set at 109ms, voxel size was 2.1mm×2.1mm×2.0mm, the number of slices was 70, slice thickness was 2.0mm, the field of view was 230mm×230mm, the matrix size was 110×110, the scan duration was 683 seconds, and a b-value ranging from 0 to 3000s/mm² was utilized (Supplementary for the b-value table).

2.4 Perivascular Space Tensor Imaging

Prior to preprocessing, visual inspection of the DSI images for noise artifacts was conducted by two neuroradiologists with over 5 years of experience. The diffusion data in DICOM format was converted to the NIFTI format using dcm2niigui. Initially, eddy current correction was applied to the DSI images within the gradient coil utilizing the FDT toolbox from FSL (http://www.fmrib.ox.ac.uk/fsl). Subsequently, the brain extraction tool (BET) was utilized to extract a brain mask with a b-value of 0 from the images. Based on the diffusion tensor model, tensor reconstruction was carried out during the process of fitting FA (Fractional Anisotropy) images using the FSL toolbox DTIFIT, providing the diffusion rates in the X, Y, and Z directions for all subjects. FA color maps were generated using FSLeyes. Subsequently, 5mm diameter regions of interest (ROIs) were positioned on the perivascular and associative fibers to obtain the diffusion rates of each fiber in the X, Y, and Z axes, with the assistance of Itk-snap software (Fig. 1).

2.5 The ALPS index measurement method:

The ALPS index, used to assess the glymphatic system's function for all subjects, was determined by substituting the diffusion rates of the projective and associative fibers of the left hemisphere on the X, Y, and Z axes into the following formula: ALPS index = mean (Dxproj, Dxassoc) / mean (Dyproj, Dzassoc). Dxproj and Dxassoc represent the diffusion rates of the projective and associative fibers in the X-axis direction, while Dyproj and Dyassoc pertain to the Y-axis direction, and Dzproj and Dzassoc relate to the Z-axis direction (Supplementary Fig. 1).

2.6 Calculation of TyG Index

Biochemical parameters, including fasting triglycerides (TG), fasting plasma glucose (FPG), and glycated hemoglobin (HbA1c), were obtained from blood samples to compute the TyG index. For T2DM patients, the TyG index was calculated using the formula: TyG = Ln[TG (mg/dL) × FPG (mg/dL) / 2](Park et al., 2019).

2.7 Statistical Analysis

Data analysis was performed using SPSS 26.0 statistical analysis software. Normality and homogeneity of variance tests were conducted for all continuous data. Normally distributed data were expressed as mean ± standard deviation and analyzed through independent sample t-tests. Non-normally distributed data were presented as the median (interquartile range), and the Mann-Whitney U test was employed for analysis. A significance level of P < 0.05 was considered statistically significant. Age was utilized as a covariate, and Spearman's partial correlation analysis was carried out to assess the relationship between diffusion rates and the ALPS index in projection and association fibers on the X, Y, and Z axes, and the TyG index. Multiple tests were corrected using the Bonferroni correction, and the significance level for all tests was set at α = 0.05, two-tailed.

3.1 Comparison of General Information and Laboratory Parameters

Age, gender, and years of education were comparable between the T2DM group and the HC group (P > 0.05). In the T2DM group, fasting triglycerides, HbA1c, fasting plasma glucose, and the TyG index were all elevated in comparison to the HC group (P < 0.01 or P < 0.05), whereas high-density lipoprotein levels were lower in the T2DM group in contrast to the HC group (P < 0.01). No statistically significant differences were observed in other laboratory parameters (P > 0.05) (Table 1).

Table 1

Comparison of General Information and Laboratory Parameters between T2DM and HC Groups
	T2DM(n=28)	HC(n=24)	T/Z/χ²	P Value
Sex (male/female)	9/19	7/17	0.540	0.817^a
Age (years)	58.5(55.50, 62.00)	54.50(52.00, 58.00)	-1.813	0.070^b
Education (years)	12.00(9.00, 15.00)	13.50(13.00, 16.00)	-1.364	0.172^b
FPG (mmol/L)	8.11 ± 2.55	5.37 ± 0.67	-5.490	< 0.001^c
HbA1c (%)	7.98 ± 1.59	5.52 ± 0.58	-7.639	< 0.001^c
TG (mmol/L)	1.80(1.24, 2.28)	1.25(1.01, 1.52)	-2.478	0.013^b
TyG index	9.33 ± 0.71	8.60 ± 0.38	-4.730	< 0.001^c
HC: Healthy Control Group; T2DM: Type 2 Diabetes Mellitus Patients; TyG: the Triglyceride-Glucose. a: Chi-square test; b: Mann-Whitney U test; c: Independent sample t-test.

3.2 Comparison of ALPS Index and Fiber Diffusivity between the Two Groups

In the T2DM group, Dxproj was lower than in the HC group (P < 0.05), while Dyproj in the T2DM group was higher than in the HC group (P < 0.05). Furthermore, the ALPS index in the T2DM group was lower than in the HC group (P < 0.05) (Table 2; Fig. 2).

Table 2

Comparison of fiber diffusivity and ALPS-index between the two groups.
		HC(n=24)	T	P Value
Dxproj (×10^− 3mm²/s)	0.333597 ± 0.033767	0. 354252 ± 0.034626	2.173	0.035^*c
Dxassoc (×10^− 3mm²/s)	0.404334 ± 0.042012	0. 402395 ± 0.036526	-0.176	0.861^c
Dyproj (×10^− 3mm²/s)	0.344206 ± 0.036083	0.315173 ± 0.044827	-2.587	0.013^*c
Dyassoc (×10^− 3mm²/s)	0.709913 ± 0.045955	0.708411 ± 0.045269	-0.118	0.906^c
Dzproj (×10^− 3mm²/s)	0.627756 ± 0.048938	0.636925 ± 0.057997	0.618	0.539^c
Dzassoc (×10^− 3mm²/s)	0.209124 ± 0.033155	0.205548 ± 0.040041	-0.352	0.726^c
ALPS index	1.338342 ± 0.121358	1.465294 ± 0.160279	3.246	0.002^*c
Note: *Statistical significance was observed between the HC and T2DM groups (P < 0.05).
Dxproj: Diffusivity of the projection fibers in the X-axis direction; Dxassoc: Diffusivity of the association fibers in the X-axis direction; Dyproj: Diffusivity of the projection fibers in the Y-axis direction; Dyassoc: Diffusivity of the association fibers in the Y-axis direction; Dzproj: Diffusivity of the projection fibers in the Z-axis direction; Dzassoc: Diffusivity of the association fibers in the Z-axis direction; c: T-test.

3.3 Correlation Analysis Between Fiber Diffusivity and TyG Index

Within this study, a substantial negative correlation was detected between Dxassoc, which represents fiber diffusivity, and the TyG index in T2DM patients (ρ = -0.563, P = 0.002). No significant correlations were evident between the remaining fiber diffusivity indices and the TyG index (P > 0.05) (Table 3; Fig. 3).

Table 3

Correlation Results Between Fiber Diffusivity and the TyG Index.
	ρ	P Value
Dxproj (×10^− 3mm²/s)	-0.234	0.241
Dxassoc (×10^− 3mm²/s)	-0.563	0.002^*
Dyproj (×10^− 3mm²/s)	0.095	0.638
Dyassoc (×10^− 3mm²/s)	0.169	0.398
Dzproj (×10^− 3mm²/s)	0.002	0.994
Dzassoc (×10^− 3mm²/s)	-0.169	0.400
Note: *Statistically significant differences between HC and T2DM groups (P < 0.05).
Dxproj: Diffusivity of the projection fibers along the X-axis; Dxassoc: Diffusivity of the association fibers along the X-axis; Dyproj: Diffusivity of the projection fibers along the Y-axis; Dyassoc: Diffusivity of the association fibers along the Y-axis; Dzproj: Diffusivity of the projection fibers along the Z-axis; Dzassoc: Diffusivity of the association fibers along the Z-axis.

3.4 Correlation Analysis Between ALPS Index and TyG Index

In T2DM patients, a significant negative correlation was established between the ALPS index and the TyG index (ρ = -0.419, P = 0.030) (Fig. 4).

In this study, we employed DSI sequences to compute the diffusion rates of periventricular projection and association fibers in T2DM patients and determined their ALPS indices. We conducted a quantitative evaluation of lymphatic system function in T2DM patients using the DTI-ALPS index. The findings revealed abnormalities in projection fibers and ALPS in T2DM patients. Furthermore, these abnormalities (Dxassoc and ALPS) exhibited a correlation with the TyG index. This discovery suggests a potential involvement of insulin resistance in lymphatic system damage in T2DM patients.

The non-invasive, in vivo assessment of the brain's lymphatic system function, as exemplified by the DTI-ALPS index, shows promise in addressing the challenges and safety concerns associated with lymphatic imaging techniques. Notably, the brain's projection fibers follow the vertical axis, while association fibers follow the anterior-posterior axis. However, both types of fibers do not run parallel to the perivascular spaces around venous vessels. Consequently, analyzing changes in the diffusion rates of these two fiber types in the X, Y, and Z axes can provide insights into the pathophysiological alterations in the perivascular spaces(Taoka et al., 2017). Previously, DTI-ALPS has found widespread applications in clinical research for conditions such as obstructive sleep apnea syndrome(Lee et al., 2022; Roy et al., 2022), epilepsy(Lee et al., 2022; Lee et al., 2022), and Parkinson's disease(Si et al., 2022). Nevertheless, the DTI model is relatively simplistic and may not effectively capture the specific characteristics of white matter fibers. In contrast, DSI represents a generalized q-space sampling imaging technique and is currently the sole method capable of non-invasively providing high-resolution whole-brain fiber tract imaging in living subjects. It offers exceptional cross-fiber resolution and high spatial resolution of diffusion data, facilitating the simultaneous fitting of various diffusion quantitative parameters(Wang et al., 2022). Research by Taoka et al.(Taoka et al., 2022) has demonstrated a strong correlation between ALPS indices acquired with different scanning parameters, affirming the feasibility of assessing lymphatic system function using multi-b-value diffusion MRI data obtained with DSI sequences. In a prior study, Qin et al.(Qin et al., 2023) obtained DSI images from subacute ischemic stroke patients, from which they derived ALPS indices. They observed a significant reduction in the ALPS index on the affected side in comparison to the unaffected side and the same-side ALPS index in healthy subjects. In this study, a total of 129 DWI images were acquired for each subject, encompassing 18 different b-values ranging from 0 to 3000 s/mm². Consequently, the diffusion rates of projection fibers and association fibers in the X, Y, and Z axes were lower than those obtained with traditional DTI sequence acquisition(Lin et al., 2023), while the ALPS index remained consistent with previous research findings.

This study uncovered that the ALPS index, determined by DSI, was lower in T2DM patients compared to healthy individuals, implying impaired lymphatic system function in the examined T2DM patient population. In a study by Yang et al.(Yang et al., 2020), DTI data were collected from 10 individuals with T2DM of over 10 years' duration, 10 with T2DM of less than 10 years duration, and 10 healthy volunteers. The research revealed that both groups of T2DM patients exhibited reduced DTI-ALPS indices. Additionally, it was observed that in T2DM patients with a longer disease duration, the diffusion rates of both projection fibers and association fibers were lower in comparison to the other two groups. In this study, the diffusion rate of T2DM patients' projection fibers along the X-axis was lower than that of normal subjects. This could potentially result from the accumulation of soluble proteins in the perivascular spaces around venous vessels, hindering the flow of water molecules in that direction. Simultaneously, the diffusion rate of T2DM patients' projection fibers along the Y-axis was higher than that of normal subjects, indicating potential compromised white matter integrity of the projection fibers in T2DM patients, leading to an increase in radial diffusion of water molecules. Utilizing DSI technology, this study further corroborated the presence of lymphatic system dysfunction in T2DM, along with structural abnormalities in projection fibers.

Moreover, of significant importance, a negative correlation was identified between the ALPS index in T2DM patients and the TyG index. This suggests that insulin resistance in T2DM patients may indirectly signify alterations in lymphatic system function. Previous research has indicated an association between elevated fasting blood glucose and reduced white matter integrity in individuals with metabolic syndrome(Huang et al., 2023; Yau et al., 2014). Jing et al.(Jing et al., 2022) emphasized that T2DM patients exhibited decreased FA values and increased mean diffusivity (MD) values in areas encompassing projection fibers, such as the bilateral anterior corona radiata, further highlighting that abnormal metabolic levels might lead to white matter damage in projection fiber regions.

In this study, a negative correlation was observed between the diffusion rate of T2DM patients' association fibers along the X-axis and the TyG index. These findings imply that insulin resistance in T2DM might be linked to restricted diffusion of water molecules within the perivascular spaces around association fibers. Insulin resistance has the potential to elevate the concentration and toxicity of β-amyloid proteins, thereby promoting their further deposition in the perivascular spaces(Michailidis et al., 2022). This suggests that insulin resistance exacerbates the deposition of β-amyloid proteins in the perivascular spaces of association fiber regions. It's essential to note that the differences in the Dxassoc group between the recruited T2DM patients and the normal subjects were not statistically significant, which does not entirely align with previous research(Yang et al., 2020). This inconsistency may be attributed to sample heterogeneity and the utilization of DSI technology. Future studies with larger sample sizes and the elimination of potential confounding factors will be required to validate the robustness of these results.

This study has several limitations: 1. Some of the recruited T2DM patients exhibited well-controlled blood sugar levels and good dietary habits, resulting in less pronounced increases in the TyG index. This could potentially influence the final experimental outcomes. 2. Due to the nature of perivascular space tensor imaging, there might be some errors in the manual delineation of ROI. 3. The relatively low b-values in the collected diffusion MRI data may have a greater influence from highly mobile water molecules. Future research should place greater emphasis on establishing appropriate b-value parameters.

In summary, the utilization of ALPS indices obtained from DSI has revealed a decline in T2DM patients, indicating potential impairment of the lymphatic system in these individuals. Moreover, the association between T2DM insulin resistance parameters (TyG) and reduced lymphatic system function indices (ALPS) suggests that this may serve as a crucial neuroimaging evidence of insulin resistance-related lymphatic system damage. These findings contribute to a deeper understanding of brain injuries associated with insulin resistance.

Acknowledgements

The authors want to thank the clinical and the nursing team of the Affiliated Zhongshan Hospital of Dalian University for their cooperation with working on patients' recruiting.

Author contributions

SX, Writing—original draft; XYW, Writing—review & editing; DY, Writing – review & editing, XYW and YY, Methodology; SX and DY, Project administration; Validation; DZ, Formal analysis, Supervision; LND and QQC, Investigation; JS and LL, Data curation, Software; JLW, Conceptualization, Funding acquisition, Resources. SX, XYW, DY, DZ, LND, QQC, YY, LL, JS, and JLW are responsible for reviewing and providing feedback on the manuscript. All authors read and approved the final manuscript.

Funding

This work was supported by National Natural Science Foundation of China (grant number: 82071911) and from the Dalian Science and Technology Innovation Fund (grant number: 2021JJ12SN38).

Data availability

Upon reasonable request, the original data set can be obtained from the corresponding author

Ethical approval

The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013). This study was approved by the Institutional Review Board of the Affiliated Zhongshan Hospital of Dalian University Prefecture (approval number: 2020017). The patients/participants provided their written informed consent to participate in this study.

Competing interests

The authors declare no competing interests.

Shen, T., Yue, Y., Ba, F., He, T., Tang, X., Hu, X., Pu, J., Huang, C., Lv, W., Zhang, B.& Lai, H. Y. (2022). Diffusion along perivascular spaces as marker for impairment of glymphatic system in Parkinson's disease. NPJ Parkinsons Dis, 8(1), 174. http://dx.doi.org/10.1038/s41531-022-00437-1.
Nedergaard, M.& Goldman, S. A. (2020). Glymphatic failure as a final common pathway to dementia. Science, 370(6512), 50-56. http://dx.doi.org/10.1126/science.abb8739.
Iliff, J. J., Wang, M., Liao, Y., Plogg, B. A., Peng, W., Gundersen, G. A., Benveniste, H., Vates, G. E., Deane, R., Goldman, S. A., Nagelhus, E. A.& Nedergaard, M. (2012). A paravascular pathway facilitates CSF flow through the brain parenchyma and the clearance of interstitial solutes, including amyloid β. Science Translational Medicine, 4(147), 147ra111. http://dx.doi.org/10.1126/scitranslmed.3003748.
Michailidis, M., Moraitou, D., Tata, D. A., Kalinderi, K., Papamitsou, T.& Papaliagkas, V. (2022). Alzheimer's Disease as Type 3 Diabetes: Common Pathophysiological Mechanisms between Alzheimer's Disease and Type 2 Diabetes. International Journal of Molecular Sciences, 23(5), http://dx.doi.org/10.3390/ijms23052687.
Mussa, B. M., Srivastava, A., Al-Habshi, A., Mohammed, A. K., Halwani, R.& Abusnana, S. (2021). Inflammatory Biomarkers Levels in T2DM Emirati Patients with Diabetic Neuropathy. Diabetes Metab Syndr Obes, 14(3389-3397. http://dx.doi.org/10.2147/dmso.S319863.
Zhang, L., Chopp, M., Jiang, Q.& Zhang, Z. (2019). Role of the glymphatic system in ageing and diabetes mellitus impaired cognitive function. Stroke Vasc Neurol, 4(2), 90-92. http://dx.doi.org/10.1136/svn-2018-000203.
Arnold, S. E., Arvanitakis, Z., Macauley-Rambach, S. L., Koenig, A. M., Wang, H. Y., Ahima, R. S., Craft, S., Gandy, S., Buettner, C., Stoeckel, L. E., Holtzman, D. M.& Nathan, D. M. (2018). Brain insulin resistance in type 2 diabetes and Alzheimer disease: concepts and conundrums. Nat Rev Neurol, 14(3), 168-181. http://dx.doi.org/10.1038/nrneurol.2017.185.
Tarasoff-Conway, J. M., Carare, R. O., Osorio, R. S., Glodzik, L., Butler, T., Fieremans, E., Axel, L., Rusinek, H., Nicholson, C., Zlokovic, B. V., Frangione, B., Blennow, K., Ménard, J., Zetterberg, H., Wisniewski, T.& de Leon, M. J. (2015). Clearance systems in the brain-implications for Alzheimer disease. Nat Rev Neurol, 11(8), 457-470. http://dx.doi.org/10.1038/nrneurol.2015.119.
Zhang, L., Li, D., Yi, P., Shi, J., Guo, M., Yin, Q., Liu, D., Zhuang, P.& Zhang, Y. (2023). Peripheral origin exosomal microRNAs aggravate glymphatic system dysfunction in diabetic cognitive impairment. Acta Pharm Sin B, 13(7), 2817-2825. http://dx.doi.org/10.1016/j.apsb.2023.03.018.
Taoka, T., Masutani, Y., Kawai, H., Nakane, T., Matsuoka, K., Yasuno, F., Kishimoto, T.& Naganawa, S. (2017). Evaluation of glymphatic system activity with the diffusion MR technique: diffusion tensor image analysis along the perivascular space (DTI-ALPS) in Alzheimer's disease cases. Jpn J Radiol, 35(4), 172-178. http://dx.doi.org/10.1007/s11604-017-0617-z.
Hsu, J. L., Wei, Y. C., Toh, C. H., Hsiao, I. T., Lin, K. J., Yen, T. C., Liao, M. F.& Ro, L. S. (2023). Magnetic Resonance Images Implicate That Glymphatic Alterations Mediate Cognitive Dysfunction in Alzheimer Disease. Annals of Neurology, 93(1), 164-174. http://dx.doi.org/10.1002/ana.26516.
Glenn, G. R., Kuo, L. W., Chao, Y. P., Lee, C. Y., Helpern, J. A.& Jensen, J. H. (2016). Mapping the Orientation of White Matter Fiber Bundles: A Comparative Study of Diffusion Tensor Imaging, Diffusional Kurtosis Imaging, and Diffusion Spectrum Imaging. AJNR: American Journal of Neuroradiology, 37(7), 1216-1222. http://dx.doi.org/10.3174/ajnr.A4714.
Tao, L. C., Xu, J. N., Wang, T. T., Hua, F.& Li, J. J. (2022). Triglyceride-glucose index as a marker in cardiovascular diseases: landscape and limitations. Cardiovascular Diabetology, 21(1), 68. http://dx.doi.org/10.1186/s12933-022-01511-x.
Yan, F., Yan, S., Wang, J., Cui, Y., Chen, F., Fang, F.& Cui, W. (2022). Association between triglyceride glucose index and risk of cerebrovascular disease: systematic review and meta-analysis. Cardiovascular Diabetology, 21(1), 226. http://dx.doi.org/10.1186/s12933-022-01664-9.
Park, K., Ahn, C. W., Lee, S. B., Kang, S., Nam, J. S., Lee, B. K., Kim, J. H.& Park, J. S. (2019). Elevated TyG Index Predicts Progression of Coronary Artery Calcification. Diabetes Care, 42(8), 1569-1573. http://dx.doi.org/10.2337/dc18-1920.
Lee, H. J., Lee, D. A., Shin, K. J.& Park, K. M. (2022). Glymphatic system dysfunction in obstructive sleep apnea evidenced by DTI-ALPS. Sleep Med, 89(176-181. http://dx.doi.org/10.1016/j.sleep.2021.12.013.
Roy, B., Nunez, A., Aysola, R. S., Kang, D. W., Vacas, S.& Kumar, R. (2022). Impaired Glymphatic System Actions in Obstructive Sleep Apnea Adults. Frontiers in Neuroscience, 16(884234. http://dx.doi.org/10.3389/fnins.2022.884234.
Lee, D. A., Park, B. S., Ko, J., Park, S. H., Park, J. H., Kim, I. H., Lee, Y. J.& Park, K. M. (2022). Glymphatic system function in patients with newly diagnosed focal epilepsy. Brain Behav, 12(3), e2504. http://dx.doi.org/10.1002/brb3.2504.
Lee, D. A., Park, B. S., Ko, J., Park, S. H., Lee, Y. J., Kim, I. H., Park, J. H.& Park, K. M. (2022). Glymphatic system dysfunction in temporal lobe epilepsy patients with hippocampal sclerosis. Epilepsia Open, 7(2), 306-314. http://dx.doi.org/10.1002/epi4.12594.
Si, X., Guo, T., Wang, Z., Fang, Y., Gu, L., Cao, L., Yang, W., Gao, T., Song, Z., Tian, J., Yin, X., Guan, X., Zhou, C., Wu, J., Bai, X., Liu, X., Zhao, G., Zhang, M., Pu, J.& Zhang, B. (2022). Neuroimaging evidence of glymphatic system dysfunction in possible REM sleep behavior disorder and Parkinson's disease. NPJ Parkinsons Dis, 8(1), 54. http://dx.doi.org/10.1038/s41531-022-00316-9.
Wang, Z. M., Wei, P. H., Zhang, M., Wu, C., Shan, Y., Yeh, F. C., Shan, Y.& Lu, J. (2022). Diffusion spectrum imaging predicts hippocampal sclerosis in mesial temporal lobe epilepsy patients. Ann Clin Transl Neurol, 9(3), 242-252. http://dx.doi.org/10.1002/acn3.51503.
Taoka, T., Ito, R., Nakamichi, R., Kamagata, K., Sakai, M., Kawai, H., Nakane, T., Abe, T., Ichikawa, K., Kikuta, J., Aoki, S.& Naganawa, S. (2022). Reproducibility of diffusion tensor image analysis along the perivascular space (DTI-ALPS) for evaluating interstitial fluid diffusivity and glymphatic function: CHanges in Alps index on Multiple conditiON acquIsition eXperiment (CHAMONIX) study. Jpn J Radiol, 40(2), 147-158. http://dx.doi.org/10.1007/s11604-021-01187-5.
Qin, Y., Li, X., Qiao, Y., Zou, H., Qian, Y., Li, X., Zhu, Y., Huo, W., Wang, L.& Zhang, M. (2023). DTI-ALPS: An MR biomarker for motor dysfunction in patients with subacute ischemic stroke. Frontiers in Neuroscience, 17(1132393. http://dx.doi.org/10.3389/fnins.2023.1132393.
Lin, L. P., Su, S., Hou, W., Huang, L., Zhou, Q., Zou, M., Qian, L., Cui, W., Yang, Z., Tang, Y.& Chen, Y. (2023). Glymphatic system dysfunction in pediatric acute lymphoblastic leukemia without clinically diagnosed central nervous system infiltration: a novel DTI-ALPS method. European Radiology, 33(5), 3726-3734. http://dx.doi.org/10.1007/s00330-023-09473-8.
Yang, G., Deng, N., Liu, Y., Gu, Y.& Yao, X. (2020). Evaluation of Glymphatic System Using Diffusion MR Technique in T2DM Cases. Front Hum Neurosci, 14(300. http://dx.doi.org/10.3389/fnhum.2020.00300.
Huang, H., Ma, X., Yue, X., Kang, S., Li, Y., Rao, Y., Feng, Y., Wu, J., Long, W., Chen, Y., Lyu, W., Tan, X.& Qiu, S. (2023). White Matter Characteristics of Damage Along Fiber Tracts in Patients with Type 2 Diabetes Mellitus. Clin Neuroradiol, 33(2), 327-341. http://dx.doi.org/10.1007/s00062-022-01213-7.
Yau, P. L., Kim, M., Tirsi, A.& Convit, A. (2014). Retinal vessel alterations and cerebral white matter microstructural damage in obese adolescents with metabolic syndrome. JAMA Pediatr, 168(12), e142815. http://dx.doi.org/10.1001/jamapediatrics.2014.2815.
Jing, J., Zhou, Y., Pan, Y., Cai, X., Zhu, W., Zhang, Z., Li, Z., Liu, C., Meng, X., Cheng, J., Wang, Y., Li, H., Wu, Z., Wang, S., Niu, H., Wen, W., Liu, T., Wei, T., Wang, Y.& Sachdev, P. S. (2022). Reduced white matter microstructural integrity in prediabetes and diabetes: A population-based study. EBioMedicine, 82(104144. http://dx.doi.org/10.1016/j.ebiom.2022.104144.

No competing interests reported.

Download PDF

Version 1

posted

You are reading this latest preprint version

Exploring the Relationship Between Lymphatic System Damage and Insulin Resistance in T2DM Based on Vascular Periadventitial Tensor Analysis and Triglyceride-Glucose Index

Status:

Version 1

Abstract

Figures

1. Introduction

2. Materials and Methods

2.1 Study Design

2.2 Study Subjects

2.3 Magnetic Resonance Imaging Equipment and Examination Sequences

2.4 Perivascular Space Tensor Imaging

2.5 The ALPS index measurement method:

2.6 Calculation of TyG Index

2.7 Statistical Analysis

3. Results

3.1 Comparison of General Information and Laboratory Parameters

3.2 Comparison of ALPS Index and Fiber Diffusivity between the Two Groups

3.3 Correlation Analysis Between Fiber Diffusivity and TyG Index

3.4 Correlation Analysis Between ALPS Index and TyG Index

4. Discussion

5. Conclusions

Declarations

References

Additional Declarations

Supplementary Files

Status:

Version 1