Abstract
Background
The aim of the study was to explore whether plasma stromal cell-derived factor 1 (SDF-1) levels are associated with the EZSCAN score and its derived indicators in patients with type 2 diabetes (T2D).
Methods
From July 2020 to December 2020, a total of 253 patients with T2D were consecutively recruited. Serum SDF-1 levels were measured by sandwich ELISA. EZSCAN test was applied to evaluate the sudomotor function of each patient, and based on the results, EZSCAN score, cardiac autonomic neuropathy risk score (CANRS) and cardiovascular risk score (CVDRS) were calculated by particular algorithms. In addition, other relevant clinical data were also collected.
Results
With increasing tertiles of serum SDF-1 levels, the CANRS and CVDRS significantly increased (both Pfor trend <0.001), while the EZSCAN score significantly decreased (Pfor trend <0.001). Moreover, serum SDF-1 levels were significantly and positively correlated with the CANRS and CVDRS (r = 0.496 and 0.510, respectively, both P < 0.001), and negatively correlated with the EZSCAN score (r = −0.391, P < 0.001). Furthermore, multivariate linear regression analyses were constructed, and after adjusting for other clinical covariates, serum SDF-1 levels were independently responsible for EZSCAN score (β = −0.273, t = −3.679, P < 0.001), CANRS (β = 0.334, t = 5.110, P < 0.001) and CVDRS (β = 0.191, t = 4.983, P = 0.003).
Conclusions
SDF-1 levels in serum were independently associated with the EZSCAN score and its derived indicators, such as CANRS and CVDRS in patients with T2D.
Introduction
The rising prevalence of type 2 diabetes (T2D) has posed a great threat to public health, affecting approximately 400 million people in 2017 (1). Uncontrolled and untreated T2D may lead to a variety of chronic complications (2). In 2015, approximately 5.0 million deaths may be attributed to T2D and its complications (3). Compared with individuals without diabetes, those with T2D suffer a severe decrease in quality of life due to the heavy burden of treatment and the development of diabetic complications (4). Therefore, in patients with T2D, identifying the risk of developing complications and timely intervention is the focus of the management of T2D.
The EZSCAN test is noninvasive detection technology for the assessment of electrochemical skin conductance (ESC) by reverse iontophoresis and chronoamperometry, which is clinically useful for evaluating sudomotor function (5). Since sudomotor function is dominated by small sympathetic C fibers, the EZSCAN test is available for assessing autonomic neuropathy and small-fiber neuropathy in diabetes (6). Cardiac autonomic neuropathy risk score (CANRS), derived from ESC, age and BMI, can serve as a good screening tool for cardiac autonomic neuropathy (CAN) (7). In addition, the EZSCAN test also functions to evaluate the risks of peripheral artery disease (PAD) (8), diabetic kidney disease (DKD) (9) and diabetic retinopathy (DR) (10) in patients with diabetes. Therefore, the EZSCAN test can be applied to evaluating the risk of diabetic complications in patients with T2D.
Stromal cell-derived factor 1 (SDF-1) belonging to the CXC chemokine family is widely expressed in various tissues and regulates several aspects of stem cell function (11). Sustainable inflammation and chronic ischemia can induce SDF-1 production in systemic tissues through hypoxia-inducible factor 1 (HIF-1) (12). It has been confirmed that elevated SDF-1 levels can promote the onset and progression of T2D by inducing islet inflammation (13) and mediating insulin desensitization in adipocytes (14). Our previous study demonstrated that serum SDF-1 levels are closely related to hyperglycemia, hypercoagulability, and inflammation in patients with T2D (15). Since chronic complications of diabetes share a common pathogenesis (16), we speculated that serum SDF-1 levels may be closely related to CAN and cardiovascular disease (CVD) in patients with T2D. However, no studies have focused on this aspect.
Hence, the present study was designed to assess whether serum SDF-1 levels are related to the EZSCAN score and its derived indicators, such as CANRS and CVD risk score (CVDRS).
Methods
Study design and participants
From July 2020 to December 2020, this observational cross-sectional study was conducted on patients with T2D at the inpatient department of the Second Affiliated Hospital of Nantong University. The diagnostic criteria for T2D were based on the statement of the American Diabetes Association in 2011 (17). The exclusion criteria were as follows: (1) type 1 diabetes (T1D); (2) foot deformity; (3) a previous history of uses of medications such as steroids; (4) previous and current malignant tumors; (5) chronic heart and renal failure; (6) acute diabetic complications, such as diabetic ketoacidosis, hyperosmolar hyperglycemic state and hypoglycemic coma; (7) complications with infection or autoimmune diseases. This study completely adhered to the Declaration of Helsinki, and written informed consent was provided by all participants. The medical research ethics committee of the Second Affiliated Hospital of Nantong University fully evaluated and approved the study protocol. Ultimately, 253 patients with T2D were enrolled in the present study.
Basic data collection
Information on demographics, including age, sex, height, weight, smoking history, drinking history, duration of diabetes, PAD, DKD, DR, antihyperglycemia therapy and use of lipid-lowering agents, was collected using self-report questionnaires with the assistance of an experienced clinician.
Assessment of serum SDF-1 level
Fasting blood samples were collected the morning after enrollment for the measurement of laboratory parameters and serum SDF-1 levels. After fully centrifuged, all blood samples were stored at –80°C. Sandwich ELISA kits purchased from Elabscience (Wuhan, China) were used to measure serum SDF-1 levels.
EZSACAN test
During the test, patients placed their hands, feet and foreheads on nickel electrodes, as the skin in these regions is rich in sweat glands. Then, an incremental low direct voltage (less than 4 V) was applied to the electrodes, and the ESC was calculated as the ratio of the resulting voltage and the generated current. Through a proprietary algorithm, an EZSCAN score was calculated based on the ESC measured on hands and feet. The CANRS and CVDRS were calculated with algorithms controlling for demographic data, including age, BMI, HbA1c and gender, and had a range from 0 to 100.
Laboratory examination and calculation
An automated biochemical analyzer (Model 7600, Hitachi) was used to detect lipid profiles, blood urea nitrogen, creatinine, cystatin C and uric acid (UA) levels. An ion exchange-based HPLC method in a hemoglobin analysis system (D-10, Bio-Rad) was adopted to measure glycosylated hemoglobin (HbA1c) levels. The estimated glomerular filtration rate (eGFR) was calculated based on the Chronic Kidney Disease Epidemiology Collaboration creatinine-cystatin C equation (2012) (18).
Statistical analyses
Normally distributed continuous variables are presented as the mean ± s.d., and skewed distributed continuous variables are presented as the median (25 and 75% interquartile). Categorical variables are described as frequencies (percentages). The total participants were divided according to the SDF-1 tertiles, and the clinical variables of each subgroup are presented. To compare differences in normally distributed data, skewed data and categorical data among the three subgroups, one-way ANOVA, the Kruskal–Wallis test, and the chi-square test were conducted. Pearson’s bivariate correlation analysis was constructed to explore the correlations of SDF-1 levels with the CANRS and CVDRS. Multiple linear regression analysis was performed to investigate the independent effects of SDF-1 levels on the CANRS and CVDRS. SPSS statistical software 18.0 (IBM SPSS Inc.) was used to data analyses. A value of P < 0.05 was identified as statistical significance.
Results
Basic characteristics
The clinical characteristics of the participants were presented in Table 1. 253 patients with T2D were recruited in the present study, and the range of SDF-1 levels was 0.54–9.29 pg/mL. From the first to third tertiles of SDF-1 levels, age, diabetes duration, the prevalence of PAD and DKD, use of insulin and insulin sensitizing agents, HbA1c level, CANRS and CVDRS significantly increased (P for trend <0.05), eGFR and EZSCAN score decreased (both P for trend <0.001). However, among the tertiles of SDF-1 levels, there were no significant differences in the gender distribution, smoking and drinking history, the prevalence of DR, blood pressure, other antidiabetic treatment, use of statin medications, lipid profile or UA level (P for trend >0.05).
Clinical characteristics of the study participants. Normally distributed values in the table are given as the mean ± s.d., skewed distributed values are given as the median (25 and 75% interquartiles), and categorical variables are given as frequency (percentage).
| Variable | Total | T1 | T2 | T3 | P for trend |
|---|---|---|---|---|---|
| SDF-1 (ng/mL) | 4.46 ± 1.49 | <3.91 | 3.91-4.94 | >4.94 | |
| n | 253 | 84 | 86 | 83 | |
| Age (years) | 56.0 (47.0–64.0) | 49.0 (37.3–55.0) | 57.0 (50.0–65.0) | 61.0 (54.0–68.0) | <0.001 |
| Male, n (%) | 172 (68.0) | 60 (71.4) | 56 (65.1) | 56 (67.5) | 0.392 |
| Diabetic duration (years) | 5.0 (1.0–10.0) | 4.0 (0.0–8.0) | 7.5 (1.0–10.0) | 8.5 (1.8–13.3) | <0.001 |
| BMI (kg/m2) | 25.48 ± 3.98 | 26.03 ± 3.82 | 25.51 ± 3.63 | 24.89 ± 4.44 | 0.191 |
| SBP (mmHg) | 133.0 (124.0–148.0) | 131.5 (120.3–143.5) | 132.5 (123.8–150.0) | 136.0 (126.0–152.0) | 0.089 |
| DBP (mmHg) | 82.43 ± 10.79 | 82.51 ± 10.72 | 82.33 ± 9.67 | 82.46 ± 12.01 | 0.993 |
| Smoking history, n (%) | 34 (13.4) | 10 (11.9) | 13 (15.1) | 11 (13.3) | 0.827 |
| Drinking history, n (%) | 29 (11.5) | 13 (15.5) | 5 (5.8) | 11 (13.3) | 0.117 |
| Antidiabetic treatments | |||||
| Insulin treatment, n (%) | 59 (23.3) | 12 (14.3) | 18 (20.9) | 29 (34.9) | 0.006 |
| Metformin, n (%) | 111 (43.9) | 40(47.6) | 40 (46.5) | 31 (37.3) | 0.340 |
| Acarbose, n (%) | 17 (6.7) | 5 (6.0) | 8 (9.3) | 4 (4.8) | 0.479 |
| Insulin-secretagogues, n (%) | 78 (30.8) | 20 (23.8) | 29 (33.7) | 29 (34.9) | 0.230 |
| Insulin-sensitisers, n (%) | 43 (17.0) | 5 (6.0) | 9 (10.5) | 29 (34.9) | <0.001 |
| DPP-4 inhibitors, n (%) | 11 (4.3) | 4 (4.8) | 3 (3.5) | 4 (4.8) | 0.886 |
| Statins medications, n (%) | 6 (2.4) | 2 (2.4) | 2 (2.3) | 2 (2.4) | 0.999 |
| HbA1c (%) | 9.51 ± 2.25 | 9.01 ± 2.05 | 9.29 ± 2.12 | 10.23 ± 2.41 | 0.001 |
| TG (mmol/L) | 1.69 (1.04–3.19) | 1.69 (1.17–2.98) | 1.69 (0.99–3.49) | 1.69 (1.00–3.79) | 0.954 |
| TC (mmol/L) | 4.52 ± 1.26 | 4.77 ± 1.31 | 4.41 ± 1.41 | 4.38 ± 0.98 | 0.082 |
| HDL-c (mmol/L) | 1.11 ± 0.24 | 1.11 ± 0.21 | 1.09 ± 0.22 | 1.13 ± 0.27 | 0.499 |
| LDL-c (mmol/L) | 2.75 ± 0.93 | 2.87 ± 0.93 | 2.63 ± 0.99 | 2.76 ± 0.86 | 0.236 |
| UA (μmol/L) | 334.02 ± 108.62 | 343.85 ± 114.28 | 321.48 ± 105.14 | 337.06 ± 106.33 | 0.388 |
| eGFR (mL/min/1.73m2) | 104.35 ± 28.48 | 123.52 ± 27.48 | 101.32 ± 17.90 | 89.08 ± 28.24 | <0.001 |
| PAD, n (%) | 121 (47.8) | 31 (36.9) | 42 (48.8) | 48 (57.8) | 0.025 |
| DKD, n (%) | 43 (17.0) | 5 (6.0) | 9 (10.5) | 29 (34.9) | <0.001 |
| DR, n (%) | 18 (7.1) | 3 (3.6) | 7 (8.1) | 8 (9.6) | 0.282 |
| EZSCAN score (%) | 40.10 ± 12.85 | 45.60 ± 10.81 | 39.72 ± 11.95 | 35.06 ± 13.56 | <0.001 |
| CANRS (%) | 59.20 ± 24.79 | 43.57 ± 21.87 | 61.71 ± 22.97 | 72.42 ± 20.61 | <0.001 |
| CVDRS (%) | 42.66 ± 15.50 | 33.36 ± 13.05 | 42.70 ± 14.59 | 52.05 ± 12.93 | <0.001 |
CANRS, cardiac autonomic neuropathy risk score; CVDRS, cardiovascular disease risk score; DR, diabetic retinopathy; DKD, diabetic kidney disease; DPP-4 inhibitors, dipeptidyl peptidase-4 inhibitors; eGFR, estimated glomerular filtration rate; HbA1c, glycosylated hemoglobin A1c; insulin-secretagogues, insulin secretagogues; insulin-sensitisers, insulin sensitizing agents; PAD, peripheral arterial disease; SBP/DBP, systolic/diastolic blood pressure; SDF-1, stromal cell-derived factor 1; TC, total cholesterol; TG, triglyceride.
Association of serum SDF-1 levels with CANRS and CVDRS
We constructed Pearson’s bivariate correlation analysis to explore the association of serum SDF-1 levels with EZSCAN score, CANRS and CVDRS. As illustrated in Figs 1, 2 and 3, serum SDF-1 levels were negatively associated with EZSCAN score (r = −0.391, P < 0.001), and positively associated with CANRS (r = 0.496, P < 0.001) and CVDRS (r = 0.510, P < 0.001).
The relationship between SDF-1 and EZSCAN score in patients with T2D.
Citation: Endocrine Connections 11, 4; 10.1530/EC-21-0629
The relationship between SDF-1 and CANRS in patients with T2D.
Citation: Endocrine Connections 11, 4; 10.1530/EC-21-0629
The relationship between SDF-1 and CVDRS in patients with T2D.
Citation: Endocrine Connections 11, 4; 10.1530/EC-21-0629
Multiple linear regression models displayed the effects of SDF-1 on EZSCAN score, CANRS and CVDRS
The associations between SDF-1 and EZSCAN score, CANRS and CVDRS evaluated by multiple linear regression analyses are shown in Table 2. In the basal unadjusted Model 0, SDF-1 was significantly associated with EZSCAN score (β= −0.391, t = −6.657, P < 0.001, adjusted R 2 = 0.153), CANRS (β= 0.496, t = 9.044, P < 0.001, adjusted R 2 = 0.246) and CVDRS (β = 0.510, t = 9.403, P < 0.001, adjusted R 2 = 0.260). The adjusted R 2 gradually increased as the other clinical covariates were gradually added to each model. In Model 3, SDF-1 was still independently associated with EZSCAN score (β= −0.273, t = −3.679, P < 0.001, adjusted R 2 = 0.268), CANRS (β= 0.334, t = 5.110, P < 0.001, adjusted R 2 = 0.439) and CVDRS (β= 0.191, t = 4.983, P < 0.01, adjusted R 2 = 0.810).
Multiple linear regression models displaying adjusted estimates for SDF-1 for EZSCAN score, CANRS and CVDRS adjusted for the other clinical covariates in each model in patients with T2D. Model 0: unadjusted model; Model 1: adjusted for age, male, diabetic duration, BMI, smoking history, drinking history; Model 2: additionally adjusted for SBP, DBP, TG, TC, HDL-c, LDL-c, UA, eGFR; Model 3: additionally adjusted for HbA1c, antidiabetic treatments, statins medications.
| Models | B (95% CI) | β | t | P | R2 for model |
|---|---|---|---|---|---|
| EZSCAN score | |||||
| Model 0 | −3.366 (−4.362 to −2.370) | −0.391 | −6.657 | <0.001 | 0.153 |
| Model 1 | −2.187 (−3.258 to −1.116) | −0.254 | −4.022 | <0.001 | 0.226 |
| Model 2 | −2.305 (−3.494 to −1.117) | −0.262 | −3.824 | <0.001 | 0.254 |
| Model 3 | −2.437 (−3.743 to −1.131) | −0.273 | −3.679 | <0.001 | 0.268 |
| CANRS | |||||
| Model 0 | 8.227 (6.435 to 10.018) | 0.496 | 9.044 | <0.001 | 0.246 |
| Model 1 | 5.034 (3.221 to 6.847) | 0.301 | 5.470 | <0.001 | 0.400 |
| Model 2 | 5.692 (3.678 to 7.707) | 0.330 | 5.569 | <0.001 | 0.432 |
| Model 3 | 5.770 (3.544 to 7.996) | 0.334 | 5.110 | <0.001 | 0.439 |
| CVDRS | |||||
| Model 0 | 5.294 (4.185 to 6.402) | 0.510 | 9.403 | <0.001 | 0.260 |
| Model 1 | 3.695 (2.617 to 4.773) | 0.355 | 6.753 | <0.001 | 0.454 |
| Model 2 | 4.449 (3.320 to 5.579) | 0.416 | 7.762 | <0.001 | 0.530 |
| Model 3 | 2.040 (1.233 to 2.846) | 0.191 | 4.983 | 0.003 | 0.810 |
Discussion
The current study revealed the association between serum SDF-1 levels and the EZSCAN score and its derived indicators, such as CANRS and CVDRS in patients with T2D. The main findings are: first, across the ascending tertiles of serum SDF-1 levels in patients with T2D, CANRS and CVDRS significantly increased, and EZSCAN score significantly decreased; second, bivariate correlation analysis showed that serum SDF-1 levels were positively associated with CANRS and CVDRS, and negatively associated with EZSCAN score; third, after adjusting for other possible factors, serum SDF-1 levels were significantly and positively associated with CANRS and CVDRS, while negatively associated with EZSCAN score.
Eccrine glands mediating sweat responses are simple tubular glands with a rich supply of blood vessels and are innervated by sympathetic fibers (19). A review published in 2019 compared different tests for the detection of sudomotor dysfunction and found that ESC results achieved by the EZSCAN test might be more sensitive, less expensive, and easier to operate than other tests (20). A study that enrolled 83 patients with diabetes found that ESC results were significantly correlated with autonomic function testing assessed by quantitative autonomic function testing (QAFT) (21). Sympathetic overactivity possibly promotes vascular muscle growth and arterial wall fibrosis by regulating the renin-angiotensin-aldosterone system and other mechanisms. These conditions eventually lead to arterial stiffness (22), which is an independent predictor of CVD. Zeng et al. found a strong correlation between the EZSCAN scores and arterial stiffness in non-T2D and non-CVD participants (23). Moreover, Yajnik et al. revealed that compared with Ewing tests, CANRS derived from ESC was suitable for early screening of CAN in patients with diabetes (24). Similarly, in a study involving 90 patients with T2D, the combination of ESC and heart rate variability was capable of accurately diagnosing and assessing CAN (25). However, no association between ESC and CAN was observed in patients with T1D (26). This discrepancy may be attributed to the different populations enrolled and the different pathogenesis of T1D and T2D. In a word, it is plausible that the CANRS and CVDRS calculated based on the ESC results reflect the risks of developing CAN and CVD in patients with T2D. In this study, we found that serum SDF-1 levels were independently associated with the EZSCAN score, CANRS and CVDRS.
CVD with atherosclerosis as the main pathological feature is the primary cause of mortality among patients with diabetes (3). SDF-1 is not expressed in normal vessels but is abundantly expressed in smooth muscle cells, endothelial cells and macrophages at human atherosclerotic plaques (27). The key to atherosclerotic plaque formation is that macrophages phagocytose oxidized low-density lipoprotein (oxLDL) to form foam cells, and SDF-1 can accelerate this process (28). As an inflammatory chemokine, SDF-1 can induce the expression of tumor necrosis factor-α (TNF-α), leading to the apoptosis of cardiomyocytes (29). In addition, SDF-1 can aggravate cardiac remodeling and worsen cardiac function by enhancing the proliferation of cardiac fibroblasts and collagen production (30). Correspondingly, plasma SDF-1 levels can serve as a predictor of myocardial infarction (MI) in patients with chronic kidney disease (CKD) (31). Thus, increased SDF-1 in serum may be closely related to CVD in patients with T2D.
SDF-1 is constitutively expressed in astrocytes, microglia and neurons (32), which proposes a basis for the close correlation between serum SDF-1 levels and CANRS in patients with T2D. SDF-1 may contribute to diabetes-induced pain by resulting in the activation of microglia, which in turn derives pronociceptive cytokines (33). Additionally, SDF-1 can induce the apoptosis of astrocytes and neurons by inducing the production of TNF-α and aggravating neurologic impairment in patients with T2D (34). In addition, SDF-1 can act as a regulatory neuropeptide that regulates both central cholinergic and dopaminergic systems in the brains of adult rats (35). Under pathological conditions, increased SDF-1 can activate the sympathetic nervous system, possibly contributing to autonomic dysfunction in patients with T2D (36). Hence, serum SDF-1 levels may be closely associated with CAN in patients with T2D.
However, SDF-1 also promotes the mobilization and migration of endothelial progenitor cells (EPCs), so a number of studies have reported that elevated SDF-1 levels may have a protective effect in vivo. There are several possible explanations for the discrepancies between these studies and our study. First, EPCs isolated from patients with diabetes are deficient in migration function in response to SDF-1, so elevated SDF-1 levels under diabetic conditions may fail to play a protective role by attracting EPCs (37). Second, due to the rapid degradation of SDF-1, serum SDF-1 levels do not reflect the local concentration of SDF-1 (27). Third, different concentrations of SDF-1 may have completely opposite effects (27).
Some limitations to our study should be addressed. First, as a cross-sectional study, the present study cannot reach a cause-effect relationship between serum SDF-1 levels and the risk indices of CAN and CVD in patients with T2D. Secondly, the EZSCAN test is not the gold standard for screening and evaluating CAN and CVD. However, the accuracy of the EZSCAN test has been well validated, so this is not a major concern. Thirdly, the present study was conducted in a Chinese population with T2D, which might limit the generalizability of our study. Therefore, further research should be conducted to validate the results of our study and to address the above limitations.
Conclusions
In summary, serum SDF-1 levels were independently related to EZSCAN score, CANRS and CVDRS evaluated by EZSCAN test in patients with T2D.
Declaration of interest
The authors declare that there is no conflict of interest that could be perceived as prejudicing the impartiality of the research reported.
Funding
The study was supported by the Medical Research Project of Health Commission of Nantong (MB2020012) and the Science and Technology Support Program of Nantong (JC2021118, HS2020005).
Ethics approval and consent to participate
The study was approved by the institutional review board of Affiliated Hospital 2 of Nantong University and First People’s Hospital of Nantong City, and written informed consent was obtained from all participants.
Availability of data and materials
The current data are available to all interested researchers upon reasonable request. Requests for access to data should be made to the principal investigators of the study.
Author contribution statement
W L, L H and F X participated in the design of the study, data collection, analysis of the data and drafting of the manuscript. J S, C L and X W conceived of the study, participated in its design and revised the manuscript. W L, S Z and F Q participated in the analysis of the data and revised the manuscript. W L and S Z participated in data collection. All authors read and approved the final manuscript.
References
- 1↑
Pham TB, Nguyen TT, Truong HT, Trinh CH, Du HNT, Ngo TT, Nguyen LH. Effects of diabetic complications on health-related quality of life impairment in Vietnamese patients with type 2 diabetes. Journal of Diabetes Research 2020 2020 4360804. (https://doi.org/10.1155/2020/4360804)
- 2↑
Chatterjee S, Khunti K, Davies MJ. Type 2 diabetes. Lancet 2017 389 2239–2251. (https://doi.org/10.1016/S0140-6736(1730058-2)
- 3↑
Zheng Y, Ley SH, Hu FB. Global aetiology and epidemiology of type 2 diabetes mellitus and its complications. Nature Reviews: Endocrinology 2018 14 88–98. (https://doi.org/10.1038/nrendo.2017.151)
- 4↑
Wong CK, Lo YY, Wong WH, Fung CS. The associations of body mass index with physical and mental aspects of health-related quality of life in Chinese patients with type 2 diabetes mellitus: results from a cross-sectional survey. Health and Quality of Life Outcomes 2013 11 142. (https://doi.org/10.1186/1477-7525-11-142)
- 5↑
Mayaudon H, Miloche PO, Bauduceau B. A new simple method for assessing sudomotor function: relevance in type 2 diabetes. Diabetes and Metabolism 2010 36 450–454. (https://doi.org/10.1016/j.diabet.2010.05.004)
- 6↑
Zeng Q, Dong SY, Wang ML, Wang F, Li JM, Zhao XL. Cardiac autonomic neuropathy risk estimated by sudomotor function and arterial stiffness in Chinese subjects. Journal of Human Hypertension 2016 30 720–725. (https://doi.org/10.1038/jhh.2015.126)
- 7↑
Yuan T, Li J, Fu Y, Xu T, Li J, Wang X, Zhou Y, Dong Y, Zhao W. A cardiac risk score based on sudomotor function to evaluate cardiovascular autonomic neuropathy in asymptomatic Chinese patients with diabetes mellitus. PLoS ONE 2018 13 e0204804. (https://doi.org/10.1371/journal.pone.0204804)
- 8↑
Sun J, Zhang Y, Xu B, Lv X, Ding L, Chen Y, Sun W, Lu J, Xu M & Bi Y et al. Autonomic dysfunction assessed by EZSCAN and subclinical atherosclerosis. Journal of Diabetes 2014 6 409–416. (https://doi.org/10.1111/1753-0407.12135)
- 9↑
Luk AO, Fu WC, Li X, Ozaki R, Chung HH, Wong RY, So WY, Chow FC, Chan JC. The clinical utility of SUDOSCAN in chronic kidney disease in Chinese patients with type 2 diabetes. PLoS ONE 2015 10 e0134981. (https://doi.org/10.1371/journal.pone.0134981)
- 10↑
Eranki VG, Santosh R, Rajitha K, Pillai A, Sowmya P, Dupin J, Calvet JH. Sudomotor function assessment as a screening tool for microvascular complications in type 2 diabetes. Diabetes Research and Clinical Practice 2013 101 e11–e13. (https://doi.org/10.1016/j.diabres.2013.07.003)
- 11↑
Kim KY, Lee HK, Kim H, Kim Y, Kim Y, Choi HH, Kim SW, Kim HK, Chae HS. Stromal cell-derived factor-1 as a serologic biomarker for the diagnosis of colon ischemia with chronic cardiovascular disease. Medicine 2020 99 e20539. (https://doi.org/10.1097/MD.0000000000020539)
- 12↑
Uematsu M, Nakamura T, Yoshizaki T, Watanabe Y, Deyama J, Watanabe K, Kobayashi T, Fujioka D, Saito Y & Nakamura K et al. High levels of stromal cell-derived factor-1α predict short-term progression of renal dysfunction in patients with coronary artery disease. Clinical and Experimental Nephrology 2019 23 920–927. (https://doi.org/10.1007/s10157-019-01722-9)
- 13↑
Derakhshan R, Arababadi MK, Ahmadi Z, Karimabad MN, Salehabadi VA, Abedinzadeh M, Khorramdelazad H, Balaei P, Kennedy D, Hassanshahi G. Increased circulating levels of SDF-1 (CXCL12) in type 2 diabetic patients are correlated to disease state but are unrelated to polymorphism of the SDF-1β gene in the Iranian population. Inflammation 2012 35 900–904. (https://doi.org/10.1007/s10753-011-9391-8)
- 14↑
Shin J, Fukuhara A, Onodera T, Kita S, Yokoyama C, Otsuki M, Shimomura I. SDF-1 is an autocrine insulin-desensitizing factor in adipocytes. Diabetes 2018 67 1068–1078. (https://doi.org/10.2337/db17-0706)
- 15↑
Lu CF, Ma JH, Su JB, Wang XQ, Liu WS, Ge XQ. XQSerum stromal cell-derived factor-1 levels are associated with diabetic kidney disease in type 2 diabetic patients. Endocrine Journal 2021 68 1101–1107. (https://doi.org/10.1507/endocrj.EJ21-0039)
- 16↑
Forbes JM, Cooper ME. Mechanisms of diabetic complications. Physiological Reviews 2013 93 137–188. (https://doi.org/10.1152/physrev.00045.2011)
- 17↑
American Diabetes Association. Diagnosis and classification of diabetes mellitus. Diabetes Care 2013 36 (Supplement 1) S67–S74. (https://doi.org/10.2337/dc13-S067)
- 18↑
Inker LA, Schmid CH, Tighiouart H, Eckfeldt JH, Feldman HI, Greene T, Kusek JW, Manzi J, Van Lente F & Zhang YL et al. Estimating glomerular filtration rate from serum creatinine and cystatin C. New England Journal of Medicine 2012 367 20–29. (https://doi.org/10.1056/NEJMoa1114248)
- 19↑
Ozaki R, Cheung KK, Wu E, Kong A, Yang X, Lau E, Brunswick P, Calvet JH, Deslypere JP, Chan JC. A new tool to detect kidney disease in Chinese type 2 diabetes patients: comparison of EZSCAN with standard screening methods. Diabetes Technology and Therapeutics 2011 13 937–943. (https://doi.org/10.1089/dia.2011.0023)
- 20↑
Novak P Electrochemical skin conductance: a systematic review. Clinical Autonomic Research 2019 29 17–29. (https://doi.org/10.1007/s10286-017-0467-x)
- 21↑
Casellini CM, Parson HK, Richardson MS, Nevoret ML, Vinik AI. Sudoscan, a noninvasive tool for detecting diabetic small fiber neuropathy and autonomic dysfunction. Diabetes Technology and Therapeutics 2013 15 948–953. (https://doi.org/10.1089/dia.2013.0129)
- 22↑
Fink GD Arthur C. Corcoran Memorial Lecture. Sympathetic activity, vascular capacitance, and long-term regulation of arterial pressure. Hypertension 2009 53 307–312. (https://doi.org/10.1161/HYPERTENSIONAHA.108.119990)
- 23↑
Zeng Q, Dong SY, Wang ML, Xiang H, Zhao XL. Association of EZSCAN values with arterial stiffness in individuals without diabetes or cardiovascular disease. PLoS ONE 2014 9 e90854. (https://doi.org/10.1371/journal.pone.0090854)
- 24↑
Yajnik CS, Kantikar V, Pande A, Deslypere JP, Dupin J, Calvet JH, Bauduceau B. Screening of cardiovascular autonomic neuropathy in patients with diabetes using non-invasive quick and simple assessment of sudomotor function. Diabetes and Metabolism 2013 39 126–131. (https://doi.org/10.1016/j.diabet.2012.09.004)
- 25↑
Lai YR, Huang CC, Cheng BC, Tsai NW, Chiu WC, Chang HW, Chen JF, Lu CH. Feasibility of combining heart rate variability and electrochemical skin conductance as screening and severity evaluation of cardiovascular autonomic neuropathy in type 2 diabetes. Journal of Diabetes Investigation 2021 12 1671–1679. (https://doi.org/10.1111/jdi.13518)
- 26↑
Ang L, Jaiswal M, Callaghan B, Raffel D, B. Brown M, Pop-Busui R. Sudomotor dysfunction as a measure of small fiber neuropathy in type 1 diabetes. Autonomic Neuroscience 2017 205 87–92. (https://doi.org/10.1016/j.autneu.2017.03.001)
- 27↑
Abi-Younes S, Sauty A, Mach F, Sukhova GK, Libby P, Luster AD. The stromal cell-derived factor-1 chemokine is a potent platelet agonist highly expressed in atherosclerotic plaques. Circulation Research 2000 86 131–138. (https://doi.org/10.1161/01.res.86.2.131)
- 28↑
Bakogiannis C, Sachse M, Stamatelopoulos K, Stellos K. Platelet-derived chemokines in inflammation and atherosclerosis. Cytokine 2019 122 154157. (https://doi.org/10.1016/j.cyto.2017.09.013)
- 29↑
Jarrah AA, Schwarskopf M, Wang ER, LaRocca T, Dhume A, Zhang S, Hadri L, Hajjar RJ, Schecter AD, Tarzami ST. SDF-1 induces TNF-mediated apoptosis in cardiac myocytes. Apoptosis 2018 23 79–91. (https://doi.org/10.1007/s10495-017-1438-3)
- 30↑
Peiró ÓM, Farré N, Cediel G, Bonet G, Rojas S, Quintern V, Bardají A. Stromal cell derived factor-1 and long-term prognosis in acute coronary syndrome. Biomarkers in Medicine 2019 13 1187–1198. (https://doi.org/10.2217/bmm-2019-0133)
- 31↑
Mehta NN, Matthews GJ, Krishnamoorthy P, Shah R, McLaughlin C, Patel P, Budoff M, Chen J, Wolman M & Go A et al. Higher plasma CXCL12 levels predict incident myocardial infarction and death in chronic kidney disease: findings from the chronic renal insufficiency Cohort study. European Heart Journal 2014 35 2115–2122. (https://doi.org/10.1093/eurheartj/eht481)
- 32↑
Zychowska M, Rojewska E, Pilat D, Mika J. The role of some chemokines from the CXC subfamily in a mouse model of diabetic neuropathy. Journal of Diabetes Research 2015 2015 750182. (https://doi.org/10.1155/2015/750182)
- 33↑
Zychowska M, Rojewska E, Kreiner G, Nalepa I, Przewlocka B, Mika J. Minocycline influences the anti-inflammatory interleukins and enhances the effectiveness of morphine under mice diabetic neuropathy. Journal of Neuroimmunology 2013 262 35–45. (https://doi.org/10.1016/j.jneuroim.2013.06.005)
- 34↑
Han Y, He T, Huang DR, Pardo CA, Ransohoff RM. TNF-alpha mediates SDF-1 alpha-induced NF-kappa B activation and cytotoxic effects in primary astrocytes. Journal of Clinical Investigation 2001 108 425–435. (https://doi.org/10.1172/JCI12629)
- 35↑
Banisadr G, Skrzydelski D, Kitabgi P, Rostène W, Parsadaniantz SM. Highly regionalized distribution of stromal cell-derived factor-1/CXCL12 in adult rat brain: constitutive expression in cholinergic, dopaminergic and vasopressinergic neurons. European Journal of Neuroscience 2003 18 1593–1606. (https://doi.org/10.1046/j.1460-9568.2003.02893.x)
- 36↑
Liu K, Yang S, Hou M, Chen T, Liu J, Yu B. Increase of circulating stromal cell-derived factor-1 in heart failure patients. Herz 2015 40 (Supplement 1) 70–75. (https://doi.org/10.1007/s00059-014-4169-z)
- 37↑
Karimabad MN, Hassanshahi G. Significance of CXCL12 in type 2 diabetes mellitus and its associated complications. Inflammation 2015 38 710–717. (https://doi.org/10.1007/s10753-014-9981-3)
