Skin Autouorescence Prole in Tunisian Subjects with and Without Metabolic Disorders

Background: The AGE Reader, as a clinical tool for non invasive assessment, measures the accumulation of advanced glycation end products (AGEs) in skin tissues shown as skin autouorescence (SAF). AGEs Accumulation has been implicated in several diseases. There is no data about SAF prole in Tunisian population, this study aimed to assess rstly SAF prole in subjects with and without metabolic disorders and secondly to examine the association between SAF and various clinical parameters. Methods: In a cross-sectional study, we included 220 participants between 19 and 86 years of age who were subdivided in two groups: a healthy group (n=93) and patients group with metabolic disorders (n=127) contains three subgroups as following: diabetic patients (n=32), hypertensive patients (n=54) and patients with both diabetes and hypertension (n=41). Skin AGEs accumulation was measured by AGE Reader and clinical data was obtained. Results: SAF was signicantly higher in patients group compared to healthy group [2.40 AU (2.10-2.60) vs. 2.00 AU (1.70-2.10) respectively; P <0.001]. Diabetic patients and hypertensive patients showed an increased level of SAF. The highest of SAF was observed in patient with both coexistence of diabetes, hypertension and dyslipidemia. SAF was associated with age, gender, BMI, duration of diabetes, HbA1c, triglyceride and obesity. Multivariate analysis showed that age and duration of diabetes were independent determinant of SAF. The ROC analysis indicated that an SAF > 2.25 AU was optimal cut-off point to predict the presence of metabolic disorders (P <0.001). Conclusion: SAF was increased in patients with diabetes and/or with hypertension and dyslipidemia. AGE Reader device is a rapid and helpful tool in clinical practice for evaluating and screening metabolic disorders in undiagnosed subjects. Linear regression analysis was performed to determine predictors of SAF. The significant variables in the univariate analysis were selected for the multivariate regression analysis. NS: not significant.


Introduction
Diabetes Mellitus and dyslipidemia as known as metabolic disorders explained by glucose and lipid metabolic abnormalities. Hypertension is related to impaired metabolic homeostasis and can be regarded as a metabolic disorder [1,2]. The numbers of subjects suffering for metabolic disorders are constantly increasing in Tunisia [3][4][5]. Type 2 diabetes, a silent disease, induces long-term vascular complications and is diagnosed too late [6,7]. Diabetic patients which are under treatment do not prevent to develop microvascular complications (retinopathy, nephropathy and neuropathy) and macrovascular complications (cerebrovascular disease, cardiovascular disease and peripheral vascular disease) which that contribute to the increased risk of morbidity and mortality [8]. It is well known that hyperglycemia induces glycation and oxidative pathways directly or indirectly on proteins, lipids and nucleic acids [9,10]. Glucose and glycated hemoglobin (HbA1c) levels are the gold reference biomarkers for detecting diabetes but these parameters are usually uctuated in the blood and do not re ect the pro le in tissues for diabetic patients with or without vascular complications. Currently, several studies have shown the role of advanced glycation end products (AGEs) accumulation in tissues as biomarker for early detection, screening and diagnosis of many diseases [11,12].
AGEs are a complex and heterogeneous group of non-uorescent and uorescent compounds becomes irreversibly cross-linked adducts which bind to amino acids (lysine/arginine residues) of proteins, and accumulate on proteins a slow turnover [13]. AGEs are formed by a non enzymatic glycation and oxidation during aging and are much rise in hyperglycemia conditions. Many different AGEs have been identi ed, such as, pentosidine, crossline, methyl-glyoxal-lysine dimmer (MOLD), glyoxal-lysine dimmer (GOLD) as uorescent AGEs, and N-carboxymethyl-lysine (CML), methyl-imidazolone, argpyrimidine, glucosepane, pyrriline as a non-uorescent AGEs [9,10].
We have shown previously that plasma AGEs has been implicated in diabetes with and without vascular complications in Tunisian patients [14][15][16]. previous studies have shown that skin AGEs accumulation was measured non invasively by AGE Reader device as a simple tool to identify those at risk for diabetes and those at risk for diabetes complications [17,18]. There is no study about skin AGEs accumulation in Tunisian subjects with and without metabolic disorders. This cross-sectional study, we have measured SAF in healthy subjects and in patients, and we have examined the relationship between the SAF and various clinical parameters.

Study population
For the current study, we have included 220 participants from Monastir region (Tunisia) who provided written informed consent. The study protocol was approved by the medical ethical review committee of the hospital Fattouma Bourguiba at Monastir. All participants are subdivided in two groups: one group for healthy control subjects between 19 and 73 years old (n=93), the second group for patients between 36 and 86 years old (n=127) without history for cardiovascular or kidney diseases and contains three subgroups as following: diabetic patients (n=32), hypertensive patients (n=54) and patients with both diabetes and hypertension (n=41). Diabetes is de ned as fasting blood glucose ≥ 7.0 mmol/L or HbA1c ≥ 6.5% and/or use of anti-diabetes medication Hypertension is de ned as systolic blood pressure ≥ 130 mmHg and/or diastolic blood pressure ≥ 85 mmHg and/or use of anti-hypertensive medication.
Dyslipidemia is de ned as hypercholesterolemia and/or hypertriglyceridemia and/or use of lipid lowering medication. Obesity is de ned as BMI ≥ 30 kg/m 2 . The three subgroup of patient are with or without dyslipidemia.

Clinical data collection
For each subject we have collected the following data: Age, sex, body mass index (BMI), duration of diabetes, diabetes, hypertension and dyslipidemia.

Skin auto uorescence measurement
For all participants, SAF was measured by AGE Reader device (Diagnotics, Groningen, Netherlands) and the method has been described previously [19,20]. The device, a non-invasive tool, evaluates AGEs in skin using the principle that several AGEs emit auto uorescence when excited by UV light. Excitation light source which wavelength is between 300 and 420 nm and the peak intensity is at 3 70 nm. This light source is projected approximately onto 4 cm 2 on the volar side of the forearm skin surface, and the intensity of any light (420-600 nm) emitted is measured with an internal spectrometer.
SAF was expressed in arbitrary units (AU) and was calculated from the mean value of the emitted light intensity divided by the excitation light intensity and multiplying by 100. The patients with Fitzpatrick phototypes V and VI were not included due their skin pigmentation, which had ultraviolet re ectance of < 6%. The reproducibility of SAF is indicated by a mean coe cient of variance ~ 5% [19]. For every participant, SAF was measured three times on normal skin without scars, tattoos, or other skin abnormalities.

Statistical analysis
Data are shown as mean ± standard deviation (SD) or median and interquartile range in case of non-normally distributed data. Between groups comparisons were performed using the Student's t-test or Mann-Whitney test, and the correlation coe cient was estimated using the Person or Spearman rankorder correlation analysis. Multivariate analysis was performed using multiple regression analyses. The receiver-operating characteristic curves were constructed to determine the optimal SAF cut-off levels for prediction of metabolic disorders. A P-value < 0.05 was considered statistically signi cant. All of statistical analyses were performed using SPSS-17.0 statistical software (IBM, USA).

Characteristics of the study population
The demographic and clinical characteristics of the study population are presented in Table 1. A signi cant increased was shown for age, BMI, glucose, HbA1c, total cholesterol and triglyceride levels in patients compared to healthy subjects. All patients showed a high proportion of hypertension, diabetes and dyslipidemia (74.8%, 57.7%, and 32.2% respectively). The median SAF value was increased in patients compared to healthy subjects, as shown in Figure 1 (P < 0.001). SAF levels in healthy controls aged between 19 and 73 years old has a mean value at 1.90 ± 0.29 AU. Compared to patients group the mean age of healthy controls was signi cantly lower. To matched age between the two groups, SAF levels in healthy group (n=67) shown that the mean value was at 2.00 ± 0.22 AU. This SAF value still signi cant lower compared to patients group (P < 0.001).

SAF levels in patient subgroups
The SAF levels in patient subgroups are presented in Table 2. For each patient subgroup SAF levels were markedly increased compared to healthy subjects. Patients with both DM and HTA showed higher age and a signi cant increased SAF levels compared to patients with HTA (P = 0.047).

Correlation of SAF levels with other variables
The relationship between SAF levels and other variables in study population was shown in table 3. For healthy subjects, univariate linear regression analysis showed a signi cant relationship between SAF, age, BMI and obesity. Multivariate analysis showed that age was independent determinant of SAF (b = 0.552, P < 0.001). For patients group, univariate analysis showed a signi cant relationship between SAF, age, gender, duration of diabetes, HbA1c and triglyceride levels. Multivariate analysis showed that age and duration of diabetes were independent determinant of SAF (b = 0.270, P = 0.008; b = 0.515, P < 0.001 respectively). For all participants a signi cant correlation was shown between SAF and HbA1c levels (r = 0.361, P < 0.001) (Figure 2).

Receiver operating characteristic curve
To predict the presence of metabolic disorders based on SAF, receiver operating characteristic curve was used and shown in gure 3. The maximum Youden index indicated that an SAF > 2.25AU was optimal cut-off point to predict the presence of metabolic disorders. Area under the curve (AUC) = 0.844 (95%CI: 0.795-0.894), P < 0.001 with sensitivity of 63% and speci city of 92%.  [21,22]. Cigarette smoking can also affect the SAF levels and previous studies showed that cigarette smokers had higher levels than nonsmokers [20,23]. Due to small size for our smokers participants data were not shown in this study and patient participants have stopped smoking for a longtime.

Discussion
Tunisian subjects with metabolic disorders showed an increased SAF levels compared to healthy subjects. Diabetic patients showed the same SAF levels with hypertensive patients, however, patients with both metabolic disorders showed an increased SAF levels. Our nding about elevated levels of SAF in diabetes and in hypertension is line with results of several previous studies [12,24]. In subjects with diabetes, the formation of AGEs is accelerated due to hyperglycemia. Glucose plays an essential role in the formation of AGEs as protein amino groups and lipids are non-enzymatically glycated to form stable structures on long lived tissues [25]. AGEs formation also produced by glucose autoxidation, lipid peroxidation and by the reactive carbonyl compounds [10]. Elevated blood pressure may well be a consequence of increased AGEs accumulation. Several AGEs are able to form crosslinks within collagen in the vascular wall, which may result in impaired vascular elasticity and increased arterial stiffness, causing blood pressure to rise [26]. Also several metabolites released from metabolic dysfunction were associated with elevated blood pressure as described recently [2]. These observations may us to suggest that AGEs could in uence directly or indirectly blood pressure via modulation of metabolic processes.
The prevalence of diabetes and hypertension in Tunisia is growing rapidly and it is so important to use non invasive technologies in clinical practice to detect early diseases and monitor patient's health over time. The AGE Reader is a non invasive device that measures tissue accumulation of AGEs provides also an immediate vascular risk prediction. Several studies have already shown the usefulness of SAF as a new marker in predicting diabetes and its complications [17][18][19].
In the present study we found signi cant associations between SAF levels and other additional factors such as age, gender, BMI, duration of diabetes, HbA1c and obesity. Our nding is a line with several studies that they have shown the same results [27][28][29][30][31]. Moreover, we found that age and diabetes duration were independent risk factors that were associated with SAF. The screening value of SAF for the presence of metabolic disorders was assessed by ROC analysis and revealed that the optimal cut-off value was 2.25 AU which suggests that it could be used as a prognostic indicator.
Some limitations of this study should be noted. The number of healthy subjects and patients seems small and the value of SAF in predicting for metabolic disorders could not con rmed in this crosssectional study, which requires further follow-up.
In conclusion, this study indicates that SAF was associated with the presence of metabolic disorders. Non invasive measurement of SAF is a good surrogate marker for evaluating metabolic disorders. Further investigations in a large number of prospective studies are required to validate the results in this study.

Declarations
The study protocol is in accordance with ethical guidelines of declaration of Helsinki and has approved by the ethics committees at the Hospital of Fattouma Bourguiba at Monastir-Tunisia. All participants signed the informed consent in writing prior to inclusion in the study. Tables   Table 1 Characteristics of the study population Page  Data are shown as the median (interquartile range), mean ± SD or number (percentage). Data are shown as the median (interquartile range), mean ± SD or number (percentage).

BMI: body mass index
a P = 0.023 compared to patients with DM b P = 0.047 compared to patients with HTA BMI: body mass index Table 3 Relative risk factors of SAF in study population NS: not significant. Figure 1 Page 13/14 SAF levels in study population.

Figure 2
Correlation between SAF and HbA1c in study population (n=220).