Skin autofluorescence, a non-invasive biomarker for advanced glycation end products, is associated with the metabolic syndrome and its individual components

Subjects included were participants from the LifeLines Cohort Study [29], a large population-based cohort study in the northern region of the Netherlands examining the interaction between genetic and environmental factors associated with chronic diseases and healthy ageing. Between 2006 and 2013, individuals from the three northern provinces of the Netherlands were invited to participate in the study. At baseline, both physical examination and extensive questionnaires were collected from more than 167,000 participants [30]. All participants have provided written informed consent before participating in the study. The study has been approved by the Medical Ethics Review Committee of the University Medical Center Groningen. For the present study, we included subjects of Western European descent between 18 and 80 years of age having a SAF measurement available (n = 82,515). We excluded subjects with either missing data for MetS status (n = 1221) and those with a serum creatinine >140 µmol/L (n = 75), as severely reduced kidney function itself increases AGE levels. Furthermore, subjects with missing data for diabetes status (n = 86) were excluded, as were those with type 1 diabetes (n = 177), type 2 diabetes (n = 2157) or previous gestational diabetes (n = 128) leaving 78,671 non-diabetic individuals for analyses.

The following clinical data were used: age, gender, body mass index (BMI), waist circumference, systolic and diastolic blood pressure, serum lipids, fasting plasma glucose, HbA1c, creatinine clearance, use of medication, and self-reported history of CVD (myocardial infarction and cerebrovascular accident). Information regarding smoking behaviour was collected by extensive questionnaires, as described earlier [10]. Pack-years of smoking were calculated as the number of cigarette packages smoked per day multiplied by the number of years an individual had smoked. Data regarding coffee consumption (cups of coffee per day) were obtained by questionnaire. We were not able to distinguish between caffeinated and decaffeinated coffee consumption.

Weight was measured to the nearest 0.1 kg and height and waist circumference to the nearest 0.5 cm by trained technicians using calibrated measuring equipment, with participants wearing light clothing and no shoes. Waist circumference was measured with a tape around the body between the lower rib margin and the iliac crest. BMI was calculated as weight divided by height squared (kg/m²). Systolic and diastolic blood pressure were measured every minute during 10 min in the supine position using an automated Dinamap Monitor (GE Healthcare, Freiburg, Germany). The average of the last three readings was recorded. SAF was assessed as the mean of three consecutive measurements using the AGE Reader (DiagnOptics Technologies BV, Groningen, the Netherlands) in all participants, as described previously [9‐11].

Blood samples were taken in the fasting state between 8.00 and 10.00 a.m. and transported to the LifeLines laboratory facility at room temperature or at 4 °C, depending on the sample requirements. On the day of collection, HbA1c (EDTA-anticoagulated) was analyzed using a NGSP-certified turbidimetric inhibition immunoassay on a Cobas Integra 800 CTS analyzer (Roche Diagnostics Nederland BV, Almere, the Netherlands). Serum creatinine was measured on a Roche Modular P chemistry analyzer (Roche, Basel Switzerland), and creatinine clearance was calculated with the chronic kidney disease epidemiology collaboration (CKD-EPI) formula [31]. Total and high density lipoprotein (HDL) cholesterol were measured using an enzymatic colorimetric method, triglycerides using a colorimetric UV method, and low density lipoprotein (LDL) cholesterol using an enzymatic method, on a Roche Modular P chemistry analyzer (Roche, Basel, Switzerland). Fasting blood glucose was measured using a hexokinase method.

Diagnosis of MetS was established if a subject satisfied at least three out of five criteria according to the revised National Cholesterol Education Programs Adults Treatment Panel III (NCEP ATPIII criteria) [22]: (1) systolic blood pressure ≥130 mmHg and/or diastolic blood pressure ≥85 mmHg and/or use of antihypertensive medication; (2) HDL cholesterol levels <1.03 mmol/L in men and <1.30 mmol/L in women and/or use of lipid-lowering medication influencing HDL levels; (3) triglyceride levels ≥1.70 mmol/L and/or use of triglyceride-lowering medication; (4) waist circumference ≥102 cm in men and ≥88 cm in women; (5) fasting glucose level between 5.6 and 7.0 mmol/L. As mentioned earlier, people with diabetes were excluded from analysis to reflect ‘true’ MetS.

Data are shown as mean ± standard deviation (SD) or median and interquartile range (IQR) in case of non-normally distributed data. Student’s unpaired t test, Analysis of Variance (ANOVA) or Chi Square test were performed to compare groups. Age-adjusted SAF levels (Z-scores) were calculated based on the total population (separated for males and females) as SAF and MetS are strongly affect by ageing [10, 32]. Next, we classified individuals into tertiles of their age-adjusted SAF Z-scores, in men: lowest SAF Z ≤ −0.59, intermediate SAF −0.60 < Z < 0.49 and highest SAF Z ≥ 0.50; in women: lowest SAF Z ≤ −0.72, intermediate SAF −0.73 < Z < 0.33 and highest SAF Z ≥ 0.34. Logistic regression analysis was performed to assess whether either of the SAF groups (low SAF was set as a reference) were associated with the presence of MetS and its components. Model 1 shows the unadjusted association between tertiles of SAF Z-scores and MetS as well as its individual components. In model 2, we adjusted for gender and BMI. In model 3, we additionally adjusted for creatinine clearance, HbA1c, current smoking, pack-years and CVD history. Linear regression analysis was performed to examine the association between each of the individual MetS components and SAF among the population with MetS. In the multivariate models, we adjusted for determinants of SAF reported in our previous study, including age, gender, BMI, creatinine clearance, HbA1c, smoking status, packyears, and CVD history [10]. SPSS (version 22, IBM, Armonk, NY, USA) was used for statistical analyses. A p value <0.001 (two-tailed) was considered statistically significant.

Table 1 shows the clinical characteristics of the study population stratified for gender and MetS status. The overall prevalence of MetS was 19% in men and 12% in women.

In both genders, individuals with MetS were older than subjects without MetS and had a significantly higher BMI and waist circumference (all p < 0.001). Both in men and women, the prevalence of MI (3.5% in men and 1.0% in women) and CVA (1.1% in men and 1.2% in women) was higher among individuals with MetS compared to subjects without MetS (p < 0.001). The percentage of current smokers as well as the number of pack-years smoked (p < 0.001) were higher in the participants with MetS. In addition, both men and women with MetS reported higher consumption of coffee compared to individuals without MetS (p < 0.001). Mean SAF levels were significantly higher among both male and female subjects with MetS compared to individuals without MetS (men: 2.07 ± 0.44 AU vs 1.94 ± 0.43 AU, p < 0.001; women: 2.07 ± 0.45 AU vs 1.86 ± 0.42 AU, p < 0.001). In subjects with MetS, mean SAF levels were not different between men and women (2.07 ± 0.44 AU vs 2.07 ± 0.45 AU, p = 0.715). However, among individuals without MetS, men had significantly higher SAF levels than women (1.94 ± 0.43 AU vs 1.86 ± 0.42 AU, p < 0.001) (data not shown). Furthermore, mean SAF was 2.33 ± 0.50 AU in subjects with a history of MI and 1.91 ± 0.43 AU in subjects without a history of MI (p < 0.001). Mean SAF was 2.22 ± 0.50 AU in subjects with a history of CVA and 1.91 ± 0.43 AU in subjects without a history of CVA (p < 0.001) (data not shown). Finally, within obese individuals, subjects with MetS had a higher SAF than individuals without MetS (2.11 ± 0.47 AU vs 1.94 v 0.41 AU, p < 0.001). However, within the population with MetS, there was no significant difference in SAF between obese and non-obese individuals (2.07 ± 0.45 AU vs 2.06 v 0.45 AU, p = 0.46) (data not shown).

In both men and women, we observed a gradual rise in age-adjusted SAF Z-scores with higher number of individuals MetS components. Compared to men without any MetS components, SAF Z-scores were significantly higher among subjects with ≥1 MetS component (p < 0.001). Men with ≥2 MetS components had significantly higher SAF Z-scores compared to men having only one MetS component (p < 0.001). No significant differences in SAF Z-scores were observed between men with two vs three MetS components as well as men with four vs five MetS components. Among women, SAF Z-scores were significantly higher among those with ≥1 MetS component compared to women without any MetS components (p < 0.001). Women with three, four or five MetS components had significantly higher SAF Z-scores compared to women with only one MetS component (p < 0.001). No differences in SAF Z-scores were found for women with two vs one MetS components as well as three vs two MetS components. Furthermore, women with either four or five MetS components had significantly higher SAF Z-scores than women with two MetS components or three MetS components (p < 0.001). In men, SAF Z-scores tended to be higher in those with one, two and three MetS components while in women SAF Z-scores were higher in three, four or five MetS components compared to men (Fig. 1).

Figure 2 shows the prevalence of the individual MetS components for both men and women in the total study population according to tertiles of age-adjusted SAF Z-scores. Among men, individuals in the highest SAF groups had a higher prevalence of elevated blood pressure (58%) compared to subjects in the middle (52%) and lowest SAF group (54%).

We observed the same trend for enlarged waist circumference, impaired fasting glucose, elevated triglycerides and low HDL cholesterol (all p < 0.001).

In contrast to men, the most frequent MetS component in women was enlarged waist circumference (high SAF 48%, intermediate SAF 41% and lowest SAF 41%). There was a clear and significant trend among women within the highest SAF group, to be associated with higher prevalence of blood pressure, elevated triglycerides, lower HDL cholesterol levels and impaired fasting glucose (all p < 0.001).

Table 2 shows the results of the logistic regression analyses describing the association between SAF tertiles and the presence of MetS in the total population. Compared to the low SAF group which was set as a reference, high SAF was significantly associated with a higher presence of MetS (odds ratio (OR) 2.61; 95% CI 2.48–2.75). After adjusting for age, gender, BMI, creatinine clearance, HbA1c, smoking status, packyears, CVD history, high SAF (OR 1.24; 95% CI 1.13–1.37) and intermediate SAF (OR 1.11; 95% CI 1.01–1.22) remained significantly associated with the presence of MetS (Table 2, model 3). Considering the individual MetS components, high SAF (OR 3.16; 95% CI 2.97–3.37 and intermediate SAF (OR 1.90; 95% CI 1.78–2.03) were significantly associated with the presence of impaired fasting glucose, although the OR attenuated after adjusting for several covariables (Table 2, models 1, 2 and 3). We also found high SAF (OR 2.93; 95% CI 2.82–3.03) and intermediate SAF (OR 1.71; 95% CI 1.65–1.77) to be associated with the presence of elevated blood pressure (Table 2, model 1). Finally, high SAF (OR 1.93; 95% CI 1.86–2.00) and intermediate SAF (OR 1.45; 95% CI 1.40–1.50) were also associated with the presence of enlarged waist circumference, which remained significant after adjusting for covariables (Table 2, models 1, 2 and 3).

The univariate associations between the individual MetS components and SAF levels in the MetS population are shown in Table 3. In subjects with MetS, the impaired fasting glucose and elevated blood pressure components gave the strongest increase in SAF. Subjects with MetS having the impaired fasting glucose component had a 0.12 AU higher SAF compared to subjects without this particular component. A similar association was observed for individuals with MetS having the elevated blood pressure who had a 0.11 AU higher SAF than those without elevated blood pressure. Multivariate analyses showed that after adjusting for several determinants of SAF, elevated blood pressure (0.05 AU) and low HDL cholesterol (0.04 AU) were significantly associated with higher SAF. Impaired fasting glucose, elevated triglycerides and enlarged waist circumference were not significantly associated with SAF. Regarding the clinical and lifestyle factors, age, HbA1c, current smoking, packyears and a history of CVD were all significantly associated with higher SAF whereas a higher creatinine clearance was associated with lower SAF.