Consistent with previous studies [
5,
14,
15], patients with type 1 diabetes showed significantly higher SAF than controls, both for the group as a whole and across all age categories. It is important to note that this was already apparent for the lowest age category (age 11–12 years). SAF appeared to increase faster in the elder adolescents for both patients and controls and with diabetes duration in patients. Differences in SAF between current HbA1c-within-target and HbA1c-above-target patients and between historical Hba1c-within-target and HbA1c-above-target patients were small. In addition, SAF was only weakly associated with diabetes duration and HbA1c (both current and historical) in our homogeneous group of Dutch Caucasians, consistent with SIF findings from Felipe et al. [
5]. This association disappeared when adjusting for diabetes duration and age. Interestingly, a subgroup of patients with a Hba1c-within-target had an elevated SAF. Previous studies showed conflicting results on the association between historical HbA1c and SAF [
7,
22]. A strong correlation between historical HbA1c and SAF would be expected, as SAF is believed to be at least partly caused by hyperglycemia-induced superoxide and carbonyl damage, resulting in permanent damage to long-lived proteins such as collagen [
3,
23]. However, in this study, a strong association between historical HbA1c and SAF could not be demonstrated. An explanation may be that the period during which historical HbA1c was determined was too short or that median intra-individual HbA1c is an inadequate parameter to express historical HbA1c. Alternatively, as sAGEs are considered to be formed by various pathways [
7,
23,
24], the influence of hyperglycemia on SAF may also be overestimated. It is intriguing to see that some patients show an elevated SAF despite having a HbA1c-within-target. This may be explained by genetic factors influencing either the level of glycation of HbA1c or by factors that influence AGE formation such as polymorphisms of the AGE-receptor (
RAGE) gene [
25] or the
NAT2 acetylator [
26]. Also, oxidative/carbonyl stress may play a role [
24,
27], which may be hypoglycemia-related [
28]. An outstanding question is if this heterogeneity in patients reflects differences in risk for complications. If so, then SAF measurement in this subgroup may provide information on risk for complications independent of HbA1c. However, one should bear in mind that Sun et al. showed in the Medalist Joslin study group that certain types of plasma AGEs are associated with risk of complications and others are protective [
29]. In addition, Conway et al. suggest that also resistance to AGEs may play a role [
30].
A strength of this study was that adjustment for skin color was not necessary, as measured patients and controls were from the same ethnic background (Caucasian). Homogeneity of the patient population supports internal validity. However, the results cannot be applied to non-Caucasians and therefore generalizability is lower. Also, we studied the age range 11–19 years in more detail when compared with previous studies [
5,
12,
31], showing clearly that children and adolescents with type 1 diabetes in the age category 11–12 years already have elevated SAF. We took into account the use of skin care products, as these can affect SAF readings [
32]. One limitation of our study as well as previous studies [
5,
15] is that we were unable to quantify measurement errors in terms of coefficient of variation. To reduce measurement error as much as possible, only one type of AGE-reader was used and SAF measurements were performed in triplicate. However, when measuring SAF is to be of use in routine clinical practice, precision CV of these measurements will have to be assessed to be able to distinguish measurement error from clinically meaningful SAF measurements. SAF readings may be confounded by a number of behavioral factors such as dietary factors and fasting state [
33]. Additional factors such as smoking and exercise are implicated in the accumulation of SAF [
20,
28]. We could not adjust for these factors. BMI may influence SAF, in particular in individuals with central obesity [
34]. We did not extend the BMI analyses as only four patients had a BMI >+2 SDS.