Skin autofluorescence in the general population: associations and prediction
van Waateringe, Robert Paul
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General discussion, summary and future perspectives
Summary
The world-wide prevalence of type 2 diabetes is increasing rapidly and estimated to be close to 650 million in 2040 (1). Cardiovascular complications are the main drivers of increased morbidity and premature mortality in diabetes. In the last two decades, the role of advanced glycation end products (AGEs) in ageing and in the pathophysiology of diabetes-related complications have been studied extensively. Advanced glycation end products (AGEs) comprise a group of largely irreversible glycated proteins, lipids and nucleic acids which represent chronic exposure to hyperglycaemia and oxidative stress. The endogenous pathways of AGEs formation consist of multistep processes by glycation and oxidation of free amino groups of proteins, lipids and nucleic acids. In addition to the classical Maillard reaction, AGEs are formed through the reaction of amino groups with α-dicarbonyls, such as 3-deoxyglucosone, methylglyoxal and glyoxal (2-4). Exogenous sources of AGEs include heat-processed foods, such as grilled meat, deep-fried food, baked products, coffee and other brewed drinks, as well as tobacco smoke (5, 6). AGEs accumulate during our life in the body as a result of normal ageing. However, the rate by which AGEs accumulate is increased in conditions of glycaemic and oxidative stress, such as in individuals with type 2 diabetes and also in situations where removal of AGEs, α-dicarbonyls and related products from the body is reduced, for instance in subjects with impaired renal function (7, 8). The accumulation of AGEs is an important contributor to the pathophysiology of cardiovascular complications in diabetes and renal failure by the formation of crosslinks within and binding to the AGEs receptor (RAGE) which provokes a cascade of inflammatory reactions and oxidative stress within the vascular wall (9). Tissue AGEs accumulation can be assessed easily and non-invasively in the skin with a device called the AGE Reader (10). The technique to estimate AGE accumulation is based on the fluorescent properties of certain AGEs in the skin, which absorb and emit the light differently, so that they can be detected. Skin autofluorescence (SAF) is the amount of emitted light or fluorescence corrected by the reflection of the skin. The higher the skin AGEs content, the higher the SAF value. Several validation studies of this technique have been conducted in which SAF was shown to be strongly related to the gold standard, i.e. direct measurement of AGE levels in skin biopsies (10). SAF levels increase with ageing and are elevated in subjects with type 2 diabetes or impaired renal function compared to age-matched controls (11, 12). Initially, studies have primarily focused on the role of SAF as a marker of cardiovascular complications in people with type 2 diabetes and impaired renal function or renal failure. It has been demonstrated that SAF is a strong predictor of micro- and macrovascular complications in type 2 diabetes, end-stage renal
failure and the need for hemodialysis (13-17). In recent years, SAF has also been studied among people with type 1 diabetes and peripheral artery disease (18, 19). Since these earlier studies have shown promising results for SAF as a valuable biomarker in selected patient populations, the goal of the present set of studies and thesis was to examine the role of SAF as a biomarker within the general population. For this reason, we utilised a large dataset of SAF measurements and clinical and biochemical measures derived from the Dutch LifeLines Cohort Study, a large population-based cohort study in the northern region of The Netherlands (20, 21). We have examined the association between several clinical and lifestyle determinants and SAF in subjects with and without type 2 diabetes. Next, we assessed the association between several cardiometabolic risk factors of the metabolic syndrome and SAF. Finally, prospective data from LifeLines were used to evaluate the value of SAF as a non-invasive screening method to predict the development of type 2 diabetes, cardiovascular disease and mortality.
When measuring and interpreting SAF levels, many factors have to be taken into account, such as age, renal function and diabetes status. Therefore, in chapter 2 we evaluated
which additional clinical and lifestyle parameters were associated with SAF, both in subjects with and without type 2 diabetes. For each parameter, we tried to assess its contribution on SAF levels. Multivariable regression analyses showed that age, male gender, body mass index (BMI), glycated haemoglobin (HbA1c), current smoking, pack-years of smoking and coffee consumption were significantly associated with higher SAF levels whereas both creatinine clearance and one specific genetic marker, i.e. the CC genotype of the NAT2 gene was negatively associated with SAF. Current smoking status was significantly and strongly associated with higher SAF, which appeared to be higher in participants with type 2 diabetes, which is in agreement with a previous study by Lutgers et al. (12). In addition to tobacco smoking, we observed a linear relationship between the daily amount of coffee consumption and higher SAF levels. This association has also been reported earlier in a Canadian study among subjects with type 1 diabetes (22). Furthermore, we observed an interesting interaction between the NAT2 gene polymorphism and coffee consumption. The increase in SAF as a result of coffee consumption was strongest for subjects with the TT genotype and weakest in individuals with the CC genotype.
In Chapter 3 we examined the effect of both active and passive smoking, as well as
smoking cessation on SAF. We collaborated with the Weill Cornell Medical College hospital in Qatar which had collected cotinine biomarkers, the main metabolite of
nicotine, in plasma, saliva and urine. Cotinine is considered to be a valid biomarker for environmental tobacco smoke exposure due to its high sensitivity, specificity and long half-life time (23, 24). Cotinine N-oxide had the highest sensitivity but relatively low specificity for current smoking. Urinary cotinine and cotinine N-oxide levels were higher among current smokers compared to never-smokers. However, only cotinine N-oxide was significantly associated with higher SAF levels. Next, a higher smoking intensity, measured as gram tobacco per day, was associated with even higher SAF levels. Similar to active smoking, passive smoking was associated with higher SAF levels as well among never-smokers and former smokers who had stopped smoking for more than 15 years. Next, we observed a clear association between the exposure to passive smoking and SAF levels in never and former smokers. Higher exposure to passive smoking was associated with even higher SAF levels. Finally, we evaluated the effect of quitting smoking on SAF in former smokers. A longer period since cessation of smoking was characterized by a larger decline of SAF levels towards the normal value for an individuals’ age. SAF levels in former smokers equalled levels of never smokers after approximately 15 years of smoking abstinence.
The metabolic syndrome (MetS) is a cluster of cardiometabolic abnormalities associated with an increased risk of cardiovascular disease (CVD) and type 2 diabetes mellitus.
Chapter 4 describes the association between SAF and MetS and its individual components,
within the LifeLines population including around 80,000 non-diabetic subjects. MetS was defined by the revised National Cholesterol Education Programs Adults Treatment Panel III criteria. Individuals with MetS had significantly higher SAF levels compared to those without MetS. Furthermore, a higher number of individual components was associated with even higher SAF levels. Our data confirm those of a previous study by Uribarri et al. who reported that serum AGEs levels were higher in obese individuals with MetS than in obese individuals without MetS (25, 26). Compared to subjects with a low SAF level, subjects with a high SAF level had an increased presence of the individual MetS components, in particular enlarged waist circumference, elevated blood pressure and impaired fasting glucose.
Previous studies regarding the predictive value of SAF have mainly focused on selected patient-populations, such as people with type 2 diabetes, renal failure and peripheral artery disease. Data regarding SAF in the general, non-diabetic population was lacking. In Chapter 5 we described the predictive value of SAF for incident type 2 diabetes,
Dutch Lifelines Cohort Study, who underwent baseline investigations between 2007-2013, and follow-up between January 2014 and January 2018. After a median follow-up of 4 years (range 1-9 years), 1056 participants (1.4%) had developed type 2 diabetes, 1258 individuals (1.7%) were diagnosed with CVD, while 928 (1.3%) had died. Baseline SAF was elevated in subjects with incident type 2 diabetes, cardiovascular disease and those who died compared to individuals who remained free of disease. The incidence of the separate outcomes increased with age. Univariate regression analyses showed that SAF alone strongly predicted type 2 diabetes (3-fold increased risk), cardiovascular disease (3-fold increased risk) and mortality (5-fold increased risk). SAF remained significantly associated with the separate outcomes even after correction for age, gender, waist circumference, fasting glucose, HbA1c and smoking status. Finally, we performed a survival analysis for mortality which showed that the third SAF tertile was associated with almost two-fold higher risk of mortality compared to the second and first tertile of SAF.
In Chapter 6 we examined whether TH levels predict 4-years risk of type 2 diabetes,
cardiovascular disease and overall mortality in the general population. We included 27,086 participants of the Dutch Lifelines Cohort Study, of whom both SAF and TH levels (TSH, FT4 and FT3) had been measured, and no history of diabetes, CVD or using medication influencing TH (including levothyroxine). Individuals who had developed type 2 diabetes had lower FT4 levels while subjects with incident cardiovascular disease had higher FT4 levels compared to those who remained free of disease. We observed no significant difference in FT3 and TSH levels for the three different outcomes. Next, we assessed whether thyroid hormone levels predicted incident type 2 diabetes and cardiovascular disease. Univariate regression showed that lower FT4 levels significantly predicted a higher risk of incident type 2 diabetes, while higher FT4 levels were associated with an increased risk of cardiovascular disease. Lower FT4 levels remained significantly associated with incident type 2 diabetes, even after correction for SAF, age, gender, BMI, glucose and smoking status. Higher FT3 levels were associated with increased risk of both type 2 diabetes and cardiovascular disease, even after correction for SAF, age and the presence of the metabolic syndrome, but lost its significance when further adjustment was performed for classic risk factors including blood pressure, cholesterol, glucose and smoking.
Chapter 7 described the association between the number of pregnancies and skin
autofluorescence. Preliminary data from our showed that SAF levels increase during pregnancy, and did not return to levels before pregnancy (Groen et al. unpublished
data). A possible mechanism include increased oxidative and glycemic stress during pregnancy which in turn, can increase the formation of AGES. Women between the age of 18 and 80 years who participated in the LifeLines study and had a SAF measurement available were included in the study. Women with type 1 and type 2 diabetes were excluded, as were as those with missing data for diabetes, leaving 47 834 individuals for analysis. Subjects completed a questionnaire on medical history, past and current diseases, use of medication, and health behavior. The questionnaire also included information on previous pregnancies and childbirth. Analyses showed that a higher number of pregnancies was associated with higher SAF levels, which remained after correction for waist circumference and renal function. However, significance was lost after further correction for age. Women in the highest quartile of the SAF Z-score had a worse cardiovascular risk profile (higher prevalence of metabolic syndrome, higher BMI, waist circumference, glucose, HbA1c, and lipids) compared to women in the other three quartiles.
As SAF is a biomarker for cardiovascular disease, and inflammation may be an important contribution to increased SAF levels, we performed a study on other biomarkers related to CVD and type 2 diabetes, specifically to evaluate the effect of storage time on the assays. This study was performed within the framework of The Biobank Standardization and Harmonization for Research Excellence in the European Union (BioSHaRE-EU) project, based on international collaborative projects between European institutes and biobanks. The project has developed and applied several methods and tools for harmonisation and standardisation in European biobanks. Chapter 8 describes the reproducibility of several
inflammatory biomarkers including high sensitive hsCRP, high sensitive Interleukin-6 (hsIL6) and high sensitive Tumor Necrosis Factor Alpha (hsTNFα) obtained from healthy individuals (n=80), individuals with type 2 diabetes (n=80) and those with a previous myocardial infarction (n=80). Samples were stored for either less than 2 years and more than 4 years at -80 o C. We examined the effect of storage time as well as inter- and
intra-assay variability for specific measurements between collaborating biobanks. Our data showed that short- to medium-term storage did not influence the plasma levels of hsCRP and hsIL6 measured by nephelometry and by ELISA. Results of hsTNFα measurements could not be evaluated fully, due to analytical problems at one laboratory, and inferior performance of one of the chosen ELISA assays, the IBL assay.
A general discussion and summary of the thesis as well as our reflection on possible future research on skin autofluorescence and the AGE Reader is given in chapter 9.
Strengths and limitations of the studies
The strengths of the above-mentioned studies include their large and well-characterised study population, including high-quality data on anthropometric and clinical measurements. This resulted in good statistical power and the ability to perform stratified analysis in pre-defined subgroups. Also, this was the first and largest study among a population of people without diabetes, while previous studies on SAF were mainly performed in selected patient populations such as type 2 diabetes, impaired renal failure and peripheral artery disease. Of course, our studies have some limitations as well. Firstly, some of the studies were based on a cross-sectional design, which did not allow us to draw any conclusions about causality in the association found. Secondly, current medication use was not included in the follow-up questionnaires from LifeLines. Medication use, in particular, oral blood glucose lowering medication or insulin, may validate the self-reported diagnosis of type 2 diabetes. Moreover, when subjects report no current disease, use of medication can act as an alternative diagnostic parameter. Thirdly, mean follow up duration in the prospective study was four years, which is relatively short. Cause of death was not collected which did not allow us to refine the predictive power of SAF for mortality. Finally, data regarding the time point individuals had developed type 2 diabetes and cardiovascular disease was lacking. As a consequence, we were not able to perform survival analyses for these two outcomes.
Discussion
In this thesis we have demonstrated that SAF is associated with several clinical and lifestyle factors. Moreover, SAF was associated with several cardiovascular and diabetic risk factors of MetS in non-diabetic subjects as well. Finally, SAF was able to predict 4-years risk of incident type 2 diabetes, cardiovascular disease and overall mortality within the general population. The final part of this chapter discusses the main findings of this thesis, including coffee consumption, tobacco smoking, MetS components and the predictive value of SAF for type 2 diabetes, cardiovascular disease and overall mortality.
Skin autofluorescence and coffee consumption
In our first study, we observed a dose-dependent association between coffee consumption and higher SAF levels. Although caffeine has been reported to decrease insulin sensitivity, several studies have reported that coffee consumption reduces the risk of type 2 diabetes (27-29). In contrast, it has also been demonstrated that coffee consumption decreases the risk of cardiovascular disease (30, 31). Coffee is a major source of the phenolic antioxidant chlorogenic acid (32). Chlorogenic acid has been reported to inhibit AGEs formation in vitro (33). As a consequence, one would expect that coffee consumption was associated with lower SAF levels. However, due to the roasting process of coffee beans, a significant amount of chlorogenic acid is lost (34). As a consequence, a reduced inhibitory effect of chlorogenic acid may result in higher AGEs formation. The roasting process of coffee beans further leads to changes in the chemical composition of coffee brew, including the formation of melanoidins as the end products of the Maillard reaction (35, 36). This might be an additional explanation how coffee consumption, as an exogenous source of AGEs, raises SAF levels. These findings implicate that coffee consumption needs to be taken into account when measuring and interpreting SAF levels. Interestingly, it has been reported that a polymorphism in the N-acetyltransferase 2 (NAT2) gene is involved in the metabolic pathway of caffeine as well (37). A recent genome-wide association study from our group reported that a polymorphism in the NAT2 gene was significantly associated with SAF (38). Similarly, Eny et al. reported that among individuals with type 1 diabetes, caffeine consumption was associated with skin intrinsic fluorescence (SIF) (which is a more or less comparable measure for AGEs like SAF), while the association was partly explained by the NAT2 gene (22). We replicated the analyses in the LifeLines Cohort and observed the same association. Furthermore, we found an interaction between coffee consumption and the NAT2 gene in the association with SAF. More specifically, individuals
with the slow acetylator genotype CC had lower SAF levels compared to subjects with the fast acetylator genotype TT (per cup of coffee).
Skin autofluorescence and tobacco smoking
In addition to coffee consumption, we observed that tobacco smoking was strongly associated with higher SAF levels, and both current smoking as well as how much a person had smoked during his or her life (the so-called number of pack-years) were independently associated with higher SAF levels. These findings are in agreement with several previous studies (11, 39). In addition to active smoking, this is the first study which demonstrates that secondhand smoking increases SAF levels both in never smokers and former smokers who had been quitting smoking for around 15 years. It has previously been reported that tobacco smoke is an exogenous source of AGEs itself (40, 41), thereby contributing to increased SAF levels. Levels of AGEs were higher in lenses and blood vessels of tobacco smokers than non-smokers (40). A second mechanism that explains higher SAF levels in tobacco smokers includes increased systemic oxidative stress as a result of smoking, which in turn enhances the formation of AGEs (42, 43).
Both active and passive smoking are associated with increased risk of type 2 diabetes and cardiovascular disease (44-46). There is a growing body of evidence which suggests that the accumulation of AGEs is associated with reduced insulin sensitivity, most likely by depletion of insulin-signalling pathways (47, 48). Moreover, the formation of AGEs cross-links within the vascular wall have been reported to increase vascular stiffness and may result in arterial hypertension (49, 50). Furthermore, uptake of AGEs through the AGE receptor (RAGE) and subsequent stimulation of post-receptor inflammatory and mitogenic processes stimulates the production of pro-inflammatory cytokines, enhances oxidative stress and causes endothelial dysfunction (9, 41, 51). Therefore, AGEs accumulation as a result of tobacco smoke may indirectly be involved in the underlying pathophysiological mechanism of both type 2 diabetes and cardiovascular disease. One of the interesting observations was that SAF levels gradually decreased as the time since smoking abstinence increased. SAF levels of former smokers dropped to levels of never smokers after around 15 years of smoking cessation, after adjustment for confounding factors as age and weight. One explanation includes the fact that the accumulation rate of AGEs is lower in former smokers compared to current smokers resulting in lower SAF levels in the former group. However, the question remains whether the effect of tobacco smoking on AGEs accumulation is reversible.
Several studies have reported that smoking cessation is associated with reduced risk of both type 2 diabetes and cardiovascular disease (52-54). A meta-analysis by Pan et al. showed that the risk of type 2 diabetes gradually decreased as the time since quitting smoking increased (52). Similar associations were found for cardiovascular risk by Mons et al. (54). Possible mechanisms for reduced risk of type 2 diabetes and cardiovascular disease after smoking cessation include improved insulin sensitivity, endothelial function and lipoprotein metabolism as well as decreased vasoconstriction (55-57). On the other hand, it is known that the effects of smoking cessation on abnormal lipid levels, like lower HDL-cholesterol levels, are already observed after a few weeks (58). Nevertheless, a reduction in AGEs accumulation rate as a consequence of smoking cessation may be another likely mechanism leading to decreased diabetes and cardiovascular risk.
Skin autofluorescence and the metabolic syndrome
MetS comprises a combination of several cardiometabolic parameters associated with an increased risk of both type 2 diabetes and cardiovascular disease (CVD) (59, 60). In our study, we observed that non-diabetic individuals with MetS had higher SAF levels than subjects without MetS, independent of other variables associated with SAF. Interestingly, SAF levels increased with a higher number of individual MetS components, which confirms that SAF is associated with cardiometabolic risk. It is expected that the association between a higher number of MetS components and higher SAF levels in subjects with MetS is a result of combined cardiometabolic risk factors, as some of them play a role in the formation and accumulation of AGEs. Regression analyses demonstrated that SAF was associated with some of the individual MetS components, particularly elevated blood pressure, waist circumference and impaired glucose levels.
Elevated blood pressure
We have shown that elevated blood pressure was the most common MetS component among men. Moreover, we observed a strong association between elevated blood pressure and SAF. The pathophysiology behind the association between blood pressure and AGEs accumulation has not been described in detail yet. However, it has been known that the accumulation of AGEs form irreversible crosslinks within the vascular wall leading to endothelial dysfunction but especially increased arterial stiffness (61, 62).
Moreover, binding of circulating AGEs to its receptor at endothelial cells leads to a cascade of inflammatory cytokines. This processes may result in arterial hypertension (63-65). McNulty et al. have demonstrated a correlation between plasma AGEs levels and pulse wave velocity (PWV), a marker of arterial stiffness. Moreover, they have
reported that plasma AGEs levels were elevated in subjects with hypertension compared to normotensive individuals (66). This finding was confirmed by another recent study which showed an independent association between plasma AGEs and higher systolic blood pressure levels in a large Chinese population (67).
SAF as a predictor of incident type 2 diabetes
As some previous cross-sectional studies already have shown promising data for the use of SAF as a screenings tool for undetected type 2 diabetes, we are the first to demonstrate that SAF indeed is a strong predictor of incident type 2 diabetes within the general population (68-70). SAF strongly predicted incident type 2 diabetes alone and after adjustment for several conventional risk factors, including age, presence of the metabolic syndrome, smoking as well as fasting glucose and HbA1c. Although waist circumference, fasting glucose, the presence of metabolic syndrome and current smoking status are known to be strong predictors of incident type 2 diabetes, SAF remained its significance in five different models either including biochemical markers, glycaemic markers and a model including all parameters. Remarkably, SAF remained its significance as well when adjusting for both fasting glucose and HbA1c. Both glycaemic parameters were also used to define type 2 diabetes at follow-up. Although this may have caused overestimation of the predictive values, it demonstrates the strength of SAF.
SAF as a predictor for cardiovascular disease and overall mortality
Within this thesis, we have also shown the strong predictive power of SAF for both incident cardiovascular disease and overall mortality in a general population. SAF alone was a stronger predictor than waist circumference, fasting glucose, blood pressure, cholesterol levels or current smoking status for incident cardiovascular disease. Also within extensive prediction models combined with several convention risk factors, SAF remained a strong predictor of incident cardiovascular disease. The most impressive finding of our study was that SAF in the univariate, as well as multivariate analyses, was also one of the strongest predictors of overall mortality in addition to a participants’ age. The predictive value of SAF was stronger than metabolic syndrome, fasting glucose, HbA1c, systolic blood pressure as well as current smoking status. Also within an extensive model including several confounding risk factors, SAF remained the strongest predictor of mortality. Since this was the first study within a general population, comparison with other studies is difficult as those are limited to selected patient populations. A recent French study showed that SAF was associated with a higher risk of incident macrovascular events among a population of people with type 1 diabetes (19). SAF has been reported
to predict risk of cardiac mortality both in type 1 and 2 diabetes as well as overall and cardiovascular mortality in hemodialysis patients (71, 72). Recently, it was shown that SAF predicts all-cause mortality and major adverse cardiovascular events in subjects with peripheral artery disease after 5 years of follow-up (73). Moreover, in the same patient population, it was shown that SAF predicts lower limb amputation independently of diabetes status and Fontaine stage classification after 6 years of follow-up (18).
Quality of questionnaires
Large population-based cohort studies, such as LifeLines, comprise large sets of health-related data which have been mainly derived from many, extensive questionnaires. With the aid of questionnaires an extensive array of information regarding the health and disease history of a certain subject can be established. Limitations of questionnaires exist as well, such as the fact that crucial data like the presence of a disease is often based on self-report. This may be overcome by validation with medication use, biochemical data or GP charts. However, if those data are lacking, as was the case in one of our studies, this may cause serious limitations for the researcher. Secondly, open questionnaires may cause problems since individuals can give unclear answers to certain questions which might lead to a wrong interpretation by the researcher. With reference to the advices of a 2009 editorial, we do confirm that the Lifelines questionnaires specifically ask for the history of disease as recognised and recalled by the patient, for instance ‘did you have a myocardial infarction’ (74). Cardiovascular disease was the topic of several questions, with a combination of questions for higher validity. Nevertheless, an intervention like CABG was not directly asked for in all questionnaires, but could be answered by a participant as ‘other CVD events’. Here, there is the possibility of underestimation. Earlier studies have suggested that for the diagnosis ‘diabetes’ overreporting is limited (75). For other diagnoses, there may be both overreporting (17% for angina), but also underreporting (8.0% for myocardial infarction, 57% for heart failure). Thirdly, it has to be recognized that questions regarding one single topic may be spread over multiple questionnaires, which sometimes results in contradictory answers. Altogether, it is very important that biobanks offer high-quantity but also high-quality data.
Future research
This thesis shows strong support for the clinical use of SAF as a screening tool to assess the risk of type 2 diabetes, cardiovascular disease and mortality both in the general population as well as in patient populations, for instance, people with metabolic syndrome. Early identification of individuals at risk for any cardiometabolic disease may help to improve and maintain a healthy lifestyle to prevent future illness and its complications. For example, screening of subjects with undiagnosed type 2 diabetes may help to delay or even prevent developing microvascular and macrovascular complications once proper treatment has been initiated. In the final paragraph of this thesis, recommendations for future research and implementation of the AGE Reader are given.
Mortality
We have shown that SAF is a strong predictor of overall mortality in the general population. Future studies need to examine the predictive power of SAF for specific causes of death. For the research described in the current thesis, information on causes of death was lacking, as these are not part of the available LifeLines dataset. Based on the strong association between SAF, mortality and cardiovascular risk factors, whereas any association between SAF and malignancies has not been reported yet, we hypothesise that in the majority of individuals from the Lifelines population who have died, this was caused by cardiovascular disease. When an individual has died, a death registration form has to be completed to register the details of the death. However, several studies have shown that errors may occur when completing the death registration forms. For example, a Dutch study which examined the cause of death from 841 individuals compared the death registration form with information of the National Central Bureau of Statistics (CBS). Similar numbers of death were found for individuals who died from malignancies, while death from heart disease was registered less often (76). Since the death registration form asks for the main cause of death and only two underlying comorbidities, it may well be that cardiovascular disease as comorbidity is less well or extensively registered compared to malignancies and infectious diseases.
The AGE Reader: future developments and opportunities
The AGE Reader is a non-invasive device, which allows an easy and reproducible measurement of skin autofluorescence to screen possible high-risk individuals. We have demonstrated that several clinical and lifestyle parameters influence the SAF measurements. The present version of the AGE Reader can account for both age and gender, and as such calculate the age-specific Z-score. As coffee consumption and a polymorphism in the NAT2 gene increase respectively decrease SAF, there is a clinical need to develop an algorithm that accounts for these two factors. Such an algorithm may result in a more balanced interpretation of SAF and consideration of traditional diabetes and cardiovascular risk factors. Furthermore, the current device is based on reference values from a population without diabetes. To my opinion, the device should be updated at least with reference values for individuals with diabetes and those with a history of cardiovascular events. The LifeLines dataset is an excellent basis for this extension of reference values.
The current version of the AGE Reader measures individual SAF levels and directly shows whether it is elevated or not. However, it does not directly reflect what exactly the risk of type 2 diabetes or cardiovascular disease is. Of course, we can choose statistical cut off levels to determine any individuals risk, such as the median, tertile or quartile. However, calculating reference levels, as well as sensitivity and specificity may be more reliable. In the United States, the Scout Diabetes Score (DS) has been used to screen individuals for type 2 diabetes or impaired fasting glucose (77). The Scout DS is a device comparable to the AGE Reader; it uses fluorescence spectroscopy to measure skin intrinsic fluorescence (SIF) from dermal tissue. The Scout DS score ranges from 0-100. Reference levels for the Scout DS are set against the 2 hour 75 grams oral glucose tolerance test (OGTT) (78, 79). SIF levels below 50 are supposed to be equal to a glucose level below 7.8 mmol/L. SIF levels equal or below 50 are considered as a “negative screen”. SIF levels between 50 and 60 are equal to glucose levels between 7.8 and 11.0 mmol/L and indicate a “positive screen” with a 2.8 fold increased risk of diabetes. Finally, SIF levels above 60 are equal to a glucose level above 11.0 mmol/L and reflect an eight-fold increased risk of diabetes compared to a level below 50. Studies which examine the sensitivity and specificity of SAF for type 2 diabetes and cardiovascular disease are on their way to further improve the screening capacities of the AGE Reader. The AGE Reader may be implemented for an initial screening of high-risk individuals, such as individuals who smoke, are obese or have a positive family history of diabetes or cardiovascular disease.
Screening can be performed in the office of the general practitioner or in non-medical settings or public locations such as pharmacies, or even supermarkets. In case of an elevated SAF level, individuals may be advised to have their blood pressure, glucose and cholesterol levels measured. In such a setup, the AGE Reader may be considered as a screening device in the so-called ‘Preventie consult’ in general practice, with laboratory assessment performed in only those with elevated SAF levels. Future studies are needed to evaluate the benefits and risks of such an approach.
We also recommend screening of women who have been diagnosed with gestational diabetes during their pregnancy. Women with a history of gestational diabetes have an increased risk of up to 50% of developing type 2 diabetes within the first 5-10 years postpartum (80). Currently, women are advised to check their glucose levels regularly. Although several studies have shown contradictory results on SAF in women with gestational diabetes, we recommend a first, non-invasive screening by the AGE Reader (81, 82). In case of an elevated SAF level, women may still be referred for a fasting glucose or oral glucose tolerance test.
In addition to the screening of healthy individuals with certain risk factors, the AGE Reader has been proposed to monitor disease process and risk estimation of cardiovascular complications in individuals with either type 1 or type 2 diabetes and mortality within subjects with existing cardiovascular disease, such as peripheral artery disease and myocardial infarction. In the clinical setting, the focus of risk factor intervention is on optimisation of blood pressure, glucose, HbA1c and cholesterol levels, supported with stop smoking and exercise programs to reduce the risk of cardiovascular complications. The question remains what to do in case of an increase in SAF despite optimal medical treatment. Currently, the role of AGE breakers and inhibitors, such as aminoguanidine, is still limited to small-scale research. Although several studies have shown encouraging results, safety and efficacy issues occur (50, 83, 84), and to our knowledge, there are, other than stopping smoking, no direct interventions which may lower SAF levels and thereby reduce cardiovascular risk. Interestingly, it has also been reported that both metformin and atorvastatin, commonly prescribed medications for people with type 2 diabetes, may have beneficial effects on AGEs inhibition. While metformin has been demonstrated to have inhibitory effects on protein glycation, atorvastatin has been shown to significantly lower serum AGEs levels (85-87). While AGE-inhibitors are still limited to research purposes, both drugs seem to be a good alternative in lowering serum AGEs levels in subjects with diabetes and cardiovascular disease.
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Nederlandse samenvatting
Dutch summary
Inleiding
Type 2 diabetes en hart- en vaatziekten
Type 2 diabetes wordt gedefinieerd als een verhoogde glucosespiegel in het bloed. Type 2 diabetes wordt in het algemeen veroorzaakt door een combinatie van ongevoeligheid van lichaamscellen voor, alsmede een relatief tekort aan het hormoon insuline. Hierdoor stijgt de glucosespiegel in het bloed. Belangrijke risicofactoren voor type 2 diabetes zijn overgewicht en obesitas, vaak als gevolg van een ongezonde leefstijl. Wereldwijd neemt het aantal mensen die gediagnosticeerd zijn met type 2 diabetes snel toe. Er wordt geschat dat er in 2040 ongeveer 650 miljoen mensen zijn met type 2 diabetes (1). Slecht gereguleerde type 2 diabetes kan op den duur leiden tot ernstige complicaties zoals hart- en vaatziekten als gevolg van schade aan de kleine en grote bloedvaten. Microvasculaire complicaties omvatten bijvoorbeeld schade aan nieren (nefropathie), zenuwen (neuropathie) en ogen (retinopathie) terwijl macrovasculaire complicaties leiden tot bijvoorbeeld een hart- of herseninfarct of vaataandoeningen in de benen (perifeer vaatlijden).
Advanced Glycation End products (AGEs)
In de afgelopen 20 jaar is er steeds meer aandacht ontstaan voor de betrokkenheid van advanced glycation end products (AGEs) in het proces dat leidt tot diabetes mellitus en hart- en vaatziekten. AGEs worden gevormd door een non-enzymatische reactie tussen eiwitten en vetten enerzijds, en glucose moleculen wat ook wel de Maillard reactie wordt genoemd (2). Voorbeelden van AGEs vorming in de voedingsindustrie zijn crème brullee, koekjes en gebakken of gegrild vlees en geroosterde koffiebonen (3). De vorming van AGEs wordt versneld door verhitting van rauwe producten die glucose, eiwitten of vetten bevatten. Ook in het menselijk lichaam worden AGEs gevormd tijdens het leven, als gevolg van de normale veroudering. Echter, bij mensen met bijvoorbeeld diabetes mellitus of een gestoorde nierfunctie stapelen de AGEs zich sneller op als gevolg van respectievelijk hoge glucose waarden in het bloed en een verminderde uitscheiding van AGEs uit het lichaam. Als gevolg van stevige verbindingen tussen AGEs en weefsels in het lichaam ontstaat er stijfheid van structuren, bijvoorbeeld in de huid en bloedvaten. Dit kenmerkt zich door de vorming van rimpels, verminderde beweeglijkheid van de gewrichten, en het ontstaan van hypertensie (4).
De receptor voor AGEs (RAGE) bevindt zich op allerlei celwanden, bijvoorbeeld op de binnenwand van bloedvaten. Daarnaast komen oplosbare vormen van RAGE voor in het bloed. Binding aan de AGE receptor leidt tot verscheidene processen die betrokken zijn bij inflammatieprocessen in het lichaam en het ontstaan van atherosclerose (5).
Skin autofluorescence (SAF)
Waar vroeger de hoeveelheid AGEs werden bepaald door het afnemen van huid biopten wordt tegenwordt gebruikt gemaakt van een niet-invasieve methode middels de AGE Reader (Diagnoptics Technologies, Groningen, Nederland) (6). Vanwege de fluorescerende eigenschappen van bepaalde AGEs in de huid wordt gebruikt gemaakt van ultraviolet licht wat berust op het principe van “excitatie en emissie”. De AGE Reader belicht een huid oppervlak van ongeveer 4 mm2 van de onderarm met een golflengte tussen de
300 en 420 nanometer (excitatie). Een spectrometer analyseert het fluorescerende licht afkomstig van de AGEs in de huid op een golflengte tussen de 420 en 600 nanometer (emissive). Vervolgens berekent de AGE Reader de ratio tussen de gemiddelde emissie en excitatie wat wordt weergegeven in “arbitrary units”. Voorgaande onderzoeken hebben aangetoond dat de SAF waarde is verhoogd in mensen met diabetes alsmede in mensen met een gestoorde nierfunctie die hemodialyse ondergaan. In deze laatste groep mensen blijkt de SAF waarde een sterke voorspeller voor het risico op overlijden als gevolg van hart- en vaatziekten (7).
LifeLines
De gegevens uit dit proefschrift zijn afkomstig van ongeveer 167 000 deelnemers van het LifeLines onderzoek, een groot bevolkingsonderzoek binnen de regio Groningen, Friesland en Drenthe. Dit bevolkingsonderzoek, waarin de deelnemers 30 jaar lang worden gevolgd, richt zich op genetische, omgevings- en sociaal-demografische factoren die van invloed zijn op, met name, multifactoriele ziekten (8). Deelnemers van 18 jaar en ouder werden tussen 2007 en 2013 geincludeerd, waarna er bloedafname en lichamelijk onderzoek werd verricht, alsmede de deelnemers vragenlijsten omtrend gezondheid beantwoorden. De SAF meting maakte uiteraard ook deel uit van het onderzoek.
Samenvatting
Wanneer men de SAF waarde meet, geldt over het algemeen, hoe hoger de leeftijd van het individu, des te hoger de waarde is. Er bestaat vrijwel een lineair verband tussen de leeftijd en de SAF waarde. In hoofdstuk 2 hebben zowel leefstijl als biochemische
9009 mensen mét en zonder type 2 diabetes die deelnemen aan het LifeLines onderzoek en waarvan een SAF meting beschikbaar was. De analyses toonden aan dat een hogere leeftijd, BMI, HbA1c en een mannelijk geslacht, alsmede actief roken, pack-years (een maat voor de hoeveelheid pakjes sigaretten die men gedurende een jaar heeft gerookt) en de hoeveelheid koffie die per dag wordt gedronken geassocieerd waren met een hogere SAF waarde. Zowel een hogere kreatine klaring, een maat voor de nierfunctie, en een specifiek genotype van het NAT2 gen bleken geassocieerd te zijn met een lagere SAF waarde. Dit hoofdstuk laat dus zien dat met bovenstaande factoren rekening moet worden gehouden bij de interpretatie van de SAF waarde.
Vervolgens zijn we in hoofdstuk 3 nader in gegaan op het effect van roken op de SAF
waarde. Behalve actief roken onderzochten we ook of er een associatie bestond tussen
passief roken en de SAF waarde, als ook het effect van stoppen met roken in 8905 mensen uit het LifeLines cohort waarvan een SAF meting beschikbaar was. Dit onderzoek betrof een samenwerking met het Weill Cornell Medical College ziekenhuis in Qatar,
alwaar verschillende afbraakproducten van nicotine waren verzameld van total 364 mensen. Het bleek dat hoe meer tabak er per dag werd gerookt, SAF ook significant meer toenam. Behalve actief roken bleek ook passief roken geassocieerd te zijn met een hogere SAF waarde in mensen die nooit hadden gerookt of al langer dan 15 jaar gestopt waren met roken. SAF waarden namen geleidelijk af naar mate men langer was gestopt met roken, en bereikte uiteindelijk na 15 jaar een waarde gelijk aan die van mensen die nooit hadden gerookt. De afbraakproducten Cotinine en Cotinine N-oxide gemeten in de urine bleken significant hoger in huidige rokers dan in mensen die nooit hadden gerookt. Alleen cotinine N-oxide in de urine bleek significant geassocieerd te zijn met een hogere SAF waarde. Behalve actief roken zijn dus ook passief roken én stoppen met roken van invloed op de SAF waarde.
In hoofdstuk 4 hebben we in 78 671 mensen zonder type 2 diabetes die deelnamen aan
het LifeLines onderzocht of er een associatie bestond tussen het metabool syndroom en de SAF waarde. Het metabool syndroom bestaat uit een vijftal risicofactoren voor zowel type 2 diabetes als hart- en vaatziekten namelijk een vergrote middelomtrek, hoge bloeddruk, een verhoogde glucose waarde, een verhoogde triglyceride waarde en een verlaagde HDL cholesterol. Wanneer men volgens de National Cholesterol Education Programs Adults Treatment Panel III richtlijnen aan 3 of meer criteria voldoet spreekt men van het metabool syndroom. Mensen met het metabool syndroom bleken een hogere SAF waarde te hebben dan mensen zonder dit syndroom. Bovendien nam de SAF
waarde toe met het aantal metabool syndroom criteria. Wanneer we de deelnemers in drie groepen verdeelden op basis van hun SAF waarde, bleek dat onder de mensen die in de hoogste groep zaten het metabool syndroom vaker voor kwam. Wat betreft de afzonderlijke metabool syndroom componenten bleek dat een vergrote middelomtrek, hoge bloeddruk en een verhoogde glucose waarde vaker voor kwamen bij mensen die in de hoogste SAF waarde groep vergeleken met mensen in de laagste groep. Dit hoofdstuk toont dus aan dat er sterke associaties bestaan tussen SAF en belangrijke risicofactoren voor het ontwikkelen van hart- en vaatziekten en/of type 2 diabetes.
Nadat we de associatie tussen de SAF waarde en de risicofactoren voor type 2 diabetes en hart- en vaatziekten in kaart hadden gebracht onderzochten we vervolgens in hoofdstuk 5 of de SAF meting ook beide ziekten alsmede overlijden kan voorspellen. Voor dit
onderzoek werden 72 880 deelnemers van LifeLines geincludeerd die tussen 2007 en 2013 een SAF meting hadden ondergaan en op dat moment geen diabetes en hart- en vaatziekten hadden. Na een gemiddelde follow-up tijd van 4 jaar hadden er 1056 mensen type 2 diabetes ontwikkeld, 1258 waren gediagnosticeerd met hart- en vaatziekten en 928 mensen waren overleden. De SAF waarde die op baseline was gemeten bleek verhoogd in mensen die type 2 diabetes en hart- en vaatziekten hadden ontwikkeld vergeleken met mensen die geen ziekte hadden ontwikkeld. Tevens bleek deze waarde verhoogd in mensen die waren overleden. Een hogere SAF waarde bleek geassocieerd met een groter risico op het ontwikkelen van type 2 diabetes, hart- en vaatziekten alsmede overlijden binnen een periode van 4 jaar, onafhankelijk van andere risicofactoren zoals leeftijd, geslacht, het metabool syndroom, glucose en roken. Wanneer we de deelnemers in drie groepen verdeelden op basis van hun SAF waarde bleek dat mensen met de hoogste waarde een grotere kans hadden om te overlijden vergeleken met mensen met een lage waarde. Uit dit hoofdstuk blijkt dat de SAF meting een goede voorspeller is voor het risico op hart- en vaatziekten, type 2 diabetes en overlijden.
Vervolgens hebben we in hoofdstuk 6 onderzoek gedaan of een te hoge of te lage
schildklier hormoon waarde het 4-jaars risico op type 2 diabetes, hart- en vaatziekten (hart infarct, stent plaatsing, vaat omleidingen ofwel by pass, TIA, CVA, etalage benen of vaatoperaties) en overlijden kan voorspellen, onafhankelijk van de SAF waarde. Van het Lifelines onderzoek werden 27 086 deelnemers zonder diabetes en hart- en vaatziekten geincludeerd van wie schildklier hormoon waarden (TSH, FT4 and FT3) en een SAF meting beschikbaar was. Zowel het FT4 als FT3 hormoon was positief geassocieerd met de SAF waarde, echter na correctie voor andere factoren (leeftijd, nierfunctie, roken, glucose
en HbA1c) bleken de associaties niet meer significant te zijn. Na ongeveer 4 jaar hadden 382 mensen type 2 diabetes ontwikkeld, 496 hart- en vaatziekten en 372 mensen waren overleden. Mensen die type 2 diabetes hadden ontwikkeld bleken op baseline lageren FT4 waarden te hebben dan mensen die geen diabetes hadden ontwikkeld. In tegenstelling tot type 2 diabetes bleken mensen die hart- en vaatziekten hadden ontwikkeld op baseline juist hogere FT4 waarden te hebben dan mensen die geen ziekte hadden ontwikkeld. Verdere analyses toonden aan mensen met een lage FT4 waarde een groter risico hebben om type 2 diabetes te ontwikkelen, zelfs na correctie voor onder andere leeftijd, geslacht, BMI, bloeddruk, cholesterol en roken. Mensen die op baseline een hoge FT4 waarde hadden bleken juist een hoger risico te hebben om hart- en vaatziekten te ontwikkelen, echter na correctie voor bovenstaande factoren bleek deze associatie niet meer significant te zijn. Concluderend kunnen we zeggen dat de FT4 waarde een tegengesteld effect heeft op het 4-jaars risico op type 2 diabetes en hart- en vaatziekten. Omdat eerder onderzoek heeft aangetoond dat SAF waarden verhoogd zijn gedurende de zwangerschap hebben we in hoofdstuk 7 onderzocht of er een associatie bestond
tussen het aantal zwangerschappen en de hoogte van de SAF waarde. Voor dit onderzoek includeerden we 47 834 vrouwen zonder diabetes mellitus die deelnamen aan het LifeLines onderzoek en een SAF meting hadden ondergaan. Een hoger aantal zwangerschappen bleek geassocieerd te zijn met een hogere SAF waarde, echter deze associatie was niet meer significant na correctie voor de leeftijd. Wanneer we de vrouwen in groepen verdeelden op basis van hun SAF waarde bleken vrouwen in de hoogste SAF groep vaker het metabool syndroom te hebben dan vrouwen in een lagere SAF groep. Tevens hadden vrouwen in de hoogste groep een hogere BMI en middelomtrek alsmede een hogere glucose, HbA1c en cholesterol waarde verleken met vrouwen in de andere groepen. Dit toont aan dat er een duidelijk verband bestaat tussen de SAF waarde en het cardiovasculaire risico profiel.
Onderzoek heeft aangetoond dat AGEs kunnen binden aan receptoren in allerlei weefsels in het lichaam, waaronder de wand van bloedvaten. De binding van AGEs moleculen aan een receptor leidt tot het vrijkomen van zogenaamde ontstekingsparameters zoals C reactive protein (CRP), interleukin-6 (IL6) en Tumor Necrosis Factor Alpha (TNFα).
Hoofdstuk 8 van dit proefschrift betreft een onderzoek naar de reproduceerbaarheid,
de invloed van opslagtijd (< 2 jaar vs > 4 jaar) alsmede de meetmethode (ELISA vs nefelometrie) van de ontstekingparameters CRP, IL-6 en TNFα. Dit onderzoek vond plaats binnen het Biobank Standardization and Harmonization for Research Excellence in the
European Union (BioSHaRE-EU) project waarin verschillende Europese biobanken met elkaar samenwerken. De ontstekingsparameters van 80 gezonde mensen, 80 mensen met type 2 diabetes alsmede 80 mensen die een hartinfarct hadden ondergaan werden bepaald in drie verschillende laboratoria. Vervolgens werd de ene helft van de samples opgeslagen voor een periode korter dan 2 jaar en de andere helft voor een periode van meer dan 4 jaar. Zowel de opslagtijd alsmede de meetmethode waren niet van invloed op de CRP en IL-6 waarden gemeten met zowel ELISA als nefelometrie. Vanwege analytische problemen op een van de laboratoria alsmede de kwaliteit van de IBL ELISA kon er geen betrouwbare evaluatie van TNFα worden verricht.
Conclusie
Samenvattend geeft dit proefschrift informatie over de rol van zowel leefstijl- als klinische factoren die van invloed zijn op de SAF meting in gezonde mensen en mensen met type 2 diabetes. Een belangrijke bevinding was dat het drinken van koffie leidt tot een hogere SAF waarde. Tevens toonde ons onderzoek aan dat naast actief en passief roken ook het stoppen met roken van invloed is op de hoogte van de SAF waarde. Vervolgens lieten we zien dat er en sterke associatie bestaat tussen het metabool syndroom, de individuele componenten zoals hypertensie, middelomtrek en verhoogde glucose waarde, en de SAF waarde. Echter de grootste bevinding beschreven in dit proefschrift bleek de sterk voorspellende waarde van SAF voor het 4-jaars risico op type 2 diabetes, hart- en vaatziekten en overlijden binnen een algemene populatie afkomstig van het LifeLines onderzoek.
Uit dit proefschrift kan worden geconcludeerd dat de SAF meting geschikt is voor een korte-termijn risico-inschatting op het ontstaan van bovengenoemde ziekten in de algemene bevolking maar dat men rekening dient te houden met een aantal klinische- en leefstijlfactoren. Toekomstig onderzoek met een langere follow-up duur van de LifeLines populatie is noodzakelijk om ook een lange-termijn risico-inschatting op type 2 diabetes, hart- en vaatziekten en overlijden te kunnen maken.
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