Skin autofluorescence predicts incident type 2 diabetes, cardiovascular disease and mortality in the general population

Participants from the Lifelines Cohort Study, a large population-based study in the northern region of the Netherlands (electronic supplementary material [ESM] Methods), were included [27]. At baseline, both physical examination and extensive questionnaire data were collected [28]. All individuals provided written informed consent before participating in the study, which was approved by the Medical Ethics Review Committee of the University Medical Center Groningen.

For the present study, we evaluated 82,904 participants of Western European descent between 18 and 90 years of age, who underwent baseline investigations between 2007 and 2013, and for whom validated SAF measurement was available at baseline and prospective follow-up was performed between January 2014 and January 2018. There were no relevant differences in sex distribution, age and glycaemic variables between those with and without SAF measurements. We excluded participants who, at baseline, had clinical CVD (n = 1861) and/or type 1 diabetes (n = 177) and/or type 2 diabetes (n = 2557) or reported a history of gestational diabetes (n = 134) or MODY (n = 4). Furthermore, individuals with new type 1 diabetes (n = 12) and a new history of gestational diabetes (n = 55) at follow-up were excluded, as well as those without documented follow-up (n = 5530). This resulted in 72,880 individuals available for analyses (ESM Fig. 1). Of these, 59,583 participated in the second screening, filled in the follow-up questionnaires and underwent follow-up examination with detailed BP measurement and laboratory examinations (ESM Methods), while only interim questionnaire data were available for 13,297 individuals. Median follow-up was 4 years, with a range of 0.5–10 years, for a total of 274,629 participant-years (ESM Fig. 2). Median follow-up for the 59,583 participants who had completed the second screening visit and measurements was 4.1 years. Follow-up measurements of fasting blood glucose and HbA_1c were available for 55,759 (76.5%) and 56,086 (77%) participants, respectively.

At both baseline and follow-up examination, participants completed a self-administered questionnaire on medical history, past and current diseases, and health behaviour. Medication use was verified at baseline by a certified research assistant and scored using the Anatomical Therapeutic Chemical (ATC) Classification System. Information regarding smoking behaviour (never, former and current smoking) and quantity smoked, as well as coffee consumption (cups/day), was collected from the questionnaires [29]. Weight was measured to the nearest 0.1 kg and height and waist circumference to the nearest 0.5 cm, with participants wearing light clothing and no shoes. BMI was calculated as kg/m². Systolic and diastolic BP and heart rate were measured every minute for 10 min in the supine position using an automated Dinamap monitor (GE Healthcare, Freiburg, Germany). The average of the last three readings was recorded for each BP variable and heart rate.

SAF was measured non-invasively using an AGE reader (Diagnoptics Technologies, Groningen, the Netherlands), as described previously [17, 20]. The AGE reader illuminates a skin surface of approximately 4 cm², guarded against surrounding light, with an excitation light source with wavelength between 300 and 420 nm (peak intensity at ~370 nm). Emission light and reflected excitation light from the skin are measured with an internal spectrometer in the range 300–600 nm. SAF was based on the ratio of the average emitted light intensity per nm in the range 420–600 nm and the average reflected light intensity per nm in the range 300–420 nm, multiplied by 100, and is expressed in arbitrary units (AU), taking skin colour into account [30]. Previous studies have shown an error rate of 5% when repeated SAF measurements were taken over a single day in control participants and individuals with diabetes [17]. More details about the number of machines and validation of measurements are given in the ESM Methods. Age-adjusted SAF levels (z scores) were calculated separately for men and women, based on the total population.

Blood samples were taken in the fasting state between 08:00 and 10:00 hours and transported to the laboratory facility at room temperature or 4°C, depending on the sample requirements. On the same day, HbA_1c (EDTA-anticoagulated) was analysed using an NGSP-certified turbidimetric inhibition immunoassay on a Cobas Integra 800 CTS analyser (Roche Diagnostics Nederland, Almere, the Netherlands). Serum creatinine was measured on a Roche Modular P chemistry analyser (Roche, Basel, Switzerland) and renal function was calculated as estimated (e)GFR with the formula developed by the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) [31]. Total cholesterol and HDL-cholesterol were measured using an enzymatic colorimetric method, triacylglycerol using a colorimetric UV method, and LDL-cholesterol using an enzymatic method, on a Roche Modular P chemistry analyser (Roche). Fasting blood glucose was measured using a hexokinase method.

Diagnosis of the metabolic syndrome was established if a participant at baseline satisfied at least three out of five criteria according to the modified guidelines of the National Cholesterol Education Programs Adults Treatment Panel III (NCEP ATPIII criteria): (1) systolic BP ≥130 mmHg and/or diastolic BP ≥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-cholesterol levels; (3) triacylglycerol levels ≥1.70 mmol/l and/or use of triacylglycerol-lowering medication; (4) waist circumference ≥102 cm in men and ≥88 cm in women; (5) fasting glucose level ≥5.6 mmol/l and/or use of blood glucose-lowering medication and/or diagnosis of type 2 diabetes [32]. Incident type 2 diabetes was based on either self-report or fasting blood glucose ≥7.0 mmol/l and/or HbA_1c ≥48 mmol/mol (≥6.5%) at follow-up evaluation. Incident CVD was defined as present when participants reported myocardial infarction, percutaneous transluminal coronary angioplasty (PTCA), stent positioning, coronary artery bypass grafting (CABG), transient ischaemic attack (TIA), cerebrovascular accident (CVA), intermittent claudication or peripheral artery vascular surgery. Vital status was ascertained with the Municipal Personal Records Database (GBA). Data on cause of death were not available. The incidence of type 2 diabetes, CVD and mortality was calculated separately and as a composite outcome for all age-decade groups (18–29, 30–39, 40–49, 50–59, 60–69, 70–79, ≥80 years)..

All analyses were conducted using PASW Statistics (Version 22, IBM, Armonk, NY, USA). Data are presented as mean ± SD, or median and interquartile range (IQR) when not normally distributed. Means were compared between groups with ANOVA. When variables were not normally distributed, medians were compared using the non-parametric Mann–Whitney U test. The χ² test was used to analyse categorical variables. Uni- and multivariate logistic regression analyses were performed to examine the association between SAF and the composite outcome of incident type 2 diabetes, CVD and mortality, as well as these outcomes separately, while adjusting for relevant clinical, biochemical and lifestyle risk factors. In our models, we adjusted for the most important determinants of SAF, i.e. age (model 1, used as a basic model), additionally adjusted for presence of the metabolic syndrome (model 2), glycaemic variables (model 3a/b), confounding non-biochemical factors (model 4) and all relevant factors (model 5). As age is an important factor influencing not only SAF measurements, but also the absolute incidence of events, we calculated the association between SAF and outcome according to four clinically relevant age groups, considered as low, intermediate, high and very high risk: ≤35 years; 36–50 years; ≥51 years; and ≥61 years. Values of p < 0.05 were considered statistically significant.

The incidence of all outcomes is shown in Table 1. An individual may have had more than one outcome. After a median follow-up of 4 years, 1056 individuals had developed type 2 diabetes (1.4%). Of those, 525 reported that they had been diagnosed with type 2 diabetes between the baseline visit and the follow-up measurement, while in those not reporting a diagnosis of diabetes, fasting blood glucose ≥7.0 mmol/l was observed in 408 participants, elevated HbA_1c ≥48 mmol/mol (≥6.5%) in 268, and either elevated blood glucose or HbA_1c in 531 participants.

Individuals with incident type 2 diabetes were significantly older at baseline than participants who did not develop type 2 diabetes (51.8 ± 11.4 years vs 43.7 ± 12.0 years, p < 0.001), and had a higher baseline BMI, fasting glucose and HbA_1c (all p < 0.001, Table 2). Moreover, the prevalence of the metabolic syndrome was also higher. As expected, the incidence of type 2 diabetes increased with age, and was between 3.3% and 4.1% in the three highest age decades (Fig. 1a). Mean baseline SAF z score was 0.16 ± 0.95 in participants with incident type 2 diabetes and −0.01 ± 0.81 in individuals who remained healthy (p < 0.001, Fig. 2).

In the same population, 1258 individuals (1.7%) had developed CVD at follow-up (Table 1). Participants with incident CVD were significantly older at baseline, had a higher waist circumference, higher systolic BP and diastolic BP, higher lipid levels and a lower eGFR (p < 0.001, Table 2). As expected, incidence of CVD increased with age, and was between 4.5% and 11.6% in the three highest age decades (Fig. 1b). Mean baseline SAF z score was 0.16 ± 0.96 among the population with incident CVD vs −0.01 ± 0.81 in participants who did not develop CVD or type 2 diabetes (p < 0.001, Fig. 2). In total, 55 individuals developed both type 2 diabetes and CVD, and these had the highest baseline SAF z scores (p < 0.001 vs no disease, p = 0.004 vs type 2 diabetes [in women only], Fig. 2).

Death was reported in 928 individuals (1.3%). As expected, mortality increased with age (Fig. 1c). Participants who died were older at baseline, had higher BP, were more likely to have impaired renal function/low eGFR and were more frequently current smokers. They also had higher SAF levels, even when corrected for age, than individuals who developed type 2 diabetes or CVD or remained without these disorders (Table 2).

ESM Table 1 details the mean age and SAF levels according to each age group. In almost all age groups, SAF was significantly higher (p < 0.0001) in those participants who developed an event (the composite outcome of incident type 2 diabetes, CVD and mortality) compared with those who remained free from these events.

Table 3 shows the results of the univariate and multivariate associations between SAF and clinical, biochemical and lifestyle factors and the composite outcome of incident type 2 diabetes, CVD and mortality. Univariate analyses showed that SAF was strongly associated with these outcomes (OR 3.84, 95% CI 3.57, 4.11, p = 1.5 × 10⁻³⁰⁷). This association remained significant after adjusting for age (model 1), age and the metabolic syndrome (model 2) and also after adjusting for age, fasting glucose (OR 1.79, 95% CI 1.64, 1.96, p = 1.1 × 10⁻³⁷) or HbA_1c (OR 1.78, 95% CI 1.63, 1.95, p = 6.6 × 10⁻³⁸). Additional regression models revealed that the association also remained significant when adjusted for sex, waist circumference and current smoking (model 4), as well as all for other variables including systolic BP, plasma lipids, eGFR and coffee consumption (model 5, OR 1.54, 95% CI 1.40, 1.70, p = 3.9 × 10⁻¹⁸). In the composite multivariate model 5, age, glucose, waist circumference, current smoking, systolic blood pressure and triacylglycerol were most strongly associated with the composite outcome (Table 3).

Additionally, we assessed the relationship between SAF and the three individual outcomes separately (Table 4). In a univariate model, SAF was most strongly associated with death (OR 5.10, 95% CI 4.56, 5.70, p = 4.1 × 10⁻¹⁸¹). This association remained highly significant after adjusting for age (model 1), presence of the metabolic syndrome (model 2), glycaemic variables (model 3a/b) and other possible confounding, non-biochemical factors (model 4). Model 5 showed that male sex, waist, systolic BP, cholesterol and current smoking, in addition to SAF and age, were independently associated with mortality. Similarly, univariate regression analyses revealed SAF to be strongly associated with both incident type 2 diabetes and incident CVD separately (Table 4). In addition to SAF and age, the strongest predictors of incident type 2 diabetes were fasting glucose, HbA_1c, triacylglycerol, BMI, waist circumference, BP and the presence of the metabolic syndrome. The strongest univariate predictors for CVD were—again in addition to SAF and age—waist circumference, BMI, glucose, HbA_1c, BP, eGFR and presence of the metabolic syndrome. SAF remained significantly associated with type 2 diabetes and incident CVD in the first four multivariate models. Also in these multivariate models, the presence of the metabolic syndrome, fasting glucose and HbA_1c levels was strongly associated with incident type 2 diabetes and moderately associated with incident CVD. In the final model (model 5), SAF still was significant, and age, glucose, waist circumference, male sex and triacylglycerol were the strongest factors associated with incident type 2 diabetes, and age, waist circumference, systolic BP and current smoking were the strongest factors associated with incident CVD.

As the time of death of all participants was recorded, we were able to show the effect of SAF on time from baseline to death. As can be seen in ESM Fig. 3, the highest SAF z score tertile was associated with an almost twofold increased risk of mortality compared with the other tertiles.

Finally, as age is an important factor influencing SAF measurements, but also the absolute incidence of outcome events (Fig. 1), we calculated the association between age-corrected SAF score and outcome according to four clinically relevant age groups (Table 5). SAF score was significantly associated with the composite outcome and with mortality in all age groups. For incident type 2 diabetes, in participants aged ≤35 years and those between 51 and 60 years SAF was not significant. For CVD, there was no significant predictive value in the lowest age group probably because of the low number of events.

We have presented data from a prospective population-based study that included almost 73,000 participants within a broad range of age and cardiovascular risk. This is the first prospective study to examine SAF as a predictor for type 2 diabetes, CVD and mortality in the general population. Although Lifelines extensively collected information on medication use at baseline, unfortunately no data were available on the use of new medications or changes in medications, as this information was not included in the follow-up questionnaires. Medication use, in particular oral blood-glucose-lowering agents and/or insulin, can validate self-reported diagnosis of type 2 diabetes, or even ascertain the presence of diabetes when a participant does not report diabetes correctly in the questionnaire. Also, data regarding the exact time of diabetes diagnosis and CVD events were not collected. As a consequence, we were not able to perform survival analyses for both diseases. We do not have follow-up blood glucose or HbA_1c measurements for 16,720 participants. This may underestimate the incidence of type 2 diabetes, and could alter the effects described.

As the study has been performed in people of Western European descent, the results may not be generalisable to other populations.

Finally, future studies need to incorporate the specific cause of death in order to further refine the predictive power of SAF.

This is the first prospective study in the general population to show the predictive value of SAF for incident type 2 diabetes, CVD and mortality. SAF significantly predicted the risk of these outcomes independently of several conventional risk factors. A longer follow-up of Lifelines participants will allow further validation and will expand the present findings.