Prediction of outcome in individuals with diabetic foot ulcers: focus on the differences between individuals with and without peripheral arterial disease. The EURODIALE Study

Aims/hypothesis Outcome data on individuals with diabetic foot ulcers are scarce, especially in those with peripheral arterial disease (PAD). We therefore examined the clinical characteristics that best predict poor outcome in a large population of diabetic foot ulcer patients and examined whether such predictors differ between patients with and without PAD.
Introduction
Diabetic foot ulcers are a common and much feared complication of diabetes, with recent studies suggesting that the lifetime risk of developing a foot ulcer in diabetic patients may be as high as 25% [1]. Foot ulceration requires long and intensive treatment, has important effects on quality of life of both patients and care-givers [2] and is associated with major healthcare costs [3–5]. Although in recent years much effort has been put into the development of international guidelines in order to stimulate the delivery of uniform and structured care [6], prospective data on outcomes and predictors of outcome in patients with diabetic foot ulcers are limited.
The population of diabetic patients who present with foot ulceration is heterogeneous: although most patients have peripheral polyneuropathy, there are several other characteristics that may vary among patients, such as the presence of peripheral arterial disease (PAD), infection and co-morbidities. PAD is present in approximately one-half of all patients with foot ulcers [7] and is considered an important predictor of outcome [8, 9]. Therefore, outcome data on this important subgroup of patients with diabetic foot disease are needed. Such a requirement is underlined by the fact that although diabetic foot ulcers are usually reported and analysed as one clinical entity, marked differences in patient, foot and ulcer characteristics can exist between patients with and without PAD [7]. These observations raise the question of whether predictors of outcome in patients with and without PAD may differ.
The aim of the present study was therefore: (1) to obtain prospective data on outcome of individuals presenting with a new diabetic foot ulcer, including patients both with and without PAD; (2) to assess clinical characteristics that best predict poor outcome (i.e. non-healing of the foot ulcer) from this large set of patients; and (3) to examine whether such predictors differ between patients with and without PAD.
Methods
Study design and population
The EURODIALE consortium is an international collaborative network that was created to stimulate further research in the field of diabetic foot disease. Its main objective was to assess outcome and the major predictors of clinical outcome in a large sample of European patients with diabetic foot ulcers. The design and rationale of this study have been described in detail elsewhere [10].
Briefly, between 1 September 2003 and 1 October 2004, 1,232 patients with a new foot ulcer were included in 14 diabetic foot centres in ten European countries. The mean (range) number of included patients per centre was 88 (40–125). All participating centres have a longstanding expertise in the field of diabetic foot disease. Patients included were those presenting for the first time with a new foot ulcer within a period of 12 months, either at the outpatient or inpatient clinics of participating centres. Excluded patients were those who had been treated at the participating centres for an ulcer on the ipsilateral foot during the previous 12 months and those with a life expectancy of less than 1 year. Participants attended follow-up visits on a monthly basis. At baseline and during all follow-up visits, data were collected and recorded on standardised case record forms. This was done by dedicated investigators in each centre who were trained during plenary meetings and on-site visits. Recorded data included demographics, data on co-morbidities and foot and ulcer characteristics, as well as management. The local ethics committees of the 14 hospitals approved the study protocol and all patients gave written informed consent.
Management of diabetic foot ulcer
All patients were treated according to protocols based on the International Consensus on the Diabetic Foot [11], which include off-loading, diagnosis and treatment of infection, assessment of vascular status, treatment of PAD and regular wound debridement.
Potential predictive factors
Potential determinants of healing were chosen on the basis of (1) current literature; (2) expert opinion after extensive discussions during EURODIALE meetings; and (3) suitability for use in daily clinical practice. In addition to sex, age at baseline and duration of diabetes, several disease-specific characteristics and co-morbidities were investigated [10].
Ulcer characteristics All patients underwent a standardised examination according to the PEDIS system. This was developed by the International Consensus on the Diabetic Foot to enable classification of patients for clinical research purposes [11, 12] and classifies foot ulcers according to five categories: perfusion, extent, depth, infection and sensation.Perfusion assessment included evaluation of the presence of pedal pulses and measurement of the ankle–brachial pressure index (ABPI) using a handheld Doppler device; PAD was considered to be present if ABPI was <0.9 and/or two foot pulses were absent.Extent (i.e. size) was determined by multiplying the largest by the second largest diameter perpendicular to the first and divided into three categories: <1 cm2, 1–5 cm2 and >5 cm2.Depth was described as either deep or superficial if a full thickness lesion of the skin was or was not extending through the subcutis, respectively.Infection was diagnosed if two or more of the following signs were present: frank purulence, local warmth, erythema, lymphangitis, oedema, pain, fever and foul smell. The term infection covers both soft tissue infection and bone infection.Evaluation of sensation (peripheral neuropathy [PNP]) included pressure sensation (10 g monofilament on plantar aspect of hallux, metatarsophalangeal joints 1 and 5), tactile sensation (cotton wisp on dorsum of foot), vibration sensation (128 Hz tuning fork on dorsum of the hallux) and blunt/sharp discrimination (dorsum of foot). PNP was diagnosed if the results of two or more of the aforementioned tests were abnormal.In addition, the location of the ulcer was divided into plantar (on the plantar toes, plantar mid- or forefoot and plantar hind foot) and non-plantar (on the dorsal or interdigital part of the toes, on the dorsal or lateral aspect of the foot and heel ulcers). Ulcer duration was divided into three categories: <1 week, between 1 week and 3 months, and >3 months.
Co-morbidities The following disabling co-morbidities were assessed: presence of severe visual impairment (defined as the inability to read a newspaper after correction), end-stage renal disease (ESRD) (defined as dependency on haemodialysis or peritoneal dialysis or a previous renal transplant procedure), heart failure (New York Heart Association [NYHA] classification III or IV), any neurological disorder (excluding diabetic polyneuropathy) resulting in loss of motor or sensory function (e.g. stroke) and inability to stand or walk without help.
Study main outcome
Main outcome was complete healing (with or without minor amputation) of the foot, within the maximum follow-up period of 1 year. Healing was defined as healing (intact skin) of the whole foot at two consecutive visits. If more than one ulcer was present, the foot was defined as healed once all ulcers were healed. Outcome information was not obtained in 144 patients (11.7% of the patients included) who dropped out of the study and were therefore excluded from the analyses. Reasons for dropout were non-compliance (n = 24), inability to follow the patient (lack of transportation, no social support, too sick to attend; n = 25) or if care had been taken over by other specialists (n = 29); in 66 patients the reason for dropout could not be discovered. At baseline these participants were slightly older and had a higher incidence of heart failure, deeper ulcers and ulcers of longer duration than those included in the analyses (n = 1,088; Table 1).
Table 1Baseline characteristics of participants included and those excluded (dropouts) from the present studyVariableIncluded (n = 1,088)Dropouts (n = 144)p valueAge (years)64.7 ± 12.568.0 ± 11.60.003Male sex, n (%)a703 (64.6)85 (59.0)0.189Duration of diabetes, n (%)a0.418<5 years148 (14.1)19 (13.5)5–10 years169 (16.1)17 (12.1)>10 years731 (69.8)105 (74.5)Deep ulcer, n (%)a476 (43.8)80 (55.6)0.007Size of ulcer, n (%)a0.843<1 cm2403 (37.2)50 (35.0)1–5 cm2563 (52.0)76 (53.1)>5 cm2117 (10.8)17 (11.9)Duration of ulcer, n (%)a<0.001<1 week184 (17.0)10 (7.0)1 week–3 months627 (58.1)68 (47.6)>3 months269 (24.9)65 (45.5)Plantar location, n (%)a493 (48.2)62 (46.3)0.675Pretibial oedema, n (%)a197 (18.2)29 (20.3)0.538Heart failure NYHA III–IV, n (%)a117 (10.9)23 (16.1)0.065Neurological disorder, n (%)a70 (6.5)9 (6.3)0.918Inability to stand or walk without help, n (%)a107 (9.9)15 (10.4)0.843Visual impairment, n (%)a164 (15.3)19 (13.2)0.507ESRD, n (%)a63 (5.8)7 (4.9)0.639Polyneuropathy, n (%)a826 (78.5)105 (76.1)0.515Infection, n (%)a591 (57.2)82 (61.2)0.380PAD, n (%)a505 (47.5)78 (56.1)0.056Unless otherwise stated, data are mean values ± SD.aPercentages may not sum to 100 due to missing information
Statistical analyses
All statistical analyses were carried using the STATA software package version 9.2 (STATA, College Station, TX, USA). Comparisons between groups’ characteristics were made with χ2 tests (frequency data) or Student’s t test (continuous data).
Multiple imputation of missing values of predictor variables Values for one (n = 188), two (n = 35) or three (n = 13) predictor variables were not available for 236 participants; the number of missing values per predictor ranged from 0 to 6%. In order to decrease bias and increase power of the analyses [13], we used multiple imputation chained equations (procedure ‘ICE’ in STATA) to impute those missing values (1.7% of all required values) rather than performing complete case analyses [14, 15]. With ICE the imputation model of a single variable uses all the other variables as predictors by appropriate regression models (i.e. linear, logistic or multinomial if imputed variable is continuous, dichotomous or categorical). We generated five imputed datasets that were used to fit the regression models of interest (in each dataset and in the final, i.e. the combined dataset). Parameter estimates and standard errors were combined across the five replicates according to the procedure described by Rubin [16] and Carlin et al. [17] (procedure ‘micombine’ in STATA).
Development of predictive models First, univariable logistic regression analyses were performed for all potential predictor variables with the outcome of interest (non-healing), with values presented as univariable odds ratios (ORs) along with the respective 95% CI. Second, all potential predictors were entered simultaneously in a multivariable logistic regression model that was reduced to a most parsimonious model using a backward selection method based on Akaike’s Information Criterion. These models yielded a set of variables that best predict (and can be regarded as independent predictors of) outcome.
Results
Clinical outcome
Within the 1 year follow-up, 77% of the 1,088 patients healed, 12% were still undergoing treatment, 5% underwent a major (i.e. above the ankle level) amputation and 6% died (before healing of the foot ulcer). Among the patients who healed, 17% underwent a minor amputation; this rate was similar to that in those patients who did not heal (20%, p = 0.425).
When stratifying patients according to the presence or absence of PAD, significantly (p < 0.001) worse healing rates were observed in patients with than in those without PAD (69% vs 84%, respectively). Major amputation and mortality rates were also higher in patients with (8% and 9%, respectively) than in patients without PAD (2% and 3% respectively; p < 0.001). Baseline characteristics of patients with PAD compared with those without PAD are provided in Table 2.
Table 2Patients’ baseline characteristics according to their PAD statusVariablePatients with PAD (n = 505)Patients without PAD (n = 558)p valueAge (years)69.1 ± 11.260.5 ± 12.3<0.001Male sex, n (%)a321 (65.6)366 (63.6)0.490Duration of diabetes, n (%)a0.265<5 years63 (12.9)80 (14.9)5–10 years72 (14.7)93 (17.4)>10 years354 (72.4)363 (67.7)Deep ulcer, n (%)a266 (52.7)200 (35.8)<0.001Size of ulcer, n (%)a0.002<1 cm2173 (34.4)219 (39.5)1–5 cm2259 (51.5)294 (53.0)>5 cm271 (14.2)42 (7.5)Duration of ulcer, n (%)a<0.001<1 week58 (11.5)120 (21.7)1 week–3 months296 (58.0)318 (57.5)>3 months148 (29.5)115 (20.8)Plantar location, n (%)a197 (40.9)284 (55.0)<0.001Pretibial oedema, n (%)a111 (22.0)83 (14.9)0.002Heart failure NYHA III–IV, n (%)a64 (12.7)47 (8.5)0.027Neurological disorder, n (%)a40 (8.0)27 (4.9)0.039Inability to stand or walk without help, n (%)a65 (12.9)36 (6.5)<0.001Visual impairment, n (%)a89 (17.9)66 (12.0)0.007ESRD, n (%)a35 (7.0)25 (4.5)0.082Polyneuropathy, n (%)a383 (77.2)424 (79.3)0.429Infection, n (%)a293 (60.9)282 (53.4)0.016Unless otherwise stated, data are mean values ± SDaPercentages may not sum to 100 due to missing information
Predictors of healing
Table 3 shows the univariable associations of the potential predictors of non-healing in the overall population and Table 4 presents the variables retained in the predictive models after backward selection in the combined imputed datasets. The estimates were similar to those obtained in the complete cases dataset (n = 854) indicating that missing values were non-selective (data not shown). These include the following eight characteristics, all of which predict lower probabilities of healing: older age, male sex, larger ulcer size, heart failure, inability to stand or walk without help, ESRD, PNP and PAD. These variables were consistently identified in all five imputed datasets.
Table 3Association of each potential predictor with non-healing in the overall population (n = 1,088)Predictor variablesOutcome: healingOR95% CIp valueAge, per 10 year increase1.321.17–1.49<0.001Sex, men vs women1.501.07–1.970.018Duration of diabetes0.712 5–10 vs <5 yearsa0.960.56–1.65 >10 vs <5 yearsa1.050.69–1.60Depth of ulcer, deep vs superficial1.661.25–2.20<0.001Size of ulcer<0.001 1–5 vs <1 cm2a2.251.60–3.17 >5 vs <1 cm2a4.222.64–6.72Duration of ulcer<0.001 1 week to 3 months vs <1 weeka1.811.15–2.85 >3 months vs <1 weeka2.611.60–4.27Location, plantar vs non-plantar0.730.55–0.980.035Pretibial oedema, yes vs no1.791.27–2.510.001Heart failure (NYHA III–IV), yes vs no2.031.35–3.050.001Neurological disorder, yes vs no1.440.85–2.460.176Inability to stand or walk without help, yes vs no2.501.62–3.79<0.001Visual impairment, yes vs no1.360.94–1.980.105ESRD, yes vs no2.201.30–3.730.004Polyneuropathy, yes vs no1.410.98–2.040.065Infection, yes vs no1.471.09–2.000.012PAD, yes vs no2.311.72–3.10<0.001aReference categoryTable 4Multivariable models with independent predictors of non-healing in the whole study population and in patients with and without PADVariableAll patientsPatients with PADPatients without PADOR95% CIp valueOR95% CIp valueOR95% CIp valueAge, per 10 year increase1.281.11–1.470.0011.420.17–1.73<0.0011.550.91–2.630.105Sex, men vs women1.721.23–2.400.0021.971.25–3.110.003–––Size of ulcer<0.001<0.0010.008 1–5 vs <1 cm2a2.261.58–3.223.221.95–5.321.250.74–2.12 >5 vs <1 cm2a3.882.37–6.343.841.97–7.483.481.62–7.46Duration of ulcer––0.086 1 week to 3 months vs <1 weeka––––2.141.05–4.36 >3 months vs <1 weeka–– ––2.180.98–4.84Heart failure (NYHA III–IV), yes vs no1.550.99–2.430.0541.540.87–2.740.141–––Inability to stand or walk without help, yes vs no2.001.27–3.140.0032.361.34–4.170.0031.910.86–4.240.112ESRD, yes vs no2.511.41–4.480.0023.041.38–6.700.0062.000.76–5.250.161Polyneuropathy, yes vs no1.420.96–2.080.078–––1.700.89–3.250.108Infection, yes vs no–––1.631.03–2.580.036–––PAD, yes vs no1.711.23–2.370.001N/AN/AaReference categoryN/A, not applicable
Since we hypothesised that, from an aetiological point of view, predictors of non-healing would differ between patients with and those without PAD, predictive models were also fitted for these two groups separately (Table 4). In patients with PAD almost all of the predictors identified in the whole study population, with the exception of PNP, were again found to be independent predictors of healing. In addition, the presence of infection emerged as an additional independent predictor of non-healing. In patients without PAD, older age, larger ulcer size, inability to stand or walk without help, ESRD, PNP and, in addition, longer ulcer duration were independent predictors of poorer healing.
The observed interaction between infection and PAD status partly supports the classification of foot ulcer disease into four stages as suggested by Armstrong et al. (University of Texas classification system) [9]. Accordingly, upon analysis of the odds of non-healing per PAD × infection status, it was only in those patients with both PAD and infection that the odds of non-healing were markedly increased compared with those without PAD or infection: OR 2.82, CI 1.88–4.22, p < 0.001 in unadjusted analyses (Fig. 1) vs OR 1.87, CI 1.20–2.91, p < 0.001 after adjustments for the other variables included in the predictive model.
Fig. 1ORs of healing per PAD and infection (Inf) status
Discussion
The EURODIALE study is one of the few large prospective, international studies on outcome and determinants of outcome in diabetic foot disease. Despite the severity of the underlying disease and the important co-morbidity [7], clinical outcome of this population within a 1 year follow-up can be considered favourable. In our cohort, 77% of the patients healed (with or without a minor amputation), 5% underwent a major amputation and 6% died. However, healing rates in patients with PAD were considerably worse. In addition, predictors of healing also differed between the groups with and without PAD. The presence of infection, which is generally regarded as an important predictor of healing, was only predictive in individuals with PAD.
With regard to the overall outcome in our cohort, two recent studies found relatively comparable outcomes. Jeffcoate et al. [18] reported healing (excluding minor amputations) rates of 66% and an amputation rate of 5% with a similar prevalence of PAD. In a German cohort, Beckert et al. [19] found healing rates between 57% and 93% and major amputation rates of 3%, although the data as presented in that report cannot be easily compared because of their unique classification system. Oyibo et al. [20] also found similar rates of major amputation in their cohort (5%).
Our study shows that the combination of PAD and infection has a major impact on healing rates (Fig. 1); this significant interaction between PAD and infection is, in our opinion, one of the major findings of this study. In the patients without PAD we did not observe an association between infection and non-healing, which suggests that in these patients current antibiotic regimens and surgical techniques seem adequate to save a limb with adequate perfusion. However, within the total population of individuals with diabetic foot disease in developed countries, the group of infected and ischaemic ulcers accounted for almost one-third of all patients in our earlier report [7]. In a recent study, a significant relation between PAD, infection and poor outcome was also observed: in that study’s large cohort of outpatients with type 2 diabetes, PAD was an independent predictor of infection-related mortality [21]. Unfortunately, there is very little insight into the pathophysiology and treatment of infection in individuals with PAD. Currently it is not clear why infection is more prevalent and more difficult to treat in individuals with PAD. Remarkably, very few patients with PAD were included in most of the randomised trials on antibiotic therapy in diabetic foot infections [22, 23]. It has previously been demonstrated that lower limb tissue levels of antibiotics can be markedly decreased as a result of impaired perfusion in PAD [24]. It is open to speculation whether aggressive revascularisation will improve control of infection in these patients.
Although some earlier studies have examined the impact of co-morbidities on ulcer healing [25], no studies, to our knowledge, have systematically assessed in a multivariable analysis the effects on ulcer healing of patient characteristics including co-morbidities, as well as foot and ulcer characteristics at baseline. In our study, older age, ulcer size and several co-morbidities were independent predictors of non-healing in patients with and without PAD. Recently a number of larger studies reported data on determinants of outcome in diabetic foot disease such as the single-centre study by Beckert in Germany and the UK multi-centre study initiated by Jeffcoate [19, 26]. The former focused on wound-based characteristics and also found that the presence of PAD (defined as absence of pedal pulses) was an independent predictor of outcome (healing), while infection was not. In the recent UK multi-centre study, ulcer area was a strong predictor of outcome, as was the presence of PAD; co-morbidities were not taken into account in the regression analyses. Surprisingly, depth of the ulcer was not associated with outcome in our multivariable model, a finding also shown by Ince et al. [26]. In a retrospective study, Miyajima et al. [27] reported on patient characteristics that determined major lower extremity amputation and found that haemodialysis was an independent predictor of major amputation; in this study wound characteristics were not part of the regression analyses. The poor prognosis of foot ulcers for individuals in our study with ESRD is in line with earlier reports, in which amputation rates of 57% in individuals on haemodialysis were observed [25]. Although in our study ESRD was a predictor of non-healing in patients with and without PAD, it seemed to have a particularly negative effect in the latter patient group. PAD is frequently diagnosed and associated with adverse outcomes in haemodialysis patients [28]. PAD in ESRD patients is more severe and is accompanied by diffuse vascular calcifications, involvement of both distal infrapopliteal and foot arteries, and by impaired microcirculatory perfusion [29–31]. The severity of PAD in ESRD may explain the importance of ESRD in our healing models; additional mechanisms are probably impaired host defences in chronic renal failure and uraemia, or the presence of more resistant micro-organisms [32, 33].
The outcome of patients without PAD in our study was relatively favourable: 84% of the patients healed with or without minor amputation, 2% underwent a major amputation and 3% died. In our multivariate models, loss of sensation was associated with a poorer outcome in these patients, suggesting that loss of protective sensation is not only a key factor in the development of an ulcer, but also affects its outcome. This may be related to the preserved mechanism of off-loading the ulcer in individuals with intact protective sensation. However, neuropathy may also have direct effects on wound healing. Although data on the effect of neuropathy on wound healing in humans are scarce, some animal studies suggest that denervation may contribute to impaired wound healing in diabetes [34, 35]. A large dataset on individuals with neuropathic ulcers comes from retrospective database analyses in which healing rates of 47% were observed [36]. Although this study reported on healing at 20 weeks (whereas the current one examined healing rates at 1 year), the different results compared with our study are striking and may be related to an increased awareness of the importance of adequate off-loading, as a result of publication of international guidelines and reports on casting techniques [37–39].
There are several limitations to our study. Individuals who were lost to follow-up were excluded from the analyses as healing status could not be obtained; these individuals were slightly older and had a greater incidence of heart failure, deep ulcers or ulcers with a longer duration and PAD at baseline. In addition, we excluded patients who had had a previous ulcer within 12 months prior to presentation (i.e. we probably excluded patients with recurrent ulcers). Also we excluded patients with a life expectancy shorter than 1 year because of anticipated problems with follow-up. The estimates obtained in our models may therefore have underestimated the probability of non-healing in patients with a recurrent foot ulcer, although in one earlier study healing rate of neuropathic foot ulcers did not decrease in patients with multiple recurrences [40]. Since our study was embedded in daily clinical practice, limitations had to be set with regard to the number and type of data collected. It was therefore not possible to record more characteristics of these patients such as medication and extensive documentation of all complications. Moreover, to facilitate data collection, some continuous data (e.g. ulcer size) had to be transformed into a limited set of categories. Nevertheless, the set of potential predictors used in the present study do cover relevant patient and disease-specific aspects that can be easily assessed and used for patient risk estimation in clinical practice. Finally, our predictive model is based on outcomes that can be obtained in developed countries with access to the necessary resources such as antibiotic treatment and revascularisation; our results, therefore, are most relevant for diabetic foot ulcer patients in developed countries.
In conclusion, the results of this study have several implications. Both ulcer characteristics and several patient-related characteristics affected the outcome of diabetic foot ulcers. Therefore, a holistic approach by healthcare professionals who are familiar with the treatment of complicated diabetic patients is essential in order to identify the high-risk patient and start appropriate treatment. We found that patients with and without PAD differ in clinical characteristics, outcome and predictors of outcome. Taking into account these findings and the different pathophysiology and treatment of PAD and non-PAD ulcers, we feel that that diabetic foot ulcer with and without PAD should be defined as two separate disease states. The prevalent combination of PAD and infection is a unique entity; an important challenge lies in the development of evidence-based strategies to improve the poor outcome of these patients. Both studies comparing different antibiotic regimens in PAD, and studies evaluating the effects of early revascularisation on control of infection are urgently needed.

outcome
diabetes
foot ulcer
peripheral arterial disease
infection
co-morbidities
non-healing
predictive model


Dipeptidyl peptidase IV inhibitors in diabetes: more than inhibition of glucagon-like peptide-1 metabolism?

Inhibitors of the protease dipeptidyl peptidase IV (DPP-IV) are promising new drugs for the treatment of type 2 diabetes. They are thought to act by inhibiting the breakdown of glucagon-like peptide-1 and, thereby, selectively enhancing insulin release under conditions when it is physiologically required. These drugs are selective for DPP-IV, but the enzyme itself has a broad range of substrates other than glucagon-like peptide-1. Other high affinity substrates of DPP-IV including peptide YY may also play a role in the regulation of energy homeostasis. Moreover, DPP-IV is also known as CD26 and considered to be a moonlighting protein because it has a wide range of other functions unrelated to energy homeostasis, e.g. in immunity. The potential role of DPP-IV inhibition on substrates other than glucagon-like peptide-1 in diabetes patients remains to be elucidated.
Glucagon-like peptide-1 (GLP-1) is a hormone which is released following meals and stimulates insulin release from the pancreas. Its effects are terminated by breakdown by the enzyme dipeptidyl peptidase IV (DPP-IV). Therefore, inhibition of DPP-IV increases GLP-1 levels in the circulation and, hence, insulin release under conditions when it is needed, i.e. after a meal but not during fasting. Consequently, inhibition of GLP-1 inactivation is an insulinotropic principle which is unlikely to cause hypoglycaemia between meals. The lower risk for hypoglycaemic events as compared with other insulinotropic or insulin-sensitising agents makes DPP-IV inhibitors very promising candidates for a more physiological treatment of type 2 diabetes (Combettes and Kargar 2008).
In recent years, a number of selective DPP-IV inhibitors such as vildagliptin and sitagliptin have been evaluated in clinical trials (Hermansen et al. 2007; Utzschneider et al. 2008) and may have a future role in the treatment of type 2 diabetes (Combettes and Kargar 2008). In this issue of the journal, a novel DPP-IV inhibitor, ASP8497, is being introduced, which is highly selective for DPP-IV as compared to other peptidases (Someya et al. 2008). However, DPP-IV itself is not selective for GLP-1 but has a wide range of other natural substrates (Boonacker and Van Noorden 2003). Therefore, we wish to highlight potential implications of this lack of selectivity of DPP-IV for the use of DPP-IV inhibitors in diabetes treatment.
Another high affinity substrate of DPP-IV is peptide YY (PYY; Mentlein et al. 1993). Whereas cleavage of GLP-1 by DPP-IV causes inactivation, cleavage of PYY yields the long C-terminal fragment PYY3–36, which is inactive at some but active at other subtypes of PYY receptors. Specifically, it converts the non-subtype-selective agonist PYY into a selective agonist at Y2 and Y5 receptors (Michel et al. 1998). Thus, DPP-IV does not inactivate PYY but, rather, qualitatively alters its biological activity.
Indeed it has been reported that peripherally administered PYY3–36 inhibits food intake in rats, whereas PYY is a potent central stimulator of food intake (Batterham et al. 2002). Given the role of obesity in type 2 diabetes, prevention of the formation of such an endogenous food intake inhibitor by a DPP-IV inhibitor may be undesirable. It has been proposed that the effects of peripherally administered PYY3–36 on central nervous functions such as food intake may be mediated by excitation of afferent vagal fibres (Koda et al. 2005). However, the majority of subsequent rodent studies did not confirm inhibition of food intake by PYY3–36, particularly not following chronic administration (Boggiano et al. 2005). On the other hand, recent studies in non-rodents such as pigs (Ito et al. 2006) or humans (Degen et al. 2005; Sloth et al. 2007a; Sloth et al. 2007b) have reported reduced food intake upon peripheral administration of PYY3–36 but typically, these effects were found only at relatively high concentrations. Moreover, the effect of PYY3–36 on food intake was biphasic, depending on the duration of its administration (Parkinson et al. 2008). Two additional findings deserve consideration. Firstly, PYY3–36 was reported to promote fat oxidation and ameliorate insulin resistance in mice even under conditions of chronic administration where it did not reduce food intake (van den Hoek et al. 2006). Secondly, PYY3–36 was reported to lower plasma glucose levels even in the absence of alterations in circulating insulin levels (Bischoff and Michel 1998).
Taken together, the presently available data on PYY3–36 on food intake and metabolic parameters are not yet conclusive. However, it is clear that PYY3–36 is largely formed by DPP-IV, raising the possibility that selective DPP-IV inhibitors may exert part of their effects by modulating the PYY/PYY3–36 ratios. Therefore, it remains to be explored how possible effects on PYY cleavage contribute to metabolic effects of DPP-IV inhibitors in diabetic patients.
Moreover, DPP-IV is not only a protease for substrates relevant to energy homeostasis, but it also has a range of additional functions (Boonacker and Van Noorden 2003). Therefore, it is considered to be a moonlighting protein. As a protease, it has several other substrates, and it also acts as a receptor and costimulatory protein in the immune system. In this regard, CD26 is considered to be an important regulator of T-cell function (Reinhold et al. 2008). These pleiotropic effects of DPP-IV or CD26 lead to numerous potential uses of its inhibitors other than type 2 diabetes including inflammatory diseases (Ohnuma et al. 2006; Reinhold et al. 2007; Thielitz et al. 2008; Thompson et al. 2007) and, perhaps, certain types of cancers (Kikkawa et al. 2005; Thompson et al. 2007). Some of these effects may manifest as useful secondary actions when being used for the treatment of diabetic patients whereas others may manifest as adverse events. Most of these potential additional effects may not yet have manifested in the currently published diabetes literature, but it appears prudent to keep an eye on them.

dipeptidyl peptidase iv
diabetes
glucagon-like peptide-1
peptide yy
cd26


Refractive properties of the healthy human eye during acute hyperglycemia

Purpose To measure the refractive properties of the healthy human eye during acute hyperglycemia by means of Scheimpflug imaging and Hartmann-Shack aberrometry.
Transient refractive changes, due to a variation in blood glucose levels, are well-known complications of diabetes mellitus (DM). Both myopic shifts [2, 6, 10, 23, 31, 36] and hyperopic shifts [9, 13, 18, 26, 32, 37] have been reported in patients with DM after several days or weeks of hyperglycemia. It has been suggested that the predominant cause of the refractive changes is a change in the thickness of the lens [16, 17, 19, 25, 29] or shape of the lens [21], and/or a change in its refractive index [24, 27, 29, 33]. Two studies have been conducted in which hyperglycemia was induced under controlled circumstances to investigate refractive changes during hyperglycemia. Firstly, Gwinup et al. [15] administered glucose intravenously to ten patients with DM. A myopic shift of maximal -0.75 D was measured with autorefractometry. Secondly, Furushima et al. [11] induced hyperglycemia in seven healthy, young subjects through somatostatin injections and an oral glucose load. By means of autorefractometry and ultrasound biometry a large change in ocular refraction (-2.0 diopters (D)) and thickness of the lens (1.0 mm) was measured within 150 minutes after administration of the glucose load (maximal hyperglycemia: 13.9 mmol/l). The results of these two studies indicated that induced hyperglycemia can cause changes in refraction and that these changes appeared to be larger in healthy subjects.
In the study of Furushima et al. [11], only the thickness of the lens was measured, and there is no information about the change in the shape and the refractive index of the lens due to acute hyperglycemia. Therefore, the aim of our study was to induce hyperglycemia in healthy subjects under controlled circumstances, and accurately measure ocular geometry and refraction by means of corrected Scheimpflug imaging and Hartmann-Shack aberrometry, in order to investigate the mechanism underlying refractive changes during hyperglycemia. From the ocular geometry and refraction, in combination with the measurement of the axial length of the eye, it is also possible to calculate the equivalent refractive index of the lens [4, 5].
Subjects and methods
Five healthy Caucasian subjects (two males and three females) were recruited for this study. Mean age was 24.8 years (SD ±4.6 and range 21.2–32.6) and mean body mass index (BMI) was 24.2 kg/m2 (SD ±3.2 and range 21.4 - 29.7). The Medical Ethics Committee of the VU University Medical Center in Amsterdam approved the study protocol, and written informed consent was obtained from all subjects after the nature of the study had been explained. Subjects with a history of diabetes mellitus (or a fasting plasma glucose >5.5 mmol/l), a BMI of >30 kg/m2, visual acuity of <0.5 (Snellen), or a history of ocular pathology were excluded from the study.
Procedure to induce hyperglycemia
After a 10-hour overnight fast, the subjects received a subcutaneous injection of 100 µg synthetic somatostatin (Sandostatin, Novartis, Basel, Switzerland) in order to suppress the endogenous insulin secretion during glucose loading. Each subject had an oral glucose tolerance test (OGTT) (glucose 75 g) 30 minutes after the somatostatin injection. Blood glucose levels were measured with a blood glucose analyzer (HemoCue Diagnostics BV, Oisterwijk, the Netherlands). Endogenous insulin levels were determined with immunometric assays (Luminescence, Bayer Diagnostics, Mijdrecht, the Netherlands) in the Laboratory of Endocrinology of the Department of Clinical Chemistry in the VU University Medical Center. The subjects remained in a fasting state during the entire procedure.
Measurement of ocular parameters
Before and 120 minutes after the OGTT, 1.0% cyclopentolate and 5.0% phenylephrine eye-drops were administered to the right eye of the subjects. Hartmann-Shack aberrometry was performed with an IRX3 aberrometer (Imagine Eye Optics, Paris, France) and Scheimpflug imaging was performed with a Topcon SL-45 Scheimpflug camera, in which the film was replaced by a charge-coupled device (CCD) camera (St-9XE, SBIG astronomical instruments) with a range of 16 bits of grey values (512 × 512 pixels, pixel size 20 × 20 µm, magnification: 1×). Before and every 30 minutes after the OGTT, three measurements were made with each apparatus. To obtain accurate measurements of the shape of the lens, ray-tracing was performed to correct the Scheimpflug images for the distortion that is inherent to this technique [4, 5]. By combining the measurements of the corneal thickness (d1), the depth of the anterior chamber (ACD), the anterior (R1) and posterior (R2) radius of the cornea, the lens thickness (d3) and the anterior (R3) and posterior (R4) radius of the lens, the axial length of the eye, and ocular refraction, it was possible to calculate the equivalent refractive index of the lens (n lens) by means of an iterative process [5]. In these calculations it was assumed that the refractive indices of the cornea, aqueous humor, and vitreous do not change, because in vitro experiments have shown that large changes in glucose levels induce negligible changes in the refractive indices of the ocular media [8, 22]. The axial length of the eye was measured with an IOL-master (Carl Zeiss Inc., North America). The equivalent refractive error (ERE) was calculated as: ERE = sphere + (cylinder/2). Furthermore, the higher order aberrations (HOA) of each eye were analyzed for a pupil size of 5.7 mm and they were summarized in root mean square errors (RMS), including the third up to the eighth Zernike orders [34]. Furthermore, the HOA, including the spherical aberrations, were analyzed separately.
Measurements at baseline and during hyperglycemia were compared in the whole group by means of Wilcoxon signed rank sum tests. A refractive change of more than 0.2 D and a change in HOA of more than 0.025 µm were considered to be meaningful, according to the precision (defined as 95% confidence interval) for measuring the ERE and HOA of the aberrometer [3, 30]. Error analysis indicated that a change in R1, R2, d1, ACD, d3, R3, R4, and n lens of more than 0.05 mm, 0.05 mm, 0.02 mm, 0.14 mm, 0.15 mm, 0.30 mm and 0.40 mm, and 0.007 respectively, could be considered as significant, according to the precision of corrected Scheimpflug imaging [4]. In each subject individually, the significance of a change was determined from the precision of the measurements and the difference in the ocular parameters at baseline and during hyperglycemia. P values ≤0.05 were considered statistically significant.
Results
Figure 1 shows the changes in blood glucose after the OGTT. In all subjects the mean blood glucose levels rose from 4.0 mmol/l (range 3.4–4.5 mmol/l) to 18.4 mmol/l (range 16.1–22.0 mmol/l) in 126 minutes (range 90–210 minutes) after the OGTT. Subject 1 had a delayed elevation of blood glucose level, and therefore this subject received a second 75 g oral glucose load at time = 30 minutes. Furthermore, venous blood samples of subject 1 were taken from the antecubital vein, which was kept open with 0.9% saline (100 cl). Endogenous insulin was suppressed by the subcutaneous injection of somatostatin during the glucose load to a mean value of 2.1 pmol/l (range 0.4–4.5 pmol/l), and remained below basal secretion level (<110.0 pmol/l) for 147 minutes (range 75–270 minutes).
Fig. 1Changes in blood glucose (BG) levels in the five subjects after the administration of somatostatin and glucose; the oral glucose load (75 g) was administered at time = 0 minutes
No significant change in ERE was found in the whole group or in four of the subjects individually. In subject 1, a hyperopic shift in ERE of 0.4 D and an increase in the spherical aberration (±SE) from 0.03 ± 0.01 µm at T = 0 to 0.11 ± 0.01 µm during hyperglycemia was measured (p < 0.001). The other HOA did not change in subject 1. Furthermore, there was no significant change in the HOA separately or the RMS values in the group or in any of the other subjects individually. No changes in corneal thickness (d1), corneal shape (R1 and R2), anterior chamber depth (ACD), lens thickness (d3), or posterior lens shape (R4) were found in the group or in any of the subjects individually. In the whole group and in four subjects, no changes were found in the anterior radius of the lens (R3) or refractive index (n lens). However, in subject 01, the R3 and n lens changed significantly during hyperglycemia, compared to normal conditions; R3 decreased from 11.65 to 9.69 mm (mean decrease R3 = 1.96 mm; p < 0.001) and n lens decreased from 1.436 to 1.422 (mean decrease n lens = 0.014; p = 0.003). Figure 2 presents graphs of the normalized ERE, ACD, d3, R3, R4 and n lens of the five subjects. Figure 3 shows two corrected Scheimpflug images and a schematic drawing of the lens of subject 1, in order to illustrate the differences in lens geometry during hyperglycemia compared to baseline. In all subjects, both hematological and ocular parameters normalized within 6 hours after the OGTT.
Fig. 2Graphs of the normalized equivalent refractive error (ERE), lens thickness (d3), anterior chamber depth (ACD), anterior (R3) and posterior (R4) radius of the lens, and the refractive index of the lens (n lens) of the five subjects. Data are normalized by subtracting the value at baseline from the measured value in each subject. In subject 01 a hyperopic shift of 0.4 D, an increase in R3 and decrease in n lens were found during hyperglycemia at T = 240 minutes (p < 0.01). The oral glucose load was administered at T = 0Fig. 3Corrected Scheimpflug images of the right eye of subject 1. a At baseline time = 0 minutes, the shape of the cornea and lens are indicated with a solid line. b During hyperglycemia at time = 240 minutes after the first oral glucose load, the shape of the cornea and lens are indicated with a line of dashes. c A drawing of the changes in the shape of the lens in hyperglycemic condition (line of dashes) compared to the normal condition (solid line)
Discussion
Refractive changes occur frequently in patients with DM. The underlying mechanism is still unclear, and therefore the aim of our study was to measure ocular refraction and geometry during hyperglycemia, in an attempt to identify a possible explanation for these refractive changes. The effect of reproducible hyperglycemia was studied in healthy subjects without the systemic effects of DM. An OGTT in combination with a somatostatin injection was used to induce hyperglycemia. In an earlier study, this was shown to induce large changes in the refractive error and lens thickness [11]. Somatostatin inhibits insulin secretion [7], and to our knowledge there are no reports of refractive errors or changes in the lens due to this agent.
In general, and in four of the five subjects individually, no changes in ocular refraction or geometry were found during hyperglycemia. It could be that a more prolonged and severe hyperglycemia is needed to induce changes in refractive error or geometry of the eye. Glucose within the lens is metabolized via the sorbitol pathway, which consists of two enzymes that catalyze the conversion of glucose into its sugar alcohol sorbitol and the further conversion of sorbitol to fructose. These sugar alcohols tend to accumulate within the lens, because they penetrate cell membranes poorly. This accumulation of sugar alcohols causes the lens to swell [12]. This process might have taken longer than the observation period of the present study. Furthermore, the subjects measured in our study were young, and it could be that their lenses, which still have a fast metabolic reaction capacity, tolerate short hyperglycemic stress without swelling.
The results of our study are not in accordance with the findings of Furushima et al. [11], who observed a large myopic shift (-2 D) and a large increase in the thickness of the lens (1 mm) during hyperglycemia in healthy subjects. It must be noted that in our study the mean increase in the blood glucose level was even higher than that of the study of Furushima et al.. One main difference between our study and the study by Furushima et al. is the methods that were used to measure ocular refraction (aberrometry versus autorefractometry) and ocular geometry (corrected Scheimpflug imaging versus ultrasound biometry). However, the precision of aberrometry and autorefractometry are comparable for the measurement of defocus, astigmatism and, consequently, the equivalent refractive error [3, 30, 35]. Furthermore, the precision of corrected Scheimpflug imaging and ultrasound biometry are comparable as well. It could be that the difference in ethnicity (Caucasian subjects in the present study versus Asian subjects in the study by Furushima et al.) caused the inconsistency between the two studies, since Asian people generally have a more myopic ocular refraction than Caucasian subjects [14, 38].
In our study, small but significant changes in ocular refraction and lens geometry were found in one subject. A hyperopic shift of 0.4 D was found, in combination with an increase in anterior convexity of the lens. A combination of hyperopia and an increase in lens thickness during hyperglycemia has been described by Kluxen et al. [21], who found a 6 D hyperopic shift and a 0.4 mm increase in lens thickness in a diabetic patient with hyperglycemia. Saito et al. [29] reported hyperopic shifts (1.1 to 4.9 D) and an increase of approximately 0.2 mm in the thickness of the lens in five diabetic subjects during hyperglycemia. Therefore, it has been suggested that hyperglycemia causes a change in the refractive index of the lens [21, 24, 27, 29, 33]. The results of our study support this hypothesis; in subject 1, a decrease in the equivalent refractive index of the lens was calculated during hyperglycemia. It could be suggested that if the change in the shape of the lens is small, hyperopia will predominate during hyperglycemia due to a decrease in the refractive index of the lens. Alternatively, if the change in the shape of the lens is large in comparison to the decrease in the refractive index of the lens, the overall refractive error will result in myopia. The controversy in the literature with regard to refractive changes during hyperglycemia could be explained by this underlying mechanism of a balance between changes in the shape or the refractive index of the lens, which eventually determine the overall refractive outcome [20, 23, 28, 37].
It is surprising that a change in refraction and ocular parameters could be determined in only one subject. It must be noted that the procedures for inducing hyperglycemia and monitoring blood glucose were, to some extent, different for subject 1 compared to the other subjects. Because of a delayed elevation in blood glucose level, a second oral glucose load (150 g instead of 75 g glucose) was administered. Nevertheless, the maximum blood glucose value of subject 1 did not exceed that of the other subjects, and the endogenous insulin level was adequately suppressed during the glucose loading. Furthermore, in order to obtain sufficient blood samples, 0.9% saline had to be administered to keep the antecubital vein open. Therefore, it could not be excluded that the administration of saline contributed to the refractive change and alterations in the lens of subject 1. However, no studies have yet reported any refractive change due to saline administration.
The change in refractive error in patients with hyperglycemia could also be caused by a change in the shape of the cornea. However, previous research has shown that hyperglycemia has no influence on the shape of the anterior corneal surface [10, 24, 29, 32]. The results of our study also agree with these findings. No change in the anterior or posterior corneal radius was found during hyperglycemia. Therefore, the cornea does not seem to play a role in the explanation of refractive changes during hyperglycemia. This also applies for the higher order aberrations. Applegate et al. [1] reported that an increase in the higher order aberrations could cause a decrease in visual acuity. However, no changes in the higher order aberrations were found in any of the subjects in our study. Therefore, it can be assumed that blurred vision in hyperglycemia cannot be explained by changes in the higher order aberrations of the eye.
In sum, the results of this study show that induced hyperglycemia generally does not cause changes in the refractive properties of the healthy human eye. However, there were interindividual variations, as illustrated by subject 1, who had a hyperopic shift of refraction and a change in shape and equivalent refractive index of the lens during hyperglycemia. This could provide an explanation for the mechanism underlying the refractive changes often experienced by patients with DM and hyperglycemia.

hyperglycemia
scheimpflug imaging
hartmann-shack aberrometry
diabetes mellitus
lens
refractive errors