Prevalence and progression of visual impairment in patients newly diagnosed with clinical type 2 diabetes: a 6-year follow up study

Study population

In the Danish Diabetes Care in General Practice study [24], 474 general practitioners agreed to include all subjects with newly diagnosed diabetes on their practice list (Figure 1). The 140 patients without a measurement of visual acuity at diagnosis did not differ from the 1,241 patients who were included in the present study regarding age (p = 0.36), sex (p = 0.31) and diagnostic plasma glucose (p = 0.81). At 6-year follow up, the 159 non-censored patients without information about visual acuity (Figure 1) did not differ from the 807 re-examined patients with regard to age (p = 0.23), sex (p = 0.82), diagnostic plasma glucose (p = 0.43), prevalence of DR at diagnosis (3.2% (5/158) vs. 4.4% (35/800), p = 0.49) and prevalence of moderately impaired vision or worse at diagnosis (7.0% (11/159) vs. 3.3% (27/807), p = 0.10). A small number started insulin treatment within 180 days of diagnosis, so 97.6% of the 1381 patients who had started in the study were considered to have type 2 diabetes [24]. Informed consent was obtained from all participants, and the protocol was approved by the ethics committee for Copenhagen and Frederiksberg.

Ophthalmologic examination

Immediately after diabetes diagnosis, the general practitioner referred the patient to a practising ophthalmologist who did a standard eye examination. The result of the funduscopy was recorded by the ophthalmologist in a multiple choice question with six response categories: no diabetic lesions, microaneurysms only, background retinopathy without or with maculopathy, and proliferative retinopathy without or with new vessels on the optic disc. In open-ended questions information about other retinal pathologies, glaucoma, and eye operations was given, and the presence of cataract was indicated in a closed question. Information about glaucoma was also given as answers to open-ended questions about reasons for impaired vision. Visual acuity with best correction was estimated with an optotype chart, usually a Snellen chart, and given for each eye as the Snellen ratio at 6 metres or 20 feet or as decimal acuity, but all measurements were converted to decimal acuity and logMAR [25]. The time (interquartile range, IQR) from diabetes diagnosis until the first eye examination was 45 (24-83) days. A total of 164 ophthalmologists participated, and 68.5% (553/807) of patients were seen by the same ophthalmologist at first and last eye examination. The eye with best visual acuity was used for evaluation of retinal pathology.

Assessments

The doctors recorded the following information about the patient: height and weight without shoes and outer garments; blood pressure and heart rate by routine methods after a 10-minute rest in a sitting position; sense of touch of cotton wool and pin prick on both feet; presence of dorsalis pedis or posterior tibial pulse on both feet; presence of patellar reflexes; history of myocardial infarction and/or stroke causing hospitalization; and amputation of any part of leg or foot before or at the time of diabetes diagnosis.

In questionnaires, patients gave information about whether they lived alone, their education, familial disposition to diabetes, smoking habits, leisure time physical activity, angina pectoris, intermittent claudication, and global self-rated health.

Definitions

The decimal acuity of patients' best seeing eye was used to categorise patients according to usual clinical and administrative practice in Denmark: normal vision (≥ 0.5), moderate visual impairment (< 0.5 and > 0.1) or blindness (≤ 0.1). Cardiovascular disease was defined as history of myocardial infarction and/or history of stroke and/or angina pectoris and/or intermittent claudication and/or absent arterial pulses on both feet and/or amputation on the lower extremities. Peripheral neuropathy was defined as lack of a sense of pin prick and/or touch of cotton wool on at least one foot and/or absent patellar reflex on at least one knee.

Assays

Laboratory analyses were centralized and quality controlled. Fasting blood samples were analysed at Odense University Hospital. Throughout the study, haemoglobin A1c (HbA_1c) was determined by the same ion-exchange, high-performance liquid chromatography method. Samples from 100 blood donors (age 20-80 years, 33 men, 67 women) were analysed, and the reference interval (mean ± 2SD) was calculated to be 5.4-7.4%. Quality assurance was obtained with commercial control preparations from Bio-Rad. In October-December 1995, the mean (SD) of low (n = 24) and high (n = 29) control samples were 6.7 (0.31)% and 10.4 (0.63)%, respectively, resulting in coefficients of variation (CV = SD × 100/mean) of 4.6% and 6.0%. Serum total cholesterols were measured enzymatically with cholesterol esterase-cholesterol oxidase-peroxidase reagent, and fasting serum triglycerides was determined enzymatically with a lipase-glycerolkinase-glycerol-3-phosphate oxidase-peroxidase reagent. Serum creatinine was determined by the Jaffe reaction. In this nationwide study a freshly voided morning urine sample was used instead of timed urine collection to determine renal involvement. Urinary albumin concentration was measured at Århus University Hospital by a polyethylene glycol radioimmunoassay. Spot urinary albumin concentration is of value in predicting both progression of renal involvement and increased mortality, as is albumin excretion rate [26].

Statistical analysis

The influence of baseline characteristics on change in visual acuity over 6 years was investigated in linear mixed models [27] using all available measurements of visual acuity from the best seeing eye. For each of the characteristics two models were constructed: one model where the change in visual acuity was described by a linear time trend (intercept and slope for the average change in visual acuity during the study) that differed for each of the categories of the characteristic, and a second model where the time trend additionally depended on age and sex in a multivariable regression fashion. Within-patient correlation of the observations was modelled by a patient random effect. The effect of the characteristic on changes in visual acuity was summarized with the fixed effect intercept and slope for each of the covariate classes from the first model (i.e. excluding age and sex). Differences between the covariate categories in intercept and slope in the two models were tested with the appropriate F-tests. In the 5.8% (72/1241) of patients who had an eye operation in the best seeing eye during follow up, measurements of visual acuity made after the operation were excluded from the analyses.

Baseline characteristics

Change in visual acuity

During the 6 years of follow up, DR had appeared in 11.7% (90/770) of patients without DR at diagnosis (Table 4). At 6-year follow up, DR and AMD were also relatively more common among the visually impaired (Table 5). The influence of eye complications at diagnosis on change in visual acuity over 6 years was investigated in linear mixed models (Table 1). The 112 patients in Table 1 with "other retinopathy" presented with133 retinal pathologies other than DR and AMD: hypertensive retinopathy (n = 43), retinal vasosclerosis (41), drusen (18) and other retinopathy (31). DR (n = 52), AMD (154), and cataract (337) were associated with the level of visual acuity and/or its annual change also in age- and sex-adjusted analyses. These effects are illustrated with median decimal acuity values in Figure 2.

	Diabetic retinopathy at 6-year follow up a
Diabetic retinopathy at diabetes diagnosis a	No diabetic retinopathy	Background retinopathy
		Microaneurysms only	Without maculopathy	With maculopathy	Proliferative retinopathy b	Total
No diabetic retinopathy	680 (88.3)	46 (6.0)	32 (4.2)	10 (1.3)	2 (0.3)	770 (96.0)
Background retinopathy
Microaneurysms only	2 (20.0)	3 (30.0)	4 (40.0)	1 (10.0)	0 (0)	10 (1.2)
Without maculopathy	8 (47.1)	1 (5.9)	3 (17.6)	1 (5.9)	4 (23.5)	17 (2.1)
With maculopathy	0 (0)	1 (20.0)	0 (0)	3 (60.0)	1 (20.0)	5 (0.6)
Proliferative retinopathy	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)	0 (0)
Total	690 (86.0)	51 (6.4)	39 (4.9)	15 (1.9)	7 (0.9)	802 (100)

Data are numbers (row- and column-%)

^a Refers to the eye with the most pronounced diabetic retinopathy at diabetes diagnosis and 6-year follow up, respectively

^b Among these, 2 patients with new vessels on the optic disc

	Visual acuity a
	Normal	Moderately impaired	Blind	All patients
Retinopathy a	n = 734	n = 54	n = 16	n = 804
Diabetic retinopathy
Microaneurysms only	46 (6.3)	5 (9.3)	0 (0)	51 (6.3)
Further diabetic retinopathy b	48 (6.5)	10 (18.5)	3 (18.8)	61 (7.6)
Age-related macular degeneration, AMD	65 (8.9)	20 (37.0)	11 (57.9)	96 (11.9)
Other retinopathy	44 (6.0)	0 (0)	2 (10.5)	46 (5.7)
No retinopathy	541 (73.7)	23 (42.6)	1 (6.3)	565 (70.3)

Data are numbers (column-%)

^a Refers to best seeing eye

^b Among patients with normal eye sight, impaired eye sight and blindness there are 4, 2 and 1 patient, respectively, with proliferative retinopathy

Similarly, some socio-demographic, clinical, biochemical, and behavioural variables affected the level of visual acuity and/or its annual change over 6 years even when the effect of age and sex was considered (Table 1). All significant effects, except that of self-rated health and triglycerides, are illustrated in Figure 2. The effect of triglycerides was not picked up by the median values. This is because the median figures in Figure 2 emphasize typical developments in visual acuity, while the result of the statistical analysis is necessarily based on logMAR values and expressed as means which are greatly affected by patients with very poor eye sight (Table 1).

When the changes in visual acuity were modelled as piecewise linear models with a breakpoint 3 years after diagnosis, the annual change in logMAR before (0.0268, p < 0.0001) and after this point (0.0231, p = 0.0008) was similar (p = 0.99 for the significance test of the time-logMAR interaction). Accordingly, visual loss during the first years after diabetes diagnosis did not seem to be greater than later on, which supports the underlying assumption in Figure 2 that visual loss follows a linear course.

Adding the identification number of the eye doctors as a random effect to the models in Table 1 did not change the associations with developments in visual acuity. Therefore, it is unlikely that our results are affected by the fact that some patients were examined by the same eye doctor at baseline and follow up, while others were not.

Strengths and limitations of the study

Our patients are likely to be representative of Danish patients with newly diagnosed, clinical, often symptomatic diabetes in this age group because of the well-defined background population in each general practice, the unchanged inclusion activity during the inclusion period, the small number of primary exclusions, and the acceptable participation in the eye examinations [24]. Furthermore, data from studies including patients with known diabetes may be misleading because of selective survival of patients with a more favourable risk factor profile [29]. Our results, however, cannot be generalised to all countries because of marked disparities in causes of reduced vision across the world [8].

Our patient sample was established in the early 90's and since then both surgical treatment of eye pathologies and pharmacological treatment of hyperglycaemia, hypertension and dyslipidaemia have been intensified, as has screening for diabetes. These initiatives to improve diabetes care and to identify patients earlier in the natural history of diabetes have probably decreased the variability of measures of treatment quality, so if a similar follow up study were to be made today, the associations between variables would perhaps be harder to identify. There is, however, no reason to suppose that the causal patterns underlying the associations that we have identified would be substantially different.

By presenting results from the best seeing eye only, we are underestimating the prevalence of eye pathologies in this patient group. This is because our main purpose is to estimate the change in visual acuity and its predictors, and eye pathologies are important in this study only insofar they are predictors of visual loss. In most studies the main pathology in the better eye is considered the cause of visual impairment [6, 7]. We analysed predictors of vision impairment prospectively. Furthermore, to improve statistical strength we chose to analyse visual acuity as a continuous outcome.

Our ability to detect a true clinical change in visual acuity is inversely related to the measurement error, i.e. the test-retest variability, of the test used [30, 31]. Unlike logMAR charts, the Snellen chart, which we had to rely on in this nationwide primary care study, has a large-scale increment resulting in a relatively high measurement error. Another factor contributing to the test-retest variability was differing routines for taking account of visual field loss [32]. The lower precision of the outcome, visual acuity, will however only tend to lessen the strength of the association between the outcome and a predictor.

On the other hand, the true incidence of vision impairment may be underestimated if those patients who missed an eye examination experienced a relatively rapidly declining vision. However there is no reason to suppose that the possible imprecision and error introduced were associated with any of the possible predictors of visual loss that were examined. Comparison of visual acuity between studies, even when categorised, is in any case not feasible [31], and our prevalence and incidence figures for visual impairment should be interpreted in this light.

Almost all Danish ophthalmologists contributed to the study increasing the inter-rater variability, and their screening by funduscopy may have overlooked 10-40% of sight-threatening eye disease [33]. Such measurement errors in the predictor variables will tend to reduce a true association between e.g. an eye pathology and the outcome, i.e. visual acuity, but it does not invalidate the associations that we actually find. It can be assumed that the detection rate for eye disease was higher in patients with low visual acuity. This may have biased the cross-sectional associations between eye disease and visual acuity in Table 2 and 5, but it does not to the same extent compromise the estimation of the predictive power of the eye pathologies at diagnosis for the change in visual acuity during the following 6 years. Therefore, our non-standardized estimation of eye pathologies at diabetes diagnosis only diminishes our ability to detect an effect of these variables on changes in visual acuity.

Comparison with existing literature

Implications for clinical practice and future research

It is evident that even in Denmark where patients can refer themselves to free eye examinations, treatable eye pathologies such as DR and cataract predict further visual loss. Our results underline the importance of eliminating barriers to efficient eye care by facilitating access to eye examination, increasing the understanding of patients and primary care practitioners of the need for regular screening and early surgical treatment, and, in some countries, addressing patients' financial burdens [49]. It should be easy to motivate a thoroughly informed patient to have regular eye examinations as many patients fear visual loss as the worst consequence of diabetes [10, 13]. The fact that almost all Danish primary care eye doctors participated in the present study demonstrates the commitment of ophthalmologists to preventive diabetes care.

In primary care, future intervention studies to reduce visual impairment in patients with diabetes may be designed primarily to overcome barriers to effective eye care, and such trials should preferably distinguish between the effect on visual acuity of changes in biological age on retina and of the interventions targeting treatable eye pathologies.