Journal of Pediatric Psychology Advance Access published online on July 17, 2008
Journal of Pediatric Psychology, doi:10.1093/jpepsy/jsn074
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Neuro-cognitive Performance in Children with Type 1 Diabetes—A Meta-analysis
1School of medicine, 2Statistical Advisory Service, Imperial College, 3Department of Psychology, Institute of Psychiatry, Kings College, and 4Academic Unit of Child and Adolescent Psychiatry, Imperial College
All correspondence concerning this article should be addressed to Professor Elena Garralda, MD, Academic Unit of Child and Adolescent Psychiatry, Imperial College, St Mary's Campus, Norfolk Place, London W21PG, UK. E-mail: e.garralda{at}imperial.ac.uk
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Abstract |
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Objective To conduct meta-analyses of neuropsychological performance in young people with type 1 diabetes. Methods Meta-analysis of 24 studies. Studies published between 1980 and 2005 were identified. The inclusion criteria were: young people who were
19 years of age with type 1 diabetes, a case–control design and standardized neuropsychological tests of seven cognitive domains. Results Diabetes was statistically associated with poorer performance on visuospatial ability (d = –0.29), motor speed (d = –0.26) and writing (d = –0.28), on sustained attention (d = –0.21) and reading (d = –0.23). Smaller effects were identified on full IQ (d = –0.14), on performance (d = –0.18) and verbal IQ (d = –0.15). Severe hypoglycemia was linked to deficits in short-term verbal memory (d = –0.14; Confidence Interval: –0.318, 0.024; p =.04). Conclusions This meta-analysis indicates that children with type 1 diabetes have mild cognitive impairments and subtly reduced overall intellectual functioning. Key words: diabetes; meta-analysis; neuro-cognitive; pediatric.
Type 1 diabetes is one of the commonest chronic diseases of childhood affecting three per 1,000 children in Europe between 0 and 15 years of age, and 1.7 per 1,000 children in USA between 0 and 19 years of age (CDC, n.d.); a prevalence that is rising (Daneman, 2006
). It is associated with total insulin deficiency, which is essential for glucose metabolism. This in turn can lead to impairments in the brain and can affect cognitive function.
Problems with cognitive function in children with chronic illness can have adverse consequences for the child's academic performance and affect illness management (Hobbs, 1985). Satisfactory metabolic control in childhood diabetes is regarded as crucial to prevent long-term microvascular (such as retinopathy) and macrovascular (such as cardiovascular disease) diabetic complications, but it requires increasingly intensified and demanding treatments involving strict dietary and medication control (De Beaufort & Swift, 2006). These may be affected by memory problems (Soutor, Chen, Streisand, Kaplowitz, & Holmes, 2004).
Recent years have seen the publication of a number of studies on cognitive functioning in children with type 1 diabetes. This is especially relevant given the vulnerability of the young brain to metabolic insult (Taylor & Alden, 1997). The studies have addressed the implications of illness features such as age at onset, glycemic control, hypoglycemic episodes, and hyperglycemia. However, results have not always been consistent and the wide variation in cognitive skills addressed by different studies makes it difficult to reach conclusions about the pattern of existing cognitive deficits. More specifically, although there are guidelines on desirable metabolic or glycemic control (Diabetes Control and Complications Research Trial [DCCT], 1996) evidence that this has cognitive implications is equivocal in children (Kaufman, Epport, Engilman & Halvorson, 1999; Kovacs, Ryan, & Obrosky, 1994; Northam, Anderson, Werther, Warne, & Andrewes, 1999; Schoenle, Schoenle, Molinari & Largo, 2002; Wysocki et al., 2003).
Desrocher and Rovet (2004) reviewed studies on the effect of a broader range of diabetic disease variables and aspects of cognitive functioning. They found associations between early illness onset and illness duration, recurrent hypoglycemic episodes and hypoglycemia, and a range of cognitive deficits in motor and visual attention and memory function, in set shifting and intelligence. However, the authors highlighted discrepancies in the way illness effects were defined across studies and size of cognitive impairments, and the review did not consider comparative studies between children with and without type 1 diabetes.
Marked variability in published data is likely to result from the relatively small numbers of subjects in each study and variation in the methods used for their evaluation. A meta-analysis of cognitive function in adult patients with type 1 diabetes identified mild to moderate cognitive deficits, involving slowing of mental speed and diminished mental flexibility (Brands, Biessels, De Haan, Kappelle, & Kessels, 2005). It is of interest to investigate whether a similar type of analysis will also identify cognitive deficits in children. Meta-analysis is a tool that helps overcome inconsistent research findings because it enables the combination of different numerical results, taking into account small effects. This helps reduce in a systematic way subjective bias in the interpretation of results, enabling a more reliable quantitative estimation of cognitive performance in children with diabetes.
The current article reports a meta-analysis of studies of cognitive function to determine the nature and severity of a variety of cognitive impairments in children with type 1 diabetes compared to child controls without diabetes. The secondary aim is to delineate cognitive impairments associated with disease variables such as age of onset and hypoglycemic episodes in children with diabetes. Our a priori hypotheses were that, as a result of metabolic changes potentially affecting brain function, specially so in the more immature brain and in the presence of severe hypoglycemia, neuro-cognitive impairments would be more commonly present in children with diabetes than in controls, and within the children with diabetes group in those with early illness onset and a history of hypoglycemic episodes.
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Methods |
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Data Sources
The databases MEDLINE, EMBASE, and PsycINFO were searched using the following terms or truncated versions: cognitive, behavioral, attention, learning, memory, executive functioning, information processing, spatial, intelligence, intellectual, and neuropsychological. These were combined in all possible combinations with the terms diabetes, type 1 diabetes, insulin-dependent diabetes, hypoglycemia, and hyperglycemia. References for retrieved papers were hand searched (ancestry method). Additionally, issues of Diabetes Care and the Journal of Pediatric Psychology were searched and authors contacted where appropriate.
Study Selection
Studies were selected and agreed by authors (J.N. and E.G.) to be included in the meta-analysis if they fulfilled a predetermined set of inclusion criteria: subjects 19-years old, diagnosed with type 1 diabetes, and systematically recruited from diabetic clinics; presence of a defined control group, either children without diabetes, or with type 1 diabetes with and without the illness characteristic under investigation (such as age of onset and hypoglycemic episodes); studies with a minimum of three subjects in each of the experimental and control groups, stated age of participants, and the use of standard neuropsychological testing methods to assess cognition. Only original studies published in full, in English, between 1980 and 2005, giving means and SD values or equivalent information in the form of t- or p-values, and published in peer-reviewed journals were included in the meta-analysis. Review papers were excluded. We chose 1980 as the starting date somewhat arbitrarily because we were interested in comparatively recent papers reflecting current practice and function.
The literature search identified a total of 37 studies appearing to meet the inclusion criteria. Of these, and in order to avoid statistical dependence between studies, five were excluded because they were updates or re-analysis of previously published data; when this was the case, the most inclusive study population was selected. Further, eight studies were excluded because they did not present the data in suitable format even after contact with the authors. This left 24 studies based on separate cohorts of children presenting data in a suitable format for meta-analytical synthesis.
Data Extraction
For each study, the type of control group (either control children without diabetes, or patients with diabetes grouped according to the presence or absence of the diabetic variable under consideration) was determined. Diabetic variables such as hypoglycemic episodes (defined as episodes with loss of consciousness and/or seizures), and age of onset (see Table I for cut-off points used), were dichotomized for statistical analysis. The choice of diabetic variables for this analysis was determined by the potential for an effect on brain function and by the availability of studies. Glycosylated hemoglobin (HbA1c), a measure of blood glucose control over the 6–8 weeks prior to testing was used as an index of long-term metabolic control: values >8% are taken as indicating poor control, those >10% as very poor metabolic control (DCCT, 1996; Sattler, 2001). Group characteristics and exclusion criteria for the children with and without diabetes groups were recorded. The data extracted were transcribed into a spreadsheet and regular checks and double entries carried out to ensure the reliability and full accuracy of the transcription.
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Since different tests were used across studies to assess similar aspects of cognition, a generic classification of cognitive functions was developed for the purpose of the meta-analysis. Data aggregation was carried out following the guidance in Lezak, Howieson, and Loring (2004
) and Sattler (2001), and in line with recommendations by Baron (2004). The classification comprised seven cognitive areas: intelligence, visuospatial, language and education, memory and learning, psychomotor activity, attention, and executive function. These were further subdivided into narrower subdomains as follows: - Intelligence
- Full IQ
- Performance IQ
- Verbal IQ
- Full IQ
- Visuospatial (visual perception and visuomotor integration)
- Language and education
- Language expression and comprehension
- Reading ability (learned verbal skills; comprehension of written language)
- Writing and written spelling (academic verbal skills and integration with visuospatial and motor abilities)
- Language expression and comprehension
- Memory and learning
- Verbal learning and immediate memory
- Visual learning and immediate memory
- Verbal delayed memory
- Visual delayed memory
- Working memory
- Verbal learning and immediate memory
- Psychomotor activity
- Motor speed
- Psychomotor efficiency (motor speed in response to information processing tasks)
- Motor speed
- Attention
- Visual attention
- Verbal attention
- Sustained attention
- Visual attention
- Executive function
- Global function
- Ability to shift concepts
- Problem solving strategies
- Planning skills
- Global function
The different neuropsychological tests used were categorized under these domains, and those measuring the same cognitive subdomain grouped together. A list of tests used for the comparison between children with and without diabetes, their categorization, and the article source is available in the Appendix (see Supplementary material online).
Paper selection and measure aggregation were conducted by the first author (J.N.) in discussion with a second author (E.G.); if there was still doubt this was discussed with a third author (C.L.) and agreement reached. Estimates of papers and measures requiring discussion between authors were 20% and 22%, respectively. Data extraction was conducted using a double entry strategy for accuracy. Data synthesis and analysis were carried out by a professional statistician specializing in meta-analysis (E.K.).
Data Synthesis
The same type of analysis was conducted to examine differences between children with and without diabetes, and children with diabetes according to characteristics such as age of onset and hypoglycemic episodes. First, Cohen's d statistic (the difference between diabetes and nondiabetes group mean scores standardized by pooled SD) (Cohen, 1988), was calculated for every test of a particular study. Cohen (1988) defined effect sizes as small (d = 0.2), moderate (d = 0.5), and large (d = 0.8). To ensure the data was in a suitable format, any variation values for original data were converted to SDs. This sometimes necessitated derivation from the published t- or p-values, or calculation from the raw data. Where data provided was not sufficient for derivation of d values, the appropriate author was contacted to try and obtain these: of eight contacted, one replied with additional data. Both significant and nonsignificant data were used. When cohort longitudinal studies were used results pertaining to cross-sectional data analysis only were entered.
The values of Cohen's d were adjusted for small sample sizes, and the arcsine transformation used for variance-stabilization (Hedges & Olkin, 1985) resulting in the transformed effects denoted by h. The direction of the effect was negative if the performance of the diabetes group was worse than the nondiabetes group. Next, the variance-stabilized effects of several tests assessing the same cognitive subdomain in the same group of children were combined in one mean h value. When calculating variance of such a mean, correlations between the tests (
) need to be taken into account (Sutton, Abrams, Jones, & Sheldon, 2000). Within a study with a sample size N, variance-stabilized effects of k tests have equal variances 1/N and their combined effect is an arithmetic mean with the variance of [1 + (k – 1) ]/Nk. Therefore, the precision of the mean h value is inversely proportionate to the unknown value of the correlation . As recommended by Rosenthal and Rubin (1986) cited in Sutton et al. (2000, Section 15.3), we carried out separate analyses with upper and lower bounds for the possible correlation. Two extreme values of (0 and 8) were used, resulting in an interval of possible values of the combined effect size. A random selection of calculations was checked to ensure consistent results to increase reliability.
For each cognitive subdomain, meta-analysis was performed where a minimum of three studies had assessed the same subdomain. Separate meta-analyses were performed for the differences between type 1 diabetes patients compared with control children without diabetes, and for differences in cognition between patients with type 1 diabetes according to the presence or absence of different disease variables. R statistical language (R Development Core Team, 2004) was used to perform the meta-analyses, using variance stabilized effect sizes weighted by the corresponding sample sizes, as recommended by Hedges and Olkin (1985) and Kulinskaya, Morgenthaler, and Staudte (2008). Combined effect was back-transformed to produce an overall effect size d and its two-sided 95% confidence interval (CI) (equivalent to a two-sided test at the 5% level) for each cognitive subdomain representing the overall magnitude of the effect, as well as a one-sided p-value for a negative difference in task performance (where zero meant that there was no differences in cognitive performance) between children with diabetes and healthy control children, and between children with diabetes in relation to illness characteristics.
Two versions of meta-analyses (for = 0 and =.8) were performed. This varied the precision of the results without noticeably affecting their significance; hence the summary effect size value was used along with the more conservative values for the upper and lower limits of the CI corresponding to =.8. The analysis described so far corresponds to the so called fixed effects model (FEM) of meta-analysis appropriate when there is no heterogeneity in effects from different studies.
To assess heterogeneity across studies the Cochran's Q test was performed using the 
2-distribution (a significant Q statistic means heterogeneity across studies) (Sutton et al., 2000). The inconsistency value I2 was also calculated to quantify the heterogeneity. Where the Q test was significant at 10% level, corresponding to I2 
40%, in accordance to the recommendation from Cochrane Handbook (Section 9.5.2, Higgins & Green, 2008), the overall effect size was recalculated using a random-effects model (REM). This model assumes an extra random variation around the common mean effect, which results in wider CIs than the fixed effects model originally used (Sutton et al., 2000). For lower values of heterogeneity, the results of the two models do not differ much and for a small number of studies the REM results may be not reliable. The method from Kulinskaya et al. (2008) was used to obtain the combined effects under the REM. A summary graph of the effect sizes and their CIs across all the cognitive domains assessed was then produced.
In order to assess the presence of publication bias, funnel plots were constructed for each cognitive domain whereby the variance stabilized effect sizes were plotted against their socioeconomic status (SES) (n–1/2) (Sutton et al., 2000; Higgins & Green, 2008, Section 10.4.1), an asymmetric or hollow plot indicating the likelihood of publication bias. In the absence of publication bias, the results from smaller studies are more widely spread around the mean effect resulting in a funnel shape. The funnel plot is an informal method for assessing the potential publication bias but the formal methods are not reliable for small number of studies k 10 in each cognitive domain.
Separate meta-analyses were performed for the differences between children with type 1 diabetes and children without diabetes and for differences in cognition between patients with type 1 diabetes according to the presence or absence of different disease variables.
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Results |
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Study Characteristics
In total, 24 suitable studies were identified and used in the meta-analysis. A number were suitable for use both in the primary analysis comparing children with type 1 diabetes and without diabetes, and in the secondary analyses into the effect of main disease variables. In total, 15 studies were used in the primary analysis, eight in the analysis of age of onset effects, eight to investigate the effects of severe hypoglycemic events (defined as episodes with loss of consciousness and/or seizures), and six to analyze gender effects. Studies assessing the effects of mild hypoglycemia could not be combined statistically because they were heterogeneous with regards to the number of episodes experienced and to whether cognitive assessment was carried out during or after the episode. None of the identified studies assessing the effects of hyperglycemia presented their data in a suitable format to allow statistical analysis.
The characteristics of studies that compared cognitive functioning in patients with diabetes and in children without diabetes are given in Table I. Sample sizes were variable across studies, but there was homogeneity with respect to gender distribution, both genders being generally split equally. Studies were heterogeneous for age, but most did age-match their groups and a number matched them on other characteristics such as SES. Age of illness onset ranged from 4.4 to 11.9 years, though the majority lay between 7 and 8 years. Most studies excluded patients with other chronic diseases and a history of psychiatric and neurological problems. HbA1c, a measure of blood glucose control over the preceding 6–8 weeks, was used as an index of long-term metabolic control. Although this is a continuous variable with higher HbA1c values indicating poorer metabolic control, those >8% are generally taken as an indication of poor control, and those >10% as of very poor metabolic control (DCCT, 1996). Only 8/15 studies provided HbA1c values for the subjects with diabetes, and all but one (Kaufman et al., 1999) had mean values indicative of poor metabolic control. Four studies had a cohort and the rest a case–control design.
Table II summarizes the results of 19 studies that examined the effects of disease variables [age of onset, severe hypoglycemic episodes, and gender] on cognitive functioning. The mean age at illness onset in the early onset groups was predominantly 2 years, in the late onset group 6–8 years. Metabolic control (HbA1c) was reported in 15 studies and was mostly indicative of poor or very poor control.
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Children with Type 1 Diabetes Versus children without Diabetes
No evidence of publication bias was found on the funnel plots for each of the subdomains (data not reproduced). Figure 1 summarizes overall effect sizes in each cognitive subdomain from the meta-analyses comparing performance in children with and without diabetes. Further details are given in Table III. Children with type 1 diabetes performed significantly worse on tasks assessing visuospatial abilities, motor speed and writing, sustained attention, and reading. There were also small negative or adverse effects on full, verbal, and performance IQ. All effect sizes were small, ranging from d = –0.29 to –0.14. There was a larger effect for shorter term verbal memory (d = –0.36), but this was not statistically significant.
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The
2-test for heterogeneity was significant for six cognitive subdomains, probably due to the wide range of neuropsychological tests used to assess a single domain, resulting in a variety of d values. This was taken into account by re-analyzing the data for these subdomains using a REM. All significant results using a FEM remained so after reanalysis, indicating that heterogeneity did not affect the significance of the results of the meta-analysis. All significant effect sizes were accounted for by over 248 participants per calculated subdomain score.
Early Versus Late Age of Onset of Type 1 Diabetes
The domains addressed by at least three studies entered into the meta-analysis included intelligence (full IQ, verbal IQ, and performance IQ), visuospatial skills, verbal language skills, and reading. No statistically significant effects emerged. The only effect close to significance was performance IQ (d = –0.38; p =.08), with poorer performance in children with an early age of onset (based on three studies and 191 participants).
History of Severe Hypoglycemic Episodes
The meta-analyses of cognitive functioning in relation to a history of severe hypoglycemic episodes identified only short-term verbal memory as a domain with statistically significant effects (d = –0.14; CI: –0.318, 0.024; p =.04) (based on six studies with 350 participants). There was a large effect for language expression and comprehension (based on four studies and 153 patients) but this was not significant (d = –0.66; p =.08). The 2-test for heterogeneity was significant for eight cognitive subdomains.
Gender Effects in Children With Type 1 Diabetes
There were sufficient studies to carry out meta-analysis of language, short-term verbal memory, psychomotor, and visuospatial abilities. No significant effect sizes were found in this analysis.
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Discussion |
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The main results from this meta-analysis comparing children with diabetes and controls without diabetes was mildly reduced intellectual quotient or IQ. The largest effects, even though still within the "small" range, were on visuospatial ability, motor speed and writing, and on sustained attention and reading. Secondary analysis indicated near significant effects of early diabetes onset on performance IQ, whereas severe hypoglycemic attacks had a small and significant effect on short-term verbal memory.
The neuro-psychological findings from the comparison of children with and without diabetes showed adverse effects on general, verbal, and performance IQ. These effects were small and unlikely to be of overall clinical significance, but they may place children with diabetes at a disadvantage in relation to peers, especially in demanding educational environments. More specifically, there were deficits in basic functions such as visuospatial ability, motor speed and sustained attention, and in the more complex practical learning tasks of writing and reading.
Visuospatial ability deficits may be presumed to underlie reduced IQ performance in children with diabetes. The latter was also the main suboptimal area identified in children with early diabetes onset and could be accounted for by an increased vulnerability for perceptual tasks due to early childhood brain insult (Taylor & Alden, 1997) and enhanced impact of diffuse diabetics-related metabolic changes on the young brain (Davis, Trundle, Ives, Robins, & Jones, 2002; Ferguson et al., 2003; Hershey et al., 2005; Schoenle et al., 2002). The poorer motor speed in children with diabetes may be the childhood equivalent of the slow mental ability which, together with reduced flexibility, was a main finding from the meta-analysis of cognition in adults with diabetes (Brands et al., 2005).
The adverse effects of diabetes on reading and writing may represent a direct consequence of the core visuospatial anomalies, reduced motor speed, and sustained attention identified in children with diabetes. Other factors such as reduced educational opportunities and increased school absence could have also contributed (Ryan, Longstreet, et al., 1985), but recent reports do not identify school absenteeism as a substantial problem likely to effect cognition in children with diabetes (McCarthy et al., 2002; Vetiska, Glaab, Perlman, & Daneman, 2000). Nevertheless, the educational significance of the mild decrements in reading and writing reported in this meta-analysis requires clarification.
In line with neuro-psychological results in adults with diabetes (Brands et al., 2005), we failed to find significant associations between a history of severe hypoglycemic episodes and reduced neuro-psychological performance, except for a small effect size on short-term verbal memory. The existing literature indicates that hypoglycemic episodes affect neuropsychological function selectively in children with an early onset of diabetes (before 5 or 6 years of age) (Ryan, 2004). The first 5 years of life are thought to constitute an especially critical period for brain development: special sensitivity to changes in glucose levels characteristic of diabetes could result in an enhanced likelihood of structural and functional brain deficits and neurocognitive deficits in this age group.
The strength of this meta-analysis is its ability to integrate findings from a number of studies adding consistency and generalizability to earlier results. Effect sizes are of similar magnitude to those for global cognitive changes in children with other pediatric hazards, such as preterm children followed up into childhood (Bhutta, Cleves, Casey, Cradock, & Anand, 2002) suggesting comparable effects across different types of comparatively common pediatric problems. Our results may be generalized to many children with type 1 diabetes since the study recruits attended diabetic hospital clinics.
There are a number of limitations to this analysis. In spite of adequately controlling for age and gender, studies did not always match their subjects on demographic variables, IQ, or psychiatric problems, all of which could affect neuro-psychological function. However, results in studies that did match for SES were comparable to the rest. The small number of studies included in some of the analyses call for caution in the interpretation of results. All studies bar one were carried out in Western countries. Studies published in other languages may differ in completeness of reporting, design characteristics and analytical approaches, but there is no empirical evidence of foreign language bias (Section 7.2.5, Sutton et al., 2000). Glycemic control was often suboptimal: nevertheless, evidence that this has cognitive implications is equivocal in children (Kaufman et al., 1999; Kovacs et al., 1994; Northam et al., 1999; Schoenle et al., 2002; Wysocki et al., 2003). It is also conceivable that in some children neuro-psychological deficits preceded the onset of diabetes. We reviewed the literature from 1980 onwards, but it is important to note that clinical practice is likely to have changed following the influential DCCT (1996): it is possible that more intensive diabetes medical regimes in current use will be associated with less neuro-cognitive deficits than reported here.
In conclusion, this meta-analysis has identified mild cognitive impairments in children with diabetes compared to children without diabetes. The extent to which educational performance and diabetes management are affected requires further examination.
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Supplementary data are available at JPEPSY online.
Conflicts of interest: None declared.
Received September 3, 2007; revision received June 19, 2008; accepted June 21, 2008
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