Diabetes Metab J > Epub ahead of print
Zhang, Shaw, and Cherbuin: Association between Type 2 Diabetes Mellitus and Brain Atrophy: A Meta-Analysis

Abstract

Type 2 diabetes mellitus (T2DM) is known to be associated with cognitive decline and brain structural changes. This study systematically reviews and estimates human brain volumetric differences and atrophy associated with T2DM. PubMed, PsycInfo, and Cochrane Library were searched for brain imaging studies reporting on brain volume differences between individuals with T2DM and healthy controls. Data were examined using meta-analysis, and association between age, sex, diabetes characteristics and brain volumes were tested using meta-regression. A total of 14,605 entries were identified; after title, abstract and full-text screening applying inclusion and exclusion criteria, 64 studies were included and 42 studies with compatible data contributed to the meta-analysis (n=31,630; mean age 71.0 years; 44.4% male, 26,942 control, 4,688 diabetes). Individuals with T2DM had significantly smaller total brain volume, total grey matter volume, total white matter volume and hippocampal volume (approximately 1% to 4%); meta-analyses of smaller samples focusing on other brain regions and brain atrophy rate in longitudinal investigations also indicated smaller brain volumes and greater brain atrophy associated with T2DM. Meta-regression suggests that diabetes-related brain volume differences start occurring in early adulthood, decreases with age and increases with diabetes duration. T2DM is associated with smaller total and regional brain volume and greater atrophy over time. These effects are substantial and highlight an urgent need to develop interventions to reduce the risk of T2DM for brain health.

INTRODUCTION

Type 2 diabetes mellitus (T2DM) is a common, chronic, and progressive metabolic disorder characterised by abnormally high blood glucose levels for a prolonged period, termed hyperglycaemia, due to insulin resistance and decreased production of insulin. Typical T2DM complications include retinopathy, kidney failure, and peripheral neuropathy [1]. Because of the high prevalence of T2DM among the elderly and the growing concern over cognitive health in older populations, there is a growing interest in how T2DM affects brain functions and related brain structures. T2DM is associated with an approximately 50% increased risk of developing dementia [2]; higher blood glucose levels in non-diabetics, which are known to be associated with increased risk of T2DM, are also associated with elevated risk of dementia [3]. Cognitive domains that may be affected by T2DM include memory, processing speed, and executive function [4]. Although the specific mechanisms that result in cognitive impairment in T2DM are not clear, hyperglycaemia, vascular disorders, hypoglycaemia, and insulin resistance are associated with increased risk; T2DM may also be involved in the pathogenesis of Alzheimer’s disease [5]. Moreover, there is emerging evidence that brain changes that lead to functional deficits may start developing well before T2DM is clinically diagnosed [3].
Many studies have used human brain magnetic resonance imaging (MRI) to measure structural changes in vivo that may be associated with T2DM. Typically, they have used brain volumetry to measure the extent of brain atrophy. Cross-sectional studies have consistently identified associations between T2DM and a decrease in mean total brain volume by 0.2 to 0.6 standard deviation units, which is comparable to 3 to 5 years of normal ageing [6-8]. More frequent brain lesions [8,9] and greater number of white matter hyperintensities [7,8] have also been identified in T2DM, likely due to the increased vascular pathology in this disease. These brain structural differences may also be associated with T2DM duration and blood glucose levels [8]. Studies focusing on specific brain regions have found negative associations between T2DM and volume of sub-regions including the hippocampus, basal ganglia, and many cortical regions among cognitively healthy individuals [6,8,10,11].
Longitudinal case-control and population-based studies have identified brain atrophy three times greater in T2DM than normal ageing [11-14]. Ventricular enlargement has also been observed [11,12,14,15], suggesting vulnerability of subcortical areas surrounding the ventricles. However, we lack robust estimates of atrophy rates attributable to T2DM as well as an understanding of which brain structures are most affected, as well as the timing of these changes across adulthood.
Therefore, the aim of this systematic review is to precisely quantify the volumetric differences and rates of brain atrophy associated with T2DM using a published methodology. We hypothesise that those with T2DM have smaller brain volumes and higher brain atrophy rate than those without T2DM.

METHODS

This systematic review and meta analysis was based on our previously published methodology [16], following predetermined search terms, inclusion and exclusion criteria, and quality assessment at the study level. This review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines [17] and was pre-registered in PROSPERO (No.: CRD42021230535).

Search strategy

PubMed, PsycInfo, and Cochrane Library (1950 to January 2020) was searched using the following terms: “(Diabetes or T2D) AND (brain or cerebrum or cerebral or cerebellum or cerebellar or hippocampus or hippocampal or subcortical or (cerebral ventricle) or ventricular or thalamus or thalamic or (basal ganglia) or striatum or (grey matter) or (white matter)) AND ((magnetic resonance imaging) or MRI or (computed tomography) or neuroimaging or morphometry or (diffusion tensor imaging) or volume or volumetric or thickness or atrophy or shrinkage).” Search included all text, all dates, full text papers in English (all article types for PubMed and PsycInfo; trials for Cochrane Library). Both literal and Medical Subject Heading searches were performed when possible. Titles and abstracts of the paper were screened by two reviewers (T.Z. and N.C./M.S.) for full text review. Full text and supplemental material of qualified studies were retrieved and examined by two reviewers against the inclusion and exclusion criteria. Disagreements between two reviewers were resolved by consensus or by a third reviewer. Citation maps of retrieved papers, previous reviews and previously identified journals were examined to identify additional journal articles that meet the criteria.

Inclusion and exclusion criteria

Studies were included if they had: (1) human adult participants; (2) at least a control group consisting of healthy participants without T2DM; (3) at least one comparison between participants diagnosed with T2DM and controls; (4) brain grey matter or white matter volume data from structural MRI or computed tomography (CT) scan data of T2DM and control participants; (5) data acquired using a validated automatic or manual segmentation method; (6) for longitudinal studies, longitudinal measurements at a minimum of two time points. Studies were excluded if they had: (1) participants with only type 1 diabetes mellitus (T1DM) or that did not differentiate T1DM and T2DM; (2) participants with only subclinical diabetes, including impaired fasting glucose, impaired glucose tolerance, insulin resistance, in the disease group or control group; (3) T2DM participants that all have major conditions other than diabetes, e.g., mental illness, behavioural problems, substance abuse, systemic illness or major brain structural abnormalities; (4) T2DM participants that are all under diabetes treatment (including placebo treatments) and being compared with those without such treatment; (5) only case studies and small samples with less than 10 participants in one group; (6) duplicate samples (for identified duplicates, the sample that best fits the study criteria will be included and the other samples will be excluded); (7) review articles, theses, unfinished studies, or entries with abstract only; (8) for longitudinal studies, samples where the total MRI follow-up period is less than 12 months.
Studies meeting the inclusion and exclusion criteria were assessed for quality using the Newcastle-Ottawa scale. Each study was evaluated on eight items classified into three categories including the selection of the study groups, the comparability of the groups, and the ascertainment of outcome of interest. Each quality item was awarded by a star (except two for comparability) and for each study up to nine stars in total.

Data extraction

Two of the authors extracted data (T.Z. and N.C.) and discrepancies were resolved by consensus. Data extracted consisted of (1) study design and number of participants in each group; (2) participants’ demographics including age, sex ratio, diagnostic criteria for T2DM, duration of T2DM, medication status, glycosylated hemoglobin (HbA1c) levels, fasting plasma glucose levels and body mass index; (3) measurement details including MRI parameters, structural measurements and segmentation method; and (4) study results including areas of interest (left and right) and effect sizes (left, right, and total). Data from studies reporting ratios relative to intracranial volume were included if a volumetric difference could be computed based on group statistics.
Multiple reports on the same cohort but on different brain structures were considered independent studies and included. Where a particular sample was reported in multiple studies on the same brain structure, the study that fit the selection criteria and provided data compatible for meta-analysis was included and other studies were excluded. Studies that reported effect sizes (or provided them after contact) were considered and from those the most recent study with the largest sample size was selected. If there was more than one study similar in sample size and time, the one with the highest quality rating was selected.

Statistical analysis

R version 3.1.1 (R Foundation for Statistical Computing, Vienna, Austria) [18] was used for statistical analysis. Meta-analyses were performed using the Metafor version 1.9-4 R package (https://www.metafor-project.org) [19]. Volumes of brain structures and annual percentage mean atrophy rate was considered as the effect size, and calculation of required standard error (SE) for meta-analysis was based on the standard deviation and number of participants in each group for each individual study. Availability of volume of a brain structure, either reported or computed based on other reported results, was the essential requirement for the meta-analysis. Atrophy rate was calculated using the formula: atrophy=[(volume_time1-volume_time2)/volume_time1]/(time1-time2) if not provided. Where insufficient data were available for inclusion in the meta-analysis, authors were contacted directly to seek additional information.
A random-effects model using a restricted maximum likelihood estimator was used for all meta-analyses. A random effects model was chosen based on the assumption that included studies are heterogeneous because they sample populations with different characteristics using a range of methodologies and therefore one cannot assume that there is a single effect size [20]. A random effects meta-analysis estimates the mean of a distribution of effects rather than estimating a unique effect [20]. We assessed heterogeneity across studies with the Q statistic (with P<0.01 being suggestive of significant heterogeneity) and the I2 statistic (values of 25%, 50%, and 75% were indicative of low, medium, and high heterogeneity). Separate meta-analyses were performed for different brain structures.
Meta-regression was used to investigate the impact of demographic and diabetes characteristics on differences in brain volumes between metabolically healthy individuals and individuals with diabetes, if there were at least 10 studies providing information of a covariate, including age (centred at 60 years), sex, diabetes duration, ratio of diabetes patients taking medication, plasma insulin levels, HbA1c levels and fasting plasma glucose levels, using linear mixed-effects models.
Sensitivity analyses were conducted using the leave-one-out method to identify studies contributing excessively to heterogeneity. Visual evaluation of asymmetry of the funnel plots was used to assess the bias in the meta-analyses results toward publication of studies with significant outcomes. The trim-and-fill method was used to estimate the number of missing studies (representative of unreported effect sizes) in the meta-analysis to estimate adjusted effect sizes.

RESULTS

Literature search and study inclusion

The systematic search identified 10,360 entries from PubMed, 664 from PsycInfo, and 3,641 from Cochrane Library; 60 duplicate entries were identified and excluded. Of these 14,605 studies, 355 studies passed title screening, and 109 studies remained after abstract screening; 64 studies remained after applying inclusion criteria in full-text assessment (Fig. 1).
Of the included studies, 40 were cross-sectional case-control, 17 cross-sectional population-based, one longitudinal case-control, and six longitudinal population-based studies. Forty-two studies (n=31,630; mean age 71.0 years; 44.4% male; 26,942 controls; 4,688 diabetes) with enough volumetric data could be considered for meta-analysis. Of these, 25 used automated segmentation, six used manual tracing, and 15 studies applied voxel-based morphometry (Tables 1 and 2) [11,21-84].
Twenty-eight studies reported disease duration, 17 medication status (number of patients taking medication, or specifically taking oral medication or insulin), five plasma insulin levels, 29 HbA1c levels, and 22 fasting plasma glucose levels (for specific demographic and T2DM-related information) (Table 2).

Meta-analysis

Of all the brain regions investigated, only total brain volume, total grey matter, total white matter, hippocampus, thalamus, caudate, putamen, globus pallidus, amygdala, nucleus accumbens, frontal lobe, superior temporal gyrus, total cerebrospinal fluid, and white matter hyperintensity were reported in a sufficient number of studies to be included in meta-analyses. Total brain volume annual atrophy rate was also reported in five longitudinal studies (see Table 1 for details).

Global brain volumes

Fifteen studies reported on total brain volume (15,937 normal; 2,277 diabetes), 24 studies on total grey matter volume (15,475 normal; 3,117 diabetes), and 22 studies on total white matter volume (14,091 normal; 2,965 diabetes). Participants with T2DM had significantly smaller total brain volume, with volumetric difference attributable to T2DM (T2DM-normal in Table 3) of 20.50 cm3 (1.81% of normal volume). They also had significantly smaller grey matter volume (−17.43 cm3; 2.88%) and white matter volume (−10.73 cm3; 2.15%) (Table 3, Fig. 2).

Subcortical volumes

Fourteen studies reported on hippocampal volume (9,935 normal; 1,319 diabetes), four studies on thalamus (9,703 normal; 625 diabetes), and three studies on caudate, putamen, globus pallidus, amygdala, and nucleus accumbens (9,350 normal; 579 diabetes). Participants with T2DM had smaller hippocampus (−0.15 cm3; 4.4%), thalamus (−0.33 cm3; 4.2%), caudate (−0.09 cm3; 2.6%), putamen (−0.14 cm3; 2.9%), globus pallidus (−0.014 cm3; 0.8%), amygdala (−0.003 cm3; 0.2%), and nucleus accumbens volume (−0.035 cm3; 7.8%); these associations were significant in hippocampus, thalamus, caudate, putamen, and nucleus accumbens (Table 3, Supplementary Fig. 1).

Local cortical volumes

Five studies reported on superior temporal gyrus volume (820 normal; 665 diabetes) and five studies on frontal lobe volume (1,617 normal; 282 diabetes). Participants with T2DM had smaller superior temporal gyrus (−0.18 cm3; 0.88%) and smaller frontal lobe volume (−1.04 cm3; 0.71%) but the association was not significant (Table 3, Supplementary Fig. 1).

Total cerebrospinal fluid volume

Ten studies reported on total cerebrospinal fluid volume (4,363 normal; 1,239 diabetes). Participants with T2DM had higher cerebrospinal fluid volume (7.15 cm3; 1%) but the association was not significant (Table 3, Supplementary Fig. 1).

White matter hyperintensities volume

Twelve studies reported on white matter hyperintensities volume (14,030 normal; 2,070 diabetes). Participants with T2DM had smaller white matter hyperintensities volume (−0.006 cm3; 0.001%) but the association was not significant (Table 3, Supplementary Fig. 1).

Total brain atrophy rate

Five longitudinal studies reported on total brain atrophy rate (3,823 normal; 778 diabetes). An initial analysis produced a paradoxical non-significant result given all studies reported a greater atrophy in T2DM, with four being highly significant (Supplementary Fig. 2). A leave-one-out analysis indicated that this perplexing finding was attributable to a single study [21], likely due to it being an outlier in the size of its estimate and having a very large confidence interval, which would unduly inflate the error variance estimate of the analysis. Consequently, a follow-up analysis excluding this study is reported. Participants with T2DM had significantly larger atrophy rate (0.072%; 13.4% larger than normal) (Table 3, Fig. 2).

Inhomogeneity and publication bias

Significant inhomogeneity was observed in Q tests of the meta-analysis, except in thalamus, caudate, putamen, globus pallidus, amygdala, nucleus accumbens, and in T2DM-normal difference in superior temporal gyrus and frontal lobe (Table 3).
Evidence of some publication bias was also detected for most brain regions investigated. Visual inspection of funnel plots revealed that studies were likely missing for total brain volume (13.3% of total), globus pallidus (40%), superior temporal gyrus (16.7%), frontal lobe (28.6%), cerebrospinal fluid (20%), and white matter hyperintensity (20%). Although asymmetry and presence of missing studies suggested some publication bias toward studies reporting higher atrophy rates, trim and fill test indicated that the differences between corrected and reported volumetric differences were generally small and therefore publication bias is unlikely to have significantly influenced the present results (Fig. 3).

Meta-regression analyses

The effect of age, sex ratio, diabetes duration, medication, fasting glucose and HbA1c on the association between T2DM and volume of total brain, grey matter, white matter and hippocampus was investigated by meta-regression analysis. A significant negative association between decreasing total brain volume difference and increasing age was detected such that every additional year in age above 60 was associated with a 4.4% smaller volumetric dif ference between individuals with and without diabetes (individuals with diabetes at age 60 years are 28.45cm3 smaller; this difference decreases by 1.24 cm3 per year) (Supplementary Table 1). A significant positive association between decreasing grey matter volume difference and increasing diabetes duration was also detected such that every additional year above mean diabetes duration (10.5 years) in age above 60 was associated with an 8.8% larger volumetric difference between individuals with and without diabetes. No significant effects were observed in other analyses (Supplementary Table 1, Supplementary Fig. 3).

DISCUSSION

The aim of this study was to synthesise the evidence on quantitative differences in brain volumes and rates of brain atrophy associated with T2DM via systematic review and meta-analysis of the published literature. In total, 42 studies including 31,630 participants were included. The main findings indicated that individuals with T2DM had significantly smaller brain volumes compared to those without T2DM, as well as larger atrophy rates. Moreover, T2DM-related volumetric differences appeared to decrease with age and increase with diabetes duration but did not differ between men and women.

Global and local brain volumes

This study’s results are consistent with the findings of previous reviews reporting that brain volumes are smaller in those who live with T2DM, but it also substantially extends our understanding of the scope of this effect. The present study was able to convincingly demonstrate volumetric differences in four brain structures while also precisely summarising their magnitude. It demonstrated that diabetes-related volumetric brain differences were substantial (total brain: 1.88%; grey matter: 2.81%; white matter: 2.15%; hippocampus: 4.4%). Indeed, in normal ageing, total brain volume shrinks by about 0.5% every year from the 40s onwards with further acceleration after age 70 [21]. Similarly, the hippocampus shrinks by about 0.3%/year before 55, 0.85%/year between 55 and 70, and 1.1%/year thereafter in those cognitively intact [16]. Thus, the differences observed in T2DM correspond to about 4 to 5 years of normal ageing, and possibly more. It is also worth noting that while these effects were relatively large across these brain regions, they were particularly strong in the hippocampus. This is noteworthy because subcortical atrophy in the hippocampus is a hallmark of Alzheimer’s disease and is one of the strongest predictors of conversion from mild cognitive impairment to Alzheimer’s disease [85,86]. Importantly, the rate of hippocampal atrophy in mild cognitive impairment has been estimated in a recent meta-analysis to be approximately 2.5%/year [87]. This may suggest that the hippocampal volumetric difference observed in T2DM might lead to an earlier conversion to Alzheimer’s disease by almost 2 years.
Furthermore, the rate of total brain atrophy in T2DM was significantly higher than in metabolically healthy individuals by 13.4%. This is consistent with cross-sectional results, and that cross-sectional volumetric differences may increase with diabetes duration according to meta-regression analysis. However, meta-regression results also revealed that the difference in total brain volume between those with T2DM and metabolically healthy individuals decreased with age (4.4% for every year above 60). This implies that a disease onset before age 60 years may have a greater impact on brain volumes. Moreover, similar consistent trends were observed for grey matter, white matter, and hippocampal volumes. Together, these may suggest that diabetes-related neurodegeneration occurs before onset of diabetes and slows down with increasing age. Hyperglycaemia is the main characteristic of T2DM pathology, and studies on metabolically healthy individuals and individuals with prediabetes have found association between higher blood glucose levels, smaller brain volumes and poorer cognitive functions [21,88,89]. Known mechanisms that may contribute to T2DM-related brain changes, such as hyperglycaemia, vascular disorders and insulin resistance, were likely present before clinical diagnosis [5]. An implication of these findings is that it will be important for future research to investigate brain changes leading to T2DM diagnosis, and to conduct more longitudinal studies following individuals with T2DM over longer periods of time to clarify these issues. From a health policy perspective, it may also suggest that more resources should be directed towards risk reduction interventions in those at risk before the disease develops, rather than mitigate the effects of T2DM when much of the damage has already taken place.

Clinical implications and moderators of T2DM-related brain atrophy

T2DM is a known risk factor of dementia, with increased risk of dementia by two-to-three fold [90]. The current study not only shows substantial brain volume differences that might result in earlier conversion to mild cognitive impairment and Alzheimer’s disease, but also indicates potential predictors and structural basis of cognitive deficit among individuals with T2DM. Previous studies showed evidence of cognitive decline in important functions that may affect self-caring ability of diabetes patients, such as executive function [91] and processing speed [89]. Our study has also found significant associations between brain volume of some subcortical structures and diabetes, even though studies on local volumes were fewer. Indeed, studies on specific cognitive deficits in T2DM are relatively few with inconsistent results, and even fewer studies on both brain volumes and cognition. Further studies with larger sample size are needed to understand how changes at local brain structures are related to cognitive functions of diabetes patients.
We conducted meta-regression analyses to investigate moderators of volumetric differences between metabolically healthy individuals and individuals with T2DM. In this study, meta-regression analyses to examine the moderating effects of age, sex ratio, diabetes duration, ratio of medication, fasting glucose and HbA1c were only possible for some brain structures. These showed that volumetric differences decreased with age, which suggests early pre-clinical occurrence of diabetes-related brain changes. However, an increase in volumetric differences was also observed with diabetes duration in grey matter, and a similar trend in white matter. A likely explanation may be that effects related to T2DM are attributable to pathogenic mechanisms but that they become obscured by the increasingly prevalent, and often related, effects of other risk factors for neurodegeneration including cardiovascular diseases, hyperlipidemia, obesity, and others. The cause of the brain differences reported in the present study may be a combination and interaction of pathogenic mechanisms of T2DM and genetic factors, environmental exposures, age, sex, comorbidities, and medication. Some anti-diabetes oral medications, such as dipeptidyl peptidase 4 inhibitors, metformin, thiazolidinediones and sulfonylurea have potential neuroprotective effects for individuals with diabetes, whereas insulin may be associated with increased risk of dementia [92]. Although lifestyle factors such as high cholesterol diet, smoking, etc. are known risk factors for cognitive decline common among people with T2DM, no conclusive evidence is available on whether cardiovascular risk factor management via lifestyle change, controlling blood pressure and cholesterol levels may reduce the risk of cognitive dysfunction in people with diabetes compared with those without diabetes [93]. While these factors are sometimes reported by studies included in our meta-analysis, usually only some of these factors were reported in one study; reports on medication were often unspecific. This limited the covariates we could control for in our meta-analysis, the types of meta-regression we could conduct and the number of studies that could be included.

Strengths and limitations

The main strengths of this review were an extensive search of the literature using a wide range of search terms across multiple databases, and inclusion of both cross-sectional and longitudinal studies across many brain structures. The main limitation was the relatively small number of studies which could be included in meta-analyses of regional volumes and particularly longitudinal atrophy. This also limited the number of moderators that could be tested in meta-regressions.

CONCLUSIONS

To our knowledge, this is the first meta-analysis that synthesises and precisely quantifies findings from both cross-sectional and longitudinal studies on the association between T2DM and brain atrophy. Results showed that T2DM is associated with smaller total and regional brain volumes with this difference decreasing at older ages. These effects are important and highlight an urgent need for the development of interventions to prevent them. How T2DM-related brain atrophy changes over time, and in the pre-clinical stages of the disease is unclear based on the available evidence and requires further investigation.

SUPPLEMENTARY MATERIALS

Supplementary materials related to this article can be found online at https://doi.org/10.4093/dmj.2021.0189.
Supplementary Table 1.
Results from meta-regression analyses for association between T2DM-normal brain volume differences and age, sex ratio, T2DM duration, fasting glucose, and HbA1c
dmj-2021-0189-suppl1.pdf
Supplementary Fig. 1.
Forest plots of differences in local brain volumes between participants with and without type 2 diabetes mellitus. (A) Hippocampus, (B) caudate, (C) thalamus, (D) putamen, (E) globus pallidus, (F) amygdala, (G) nucleus accumbens, (H) superior temporal gyrus, (I) frontal lobe, (J) cerebrospinal fluid, and (K) white matter hyperintensity. CI, confidence interval.
dmj-2021-0189-suppl2.pdf
Supplementary Fig. 2.
Forest plot of difference in brain atrophy rates between participants with and without type 2 diabetes mellitus, including the excluded study [21]. CI, confidence interval.
dmj-2021-0189-suppl3.pdf
Supplementary Fig. 3.
Association between type 2 diabetes mellitus-normal brain volume differences and age, sex ratio, diabetes duration, medication, fasting glucose, and glycosylated hemoglobin. (A) Age and total brain volume, (B) age and grey matter volume, (C) age and white matter volume, (D) age and hippocampal volume, (E) age and cerebrospinal fluid volume, (F) age and white matter hyperintensity volume, (G) sex ratio and total brain volume, (H) sex ratio and grey matter volume, (I) sex ratio and white matter volume, (J) sex ratio and hippocampal volume, (K) sex ratio and cerebrospinal fluid volume, (L) sex ratio and white matter hyperintensity volume, (M) diabetes duration and grey matter volume, (N) diabetes duration and white matter volume, (O) diabetes duration and hippocampal volume, (P) fasting glucose and grey matter volume, (Q) fasting glucose and white matter volume, (R) HbA1c and grey matter volume, and (S) HbA1c and white matter volume.
dmj-2021-0189-suppl4.pdf

ACKNOWLEDGMENTS

The authors are grateful to Dr. Simon Cox of University of Edinburgh, Prof. Jose Luchsinger and Mr. Brady Rippon of University of Colombia (support for the reported work was provided by United States National Institutes of Health grants R01AG050440, RF1AG051556, and K24AG045334), and Assis. Prof. Hideaki Suzuki of Tohoku University for providing additional data for meta-analysis.

NOTES

CONFLICTS OF INTEREST

No potential conflict of interest relevant to this article was reported.

FUNDING

The study was supported by Australian Research Council grant No. 120100227, 130101705, and National Health and Medical Research Council of Australia grant No. 1063907.

Fig. 1
Flowchart of the screening and inclusion of studies into the systematic review and meta-analysis.
dmj-2021-0189f1.jpg
Fig. 2
Forest plots of differences in global brain volumes and total brain atrophy rate between participants with and without type 2 diabetes mellitus. (A) Total brain volume difference, (B) grey matter volume difference, (C) white matter volume difference, (D) total brain atrophy difference. CI, confidence interval.
dmj-2021-0189f2.jpg
Fig. 3
Funnel plots of brain volumes and atrophy rate assessing possible publication bias using the trim and fill method. Filled circles represent studies included in the meta-analysis. Open circles represent possible missing studies. Brain volumes: (A) total brain volume, (B) grey matter, (C) white matter, (D) hippocampus, (E) thalamus, (F) caudate, (G) putamen, (H) globus pallidus, (I) amygdala, (J) nucleus accumbens, (K) superior temporal gyrus, (L) frontal lobe, (M) cerebrospinal fluid, (N) white matter hyperintensity, and (O) total brain atrophy rate._(Continued to the next page)
dmj-2021-0189f3.jpg
Table 1
Studies included in the review
No. Study Study design Cohort Newcastle-Ottawa quality assessment scalea Compatibility with meta-analysis


Selection Comparability Outcome In




Q1 Q2 Q3 Q4 Q5 Q6 Q7 Yes/No Structures
1 Ajilore et al. (2010) [22]a CCC * * ** * Yes Total brain volume

2 Ajilore et al. (2015) [23] CCC * * * * * * Yes Hippocampus

3 Armstrong et al. (2019) [24] LP BLSA * * * ** ** * * No

4 van Bloemendaal et al. (2016) [25] CCC * * * * No

5 Bruehl et al. (2009I) [26] CCC * * * * Yes Hippocampus, superior temporal gyrus

6 Bruehl et al. (2009II) [27] CCC * * * * ** * * Yes Superior temporal gyrus, frontal lobe, cerebrospinal fluid

7 Brundel et al. (2010) [28] CCC UDES * * * * ** * * Yes Grey matter, hippocampus

8 Brundel et al. (2014) [29] CCC UDES * * * * ** * * Yes White matter hyperintensity

9 Callisaya et al. (2019) [30] LP CDOT * * * * ** * * Yes Total brain volume, white matter hyperintensity

10 Chen et al. (2006) [31] CP PATH * * * * ** * * No

11 Chen et al. (2012) [32] CCC * * ** ** * * Yes Grey matter, white matter

12 Chen et al. (2014) [33] CCC * * ** * * Yes Grey matter, white matter

13 Chen et al. (2017) [34] CCC * ** * * Yes Grey matter, white matter, hippocampus, thalamus, caudate, putamen, globus pallidus, amygdala, nucleus accumbens

14 Climie et al. (2014) [35] CCC * * ** ** * * Yes Grey matter, white matter, hippocampus, white matter hyperintensity

15 Cox et al. (2019) [36] CP UK Biobank * * * ** * * Yes Total brain volume, grey matter, hippocampus, thalamus, caudate, putamen, globus pallidus, amygdala, nucleus accumbens

16 Cui et al. (2014) [37] CCC * * * * ** * * Yes Grey matter, white matter, cerebrospinal fluid

15 Cui et al. (2017) [38] CCC * * ** ** * * Yes Grey matter, white matter, cerebrospinal fluid

16 Cui et al. (2020) [39] CCC * * * ** * * No

17 de Bresser et al. (2010) [12] LCC * * * ** * * * Yes Total brain atrophy rate

18 de Bresser et al. (2018) [40] LP BLSA * * * * * * * No

19 den Heijer et al. (2003) [41] CP Rotterdam Study * * * ** ** * * No

20 Espeland et al. (2013) [11] LP WHIMS-MRI * * * ** * * Yes Total brain volume, grey matter, white matter, cerebrospinal fluid, total brain atrophy rate

21 Fang et al. (2018) [42] CCC * * * No

22 Fang et al. (2019) [43] CCC * * * ** * * Yes Grey matter, white matter

23 Ferreira et al. (2017I) [44] CCC * * * * * * * No

24 Ferreira et al. (2017II) [45] CCC * * * * * * * No

25 Gold et al. (2007) [46] CCC * * * * * * * Yes Hippocampus, superior temporal gyrus, frontal lobe

26 Hempel et al. (2012) [47] CCC * * * * * * Yes Hippocampus, superior temporal gyrus, frontal lobe

27 Hirabayashi et al. (2016) [48] CP Hisayama Study * * * * ** * * No

28 Hoogendam et al. (2012) [49] CP Rotterdam Study * * * * * * * No

29 Hsu et al. (2012) [50] CCC * * * ** * * Yes Grey matter, white matter, cerebrospinal fluid

30 Jongen et al. (2007) [51] CCC UDES * * * * ** * * Yes Total brain volume, grey matter, white matter, white matter hyperintensity, cerebrospinal fluid

31 Kumar et al. (2008I) [52] CP PATH * * * * * Yes Total brain volume, grey matter, white matter, hippocampus, cerebrospinal fluid

32 Kumar et al. (2008II) [53] CCC * * * * * Yes Grey matter, white matter

33 Last et al. (2007) [54] CCC * * ** ** * * Yes Total brain volume

34 Launer et al. (2015) [55] CP CARDIA * * * ** * * No

35 Lee et al. (2013) [56] CCC ** * * * No

36 Li et al. (2016) [57] CP ADNI * * * * ** * * Yes Total brain volume

37 Li et al. (2018) [58] CCC * * * * No

38 Liu et al. (2018) [59] CCC * * * ** * * No

39 Lucatelli et al. (2016) [60] CCC * * * * ** * * No

40 Luchsinger et al. (2020) [61] CCC * * ** * * Yes Total brain volume, white matter hyperintensity

41 Maldijan et al. (2013) [62] CP Diabetes Heart Study-Mind * * * * * * Yes White matter hyperintensity

42 Manor et al. (2012) [63] CCC * * * * * * * No

43 Moran et al. (2015) [64] CP ADNI * * * * ** * * No

44 Moran et al. (2016) [65] CCC * * * * ** * * Yes Grey matter, white matter, white matter hyperintensity

45 Musen et al. (2012) [66] CCC ** * * Yes Hippocampus

46 Novak et al. (2011) [67] CCC * * * ** * * No

47 Peng et al. (2015) [68] CCC * ** * * No

48 Qiu et al. (2014) [69] CP AGES-Reykjavik Study * * * * ** * * Yes Total brain volume, grey matter, white matter, white matter hyperintensity, cerebrospinal fluid

49 Raffield et al. (2016) [70] CP Diabetes Heart Study-Mind * * * * ** * * Yes Grey matter, white matter

50 Redel et al. (2018) [71] CCC * ** * * Yes Total brain volume, grey matter

51 Reinhard et al. (2012) [72] CCC * * * * * * * Yes Total brain volume, grey matter, white matter, white matter hyperintensity

52 Rensma et al. (2020) [73] LP AGES-Reykjavik Study * * * ** * * Yes Total brain atrophy rate

53 Roberts et al. (2014) [74] CP MCSA * * * * ** * * No

54 Roy et al. (2020) [75] CCC * * * * * * No

55 Saczynski et al. (2009) [76] CP AGES-Reykjavik Study * * * * ** * * No

56 Samaras et al. (2014) [21] LP Sydney Memory and Aging Study * * * ** ** * * Yes Total brain volume, hippocampus, frontal lobe, cerebrospinal fluid, total brain atrophy rate

57 Shibata et al. (2019) [77] CP Strong Heart Study (CDCAI) * * * * ** * * No

58 Sun et al. (2018) [78] CCC * * ** * * Yes Total brain volume, grey matter, white matter

59 Suzuki et al. (2019) [79] CP UK Biobank * * * ** * * Yes White matter volume

60 Walsh et al. (2019) [80] CP PATH * * * ** ** * * Yes Total brain volume, grey matter, white matter, thalamus

61 Wood et al. (2016) [81] CCC * * * * * * Yes Grey matter, white matter, hippocampus, white matter hyperintensity

62 Yau et al. (2014) [82] CCC * * * ** * * * Yes Hippocampus, superior temporal gyrus

63 Zhang et al. (2014) [83] CCC * ** * * Yes Grey matter, white matter

64 Zhang et al. (2015) [84] CCC * * * * ** * * Yes Grey matter, white matter, hippocampus, white matter hyperintensity, thalamus, caudate, putamen, globus pallidus, amygdala, nucleus accumbens

CCC, cross-sectional case-control studies; CP, cross-sectional population-based studies; LCC, longitudinal case-control studies; LP, longitudinal population-based studies; abbreviations of cohort studies, in order of appearance: BLSA, Baltimore Longitudinal Study of Aging; UDES, Utrecht Diabetic Encephalopathy Study; CDOT, Cognition and Diabetes in Older Tasmanians Study; PATH, Personality and Total Health Through Life Study; WHIMS-MRI, Women’s Health Initiative Memory Study; CARDIA, Coronary Artery Risk Development in Young Adults; ADNI, Alzheimer’s Disease Neuroimaging Initiative; MCSA, The Mayo Clinic Study of Aging; CDCAI, Cerebrovascular Disease and its Consequences in American Indians Study.

a A ‘star system’ for a quick visual assessment. Stars awarded for each quality item. Questions: (1) Is the case definition adequate; (2) Representativeness of the cases (cross-sectional)/exposed cohort (longitudinal); (3) Selection of controls (cross-sectional)/non-exposed cohort (longitudinal); (4) Definition of controls (cross-sectional)/adequacy of follow-up of cohorts (longitudinal); (5) The participants in different outcome groups are comparable, based on the study design or analysis. Confounding factors are controlled for; (6) Measurement of the outcome (brain volume); (7) Statistical test is clearly described and appropriate.

Table 2
Demographic and type 2 diabetes mellitus characteristics for studies included in meta-analysis
No. Study Total No. Age, yr Male, % T2DM duration, yr Oral No. Insulin No. Medication, % Serum insulin, μU/mL HbA1c, % Fasting glucose, mmol/L MRI strength Measurement
1 Ajilore et al. (2010) [22] 46 56.6±8.4 28.3 1.5 Volumetry (automated)
Normal 20 55.2±8.2 25.0 5.6±1.1
T2DM 26 57.8±8.5 30.8 9.8±8.2 7.1±1.1

2 Ajilore et al. (2015) [23] 56 55.7±9.8 32.1 1.5 Volumetry (automated)
Normal 32 53.5±10.3 28.1 5.5±0.4
T2DM 24 58.9±8.3 37.5 9.7±7.8 7±1.1

3 Bruehl et al. (2009I) [26] 30 59.8±8 60.0 1.5 Volumetry (manual)
Normal 12 63.0±6.8 66.7 6.9±3.3 5.3±0.5 4.5±0.5
T2DM 18 57.7±8.2 55.6 5.9±3.7 17 94.4 19.6±13.1 8±2 7.5±3

4 Bruehl et al. (2009II) [27] 88 59.5±8.1 52.3 1.5 Volumetry (manual)
Normal 47 60.0±8.0 51.1 5.6±1.7 5.2±0.4 4.5±0.5
T2DM 41 59.0±8.4 53.7 7±6.4 14.1±10.4 7.9±1.8 7.8±2.9

5 Brundel et al. (2010) [28] 86 69.3±4.9 46.5 1.5 Volumetry (automated)
Normal 30 68.1±4.3 43.3 5.7±0.5 5.6±0.8
T2DM 56 70.0±5.2 48.2 13.6±6.8 27 48.2 8.1±6.8 7.1±1 8.3±3

6 Brundel et al. (2014) [29] 97 70.7±4.3 57.7 3.0 Volumetry (manual; automated)
Normal 49 71.1±4.5 61.2 5.7±0.4 5.6±0.7
T2DM 48 70.3±4.1 54.2 11±9.3 6.8±0.8 8±2

7 Callisaya et al. (2019) [30] 705 70.4±7.4 57.1 1.5 Volumetry (automated)
Normal 357 72.5±7.1 53.2 5.6±0.3 5.3±0.5
T2DM 348 68.2±7.0 60.1 9.5±9.4 234 71 67.2 7.2±1.2 7.7±2.3

8 Chen et al. (2012) [32] 32 60.4±7 75.0 1.5 VBM
Normal 16 59.6±6.1 75.0
T2DM 16 61.2±7.8 75.0 13.2±5.6

9 Chen et al. (2014) [33] 22 58.7±2.5 27.3 3.0 VBM
Normal 11 56.2 27.3
T2DM 11 61.2 27.3 13.9 8.3±1.9 9.9±2.4

10 Chen et al. (2017) [34] 47 58.8±8.1 51.1 3.0 Volumetry (automated)
Normal 24 57.0±7.5 50.0 8.8±4.5 5.8±0.2 5.3±0.3
T2DM 23 60.8±8.3 52.2 8.9±4.8 23 12 100.0 12.3±5.8 8.6±2.2 9±2.7

11 Climie et al. (2014) [35] 80 57.5±10.1 45.0 1.5 Volumetry (manual; semi-auto; automated)
Normal 40 52.0±8.0 47.5 2.4±4.7 5.5±0.3 4.7±0.4
T2DM 40 63.0±9.0 42.5 6±6 10.2±8.6 7.2±0.8 7.5±1.8

12 Cox et al. (2019) [36] 9,722 62.0±7.5 47.5 3.0 Volumetry (automated)
Normal 9,246 61.8±7.5 46.6
T2DM 476 64.5±6.9 65.1

13 Cui et al. (2014) [37] 69 65.4±9.3 49.3 3.0 VBM
Normal 26 65.2±10.2 53.8 5.7±0.3 5.0±0.6
T2DM 43 65.5±8.7 42.5 13.3±6.8 34 11 79.1 7.1±1.1 6.9±2.6

14 Cui et al. (2017) [38] 81 59.2±6.8 42.0 3.0 VBM
Normal 41 57.9±6.5 31.7 5.6±0.3 5.4±0.3
T2DM 40 60.5±6.9 52.5 8.9±5 8 20.0 7.7±1.6 7.8±2.1

15 de Bresser et al. (2018) [40] 83 65.3±5.1 45.8 1.5 Volumetry (automated)
Normal 28 64.2±4.3 42.9
T2DM 55 65.9±5.4 47.3 9.5±6.6 31 56.4 7.0±1.1 5.6±0.6

16 Espeland et al. (2013) [11] 1,366 78.5±0.2 0.0 1.5 Volumetry (automated)
Normal 1,221 78.6±0.1 0.0
T2DM 145 78.1±0.3 0.0

17 Fang et al. (2019) [43] 67 33.1±5.2 64.2 3.0 VBM
Normal 32 34.1±4.8 59.4 5.5±0.3 4.8±0.5
T2DM 35 32.1±5.3 68.6 1 33 25 94.2 10.4±2.4 8.5±3.8

18 Gold et al. (2007) [46] 46 59.5±8.5 47.8 1.5 Volumetry (automated); VBM
Normal 23 59.9±8.6 47.8 5.1±0.4 4.5±0.5
T2DM 23 59.2±8.4 47.8 6±6.3 6.9±0.8 6.7±1.8

19 Hempel et al. (2012) [47] 87 59.4±11.7 52.9 1.5 Volumetry (manual)
Normal 47 60.0±11.3 51.1 5.6±2.5 5.2±0.5 4.5±0.7
T2DM 40 58.9±12.2 55.0 14.6±14.9 7.7±2.3 7.7±3.9

20 Hsu et al. (2012) [50] 137 56.3±4.9 57.7 1.5 VBM
Normal 97 56.2±4.7 55.7 5.5±0.3 5.0±0.6
T2DM 40 56.8±5.5 62.5 5.1±4.7 29 72.5 7.7±1.7 7.8±2.4

21 Jongen et al. (2007) [51] 145 65.5±5.6 47.6 1.5 Volumetry (automated)
Normal 46 64.9±5.6 43.5 5.5±0.3
T2DM 99 65.9±5.6 49.5 8.7±6.1 29 29.3 6.8±1.2

22 Kumar et al. (2008I) [52] 467 62.6±1.1 51.8 1.5 VBM; Volumetry (manual)
Normal 428 62.6±1.5 51.2
T2DM 39 62.6±1.2 59.0 19 7 49

23 Kumar et al. (2008II) [53] 51 55.5±9.1 23.5 9.8±8.2 1.5 Volumetry (automated)
Normal 25 53.2±9.1 20.0 5.3±0.4
T2DM 26 57.9±8.5 26.9 16 9 96.2 7.1±1.1

24 Last et al. (2007) [54] 51 61±7.6 51.0 1.5 Volumetry (automated)
Normal 25 60.4±8.6 52.0 5.5±0.4 4.4±0.9
T2DM 26 61.6±6.6 50.0 12.9±11.3 7.1±0.1 7.4±4.3

25 Li et al. (2016) [57] 429 74.3±0.5 49.7 1.5, 3.0 Volumetry (automated)
Normal 398 74.3±0.4 48.0
T2DM 31 74.8±1.3 71.0

26 Luchsinger et al. (2020) [61] 250 64.1±3.5 28.0 73.0 7.6±1.7 3.0 Volumetry (automated)
Normal 139 63.7±3.4 24.6 5.4±0.2
T2DM 111 64.7±3.5 33.3 7.6±1.7

27 Maldijan et al. (2013) [62] 200 67.6±9.3 19.0 1.5 Volumetry (manual; automated)
Normal 100 67.5±9.4 15.0 5.9±0.3
T2DM 100 67.7±9.2 23.0 7.6±1.4

28 Moran et al. (2016) [65] 451 69.5±7.2 56.8 1.5 VBM
Normal 181 72.9±6.7 53.6 5.6±0.3 5.3±0.6
T2DM 270 67.3±6.7 58.9 53 19.6 7.1±1.2 7.7±2.1

29 Musen et al. (2012) [66] 21 54.9±2.2 66.7 1.5 Volumetry (automated)
Normal 11 54.0±1.8 63.6 11.9±2.1 5.6±0.1 4.8±0.2
T2DM 10 56.0±2.2 70.0 6.1±0.9 19.7±3.6 7.5±0.5 8.4±1.3

30 Qiu et al. (2014) [69] 4,206 76.1±5.3 41.6 1.5 Volumetry (automated)
Normal 3,744 76.2±5.4 39.9
T2DM 462 76.0±5.1 55.2

31 Raffield et al. (2016) [70] 784 65.8±9.8 45.9 1.5, 3.0 VBM
Normal 102 66.7±10.0 34.3 3 2.9 5.9±0.3 5.4±0.6
T2DM 682 65.8±9.8 47.7 15.2±7.7 445 227 65.3 7.5±1.4 8.2±3.0

32 Redel et al. (2018) [71] 40 16.7±2.3 25.0 3.0 VBM
Normal 20 16.7±2.6 25.0
T2DM 20 16.7±2.0 25.0

33 Reinhard et al. (2012) [72] 46 54.1±13.2 82.6 3.0 Volumetry (automated)
Normal 26 52.0±15.0 80.8
T2DM 20 57.0±10.0 85.0 12.0±6.0 18 14 90.0 7.9

34 Rensma et al. (2020) [73] 2135 74.5±4.6 41.7 1.5 Volumetry (automated)
Normal 1,938 74.5±4.6 41.4 5.6±0.3
T2DM 197 74.6±4.3 52.3 6.5±0.9

35 Samaras et al. (2014) [21] 312 78.4±4.7 51.6 3.0 Volumetry (automated); VBM
Normal 279 78.4±4.8 53.0
T2DM 33 78.4±4.7 39.4

36 Sun et al. (2018) [78] 36 66.9±5.2 33.3 3.0 VBM
Normal 24 66.7±5.4 16.7
T2DM 12 67.3±4.7 66.7 7.9±5.3

37 Suzuki et al. (2019) [79] 8,312 62.3±7.4 47.6 3.0 Volumetry (automated)
Normal 7,912 62.2±7.4 46.8
T2DM 400 64.8±7.0 63.5

38 Walsh et al. (2019) [80] 399 64.4±9.7 46.9 1.5 Volumetry (automated)
Normal 353 63.7±9.6 45.0 5.4±0.2
T2DM 46 70.0±8.7 60.9 7.5±2.7

39 Wood et al. (2016) [81] 44 59.5±5.3 45.5 3.0 VBM
Normal 22 59.5±5.3 36.4 5.7±0.8
T2DM 22 59.5±5.3 54.5 10.1±9.7 20 4 90.9 7.0±1.5

40 Yau et al. (2014) [82] 96 58.7±8 44.8 1.5 Volumetry (manual)
Normal 50 58.8±7.9 44.0 5.3±0.4 4.3±0.5
T2DM 46 58.8±8.2 7.5±6.7 7.8±1.8 7.9±3

41 Zhang et al. (2014) [83] 54 53.9±9.3 48.1 3.0 VBM
Normal 29 55.5±9.1 41.4
T2DM 25 52.2±9.2 56.0 6.4±5.3 7.3±1.3

42 Zhang et al. (2015) [84] 160 57.6±9.6 40.0 3.0 Volumetry (automated)
Normal 80 57.8±10.3 36.3 5.6±0.4
T2DM 80 57.5±9.0 43.8 7.0±6.7 7.5±1.5

Values are presented as mean±standard deviation.

T2DM, type 2 diabetes mellitus; HbA1c, glycosylated hemoglobin; MRI, magnetic resonance imaging; VBM, voxel-based morphometry.

Table 3
Random-effect models of brain volumes and atrophy rates in normal controls and type 2 diabetes mellitus patients, including total volumes and differences between groups
Brain areas Group k No. Male, % Age, yr Volume, cm3 SE 95% CI Sig tau2 tau I2 QE
Total brain volume All 30 18,214 42.863 67.392 1120.675 30.243 1,061.400 to 1,179.950 a 27,198.99 164.9212 99.98716 76,018.724
Normal 15 15,937 42.731 67.328 1132.803 44.902 1,044.797 to 1,220.808 a 30,041.44 173.3247 99.99302 66,446.501
T2DM 15 2,277 43.786 67.846 1108.552 41.846 1,026.535 to 1,190.569 a 25,986.99 161.2048 99.91959 8,472.641
T2DM-normal 15 18,214 42.863 67.392 −20.502 5.851 −31.970 to −9.034 a 439.6137 20.96697 99.57289 487.3966

Grey matter volume All 48 18,592 42.938 66.513 596.956 12.889 571.694 to 622.218 a 7,886.426 88.80555 99.91265 121,045.52
Normal 24 15,475 42.488 66.608 605.984 18.299 570.119 to 641.849 a 7,947.538 89.14896 99.94295 101,310.41
T2DM 24 3,117 45.172 66.039 587.935 18.363 551.944 to 623.925 a 8,005.686 89.4745 99.77253 19,310.03
T2DM-normal 24 18,592 42.938 66.513 −17.433 4.296 −25.853 to −9.013 a 405.1253 20.12772 99.13594 808.9859

White matter volume All 44 17,056 42.642 67.164 493.281 10.613 472.481 to 514.081 a 4,874.427 69.8171 99.89194 25,351.214
Normal 22 14,091 42.247 67.330 499.042 14.641 470.346 to 527.739 a 4,645.458 68.1576 99.92809 16,527.384
T2DM 22 2,965 44.519 66.376 487.500 15.610 456.904 to 518.095 a 5,269.203 72.58927 99.66385 7,907.034
T2DM-normal 22 17,056 42.642 67.164 −10.733 2.864 −16.347 to −5.119 a 148.5759 12.18917 98.16723 251.0688

Hippocampal volume All 28 11,254 47.672 62.288 3.318 0.113 3.097 to 3.540 a 0.3543962 0.5953118 99.72612 7,037.232
Normal 14 9,935 48.676 62.175 3.394 0.161 3.078 to 3.710 a 0.3602119 0.6001765 99.69904 2,707.878
T2DM 14 1,319 40.106 63.134 3.243 0.162 2.926 to 3.561 a 0.3639983 0.6033227 99.54532 2,151.285
T2DM-normal 14 11,254 47.672 62.288 −0.151 0.045 −0.239 to −0.063 a 0.02663409 0.1631996 97.89546 401.8367

Thalamus volume All 8 10,328 47.366 61.983 7.562 0.089 7.388 to 7.736 a 0.05621649 0.2371002 96.45107 123.66
Normal 4 9,703 46.450 61.861 7.723 0.112 7.503 to 7.942 a 0.04570418 0.2137854 94.21684 25.00385
T2DM 4 625 61.600 63.878 7.387 0.074 7.242 to 7.532 a 0.01404856 0.1185266 70.47676 10.83599
T2DM-normal 4 10,328 47.366 61.983 −0.325 0.062 −0.447 to −0.203 a 0.01298164 0.113937 88.01778 20.0754

Caudate volume All 6 9,929 47.386 61.886 3.353 0.042 3.270 to 3.436 a 0.009062094 0.09519503 93.94834 74.60724
Normal 3 9,350 46.503 61.793 3.408 0.049 3.312 to 3.504 a 0.005463963 0.07391862 79.6928 12.1904
T2DM 3 579 61.658 63.392 3.302 0.065 3.174 to 3.430 a 0.011110639 0.10540702 89.75669 20.24112
T2DM-normal 3 9,929 47.386 61.886 −0.090 0.025 −0.139 to −0.041 a 0.001316991 0.03629038 73.38077 5.694014

Putamen volume All 6 9,929 47.386 61.886 4.745 0.050 4.647 to 4.843 a 1.21E-02 0.110161803 92.06085 56.336064
Normal 3 9,350 46.503 61.793 4.814 0.081 4.656 to 4.973 a 1.71E-02 0.13082691 89.303203 11.837765
T2DM 3 579 61.658 63.392 4.689 0.023 4.644 to 4.735 a 8.96E-05 0.009463786 3.049979 1.536428
T2DM-normal 3 9,929 47.386 61.886 −0.139 0.062 −0.260 to −0.017 c 0.009963195 0.09981581 88.87643 10.80907

Globus pallidus volume All 6 9,929 47.386 61.886 1.762 0.016 1.731 to 1.793 a 0.000838993 0.02896537 79.07953 37.0150961
Normal 3 9,350 46.503 61.793 1.782 0.002 1.778 to 1.786 a 0 0 0 0.5905017
T2DM 3 579 61.658 63.392 1.766 0.043 1.681 to 1.851 a 0.004061401 0.06372913 81.56729 6.5953594
T2DM-normal 3 9,929 47.386 61.886 −0.014 0.038 −0.089 to 0.060 0.003180063 0.05639205 91.2341 14.47432

Amygdala volume All 6 9,929 47.386 61.886 1.299 0.037 1.226 to 1.372 a 0.007582182 0.08707572 97.63179 39.40874
Normal 3 9,350 46.503 61.793 1.298 0.055 1.190 to 1.405 a 0.008433407 0.09183358 95.38069 21.78316
T2DM 3 579 61.658 63.392 1.303 0.065 1.175 to 1.431 a 0.011780498 0.108538 95.01308 17.51575
T2DM-normal 3 9,929 47.386 61.886 −0.003 0.002 −0.007 to 0.002 0 0 0 0.872544

Nucleus accumbens volume All 6 9,929 47.386 61.886 0.429 0.010 0.409 to 0.448 a 0.000398667 0.01996666 90.67598 107.1207294
Normal 3 9,350 46.503 61.793 0.446 0.011 0.424 to 0.468 a 0.000247974 0.01574719 68.49809 7.0336341
T2DM 3 579 61.658 63.392 0.415 0.004 0.406 to 0.423 a 0 0 0 0.7617099
T2DM-normal 3 9,929 47.386 61.886 −0.035 0.012 −0.059 to −0.012 b 0.000331685 0.01821223 83.76001 12.15621

Nucleus accumbens volume All 6 9,929 47.386 61.886 0.429 0.010 0.409 to 0.448 a 0.000398667 0.01996666 90.67598 107.1207294
Normal 3 9,350 46.503 61.793 0.446 0.011 0.424 to 0.468 a 0.000247974 0.01574719 68.49809 7.0336341
T2DM 3 579 61.658 63.392 0.415 0.004 0.406 to 0.423 a 0 0 0 0.7617099
T2DM-normal 3 9,929 47.386 61.886 −0.035 0.012 −0.059 to −0.012 b 0.000331685 0.01821223 83.76001 12.15621

Superior temporal gyrus volume All 10 347 50.432 59.351 20.376 2.597 15.286 to 25.465 a 66.93965 8.181666 99.64734 1,633.6882
Normal 5 179 49.721 59.856 20.570 3.961 12.807 to 28.333 a 77.96256 8.829641 99.66233 841.5952
T2DM 5 168 51.190 58.812 20.189 3.827 12.689 to 27.690 a 72.71978 8.527589 99.64757 777.338
T2DM-normal 5 347 50.432 59.351 −0.183 0.153 −0.482 to 0.116 0 0 0 3.195813

CSF volume All 20 5,602 44.199 73.170 355.538 42.541 272.159 to 438.917 a 35,986.88 189.7021 99.93566 10,942.665
Normal 10 4,363 46.161 74.898 353.829 62.682 230.975 to 476.682 a 39,060.57 197.6375 99.91702 5,588.27
T2DM 10 1,239 37.288 67.084 357.362 60.961 237.881 to 476.844 a 36,975.35 192.2898 99.90901 4,253.568
T2DM-normal 10 5,602 44.199 73.170 7.145 5.092 −2.835 to 17.126 210.2279 14.49924 93.53862 101.4667

WMH volume All 24 16,100 46.043 66.355 6.040 1.101 3.882 to 8.198 a 27.38855 5.233407 99.72127 3,723.2096
Normal 12 14,030 44.647 66.114 6.282 1.509 3.324 to 9.241 a 24.58371 4.958196 99.71089 2,849.344
T2DM 12 2,070 55.507 67.986 5.824 1.659 2.571 to 9.076 a 32.50942 5.701703 99.54469 873.7837

Atrophy rate (%/year) All 8 4,283 30.983 74.948 −0.00565 0.00080 −0.00722 to −0.00408 a 4.78E-06 0.00218744 99.34591 256.421
Normal 4 3,544 27.906 75.651 −0.00537 0.00129 −0.00790 to −0.00285 a 6.32E-06 0.002513821 99.53845 189.94315
T2DM 4 739 45.737 71.774 −0.00598 0.00116 −0.00825 to −0.00370 a 4.97E-06 0.002228805 98.69538 63.01815
T2DM-normal 4 4,283 30.983 74.948 −0.00072 0.00006 −0.00083 to −0.00062 a 0.00E+00 0 0 0.8717236

k, number of studies; SE, standard error; CI, confidence interval; Sig, statistical significance; QE, test statistic of Cochran’s test of heterogeneity; T2DM, type 2 diabetes mellitus; CSF, cerebrospinal fluid; WMH, white matter hyperintensity.

a P<0.001,

b P<0.01,

c P<0.05.

REFERENCES

1. Forbes JM, Cooper ME. Mechanisms of diabetic complications. Physiol Rev 2013;93:137-88.
Article  PubMed 
2. Cheng G, Huang C, Deng H, Wang H. Diabetes as a risk factor for dementia and mild cognitive impairment: a meta-analysis of longitudinal studies. Intern Med J 2012;42:484-91.
Article  PubMed 
3. Cherbuin N, Walsh EI. Sugar in mind: untangling a sweet and sour relationship beyond type 2 diabetes. Front Neuroendocrinol 2019;54:100769.
Article  PubMed 
4. Kodl CT, Seaquist ER. Cognitive dysfunction and diabetes mellitus. Endocr Rev 2008;29:494-511.
Article  PubMed  PMC 
5. Kawamura T, Umemura T, Hotta N. Cognitive impairment in diabetic patients: can diabetic control prevent cognitive decline? J Diabetes Investig 2012;3:413-23.
Article  PubMed  PMC 
6. van Harten B, de Leeuw FE, Weinstein HC, Scheltens P, Biessels GJ. Brain imaging in patients with diabetes: a systematic review. Diabetes Care 2006;29:2539-48.
PubMed 
7. Manschot SM, Brands AM, van der Grond J, Kessels RP, Algra A, Kappelle LJ, et al. Brain magnetic resonance imaging correlates of impaired cognition in patients with type 2 diabetes. Diabetes 2006;55:1106-13.
Article  PubMed 
8. Tiehuis AM, van der Graaf Y, Visseren FL, Vincken KL, Biessels GJ, Appelman AP, et al. Diabetes increases atrophy and vascular lesions on brain MRI in patients with symptomatic arterial disease. Stroke 2008;39:1600-3.
Article  PubMed 
9. Manschot SM, Biessels GJ, de Valk H, Algra A, Rutten GE, van der Grond J, et al. Metabolic and vascular determinants of impaired cognitive performance and abnormalities on brain magnetic resonance imaging in patients with type 2 diabetes. Diabetologia 2007;50:2388-97.
Article  PubMed  PMC 
10. Bruehl H, Sweat V, Tirsi A, Shah B, Convit A. Obese adolescents with type 2 diabetes mellitus have hippocampal and frontal lobe volume reductions. Neurosci Med 2011;2:34-42.
Article  PubMed  PMC 
11. Espeland MA, Bryan RN, Goveas JS, Robinson JG, Siddiqui MS, Liu S, et al. Influence of type 2 diabetes on brain volumes and changes in brain volumes: results from the Women’s Health Initiative Magnetic Resonance Imaging studies. Diabetes Care 2013;36:90-7.
PubMed 
12. de Bresser J, Tiehuis AM, van den Berg E, Reijmer YD, Jongen C, Kappelle LJ, et al. Progression of cerebral atrophy and white matter hyperintensities in patients with type 2 diabetes. Diabetes Care 2010;33:1309-14.
Article  PubMed  PMC 
13. van Elderen SG, de Roos A, de Craen AJ, Westendorp RG, Blauw GJ, Jukema JW, et al. Progression of brain atrophy and cognitive decline in diabetes mellitus: a 3-year follow-up. Neurology 2010;75:997-1002.
Article  PubMed 
14. Kooistra M, Geerlings MI, Mali WP, Vincken KL, van der Graaf Y, Biessels GJ, et al. Diabetes mellitus and progression of vascular brain lesions and brain atrophy in patients with symptomatic atherosclerotic disease: the SMART-MR study. J Neurol Sci 2013;332:69-74.
Article  PubMed 
15. Debette S, Seshadri S, Beiser A, Au R, Himali JJ, Palumbo C, et al. Midlife vascular risk factor exposure accelerates structural brain aging and cognitive decline. Neurology 2011;77:461-8.
Article  PubMed  PMC 
16. Fraser MA, Shaw ME, Cherbuin N. A systematic review and meta-analysis of longitudinal hippocampal atrophy in healthy human ageing. Neuroimage 2015;112:364-74.
Article  PubMed 
17. Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ 2021;372:n71.
Article  PubMed  PMC 
18. R Development Core Team. R: A language and environment for statistical computing. Vienna: R Foundation for Statistical Computing; 2014.
19. Viechtbauer W. Conducting meta-analyses in R with the metafor package. J Stat Softw 2010;36:1-48.
20. Borenstein M, Hedges LV, Higgins JPT, Rothstein HR. Introduction to meta-analysis. Chichester: Wiley; 2011.
21. Samaras K, Lutgers HL, Kochan NA, Crawford JD, Campbell LV, Wen W, et al. The impact of glucose disorders on cognition and brain volumes in the elderly: the Sydney Memory and Ageing Study. Age (Dordr) 2014;36:977-93.
Article  PubMed  PMC 
22. Ajilore O, Narr K, Rosenthal J, Pham D, Hamilton L, Watari K, et al. Regional cortical gray matter thickness differences associated with type 2 diabetes and major depression. Psychiatry Res 2010;184:63-70.
Article  PubMed  PMC 
23. Ajilore O, Lamar M, Medina J, Watari K, Elderkin-Thompson V, Kumar A. Disassociation of verbal learning and hippocampal volume in type 2 diabetes and major depression. Int J Geriatr Psychiatry 2015;30:393-9.
Article  PubMed 
24. Armstrong NM, An Y, Beason-Held L, Doshi J, Erus G, Ferrucci L, et al. Predictors of neurodegeneration differ between cognitively normal and subsequently impaired older adults. Neurobiol Aging 2019;75:178-86.
Article  PubMed 
25. van Bloemendaal L, Ijzerman RG, Ten Kulve JS, Barkhof F, Diamant M, Veltman DJ, et al. Alterations in white matter volume and integrity in obesity and type 2 diabetes. Metab Brain Dis 2016;31:621-9.
Article  PubMed  PMC 
26. Bruehl H, Wolf OT, Convit A. A blunted cortisol awakening response and hippocampal atrophy in type 2 diabetes mellitus. Psychoneuroendocrinology 2009;34:815-21.
Article  PubMed  PMC 
27. Bruehl H, Wolf OT, Sweat V, Tirsi A, Richardson S, Convit A. Modifiers of cognitive function and brain structure in middle-aged and elderly individuals with type 2 diabetes mellitus. Brain Res 2009;1280:186-94.
Article  PubMed  PMC 
28. Brundel M, van den Heuvel M, de Bresser J, Kappelle LJ, Biessels GJ; Utrecht Diabetic Encephalopathy Study Group. Cerebral cortical thickness in patients with type 2 diabetes. J Neurol Sci 2010;299:126-30.
Article  PubMed 
29. Brundel M, Reijmer YD, van Veluw SJ, Kuijf HJ, Luijten PR, Kappelle LJ, et al. Cerebral microvascular lesions on high-resolution 7-Tesla MRI in patients with type 2 diabetes. Diabetes 2014;63:3523-9.
Article  PubMed 
30. Callisaya ML, Beare R, Moran C, Phan T, Wang W, Srikanth VK. Type 2 diabetes mellitus, brain atrophy and cognitive decline in older people: a longitudinal study. Diabetologia 2019;62:448-58.
Article  PubMed 
31. Chen X, Wen W, Anstey KJ, Sachdev PS. Effects of cerebrovascular risk factors on gray matter volume in adults aged 60-64 years: a voxel-based morphometric study. Psychiatry Res 2006;147:105-14.
Article  PubMed 
32. Chen Z, Li L, Sun J, Ma L. Mapping the brain in type II diabetes: voxel-based morphometry using DARTEL. Eur J Radiol 2012;81:1870-6.
Article  PubMed 
33. Chen Z, Li J, Sun J, Ma L. Brain expansion in patients with type II diabetes following insulin therapy: a preliminary study with longitudinal voxel-based morphometry. J Neuroimaging 2014;24:484-91.
Article  PubMed 
34. Chen J, Zhang J, Liu X, Wang X, Xu X, Li H, et al. Abnormal subcortical nuclei shapes in patients with type 2 diabetes mellitus. Eur Radiol 2017;27:4247-56.
Article  PubMed 
35. Climie RE, Srikanth V, Beare R, Keith LJ, Fell J, Davies JE, et al. Aortic reservoir characteristics and brain structure in people with type 2 diabetes mellitus; a cross sectional study. Cardiovasc Diabetol 2014;13:143.
Article  PubMed  PMC 
36. Cox SR, Lyall DM, Ritchie SJ, Bastin ME, Harris MA, Buchanan CR, et al. Associations between vascular risk factors and brain MRI indices in UK Biobank. Eur Heart J 2019;40:2290-300.
Article  PubMed  PMC 
37. Cui X, Abduljalil A, Manor BD, Peng CK, Novak V. Multi-scale glycemic variability: a link to gray matter atrophy and cognitive decline in type 2 diabetes. PLoS One 2014;9:e86284.
Article  PubMed  PMC 
38. Cui Y, Liang X, Gu H, Hu Y, Zhao Z, Yang XY, et al. Cerebral perfusion alterations in type 2 diabetes and its relation to insulin resistance and cognitive dysfunction. Brain Imaging Behav 2017;11:1248-57.
Article  PubMed 
39. Cui Y, Tang TY, Lu CQ, Cai Y, Lu T, Wang YC, et al. Abnormal cingulum bundle induced by type 2 diabetes mellitus: a diffusion tensor tractography study. Front Aging Neurosci 2020;12:594198.
Article  PubMed  PMC 
40. de Bresser J, Kuijf HJ, Zaanen K, Viergever MA, Hendrikse J, Biessels GJ, et al. White matter hyperintensity shape and location feature analysis on brain MRI; proof of principle study in patients with diabetes. Sci Rep 2018;8:1893.
Article  PubMed  PMC 
41. den Heijer T, Vermeer SE, van Dijk EJ, Prins ND, Koudstaal PJ, Hofman A, et al. Type 2 diabetes and atrophy of medial temporal lobe structures on brain MRI. Diabetologia 2003;46:1604-10.
Article  PubMed 
42. Fang F, Zhan YF, Zhuo YY, Yin DZ, Li KA, Wang YF. Brain atrophy in middle-aged subjects with type 2 diabetes mellitus, with and without microvascular complications. J Diabetes 2018;10:625-32.
Article  PubMed 
43. Fang F, Lai MY, Huang JJ, Kang M, Ma MM, Li KA, et al. Compensatory hippocampal connectivity in young adults with early-stage type 2 diabetes. J Clin Endocrinol Metab 2019;104:3025-38.
Article  PubMed 
44. Ferreira FS, Pereira JM, Reis A, Sanches M, Duarte JV, Gomes L, et al. Early visual cortical structural changes in diabetic patients without diabetic retinopathy. Graefes Arch Clin Exp Ophthalmol 2017;255:2113-8.
Article  PubMed 
45. Ferreira FS, Pereira JM, Duarte JV, Castelo-Branco M. Extending inferential group analysis in type 2 diabetic patients with multivariate GLM implemented in SPM8. Open Neuroimag J 2017;11:32-45.
Article  PubMed  PMC 
46. Gold SM, Dziobek I, Sweat V, Tirsi A, Rogers K, Bruehl H, et al. Hippocampal damage and memory impairments as possible early brain complications of type 2 diabetes. Diabetologia 2007;50:711-9.
Article  PubMed 
47. Hempel R, Onopa R, Convit A. Type 2 diabetes affects hippocampus volume differentially in men and women. Diabetes Metab Res Rev 2012;28:76-83.
Article  PubMed  PMC 
48. Hirabayashi N, Hata J, Ohara T, Mukai N, Nagata M, Shibata M, et al. Association between diabetes and hippocampal atrophy in elderly Japanese: the Hisayama Study. Diabetes Care 2016;39:1543-9.
Article  PubMed 
49. Hoogendam YY, van der Geest JN, van der Lijn F, van der Lugt A, Niessen WJ, Krestin GP, et al. Determinants of cerebellar and cerebral volume in the general elderly population. Neurobiol Aging 2012;33:2774-81.
Article  PubMed 
50. Hsu JL, Chen YL, Leu JG, Jaw FS, Lee CH, Tsai YF, et al. Microstructural white matter abnormalities in type 2 diabetes mellitus: a diffusion tensor imaging study. Neuroimage 2012;59:1098-105.
Article  PubMed 
51. Jongen C, van der Grond J, Kappelle LJ, Biessels GJ, Viergever MA, Pluim JP, et al. Automated measurement of brain and white matter lesion volume in type 2 diabetes mellitus. Diabetologia 2007;50:1509-16.
Article  PubMed  PMC 
52. Kumar R, Anstey KJ, Cherbuin N, Wen W, Sachdev PS. Association of type 2 diabetes with depression, brain atrophy, and reduced fine motor speed in a 60- to 64-year-old community sample. Am J Geriatr Psychiatry 2008;16:989-98.
PubMed 
53. Kumar A, Haroon E, Darwin C, Pham D, Ajilore O, Rodriguez G, et al. Gray matter prefrontal changes in type 2 diabetes detected using MRI. J Magn Reason Imaging 2008;27:14-9.
54. Last D, Alsop DC, Abduljalil AM, Marquis RP, de Bazelaire C, Hu K, et al. Global and regional effects of type 2 diabetes on brain tissue volumes and cerebral vasoreactivity. Diabetes Care 2007;30:1193-9.
Article  PubMed 
55. Launer LJ, Lewis CE, Schreiner PJ, Sidney S, Battapady H, Jacobs DR, et al. Vascular factors and multiple measures of early brain health: CARDIA brain MRI study. PLoS One 2015;10:e0122138.
Article  PubMed  PMC 
56. Lee JH, Yoon S, Renshaw PF, Kim TS, Jung JJ, Choi Y, et al. Morphometric changes in lateral ventricles of patients with recent-onset type 2 diabetes mellitus. PLoS One 2013;8:e60515.
Article  PubMed  PMC 
57. Li W, Risacher SL, Huang E, Saykin AJ; Alzheimer’s Disease Neuroimaging Initiative. Type 2 diabetes mellitus is associated with brain atrophy and hypometabolism in the ADNI cohort. Neurology 2016;87:595-600.
Article  PubMed  PMC 
58. Li C, Li C, Yang Q, Wang B, Yin X, Zuo Z, et al. Cortical thickness contributes to cognitive heterogeneity in patients with type 2 diabetes mellitus. Medicine (Baltimore) 2018;97:e10858.
Article  PubMed  PMC 
59. Liu J, Rutten-Jacobs L, Liu M, Markus HS, Traylor M. Causal impact of type 2 diabetes mellitus on cerebral small vessel disease: a Mendelian randomization analysis. Stroke 2018;49:1325-31.
Article  PubMed  PMC 
60. Lucatelli P, Montisci R, Sanfilippo R, Sacconi B, Suri JS, Catalano C, et al. Is there an association between leukoaraiosis volume and diabetes? J Neuroradiol 2016;43:273-9.
Article  PubMed 
61. Luchsinger JA, Palta P, Rippon B, Sherwood G, Soto L, Ceballos F, et al. Pre-diabetes, but not type 2 diabetes, is related to brain amyloid in late middle-age. J Alzheimers Dis 2020;75:1241-52.
PubMed  PMC 
62. Maldjian JA, Whitlow CT, Saha BN, Kota G, Vandergriff C, Davenport EM, et al. Automated white matter total lesion volume segmentation in diabetes. AJNR Am J Neuroradiol 2013;34:2265-70.
Article  PubMed  PMC 
63. Manor B, Newton E, Abduljalil A, Novak V. The relationship between brain volume and walking outcomes in older adults with and without diabetic peripheral neuropathy. Diabetes Care 2012;35:1907-12.
Article  PubMed  PMC 
64. Moran C, Beare R, Phan TG, Bruce DG, Callisaya ML, Srikanth V, et al. Type 2 diabetes mellitus and biomarkers of neurodegeneration. Neurology 2015;85:1123-30.
Article  PubMed  PMC 
65. Moran C, Tapp RJ, Hughes AD, Magnussen CG, Blizzard L, Phan TG, et al. The association of type 2 diabetes mellitus with cerebral gray matter volume is independent of retinal vascular architecture and retinopathy. J Diabetes Res 2016;2016:6328953.
Article  PubMed  PMC 
66. Musen G, Jacobson AM, Bolo NR, Simonson DC, Shenton ME, McCartney RL, et al. Resting-state brain functional connectivity is altered in type 2 diabetes. Diabetes 2012;61:2375-9.
Article  PubMed  PMC 
67. Novak V, Zhao P, Manor B, Sejdic E, Alsop D, Abduljalil A, et al. Adhesion molecules, altered vasoreactivity, and brain atrophy in type 2 diabetes. Diabetes Care 2011;34:2438-41.
Article  PubMed  PMC 
68. Peng B, Chen Z, Ma L, Dai Y. Cerebral alterations of type 2 diabetes mellitus on MRI: a pilot study. Neurosci Lett 2015;606:100-5.
Article  PubMed 
69. Qiu C, Sigurdsson S, Zhang Q, Jonsdottir MK, Kjartansson O, Eiriksdottir G, et al. Diabetes, markers of brain pathology and cognitive function: the Age, Gene/Environment Susceptibility-Reykjavik Study. Ann Neurol 2014;75:138-46.
PubMed  PMC 
70. Raffield LM, Cox AJ, Freedman BI, Hugenschmidt CE, Hsu FC, Wagner BC, et al. Analysis of the relationships between type 2 diabetes status, glycemic control, and neuroimaging measures in the Diabetes Heart Study Mind. Acta Diabetol 2016;53:439-47.
Article  PubMed 
71. Redel JM, DiFrancesco M, Vannest J, Altaye M, Beebe D, Khoury J, et al. Brain gray matter volume differences in obese youth with type 2 diabetes: a pilot study. J Pediatr Endocrinol Metab 2018;31:261-8.
Article  PubMed 
72. Reinhard H, Garde E, Skimminge A, Akeson P, Ramsoy TZ, Winther K, et al. Plasma NT-proBNP and white matter hyperintensities in type 2 diabetic patients. Cardiovasc Diabetol 2012;11:119.
Article  PubMed  PMC 
73. Rensma SP, van Sloten TT, Ding J, Sigurdsson S, Stehouwer CD, Gudnason V, et al. Type 2 diabetes, change in depressive symptoms over time, and cerebral small vessel disease: longitudinal data of the AGES-Reykjavik Study. Diabetes Care 2020;43:1781-7.
Article  PubMed  PMC 
74. Roberts RO, Knopman DS, Przybelski SA, Mielke MM, Kantarci K, Preboske GM, et al. Association of type 2 diabetes with brain atrophy and cognitive impairment. Neurology 2014;82:1132-41.
Article  PubMed  PMC 
75. Roy B, Ehlert L, Mullur R, Freeby MJ, Woo MA, Kumar R, et al. Regional brain gray matter changes in patients with type 2 diabetes mellitus. Sci Rep 2020;10:9925.
Article  PubMed  PMC 
76. Saczynski JS, Siggurdsson S, Jonsson PV, Eiriksdottir G, Olafsdottir E, Kjartansson O, et al. Glycemic status and brain injury in older individuals: the age gene/environment susceptibility-Reykjavik study. Diabetes Care 2009;32:1608-13.
PubMed  PMC 
77. Shibata D, Suchy-Dicey A, Carty CL, Madhyastha T, Ali T, Best L, et al. Vascular risk factors and findings on brain MRI of elderly adult American Indians: the Strong Heart Study. Neuroepidemiology 2019;52:173-80.
Article  PubMed 
78. Sun Q, Chen GQ, Wang XB, Yu Y, Hu YC, Yan LF, et al. Alterations of white matter integrity and hippocampal functional connectivity in type 2 diabetes without mild cognitive impairment. Front Neuroanat 2018;12:21.
Article  PubMed  PMC 
79. Suzuki H, Venkataraman AV, Bai W, Guitton F, Guo Y, Dehghan A, et al. Associations of regional brain structural differences with aging, modifiable risk factors for dementia, and cognitive performance. JAMA Netw Open 2019;2:e1917257.
Article  PubMed  PMC 
80. Walsh EI, Shaw M, Sachdev P, Anstey KJ, Cherbuin N. The impact of type 2 diabetes and body mass index on cerebral structure is modulated by brain reserve. Eur J Neurol 2019;26:121-7.
Article  PubMed 
81. Wood AG, Chen J, Moran C, Phan T, Beare R, Cooper K, et al. Brain activation during memory encoding in type 2 diabetes mellitus: a discordant twin pair study. J Diabetes Res 2016;2016:3978428.
Article  PubMed  PMC 
82. Yau PL, Kluger A, Borod JC, Convit A. Neural substrates of verbal memory impairments in adults with type 2 diabetes mellitus. J Clin Exp Neuropsychol 2014;36:74-87.
Article  PubMed  PMC 
83. Zhang Y, Zhang X, Zhang J, Liu C, Yuan Q, Yin X, et al. Gray matter volume abnormalities in type 2 diabetes mellitus with and without mild cognitive impairment. Neurosci Lett 2014;562:1-6.
Article  PubMed 
84. Zhang YW, Zhang JQ, Liu C, Wei P, Zhang X, Yuan QY, et al. Memory dysfunction in type 2 diabetes mellitus correlates with reduced hippocampal CA1 and subiculum volumes. Chin Med J (Engl) 2015;128:465-71.
Article  PubMed  PMC 
85. Peters R. Ageing and the brain. Postgrad Med J 2006;82:84-8.
Article  PubMed  PMC 
86. Tabatabaei-Jafari H, Shaw ME, Cherbuin N. Cerebral atrophy in mild cognitive impairment: a systematic review with meta-analysis. Alzheimers Dement (Amst) 2015;1:487-504.
Article  PubMed  PMC 
87. Henneman WJ, Sluimer JD, Barnes J, van der Flier WM, Sluimer IC, Fox NC, et al. Hippocampal atrophy rates in Alzheimer disease: added value over whole brain volume measures. Neurology 2009;72:999-1007.
Article  PubMed  PMC 
88. Cherbuin N, Sachdev P, Anstey KJ. Higher normal fasting plasma glucose is associated with hippocampal atrophy: the PATH Study. Neurology 2012;79:1019-26.
Article  PubMed 
89. Garfield V, Farmaki AE, Eastwood SV, Mathur R, Rentsch CT, Bhaskaran K, et al. HbA1c and brain health across the entire glycaemic spectrum. Diabetes Obes Metab 2021;23:1140-9.
Article  PubMed  PMC 
90. Li J, Shao YH, Gong YP, Lu YH, Liu Y, Li CL. Diabetes mellitus and dementia: a systematic review and meta-analysis. Eur Rev Med Pharmacol Sci 2014;18:1778-89.
PubMed 
91. Vincent C, Hall PA. Executive function in adults with type 2 diabetes: a meta-analytic review. Psychosom Med 2015;77:631-42.
PubMed 
92. Zhou JB, Tang X, Han M, Yang J, Simo R. Impact of antidiabetic agents on dementia risk: a Bayesian network meta-analysis. Metabolism 2020;109:154265.
Article  PubMed 
93. Srikanth V, Sinclair AJ, Hill-Briggs F, Moran C, Biessels GJ. Type 2 diabetes and cognitive dysfunction-towards effective management of both comorbidities. Lancet Diabetes Endocrinol 2020;8:535-45.
Article  PubMed 


ABOUT
BROWSE ARTICLES
FOR CONTRIBUTORS
Editorial Office
101-2104, Lotte Castle President, 109 Mapo-daero, Mapo-gu, Seoul 04146, Korea​
Tel: +82-2-714-9064    Fax: +82-2-714-9084    E-mail: diabetes@kams.or.kr                

Copyright © 2022 by Korean Diabetes Association.

Developed in M2PI

Close layer