Skip to main content
Download PDF
  • Systematic review update
  • Open access
  • Published:

Effect of glycemic control on cognitive function in patients with type 1 diabetes mellitus: a systematic review and meta-analysis

Systematic Reviews volume 13, Article number: 10 (2024) Cite this article

Abstract

Background

It is controversial whether the level of glycemic control in patients with type 1 diabetes mellitus (T1DM) correlates with reduced cognitive function. This study explored the influence of glycemic management quality on cognitive function in T1DM patients by examining the association between glycemic control level and impaired cognitive function.

Methods

The electronic databases PubMed, Embase, Cochrane Library, China National Knowledge Infrastructure, China Science and Technology Journal database, Wanfang database, and China Biology Medicine disc database were systematically searched to identify eligible studies published before January 2023. Search, selection, and data extraction were performed by two independent reviewers. RevMan 5.4 software was used for meta-analysis, and standardized mean difference (SMD) between groups was calculated.

Results

Six studies involving 351 patients with T1DM were included in this study. Compared with T1DM subjects with good glycemic control, those with poor glycemic control performed worse in full-scale intellectual quotient (P = 0.01, SMD = −0.79, 95%CI = −1.42 to −0.17), but no significant differences were observed in verbal intellectual quotient (P = 0.08, SMD = −1.03, 95%CI = −2.20 to 0.13), memory (P = 0.05, SMD = −0.41, 95%CI = −0.82 to 0.00), and attention (P = 0.23, SMD = −0.26, 95%CI = −0.69 to 0.16).

Conclusions

T1DM patients with suboptimal glycemic control may have a worse cognitive function, mainly focusing on the full-scale intellectual quotient. The current study highlights the significance of maintaining satisfactory glycemic control in T1DM patients to improve their health status and quality of life. Standardized tests should be employed in clinical neuropsychological practice to provide early and complete cognitive assessment of individuals with poor glycemic control.

Systematic review registration

The study protocol has been registered in the PROSPERO database (CRD42023390456).

Peer Review reports

Introduction

Diabetes mellitus (DM) is a chronic metabolic disease caused by insufficient insulin secretion or deficiencies in insulin action or both, mainly characterized by hyperglycemia [1]. Based on the latest survey by the International Diabetes Federation (IDF), 537 million adults suffer from DM, which is predicted to rise to 700 million in 2045 [2]. DM is classified as type 1 diabetes mellitus (T1DM), type 2 diabetes mellitus (T2DM) depending on various pathogenesis, and gestational diabetes mellitus, etc. [3]. T1DM is an autoimmune disease leading to the destruction of β cells, absolute insulin deficiency and hyperglycemia, with approximately 5–10% of all diabetes cases [3]. However, T1DM can occur at any age, mainly involving children and adolescents, of which more than 1.2 million are living with T1DM [2]. Currently, insulin replacement is still considered as the best therapy for patients controlling blood glucose in T1DM [4].

Cognition function is an advanced neurological function and an important ability in the brain, which acquiring knowledge and understanding through thought, experience, and senses [5]. With the improvement in medicine, a growing body of evidence suggests that various pathologies or diseases can impair cognitive function [6, 7]. One of the most common diseases in the field of endocrine and metabolic diseases, DM has been shown to cause cognitive impairment in the brain. A longitudinal cohort study found that T2DM patients had cognitive decline in executive function, concentration, and attention [7]. It is suggested by some clinical studies hyperglycemia is a potential risk factor for mild cognitive impairment (MCI) or Alzheimer’s disease, and MCI patients are more likely to develop dementia than the general population [8]. Moreover, reports have confirmed that people with diabetes have twice the risk of developing dementia as people without diabetes [9]. This may be related to the fact that the hyperglycemia can increase the accumulation of β-amyloid in brain lesions, aggravating oxidative stress, neuroinflammation and mitochondrial dysfunction, and damage neuronal integrity [10]. With significant improvements in survival for patients with T1DM [11], the problem of dementia associated with T1DM has attracted attention. Patients with T1DM perform worse than normal on neuropsychological tests in the areas of memory, learning, and executive function [12]. The results of one study showed that nearly half of patients with childhood or adult-onset T1DM had clinically significant cognitive impairment at an average age of 68 years [13]. Recent studies have suggested that T1DM patients with suboptimal glycemic control perform worse in terms of psychomotor speed, language, and overall cognitive performance [14]. Interestingly, Ohmann et al. proposed that there was no significant association between the level of glycemic control and brain cognitive function [15].

Therefore, this study conducted a systematic review and meta-analysis of the currently available research evidence to investigate the potential correlation between cognitive impairment and the quality of diabetes management in patients with T1DM by analyzing the association between glycemic control level and impaired cognitive function.

Methods

The present review was conducted according to the Joanna Briggs Institute (JBI) methodology for systematic reviews of etiology and risk [16] and is reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses [17], and the checklist is shown in Additional file 1. The study protocol has been registered in the PROSPERO database (CRD42023390456).

Eligibility and exclusion criteria

Eligibility criteria

Observational studies that met the following "PEO" structure were included.

Participants (P): Patients with T1DM.

Exposure of interest (E): The exposure of interest was the level of glycemic control. Patients were divided into good controlled and poorly controlled groups according to glycated hemoglobin (HbA1c) levels.

Outcomes (O): Cognitive function. The study protocol included at least one measure of cognitive function, such as intelligence, memory, attention, psychomotor speed and so on.

Exclusion criteria

Duplicate literatures, reviews, animal studies, reviews, conference abstracts, academic articles, and non-Chinese or English publications were excluded.

Search

The electronic databases PubMed, Embase, Cochrane Library, Web of Science, China National Knowledge Infrastructure (CNKI), China Science and Technology Journal database, Wanfang database, and China Biology Medicine disc database were systematically searched to identify eligible studies published before January, 2023. Synonyms of “type 1 diabetes,” “cognition,” and “glycemic control” were searched by combining subject headings (i.e., MeSH) and free text words, the detailed search strategy was described in Additional file 2.

Study selection

After removing duplicate search results using EndNote X9 (Thomson Corporation, USA), the remaining articles were screened by two reviewers based on titles and abstracts. Then, the initially included articles were screened on the basis of full text to assess whether they met the inclusion criteria. The reference lists of the included articles were examined to identify any additional relevant literature. A third reviewer was consulted when two reviewers disagreed. Reasons for exclusion were recorded for all excluded literature.

Data extraction

Two reviewers independently extracted the relevant data. Any disagreements were arbitrated by a third reviewer. For each included study, the following data were extracted: study characteristics (publication year, name of author, country), study design, sample characteristics (sample size, age, country, duration of diabetes), and raw scores of cognitive function tests, including means and standard deviations of the good and poor glycemic control samples.

This review classified cognitive function into more specific cognitive domains of intelligence, memory, and attention. In addition, some studies included cognitive tests that did not fit into any of these domains; these were classified into categories of other cognitive functions. Among the included literatures, one study [18] was divided into three groups according to HbA1c, and the standard formula see formula (1)–(3) [19] was used to combine the relevant research indicators \(\overline{x}\pm\)s in two groups with 7.5% as the boundary.

$$N=N1+{N}_2$$
(1)
$$M=\left(N1M1+N2M2\right)/\left(N1+N2\right)$$
(2)
$$SD=\sqrt{\frac{\left({N}_1-1\right){SD}_1^2+\left({N}_2-1\right){SD}_2^2+\frac{N_1{N}_2}{N_1+{N}_2}\left({M}_1^2+{M}_2^2-2{M}_1{M}_2\right)}{N_1+{N}_2-1}}$$
(3)

Equations (1)-(3), N is the combined sample size, and N1 and N2 are the sample size of the two groups; M is the combined mean, and M1 and M2 are the mean of the two groups; SD is the combined standard deviation, and SD1 and SD2 are the standard deviation of the two groups.

Assessment of risk of bias

Risk of bias for the included studies was assessed by two reviewers using the critical appraisal tool provided by the JBI. The tool included a total of eight items, and each item was rated as “low risk,” “high risk,” or “unclear” [16].

Synthesis of evidence

Meta-analysis was performed when two or more studies with similar study designs, and outcome measures could be combined. The meta-analysis was conducted using RevMan 5.4 [20]. Regarding cognition, studies were grouped by cognitive domains and standardized mean difference (SMD) were calculated. All measures were reported with the 95% confidence interval (CI). The heterogeneity between studies was assessed using the I2 statistic, and an I2 value > 50% was considered as highly heterogeneous and a random effects model would be used. In the case of high heterogeneity, sensitivity analyses were performed by excluding one study at a time to explore whether individual studies accounted for heterogeneity. P < 0.05 was considered statistically significant.

For I2 > 30% and more than 5 studies included, the prediction interval (PI) from the random-effects meta-analyses is used. It predicts the potential underlying effect in a new study that is different from the average effect from the meta-analyses [21].

Quality of the evidence

The Grading of Recommendations Assessment, Development and Evaluation (GRADE) criteria were used to assess the quality of the evidence for each outcome [22].

Result

Study selection

A total of 3359 records were retrieved. After eliminating duplicates, a screening of the remaining 3096 studies was performed based on titles and abstracts, of which 3038 were excluded. The remaining 58 articles were subsequently read in full text. Ultimately, six studies were eligible for the meta-analyses. The PRISMA flow chart of study selection is shown in Fig. 1.

Fig. 1
figure 1

Flow diagram of the study selection

Full size image

Study characteristics

A total of six studies [15, 18, 23,24,25,26] were included, all of which were cross-sectional. They were published from 2009 to 2018, and the study areas were distributed in Austria [15, 26], China [25], Poland [18], Egypt [24], and Germany [23]. Studies provided data on the full-scale intellectual quotient (FSIQ) [18, 24,25,26], verbal intellectual quotient (VIQ) [18, 24], memory [15, 18, 23,24,25,26], and attention [18, 23]. According to HbA1c grouping, 8.0% was used as the cutoff in two studies [15, 26], and 7.5% was used in the rest [18, 23,24,25]. Five studies included adolescent patients [15, 18, 24,25,26], and one study included adult patients [23]. A total of 351 patients with T1DM was involved, and two of the studies had small sample sizes [23, 26]. The basic characteristics of the included studies were shown in Table 1.

Table 1 Characteristics of the included studies
Full size table

Risk of bias

Almost all studies had clear inclusion and exclusion criteria for participants. However, there is an unclear risk of bias in the measurement of outcomes due to some subjectivity in using scales to assess cognitive function. The risk of bias in the included studies was depicted in Table 1 (Additional file 3) and Fig. 2.

Fig. 2
figure 2

Risk of bias of included studies

Full size image

Association between glycemic control and cognition in patients with T1DM

Full-scale intellectual quotient

Four studies [18, 24,25,26] examined the FSIQ. Meta-analysis showed that patients with poor glycemic control scored lower on the FSIQ compared to those with good glycemic control (P = 0.01, SMD = −0.79, 95%CI = −1.42 to −0.17, I2 = 79%) (Fig. 3).

Fig. 3
figure 3

Forest plots of the relationship between glycemic control and cognitive outcomes

Full size image

Verbal intellectual quotient

Two studies [18, 24] examined the VIQ. The results of meta-analysis showed that there was no significant difference between the two groups (P = 0.08, SMD = −1.03, 95%CI = −2.20 to 0.13, I2 = 87%) (Fig. 3).

Memory

Six studies [15, 18, 23,24,25,26] examined memory. Meta-analysis found that patients with poor glycemic control scored similarly on memory compared to those with good glycemic control (P = 0.05, SMD = −0.41, 95%CI = −0.82 to 0.00, I2 = 69%, PI = −1.76 to 0.94) (Fig.1 (Additional file 3) and Fig. 3 ).

Attention

Two studies [18, 23] examined attention. Meta-analysis showed that patients with poor glycemic control scored similarly on attention compared to those with good glycemic control (P = 0.23, SMD = −0.26, 95%CI = −0.69 to 0.16, I2 = 42%) (Fig. 3).

Sensitivity analyses

For the FSIQ, the heterogeneity was 0% after removing Abo-el-Asrar’ study [24] (P < 0.001, SMD = −0.52, 95%CI = −0.82 to −0.22), and for the memory, heterogeneity was fully explained by STANISŁAWSKA-KUBIAK’ study [18] (P = 0.09, SMD = −0.21, 95%CI = −0.46 to 0.03, I2 = 0%). After exclusion, a meta-analysis of the remaining studies showed that the results changed in the same direction as before exclusion.

Grading of the evidence

The summary of the GRADE assessment for each outcome was shown in Table 2. The evidence certainty was very low for all outcomes assessed in this systematic review, starting with a low rating because the data were from observational studies, and the certainty of the evidence was further downgraded for inconsistency or imprecision.

Table 2 Quality of evidence according to GRADE approach
Full size table

Discussion

In recent years, the impact of T1DM on the brain cognitive function has received extensive attention. However, the relationship between glycemic control level and cognitive function in T1DM patients is still unclear according to current research. In this study, a meta-analysis was conducted to compare the cognitive function of T1DM patients with satisfactory and suboptimal glycemic control. The neuropsychological results showed that poor glycemic control had a significant adverse effect on FSIQ. Although no statistically significant correlation was found with VIQ, memory, and attention, the combined estimates pointed to a negative association. These findings are consistent with previous studies showing that suboptimal glycemic control has a negative impact on cognitive function in patients with T1DM [27,28,29].

Patients with T1DM have large fluctuations in blood glucose levels due to absolute insulin deficiency and lifelong dependence on insulin therapy [30]. Therefore, satisfactory or stable glycemic control is particularly important. In a large cohort study, patients with T1DM who had a mean HbA1c concentration of less than 7.4% performed significantly better on tests of the speed of thought and visual-motor integration than those with mean HbA1c concentration of more than 8.8% [28]. As the data were not available, we were unable to merge to obtain consistent results. It has been reported that suboptimal glycemic control can negatively affect cognitive function in young patients with T1DM [27]. In a prospective longitudinal study, cognitive decline in patients with early diagnosed T1DM was associated with chronically high HbA1c levels [31]. Mauras and colleagues found that brain volume and cognitive scores were inversely associated with HbA1c levels in children with T1DM [32]. At the same time, previous research has shown that patients with well controlled diabetes did not have a significantly increased risk of cognitive impairment compared with healthy individuals without diabetes. Diabetes and glycemic control were strongly associated with incident MCI in people with normal cognition at baseline [8]. However, Ohmann et al. found that cognitive function was significantly impaired in children and adolescents with T1DM, and it was not associated with the quality of glycemic control [15]. Preclinical animal models of T1DM have also highlighted that hyperglycemia increases the risk of cognitive impairment. In the streptozotocin (STZ) model, chronic hyperglycemia tends to result in decreased performance on behavioral tests of spatial learning and memory (e.g., Morris water maze, Y-maze, and conditional active avoidance) and visuospatial object recognition memory [33, 34]. Biessels et al. demonstrated that the administration of insulin effectively mitigated spatial learning and synaptic plasticity impairments in STZ-induced diabetic rats, but only when insulin treatment was initiated immediately after the induction of hyperglycemia [35]. This also suggests the importance of early standardized treatment to reduce exposure to hyperglycemia.

The current research supports the concept that insufficient glycemic control increases vulnerability to cognitive decline, and there are plausible mechanisms that can explain the influence of suboptimal glycemic regulation on cognitive function. First, poor glycemic control means that the body is in a state of hyperglycemia for a long time, and in this state, the cells of the body are susceptible to stress (including oxidative stress and endoplasmic reticulum stress) and inflammatory response, which will lead to cell dysfunction, thereby impairing the normal function of brain cells. Second, an increase in glucose levels can damage the lining of blood vessels, these injuries may lead to cerebral vascular diseases, such as cerebral hemorrhage and cerebral infarction. Third, in a state of long-term hyperglycemia, brain neurons are vulnerable to damage and death [36,37,38,39]. These structural changes in the brain caused by cellular and vascular alterations resulting from chronic hyperglycemia may underlie the pathophysiology of cognitive impairment. In functional magnetic resonance imaging, higher HbA1c levels are associated with lower brain activation, This may reflect the upregulation of glucose transport in the brain [40]. Data from the current study suggest that controlling blood glucose within the near-normal range may reduce the risk of cognitive decline in patients with diabetes. When blood glucose control is suboptimal and the brain is in a hyperglycemic state for a long time, it can affect cognitive function. Cognitive function is particularly important for glycemic control in patients with T1DM, as poor cognitive function may affect patients’ understanding and implementation of self-management, as well as their adherence to medication use and diet control, leading to suboptimal glycemic control, which in turn affects cognitive function, forming a vicious circle.

The strength of this review is that it follows the best practice guidelines for systematic reviews and meta-analyses, combining the available research evidence and clarifying the conclusions. To our knowledge, few studies have reported the relationship between glycemic control level and cognitive function in T1DM patients. There are several limitations that should be acknowledged. First, in present study, the included studies were cross-sectional and lacked high-quality longitudinal studies. Second, HbA1c is the best response to glycemic control level, but does not reflect blood glucose fluctuations and the risks associated with extreme hypoglycemia and hyperglycemia. Third, due to the limitations on the number of studies included, we were unable to do subgroup analyses to assess the effect of confounding factors (e.g., age, disease duration). Therefore, more relevant studies will be needed to validate in the future.

Conclusion

The current study suggests that T1DM patients with suboptimal glycemic control have a worse cognitive function, but only based on the scale of FSIQ. Moreover, a number of longitudinal studies are needed to further illuminate these present results. In this case, a comprehensive neuropsychological evaluation should be performed early in T1DM patients with poor glycemic control using standardized detection methods. This study highlights the importance of maintaining satisfactory glycemic control in patients with T1DM to improve their health status and quality of life. Future studies are needed to more precisely identify risk and protective factors for cognitive deficits in T1DM, and these results have the potential to influence patient treatment standards and guide treatment decisions.

Availability of data and materials

Data were extracted from published sources.

Abbreviations

CNKI:

China National Knowledge Infrastructure

CI:

Confidence interval

DM:

Diabetes mellitus

FSIQ:

Full-scale intellectual quotient

GRADE:

Grading of Recommendations Assessment, Development and Evaluation

HbA1c:

Glycated hemoglobin

IDF:

International Diabetes Federation

JBI:

Joanna Briggs Institute

MCI:

Mild cognitive impairment

PROSPERO:

International Prospective Register of Systematic Reviews

PI:

Prediction interval

SMD:

Standardized mean difference

STZ:

Streptozotocin

T1DM:

Type 1 diabetes mellitus

T2DM:

Type 2 diabetes mellitus

VIQ:

Verbal intellectual quotient

References

  1. Cloete L. Diabetes mellitus: an overview of the types, symptoms, complications and management. Nurs Stand. 2022;37(1):61–6. https://doiorg.publicaciones.saludcastillayleon.es/10.7748/ns.2021.e11709.

    Article  PubMed  Google Scholar 

  2. International Diabetes Federation. IDF Diabetes Atlas 10th Edition 2021. https://www.idf.org/aboutdiabetes/what-is-diabetes/facts-figures.html.

  3. Classification and diagnosis of diabetes: standards of medical care in diabetes-2022. Diabetes Care. 2022;45(Suppl 1):S17–s38. https://doiorg.publicaciones.saludcastillayleon.es/10.2337/dc22-S002.

  4. Syed FZ. Type 1 Diabetes mellitus. Ann Int Med. 2022;175(3):ITC33–48. https://doiorg.publicaciones.saludcastillayleon.es/10.7326/aitc202203150.

    Article  PubMed  Google Scholar 

  5. Pan WT, Liu PM, Ma D, Yang JJ. Advances in photobiomodulation for cognitive improvement by near-infrared derived multiple strategies. J Transl Med. 2023;21(1):135. https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12967-023-03988-w.

    Article  PubMed  PubMed Central  Google Scholar 

  6. Yuan L, Luan D, Xu X, Yang Q, Huang X, Zhao S, et al. Altered attention networks in patients with thyroid dysfunction: a neuropsychological study. Horm Behav. 2020;121:104714. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.yhbeh.2020.104714.

    Article  PubMed  Google Scholar 

  7. Yu JH, Kim REY, Park SY, Lee DY, Cho HJ, Kim NH, et al. Association of long-term hyperglycaemia and insulin resistance with brain atrophy and cognitive decline: a longitudinal cohort study. Diabetes Obes Metab. 2022; https://doiorg.publicaciones.saludcastillayleon.es/10.1111/dom.14958.

  8. Rawlings AM, Sharrett AR, Albert MS, Coresh J, Windham BG, Power MC, et al. The association of late-life diabetes status and hyperglycemia with incident mild cognitive impairment and dementia: the ARIC study. Diabetes Care. 2019;42(7):1248–54. https://doiorg.publicaciones.saludcastillayleon.es/10.2337/dc19-0120.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Zhou C, Dong C, Xie Z, Hao W, Fu C, Sun H, et al. Sex-specific associations between diabetes and dementia: the role of age at onset of disease, insulin use and complications. Biol Sex Differ. 2023;14(1):9. https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s13293-023-00491-1.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Sharma S, Brown CE. Microvascular basis of cognitive impairment in type 1 diabetes. Pharmacol Ther. 2022;229:107929. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.pharmthera.2021.107929.

    Article  CAS  PubMed  Google Scholar 

  11. Arffman M, Hakkarainen P, Keskimäki I, Oksanen T, Sund R. Long-term and recent trends in survival and life expectancy for people with type 1 diabetes in Finland. Diabetes Res Clin Pract. 2023;198:110580. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.diabres.2023.

    Article  PubMed  Google Scholar 

  12. Macedo LBC, Foss MP, Galera CA. Cognitive impairments in type 1 diabetes mellitus: integrative review. Psicologia - Teoria e Prática. 2023;25(1) https://doiorg.publicaciones.saludcastillayleon.es/10.5935/1980-6906/ePTPPA14344.en.

  13. Chaytor NS, Barbosa-Leiker C, Ryan CM, Germine LT, Hirsch IB, Weinstock RS. Clinically significant cognitive impairment in older adults with type 1 diabetes. J Diabetes Complicat. 2019;33(1):91–7. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.jdiacomp.2018.04.003.

    Article  Google Scholar 

  14. Shigemoto S, Imbe H, Fujisawa R, Sasagawa A, Watanabe D, Tachibana M, et al. Decreased cognitive function is associated with preceding severe hypoglycemia and impaired blood glucose control in the elderly individuals with type 1 diabetes. Diabetol Int. 2022;13(4):679–86. https://doiorg.publicaciones.saludcastillayleon.es/10.1007/s13340-022-00588-9.

    Article  PubMed  PubMed Central  Google Scholar 

  15. Ohmann S, Popow C, Rami B, König M, Blaas S, Fliri C, et al. Cognitive functions and glycemic control in children and adolescents with type 1 diabetes. Psychol Med. 2009;40(1):95–103. https://doiorg.publicaciones.saludcastillayleon.es/10.1017/s0033291709005777.

    Article  PubMed  Google Scholar 

  16. Moola SMZ, Tufanaru C, Aromataris E, Sears K, Sfetcu R, Currie M, et al. Chapter 7: systematic reviews of etiology and risk. In: Aromataris E, Munn Z, editors. JBI Manual for Evidence Synthesis JBI; 2020. https://doiorg.publicaciones.saludcastillayleon.es/10.46658/JBIMES-20-08.

    Chapter  Google Scholar 

  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. https://doiorg.publicaciones.saludcastillayleon.es/10.1136/bmj.

    Article  PubMed  PubMed Central  Google Scholar 

  18. Stanisławska-Kubiak M, Mojs E, Wójciak RW, Piasecki B, Matecka M, Sokalski J, et al. An analysis of cognitive functioning of children and youth with type 1 diabetes (T1DM) in the context of glycemic control. Eur Rev Med Pharmacol Sci. 2018;22(11):3453–60. https://doiorg.publicaciones.saludcastillayleon.es/10.26355/eurrev_201806_15170.

    Article  PubMed  Google Scholar 

  19. Higgins JPT, Li T, Deeks JJ, editors. Chapter 6: Choosing effect measures and computing estimates of effect. In: Higgins JPT, Thomas J, Chandler J, Cumpston M, Li T, Page MJ, Welch VA, editors. Cochrane Handbook for Systematic Reviews of Interventions version 6.4 (updated August 2023). Cochrane, 2023. Available from www.training.cochrane.org/handbook.

  20. The Cochrane Collaboration. Review Manager (RevMan) [computer program]. Version 5.4: The Cochrane Collaboration. 2020.

  21. Riley RD, Higgins JP, Deeks JJ. Interpretation of random effects meta-analyses. Bmj. 2011;342:d549. https://doiorg.publicaciones.saludcastillayleon.es/10.1136/bmj.d549.

    Article  PubMed  Google Scholar 

  22. Guyatt G, Oxman AD, Akl EA, Kunz R, Vist G, Brozek J, et al. GRADE guidelines: 1. Introduction-GRADE evidence profiles and summary of findings tables. J Clin Epidemiol. 2011;64(4):383–94. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.jclinepi.2010.04.026.

    Article  PubMed  Google Scholar 

  23. Zihl J, Schaaf L, Zillmer EA. The relationship between adult neuropsychological profiles and diabetic patients’ glycemic control. Appl Neuropsychol. 2010;17(1):44–51. https://doiorg.publicaciones.saludcastillayleon.es/10.1080/09084280903526133.

    Article  PubMed  Google Scholar 

  24. Abo-El-Asrar M, Andrawes NG, Rabie MA, El-Gabry DA, Khalifa AG, El-Sherif M, et al. Cognitive functions in children and adolescents with early-onset diabetes mellitus in Egypt. Appl Neuropsychol Child. 2016;7(1):21–30. https://doiorg.publicaciones.saludcastillayleon.es/10.1080/21622965.2016.1224186.

    Article  PubMed  Google Scholar 

  25. He J, Li S, Liu F, Zheng H, Yan X, Xie Y, et al. Glycemic control is related to cognitive dysfunction in Chinese children with type 1 diabetes mellitus. J Diabetes. 2018;10(12):948–57. https://doiorg.publicaciones.saludcastillayleon.es/10.1111/1753-0407.12775.

    Article  PubMed  Google Scholar 

  26. Kaufmann L, Pixner S, Starke M, Zotter S, Köhle J, Meraner D, et al. Neurocognition and brain structure in pediatric patients with type 1 diabetes. Journal of Pediatric. Neuroradiol. 2012;1(1):25–35. https://doiorg.publicaciones.saludcastillayleon.es/10.3233/PNR-2012-005.

    Article  Google Scholar 

  27. Kirchhoff BA, Jundt DK, Doty T, Hershey T. A longitudinal investigation of cognitive function in children and adolescents with type 1 diabetes mellitus. Pediatr Diabetes. 2016;18(6):443-i449 https://doiorg.publicaciones.saludcastillayleon.es/10.1111/pedi.12414.

  28. Jacobson AM, Musen G, Ryan CM, Silvers N, Cleary P, Waberski B, et al. Long-term effect of diabetes and its treatment on cognitive function. New England j med. 2007;356(18):1842–52. https://doiorg.publicaciones.saludcastillayleon.es/10.1056/NEJMoa066397.

    Article  Google Scholar 

  29. Lancrei HM, Yeshayahu Y, Grossman ES, Berger I. Sweet but sour: impaired attention functioning in children with type 1 diabetes mellitus. Front Human Neurosci. 2022;16 https://doiorg.publicaciones.saludcastillayleon.es/10.3389/fnhum.2022.895835.

  30. Bolli GB, Porcellati F, Lucidi P, Fanelli CG. The physiological basis of insulin therapy in people with diabetes mellitus. Diabetes Res Clin Pract. 2021;175:108839. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.diabres.2021.

    Article  CAS  PubMed  Google Scholar 

  31. Schoenle EJ, Schoenle D, Molinari L, Largo RH. Impaired intellectual development in children with type I diabetes: association with HbA(1c), age at diagnosis and sex. Diabetologia. 2002;45(1):108–14. https://doiorg.publicaciones.saludcastillayleon.es/10.1007/s125-002-8250-6.

    Article  CAS  PubMed  Google Scholar 

  32. Mauras N, Buckingham B, White NH, Tsalikian E, Weinzimer SA, Jo B, et al. Impact of type 1 diabetes in the developing brain in children: a longitudinal study. Diabetes Care. 2021;44(4):983–92. https://doiorg.publicaciones.saludcastillayleon.es/10.2337/dc20-2125.

    Article  PubMed  PubMed Central  Google Scholar 

  33. Rom S, Zuluaga-Ramirez V, Gajghate S, Seliga A, Winfield M, Heldt NA, et al. Hyperglycemia-driven neuroinflammation compromises BBB leading to memory loss in both diabetes mellitus (DM) type 1 and type 2 mouse models. Mol Neurobiol. 2019;56(3):1883–96. https://doiorg.publicaciones.saludcastillayleon.es/10.1007/s12035-018-1195-5.

    Article  CAS  PubMed  Google Scholar 

  34. Ahmed A, Zeng G, Jiang D, Lin H, Azhar M, Farooq AD, et al. Time-dependent impairments in learning and memory in Streptozotocin-induced hyperglycemic rats. Metab Brain Dis. 2019;34(5):1431–46. https://doiorg.publicaciones.saludcastillayleon.es/10.1007/s11011-019-00448-7.

    Article  CAS  PubMed  Google Scholar 

  35. Biessels GJ, Kamal A, Urban IJ, Spruijt BM, Erkelens DW, Gispen WH. Water maze learning and hippocampal synaptic plasticity in streptozotocin-diabetic rats: effects of insulin treatment. Brain Res. 1998;800(1):125–35. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/s0006-8993(98)00510-1.

    Article  CAS  PubMed  Google Scholar 

  36. Ma WX, Tang J, Lei ZW, Li CY, Zhao LQ, Lin C, et al. Potential biochemical mechanisms of brain injury in diabetes mellitus. Aging Dis. 2020;11(4):978–87. https://doiorg.publicaciones.saludcastillayleon.es/10.14336/ad.2019.0910.

    Article  PubMed  PubMed Central  Google Scholar 

  37. Muriach M, Flores-Bellver M, Romero FJ, Barcia JM. Diabetes and the brain: oxidative stress, inflammation, and autophagy. Oxid Med Cell Longev. 2014;2014:102158. https://doiorg.publicaciones.saludcastillayleon.es/10.1155/2014/102158.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Shukla V, Shakya AK, Perez-Pinzon MA, Dave KR. Cerebral ischemic damage in diabetes: an inflammatory perspective. J Neuroinflammation. 2017;14(1):21. https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12974-016-0774-5.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Lima J, Moreira NCS, Sakamoto-Hojo ET. Mechanisms underlying the pathophysiology of type 2 diabetes: from risk factors to oxidative stress, metabolic dysfunction, and hyperglycemia. Mutat Res Genet Toxicol. Environ Mutagen. 2022;874-875:503437. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.mrgentox.2021.503437.

    Article  CAS  Google Scholar 

  40. Bolo NR, Musen G, Jacobson AM, Weinger K, McCartney RL, Flores V, et al. Brain activation during working memory is altered in patients with type 1 diabetes during hypoglycemia. Diabetes. 2011;60(12):3256–64. https://doiorg.publicaciones.saludcastillayleon.es/10.2337/db11-0506.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

Not applicable.

Funding

This study was supported by the Science and Technology Major Project, Gansu Province (No.22ZD6FA033).

Author information

Author notes

  1. Wenting Hua and Zouxi Du authors contributed equally to this work.

Authors and Affiliations

  1. The First School of Clinical Medicine, Lanzhou University, Lanzhou, 730000, China

    Wenting Hua, Zouxi Du & Limin Tian

  2. Clinical Research Center for Metabolic Diseases, Gansu Province, Lanzhou, 730000, China

    Wenting Hua, Zouxi Du & Limin Tian

  3. Institution of Clinical Research and Evidence-Based Medicine, Gansu Provincial Hospital, Lanzhou, 730000, China

    Tingting Lu

  4. Department of Endocrinology, Gansu Provincial Hospital, Lanzhou, 730000, China

    Limin Tian

Authors
  1. Wenting Hua
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Zouxi Du
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Tingting Lu
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Limin Tian
    View author publications

    You can also search for this author inPubMed Google Scholar

Contributions

All authors contributed to the study conception and design. Material preparation, data collection, and analysis were performed by Wenting Hua and Zouxi Du. The first draft of the manuscript was written by Wenting Hua. All authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Limin Tian.

Ethics declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Additional file 1.

PRISMA Checklist.

Additional file 2.

Search strategy.

Additional file 3:

Table 1. Risk of bias in the included studies. Figure 1. The prediction intervals for memory.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Hua, W., Du, Z., Lu, T. et al. Effect of glycemic control on cognitive function in patients with type 1 diabetes mellitus: a systematic review and meta-analysis. Syst Rev 13, 10 (2024). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s13643-023-02433-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s13643-023-02433-9

Keywords

Systematic Reviews

ISSN: 2046-4053