Skip to main content
Download PDF
Download ePub

Effect of glycemic status on myocardial deformation and microvascular function in uncomplicated pediatric type 1 diabetes mellitus: cardiac magnetic resonance imaging

Cardiovascular Diabetology volume 24, Article number: 215 (2025) Cite this article

Abstract

Background

Cardiovascular disease remains the leading cause of morbidity and mortality among individuals with type 1 diabetes mellitus (T1DM). Individuals with hyperglycemia are at great risk of cardiovascular complications. This study investigated the impact of glycemic control on left ventricular (LV) microvascular perfusion and myocardial deformation in uncomplicated pediatric T1DM using cardiac magnetic resonance (CMR) imaging.

Methods

A total of 100 uncomplicated pediatric patients with T1DM and 35 controls were enrolled and underwent 3.0 T CMR examinations. Patients were divided into two groups according to HbA1c levels of 7.0% (HbA1c < 7.0%, n = 25; HbA1c ≥ 7.0%, n = 75). Subclinical systolic and diastolic function were evaluated using peak strain and strain rate based on myocardial deformation analysis. Myocardial perfusion upslope and maximum signal intensity (MaxSI) were assessed via first-pass perfusion imaging at rest. Multivariable linear regression analyses identified the independent factors of reduced myocardial perfusion and deformation in T1DM patients.

Results

Among the three groups, longitudinal peak diastolic strain rate (PDSR) deteriorated gradually from controls through patients with HbA1c < 7.0% to patients with HbA1c ≥ 7.0% (all p < 0.05). Upslope in patients with HbA1c ≥ 7.0% was decreased compared to patients with HbA1c < 7.0% (p = 0.007) and controls (p < 0.001). Compared to controls, both MaxSI and circumferential PDSR were reduced in patients with HbA1c ≥ 7.0% (p = 0.025 and 0.016, respectively), but not in patients with HbA1c < 7.0% (p = 0.566 and 0.379, respectively). In multivariable analysis, elevated HbA1c level was independently associated with reduced upslope (β = − 2.53, p < 0.001) and longitudinal PDSR (β = − 0.02, p = 0.007). When the perfusion indices were included in the multivariable analysis for diastolic dysfunction, upslope (β = 0.10, p = 0.016) and MaxSI (β = − 0.02, p = 0.006) were associated with reduced longitudinal PDSR.

Conclusion

Pediatric T1DM with higher HbA1c showed worse myocardial perfusion and subclinical diastolic dysfunction. Microvascular dysfunction was associated independently with cardiac dysfunction.

Trial registration: retrospectively registered ChiCTR2100043799.

Introduction

Type 1 diabetes mellitus (T1DM) is the most common type of childhood diabetes, accounting for approximately 90% of all pediatric diabetes cases [1]. Cardiovascular diseases (CVD) including epicardial coronary artery disease and heart failure remain the leading cause of morbidity and mortality among individuals with T1DM [2, 3]. Individuals diagnosed with T1DM before age 10 face a sevenfold higher CVD mortality risk compared to those non-diabetic peers [4], suggesting that myocardial changes may begin early in childhood-onset T1DM. Early detection of subclinical myocardial injury biomarkers in these patients is crucial for preventing and managing CVD [5]. Studies reporting myocardial microcirculation and subclinical dysfunction in pediatric T1DM patients remain scarce.

Myocardial deformation analysis enables detection of subclinical cardiac dysfunction before left ventricular (LV) ejection fraction (EF) declines, offering insights into early-stage diabetic cardiomyopathy. Additionally, first-pass myocardial perfusion CMR could monitor myocardial microvascular dysfunction non-invasively. Cardiac magnetic resonance (CMR) imaging serves as a sensitive and non-invasive method for detecting myocardial alterations, as recommended for young individuals with T1DM [6].

A large prospective study demonstrated that a 1% increase in HbA1c level was associated with a 30% higher risk of heart failure independently of other CVD risk factors [7]. Poor glycemic control increases microvascular complications in the T1DM population in a 30-year follow-up study [8]. Chronic hyperglycemia is the most critical modifiable factor for CVD in diabetes. However, the relationship between hyperglycemia and diabetic myocardial injury in pediatric T1DM requires further investigation. Therefore, we used CMR to evaluate the effect of glycemic status on LV myocardial deformation in uncomplicated pediatric T1DM. We further incorporated myocardial perfusion imaging to unmask subtle changes in myocardial microvascular function that might compromise cardiac performance.

Materials and methods

This study was approved by the Biomedical Research Ethics Committee of our hospital and conducted in accordance with the Declaration of Helsinki. Written informed consent was obtained from all the participants’ legal guardian prior to inclusion in the study.

Study population

From June 2019 to May 2024, pediatric T1DM patients (< 18 years) were enrolled at our institution. The inclusion criterion was the diagnosis of T1DM based on the guidelines by the American Diabetes Association guidelines. The exclusion criteria were as follows: (1) individuals with established cardiovascular disease (e.g., myocarditis and congenital cardiovascular); (2) those with contraindications to CMR imaging (claustrophobia, implanted pacemakers); (3) individuals with diabetic macrovascular and microvascular complications (retinopathy, nephropathy, peripheral neuropathy, autonomic neuropathy) as assessed by the endocrinologists; (4) incomplete imaging results or poor CMR image quality that affecting LV measurements; (5) missing biochemical data.

Using G*Power 3.1.9.7 (Heinrich-Heine-Universität Düsseldorf, Germany), we performed a one-way ANOVA based on preliminary longitudinal peak diastolic strain rate data [9]. With α = 0.05 (95% confidence) and power = 0.80, the analysis required 22 participants per group. Accounting for a 10% dropout rate, we included 75 participants (25 per group). Of 110 screened T1DM patients, 100 met eligibility criteria (see Fig. 1 for flowchart).

Fig. 1
figure 1

Flowchart of the cohort study. T1DM, type 1 diabetes mellitus; CMR, cardiac magnetic resonance

Full size image

Clinical and biochemical assessments

Before CMR examinations, we recorded blood pressure [systolic blood pressure (SBP) and diastolic blood pressure (DBP)] and anthropometric measurements [body weight, height, hip, waist circumference, body mass index (BMI), and body surface area (BSA)] and collected fasting blood samples for analysis. Laboratory measurements included glucose metabolism [Fasting plasma glucose (FPG) and glycated hemoglobin (HbA1c)], lipid profile [Total cholesterol (TC), triglycerides (TG), high-density lipoprotein (HDL), low-density lipoprotein (LDL), and apolipoprotein A1 (ApoA1)], renal function (Urea nitrogen, creatinine, uric acid, and cystatin C).

Patient stratification

Per the 2024 American Diabetes Association and 2020 Chinese guidelines [10, 11], which both recommend a glycemic target of < 7.0% HbA1c for pediatric T1DM, we stratified patients into two groups: HbA1c < 7.0% and HbA1c ≥ 7.0%. Age- and sex-matched controls were also enrolled and underwent the same CMR protocol.

CMR protocols

All CMR scans were performed using a 3.0T scanner (MAGNETOM Skyra, Siemens Healthcare, Erlangen, Germany) equipped with an 18-channel receive coil. ECG-gated cine images were acquired using a balanced steady-state free-precession sequence during breath-holds (5–8 s), covering the LV with 8–12 contiguous short-axis slices from mitral valve to apex (thickness 6 mm, TR/TE 3.42/1.48 ms, matrix 126 × 224, FOV 300 × 320 mm2). First-pass perfusion imaging was obtained at rest following intravenous administration of gadobutrol (Gadovist, Bayer Healthcare, 0.1 mL/kg at 1–2 mL/s) using an inversion recovery echo-planar sequence in three short-axis (thickness 6 mm, TR/TE 2.20/1.10 ms, flip angle 10°, matrix 98 × 192, FOV 270 × 360 mm2). LGE images were acquired 5–10 min post-contrast using phase-sensitive inversion recovery (thickness 6 mm, TR/TE 2.55/1.10 ms, flip angle 55°, matrix 128 × 192, FOV 340 × 360 mm2).

Postprocessing of CMR images

All CMR parameters were measured using commercially available software (CVI42; Circle Cardiovascular Imaging, Inc., Calgary, Canada) by two experienced CMR radiologists. LV endocardial and epicardial borders were initially delineated automatically followed by manual adjustment at end-diastolic and end-systolic phases. LV end-diastolic volume (EDV), end-systolic volume (ESV), and mass (LVM) were measured and indexed to body surface area (BSA) as LVEDVi, LVESVi, and LVMi. Myocardial strain analysis employed feature-tracking technology to derive global peak strain, peak diastolic (PDSR) and systolic (PSSR) strain rates in radial, circumferential, and longitudinal dimensions. Perfusion parameters were measured through time-intensity curve analysis of basal, mid, and apical slices, generating upslope, maximum signal intensity (MaxSI), time-to-MaxSI (TTM), and perfusion index (myocardial-to-blood pool upslope ratio).

Reproducibility of LV strain and first-pass myocardial perfusion parameters

The reproducibility of CMR parameters was assessed by re-measuring LV global strain and myocardial perfusion parameters in 40 randomly selected cases. Intra-observer variability was determined by reanalyzed after 1 month by the original investigator (L.Z. with 6 years of experience). Interobserver variability was determined by a second blinded investigator (S.H. with 6 years of experience) evaluating the same cohort independently.

Statistical analysis

Statistical analyses were performed using SPSS version 23.0 (IBM, Armonk, New York, USA), GraphPad Prism 9 (GraphPad Software, San Diego, CA, USA), and R studio (version 4.1.2, http://www.r-project.org/). Normally distributed continuous variables are presented as mean ± standard deviation (SD), while non-normally distributed variables are expressed as median with interquartile range. Non-normally distributed data were log-transformed prior to analysis. Group comparisons were conducted using one-way ANOVA with Bonferroni correction for normally distributed variables and Kruskal-Wallis test for non-normally distributed variables. Categorical variables are presented as percentages and analyzed using Chi-square tests. Associations between clinical variables and CMR parameters were also examined using Pearson’s or Spearman’s coefficients. Multivariable linear regression analysis was employed to identify independent predictors of myocardial perfusion and strain parameters (positive values) in T1DM. Potential predictors without multicollinearity were selected based on a p-value < 0.1 in the univariable linear regression analysis or clinical relevance. If the variance inflation factor (VIF) ≥ 5 and tolerance (1/VIF) < 0.1, multicollinearity exists. The intraclass correlation coefficient (ICC) was used to assess the inter- and intra- observer reproducibility. An ICC above 0.8 was considered to be excellent. ICC was interpreted with: < 0.50 = poor; 0.50 ≤ ICC < 0.75 = moderate; 0.75 ≤ ICC <0.90 = good; > 0.90 = excellent [95% confidence interval (CI) calculated from 2-way random-effects models [12]. All statistical tests were two-sided, with p < 0.05 considered statistically significant.

Results

Baseline characteristics of the participants

The T1DM cohort included 100 T1DM stratified by glycemic control [HbA1c < 7.0% (n = 25) vs. HbA1c ≥ 7.0% (n = 75)] and 35 age- and sex-matched controls. The demographic and clinical characteristics of the enrolled individuals are presented in Table 1. Age, BMI, waist-to-hip ratio, BSA, and SBP were comparable among the three groups (all p > 0.05). Median diabetes duration did not differ significantly between T1DM subgroups (p = 0.267). FBG, TG, HDL, and LDL levels were significantly higher in patients with HbA1c ≥ 7.0% compared to patients with HbA1c < 7.0% (all p > 0.05).

Table 1 Baseline characteristics of the included participants
Full size table

Comparisons of CMR-derived LV geometry, deformation, and perfusion parameters

Comparisons of CMR parameters among three groups are summarized in Table 2. No significant differences were observed in LVEDVi, LVESVi, LVEF, LVMi, or LVRI among the groups (all p > 0.05). Regarding the strain parameters (Fig. 2 A-C), LV radial and longitudinal peak strain were significantly higher in both patient groups compared to controls, without significant differences between patient subgroups (radial, 36.8 ± 8.4% vs. 45.2 ± 4.6% vs 43.9 ± 11.9%, p = 0.009; longitudinal, − 13.4 ± 2.8% vs. − 15.1 ± 3.7% vs. − 15.1 ± 2.9%, p = 0.002). Conversely, LV longitudinal PDSR was reduced progressively from controls through patients with HbA1c < 7.0% to patients with HbA1c ≥ 7.0% (1.5 ± 0.5 1/s vs. 1.2 ± 0.2 1/s vs. 1.0 ± 0.2 1/s, p < 0.001). Additionally, compared to controls, LV circumferential PDSR was reduced in patients with HbA1C ≥ 7.0% (1.3 ± 0.3 1/s vs. 1.5 ± 0.41/s, p = 0.016) but preserved in patients with HbA1c < 7.0% (1.4 ± 0.4 1/s vs. 1.5 ± 0.4 1/s, p = 0.379). No significant differences were observed in circumferential peak strain, PSSR from three directions, or radial PDSR (all p > 0.05).

Table 2 CMR characteristics compared among type 1 diabetics and controls
Full size table
Fig. 2
figure 2

Comparisons of myocardial strain (the absolute values) and perfusion parameters among type 1 diabetics and controls. Abbreviations: TTM, time to maximal signal intensity; Max SI, maximal signal intensity. * p < 0.05.

Full size image

First-pass perfusion analysis (Fig. 2D) showed graded microvascular impairment with worsening glycemic control. Patients with HbA1c ≥ 7.0% showed significantly reduced perfusion upslope compared to patients with HbA1c < 7.0% (p = 0.007) and controls (p < 0.001), along with decreased MaxSI compared to controls (p = 0.025). MaxSI and upslope in patients with HbA1c < 7.0% remained comparable to controls (MaxSI, p = 0.566; upslope, p = 1.000, respectively). Representative CMR images illustrating these findings were presented in Fig. 3.

Fig. 3
figure 3

Representative cases showed myocardial perfusion function and longitudinal peak diastolic strain rate (PDSR-L) in a control (top row), a patient with HbA1c < 7.0% (middle row), and a patient with HbA1c ≥ 7.0% (bottom row). First-pass myocardial perfusion MR images (A, E, and I) and signal intensity-time curves (B, F, and J) were obtained from the left mid-ventricular slices. PDSR-L was analyzed using Bull's-eye plots (D, H, and L) derived from long-axis cine images (C, G, and K). Max SI, maximum signal intensity; TTM, time to maximum signal intensity

Full size image

Univariable and multivariable regression analyses of reduced myocardial perfusion upslope and longitudinal PDSR

Spearman correlation analysis demonstrated a significant inverse correlation between HbA1c levels and myocardial perfusion upslope (r = − 0.482, p < 0.001). Multivariable analyses showed that BMI (β = − 0.11, p = 0.005), HbA1c level (β = − 2.53, p < 0.001), and LDL (β = 0.40, p = 0.021) were independently associated with upslope after adjustment for other variables (Table 3).

Table 3 Univariable and multivariable regression analyses of upslope
Full size table

Spearman correlation analysis demonstrated that myocardial perfusion upslope was negatively correlated with longitudinal PDSR (r = − 0.246, p = 0.023). Multivariable analyses showed that BMI (β = − 0.02, p = 0.030), HbA1c level (β = − 0.02, p = 0.007), and SBP (β = − 0.01, p = 0.014) were independently associated with longitudinal PDSR after adjustment for other variables (Table 4). When myocardial perfusion parameters upslope and MaxSI were included in multivariable analyses, upslope (β = 0.10, p = 0.016), MaxSI (β = − 0.02, p = 0.006), and LDL (β = − 0.11, p = 0.013) were independently associated with longitudinal PDSR after adjustment for other variables (Table 4).

Table 4 Univariable and multivariable regression analyses of longitudinal PDSR
Full size table

Inter- and intra-observer variability of CMR-derived parameters

The intra-observer and inter-observer correlation coefficients of the measurement of LV global peak strain, PDSR, and PSSR in three directions (radial, circumferential, and longitudinal), upslope, TTM, and maxSI were considered good (Table 5).

Table 5 Intra-observer and interobserver reproducibility of CMR parameters
Full size table

Discussion

In this study, we included pediatric T1DM patients without other diabetes-related complications to investigate the effect of glycemic levels on the myocardium. We found that (1) pediatrics T1DM with HbA1c ≥ 7.0% showed worse myocardial microvascular dysfunction and subclinical diastolic dysfunction. (2) Hyperdynamic systolic function but decreased diastolic function was observed in T1DM with poor control. (3) HbA1c, BMI, and LDL were independently associated with microvascular dysfunction after adjusting for other variables; 4) Myocardial microcirculation dysfunction and LDL were independently associated with reduced LV longitudinal PDSR.

Although the progress of diabetic cardiomyopathy can develop independently of macrovascular complications of the disease, structural and functional changes at the level of the coronary vasculature are common comorbidities in diabetic patients, which can further exacerbate diabetic cardiomyopathy. Currently, most studies have focused on myocardial perfusion in patients with type 2 diabetes mellitus, while research on myocardial perfusion in T1DM is relatively limited and yields conflicting results [13,14,15]. Shivu et al. [15] found the average myocardial perfusion reserve index in young asymptomatic subjects with T1DM was significantly lower than that of healthy controls utilizing magnetic resonance spectroscopy and stress magnetic resonance imaging. Our study further confirms that myocardial perfusion dysfunction can be observed at an earlier stage in T1DM patients. Furthermore, higher HbA1c level was associated with worse microvascular dysfunction. Persistent hyperglycemia is associated with endothelial dysfunction [16]. Autopsy studies of ventricular myocardial samples have confirmed the presence of microvascular abnormalities in diabetic patients, including capillary basement membrane thickening, arteriolar intimal thickening, and increased perivascular fibrosis [17, 18]. These microvascular changes are believed to be driven by hyperglycemia, dyslipidemia, and neurohormonal system activation. In the present study, T1DM patients with microcirculatory impairment exhibited poorer control of blood lipids and glucose, further supporting the proposed mechanisms underlying microvascular pathology [16].

Chronic hyperglycemia exacerbates pathological molecular processes such as non-enzymatic glycation, the formation of advanced glycation end products, and oxidative stress. These processes trigger myocardial inflammation, leading to fibrosis, cardiac remodeling, disruption of calcium homeostasis, endothelial dysfunction, and ultimately a decline in myocardial function [19, 20]. Our study revealed impaired longitudinal diastolic myocardial function in the patient groups. Impaired myocardial perfusion was an independent risk factor of longitudinal PDSR. Longitudinal cardiac function of LV depends on the integrity of the subendocardial myocardial fibers, which are more susceptible to microcirculatory damage. LV diastolic dysfunction was potentially driven by microvascular impairment. Furthermore, persistent hyperglycemia is closely linked to endothelial dysfunction, which increases the risk of microvascular permeability, impairs microvascular blood flow, and may lead to tissue ischemia and heart failure [21,22,23].

Interestingly, our study demonstrated increased longitudinal myocardial contraction accompanied by impaired diastolic function in pediatric T1DM. This observation suggests that diastolic dysfunction may precede systolic impairment in diabetes-related heart disease [24]. The paradoxical hyperdynamic systolic contraction and impaired diastolic function are consistent with previous echocardiographic findings. In the prospective blinded speckle tracking stress echocardiography study, Hensel et al. [25] reported similar results in a cohort of pediatric T1DM with comparable age (mean age 11.5 ± 3.1 years) and disease duration (mean duration 4.3 ± 3.5 years). Their findings showed that patients with poor glycemic control exhibited increased resting circumferential and longitudinal peak strain despite evidence of reduced diastolic function, as indicated by a decreased E/A ratio. It has also been reported that increased left ventricular torsion in young patients with type 1 diabetes is associated with impaired myocardial perfusion and early diastolic filling abnormalities [15, 26, 27]. This hyperdynamic myocardial contraction in the early stages of diabetes may represent a transient compensatory mechanism in the progression of non-ischemic cardiomyopathy, indicating the onset of the asymptomatic subclinical phase of heart failure [25]. Animal model studies suggest that this phenomenon may be linked to altered myocardial substrate utilization and reduced cardiac efficiency, further deteriorating LV remodeling and systolic function [29, 30].

Existing evidence indicates that patients with diabetic microvascular diseases are prone to have worse clinical adverse outcomes [31]. The screening strategy for T1DM population should include myocardial microvascular disease and not only neuropathy, retinopathy, and nephropathy. These findings highly underscore the critical need for optimizing glycemic control and exploring potential therapeutic strategies to address microvascular dysfunction. Furthermore, CMR offers a noninvasive quantitative method to perform risk stratification in diabetics.

Limitations

The current study has several limitations. First, only CMR perfusion at rest was performed due to the restrictions on medication and the potential risks associated with cardiac stress tests for children. However, the value of rest myocardial perfusion compared to stress perfusion has been validated in a variety of studies with T2DM [13, 32]. Our study has also shown that microvascular function was reduced in T1DM subgroups compared to controls and deteriorated along with elevated HbA1c. The clinical significance of resting myocardial perfusion assessment in T1DM population warrants further attention and validation.

Second, although HbA1c is the most widely used clinical indicator of glycemic control, it may not fully capture glycemic variability or cumulative hyperglycemic exposure. The relationship between cumulative hyperglycemic burden during follow-up and cardiovascular risk requires further investigation.

Third, our single-center design resulted in a modest sample size, though our cohort's glycemic control distribution (75% above target HbA1c) reflects real-world epidemiologic patterns in China [33]. Multicenter prospective studies with larger samples are needed to confirm these findings.

Finally, as all scans were performed at 3.0 T, the potential influence of magnetic field strength on perfusion and strain measurements remains unexplored [34, 35]. Systematic comparisons between 1.5 T and 3.0 T systems in diabetic populations would help establish measurement consistency across platforms.

Conclusions

Pediatric T1DM with higher levels of HbAlc exhibited worse myocardial microvascular function and subclinical diastolic function. HbA1c was independently associated with microvascular dysfunction. Microvascular impairment was an independent risk factor related to reduced subclinical diastolic function.

Availability of data and materials

No datasets were generated or analysed during the current study.

Abbreviations

T1DM:

Type 1 diabetes mellitus

CMR:

Cardiac magnetic resonance

HbA1c:

Hemoglobin A1c

BSA:

Body surface area

BMI:

Body mass index

SBP:

Systolic blood pressure

DBP:

Diastolic blood pressure

FBG:

Fast blood glycemia

TC:

Total cholesterol

TG:

Triglycerides

HDL:

High-density lipoprotein

LDL:

Low-density lipoprotein

LV:

Left ventricular

LVEDVi:

Indexed left ventricular end-diastolic volume

LVESVi:

Indexed left ventricular end-systolic volume

LVEF:

Left ventricular ejection fraction

LVRI:

Left ventricular remodeling index

LVMi:

Indexed LV mass

PSSR:

Global peak systolic strain rate

PDSR:

Global peak diastolic strain rate

TTM:

Time to maximal signal intensity

Max SI:

Maximal signal intensity

References

  1. NCD Risk Factor Collaboration (NCD-RisC). Worldwide trends in diabetes prevalence and treatment from 1990 to 2022: a pooled analysis of 1108 population-representative studies with 141 million participants. Lancet. 2024;404(10467):2077–2093.

  2. Marx N, Federici M, Schütt K, et al. 2023 ESC Guidelines for the management of cardiovascular disease in patients with diabetes: Developed by the task force on the management of cardiovascular disease in patients with diabetes of the European Society of Cardiology (ESC). Eur Heart J. 2023;44(39):4043–140.

    Article  CAS  PubMed  Google Scholar 

  3. Dabelea D, Stafford JM, Mayer-Davis EJ, et al. Association of type 1 diabetes vs type 2 diabetes diagnosed during childhood and adolescence with complications during teenage years and young adulthood. JAMA. 2017;317(8):825–35.

    Article  PubMed  PubMed Central  Google Scholar 

  4. Rawshani A, Sattar N, Franzén S, et al. Excess mortality and cardiovascular disease in young adults with type 1 diabetes in relation to age at onset: a nationwide, register-based cohort study. Lancet. 2018;392(10146):477–86.

    Article  PubMed  PubMed Central  Google Scholar 

  5. Manrique-Acevedo C, Hirsch IB, Eckel RH. Prevention of cardiovascular disease in type 1 diabetes. N Engl J Med. 2024;390(13):1207–17.

    Article  CAS  PubMed  Google Scholar 

  6. Bjornstad P, Donaghue KC, Maahs DM. Macrovascular disease and risk factors in youth with type 1 diabetes: time to be more attentive to treatment? Lancet Diabetes Endocrinol. 2018;6(10):809-820

    Article  PubMed  PubMed Central  Google Scholar 

  7. Lind M, Bounias I, Olsson M, Gudbjörnsdottir S, Svensson AM, Rosengren A. Glycaemic control and incidence of heart failure in 20,985 patients with type 1 diabetes: an observational study. Lancet. 2011;378(9786):140–6.

    Article  PubMed  Google Scholar 

  8. Diabetes Control and Complications Trial (DCCT)/Epidemiology of Diabetes Interventions and Complications (EDIC) Study Research Group. Intensive Diabetes Treatment and Cardiovascular Outcomes in Type 1 Diabetes: The DCCT/EDIC Study 30-Year Follow-up. Diabetes Care. 2016;39(5):686–693.

  9. Faul F, Erdfelder E, Lang AG, Buchner A. G*Power 3: a flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behav Res Methods. 2007;39(2):175–91.

    Article  PubMed  Google Scholar 

  10. American Diabetes Association Professional Practice Committee. 14. Children and Adolescents: Standards of Care in Diabetes-2024. Diabetes Care. 2024;47(Suppl 1):S258-S281.

  11. Subspecialty Group of Endocrinologic, Hereditary and Metabolic Diseases, the Society of Pediatrics, Chinese Medical Association, Editorial Board, Chinese Journal of Pediatrics. [Expert consensus on the standardized diagnosis and management of type 1 diabetes mellitus in Chinese children (2020)]. Zhonghua Er Ke Za Zhi. 2020;58(6):447–454.

  12. Koo TK, Li MY. A guideline of selecting and reporting intraclass correlation coefficients for reliability research. J Chiropr Med. 2016;15(2):155–63.

    Article  PubMed  PubMed Central  Google Scholar 

  13. Wang J, Yang ZG, Guo YK, et al. Incremental effect of coronary obstruction on myocardial microvascular dysfunction in type 2 diabetes mellitus patients evaluated by first-pass perfusion CMR study. Cardiovasc Diabetol. 2023;22(1):154.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Byrne C, Jensen T, Hjortkjær HØ, et al. Myocardial perfusion at rest in patients with Diabetes Mellitus Type 1 compared with healthy controls assessed with Multi Detector Computed Tomography. Diabetes Res Clin Pract. 2015;107(1):15–22.

    Article  PubMed  Google Scholar 

  15. Shivu GN, Abozguia K, Phan TT, et al. Increased left ventricular torsion in uncomplicated type 1 diabetic patients: the role of coronary microvascular function. Diabetes Care. 2009;32(9):1710–2.

    Article  PubMed  PubMed Central  Google Scholar 

  16. Farhangkhoee H, Khan ZA, Kaur H, Xin X, Chen S, Chakrabarti S. Vascular endothelial dysfunction in diabetic cardiomyopathy: pathogenesis and potential treatment targets. Pharmacol Ther. 2006;111(2):384–99.

    Article  CAS  PubMed  Google Scholar 

  17. Factor SM, Okun EM, Minase T. Capillary microaneurysms in the human diabetic heart. N Engl J Med. 1980;302(7):384–8.

    Article  CAS  PubMed  Google Scholar 

  18. Kawaguchi M, Techigawara M, Ishihata T, et al. A comparison of ultrastructural changes on endomyocardial biopsy specimens obtained from patients with diabetes mellitus with and without hypertension. Heart Vessels. 1997;12(6):267–74.

    Article  CAS  PubMed  Google Scholar 

  19. Huynh K, Bernardo BC, McMullen JR, Ritchie RH. Diabetic cardiomyopathy: mechanisms and new treatment strategies targeting antioxidant signaling pathways. Pharmacol Ther. 2014;142(3):375–415.

    Article  CAS  PubMed  Google Scholar 

  20. Marwick TH, Ritchie R, Shaw JE, Kaye D. Implications of underlying mechanisms for the recognition and management of diabetic cardiomyopathy. J Am Coll Cardiol. 2018;71(3):339–51.

    Article  PubMed  Google Scholar 

  21. Di Carli MF, Janisse J, Grunberger G, Ager J. Role of chronic hyperglycemia in the pathogenesis of coronary microvascular dysfunction in diabetes. J Am Coll Cardiol. 2003;41(8):1387–93.

    Article  PubMed  Google Scholar 

  22. Camici PG, Tschöpe C, Di Carli MF, Rimoldi O, Van Linthout S. Coronary microvascular dysfunction in hypertrophy and heart failure. Cardiovasc Res. 2020;116(4):806–16.

    Article  CAS  PubMed  Google Scholar 

  23. Oltman CL, Richou LL, Davidson EP, Coppey LJ, Lund DD, Yorek MA. Progression of coronary and mesenteric vascular dysfunction in Zucker obese and Zucker diabetic fatty rats. Am J Physiol Heart Circ Physiol. 2006;291(4):H1780-1787.

    Article  CAS  PubMed  Google Scholar 

  24. Ho KL, Karwi QG, Connolly D, et al. Metabolic, structural and biochemical changes in diabetes and the development of heart failure. Diabetologia. 2022;65(3):411–23.

    Article  PubMed  Google Scholar 

  25. Hensel KO, Grimmer F, Roskopf M, Jenke AC, Wirth S, Heusch A. Subclinical alterations of cardiac mechanics present early in the course of pediatric type 1 diabetes mellitus: a prospective blinded speckle tracking stress echocardiography study. J Diabetes Res. 2016;2016:2583747.

    Article  PubMed  Google Scholar 

  26. Shivu GN, Abozguia K, Phan TT, Narendran P, Stevens M, Frenneaux M. Left ventricular filling patterns and its relation to left ventricular untwist in patients with type 1 diabetes and normal ejection fraction. Int J Cardiol. 2013;167(1):174–9.

    Article  PubMed  Google Scholar 

  27. Piya MK, Shivu GN, Tahrani A, et al. Abnormal left ventricular torsion and cardiac autonomic dysfunction in subjects with type 1 diabetes mellitus. Metabolism. 2011;60(8):1115–21.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Hensel KO, Grimmer F, Roskopf M, Jenke AC, Wirth S, Heusch A. Subclinical alterations of cardiac mechanics present early in the course of pediatric type 1 diabetes mellitus: a prospective blinded speckle tracking stress echocardiography study. J Diabetes Res. 2016;2016:2583747.

    Article  PubMed  Google Scholar 

  29. Buchanan J, Mazumder PK, Hu P, et al. Reduced cardiac efficiency and altered substrate metabolism precedes the onset of hyperglycemia and contractile dysfunction in two mouse models of insulin resistance and obesity. Endocrinology. 2005;146(12):5341–9.

    Article  CAS  PubMed  Google Scholar 

  30. Levelt E, Mahmod M, Piechnik SK, et al. Relationship between left ventricular structural and metabolic remodeling in type 2 diabetes. Diabetes. 2016;65(1):44–52.

    Article  CAS  PubMed  Google Scholar 

  31. Tromp J, Lim SL, Tay WT, et al. Microvascular disease in patients with diabetes with heart failure and reduced ejection versus preserved ejection fraction. Diabetes Care. 2019;42(9):1792–17995.

    Article  PubMed  Google Scholar 

  32. Cao L, Liu C, Ou C, et al. Impact of pretransplant T2DM on left ventricular deformation and myocardial perfusion in heart transplanted recipients: a 3.0 T cardiac magnetic resonance study. Cardiovasc Diabetol. 2024;23(1):216.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Huo L, Deng W, Shaw JE, et al. Factors associated with glycemic control in type 1 diabetes patients in China: A cross-sectional study. J Diabetes Investig. 2020;11(6):1575–82.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Holtackers RJ, Wildberger JE, Wintersperger BJ, Chiribiri A. Impact of field strength in clinical cardiac magnetic resonance imaging. Investig Radiol. 2021;56(11):764–72.

    Article  Google Scholar 

  35. Yang W, Xu J, Zhu L, et al. Myocardial strain measurements derived from MR feature-tracking: influence of sex, age, field strength, and vendor. JACC Cardiovasc Imaging. 2024;17(4):364–79.

    Article  PubMed  Google Scholar 

Download references

Acknowledgements

Not applicable.

Funding

This work was supported by the National Natural Science Foundation of China (82120108015, 82271981, 82402251, 82304078, 82302168, and 824B2052), Sichuan Science and Technology Program (2023NSFSC1715, 25QNJJ1397, 2024YFFK0259, and 2023ZYD0120), China Postdoctoral Science Foundation (2023M732453), Postdoctoral Fellowship Program of CPS Funder Grant (GZC20231830), and Sichuan University Interdisciplinary Innovation Fund.

Author information

Author notes

  1. Lu Zhang and Shan Huang are co-first authors.

Authors and Affiliations

  1. Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), Ministry of Education, West China Second University Hospital, Sichuan University, Chengdu, 614001, Sichuan, China

    Lu Zhang, Shan Huang, Pengfei Ye, Ran Sun, Huayan Xu, Rong Xu, Meng Zhang & Yingkun Guo

  2. Department of Radiology, West China Second University Hospital, Sichuan University, Chengdu, 614001, Sichuan, China

    Lu Zhang, Shan Huang, Pengfei Ye, Huayan Xu, Rong Xu & Yingkun Guo

  3. Department of Radiology, West China Hospital, Sichuan University, Chengdu, 614001, Sichuan, China

    Xueming Li

  4. Department of Radiology, Chengdu Fifth People’s Hospital, Chengdu, 611130, Sichuan, China

    Zhi Yang

  5. Department of Pediatrics, West China Second University Hospital, Sichuan University, Chengdu, 614001, Sichuan, China

    Ying Liu, Chuanjie Yuan & Jin Wu

Authors
  1. Lu Zhang
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Shan Huang
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Xueming Li
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Zhi Yang
    View author publications

    You can also search for this author inPubMed Google Scholar

  5. Pengfei Ye
    View author publications

    You can also search for this author inPubMed Google Scholar

  6. Ran Sun
    View author publications

    You can also search for this author inPubMed Google Scholar

  7. Huayan Xu
    View author publications

    You can also search for this author inPubMed Google Scholar

  8. Rong Xu
    View author publications

    You can also search for this author inPubMed Google Scholar

  9. Meng Zhang
    View author publications

    You can also search for this author inPubMed Google Scholar

  10. Ying Liu
    View author publications

    You can also search for this author inPubMed Google Scholar

  11. Chuanjie Yuan
    View author publications

    You can also search for this author inPubMed Google Scholar

  12. Jin Wu
    View author publications

    You can also search for this author inPubMed Google Scholar

  13. Yingkun Guo
    View author publications

    You can also search for this author inPubMed Google Scholar

Contributions

Lu Zhang, Conceptualization, Methodology, Formal analysis, Writing-Original draft preparation, Visualization. Shan Huang: Conceptualization, Methodology, Writing-Original draft preparation, Draft revising. Yingkun Guo, Supervision, Project administration, Funding acquisition. Jin Wu, Resources, Conceptualization, Writing-Review & Editing. Pengfei Ye, Resources, Conceptualization. Xueming Li & Ran Sun, Conceptualization, Writing-Review & Editing. Zhi Yang, Resources, Conceptualization. Huayan Xu, Writing-Review & Editing. Rong Xu, Funding acquisition, Writing-Review & Editing. Meng Zhang, Resources, Conceptualization. Chuanjie Yuan, Resources. Ying Liu, Resources.

Corresponding authors

Correspondence to Jin Wu or Yingkun Guo.

Ethics declarations

Ethics approval and consent to participate

This study was approved by the Biomedical Research Ethics Committee of our hospital (K2019053) and conducted in accordance with the Declaration of Helsinki. Written informed consent was obtained from all the participants’ legal guardian prior to inclusion in the study.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Additional information

Publisher's Note

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

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, 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 you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. 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-nc-nd/4.0/.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhang, L., Huang, S., Li, X. et al. Effect of glycemic status on myocardial deformation and microvascular function in uncomplicated pediatric type 1 diabetes mellitus: cardiac magnetic resonance imaging. Cardiovasc Diabetol 24, 215 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12933-025-02757-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12933-025-02757-x

Keywords

Cardiovascular Diabetology

ISSN: 1475-2840