Impact of Comprehensive Metabolic Optimization on Myocardial steatosis and Cardiac Remodeling in newly diagnosed type 2 diabetes: a longitudinal study
ÁngelRosales-Rojas
MD
1AlbertTeis
MD, PhD
2,3PedroGil-Millan
MD
4,5JoanaRossell
PhD
6,7BrunoPedraz-Petrozzi
MD, PhD
8,10,11DavidVilades
MD, PhD
3,12,13JoseLuisSanchez-Quesada
PhD
6,14AlvaroGarcía-Osuna
MD
14DídacMauricio
MD, PhD
6,7,15,16JosepJulve
PhD
6,7,18✉Emailjjulve@santpau.catAntonioPerez
MD
6,7,15NuriaAlonso
MD, PhD
1,5,6,17✉Phone00-34-93497XXXXEmailnalonso.germanstrias@gencat.catSantPau1
IR-SantPau1
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Department of EndocrinologyGermans Trias University Hospital. Badalona. BarcelonaSpain2Heart Institute. Cardiology DepartmentGermans Trias University HospitalBadalona. BarcelonaSpain
3Clinica Creu BlancaBarcelonaSpain
4Endocrinology DepartmentHospital Vall d´Hebron08035BarcelonaSpain
5Department of MedicineUniversitat Autònoma de Barcelona08193BarcelonaSpain
6Centro de Investigación Biomédica en Red de Diabetes y Enfermedades Metabólicas Asociadas (CIBERDEM)BarcelonaSpain
7Research Group in Endocrinology, Diabetes and NutritionInstitut de Recerca SANT PAUBarcelonaSpain
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Department of Psychiatry and PsychotherapyCentral Institute of Mental HealthJ59Medical Faculty MannheimUniversity of Heidelberg68159Mannheim, MannheimGermany
10Research Group of Stress-related Disorders, Department of Psychiatry and Psychotherapy, Clinical Faculty MannheimCentral Institute of Mental Health, University of HeidelbergMannheimGermany
11partner site MannheimGerman Center for Mental Health (DZPG)
12Department of CardiologyHospital de SANT PAU08041BarcelonaSpain
13Centro de Investigación Biomédica en Red en Enfermedades Cardiovasculares (CIBERCV)BarcelonaSpain
14Cardiovascular BiochemistryInstitut de Recerca
15Department of EndocrinologyHospital de la Santa Creu I Sant Pau. BarcelonaSpain
16Department of MedicineUniversity of Vic - Central University of CataloniaVicSpain
17Endocrinology DepartmentGermans Trias University Hospital. Badalona. BarcelonaSpain
18Research Group in Endocrinology, DiabetesInstitut de Recerca SANT PAUBarcelonaSpain
Ángel Rosales-Rojas, MD1*, Albert Teis, MD, PhD2,3*, Pedro Gil-Millan MD4,5 Joana Rossell, PhD6,7, Bruno Pedraz-Petrozzi MD, PhD8,9,10, David Vilades, MD, PhD3,11,12, Jose Luis Sanchez-Quesada PhD6,13, Alvaro García-Osuna MD13, Dídac Mauricio MD, PhD6,7,14,15, Josep Julve, PhD6,7, Antonio Perez MD6,7,14,Nuria Alonso MD, PhD1,5,6.
1Department of Endocrinology. Germans Trias University Hospital. Badalona. Barcelona. Spain.
2Heart Institute. Cardiology Department. Germans Trias University Hospital. Badalona. Barcelona. Spain.
3Clinica Creu Blanca, Barcelona, Spain
4Endocrinology Department, Hospital Vall d´Hebron, 08035 Barcelona, Spain.
5Department of Medicine, Universitat Autònoma de Barcelona, 08193 Barcelona, Spain.
6Centro de Investigación Biomédica en Red de Diabetes y Enfermedades Metabólicas Asociadas (CIBERDEM), Barcelona, Spain
7Research Group in Endocrinology, Diabetes and Nutrition, Institut de Recerca SANT PAU, Barcelona, Spain
8Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, J5, 68159 Mannheim, Medical Faculty Mannheim - University of Heidelberg, Mannheim, Germany
9Research Group of Stress-related Disorders, Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Clinical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
10German Center for Mental Health (DZPG), partner site Mannheim
11Department of Cardiology. Hospital de SANT PAU, 08041 Barcelona, Spain.
12Centro de Investigación Biomédica en Red en Enfermedades Cardiovasculares (CIBERCV), Barcelona, Spain.
13Cardiovascular Biochemistry, Institut de Recerca Sant Pau (IR-Sant Pau)
14 Department of Endocrinology. Hospital de la Santa Creu I Sant Pau. Barcelona. Spain.
15Department of Medicine, University of Vic - Central University of Catalonia, Vic, Spain.
Corresponding authors:
1.Nuria Alonso, MD, PhD
Endocrinology Department. Germans Trias University Hospital. Badalona. Barcelona. Spain
Tel.: 00-34-93497XXXX
e-mail: nalonso.germanstrias@gencat.cat
2. Josep Julve PhD
Research Group in Endocrinology, Diabetes and, Institut de Recerca SANT PAU, Barcelona, Spain
e-mail: jjulve@santpau.cat
*Ángel Rosales-Rojas and Albert Teis contributed equally to this study.
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ABSTRACT
Background
Cardiac remodeling is common in individuals with type 2 diabetes (T2D) and may be influenced by both glycemic and metabolic factors. On the other hand, myocardial steatosis, a hallmark of diabetic cardiomyopathy, has been inconsistently linked to glycemic control. This study aimed to evaluate the impact of comprehensive metabolic optimization—targeting improvements in both glycemic control and adiposity— on myocardial triglyceride content (MTGC) and cardiac remodeling in newly diagnosed.
Methods
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Twenty adults with newly diagnosed T2D underwent a 12-month standardized metabolic optimization protocol including insulin, metformin, and empagliflozin therapy, alongside nutritional and lifestyle counseling. Cardiac magnetic resonance imaging (CMR) and proton magnetic resonance spectroscopy (1H-MRS) were performed at baseline and after 12 months to assess cardiac structure, function, and myocardial triglyceride content (MTGC). Analyses assessed longitudinal changes and explored associations among clinical, biochemical, and imaging parameters.Results
Participants (mean age 54.8 ± 9 years, 72.3% male) achieved significant reduction in HbA1c, body mass index (BMI) and waist circumference (WC) after 12 months of glycemic optimization. No significant changes in MTGC were found at follow up (0.52% [0.25–1.44] vs 1.05% [0.43–3.06]; p = 0.23). In contrast, CMR parameters showed favorable remodeling with improved left ventricular ejection fraction Left Ventricle(LV) Ejection Fraction (59.0% [54.8–61.5] vs 63.1%[56.9–66.3], p = 0.01) and reduced ventricular volumes (LV) End-systolic volume (29.9 mL/m2 [26.4–35.1] vs 27.3 mL/m2 [22.5–31.7]; p = 0.007), LV mass (46.1 g/m2 [35.1–54.2] vs 49.5 [39.5–54.3], p = 0.006), right ventricular (RV) End-systolic volume (30.6 mL/m2 [25.9–35.7] vs 28.7 mL/m2 [25.5–32.6], p = 0.02) and RV End-diastolic volume (76.5 mL/m2 [64.6–82.4] vs 72.4 mL/m2 [66.1–77.7], p = 0.03). No associations were found between HbA1c improvement and MTGC or CMR parameters. Changes in BMI and WC strongly correlated with improved left atrial strain (ρ = − 0.78 and − 0.77; p < 0.001), whereas WC correlated with LV End-diastolic volume (ρ = -0.59, p = 0.024).
Conclusions
In newly diagnosed T2D, 12 months of comprehensive metabolic optimization improved cardiac remodeling parameters without altering myocardial steatosis. The cardiac benefits observed were closely related to reductions in adiposity rather than glycemic normalization, emphasizing the importance of weight management as a key therapeutic target for early prevention of diabetic cardiomyopathy
Keywords:
Type 2 diabetes. Myocardial steatosis. Proton magnetic resonance spectroscopy. Glycemic optimization. Cardiac remodeling. Ectopic fat
Research Insights
What is currently known about this topic? Diabetes increases cardiovascular risk. Impact of comprehensive metabolic optimization on myocardial fat remains unclear. |
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What is the key research question? Does a comprehensive metabolic optimization modify myocardial steatosis and improve cardiac remodeling? |
What is new? Cardiac remodeling improves after a comprehensive metabolic optimization. Myocardial steatosis did not change with therapy. |
How might this study influence clinical practice? Glycemic optimization alongside with weight reduction are keys for cardiac health in early diabetes. |
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INTRODUCTION
Type 2 diabetes (T2D) represents a significant challenge to cardiovascular health, as it is associated with increased risk of heart failure (HF) and cardiovascular mortality (1–3). Among the T2D population, the most prevalent HF phenotypes are asymptomatic left ventricular diastolic dysfunction and HF with preserved ejection fraction affecting 43% and 17% of individuals, respectively (4, 5). Diabetes duration, obesity, and smoking habit are known independent risk factors for heart failure, contributing alongside arterial hypertension and coronary artery disease to its development.
Beyond these classical factors, myocardial steatosis, defined as excessive triglyceride (TG) deposition within cardiomyocytes, has emerged as a distinct pathological factor involved in cardiac dysfunction in subjects with T2D (6–10). Lipid accumulation in the myocardium can induce lipotoxic damage via the accumulation of ceramides or diacylglycerol, triggering oxidative stress and mitochondrial dysfunction, eventually leading to myocyte apoptosis, cardiac fibrosis, and ultimately, cardiac dysfunction (11–13). The myocardial triglyceride content (MTGC) varies widely among individuals, and is increased in people with T2D, metabolic syndrome, and obesity. However, the mechanisms underlying this increase are not yet fully understood (14, 15).
The gold standard for evaluating MTGC is a cardiac biopsy, an invasive technique associated with complications which limits its use in clinical research for ethical reasons. Therefore, Proton magnetic resonance spectroscopy (1H-MRS) techniques have therefore become the non-invasive method of choice for quantify myocardial lipid content in experimental animal models and in humans, demonstrating good sensitivity and reproducibility compared to cardiac biopsy (16, 17). However, few studies have evaluated myocardial steatosis in individuals with T2D using 1H-MRS so far, yielding mixed results (18–23).
Although improved glycemic control has been shown to have a favorable impact on cardiac structure and function as assessed by echocardiography in subjects with T2D (24, 25), its specific impact on myocardial steatosis remains uncertain. Most existing data derive from cross-sectional or short-term studies, and the longitudinal relationship between changes in metabolic control, myocardial lipid content, and cardiac remodeling in newly diagnosed T2D has not been fully elucidated. Therefore, we aimed to assess changes in myocardial steatosis and their association with cardiac structure and functional parameters after 12 months of comprehensive metabolic optimization in individuals with newly diagnosed T2D
MATERIALS AND METHODS
Study subjects
This substudy was conducted in a subset of participants from a previously published cohort of individuals with newly diagnosed T2D recruited from the diabetes outpatient clinic at Hospital de la Santa Creu i Sant Pau (Barcelona, Spain), between 2018 and 2020 (26, 27). Inclusion criteria were: age ≥ 18 years and diagnosis of T2D according to the American Diabetes Association (ADA) criteria at the time of the study (28). We excluded individuals with GAD65 antibodies, on treatment with steroids or immunosuppressant therapy, decreased renal function (creatinine clearance < 60 mL/min/1.73 m2), elevated liver enzymes ≥ 3 times above the upper limit of normal, liver cirrhosis, pancreas disease, previously known coronary artery disease, HF or active oncologic disease.
Study design
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This was a prospective before-after longitudinal study including 4 visits: an initial one the time of T2D diagnosis (baseline), and at 3, 6, and 12 months of follow-up. A
All participants received a structured program of lifestyle changing, physical activity and pharmacology therapy based on clinical guidelines recommendations. The initial therapy included glargine U-100 at a dose of 0.2 UI/kg/d, sitagliptin 100 mg/d, and metformin 1700 mg/d at the first visit. Then, Basal insulin was suspended after 2 weeks, and sitagliptin was replaced by empagliflozin 10 mg/day.Anthropometric and biochemical evaluations were performed at each visit, and cardiac magnetic resonance imaging (CMR) with proton magnetic resonance spectroscopy (1H-MRS) was conducted at baseline and after 12 months.
Clinical and biochemical evaluation
For all subjects, the following clinical data were collected: age, gender, blood pressure, smoking status, dyslipidemia, adiposity measures, i.e., body mass index (BMI) and waist circumference (WC). Fasting blood samples were analyzed for lipid profile (total cholesterol, triglycerides, High-density lipoprotein cholesterol [HDL-c], low-density lipoprotein cholesterol [LDL-c]), glucose profile (fasting plasma glucose [FPG], HbA1c, and fasting plasma C-peptide), Lipoprotein (Lp)(a) and Apolipoprotein B (ApoB), and C-reactive protein (CRP).
Hypertension was defined according to the criteria established by the European Society of Hypertension/European Society of Cardiology(29).
Dyslipidemia was defined as the presence of any of the following: triglyceride concentrations of 150 mg/dL or greater, HDLc concentrations of less than 40 mg/dL in men or less than 45 mg/dL in women, or LDLc concentrations greater than 160mg/dL(30).
Insulin resistance (IR) was estimated using the triglyceride and glucose index (TyG), calculated as: (Ln [fasting triglycerides] [mg/dl]) × fasting glucose [mg/d])/2). IR was defined as a TyG ≥ 4.68 (31).
Remnant cholesterol (Rem-c) was calculated as Total cholesterol – [HDL-c -LDL-c](32)
Cardiovascular magnetic resonance data acquisition and analysis
CMR imaging was performed on a 3T scanner (Verio, Siemens, Erlangen, Germany) following a standardized protocol, with the patient in a supine position and a 16-element phased-array coil placed over the chest. Images were acquired during breath-holds with electrocardiographic gating. Cine long-axis (2-, 3-, and 4-chamber views) and short-axis reconstructions (contiguous slices of 8-mm thickness covering from base to apex) were acquired using segmented k-space steady-state free precession (SSFP) sequences. Delayed enhancement images were acquired with a segmented gradient-echo inversion-recovery sequence at matching cine-image slice locations 10 to 20 minutes after intravenous gadolinium-diethylenetriamine penta-acetic acid (DTPA) administration (Gadovist, 0.15 mmol/kg). Inversion time was optimized to achieve myocardial nulling.
All images were analyzed offline with commercially available post-processing software (QMass-MR, v.8.1; Medis Medical Imaging Systems, Leiden, the Netherlands). A CMR expert performed CMR data analysis. Left (LV) and right ventricular (RV) volumes were obtained from the short-axis cine images tracing the endocardial borders in the end-diastolic and end-systolic frames, excluding the papillary muscles from the tracing. LV mass was calculated by subtracting the endocardial volume from the epicardial volume at the end diastole and multiplying by the tissue density (1.05 g/ml). According to current recommendations, LV mass and volumes were indexed to body surface area (33). Myocardial fibrosis was assessed visually by signal intensity on late gadolinium enhancement (LGE) sequences, and the distribution, location, and number of segments affected by LGE were reported.
Feature-tracking CMR-derived analysis of the LV, RV, and Left Atrium (LA) was performed to measure global longitudinal strains, utilizing commercially available software (QStrain Version 8.1, Medis, Leiden, The Netherlands). For LV global longitudinal strain (GLS), endocardial borders were automatically traced in cine longitudinal 4-, 2- and 3-chamber views. For RV-GLS, endocardial borders were automatically traced in a cine longitudinal 4-chamber view. Finally, for LA-GLS, LA endocardial borders were automatically traced in a cine longitudinal 2-chamber view, excluding LA appendage and pulmonary veins. The tracking quality was visually assessed and manual adjustments were made for all strain analyses, if necessary.
Cardiac 1H-MRS protocol
Cardiac 1H-MRS was performed at the same time as CMR. A single voxel, ECG-triggered, and self-navigated 1H-MRS technique was used placing the region of interest at the septum. To avoid voxel contamination from the blood pool signal, a short axis view and a 4ch view were used for planning (34).
The resulting 1H-MRS spectrum was analyzed using dedicated spectroscopy software (SyngoVia, Siemens, Erlangen, Germany). Water peaks were assessed at 4.7 ppm (W), while fat peaks were assessed at 0.9 ppm (Fat1) and 1.3 ppm (Fat2). Total cardiac fat content was calculated as MTGC (%) = ([Fat1 + Fat2*]/W) ∗ 100, as previously described (34).
Statistical analysis
All adiposity measures, biochemical, and imaging variables were described at baseline (T0) and after 12 months of treatment (T12). Given the non-parametric distribution and the small sample size, all variables were reported as medians and interquartile ranges, unless otherwise specified. To assess longitudinal changes, the Wilcoxon signed-rank test was applied to examine differences in adiposity, biochemical, and imaging variables between time points. For each variable, the differences before and after treatment were calculated as T12-T0. In addition, Spearman's rho correlations were calculated to explore the correlations between changes in adiposity or laboratory assessments with imaging parameters. Correlation heat maps were created to graphically illustrate the correlations between variables. Significance was defined as a two-tailed p-value of < 0.05. Due to the exploratory nature of this study, no post hoc corrections for multiple comparisons were performed. Statistical analyses were conducted using the R-based software jamovi v. 2.5, while graphs were generated using the ggplot2, corrplot, and Hmisc functions of RStudio version 4.4.2 (35).
Results
Sample description and characteristics
Of 25 eligible individuals, 20 completed both baseline and follow-up CMR studies and were included in the analysis (Fig. 1). Study subject characteristics are detailed in Table 1. In brief, participants were middle-aged adults (median age 55 years), predominantly male (80%), with a high prevalence of hypertension (45%) and dyslipidemia (40%). Forty percent were active smokers, and 35% reported regular alcohol consumption (median 0 [0–5] standard drinks per week).
Fig. 1
Flowchart of study participants
New onset T2D (n = 20) | |
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Sex (male), n (%) | 16 (80) |
Age (years) | 55.5 (48.5–60.5) |
Smoking status – - Non-smoker, n (%) - Former smoker, n (%) - Active smoker, n (%) | 6 (30) 6 (30) 8 (40) |
Alcohol consumption, n (%) | 7 (35) |
Hypertension, n (%) | 9 (45) |
Dyslipidemia, n (%) | 6 (40) |
TREATMENT | |
ACEi, n (%) | 6 (30) |
ARB, n (%) | 2 (10) |
Calcium channel blocker, n (%) | 4 (20) |
Beta blocker, n (%) | 1 (5) |
Statins, n (%) | 1 (5) |
ACEi = angiotensin II converting enzyme inhibitor. ARB = angiotensin II receptor blocker
Changes in adiposity and biochemical parameters during glycemic optimization
Adiposity and biochemical parameters at baseline and at 12 months of follow-up are shown in Table 2. Briefly, HbA1c levels declined substantially with treatment with 90% of patients achieving optimal glycemic control (HbA1c < 7%). Reductions in adiposity measures were observed, along with improvements in HDLc and ApoB. Liver enzymes and CRP also reduced, whereas other biochemical parameters remain unchanged.
T0 (N = 20) | T12 (N = 20) | p-value | |
|---|---|---|---|
Weight (kg) | 96.50 (80.7-114.1) | 93.40 (83.3-102.9) | 0.016 |
WC (cm) | 112.5 (102.5-125.5) | 107.25 (101.5-117.5) | 0.040 |
BMI (kg/m2) | 32.83 (29.7–39.3) | 32.56 (29.6–34.3) | 0.011 |
HbA1c (%) | 11.10 (10.2–13.1) | 6.05 (5.7–6.5) | < 0.001 |
FPG (mg/dL) | 122.94 (110.3-208.7) | 109.80 (104.1-131.1) | 0.044 |
FCP (pmol/L) | 944.00 (761.15) | 1070.00 (287.98) | 0.594 |
Creatinine (mg/dL) | 0.82 (0.71–0.98) | 0.81 (0.73–0.94) | 0.271 |
Total cholesterol (mg/dL) | 189.25 (158.9-215.8) | 175.89 (148.4-206.5) | 0.546 |
HDL-c (mg/dL) | 41.02 (32.5–44.9) | 41.22 (37.7–50.3) | 0.036 |
LDL-c (mg/dL) | 125.15 (92.5-144.3) | 101.00 (85.0-123.3) | 0.104 |
TG (mg/dL) | 111.52 (99.1-193.8) | 132.75 (90.3-188.9) | 0.481 |
Rem-c (mg/dL) | 29.81 (24.9–39.2) | 30.38 (20.1–41.1) | 0.622 |
ApoB (mg/dL) | 1.02 (0.87–1.25) | 0.91 (0.76–1.05) | 0.048 |
Lp(a) (mg/dL) | 11.8 (5.9–27.9) | 11.7 (6.8–43.9) | 0.922 |
Total bilirubin (µmol/L) | 12.00 (8–14) | 10.50 (6.5–13) | 0.266 |
AST (UI/L) | 29.50 (23.5–53) | 19.00 (17.5–24.5) | 0.001 |
ALT (UI/L) | 38.00 (34-66.5) | 21.50 (16-31.5) | 0.002 |
GGT (UI/L) | 35.00 (28-69.5) | 19.50 (7–38) | 0.002 |
ALP (UI/L) | 94.00 (83–107) | 78.50 (73.5–102) | 0.016 |
TyG index | 4.83 (4.7–5.2) | 4.83 (4.5-5.0) | 0.388 |
CRP (mg/L) | 7.85 (4.55–11.9) | 2.35 (1.15–4.75) | < 0.001 |
T0 (N = 20) | T12 (N = 20) | p-value | |
|---|---|---|---|
MTGC - % | 0.52(0.25–1.44) | 1.05(0.43–3.06) | 0.225 |
LVEDVi - ml/m2 | 74.1(69.1–80.9) | 72.6(68.3–76.5) | 0.097 |
LVESVi - ml/m2 | 29.9(26.4–35.1) | 27.3(22.5–31.7) | 0.007 |
LVEF - % | 59.0(54.8–61.5) | 63.1(56.9–66.3) | 0.012 |
LVMi - g/m2 | 46.1(35.1–54.2) | 49.5(39.5–54.3) | 0.006 |
RVEDVi - ml/m2 | 76.5(64.6–82.4) | 72.4(66.1–77.7) | 0.033 |
RVESVi - ml/m2 | 30.6(25.9–35.7) | 28.7(25.5–32.6) | 0.021 |
RVEF - % | 59.3(55.2–61.6) | 60.5(55.9–61.1) | 0.277 |
LA GLS - % | 32.8(27.3–36.8) | 33.1(28.4–41.2) | 0.648 |
LV GLS - % | 23.5(21.9–26.2) | 23.3(0.8–26.3) | 0.765 |
RV GLS - % | 28.3(26.0-31.9) | 29.9(28.4–34.5) | 0.090 |
Values are expressed as median (interquartile range). Two-tailed p-values are reported in the table, with significant values (p < 0.05) shown in bold. MTGC = Myocardial Triglyceride Content. LVMi = Left ventricular mass index. EF: ejection fraction. RVESVi = right ventricular end-systolic volume index, RVEDVi = Right ventricle end-diastolic volume index, LVESVi = Left ventricular end-systolic volume index, LVEDVi = Left ventricular end-diastolic volume index. LA GLS = Left atrium Global Longitudinal Strain. LV GLS = Left Ventricular Global Longitudinal Strain. RV GLS = Right ventricle Global Longitudinal Strain | |||
| Correlations between adiposity, biochemical and imaging variables | |||
| A correlation heatmap between MTGC, CMR parameters, adiposity and laboratory parameters is presented in Fig. 2. All Spearman rho coefficients and p-values are detailed in the supplementary material. No associations were found between baseline and 12-month changes in HbA1c and MTGC, HbA1c and CMR parameters, or MTGC and CMR parameters (Fig. 2). Similarly, no correlations were detected between changes in adiposity measures and changes in MTGC. In contrast, significant correlations were observed between adiposity measures and some CMR parameters. Changes in BMI and WC significantly correlated with changes in LA GLS, (ρ =-0.78, p < 0.001 and ρ = -0.77, p < 0.001, respectively). Changes in WC also significantly correlated with changes in LVEDVi (ρ = -0.5, p = 0.024). | |||
| Overall, these findings suggest that the improvement in cardiac structure and function was primarily related to changes in adiposity rather than to glycemic control per se. | |||
Values are expressed as median (interquartile range). Two-tailed p-values are reported in the table, with significant values (p < 0.05) shown in bold.
Abbreviations:
WC
waist circumference
BMI
Body mass index
HbA1c
glycated hemoglobin
FGC
fasting plasma glucose
FCP
fasting C-peptide
HDL-c
High-density lipoprotein cholesterol
LDL-c
low-density lipoprotein cholesterol. VLDLc = Very low-density lipoprotein cholesterol
TG
Triglycerides
RC
remnant cholesterol
Lp(a)
Lipoprotein(a)
ApoB
Apolipoprotein B
GGT
gamma glutamyltransferase
AST
aspartate transaminase
ALT
alanine transaminase
ALP
alkaline phosphatase
TyG index
triglyceride-glucose index and CRP = C-reactive protein.
Changes in MTGC and cardiac structure and function
Medians of the 1H-MRS and CMR imaging results are represented in Table 3. No significant change was observed in MTGC after glycemic optimization (T0: 0.52% [0.25–1.44] vs T12: 1.05% [0.43–3.06], p = 0.225). However, regarding CMR parameters, several significant changes were observed (data shown in Table 3). In particular, a significant improvement on LV ejection fraction (LVEF), LV end-diastolic volume index (LVEDVi) and RV end-diastolic and systolic volumes (RVEDVi and RVESVi, respectively) after 12 months of glycemic optimization were observed. A mild increase on LV mass index was also observed.
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Fig. 2
Correlation heatmap between Myocardial Fat content, cardiac structure and function parameters and adiposity and laboratory parameters.
MTGC = Myocardial Triglyceride Content. LVMi = Left ventricular mass index. EF: ejection fraction. RVESVi = right ventricular end-systolic volume index, RVEDVi = Right ventricle end-diastolic volume index, LVESVi = Left ventricular end-systolic volume index, LVEDVi = Left ventricular end-diastolic volume index. LA GLS = Left atrium Global Longitudinal Strain. LV GLS = Left Ventricular Global Longitudinal Strain. RV GLS = Right ventricle Global Longitudinal Strain. WC = waist circumference, BMI = Body mass index, HbA1c = glycated hemoglobin, FGC = fasting plasma glucose, FCP = fasting C-peptide, HDLc = High-density lipoprotein cholesterol, LDL-c = low-density lipoprotein cholesterol. VLDLc = Very low-density lipoprotein cholesterol, TG = Triglycerides, REM-C = remnant cholesterol, Lp(a) = Lipoprotein(a), ApoB = Apolipoprotein B, GGT = gamma glutamyltransferase, AST = aspartate transaminase, ALT = alanine transaminase, ALP = alkaline phosphatase, TyG index = triglyceride-glucose index and CRP = C-reactive protein.
Discussion
The present study shows that, in individuals with newly diagnosed T2D, twelve months of comprehensive metabolic optimization improved cardiac remodeling parameters without altering myocardial triglyceride content (MTGC). The cardiac benefits observed were closely related to reductions in adiposity rather than glycemic normalization, emphasizing the importance of weight management as a key therapeutic target that may drive early beneficial in cardiac health.
Effect of optimized glycemic control on myocardial steatosis in newly diagnosed T2D
To the best of our knowledge, this is the first study to use 1H-MRS to assess myocardial steatosis and its impact on cardiac remodeling in individuals with newly diagnosed T2D after a comprehensive metabolic optimization. Our findings did not find any association between changes in HbA1c and MTGC, consistent with previous studies using different treatment protocols and populations (19–23). Only Zib et al. reported a reduction in MTGC following HbA1c improvement with addition of pioglitazone, a drug known to reduce ectopic fat deposition (18). This supports the notion that factors beyond glycemic control, such as treatment class, may play a greater role in regulating myocardial fat than the magnitude of HbA1c reduction alone. Supporting this view, a recent report 12-month randomized trial evaluated the effect of a fasting-mimicking diet in patients with type 2 diabetes using 1H-MRS to measure myocardial triglyceride content. The results showed a significant reduction in myocardial triglyceride content following the intervention, suggesting that lifestyle modification may influence myocardial steatosis in this patient population (36). In another randomized study investigating the effect of intensive glycemic control in patients with HF and T2D, although myocardial steatosis was not directly measured, improvements in muscle strength and body composition were observed in the intensive glycemic control group, suggesting possible indirect effects on myocardial health (37). Although previous cross-sectional studies (11) have reported associations between MTGC and BMI, we did not observe this in our cohort. A possible explanation is our small sample size and the modest 6% reduction in adiposity measures, which may have been insufficient to modify MTGC. Indeed, is the extent of fold changes in MTGC were considerably lower than other ectopic fat depots, such as the liver, where lipid content is four- to tenfold higher (7). No association was detected between IR with MTGC. This finding is consistent with that of Krššák et al, who reported that IR was not associated with MTGC in either healthy and T2D women (38). Along the same lines, Van der Meer did not observe a reduction in MTGC following improvement in glycemic control and IR, suggesting that myocardial fat may respond differently than other fat depots with established relationship with IR, such as liver or skeletal muscle (19).
Effect of optimized glycemic control on cardiac remodeling and function in newly diagnosed T2D
Suboptimal glycemic control has been associated with an increased risk of cardiovascular events (39, 40). Our study showed significant improvements in cardiac structure and function after glycemic control; however, these changes were not directly related to reductions in HbA1c but rather to the TyG index. The TyG index is regarded as a reliable surrogate marker for insulin resistance, demonstrating good correlation with more direct measures such as HOMA‑IR and the hyperinsulinemic-euglycemic clamp (37–39). This finding suggests that the relationship between glycemic control and cardiac function is more complex and cannot be fully explained solely by favorable changes in HbA1c. In this line, intensive glycemic therapy in T2D have shown mixed effects on cardiac remodeling. Although accumulating evidence from echocardiographic studies suggests that improving glycemic control may have a beneficial effect on cardiac structure and function in individuals with T2D (24, 25, 41), other studies have reported no diastolic improvement which could be partly attributed to shorter follow-up periods (i.e., 4 and 6 months, respectively); neither was an association with HbA1c reduction observed (42, 43).
The current use of empagliflozin, an SGLT2 inhibitor, might also have contributed to the observed results, as SGLT2 inhibitors have shown benefits on cardiac remodeling in several studies (44–46).
A mild increase in LV mass index after optimization of glycemic control was observed. As increased LV mass is indicative of left ventricular hypertrophy (47), this finding warrants attention. Nevertheless, it did not appear to have impact on cardiac function. A similar observation was reported by Jankovic et al. (22), who described an acute and sustained increase in LV mass after insulin initiation, without functional impairment. In contrast, Shi et al (48) described that insulin-treated patients with HF with reduced EF were associated with LV dilation, increased LV mass and spherical remodeling, accompanied by reduced LV contractility. However, these patients had longer diabetes duration and greater metabolic impairment, making it difficult to attribute causality solely to insulin use.
In animal models, insulin signaling through the PI3K/Akt-1 pathway has been implicated in both cardiomyocyte hypertrophy and apoptosis prevention (49, 50), while also improving sarcoplasmic reticulum function and calcium handling(51), thereby supporting a potential cardioprotective role. Furthermore, large cardiovascular outcome trials such as ORIGIN(52) and DEVOTE(53) reported no excess cardiovascular risk with insulin therapy when glycemic control was appropriately achieved in patients at high cardiovascular risk.
Finally, we observed that reductions in adiposity parameters were associated with improvements of LA GLS and LV EDVi. Excess adiposity plays an increasingly recognized role in the pathophysiology of heart failure and other cardiac diseases (54). Adipose tissue dysfunction leads to altered adipokine secretion, low-grade inflammation, activation of the renin–angiotensin–aldosterone system, and insulin resistance, promoting left ventricular hypertrophy, microvascular dysfunction, and fibrosis, ultimately impairing ventricular filling (as reflected by reduced LA GLS) (55). Our findings are consistent with current evidence emphasizing obesity (i.e. excess adiposity) as a key pathogenic driver of heart failure (56–59).
This study has several limitations. The single-center design and small cohort size limit statistical power and generalizability, and the predominance of male participants reduced the representativeness of women. The naturalistic design did not allow us to fully control for potential confounders such as concomitant use of lipid-lowering agents, antihypertensive, diet, or physical activity. In addition, the absence of a control group, did not allow us to compare these findings with non-diabetic individuals. Finally, the use GLP-1 receptor agonists— a drug class with proven cardiovascular benefits and weight reduction—were not available due to national health insurance restrictions at the time of the study.
Despite these limitations, our study has notable strengths. It is the first to use ¹H-MRS to evaluate longitudinal changes in myocardial steatosis together with detailed CMR-derived measures of structure and function in individuals with newly diagnosed T2D. The standardized treatment protocol and comprehensive imaging approach provide valuable insights into the interplay between glycemic optimization, adiposity, and cardiac remodeling.
Conclusions:
In individuals with newly diagnosed T2D, reductions in adiposity parameters achieved during glycemic optimization were the main determinants of improved cardiac remodeling, whereas myocardial steatosis remained unchanged. These findings indicate that favorable cardiac adaptations in early diabetes are primarily related to decreases in body fat and improvements in overall metabolic balance rather than to glycemic normalization alone.
Therefore, therapeutic strategies that combine glycemic improvement with effective adiposity reduction—including lifestyle interventions and pharmacologic agents with metabolic benefits such as SGLT2 inhibitors or GLP-1 receptor agonists—may play a pivotal role in preventing or delaying cardiac damage from the onset of T2D.
Further studies with larger, controlled cohorts and longer follow-up are needed to determine the time course of myocardial lipid adaptation and to confirm the independent contribution of adiposity reduction to early cardiac remodeling in diabetes.
Abbreviation List
ApoB
ApoLipoprotein B
BMI
Body Mass Index
CMR
Cardiovascular Magnetic Resonance
EF
Ejection Fraction
FCP
Fasting C-peptide
FPG
Fasting Plasma Glucose
HbA1c
Glycosylated Hemoglobin
HF
Heart failure
HDL-c
High Density Lipoprotein Cholesterol
CRP
C-reactive Protein
LA GLS
Left Atrial Global Longitudinal Strain
LDL-c
Low Density Lipoprotein Cholesterol
LGE
Late Gadolinium Enhancement
LVEDVi
Left Ventricular End Diastolic Volume Index
LVESVi
Left Ventricular End Systolic Volume Index
LVMi
Left Ventricular Mass Index
LP(a)
Lipoprotein(a)
LV GLS
Left Ventricular Global Longitudinal Strain
MTGC
Myocardial Triglyceride Content
Rem-c
Remnant Cholesterol
RVEDVi
Right Ventricular End Diastolic Volume Index
RVESVi
Right Ventricular End Systolic Volume Index
RV GLS
Right Ventricular Global Longitudinal Strain
TC
Total Cholesterol
TG
Triglycerides
T2DM
Type 2 Diabetes Mellitus
WC
Waist Circumference
TyG
Triglycerides and Glucose Index
1H-MRS
Proton Magnetic Resonance Spectroscopy
Declarations
Ethic approval and consent to participate
A
A
The study protocol was approved by our local Ethics Committee of the Hospital de SANT PAU (IIBSP-REL-2017-27, date: Jul 26, 2017); all procedures fully complied with the Declaration of Helsinki. A
All subjects gave written informed consent.A
Consent of publicationNo applicable
A
Data Availability
Data of the study may be available for research collaboration purpose upon reasonable request to the corresponding authors and will require the completion of a data processing agreement.
Competing interests
The authors declare no conflict of interest concerning the article
A
Funding:
This research was funded by grants FIS PI15/00625, PI18/00328, and PI21/01163 (to D.M.), PI16/00471 (to J.L.S.-Q.), PI17/00232, PI21/00770, and PI24/00156 (to J.J.) and PI17/01362 (to N.A.)
A
from the Instituto de Salud Carlos III (ISCIII) (cofinanced by the European Regional Development Fund) and by grants from Fundació La MARATÓ de TV3 (201602.30.31 to N.A. and J.J.). We also acknowledge the support received from CIBER de Diabetes y Enfermedades Metabólicas Asociadas (CIBERDEM) (leading group CB15/00071), and CIBER de Enfermedades Cardiovasculares (CIBERCV), which are projects of the ISCIIII. J.J. was a recipient of a Miguel Servet Type 1 contract (CP13/00070; ISCIII), Miguel Servet Type 2 contract (CPII18/00004; ISCIII), and has received financial support from Agencia Estatal de Investigación (MCIN/AEI/10.13039/501100011033 and European Union “NextGeneration EU”/PRTR) within the action “Consolidación Investigadora 2022” (CNS2022-135559). D.M., J.J., J.R. and N.A. are members of the coordinated consolidated quality research group of the Agència de Gestió d’Ajuts Universitaris i de Recerca (AGAUR) (2021 SGR 00857, and 2021 SGR 01211) from Generalitat de Catalunya. N.A., D.M. were members of the Quality Research Group 2017-SGR-1149 from Generalitat de Catalunya. Additionally, J.R., D.M. and J.J. also belong to the XARTEC Salut network. The Institut de Recerca de l'Hospital de la Santa Creu i Sant Pau and the Germans Trias i Pujol Research Institute are accredited by the Generalitat de Catalunya as Centres de Recerca de Catalunya (CERCA)A
Author Contribution
AR-R and AT contributed equally to this study. JJ, and NA participated and designed the study. NA, and J.J. conceptualized the study. All authors contributed to researched data. AR-R, AT, PG-M , AP, JLS-Q, JJ contributed to the clinical assessment and management and buildup of databases. AR-R, AT, JR, BP-P, DM, JJ, and NA analyzed and interpreted data. BP-P conducted statistical analysis. All authors contributed to the discussion and reviewed the manuscript; AR-R, AT, BP-P, JR, JJ and NA wrote the original draft of the manuscript; AR-R, AT, JR, DM, JJ, and NA reviewed/edited the manuscript; DM supervised the study. All authors read and approved the manuscript. D.M., J.J. and NA provided funding support.
A
Acknowledgement
The authors would like to thank the participants of the study. We would also acknowledge the support of the nursing staff and endocrinology residents who actively contribute to the conduct of the study.
Electronic Supplementary Material
Below is the link to the electronic supplementary material
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