Journal of Medical Sciences

: 2023  |  Volume : 43  |  Issue : 1  |  Page : 18--27

Identification of metabolite shifts and early serum predictors for indicators of remodelling in diabetes and nondiabetic models of cardiac hypertrophy

Dharaniyambigai Kuberapandian, Victor Arokia Doss 
 Department of Biochemistry, PSG College of Arts and Science, Coimbatore, Tamil Nadu, India

Correspondence Address:
Dr. Victor Arokia Doss
Department of Biochemistry, PSG College of Arts and Science, Coimbatore - 641 014, Tamil Nadu


Background: Cardiac hypertrophy (CH) is the asymptomatic enlargement of ventricular walls witnessed in diabetes and hypertension, for which early metabolite differences and prediction are less stated previously. Aim: The aim of the study was (i) to understand the metabolic and ventricular events in diabetes and nondiabetes induced CH at the end of 2 weeks and (ii) to identify significant metabolite predictors and pathways that influence the seven metabolic and physiological responders of CH, namely, 3-hydroxybutyrate (3-HB); lactic acid; urea; and electrocardiography (ECG) waves (QRS complex, R amplitude, R-R interval, and heart rate). Methods: Diabetic rat models of CH using streptozotocin (40 mg/kg, i. p., single dose), and nondiabetic models using adrenaline (0.3 mg/kg, i. p, 2 weeks) were developed. Blood glucose levels, ECG, heart weight/body weight ratio, histopathological analysis, and serum metabolite analysis using gas chromatography mass spectrometry were performed at the end of 2 weeks. Strong metabolite predictors and pathways were identified using Pearson's correlation, multiple regression (MRA) and metabolite set enrichment (MSEA) analyses. Results: The prevalence of CH was observed through preliminary screenings at the end of 2 weeks. Galactose, leucine, erythrose, sorbitol, and valine were identified as significant (P < 0.05) predictors in SZ model, whereas isoleucine, galactose, leucine, inositol, and palmitic acid were identified in ADR model. However, galactose metabolism, branched-chain amino acid, and lactose degradation pathways were mapped as the highly influential apparent pathways during early CH remodeling in both the models. Conclusion: This study identified putative initial metabolite shifts, significant predictors pathways that can aid in forecasting, intervention, and prevention of CH.

How to cite this article:
Kuberapandian D, Doss VA. Identification of metabolite shifts and early serum predictors for indicators of remodelling in diabetes and nondiabetic models of cardiac hypertrophy.J Med Sci 2023;43:18-27

How to cite this URL:
Kuberapandian D, Doss VA. Identification of metabolite shifts and early serum predictors for indicators of remodelling in diabetes and nondiabetic models of cardiac hypertrophy. J Med Sci [serial online] 2023 [cited 2023 Mar 29 ];43:18-27
Available from:

Full Text


Cardiac hypertrophy (CH) is characterized by enlarged ventricles due to various physiological and pathological stressors such as chronic vigorous exercises, comorbidities like diabetes, hypertension, chronic kidney disease, obesity, and intake of anthracyclines by cancer patients.1-3 Chronic energy deficiency due to the imbalance between glucose and fatty acid oxidation ultimately causes cardiac dysfunctions that are diagnosed only after intense cardiac impairment by echocardiography or sudden cardiac death (SCD), for which adrenaline is administered during emergency with typical treatments that include β-adrenergic receptor blockers and angiotensin-II receptor antagonists.[4],[5] Electrocardiography (ECG) parameters were reported as the inexpensive markers of myocardial global electric heterogeneity that can predict the ventricular structural and functional abnormalities and SCD. As lead II is the elemental ECG wave for CH,[6],[7] this study focused on the QRS complex, R amplitude, R-R interval, heart rate, and screening of β-hydroxybutyrate (3-HB), lactic acid (LA), urea as the seven responders of metabolic and physiological homeostasis.8-10 Hence, the objective of this study was to screen the metabolite shifts and identify early predictors for the seven responders of CH.

 Materials and Methods


All chemicals and reagents used were of analytical grade from Hi Media Pvt Ltd., India. Streptozotocin (SZ) was purchased from SRL Pvt. Ltd., India. N-Trimethylsilyl-N-methyl trifluoroacetamide (MSTFA) and methoxyamine hydrochloride were purchased from Sigma-Aldrich. Adrenaline (Adrenicure 1 mg/ml injection ampoules) was purchased ethically from the licensed pharmacy shop at Coimbatore.

Experimental rats

Male Sprague Dawley rats weighing between 200 and 210 g procured after ethical clearance (CPCSEA/No: 399/2018/IAEC) were acclimatized for 3 days under controlled temperature of 29°C ± 5°C, humidity at 55% ± 5%, and 12 h of light/dark cycles. They were divided into two groups with five rats per group and were subjected to induction and development of diabetes for 2 weeks as follows and after which the fasting blood glucose was measured using AccuCheck Active glucometer.

Group 1 – normal (control – NOR)Group 2 – SZ (SZ – 40 mg/kg, i. p., single dose, 2 weeks)[11]Group 3 – adrenaline (ADR – 0.3 mg/kg, i. p, 2 weeks).[4]

Electrocardiography analysis of cardiac hypertrophy underlying DC

To monitor the cardiac function in vivo after 2 weeks, ECG analysis of the conventional bipolar limb lead II using BITalino ECG Sensor-OpenSignals (r) evolution software was performed for 6 min in unanesthetized rats for recording the changes of QRS complex, R amplitude, R–R interval, and pulse/heart rate (HR). The percentage changes in these parameters were calculated as: ([ECG parameter value in SZ-in NOR]/[in NOR] × 100).[5],[12]

Assessment of heart weight/body weight ratio

The enlarged heart sizes in each rat models were determined using the heart weight (HW)/body weight (BW) ratio.[5]

Histopathological analysis of left and right ventricles

The heart tissues were excised and initially preserved in 10% formalin until their left and right ventricles were processed and stained with hematoxylin and eosin (H & E) to examine their cellular architecture at 40X.[13]

Gas chromatography-mass spectroscopy analysis of serum metabolites

Serum was isolated from the blood through cardiac-puncture after overnight fasting and derivatized as per the modified protocol.[14] Briefly, 100 μl serum was precipitated using 250 μl acetonitrile, evaporated to dryness using N2 gas. 20 mg/ml methoxylamine hydrochloride dissolved in pyridine was added and incubated at 70°C for 60 min followed by 50 μl MSTFA and incubated 40°C for 90 min. 1 μl derivatized sample was injected into the inlet port at 10:1 split mode and analyzed using a Shimadzu GC-2010 plus gas chromatography instrument coupled to a Shimadzu QP2010 mass spectrometer (Shimadzu, Japan). Helium served as carrier gas at flow rate of 1 ml/min with the initial temperature as 100°C for 4 min that was elevated to 270°C at the rate of 5°C/min. The temperatures of injection were 280°C, interface was 250°C, and ion source was 200°C with a solvent delay of 9 min. The same procedure was followed for the reference compound ribitol. MS was operated in electron ionisation mode of 70 eV, and scan range was between 35 and 800 m/z followed by the identification of metabolites based on NIST and WILEY mass library.

Data analysis

All the data were expressed as mean ± standard error of the mean with significance at P < 0.05 using analysis of variance.[5],[14] Pearson's correlation analysis along with pattern search analysis was performed to identify metabolites strongly related between themselves and with the seven responders (QRS complex, R-amplitude, R-R interval, HR, 3-HB, LA, and urea) of this study. The list of a few strongly related metabolites was then subjected to multiple regression analysis (MRA) to identify the significantly potent predictor metabolite for each of the seven responders, respectively. Similarly, metabolite set enrichment analysis (MSEA using SMPDB library) was also performed to identify highly significant probable metabolic pathways, in which the identified predictors participate and might exert cumulative effects during early remodeling. MRA was performed using IBM SPSS 26.0, and the Pearson's correlation, pattern hunting, and MSEA were performed using MetaboAnalyst 4.0.[15],[16]


Electrocardiography screening for cardiac hypertrophy in DC

Primary CH features such as widened QRS complex, elevated R-amplitude, and prolonged R-R interval were observed in SZ and ADR administered rats when compared to normal as seen in [Figure 1] and [Table 1]. Although the changes in QRS complex were insignificant statistically, other ECG parameters of this study were significant, thereby indicating the commencement and early stage of CH at the end of 2 weeks.{Figure 1}{Table 1}

Blood glucose estimation and hypertrophic index (heart weight/body weight ratio)

Blood glucose was significantly increased at the end of 2 weeks accompanied by elevated HW in streptozotocin and adrenaline administered rats. Though statistically insignificant, considerable reduction in BW was observed in both groups accompanied by significant mild increase in HW/BW ratio [Table 2] and enlarged heart sizes [Figure 2].{Table 2}{Figure 2}

Histopathological analysis of ventricles

H and E staining revealed the distorted and degenerating cellular architecture of the left and right ventricles in diabetic SZ (Group II) and nondiabetic ADR (Group III) models when compared to the normal heart (Group I) as seen in [Figure 3]. Although both models express thick myocardial fibers and hypertrophic nuclei with abnormal connective tissue separations, the different pathophysiological impacts of SZ and ADR can be well visualized with the uniquely distorted cellular architecture by each inducer.{Figure 3}

Metabolite profiling and correlation analysis to identify relationship between metabolites and the responders

Upon comparing the serum of normal (NOR) with the sera of SZ and adrenaline (ADR) administered rats, gas chromatography mass spectrometry (GC-MS) analysis revealed a panel of serum metabolites, wherein L-valine, L-isoleucine, L-alanine, L-proline, and D-mannose were comparatively low in SZ-induced diabetic rats, whereas L-proline was the only prominently low serum metabolite in ADR model. Elevated serum glucose levels along with the apparent presence of D-ribose, D-erythrose, and D-sorbitol were also observed in SZ-administered rats, whereas L-aspartic acid, L-glutamic acid, D-ribose, D-succinic acid, D-sorbitol, butanoic acid, and myristamide were seen observed highly elevated in ADR model when compared to normal serum as shown in [Figure 4]. [Figure 5] shows the Pearson's correlation for the relationship strength among metabolites and between the seven responders of this study. Their better visualization through pattern hunting as indicated in green boxes shown in [Supplementary Figure 1] and [Supplementary Figure 2] indicate metabolites that were strongly correlated with each metabolic responder. Each responder was considered as independent variable and proceeded with multiple regression analysis to identify a single potent putative predictor for each of the seven dependent variable responders.{Figure 4}{Figure 5}[INLINE:1][INLINE:2]

Identification of specific putative predictors for the metabolic responders using multiple regression analysis

Among the metabolites listed in [Figure 4], galactose, leucine, erythrose, sorbitol, and valine were found to be the highly potent predictors for the SZ-induced diabetes-associated CH, whereas isoleucine, galactose, leucine, inositol, and palmitic acid (PA) were identified as significant predictor metabolites for ADR-induced CH at the end of 2 weeks. In SZ model, all predictors except galactose exhibited very good regression fit (R[2] ≥ 0.80), but due to the strong significance (P < 0.05) by all these five predictors, they were further considered for MSEA similar to the ADR model in order to check pathways that are probably influenced by these predictors.

MSEA of predictors for identification of enriched pathways

Galactose metabolisms, branched-chain amino acid (BCAA), and lactose degradation pathways were mapped as the highly impacted and enriched pathways by the five significant predictors of each CH models that, besides, their varying enrichment scores indicate the metabolic pathways affected by the two inducers vary that might have effective roles and that can be affected during SZ-induced diabetes-associated CH [Figure 5].


CH is the initial remodelling stage in diabetes-induced cardiomyopathy (diabetic cardiomyopathy) and a crucial clinical subset of cardiac diseases including myocardial ischemia or infarction developed due to various clinical and physiological conditions. CH initially develops as a reversible cellular adaptative condition to combat physiologically or pathologically induced stress which transforms into irreversible cardiac abnormality, wherein SCD has been reported as one of the common end points. CH has been previously studied extensively in terms of oxidative and reductive stress and their regulation by Nrf-2-associated antioxidant systems.[8],[17],[18] Recently, deep learning computational methods and modern statistical methods combined with Artificial Intelligence were used in precision medicine to accurately predict blood sugar levels from ECG observations itself.[19] Thus, the influences of metabolite perturbations that underlie disease development and progression to cardiovascular dysfunction, if predicted using metabolites, can portend the commencement of CH much earlier and those metabolites can be called as early predictors.

This study used two modes for CH development; one was SZ- provoked diabetes-induced cardiac impairments and the other was adrenaline (ADR) administration which was considered as nondiabetes model that contributes to CH. SZ has been previously shown to induce changes in the cardiac electrophysiology and ventricular events after its 5th day of administration,[20] yet, its earlier metabolic events before and after day 5 before the advanced stage are scarcely understood. Adrenaline (ADR) or epinephrine is a nonselective α-and β-adrenergic agonist administered as a life support along with cardiopulmonary resuscitation during cardiac emergencies. ADR has been previously associated with increased myocardial dysfunction and cerebral ischemia in response to postresuscitation and even in vigorous exercises that in turn were chronically linked with SCD.[18]

In this study, impaired QRS complex, R-amplitude, R-R interval, heart rate (HR), abnormal levels of 3-hydroxybutyrate (3-HB), LA, and urea were considered as applicable responders for metabolic stress during CH. We hypothesized that an easily traceable serum metabolite with strong influence for those seven responders at the end of 2 weeks can possibly become early predictors that influence and showcase the progression of cardiac remodeling. The unique presence of elevated ribose, erythrose, and sorbitol along with traces of valine, isoleucine, alanine, proline, and mannose in SZ model, whereas increased aspartic acid, glutamic acid, ribose, succinic acid, sorbitol, butanoic acid, and myristamide accompanied by a lower concentration of proline in ADR model witnessed in this study show the metabolite profile variations between the two modes of CH routes during early events. However, as seen in [Table 3] and [Table 4], galactose, leucine, erythrose, sorbitol, and valine of SZ model, and isoleucine, galactose, leucine, inositol, and PA of ADR model were identified as significant predictor metabolites for their respective responders during early CH events that were confirmed primarily through ECG and hypertrophic indices.{Table 3}{Table 4}

Although the blood glucose was found elevated in both SZ and ADR models, the prominent hyperglycemic condition was observed in SZ primarily due to its direct toxic potential on pancreas that affects insulin secretion[13] unlike ADR that increases blood glucose through secondary responses such as enhanced glycogenolysis and gluconeogenesis.[8] ADR though well established to develop hypertension-based CH models[4] was not evaluated for high blood-pressure analysis in this study because the objective was only to understand the metabolite shifts and predictors after ADR administration irrespective of blood pressure effects with comparison to SZ-induced diabetes-based CH model at the end of 2 weeks. In addition, this study showcased the three most impacted metabolic pathways, thereby hinting the convergence during metabolic remodeling underlying two different pathophysiology by diabetes (SZ model) and nondiabetes (ADR model) of CH progression and intervention.{Figure 6}

Galactose and its metabolism

In this study, serum galactose in SZ and ADR models had been identified as a predictor metabolite that influences ventricular depolarization-QRS complex and R amplitude, respectively. QRS complex abnormalities of ventricular dysfunction are seen in Fabry's disease, an α-galactosidase deficiency that manifests as CH. Galactose was previously reported in association with aggravated inflammation, calcium signaling, ROS, oxidative stress, apoptosis, advanced glycation end products (AGEs) that lead to cardiac fibrosis, hypertrophy, senescence, and dysfunction.[21],[22] This correlation between the early galactose metabolic perturbations seen in the serum and the ventricular abnormalities of CH is a significant finding of this study.

Lactose degradation pathway

Besides, the galactose-induced metabolic perturbations aforementioned, lactose degradation pathway had been mapped as the most significantly impacted pathway in SZ and ADR models of CH. Lactose overconsumption, impairment of enzymes of lactose degradation either due to inherited or intestinal damage from viral infections, lactose intolerance, lactose malabsorption, shifts in lactose degradation products, namely, galactose and glucose are all hallmark of diabetes, hypertension, and CH pathophysiology.[23] Thus, this study recommends detailed exploration of the lactose degradation pathway contributing to cardiac remodeling.

Branched chain amino acid

Among the three BCAA, leucine and valine were identified as predictors for R-amplitude and urea, respectively, in SZ model, whereas isoleucine and leucine as predictors for QRS complex and R-R interval, respectively, in ADR model. Valine and isoleucine were found elevated in ADR model, whereas trace in SZ model was accompanied by elevated levels of leucine in both the models. Degradation pathway of valine, leucine, and isoleucine, the BCAAs have been mapped as highly impacted pathway by MSEA in this study. Leucine levels have been previously shown to reduce the occurrence of cardiomyocyte apoptosis in cancer-cachexia model and thereby it could be the reason why leucine has been identified as a significant regulator of ventricular depolarization events (R-amplitude and R-R interval) as seen in this study.

Earlier studies have reported defective isoleucine metabolism in a condition called propionic aciduria that manifests left ventricular dysfunction indicated by prolonged QRS complex. This hereby advocates the identification of isoleucine as a predictor in the present study which warrants direct investigation of it in CH pathophysiology. Even though plasma valine levels are reported to increase in the type 2 diabetes after 4 weeks of high-fat diet, elevated leucine decreased the valine and isoleucine levels in this study. It may be probably due to the excess utilization of ketone bodies as rightly obtained predictor-responder “relationship” between the valine and 3-HB. The low serum concentration of the predictor, valine in SZ-administered diabetic rats after 2 weeks may be due to the influence of gut microbial community during diabetes and hence needs further analysis of the same. A previous study highlighted the relationship between valine oxidation and blood urea nitrogen. To the best of our knowledge, this is the first report of an association between the valine with urea in rat models identified through GC-MS-based MRA and MSEA. Similarly, association between valine and LA in a previous study had been identified as putative biomarker for brain tumour and abscess discrimination.[24],[25],[26]


Primarily, erythrose-4-phosphate has been associated with Nrf-2-mediated regulation of metabolic enzymes and reducing equivalents such as NADPH that play a key role in hypoxia and CH. Rightly, in this study too, erythrose has been mapped as a predictor of R-R interval in SZ model.[25]

Sorbitol and Inositol

This study identified sorbitol as a predictor of heart rate (HR) in SZ model and inositol in ADR model. Chronically increased blood glucose activates the sorbitol pathway, thereby converting glucose to sorbitol by aldose reductase. High sorbitol concentration contributes to the release AGEs, namely, 3-deoxyglucosone and glyoxal production. These enhance collagen accumulation and cardiac fibrosis which in turn impairs cardiac electrical conductivity and affects heart rate (HR) possibly through PKA/PKC-β signaling mechanisms[8] whose detailed mechanistic investigation is warranted. Inositol besides as a predictor for HR was also identified as a predictor for LA and urea. Inositol 1,4,5 triphosphate (IP3) receptor signaling has been enormously studied earlier in terms of cardiac excitation-contraction coupling mechanisms and arrhythmias through α/β-adrenergic receptors that hereby explains the background of inositol as a predictor of HR or cardiac rhythm. Inositol and urea were previously related to each other by early growth response protein-1 that can be activated by urea which in turn stimulated inositol phosphates. During acute kidney injury, increased blood urea was accompanied by elevated levels of an enzyme, myoinositol oxygenase that catabolizes myoinositol. Similarly, in a previous study, the levels of inositol and lactate were found elevated during fetal metabolism, a metabolic characteristic in CH. A common event between the responder LA and predictor inositol is the insulin-glucose signaling mechanism as identified in earlier studies,27-29 and therefore, this study recommends a direct investigation between the responders and inositol in terms of CH pathophysiology.

Palmitic acid

Elevated PA in ADR-administered rats was identified as a predictor of 3-HB. PA, a long-chain saturated fatty acid synthesized from acetyl coenzyme A and malonyl coenzyme A by fatty acid synthase had been previously shown to be affected by ADR during which increased oxidation of PA was reported to occur through ketone bodies like 3-HB. Palmitate inhibition by ketone bodies, especially 3-HB, was reported as competitors of myocardium fuels[30] that herein explains their relationship, thereby validating the prediction of 3-HB using palmitate levels in serum of ADR induced CH.


Hence, this study identified metabolites and pathways with crucial interplay in CH developed by diabetic (streptozotocin model) and non-diabetic (adrenaline model) conditions vary. The linear equation for each predictor–responder model along with the concentration of the predictor metabolite can be used to predict the status of the respective responder; thereby, we can decipher the stages of remodeling that occurs gradually and sequentially. The mapping of the three common metabolic pathways by the different predictor metabolites for their respective responders herein indicates that though the modes of CH pathophysiology for diabetic (SZ) and nondiabetic (adrenaline) vary, yet they converge mechanistically. This fundamental yet key observation makes it promising for future studies with integrated approach of metabolomics and machine-learning statistical methods. Such as approach can predict the levels of pathophysiological responders merely based on the concentration of predictor metabolites. This may enable detection and forecasting of early events of CH. Besides, the mechanistic details of how these early changes in metabolite patterns translate into CH pathophysiology can be explored more extensively.


We express our gratitude to the Department of Science and Technology (DST), Government of India for the Proposal Grant (SR/WOS-A/LS-480/2018) which is a part of this study. We thank CPCSEA (India) and PSG Institutional Animal Ethics Committee (Coimbatore), PSGIMS & R for animal ethical clearance. We also thank DST-FIST for rendering support to PSG College of Arts & Science with research infrastructure. We thank PSG College of Arts & Science for providing necessary administrative support.

Financial support and sponsorship

This study is a part of the sanctioned Research Grant Proposal SR/WOS-A/LS-480 by the Department of Science & Technology (DST), Government of India. All chemicals and reagents used were analytical grade from Hi Media Pvt Ltd., India. Streptozotocin was purchased from SRL Pvt. Ltd., India. N-Trimethylsilyl-N-methyl trifluoroacetamide (MSTFA) and methoxyamine hydrochloride were purchased from Sigma-Aldrich. Adrenaline (Adrenicure 1mg/ml injection ampoules) was purchased ethically from the licensed pharmacy shop at Coimbatore.

Conflicts of interest

There are no conflicts of interest.


1Wu R, Wang HL, Yu HL, Cui XH, Xu MT, Xu X, et al. Doxorubicin toxicity changes myocardial energy metabolism in rats. Chem Biol Interact 2016;244:149-58.
2Xu J, Kulkarni SR, Donepudi AC, More VR, Slitt AL. Enhanced Nrf2 activity worsens insulin resistance, impairs lipid accumulation in adipose tissue, and increases hepatic steatosis in leptin-deficient mice. Diabetes 2012;61:3208-18.
3Gibb AA, Hill BG. Metabolic coordination of physiological and pathological cardiac remodeling. Circ Res 2018;123:107-28.
4James O, Friday EA, Unekwuojo EG. Antihypertensive effect of methanol extract of Napoleona imperialis (p. beauv) in adrenaline induced hypertensive albino rats. Int J Biochem Res Rev 2011;1:47-57.
5Doss VA, Kuberapandian D. Evaluation of anti-hypertrophic potential of Enicostemma littorale blume on isoproterenol induced cardiac hypertrophy. Indian J Clin Biochem 2021;36:33-42.
6Biering-Sørensen T, Kabir M, Waks JW, Thomas J, Post WS, Soliman EZ, et al. Global ECG measures and cardiac structure and function: The ARIC study (atherosclerosis risk in communities). Circ Arrhythm Electrophysiol 2018;11:e005961.
7Farraj AK, Hazari MS, Cascio WE. The utility of the small rodent electrocardiogram in toxicology. Toxicol Sci 2011;121:11-30.
8Asbun J, Villarreal FJ. The pathogenesis of myocardial fibrosis in the setting of diabetic cardiomyopathy. J Am Coll Cardiol 2006;47:693-700.
9Almoro AL, Tunon J, Orejas M, Cortes M, Egido J, Lorenzo O. Diagnostic approaches for diabetic cardiomyopathy. Cardiovasc Diabetol 2017;16:1-14.
10Siedlecki AM, Jin X, Muslin AJ. Uremic cardiac hypertrophy is reversed by rapamycin but not by lowering of blood pressure. Kidney Int 2009;75:800-8.
11Mostafavinia A, Amini A, Ghorishi SK, Pouriran R, Bayat M. The effects of dosage and the routes of administrations of streptozotocin and alloxan on induction rate of type 1 diabetes mellitus and mortality rate in rats. Lab Anim Res 2016;32:160-5.
12Konopelski P, Ufnal M. Electrocardiography in rats: A comparison to human. Physiol Res 2016;65:717-25.
13Ajibabe AJ, Fakunle PB, Adewsi MO, Oyewo OO. Some morphological findings on the heart of adult wistar rats following experimental artesunate administration. Curr Res Cardiovasc Pharmacol 2012;1:1-9.
14Sowndarya R, Doss VA. Identification of metabolomic changes before and after exercise regimen in stress induced rats. J Enivorn Biol 2017;38:517-22.
15Dhakal CP. Interpreting the basic output (SPSS) of multiple linear regression. IJSR 2019;8:1448-52.
16Chong J, Wishart DS, Xia J. Using MetaboAnalyst 4.0 for comprehensive and integrative metabolomics data analysis. Curr Protoc Bioinformatics 2019;68:e86.
17Filardi T, Ghinassi B, Di Baldassarre A, Tanzilli G, Morano S, Lenzi A, et al. Cardiomyopathy associated with diabetes: The central role of the cardiomyocyte. Int J Mol Sci 2019;20:E3299.
18Ilicki J, Bruchfeld S, Djärv T. Can epinephrine therapy be detrimental to patients with hypertrophic cardiomyopathy with hypotension or cardiac arrest? A systematic review. Eur J Emerg Med 2019;26:150-7.
19Porumb M, Stranges S, Pescapè A, Pecchia L. Precision medicine and artificial intelligence: A pilot study on deep learning for hypoglycemic events detection based on ECG. Sci Rep 2020;10:170.
20Howarth FC, Jacobson M, Naseer O, Adeghate E. Short-term effects of streptozotocin-induced diabetes on the electrocardiogram, physical activity and body temperature in rats. Exp Physiol 2005;90:237-45.
21Frustaci A, Chimenti C, Ricci R, Natale L, Russo MA, Pieroni M, et al. Improvement in cardiac function in the cardiac variant of Fabry's disease with galactose-infusion therapy. N Engl J Med 2001;345:25-32.
22Chang YM, Chang HH, Lin HJ, Tsai CC, Tsai CT, Chang HN, et al. Inhibition of cardiac hypertrophy effects in D-galactose-induced senescent hearts by Alpinate Oxyphyllae Fructus treatment. Evid Based Complement Alternat Med 2017;2017:2624384.
23Craveiro Barra SN, Gomes P, Leitão Marques A. Severe lactose intolerance in a patient with coronary artery disease and ischemic cardiomyopathy. Rev Port Cardiol 2012;31:821-4.
24Ardoin KB, Moodie DS, Snyder CS. Rate-dependent left bundle-branch block in a child with propionic aciduria. Ochsner J 2009;9:65-7.
25Kuberapandian D, Doss VA. Identification of serum predictors of n-acetyl-l-cysteine and isoproterenol induced remodelling in cardiac hypertrophy. Turk J Biol 2021;45:323-32.
26Lange T, Ko C, Lai P, Dacko M, Tsai SY, Buechart M. Simultansous detection of valine and lactate using MEGA-PRESS editing in pyogenic brain abscess. NMR Biomed 2016;29:1739-47.
27Luo DL, Gao J, Lan XM, Wang G, Wei S, Xiao RP, et al. Role of inositol 1,4,5-triphosphate receptors in α1-adrenergic receptor-induced cardiomyocyte hypertrophy. Acta Pharmacol Sin 2006;27:895-900.
28Mertoglu C, Gunay M, Gurel A, Gungor M. Myo-inositol oxygenase as a novel marker in the diagnosis of acute kidney injury. J Med Biochem 2018;37:1-6.
29Contreras-Ferrat AE, Toro B, Bravo R, Parra V, Vásquez C, Ibarra C, et al. An inositol 1,4,5-triphosphate (IP3)-IP3 receptor pathway is required for insulin-stimulated glucose transporter 4 translocation and glucose uptake in cardiomyocytes. Endocrinology 2010;151:4665-77.
30Akpa MM, Point F, Sawadogo S, Radenne A, Mounier C. Inhibition of insulin and T3-induced fatty acid synthase by hexanoate. Lipids 2010;45:997-1009.