Altered Carnitine Profiles and Amino Acids in Obese Hypertensive Children: A Comparative Study with Healthy Controls

Altered Carnitine Profiles and Amino Acids in Obese Hypertensive Children: A Comparative Study with Healthy Controls | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Altered Carnitine Profiles and Amino Acids in Obese Hypertensive Children: A Comparative Study with Healthy Controls Seçil Kezer¹, Arzu Selimoğlu³, Tarık Yıldırım³ This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8455780/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Objective Early detection of hypertension and characterization of metabolic alterations in obese children are critical for timely intervention. This study aimed to evaluate amino acid profiles and carnitine levels in pediatric patients with secondary hypertension due to exogenous obesity, compare these findings with healthy controls, and identify potential early biomarkers of hypertension. Materials and Methods Thirty hypertensive children (19 males; mean age 13.4 years; mean BMI 2.95 SDS) attending the Pediatric Nephrology Clinic of Bağcılar Training and Research Hospital between August 2024 and January 2025 were enrolled. Age-matched healthy children served as controls. Participants with chronic illnesses or regular medication use were excluded. All underwent anthropometric assessment, biochemical and hormonal testing, lipid profiling, vitamin evaluation, abdominal and renal ultrasonography, ambulatory blood pressure monitoring, cardiac assessment, and fundoscopic examination. Quantitative amino acid and acylcarnitine analyses were performed using tandem mass spectrometry. Results Antihypertensive therapy was initiated in 25 patients. Significant elevations in C3, C5-Iso, C5-DC, C16 (p < 0.005), C5:1 (p = 0.012), and C18 (p = 0.013) were observed in hypertensive children compared with controls. Branched-chain and aromatic amino acids were also elevated. C16 carnitine strongly correlated with BMI (p = 0.003), while C8:0 (octanoylcarnitine) was most strongly associated with HOMA-IR (r = 0.62; p < 0.001). Insulin resistance was identified in 60% of cases. Conclusion Systemic inflammation in obese children contribute to hypertension development. Early assessment of metabolic markers using tandem MS may enable detection of preclinical alterations and guide timely intervention. Elevated C16 and C8:0 carnitine levels may serve as early indicators of metabolic dysfunction in hypertensive obese children. Figures Figure 1 INTRODUCTION Hypertension, one of the leading risk factors for cardiovascular disease and a major cause of global mortality, can originate during childhood. Changes in dietary habits and reduced physical activity among children and adolescents have contributed to a rise in obesity prevalence. The increasing frequency of insulin resistance and metabolic syndrome (MS) in obese pediatric populations is paralleled by a heightened prevalence of hypertension. While the prevalence of hypertension in the general pediatric population ranges between 1–5%, this rate increases to 11–30% among obese children ( 1 , 2 ). Recent research has demonstrated that hypertension and metabolic syndrome in obese individuals are closely associated with endothelial dysfunction, oxidative stress, and chronic low-grade inflammation ( 3 ). Furthermore, studies report that both systolic and diastolic nighttime ambulatory blood pressure (ABPM) values are significantly higher in children with metabolic syndrome ( 4 ). Even obese children with normal office blood pressure measurements may exhibit elevated blood pressure on ABPM ( 5 ). Therefore, early diagnosis of hypertension in this population is of vital importance. In children, overweight is defined as a BMI between the 85th and 95th percentiles, whereas obesity is defined as BMI > 95th percentile. Metabolic syndrome is characterized by the co-existence of abdominal obesity, insulin resistance, atherogenic dyslipidemia (high triglycerides and low HDL cholesterol), and hypertension. The pathophysiology of obesity-related hypertension involves increased autonomic nervous system activation, hyperinsulinemia, elevated leptin levels, activation of the renin–angiotensin–aldosterone system (RAAS), endothelial dysfunction, hyperuricemia, high fructose intake, and chronic inflammation ( 6 ). Accumulation of toxic lipids in the liver and skeletal muscle impairs insulin signaling, resulting in reduced glucose uptake, increased gluconeogenesis, and hyperglycemia. Leptin and hyperinsulinemia stimulate hypothalamic centers, increasing sympathetic nervous system activity and vascular smooth muscle tone; simultaneously, enhanced renal sympathetic activity and elevated aldosterone reduce natriuresis and promote sodium retention and plasma volume expansion ( 7 ). Perirenal and renal sinus fat may exert mechanical compression, increasing intrarenal pressure and further activating RAAS ( 8 ). Under normal physiological conditions, insulin induces endothelial nitric oxide (NO) release, leading to vasodilation; however, NO production decreases in insulin resistance ( 9 ). Genetic factors also contribute to hypertension susceptibility, including ACE (I/D) ( 10 ), AGT ( 11 ), ADRB2 ( 12 ), NOS3 ( 13 ), PPAR ( 14 ), and IRS1 ( 15 ) polymorphisms. Individuals with genetically increased RAAS activity, impaired NO synthesis, or salt sensitivity are at heightened risk of hypertension. Ultimately, sodium and water retention, increased vascular resistance, and plasma volume expansion lead to glomerular hyperfiltration, hypertrophy, and microalbuminuria over time ( 7 ). Visceral adipose tissue acts as a metabolically active endocrine organ that promotes local hypoxia and inflammation; it secretes pro-inflammatory mediators such as IL-6, TNF-α, and PAI-1, adipokines, RAAS components, and free fatty acids, and contributes to mitochondrial dysfunction and reactive oxygen species (ROS) accumulation. This cascade results in oxidative stress, vascular remodeling, and apoptosis ( 16 – 18 ). The gut microbiota is also closely linked to metabolic health. Through metabolism of phosphatidylcholine, choline, L-carnitine, and betaine, the microbiota produces trimethylamine N-oxide (TMAO), which enhances angiotensin II-mediated vasoconstriction and contributes to hypertension and accelerated atherosclerosis. While HOMA-IR, QUICKI, and the Matsuda index reflect insulin resistance, the triglyceride-glucose (TyG) index has emerged as a simple and practical marker of cardiometabolic risk ( 19 ). Alterations in carnitine and acylcarnitine levels indicate disturbances in fatty acid oxidation and mitochondrial energy metabolism, serving as potential early markers of cardiometabolic risk ( 20 ). Elevated branched-chain amino acids (leucine, isoleucine, valine) and aromatic amino acids (phenylalanine, tyrosine) have been associated with insulin resistance and metabolic syndrome ( 21 , 22 ). Reduced arginine levels impair NO bioavailability and contribute to endothelial dysfunction ( 23 ). Decreases in glycine and serine are typical in obesity and insulin resistance due to reduced anti-inflammatory amino acid availability. Therefore, evaluating amino acid and carnitine profiles in obese and hypertensive children and elucidating their relationship with metabolic syndrome components may provide valuable insight for early detection of cardiometabolic risk. Screening dried blood spots using tandem MS in obese children may help identify metabolic markers that predict hypertension development and may assist future population-based screening strategies. METHODS A total of 30 pediatric patients diagnosed with obesity and hypertension and 30 age- and sex-matched healthy individuals were included in the study. The diagnosis of hypertension was established according to the criteria of the American Academy of Pediatrics. Body mass index (BMI), blood pressure measurements, biochemical parameters (insulin, lipid profile, uric acid, liver function tests), anemia indices, and metabolic markers were evaluated. Endocrine assessments—including thyroid function tests, insulin–glucose homeostasis, cortisol, renin, aldosterone, metanephrine, adrenaline, and dopamine—were performed, and secondary causes of hypertension were excluded. Only patients whose hypertension was determined to be secondary to exogenous obesity were enrolled. Patients who met the diagnostic criteria on 24-hour ambulatory blood pressure monitoring (ABPM) underwent additional evaluations including urinary ultrasound, abdominal ultrasound for hepatic steatosis, renal Doppler ultrasonography, echocardiography, and fundoscopy for hypertensive retinopathy. A complete urinalysis was also obtained. Carnitine and amino acid analyses were performed using LC–MS/MS. Statistical Analysis All statistical analyses were conducted using IBM SPSS Statistics for Windows, Version 25.0 (IBM Corp., Armonk, NY, USA). Continuous variables were expressed as mean ± standard deviation (SD). Since the distributional characteristics were similar between the obese-hypertensive group and healthy controls, parametric tests were used for comparisons. Differences between groups were assessed using the independent samples t-test (Student’s t-test). A p-value < 0.05 was considered statistically significant. Associations between variables were evaluated using Pearson or Spearman correlation analyses, where appropriate. Correlations between BMI and serum metabolites (carnitines and amino acids), as well as correlations between HOMA-IR and serum metabolites, were assessed using Pearson correlation analysis. Correlation strength was interpreted as follows: r = 0.1–0.3 (weak), r = 0.3–0.5 (moderate), and r > 0.5 (strong). Results were visualized using tables and graphs, with significant differences highlighted accordingly. RESULTS Of the 30 patients included in the study, 19 were male. The mean BMI was 2.95 SDS. Antihypertensive therapy was initiated in 25 patients. Five patients were diagnosed with Stage 1 hypertension based on ABPM and were followed with lifestyle modification alone, as both echocardiography and fundoscopy were normal. The comparison of carnitine and amino acid levels between patients and healthy children is presented in Table 1 . Table 1 Comparison of Metabolic Parameters Between Children With Obesity-Related Hypertension and Healthy Controls Acylcarnitine Profile (mmol/L) Metabolite (Reference Range) Obesity-Related Hypertension Healthy Controls P-value C0 (9–65) 31.2 ± 8.7 24.9 ± 5.1 0.021 C2 (5–52) 12.6 ± 5.9 12.8 ± 4.1 0.875 C3 (0–5.5) 1.84 ± 0.8 1.12 ± 0.5 0.0003 C4 (0–0.75) 0.14 ± 0.01 0.14 ± 0.01 0.926 C5:1 (0–0.25) 0.10 ± 0.01 0.08 ± 0.01 0.012 C5-Iso (0–0.39) 0.10 ± 0.01 0.07 ± 0.01 0.0002 C6 (0–0.18) 0.03 ± 0.02 0.02 ± 0.01 0.369 C5-OH (0–0.38) 0.11 ± 0.03 0.13 ± 0.03 0.416 C8:1 (0–0.40) 0.06 ± 0.01 0.04 ± 0.01 0.205 C8:0 (0–0.71) 0.07 ± 0.01 0.07 ± 0.01 0.959 C10 (0–0.18) 0.06 ± 0.01 0.02 ± 0.01 0.078 C5-DC (0–0.21) 0.095 ± 0.01 0.059 ± 0.01 0.002 C12 (0–0.41) 0.06 ± 0.01 0.045 ± 0.01 0.038 C6-DC (0–0.20) 0.028 ± 0.01 0.023 ± 0.01 0.402 C14:2 (0–0.25) 0.016 ± 0.01 0.030 ± 0.01 0.026 C14:1 (0–0.37) 0.029 ± 0.01 0.039 ± 0.01 0.240 C14-MYC (0–0.5) 0.05 ± 0.01 0.05 ± 0.01 0.289 C4-DC (0–0.71) 0.18 ± 0.05 0.21 ± 0.06 0.285 C16:1 (0–0.10) 0.0372 ± 0.01 0.0308 ± 0.01 0.091 C16 (0–1.51) 1.05 ± 0.26 0.68 ± 0.18 0.001 C18:2 (0–0.24) 0.036 ± 0.01 0.052 ± 0.01 0.367 C18:1 (0.02–0.25) 0.093 ± 0.01 0.072 ± 0.01 0.177 C18 (0.01–0.6) 0.547 ± 0.17 0.424 ± 0.15 0.013 C18:1-OH (0–0.06) 0.052 ± 0.01 0.004 ± 0.01 0.516 Branched-chain amino acids (BCAAs) were elevated compared with controls. Significant increases in acylcarnitines C3, C5:1, C5-Iso, C5-DC and C16 were observed in the patient group (p < 0.005). Aromatic amino acids (tyrosine and phenylalanine) and alanine were also higher than in controls (BCAA p = 0.161; phenylalanine p = 0.025; alanine p = 0.009; tyrosine p = 0.052). Hyperuricemia was detected in 12 patients, and hepatic steatosis was present in 9 patients. Vitamin D deficiency was identified in 10 patients, and anemia in 18 patients. Hyperuricemia was again confirmed in 12 patients, and hepatic steatosis in 9 patients. Dyslipidemia was prevalent: HDL 130 mg/dL in 10 patients, triglycerides >130 mg/dL in 10 patients. ALT >40 IU/mL was found in 7 patients, TSH >4 IU/mL in 8 patients, and insulin resistance (HOMA-IR >2.5) in 18 patients. Among patients with hypertension confirmed by ambulatory blood pressure monitoring (ABPM), hypertensive retinopathy was identified in three patients, and left ventricular hypertrophy was detected in two patients by echocardiography. Correlations Between BMI and Serum Metabolites In obese children, BMI showed weak-to-moderate positive correlations with several metabolites, including C0 (r = 0.28, p = 0.134), C2 (r = 0.11, p = 0.563), C3 (r = 0.33, p = 0.075), C14:0 (myristoylcarnitine) (r = 0.36, p = 0.051), and C16 (r = 0.52, p = 0.003). Among these, only the correlation with C16 reached statistical significance (p < 0.01). Among amino acids, arginine (r = − 0.32, p = 0.085) and proline (r = − 0.29, p = 0.120) showed negative correlations with BMI, though neither reached statistical significance. Serine exhibited a weak, nonsignificant positive correlation (r = 0.23, p = 0.221). Correlations Between HOMA-IR and Serum Metabolites Correlation analyses revealed weak-to-moderate and some strong associations between HOMA-IR and serum metabolites. Among acylcarnitines, C8:0 demonstrated the strongest positive correlation with HOMA-IR (r = 0.62, p < 0.001). This was followed by C12 (r = 0.41, p = 0.024) and C14:1 (r = 0.35, p = 0.058); C12 reached statistical significance whereas C14:1 showed only a trend toward significance. Overall, C8:0 emerged as the metabolite most strongly associated with insulin resistance. Other acylcarnitines—C3, C5-OH, C5-DC, C14-Myc, and C16:1—displayed weak-to-moderate positive correlations with HOMA-IR, though none achieved statistical significance (all p > 0.05). Among amino acids, glycine (r = − 0.32, p = 0.085) and serine (r = − 0.36, p = 0.051) showed moderate negative correlations with HOMA-IR, indicating a trend toward lower levels with increasing insulin resistance. Arginine showed no meaningful correlation (r = 0.01, p = 0.958). ROC curve analysis of the C16 carnitine variable demonstrated an area under the curve (AUC) of 0.869 (95% CI: 0.773–0.964; p < 0.001)as shown in Fig. 1 . The optimal cut-off value was determined to be 0.825. DISCUSSION Childhood obesity represents a complex condition in which metabolic disturbances emerge early and predispose affected individuals to long-term cardiometabolic diseases ( 24 ). In this study, we comprehensively evaluated alterations in amino acid and acylcarnitine profiles among obese, hypertension-prone children and examined their associations with BMI, insulin resistance (HOMA-IR), and blood pressure parameters. Our findings demonstrate that several metabolites, which may serve as early biochemical indicators of metabolic stress, are significantly altered in obese children and may contribute to the development of hypertension. The prevalence of metabolic syndrome (MS) is markedly higher in obese children than in the general pediatric population. Whereas the prevalence of hypertension is approximately 1.9% in normal-weight children, it increases to 5% in overweight and up to 15% in obese children ( 1 ). WHO data indicate that MS affects only 3–5% of children overall but rises to 30–39% among those with obesity. Turkish data show a similar pattern ( 25 ). These high rates underscore the need for early cardiometabolic screening in obese pediatric populations. The predominance of normal echocardiographic and ophthalmologic findings may reflect early-stage diagnosis in most patients. The ability to detect early-stage findings and achieve an early diagnosis is of considerable clinical value, especially in obese children. One of the most notable findings of our study is the significant elevation of C16 carnitine and C8:0 (octanoylcarnitine) levels in the hypertensive obese group. The positive correlation between C16 and BMI (p = 0.003), as well as the strong association between C8:0 and HOMA-IR (r = 0.62, p < 0.001), highlights a mechanistic link between impaired mitochondrial β-oxidation and insulin resistance. As adipose tissue expands in obesity, increased flux of free fatty acids overwhelms mitochondrial oxidative capacity, leading to incomplete β-oxidation and the accumulation of medium- and long-chain acylcarnitines ( 26 ). C8:0 accumulation is widely recognized as a classical marker of metabolic “bottlenecking,” reflecting mitochondrial overload and metabolic stress. Our results suggest that rising C8:0 levels may accompany worsening insulin resistance and contribute to vascular dysfunction. These observations align with the findings of Adams et al. ( 20 ), who reported elevated short- and medium-chain acylcarnitines in obese children, and with Kepka et al. ( 27 ), who demonstrated increased urinary carnitine excretion in adolescents with primary hypertension. Metabolites such as C12 and C14:1, which showed moderate correlations with HOMA-IR, further suggest dysfunction at specific stages of fatty acid β-oxidation. Other acylcarnitines with weaker correlations (C3, C5-OH, C5-DC, C14-MYC, C16:1) collectively support the concept of a broad, multisystem disturbance in energy metabolism among obese children. Elevations in short-chain carnitines—specifically C3, C5:1, C5-iso, and C5-DC—correspond with impaired branched-chain amino acid (BCAA) catabolism. In obesity, reduced activity of the BCKDH enzyme complex leads to BCAA accumulation and increased production of their corresponding acylcarnitines. Numerous studies have shown that elevated BCAA levels are among the strongest metabolic predictors of insulin resistance ( 28 , 29 ), operating partly through mTOR pathway activation and impaired insulin signaling ( 30 ). The elevated BCAA levels observed in our cohort are consistent with this mechanistic framework. We also observed increases in aromatic amino acids, particularly phenylalanine and tyrosine, in the hypertensive obese group. These amino acids participate in catecholamine and neurotransmitter biosynthesis and may reflect altered sympathetic drive or neuroendocrine activation in obesity. Consistent with previous literature linking aromatic amino acids to hypertension, phenylalanine levels were significantly higher (p = 0.036), potentially indicating increased sympathetic tone and early endothelial dysfunction. The negative correlation between BMI and arginine (r = − 0.32) is clinically meaningful. Arginine is the substrate for endothelial nitric oxide (NO) synthesis; reduced levels impair NO bioavailability and contribute to endothelial dysfunction—one of the earliest steps in obesity-related hypertension ( 23 ). Reduced arginine may also reflect increased levels of endogenous NOS inhibitors such as ADMA, which rise in high-fat and high-carbohydrate dietary environments and contribute to vascular tone dysregulation ( 31 ). The presence of elevated HOMA-IR, dyslipidemia, elevated ALT, and hyperuricemia in our cohort highlights the multifactorial nature of metabolic syndrome. Uric acid has been shown to promote insulin resistance and contribute to hypertension via renal microvascular effects ( 32 ). Dyslipidemia and hepatic steatosis, both common in our cohort, further compound cardiometabolic risk. Glycine and serine, both of which displayed moderate negative correlations with HOMA-IR in our study, play key roles in glutathione synthesis and oxidative stress regulation. Their reduction suggests impaired trans-sulfuration pathway activity and increased oxidative stress, findings consistent with reports in children with type 2 diabetes ( 33 ). Evidence that glycine/N-acetylcysteine supplementation reduces insulin resistance in adults ( 34 ) further supports the metabolic significance of these pathways. Renin elevation in nine of our patients and the observed reduction following weight loss underscore the role of RAAS activation in obesity-related hypertension. Adipose tissue contains a functional RAAS capable of producing angiotensin II, which contributes to sodium retention and vascular tone dysregulation ( 8 ). This aligns with clinical evidence supporting the use of ACE inhibitors and ARBs in hypertensive obese children. The pubertal period may amplify these metabolic disturbances; physiological insulin resistance, heightened sympathetic responsiveness, and hormonal variability may collectively accelerate the development of hypertension. Furthermore, systemic inflammatory indices such as SII, NLR, and LMR have been associated with hypertension in obese adolescents ( 35 ), supporting the role of inflammation in vascular dysfunction. Emerging evidence also links pediatric obesity to epigenetic modifications, including altered DNA methylation and microRNA profiles, some of which are reversible with weight loss ( 36 ). Thus, amino acid and acylcarnitine profiles may serve not only as metabolic biomarkers but also as early indicators of epigenetic dysregulation. Specific metabolites such as α-hydroxybutyric acid (α-HB) and oleic acid have been proposed as early markers of insulin resistance in metabolomic studies ( 37 ). Our findings are consistent with this literature, suggesting a multifaceted pattern of early metabolic stress in obese children. Taken together, our results support previous observations from Adams et al. (2009), Kepka et al. (2015), Cobb et al. (2016),), and Gallardo-Escribano et al. (2022), Wu yan et al ( 38 ) and Mihalik et al. ( 39 )collectively demonstrate that amino acid and acylcarnitine profiling may provide important early biochemical markers of metabolic deterioration in obese hypertensive children. Metabolites such as C8:0, C16, BCAAs, and indicators of arginine-NO imbalance may be valuable targets for early risk stratification and preventive interventions. C16 cutt of value 0.82 is crucial for healthy of vascular state. Limitations of this study include the relatively small sample size, single-center design, and cross-sectional nature, which preclude causal inference. Dietary intake and physical activity were not quantitatively assessed. However, the comprehensive metabolic profiling using tandem MS/MS and detailed endocrine/metabolic evaluation represent important strengths. Most metabolite values in these children without any inborn metabolic disease remained within normal reference ranges; however, their differences compared with healthy controls suggest that these markers may still carry potential diagnostic value for early screening. Future studies integrating metabolomic findings with genomic, epigenetic, and microbiome analyses through multi-omics approaches are expected to provide a more comprehensive understanding of the biological mechanisms underlying obesity-related hypertension. The absence of left ventricular hypertrophy in most patients indicates that the cohort consists of adolescents with early-stage hypertension and that metabolic alterations emerge before the development of target organ damage. In conclusion, our findings indicate that metabolic screening in obese children should not be limited to conventional glucose and lipid panels; the early assessment of amino acid and acylcarnitine profiles may offer significant clinical benefits. This approach could contribute to the early identification of childhood hypertension and cardiometabolic risk and support the development of individualized preventive strategies. Declarations Author Contributions, Ethics Statement, Funding, and Conflict of Interest Author Contributions: S.K., A.S., and T.Y. conceptualized and designed the study. S.K., A.S., and T.Y. collected the data. S.K. performed the statistical analyses and contributed to data interpretation. S.K. contributed to the critical evaluation of the findings and manuscript drafting. All authors participated in writing and revising the manuscript and approved the final version for submission. Ethics Statement: The study was conducted in accordance with the Declaration of Helsinki and was approved by the Institutional Review Board of the Bağcılar Training and Research Hospital – Non-Interventional Clinical Research Ethics Committee (Approval No.: 2025/05/04/047). Written informed consent was obtained from all participants and/or their legal guardians before enrollment. Funding: This research did not receive any specific funding from public, commercial, or nonprofit organizations. Conflict of Interest: The authors declare no commercial or financial relationships that could be perceived as potential conflicts of interest. Corresponding Author Dr. Seçil Kezer [email protected] İstanbul University, Chıld Health Institute, PhD program in Pediatric Kıdney Transplantation research, İstanbul, Türkiye References Song P, Zhang Y, Yu J, Zha M, Zhu Y, Rahimi K, Rudan I (2019) Global prevalence of hypertension in children: a systematic review and meta-analysis. JAMA Pediatr 173(12):1154–1163 Ibrahim MM, Damasceno A (2012) Hypertension in developing countries. 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PMID: 20111019; PMCID: PMC3984458 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8455780","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":588430871,"identity":"2dbe79b3-9675-4216-a405-685942962953","order_by":0,"name":"Seçil Kezer¹","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1klEQVRIiWNgGAWjYHACxgMgkp+BgY14PWAtkg0kazE4QKwW+QbmB4cLamrljG8kP3vwoYJBnl/sAH4tBgfYDA7POHbc2OxGmrnhjDMMhjNnJxDQAkSHediOJW67kWAmzdvGkGBwm4AW+Qb2D4d5/h1L3Dwj/RtxWhgO8Bgc5m2rSdwgkUOkLQYHeAoOz+w7YCxx5k2Z5IwzEoT9AnTYxscF3+rk+NvTt0l8qLCR55cm5DD5BwzMDAyHGRgEwColCCiHAqCWOmCKOUCc6lEwCkbBKBh5AAAQX0Qu+qxvogAAAABJRU5ErkJggg==","orcid":"","institution":"Sağlık Bilimleri Üniversitesi","correspondingAuthor":true,"prefix":"","firstName":"Seçil","middleName":"","lastName":"Kezer¹","suffix":""},{"id":588430874,"identity":"871fc357-6821-4942-8aa8-631687d6fb67","order_by":1,"name":"Arzu Selimoğlu³","email":"","orcid":"","institution":"Sağlık Bilimleri Üniversitesi","correspondingAuthor":false,"prefix":"","firstName":"Arzu","middleName":"","lastName":"Selimoğlu³","suffix":""},{"id":588430876,"identity":"eacc6fcc-c72b-4d44-87d1-99672dcd1075","order_by":2,"name":"Tarık Yıldırım³","email":"","orcid":"","institution":"Sağlık Bilimleri Üniversitesi","correspondingAuthor":false,"prefix":"","firstName":"Tarık","middleName":"","lastName":"Yıldırım³","suffix":""}],"badges":[],"createdAt":"2025-12-26 13:53:31","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8455780/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8455780/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102377390,"identity":"3bb039bb-dce8-4516-8ece-f68b918ba4a4","added_by":"auto","created_at":"2026-02-11 05:47:43","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":41844,"visible":true,"origin":"","legend":"\u003cp\u003eReceiver operating characteristic (ROC) curve of C16 for discriminating patients from controls.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8455780/v1/424d2875cc6ce2644fff71fe.png"},{"id":103541145,"identity":"f0716b60-da77-417a-9560-ab6c7e68f458","added_by":"auto","created_at":"2026-02-26 20:24:46","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":725229,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8455780/v1/b2014602-221d-4230-841a-a291a8ec6b30.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Altered Carnitine Profiles and Amino Acids in Obese Hypertensive Children: A Comparative Study with Healthy Controls","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eHypertension, one of the leading risk factors for cardiovascular disease and a major cause of global mortality, can originate during childhood. Changes in dietary habits and reduced physical activity among children and adolescents have contributed to a rise in obesity prevalence. The increasing frequency of insulin resistance and metabolic syndrome (MS) in obese pediatric populations is paralleled by a heightened prevalence of hypertension. While the prevalence of hypertension in the general pediatric population ranges between 1\u0026ndash;5%, this rate increases to 11\u0026ndash;30% among obese children (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eRecent research has demonstrated that hypertension and metabolic syndrome in obese individuals are closely associated with endothelial dysfunction, oxidative stress, and chronic low-grade inflammation (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Furthermore, studies report that both systolic and diastolic nighttime ambulatory blood pressure (ABPM) values are significantly higher in children with metabolic syndrome (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Even obese children with normal office blood pressure measurements may exhibit elevated blood pressure on ABPM (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Therefore, early diagnosis of hypertension in this population is of vital importance.\u003c/p\u003e \u003cp\u003eIn children, overweight is defined as a BMI between the 85th and 95th percentiles, whereas obesity is defined as BMI \u0026gt;\u0026thinsp;95th percentile. Metabolic syndrome is characterized by the co-existence of abdominal obesity, insulin resistance, atherogenic dyslipidemia (high triglycerides and low HDL cholesterol), and hypertension.\u003c/p\u003e \u003cp\u003eThe pathophysiology of obesity-related hypertension involves increased autonomic nervous system activation, hyperinsulinemia, elevated leptin levels, activation of the renin\u0026ndash;angiotensin\u0026ndash;aldosterone system (RAAS), endothelial dysfunction, hyperuricemia, high fructose intake, and chronic inflammation (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Accumulation of toxic lipids in the liver and skeletal muscle impairs insulin signaling, resulting in reduced glucose uptake, increased gluconeogenesis, and hyperglycemia. Leptin and hyperinsulinemia stimulate hypothalamic centers, increasing sympathetic nervous system activity and vascular smooth muscle tone; simultaneously, enhanced renal sympathetic activity and elevated aldosterone reduce natriuresis and promote sodium retention and plasma volume expansion (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). Perirenal and renal sinus fat may exert mechanical compression, increasing intrarenal pressure and further activating RAAS (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eUnder normal physiological conditions, insulin induces endothelial nitric oxide (NO) release, leading to vasodilation; however, NO production decreases in insulin resistance (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). Genetic factors also contribute to hypertension susceptibility, including ACE (I/D) (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e), AGT (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e), ADRB2 (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e), NOS3 (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e), PPAR (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e), and IRS1 (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e) polymorphisms. Individuals with genetically increased RAAS activity, impaired NO synthesis, or salt sensitivity are at heightened risk of hypertension. Ultimately, sodium and water retention, increased vascular resistance, and plasma volume expansion lead to glomerular hyperfiltration, hypertrophy, and microalbuminuria over time (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eVisceral adipose tissue acts as a metabolically active endocrine organ that promotes local hypoxia and inflammation; it secretes pro-inflammatory mediators such as IL-6, TNF-α, and PAI-1, adipokines, RAAS components, and free fatty acids, and contributes to mitochondrial dysfunction and reactive oxygen species (ROS) accumulation. This cascade results in oxidative stress, vascular remodeling, and apoptosis (\u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). The gut microbiota is also closely linked to metabolic health. Through metabolism of phosphatidylcholine, choline, L-carnitine, and betaine, the microbiota produces trimethylamine N-oxide (TMAO), which enhances angiotensin II-mediated vasoconstriction and contributes to hypertension and accelerated atherosclerosis.\u003c/p\u003e \u003cp\u003eWhile HOMA-IR, QUICKI, and the Matsuda index reflect insulin resistance, the triglyceride-glucose (TyG) index has emerged as a simple and practical marker of cardiometabolic risk (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). Alterations in carnitine and acylcarnitine levels indicate disturbances in fatty acid oxidation and mitochondrial energy metabolism, serving as potential early markers of cardiometabolic risk (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). Elevated branched-chain amino acids (leucine, isoleucine, valine) and aromatic amino acids (phenylalanine, tyrosine) have been associated with insulin resistance and metabolic syndrome (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). Reduced arginine levels impair NO bioavailability and contribute to endothelial dysfunction (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). Decreases in glycine and serine are typical in obesity and insulin resistance due to reduced anti-inflammatory amino acid availability.\u003c/p\u003e \u003cp\u003eTherefore, evaluating amino acid and carnitine profiles in obese and hypertensive children and elucidating their relationship with metabolic syndrome components may provide valuable insight for early detection of cardiometabolic risk. Screening dried blood spots using tandem MS in obese children may help identify metabolic markers that predict hypertension development and may assist future population-based screening strategies.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cp\u003eA total of 30 pediatric patients diagnosed with obesity and hypertension and 30 age- and sex-matched healthy individuals were included in the study. The diagnosis of hypertension was established according to the criteria of the American Academy of Pediatrics. Body mass index (BMI), blood pressure measurements, biochemical parameters (insulin, lipid profile, uric acid, liver function tests), anemia indices, and metabolic markers were evaluated.\u003c/p\u003e \u003cp\u003eEndocrine assessments\u0026mdash;including thyroid function tests, insulin\u0026ndash;glucose homeostasis, cortisol, renin, aldosterone, metanephrine, adrenaline, and dopamine\u0026mdash;were performed, and secondary causes of hypertension were excluded. Only patients whose hypertension was determined to be secondary to exogenous obesity were enrolled.\u003c/p\u003e \u003cp\u003ePatients who met the diagnostic criteria on 24-hour ambulatory blood pressure monitoring (ABPM) underwent additional evaluations including urinary ultrasound, abdominal ultrasound for hepatic steatosis, renal Doppler ultrasonography, echocardiography, and fundoscopy for hypertensive retinopathy. A complete urinalysis was also obtained. Carnitine and amino acid analyses were performed using LC\u0026ndash;MS/MS.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eAll statistical analyses were conducted using IBM SPSS Statistics for Windows, Version 25.0 (IBM Corp., Armonk, NY, USA). Continuous variables were expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD). Since the distributional characteristics were similar between the obese-hypertensive group and healthy controls, parametric tests were used for comparisons.\u003c/p\u003e \u003cp\u003eDifferences between groups were assessed using the independent samples t-test (Student\u0026rsquo;s t-test). A p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant. Associations between variables were evaluated using Pearson or Spearman correlation analyses, where appropriate.\u003c/p\u003e \u003cp\u003eCorrelations between BMI and serum metabolites (carnitines and amino acids), as well as correlations between HOMA-IR and serum metabolites, were assessed using Pearson correlation analysis. Correlation strength was interpreted as follows: r\u0026thinsp;=\u0026thinsp;0.1\u0026ndash;0.3 (weak), r\u0026thinsp;=\u0026thinsp;0.3\u0026ndash;0.5 (moderate), and r\u0026thinsp;\u0026gt;\u0026thinsp;0.5 (strong). Results were visualized using tables and graphs, with significant differences highlighted accordingly.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cp\u003eOf the 30 patients included in the study, 19 were male. The mean BMI was 2.95 SDS. Antihypertensive therapy was initiated in 25 patients. Five patients were diagnosed with Stage 1 hypertension based on ABPM and were followed with lifestyle modification alone, as both echocardiography and fundoscopy were normal.\u003c/p\u003e \u003cp\u003eThe comparison of carnitine and amino acid levels between patients and healthy children is presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of Metabolic Parameters Between Children With Obesity-Related Hypertension and Healthy Controls Acylcarnitine Profile (mmol/L)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMetabolite (Reference Range)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eObesity-Related Hypertension\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHealthy Controls\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eC0 (9\u0026ndash;65)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e31.2\u0026thinsp;\u0026plusmn;\u0026thinsp;8.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e24.9\u0026thinsp;\u0026plusmn;\u0026thinsp;5.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.021\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eC2 (5\u0026ndash;52)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e12.6\u0026thinsp;\u0026plusmn;\u0026thinsp;5.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e12.8\u0026thinsp;\u0026plusmn;\u0026thinsp;4.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.875\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eC3 (0\u0026ndash;5.5)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e1.84\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e1.12\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.0003\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eC4 (0\u0026ndash;0.75)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.14\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e 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(0\u0026ndash;0.39)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.10\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.07\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.0002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eC6 (0\u0026ndash;0.18)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.03\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.02\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.369\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eC5-OH (0\u0026ndash;0.38)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.11\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.13\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.416\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eC8:1 (0\u0026ndash;0.40)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.06\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.04\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.205\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eC8:0 (0\u0026ndash;0.71)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.07\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.07\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.959\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eC10 (0\u0026ndash;0.18)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.06\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.02\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.078\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eC5-DC (0\u0026ndash;0.21)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.095\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.059\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eC12 (0\u0026ndash;0.41)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.06\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.045\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.038\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eC6-DC (0\u0026ndash;0.20)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.028\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.023\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.402\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eC14:2 (0\u0026ndash;0.25)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.016\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.030\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.026\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eC14:1 (0\u0026ndash;0.37)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.029\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.039\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.240\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eC14-MYC (0\u0026ndash;0.5)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.05\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.05\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.289\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eC4-DC (0\u0026ndash;0.71)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.18\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.21\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.285\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eC16:1 (0\u0026ndash;0.10)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.0372\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.0308\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.091\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eC16 (0\u0026ndash;1.51)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e1.05\u0026thinsp;\u0026plusmn;\u0026thinsp;0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.68\u0026thinsp;\u0026plusmn;\u0026thinsp;0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eC18:2 (0\u0026ndash;0.24)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.036\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.052\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.367\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eC18:1 (0.02\u0026ndash;0.25)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.093\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.072\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.177\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eC18 (0.01\u0026ndash;0.6)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.547\u0026thinsp;\u0026plusmn;\u0026thinsp;0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.424\u0026thinsp;\u0026plusmn;\u0026thinsp;0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.013\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eC18:1-OH (0\u0026ndash;0.06)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.052\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.004\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.516\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\u003cp\u003eBranched-chain amino acids (BCAAs) were elevated compared with controls. Significant increases in acylcarnitines C3, C5:1, C5-Iso, C5-DC and C16 were observed in the patient group (p \u0026lt; 0.005). Aromatic amino acids (tyrosine and phenylalanine) and alanine were also higher than in controls (BCAA p = 0.161; phenylalanine p = 0.025; alanine p = 0.009; tyrosine p = 0.052).\u003c/p\u003e\n\u003cp\u003eHyperuricemia was detected in 12 patients, and hepatic steatosis was present in 9 patients. Vitamin D deficiency was identified in 10 patients, and anemia in 18 patients. Hyperuricemia was again confirmed in 12 patients, and hepatic steatosis in 9 patients. Dyslipidemia was prevalent: HDL \u0026lt; 44 mg/dL in 9 patients, LDL \u0026gt;130 mg/dL in 10 patients, triglycerides \u0026gt;130 mg/dL in 10 patients. ALT \u0026gt;40 IU/mL was found in 7 patients, TSH \u0026gt;4 IU/mL in 8 patients, and insulin resistance (HOMA-IR \u0026gt;2.5) in 18 patients.\u003c/p\u003e\n\u003cp\u003eAmong patients with hypertension confirmed by ambulatory blood pressure monitoring (ABPM), hypertensive retinopathy was identified in three patients, and left ventricular hypertrophy was detected in two patients by echocardiography.\u003c/p\u003e\n\u003ch3\u003eCorrelations Between BMI and Serum Metabolites\u003c/h3\u003e\n\u003cp\u003eIn obese children, BMI showed weak-to-moderate positive correlations with several metabolites, including C0 (r\u0026thinsp;=\u0026thinsp;0.28, p\u0026thinsp;=\u0026thinsp;0.134), C2 (r\u0026thinsp;=\u0026thinsp;0.11, p\u0026thinsp;=\u0026thinsp;0.563), C3 (r\u0026thinsp;=\u0026thinsp;0.33, p\u0026thinsp;=\u0026thinsp;0.075), C14:0 (myristoylcarnitine) (r\u0026thinsp;=\u0026thinsp;0.36, p\u0026thinsp;=\u0026thinsp;0.051), and C16 (r\u0026thinsp;=\u0026thinsp;0.52, p\u0026thinsp;=\u0026thinsp;0.003). Among these, only the correlation with C16 reached statistical significance (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01).\u003c/p\u003e \u003cp\u003eAmong amino acids, arginine (r = \u0026minus;\u0026thinsp;0.32, p\u0026thinsp;=\u0026thinsp;0.085) and proline (r = \u0026minus;\u0026thinsp;0.29, p\u0026thinsp;=\u0026thinsp;0.120) showed negative correlations with BMI, though neither reached statistical significance. Serine exhibited a weak, nonsignificant positive correlation (r\u0026thinsp;=\u0026thinsp;0.23, p\u0026thinsp;=\u0026thinsp;0.221).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eCorrelations Between HOMA-IR and Serum Metabolites\u003c/h2\u003e \u003cp\u003eCorrelation analyses revealed weak-to-moderate and some strong associations between HOMA-IR and serum metabolites. Among acylcarnitines, C8:0 demonstrated the strongest positive correlation with HOMA-IR (r\u0026thinsp;=\u0026thinsp;0.62, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). This was followed by C12 (r\u0026thinsp;=\u0026thinsp;0.41, p\u0026thinsp;=\u0026thinsp;0.024) and C14:1 (r\u0026thinsp;=\u0026thinsp;0.35, p\u0026thinsp;=\u0026thinsp;0.058); C12 reached statistical significance whereas C14:1 showed only a trend toward significance. Overall, C8:0 emerged as the metabolite most strongly associated with insulin resistance.\u003c/p\u003e \u003cp\u003eOther acylcarnitines\u0026mdash;C3, C5-OH, C5-DC, C14-Myc, and C16:1\u0026mdash;displayed weak-to-moderate positive correlations with HOMA-IR, though none achieved statistical significance (all p\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003eAmong amino acids, glycine (r = \u0026minus;\u0026thinsp;0.32, p\u0026thinsp;=\u0026thinsp;0.085) and serine (r = \u0026minus;\u0026thinsp;0.36, p\u0026thinsp;=\u0026thinsp;0.051) showed moderate negative correlations with HOMA-IR, indicating a trend toward lower levels with increasing insulin resistance. Arginine showed no meaningful correlation (r\u0026thinsp;=\u0026thinsp;0.01, p\u0026thinsp;=\u0026thinsp;0.958).\u003c/p\u003e \u003cp\u003eROC curve analysis of the C16 carnitine variable demonstrated an area under the curve (AUC) of 0.869 (95% CI: 0.773\u0026ndash;0.964; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001)as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The optimal cut-off value was determined to be 0.825.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eChildhood obesity represents a complex condition in which metabolic disturbances emerge early and predispose affected individuals to long-term cardiometabolic diseases (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). In this study, we comprehensively evaluated alterations in amino acid and acylcarnitine profiles among obese, hypertension-prone children and examined their associations with BMI, insulin resistance (HOMA-IR), and blood pressure parameters. Our findings demonstrate that several metabolites, which may serve as early biochemical indicators of metabolic stress, are significantly altered in obese children and may contribute to the development of hypertension.\u003c/p\u003e \u003cp\u003eThe prevalence of metabolic syndrome (MS) is markedly higher in obese children than in the general pediatric population. Whereas the prevalence of hypertension is approximately 1.9% in normal-weight children, it increases to 5% in overweight and up to 15% in obese children (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). WHO data indicate that MS affects only 3\u0026ndash;5% of children overall but rises to 30\u0026ndash;39% among those with obesity. Turkish data show a similar pattern (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). These high rates underscore the need for early cardiometabolic screening in obese pediatric populations.\u003c/p\u003e \u003cp\u003eThe predominance of normal echocardiographic and ophthalmologic findings may reflect early-stage diagnosis in most patients. The ability to detect early-stage findings and achieve an early diagnosis is of considerable clinical value, especially in obese children. One of the most notable findings of our study is the significant elevation of C16 carnitine and C8:0 (octanoylcarnitine) levels in the hypertensive obese group. The positive correlation between C16 and BMI (p\u0026thinsp;=\u0026thinsp;0.003), as well as the strong association between C8:0 and HOMA-IR (r\u0026thinsp;=\u0026thinsp;0.62, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), highlights a mechanistic link between impaired mitochondrial β-oxidation and insulin resistance. As adipose tissue expands in obesity, increased flux of free fatty acids overwhelms mitochondrial oxidative capacity, leading to incomplete β-oxidation and the accumulation of medium- and long-chain acylcarnitines (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). C8:0 accumulation is widely recognized as a classical marker of metabolic \u0026ldquo;bottlenecking,\u0026rdquo; reflecting mitochondrial overload and metabolic stress. Our results suggest that rising C8:0 levels may accompany worsening insulin resistance and contribute to vascular dysfunction.\u003c/p\u003e \u003cp\u003eThese observations align with the findings of Adams et al. (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e), who reported elevated short- and medium-chain acylcarnitines in obese children, and with Kepka et al. (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e), who demonstrated increased urinary carnitine excretion in adolescents with primary hypertension. Metabolites such as C12 and C14:1, which showed moderate correlations with HOMA-IR, further suggest dysfunction at specific stages of fatty acid β-oxidation. Other acylcarnitines with weaker correlations (C3, C5-OH, C5-DC, C14-MYC, C16:1) collectively support the concept of a broad, multisystem disturbance in energy metabolism among obese children.\u003c/p\u003e \u003cp\u003eElevations in short-chain carnitines\u0026mdash;specifically C3, C5:1, C5-iso, and C5-DC\u0026mdash;correspond with impaired branched-chain amino acid (BCAA) catabolism. In obesity, reduced activity of the BCKDH enzyme complex leads to BCAA accumulation and increased production of their corresponding acylcarnitines. Numerous studies have shown that elevated BCAA levels are among the strongest metabolic predictors of insulin resistance (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e), operating partly through mTOR pathway activation and impaired insulin signaling (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). The elevated BCAA levels observed in our cohort are consistent with this mechanistic framework.\u003c/p\u003e \u003cp\u003eWe also observed increases in aromatic amino acids, particularly phenylalanine and tyrosine, in the hypertensive obese group. These amino acids participate in catecholamine and neurotransmitter biosynthesis and may reflect altered sympathetic drive or neuroendocrine activation in obesity. Consistent with previous literature linking aromatic amino acids to hypertension, phenylalanine levels were significantly higher (p\u0026thinsp;=\u0026thinsp;0.036), potentially indicating increased sympathetic tone and early endothelial dysfunction.\u003c/p\u003e \u003cp\u003eThe negative correlation between BMI and arginine (r = \u0026minus;\u0026thinsp;0.32) is clinically meaningful. Arginine is the substrate for endothelial nitric oxide (NO) synthesis; reduced levels impair NO bioavailability and contribute to endothelial dysfunction\u0026mdash;one of the earliest steps in obesity-related hypertension (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). Reduced arginine may also reflect increased levels of endogenous NOS inhibitors such as ADMA, which rise in high-fat and high-carbohydrate dietary environments and contribute to vascular tone dysregulation (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe presence of elevated HOMA-IR, dyslipidemia, elevated ALT, and hyperuricemia in our cohort highlights the multifactorial nature of metabolic syndrome. Uric acid has been shown to promote insulin resistance and contribute to hypertension via renal microvascular effects (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). Dyslipidemia and hepatic steatosis, both common in our cohort, further compound cardiometabolic risk.\u003c/p\u003e \u003cp\u003eGlycine and serine, both of which displayed moderate negative correlations with HOMA-IR in our study, play key roles in glutathione synthesis and oxidative stress regulation. Their reduction suggests impaired trans-sulfuration pathway activity and increased oxidative stress, findings consistent with reports in children with type 2 diabetes (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). Evidence that glycine/N-acetylcysteine supplementation reduces insulin resistance in adults (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e) further supports the metabolic significance of these pathways.\u003c/p\u003e \u003cp\u003eRenin elevation in nine of our patients and the observed reduction following weight loss underscore the role of RAAS activation in obesity-related hypertension. Adipose tissue contains a functional RAAS capable of producing angiotensin II, which contributes to sodium retention and vascular tone dysregulation (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). This aligns with clinical evidence supporting the use of ACE inhibitors and ARBs in hypertensive obese children.\u003c/p\u003e \u003cp\u003eThe pubertal period may amplify these metabolic disturbances; physiological insulin resistance, heightened sympathetic responsiveness, and hormonal variability may collectively accelerate the development of hypertension. Furthermore, systemic inflammatory indices such as SII, NLR, and LMR have been associated with hypertension in obese adolescents (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e), supporting the role of inflammation in vascular dysfunction.\u003c/p\u003e \u003cp\u003eEmerging evidence also links pediatric obesity to epigenetic modifications, including altered DNA methylation and microRNA profiles, some of which are reversible with weight loss (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). Thus, amino acid and acylcarnitine profiles may serve not only as metabolic biomarkers but also as early indicators of epigenetic dysregulation.\u003c/p\u003e \u003cp\u003eSpecific metabolites such as α-hydroxybutyric acid (α-HB) and oleic acid have been proposed as early markers of insulin resistance in metabolomic studies (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e). Our findings are consistent with this literature, suggesting a multifaceted pattern of early metabolic stress in obese children.\u003c/p\u003e \u003cp\u003eTaken together, our results support previous observations from Adams et al. (2009), Kepka et al. (2015), Cobb et al. (2016),), and Gallardo-Escribano et al. (2022), Wu yan et al (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e) and Mihalik et al. (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e)collectively demonstrate that amino acid and acylcarnitine profiling may provide important early biochemical markers of metabolic deterioration in obese hypertensive children. Metabolites such as C8:0, C16, BCAAs, and indicators of arginine-NO imbalance may be valuable targets for early risk stratification and preventive interventions. C16 cutt of value 0.82 is crucial for healthy of vascular state.\u003c/p\u003e \u003cp\u003eLimitations of this study include the relatively small sample size, single-center design, and cross-sectional nature, which preclude causal inference. Dietary intake and physical activity were not quantitatively assessed. However, the comprehensive metabolic profiling using tandem MS/MS and detailed endocrine/metabolic evaluation represent important strengths.\u003c/p\u003e \u003cp\u003eMost metabolite values in these children without any inborn metabolic disease remained within normal reference ranges; however, their differences compared with healthy controls suggest that these markers may still carry potential diagnostic value for early screening. Future studies integrating metabolomic findings with genomic, epigenetic, and microbiome analyses through multi-omics approaches are expected to provide a more comprehensive understanding of the biological mechanisms underlying obesity-related hypertension.\u003c/p\u003e \u003cp\u003eThe absence of left ventricular hypertrophy in most patients indicates that the cohort consists of adolescents with early-stage hypertension and that metabolic alterations emerge before the development of target organ damage.\u003c/p\u003e \u003cp\u003eIn conclusion, our findings indicate that metabolic screening in obese children should not be limited to conventional glucose and lipid panels; the early assessment of amino acid and acylcarnitine profiles may offer significant clinical benefits. This approach could contribute to the early identification of childhood hypertension and cardiometabolic risk and support the development of individualized preventive strategies.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor Contributions, Ethics Statement, Funding, and Conflict of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions:\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;S.K., A.S., and T.Y. conceptualized and designed the study. S.K., A.S., and T.Y. collected the data. S.K. performed the statistical analyses and contributed to data interpretation. S.K. contributed to the critical evaluation of the findings and manuscript drafting. All authors participated in writing and revising the manuscript and approved the final version for submission.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Statement:\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;The study was conducted in accordance with the Declaration of Helsinki and was approved by the Institutional Review Board of the Bağcılar Training and Research Hospital \u0026ndash; Non-Interventional Clinical Research Ethics Committee (Approval No.: 2025/05/04/047). Written informed consent was obtained from all participants and/or their legal guardians before enrollment.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;This research did not receive any specific funding from public, commercial, or nonprofit organizations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest:\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;The authors declare no commercial or financial relationships that could be perceived as potential conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorresponding Author\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDr. Se\u0026ccedil;il Kezer\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;[email protected]\u003cbr\u003e\u0026nbsp;İstanbul University, Chıld Health Institute, PhD program in Pediatric Kıdney Transplantation research, İstanbul, T\u0026uuml;rkiye\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSong P, Zhang Y, Yu J, Zha M, Zhu Y, Rahimi K, Rudan I (2019) Global prevalence of hypertension in children: a systematic review and meta-analysis. 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J Diabetes 15(12):1020\u0026ndash;1028. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/1753-0407.13460\u003c/span\u003e\u003cspan address=\"10.1111/1753-0407.13460\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003eEpub 2023 Aug 25. PMID: 37622725; PMCID: PMC10755598\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMihalik SJ, Goodpaster BH, Kelley DE, Chace DH, Vockley J, Toledo FG, DeLany JP (2010) Increased levels of plasma acylcarnitines in obesity and type 2 diabetes and identification of a marker of glucolipotoxicity. Obes (Silver Spring) 18(9):1695\u0026ndash;1700. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/oby.2009.510\u003c/span\u003e\u003cspan address=\"10.1038/oby.2009.510\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003eEpub 2010 Jan 28. PMID: 20111019; PMCID: PMC3984458\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-8455780/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8455780/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eEarly detection of hypertension and characterization of metabolic alterations in obese children are critical for timely intervention. This study aimed to evaluate amino acid profiles and carnitine levels in pediatric patients with secondary hypertension due to exogenous obesity, compare these findings with healthy controls, and identify potential early biomarkers of hypertension.\u003c/p\u003e\u003ch2\u003eMaterials and Methods\u003c/h2\u003e \u003cp\u003eThirty hypertensive children (19 males; mean age 13.4 years; mean BMI 2.95 SDS) attending the Pediatric Nephrology Clinic of Bağcılar Training and Research Hospital between August 2024 and January 2025 were enrolled. Age-matched healthy children served as controls. Participants with chronic illnesses or regular medication use were excluded. All underwent anthropometric assessment, biochemical and hormonal testing, lipid profiling, vitamin evaluation, abdominal and renal ultrasonography, ambulatory blood pressure monitoring, cardiac assessment, and fundoscopic examination. Quantitative amino acid and acylcarnitine analyses were performed using tandem mass spectrometry.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eAntihypertensive therapy was initiated in 25 patients. Significant elevations in C3, C5-Iso, C5-DC, C16 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.005), C5:1 (p\u0026thinsp;=\u0026thinsp;0.012), and C18 (p\u0026thinsp;=\u0026thinsp;0.013) were observed in hypertensive children compared with controls. Branched-chain and aromatic amino acids were also elevated. C16 carnitine strongly correlated with BMI (p\u0026thinsp;=\u0026thinsp;0.003), while C8:0 (octanoylcarnitine) was most strongly associated with HOMA-IR (r\u0026thinsp;=\u0026thinsp;0.62; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Insulin resistance was identified in 60% of cases.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eSystemic inflammation in obese children contribute to hypertension development. Early assessment of metabolic markers using tandem MS may enable detection of preclinical alterations and guide timely intervention. Elevated C16 and C8:0 carnitine levels may serve as early indicators of metabolic dysfunction in hypertensive obese children.\u003c/p\u003e","manuscriptTitle":"Altered Carnitine Profiles and Amino Acids in Obese Hypertensive Children: A Comparative Study with Healthy Controls","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-11 05:47:39","doi":"10.21203/rs.3.rs-8455780/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"efd7be45-88ba-4d0a-bdd7-28267201f67d","owner":[],"postedDate":"February 11th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-02-26T20:24:17+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-11 05:47:39","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8455780","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8455780","identity":"rs-8455780","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}