Metabolic, inflammatory and adipokine differences on overweight/obese children with and without metabolic syndrome: A cross-sectional study

Idalia Cura–Esquivel; Marlene Marisol Perales-Quintana; Liliana Torres-González; Katia Guzmán-Avilán; Linda Muñoz-Espinosa; Paula Cordero-Pérez

doi:10.1371/journal.pone.0281381

. 2023 Mar 15;18(3):e0281381. doi: 10.1371/journal.pone.0281381

Metabolic, inflammatory and adipokine differences on overweight/obese children with and without metabolic syndrome: A cross-sectional study

Idalia Cura–Esquivel ^1,^#, Marlene Marisol Perales-Quintana ^2,^#, Liliana Torres-González ³, Katia Guzmán-Avilán ¹, Linda Muñoz-Espinosa ³, Paula Cordero-Pérez ^3,^*

Editor: Omar Yaxmehen Bello-Chavolla⁴

PMCID: PMC10016645 PMID: 36920931

Abstract

Background

Obesity is associated with low-grade inflammation and metabolic syndrome (MetS) in both children and adults. Our aim was to describe metabolic, inflammatory and adipokine differences on overweight/obese children with and without MetS.

Methods

This was an observational study. A total of 107 children and adolescents aged 6–18 years were included. Among this sample, n = 21 had normal body weight, n = 22 had overweight/obesity without MetS, and n = 64 had overweight/obesity with MetS. Anthropometric data and biochemical, adipokine, and inflammatory markers were measured. Different ratios were then assessed for estimate the probability of MetS. ROC analysis was used to estimate the diagnostic accuracy and optimal cutoff points for ratios.

Results

Serum CRP levels were higher among children with overweight/obesity with MetS. Adipokines like PAI-1 and leptin were significantly lower in children with normal body weight. The Adipo/Lep ratio was highest in the group with normal body weight. TG/HDL-C and TC/HDL-C ratios were significantly correlated with BMI, DBP, PCR, and PAI-1. TC/HDL-C ratio was significantly correlated with SBP and resistin. TGL/HDL-C ratio was significantly correlated with waist and hip circumferences, fasting glucose, and MCP-1. The AUC for TG/HDL-C at the optimal cutoff of 2.39 showed 85.71% sensitivity and 71.43% specificity. CT/HDL-C at the optimal cutoff of 3.70 showed 65.08% sensitivity and 81.82% specificity. Levels of both ratios increased significantly as additional MetS criteria were fulfilled.

Conclusion

Low-grade inflammation is correlated with MetS in children with overweight/obesity. TGL, HDL-C and TGL/HDL-C ratio, obtainable from routine lab tests, allows identification of MetS in children with overweight or obesity.

Introduction

Childhood obesity is a global public health problem. Obesity, a state of chronic low-grade inflammation, results from accumulation of visceral fat, which leads to complications such as metabolic syndrome (MetS). There is currently no clear consensus on the definition of pediatric MetS [1]; however, the term refers to a set of metabolic risk factors that include obesity, dyslipidemia, hypertension, and type 2 diabetes mellitus [2]. The prevalence of MetS among children in Mexico has been reported to be as high as 54.6% [3]. Its increase in recent decades has also raised the prevalence of associated comorbidities and, since it is also considered a predictor of cardiometabolic diseases in adulthood, identification and early therapeutic intervention are crucial. It has been postulated that peripheral insulin resistance (IR) and abdominal obesity are the main factors contributing to MetS, and that its metabolic changes affect lipid metabolism due to increased low-density lipoprotein cholesterol (LDL-C), decreased high-density lipoprotein cholesterol (HDL-C), increased triglycerides (TGL), and increased fatty acids [4].

Obesity is also linked to changes in serum lipoproteins, which are in turn associated with the development of atherosclerosis. Evidence suggests that the atherosclerotic process begins in childhood. The prevalence of atherogenic dyslipidemia is increasing among children and adolescents with obesity, and is characterized by hypertriglyceridemia, increased very-low-density lipoprotein cholesterol (VLDL-C), and reduced HDL-C; its association with MetS also increases cardiovascular disease risk [5].

In adults, the relation between lipids such as TGL and HDL-C and the ratio of total cholesterol (TC) and HDL-C are widely used to assess MetS. These ratios indicate balance between all atherogenic cholesterols (including VLDL-C and HDL-C) and are thus important determinants of cardiovascular risk.

Obesity is related to both classic and novel risk factors, including prothrombotic factors (fibrinogen, plasminogen activator inhibitor-1 [PAI-1], homocysteine), inflammatory factors (interleukin 10 [IL-10], interleukin 6 [IL-6], tumor necrosis factor alpha [TNF-α], monocyte chemoattractant protein 1 (MCP-1), C-reactive protein [CRP]), and some adipocytokines (leptin, adiponectin) [6, 7]. Inflammation arising from adipose tissue has been identified as an important source of systemic inflammation and may be associated with IR. The complex MetS pathophysiology is also associated with hormone (adipokines) changes and inflammatory markers [8].

Adiponectin plays a protective role against IR and cardiovascular diseases (CVD) [9]. In contrast, leptin has proinflammatory effects; high levels are associated with development of IR and CVD. Hypoadiponectinemia and hyperleptinemia are observed in both adults and children with obesity, and the adiponectin/leptin (Adipo/Lep) ratio has been proposed as a sensitive MetS marker in children and adolescents [10].

The state of low-grade inflammation in obesity is exacerbated in individuals with MetS. Specifically, increased levels of inflammatory markers, including CRP, have been detected in children, adolescents, and adults with obesity and MetS [11]. Thus, recent attention has focused on the relations between inflammation and hormonal dysfunction (adipokines), and their relations with MetS. As such, the objective herein was to describe metabolic, inflammatory and adipokine differences on overweight/obese children with and without MetS.

Material and methods

Design of the study

This was a cross-sectional analytical study of children and adolescents attending pediatric consultation at the University Hospital “Dr. José Eleuterio González” of the Autonomous University of Nuevo León in Monterrey, N.L., Mexico conducted between January 2017 and December 2019. This public hospital, with 500 beds, is the largest in Northeast Mexico with patients coming principally from the State of Nuevo Leon and surrounding states in Northern Mexico (Coahuila, Tamaulipas, and San Luis Potosi).

The institutional ethics committee approved the study (PE17-00010). A detailed letter explaining the study aims was provided to all parents or guardians and informed consent was obtained.

Study population

Three groups were included: (I) Normal weight children: healthy children with adequate weight and height for age; (II) Obese / overweight children without metabolic syndrome; (III) Obese / overweight children without metabolic syndrome. The inclusion criteria for group (II) and (III) were: younger than age 18 years; and body mass index (BMI) ≥85th percentile according to the Centers for Disease Control and Prevention (CDC). The exclusion criteria were: congenital malformation; previous diagnosis with endocrinological, kidney, or hepatic disorder; use of any medication affecting serum lipid concentration; and refusal to participate in the study.

Definitions

Overweight and obesity were defined according to the criteria established by the CDC. Overweight was considered a BMI between the 85th and 95th percentiles. Obesity was considered a BMI ≥95th percentile.

MetS was defined according to the de Ferranti criteria [12] and was considered present when the patient met three or more of the following criteria: (I) abdominal obesity defined as a waist circumference (WC) >75th percentile; (II) hypertension defined as a blood pressure >90th percentile; (III) TGL >100 mg/dL; (IV) HDL-C <50 mg/dL; and (V) fasting glucose >100 mg/dL.

The Adipo/Lep ratio was obtained as (Serum adiponectin levels) / (Serum leptin levels). The TC/HDL-C was calculated as (Total cholesterol) / (High-density lipoprotein cholesterol). The TGL/HDL-C was obtained as (Triglycerides) / (High-density lipoprotein cholesterol). While the RCP/HDL-C was obtained as (C-reactive protein) / (High-density lipoprotein cholesterol).

Data collection

At the hospital visit, sex and age were recorded and anthropometrics (height, weight and waist and hip circumferences) were measured. BMI was calculated as body weight (kg)/height² (m). Blood pressure was measured using a sphygmomanometer while the child was seated.

Biochemical and inflammatory parameters

Blood samples were taken to measure biochemical and inflammatory parameters, and adipokines. TC, HDL-C, TGL, and glucose levels were determined using an ILAB-Aries self-analyzer spectrophotometer and diagnostic kits (Instrumentation Laboratory, Bedford, MA, USA) according to the supplier’s specifications. Cytokine (IL-6, TNF-α, MCP-1) concentrations were measured using a commercially available enzyme-linked immunoassays (Human IL-6 Immunoassay, Quantikine ELISA Kit; Human TNF-α Quantikine ELISA Kit; and Human CCL2/MCP-1 Immunoassay, respectively, Bio-Techne, Minneapolis, MN, USA) and are reported in pg/mL.

Inflammatory marker CRP was measured by human CRP ELISA kit (Bio-Techne, Minneapolis, MN, USA) and is reported in mg/L.

Adipokine (adiponectin, leptin), resistin, and PAI-1 levels were measured using an enzyme-linked immunoassay kit. Serum leptin level was measured by human leptin ELISA, Clinical Range kit and is reported in ng/mL (BioVendor Research and Diagnostic products, Karasek, Czech Republic). Adiponectin was measured by a Human Adiponectin/Acrp30 DuoSet ELISA kit and is reported in mg/mL and resistin was measured by a Human Resistin Quantikine ELISA Kit (both from R&D Systems, Minneapolis, MN, USA) and are reported in ng/mL. PAI-1 was measured by a PAI1 Human ELISA Kit and is reported in ng/mL (Thermo Fisher Scientific, Waltham, MA, USA).

Ratios previously described within populations of patients who with overweight and obesity were also evaluated: Adipo/Lep, TC/HDL-C, TGL/HDL-C.

Statistical analysis

Analyses were performed using GraphPad Prism software (v. 6.0; GraphPad, San Diego, CA, USA) or SPSS software (v.22.0; Chicago, Ill., USA) and MedCalc Statistical Software version 20.009 (MedCalc Software bvba, Ostend, Belgium). Normally distributed variables are presented as means and standard deviations and were analyzed by ANOVA-tests. Non-normally distributed variables are presented as medians and interquartile ranges and were compared by Kruskal-Wallis tests.

Bivariate and multivariate logistic regression analyses were conducted to determine factors associated with MetS, variables with p<0.05 in bivariate analysis were included in multivariate analysis.

Receiver operating characteristic (ROC) analysis was performed to determine the area under the curve (AUC) to assess the precision of the TGL/HDL and TC/HDL ratios for identify children with overweight/obesity, with and without MetS. To determine the optimal cutoff point, the Younden index was used. Sensitivity and specificity of the cutoff points were calculated. A correlation study for TGL/HDL-C and TC/HDL-C was carried out using the Spearman correlation. For all analyses, p<0.05 was considered statistically significant.

Results

Sample characteristics

The total sample was 107 patients, among whom 63 were male (58.80%) and 44 were female (41.10%); their mean age was 10.52(1.76) years. Among the total sample, 21 children (19.60%) had normal body weight and 86 (80.40%) had overweight/obesity.

MetS diagnosis

Among the study sample, 64 (59.81%) had obesity or overweight and met the MetS diagnostic criteria of three of the five Ferranti criteria. In this subgroup, 76.64% had abdominal obesity, 33.64% presented arterial hypertension, 60.75% had elevated TGL levels, 81.31% had low HDL-C levels, and 4.67% presented with hyperglycemia (Fig 1).

Anthropometric, biochemical, adipokine, and cytokine characteristics

Sample anthropometric, biochemical, adipokine, and cytokine characteristics are described in Table 1. Compared with children with normal body weight, those with overweight/obesity and MetS had significantly higher BMI, systolic blood pressure (SBP), diastolic blood pressure (DBP), TGL, and MCP-1, and significantly lower HDL-C and TGL/HDL-C ratio (4.51(3.15); p<0.0001). There were no significant differences on indices between the children with overweight/obesity without MetS and those with normal body weight (p>0.05) (Table 1).

Table 1. Anthropometric and laboratory parameters of the children.

	Normal weight children (n = 21)	Obese/overweight children without metabolic syndrome (n = 22)	Obese/overweight children with metabolic syndrome (n = 64)	p
Age in years, mean (age range)	8.70 (6–11)	10.00 (6–15)	11.00 (8–15)
Sex Male/Female, n (%)	12/9 (57.14/42.86)	14/8 (63.64/36.36)	37/27 (57.81 / 42.19)
Obese / Overweight		17/5 (77.27 / 22.73)	54/10 (84.38 / 15.63)
Anthropometric variables, mean (SD)
Height (cm)	134.00 (9.30)	149.00 (11.00)	147.00 (8.70)	< 0.001 ^† ^‡
Weight (kg)	28.00 (4.80)	61.00 (17.00)	64.00 (16.00)	< 0.001 ^† ^‡
BMI (kg/m²)	16.00 (2.10)	27.00 (4.50)	29.00 (5.30)	< 0.001 ^† ^‡
Waist circunference (cm)	57.00 (6.40)	91.00 (12.00)	89.00 (9.20)	< 0.001 ^† ^‡
Hip circunference(cm)	67.00 (7.40)	97.00 (12.00)	99.00 (11.00)	< 0.001 ^† ^‡
Blood pressure, mean (SD)
SBP (mmHg)	98.00 (7.90)	108.00 (11.00)	111.00 (16.00)	0.001 ^† ^‡
DBP (mmHg)	60.00 (8.00)	59.00 (10.00)	69.00 (10.00)	< 0.001 ^‡ ^§
Biochemical variables, median (IQR)
Total cholesterol (mg/dL)	150.0 (130.0–173.0)	141.00 (116.0–160.0)	157.00 (134.0–184.0)	0.075
HDL cholesterol (mg/dL)	49.00 (55.00–41.00)	46.00 (34.00–51.00)	39.00 (34.00–43.00)	< 0.001 ^‡ ^§
Triglycerides (mg/dL)	76.00 (57.00–99.00)	75.00 (51.00–101.00)	183.00 (123.00–223.00)	< 0.001 ^‡ ^§
Fasting glucose (mg/dL)	80.00 (78.00–85.00)	78.00 (74.00–85.00)	83.00 (77.00–87.00)	0.052
CRP (mg/L)	0.10 (0.10–0.38)	0.50 (0.15–0.89)	1.10 (0.33–3.50)	< 0.001 ^† ^‡
Adipokines, median (IQR)
Adiponectin (mg/mL)	31.13 (12.46–36.90)	25.42 (17.90–36.83)	23.82 (15.56–32.87)	0.258
Resistin (ng/mL)	17.43 (12.05–19.60)	19.68 (16.55–29.30)	21.35 (18.31–28.61)	0.020 ^‡
PAI-1 (ng/mL)	19.18 (15.31–22.58)	27.61 (23.20–37.38)	27.37 (17.89–41.26)	<0.001 ^† ^‡
Leptin (ng/mL)	2.31 (1.47–4.90)	18.17 (11.74–29.17)	16.89 (2.52–23.62)	< 0.001 ^† ^‡
Cytokines, median (IQR)
IL-6 (pg/mL)	16.76 (16.66–17.16)	17.16 (16.71–18.01)	17.60 (16.76–18.75)	0.007 ^‡
TNF-α (pg/mL)	17.65 (13.51–20.31)	17.65 (16.02–21.45)	18.68 (16.30–127.30)	0.018 ^‡
MCP-1 (pg/mL)	331.6 (182.7–405.3)	279.8 (395.4–231.3)	372.9 (272.2–770.60)	0.033 ^‡ ^§
Ratio, mean (SD)
Adipo/Lep ratio	6.68 (7.89)	1.08 (1.62)	1.08 (0.85)	< 0.001 ^† ^‡
RCP/HDL-C ratio	0.002 (0.001)	0.01 (0.01)	0.02 (0.05)	< 0.001 ^† ^‡
CT/HDL-C ratio	3.08 (0.98)	3.28 (0.53)	4.24 (1.16)	< 0.001 ^‡ ^§
TGL/HDL-C ratio	1.46 (0.91)	1.78 (1.84)	4.51 (3.15)	< 0.001 ^‡ ^§

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‡ showed significant difference between obese/overweight with metabolic syndrome and normal weight children; † showed significant difference between obese/overweight without metabolic syndrome and normal weight children

§ showed significant difference between obese/overweight with metabolic syndrome and obese/overweight without metabolic syndrome.

After bivariate analysis, we found that children with high levels of HDL-C have lower probability of having MetS (OR = 0.88, 95% CI = 0.81–0.95, p = 0.002). In addition, children with high levels of TGL have lower probability of having MetS (OR = 1.022, 95% CI = 1.01–1.033, p<0.001).

Serum leptin, resistin, PAI-1, and CRP levels differed significantly between children with normal body weight and those with overweight/obesity, and between those with and without MetS (all p<0.05), being higher in children with overweight/obesity and MetS.

The highest adiponectin levels were in children with normal body weight (31.13; IQR 12.46–36.90 mg/mL) and the lowest were in children with overweight/obesity with MetS (23.82; IQR 15.56–32.87 mg/mL). In contrast, the highest leptin levels were in children with overweight/obesity without MetS (18.17; IQR 11.74–29.17 ng/mL) and the lowest were in those with normal body weight (2.31; IQR 1.47–4.90 ng/mL) (Table 1). Consequently, the Adipo/Lep ratio was highest in children with normal body weight (6.68(7.89)), lower in those with overweight/obesity, and not significantly different between those without and with MetS (1.08(1.62) vs 1.08(0.85), respectively) (Table 1).

Serum CRP levels in children with normal body weight (0.10; IQR 0.10–0.38 mg/L) were significantly lower than in those with obesity and MetS (1.10; IQR 0.33–3.50 mg/L, p<0.0001).

Among the ratios evaluated (Table 1), only TC/HDL-C and TGL/HDL-C differed significantly between children with overweight/obesity with and without MetS (p<0.0001).

TC/HDL-C and TGL/HDL-C ROC curve analyses differentiated between children with overweight/obesity with MetS, as shown in Fig 2 and Table 2. TGL/HDL-C had the highest probability for MetS with an AUC of 0.85 and a cutoff value >2.39. Correlations between TG/HDL-C and TC/HDL-C and important variables are shown in Table 3. Both ratios were correlated with BMI, DBP, PCR, and PAI-1. Only the TC/HDL-C ratio was significantly correlated with SBP and resistin. Only the TGL/HDL-C ratio was significantly correlated with waist and hip circumferences, fasting glucose, and MCP-1.

Table 2. AUCROC and cutoff values of TGL/HDL-C and TC/HDL-C ratios to estimate probability of metabolic syndrome in obese and overweight children.

Area under the ROC curve					Younden index
	Area	Error	95% CI	p	Criterion	J	Sensivity	Specificity
TC/HDL-C ratio	0.76	0.05	0.66–0.85	<0.001	>3.71	0.47	65.08	81.82
TGL/HDL-C ratio	0.85	0.05	0.75–0.92	<0.001	>2.39	0.57	85.71	71.43

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Table 3. Correlation between TGL/HDL-C and TC/HDL-C ratios with variables.

TC/HDL-C ratio				TGL/HDL-C ratio
	r	p	CI– 95%	r	p	CI-95%
Pearson
Waist circumference (cm)	0.192	0.054	-0.003 to 0.374	0.353	<0.001	0.169 to 0.514
Hip circumference(cm)	0.153	0.125	-0.043 to 0.339	0.410	< 0.001	0.232 to 0.561
BMI (kg/m²)	0.264	0.006	0.077 to 0.433	0.440	< 0.001	0.169 to 0.514
SBP (mmHg)	0.304	0.003	0.109 to 0.476	-0.056	0.589	-0.256 to 0.148
DBP (mmHg)	0.356	<0.001	0.166 to 0.520	0.274	0.008	0.075 to 0.451
Spearman
Fasting glucose (mg/dL)	0.167	0.086	-0.030 to 0.352	0.247	0.011	0.052 to 0.424
PCR (mg/L)	0.207	0.039	0.005 to 0.392	0.611	< 0.001	0.466 to 0.724
Adiponectin (mg/mL)	-0.115	0.244	-0.305 to 0.084	-0.149	0.130	-0.338 to 0.0502
Resistin (ng/mL)	0.203	0.037	0.006 to 0.385	0.112	0.258	-0.088 to 0.303
PAI-1 (ng/mL)	0.236	0.015	0.040 to 0.414	0.232	0.018	0.036 to 0.412
Leptin (ng/mL)	0.077	0.438	-0.123 to 0.271	0.055	0.580	-0.146 to 0.251
IL-6 (pg/mL)	0.034	0.732	-0.164 to 0.228	0.127	0.197	-0.072 to 0.316
TNF-α (pg/mL)	0.078	0.456	-0.133 to 0.283	-0.062	0.559	-0.269 to 0.151
MCP-1 (pg/mL)	0.069	0.487	-0.132 to 0.265	0.199	0.045	-0.0008 to 0.384

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Levels of both ratios increased significantly as increasing numbers of Ferranti criteria were fulfilled as shown in Fig 3.

Discussion

Obesity-related diseases and complications were previously considered exclusive to the adult population, so evaluating them in childhood or adolescence was not routine practice. Nevertheless, many studies have now shown that childhood obesity tends to perpetuate into adulthood; this favors early development of metabolic disease and increases risk for CVD and diabetes, which in turn decreases life expectancy [13, 14].

Herein, we evaluated children with overweight/obesity and identified that in addition to abnormal weight, they typically present with at least one metabolic alteration, of which the most prevalent are dyslipidemia (60.75%) and arterial hypertension (33.64%).

The prevalence of pediatric MetS is variable and depends on the diagnostic criteria and component cutoff values [1]. We found a MetS prevalence of 58.81% using the Ferranti criteria, consistent with the meta-analysis by Bitew et al. showing a general prevalence of 56.32% in studies using the same criteria [15]. The most prevalent MetS criterion herein were low HDL-C (81.31%), central obesity (76.64%), and high total TGL (60.75%).

Obesity is a state of chronic low-grade inflammation, in which nutrient overload, increased metabolic demands, and lipotoxicity at the adipose level contribute to production of inflammatory mediators. Some of these markers are synthesized by adipocytes, including acute phase proteins such as CRP, haptoglobin, PAI-1, TNF-α, resistin, and cytokines (IL-1b, IL-6, IL-8, IL-10) [16, 17]. MetS is characterized by multiple cardiovascular risk factors; the endothelial dysfunction from this prothrombotic, inflammatory state is caused by the expression of inflammatory cytokines and cell adhesion molecules [17, 18].

As the main inhibitor of fibrinolysis, high levels of PAI-1 can increase coronary heart disease risk. Increased PAI-1 is involved with control of insulin signaling in adipocytes and can be considered a component of MetS [19]. IR states have been associated with elevated PAI-1 levels and altered plasma lipids, which helps explain the characteristic prothrombotic state of these pathologies [19, 20]. Children in our sample with overweight or obesity showed elevated PAI-1 levels compared with those with normal body weight, and similarly elevated TGL and blood pressure. In contrast, PAI-1 levels did not differ between those with and without MetS, providing further evidence of its association with plasma lipids but not necessarily MetS.

However, MCP-1 levels were considerably elevated in children with obesity and MetS compared with those with normal body weight. MCP-1 levels were also correlated with waist and hip circumferences, BMI, and DBP. Kim et al. found similar correlations with BMI and WC in young Koreans [21].

Resistin, a protein suspected to be related to obesity and IR, is reportedly increased in children with central obesity [22]. Herein, resistin was elevated in children with overweight/obesity, both with and without MetS; however, the only significant difference was between children with MetS and those with normal body weight. That no difference was found between the groups with obese/overweight with and without MetS suggests that plasma resistin may be a weak biochemical marker of metabolic dysfunction. This supports the notion that only a small proportion of variance in resistin can be explained by MetS-related factors.

Although central obesity assessed with WC is considered a better marker of metabolic risk than high BMI in adults, pediatric results have been contradictory [23, 24]. Our subsample with overweight/obesity showed significant differences on various inflammatory markers, and those with abdominal obesity had higher CRP levels compared with those without. Findings were consistent for PAI-1 and resistin, but not MCP-1, suggesting that WC may be correlated with inflammation and metabolic risk regardless of MetS status.

Other adipokines evaluated herein were adiponectin and leptin. While leptin acts primarily in the hypothalamus to control food intake, satiety, and energy expenditure, adiponectin is associated with reduced total body fat mass and promotes insulin sensitivity [25, 26]. Obesity and MetS are characterized by decreased serum adiponectin in parallel with increased concentrations of circulating leptin. Consequently, the Adipo/Lep ratio is associated with BMI and MetS status [26, 27]. The results herein show a negative correlation between Adipo/Lep ratio and BMI, meaning that Adipo/Lep ratio is significantly lower in children and adolescents with obesity, with or without MetS, compared with children with normal body weight. This biomarker decreases with increasing metabolic risk factors, which is why it has been proposed as predictive of MetS [25, 26].

Herein, adiponectin concentrations were lower in children with overweight/obesity with MetS compared with those without MetS, providing further evidence that adiponectin decreases in the presence of previously identified MetS parameters [28].

Dyslipidemia, particularly TC and TGL levels, has been described as an important risk factor for CVD, based on various indices [29, 30]. TC and TGL reflect the concentrations of the lipoproteins that transport them. HDL-C has antiatherogenic activity. Together, TC/HDL-C and TGL/HDL-C ratios may reflect the balance between these lipoproteins and could serve as a useful marker for cardiovascular risk. These relations have been evaluated in adults and children with and without obesity [31–33].

In an otherwise adult healthy Mexican sample, TGL/HDL-C ratio was associated with low insulin sensitivity and MetS, suggesting that it may serve as a reference index for MetS [32]. Herein, we found that TGL/HDL-C ratio was higher in children with overweight/obesity compared with children without overweight/obesity. When evaluating this in children with obesity with and without MetS, the association was stronger in the presence of MetS. This index also rose with increased numbers of fulfilled MetS criteria. In bivariate analysis and multivariate logistic regression, only HDL-C and TGL showed a significant correlation with MetS. This confirms that both biochemical markers are relevant in the pathophysiology of this syndrome, thus contributing to the usefulness of TC/HDL-C and TGL/HDL-C ratios for MetS screening in obese children, such as has previously been reported [34].

The TGL/HDL-C ratio was predictive of MetS with an AUC of 0.848 (95% confidence interval [CI]: 0.753–0.917). The optimal cutoff value was >2.390. Our TGL/HDL-C cutoff value for MetS identification was higher than the values of 1.25 reported for Chinese children with obesity [34] and 2.0 for Korean children with overweight [35]. These differences may be attributable to population-based ethnic and genetic variations. Herein, TGL/HDL-C ratio was correlated with BMI, WC, fasting glucose, and the inflammatory parameters CRP and PAI-1, suggesting its value for identifying MetS.

Elevated TC/HDL-C has been associated with a proinflammatory state in adults and adolescents, as it is strongly related to elevated CRP levels [33]. Herein, TC/HDL-C was evaluated as an indicator of cardiovascular risk. Consistent with other studies, it was significantly correlated with BMI, hypertension, and systemic inflammatory parameters: CRP, resistin, and PAI-1. This evidence confirms TC/HDL-C as a significant low-grade inflammation parameter. As such, it can be used to predict cardiovascular risk, as it reflects an imbalance between cholesterol transported by atherogenic lipoproteins and protective lipoproteins. It is widely accepted that obesity induces lipid biochemistry alterations in the development of atherogenic dyslipidemia, a critical factor in cardiovascular events among adults. The presence of atherosclerotic plaques has been reported in autopsies of children as young as age two years in the Bogalusa Heart Study [36]. Early identification is a crucial step toward reducing related morbidity and mortality. Herein, metabolic risk factors like obesity, atherogenic dyslipidemia (high TGL, low HDL-C), and high blood pressure were the most common MetS parameters (Fig 1).

RCP levels differed significantly between children with and without MetS, and were positively correlated with metabolic risk factors such as TGL/HDL-C and TC/HDL-C. It is known that childhood dyslipidemia can trigger low-grade inflammation even in the absence of obesity, since these parameters are increased even in children who without overweight. However, in children with obesity, these parameters are considerably increased in the presence of MetS.

In sum, the main risk factors correlated with metabolic disease and cardiovascular risk were WC, hypertension, atherogenic dyslipidemia (elevated TC, low HDL-C), HDL-C, TGL and inflammatory parameters (CRP, PAI-1). Of note, HDL-C, TGL, TGL/HDL-C ratio and TC/HDL-C ratio are available from routine lab tests, simplifying surveillance for cardiovascular risk among children with overweight or obesity.

To date, few studies in Mexico have evaluated the cardiovascular risk indices in children. Therefore, some limitations of the present study must be acknowledged. This study was based on a population of children and adolescents who attended an obesity consultation motivated by themselves or their parents to receive treatment, consequently, the results cannot be generalized since the sample consisted mainly of children of a medium-low socioeconomic level who were more predisposed to the development of metabolic disorders. Moreover, a weakness of this study was the comparison of our group of children with populations of different ethnic groups, making it difficult to compare the results obtained with those of other authors, especially considering the population differences and various methodologies used. Despite these limitations, we must highlight that Mexico is one of the first places in childhood obesity; therefore, this study is extremely important for the recognition of risk factors since childhood that influence the appearance of chronic-degenerative diseases in adulthood.

In conclusion in the population evaluated HDL-C, TGL, TGL/HDL-C ratio and TC/HDL-C ratio shown major alteration in overweight and obese children with MetS, which can be explained because the lipid parameters are part of the MetS diagnostic criteria; however, the inflammatory parameters and adipokines evaluated did not shown a difference in overweight or obese children with/without MetS, confirming the chronic inflammation state that has been previously described in patients under these conditions.

Supporting information

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S1 File

(XLSX)

Click here for additional data file.^{(30.7KB, xlsx)}

S2 File

(SPV)

Click here for additional data file.^{(66.6KB, spv)}

Acknowledgments

We acknowledge all staff and patients who offered help for this study.

Data Availability

All relevant data are within the paper and its Supporting Information files.

Funding Statement

The author(s) received no specific funding for this work.

References

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PLoS One. doi: 10.1371/journal.pone.0281381.r001

Decision Letter 0

Omar Yaxmehen Bello-Chavolla

2 Aug 2022

PONE-D-22-12285Assessment of cardiometabolic risk in children with obesity: adipokines, lipoprotein ratios, and inflammatory markersPLOS ONE

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Reviewer #1: The present work entitled "Assessment of cardiometabolic risk in children with obesity: adipokines, lipoprotein ratios, and inflammatory markers", which aimed "to explore the profile of adipokines, inflammatory markers, and lipid ratios to identify children with cardiometabolic risk" shows an interesting knowledge gap that could be useful in the search for information on cardiovascular risk, however, the submitted manuscript has various errors that must be resolved during a review.

My comments are as follows

Major Comments:

1.- The objective of the study is very ambiguous, "to explore the profile of adipokines, lipoprotein ratios, and inflammatory markers", with cardiometabolic risk, it is interpreted that each of these variables is associated with "cardiometabolic risk", but never cardiometabolic risk was defined, but only mention is made of “cardiometabolic risk factors”, so the objective and the rest of your work must be adjusted to a specific objective of what is sought to be carried out, whether it is the case of associating certain variables independent with cardiometabolic risk factors or with metabolic syndrome, as is.

2.- The type of study is not very clear, it is only mentioned as an observational study, but it really seems that it is a cross-sectional analytical study, so it should be classified that way, or give reasons why it is another type of study.

3.- During the manuscript there is talk about cardiometabolic risk, but in reality only metabolic syndrome was explored, the introduction and discussion should be changed so that the terms and references are appropriate to what was done, following the definitions of the study itself.

4.- It must be defined how each of the presented indices and the units of the variables used for their calculation were calculated, this is because these are their independent variables, and they should be considered with importance throughout the manuscript, and identified as such, so that the reader can understand what the authors want to do.

In the statistical analyzes it is mentioned that only comparisons were applied through the Student's t and Mann-Whitney U tests, but in the tables 3 groups are being presented in which the sample was divided, the comparisons should not be made only by bivariate tests, but by analysis of variance or the Kruskall-Wallis test if the ANOVA assumptions are not met.

5.- Table 3 mentions the word "prediction" referring to a prediction of metabolic syndrome by lipoprotein indices, but in reality they do not make a prediction, only an estimate of the probability of presenting metabolic syndrome at the same time of the measurement of the variables, and a prediction is related to the probability of estimating the presentation of a future condition. Therefore, I recommend avoiding the term throughout the manuscript.

6.- The reason why the correlations of the lipoprotein indices with all the variables were made is not understood, it would be interesting if the authors explained them and if it was part of a secondary objective, mention it as such in the text.

Minor Comments:

1.- The title of the study is ambiguous, because once the body of the manuscript is read, it is observed that the population studied is not only obese children but also overweight and obese, in the same way a cardiometabolic risk assessment was not made per se, but only its association with the metabolic syndrome. The function for which the words “adipokines, lipoprotein ratios, and inflammatory markers” are placed in the title is not clear, because the reader understands that these markers are the ones associated with cardiometabolic risk, but in reality it was never carried out, only the lipoprotein indices were associated with metabolic syndrome, the title will have to be restructured, following the recommendations of STROBE, as well as indicating the study population, the dependent and independent variables and the type of study.

2.- There are no references to the definitions presented, the references to each definition must be attached.

3.- Throughout the manuscript, the terms “abdominal adiposity” and “hypertension” are presented, which are mentioned in the results, but were never defined as such in the methodology, this error must be resolved.

4.- The symbol ± is used throughout the text to show the standard deviation, please avoid it and place the standard deviation only in parentheses or with the abbreviation "SD".

5.- In line 202 of the text the word associated is mentioned to show the relationship between certain variables, I consider the term “correlated” to be more optimal.

Reviewer #2: I want to thank the opportunity to review this manuscript. Cura–Esquivel Idalia and Perales-Quintana Marlene Marisol et al performed a cross-sectional study design to evaluate the metabolic, adipokine and inflammatory profile among overweight and obese pediatrics with cardiometabolic risk (classified with Mets according to the Ferranti criteria). The authors found that CRP, PAI-, Adipo-Lep and Tg/HDL and TC-HDL-C were different among patients living with MetS. Furthermore, the authors estimate the optimal threshold for Tg/HDL and TC-HDL-C to identify MetS. The novelty of this manuscript relies on the population and adipokines and inflammatory markers measured. A potential limitation is the relatively small sample size and the single center recruitment facility. Overall, the manuscript has an important message and delivers relevant findings in the field of endocrinology. Nevertheless, some findings have a lack of rational to demonstrate the objective. Furthermore, there are some methological and statistical issues that needs to be solved prior to the recommendation of acceptance of this manuscript. My suggestions are appended below.

Introduction

• A main concern of the objective of this study is that the authors shough to assess cardiometabolic risk using a proxy metabolic syndrome among children. Although it is a simplified and relevant concept, I would suggest to clearly specify in the objective that the authors used metabolic syndrome as their main dependent variable.

Methods

• Please specify the type of study the authors used (E.g., Cross-sectional recruitment).

• Please provide a more detailed context of the center of reference where the authors recruited the patients.

• For this reviewer, it is quite unclear why the authors sought to explore a cut-off point to determine TGL/HDL and TC/HDL with and without MetS. Overall, I could interpret this to explore a tress-hold to define as a proxy of insulin resistance using MetS as their outcome variable in children. Please explain the purpose of this analysis. Furthermore, I would suggest to perform a stratification of this threshold among children and adolescence.

• The authors state that part of the inclusion criteria where patients ≥85th percentile of BMI (definition of overweight), but in table 1 they displayed 21 patients with normal weight (patients <85th percentile?). Please clarify.

Results

• A main issue with table 1, is that the authors sought to compare the cardiometabolic risk using as a proxy the MetS construct, which sought to identify the profile among the subjects that fulfill the Ferranti criteria. A better approach to describe the results could be to display the overall population (n=107) comparing between subjects with MetS (n=64) and without MetS (n=43).

• Another mayor issue is that the manuscript does not fully demonstrate the objective posed by the authors. As for what this reviewer understood, the sought to describe the metabolic and biochemical characteristics of patients with either obesity/overweigh with MetS and without MetS. Then, they sought to evaluate the adipokine and inflammatory profile, for which they found that patients with MetS had increased inflammation markers and lowest Adipo/Lep, which goes in line that MetS is mainly attributable to IR. Then, to prove this, the authors sought to evaluate the use of TGL/HDL and TC/HD-C ratios, for which found a cut-off value to determinate MetS. The structure of the results seems more like the objective was to describe metabolic, inflammatory and adipokine differences on overweight/obese children with and without MetS.

• I would suggest exploring a logistic regression model to assess whether metabolic, inflammatory and adipokine parameters predicts MetS.

• For what it is observed, the higher MetS criteria was hypoalfaproteinemia and abdominal obesity. I would suggest performing a sensitivity analysis to evaluate whether the predictors identified by logistic regression, are really driven by these two main components.

• Please describe the rationale and purpose of including the variables in Table 4.

Discussion

• Overall, a well-written discussion regarding the main findings.

• A consideration would be to evaluate whether the causality of having MetS are all the metabolic dysfunctions caused by environmental or nutritional factors linked by the burden of obesity observed in Mexico.

Conclusion

• Please let the conclusion statement at the end of the manuscript.

Minor comments

• Please round all the result for two decimals

• The p-value in table 1 are not displayed in the pdf proof.

• Please include headings in the methods and result section to separate the main ideas across the manuscript.

• Suggest including the waist-to-height ratio, waist-to-hip ratio, and non-HDL cholesterol estimation as part of table 1.

• The ratios estimated in table 2 could be included as part of table 1.

• In figure 1, please modify hypertension to high-blood pressure, TGL to hypertriglyceridemia, fasting glucose to hyperglycemia, HDL to hypoalfaproteinemia. Also suggest including an additional label with the actual prevalence.

• In figure 3, please include the trending p-value to observe a potential dose-relationship progression with this two markers and Ferranti criteria.

• Please check for typos of TC-HDL-C in table 3 and 4.

**********

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Reviewer #1: Yes: Ashuin Kammar-García

Reviewer #2: Yes: Neftali Eduardo Antonio-Villa, MD

**********

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PLoS One. 2023 Mar 15;18(3):e0281381. doi: 10.1371/journal.pone.0281381.r002

Author response to Decision Letter 0

19 Sep 2022

Reviewer 1

The objective of the study is very ambiguous, "to explore the profile of adipokines, lipoprotein ratios, and inflammatory markers", with cardiometabolic risk, it is interpreted that each of these variables is associated with "cardiometabolic risk", but never cardiometabolic risk was defined, but only mention is made of “cardiometabolic risk factors”, so the objective and the rest of your work must be adjusted to a specific objective of what is sought to be carried out, whether it is the case of associating certain variables independent with cardiometabolic risk factors or with metabolic syndrome, as is.

After discussing the point with the co-authors and reviewing the bibliography, we have decided to change the objective of the work since, as you have rightly commented, we evaluate risk in overweight/obese children with and without MetS.

These changes are observed in the abstract (lines 3-5) and in the introduction (lines 74-75).

The aim of the study was changed to: Describe the metabolic, inflammatory, and adipokine differences in overweight/obese children with and without metabolic syndrome.

However, it should be noted that "cardiometabolic risk" is a term that is used to describe the metabolic alterations, mainly in lipids, that overweight/obese children have and their relationship with the appearance of early atherosclerosis in adulthood.

The type of study is not very clear, it is only mentioned as an observational study, but it really seems that it is a cross-sectional analytical study, so it should be classified that way, or give reasons why it is another type of study.

We appreciate the observation, and this has already been corrected in the material and methods section (line 78)

During the manuscript there is talk about cardiometabolic risk, but in reality only metabolic syndrome was explored, the introduction and discussion should be changed so that the terms and references are appropriate to what was done, following the definitions of the study itself.

The word “cardiometabolic risk” was replace by “metabolic risk”.

However, it should be noted that "cardiometabolic risk" is a term that is used to describe the metabolic alterations, mainly in lipids,associated with hypertension that overweight /obese children have and their relationship with the appearance of early atherosclerosis in adulthood.

It must be defined how each of the presented indices and the units of the variables used for their calculation were calculated, this is because these are their independent variables, and they should be considered with importance throughout the manuscript, and identified as such, so that the reader can understand what the authors want to do.

We appreciate the observation; we have decided to include the way to obtain these ratios (lines 107 - 111). Throughout the manuscript the word "ratio" has been added whenever reference is made to the indices.

This analysis had already been done but there was an omission in the statistical analysis section. This has already been corrected in this material and methods section (lines 145 and 147)

Table 3 mentions the word "prediction" referring to a prediction of metabolic syndrome by lipoprotein indices, but in reality they do not make a prediction, only an estimate of the probability of presenting metabolic syndrome at the same time of the measurement of the variables, and a prediction is related to the probability of estimating the presentation of a future condition. Therefore, I recommend avoiding the term throughout the manuscript.

We appreciate the observation, we agree and have already made the change throughout the document and in the header of Table 3.

The reason why the correlations of the lipoprotein indices with all the variables were made is not understood, it would be interesting if the authors explained them and if it was part of a secondary objective, mention it as such in the text.

This analysis was used with the objective of showing that these indices are indeed useful to suspect or identify the presence of MetS by correlating with the diagnostic, anthropometric and biochemical criteria proposed by the various societies for the diagnosis of MetS. Evaluating the indices is more practical in the daily than determining the levels of PAI-1, MCP-1 and Resistin which are not easy to access in public institutions (lines 366-371).

The title of the study is ambiguous, because once the body of the manuscript is read, it is observed that the population studied is not only obese children but also overweight and obese, in the same way a cardiometabolic risk assessment was not made per se, but only its association with the metabolic syndrome. The function for which the words “adipokines, lipoprotein ratios, and inflammatory markers” are placed in the title is not clear, because the reader understands that these markers are the ones associated with cardiometabolic risk, but in reality it was never carried out, only the lipoprotein indices were associated with metabolic syndrome, the title will have to be restructured, following the recommendations of STROBE, as well as indicating the study population, the dependent and independent variables and the type of study.

Once the STROBE guidelines were reviewed, it was decided to change the title of the document to: Metabolic, inflammatory and adipokine differences on overweight/obese children with and without metabolic syndrome: a cross-sectional study.

There are no references to the definitions presented, the references to each definition must be attached.

The reference used for the diagnosis of metabolic syndrome in children was added, this defines each of the components and cut-off criteria for metabolic syndrome (line 102)

Throughout the manuscript, the terms “abdominal adiposity” and “hypertension” are presented, which are mentioned in the results, but were never defined as such in the methodology, this error must be resolved.

The definitions for the diagnosis of metabolic syndrome according to the Ferranti criteria have been added in the material and methods section (lines 103 - 106).

The symbol ± is used throughout the text to show the standard deviation, please avoid it and place the standard deviation only in parentheses or with the abbreviation "SD".

The expression of the standard deviation has already been unified.

In line 202 of the text the word associated is mentioned to show the relationship between certain variables, I consider the term “correlated” to be more optimal.

We appreciate the comment and believe that the suggested term is more appropriate. The change has been made.

Reviewer 2

Please specify the type of study the authors used (E.g., Cross-sectional recruitment).

We appreciate the observation, and this has already been corrected in the material and methods section (line 78)

Please provide a more detailed context of the center of reference where the authors recruited the patients.

Information from the hospital where the study was conducted has been added (lines 81 – 84)

For this reviewer, it is quite unclear why the authors sought to explore a cut-off point to determine TGL/HDL and TC/HDL with and without MetS. Overall, I could interpret this to explore a tress-hold to define as a proxy of insulin resistance using MetS as their outcome variable in children. Please explain the purpose of this analysis. Furthermore, I would suggest to perform a stratification of this threshold among children and adolescence.

We appreciate the observation, but we consider that stratification by age group (children and adolescents) would not be appropriate because the population is not large enough to make this division.

The authors state that part of the inclusion criteria where patients ≥85th percentile of BMI (definition of overweight), but in table 1 they displayed 21 patients with normal weight (patients <85th percentile?). Please clarify.

In the material and methods section (lines 89-91) the groups studied were adequately described. Overweight and obese children were included and the results were contrasted with a group of normal weight children that was the control group

A main issue with table 1, is that the authors sought to compare the cardiometabolic risk using as a proxy the MetS construct, which sought to identify the profile among the subjects that fulfill the Ferranti criteria. A better approach to describe the results could be to display the overall population (n=107) comparing between subjects with MetS (n=64) and without MetS (n=43).

It has already been described that overweight children present morbidities more frequently than children with normal weight and healthy (Skinner AC, et al 2008). Generally, in studies where differences are established, such as those of the present study, the results are compared separately and are counteracted with a group healthy control to demonstrate the effect of overweight/obesity on the presence of MetS (Gökçe S, et al. 2013; Donma M, et al. 2015).

Skinner AC, Mayer ML, Flower K, Weinberger M. Health status and health care expenditures in a nationally representative sample: how do overweight and healthy-weight children compare? Pediatrics. 2008 Feb;121(2):e269-77. doi: 10.1542/peds.2007-0874. Epub 2008 Jan 14.)

Gökçe S, Atbinici Z, Aycan Z, Cınar HG, Zorlu P. The relationship between pediatric nonalcoholic fatty liver disease and cardiovascular risk factors and increased risk of atherosclerosis in obese children. Pediatr Cardiol. 2013 Feb;34(2):308-15. doi: 10.1007/s00246-012-0447-9. Epub 2012 Aug 9. PMID: 22875138.

Donma M, Karasu, E., Ozdilek, B. et al. CD4+, CD25+, FOXP3+ T Regulatory Cell Levels in Obese, Asthmatic, Asthmatic Obese, and Healthy Children. Inflammation 38, 1473–1478 (2015). https://doi.org/10.1007/s10753-015-0122-4

Another mayor issue is that the manuscript does not fully demonstrate the objective posed by the authors. As for what this reviewer understood, the sought to describe the metabolic and biochemical characteristics of patients with either obesity/overweigh with MetS and without MetS. Then, they sought to evaluate the adipokine and inflammatory profile, for which they found that patients with MetS had increased inflammation markers and lowest Adipo/Lep, which goes in line that MetS is mainly attributable to IR. Then, to prove this, the authors sought to evaluate the use of TGL/HDL and TC/HD-C ratios, for which found a cut-off value to determinate MetS. The structure of the results seems more like the objective was to describe metabolic, inflammatory and adipokine differences on overweight/obese children with and without MetS.

We understand the confusion and after discussing with the co-authors and reviewing the bibliography, we modified the objective of the work in the hope that it would be more understandable and descriptive, in addition to correlating with the results described. These changes are noted in the abstract (lines 3-5) and in the introduction (lines 74-75).

The aim of the study was changed to: Describe the metabolic, inflammatory and adipokine differences in overweight/obese children with and without metabolic syndrome.

I would suggest exploring a logistic regression model to assess whether metabolic, inflammatory and adipokine parameters predicts MetS.

Bivariate and multivariate logistic regression analyses were conducted to determine factors associated with MetS, variables with p<0.05 in bivariate analysis were included in multivariate analysis.

The only variables that showed a correlation were HDL and TGL. This information was added to section Material and Methods (lines 148-150) as well as in Results (lines 183-186) and discussion (lines 327-332).

Please describe the rationale and purpose of including the variables in Table 4

A consideration would be to evaluate whether the causality of having MetS are all the metabolic dysfunctions caused by environmental or nutritional factors linked by the burden of obesity observed in Mexico.

A direct antecedent for this study is the results previously published by the national health survey where the main factors that contribute to obesity are described, these are the same ones that affect the population studied in our report.

Please let the conclusion statement at the end of the manuscript

A conclusion was added at the end of the work (lines 386 - 388)

Please round all the result for two decimals

The expression of the decimals has already been unified

The p-value in table 1 are not displayed in the pdf proof.

The margins of the table were modified for the visualization of the p value in table 1.

Please include headings in the methods and result section to separate the main ideas across the manuscript.

This has been done

Suggest including the waist-to-height ratio, waist-to-hip ratio, and non-HDL cholesterol estimation as part of table 1.

This study only evaluates the components of the metabolic syndrome, so we do not consider it necessary to include the other indices mentioned.

The estimated ratios in table 2 could be included as part of table 1. We think it is a good idea to put the data together. Tables 1 and 2 have been merged.

The ratios estimated in table 2 could be included as part of table 1.

We think it's a good idea to put the information together. Table 1 and 2 have been merged.

In figure 1, please modify hypertension to high-blood pressure, TGL to hypertriglyceridemia, fasting glucose to hyperglycemia, HDL to hypoalfaproteinemia. Also suggest including an additional label with the actual prevalence.

We appreciate the comments, the suggested changes were made including the term "hypoalphaproteinemia", although this is a more descriptive term and it is understood that in the criteria for metabolic syndrome the definition includes low HDL (HDL ≤ 40 mg/dl). We consider your suggestion and make the change.

In figure 3, please include the trending p-value to observe a potential dose-relationship progression with this two markers and Ferranti criteria.

The figure and figure caption have been modified.

Please check for typos of TC-HDL-C in table 3 and 4.

This expression has already been unified.

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PLoS One. doi: 10.1371/journal.pone.0281381.r003

Decision Letter 1

Omar Yaxmehen Bello-Chavolla

17 Nov 2022

PONE-D-22-12285R1Metabolic, inflammatory and adipokine differences on overweight/obese children with and without metabolic syndrome: a cross-sectional studyPLOS ONE

Dear Dr. Cordero Pérez,

Please submit your revised manuscript by Jan 01 2023 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

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Academic Editor

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Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

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Authors should just address the minor concerns put forth by Reviewer 2 prior to acceptance of the manuscript.

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

Reviewer #2: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #1: Yes

Reviewer #2: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

Reviewer #1: Yes

Reviewer #2: No

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

Reviewer #1: Yes

Reviewer #2: Yes

**********

6. Review Comments to the Author

Reviewer #1: My comments were answered correctly and the changes suggested in the manuscript were made. I have no additional comments

Reviewer #2: I would like to congratulate the efforts from the authors to improve the content and address the issues by both revisors in this manuscript. Significant and mayor improvements have been made. I have some specific issues that could be addresses within minor comments.

Results

• Please, move the p-value at the end of each sentence. Eg. “(p=0.002, OR=0.88, 95% CI=0.81-0.95)” should be “(OR=0.88, 95% CI=0.81-0.95, p=0.002)”

• In the Adipo/Lep ratio 6.68(7.89), what does the number in parenthesis represents? If it’s the standard deviations, please clarify.

• In table 3, please round to three decimals the correlation coefficient and the p-value. Additionally, please include the 95% confidence intervals within both types of correlations.

• In Page 19 and Line 349-350: The label “Fig 1. Frequency of the Ferranti criteria used for the diagnosis of metabolic syndrome.” seems out of line.

• In figure 1, please specify in the x-axis that this is the prevalence.

Although the conclusion passed by the authors is supported by their results, it does not go in line with the main objective. In a brief sentence, please give a response to the following question: what were the metabolic, inflammatory and adipokine differences among patients with overweight/obese children with and without MetS? After responding this question, the authors could include the statement in the original conclusion.

**********

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Reviewer #1: Yes: Ashuin Kammar García

Reviewer #2: Yes: Neftali Eduardo Antonio-Villa, MD

**********

PLoS One. 2023 Mar 15;18(3):e0281381. doi: 10.1371/journal.pone.0281381.r004

Author response to Decision Letter 1

30 Dec 2022

• Please, move the p-value at the end of each sentence. Eg. “(p=0.002, OR=0.88, 95% CI=0.81-0.95)” should be “(OR=0.88, 95% CI=0.81-0.95, p=0.002)”

We appreciate the observation, and this has already been corrected in the lineS 180 to 182

• In the Adipo/Lep ratio 6.68(7.89), what does the number in parenthesis represents? If it’s the standard deviations, please clarify.

We appreciate the observation, and this has been clarify in the Table 1.

• In table 3, please round to three decimals the correlation coefficient and the p-value. Additionally, please include the 95% confidence intervals within both types of correlations.

The expression of the decimals has already been unified, and 95% confidence intervals have been added.

• In Page 19 and Line 349-350: The label “Fig 1. Frequency of the Ferranti criteria used for the diagnosis of metabolic syndrome.” seems out of line.

Figure captions were inserted immediately after the first paragraph in which the figure was cited, and these captions were aligned to the text.

• In figure 1, please specify in the x-axis that this is the prevalence.

We appreciate the observation, and this has already been corrected

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PLoS One. doi: 10.1371/journal.pone.0281381.r005

Decision Letter 2

Omar Yaxmehen Bello-Chavolla

23 Jan 2023

Metabolic, inflammatory and adipokine differences on overweight/obese children with and without metabolic syndrome: a cross-sectional study

PONE-D-22-12285R2

Dear Dr. Cordero Pérez,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

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Kind regards,

Omar Yaxmehen Bello-Chavolla, MD, PhD

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

All comments have been adequately addressed.

Reviewers' comments:

PLoS One. doi: 10.1371/journal.pone.0281381.r006

Acceptance letter

Omar Yaxmehen Bello-Chavolla

26 Jan 2023

PONE-D-22-12285R2

Metabolic, inflammatory and adipokine differences on overweight/obese children with and without metabolic syndrome: a cross-sectional study

Dear Dr. Cordero-Pérez:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

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Supplementary Materials

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Data Availability Statement

All relevant data are within the paper and its Supporting Information files.

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PERMALINK

Metabolic, inflammatory and adipokine differences on overweight/obese children with and without metabolic syndrome: A cross-sectional study

Idalia Cura–Esquivel

Marlene Marisol Perales-Quintana

Liliana Torres-González

Katia Guzmán-Avilán

Linda Muñoz-Espinosa

Paula Cordero-Pérez

Roles

Abstract

Background

Methods

Results

Conclusion

Introduction

Material and methods

Design of the study

Study population

Definitions

Data collection

Biochemical and inflammatory parameters

Statistical analysis

Results

Sample characteristics

MetS diagnosis

Fig 1. Frequency of the Ferranti criteria used for the diagnosis of metabolic syndrome.

Anthropometric, biochemical, adipokine, and cytokine characteristics

Table 1. Anthropometric and laboratory parameters of the children.

Fig 2. ROC curves for TGL/HDL-C and TC/HDL-C ratios in predicting metabolic syndrome in children with overweigh or obesity.

Table 2. AUCROC and cutoff values of TGL/HDL-C and TC/HDL-C ratios to estimate probability of metabolic syndrome in obese and overweight children.

Table 3. Correlation between TGL/HDL-C and TC/HDL-C ratios with variables.

Fig 3. Levels of TGL/HDL-C and TC/HD-C ratios in relation to the number of fulfilled Ferranti criteria.

Discussion

Supporting information

Acknowledgments

Data Availability

Funding Statement

References

Decision Letter 0

Omar Yaxmehen Bello-Chavolla

Roles

Author response to Decision Letter 0

Decision Letter 1

Omar Yaxmehen Bello-Chavolla

Roles

Author response to Decision Letter 1

Decision Letter 2

Omar Yaxmehen Bello-Chavolla

Roles

Acceptance letter

Omar Yaxmehen Bello-Chavolla

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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