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Uric Acid Index as a Prognostic Factor in Adolescents with Obesity after Lifestyle Intervention
ORIGINAL ARTICLES Resistin/Uric Acid Index as a Prognostic Factor in Adolescents with Obesity after Lifestyle Intervention Jessie Zurita-Cruz, MD, PhD1, Miguel Villasis-Keever, MD, MSc2, Leticia Manuel-Apolinar, PhD3, Leticia Damasio-Santana, RA3, Guillermo Hideo Wakida-Kusunoki, MD4, Michel Padilla-Rojas, MD4, and Cesar Maldonado-Rivera, MD4 Objective To evaluate the resistin/uric acid index as a prognostic factor associated with body mass index (BMI) z-score change after 1 year of lifestyle interventions for obesity. Study design In this prospective cohort study, we included 102 adolescents with overweight or obesity (BMI ³85th percentile). Weight and height were measured at the start of the lifestyle change intervention and 12 months later. Serum levels of resistin and uric acid were quantified at the beginning of the intervention. The intervention consisted of nutrition education, exercise, and physical activity promotion. Results The sample included 54 girls and 48 boys; the median age was 11 years (range 10-16 years). The BMI z-score decreased during follow-up (median BMI z-score at baseline was 1.81 vs 1.70 after 1 year, P < .001). The resistin/uric acid index was positively correlated with BMI z-score change (r = 0.27, P < .01). In the linear regression analysis, the resistin/uric acid index was significantly associated with BMI z-score modification at the 12-month follow-up (b = 0.17; 95% CI 0.08-0.26; P < .01). Conclusions The resistin/uric acid index can be considered a prognostic factor for identifying adolescents with overweight or obesity with a greater probability of improving their BMI. This index could help establish different interventions for adolescents with overweight and obesity; however, additional studies are needed to confirm the usefulness of this index. (J Pediatr 2019;-:1-5).
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dipose tissue is an endocrine organ that produces proteins with biological activity, including adipocytokines, which reflect a proinflammatory environment that is present in insulin resistance.1 Resistin has been linked to obesity and associated comorbidities2; studies in humans have reported significantly greater resistin levels in individuals with obesity than in subjects of normal weight. In addition, a positive correlation between resistin concentrations and the presence of cardiometabolic alterations has been identified.3,4 Uric acid also has been linked to obesity. It is known that uric acid produces endothelial dysfunction and vascular damage via several mechanisms; a reduction in the bioavailability of nitric oxide and activation of the angiotensin system are the main mechanisms. Uric acid concentrations correlate with intima-media thickness in patients with hypertension, suggesting that uric acid participates in the process of atherosclerosis.5,6 As in adults, a positive association between high levels of uric acid and the presence of systemic hypertension has been demonstrated in the pediatric population.7 In addition, an increase in uric acid can cause weight gain and obesity by accelerating lipogenesis in the liver and peripheral tissue,8,9 as well as inducing proinflammatory changes in adipocytes that are similar to those observed in subjects with prediabetes.8 The objective of this study was to identify whether the resistin/uric acid index may be a prognostic factor associated with body mass index (BMI) z-score change after 1 year of lifestyle interventions in adolescents with overweight and obesity.
Methods The present study was a prospective cohort study of adolescents with overweight or obesity who were included in a pilot program called the “Pediatric Health Clinic,” which was carried out between January 2016 and January 2017, at the High Specialty South Central Hospital of Petroleos Mexicanos in Mexico City. Patients 10-17 years of age with a diagnosis of overweight or obesity, based on a BMI ³85th percentile, were included. Exclusion criteria were the presence of any associated condition or use of medications that potentially influenced weight or appetite (genetic syndromes, use of steroids, fluoxetine, From the Unit of Nutrition, Unit of Medical Research in 1
BMI BMI z-score DBMI z-score PEMEX
Body mass index Body mass index z-score Change in body mass index z-score Petroleos Mexicanos
2
Clinical Epidemiology, and 3Department of Endocrinology Research, Hospital of Medical Specialties, National Medical Center XXI Century, Instituto Mexicano del Seguro Social; and 4Pediatrics Service, South Central Hospital of High Specialty of Petroleos Mexicanos, Health Services of Petroleos xico Mexicanos, Mexico City, Me The authors declare no conflicts of interest. 0022-3476/$ - see front matter. ª 2019 Elsevier Inc. All rights reserved. https://doi.org/10.1016/j.jpeds.2019.12.006
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insulin sensitizers, anorexigenics, or intestinal fat absorption inhibitors) or refusal to participate. The research protocol was approved by the hospital’s research and ethics committee. Parents and patients signed informed consent and assent forms, respectively. The anthropometric variables were weight, height, and waist circumference. These measurements were performed using a seca-brand scale stadiometer and tape measure (seca, Chino, California). The anthropometric measurements were collected in the morning by a certified nutritionist while the participants were fasting and wearing underwear with a light dressing gown without shoes. These measurements were taken at the beginning of the study and at 6 and 12 months of follow-up. The “Pediatric Health Clinic” was a program for pediatric patients with overweight or obesity that included individuals between 10 and 16 years of age. The patients included were children of workers of the Petroleos de Mexico (PEMEX) company, who have a medium socioeconomic level (deciles 5 and 6 of the Mexican net household income) and reside in Mexico City. The High Specialty South Central Hospital is exclusive to PEMEX workers and their families, who do not pay for the health services provided. As part of the programs to improve the health of its workers, PEMEX has programs related to overweight/obesity, which include their children. Therefore, it is unlikely that these families had economic problems that would limit them to follow dietary recommendations. The program was ambulatory and was carried out by a multidisciplinary team that included pediatricians, pediatric endocrinologists, pediatric gastroenterologists, psychologists, a physical trainer, and nutritionists. An initial assessment was made that included the participants’ physical, medical, and psychological histories and eating and exercise habits. Based on this information, a complete diagnosis was made, including the comorbidities associated with obesity, and a nutrition plan was implemented. The clinic employed cognitive–behavioral techniques to encourage the participants to incorporate self-control, nutritional education, and promotion of physical activity into daily living, which includes limiting screen time, participating in outdoor activities, and scheduling time for sports activities. The program required the active participation of parents, grandparents, and caregivers. The caloric requirements of each participant were estimated based on resting energy expenditure (calculated using the equation from the Institute of Medicine that is used to predict the Dietary Reference Intake) and physical activity levels. Nutritionists designed their diets on daily calorie dietary requirements. The diets consisted of 50% carbohydrates, 20% protein, and 30% total fat; additionally, the diets were rich in fruits, vegetables, whole grains, and low-fat dairy products and low in cholesterol and refined grains. In addition, nutritionists provided nutrition lectures. The patients were followed biweekly for the first 6 months and then once a month until completion of the one-year follow-up. 2
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Blood samples were obtained after a minimum of 12 hours of fasting between 7:00 and 8:00 a.m. Serum samples were frozen at 20 C until analysis. Glucose, uric acid, triglycerides, and high-density lipoprotein cholesterol were determined by a colorimetric enzymatic method (IN-REACT, SPIM120, Spinreact SAU, Girona, Spain). Low-density lipoprotein cholesterol was calculated with the Friedewald formula. Resistin, leptin, leptin receptor, and adiponectin levels were measured using ELISAs: Human Resistin DY1359, Human Adiponectin/Acrp30 Duo Set Elisa DY1065, Human Leptin Duo Set Elisa DY398, and Human Leptin Receptor (Leptin R) Duo Set Elisa DY389 (R&D Systems, Minneapolis, Minnesota). Plates were read using an ELISA microplate reader (Labsystems Multiskan EX; MTX Labsystems, Inc, Vienna, Virginia) and were assessed in duplicate according to the manufacturer’s instructions. Intraand interassay coefficients of variation <7% were considered. A standard curve also was included within each assay. Overweight was defined as BMI between the ³85th and <95th percentiles, and obesity was defined as BMI ³95th percentile. Definitions for the biochemical profile were impaired fasting glucose (plasma glucose ³100 mg/dL),10 hypertriglyceridemia (plasma triglycerides ³150 mg/dL),11 hypoalphalipoproteinemia (high-density lipoprotein cholesterol <50 mg/dL, except in boys aged 15-19 years, in whom the cutoff was <45 mg/dL),12 elevated low-density lipoprotein cholesterol (>100 mg/dL), and high uric acid (>6 mg/dL).13 The data were analyzed using the statistical package Stata, version 11.0 (StataCorp LLC, College Station, Texas). Because the data were not normally distributed, the data were normalized through natural logarithmic transformation. Thus, for the quantitative variables, the mean and SD were calculated. The paired Student t test was used to analyze the change between the baseline z-score and the BMI z-score after 12 months of follow-up. The change in BMI z-score (DBMI z-score) was calculated by subtracting the BMI z-score at 12 months from that at baseline. Patients were divided into groups by those with and without a decrease in BMI z-score (decrease of ³0.5 in BMI z-score) after 12 months of follow-up. These 2 groups were compared in relation to the baseline anthropometric and biochemical variables. The correlations of the levels of uric acid, resistin, leptin, leptin adiponectin receptor, leptin/adiponectin index, and resistin/uric acid index with BMI z-score were determined using the Pearson coefficient. Linear regression models were performed to identify the independent variables associated with DBMI z-score and Dweight. The models comply with the assumptions of independence, heterogeneity, normality, and noncollinearity.
Results We identified 147 adolescents with overweight or obesity; 27 patients were not included because 10 were being treated with Zurita-Cruz et al
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Table I. Baseline comparison of anthropometric and clinical characteristics of study subjects BMI z-score reduction Characteristics
Total n = 102
With n = 68
Without n = 34
Age, y Sex, n of boys (%) Weight, kg Height, cm BMI, kg/m2 BMI, z-score Pubertal development (Tanner), n (%) 1 2 3 4 5
11.77 2.2 48 (47.1) 57.4 23.1 146.1 18.7 25.56 4.7 1.86 0.39
11.97 2.3 28 (41.1) 57.8 23.7 146.5 16.3 25.65 4.8 1.87 0.40
11.29 1.9 20 (58.8) 56.8 22.1 145.5 19.9 25.39 4.7 1.83 0.38
24 (23.53) 22 (21.57) 22 (21.57) 32 (31.37) 2 (1.96)
16 (23.53) 14 (20.59) 14 (20.59) 22 (32.35) 2 (2.94)
P value .14 .09 .72 .94 .71 .60 .87
8 (23.53) 8 (23.53) 8 (23.53) 10 (29.41) -
Values are mean SD.
metformin, 5 used steroids, and 12 did not agree to participate. Ultimately, a total of 102 patients were included, after excluding 18 more patients who did not comply with the 12-month follow-up. The ages ranged from 10 to 16 years (median 11 years), and 58.6% were female. Approximately 82.2% of patients had already entered puberty. The median baseline BMI z-score was 1.81; 31.3% (n = 32) of participants were overweight, and the rest were obese (Table I). At the beginning of the study, 7.8% (n = 8) of participants had impaired fasting glucose, 27.4% (n = 28) had elevated uric acid, 27.4% (n = 28%) had hypertriglyceridemia, 68.6% (n = 70) had hypoalphalipoproteinemia, and 36.2% (n = 36) had elevated low-density lipoprotein (LDL) (Tables I and II). At the end of the follow-up, increases in height (baseline 146.1 18.7 cm vs follow-up 149.4 18.0 cm; P < .001) and weight (baseline 57.4 23.1 kg vs follow-up 60.3 22.4 kg; P < .001) were observed, with a significant decrease in BMI z-score (baseline 1.86 0.39 vs follow-up 1.77 0.39; P = .002). Patients were divided into 2 groups: those with a reduced BMI z-score (n = 68) and those with no reduc-
tion in BMI z-score (n = 34). As observed in Tables I and II, there was no difference between the groups in the baseline clinical characteristics. However, analysis of the biochemical measures at the beginning of the follow-up showed that patients who did not lose weight after 1 year had lower serum levels of uric acid and free leptin in addition to a greater resistin/uric acid index and increased levels of LDL and leptin receptor (Tables I and II). An exploratory analysis was carried out to identify which biochemical studies at the time of the start of the intervention could predict the modification of BMI z-score and weight after 1 year of follow-up (DBMI z-score and Dweight), for which 2 indices were generated: the leptin/uric acid index and the resistin/uric acid index. These data demonstrated that both DBMI z-score and Dweight had a statistically significant correlation with resistin/uric acid index (Figure and Table III [available at www.jpedscom]). In addition, patients who did not lose weight had greater values of the resistin/uric acid index than those who lost weight (resistin/uric acid index 3.07 0.47 vs 2.7 0.93; P = .02) (Table II).
Table II. Baseline metabolic characteristics of study subjects BMI z-score reduction Characteristics
Total n = 102
With n = 68
Without n = 34
P value
Glucose, mg/dL Uric acid, mg/dL Triglycerides, mg/dL HDL cholesterol, mg/dL VLDL cholesterol, mg/dL LDL cholesterol, mg/dL Leptin, ng/mL Adiponectin, mg/mL Resistin, pg/mL Leptin receptor Free leptin Resistin/uric acid index Leptin/uric acid index
89.0 8.2 6.0 1.5 124.8 64.6 42.8 10.9 24.8 13 101.0 52.4 12.0 2.7 6.6 2.1 15.9 2.8 12.6 7.4 1.9 2.7 2.9 0.8 2.1 0.6
89.2 9.5 6.3 1.7 125.0 72.3 42.1 11.5 24.7 14.6 93.5 25.6 12.2 2.3 6.5 1.7 15.8 3.2 11.6 7.6 2.3 3.1 2.7 0.9 2.1 0.6
88.7 4.5 5.3 0.7 124.5 46.6 44.0 9.4 25.0 9.3 116.1 82.0 11.4 3.3 6.8 2.8 16.1 1.4 14.6 6.7 1.1 0.8 3.0 0.4 2.2 0.9
<.001 .51 .20 .50 .01 .06 .23 .34 .02 .01 .02 .41
HDL, high-density lipoprotein; LDL, low-density lipoprotein; VLDL, very-low-density lipoprotein. Values are mean SD.
Resistin/Uric Acid Index as a Prognostic Factor in Adolescents with Obesity after Lifestyle Intervention
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Figure. Correlation between resistin levels, uric acid levels, resistin/uric acid index, and DBMI z-score–Dweight.
The multivariate analysis revealed that both the resistin/ uric acid index and male sex were independent variables that were associated with an increase in DBMI z-score (resistin/uric acid index beta coefficient 0.17 [95% CI 0.08-0.23] and male sex beta coefficient 0.15 [95% CI 0.04-0.26]) and Dweight (resistin/uric acid index beta coefficient 1.2 [95% CI 0.13-2.28] and male sex beta coefficient 2.89 [95% CI 1.1-4.68]) after 12 months of follow-up (Table IV). When we adjusted for puberty and sex (Tables V and VI [available at www.jpeds.com]), the model including only pubescent male subjects (n = 34) demonstrated that the beta coefficient of the resistin/uric acid index was greater than that in the model that included all subjects to predict an increase in DBMI z-score and Dweight (DBMI z-score = resistin/uric acid index beta coefficient 0.20; P < .001 and Dweight = resistin/uric acid index beta coefficient 4.88; P = .001). This supports the concept that this index is useful for predicting the modification of BMI z-score and weight in the group of pubescent male subjects.
Table IV. Linear regression analyses for DBMI z-score and Dweight (kg) in 102 patients with obesity Analyses Linear regression analysis for D BMI z-score Resistin/uric acid index Age, y Puberty presence Sex male Basal BMI z-score >2 Linear regression analysis for D weight (kg) Resistin/uric acid index Age, y Puberty presence Sex male Basal BMI z-score >2 4
95% CI
P value
0.17 0.00 0.10 0.15 0.10
0.10-0.23 0.02 to 0.02 0.02 to 0.24 0.04-0.26 0.20 to 0.00
<.001 .954 .108 .006 .055
1.20 1.20 2.10 2.89 0.19
0.13-2.28 1.65 to –0.76 0.02 to 4.23 1.10-4.68 1.45 to 1.84
.028 <.001 .052 .002 .818
Coefficient
Discussion The resistin/uric acid index may be useful for identifying adolescents who should receive other interventions, such as pharmacologic interventions, from the beginning, in addition to the recommended lifestyle interventions. Elevated levels of uric acid (>5.5 mg/dL) in adolescents have been associated with high blood pressure (OR 2.03, 95% CI 1.38-3.00).7 Elevated levels of uric acid are related to the presence of metabolic syndrome.6 In our study, we observed that in adolescents with obesity, greater levels of uric acid before dietary interventions were associated with a decrease in BMI z-score, supporting the idea that levels of uric acid may be a marker for identifying changes in metabolism after interventions to improve the nutritional status of adolescents with overweight/obesity. Resistin is a hormone produced by adipocytes that acts in peripheral tissues to influence insulin sensitivity and is suggested to have a proinflammatory effect.14 Resistin is related to the components of metabolic syndrome.15 Notably, in the present study, patients with obesity who did not lose weight had greater levels of resistin at the start of lifestyle interventions. This resistin/acid uric index could be used to differentiate the patients who had a decreased BMI z-score with interventions from those who did not exhibit changes in their BMI z-score. Thus, adolescents who improved their nutritional status had lower values of the resistin/uric acid index (2.7 0.9) than those whose BMI z-score scores remained the same or worsened (3.0 0.4). In male adolescents, the index was more reliable, as shown in the multivariate analysis (Table IV). The theoretical basis of the inverse relationship between uric acid and resistin may be multifactorial. Uric acid levels are significantly influenced by the type of diet in patients with obesity,16,17 which could mean that patients with obesity and elevated uric acid are more likely to lose weight after Zurita-Cruz et al
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changing their diet. Resistin expression also has been identified in the nonadipocyte stromal vascular fraction of white adipose tissue, fibrotic liver, and atherosclerotic lesions. Resistin upregulates the expression of proinflammatory cytokines such as tumor necrosis factor-a, interleukin-6, and interleukin-12.18,19 Patients with high levels of resistin were less likely to lose weight, despite making changes in diet and increasing physical activity. The resistin/uric acid index may be a marker that would help clinicians to individualize the management of children and adolescents with overweight and obesity. To achieve this goal, based on receiver operating characteristic curves, we found that the optimal cutoff point for the resistin/uric acid index was ³2.72 (95% CI 2.57-2.89). At this cutoff point, the sensitivity was 82.3%, and the specificity was 52.9% for identifying those adolescents with obesity who did not reduce their BMI z-score after 12 months of follow-up. However, the same cutoff value ³2.72 (95% CI 2.49-2.96) only among male subjects decreased specificity to 70% but substantially increased the specificity to 92.8%. However, these cutoff values should be taken with caution, because overlapping values between categories (with and without a decrease in BMI z-score) can limit their clinical usefulness. More studies are needed to validate our results. Likewise, we recognize the limitations of the study. The sample size was small and the follow-up time was relatively short; it is possible that during a longer follow-up period, the resistin/ uric acid values might behave differently. In addition, we did not objectively measure how our intervention modified the adolescents’ lifestyles, so it is possible that the resistin/ uric acid index can be more useful in those who adhere to the recommendations. After performing a multidisciplinary intervention involving lifestyle changes in adolescents with overweight and obesity, we conclude that the resistin/uric acid index seems to be a prognostic marker in the modification of BMI z-score 12 months. n Submitted for publication Aug 1, 2019; last revision received Dec 3, 2019; accepted Dec 4, 2019. Reprint requests: Miguel A. Villasis-Keever, Unit of Medical Research in Clinical Epidemiology, National Medical Center XXI Century, Instituto Mexicano del Seguro Social. Av. Cuauhtemoc 330, Col. Doctores, CP 06720, xico City, Me xico. E-mail: [email protected] Me
References 1. Shimobayashi M, Albert V, Woelnerhanssen B, Frei IC, Weissenberger D, Meyer-Gerspach AC, et al. Insulin resistance causes inflammation in adipose tissue. J Clin Invest 2018;128:1538-50. 2. Szalowska E, Elferink M, Hoek A, Groothuis G, Vonk RJ. Resistin is more abundant in liver than adipose tissue and is not up-
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regulated by lipopolysaccharide. J Clin Endocrinol Metab 2009;94: 3051-7. Piestrzeniewicz K, quczak K, Komorowski J, Maciejewski Wika J, Goch JH. Resistin increases with obesity and atherosclerotic risk factors in patients with myocardial infarction. Metabolism 2008;57:488-93. Yannakoulia M, Yiannakouris N, Bl€ uher S, Matalas AL, Klimis-Zacas D, Mantzoros CS. Body fat mass and macronutrient intake in relation to circulating soluble leptin receptor, free leptin index, adiponectin, and resistin concentrations in healthy humans. J Clin Endocrinol Metab 2003;88:1730-6. Hsia S, Chou I, Kuo C, See L, Huang J, Yu KH, et al. Survival impact of serum uric acid levels in children and adolescents. Rheumatol Int 2013;33:2797-802. Safiri S, Qorbani M, Heshmat R, Tajbakhsh R, Eslami A, Babaki S, et al. Association of serum uric acid with cardiometabolic risk factors and metabolic syndrome in Iranian adolescents: the CASPIAN-III Study. Iran J Kidney Dis 2016;10:6-10. Loeffler LF, Navas-Acien A, Brady TM, Miller ER 3rd, Fadrowski JJ. Uric acid level and elevated blood pressure in US Adolescents: National Health and Nutrition Examination Survey, 1999-2006. Hypertension 2012;59:811-7. Sautin YY, Nakagawa T, Zharikov S, Johnson RJ. Adverse effects of the classic antioxidant uric acid in adipocytes: NADPH oxidasemediated oxidative/nitrosative stress. Am J Physiol Cell Physiol 2007;293:C584-96. Johnson RJ, Lanaspa MA, Gaucher E. Uric acid: a danger signal from the RNA world that may have a role in the epidemic of obesity, metabolic syndrome, and cardiorenal disease: evolutionary considerations. Semin Nephrol 2011;31:394-9. Zimmet P, Alberti G, Kaufman F, Tajima N, Silink M, Arslanian S, et al. The metabolic syndrome in children and adolescents. Lancet 2007;369: 2059-61. Pollack MM, Holubkov R, Funai T, Dean JM, Berger JT, Wessel DL, et al. The pediatric risk of mortality score: update 2015. Pediatr Crit Care Med 2016;17:2-9. De Ferranti SD, Gauvreau K, Ludwig DS, Neufeld EJ, Newburger JW, Rifai N. Prevalence of the metabolic syndrome in American adolescents: findings from the Third National Health and Nutrition Examination Survey. Circulation 2004;1:2494-7. De Miguel E, Puig JG, Castillo C, Peiteado D, Torres RJ, Martin-Mola E. Diagnosis of gout in patients with asymptomatic hyperuricaemia: a pilot ultrasound study. Ann Rheum Dis 2012;71:157-8. McTernan PG, Kusminski CM, Kumar S. Resistin. Curr Opin Lipidol 2006;17:170-5. Mostafazadeh M, Haiaty S, Rastqar A, Keshvari M. Correlation between resistin level and metabolic syndrome component: a review. Horm Metab Res 2018;50:521-36. Becerra-Tomas N, Mena-Sanchez G, Dıaz-L opez A, Martınez Babio N, Corella D, et al. Cross-sectional association beGonzalez MA, tween non-soy legume consumption, serum uric acid and hyperuricemia: the PREDIMED-Plus study. Eur J Nutr 2019. in press. Jakse B, Jakse B, Pajek M, Pajek J. Uric acid and plant-based nutrition. Nutrients 2019;11:E1736. Jamaluddin MS, Weakley SM, Yao Q, Chen C. Resistin: functional roles and therapeutic considerations for cardiovascular disease. Cardiovasc Res 2006;69:76-85. Bokarewa M, Nagaev I, Dahlberg L, Smith U, Tarkowski A. Resistin, an adipokine with potent proinflammatory properties. J Immunol 2005;174:5789-95.
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Table VI. Linear regression analyses for Dweight (kg)
Table III. Correlation between basal metabolic variables and DBMI z-score–Dweight
Analyses
r Characteristics Uric acid, mg/dL Leptin, ng/mL Adiponectin, mg/mL Resistin, pg/mL Leptin receptor, ng/mL Free leptin, ng/mL Resistin/uric acid index Leptin/uric acid index
D BMI score*
Dweight
0.401† 0.265† 0.032 0.264† 0.123 0.127 0.407† 0.070
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0.398† 0.178 0.061 0.060 0.334† 0.377† 0.315† 0.139
*DBMI z-score = 12 months’ BMI z-score–baseline BMI z-score. †P < .01.
Linear regression analysis in pubertal patients (n = 78) Resistin/uric acid index Age, y Sex male Basal BMI z-score >2 Linear regression analysis in female pubertal patients (n = 44) Resistin/uric acid index Age, y Basal BMI z-score >2 Linear regression analysis in male pubertal patients (n = 34) Resistin/uric acid index Age, y Basal BMI z-score >2
95% CI
P value
3.08 0.93 3.48 1.39
1.27-4.89 1.44 to -0.42 1.17-5.79 3.44 to 0.66
.001 .001 .004 .372
2.21 0.86 0.27
0.17-4.24 1.48 to 0.25 2.36 to 2.90
.034 .007 .836
4.88 0.81 3.27
1.44-8.31 1.74 to 0.11 6.77 to 0.22
.007 .084 .065
Coefficient
Table V. Linear regression analysis for DBMI z-score Analyses Linear regression analysis in pubertal patients (n = 78) Resistin/uric acid index Age, y Sex male Basal BMI z-score >2 Linear regression analysis in female pubertal patients (n = 44) Resistin/uric acid index Age, y Basal BMI z-score >2 Linear regression analysis in male pubertal patients (n = 34) Resistin/uric acid index Age, y Basal BMI z-score >2 5.e1
Coefficient
95% CI
P value
0.20 0.008 0.14 0.07
0.90-0.31 0.02 to 0.03 0.003-0.28 0.20 to 0.05
<.001 .601 .045 .244
0.18 0.009 0.10
0.0006-0.36 0.04 to 0.06 0.33 to 0.11
.042 .716 .339
0.22 0.0006 0.04
0.09-0.35 0.03 to 0.03 0.17 to 0.08
.001 .970 .499
Zurita-Cruz et al
A
dipose tissue is an endocrine organ that produces proteins with biological activity, including adipocytokines, which reflect a proinflammatory environment that is present in insulin resistance.1 Resistin has been linked to obesity and associated comorbidities2; studies in humans have reported significantly greater resistin levels in individuals with obesity than in subjects of normal weight. In addition, a positive correlation between resistin concentrations and the presence of cardiometabolic alterations has been identified.3,4 Uric acid also has been linked to obesity. It is known that uric acid produces endothelial dysfunction and vascular damage via several mechanisms; a reduction in the bioavailability of nitric oxide and activation of the angiotensin system are the main mechanisms. Uric acid concentrations correlate with intima-media thickness in patients with hypertension, suggesting that uric acid participates in the process of atherosclerosis.5,6 As in adults, a positive association between high levels of uric acid and the presence of systemic hypertension has been demonstrated in the pediatric population.7 In addition, an increase in uric acid can cause weight gain and obesity by accelerating lipogenesis in the liver and peripheral tissue,8,9 as well as inducing proinflammatory changes in adipocytes that are similar to those observed in subjects with prediabetes.8 The objective of this study was to identify whether the resistin/uric acid index may be a prognostic factor associated with body mass index (BMI) z-score change after 1 year of lifestyle interventions in adolescents with overweight and obesity.
Methods The present study was a prospective cohort study of adolescents with overweight or obesity who were included in a pilot program called the “Pediatric Health Clinic,” which was carried out between January 2016 and January 2017, at the High Specialty South Central Hospital of Petroleos Mexicanos in Mexico City. Patients 10-17 years of age with a diagnosis of overweight or obesity, based on a BMI ³85th percentile, were included. Exclusion criteria were the presence of any associated condition or use of medications that potentially influenced weight or appetite (genetic syndromes, use of steroids, fluoxetine, From the Unit of Nutrition, Unit of Medical Research in 1
BMI BMI z-score DBMI z-score PEMEX
Body mass index Body mass index z-score Change in body mass index z-score Petroleos Mexicanos
2
Clinical Epidemiology, and 3Department of Endocrinology Research, Hospital of Medical Specialties, National Medical Center XXI Century, Instituto Mexicano del Seguro Social; and 4Pediatrics Service, South Central Hospital of High Specialty of Petroleos Mexicanos, Health Services of Petroleos xico Mexicanos, Mexico City, Me The authors declare no conflicts of interest. 0022-3476/$ - see front matter. ª 2019 Elsevier Inc. All rights reserved. https://doi.org/10.1016/j.jpeds.2019.12.006
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insulin sensitizers, anorexigenics, or intestinal fat absorption inhibitors) or refusal to participate. The research protocol was approved by the hospital’s research and ethics committee. Parents and patients signed informed consent and assent forms, respectively. The anthropometric variables were weight, height, and waist circumference. These measurements were performed using a seca-brand scale stadiometer and tape measure (seca, Chino, California). The anthropometric measurements were collected in the morning by a certified nutritionist while the participants were fasting and wearing underwear with a light dressing gown without shoes. These measurements were taken at the beginning of the study and at 6 and 12 months of follow-up. The “Pediatric Health Clinic” was a program for pediatric patients with overweight or obesity that included individuals between 10 and 16 years of age. The patients included were children of workers of the Petroleos de Mexico (PEMEX) company, who have a medium socioeconomic level (deciles 5 and 6 of the Mexican net household income) and reside in Mexico City. The High Specialty South Central Hospital is exclusive to PEMEX workers and their families, who do not pay for the health services provided. As part of the programs to improve the health of its workers, PEMEX has programs related to overweight/obesity, which include their children. Therefore, it is unlikely that these families had economic problems that would limit them to follow dietary recommendations. The program was ambulatory and was carried out by a multidisciplinary team that included pediatricians, pediatric endocrinologists, pediatric gastroenterologists, psychologists, a physical trainer, and nutritionists. An initial assessment was made that included the participants’ physical, medical, and psychological histories and eating and exercise habits. Based on this information, a complete diagnosis was made, including the comorbidities associated with obesity, and a nutrition plan was implemented. The clinic employed cognitive–behavioral techniques to encourage the participants to incorporate self-control, nutritional education, and promotion of physical activity into daily living, which includes limiting screen time, participating in outdoor activities, and scheduling time for sports activities. The program required the active participation of parents, grandparents, and caregivers. The caloric requirements of each participant were estimated based on resting energy expenditure (calculated using the equation from the Institute of Medicine that is used to predict the Dietary Reference Intake) and physical activity levels. Nutritionists designed their diets on daily calorie dietary requirements. The diets consisted of 50% carbohydrates, 20% protein, and 30% total fat; additionally, the diets were rich in fruits, vegetables, whole grains, and low-fat dairy products and low in cholesterol and refined grains. In addition, nutritionists provided nutrition lectures. The patients were followed biweekly for the first 6 months and then once a month until completion of the one-year follow-up. 2
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Blood samples were obtained after a minimum of 12 hours of fasting between 7:00 and 8:00 a.m. Serum samples were frozen at 20 C until analysis. Glucose, uric acid, triglycerides, and high-density lipoprotein cholesterol were determined by a colorimetric enzymatic method (IN-REACT, SPIM120, Spinreact SAU, Girona, Spain). Low-density lipoprotein cholesterol was calculated with the Friedewald formula. Resistin, leptin, leptin receptor, and adiponectin levels were measured using ELISAs: Human Resistin DY1359, Human Adiponectin/Acrp30 Duo Set Elisa DY1065, Human Leptin Duo Set Elisa DY398, and Human Leptin Receptor (Leptin R) Duo Set Elisa DY389 (R&D Systems, Minneapolis, Minnesota). Plates were read using an ELISA microplate reader (Labsystems Multiskan EX; MTX Labsystems, Inc, Vienna, Virginia) and were assessed in duplicate according to the manufacturer’s instructions. Intraand interassay coefficients of variation <7% were considered. A standard curve also was included within each assay. Overweight was defined as BMI between the ³85th and <95th percentiles, and obesity was defined as BMI ³95th percentile. Definitions for the biochemical profile were impaired fasting glucose (plasma glucose ³100 mg/dL),10 hypertriglyceridemia (plasma triglycerides ³150 mg/dL),11 hypoalphalipoproteinemia (high-density lipoprotein cholesterol <50 mg/dL, except in boys aged 15-19 years, in whom the cutoff was <45 mg/dL),12 elevated low-density lipoprotein cholesterol (>100 mg/dL), and high uric acid (>6 mg/dL).13 The data were analyzed using the statistical package Stata, version 11.0 (StataCorp LLC, College Station, Texas). Because the data were not normally distributed, the data were normalized through natural logarithmic transformation. Thus, for the quantitative variables, the mean and SD were calculated. The paired Student t test was used to analyze the change between the baseline z-score and the BMI z-score after 12 months of follow-up. The change in BMI z-score (DBMI z-score) was calculated by subtracting the BMI z-score at 12 months from that at baseline. Patients were divided into groups by those with and without a decrease in BMI z-score (decrease of ³0.5 in BMI z-score) after 12 months of follow-up. These 2 groups were compared in relation to the baseline anthropometric and biochemical variables. The correlations of the levels of uric acid, resistin, leptin, leptin adiponectin receptor, leptin/adiponectin index, and resistin/uric acid index with BMI z-score were determined using the Pearson coefficient. Linear regression models were performed to identify the independent variables associated with DBMI z-score and Dweight. The models comply with the assumptions of independence, heterogeneity, normality, and noncollinearity.
Results We identified 147 adolescents with overweight or obesity; 27 patients were not included because 10 were being treated with Zurita-Cruz et al
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Table I. Baseline comparison of anthropometric and clinical characteristics of study subjects BMI z-score reduction Characteristics
Total n = 102
With n = 68
Without n = 34
Age, y Sex, n of boys (%) Weight, kg Height, cm BMI, kg/m2 BMI, z-score Pubertal development (Tanner), n (%) 1 2 3 4 5
11.77 2.2 48 (47.1) 57.4 23.1 146.1 18.7 25.56 4.7 1.86 0.39
11.97 2.3 28 (41.1) 57.8 23.7 146.5 16.3 25.65 4.8 1.87 0.40
11.29 1.9 20 (58.8) 56.8 22.1 145.5 19.9 25.39 4.7 1.83 0.38
24 (23.53) 22 (21.57) 22 (21.57) 32 (31.37) 2 (1.96)
16 (23.53) 14 (20.59) 14 (20.59) 22 (32.35) 2 (2.94)
P value .14 .09 .72 .94 .71 .60 .87
8 (23.53) 8 (23.53) 8 (23.53) 10 (29.41) -
Values are mean SD.
metformin, 5 used steroids, and 12 did not agree to participate. Ultimately, a total of 102 patients were included, after excluding 18 more patients who did not comply with the 12-month follow-up. The ages ranged from 10 to 16 years (median 11 years), and 58.6% were female. Approximately 82.2% of patients had already entered puberty. The median baseline BMI z-score was 1.81; 31.3% (n = 32) of participants were overweight, and the rest were obese (Table I). At the beginning of the study, 7.8% (n = 8) of participants had impaired fasting glucose, 27.4% (n = 28) had elevated uric acid, 27.4% (n = 28%) had hypertriglyceridemia, 68.6% (n = 70) had hypoalphalipoproteinemia, and 36.2% (n = 36) had elevated low-density lipoprotein (LDL) (Tables I and II). At the end of the follow-up, increases in height (baseline 146.1 18.7 cm vs follow-up 149.4 18.0 cm; P < .001) and weight (baseline 57.4 23.1 kg vs follow-up 60.3 22.4 kg; P < .001) were observed, with a significant decrease in BMI z-score (baseline 1.86 0.39 vs follow-up 1.77 0.39; P = .002). Patients were divided into 2 groups: those with a reduced BMI z-score (n = 68) and those with no reduc-
tion in BMI z-score (n = 34). As observed in Tables I and II, there was no difference between the groups in the baseline clinical characteristics. However, analysis of the biochemical measures at the beginning of the follow-up showed that patients who did not lose weight after 1 year had lower serum levels of uric acid and free leptin in addition to a greater resistin/uric acid index and increased levels of LDL and leptin receptor (Tables I and II). An exploratory analysis was carried out to identify which biochemical studies at the time of the start of the intervention could predict the modification of BMI z-score and weight after 1 year of follow-up (DBMI z-score and Dweight), for which 2 indices were generated: the leptin/uric acid index and the resistin/uric acid index. These data demonstrated that both DBMI z-score and Dweight had a statistically significant correlation with resistin/uric acid index (Figure and Table III [available at www.jpedscom]). In addition, patients who did not lose weight had greater values of the resistin/uric acid index than those who lost weight (resistin/uric acid index 3.07 0.47 vs 2.7 0.93; P = .02) (Table II).
Table II. Baseline metabolic characteristics of study subjects BMI z-score reduction Characteristics
Total n = 102
With n = 68
Without n = 34
P value
Glucose, mg/dL Uric acid, mg/dL Triglycerides, mg/dL HDL cholesterol, mg/dL VLDL cholesterol, mg/dL LDL cholesterol, mg/dL Leptin, ng/mL Adiponectin, mg/mL Resistin, pg/mL Leptin receptor Free leptin Resistin/uric acid index Leptin/uric acid index
89.0 8.2 6.0 1.5 124.8 64.6 42.8 10.9 24.8 13 101.0 52.4 12.0 2.7 6.6 2.1 15.9 2.8 12.6 7.4 1.9 2.7 2.9 0.8 2.1 0.6
89.2 9.5 6.3 1.7 125.0 72.3 42.1 11.5 24.7 14.6 93.5 25.6 12.2 2.3 6.5 1.7 15.8 3.2 11.6 7.6 2.3 3.1 2.7 0.9 2.1 0.6
88.7 4.5 5.3 0.7 124.5 46.6 44.0 9.4 25.0 9.3 116.1 82.0 11.4 3.3 6.8 2.8 16.1 1.4 14.6 6.7 1.1 0.8 3.0 0.4 2.2 0.9
<.001 .51 .20 .50 .01 .06 .23 .34 .02 .01 .02 .41
HDL, high-density lipoprotein; LDL, low-density lipoprotein; VLDL, very-low-density lipoprotein. Values are mean SD.
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Figure. Correlation between resistin levels, uric acid levels, resistin/uric acid index, and DBMI z-score–Dweight.
The multivariate analysis revealed that both the resistin/ uric acid index and male sex were independent variables that were associated with an increase in DBMI z-score (resistin/uric acid index beta coefficient 0.17 [95% CI 0.08-0.23] and male sex beta coefficient 0.15 [95% CI 0.04-0.26]) and Dweight (resistin/uric acid index beta coefficient 1.2 [95% CI 0.13-2.28] and male sex beta coefficient 2.89 [95% CI 1.1-4.68]) after 12 months of follow-up (Table IV). When we adjusted for puberty and sex (Tables V and VI [available at www.jpeds.com]), the model including only pubescent male subjects (n = 34) demonstrated that the beta coefficient of the resistin/uric acid index was greater than that in the model that included all subjects to predict an increase in DBMI z-score and Dweight (DBMI z-score = resistin/uric acid index beta coefficient 0.20; P < .001 and Dweight = resistin/uric acid index beta coefficient 4.88; P = .001). This supports the concept that this index is useful for predicting the modification of BMI z-score and weight in the group of pubescent male subjects.
Table IV. Linear regression analyses for DBMI z-score and Dweight (kg) in 102 patients with obesity Analyses Linear regression analysis for D BMI z-score Resistin/uric acid index Age, y Puberty presence Sex male Basal BMI z-score >2 Linear regression analysis for D weight (kg) Resistin/uric acid index Age, y Puberty presence Sex male Basal BMI z-score >2 4
95% CI
P value
0.17 0.00 0.10 0.15 0.10
0.10-0.23 0.02 to 0.02 0.02 to 0.24 0.04-0.26 0.20 to 0.00
<.001 .954 .108 .006 .055
1.20 1.20 2.10 2.89 0.19
0.13-2.28 1.65 to –0.76 0.02 to 4.23 1.10-4.68 1.45 to 1.84
.028 <.001 .052 .002 .818
Coefficient
Discussion The resistin/uric acid index may be useful for identifying adolescents who should receive other interventions, such as pharmacologic interventions, from the beginning, in addition to the recommended lifestyle interventions. Elevated levels of uric acid (>5.5 mg/dL) in adolescents have been associated with high blood pressure (OR 2.03, 95% CI 1.38-3.00).7 Elevated levels of uric acid are related to the presence of metabolic syndrome.6 In our study, we observed that in adolescents with obesity, greater levels of uric acid before dietary interventions were associated with a decrease in BMI z-score, supporting the idea that levels of uric acid may be a marker for identifying changes in metabolism after interventions to improve the nutritional status of adolescents with overweight/obesity. Resistin is a hormone produced by adipocytes that acts in peripheral tissues to influence insulin sensitivity and is suggested to have a proinflammatory effect.14 Resistin is related to the components of metabolic syndrome.15 Notably, in the present study, patients with obesity who did not lose weight had greater levels of resistin at the start of lifestyle interventions. This resistin/acid uric index could be used to differentiate the patients who had a decreased BMI z-score with interventions from those who did not exhibit changes in their BMI z-score. Thus, adolescents who improved their nutritional status had lower values of the resistin/uric acid index (2.7 0.9) than those whose BMI z-score scores remained the same or worsened (3.0 0.4). In male adolescents, the index was more reliable, as shown in the multivariate analysis (Table IV). The theoretical basis of the inverse relationship between uric acid and resistin may be multifactorial. Uric acid levels are significantly influenced by the type of diet in patients with obesity,16,17 which could mean that patients with obesity and elevated uric acid are more likely to lose weight after Zurita-Cruz et al
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changing their diet. Resistin expression also has been identified in the nonadipocyte stromal vascular fraction of white adipose tissue, fibrotic liver, and atherosclerotic lesions. Resistin upregulates the expression of proinflammatory cytokines such as tumor necrosis factor-a, interleukin-6, and interleukin-12.18,19 Patients with high levels of resistin were less likely to lose weight, despite making changes in diet and increasing physical activity. The resistin/uric acid index may be a marker that would help clinicians to individualize the management of children and adolescents with overweight and obesity. To achieve this goal, based on receiver operating characteristic curves, we found that the optimal cutoff point for the resistin/uric acid index was ³2.72 (95% CI 2.57-2.89). At this cutoff point, the sensitivity was 82.3%, and the specificity was 52.9% for identifying those adolescents with obesity who did not reduce their BMI z-score after 12 months of follow-up. However, the same cutoff value ³2.72 (95% CI 2.49-2.96) only among male subjects decreased specificity to 70% but substantially increased the specificity to 92.8%. However, these cutoff values should be taken with caution, because overlapping values between categories (with and without a decrease in BMI z-score) can limit their clinical usefulness. More studies are needed to validate our results. Likewise, we recognize the limitations of the study. The sample size was small and the follow-up time was relatively short; it is possible that during a longer follow-up period, the resistin/ uric acid values might behave differently. In addition, we did not objectively measure how our intervention modified the adolescents’ lifestyles, so it is possible that the resistin/ uric acid index can be more useful in those who adhere to the recommendations. After performing a multidisciplinary intervention involving lifestyle changes in adolescents with overweight and obesity, we conclude that the resistin/uric acid index seems to be a prognostic marker in the modification of BMI z-score 12 months. n Submitted for publication Aug 1, 2019; last revision received Dec 3, 2019; accepted Dec 4, 2019. Reprint requests: Miguel A. Villasis-Keever, Unit of Medical Research in Clinical Epidemiology, National Medical Center XXI Century, Instituto Mexicano del Seguro Social. Av. Cuauhtemoc 330, Col. Doctores, CP 06720, xico City, Me xico. E-mail: [email protected] Me
References 1. Shimobayashi M, Albert V, Woelnerhanssen B, Frei IC, Weissenberger D, Meyer-Gerspach AC, et al. Insulin resistance causes inflammation in adipose tissue. J Clin Invest 2018;128:1538-50. 2. Szalowska E, Elferink M, Hoek A, Groothuis G, Vonk RJ. Resistin is more abundant in liver than adipose tissue and is not up-
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regulated by lipopolysaccharide. J Clin Endocrinol Metab 2009;94: 3051-7. Piestrzeniewicz K, quczak K, Komorowski J, Maciejewski Wika J, Goch JH. Resistin increases with obesity and atherosclerotic risk factors in patients with myocardial infarction. Metabolism 2008;57:488-93. Yannakoulia M, Yiannakouris N, Bl€ uher S, Matalas AL, Klimis-Zacas D, Mantzoros CS. Body fat mass and macronutrient intake in relation to circulating soluble leptin receptor, free leptin index, adiponectin, and resistin concentrations in healthy humans. J Clin Endocrinol Metab 2003;88:1730-6. Hsia S, Chou I, Kuo C, See L, Huang J, Yu KH, et al. Survival impact of serum uric acid levels in children and adolescents. Rheumatol Int 2013;33:2797-802. Safiri S, Qorbani M, Heshmat R, Tajbakhsh R, Eslami A, Babaki S, et al. Association of serum uric acid with cardiometabolic risk factors and metabolic syndrome in Iranian adolescents: the CASPIAN-III Study. Iran J Kidney Dis 2016;10:6-10. Loeffler LF, Navas-Acien A, Brady TM, Miller ER 3rd, Fadrowski JJ. Uric acid level and elevated blood pressure in US Adolescents: National Health and Nutrition Examination Survey, 1999-2006. Hypertension 2012;59:811-7. Sautin YY, Nakagawa T, Zharikov S, Johnson RJ. Adverse effects of the classic antioxidant uric acid in adipocytes: NADPH oxidasemediated oxidative/nitrosative stress. Am J Physiol Cell Physiol 2007;293:C584-96. Johnson RJ, Lanaspa MA, Gaucher E. Uric acid: a danger signal from the RNA world that may have a role in the epidemic of obesity, metabolic syndrome, and cardiorenal disease: evolutionary considerations. Semin Nephrol 2011;31:394-9. Zimmet P, Alberti G, Kaufman F, Tajima N, Silink M, Arslanian S, et al. The metabolic syndrome in children and adolescents. Lancet 2007;369: 2059-61. Pollack MM, Holubkov R, Funai T, Dean JM, Berger JT, Wessel DL, et al. The pediatric risk of mortality score: update 2015. Pediatr Crit Care Med 2016;17:2-9. De Ferranti SD, Gauvreau K, Ludwig DS, Neufeld EJ, Newburger JW, Rifai N. Prevalence of the metabolic syndrome in American adolescents: findings from the Third National Health and Nutrition Examination Survey. Circulation 2004;1:2494-7. De Miguel E, Puig JG, Castillo C, Peiteado D, Torres RJ, Martin-Mola E. Diagnosis of gout in patients with asymptomatic hyperuricaemia: a pilot ultrasound study. Ann Rheum Dis 2012;71:157-8. McTernan PG, Kusminski CM, Kumar S. Resistin. Curr Opin Lipidol 2006;17:170-5. Mostafazadeh M, Haiaty S, Rastqar A, Keshvari M. Correlation between resistin level and metabolic syndrome component: a review. Horm Metab Res 2018;50:521-36. Becerra-Tomas N, Mena-Sanchez G, Dıaz-L opez A, Martınez Babio N, Corella D, et al. Cross-sectional association beGonzalez MA, tween non-soy legume consumption, serum uric acid and hyperuricemia: the PREDIMED-Plus study. Eur J Nutr 2019. in press. Jakse B, Jakse B, Pajek M, Pajek J. Uric acid and plant-based nutrition. Nutrients 2019;11:E1736. Jamaluddin MS, Weakley SM, Yao Q, Chen C. Resistin: functional roles and therapeutic considerations for cardiovascular disease. Cardiovasc Res 2006;69:76-85. Bokarewa M, Nagaev I, Dahlberg L, Smith U, Tarkowski A. Resistin, an adipokine with potent proinflammatory properties. J Immunol 2005;174:5789-95.
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Table VI. Linear regression analyses for Dweight (kg)
Table III. Correlation between basal metabolic variables and DBMI z-score–Dweight
Analyses
r Characteristics Uric acid, mg/dL Leptin, ng/mL Adiponectin, mg/mL Resistin, pg/mL Leptin receptor, ng/mL Free leptin, ng/mL Resistin/uric acid index Leptin/uric acid index
D BMI score*
Dweight
0.401† 0.265† 0.032 0.264† 0.123 0.127 0.407† 0.070
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0.398† 0.178 0.061 0.060 0.334† 0.377† 0.315† 0.139
*DBMI z-score = 12 months’ BMI z-score–baseline BMI z-score. †P < .01.
Linear regression analysis in pubertal patients (n = 78) Resistin/uric acid index Age, y Sex male Basal BMI z-score >2 Linear regression analysis in female pubertal patients (n = 44) Resistin/uric acid index Age, y Basal BMI z-score >2 Linear regression analysis in male pubertal patients (n = 34) Resistin/uric acid index Age, y Basal BMI z-score >2
95% CI
P value
3.08 0.93 3.48 1.39
1.27-4.89 1.44 to -0.42 1.17-5.79 3.44 to 0.66
.001 .001 .004 .372
2.21 0.86 0.27
0.17-4.24 1.48 to 0.25 2.36 to 2.90
.034 .007 .836
4.88 0.81 3.27
1.44-8.31 1.74 to 0.11 6.77 to 0.22
.007 .084 .065
Coefficient
Table V. Linear regression analysis for DBMI z-score Analyses Linear regression analysis in pubertal patients (n = 78) Resistin/uric acid index Age, y Sex male Basal BMI z-score >2 Linear regression analysis in female pubertal patients (n = 44) Resistin/uric acid index Age, y Basal BMI z-score >2 Linear regression analysis in male pubertal patients (n = 34) Resistin/uric acid index Age, y Basal BMI z-score >2 5.e1
Coefficient
95% CI
P value
0.20 0.008 0.14 0.07
0.90-0.31 0.02 to 0.03 0.003-0.28 0.20 to 0.05
<.001 .601 .045 .244
0.18 0.009 0.10
0.0006-0.36 0.04 to 0.06 0.33 to 0.11
.042 .716 .339
0.22 0.0006 0.04
0.09-0.35 0.03 to 0.03 0.17 to 0.08
.001 .970 .499
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