Central obesity and dyslipidemia in pediatric patients with psoriasis: An observational study from India

To the Editor: Several studies have highlighted the link between psoriasis and systemic comorbidities. However, there is a relative paucity of data on childhood psoriasis and metabolic syndrome (MS). In this single-center, cross-sectional study we assessed the frequency of MS and its individual components in children (6-17 years of age) with psoriasis compared with apparently healthy control subjects with minor dermatologic ailments like molluscum contagiosum, warts, pityriasis alba, and milia. Adipose tissue in our body is the source of leptin, a proinflammatory adipokine that induces the production of cytokines, such as tumor necrosis factor and CXCL8 which are involved in the pathogenesis of psoriasis. A recent meta-analysis showed higher levels of serum leptin in adult patients with psoriasis,1Kyriakou A. Patsatsi A. Sotiriadis D. Goulis D.G. Serum leptin, resistin, and adiponectin concentrations in psoriasis: a meta-analysis of observational studies.Dermatology. 2017; 233: 378-389Crossref PubMed Scopus (39) Google Scholar and such an association has not yet been reported in pediatric patients with psoriasis. Therefore, we also studied the alteration of serum leptin in patients with childhood psoriasis and its correlation with disease severity. One hundred four children with psoriasis and 50 age- and sex-matched control subjects were recruited. A thorough history was taken and examination included a general physical examination, waist circumference, body mass index, and blood pressure measurement. Disease severity was defined according to the 2009 British Association of Dermatologists guidelines. International Diabetes Foundation criteria were used for diagnosing MS.2Zimmet P. Alberti K.G. Kaufman F. et al.The metabolic syndrome in children and adolescents-an IDF consensus report.Pediatr Diabetes. 2007; 8: 299-306Crossref PubMed Scopus (1233) Google Scholar Laboratory analysis included the measurement of fasting blood sugar, lipid profile, and serum leptin levels (using enzyme-linked immunosorbent assay). Two children (1.9%) with psoriasis fulfilled the criteria for MS compared with a single control subject (2%). Children with psoriasis had a significantly higher prevalence of central obesity (24% vs 8%, P = .017). Eighteen (17.3%) children with psoriasis were overweight or obese compared with 5 (10%) among control subjects. Children with psoriasis were more likely to have low high-density lipoprotein levels compared with control subjects (37.5% vs 12%, P = .001). Nine (8.6%) children with psoriasis had raised low-density lipoprotein values compared with 1 (2%) child among the control subjects (P = .318). The median serum levels of leptin among cases were significantly higher compared with control subjects (7.96 in patients vs 1.99 in control subjects, P = .02). On multinomial logistic regression analysis, the highest strength of association was found between abnormal high-density lipoprotein levels and psoriasis compared with leptin and waist circumference (Table I). Severity of disease correlated with higher body mass index (P = .033), higher waist circumference (P = .047), and lower high-density lipoprotein levels (P = .056; Table II).Table IComparison of demographic, anthropometric, and laboratory parameters between cases and control subjectsVariable assessedCases (N = 104)Controls subjects (N = 50)P value∗χ2 test used for statistical analysis.SMDAge category, n (%)0.204 6-10 years28 (23.9)17 (34) 11-16 years73 (70.1)32 (64) >16 years3 (2.8)1 (2)Average age (y), mean ± SD11.37 ± 2.710.6 ± 2.910.278Gender, n (%)−0.010 Male65 (62.5)31 (62) Female39 (37.5)19 (38)Family history of psoriasis, %5.70Age at onset of psoriasis (y), mean ± SD8.14 ± 3.36—Duration of psoriasis (y), range1 (6 months to 5 years)—BMI (kg/m2), n (%) <23 (healthy weight)86 (82.7)45 (90).387 23-27 (overweight)11 (10.57)4 (8).384 >27 (obese)7 (6.73)1 (2).224Median BMI (IQR)17.9 (15.83-21.15)16.85 (14.78-19.18).180WC.017†Statistically significant (P < .05). <90th percentile for age79 (76)46 (92) >90th percentile for age25 (24)4 (8)SBP.591 >95th percentile for age, gender, and height5 (4.8)2 (4) <95th percentile for age, gender, and height99 (95.19)48 (96)DBP.276 >95th percentile for age, gender, and height4 (3.8)4 (8) <95th percentile for age, gender, and height100 (96.15)46 (92)Laboratory investigationsHDL <40 mg/dL, n (%)39 (37.5)6 (12).001†Statistically significant (P < .05). >40 mg/dL, n (%)65 (62.5)44 (88) Mean HDL ± SD45.20 ± 10.7849.26 ± 8.88.039†Statistically significant (P < .05).TG <150 mg/dL, n (%)99 (95.2)48 (96).822 >150 mg/dL, n (%)5 (4.8)2 (4) Mean TG ± SD100 ± 37104.78 ± 29.7.433FBS <100 mg/dL, n (%)96 (92.3)47 (94).703 >100 mg/dL, n (%)8 (7.7)3 (6) Mean FBS ± SD88.42 ± 7.8687.08 ± 9.47.355LDL <130 mg/dL, n (%)95 (91.3)49 (98).318 >130 mg/dL, n (%)9 (8.6)1 (2)Median serum leptin levels, μg/mL (IQR)7.96 (3.15-20.89)1.99 (4.78-9.91).02†Statistically significant (P < .05).BMI percentile was assigned to each subject using the 2015 Indian Academy of Pediatrics growth charts for Indian children3Khadilkar V.V. Khadilkar A.V. Revised Indian Academy of Pediatrics 2015 growth charts for height, weight and body mass index for 5-18-year-old Indian children.Indian J Endocr Metab. 2015; 19: 470-476Crossref PubMed Scopus (52) Google Scholar (BMI values ≥23 adult equivalents were classified as overweight and those with values ≥27 adult equivalents were classified as obese). Hypertension was defined as per the American Academy of Pediatrics guidelines.4Flynn J.T. Kaelber D.C. Baker-Smith C.M. et al.Clinical practice guideline for screening and management of high blood pressure in children and adolescents.Pediatrics. 2017; 140: e20171904Crossref PubMed Scopus (1163) Google Scholar Age- and sex-specific WC percentile curves developed by Khadilkar et al5Khadilkar A. Ekbote V. Chiplonkar S. et al.Waist circumference percentiles in 2-18 year old Indian children.J Pediatr. 2014; 164: 1358-1362Abstract Full Text Full Text PDF PubMed Scopus (43) Google Scholar for Indian Children were taken as reference.BMI, Body mass index; DBP, diastolic blood pressure; FBS, fasting blood sugar; HDL, high-density lipoprotein; IQR, interquartile range; LDL, low-density lipoprotein; SBP, systolic blood pressure; SD, standard deviation; SMD, standardized mean difference; TG, triglycerides; WC, waist circumference.∗ χ2 test used for statistical analysis.† Statistically significant (P < .05). Open table in a new tab Table IIAnthropometric and laboratory parameter comparisons among cases with mild disease versus cases with moderate to severe diseaseVariableMild disease, n = 83Moderate to severe disease, n = 21P value∗χ2 test used for statistical analysis.BMI >23 kg/m2 (overweight/obese), n (%)12 (14.4)6 (28.5).033 Median BMI (IQR)17.75 (15.75-28)19.4 (16.4-32)WC >90th percentile for age and sex, n (%)16 (19.2)9 (42.8).047HDL <40 mg/dL, n (%)32 (38.5)7 (33.3).056 Mean HDL ± SD46.22 ± 10.741.19 ± 10.0LDL >130 mg/dL, n (%)6 (7.2)3 (14.2).318 Mean LDL ± SD93.35 ± 21.498.86 ± 26.07TG >150 mg/dL, n (%)5 (6.02)0.143 Mean TG ± SD102.73 ± 39.289.29 ± 27.3FBS >100 mg/dL, n (%)6 (7.2)2 (9.5).954 Mean FBS ± SD88.45 ± 8.0188.3 ± 7.4Median serum leptin concentrations (μg/mL)7.4317.19.07BMI, Body mass index; DBP, diastolic blood pressure; FBS, fasting blood sugar; HDL, high-density lipoprotein; IQR, interquartile range; LDL, low-density lipoprotein; SBP, systolic blood pressure; SD, standard deviation; TG, triglycerides; WC, waist circumference.∗ χ2 test used for statistical analysis. Open table in a new tab BMI percentile was assigned to each subject using the 2015 Indian Academy of Pediatrics growth charts for Indian children3Khadilkar V.V. Khadilkar A.V. Revised Indian Academy of Pediatrics 2015 growth charts for height, weight and body mass index for 5-18-year-old Indian children.Indian J Endocr Metab. 2015; 19: 470-476Crossref PubMed Scopus (52) Google Scholar (BMI values ≥23 adult equivalents were classified as overweight and those with values ≥27 adult equivalents were classified as obese). Hypertension was defined as per the American Academy of Pediatrics guidelines.4Flynn J.T. Kaelber D.C. Baker-Smith C.M. et al.Clinical practice guideline for screening and management of high blood pressure in children and adolescents.Pediatrics. 2017; 140: e20171904Crossref PubMed Scopus (1163) Google Scholar Age- and sex-specific WC percentile curves developed by Khadilkar et al5Khadilkar A. Ekbote V. Chiplonkar S. et al.Waist circumference percentiles in 2-18 year old Indian children.J Pediatr. 2014; 164: 1358-1362Abstract Full Text Full Text PDF PubMed Scopus (43) Google Scholar for Indian Children were taken as reference. BMI, Body mass index; DBP, diastolic blood pressure; FBS, fasting blood sugar; HDL, high-density lipoprotein; IQR, interquartile range; LDL, low-density lipoprotein; SBP, systolic blood pressure; SD, standard deviation; SMD, standardized mean difference; TG, triglycerides; WC, waist circumference. BMI, Body mass index; DBP, diastolic blood pressure; FBS, fasting blood sugar; HDL, high-density lipoprotein; IQR, interquartile range; LDL, low-density lipoprotein; SBP, systolic blood pressure; SD, standard deviation; TG, triglycerides; WC, waist circumference. Psoriasis and these comorbidities—most notably obesity—are believed to share common pathogenetic mechanisms that include presence of chronic low-grade inflammation with increased levels of tumor necrosis factor, C-reactive protein, and interleukin-6. Our observations suggest the association of psoriasis severity with excess adiposity and the role of adipose tissue as a source of low-grade inflammation in psoriasis. Findings from this study support the notion that psoriasis is a condition that predisposes to atherogenic profile early in the course of the disease. The institution of primary preventive measures to prevent these comorbidities, screening of children, and counseling regarding maintenance of a healthy weight therefore become integral parts of the management of psoriasis in children. None disclosed. We thank Mrs Kusum Chopra for her statistical analysis of the data.