DNB Thesis

Significance of waist to height ratio as an early predictor in developing metabolic syndrome in children of age group 5-12 years in a tertiary care centre in Trichy: Part III

Dissertation Submitted to the National Board of Examinations, New Delhi

Dr. Nimisha PV*

Department of Pediatrics, KMC Speciality Hospital, Trichy, Tamil Nadu

*Correspondence: [email protected]

Discussion

This study was conducted to find the significance of waist to height ratio in children of age group 5 to 12 years, prone for developing metabolic syndrome. There were 170 children for final evaluation of which 51 were from five years to seven years, 73 belonged to eight years to ten years and 46 children from ten to twelve years. The data was analysed in terms of frequencies and percentages. The data collected was statistically analysed and the findings were compared and contrasted with several other studies which were reviewed.70 children were identified as having metabolic syndrome.

Bitew ZW et al conducted a study of Metabolic syndrome among children and adolescents in low- and middle-income countries. 142,142 children and adolescents from 76 eligible articles were included to compute the pooled prevalence of MetS and its components. MetS among overweight and obese population was computed to be 24.09%, 36.5%, and 56.32% using IDF, ATP III and de Ferranti criteria, respectively [33]. Regarding the components of MetS, abdominal obesity was the major component in overweight and obese population and low HDL was the most common component in the general population. This study also revealed that males were highly affected by MetS than females like in our study and HDL was low too in our study population. However, both parameters did not show any statistical significance in our study.

Ahmadi et al [34] conducted a cross sectional study aimed to determine the prevalence of abdominal obesity and metabolic syndrome among children and adolescents in Yazd Greater Area, Iran; over the period of 2016-2017.1035 children, and adolescents of both sexes aged 6-18 year were randomly selected from rural and urban districts in Yazd Greater Area, Iran. Low HDL-cholesterol levels and abdominal obesity were the most common component, and family history of heart disease, BMI, and male gender were the main determinants of metabolic syndrome in adolescents whereas high triglyceride value was identified as the highest risk factor in this study. Our study showed that abdominal obesity as evidenced by high WHtR and low HDL were associated with MetS, but other findings were not corroborated.

A study aimed to investigate the prevalence of different combinations of the risk factors for developing metabolic syndrome, among a nationally representative sample of adolescents in the Middle East and North Africa (MENA) was done in 2009 by Khashayar et al [35]. The study sample, obtained as part of the third study of the school-based surveillance system entitled CASPIAN III, was representative of the Iranian adolescent population aged from 10 to 18 years. Study participants consisted of 5738 students (2875 girls) with mean age of 14.7± 2.4 years) living in 23 provinces in Iran; 17.4% of participants were underweight and 17.7% were overweight or obese. Overweight / obese subjects had 9.68 increased odds of (95% CI: 6.65-14.09) the MetS compared to their normal-weight counterparts. Low HDL-C, as seen in our study was the most common component (43.2% among the overweight/obese versus 34.9% of the normal-weight participants), whereas high blood pressure, as in the current study was the least common component.

Lambert et al conducted a school-based survey of a representative sample of youth aged 9, 13 and 16 y in Quebec, Canada in 1999. The overall prevalence of Insulin resistance syndrome defined as hyperinsulinemia combined with two or more risk factors including overweight, high systolic BP, impaired fasting glucose, high TG and low HDL-C, and was 11.5% (95% CI: 10.2-12.9) from the study and it also concluded that BMI only indirectly assess adiposity and reflects total rather than regional fatness [36]. Children in the age groups 6-8, 8-10, and 10-12 years were selected at random from school classes in The Belgian Luxembourg Child Study. Anthropometric factors, blood pressure, and fasting blood glucose, plasma cholesterol, triglyceride, and insulin levels were measured in 1992 [37]. Girls had higher triglyceride, insulin levels and metabolic syndrome in the 10- to 12-year age group, like the present study which showed 45% girls with MetS were in the group of 10-12 years. They also concluded that obesity, blood glucose, triglycerides, insulin, and blood pressure were highly interrelated and Cholesterol, triglycerides, insulin, and blood pressure values were all among the highest of values previously reported in other studies, which argues strongly for the development of a standard pediatric definition for pediatric MetS.

Present study also analysed the significance of LDL level in children with metabolic syndrome. High LDL was reported in 37 children with MetS, which accounts for 52.9% of the children with MetS. It was statistically insignificant (p value 0.8). Calcaterra V et al analysed the relation between circulating oxidized low-density lipoproteins (Ox-LDL) and MetS in pediatric ages in order to define whether plasma Ox-LDL levels are correlated to obesity and whether oxidative damage, using serum Ox-LDL levels as a proxy, are associated with MetS. They enrolled 178 children (11.8 ± 2.6 years). Obese children showed increased MetS prevalence (p = 0.001) and higher Ox-LDL levels compared to normal and overweight subjects (p < 0.05), with a limited relation between Ox-LDL and MetS (p = 0.06). Waist-to-height ratio (WHtR) (p = 0.02), triglycerides (TG) (p = 0.001) and LDL-cholesterol (p < 0.001) resulted independent predictors of increased plasma Ox-LDL levels [38]. Our study among south Indian children showed only WHtR as a significant predictor.

Nambiar et al [39] demonstrated that the waist-height ratio is a simple and effective screening tool that could be used to identify obese children with the metabolic syndrome from the Eat Smart study conducted in Australia. Data from 109 obese boys and girls, aged 10-16.50 years were collected. These measurements were used to calculate WHtR, BMI, Z-scores for BMI, WC, weight and homeostatic model assessment for insulin resistance (HOMA-IR).WHtR and BMI Z-score were positively correlated with insulin, HOMA-IR and TG (P < 0.05) among boys from the study and WHtR, BMI Z-score and WC Z-score were positively correlated with insulin and HOMA-IR and negatively correlated with high-density lipoprotein-cholesterol (P < 0.05) in girls. Compared to this, our study showed similar findings with regards to WHtR, but other correlation was not found.

Present study suggested 0.58 as the cut off WHtR with sensitivity and specificity of 62% in the population. This provides an additional information from the regular OPD whether the child is at risk for developing MetS. However, the significance of WHtR varies according to race, culture. Kruger HS et al proposed a cut-off point of WHtR for metabolic risk in African township adolescents. They collected data from 178 black South African 14 to18 years old adolescents (69 boys, 109 girls). The WHtR cut-off points derived from the receiver operating characteristics curves ranged from 0.40 to 0.41, with best diagnostic value at 0.41, which is lower than the proposed international cutoff of 0.5. Adolescents with a WHtR higher than 0.41 had an odds ratio of 2.46 (95% confidence interval 0.96-6.30) for having a higher HOMA-IR value [40]

A cross-sectional study was undertaken with 175 subjects selected from the Reference Centre for the Treatment of Children and Adolescents in Campos, Rio de Janeiro, Brazil by Kuba et al. [41] The subjects were classified according to the 2007 WHO standard as normal-weight (BMI z score>-1 and<1) or overweight/obese (BMI z score≥1). Systolic blood pressure, diastolic blood pressure, fasting glycemia, low-density lipoprotein, high-density lipoprotein, triglyceride, Homeostatic Model Assessment-Insulin Resistance (HOMA-IR), leukocyte count and ultrasensitive C-reactive protein (CRP) were also analysed. The study concluded that WHtR was as sensitive as the 2007 WHO BMI in screening for metabolic risk factors in 6-10-year-old children. The public health message “keep your waist to less than half your height” can be effective in reducing cardio-metabolic risk because most of these risk factors are already present at a cut point of WHtR ≥ 0.5 from the study. A WHtR cut-off value of >0.47 was sensitive for screening insulin resistance and any one of the cardio-metabolic parameters.

There are many studies in adult population about the significance of WHtR. One of them done by Naval K Vikram et al compared the discriminatory ability of BMI, WC, waist-to-hip ratio, and WHtR) in identifying the presence of cardiometabolic risk factors in Asian Indians. This cross-sectional study involved 509 subjects (278 males and 231 females) aged 20-60 years from New Delhi, India. Receiver operating characteristic curve analyses were performed to compare predictive validity of various adiposity measures against the cardiometabolic risk factors (dyslipidemia, hyperinsulinemia, impaired fasting glucose, hypertension, and metabolic syndrome). The odds ratio for the presence of individual cardiometabolic risk factors in the presence of overweight, abdominal obesity, and high WHtR were calculated using logistic regression analysis. Their study concluded that WC had the highest area under ROC for all other cardiometabolic risk factors except hyperinsulinemia in males and for dyslipidemia, metabolic syndrome and presence of at least one cardiometabolic risk factor in females. For metabolic syndrome, WC, followed by WHtR, was observed to be the better predictor than other measures of adiposity, and WHtR appeared to be the best predictor for hypertension in both genders, particularly in women [42]. However among children, this study shows that WHtR is the only consistent predictor of MetS.

Another systematic literature review was carried out by Marcia Mara et al aiming to collect evidence on the use of the waist-to-height ratio (WHtR) on the elderly population, focusing on validity measures to identify the best anthropometric indicator in assessing obesity associated with non-communicable diseases. The review, with no restriction regarding period of publication, consisted in a search of papers published on the databases Pubmed, Web of Science, and Lilacs, using the following combinations: abdominal fat or overweight or obesity and waist-to-height ratio or waist to stature ratio or WSR or stature and girth. Sixteen papers were selected, most of them were with high methodological quality. The receiver-operating characteristic (ROC) curves was the validity measure explored in 13 papers, followed by sensitivity and specificity measures. In all studies, the body mass index (BMI) and waist circumference (WC) received special attention for analysis along with WHtR. Five manuscripts showed evidence of WHtR being the best anthropometric index when used alone, four showed that both WHtR and WC had the best discriminatory power in predicting cardiovascular risk factors [43].

Present study didn’t show any significant association to individual biochemical factors and a high WHtR, except for a high triglyceride (p value <0.00) in children with metabolic syndrome. Similarly, study done with a cohort of 883 obese children and adolescents (age 8-18 years) in Italy by Morandi A et al concluded that BMI, Z score of the BMI, waist circumference, and waist-to-height ratio were associated with metabolic impairments but showed low to moderate accuracy in discriminating both single and clustered metabolic impairments [44].

A cross-sectional study conducted with 1,069 participants of the Cardiovascular Risk in Adolescents Study aged 12-17 years in Brazil by Silva KC et al. Receiver operating characteristics curves were plotted, and area under curve (AUC) was calculated for body mass index (BMI), waist circumference (WC), waist-to-height ratio (WHR), conicity index (CoI), body shape index (BSI), adjusted BSI for adolescents (adjusted BSI), and body roundness index (BRI). They found that the reliability of CoI, BSI, adjusted BSI, and BRI was not superior to BMI, WC, and WHR in predicting cardiovascular risk markers and MetS. All the anthropometric indices had excellent predictive capacity for MetS, but limited accuracy for cardiovascular risk markers. Among the evaluated indices, they recommended the use of cutoff point WHR ≥.55 to screening MetS in girls and boys because of its easy to measure and interpretation which is almost similar to the result of current study [45].

There are not many meta-analytical studies to evaluate discriminatory power of WHtR in children and adolescents. Lo K et al conducted a meta-analysis using multiple databases, including Embase and Medline. Studies were included if they utilized receiver-operating characteristics curve analysis and published area under the receiver-operating characteristics curves (AUC) for adiposity indicators with hyperglycaemia, elevated blood pressure, dyslipidemia, metabolic syndrome and other cardio-metabolic outcomes. 34 studies met the inclusion criteria. AUC values were extracted and pooled using a random effects model and were weighted using the inverse variance method. They found WHtR did not have significantly better screening power than other two indexes in most outcomes, except for elevated triglycerides when compared with body mass index and high metabolic risk score when compared with waist circumference. It was also concluded that WHtR was convenient in terms of measurement and interpretation, although not being superior in discriminatory power, as it was advantageous in practice and allowed for the quick identification of children with cardio-metabolic risk factors at an early age [46]

Present study found no significant association (p value 0.14) between obese as per BMI standards and metabolic syndrome. From total of 70 children with metabolic syndrome, BMI was found to be in obese in only 56 children, missing the 14 children with high metabolic score. BMI classification misses every fifth child with MetS, making WHtR a significant tool in early identification of MetS.

Sharma AK et al created smoothed centile charts and tables for WHtR and WC based on data from the US National Health and Nutrition Survey, cycle III (NHANES III, N = 11,930 aged 2-24 y 1988-1994) for North American children. In all cases, WC-Z score and WHtR-Z score are more strongly associated with metabolic risk factors than BMI-Z score. From the formal pairwise comparisons, they showed that the WHtR-Z outperforms BMI-Z for abnormal total cholesterol, low density lipoprotein cholesterol, high-density lipoprotein cholesterol, and triglycerides (P < 0.05) and WC-Z is superior to BMI as a predictor for abnormal HDL and TG. They concluded that compared to BMI, WC and WHtR are both stronger predictors for Metabolic syndrome in North American children [47]. Our study didn’t include WC as an independent predictor, but WHtR showed a significant association with MetS.

There are various other studies which compared WHtR and MetS. Ashwell M et al have done a study aimed to differentiate the screening potential of WHtR and WC for adult cardio-metabolic risk in people of different nationalities and to compare both with BMI. They undertook a systematic review and meta-analysis of studies that used receiver operating characteristics (ROC) curves for assessing the discriminatory power of anthropometric indices in distinguishing adults with hypertension, type-2 diabetes, dyslipidaemia, metabolic syndrome and general cardiovascular outcomes. Study showed the superiority of WHtR over WC and BMI for detecting cardio-metabolic risk factors in both sexes [48].

References

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  2. Khashayar P, Heshmat R, Qorbani M, et al. Metabolic syndrome and cardiovascular risk factors in a national sample of adolescent population in the middle east and north Africa: the CASPIAN III study. Int J Endocrinol. 2013;2013.
  3. Lambert M, Paradis G, O’loughlin J, et al. Insulin resistance syndrome in a representative sample of children and adolescents from Quebec, Canada. Int J Obes. 2004;28(7):833-41.
  4. Guillaume M, Lapidus L, Beckers F, et al. Cardiovascular risk factors in children from the Belgian province of Luxembourg: The Belgian Luxembourg Child Study. Am J Epidemiol. 1996;144(9):867-80.
  5. Calcaterra V, De Giuseppe R, Biino G, et al. Relation between circulating oxidized-LDL and metabolic syndrome in children with obesity: the role of hypertriglyceridemic waist phenotype. J Pediatric Endocrinol Metabol. 2017;30(12):1257-63.
  6. Nambiar S, Truby H, Davies PS, et al. Use of the waist-height ratio to predict metabolic syndrome in obese children and adolescents. J Paediatrics Child Health. 2013;49(4):E281-7.
  7. Kruger HS, Faber M, Schutte AE, et al. A proposed cutoff point of waist-to-height ratio for metabolic risk in African township adolescents. Nutrition. 2013;29(3):502-7.
  8. Kuba VM, Leone C, Damiani D. Is waist-to-height ratio a useful indicator of cardio-metabolic risk in 6-10-year-old children?. BMC Pediatrics. 2013;13(1):1-6.
  9. Vikram NK, Latifi AN, Misra A, et al. Waist-to-height ratio compared to standard obesity measures as predictor of cardiometabolic risk factors in Asian Indians in North India. Metab Syndr Relat Disord. 2016;14(10):492-9.
  10. Correa MM, Thume E, De Oliveira ER, et al. Performance of the waist-to-height ratio in identifying obesity and predicting non-communicable diseases in the elderly population: A systematic literature review. Arch Gerontol Geriatr. 2016;65:174-82.
  11. Morandi A, Del Giudice EM, Martino F, et al. Anthropometric indices are not satisfactory predictors of metabolic comorbidities in obese children and adolescents. J Pediatrics. 2014;165(6):1178-83.
  12. Silva KC, Paiva NS, de Faria FR, et al. Predictive ability of seven anthropometric indices for cardiovascular risk markers and metabolic syndrome in adolescents. J Adol Health. 2020;66(4):491-8.
  13. Lo K, Wong M, Khalechelvam P, et al. Waist-to-height ratio, body mass index and waist circumference for screening paediatric cardio-metabolic risk factors: a meta-analysis. Obes Rev. 2016;17(12):1258-75.
  14. Sharma AK, Metzger DL, Daymont C, et al. LMS tables for waist-circumference and waist-height ratio Z-scores in children aged 5-19 y in NHANES III: association with cardio-metabolic risks. Pediatric Res. 2015;78(6):723-9.
  15. Ashwell M, Gunn P, Gibson S. Waist-to-height ratio is a better screening tool than waist circumference and BMI for adult cardiometabolic risk factors: systematic review and meta-analysis. Obes Rev. 2012;13(3):275-86.
Dr.-Nimisha-PV

Dr. Nimisha PV

DNB PG

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