DOI: https://doi.org/10.21203/rs.3.rs-2223250/v1
Higher childhood overweight/obesity has been associated with an elevated risk of insulin resistance and cardiometabolic disorders. Waist-to-height ratio (WHtR) may be a simple screening tool to identify children at risk for cardiometabolic associated obesity. This study investigated whether being in the upper tertile for WHtR predicted the odds of insulin resistance, elevated liver enzyme concentrations, and cardiometabolic risk factor measures using cross-sectional data from the Family Weight Management Study randomized controlled trial.
Included was baseline data (n = 360, 7–12 years, mean Body Mass Index ≥ 85th percentile for age and sex). WHtR were grouped into tertiles by sex, male: ≤0.55(T1), > 0.55-≤0.59(T2), > 0.59(T3); female: ≤0.56(T1),>0.56-≤0.6(T2), > 0.6(T3). The Homeostatic Model Assessment for Insulin Resistance (HOMA-IR) was used to categorize participants as insulin-resistant (HOMA-IR ≥ 2.6) and insulin-sensitive (HOMA-IR < 2.6). Liver enzymes aspartate aminotransferase (AST) and alanine aminotransferase (ALT) were categorized as normal vs. elevated (AST of < 36.0 µkat/L or ≥ 36.0 µkat/L; ALT of < 30.0 µkat/L or ≥ 30.0 µkat/L). We examined differences in baseline cardiometabolic risk factors by WHtR tertiles and sex-specific multivariable logistic regression models to predict IR and elevation of liver enzymes.
Study participants had a mean WHtR of 0.59 ([SD: 0.06)]). Irrespective of sex, children in WHtR T3 had higher BMIz scores, blood pressure, triglycerides, 2-hr glucose, fasting, 2-hr insulin and lower HDL-C concentrations compared to those in T2 and T1. After adjusting for covariates, the odds of elevated IR (using HOMA-IR > 2.6) were over 5fold higher among children in T3 versus T1 (males) and T2 and T3 versus T1 (females). The odds of elevated ALT values (≥ 30) were 2.9 fold higher among female children in T3 compared to T1.
WHtR may be a practical screening tool in pediatric populations with overweight/obesity to identify children at risk of IR and cardiometabolically unhealthy phenotypes in public health settings.
The prevalence of childhood overweight/obesity and elevated cardiometabolic biomarkers have increased dramatically over the past few decades [1]. Obesity, characterized by the excessive accumulation of adipose tissue, increases cardiometabolic risk factors such as hyperglycemia, dyslipidemia, and insulin resistance (IR) [2]. The Centers for Disease Control and Prevention (CDC) reports that more than a third of American children are overweight or obese. Lacking in the CDC obesity report is the prevalence of metabolically unhealthy obesity/overweight. Risk phenotyping is based on clustering risk biomarkers with waist circumference as a “hallmark” characteristic. Our analyses explore the potential to identify the risk phenotype using the waist-to-height ratio (WHtR) [3].
Insulin resistance (IR) is strongly associated with cardiometabolic risk, characterized by elevated insulin compared to glucose concentrations [4–6], and strongly associated with the prevalence of type 2 diabetes mellitus later in life. IR appears to have a significant role in the pathogenesis of MetS [7] and is a current health risk to children who are overweight and obese [8]. Currently, there is no standard diagnostic test for IR; the Homeostatic Model Assessment of Insulin Resistance (HOMA-IR) has widely been used to assess IR status [9, 10]. Various anthropometric and biochemical markers are viable markers for cardiometabolic risk detection in adults; however, there is a lack of substantial research examining the accuracy of these measures as predictors of IR in children [11]. Furthermore, cutoff values for HOMA-IR in pediatric clinical studies vary depending on the criteria for metabolic syndrome and other conditions[11].
Childhood obesity is also associated with an increased risk of developing nonalcoholic fatty liver disease (NAFLD) later in life, with one of the histological stages being liver fibrosis [12]. The ectopic fat accumulation in obese adults is molecularly caused by the disruption of intracellular lipid homeostasis, preventing organ lipotoxicity by storing excess lipids in white adipocytes [13]. An excessive weight gain characteristic of obesity results in fat infiltration of vital organs such as the liver, forming ectopic lipids [14]. Hepatic ectopic lipid deposition can result in inflammation and liver fibrosis [15]. Obesity and IR increase liver fibrosis risk [16, 17]. Measures of liver function, such as aspartate aminotransferase (AST) and alanine aminotransferase (ALT), are indicators of cellular liver injury [18]. The lack of research on the pathogenesis of liver fibrosis in children and the unavailability of current assessment methods further emphasizes the need for simple, noninvasive methods to assess this condition[19, 20].
Waist-to-height ratio (WHtR), derived as a ratio of waist circumference in centimeters (cms) by height (cms); a simple screening tool [21], has recently been established as a highly indicative measure of early health risk in adults [22]. WHtR is more strongly associated with cardiovascular and cardiometabolic risk factors than other anthropometric measures, such as waist circumference, BMI, and waist-hip ratio [23–26]. Much of the research on the prediction of cardiometabolic risks using anthropometric measures has focused on BMI as the standard measure of obesity despite certain limitations, such as its inability to predict the percent of body fat mass or distribution [21, 22]. Systematic reviews and meta-analyses of WHtR have concluded that a cutoff value of 0.5 represents an increased cardiometabolic risk in adults across global populations [3, 27]. However, there is little consensus on appropriate cutoff value for children. [3, 7, 28] The primary objective of the present study was to assess the relation among sex-specific tertiles for WHtR and odds of insulin resistance, and elevated liver enzyme concentrations and cardiometabolic risk factors in children at risk of having IR, elevation liver enzyme concentrations and cardiometabolic risk factors.
The study utilized baseline data from the Family Weight Management Study (also known as the Fun Healthy Families study), a randomized controlled trial [29] conducted from 2009 to 2013 in a pediatric ambulatory program of an urban hospital that provides safety-net primary care services in the Bronx, New York, United States.
Study participants (N = 360) included children aged 7–12 years with a BMI ≥ 85th percentile for age and sex [30]. Exclusion criteria for the participants include any chronic illnesses, a physical, cognitive, or emotional impairment that would impact the safety of participants during study procedures, medical treatment causing fluctuations in body weight, inconvenient transportation distances, involvement in a separate weight management program, and unwillingness or inability of the parents or child to provide consent and assent, respectively. The trial design with CONSORT diagram and study process are described in detail elsewhere [29]. The Albert Einstein College of Medicine Institutional Review Board (IRB) approved all study protocols; all study participants provided written consent (parent or guardian) or assent (children).
Height and weight were measured in light clothing and without shoes. A stadiometer was used to obtain height, and a digital scale for weight. The waist circumference was measured using an elastic tape at the iliac crest and the hip circumference at the point of maximal protrusion of the gluteal muscles in the lateral position. Both were recorded to the nearest centimeter. Scales and stadiometer were calibrated, and anthropometry tapes were examined for signs of wear weekly using standardized protocols.
As previously reported in Wylie-Rosett et al., [29], systolic and diastolic blood pressures were measured three times according to traditional pediatric standards using appropriate cuff size with a manual sphygmomanometer after sitting for 2 minutes. Blood specimens were obtained after a minimum of an 8-hour fast. Fasting glucose, triglyceride (TG), total cholesterol (TC), low-density lipoprotein (LDL) cholesterol, and high-density lipoprotein (HDL) cholesterol concentrations were measured spectrophotometrically using a Beckman-Coulter LX-20 auto-analyzer (Brea, CA). A glucose amount of 1.75 g/kg body weight (GlucolaTM) was administered for the 2-hour Oral Glucose Tolerance Test. The liver enzymes, alanine transaminase (ALT/SGPT) and aspartate aminotransferase (AST/SGOT), concentrations were measured using an Immulite 2000 analyzer (Bio-DPC; Siemens Medical, Gwynedd, UK).
The following variables were used as markers for increased cardiometabolic risk
WHtR parameters: With no consensus on the appropriate cut points of WHtR in pediatric populations [7] and the recent proposals of various cutoff values in adult populations [31], we derived WHtR and grouped them by sex-specific tertile scores. Previous reviews and analyses have indicated that WHtR values < 0.5 and ≥ 0.5 are surrogates of increased risk in children; however, they may currently be insignificant when assessed for sensitivity and specificity to certain variables[31–33]. Therefore, the following cut points by sex were used based on the sample data to group them into three categories: females WHtR ≤ 0.56 (T1), WHtR > 0.56 - ≤ 0.60 (T2), and WHtR > 0.60 (T3); males WHtR ≤ 0.55 (T1), WHtR > 0.55 - ≤ 0.59 (T2), and WHtR > 0.59 (T3).
Insulin Resistance (IR): IR is a critical component of cardiovascular disease and MetS[2, 34]. Though increased HOMA-IR values are associated with higher risk, no clear cutpoint is used to assess IR in pediatric clinical studies [34]. HOMA-IR values < 2.6 and ≥ 2.6 have been considered cutpoints to evaluate increased risk based on prior published research from the Family Weight Management Study to examine cardiometabolic markers [28, 35].
Liver enzymes: Measures of serum AST(SGOT) and ALT(SGPT) levels have been used extensively in studies to assess liver damage [18]. AST values of < 36.0 and ≥ 36.0 and ALT values of < 30.0 and ≥ 30.0 ULT are proposed as cutpoints associated with increased risk of liver injury in children [36] and adopted for this study.
A sex-specific demographic, anthropometric, and cardiometabolic biomarker distribution was summarized using descriptive statistics; the normally distributed continuous variables were numerically summarized using mean (standard deviation), while non-normally distributed were presented with median (interquartile range). The categorical variables were presented as frequency counts and percentages. The difference in child characteristics among the WHtR tertile groups (sex specific) was assessed using analysis of variance, Kruskal-Wallis test, or the Pearson chi-square test. We modeled HOMA-IR, AST, and ALT values as binary variables for association models. The association between WHtR tertile categories (sex specific) and outcome variables HOMA-IR, AST, and ALT, adjusting for the other covariates, was examined using a multivariable logistic regression model. Covariates that were significant at the 20% level at the univariable model and those that were demographic confounders were considered for the multivariable model. All models were adjusted for the child's age, sex, race and ethnicity, and parent’s education and occupation.
Three hundred and sixty children participated in the study, of which 52% (n = 185) were females, and 48% (n = 175) were males. Seventy-four percent (n = 267) self-identified as Hispanic, 17.5% (n = 63) as non-Hispanic African American or Black, and 8.3% as non-Hispanic origin, others including Caucasian or White, Asian, Hawaiian, and multiracial. The average age of children was 9.3 (SD: 1.7); a more detailed summary of participant characteristics are presented elsewhere[29]. The average WHtR among the participants was 0.59 (SD: 0.06). The derived WHtR were grouped into tertiles by sex, male: ≤0.55(T1), > 0.55-≤0.59(T2), > 0.59(T3); female: ≤0.56(T1),>0.56-≤0.6(T2), > 0.6(T3). The average HOMA-IR, AST(SGOT), and ALT (SGPT) values were 3.68(SD: 2.58), 25.83 (SD: 17.57), and 30.96(SD: 9.40), respectively.
The distribution of demographic, anthropometric, and cardiometabolic characteristics between the sex specific WHtR tertile groups is presented in Table 1. The demographic characteristics were not statistically different, showing the groups are comparable.
|
Variable |
Waist to Height Ratio# |
|||||||
|---|---|---|---|---|---|---|---|---|
|
Male (n = 175) |
Female (n = 185) |
|||||||
|
T1 (n = 58) |
T2 (n = 59) |
T3 (n = 58) |
P-value |
T1 (n = 61) |
T2 (n = 62) |
T3 (n = 62) |
P Value† |
|
|
Age mean (SD) |
9.2 (1.8) |
9.2 (1.7) |
9.3 (1.6) |
0.95 |
9.0 (1.8) |
9.5 (1.8) |
9. 5 (1.7) |
0.19 |
|
Race/ethnicity n (%) Hispanic Non Hispanic AA Non Hispanic White & Others |
45 (77.6) 8 (13.8) 5 (8.6) |
44 (74.6) 11 (18.6) 4 (6.8) |
43 (74.1) 10 (17.2) 5 (8.6) |
0.96§ |
44 (72.1) 11 (18.0) 6 (9.8) |
47 (75.8) 9 (14.5) 6 (9.7) |
44 (71.0) 14 (22.6) 4 (6.5) |
0.78§ |
|
Parent education n (%) < High School High school or GED > High school |
30 (51.7) 13 (22.4) 15 (25.9) |
27 (45.8) 17 (28.8) 15 (25.4) |
28 (48.3) 18 (31.0) 12 (20.7) |
0.83§ |
29 (47.5) 15 (24.6) 17 (27.9) |
29 (46.8) 21 (33.9) 12 (19.4) |
32 (51.6) 12 (19.4) 18 (29.0) |
0.39§ |
|
Parent occupation n (%) Employed full time Employed part time Other (retire Homemaker unemployed) |
9 (15.5) 10 (17.2) 39 (67.2) |
10 (17.0) 9 (15.3) 40 (67.8) |
10 (17.2) 9 (15.5) 39 (67.2) |
1.00§ |
11 (18.0) 7 (11.5) 43 (70.5) |
13 (21.0) 13 (21.0) 36 (58.1) |
16 (25.8) 9 (14.5) 37 (59.7) |
0.46§ |
|
Height (cm) mean (SD) |
140.6 (12.2) |
139.2 (10.6) |
141.8 (11.7) |
0.50 |
138.6 (11.9) |
142.2 (11.4) |
142.2(11.5) |
0.14 |
|
Weight (lbs) mean (SD) |
98.4 (23.9) |
107.5 (26.1) |
131.5 (35.0) |
< .0001 |
95.5 (23.1) |
113.1 (28.3) |
134.0 (37.4) |
< .0001 |
|
BMI Z score mean (SD) |
1.7 (0.29) |
2.1 (0.3) |
2.4 (0.2) |
< .0001 |
1.6 (0.3) |
1.9 (0.3) |
2.3 (0.2) |
< .0001 |
|
SBP mean (SD) |
104.0 (7.5) |
106.6 (9.3) |
111.4 (11.8) |
0.0002 |
102.2 (8.8) |
106.3 (10.7) |
109.5 (12.3) |
0.001 |
|
DBP mean (SD) |
57.4 (5.2) |
57. 7 (5.3) |
59.8 (6.1) |
0.05¥ |
57.1 (4.8) |
58.5 (6.1) |
60.0 (5.6) |
0.02 |
|
Triglycerides median (IQR) |
63 (47–90) |
73(49–101) |
72(59–98) |
0.16¥ |
71 (52–113) |
79(60–113) |
86(61–126) |
0.07¥ |
|
Total cholesterol mean (SD) |
154.5 (27. 7) |
153.5 (29.2) |
159.5 (26.1) |
0.46 |
157.6 (26.9) |
151.3 (30.6) |
158.1 (29. 4) |
0.35 |
|
HDL mean (SD) |
49.9 (10.2) |
46.6 (7.9) |
45.4 (9.2) |
0.03 |
48.1 (9.8) |
44.3 (9.0) |
43.3 (9.7) |
0.01 |
|
LDL mean (SD) |
90.5 (23.1) |
91.4 (25.1) |
97.0 (20.4) |
0.26 |
93.3 (23.2) |
91.2 (20.7) |
93.6 (25.5) |
0.83 |
|
Fasting glucose mean (SD) |
84.6 (7.1) |
86.2 (7.1) |
86.0 (10.5) |
0.55 |
84.0 (8.1) |
84. 6 (13.4) |
84.6 (7.4) |
0.93 |
|
Glucose 2 HR mean (SD) |
93.3 (13. 7) |
96.7 (15.0) |
105. 8 (18.6) |
0.0001 |
92.30 (16.4) |
95.2 (20. 0) |
100.9 (17.5) |
0.03 |
|
Fasting insulin median (IQR) |
10.3 (7.0-15.5) |
12.5(9.9–18.2) |
16.5 (11.6–23.4) |
0.0001¥ |
11.8 (8.2–16.5) |
18.0 (11.5–28.0) |
18.2(13.0-27.9) |
< .0001¥ |
|
Fasting Insulin 2 HR median (IQR) |
49.3 (26.3–72.1) |
47.0 (27.5–74.1) |
76.7(49.2–135.0) |
0.0003¥ |
46.9 (34.3–77.5) |
92.9(63.8-157.1) |
99.9(44.7-204.1) |
< .0001¥ |
|
HOMA-IR median (IQR) |
2.0 (1.40–3.37) |
2.5 (2.2-4.0) |
3.5(2.3–5.3) |
0.0002¥ |
2.3 (1.5–3.4) |
3.8 (2.5–5.9) |
3.9 (2.6–6.1) |
< .0001¥ |
|
ALT (SGPT) median (IQR) |
21 (19–28) |
24 (19–29) |
27(20–35) |
0.03¥ |
20 (17–24) |
22(18–25) |
22(18–29) |
0.08¥ |
|
AST (SGOT) median (IQR) |
31 (26–35) |
32(27–35) |
32(26–36) |
0.82¥ |
30 (27–34) |
27(25–33) |
28(25–32) |
0.10¥ |
| †- Analysis of Variance, §- Pearson Chi-square test, ¥ - Kruskal Wallis Test, # waist-to-height ratio tertiles: male: ≤0.55(T1), > 0.55-≤0.59(T2), > 0.59(T3); female: ≤0.56(T1),>0.56-≤0.6(T2), > 0.6(T3). | ||||||||
The differences for the cardiometabolic risk markers of BMI- z score, SBP, DBP, and triglycerides concentrations in females were significant different, with markers showing dose-response relation among the WHtR tertiles in both sexes. HDL-cholesterol concentration was lowest in WHtR T3 category in both males and females, given that HDL-cholesterol tends to decrease with increasing adiposity [37]. The liver biomarker (SGPT/ALT) also showed a statistically significant positive dose-response relation with increasing WHtR categories.
We modeled HOMA-IR, AST, and ALT as the primary indicators of cardiometabolic risk adjusting for socio-demographic variables using a multivariable logistic regression model for each sex separately. The effect measures are presented in Table 2–4. Female children in T3 (WHtR > 0.60) had a 5.0 (95% CI: 2.09, 12.05) fold higher odds of being insulin resistant (HOMA-IR > 2.6) than those in T1 (WHtR ≤ 0.56). The odds of insulin resistance were 4.77 (95%CI: 1.95, 11.63) fold higher among T2 than T1. Similarly, the odds of insulin resistance were 5.25 (95%CI: 2.17, 12.72) fold higher among T3 (WHtR > 0.59) group than T1 (WHtR ≤ 0.55) among the males. The effect size was not statistically significant reduced to half for those in T2 (WHtR: >0.55-≤0.59).
|
Variable |
Male |
Female |
|||||
|---|---|---|---|---|---|---|---|
|
Odds Ratio |
95% CI |
P-value |
Odds Ratio |
95% CI |
P-value |
||
|
WHtR # |
T1 (ref) T2 T3 |
1 1.87 5.25 |
0.80–4.36 2.17–12.72 |
0.15 0.0002 |
1 4.77 5.02 |
1.95–11.63 2.09–12.05 |
0.001 0.0003 |
|
Age (years) |
1.67 |
1.34–2.08 |
< .0001 |
1.89 |
1.48–2.40 |
< .0001 |
|
|
Race/ethnicity |
Hispanic (ref) Non-Hispanic AA Non-Hispanic White & Others |
1 1.44 4.13 |
0.55–3.77 0.96–17.70 |
0.46 0.06 |
1 0.93 1.14 |
0.35–2.51 0.30–4.29 |
0.89 0.85 |
|
Parent education |
< High School (ref) High school or GED > High school |
1 1.18 0.52 |
0.50–2.77 0.20–1.36 |
0.71 0.18 |
1 0.73 1.91 |
0.29–1.85 0.72–5.10 |
0.51 0.19 |
|
Parent occupation |
Employed full time (ref) Employed part time Rest (retire, Homemaker, unemployed) |
1 2.33 1.97 |
0.66–8.25 0.72–5.40 |
0.19 0.19 |
1 1.49 1.18 |
0.45–4.89 0.46–3.01 |
0.51 0.73 |
| # waist-to-height ratio tertiles: male: ≤0.55(T1), > 0.55-≤0.59(T2), > 0.59(T3); female: ≤0.56(T1),>0.56-≤0.6(T2), > 0.6(T3); ref-reference category | |||||||
|
Variable |
Male |
Female |
|||||
|---|---|---|---|---|---|---|---|
|
Odds Ratio |
95% CI |
P-value |
Odds Ratio |
95% CI |
P-value |
||
|
WHtR# |
T1 (ref) T2 T3 |
1 1.13 1.87 |
0.46–2.80 0.79–4.40 |
0.78 0.16 |
1 1.77 2.92 |
0.59–5.35 1.01–8.41 |
0.31 0.05 |
|
Age (Years) |
1.01 |
0.82–1.25 |
0.91 |
0.73 |
0.56–0.95 |
0.02 |
|
|
Race/ethnicity |
Hispanic (ref) Non-Hispanic AA Non-Hispanic White & Others |
1 0.68 0.45 |
0.24–1.88 0.09–2.19 |
0.45 0.32 |
1 0.09 2.19 |
0.01–1.42 0.63–7.64 |
0.09 0.22 |
|
Parent education |
< High School (ref) High school or GED > High school |
1 1.05 1.04 |
0.44–2.53 0.40–2.68 |
0.91 0.94 |
1 0.55 0.56 |
0.18–1.75 0.18–1.73 |
0.31 0.31 |
|
Parent occupation |
Employed full time (ref) Employed part time Rest (retire, Homemaker, unemployed) |
1 2.33 1.18 |
0.67–8.11 0.41–3.40 |
0.18 0.76 |
1 1.73 0.99 |
0.43–6.93 0.29–3.29 |
0.44 0.98 |
| # waist-to-height ratio tertiles: male: ≤0.55(T1), > 0.55-≤0.59(T2), > 0.59(T3); female: ≤0.56(T1),>0.56-≤0.6(T2), > 0.6(T3); ref-reference category | |||||||
Among the females, the odds of elevated ALT ( > = 30) were 2.9-fold higher among T3 compared to T1 WHtR group (aOR, 2.92; 95% CI: 1.01, 8.41); while there was an elevated association (aOR = 1.77; 95%CI: 0.59, 5.35) in the T2 WHtR group, this was not statistically significant. Among males, there was a non-significant elevated association between T3 WHtR (aOR = 1.87) and T2 WHtR tertile (aOR = 1.13), and an elevated association was observed between AST and WHtR among males but was not statistically significant.
Table 4: Odds Ratio, 95% confidence interval, and P-value from multivariable logistic regression for AST(SGOT)
|
Variable |
Male |
Female |
||||||
|
Odds Ratio |
95% CI |
P-value |
Odds Ratio |
95% CI |
P-value |
|||
|
WHtR sex specific |
T1 (ref) T2 T3 |
1 1.10 1.83 |
0.44-2.70 0.77-4.36 |
0.84 0.18 |
1 1.40 1.03 |
0.48-4.06 0.35-3.02 |
0.54 0.96 |
|
|
Age |
0.82 |
0.66-1.02 |
0.07 |
0.59 |
0.44-0.80 |
0.001 |
||
|
Race/ethnicity |
Hispanic (ref) Non-Hispanic AA Non-Hispanic White & Others |
1 0.54 0.16 |
0.19-1.53 0.02-1.27 |
0.25 0.08 |
1 0.13 1.59 |
0.01-1.94 0.40-6.29 |
0.14 0.51 |
|
|
Parent education |
< High School (ref) High school or GED > High school |
1 1.43 2.53 |
0.59-3.49 0.99-6.48 |
0.43 0.05 |
1 0.29 0.97 |
0.07-1.19 0.33-2.88 |
0.09 0.95 |
|
|
Parent occupation |
Employed full time (ref) Employed part time Rest (retire, Homemaker, unemployed) |
1 1.93 1.73 |
0.50-7.41 0.57-5.32 |
0.34 0.34 |
1 1.76 1.53 |
0.36-8.63 0.41-5.79 |
0.49 0.53 |
|
# waist-to-height ratio tertiles: male: ≤0.55(T1), >0.55-≤0.59(T2), >0.59(T3); female: ≤0.56(T1),>0.56-≤0.6(T2), >0.6(T3); ref-reference category
As the WHtR tertile cut points were similar in both sexes, we also examined the association between common WHtR tertile cut points and HOMA-IR and Liver enzymes. The results suggested a similar pattern to sex specific results. (Supplemental tables 1)
The study's primary finding validates our hypothesis and suggests that the WHtR is associated with cardiometabolic biomarkers, specifically insulin resistance and elevated liver biomarkers. The magnitude of association with WHTR tertiles was stronger in female than male children.
Within this cohort of 7–12-year-old children with a BMI ≥ 85th percentile for age and sex, children in the upper tertile for WHtR had almost 1.83 -5.3-fold higher odds of elevated liver enzyme levels and insulin resistance than children in the lowest tertile in both sexes. These results are consistent with previous studies in adults [24, 26, 34] analyzing various WHtR thresholds predictive of concerning cardiometabolic risk, adding to the research on anthropometric predictor value and cardiometabolic risk in pediatric populations. Ashwell and Hsieh [21] suggested dichotomized optimal WHtR cut point of 0.5 for both children and adults among different ethnic groups between both sexes. While Khoury et al. [3, 38], using arbitrary cut points < 0.5, 0.5-<0.6, ≥ 0.6 in combination with BMI, showed higher WHtR categories were significant risk factors for lipid and cardiometabolic markers in obese children. Another study [39] used 0.512 as the WHtR cut point ignoring the child’s sex, concluded there is little difference between BMI and WHtR but preferred WHtR in identifying children with adverse CVD risk factors. Our study presents a novel finding utilizing sex-specific tertile cut points predicting IR and liver enzyme levels in a cohort of predominantly Hispanic and Black children with a greater metabolic burden. The study also identified a higher threshold WHtR cut point among children.
ALT and AST are widely used as noninvasive screening tools for NAFLD and nonalcoholic steatohepatitis (NASH) in the pediatric population [40]. Of note, ALT and AST are not efficient diagnostic tools on their own due to confounding liver functions other than fibrosis. While proportional relationships between HOMA-IR and the liver enzymes and varying WHtR thresholds have been established [21, 26, 34], this was not observed in our study population, with the exception of ALT, which was 3-fold higher in female children in WHtR T3 compared to T1. Little is known concerning the correlation between other prominent cardiometabolic phenotypes such as BMI, other liver enzymes (ALP), and HOMA-IR. A more thorough assessment of current liver fibrosis indicators including tumor necrosis factor (TNF), interleukin-6 (IL-6), IL-8, and C-reactive protein (CRP) [36] could serve as biomarkers of NASH in pediatric populations with obesity, but need to be evaluated for diagnostic accuracy.
Obesity screening programs could be incorporated into pediatric settings such as schools and conducted with protocols similar to those used in school fitness grams, fitness evaluations, and physicals[41, 42]. Such population-based intervention programs conducted in safe, confidential spaces can prevent cardiometabolic risk development and reduce the stigma surrounding overweight and obese youth. This study indicates that WHtR would be an excellent screener for unhealthy phenotypes and can be included in the regular public health screening programs for children
Strengths of this study include the scientific rigor of data collection, the availability of a database with an adequate sample size for testing hypotheses, the interface with safety-net primary care, and the availability of relevant cardiometabolic biomarkers.
Study limitations include a single measure of biochemical variables obtained at baseline from a clinical trial and the cross-sectional design. Also, all the children in the clinical trial were overweight or obese, so we did not have a normal weight control group to perform a comparative analysis. The lack of statistical significance between WHtR and cardiometabolic risk markers among males could be due to the small sample size. Additionally, the population's demographic characteristics (majority of parents/guardians identified as Hispanic and were born outside of the continental United States) and setting (pediatric safety-net primary care) potentially limit the generalizability of our results.
Assessing WHtR may improve the ability of pediatric obesity screening programs to identify which children, with overweight and obesity, have greater health risks. Screening programs need to develop assessment methods that identify health needs without increasing stigma or social disparities associated with obesity. These programs conducted in schools and other pediatric environments, such as fitness grams and evaluations, are essential in preventing widespread cardiometabolic risk development in overweight and obese youth. The study suggests that WHtR may be effective screening tools for IR in children and adolescents in public health settings.
Ethics approval and consent to participate
The study was conducted in accordance with the Declaration of Helsinki guidelines, and the Albert Einstein College of Medicine (IRB# 2005-582) Institutional Review Board approved all study procedures. Written informed consent was obtained from all parents/guardians, and assent was obtained from children who entered the study.
Consent for publication
Not applicable.
Availability of data and materials:
The datasets generated during and/or analyzed during the current study are not publicly available. Questions about access can be directed to the Study PI’s Drs. Wylie-Rosett (5R18DK075981) and Lichtenstein (5R01HL101236).
Competing interests
The authors have no conflict of interest to disclose, and the authors have no financial relationships relevant to this article to disclose.
Funding
This study was made possible with funding from the National Institute of Diabetes Digestive and Kidney Diseases (5R18DK075981 and P30DK111022) and the National Heart Lung and Blood Institute (5R01HL101236); the content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
Author contributions
J.W-R., A.E.G-P, P.M.D., C.R.I., and M.G., contributed to designing and conducting the parent study. T.E.U conducted the current examination under the supervision of J. W.-R and V.S. The manuscript was written by T.E.U under the supervision of J.W-.R and V.S. The manuscript was reviewed and edited by J.W-R., V.S, A.E.G-P, P.M.D., J.R., N.R.M., and A.H.L. All authors read and approved the final manuscript.
ACKNOWLEDGMENTS
Not applicable.