Abstract
Background:
Metabolic syndrome (MetS) affects ~10% of US adolescents. Abdominal obesity is the most prevalent component and may indicate MetS risk in adolescents undergoing weight-loss surgery.
Objectives:
Assess MetS risk/severity and its association with abdominal obesity (measured by sagittal abdominal diameter, SAD) before and after weight-loss surgery in adolescents to determine whether SAD predicts MetS risk in this population.
Setting:
Data were collected in the Teen Longitudinal Assessment of Bariatric Surgery (Teen-LABS) study at 5 sites (US children’s hospitals) performing weight-loss surgery in adolescents. The current study is a secondary analysis of these data.
Methods:
We examined data collected pre-surgery through 5 years post-surgery. MetS risk/severity was defined using the MetS severity z-score (MetS-z), and MetS prevalence was determined using age-appropriate criteria. Association between SAD and MetS-z was evaluated with an adjusted linear mixed model.
Results:
Among 228 individuals (75% female, 72% white), mean age 16.5y and BMI 53 kg/m2, 79% met MetS criteria pre-surgery. MetS-z (1.5) and SAD (32cm) were correlated (r=0.6, p<0.0001) pre-surgery, and both improved significantly at 6 months, 1, and 5 years post-surgery, remaining highly correlated at each timepoint. SAD predicted MetS-z (β=0.118, 95% CI 0.109, 0.127) after adjustment for age, visit, surgery type, and caregiver education.
Conclusions:
Abdominal obesity is a key MetS risk marker in youth undergoing weight-loss surgery. Both SAD and Met-z measures may be useful for MetS risk assessment and tracking post-surgery changes in this population, but more research is needed to identify effective lifestyle interventions targeting abdominal obesity.
Keywords: adolescent, metabolic syndrome, bariatric surgery, abdominal obesity, sagittal abdominal diameter
Introduction
The metabolic syndrome (MetS) is a cluster of abnormalities including abdominal obesity, low high-density lipoprotein cholesterol (HDL-C), and elevated blood pressure, triglycerides, and glucose(1). A diagnosis of MetS requires the presence of at least 3 of these abnormalities, and signals increased risk for diabetes and heart disease. Approximately 35% of adults(2) and 10% of adolescents(3) in the US are affected according to commonly applied criteria(1, 4). While a relatively small proportion of adolescents meet criteria for MetS, many more may be at risk, in particular those with obesity(5). Gurka and colleagues developed and validated MetS z-score formulae for adults and adolescents as tools for determining risk and severity, and tracking changes over time(6, 7). The z-score is sex- and race-specific, and although not originally developed as a risk score, it has been shown to correlate strongly with several MetS risk factors in adolescents(8). According to an analysis of National Health and Nutrition Examination Survey (NHANES) data, 2011–2016, the mean MetS z-score in US adolescents is −0.08 (range −2.4 to 4.0)(9).
Abdominal obesity plays a predominant role in the development of MetS and subsequent chronic disease(5, 10), making it an important target for screening and risk reduction for all ages. Two types of fat tissue – subcutaneous and visceral – can accumulate in the mid-section and contribute to abdominal obesity, but visceral fat has the stronger assocation with cardiometabolic complications(10). Common methods for assessing abdominal obesity include waist circumference (WC) and body mass index (BMI), yet neither method differentiates between the 2 types of fat. Sagittal abdominal diameter (SAD), or the distance from the back to the front of the abdomen when in the supine position(11), is an alternative means of assessing abdominal obesity. Studies have shown that both BMI and WC are well correlated with subcutaneous fat measured by computed tomography (CT)(12, 13), but SAD has a higher correlation with visceral fat than BMI or WC when measured by both CT(12, 14) and magnetic resonance imaging (MRI)(15). This may explain why SAD is more predictive of metabolic disease risk than BMI or WC in adults(12, 14). Data in adolescents, however, are inconclusive. One study found that SAD is superior in assessing cardiometabolic risk and visceral fat(16), while others found it to be no better than other methods(3, 17).
Obesity in adolescence increases the risk for adult obesity and related diseases(18). Interventions focusing on weight loss and chronic disease prevention in adolescents are urgently needed. Weight-loss surgery presents an opportunity to achieve both, and improves the health of teens as they progress into adulthood. The purpose of this study was to estimate MetS z-score and prevalence of MetS in adolescents before and after weight-loss surgery, and to explore how changes in abdominal obesity that accompany weight loss affect MetS risk in adolescents.
Materials and Methods
Study Design
This study involved analysis of data collected prospectively on individuals enrolled in the Teen Longitudinal Assessment of Bariatric Surgery (Teen-LABS) study who were 5 or more years post-surgery. Teen-LABS is a multi-year, multi-site observational study assessing the health benefits and risks of weight-loss surgery for adolescents(19). Anthropometric and metabolic measurements were collected on Teen-LABS participants at baseline and annual post-surgery assessments. This secondary data analysis was approved by the local Institutional Review Board.
Subjects
To be eligible for Teen-LABS, adolescents had to be approved for any type of bariatric surgery at one of 5 participating study sites, and consent to be in the study. Data from all Teen-LABS participants who had undergone Roux en Y Gastric Bypass (RYGB) or Sleeve Gastrectomy (SG) were potentially eligible for inclusion in the present study. Those who had undergone laparoscopic adjustable gastric banding were excluded from analysis due to the small number of individuals (n=14) in that group. Data from women in their second or third trimester of pregnancy and through 6 months post-partum at a given timepoint (n=1 at 6 months, n=4 at 1 year, and n=14 at 5 years post-surgery) were excluded from analysis at that timepoint
Measures
For the present analysis, the following Teen-LABS variables (all collected at baseline and some at 6 months, 1 year, and 5 years post-surgery) were extracted from the study database: age, sex, race, ethnicity, education of caregiver, type of surgery, date of surgery, BMI, WC, SAD, systolic blood pressure (SBP), diastolic blood pressure (DBP), SBP percentile, triglycerides, HDL-C, and glucose, plus use of lipid-lowering, anti-hypertensive, and anti-diabetes medications.
For children and adolescents, a BMI z-score is typically reported to reflect weight in terms of age, sex, and height. However, research has demonstrated that the BMI z-score is inaccurate for youth with severe obesity, prompting experts to caution against using it in this population(20). Therefore, we instead used the absolute BMI value (kg/m2) in our analysis. WC was measured in centimeters (cm) using a non-stretching measuring tape (Gulick II, Country Technology, Gays Mills, WI) and SAD (cm) was measured using an abdominal caliper (Holtain-Kahn, Seritex, Wales) with the participant lying supine (Supplemental Figure 1). Both measurements were performed by two trained examiners following the methods used in NHANES(21). Blood pressure and laboratory methods are described elsewhere(19). The MetS z-score was calculated using race- and sex-specific equations developed by Gurka and colleagues, which comprise measures of BMI, SBP, TG, HDL-C, and glucose as equation terms(7). For our final MetS z-score calculations, we used the MetS z-score equations intended for adults which include absolute BMI (kg/m2) instead of the adolescent versions that use BMI z-scores, for reasons noted previously about use of BMI z-scores in severe obesity. Presence of metabolic syndrome was determined according to age at the time of observation. For those 18y and younger, modified pediatric criteria for MetS were used(4), while for those older than 18y, adult criteria were applied(1) (Supplemental Table 1). To define abdominal obesity for MetS diagnosis, the pediatric criteria employ a cut-off for WC at the 75th percentile for age and sex. To determine who exceeded the 75th percentile in those 18y and younger, we set the percentile values published by Fernandez(22) as cutpoints in our data.
Data Analysis
To characterize the study sample at baseline, pre-surgery age and BMI (mean, SD) along with frequency counts (n, % of total) of categorical variables are reported. Least squares (LS) means and 95% confidence intervals for labs and physical measurements at baseline and 0.5, 1, and 5 years post-surgery were estimated and between-visit differences compared using linear mixed modeling without covariates. Pearson’s correlation coefficients (r) for SAD and MetS z-score (overall, and at each timepoint) were calculated to examine their association. The study’s primary outcome was MetS z-score. A linear mixed model with SAD as the independent predictor and MetS z-score as the outcome was used to further examine the relationship between these two measures. Surgery location (center) was treated as a random effect, while age at surgery, study visit, type of surgery, and education of primary caregiver were included as covariates. We further adjusted the model for sex and race to see if either of these affected results. Similar models with WC or BMI as the independent predictor were also tested, and the Akaike Information Criterion (AIC) was used to determine the best-fitting model. Unadjusted LS means for percent change in all three adiposity measures (SAD, BMI, and WC), from baseline to 0.5, 1, and 5 years post-surgery, were also estimated. Maximum likelihood estimates were used in the models to account for missing data. Missing data were assumed to be missing-at-random, and this assumption was supported with sensitivity analyses reported in an earlier Teen-LABS publication(19). SAS Software version 9.4 (SAS Institute Inc., Cary, NC) was used for analyses.
Results
There were 228 individuals (75% female) included in the analysis at baseline; of these, 161 (71%) had RYGB and 67 (29%) had SG. Distribution of race/ethnicity characteristics were as follows: white (72%), black (22%), multi-race (5%), and other race (1%), with 7% reporting Hispanic ethnicity. Ten percent of primary caregivers reported not completing high school, but the majority, 71%, had graduated high school or completed some college and 19% were college graduates (Supplemental Table 2). Twenty-three (10%) participants underwent panniculectomy or removal of excess abdominal skin between 1 and 5 years post-surgery. Baseline and post-surgery lab and physical measurements are in Table 1. MetS z-score and SAD were highly correlated overall (r=0.73) and at each timepoint (baseline, r=0.58; +6 months, r=0.63; +1 year, r=0.56; +5 years, r=0.63), with p<0.0001 for all correlations (Supplemental Figure 2). Before surgery, the mean MetS z-score was 1.5 and MetS prevalence was 79%. Six months post-surgery, the MetS z-score had improved significantly to 0.3 and was maintained at 0.1 at 5 years post-surgery (both p<0.0001 compared to baseline). Similarly, MetS prevalence declined by 6 months and 5 years after surgery to 36% and 26%, respectively (both p<0.0001 compared to baseline).
Table 1.
Trends in anthropometric and metabolic measurements, baseline through 5 years post-surgery, unadjusted LS mean (95% Confidence Interval)
| Baseline | +6 months | +12 months | +5 years | |
|---|---|---|---|---|
| SAD (cm) | 31.8 (31.3, 32.4) n = 219 |
25.0 (24.4, 25.5)a n = 186 |
23.2 (22.6, 23.8)a n = 182 |
25.3 (24.5, 26.0)a n = 141 |
| BMI (kg/m2) | 52.6 (51.3, 53.9) n = 228 |
39.1 (37.8, 40.5)a n = 197 |
36.4 (35.1, 37.8)a n = 200 |
39.8 (38.4, 41.1)a n = 174 |
| WC (cm) | 147.7 (145.5, 149.8) n = 225 |
122.3 (120.0, 124.6)a n = 191 |
117.0 (114.6, 119.4)a n = 188 |
119.6 (116.5, 122.6)a n = 141 |
| SBP (mmHg) | 125.3 (123.5, 127.0) n = 224 |
118.0 (116.3, 120.2)a n = 196 |
116.0 (114.3, 117.8)a n = 198 |
121.9 (119.9, 123.9)b n = 172 |
| DBP (mmHg) | 74.2 (72.9, 75.5) n = 224 |
69.6 (68.3, 70.8)a n = 196 |
69.8 (68.6, 71.0)a n = 198 |
74.6 (73.0, 76.1) n = 172 |
| Fasting glucose (mg/dL) | 97.8 (94.3, 101.2) n = 225 |
86.4 (84.1, 88.8)a n = 191 |
84.9 (82.5, 87.3)a n = 195 |
88.3 (84.7, 91.8)a n = 156 |
| Fasting TG (mg/dL) | 132.6 (123.1, 142.1) n = 225 |
89.2 (83.9, 94.4)a n = 191 |
78.3 (73.6, 83.1)a n = 195 |
79.2 (72.8, 85.5)a n = 156 |
| HDL-C (mg/dL) | 37.5 (36.4, 38.7) n = 225 |
43.1 (41.6, 44.5)a n = 191 |
48.5 (47.0, 50.0)a n = 195 |
54.4 (52.4, 56.5)a n = 156 |
| MetS z-score | 1.5 (1.4, 1.6) n = 207 |
0.3 (0.2, 0.4)a n = 177 |
−0.0 (−0.1, 0.1)a n = 180 |
0.1 (−0.0, 0.3)a n = 144 |
| MetS prevalence, n (%) | 176 (79) | 67 (36)a | 44 (24)a | 33 (26)a |
| Panniculectomy, n (%) | - | - | - | 23 (10%) |
p <0.0001 compared to baseline;
p <0.01 compared to baseline
BMI, body mass index; WC, waist circumference; SAD, sagittal abdominal diameter; SBP, systolic blood pressure; DBP, diastolic blood pressure; TG, triglycerides; HDL-C, high density lipoprotein cholesterol; MetS, metabolic syndrome
Measures of abdominal obesity (SAD, BMI, and WC) each declined significantly from baseline to 5 years post-surgery (Table 1). When examining their percent change over time, we found that SAD declined to a greater degree than WC – but not BMI – at each timepoint (Figure 1). All three measures were significant predictors of the MetS z-score after adjustment for visit, age at surgery, type of surgery, and caregiver education. Excluding the participants who had undergone panniculectomy did not affect results. Results of the linear mixed models were: SAD (β=0.118, 95% CI 0.109, 0.127); BMI (β=0.058, 95% CI 0.054, 0.062); and WC (β=0.027, 95% CI 0.025, 0.03), all p<0.0001. Model fit statistics (AIC) indicated that SAD had the best fit, followed by WC, then BMI. Adjusting for race or sex did not affect these results, and there was no significant interaction with time (visit) for any of the three measurements.
Figure 1.
Percent changes in adiposity measures (BMI, SAD, WC) from baseline to 0.5, 1, and 5 years post-surgery. Open circles are LS mean values at each timepoint; error bars represent the 95% confidence intervals.
Discussion
This report described the use of SAD for assessing abdominal obesity and examined its association with the MetS z-score in an adolescent bariatric surgery cohort. We found that both measures significantly improved from baseline to 5 years post-surgery, and that an adjusted model with SAD as the independent predictor of MetS z-score had a better fit than using either WC or BMI. New information is presented on the MetS z-score applied to adolescents before and after weight-loss surgery. The mean pre-surgery z-score of 1.5 indicates that these adolescents were either at higher risk for, or had greater severity of, MetS compared to their peers (population mean z-score −0.08(9)). It also confirms other reports that obesity increases one’s likelihood of developing MetS, which itself predicts future diabetes and heart disease. Thus, the significant reduction in the MetS z-score to 0.1 at 5 years post-surgery in this cohort demonstrates the potential for weight-loss surgery to improve cardiometabolic health outcomes in adolescents.
Abdominal obesity is the most common MetS component affecting this population, yet there is ongoing debate about the utility of SAD to indicate obesity-related risk above and beyond that of BMI or WC in adolescents. Our results suggest that SAD is a better predictor of MetS risk compared to the other 2, but differences were minimal. Weber and colleagues studied all three measures of obesity and their association with cardiometabolic risk in adolescents(17). They found that each was correlated with individual risk factors, but neither SAD nor WC were superior to BMI for identifying MetS in adolescents. Likewise, in an analysis of NHANES data (2011–2016), Gaston and colleagues estimated MetS prevalence in adolescents using a variety of definitions, and association of MetS with SAD and WC(3). They found that while the association of abdominal obesity with MetS varied based on race/ethnicity and which definition was used, both SAD and WC similarly predicted MetS. A valid criticism of WC is that it cannot differentiate between visceral and subcutaneous fat in the abdominal region(23), while SAD is highly correlated with visceral fat specifically(12, 14). Interestingly, WC did not decrease as much as SAD or BMI in our adolescent cohort after surgery. This may be due in part to WC measurements capturing excess loose skin around the abdomen following surgical weight loss, a common condition reported by patients post-surgery(24). Thus, SAD may be a more precise measure of abdominal visceral obesity following weight-loss surgery than WC. For a brief discussion of WC vs SAD measurement, see Supplement 3.
The prevalence of MetS in our cohort at baseline was 79%, much greater than the reported prevalence of 10.1% in US adolescents(3), but by 5 years after surgery this proportion declined to just 26% meeting MetS criteria. Some pediatric health experts advise against using the binary MetS diagnosis in children and adolescents, instead advocating for treating individual abnormalities as they occur(25). This recommendation may offer further rationale for the use of the continuous MetS z-score to identify and treat those at risk or with greater severity of abnormalities in MetS factors.
While the analysis of data collected from an established national cohort is a strength of the study, there were also limitations. We did not have a non-surgical weight-loss control group against whom to compare SAD and MetS z-score results to determine if mode of weight loss played a role. In addition, our study sample was predominantly female and white. In a 2020 study using national electronic health record data, Courcoulas reported that of 33,560 adults who underwent bariatric surgery, 80% were female and 66% were white(26), and these demographics are largely reflected in our adolescent surgery population. Therefore our results may not be generalizable to all adolescents with severe obesity, but can still be applicable to the majority of bariatric surgery candidates in that age group. We had limited data on puberty status in our cohort; puberty affects hormone levels and may have had an effect on some of our analysis measures, notably lab values (e.g. cholesterol) and abdominal fat. Puberty onset in females and males occurs between the ages of 8–13 y and 9–14 y, respectively(27) and the average age at surgery in our population was 16.5 y (range 13–20), with 95% of females having begun menses prior to surgery. While we did not have an equivalent data point for males, the age range at surgery for males was 13–19 y with only 1 male participant under the age of 14. Thus, we estimate that most or all participants had achieved puberty prior to surgery and that puberty status did not impact results.
Finally, dietary intake was not assessed in our study and this is a limitation because of the key role that diet plays in both metabolic syndrome and abdominal obesity. In cross-sectional analyses, a healthy dietary pattern rich in vegetables, fruit, and whole grains and low in saturated fat, sodium, and added sugar was inversely associated with MetS in children and adolescents(9, 28). A 2016 meta-analysis found that a reduced-energy diet (alone or with exercise) resulted in loss of weight and abdominal (visceral) fat(29). An ancillary TeenLABS study measured loss of control-type eating behaviors before and after surgery and found that episodes of either eating large amounts at one time or continuous eating both declined after surgery(30). Adolescents enrolled in TeenLABS receive intensive diet therapy prior to and following surgery which may have contributed to the improvements in these eating behaviors and indeed, in the decline in MetS severity after surgery.
Conclusions
Adolescents with severe obesity can achieve significant reductions in MetS risk and SAD following weight-loss surgery. Large-scale, population-level data pairing SAD measurements with co-morbidities is needed to establish healthy and at-risk SAD cutoff values, while further clinical research is needed to identify lifestyle interventions (diet, exercise) for abdominal visceral fat reduction. Both SAD and the MetS z-score may be useful tools to assess MetS risk in this population and for tracking changes following surgery.
Supplementary Material
Supplemental Figure 1. Measuring sagittal abdominal diameter (SAD) using calipers fitted with a water level (from NHANES 2015–2016 Anthropometry Manual(21). Upper left inset shows the water level with bubble centered to confirm a level surface prior to measuring.
Supplemental Figure 2. Scatter plots of Sagittal Abdominal Diameter (SAD) and Metabolic Syndrome z-score (MetS z-score) correlation at baseline (pre-surgery), and 6 months, 1 year, and 5 years after surgery. Open circles are individual data points; solid line is a regression curve fitted to the data; and the shaded area around the curve represents the 95% confidence limits. Correlation coefficients (r) were: Baseline, r = 0.58; 6 months, r = 0.63; 1 year, r = 0.56; and 5 years, r = 0.63. P < 0.0001 at all timepoints.
SOARD Highlights (Summer et al, 2022):
Metabolic syndrome (MetS) is prevalent in adolescents undergoing bariatric surgery
Abdominal obesity is strongly associated with MetS risk in this population
Major reductions in MetS risk and abdominal obesity persist 5 years post-surgery
Interventions targeting abdominal obesity may reduce MetS risk in youth
Acknowledgements
We would like to thank the Teen-LABS study coordinators, participants, and data coordinating center staff for their ongoing dedication to the successful conduct of this study.
Funding Sources:
This study was supported in part by The Teen-LABS consortium, which is funded by cooperative agreements with the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK), through grants UM1DK072493 and UM1DK095710. Additional support was provided by the Center for Clinical and Translational Science and Training (CCTST) at the University of Cincinnati, which is funded by the National Institutes of Health (NIH) Clinical and Translational Science Award (CTSA) program, grant 2UL1TR001425.
Footnotes
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None of the authors report a conflict of interest in association with this research.
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Associated Data
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Supplementary Materials
Supplemental Figure 1. Measuring sagittal abdominal diameter (SAD) using calipers fitted with a water level (from NHANES 2015–2016 Anthropometry Manual(21). Upper left inset shows the water level with bubble centered to confirm a level surface prior to measuring.
Supplemental Figure 2. Scatter plots of Sagittal Abdominal Diameter (SAD) and Metabolic Syndrome z-score (MetS z-score) correlation at baseline (pre-surgery), and 6 months, 1 year, and 5 years after surgery. Open circles are individual data points; solid line is a regression curve fitted to the data; and the shaded area around the curve represents the 95% confidence limits. Correlation coefficients (r) were: Baseline, r = 0.58; 6 months, r = 0.63; 1 year, r = 0.56; and 5 years, r = 0.63. P < 0.0001 at all timepoints.