This is the first ExWAS study in NHANES describing associations between zBMI, nutritional and clinical factors in adolescents. In effect, we conducted an exposome-wide association study exploring 121 explanatory variables with respect to (sex-specific) BMI-for-age in non-diabetic and non-pregnant adolescents aged 12–18 years old in the 2003–2004 NHANES survey and used the 2013–2014 survey for validation. After adjusting for age, sex, race-ethnicity, household smoking, and income to poverty ratio, we found ALT, GGT, mean cell volume, segmented neutrophils number, triglycerides, serum uric acid and white blood cell count to have statistically significant associations with zBMI after adjusting for FDR in both the discovery and replication datasets, being in line with literature findings for our study age group.
Uric acid is the end product of purine metabolism in humans and its magnitude depends on dietary purines (from animal proteins, meat, seafood, beer and fructose sources), the degradation of endogenous purines as well as the renal and intestinal excretion of urate (Gustafsson and Unwin 2013). Although serum uric acid levels increase differently by sex from birth till adolescence, there is still no universally accepted threshold for defining hyperuricemia, or excess concentrations of serum uric acid in children and adolescents (Kubota 2019). Elevated levels of uric acid have been associated with obesity and non-communicable diseases, such as kidney and cardiovascular diseases in children and adolescents (Bussler et al. 2017; Kubota 2019). Recent literature emphasized the association between uric acid and metabolic syndrome (MetS) outcomes in children and adolescents, such as glucose intolerance, central obesity, hypertension, and dyslipidemia (Bussler et al. 2017; Goodman 2020; Kong et al. 2013). Uric acid was associated with the prevalence of metabolic syndrome and its components, as shown in a cross-sectional analysis of 1370 adolescents (12–17 years of age) using data from NHANES 1999–2002; the unweighted prevalence of metabolic syndrome was ≈ 21% in the highest quartile (> 339 µmol/L) as compared to ≈ 10% in the third quartile (≤ 339 µmol/L), ≈ 4% in the second quartile (≤ 291 µmol/L) and < 1% among participants in the unweighted lowest quartile of serum concentrations of uric acid (≤ 250 µmol/L) (Ford et al. 2007). A similar distribution of serum uric acid levels was observed in this study targeting non-diabetic adolescents for the 2003–2004 dataset (median: 297 µmol/L, interquartile range (IQR): [250 µmol/L, 351 µmol/L]) and 2013–2014 dataset (median: 297.4 µmol/L, IQR: [244 µmol/L, 351 µmol/L]). In the adjusted multivariable analysis, the strongest adjusted effect size of the association between various exposomic variables with zBMI was observed for uric acid in both discovery (estimate = 0.452) and replication (estimate = 0.707) analyses of both surveys. After excluding non-obese participants, the association between zBMI and uric acid was no longer statistically significant, although it still showed the strongest effect size in both discovery and replication subsets (adjusted estimates of 0.24 and 0.39, respectively). Thus, our findings highlight the pathogenic role of elevated concentrations of uric acid in young obese age groups, as showcased in different studies. In a case-control study conducted in Italy among 120 children and adolescents with primary obesity (zBMI ≥ 97th percentile) and 50 healthy controls, carotid intima-media thickness was significantly correlated (r = 0.61; 95% CI, 0.58–0.64) with the fourth quartile of uric acid among obese children regardless of the presence of metabolic syndrome, defined in the study as ≥ 3 or more of the following criteria: obesity, hypertension, low HDL cholesterol, elevated triglycerides, and impaired fasting glucose and/or insulin resistance (Pacifico et al. 2008). On the other hand, the association between serum uric acid and cardiovascular diseases, irrespectively of BMI, has also been documented in a study conducted on an 1999–2006 unweighted NHANES sample of 12–17 years old adolescents; after adjusting for age, sex, race/ethnicity and BMI, the odds of having elevated blood pressure (mean systolic and/or diastolic blood pressure percentile ≥ 95th percentile) was 1.38 (95% CI, 1.16 to 1.65) for each 0.1 mg/dL increase in uric acid (Loeffler et al. 2012). In a randomized, double-blinded trial among pre-hypertensive obese adolescents (11–17 years old), patients treated with two mechanisms of urate reduction (allopurinol and probenecid) did not continue to gain weight during the 3-months study period and showed a similar and significant reduction in their systolic blood pressure by 10.2 mm Hg and their diastolic by 9.0 mm Hg in the two treatment groups as compared to the placebo group; thus highlighting the role of uric acid as a biochemical mediator of increased blood pressure (Soletsky and Feig 2012).
The observed association of both uric acid and GGT with obesity and other cardiovascular risk factors has been previously documented in the literature. In a cross-sectional study on 2067 children and adolescents (6–20 years) in Hong Kong, a combined effect of the upper quartiles of both uric acid and GGT in association with obesity, low high-density lipoprotein cholesterol (HDL-C) level and high blood pressure (adjusted odds ratios ranged from 1.63 to 5.82, all p < 0.005) (Kong et al. 2013). GGT, a liver enzyme implicated in the degradation of glutathione is associated with BMI, total cholesterol, diabetes mellitus (all components of MetS) as well as cardiovascular disease and all-cause mortality in adults (Mason et al. 2010). The correlation between GGT and MetS and hypertension among younger age groups was demonstrated in a 10-year longitudinal study in Taiwan, where subjects (10–15 years) with higher baseline levels of GGT were at least twice more likely to develop MetS and hypertension during the follow-up period (Lin et al. 2017). In our analysis, we showed a positive correlation, albeit weak, between uric acid and GGT in both discovery and replication datasets, without adjustment for zBMI.
Also, ALT had a statistically significant association with zBMI in the multivariable analysis using both the discovery and replication datasets. ALT is a liver enzyme related to fat liver accumulation and considered a useful biomarker for non-alcoholic fatty liver disease (NAFLD) (Liu et al. 2014). The association between serum ALT and zBMI was previously documented in a study among adolescents (12–18 years) from NHANES III (1988–1994), in which overweight and obese study subjects were three to six times more likely to have higher levels of ALT (> 30 U/L) as compared to those with normal weight (Strauss et al. 2000). Similarly, a significant correlation between ALT, zBMI and metabolic syndrome was found among 5411 adolescents aged 12–19 years from NHANES 1999–2014; yet, with no significance increase in the prevalence of increased ALT over time (Fermin et al. 2017). On the other hand, elevated serum ALT levels (> 40 U/L) were also associated with markers of metabolic syndrome, as demonstrated in a study among adolescents 10–19 years old from the Korean National Health and Nutrition Examination Survey 1998 (Park et al. 2005).
Elevated triglycerides or hypertriglyceridemia is common among obese children and adolescents, and this component of metabolic syndrome is a known biomarker of cardiovascular disease risk (Jung and Yoo 2018). In our analysis, a positive association between triglycerides and zBMI was found in each of the discovery (estimate = 0.285) and replication dataset (estimate = 0.444), but not in the sensitivity analysis in non-obese adolescents. This positive association found in our ExWAS study between triglycerides and zBMI is in line with the findings of a study on abnormal lipid levels among adolescents (12–19 years) in NHANES 1999–2006, in which 22% of overweight and 43% of obese had at least one abnormal lipid level including elevated triglycerides, the most common lipid abnormality associated with excess weight (Centers for Disease Control and Prevention (CDC) 2010).
The inverse association between mean cell volume and zBMI found here was previously documented in a study of 210 female adolescents (12–17 years) using NHANES 2003–2004; lower mean cell volume, transferrin saturation, and higher serum transferrin receptor were found among overweight and obese female study participants, indicative of iron deficiency; these findings, consistent with those among obese adults, suggest that obesity-associated anemia reported in adults and children also occur in female adolescents (Tussing-Humphreys et al. 2009).
Our analysis also showed the association between zBMI and inflammatory markers, such as white blood cell count and segmented neutrophils number. Such findings were also documented among adolescents (Hsieh et al. 2007; Reyes et al. 2015; Wu et al. 2010), suggesting that obesity-induced inflammation could start in early childhood (Singer and Lumeng 2017).
None of the dietary variables remained significant at multivariable analysis in the discovery and replication datasets. Additionally, weak correlations were found between the laboratory and dietary variables in each of the discovery and replication datasets.
The associations found between uric acid, GGT or ALT with zBMI using the whole study population were no longer statistically significant in the sensitivity analysis that included non-obese adolescents. This observation warrants for further investigation on the potential use of these biochemical parameters as biomarkers in the early stages of obesogenesis in adolescence or childhood. The sex specific trends observed in the association between the three aforementioned biochemical parameters and zBMI are also worth of detailed investigation in other population studies as they might be useful in future obesity screening and prevention programs in adolescence and/or earlier life stages.
The strength of this study lies in the agnostic nature of the ExWAS approach which allows for the simultaneous assessment of multiple parameters and their associations with different outcomes. The NHANES dataset is considered representative of the US population; in effect, the weighted estimates of overweight and obesity prevalence in this US study population (12–18 years old) were similar in the 2003–2004 (18.4% and 18.1%, respectively) and 2013–2014 survey datasets (18.5% and 20.6%, respectively). Moreover, the obesity prevalence estimates in both datasets were similar to the weighted estimates by the U.S. CDC of 17.4 % (13.9% − 21.3%) for the 2003–2004 survey and 20.6% (16.2% − 25.6%) for the 2013–2014 survey (Ogden et al., 2016). The created models are considered as robust, being tested on two NHANES datasets, ten years apart. Another strong feature of this ExWAS study was the inclusion of variables belonging to all exposome domains; the general external (individual household income), the specific external (dietary variables; education; household smoking and physical activity) and the internal domain (intrinsic and laboratory variables). Yet, in order to fully explore the exposome’s utility, it is encouraged to include additional groups of environmental components in relation to the studied health outcome (Haddad et al. 2019).
Due to the cross-sectional study design of NHANES causal associations cannot be established and although the approach was as inclusive as possible, not all exposome parameters were available or could be included, e.g. chemical exposure data were not fully available in these NHANES surveys. In addition, dietary assessment using self-reported food frequency might be subject to recall bias, underreporting, over reporting, or omission of foods (Raatz et al. 2017); furthermore, because of day-to-day variation or within-person variability in dietary data, multiple measures of daily intake are recommended to ensure sufficient reliability (Institute of Medicine (US) Committee on Dietary Risk Assessment in the WIC Program, 2005). Another possible limitation would be the use of only two surveys out of the total available NHANES year surveys; ExWAS studies integrating additional NHANES datasets as well as a bigger number of environmental exposure variables are warranted to improve our knowledge in the environmental determinants of obesogenesis.
Adolescence is a critical life window of susceptibility to metabolic diseases, such as, overweight and obesity during which ongoing children’s development may be perturbed by a suite of environmental stressors, including lifestyle/behavioral factors and dietary habits (Schneider et al. 2017). The methodological framework of the human exposome and its tools allow for a comprehensive assessment of multiple factors with respect to disease outcomes through an agnostic, untargeted and hypothesis-generating approach. The NHANES-based discovered and replicated predictors of zBMI among U.S. adolescents seem to be in line with the global literature and further highlight their importance as potential early-stage biomarkers of excess weight. Additional studies at younger age groups are warranted to better elucidate the implication of these biomarkers in metabolic disease pathogenesis.