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. 2025 Dec 3;17:100239. doi: 10.1016/j.obpill.2025.100239

Adolescent obesity: Biochemical and dietary disparities in U.S. youth

John T Stutts a, Yong S Choe a, Ashley Lynn Bronston b,⁎
PMCID: PMC12768949  PMID: 41502847

Abstract

Background

Adolescence is a critical period of growth with elevated nutritional demands, yet many U.S. adolescents fail to meet recommended dietary intakes. This study analyzed data from three NHANES cycles (2013–2018) to assess obesity trends among adolescents aged 12–18, compare dietary intake by BMI, and evaluate nutritional biomarkers.

Methods

This retrospective observational study analyzed data from three NHANES cycles to estimate obesity prevalence among U.S. adolescents aged 12–18 years. Dietary intake and blood concentrations of micro- and macronutrients were compared between adolescents with and without obesity. Obesity was defined as BMI ≥95th percentile per CDC growth charts. Nutrient intake was assessed against Estimated Average Requirement (EAR) and Adequate Intake (AI) benchmarks, and biomarker levels were evaluated using established clinical thresholds. All statistical analyses were conducted using SAS version 9.4 and SAS Enterprise Guide version 8.3.

Results

Obesity prevalence increased slightly from 20.3% to 21.3% across cycles. Among all dietary intake variables analyzed, only one demonstrated a statistically significant difference between those with obesity and those without. The adolescents with obesity were significantly more likely to have protein intakes below the EAR (50.3% vs. 22.9%, P < 0.001). Over half of all adolescents failed to meet recommended intake levels for several nutrients, regardless of their BMI categories. The blood biomarker analysis revealed significantly lower levels of vitamin D, vitamin C, serum iron, albumin, and MCV in those with obesity, alongside higher levels of total protein, globulin, and hs-CRP (P ≤ 0.05). The prevalence of low vitamin D, low hemoglobin, low MCV, and iron deficiency anemia was also significantly higher in the group with obesity.

Conclusions

This study reveals that despite similar caloric intakes, adolescents with obesity exhibited higher rates of nutrient deficiencies and markers of inflammation. This work underscores the importance of recognizing nutrition gaps in adolescent care.

Keywords: Adolescence, Biomarkers, Dietary intake, Micronutrients, Obesity, Public health

Graphical abstract

Summary of Dietary Intake and Blood Biomarker Differences in Adolescents with Obesity Compared to Peers Without ObesityAbbreviations: EAR, Estimated Average Requirement; MCV, Mean Corpuscular Volume; hs-CRP, High-Sensitivity C-Reactive Protein. Created in BioRender. Bronston, A. (2025) https://BioRender.com/7d79gn2.

Image 1

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1. Introduction

Adolescence is a crucial stage in life, marking the transition from childhood to adulthood. This period, which typically occurs between the ages of 10 and 19, is characterized by rapid biological, psychological, emotional, and social growth [1]. Consequently, nutritional requirements during adolescence are elevated to accommodate the accelerated growth and physiological changes in body composition that occur during puberty. Throughout these years, adolescents typically attain approximately 20% of their adult height and 50% of their adult weight, while also undergoing considerable skeletal remodeling and experiencing up to a 40% increase in bone mass [2,3].

Diet quality during the formative years of adolescence has long-term effects on health outcomes, particularly in the prevention or development of non-communicable chronic diseases [4]. Excess weight during adolescence is linked to an increased risk of various chronic conditions, such as type 2 diabetes, hypertension, coronary heart disease, and metabolic-associated steatotic liver disease [5]. Yet, despite the importance of consuming a nutritious diet during this stage, many adolescents fail to meet the Dietary Reference Intakes (DRIs) for key nutrients [6,7]. These inadequacies are prevalent across body mass index (BMI) categories, but emerging data indicate that adolescents with obesity may be at heightened risk for specific nutrient shortfalls [6].

To date, few studies have comprehensively examined the intersection of obesity, dietary intake, and biochemical markers of nutritional status in U.S. adolescents using nationally representative data. The present study addresses this gap by analyzing National Health and Nutrition Examination Survey (NHANES) data to (1) assess trends in obesity prevalence among adolescents aged 12–18 years, (2) compare dietary intake between adolescents with and without obesity, and (3) evaluate differences in blood biomarker concentrations and the prevalence of low biomarker levels across BMI categories. These findings aim to inform targeted nutritional interventions and public health strategies to address both overt and hidden forms of malnutrition in adolescents.

2. Methods

This retrospective observational study utilized secondary data from the NHANES to estimate the prevalence of obesity among U.S. adolescents aged 12–18 years. Additionally, the study examined differences in dietary intake and circulating concentrations of micro- and macronutrients between adolescents with and without obesity.

NHANES data, representative of the non-institutionalized U.S. population, are released in biennial cycles and organized into five primary components: Demographics, Dietary, Examination, Laboratory, and Questionnaire. Three NHANES survey cycles—2013–2014, 2015–2016, and 2017–2018—were selected to evaluate trends in obesity prevalence among U.S. adolescents aged 12–18 years. Comparisons of dietary intake (based on total one-day nutrient intakes) and blood concentrations of micro- and macronutrients were conducted between adolescents with and without obesity using data from the 2017–2018 cycle. Although more recent cycles exist, they do not encompass all variables included in our study. Continuous variables are presented as means with standard errors (mean ± SE), while categorical variables are reported as percentages with corresponding 95% confidence intervals (CIs).

2.1. Study population

The study sample from the 2017–2018 survey cycle comprised adolescents aged 12–18 years who had not consumed any dietary supplements (defined as a product intended to supplement the diet containing one or more dietary ingredients [i.e., vitamins, minerals, herbs, botanicals, amino acids, or other substances] [8]) within the 30 days preceding data collection. Participants were divided into two cohorts based on BMI: those with obesity and those without. Obesity was defined as having a BMI at or above the 95th percentile according to the Centers for Disease Control and Prevention (CDC) sex-specific BMI-for-age growth charts [9]. Fig. 1 displays a flow diagram for participant selection.

Fig. 1.

Fig. 1

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Participant Selection Flowchart

National Health and Nutrition Examination Survey 2017–2018 participant selection flowchart. Abbreviation: BMI, Body Mass Index.

The initial study cohort comprised 1,051 adolescents aged 12–18 years. Of these, 58 participants were excluded due to missing BMI data, resulting in a sample of 993 adolescents. Subsequently, 255 participants were excluded due to unconfirmed ‘no dietary supplement usage’, yielding a final analytic sample of 738 adolescents. This cohort was stratified into two groups based on BMI: adolescents with obesity (n = 205) and adolescents without obesity (n = 533).

The demographic composition of the study population was as follows: 51.1% were male (n = 531) and 48.9% were female (n = 520). At the time of screening, the mean age of the population was 15.0 years, with a standard error of 0.1 years. The racial and ethnic distribution included 15.9% Mexican American, 6.9% Other Hispanic, 50.8% Non-Hispanic White, 13.7% Non-Hispanic Black, 5.3% Non-Hispanic Asian, and 7.4% individuals identifying as Other Race, including multiracial individuals (Table 1).

Table 1.

Demographic Characteristics of U.S. Adolescents Aged 12–18 Years (NHANES 2017–2018).

Age During NHANES Screening (in years)
Mean ± Standard Error (Minimum, Maximum) n		 15.0 ± 0.1 (12, 18) 1,051
Gender
Percent (95% CI) n		 Male 51.1 (45.9, 56.2) 531
Female 48.9 (43.8, 54.1) 520
Race/Ethnicity
Percent (95% CI) n		 Mexican American 15.9 (10.5, 21.4) 192
Other Hispanic 6.9 (4.3, 9.6) 70
Non-Hispanic White 50.8 (43.5, 58.1) 318
Non-Hispanic Black 13.7 (9.7, 17.7) 238
Non-Hispanic Asian 5.3 (2.8, 7.9) 136
Other Race - Including Multi-Racial 7.4 (4.8, 9.9) 97
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Abbreviations: NHANES, National Health and Nutrition Examination Survey; Cl, Confidence Interval; n, total sample size in data.

2.2. Outcome variables

The first outcome variable was the prevalence of obesity among adolescents aged 12–18 years, assessed using NHANES data from the 2013–2014, 2015–2016, and 2017–2018 cycles.

The second set of outcome variables pertained to dietary intake and the proportion of individuals with intakes below the Estimated Average Requirement (EAR) or Adequate Intake (AI), based on data from the NHANES 2017–2018 cycle. Nutrients assessed included: vitamin D (D2 + D3) (mcg), vitamin E (α-tocopherol) (mg), vitamin C (mg), potassium (mg), calcium (mg), food folate (mcg), vitamin A (retinol activity equivalents) (mcg), protein (gm), iron (mg), and dietary fiber (gm). Reference values for EAR and AI were obtained from the National Academies Press [10].

The third set of outcome variables was blood-based biomarkers, and the prevalence of values below established clinical thresholds. Biomarkers analyzed were: 25-hydroxyvitamin D2 + D3 (nmol/L), α-tocopherol (μmol/L), vitamin C (μmol/L), potassium (mmol/L), total calcium (mmol/L), red blood cell (RBC) folate (nmol/L), retinol (μmol/L), serum iron (μmol/L), total protein (g/L), serum albumin (g/L), globulin (g/L), high-sensitivity C-reactive protein (hs-CRP, mg/L), hemoglobin (g/dL), and mean corpuscular volume (MCV) (fL).

The prevalence of low biomarker levels was assessed using the following thresholds: 25-hydroxyvitamin D2 + D3 <50 nmol/L (Harriet Lane Handbook, 22nd Edition) [11], hemoglobin <13 g/dL for males and <12 g/dL for females, and MCV <79 fL for males and <78 fL for females (Children's Minnesota) [12]. The co-occurrence of low hemoglobin and low MCV was also evaluated.

All variables were analyzed using survey methodology. Continuous variables were examined through regression analyses adjusted for age, gender, and race/ethnicity. Categorical variables were assessed using logistic regression models, adjusted for age, gender, and race/ethnicity. Missing values in the variance computation were treated as not missing completely at random (NOMCAR) for the Taylor series variance estimation in the SAS survey procedures. All statistical analyses were conducted using SAS version 9.4 and SAS Enterprise Guide version 8.3 (SAS Institute Inc., Cary, NC). Hypothesis testing was performed using two-sided tests with a significance level set at 0.05.

3. Results

3.1. Obesity prevalence

The prevalence of obesity among U.S. adolescents aged 12–18 years remained consistently high across three NHANES cycles, ranging from ∼20% to ∼21% (Table 2).

Table 2.

Obesitya Prevalence Among U.S. Adolescents Aged 12–18 (NHANES 2013–2018).

NHANES Survey Cycle Percent (95% CI) n
2013–2014 20.3 (16.0, 24.7) 1232
2015–2016 20.5 (16.3, 24.7) 1129
2017–2018 21.3 (18.5, 24.0) 993
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a

Obesity is defined as a body mass index at or above the 95th percentile of the CDC sex-specific BMI-for-age growth charts. Abbreviations: NHANES, National Health and Nutrition Examination Survey; Cl, Confidence Interval; n, total sample size in data.

3.2. Dietary intake

Among all dietary intake variables analyzed, only one demonstrated a statistically significant difference (P ≤ 0.05) between adolescents with obesity and those without. Specifically, the adolescents with obesity had a higher prevalence of protein intake below the EAR compared to their counterparts without obesity [50.3% (95% CI: 39.9 to 60.7) vs. 22.9% (95% CI: 17.3 to 28.5), P < 0.001]. Of note, no significant differences were found between groups in total energy intake or the caloric contribution of macronutrients (Table 3).

Table 3.

Macronutrient (kcal) and Total Caloric Intake Among U.S. Adolescents Aged 12–18 (NHANES 2017–2018, no supplement).

Variable With Obesity LSM ± SE (n) Without Obesity LSM ± SE (n) Difference LSM ± SE P Value
Energy (kcal) 1823 ± 70 (193) 1948 ± 46 (488) −124 ± 80 0.143
Protein (kcal) 264 ± 9 (193) 275 ± 10 (488) −11 ± 14 0.439
Carbohydrate (kcal) 932 ± 39 (193) 1001 ± 26 (488) −69 ± 56 0.240
Total fat (kcal) 642 ± 36 (193) 688 ± 17 (488) −46 ± 32 0.166
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Regression analysis adjusted for Age, Gender, and Race/Ethnicity. Obesity is defined as a body mass index (BMI) at or above the 95th percentile based on CDC sex-specific BMI-for-age growth charts. No supplement refers to participants who reported no use of dietary supplements in the past 30 days. Protein (kcal) = 4 x Protein (g), Carbohydrate (kcal) = 4 x Carbohydrate (g), Total Fat (kcal) = 9 x Total Fat (g). Abbreviations: NHANES, National Health and Nutrition Examination Survey; LSM, Least Squares Mean; SE, Standard Error; n, sample size included in the analysis.

Although no other nutrient intake differences reached statistical significance between the two groups (all P > 0.20), more than 50% of adolescents—regardless of BMI categories—failed to meet the recommended intake levels (EAR or AI) for several key nutrients, including vitamin D, vitamin E, vitamin C, potassium, calcium, food folate, vitamin A, and dietary fiber.

3.3. Blood biomarkers

Compared to their peers without obesity, adolescents with obesity exhibited significantly lower mean concentrations (P ≤ 0.05) of several blood biomarkers, including 25-hydroxyvitamin D2 + D3, vitamin C, serum iron, serum albumin, and MCV. Additionally, there were non-significant trends (P ≤ 0.10) toward lower levels of total calcium and hemoglobin in the group with obesity (Table 4).

Table 4.

Blood Biomarkers Among U.S. Adolescents Aged 12–18 (NHANES 2017–2018, no supplement).

Variable With Obesity LSM ± SE (n) Without Obesity LSM ± SE (n) Difference LSM ± SE P Value
25-hydroxyvitamin D2 + D3 (nmol/L) 46.7 ± 1.7 (179) 53.8 ± 1.1 (469) −7.1 ± 1.8 0.001 §
α-tocopherol (μmol/L) 19.5 ± 0.4 (174) 19.3 ± 0.4 (457) 0.1 ± 0.5 0.777
Vitamin C (μmol/L) 48.5 ± 2.2 (175) 53.9 ± 1.7 (463) −5.5 ± 2.5 0.049 §
Potassium (mmol/L) 4.17 ± 0.04 (176) 4.20 ± 0.05 (450) −0.03 ± 0.03 0.282
Total Calcium (mmol/L) 2.37 ± 0.01 (176) 2.39 ± 0.00 (451) −0.02 ± 0.01 0.094
RBC Folate (nmol/L) 968.3 ± 42.5 (136) 990.1 ± 24.5 (351) −21.8 ± 49.5 0.667
Retinol (μmol/L) 1.51 ± 0.04 (179) 1.44 ± 0.02 (468) 0.07 ± 0.04 0.104
Iron, refrigerated serum (μmol/L) 13.9 ± 0.4 (176) 15.5 ± 0.3 (451) −1.6 ± 0.5 0.006 §
Total Protein (g/L) 73.6 ± 0.4 (176) 72.7 ± 0.2 (451) 0.9 ± 0.4 0.038 §
Albumin, refrigerated serum (g/L) 41.7 ± 0.3 (177) 43.1 ± 0.2 (451) −1.4 ± 0.3 <.001 §
Globulin (g/L) 31.9 ± 0.3 (176) 29.6 ± 0.3 (451) 2.4 ± 0.4 <.001 §
Hs-CRP (mg/L) 3.4 ± 0.3 (177) 1.3 ± 0.1 (450) 2.1 ± 0.3 <.001 §
Hemoglobin (g/dL) 13.7 ± 0.1 (182) 13.9 ± 0.1 (475) −0.2 ± 0.1 0.089
Mean Cell Volume (fL) 83.1 ± 0.5 (182) 85.2 ± 0.3 (475) −2.0 ± 0.5 0.001 §
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Regression analysis adjusted for Age, Gender, and Race/Ethnicity. Obesity is defined as a body mass index (BMI) at or above the 95th percentile based on CDC sex-specific BMI-for-age growth charts. No supplement refers to participants who reported no use of dietary supplements in the past 30 days. Abbreviations: NHANES, National Health and Nutrition Examination Survey; LSM, Least Squares Mean; SE, Standard Error; n, sample size included in the analysis; §, Statistically Significant; RBC, Red Blood Cell; hs-CRP, High-Sensitivity C-Reactive Protein.

Conversely, adolescents with obesity had significantly higher mean levels of total protein, globulin, and hs-CRP compared to their peers without obesity (Table 4). No other blood biomarkers showed statistically significant differences between the two groups (all P > 0.10).

The prevalence of low biomarker levels was also significantly higher among adolescents with obesity compared to those without. Specifically, the prevalence of low 25-hydroxyvitamin D2 + D3 was 51.9% in the group with obesity versus 26.8% in the group without obesity (P = 0.001). Similarly, the prevalence of low hemoglobin was significantly higher in adolescents with obesity (16.3%) than in their peers without obesity (7.5%) (P = 0.023). The prevalence of low MCV was also elevated in the group with obesity (13.4%) compared to the group without obesity (5.9%) (P = 0.017). Furthermore, the combined prevalence of low hemoglobin and low MCV was significantly higher among adolescents with obesity (11.2%) than among those without obesity (3.3%) (P = 0.020) (Table 5).

Table 5.

Prevalence of Low Blood Biomarkers Among U.S. Adolescents Aged 12–18 (NHANES 2017–2018, no supplement).

Variable With Obesity
Percent (95% CI)		 Without Obesity
Percent (95% CI)		 Ratio (n) Odds Ratio (95% CI) P Value
Low 25-hydroxyvitamin D2 + D3 (<50 nmol/L) 51.9 (39.9, 63.9) 26.8 (18.9, 34.8) With Obesity (179)/Without Obesity (469) 2.81 (1.60, 4.97) 0.001 §
Low Hemoglobin:
<13 g/dL for male,
<12 g/dL for female		 16.3 (9.7, 22.9) 7.5 (3.8, 11.3) With Obesity (182)/Without Obesity (475) 2.45 (1.15, 5.19) 0.023 §
Low Mean Cell Volume:
<79 fL for male,
<78 fL for female		 13.4 (6.2, 20.7) 5.9 (3.8, 8.0) With Obesity (182)/Without Obesity (475) 2.48 (1.21, 5.08) 0.017 §
Low Hemoglobin and Low Mean Cell Volume 11.2 (5.0, 17.5) 3.3 (1.2, 5.3) With Obesity (182)/Without Obesity (475) 3.70 (1.27, 10.79) 0.020 §
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Logistic regression analysis was adjusted for Age, Gender, and Race/Ethnicity. Obesity is defined as a body mass index (BMI) at or above the 95th percentile based on CDC sex-specific BMI-for-age growth charts. No supplement refers to participants who reported no use of dietary supplements in the past 30 days. Abbreviations: NHANES, National Health and Nutrition Examination Survey; Cl, Confidence Interval; n, sample size included in the analysis; §, Statistically Significant.

4. Discussion

This study provides a comprehensive analysis of the nutritional status of U.S. adolescents aged 12–18 years, with a specific focus on differences between those with and without obesity. Using NHANES data, we observed a slight upward trend in the prevalence of obesity in this age group, from 20.3% (2013–2014 cycle) to 21.3% (2017–2018 cycle). These findings highlight the persistent nature of adolescent obesity as a public health issue and reinforce the need for targeted interventions. It is important to note that obesity is a multifactorial condition that results from a complex interplay between genetic, socioeconomic, dietary, and lifestyle factors – rather than a single cause [13]. This complexity may help explain why, despite no significant differences in caloric intake between the two groups, our analysis revealed a pattern of nutrient inadequacy among adolescents with obesity. Notably, more than half of all adolescents, regardless of BMI categories, failed to meet the EAR or AI levels for several key nutrients, including vitamin D, vitamin E, vitamin C, vitamin A, potassium, calcium, food folate, and dietary fiber. These data align with previous research suggesting that U.S. adolescents, regardless of their BMI, fail to meet requirements for key nutrients and are susceptible to deficiencies [[14], [15], [16], [17], [18]].

However, one dietary intake variable—protein—stood out. Adolescents with obesity were significantly more likely to consume protein below the EAR compared to their peers without obesity (50.3% vs. 22.9%, P < 0.001). Given that the EAR is based on per-kilogram body weight, adolescents with higher body weight have greater absolute protein requirements. However, our findings revealed no significant difference in absolute protein intake between groups, suggesting that adolescents with obesity may not be adjusting their intake to meet increased needs. This discrepancy could have implications for growth, metabolic health, and weight management, as inadequate protein intake may affect lean mass preservation and satiety.

Biochemical analyses provided further evidence of nutritional disparities. Adolescents with obesity exhibited significantly lower mean concentrations (P ≤ 0.05) of several nutrition-related blood biomarkers, including 25-hydroxyvitamin D2 + D3, vitamin C, and serum iron.

Notably, adolescents with obesity are at a heightened risk for vitamin D insufficiency [19]. Although vitamin D stored in adipose tissue retains metabolic activity and may influence inflammatory pathways [20], the extent to which it mirrors the activity of circulating vitamin D remains unclear. Additionally, factors such as reduced sunlight exposure and lower dietary intake may contribute to this insufficiency [21]. Interestingly, our analysis found no significant differences in vitamin D intake between adolescents with and without obesity, suggesting that metabolic differences may underlie the observed disparities in serum vitamin D levels.

In addition to lower vitamin D concentrations, adolescents with obesity exhibited significantly lower serum vitamin C levels despite no significant differences in dietary intake between groups. This finding is noteworthy because low circulating vitamin C levels have been consistently associated with metabolic syndrome [[22], [23], [24]]. Vitamin C deficiency may promote endotoxemia, contributing to metabolic dysfunction [25], whereas supplementation has been shown to modulate adipocyte lipolysis [26] and protect adipose tissue from inflammation, hypoxia, and endoplasmic reticulum stress [27,28]. A 2024 systematized review [29] highlighted several proposed mechanisms that may explain the reduced vitamin C status in individuals with obesity, including volumetric dilution (where increased body mass lowers plasma concentrations despite similar intake) [30], metabolic alterations such as increased oxidative stress and accelerated turnover, and gut microbial dysbiosis [25], which may impair absorption or utilization. Collectively, these factors suggest that obesity may increase vitamin C requirements or reduce its bioavailability, independent of dietary intake.

To act effectively as an antioxidant, relatively high concentrations of vitamin C must be maintained in the body [31]. An individual's inability to sustain adequate serum levels may have long-term health implications, particularly with conditions driven by oxidative stress [31] (i.e., atherosclerosis, hypertension) [32]. A 2024 study found a negative correlation between serum vitamin C levels and high-sensitivity C-reactive protein (hs-CRP) in U.S. adolescents [33], which is consistent with our findings. Existing literature suggests that vitamin C supplementation or sufficient dietary intake may reduce levels of CRP and other inflammatory markers, but further research is warranted [[34], [35], [36]].

Our research also highlighted a concerning prevalence of iron deficiency. Iron is essential for cognitive development, energy metabolism, and immune function [21,37,38]. Adolescents with obesity may experience iron deficiency due to a combination of poor dietary intake, inflammation-induced sequestration of iron, and potential gastrointestinal blood loss [21,37]. Our findings further revealed a higher prevalence of low hemoglobin and low MCV in this population, indicating an elevated risk for iron deficiency anemia.

Several additional blood biomarkers supported the notion of chronic inflammation in adolescents with obesity. Compared to their peers, these adolescents exhibited significantly lower serum albumin levels and significantly higher levels of globulin and hs-CRP. Albumin, a negative acute-phase reactant, tends to decrease during inflammatory states [39]. However, its levels are not specific to inflammation alone, as they can also be influenced by liver function, kidney health, and protein losses [40]. Contrary to earlier assumptions, albumin is no longer considered a reliable marker of nutritional status, and its use in this context is being phased out. Elevated globulin levels can indicate various conditions, including acute infections and chronic diseases [41]. The literature shows higher levels of inflammation in those individuals with obesity [[42], [43], [44], [45], [46]]. In this study, we theorized that the high levels of globulin are a result of chronic inflammation associated with obesity. Lastly, elevated hs-CRP levels are a strong indicator of inflammation in the body [47]. The concurrent presence of inflammation and micronutrient deficiencies may reflect complex interactions between diet, adiposity, and immune function in adolescents with obesity.

These findings have important implications for clinical practice and public health efforts. First, they emphasize the importance of considering not only nutrient intake, but also blood-based biomarkers. This combination of assessments may be necessary for accurately identifying hidden forms of malnutrition, understanding metabolic and inflammatory status, and guiding more targeted nutritional interventions. Second, they suggest a need to shift the focus away from reducing excess energy intake to improving the overall quality of the diet. Thirdly, the observed disparities highlight the importance of implementing targeted strategies to address the “hidden” forms of malnutrition during adolescence.

5. Limitations

Several limitations should be acknowledged. The retrospective observational design using NHANES data precludes the ability to make causal inferences. Additionally, dietary intake was assessed using a single 24-h recall, which may not accurately represent habitual intake and is subject to both recall and reporting biases. Although participants who reported supplement use were excluded, unreported use may have influenced biomarker levels. The NHANES database did not include information on anti-obesity medication use within our population. These medications can suppress appetite and alter dietary patterns, which may have influenced results. The study did not look at access to healthy foods, which may be a determinant of dietary quality. While discussions about food security and environmental factors are beyond the scope of this analysis, they are important contributors to obesity and other non-communicable diseases.

6. Conclusion

This study highlights a nutrition paradox in adolescent health: despite similar caloric intake, adolescents with obesity face disproportionately higher rates of micronutrient deficiencies and biochemical markers of poor nutritional status and inflammation. Using nationally representative NHANES data, we found that obesity in U.S. adolescents remains prevalent and is accompanied by significant disparities in both dietary intake and blood biomarkers. These findings underscore the complexity of pediatric obesity, which encompasses not only excess weight but also hidden forms of malnutrition and chronic inflammation.

Adolescents with obesity may appear overnourished by weight-based metrics, yet suffer from nutrient inadequacies that can impair growth, development, and long-term health outcomes. This calls for a paradigm shift in how we address adolescent nutrition. To effectively address these nutrition challenges, public health strategies must prioritize early, comprehensive, and individualized interventions. Emphasis should be placed on improving diet quality in this population and addressing the inflammatory and metabolic consequences of obesity.

Future research should explore the long-term impact of such interventions and refine clinical guidelines to better capture the nuanced nutritional needs of adolescents with obesity. By recognizing and addressing both overt and hidden forms of malnutrition, we can better support the health and development of current and future generations.

6.1. Key takeaway clinical messages

  • •

    Public health efforts and early interventions are needed to address adolescent obesity: the prevalence of obesity among U.S. adolescents aged 12–18 years remained consistently high across three NHANES cycles: ∼20–21%.

  • •

    Poor diet quality is a widespread problem among U.S. adolescents, regardless of their weight status: over 50% of adolescents failed to meet EAR or AI for many nutrients critical for growth and development.

  • •

    Adolescents with obesity may require additional nutritional monitoring and individualized care: those with obesity faced disproportionately higher rates of micronutrient deficiencies and biochemical markers of poor nutritional status and inflammation.

CRediT author statement

Conceptualization, J.S., Y.C.; Methodology, Y.C.; Software, Y.C.; Formal Analysis, Y.C.; Resources, J.S.; Data Curation, Y.C.; Writing - Original Draft, A.B.; Writing - Review & Editing, A.B.; Supervision, J.S.; Project Administration, A.B.

Declaration of artificial intelligence (AI) and AI-assisted technologies

During the preparation of this work, the authors used Grammarly to review spelling and grammar. After using this tool, the authors reviewed and edited the content as needed and take full responsibility for the content of the publication.

Source of funding

This manuscript was not funded by any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. As employees of Abbott Laboratories, J.S., Y.C., and A.B. receive salaries for their professional responsibilities.

Declaration of competing interests

All the authors listed have approved the manuscript and have no conflicts of interest in this paper. J.S., Y.C., and A.B. are employees and stockholders of Abbott (Abbott Park, IL, USA).

Contributor Information

John T. Stutts, Email: John.Stutts1@abbott.com.

Yong S. Choe, Email: Yong.Choe@abbott.com.

Ashley Lynn Bronston, Email: Ashley.Bronston@abbott.com.

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