Disparities in the Cardiometabolic Impact of Adiposity among African American and Hispanic Adolescents
by
, , , , and
Pedro A. Velásquez-Mieyer
1,2,
Ramfis Nieto-Martinez
1,3,
Andres E. Velasquez
2,*,
Xichen Mou
4,
Stephanie Young-Moss
5,
Jeffrey I. Mechanick
6,
Cori Cohen Grant
7 and
Claudia P. Neira
1,2 1
Lifedoc Health, Memphis, TN 38115, USA
2
Lifedoc Research, 6625 Lenox Park Drive, Suite 205, Memphis, TN 38115, USA
3
Departments of Global Health and Population and Epidemiology, Harvard TH Chan School of Public Health, Harvard University, Boston, MA 02115, USA
4
School of Public Health, University of Memphis, Memphis, TN 38152, USA
5
Independent Researcher, Indianapolis, IN 47906, USA
6
The Marie-Josée and Henry R. Kravis Center for Cardiovascular Health at Mount Sinai Heart, Division of Endocrinology, Diabetes and Bone Disease, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
7
Department of Preventive Medicine and Tennessee Population Health Consortium, The University of Tennessee Health Science Center, Memphis, TN 38103, USA
*
Author to whom correspondence should be addressed.
Nutrients 2024, 16(18), 3143; https://doi.org/10.3390/nu16183143
Submission received: 14 August 2024
/
Revised: 3 September 2024
/
Accepted: 11 September 2024
/
Published: 18 September 2024
(This article belongs to the Special Issue Featured Articles on Nutrition and Obesity Management (2nd Edition))
Abstract
:As adiposity increases in youth, so does the prevalence of cardiometabolic risk factors (CMRFs). The etiology of adiposity-based chronic disease and CMRFs includes ethnoracial disparities that are rarely considered in current treatment approaches. Precision interventions require further characterization of these disparities among high-risk youth. The objective of this study was to characterize differences in CMRF among African American (AA) and Hispanic (H) adolescents with varying levels of adiposity. A cross-sectional analysis of 2284 adolescents aged 12–17 was conducted using 3-year clinical data from Lifedoc Health. CMRF prevalence were compared using χ2, with logistic regression models (LRM) applied to explore the relationships between exposures (age, sex, ethnoracial group, adiposity) and CMRF outcomes. Prevalence of CMRF rose with increasing adiposity, which was the strongest determinant of risk overall. However, individual risk profiles differed between the two groups, with H having higher prevalence of metabolic syndrome (MetS), higher triglycerides and liver enzymes, and low high-density lipoprotein cholesterol (HDL-c). Meanwhile, AA had higher prevalence of elevated blood pressure (BP) in the overweight category, prediabetes in overweight to severe obesity, and type 2 diabetes in obesity. LRM showed 3.0-fold greater chance of impaired glucose metabolism in AA than H, who were 1.7, 5.9, and 8.3 times more likely to have low HDL-c, high liver enzymes, and high triglycerides, respectively. Overweight/obesity prevalence was very high among AA and H adolescents. Excess adiposity was associated with an increased prevalence of CMRF, with individual risk factors differing between groups as adiposity increased. Research within routine clinical settings is required to better characterize these discrepancies and ameliorate their adverse impact on health in the transition to adulthood.
Keywords:
pediatric; obesity; cardiometabolic; risk factors; adiposity; racial disparities; chronic disease1. Introduction
The rate of obesity among children and adolescents aged 10–17 years in the US was reported to be 16.2% in the 2019–2020 National Survey of Children’s Health [1]. The prevalence of overweight/obesity varies between ethnoracial groups. Data from the National Health and Nutrition Survey (NHANES) (2015–2016) show that rates in African American and Hispanic children/adolescents aged 2–19 years exceed the national average and are higher than for other ethnoracial groups [2].
As adiposity increases in children and adolescents, so does the prevalence of cardiometabolic risk factors (CMRFs), including hypertension, dyslipidemia, and type 2 diabetes (T2D), all of which collectively make up metabolic syndrome [3]. The etiology of adiposity-based chronic disease and CMRFs includes ethnoracial disparities but these are rarely considered in current treatment approaches. As such, describing the prevalence, distribution, and factors associated with childhood obesity in more vulnerable groups may help to design effective interventions. Pragmatic real-world data from clinical settings that assess differences in the impact of adiposity on CMRFs in ethnoracially diverse adolescents are needed.
In Tennessee, rates of obesity among adolescents (20.8%) exceeded the national average [1], while 35% of adults were living with obesity in 2021 [4]. Compared with other US cities, Memphis, TN has the seventh highest proportion of African Americans (64.6%) but under-representation of Hispanics (7.7%) [3,5]. Lifedoc Health (LDH), a multidisciplinary healthcare organization, successfully implemented a data-driven model and clinical protocols to attenuate the burden of cardiometabolic-based chronic disease in children and adolescents in the Greater Memphis area [6]. However, the implementation of a more precise transcultural model requires the characterization of different CMRF phenotypes in high-risk minority groups. Thus, this study aims to elucidate disparities in the CMRFs of African American and Hispanic adolescents with varying levels of adiposity in a real-world setting.
2. Materials and Methods
2.1. Study Design and Sampling
A cross-sectional retrospective analysis was conducted of 3-year clinical data (2018–2020 ± 6 weeks) from the patient records of AA and H adolescents aged 12–17 years under LDH. Patients were included regardless of insurance coverage (private, Medicaid, or none) or whether they were assigned to the practice by the payer or referred from outside primary care for co-management [6]. Patients who were pregnant, underweight (body mass index-for-age percentile [BMI%] < 5.0%), or had any known genetic cause of obesity were excluded. All procedures were performed in accordance with the Declaration of Helsinki. Prior to data collection, informed consent was obtained. No independent ethics committee or institutional review board was sought, as this study does not meet the definition of human subject research as defined in Federal Regulation 45 CFR 46.102 [7].
2.2. Physical and Biochemical Parameters
Weight was measured in light clothing, without shoes, using a calibrated scale (Digital Platform Scale Pro Plus 2101KL, Health-o-meter®, McCook, IL, USA). Height was measured using a digital stationary stadiometer (Seca® 264, Chino, CA, USA). Blood pressure (BP) was measured in the right arm, using appropriate cuff size, in a sitting position, with a aneroid sphygmomanometer (McKesson®, Irving, TX, USA) [8]. Blood glucose, glycated hemoglobin (HbA1c), high-sensitivity C-reactive protein (hs-CRP), fibrinogen, triglycerides (TG), total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), aspartate aminotransferase (AST), alanine transaminase (ALT), and 25-OH-vitamin D were measured using random blood samples in a single certified laboratory (LabCorp, Burlington, NC, USA) [9].
2.3. Study Variables and Definitions
Participants self-reported as male or female and as African American and Hispanic. BMI% percentile was determined using age- and sex-based definitions of BMI, with individuals classified as having normal weight (<85th BMI%), overweight (≥85th and <95th BMI%), moderate obesity (≥95th and <99th BMI%), or severe obesity (≥99th BMI%) [10,11]. This modified version of the World Health Organization criteria (BMI% ≥95th) was used to define obesity due to limitations in using waist circumference (WC) to measure excess adiposity, including variability in cut-off points in age, race and ethnicity. Elevated blood pressure (BP) was defined for children aged 1–<13 years as ≥90th percentile or 120/80 mmHg (whichever was lower) and for children ≥13 years ≥120/<80 mmHg. Hypertension was defined for children aged <13 years as ≥95th percentile or ≥130/80 mmHg (whichever was lower) and for children ≥13 years as ≥130/80 mmHg or taking BP-lowering medication [12]. Dyslipidemia was defined as TC ≥ 200 mg/dL, LDL-C ≥ 130 mg/dL, TG ≥ 130 mg/dL, HDL-C < 40 mg/dL, or taking any lipid-lowering medications. Prediabetes was defined as HbA1c ≥ 5.7% and <6.5%, and T2D as either HbA1c ≥ 6.5% or a personal history of diabetes [13]. Fasting blood glucose was not used to define dysglycemia status due to practical difficulties in verifying the fasted state in a clinical setting. Metabolic syndrome (MetS) was defined as any ≥3 of the following criteria: overweight/obesity/severe obesity [14], elevated BP/hypertension, elevated TG, low HDL-C, and prediabetes/T2D [15], while elevated levels of liver enzymes, hs-CRP and fibrinogen, or vitamin D insufficiency were additional CMRFs [16,17]. High liver enzymes were defined as AST > 40 IU/L or ALT > 32 IU/L [9]. Vitamin D insufficiency was established if 25-OH-vitamin D was <30 ng/mL [18].
2.4. Statistical Analysis
Data were analyzed using R (version 3.6.2). Variables with normal distribution were presented as mean ± standard error, with differences between groups assessed by the student t-test. Frequencies were presented as percentages and 95% confidence intervals (CIs). Groups were compared using the χ2 test. Multivariate logistic regression models were applied to explore the relationships between exposures (age, sex, ethnoracial group, and adiposity) and outcomes (impaired glucose metabolism [IGM], elevated BP, high TC, low HDL-C, high LDL-C, high TG, high AST/ALT, inflammation, vitamin D insufficiency), adjusted by age and sex. Odds ratios and 95% CIs were estimated across different groups. The significance threshold was 0.05 for all analyses.
3. Results
3.1. Population Characteristics
Overall, the analysis cohort consisted of 2284 adolescents meeting inclusion criteria. Of these, 25.8% had private health insurance, 67.6% had a Medicaid healthcare plan, and 6.6% were uninsured. Overall, 687 (30.1%) were African American and 1597 (69.9%) were Hispanic, 50.3% were female, and mean age, weight, and BMI was 13.9 years, 70.9 kg, and 26.9 kg/m2, respectively. Sex distribution was similar between groups. Males had higher mean weight, height, BP, TG, blood glucose, AST, ALT, and vitamin D, while females had higher mean BMI, HDL-C, hs-CRP, and fibrinogen. African American adolescents had higher mean weight, height, BMI, BP, LDL-C, hs-CRP, and HbA1c, and lower vitamin D, while Hispanic adolescents had higher TG, AST, and ALT. Hispanic males had lower HDL-C and higher blood glucose than African American males (Table 1).
3.2. CMRF Prevalence by Ethnoracial Group, Sex, and Adiposity
Of the total cohort, 40.7% were a normal weight, 20.2% were overweight, 25.0% had obesity, and 14.2% had severe obesity (Table 2). Prevalence was high for the following CMRFs: vitamin D insufficiency, inflammation, dyslipidemia, MetS, high TG, low HDL-C, IGM, prediabetes, hypertension, high liver enzymes, elevated BP, high TC, high LDL-C, and T2D (92.4%, 55.6%, 46.2%, 34.1%, 29.2%, 24.6%, 18.0%, 16.3%, 12.3%, 12.0%, 8.5%, 7.1%, 4.9%, and 1.7%, respectively; Table 2). Males had a higher frequency of severe obesity, elevated BP, hypertension, dyslipidemia, high TG, low HDL-C, MetS, and high liver enzymes, while females had significantly higher rates of inflammation and vitamin D insufficiency (Table 2).
A higher proportion of Hispanic than African American males had overweight (20.7% vs. 12.6%) or obesity (27.6% vs. 17.7%), although severe obesity was more frequent in African American males (11.4% vs. 26.2%). IGM was also more frequent in African American males (primarily driven by prediabetes as T2D prevalence was similar between the groups). Hispanic males had higher prevalence of dyslipidemia, high TG, and high liver enzymes. Among females, overweight was more frequent in Hispanic than African American adolescents (25.9% vs. 13.8%), no difference in obesity was found, and severe obesity was more frequent in African American females (5.4% vs. 27.3%). Hispanic females also had a higher prevalence of dyslipidemia, high TG, and high liver enzymes, while African American females had a higher prevalence of prediabetes, T2D, elevated BP, hypertension, high LDL-C, and vitamin D insufficiency (Table 2).
In both groups, adiposity was the strongest determinant of cardiometabolic risk. CMRFs became more prevalent as severity of adiposity increased, except by high TG levels that were more prevalent in Hispanic adolescents regardless of level of adiposity (Table 3). As adiposity increased from overweight to severe obesity, Hispanic adolescents exhibited a significantly higher prevalence of dyslipidemia, lower HDL-C, and higher liver enzymes, with MetS prevalence also significantly higher in those with obesity and severe obesity. African American adolescents demonstrated a higher prevalence of elevated BP in the overweight category, prediabetes in the overweight to severe obesity categories, and T2D in the obesity category (Table 3).
3.3. Association between Demographics and BMI Categories with Cardiometabolic Abnormalities
Males were significantly more likely to have elevated BP, low HDL-C, high TG, and high liver enzymes, while inflammation and vitamin D insufficiency were more likely in females (Table 4). Older adolescents (aged 15–17 years) had 3.4-fold higher odds of hypertension and were more likely to have high TC, high LDL-C, and high liver enzymes than younger adolescents (12–14 years) (Table 4). Compared with Hispanic, African American ethnicity was associated with 3.0-fold greater odds of IGM, whereas Hispanic adolescents were more likely to have low HDL-C (1.7-fold higher odds), high liver enzymes (5.9-fold higher odds), and high TG (8.3-fold higher odds) than African American adolescents (Table 4).
All CMRFs tended to increase with adiposity, with a significantly increased risk of IGM, elevated BP, low HDL-C, high TG, and inflammation in overweight vs. normal weight individuals. The increased risk of high liver enzymes and vitamin D insufficiency was significant in adolescents with overweight vs. normal weight, while risk of high TC and high LDL-C became significant with severe obesity (Table 4).
4. Discussion
This study illustrates ethnoracial differences in the association of the progressive increase in adiposity with the CMRF profile of African American and Hispanic adolescents receiving care at LDH. The main finding of this study was that, independently of ethnoracial group, age, and sex, adiposity was the strongest determinant of cardiometabolic risk. Another relevant finding was that BMI% thresholds used in clinical practice were not sufficient at highlighting health implications in adolescents with adiposity in an appropriate amount of time. By the time individuals were submitted for evaluation (≥85th BMI%, classified as overweight) [14], the prevalence of IGM, elevated BP, low HDL-C, and high TG were already elevated and continued to progress with increasing adiposity. Established clinical guidelines recommend the evaluation and treatment of individuals only once they are overweight [14]. Thus, early identification of high-risk individuals in these minority groups is lacking and ultimately precludes the potential for earlier and more proactive interventions [19,20].
The rate of increase in obesity has been greater in children than in adults in many countries [21], The frequency of obesity in our study (39.2%) was higher than the 20.6% reported for US adolescents (12–19 years) in NHANES (2015–2016) [22]. In NHANES, no overall difference was found in the prevalence of obesity between Hispanic adolescents (25.8%) and African American [22] adolescents (22.0%), although obesity was more frequent in Hispanic than African American males (28% vs. 19%). We found that African American adolescents had a higher prevalence of severe obesity in both sexes, although overweight was more prevalent in Hispanic adolescents of both sexes. Our study is not a population-based sampling analysis like NHANES, but a representation of a multi-specialty clinical setting where most patients were assigned by insurance companies, which may explain these differences. Several factors may also account for the higher prevalence of obesity in our study. First, the sample was drawn from a predominantly minority population where the estimated prevalence of obesity exceeds the national average [1]. Moreover, subjects from low-income families that were uninsured or Medicaid-covered represented 74.2% of our sample and it is known that prevalence of obesity is higher among these groups [14,21,23]. Furthermore, LDH has implemented a diabetes risk stratification program, thus patients may have been referred from outside primary care for co-management [8]. Finally, the lifestyle impact of the COVID-19 pandemic could have exacerbated obesity in those evaluated in 2020 [14,24].
The overall obesity prevalence in our study (39.2%) was similar to that reported for adults in NHANES (2015–2016; 39.8%) [22]. A meta-analysis involving 200,777 subjects reported that approximately 80% of adolescents with obesity remain with obesity into adulthood [25]. Data from the Bogalusa Heart Study which followed 2392 children (aged 5–14 years) for 17.1 years showed that African American children with obesity were more likely to remain with obesity as adults (83%) than their non-Hispanic White counterparts (68%) [26]. The cumulative effect of multiple CMRFs associated with increased adiposity in childhood and adolescence may lead to adult cardiovascular disease (CVD). Earlier, more intensive interventions may be needed to ameliorate the impact of childhood obesity on adult cardiovascular outcomes [27].
Although only 1 ethnoracial disparity in CMRFs (high TG) was found in normal weight adolescents, more marked phenotypic differences were expressed as the degree of adiposity increased. Similar results have been reported in other studies using representative national samples of adolescents [28,29]. MetS is a cluster of risk factors for cardiometabolic diseases, including heart disease, stroke, and diabetes [30]. In NHANES (1999–2008), MetS in adolescents (n = 3385) was reported as 35.4% and 24.6% in boys and girls with Obesity, respectively, per CDC classification standards, compared to 0.8% and 1.7% in their normal weight counterparts [31]. Like NHANES, we found increases in the prevalence of MetS as severity of adiposity increased. We previously evaluated the prevalence of CMRFs in 759 adolescents and adults with overweight/obesity undergoing LDH’s Diabetes Risk Stratification Program. Findings showed that once obesity is present, the proportion of subjects with ≥3 CMRFs was similar regardless of age, suggesting that obesity similarly impacts the CMRF profile of adolescents and adults [32].
Dyslipidemia prevalence has been reported to be between 19–25% in US adolescents when defined as any abnormal lipid or apolipoprotein B [33]. We found a higher prevalence of dyslipidemia (46.2%), which was greater in Hispanic than African American adolescents. Compared with non-Hispanic White adolescents, both African American and Hispanic adolescents exhibit a higher degree of insulin resistance in response to increased adiposity [34,35], which has been linked to high TG and low HDL-C, referred to as metabolic dyslipidemia [36,37]. However, the NHANES (2001–2012) study showed that in African American adolescents the impact of obesity-induced insulin resistance is expressed distinctively and with fewer adverse lipid effects than in Mexican American adolescents [36,37]. In our study, African American subjects exhibited a lower baseline TG level and lower high TG prevalence compared with Hispanic subjects across all adiposity categories.
Metabolic-associated fatty liver disease (MAFLD) is associated with both metabolic dyslipidemia and progression to prediabetes and T2D [38]. A meta-analysis reported that MAFLD prevalence is higher in children with obesity than normal weight (34.2% vs. 7.6%), higher in males with obesity than females, and increases with severity of adiposity [39]. In our study, the prevalence of high liver enzymes was 12%, with a three-fold higher prevalence in males and a >two-fold higher prevalence in Hispanic subjects, regardless of sex. Although the NHANES (1999–2004) survey reported a lower prevalence of elevated ALT >40 U/L (3.6%) in adolescents aged 12–19 years and significantly higher prevalence in Mexican American adolescents (6.1%) than African American adolescents (2.3%) [40], another NHANES survey (1999–2010) in normal weight 12–18-year-old adolescents detected no ethnoracial differences in ALT levels after adjusting for sex, WC, and weight [41]. Similarly, our study found no ethnoracial differences in the prevalence of high liver enzymes in normal weight adolescents, although prevalence increased in Hispanic adolescents with increasing adiposity. The true prevalence of high liver enzymes could be even higher than reported here, since lower pediatric cut-offs for ALT (males, 25.8 U/L; females, 22.1 U/L) have been proposed [42].
Inflammation is another common pathophysiological risk factor for CVD [43]. In our study prevalence was higher among African American adolescents regardless of sex. We have previously reported that comparable levels of adiposity and insulin resistance result in a higher level of subclinical inflammation in African American adolescents than in non-Hispanic White adolescents [44].
The most prevalent CMRF was vitamin D insufficiency (92.4%), which was most frequent in African American females. Vitamin D deficiency has previously been reported as more prevalent in female adolescents than males [45], and NHANES data have consistently shown a higher prevalence of vitamin D deficiency in African American and Hispanic adolescents compared with non-Hispanic White adolescents [46]. Disparities between African American and Hispanic subjects may be attributed to several factors, including darker skin pigmentation, amount of sun exposure required to produce vitamin D [45], and higher adiposity.
There are some limitations to this study. This retrospective analysis is liable to residual confounders and causal inferences cannot be established. Since these data were drawn from a specific clinical setting, these results cannot be extrapolated to the general population. IGM was defined only using HbA1c, which can cause bias IGM estimates in African American subjects; however, it is currently used by the American Diabetes Association to define prediabetes [13]. Moreover, not all blood samples were collected in a fasted state, which introduces variations in the lipid profile. However, strengths of this study include its large sample size and conduct in a translational clinical practice designed to approach patients with cardiometabolic conditions under a standardized protocol for screening, management, and data collection. The study also used the same laboratory testing protocol and analytic methodology throughout the 3-year observation period regardless of patient payment capacity. Importantly, most adolescents were from low-income ethnoracial minority groups, who are usually under-represented in clinical trials.
5. Conclusions
The prevalence of overweight/obesity in the LDH system is very high among African American and Hispanic adolescents. Increased adiposity was the most impactful determinant of the CMRF profile in this population, even at lower levels of excess adiposity. The current BMI-based approach is not sensitive enough to allow the early identification of adolescents at higher risk, and differences between CMRF profiles in African American and Hispanic adolescents in response to increased adiposity are not addressed in current management guidelines, delaying the implementation of appropriate primary preventive measures. Research within routine clinical settings, particularly in minority groups, is required to characterize the nature of these ethnoracial discrepancies. This will better guide healthcare providers in more precise interventions to ameliorate any adverse health impact or underlying burden to the health system as they transition to adulthood.
Author Contributions
P.A.V.-M. had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. He directed the study and contributed to the analysis plan, discussion, drafting, and revision of the manuscript. R.N.-M. contributed to design of the data analysis, contributed on the writing of the first draft, discussion, and review. A.E.V. contributed to the concept and design, interpretation of data, drafting of the manuscript, and critical revision of the manuscript for important intellectual content. X.M. designed the statistical models and performed the statistical analyses. S.Y.-M. was formally working with Novo Nordisk at the time the research was conducted and contributed on the interpretation of data, drafting of the manuscript, discussion, and review. J.I.M. contributed to concept and design, interpretation of data, drafting of the manuscript, and critical revision of the manuscript for important intellectual content. C.C.G. contributed to concept and design, interpretation of data, and critical revision of the manuscript for important intellectual content. C.P.N. contributed to implementing the clinical protocol, collecting the data, training and data recording, and reviewing of the manuscript. All authors have read and agreed to the published version of the manuscript.
Funding
This was a collaborative research study run with involvement across the Lifedoc Research team and Novo Nordisk Inc. The funder had a role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.
Institutional Review Board Statement
Based on federal regulation 45 CFR 46, no IEC/IRB review was sought as this does not meet the definition of human subject research as defined in 45 CFR 46.102. Specifically, this study was designed as a retrospective review using historical datasets of proprietary, de-identified data that have been previously collected.
Informed Consent Statement
Written informed consent was obtained from all youth or parents/legal guardians as applicable, in the four original studies. This study, which used de-identified data from the previously conducted studies, was considered Non-Human Subject Research.
Data Availability Statement
Data are not publicly available due to legal restrictions on sharing/distribution of study data requiring Sponsor approval on a case-by-case basis. The data that support the findings of this study can be provided by the corresponding author (Velásquez) upon reasonable request.
Acknowledgments
The authors are grateful to all participants of the study. The authors would like to thank Marian Levy and Stephanie Watkins for their contributions to the development of the manuscript, and funded by Novo Nordisk, in accordance with Good Publication Practice (GPP) guidelines (www.ismpp.org/gpp-2022 accessed on 10 September 2024).
Conflicts of Interest
The authors declare no conflict of interest. The authors declare that this study received funding from Novo Nordisk Inc. The funder was not involved in the study design, data collection, or analysis. The funder only consulted on the interpretation of data, writing of this article, and the decision to submit it for publication.
References
- Robert Wood Johnson Foundation. From Crisis to Opportunity: Reforming Our Nation’s Policies to Help All Children Grow Up Healthy. State of Childhood Obesity. Available online: https://stateofchildhoodobesity.org/wp-content/uploads/2021/10/State-of-Childhood-Obesity-10-13-21-Final-WEB.pdf (accessed on 25 April 2023).
- Skinner, A.C.; Ravanbakht, S.N.; Skelton, J.A.; Perrin, E.M.; Armstrong, S.C. Prevalence of obesity and severe obesity in US children, 1999–2016. Pediatrics 2018, 141, e20173459. [Google Scholar] [CrossRef]
- Pratt, C.A.; Arteaga, S.; Loria, C. Forging a future of better cardiovascular health: Addressing childhood obesity. J. Am. Coll. Cardiol. 2014, 63, 369–371. [Google Scholar] [CrossRef]
- Trust for America’s Health. The State of Obesity: Better Policies for a Healthier America. Special Feature: Food and Nutrition Insecurity among Youth and Families. Available online: https://www.tfah.org/wp-content/uploads/2022/09/2022ObesityReport_FINAL3923.pdf (accessed on 25 April 2023).
- United States Census Bureau. QuickFacts Memphis City, Tennessee; United States. Available online: https://www.census.gov/quickfacts/fact/table/memphiscitytennessee,US/PST045221 (accessed on 23 June 2022).
- Nieto-Martinez, R.; Neira, C.; de Oliveira, D.; Velasquez-Rodriguez, A.; Neira, A.; Velasquez-Rodriguez, P.; Garcia, G.; González-Rivas, J.P.; Mechanick, J.I.; Velasquez-Mieyer, P. Lifestyle medicine in diabetes care: The Lifedoc Health model. Am. J. Lifestyle Med. 2022, 17, 336–354. [Google Scholar] [CrossRef]
- National Archives. eCFR: 46.102 Definitions for Purposes of This Policy. Available online: https://www.ecfr.gov/current/title-45/subtitle-A/subchapter-A/part-46/subpart-A/section-46.102 (accessed on 18 July 2023).
- Nieto-Martínez, R.; Velásquez-Rodríguez, A.; Neira, C.; Neira, C.; Mou, X.; Neira, A.; Garcia, G.; Velásquez-Rodríguez, P.; Levy, M.; Mechanick, J.I.; et al. Impact of a multidisciplinary approach on cardiometabolic risk reduction in a multiracial cohort of adults: A 1-year pilot study. Nutrients 2022, 14, 3391. [Google Scholar] [CrossRef]
- Laboratory Corporation of America® Holdings (Labcorp). Diagnostics. Pediatric Testing Reference Ranges. Available online: https://files.labcorp.com/labcorp-d8/2022-09/178250_DX_TL_PediatricTestRef_Final.pdf (accessed on 25 April 2023).
- Centers for Diseases Control and Prevention. National Center for Health Statistics: Growth Charts. Available online: https://www.cdc.gov/growthcharts/ (accessed on 25 April 2023).
- Gulati, A.K.; Kaplan, D.W.; Daniels, S.R. Clinical tracking of severely obese children: A new growth chart. Pediatrics 2012, 130, 1136–1140. [Google Scholar] [CrossRef]
- Flynn, J.T.; Kaelber, D.C.; Baker-Smith, C.M.; Blowey, D.; Carroll, A.E.; Daniels, S.R.; De Ferranti, S.D.; Dionne, J.M.; Falkner, B.; Flinn, S.K.; et al. Clinical practice guideline for screening and management of high blood pressure in children and adolescents. Pediatrics 2017, 140, e20171904. [Google Scholar] [CrossRef]
- American Diabetes Association Professional Practice Committee. 2. Classification and diagnosis of diabetes: Standards of Medical Care in Diabetes-2022. Diabetes Care 2022, 45 (Suppl. 1), S17–S38. [Google Scholar] [CrossRef]
- Hampl, S.E.; Hassink, S.G.; Skinner, A.C.; Armstrong, S.C.; Barlow, S.E.; Bolling, C.F.; Avila Edwards, K.C.; Eneli, I.; Hamre, R.; Joseph, M.M.; et al. Clinical practice guideline for the evaluation and treatment of children and adolescents with obesity. Pediatrics 2023, 151, e2022060640. [Google Scholar] [CrossRef]
- Weiss, R.; Dzuira, J.; Burgert, T.S.; Tamborlane, W.V.; Taksali, S.E.; Yeckel, C.W.; Allen, K.; Lopes, M.; Savoye, M.; Morrison, J.; et al. Obesity and the metabolic syndrome in children and adolescents. N. Engl. J. Med. 2004, 350, 2362–2374. [Google Scholar] [CrossRef]
- Azevedo, W.F.; Cantalice, A.S.; Gonzaga, N.C.; Simões, M.O.d.S.; Guimarães, A.L.V.; de Carvalho, D.F.; Medeiros, C.C.M. Fibrinogen: Cardiometabolic risk marker in obese or overweight children and adolescents. J. De Pediatr. 2015, 91, 464–470. [Google Scholar] [CrossRef]
- Huang, R.C.; Prescott, S.L.; Godfrey, K.M.; Davis, E.A. Assessment of cardiometabolic risk in children in population studies: Underpinning developmental origins of health and disease mother-offspring cohort studies. J. Nutr. Sci. 2015, 4, e12. [Google Scholar] [CrossRef] [PubMed]
- Holick, M.F. Vitamin D deficiency. N. Engl. J. Med. 2007, 357, 266–281. [Google Scholar] [CrossRef] [PubMed]
- Velasquez-Mieyer, P.A.; Perez-Faustinelli, S.; Cowan, P. Identifying children at risk for obesity, type 2 diabetes and cardiovascular disease. Diabetes Spectr. 2005, 18, 213–220. [Google Scholar] [CrossRef]
- Nieto-Martínez, R.; González-Rivas, J.P.; Mechanick, J.I. Cardiometabolic risk: New chronic care models. JPEN J. Parenter. Enteral Nutr. 2021, 45, 85–92. [Google Scholar] [CrossRef] [PubMed]
- Afshin, A.; Forouzanfar, M.; Reitsma, M.; Sur, P.; Estep, K.; Lee, A.; Marczak, L.; Mokdad, A.; Moradi-Lakeh, M.; Naghavi, M.; et al. Health effects of overweight and obesity in 195 countries over 25 years. N. Engl. J. Med. 2017, 377, 13–27. [Google Scholar] [CrossRef]
- Hales, C.; Carroll, M.; Fryar, C.; Ogden, C. Prevalence of Obesity among Adults and Youth: United States, 2015–2016. NCHS Data Brief, No 288. Available online: https://www.cdc.gov/nchs/data/databriefs/db288.pdf (accessed on 25 April 2023).
- Mylona, E.K.; Benitez, G.; Shehadeh, F.; Fleury, E.; Mylonakis, S.C.; Kalligeros, M. The association of obesity with health insurance coverage and demographic characteristics: A statewide cross-sectional study. Medicine 2020, 99, e21016. [Google Scholar] [CrossRef]
- Zachary, Z.; Brianna, F.; Brianna, L.; Garrett, P.; Jade, W.; Alyssa, D.; Mikayla, K. Self-quarantine and weight gain related risk factors during the COVID-19 pandemic. Obes. Res. Clin. Pract. 2020, 14, 210–216. [Google Scholar] [CrossRef]
- Simmonds, M.; Llewellyn, A.; Owen, C.G.; Woolacott, N. Predicting adult obesity from childhood obesity: A systematic review and meta-analysis. Obes. Rev. Off. J. Int. Assoc. Study Obes. 2016, 17, 95–107. [Google Scholar] [CrossRef]
- Freedman, D.S.; Khan, L.K.; Serdula, M.K.; Dietz, W.H.; Srinivasan, S.R.; Berenson, G.S. The relation of childhood BMI to adult adiposity: The Bogalusa Heart Study. Pediatrics 2005, 115, 22–27. [Google Scholar] [CrossRef]
- Velasquez-Mieyer, P.; Neira, C.P.; Nieto, R.; Cowan, P.A. Obesity and cardiometabolic syndrome in children. Ther. Adv. Cardiovasc. Dis. 2007, 1, 61–81. [Google Scholar] [CrossRef]
- Liu, J.; Ma, J.; Orekoya, O.; Vangeepuram, N.; Liu, J. Trends in metabolic syndrome among US youth, from 1999 to 2018. JAMA Pediatr. 2022, 176, 1043–1045. [Google Scholar] [CrossRef] [PubMed]
- Caprio, S.; Daniels, S.R.; Drewnowski, A.; Kaufman, F.R.; Palinkas, L.A.; Rosenbloom, A.L.; Schwimmer, J.B.; Kirkman, M.S. Influence of race, ethnicity, and culture on childhood obesity: Implications for prevention and treatment. Obesity 2008, 16, 2566–2577. [Google Scholar] [CrossRef] [PubMed]
- Alberti, K.G.; Eckel, R.H.; Grundy, S.M.; Zimmet, P.Z.; Cleeman, J.I.; Donato, K.A.; Fruchart, J.C.; James, W.P.T.; Loria, C.M.; Smith, S.C., Jr. Harmonizing the metabolic syndrome: A joint interim statement of the International Diabetes Federation Task Force on Epidemiology and Prevention; National Heart, Lung, and Blood Institute; American Heart Association; World Heart Federation; International Atherosclerosis Society; and International Association for the Study of Obesity. Circulation 2009, 120, 1640–1645. [Google Scholar] [CrossRef] [PubMed]
- Laurson, K.R.; Welk, G.J.; Eisenmann, J.C. Diagnostic performance of BMI percentiles to identify adolescents with metabolic syndrome. Pediatrics 2014, 133, e330–e338. [Google Scholar] [CrossRef]
- Velasquez, P.; Neira, C.; Velaquez, A.; Velaquez, A.; Christensen, M. Abstract #1238: Comparison of cardiovascular risk factors in children and adults stratified by age and severity of overweight. Endocr. Pract. 2015, 21 (Suppl. 2), 294–295. [Google Scholar] [CrossRef]
- Perak, A.M.; Ning, H.; Kit, B.K.; de Ferranti, S.D.; Van Horn, L.V.; Wilkins, J.T.; Lloyd-Jones, D.M. Trends in levels of lipids and apolipoprotein B in US youths aged 6 to 19 years, 1999–2016. JAMA 2019, 321, 1895–1905. [Google Scholar] [CrossRef]
- Goran, M.I.; Ball, G.D.; Cruz, M.L. Obesity and risk of type 2 diabetes and cardiovascular disease in children and adolescents. J. Clin. Endocrinol. Metab. 2003, 88, 1417–1427. [Google Scholar] [CrossRef]
- Goran, M.I.; Bergman, R.N.; Cruz, M.L.; Watanabe, R. Insulin resistance and associated compensatory responses in African-American and Hispanic children. Diabetes Care 2002, 25, 2184–2190. [Google Scholar] [CrossRef] [PubMed]
- Sumner, A.E. Ethnic differences in triglyceride levels and high-density lipoprotein lead to underdiagnosis of the metabolic syndrome in Black children and adults. J. Pediatr. 2009, 155, e7–e11. [Google Scholar] [CrossRef]
- Dhuper, S.; Bayoumi, N.S.; Shah, Y.D.; Mehta, S. Ethnic differences in lipid profiles of overweight, obese, and severely obese children and adolescents 6-19 years of age. Child. Obes. 2017, 13, 236–241. [Google Scholar] [CrossRef]
- Bellentani, S.; Marino, M. Epidemiology and natural history of non-alcoholic fatty liver disease (NAFLD). Ann. Hepatol. 2009, 8, S4–S8. [Google Scholar] [CrossRef] [PubMed]
- Anderson, E.L.; Howe, L.D.; Jones, H.E.; Higgins, J.P.; Lawlor, D.A.; Fraser, A. The prevalence of non-alcoholic fatty liver disease in children and adolescents: A systematic review and meta-analysis. PLoS ONE 2015, 10, e0140908. [Google Scholar] [CrossRef] [PubMed]
- Fraser, A.; Longnecker, M.P.; Lawlor, D.A. Prevalence of elevated alanine aminotransferase among US adolescents and associated factors: NHANES 1999-2004. Gastroenterology 2007, 133, 1814–1820. [Google Scholar] [CrossRef] [PubMed]
- Kliethermes, S.; Ma, M.; Purtell, C.; Balasubramanian, N.; Gonzalez, B.; Layden, T.J.; Cotler, S.J. An assessment of racial differences in the upper limits of normal ALT levels in children and the effect of obesity on elevated values. Pediatr. Obes. 2017, 12, 363–372. [Google Scholar] [CrossRef]
- Schwimmer, J.B.; Dunn, W.; Norman, G.J.; Pardee, P.E.; Middleton, M.S.; Kerkar, N.; Sirlin, C.B. SAFETY study: Alanine aminotransferase cutoff values are set too high for reliable detection of pediatric chronic liver disease. Gastroenterology 2010, 138, 1357–1364. [Google Scholar] [CrossRef]
- Mechanick, J.I.; Farkouh, M.E.; Newman, J.D.; Garvey, W.T. Cardiometabolic-based chronic disease, adiposity and dysglycemia drivers: JACC state-of-the-art review. J. Am. Coll. Cardiol. 2020, 75, 525–538. [Google Scholar] [CrossRef]
- Velásquez-Mieyer, P.A.; Cowan, P.A.; Pérez-Faustinelli, S.; Nieto-Martínez, R.; Villegas-Barreto, C.; Tolley, E.A.; Lustig, R.H.; Alpert, B.S. Racial disparity in glucagon-like peptide 1 and inflammation markers among severely obese adolescents. Diabetes Care 2008, 31, 770–775. [Google Scholar] [CrossRef]
- Varghese, S.B.; Benoit, J.; McIntyre, T. Vitamin D levels in ethnic minority adolescents in primary care. J. Pediatr. Health Care 2022, 36, 443–448. [Google Scholar] [CrossRef]
- Cui, A.; Xiao, P.; Ma, Y.; Fan, Z.; Zhou, F.; Zheng, J.; Zhang, L. Prevalence, trend, and predictor analyses of vitamin D deficiency in the US population, 2001-2018. Front. Nutr. 2022, 9, 965376. [Google Scholar] [CrossRef]
Table 1.
Cardiometabolic risk factors in adolescents by sex and ethnoracial group.
| Male (M) | Female (F) | Total | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| African American (AA) | Hispanic (H) | Total | p Value AA vs. H | African American (AA) | Hispanic (H) | Total | p Value AA vs. H | Total | p Value M vs. F | |
| n (%) | 2284 (100.0) | |||||||||
| Age, years | 13.9 ± 0.05 | 13.9 ± 0.05 | 13.9 ± 0.04 | |||||||
| Weight a, kg | 82.2 ± 1.8 | 69.6 ± 0.7 | 73.3 ± 0.8 | ≤0.001 | 81.4 ± 1.5 | 62.2 ± 0.6 | 68.6 ± 0.7 | ≤0.001 | 70.9 ± 0.5 | ≤0.001 |
| Height a, cm | 169.7 ± 0.6 | 163.3 ± 0.4 | 165.2 ± 0.3 | ≤0.001 | 161.8 ± 0.4 | 155.2 ± 0.2 | 157.4 ± 0.2 | ≤0.001 | 161.3 ± 0.2 | ≤0.001 |
| BMI a, kg/m2 | 28.2 ± 0.6 | 25.8 ± 0.2 | 26.5 ± 0.2 | ≤0.001 | 30.8 ± 0.5 | 25.6 ± 0.2 | 27.4 ± 0.2 | ≤0.001 | 26.9 ± 0.2 | 0.01 |
| SBP, mmHg b | 112.5 ± 0.7 | 108.8 ± 0.4 | 109.8 ± 0.3 | ≤0.001 | 110.0 ± 0.6 | 104.3 ± 0.3 | 106.2 ± 0.3 | ≤0.001 | 108.0 ± 0.2 | ≤0.001 |
| DBP, mmHg b | 70.0 ± 0.5 | 67.9 ± 0.3 | 68.5 ± 0.3 | ≤0.001 | 70.3 ± 0.5 | 65.2 ± 0.3 | 66.9 ± 0.3 | ≤0.001 | 67.7 ± 0.2 | ≤0.001 |
| Total cholesterol, mg/dL | 153.3 ± 2.1 | 154.7 ± 1.4 | 154.8 ± 1.1 | 0.58 | 156.4 ± 1.9 | 154.9 ± 1.21 | 155.4 ± 1.0 | 0.51 | 155.1 ± 0.8 | 0.70 |
| LDL-C, mg/dL | 88.2 ± 1.9 | 82.9 ± 1.1 | 84.8 ± 0.1 | 0.02 | 90.5 ± 1.8 | 83.0 ± 1.1 | 85.4 ± 0.9 | ≤0.001 | 85.1 ± 0.7 | 0.64 |
| HDL-C, mg/dL | 47.9 ± 0.9 | 44.9 ± 0.5 | 45.7 ± 0.4 | ≤0.001 | 50.5 ± 0.8 | 49.2 ± 0.5 | 49.6 ± 0.4 | 0.14 | 47.7 ± 0.3 | ≤0.001 |
| TG, mg/dL | 87.6 ± 4.0 | 139.8 ± 3.7 | 125.8 ± 3.0 | ≤0.001 | 78.3 ± 2.5 | 117.2 ± 2.7 | 104.5 ± 2.1 | ≤0.001 | 114.7 ± 1.8 | ≤0.001 |
| hs-CRP, mg/dL | 3.42 ± 0.4 | 2.54 ± 0.2 | 2.82 ± 0.2 | 0.04 | 4.96 ± 0.4 | 2.94 ± 0.2 | 3.79 ± 0.2 | ≤0.001 | 3.3 ± 0.1 | ≤0.001 |
| Fibrinogen, mg/dL | 316.3 ± 6.5 | 318.3 ± 3.8 | 318.5 ± 3.3 | 0.77 | 368.3 ± 6.1 | 355.2 ± 3.8 | 360.6 ± 3.3 | 0.07 | 338.9 ± 2.4 | ≤0.001 |
| Blood glucose, mg/dL | 95.7 ± 1.2 | 99.7 ± 0.7 | 98.6 ± 0.6 | ≤0.001 | 95.4 ± 1.1 | 95.1 ± 0.6 | 95.2 ± 0.6 | 0.82 | 96.8 ± 0.4 | ≤0.001 |
| HbA1c, % | 5.54 ± 0.03 | 5.38 ± 0.02 | 5.42 ± 0.01 | ≤0.001 | 5.56 ± 0.03 | 5.36 ± 0.02 | 5.43 ± 0.02 | ≤0.001 | 5.42 ± 0.01 | 0.55 |
| AST, IU/L | 23.4 ± 0.6 | 26.1 ± 0.5 | 25.4 ± 0.4 | ≤0.001 | 19.2 ± 0.4 | 20.9 ± 0.4 | 20.4 ± 0.3 | ≤0.001 | 22.8 ± 0.3 | ≤0.001 |
| ALT, IU/L | 20.3 ± 0.9 | 28.8 ± 1.0 | 26.6 ± 0.8 | ≤0.001 | 15.7 ± 0.8 | 18.8 ± 0.7 | 18.0 ± 0.5 | ≤0.001 | 22.2 ± 0.5 | ≤0.001 |
| 25-OH-Vitamin D, ng/mL | 17.3 ± 0.6 | 19.4 ± 0.4 | 19.1 ± 0.3 | ≤0.001 | 15.0 ± 0.5 | 17.8 ± 0.4 | 16.9 ± 0.3 | ≤0.001 | 18.0 ± 0.2 | ≤0.001 |
Variables are expressed as mean ± standard error. Sex (male vs female for total sample) and ethno-racial group (African American vs Hispanic for each sex) groups were compared using t-test. The sample size for each variable differed between groups; therefore, the mean (standard deviation) was calculated based on observed numbers and not the total cohort. a These variables were not adjusted by adiposity. b SBP/DBP were adjusted by height (cm). Abbreviations: ALT, alanine transaminase; AST, aspartate aminotransferase; BMI, body mass index; DBP, diastolic blood pressure; HbA1c, glycated hemoglobin; HDL-C, high-density lipoprotein cholesterol; hs-CRP, high-sensitivity C-reactive protein; LDL-C, low-density lipoprotein cholesterol; SBP, systolic blood pressure; TG, triglycerides.
Table 2.
Prevalence of cardiometabolic risk factors, high liver enzymes, and vitamin D insufficiency in adolescents by sex and ethnoracial group.
Table 2.
Prevalence of cardiometabolic risk factors, high liver enzymes, and vitamin D insufficiency in adolescents by sex and ethnoracial group.
| Male (M) | Female (F) | Total | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| African American (AA) | Hispanic (H) | Total | p Value AA vs. H | African American | Hispanic | Total | p Value AA vs. H | Total | p Value M vs. F | ||
| Overweight a | 12.6 | 20.7 | 18.4 | ≤0.001 | 13.8 | 25.9 | 21.8 | ≤0.001 | 20.2 | 0.05 | |
| (9.3, 16.9) | (18.0, 23.7) | (16.3, 20.8) | (10.5, 17.8) | (22.9, 29.2) | (19.5, 24.3) | (18.6, 21.9) | |||||
| Obesity a | 17.7 | 27.6 | 24.8 | ≤0.001 | 25.7 | 24.6 | 25.1 | 0.75 | 25.0 | 0.92 | |
| (13.7, 22.4) | (24.6, 30.9) | (22.4, 27.4) | (21.4, 30.5) | (21.7, 27.8) | (22.6, 27.7) | (23.2, 26.8) | |||||
| Severe obesity a | 26.2 | 11.4 | 15.8 | ≤0.001 | 27.3 | 5.4 | 12.5 | ≤0.001 | 14.2 | 0.03 | |
| (21.5, 31.5) | (9.4, 13.9) | (13.8, 18.0) | (22.9, 32.2) | (4.0, 7.3) | (10.7, 14.6) | (12.8, 15.6) | |||||
| Abnormal BP | Elevated | 13.2 | 9.3 | 10.4 | 0.08 | 9.4 | 5.2 | 6.6 | 0.02 | 8.5 | ≤0.001 |
| (9.6, 18.0) | (7.3, 11.6) | (8.7, 12.5) | (6.5, 13.2) | (3.8, 7.2) | (5.2, 8.3) | (7.3, 9.8) | |||||
| HTN | 15.8 | 14.6 | 14.9 | 0.69 | 17.2 | 6.2 | 9.7 | ≤0.001 | 12.3 | ≤0.001 | |
| (11.8, 20.8) | (12.2, 17.3) | (12.8, 17.3) | (13.3, 21.9) | (4.6, 8.3) | (8.0, 11.7) | (10.9, 13.8) | |||||
| Abnormal HbA1c | Pre-DM | 33.1 | 11.2 | 17.1 | ≤0.001 | 27.5 | 8.9 | 15.5 | ≤0.001 | 16.3 | 0.53 |
| (25.7, 41.4) | (8.3, 14.8) | (14.1, 20.5) | (21.4, 34.6) | (6.2, 12.5) | (12.6, 18.8) | (14.2, 18.6) | |||||
| T2D | 2.7 | 0.8 | 1.3 | 0.09 | 4.8 | 0.9 | 2.2 | 0.01 | 1.7 | 0.34 | |
| (0.9, 7.2) | (0.2, 2.4) | (0.6, 2.7) | (2.3, 9.1) | (0.2, 2.7) | (1.2, 3.9) | (1.1, 2.7) | |||||
| IGM | 35.8 | 11.9 | 18.3 | ≤0.001 | 32.3 | 9.8 | 17.7 | ≤0.001 | 18.0 | 0.84 | |
| (28.2, 44.1) | (9.0, 15.6) | (15.2, 21.8) | (25.8, 39.5) | (7.0, 13.5) | (14.6, 21.2) | (15.8, 20.4) | |||||
| Dyslipidemia | 41.8 | 58.5 | 54.3 | ≤0.001 | 33.7 | 41.8 | 38.9 | 0.04 | 46.2 | ≤0.001 | |
| (34.7, 49.2) | (54.1, 62.8) | (50.5, 57.9) | (28.0, 40.0) | (37.5, 46.2) | (35.5, 42.5) | (43.7, 48.8) | |||||
| High total cholesterol | 8.5 | 7.2 | 8.1 | 0.7 | 6.8 | 6.2 | 6.2 | 0.86 | 7.1 | 0.19 | |
| (5.1, 13.6) | (5.2, 9.9) | (6.2, 10.4) | (4.1, 10.9) | (4.3, 8.7) | (4.7, 8.2) | (5.9, 8.6) | |||||
| High LDL-C | 5.3 | 4.9 | 5.2 | 0.98 | 8.0 | 3.3 | 4.7 | 0.01 | 4.9 | 0.76 | |
| (2.7, 9.8) | (3.3, 7.3) | (3.7, 7.1) | (5.1, 12.3) | (2.0, 5.3) | (3.4, 6.5) | (3.9, 6.2) | |||||
| High TG | 13.2 | 44.0 | 35.8 | ≤0.001 | 7.6 | 30.6 | 23.2 | ≤0.001 | 29.2 | ≤0.001 | |
| (8.9, 19.1) | (39.7, 48.5) | (32.3, 39.5) | (4.8, 11.8) | (26.7, 34.8) | (20.3, 26.3) | (26.9, 31.6) | |||||
| Low HDL-C | 26.5 | 32.9 | 31.4 | 0.12 | 18.9 | 18.2 | 18.3 | 0.89 | 24.6 | ≤0.001 | |
| (20.4, 33.4) | (28.9, 37.2) | (28.0, 34.9) | (14.3, 24.4) | (15.0, 21.8) | (15.7, 21.2) | (22.4, 26.8) | |||||
| Inflammation | 55.3 | 43.8 | 47.6 | 0.03 | 68.9 | 60.6 | 64.1 | 0.1 | 55.6 | ≤0.001 | |
| (46.4, 63.9) | (38.2, 49.5) | (42.9, 52.3) | (61.1, 75.9) | (54.4, 66.6) | (59.4, 68.6) | (52.3, 58.9) | |||||
| Metabolic syndrome | 35.8 | 42.6 | 41.1 | 0.18 | 30.1 | 24.3 | 27.0 | 0.18 | 34.1 | ≤0.001 | |
| (28.2, 44.1) | (37.8, 47.7) | (37.0, 45.3) | (23.7, 37.3) | (20.0, 29.2) | (23.4, 30.9) | (31.3, 37.0) | |||||
| High liver enzymes | 9.0 | 21.2 | 18.1 | ≤0.001 | 2.1 | 7.8 | 6.3 | ≤0.001 | 12.0 | ≤0.001 | |
| (5.5, 14.3) | (17.8, 25.1) | (15.4, 21.1) | (0.8, 5.1) | (5.7, 10.6) | (4.7, 8.3) | (10.4, 13.7) | |||||
| Vitamin D insufficiency | 93.5 | 90.6 | 90.8 | 0.32 | 96.9 | 92.5 | 93.9 | 0.04 | 92.4 | 0.04 | |
| (88.4, 96.5) | (87.4, 93.0) | (88.2, 92.8) | (93.4, 98.6) | (89.6, 94.6) | (91.8, 95.5) | (90.8, 93.7) | |||||
Frequencies are expressed as percentages and 95% confidence intervals. Groups were compared using the χ² test. a These variables were not adjusted for adiposity. Abbreviations: BP, blood pressure; HbA1c, glycated hemoglobin; HDL-C, high-density lipoprotein cholesterol; HTN, hypertension; IGM, impaired glucose metabolism; LDL-C, low-density lipoprotein cholesterol; pre-DM, prediabetes mellitus; T2D, type 2 diabetes; TG, triglycerides.
Table 3.
Prevalence of cardiometabolic risk factors, high liver enzymes, and vitamin D insufficiency by severity of adiposity (via BMI%) and ethnoracial group in the total sample.
Table 3.
Prevalence of cardiometabolic risk factors, high liver enzymes, and vitamin D insufficiency by severity of adiposity (via BMI%) and ethnoracial group in the total sample.
| % | Normal Weight | Overweight | ||||||||||||||
| African American | Hispanic | Total, % (Normal Weight) | p Value | African American | Hispanic | Total, % (Overweight) | p Value | |||||||||
| Abnormal BP | Elevated | 4.9 (2.6, 8.8) | 3.3 (2.1, 5.1) | 3.7 (2.5, 5.2) | 0.38 | 15.4 (8.5, 25.7) | 5.9 (3.7, 9.1) | 7.5 (5.3, 10.6) | 0.01 | |||||||
| Hypertension | 6.2 (3.6, 10.4) | 3.8 (2.5, 5.7) | 4.4 (3.1, 6.0) | 0.19 | 3.8 (1.0, 11.6) | 6.2 (4.0, 9.5) | 5.9 (3.9, 8.7) | 0.59 | ||||||||
| Abnormal HbA1c | Pre-DM | 2.4 (0.1, 14.4) | 2.3 (0.6, 7.0) | 2.3 (0.7, 6.1) | 1.00 | 15.2 (5.7, 32.7) | 7.6 (4.5, 12.5) | 8.6 (5.5, 13.1) | 0.18 | |||||||
| DM | NA | 1.5 (0.3, 5.9) | 1.1 (0.2, 4.5) | 1.00 | NA | 0.5 (0.0, 3.2) | 0.4 (0.0, 2.7) | 1.00 | ||||||||
| IGM | 2.4 (0.1, 14.4) | 3.8 (1.4, 9.1) | 3.4 (1.4, 7.6) | 1.00 | 15.2 (5.7, 32.7) | 8.1 (4.9, 13.1) | 9.0 (5.8, 13.6) | 0.2 | ||||||||
| Metabolic syndrome | 2.6 (0.1, 15.1) | 1.5 (0.3, 6.0) | 1.7 (0.5, 5.4) | 0.55 | 12.1 (4.0, 29.1) | 23.3 (17.8, 29.8) | 21.8 (16.9, 27.7) | 0.23 | ||||||||
| Dyslipidemia | 24.3 (17.0, 33.4) | 30.1 (25.3, 35.4) | 28.3 (24.3, 32.7) | 0.29 | 20.0 (10.5, 34.1) | 41.7 (35.7, 48.0) | 38.1 (32.8, 43.7) | 0.01 | ||||||||
| High total cholesterol | 6.1 (2.7, 12.6) | 4.8 (2.8, 7.8) | 5.0 (3.3, 7.5) | 0.76 | 12.0 (5.0, 25.0) | 5.8 (3.4, 9.6) | 7.0 (4.5, 10.5) | 0.12 | ||||||||
| High LDL-C | 5.2 (2.1, 11.5) | 3.0 (1.5, 5.6) | 3.5 (2.1, 5.7) | 0.25 | 4.0 (0.7, 14.9) | 4.3 (2.3, 7.7) | 4.5 (2.6, 7.5) | 1 | ||||||||
| High TG | 4.3 (1.6, 10.3) | 20.9 (16.7, 25.7) | 16.3 (13.1, 20.1) | ≤0.001 | 2.0 (0.1, 12.0) | 32.4 (26.8, 38.6) | 27.6 (22.8, 33.0) | ≤0.001 | ||||||||
| Low HDL-C | 10.4 (5.7, 17.9) | 8.9 (6.2, 12.6) | 9.3 (6.9, 12.5) | 0.77 | 6.0 (1.6, 17.5) | 20.5 (15.8, 26.0) | 18.1 (14.1, 22.9) | 0.03 | ||||||||
| Inflammation | NA | 11.1 (2.9, 30.3) | 7.0 (1.8, 20.1) | 0.54 | 17.4 (5.7, 39.5) | 36.8 (29.3, 45.1) | 34.1 (27.2, 41.7) | 0.11 | ||||||||
| High liver enzymes | 1.9 (0.3, 7.5) | 4.5 (2.6, 7.4) | 4.0 (2.5, 6.4) | 0.38 | NA | 8.9 (5.8, 13.3) | 7.3 (4.7, 11.0) | 0.03 | ||||||||
| Vitamin D insufficiency | 91.2 (82.3, 96.1) | 85.1 (80.2, 89.0) | 86.2 (82.1, 89.6) | 0.22 | 95.2 (82.6, 99.2) | 89.7 (84.8, 93.1) | 90.3 (86.1, 93.4) | 0.39 | ||||||||
| % | Obesity | Severe Obesity | Sample | |||||||||||||
| African American | Hispanic | Total, % (Obesity) | p Value | African American | Hispanic | Total, % (Severe Obesity) | p Value | % | p Value | |||||||
| Abnormal BP | Elevated | 8.7 (4.6, 15.3) | 11.9 (8.9, 15.6) | 11.8 (9.2, 14.9) | 0.4 | 19.8 (14.1, 26.9) | 16.3 (10.6, 24.0) | 17.8 (13.7, 22.7) | 0.54 | 8.5 (7.3, 9.8) | ≤0.001 | |||||
| Hypertension | 20.5 (14.0, 28.7) | 17.3 (13.7, 21.5) | 18.0 (14.9, 21.6) | 0.5 | 34.0 (26.8, 41.9) | 33.3 (25.4, 42.2) | 33.9 (28.6, 39.6) | 1.00 | 12.3 (10.9, 13.8) | ≤0.001 | ||||||
| Abnormal HbA1c | Pre-DM | 29.9 (21.6, 39.6) | 11.4 (8.1, 15.6) | 16.2 (12.9, 20.1) | ≤0.001 | 40.4 (32.7, 48.5) | 21.0 (13.9,30.2) | 32.1 (26.7, 38.1) | ≤0.001 | 16.3 (14.2, 18.6) | ≤0.001 | |||||
| DM | 6.5 (2.9, 13.5) | 0.3 (0.0, 2.1) | 1.9 (0.9, 3.8) | ≤0.001 | 3.8 (1.6, 8.6) | 1.9 (0.3, 7.4) | 3.0 (1.4, 6.0) | 0.48 | 1.7 (1.1, 2.7) | 0.17 | ||||||
| IGM | 36.4 (27.5, 46.4) | 11.7 (8.4, 15.9) | 18.1 (14.6, 22.1) | ≤0.001 | 44.2 (36.4, 52.4) | 22.9 (15.5, 32.3) | 35.1 (29.4, 41.1) | ≤0.001 | 18.0 (15.8, 20.4) | ≤0.001 | ||||||
| Metabolic syndrome | 29.8 (21.4, 39.7) | 44.2 (38.6, 49.8) | 41.1 (36.5, 45.9) | 0.01 | 46.2 (38.3, 54.3) | 64.2 (54.2, 73.1) | 54.0 (47.9, 60.0) | 0.01 | 34.1 (31.3, 37.0) | ≤0.001 | ||||||
| Dyslipidemia | 38.4 (29.5, 48.1) | 68.1 (62.7, 73.1) | 60.4 (55.7, 65.0) | ≤0.001 | 50.9 (43.0, 58.8) | 77.8 (68.6, 85.0) | 62.0 (56.0, 67.7) | ≤0.001 | 46.2 (43.7, 48.8) | ≤0.001 | ||||||
| High total cholesterol | 6.2 (2.8, 12.9) | 7.1 (4.6, 10.5) | 6.9 (4.8, 9.7) | 0.94 | 8.1 (4.5, 13.7) | 13.9 (8.2, 22.2) | 11.1 (7.8, 15.5) | 0.18 | 7.1 (5.9, 8.6) | 0.02 | ||||||
| High LDL-C | 5.4 (2.2, 11.8) | 3.1 (1.6, 5.8) | 3.8 (2.3, 6.1) | 0.26 | 10.0 (6.0, 16.0) | 10.3 (5.5, 18.0) | 9.7 (6.6, 14.0) | 1.00 | 4.9 (3.9, 6.2) | ≤0.001 | ||||||
| High TG | 8.9 (4.6, 16.2) | 50.0 (44.6, 55.4) | 39.1 (34.6, 43.8) | ≤0.001 | 17.4 (12.1, 24.3) | 61.1 (51.2, 70.2) | 36.2 (30.6, 42.2) | ≤0.001 | 29.2 (26.9, 31.6) | ≤0.001 | ||||||
| Low HDL-C | 23.2 (16.0, 32.3) | 39.1 (33.9, 44.7) | 35.0 (30.7, 39.7) | ≤0.001 | 34.8 (27.6, 42.7) | 47.7 (38.1, 57.4) | 40.0 (34.3, 46.0) | 0.05 | 24.6 (22.4, 26.8) | ≤0.001 | ||||||
| Inflammation | 55.0 (44.8, 64.9) | 53.4 (47.5, 59.3) | 54.1 (49.1, 59.1) | 0.88 | 80.6 (73.4, 86.4) | 77.7 (68.2, 85.0) | 79.9 (74.4, 84.4) | 0.67 | 55.6 (52.3, 58.9) | 0.02 | ||||||
| High liver enzymes | 3.5 (1.1, 9.4) | 20.9 (16.7, 25.8) | 16.4 (13.2, 20.2) | ≤0.001 | 9.9 (6.0, 15.9) | 39.1 (30.1, 48.9) | 22.4 (17.8, 27.8) | ≤0.001 | 12.0 (10.4, 13.7) | ≤0.001 | ||||||
| Vitamin D insufficiency | 94.5 (88.0, 97.8) | 95.9 (93.0, 97.7) | 95.2 (92.7, 97.0) | 0.59 | 98.1 (94.2, 99.5) | 98.2 (92.9, 99.7) | 97.9 (95.2, 99.1) | 1.00 | 92.4 (90.8, 93.7) | ≤0.001 | ||||||
Frequencies are expressed as percentages and 95% confidence intervals, and groups were compared using the χ² test. Abbreviations: BMI, body mass index; BP, blood pressure; DM, diabetes mellitus; HDL-C, high-density lipoprotein cholesterol; HbA1c: glycated hemoglobin; IGM, impaired glucose metabolism; LDL-C, low-density lipoprotein cholesterol; NA, not available; TG, triglycerides.
Table 4.
Association between demographics and BMI categories with cardiometabolic abnormalities in the total sample (logistic regression analysis adjusted by age and sex).
Table 4.
Association between demographics and BMI categories with cardiometabolic abnormalities in the total sample (logistic regression analysis adjusted by age and sex).
| IGM | Elevated BP a | High TC | Low HDL-C | High LDL-C | High TG | High AST/ALT | Inflammation | Vitamin D Insufficiency | ||
|---|---|---|---|---|---|---|---|---|---|---|
| Sex | Male | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
| Female | 0.88 | 0.48 | 0.80 | 0.54 | 0.97 | 0.63 | 0.36 | 2.51 | 1.75 | |
| (0.63, 1.23) | (0.39, 0.60) | (0.54, 1.20) | (0.42, 0.70) | (0.61, 1.56) | (0.50, 0.81) | (0.25, 0.52) | (1.85, 3.39) | (1.15, 2.65) | ||
| Age | 12–14 years | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
| 15–17 years | 1.27 | 3.38 | 1.68 | 1.00 | 1.83 | 1.05 | 1.65 | 1.17 | 1.33 | |
| (0.89, 1.81) | (2.69, 4.24) | (1.11, 2.54) | (0.75, 1.32) | (1.14, 2.95) | (0.79, 1.38) | (1.13, 2.40) | (0.83, 1.65) | (0.80, 2.19) | ||
| Ethnoracial group | Hispanic | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
| African American | 3.01 | 1.13 | 0.87 | 0.58 | 1.16 | 0.12 | 0.17 | 0.80 | 1.30 | |
| (2.09, 4.32) | (0.83, 1.53) | (0.54, 1.40) | (0.43, 0.79) | (0.68, 1.98) | (0.08, 0.18) | (0.19, 0.27) | (0.56, 1.15) | (0.75, 2.26) | ||
| BMI percentile | Normal weight | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 |
| Overweight | 3.43 | 2.17 | 1.50 | 2.12 | 1.43 | 1.82 | 1.88 | 6.33 | 1.6 | |
| (1.34, 8.79) | (1.56, 3.03) | (0.82, 2.76) | (1.39, 3.23) | (0.68, 2.99) | (1.27, 2.61) | (0.98, 3.61) | (1.85, 21.61) | (0.97, 2.65) | ||
| Obesity | 6.58 | 5.23 | 1.49 | 5.11 | 1.28 | 3.62 | 4.90 | 16.6 | 3.45 | |
| (2.79, 15.52) | (3.92, 6.98) | (0.85, 2.59) | (3.54, 7.37) | (0.65, 2.53) | (2.61, 5.01) | (2.84, 8.46) | (4.98, 55.19) | (2.02, 5.90) | ||
| Severe obesity | 11.30 | 11.50 | 2.45 | 7.17 | 2.84 | 5.91 | 10.50 | 62.9 | 7.15 | |
| (4.79, 26.82) | (8.35, 5.96) | (1.38, 4.38) | (4.77, 10.76) | (1.47, 5.49) | (3.94, 8.86) | (5.84, 18.81) | (18.39, 215.22) | (2.97, 17.23) |
Data are odds ratios and 95% confidence intervals. Logistic regression analysis adjusted by sex and age was applied. Reference groups in the regression model were selected based on the lowest levels of perfect separation. a Elevated BP includes stage 1 and stage 2 hypertension. Abbreviations: ALT, alanine transaminase; AST, aspartate aminotransferase; BMI, body mass index; BP, blood pressure; HDL-C, high-density lipoprotein cholesterol; IGM, impaired glucose metabolism; LDL-C, low-density lipoprotein cholesterol; TC, total cholesterol; TG, triglycerides.
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Velásquez-Mieyer, P.A.; Nieto-Martinez, R.; Velasquez, A.E.; Mou, X.; Young-Moss, S.; Mechanick, J.I.; Grant, C.C.; Neira, C.P. Disparities in the Cardiometabolic Impact of Adiposity among African American and Hispanic Adolescents. Nutrients 2024, 16, 3143. https://doi.org/10.3390/nu16183143
AMA Style
Velásquez-Mieyer PA, Nieto-Martinez R, Velasquez AE, Mou X, Young-Moss S, Mechanick JI, Grant CC, Neira CP. Disparities in the Cardiometabolic Impact of Adiposity among African American and Hispanic Adolescents. Nutrients. 2024; 16(18):3143. https://doi.org/10.3390/nu16183143
Chicago/Turabian StyleVelásquez-Mieyer, Pedro A., Ramfis Nieto-Martinez, Andres E. Velasquez, Xichen Mou, Stephanie Young-Moss, Jeffrey I. Mechanick, Cori Cohen Grant, and Claudia P. Neira. 2024. "Disparities in the Cardiometabolic Impact of Adiposity among African American and Hispanic Adolescents" Nutrients 16, no. 18: 3143. https://doi.org/10.3390/nu16183143
APA StyleVelásquez-Mieyer, P. A., Nieto-Martinez, R., Velasquez, A. E., Mou, X., Young-Moss, S., Mechanick, J. I., Grant, C. C., & Neira, C. P. (2024). Disparities in the Cardiometabolic Impact of Adiposity among African American and Hispanic Adolescents. Nutrients, 16(18), 3143. https://doi.org/10.3390/nu16183143
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