(2024) 22:245
Qu et al. BMC Medicine
https://doi.org/10.1186/s12916-024-03453-5

BMC Medicine

RESEARCH ARTICLE

Open Access

Trends in cardiovascular risk factor
prevalence, treatment, and control among US
adolescents aged 12 to 19 years, 2001 to March
2020
Qiang Qu1†, Qixin Guo1†, Jinjing Shi1†, Ziqi Chen1, Jinyu Sun1, Iokfai Cheang1, Rongrong Gao1, Yanli Zhou1,
Haifeng Zhang2,3, Shengen Liao1*, Wenming Yao1* and Xinli Li1*

Abstract
Background Early-life cardiovascular risk factors (CVRFs) are known to be associated with target organ damage during adolescence and premature cardiovascular morbidity and mortality during adulthood. However, contemporary
data describing whether the prevalence of CVRFs and treatment and control rates have changed are limited. This
study aimed to examine the temporal trends in the prevalence, treatment, and control of CVRFs among US adolescents over the past 2 decades.
Methods This is a serial cross-sectional study using data from nine National Health and Nutrition Examination Survey
cycles (January 2001—March 2020). US adolescents (aged 12 to 19 years) with information regarding CVRFs (including hypertension, elevated blood pressure [BP], diabetes, prediabetes, hyperlipidemia, obesity, overweight, cigaretteuse, inactive physical activity, and poor diet quality) were included. Age-adjusted trends in CVRF prevalence, treatment, and control were examined. Joinpoint regression analysis was performed to estimate changes in the prevalence, treatment, and control over time. The variation by sociodemographic characteristics were also described.
Results A total of 15,155 US adolescents aged 12 to 19 years (representing ≈ 32.4 million people) were included.
From 2001 to March 2020, there was an increase in the prevalence of prediabetes (from 12.5% [95% confidence
interval (CI), 10.2%-14.9%] to 37.6% [95% CI, 29.1%-46.2%]) and overweight/obesity (from 21.1% [95% CI, 19.3%-22.8%]
to 24.8% [95% CI, 21.4%-28.2%]; from 16.0% [95% CI, 14.1%-17.9%] to 20.3% [95% CI, 17.9%-22.7%]; respectively),no improvement in the prevalence of elevated BP (from 10.4% [95% CI, 8.9%-11.8%] to 11.0% [95% CI, 8.7%-13.4%]),diabetes (from 0.7% [95% CI, 0.2%-1.2%] to 1.2% [95% CI, 0.3%-2.2%]), and poor diet quality (from 76.1% [95% CI,
74.0%-78.2%] to 71.7% [95% CI, 68.5%-74.9%]), and a decrease in the prevalence of hypertension (from 8.1% [95%CI, 6.9%-9.4%] to 5.5% [95% CI, 3.7%-7.3%]), hyperlipidemia (from 34.2% [95% CI, 30.9%-37.5%] to 22.8% [95% CI,
18.7%-26.8%]), cigarette use (from 18.0% [95% CI, 15.7%-20.3%] to 3.5% [95% CI, 2.0%-5.0%]), and inactive physical†

Qiang Qu, Qixin Guo and Jinjing Shi contributed equally.

*Correspondence:
Shengen Liaoshengenliao@163.com
Wenming Yao
yaowenming1st@163.com
Xinli Li
xinli3267@njmu.edu.cn
Full list of author information is available at the end of the article© The Author(s) 2024. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which
permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to theoriginal author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images orother third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line
to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory
regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of thislicence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Qu et al. BMC Medicine(2024) 22:245

Page 2 of 16

activity (from 83.0% [95% CI, 80.7%-85.3%] to 9.5% [95% CI, 4.2%-14.8%]). Sex and race/ethnicity affected the evolution of CVRF prevalence differently. Whilst treatment rates for hypertension and diabetes did not improve significantly(from 9.6% [95% CI, 3.5%-15.8%] to 6.0% [95% CI, 1.4%-10.6%]; from 51.0% [95% CI, 23.3%-78.7%] to 26.5% [95% CI,
0.0%-54.7%]; respectively), BP control was relatively stable (from 75.7% [95% CI, 56.8%-94.7%] to 73.5% [95% CI, 40.3%100.0%]), while glycemic control improved to a certain extent, although it remained suboptimal (from 11.8% [95% CI,
0.0%-31.5%] to 62.7% [95% CI, 62.7%-62.7%]).
Conclusions From 2001 to March 2020, although prediabetes and overweight/obesity increased, hypertension,
hyperlipidemia, cigarette use, and inactive physical activity decreased among US adolescents aged 12 to 19 years,
whereas elevated BP, diabetes, and poor diet quality remained unchanged. There were disparities in CVRF prevalence
and trends across sociodemographic subpopulations. While treatment and control rates for hypertension and diabetes plateaued, BP control were stable, and improved glycemic control was observed.
Keywords Cardiovascular risk factor, Prevalence, Treatment, Control, Pediatrics, NHANES

Background
Cardiovascular diseases (CVDs) have been the leading
cause of deaths and disabilities for several decades [1].
Despite substantial declines in cardiovascular mortality
since 1950, CVDs remain the most common source ofmortality in the US and accounts for up to 928,741 deathsin 2020 [2]. CVDs are estimated to result in direct andindirect costs of over $407.3 billion between 2018 and2019, constituting a considerable economic and social
burden [3].
Recent meta-analyses show that a substantial proportion of boys (6%-39%) and girls (6%-86%) exhibit cardiovascular risk factors (CVRFs) [4]. Boys and girls withpoor health status have 5.7- and 3.6-fold higher CVD
risks, respectively [4]. Among adolescents, CVRFs arestrongly correlated with early indicators of target organ
damage [5–8]. Without appropriate and timely intervention, conventional CVRFs (including hypertension,
elevated blood pressure [BP], diabetes, prediabetes,
hyperlipidemia, obesity, overweight, cigarette use, inactive physical activity, and poor diet quality) during adolescence may persist [9, 10], and are associated withhigher mortality and morbidity risks during adulthood[11–19]. Thus, mitigating CVRFs in adolescents could
potentially result in a reduced number of individuals
affected by CVDs later in life. The magnitude of CVRF
burden in adolescents may have an increasingly profound impact on public health as the population ages.
However, trends in CVRF prevalence, treatment, andcontrol in US adolescents are as yet unclear. Moreover,most previous studies do not provide recent estimatedrates, ignore information on several important subgroups(i.e., sociodemographic status), or have methodologicallimitations (e.g., unweighted or unadjusted estimates fordesign variables) [20–24], which are essential for predicting population-level complications and helping develop
effective national public health policies [25, 26].

The primary purpose of this study was to determine
how CVRF prevalence changed among US adolescents
aged 12 to 19 years in the past 2 decades. A secondary
purpose was to determine whether CVRF treatment andcontrol rates were improved or deteriorative during thisperiod, and whether these trends varied across sociodemographic subgroups. To achieve these goals, data from
9 cycles of the National Health and Nutrition Examination Survey (NHANES) were analyzed.

Methods
Data collection

The NHANES, administered by the National Centerfor Health Statistics (NCHS) of the Centers for Disease
Control and Prevention (CDC), comprises a series ofpublicly available, cross-sectional, nationwide surveys ofcivilian, noninstitutionalized US population [27, 28]. To
obtain nationally representative estimates, the NHANES
selects participants aged 1 to 80 years using a complex,
multistage, probability sampling design. Most questionnaire data are collected through in-home interviews,
and physical examinations and laboratory testing data
are collected through mobile examinations; the data
are released every 2 years since 1999. Details regarding
design, weighting, and methodology have been described
elsewhere [29]. This study included 9 cycles from 2001–
2002 to 2017-March 2020, focusing on participants
aged 12–19 years. Among the adolescent participants,
unweighted response rates ranged from 54.4% to 88.9%
for in-home interviews and 50.7% to 86.4% for mobile
examinations (Additional file 1: eTable 1). The NCHS
Ethics Review Board approved the study protocol, andwritten informed consent was acquired from all individuals [30]. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting
guideline was followed throughout the study (Additional
file 1: eTable 2) [31].

Qu et al. BMC Medicine(2024) 22:245

During the in-home interviews, information regarding
age, sex, race/ethnicity, educational level, income to poverty ratio (IPR), insurance status, and medical conditions(including medical history and medication use) was collected. Race/ethnicity categories included non-Hispanic
White, non-Hispanic Black, Mexican American, andother races/ethnicities (including other Hispanic andother/multiple races or ethnicities). Medical history andmedication use were self-reported. Medication use wasdetermined by the responses to questions about taking
prescription drugs. Participants who answered ‘yes’ wereasked to show all drug containers; when not available,
participants were asked to verbally list all drug names.
Participants were asked whether they had ever smokedcigarettes, how old they were when they first smoked,
and whether they smoked during the past month todetermine smoking status. Participants were also asked
to report the frequency and duration of moderate- and,separately, vigorous-intensity physical activity (including work- and transportation-related and leisure-time
domains) during a typical week. Weekly exercise time
was calculated as the minutes of moderate-intensity
physical activity plus twice the minutes of vigorousintensity physical activity per week. Dietary information was obtained from 24-h recall interviews during themobile examinations. The simple Healthy Eating Index2015 (HEI-2015) scoring algorithm (per day), based on
13 dietary components (including total fruit, whole fruit,total vegetables, greens or beans, whole grains, dairy,
total protein foods, seafood or plant protein, fatty acids,
refined grains, sodium, added sugars, and saturated fat),
was used to indicate overall diet quality [32]. Anthropometry parameters including weight, height, and BP
were measured using standard protocols. Body massindex (BMI) was calculated by dividing weight by heightsquared. Systolic and diastolic BP were calculated as themean of 3 (sometimes 4) BP determinations.
Non-fasting laboratory testing was used to measureserum levels of hemoglobin A­ 1c ­(HbA1c), total cholesterol(TC) and high-density lipoprotein cholesterol (HDLC). The non-high-density lipoprotein cholesterol (nonHDL-C) was calculated by subtracting HDL-C from TC.
Approximately half of the participants were sampled
to attend a morning examination, during which fasting
plasma glucose (FPG), low-density lipoprotein cholesterol (LDL-C), and triglycerides levels were measured
after fasting for 8.5 to less than 24 h.
Assessment of CVRF prevalence, treatment, and control

The prevalence of CVRFs was evaluated through theabove parameters derived from in-home interviews andmobile examinations. According to the 2017 American
Academy of Pediatrics and 2017 American College ofPage 3 of 16

Cardiology/American Heart Association guidelines [33,34], age-, sex-, and height-specific BP percentiles and
130/80 mmHg were used to define high BP in adolescents aged < 18 and 18–19 years, respectively (Additional
file 1: eTable 3). Hypertension was defined as stage 1 or
2 levels and/or current use of antihypertensive medications (Additional file 1: eMethod 1), whereas elevated BP
was defined as an elevated level. Diabetes was defined
as a H­ bA1c of ≥ 6.5%, FPG of ≥ 126 mg/dL, self-reportof previous diagnosis, and/or current use of antidiabetic medications, whereas prediabetes was defined asa ­HbA1c of 5.7%-6.4% and/or FPG of 100–125 mg/dL[35]. Hyperlipidemia was defined as a TC of ≥ 200 mg/
dL, HDL-C of < 40 mg/dL, non-HDL-C of ≥ 145 mg/dL,
LDL-C of ≥ 130 mg/dL, triglycerides of ≥ 130 mg/dL,
and/or current use of antihyperlipidemic medications[36, 37]. Obesity and overweight were defined basedon BMI using the Lambda Mu Sigma method [38]. In
accordance with CDC’s standard [39, 40], cigarette use
was defined as smoking cigarettes within the previous
30 days. Based on the 2018 Physical Activities Guidelines
for Americans [41], inactive physical activity was defined
as a weekly exercise time of < 420 and < 150 min/wk inadolescents aged < 18 and 18–19 years, respectively. Diet
quality was broadly classified according to HEI-2015, anda score of < 51 points was considered as poor diet quality[42]. Consistent definitions were applied for the CVRFs
throughout the study.
CVRF treatment and control rates were also assessedin adolescents aged 12–19 years. Hypertension treatment was defined as current use of antihypertensive
medications. Hypertension was considered controlled
if (1) BP was reduced to < 90th percentile in adolescents
aged < 13 years, (2) BP was reduced to < 90th percentileand < 130/80 mmHg in adolescents aged 13–17 years,or (3) BP was reduced to < 130/80 mmHg in adolescents aged 18–19 years [33, 34]. Diabetes treatment was
defined as current use of antidiabetic medications. Diabetes was considered controlled if ­HbA1c was reduced
to < 7% [43, 44]. Hyperlipidemia treatment and control
rates were not analyzed because the updated guidelines
no longer recommended lipid-level targets for treatment
and the number of adolescents receiving lipid-lowering
medications was small [36, 37].
Statistical analysis

To ensure that the estimates accurately represented thenoninstitutionalized US population, weights for the interview sample, examination sample, fasting subsample, anddietary sample were appropriately used for all analyses.
Baseline characteristics of adolescents aged 12–19 years
were presented as means or proportions with 95% confidence intervals (CIs). A linear trend in the weighted

Qu et al. BMC Medicine(2024) 22:245

means and proportions over time was tested using F test
based on weighted linear regression or Wald test basedon logistic regression, with time treated as a continuous
variable. Estimates for the prevalence, treatment, andcontrol of CVRF were age-adjusted to the 2000 Census
population, using the age groups of 12 to 14, 15 to 17, and18 to 19 years. We calculated relative % change per 4-year
cycle and P for trend using a joinpoint regression modelwith heteroscedastic and uncorrected errors, as previously described [26, 45]. The default maximum numberof joinpoints (0 joinpoints, corresponding to a straightline) was allowed to avoid possible overfitting. The optimal fitting model was chosen by performing 4499 permutation tests based on the Monte Carlo method, adjustingfor multiple tests. Parameters were estimated using
weighted least squares, with weights proportional to theinverse of the variance of ln-transformed age-standardized prevalence rate at each 4-year cycle. Furthermore,logistic regression analyses were conducted, adjusting forage, sex, and race/ethnicity, to identify factors associatedwith the prevalence, treatment, and control of CVRFs.
To assess clinical implications of the updated guidelines for high BP on the prevalence, treatment, and control of high BP among adolescents, we also performed asensitivity analysis by defining hypertension and elevated
BP following the 2003 National Institutes of Health’s
National Heart, Lung, and Blood Institute (NIH/NHLBI)
and 2004 NIH/NHLBI guidelines (Additional file 1: eTable 3) [46, 47]. The definition of hypertension treatment
was identical to that used in the main analysis. Hypertension was considered controlled if (1) BP was reduced
to < 95th percentile in adolescents aged < 18 years or (2)BP was reduced to < 140/90 mmHg in adolescents aged
18–19 years [46, 47].
All analyses were performed using R software 4.2.3(R Foundation) and Joinpoint Regression Program 5.0.2(National Cancer Institute). A two-sided P-value of lessthan 0.05 was considered statistically significant.

Results
Baseline characteristics

There were 97,657 individuals initially identified from2001-March 2020 NHANES. After exclusions for age < 12or ≥ 20 years (n = 81,980), unavailable information on
all CVRF components (n = 369), or pregnancy at thetime of examination (n = 153), 15,155 adolescents aged
12–19 years were finally included, representing approximately 32.4 million noninstitutionalized and nonpregnant US population (Additional file 1: eFigure 1).
Table 1 presents the descriptive characteristics of theindividuals stratified by survey periods. The mean age
was 15.4 years, and 51.3% were boys. The racial andethnic distribution was as follows: 57.4% non-Hispanic

Page 4 of 16

White, 14.4% non-Hispanic Black, 13.3% Mexican American, and 14.9% from other races/ethnicities; over time,
the proportions of Mexican Americans and other races/
ethnicities increased significantly (P for trend = 0.02
and < 0.001, respectively), whereas the proportion of
non-Hispanic Whites decreased (P for trend < 0.001). The
proportions of individuals who were born outside the US
varied from 7.3% to 10.1%, and those who lived in poverty
from 28.5% to 32.3%. The proportions of individuals withhealth insurance increased from 85.5% in 2001–2004 to
91.8% in 2017-March 2020 (P for trend < 0.001).
Compared with earlier years, individuals in the recent
survey cycles were more likely to have normal diastolicBP, improved lipid profiles (including TC, HDL-C, nonHDL-C, LDL-C, and triglycerides), more exercise time,and an increased HEI-2015 score, but were prone to ahigher BMI, increased H­ bA1c level, and impaired fasting
glucose. Age-adjusted trends in mean BP, H­ bA1c, FPG,
TC, HDL-C, non-HDL-C, LDL-C, triglycerides levels,
BMI, weekly exercise time, and HEI-2015 for all individuals and pivotal subgroups (age and race/ethnicity) are displayed in Additional file 1: eFigures 2–4 and eTable 4.
CVRF prevalence rates

The prevalence rates of CVRFs among adolescents aged
12–19 years are summarized for age-adjusted analysis in Table 2 and for demographics-adjusted analysis in
Table 3, and the secular trends are summarized in Additional file 1: eTable 4.
The age-adjusted prevalence of hypertension among
adolescents aged 12–19 years significantly decreased from
8.1% (95% CI, 6.9%-9.4%) in 2001–2004 to 5.5% (95% CI,
3.7%-7.3%) in 2017-March 2020 (Fig. 1A), with a -15.3%
relative decrease (95% CI, -26.8% to -1.9%) per 4-year
cycle (P for trend = 0.04). The age-adjusted prevalence ofelevated BP and diabetes did not change over this period(P for trend = 0.73 and 0.27, respectively) (Figs. 1B-C). The
age-adjusted prevalence of prediabetes was numericallyhigher in 2017-March 2020 than in 2001–2004 (37.6%[95% CI, 29.1%-46.2%] versus 12.5% [95% CI, 10.2%14.9%], Fig. 1D), although the difference did not reach significance (P for trend = 0.08). The age-adjusted prevalenceof hyperlipidemia was lower in 2017-March 2020 (22.8%[95% CI, 18.7%-26.8%]) than in 2001–2004 (34.2% [95% CI,
30.9%-37.5%]) (Fig. 1E), with a -9.8% relative decrease (95%CI, -15.2% to -4.0%) per 4-year cycle (P for trend = 0.01).
The age-adjusted prevalence of obesity significantly
increased from 16.0% (95% CI, 14.1%-17.9%) in 2001–
2004 to 20.3% (95% CI, 17.9%-22.7%) in 2017-March
2020 (Fig. 1F), with a 6.4% relative increase (95% CI,
4.2%-8.6%) per 4-year cycle (P for trend = 0.002); as did
overweight (from 21.1% [95% CI, 19.3%-22.8%] in 2001–
2004 to 24.8% [95% CI, 21.4%-28.2%] in 2017-MarchQu et al. BMC Medicine(2024) 22:245

Page 5 of 16

Table 1 Baseline Characteristics of US Adolescents Aged 12 to 19 Years, 2001 to March 2­ 020a
Characteristics

2001–2004(n = 4591)b

2005–2008(n = 3404)b

2009–2012(n = 2563)b

2013–2016(n = 2693)b

2017-March 2020
(n = 1904)b

P for ­trendc

Age, mean, y15.4 (15.3–15.5)

15.5 (15.3–15.6)

15.4 (15.3–15.5)

15.4 (15.2–15.5)

15.4 (15.3–15.5)

.83

12–14

39.7 (37.2–42.1)

37.0 (34.2–39.7)

38.7 (36.3–41.0)

38.9 (36.1–41.7)

39.3 (37.2–41.4)

.77

15–17

37.0 (34.4–39.6)

39.5 (37.2–41.9)

38.5 (36.0–41.0)

39.2 (37.4–40.9)

37.4 (34.9–39.9)

.84

18–19

23.3 (20.7–26.0)

23.5 (21.3–25.6)

22.8 (20.0–25.7)

22.0 (20.1–23.8)

23.3 (21.3–25.3).60

Female48.6 (46.9–50.3)

48.5 (46.4–50.7)

48.5 (46.1–50.9)

48.8 (46.8–50.9)

49.1 (44.8–53.4)

.80

Male

51.4 (49.7–53.1)

51.5 (49.3–53.6)

51.5 (49.1–53.9)

51.2 (49.1–53.2)

50.9 (46.6–55.2).80

Non-Hispanic White

63.1 (57.7–68.6)

61.9 (56.9–66.9)

56.7 (50.8–62.6)

53.3 (46.1–60.6)

50.8 (44.7–56.9)< .001

Non-Hispanic Black14.2 (11.1–17.3)

15.1 (11.6–18.6)

14.8 (11.0–18.6)

14.3 (10.5–18.2)

13.4 (9.3–17.5).71

Mexican American

10.9 (8.0–13.7)

11.5 (9.0–13.9)

13.8 (10.2–17.3)

15.1 (10.7–19.5)

15.8 (11.4–20.2)

.02

Other

11.8 (8.6–15.0)

11.6 (8.7–14.5)

14.7 (12.1–17.3)

17.3 (14.9–19.7)

20.0 (17.0–22.9)< .001(n = 4591)(n = 3403)(n = 2560)(n = 2692)(n = 1904)

US born91.0 (89.2–92.8)

91.7 (89.8–93.5)

89.9 (87.9–91.9)

92.7 (91.2–94.2)

92.1 (89.8–94.3).31

Non-US born

9.0 (7.2–10.8)

8.3 (6.5–10.2)

10.1 (8.1–12.1)

7.3 (5.8–8.8)

7.9 (5.7–10.2).31

Income to poverty
ratio, %(n = 4318)(n = 3186)(n = 2316)(n = 2450)(n = 1680)   < 13031.1 (27.9–34.3)

28.5 (24.9–32.0)

32.3 (26.9–37.6)

31.0 (26.3–35.8)

29.2 (25.8–32.7).92

36.4 (34.1–38.6)

35.8 (32.5–39.2)

36.4 (31.9–40.9)

39.2 (35.1–43.2)

37.4 (34.0–40.7).25  ≥ 350

32.5 (29.1–35.9)

35.7 (31.1–40.4)

31.3 (26.0–36.6)

29.8 (24.9–34.7)

33.4 (29.3–37.5).43

Insurance status(n = 4514)(n = 3377)(n = 2548)(n = 2683)(n = 1894)

Uninsured

14.5 (12.1–16.9)

14.5 (12.4–16.7)

12.2 (9.6–14.8)

10.2 (8.5–11.9)

8.2 (6.3–10.2)

Insured85.5 (83.1–87.9)

85.5 (83.3–87.6)

87.8 (85.2–90.4)

89.8 (88.1–91.5)

91.8 (89.8–93.7)< .001

Body mass index,
mean, kg/m2e

23.3 (23.0–23.7)

23.4 (23.1–23.8)

23.8 (23.4–24.2)

24.1 (23.7–24.6)

24.4 (23.9–24.8)< .001

Weight ­statusf

Age group, ySexRace/ethnicityd

Birth country

130–349< .001(n = 4464)(n = 3331)(n = 2507)(n = 2627)(n = 1832)

Normal63.0 (60.3–65.8)

61.9 (59.4–64.4)

60.6 (57.9–63.3)

57.6 (54.7–60.5)

55.1 (51.2–59.0)< .001

Overweight21.0 (19.3–22.8)

21.8 (20.2–23.3)

22.1 (20.2–24.1)

23.7 (21.8–25.5)

24.8 (21.5–28.2).02

Obesity

15.9 (14.0–17.8)

16.3 (13.8–18.8)

17.3 (15.1–19.5)

18.7 (16.0–21.4)

20.1 (17.7–22.5).004

Blood ­pressureg(n = 4371)(n = 3211)(n = 2438)(n = 2554)(n = 1653)  Normal

Systolic

85.1 (83.4–86.8)

83.6 (80.4–86.7)

85.6 (83.6–87.5)

87.1 (85.7–88.6)

85.3 (82.1–88.4).26  Elevated

11.2 (10.1–12.3)

11.6 (9.4–13.7)

10.7 (8.8–12.6)

10.2 (8.9–11.6)

12.2 (9.8–14.6).93  Stage 1

3.4 (2.5–4.2)

4.3 (3.0–5.6)

3.1 (2.2–4.0)

2.2 (1.5–2.9)

2.5 (1.2–3.8).02  Stage 2

0.3 (0.1–0.6)

0.5 (0.3–0.8)

0.6 (0.2–1.1)

0.4 (0.2–0.7)

0.1 (0.0–0.1).10

Diastolic(n = 4349)(n = 3202)(n = 2423)(n = 2539)(n = 1653)  Normal

95.2 (94.0–96.3)

96.9 (96.0–97.7)

97.9 (96.9–98.8)

98.6 (98.1–99.1)

97.0 (96.0–97.9)< .001  Elevated

0.5 (0.2–0.8)

0.2 (0.0–0.4)

0.4 (0.0–0.8)

0.0 (0.0–0.1)

0.1 (0.0–0.2).01  Stage 1

3.9 (3.0–4.8)

2.7 (1.9–3.5)

1.5 (0.8–2.2)

1.2 (0.8–1.7)

2.6 (1.7–3.5).002  Stage 2

0.5 (0.2–0.7)

0.3 (0.0–0.5)

0.3 (0.0–0.6)

0.1 (0.0–0.2)

0.4 (0.0–1.0).56

Hemoglobin ­A1c, %(n = 4209)(n = 3015)(n = 2303)(n = 2375)(n = 1656)   < 5.7

97.0 (96.4–97.6)

95.9 (94.8–97.0)

93.2 (92.0–94.5)

94.0 (92.8–95.2)

93.2 (91.2–95.2)

2.4 (1.7–3.0)

3.6 (2.7–4.6)

6.3 (5.1–7.4)

5.7 (4.5–6.9)

6.2 (4.4–8.1)< .0010.6 (0.3–0.9)

0.5 (0.2–0.8)

0.5 (0.1–0.9)

0.3 (0.0–0.7)

0.6 (0.0–1.2).75

5.7–6.4   ≥ 6.5< .001

FPG, mg/dLh(n = 2037)(n = 1425)(n = 1161)(n = 1118)(n = 740)   < 100

88.2 (85.8–90.5)

76.2 (72.6–79.9)

83.8 (80.7–86.9)

75.0 (70.6–79.4)

65.4 (57.6–73.2)

11.3 (8.9–13.8)

23.0 (19.3–26.7)

16.0 (12.9–19.1)

24.2 (19.7–28.7)

34.2 (26.4–42.0)< .0010.5 (0.0–0.9)

0.8 (0.2–1.3)

0.2 (0.0–0.4)

0.8 (0.0–1.6)

0.4 (0.0–0.8).89

100–125   ≥ 126< .001

Qu et al. BMC Medicine(2024) 22:245

Page 6 of 16

Table 1 (continued)
P for ­trendc

Characteristics

2001–2004(n = 4591)b

2005–2008(n = 3404)b

2009–2012(n = 2563)b

2013–2016(n = 2693)b

2017-March 2020
(n = 1904)bTC(n = 4130)(n = 2989)(n = 2264)(n = 2331)(n = 1593)

62.1 (59.6–64.5)

64.5 (61.9–67.1)

69.1 (66.6–71.6)

69.3 (67.1–71.4)

71.8 (68.3–75.3)< .00125.9 (23.5–28.2)

25.3 (23.2–27.5)

22.2 (20.1–24.4)

21.6 (19.8–23.3)

22.1 (18.8–25.4).01

12.1 (10.1–14.1)

10.2 (8.7–11.6)

8.6 (6.7–10.5)

9.2 (7.8–10.6)

6.1 (4.8–7.5)< .001(n = 4133)(n = 3001)(n = 2276)(n = 2339)(n = 1606)

Ideal (≥ 45 mg/dL)

66.0 (64.1–68.0)

68.7 (66.5–70.9)

71.4 (69.0–73.9)

71.4 (68.6–74.2)

69.4 (65.4–73.4).03

Intermediate (40–44
mg/dL)

16.3 (15.2–17.4)

16.4 (14.6–18.2)

13.1 (11.4–14.9)

13.0 (11.3–14.8)

16.5 (13.7–19.3)

.24
.06

Ideal (< 170 mg/dL)
Intermediate(170–199 mg/dL)
Poor (≥ 200 mg/dL)
HDL-C

Poor (< 40 mg/dL)
Non-HDL-Ci
Ideal (< 120 mg/dL)
Intermediate(120–144 mg/dL)
Poor (≥ 145 mg/dL)
LDL-ChIdeal (< 110 mg/dL)
Intermediate(110–129 mg/dL)
Poor (≥ 130 mg/dL)
Triglyceridesh
Ideal (< 90 mg/dL)
Intermediate(90–129 mg/dL)
Poor (≥ 130 mg/dL)
Weekly exercise time,mean, min/wkj

17.6 (16.0–19.3)

14.9 (13.5–16.4)

15.4 (13.6–17.2)

15.6 (13.0–18.2)

14.1 (11.8–16.4)(n = 4125)(n = 2989)(n = 2263)(n = 2330)(n = 1591)

62.7 (60.8–64.7)

66.5 (63.5–69.6)

71.9 (69.2–74.6)

72.4 (70.1–74.7)

75.3 (71.7–79.0)< .001

23.2 (21.3–25.1)

21.1 (18.5–23.7)

17.8 (15.8–19.7)

17.6 (15.7–19.4)

16.8 (13.6–19.9)< .001< .001

14.1 (12.3–15.9)

12.4 (10.6–14.1)

10.3 (8.0–12.7)

10.0 (8.5–11.6)

7.9 (6.1–9.7)(n = 1904)(n = 1402)(n = 1143)(n = 1061)(n = 715)

75.9 (73.0–78.8)

80.0 (77.0–83.0)

80.7 (78.6–82.8)

83.7 (80.6–86.8)

83.7 (79.1–88.2).001

16.2 (13.8–18.6)

13.3 (10.7–15.8)

12.0 (10.2–13.9)

10.7 (8.0–13.5)

11.5 (7.4–15.7)

.02
.02

7.9 (6.0–9.8)

6.7 (5.0–8.5)

7.2 (5.3–9.2)

5.6 (3.7–7.4)

4.8 (2.9–6.7)(n = 2003)(n = 1406)(n = 1146)(n = 1062)(n = 716)

62.0 (57.9–66.0)

64.7 (61.3–68.2)

71.8 (68.3–75.2)

78.6 (74.6–82.5)

77.9 (72.1–83.6)< .001

20.8 (17.6–23.9)

21.1 (18.1–24.0)

17.3 (14.1–20.5)

11.7 (8.6–14.7)

15.4 (10.4–20.4).002

17.3 (14.2–20.3)

14.2 (10.8–17.6)

10.9 (9.1–12.7)

9.8 (7.0–12.6)

6.7 (4.8–8.6)< .001

185.6 (159.3–212.0) 836.3 (763.4–909.2) 993.0 (906.7–1079.4) 1044.4 (974.3–1114.6) 1617.5 (1439.2–1795.8) < .001

HEI-2015, mean, ­pointsk 43.5 (42.9–44.2)

43.8 (43.0–44.6)

45.9 (45.0–46.8)

46.0 (45.2–46.8)

43.7 (42.6–44.8)

Diet ­qualityl(n = 4357)(n = 3228)(n = 2417)(n = 2492)(n = 1740)

Ideal

0.0 (0.0–0.1)

0.2 (0.0–0.4)

0.1 (0.0–0.3)

0.8 (0.3–1.3)

0.7 (0.1–1.3).03

Intermediate

23.9 (21.8–25.9)

26.4 (23.3–29.4)

32.0 (28.4–35.7)

31.7 (28.8–34.6)

27.6 (24.5–30.8)

.002

Poor

76.1 (74.0–78.1)

73.5 (70.4–76.5)

67.8 (64.1–71.5)

67.5 (64.5–70.5)

71.7 (68.6–74.9)< .001.03

SI conversions: to convert glucose to mmol/L, multiply by 0.0555; TC, HDL-C, non-HDL-C, and LDL-C to mmol/L, multiply by 0.0259; triglycerides to mmol/L, multiply
by 0.0113
Abbreviations: CI Confidence interval, FPG Fasting plasma glucose, HDL-C High-density lipoprotein cholesterol, HEI-2015 Healthy Eating Index-2015, LDL-C Low-density
lipoprotein cholesterol, NHANES National Health and Nutrition Examination Survey, non-HDL-C Non-high-density lipoprotein cholesterol, TC Total cholesterolaNationally representative estimates of US adolescents aged 12–19 years from the 2001-March 2020 NHANES. The sample size for each 4-year interval was
unweighted, whereas all other numbers were weighted means or percentages with 95% CIsb

Unweighted sample sizecF test based on weighted linear regression or Wald test based on logistic regressiondRace/ethnicity was based on self-report. The non-Hispanic Asian category was not available before 2011 due to the survey design, and thus estimates could not bepresented separately. All other racial/ethnic groups were grouped as ‘Other’eBody mass index was missing for 394 (2.6%) participants among 15,155 included from the examination samplefWeight status (normal, overweight, and obesity) was based on Ref [38]g

Blood pressure (normal, elevated, stage 1, and stage 2) were based on Ref [33, 34]hFPG, LDL-C, and triglycerides were based on fasting laboratory testingi

Non-HDL-C was calculated as the difference between serum TC and HDL-C

j

Weekly exercise time was calculated as the minutes of moderate-intensity physical activity plus twice the minutes of vigorous-intensity physical activity per week.
During 2001–2006, physical activity levels were underestimated due to a lack of data on home/yard tasks for adolescents aged 12–15 years and a lack of time data on
muscle-strengthening activities across the age spectrum; data on weekly exercise time for participants aged 12–17 years were not available during 2017-March 2020,
and thus estimates only represented those aged 18–19 years throughout this period. Weekly exercise time was missing for 5050 (33.3%) participants among 15,155
included from the examination samplek

HEI-2015 was missing for 292 (2.0%) participants among 14,526 included from the dietary day one samplelDiet quality (ideal, intermediate, and poor) was based on Ref [42]

6.7 (5.8–7.6)

9.4 (8.0–10.8)

18–19

8.2 (7.3–9.1)Male

8.6 (7.5–9.8)

4.9 (4.0–5.8)

4.9 (3.8–6.0)

Non-Hispanic Black

Mexican American

Other

3.9 (2.9–4.9)

Non-US born

5.0 (4.0–6.1)≥ 350

6.1 (5.5–6.8)

Insured10.3 (9.5–11.2)

11.1 (9.0–13.2)

9.9 (8.3–11.4)

11.3 (9.9–12.6)

10.3 (9.1–11.4)

9.5 (7.0–12.0)

10.7 (9.8–11.5)

8.6 (7.2–10.1)

10.3 (8.7–11.9)

13.6 (12.5–14.7)

10.3 (9.1–11.5)

15.4 (14.0–16.7)

5.5 (4.7–6.2)

14.1 (12.1–16.1)

11.4 (10.3–12.5)

7.3 (6.2–8.3)

1518/14247

Elevated ­BPc

11.0 (10.2–11.8)

16.4 (14.2–18.5)

9.9 (8.4–11.4)

11.8 (10.3–13.3)

14.9 (13.4–16.4)

9.2 (7.3–11.1)

12.2 (11.3–13.1)

9.4 (7.7–11.1)

9.4 (8.1–10.6)

7.0 (6.1–7.9)

14.3 (12.9–15.7)

13.0 (11.8–14.3)

10.6 (9.7–11.5)

24.4 (22.3–26.5)

13.1 (11.9–14.4)

2.2 (1.7–2.8)

1553/13865

Cigarette ­useg

47.7 (45.9–49.6)

52.6 (48.4–56.9)

47.2 (43.7–50.6)

48.7 (46.2–51.3)

49.7 (47.5–51.9)

55.3 (51.4–59.3)

47.8 (46.1–49.5)

47.8 (44.7–51.0)

52.3 (49.7–54.9)

51.6 (48.4–54.7)

46.7 (44.1–49.3)

42.7 (40.6–44.8)

54.8 (52.5–57.0)

24.7 (22.5–27.0)

52.9 (50.4–55.4)

59.8 (57.3–62.3)

5579/10105

Inactive physical­activityh

71.7 (70.2–73.2)

67.4 (63.9–70.9)

69.6 (66.5–72.7)

72.8 (71.1–74.6)

72.9 (70.7–75.0)

59.9 (55.7–64.1)

72.3 (70.9–73.8)

66.7 (63.7–69.7)

64.9 (62.4–67.4)

75.2 (73.1–77.3)

72.9 (70.7–75.2)

73.1 (71.3–74.8)

69.5 (67.7–71.3)

71.9 (69.1–74.7)

72.1 (70.3–73.9)

70.0 (67.7–72.3)

10,262/14234

Poor diet q­ ualityi

Cigarette use was defined as smoking cigarettes within the previous 30 daysg

Unweighted number of cases and sample size

Race/ethnicity was based on self-report. The non-Hispanic Asian category was not available before 2011 due to the survey design, and thus estimates could not be presented separately. All other racial/ethnic groups were groupedas ‘Other’kPoor diet quality was defined as a Healthy Eating Index-2015 score of < 51 pointsj(2024) 22:245

i

Inactive physical activity was defined as a weekly exercise time of < 420 and < 150 min/wk in adolescents aged < 18 and 18–19 years, respectively. Weekly exercise time was calculated as the minutes of moderate-intensity physicalactivity plus twice the minutes of vigorous-intensity physical activity per week. During 2001–2006, physical activity levels were underestimated due to a lack of data on home/yard tasks for adolescents aged 12–15 years and a lack of time
data on muscle-strengthening activities across the age spectrum; data on weekly exercise time for participants aged 12–17 years were not available during 2017-March 2020, and thus estimates only represented those aged 18–19 years
throughout this periodhObesity and overweight were defined based on body mass index using the Lambda Mu Sigma method (see Ref [38])fHyperlipidemia was defined as a total cholesterol of ≥ 200 mg/dL, high-density lipoprotein cholesterol of < 40 mg/dL, non-high-density lipoprotein cholesterol of ≥ 145 mg/dL, low-density lipoprotein cholesterol of ≥ 130 mg/dL,
triglycerides of ≥ 130 mg/dL, and/or current use of antihyperlipidemic medicationsedDiabetes was defined as a hemoglobin A­ 1c of ≥ 6.5%, fasting plasma glucose of ≥ 126 mg/dL, self-report of previous diagnosis, and/or current use of antidiabetic medications, whereas prediabetes was defined as a hemoglobin A­ 1c of
5.7%-6.4%

All estimates were age-standardized to the 2000 Census population using the age groups of 12 to 14, 15 to 17, and 18 to 19 years

22.5 (21.4–23.6)

23.5 (21.2–25.8)

20.5 (18.6–22.4)

23.4 (21.8–25.0)

24.1 (22.5–25.7)

19.0 (16.9–21.2)

23.0 (21.9–24.0)

21.7 (19.6–23.8)

26.8 (25.3–28.3)

22.1 (20.8–23.5)

22.0 (20.6–23.5)

23.3 (22.0–24.7)

21.8 (20.5–23.1)

22.7 (20.7–24.7)

21.5 (20.0–22.9)

23.7 (22.2–25.3)

3358/14761

Overweightf

Hypertension was defined as stage 1 or 2 levels and/or current use of antihypertensive medications, whereas elevated BP was defined as an elevated level (see Ref. [33, 34])17.3 (16.2–18.4)

20.3 (17.5–23.1)

12.4 (10.9–14.0)

19.1 (17.4–20.8)

21.5 (19.9–23.0)

12.0 (9.8–14.2)

18.3 (17.1–19.4)

16.6 (14.7–18.5)

22.9 (21.0–24.9)

22.6 (21.0–24.3)

15.5 (13.9–17.1)

17.3 (16.0–18.5)

18.2 (16.8–19.6)

19.8 (17.7–21.9)

17.8 (16.4–19.2)

16.1 (14.7–17.6)

2815/14761

Obesityfc26.9 (25.1–28.8)

31.4 (26.9–35.9)

24.9 (21.1–28.7)

28.2 (25.4–31.1)

29.7 (27.2–32.2)

26.1 (21.9–30.2)

27.7 (26.0–29.4)

27.2 (23.6–30.7)

28.2 (25.3–31.1)

21.4 (19.0–23.8)

28.8 (26.2–31.4)

30.7 (28.2–33.1)

24.0 (21.5–26.5)

35.1 (31.5–38.8)

25.4 (23.1–27.7)

24.3 (21.9–26.7)

1708/6192

Hyperlipidemiae

Nationally representative estimates of US adolescents aged 12–19 years from the 2001-March 2020 National Health and Nutrition Examination Survey

23.6 (21.4–25.8)

26.3 (22.0–30.7)

20.5 (16.2–24.8)

24.7 (21.7–27.8)

26.2 (23.7–28.7)

29.0 (24.0–33.9)

23.8 (21.5–26.0)

26.0 (22.6–29.5)

30.6 (26.4–34.7)

25.1 (22.0–28.2)

21.9 (18.5–25.3)

31.3 (28.5–34.1)

16.8 (14.6–19.0)

22.3 (19.1–25.4)

21.7 (19.1–24.4)

28.0 (25.1–30.9)

1504/6461

Prediabetesdb

1.0 (0.7–1.4)

1.0 (0.0–2.5)

0.8 (0.1–1.5)

0.9 (0.4–1.4)

1.3 (0.6–1.9)

0.4 (0.0–0.8)

1.0 (0.7–1.4)

0.5 (0.1–1.0)

1.9 (0.8–2.9)

1.4 (0.7–2.2)

0.8 (0.3–1.2)

1.1 (0.6–1.6)

0.9 (0.4–1.3)

1.4 (0.7–2.1)

0.8 (0.4–1.2)

0.9 (0.3–1.4)

84/6461

DiabetesdaAbbreviations: BP Blood pressure, CI Confidence interval

5.9 (4.5–7.3)

Uninsured

Insurance status

6.4 (5.4–7.5)

6.7 (5.9–7.5)

130–349   < 130

Income to poverty ratio, %6.4 (5.7–7.1)

US born

Birth country

6.2 (5.2–7.1)

Non-Hispanic White

Race/ethnicityk

4.0 (3.3–4.7)

FemaleSex

3.4 (2.7–4.1)

15–17

901/14247

Hypertensionc

Adolescents with cardiovascular risk factors, % (95% CIs)b

12–14

Age group, yCases/No.j

Characteristics

Table 2 Age-Adjusted Prevalence of Cardiovascular Risk Factors by Subgroups Among US Adolescents Aged 12 to 19 Years, 2001 to March 2­ 020a

Qu et al. BMC Medicine
Page 7 of 16

2.03 (1.61, 2.56)

2.90 (2.24, 3.75)

18–19

2.12 (1.70, 2.63)

Male

1.46 (1.16, 1.84)

0.77 (0.61, 0.99)

0.78 (0.58, 1.05)

Non-Hispanic Black

Mexican American

Other

0.68 (0.50, 0.92)

Non-US born

0.92 (0.75, 1.14)

0.71 (0.56, 0.91)

130–349≥ 350

0.93 (0.70, 1.23)

Insured0.90 (0.71, 1.14)

1 [Reference]

0.93 (0.75, 1.15)

1.12 (0.94, 1.33)

1 [Reference]

0.92 (0.65, 1.28)

1 [Reference]

0.83 (0.67, 1.04)

0.99 (0.81, 1.22)

1.40 (1.22, 1.62)

1 [Reference]

3.18 (2.72, 3.73)

1 [Reference]

2.08 (1.66, 2.59)

1.67 (1.39, 2.00)

1 [Reference]

1518/14247

Elevated ­BPb

1.48 (0.42, 5.22)

1 [Reference]

0.74 (0.27, 2.04)

0.76 (0.38, 1.53)

1 [Reference]

0.37 (0.13, 1.06)

1 [Reference]

0.70 (0.24, 2.03)

2.54 (1.12, 5.74)

1.90 (0.88, 4.12)

1 [Reference]

1.27 (0.64, 2.53)

1 [Reference]

1.64 (0.69, 3.89)

0.96 (0.41, 2.25)

1 [Reference]

84/6461

Diabetesc

1.00 (0.87, 1.16)

1 [Reference]

0.83 (0.72, 0.96)

0.97 (0.85, 1.11)

1 [Reference]

0.72 (0.62, 0.84)

1 [Reference]

0.98 (0.84, 1.15)

1.29 (1.16, 1.44)

1.01 (0.91, 1.13)

1 [Reference]

1.08 (0.97, 1.20)

1 [Reference]

0.94 (0.81, 1.09)

0.88 (0.78, 1.00)

1 [Reference]

3358/14761

Overweighte

0.51 (0.42, 0.61)

1 [Reference]

0.44 (0.34, 0.55)

0.65 (0.52, 0.80)

1 [Reference]

0.87 (0.66, 1.14)

1 [Reference]

0.60 (0.46, 0.78)

0.59 (0.49, 0.73)

0.42 (0.35, 0.51)

1 [Reference]

1.26 (1.08, 1.47)

1 [Reference]

14.20 (10.65, 18.94)

6.56 (5.04, 8.54)

1 [Reference]

1553/13865

Cigarette ­usef

0.84 (0.68, 1.03)

1 [Reference]

1.01 (0.83, 1.23)

1.02 (0.90, 1.15)

1 [Reference]

1.36 (1.12, 1.64)

1 [Reference]

1.04 (0.88, 1.23)

1.28 (1.08, 1.51)

1.23 (1.01, 1.49)

1 [Reference]

0.59 (0.52, 0.67)

1 [Reference]

0.22 (0.19, 0.26)

0.76 (0.67, 0.86)

1 [Reference]

5579/10105

Inactive physical­activityg

1.10 (0.93, 1.31)

1 [Reference]

0.77 (0.63, 0.93)

0.96 (0.83, 1.10)

1 [Reference]

0.66 (0.53, 0.81)

1 [Reference]

0.75 (0.61, 0.92)

0.69 (0.59, 0.80)

1.13 (0.96, 1.33)

1 [Reference]

1.20 (1.07, 1.34)

1 [Reference]

1.09 (0.92, 1.29)

1.10 (0.96, 1.27)

1 [Reference]

10,262/14234

Poor diet q­ ualityhfPoor diet quality was defined as a Healthy Eating Index-2015 score of < 51 points

Unweighted number of cases and sample size

Race/ethnicity was based on self-report. The non-Hispanic Asian category was not available before 2011 due to the survey design, and thus estimates could not be presented separately. All other racial/ethnic groups were grouped as ‘Other’

h

i

j

Inactive physical activity was defined as a weekly exercise time of < 420 and < 150 min/wk in adolescents aged < 18 and 18–19 years, respectively. Weekly exercise time was calculated as the minutes of moderate-intensity physicalactivity plus twice the minutes of vigorous-intensity physical activity per week. During 2001–2006, physical activity levels were underestimated due to a lack of data on home/yard tasks for adolescents aged 12–15 years and a lack of time
data on muscle-strengthening activities across the age spectrum; data on weekly exercise time for participants aged 12–17 years were not available during 2017-March 2020, and thus estimates only represented those aged 18–19 years
throughout this periodgObesity and overweight were defined based on body mass index using the Lambda Mu Sigma method (see Ref [38])

Cigarette use was defined as smoking cigarettes within the previous 30 dayseHyperlipidemia was defined as a total cholesterol of ≥ 200 mg/dL, high-density lipoprotein cholesterol of < 40 mg/dL, non-high-density lipoprotein cholesterol of ≥ 145 mg/dL, low-density lipoprotein cholesterol of ≥ 130 mg/dL,
triglycerides of ≥ 130 mg/dL, and/or current use of antihyperlipidemic medicationsdDiabetes was defined as a hemoglobin A­ 1c of ≥ 6.5%, fasting plasma glucose of ≥ 126 mg/dL, self-report of previous diagnosis, and/or current use of antidiabetic medications, whereas prediabetes was defined as a hemoglobin A­ 1c of
5.7%-6.4%cHypertension was defined as stage 1 or 2 levels and/or current use of antihypertensive medications, whereas elevated BP was defined as an elevated level (see Ref [33, 34])

0.90 (0.75, 1.08)

1 [Reference]

0.57 (0.49, 0.67)

0.91 (0.80, 1.03)

1 [Reference]

0.51 (0.41, 0.64)

1 [Reference]

1.10 (0.91, 1.32)

1.63 (1.40, 1.90)

1.60 (1.36, 1.88)

1 [Reference]

0.94 (0.84, 1.06)

1 [Reference]

1.29 (1.11, 1.51)

1.14 (1.00, 1.31)

1 [Reference]

2815/14761

Obesitye

Nationally representative estimates of US adolescents aged 12–19 years from the 2001-March 2020 National Health and Nutrition Examination Survey. Adjusted ORs with 95% CIs were adjusted for age, sex, and race/ethnicity groups

0.81 (0.63, 1.04)

1 [Reference]

0.68 (0.53, 0.87)

0.88 (0.73, 1.05)

1 [Reference]

0.85 (0.67, 1.08)

1 [Reference]

0.93 (0.74, 1.17)

0.97 (0.81, 1.17)

0.67 (0.55, 0.82)

1 [Reference]

1.40 (1.16, 1.70)

1 [Reference]

1.67 (1.38, 2.02)

1.06 (0.88, 1.28)

1 [Reference]

1708/6192

Hyperlipidemiadb0.85 (0.67, 1.07)

1 [Reference]

0.76 (0.58, 1.00)

0.95 (0.78, 1.16)

1 [Reference]

1.15 (0.86, 1.53)

1 [Reference]

1.25 (0.94, 1.64)

1.58 (1.20, 2.09)

1.21 (0.90, 1.62)

1 [Reference]

2.28 (1.94, 2.69)

1 [Reference]

0.71 (0.58, 0.88)

0.73 (0.61, 0.86)

1 [Reference]

1504/6461

PrediabetescaAbbreviations: BP Blood pressure, CI Confidence interval, OR Odds ratio

1 [Reference]

Uninsured

Insurance status

1 [Reference]< 130

Income to poverty ratio, %1 [Reference]

US born

Birth country

1 [Reference]

Non-Hispanic White

Race/ethnicityj

1 [Reference]

FemaleSex

1 [Reference]

15–17

901/14247

Hypertensionb

Adolescents with cardiovascular risk factors, adjusted ORs (95% CIs)

12–14

Age group, yCases/No.i

Characteristics

Table 3 Adjusted ORs for Cardiovascular Risk Factors by Subgroups Among US Adolescents Aged 12 to 19 Years, 2001 to March 2­ 020a

Qu et al. BMC Medicine(2024) 22:245
Page 8 of 16

Qu et al. BMC Medicine(2024) 22:245

2020) (Fig. 1G), with a 3.9% relative increase (95% CI,
2.3%-5.5%) per 4-year cycle (P for trend = 0.004). The
age-adjusted prevalence of cigarette use was numerically lower in 2017-March 2020 than in 2001–2004 (3.5%[95% CI, 2.0%-5.0%] versus 18.0% [95% CI, 15.7%-20.3%],
Fig. 1H), although the difference was not statistically significant (P for trend = 0.07). The age-adjusted prevalenceof inactive physical activity was lower in 2017-March
2020 (9.5% [95% CI, 4.2%-14.8%]) than in 2001–2004(83.0% [95% CI, 80.7%-85.3%]) (Fig. 1I), with a -29.0%
relative decrease (95% CI, -41.0% to -14.7%) per 4-year
cycle (P for trend = 0.01). There was no significant change
in the age-adjusted prevalence of poor diet quality (P for
trend = 0.13) (Fig. 1J).
The adjusted prevalence of hypertension or elevated
BP was higher among older individuals, boys, and nonHispanic Blacks; individuals who were Mexican American, who were born outside the US, or who were fromhigh-income families were less likely to have hypertension. Mexican Americans were more likely to have bothdiabetes and prediabetes; the adjusted prevalence ofprediabetes was also higher among boys, while being
lower among older individuals. Hyperlipidemia wasmore likely among older individuals and boys, and wasless likely among non-Hispanic Blacks and affluent individuals. Obesity and overweight were more common
among Mexican Americans, while being less common
among non-US-born or affluent individuals; older individuals and non-Hispanic Blacks were also more likely
to have obesity. The adjusted prevalence of cigarette use
correlated positively with being older and male, while itcorrelated negatively with being non-Hispanic Black,
Mexican American, or the other racial/ethnic group,

Page 9 of 16

coming from middle- or high-income families, and having health insurance. Inactive physical activity was more
likely among non-Hispanic Blacks or Mexican Americans
and non-US-born individuals, and was less likely amongolder individuals and boys. The adjusted prevalence ofpoor diet quality was higher among boys, while it was
lower among individuals who were Mexican American or
the other racial/ethnic group, who were born outside theUS, or who were from high-income families.
Age-adjusted trends in CVRF prevalence rates weregenerally similar between boys and girls, whereas there
were some disparities across races/ethnicities (Additional
file 1: eFigures 5–6). Non-Hispanic Whites experienced asignificant decrease in hyperlipidemia (2001–2004: 36.6%[95% CI, 31.9%-41.4%]; 2017-March 2020: 21.3% [95% CI,
13.7%-28.8%]; P for trend = 0.02), whereas an increasein diabetes was observed among non-Hispanic Blacks(2001–2004: 0.6% [95% CI, 0.0%-1.3%]; 2017-March
2020: 3.6% [95% CI, 0.8%-6.4%]; P for trend = 0.005).
Mexican Americans experienced a significant decreasein hyperlipidemia (2001–2004: 32.4% [95% CI, 29.1%35.6%]; 2017-March 2020: 25.7% [95% CI, 17.9%-33.5%];
P for trend < 0.001), but also an increase in obesity (2001–
2004: 16.7% [95% CI, 14.3%-19.0%]; 2017-March 2020:
29.4% [95% CI, 22.6%-36.1%]; P for trend = 0.009) andoverweight (2001–2004: 24.0% [95% CI, 21.6%-26.5%];
2017-March 2020: 28.8% [95% CI, 24.5%-33.2%]; P for
trend = 0.04), whereas a decrease in hypertension wasobserved in the other racial/ethnic group (2001–2004:
6.3% [95% CI, 2.9%-9.7%]; 2017-March 2020: 3.6% [95%CI, 1.7%-5.4%]; P for trend = 0.03). Trends in the prevalence of elevated BP, prediabetes, cigarette use, inactive
physical activity, and poor diet quality were generally(See figure on next page.)
Fig. 1 Age-Adjusted Trends in the Prevalence of Hypertension, Elevated BP, Diabetes, Prediabetes, Hyperlipidemia, Obesity, Overweight,
Cigarette Use, Inactive Physical Activity, and Poor Diet Quality Among US Adolescents Aged 12 to 19 Years, 2001 to March ­2020a−i. Abbreviations:
BP Blood pressure, CI Confidence interval.a Nationally representative estimates of US adolescents aged 12–19 years from the 2001-March 2020
National Health and Nutrition Examination Survey. Whiskers indicate 95% CIs. P for trend was calculated by the Joinpoint Regression Program:
P = .04 for hypertension in panel A; P = .73 for elevated BP in panel B; P = .46 for diabetes in panel C; P = .08 for prediabetes in panel D; P = .01
for hyperlipidemia in panel E; P = .002 for obesity in panel F; P = .004 for overweight in panel G; P = .07 for cigarette use in panel H; P = .01
for inactive physical activity in panel I; and P = .13 for poor diet quality in panel J. Specific estimates are presented in Additional file 1: eTable 4. b
All estimates were age-standardized to the 2000 Census population using the age groups of 12 to 14, 15 to 17, and 18 to 19 years. c Hypertension
was defined as stage 1 or 2 levels and/or current use of antihypertensive medications, whereas elevated BP was defined as an elevated level (seeRef [33, 34]). d Diabetes was defined as a hemoglobin ­A1c of ≥ 6.5%, fasting plasma glucose of ≥ 126 mg/dL, self-report of previous diagnosis, and/or current use of antidiabetic medications, whereas prediabetes was defined as a hemoglobin A­ 1c of 5.7%-6.4%.e Hyperlipidemia was defined
as a total cholesterol of ≥ 200 mg/dL, high-density lipoprotein cholesterol of < 40 mg/dL, non-high-density lipoprotein cholesterol of ≥ 145 mg/
dL, low-density lipoprotein cholesterol of ≥ 130 mg/dL, triglycerides of ≥ 130 mg/dL, and/or current use of antihyperlipidemic medications. f
Obesity and overweight were defined based on body mass index using the Lambda Mu Sigma method (see Ref. [38]). g Cigarette use was definedas smoking cigarettes within the previous 30 days. h Inactive physical activity was defined as a weekly exercise time of < 420 and < 150 min/wk
in adolescents aged < 18 and 18–19 years, respectively. Weekly exercise time was calculated as the minutes of moderate-intensity physical activity
plus twice the minutes of vigorous-intensity physical activity per week. During 2001–2006, physical activity levels were underestimated due
to a lack of data on home/yard tasks for adolescents aged 12–15 years and a lack of time data on muscle-strengthening activities across the age
spectrum; data on weekly exercise time for participants aged 12–17 years were not available during 2017-March 2020, and thus estimates
only represented those aged 18–19 years throughout this period. i Poor diet quality was defined as a Healthy Eating Index-2015 score of < 51 points

Qu et al. BMC Medicine(2024) 22:245

Fig. 1 (See legend on previous page.)

Page 10 of 16

Qu et al. BMC Medicine(2024) 22:245

Page 11 of 16

Fig. 2 Age-Adjusted Trends in the Rates of Hypertension and Diabetes Treatment and Control Among US Adolescents Aged 12 to 19 Years,
2001 to March ­2020a−d. Abbreviations: BP Blood pressure, CI Confidence interval. a Nationally representative estimates of US adolescents aged
12–19 years from the 2001-March 2020 National Health and Nutrition Examination Survey. Whiskers indicate 95% CIs. P for trend was calculatedby the Joinpoint Regression Program: P = .79 for hypertension treatment and P = .60 for diabetes treatment in panel A; P = .66 for BP control
and P value was not applicable for glycemic control in panel B. Specific estimates are presented in Additional file 1: eTable 4. b All estimates
were age-standardized to the 2000 Census population using the age groups of 12 to 14, 15 to 17, and 18 to 19 years. c Hypertension treatment
was defined as current use of antihypertensive medications and was evaluated among adolescents with hypertension (n = 901). Diabetes treatment
was defined as current use of antidiabetic medications and was evaluated among adolescents with diabetes (n = 84). d Control was evaluatedamong adolescents receiving treatment (n = 68 for hypertension and n = 40 for diabetes). Hypertension was considered controlled if (1) BP
was reduced to < 90th percentile in adolescents aged < 13 years, (2) BP was reduced to < 90th percentile and < 130/80 mmHg in adolescents aged
13–17 years, or (3) BP was reduced to < 130/80 mmHg in adolescents aged 18–19 years. Diabetes was considered controlled if hemoglobin A­ 1c
was reduced to < 7%comparable across racial/ethnic groups over the study
period.
CVRF treatment and control rates

Among adolescents aged 12–19 years with hypertension, the use of any antihypertensive medication did notchange significantly, from 9.6% (95% CI, 3.5%-15.8%) in
2001–2004 to 6.0% (95% CI, 1.4%-10.6%) in 2017-March
2020 (P for trend = 0.79) (Fig. 2A and Additional file 1:eTable 4). Among those receiving pharmacologic therapy, there was also no significant change in age-adjusted
hypertension control rates, from 75.7% (95% CI, 56.8%94.7%) in 2001–2004 to 73.5% (95% CI, 40.3%-100.0%)
in 2017-March 2020 (P for trend = 0.66). Hypertension
treatment was generally more likely among older individuals, and was less likely among boys (Additional file 1:eTables 5–6). There was no significant difference in BP
control across different sociodemographic subpopulations after adjusting for other factors.
Among adolescents with diabetes, the use of any antidiabetic medication did not change significantly, from
51.0% (95% CI, 23.3%-78.7%) in 2001–2004 to 26.5% (95%CI, 0.0%-54.7%) in 2017-March 2020 (P for trend = 0.60)(Fig. 2B and Additional File 1: eTable 4). Among thosereceiving pharmacologic therapy, higher age-adjusted
diabetes control rates were observed in 2017-March 2020
(62.7% [95% CI, 62.7%-62.7%]) than in 2001–2004 (11.8%[95% CI, 0.0%-31.5%]), although the difference did not

attain significance (P value was not applicable). No significant difference was seen in diabetes treatment acrossdifferent sociodemographic subpopulations after adjusting for other factors (Additional file 1: eTables 5–6).
Compared with younger individuals, older individuals
were more likely to achieve individualized ­HbA1c targets.
Treatment and control rates for hypertension and diabetes by age and race/ethnicity were not evaluated due tolimited sample size.
Sensitivity analysis

When using the 2003 NIH/NHLBI and 2004 NIH/
NHLBI guidelines, the age-adjusted prevalence of hypertension decreased (2001–2004: 4.8% [95% CI, 3.8%5.7%]; 2017-March 2020: 3.0% [95% CI, 1.8%-4.1%]; P
for trend = 0.04), whereas the age-adjusted prevalenceof elevated BP increased (2001–2004: 14.8% [95% CI,
13.1%-16.6%]; 2017-March 2020: 14.6% [95% CI, 11.7%17.5%]; P for trend = 0.27), although trends over time
were similar (Additional file 1: eFigure 7 and eTable 4).
The use of any antihypertensive medication was substantially higher among adolescents with hypertension
based on older guidelines (2001–2004: 19.3% [95% CI,
10.3%-28.4%]; 2017-March 2020: 28.3% [95% CI, 21.4%35.1%]; P for trend = 0.17), as was hypertension control
rates (2001–2004: 90.5% [95% CI, 75.9%-100.0%]; 2017March 2020: 82.8% [95% CI, 57.1%-100.0%]; P value was
not applicable).

Qu et al. BMC Medicine(2024) 22:245

Discussion
Between 2001 and March 2020, prediabetes and overweight/obesity increased among US adolescents aged
12–19 years, while hypertension, hyperlipidemia, cigarette use, and inactive physical activity decreased, andelevated BP, diabetes, and poor diet quality did notchange markedly. Boys experienced a higher prevalenceof hypertension, elevated BP, prediabetes, hyperlipidemia,
cigarette use, and poor diet quality, and a lower prevalence of inactive physical activity than girls. Furthermore,among racial/ethnic groups, non-Hispanic Whites had asignificant decrease in hyperlipidemia, while an increasein diabetes was seen among non-Hispanic Blacks. Mexican Americans had a significant decrease in hyperlipidemia, but also an increase in overweight/obesity, while
a decrease in hypertension was seen in the other racial/ethnic group. Trends in the prevalence of elevated BP,
prediabetes, cigarette use, inactive physical activity, andpoor diet quality were generally similar across racial/ethnic groups. Whilst treatment rates for hypertension
and diabetes did not show improvement over time, BP
control remained relatively stable, and there were some
improvements in glycemic control, although it remained
suboptimal.
The increase in overweight/obesity prevalence (to24.8% and 20.3%, respectively) among adolescents wasconsistent with that previously observed [48–51]. This
rise in overweight/obesity rates may have a negative
impact on public health and healthcare budgets because
obesity is strongly correlated with other CVRFs such as
hypertension, diabetes, and hyperlipidemia, which could
result in premature CVDs and cardiovascular death
during adulthood [13, 16, 52–54]. Several factors may
contribute to the increase in overweight/obesity prevalence, including larger food portion sizes, consumptionof energy-dense, nutrient-poor foods, changing modesof transportation, and sedentary behaviors [55–58]. The
prevalence of prediabetes increased from 12.5% to 37.6%
during this period, although the difference did not reachsignificance, whereas there was no noticeable changeover time in the prevalence of diabetes. Additionally, theprevalence of poor diet quality decreased from 76.1% to67.8% from 2001–2004 to 2009–2012 but then increased
to 71.7% in 2017-March 2020. Multicomponent behavior-changing interventions (e.g., increased physical activity [regardless of light or moderate-to-vigorous intensity],
reduced sedentary behavior, adequate sleep, and highquality diets) [59–63], in combination with improved
parent support behaviors [64], could help ease the burden of overweight/obesity for this population.
Despite the increased overweight/obesity prevalence,
there was a gradual decrease in the prevalence of hypertension, from 8.1% to 5.5%, with similar results seen in

Page 12 of 16

a sensitivity analysis using the older guidelines (from4.8% to 3.0%). This decrease was consistent with thatseen previous reports [65, 66]. Actually, obesity appears
to be more relevant to wide pulse pressure rather than
mean arterial pressure [67]. The contrary trends may
be partly explained by reduced lead exposure acting on
small resistance arteries, improved screening programs,
and earlier pharmacologic or lifestyle interventions suchas increased physical activity, smoking cessation, andsodium restriction [68–72]. The prevalence of inactive
physical activity significantly decreased from 83.0% to
9.5%, as did the prevalence of cigarette use (from 18.0%
to 3.5%, although the difference was not statistically significant). Hyperlipidemia also showed a relative decreaseof -33.3% over the past two decades, possibly due toreduced levels of trans-fatty acids in the food supply anda decline in smoking prevalence [73, 74]. These findings
suggest that interventions targeting hypertension, hyperlipidemia, cigarette use, and physical activity have been
more effective in comparison to interventions targetingoverweight/obesity, prediabetes/diabetes, and diet quality. In light of the strong association between CVRFs andtarget organ damage during adolescence, as well as premature cardiovascular morbidity and mortality during
adulthood, our data support the need for strengthening
public health planning and individualized clinical interventions [5, 11–19].
Considerable disparities exist regarding the prevalence
rates and trends of CVRFs across different sex and racial/ethnic groups. On the one hand, boys were consistently
at higher risk for hypertension, elevated BP, prediabetes, hyperlipidemia, cigarette use, and poor diet quality;
however, on the other hand, boys were at lower risk forinactive physical activity. Men presented a similar risk forthe aforementioned CVRFs compared to women as boys
were to girls, and were more likely to experience severe
adverse events (e.g., myocardial infarction, stroke, heart
failure, and cardiovascular death) [75–77]. The exact
mechanisms underlying such inequalities are not fully
understood, but are likely influenced by differences ininherent physiology, anti-inflammatory immune profiles,
sex steroid hormone levels, and lifestyles (e.g., sodium
intake) [78, 79]. Thus, it is necessary to adopt more effective strategies that consider sex differences in the management of CVRFs.
Unlike other races/ethnicities, non-Hispanic Blacks
experienced a significant increase in diabetes, which may
partly be attributed to early-life exposures, education,
availability of services, and other factors potentially related
to structural racism [80]. Planning future efforts to address
and mitigate these inequalities across racial/ethnic groups
should be prioritized in the management of CVRFs among
US adolescents. Besides, a clinically relevant increase inQu et al. BMC Medicine(2024) 22:245

Page 13 of 16

overweight/obesity prevalence was observed in Mexican
Americans during the study period, which was generally
consistent with previous reports [48, 49]. While disparities
in overweight/obesity between Mexican Americans andnon-Hispanic Whites used to be specific to US-born Mexican Americans, the disparities have expanded to non-USborn Mexican Americans over recent years [81]. Possible
reasons include the recent and rapid nutrition transition,changing selection migration dynamics, and longer time tolive in the US [82–84]. However, in contrast to the increasein obesity, Mexican Americans, together with non-Hispanic Whites, had a significant decrease in hyperlipidemia.
Further studies are needed to explore the contributing factors for the fluctuation.
Throughout the survey period, hypertension treatment rate remained low (< 15%), albeit this may bepartly explained by earlier lifestyle modifications. Ina retrospective study including 15,422 children (aged
3–17 years) with BP equal to or greater than 95th percentile, 14,841 (96.2%) children sought lifestyle counseling,whereas 831 (5.4%) children received antihypertensive
medications, and 848 (5.5%) children received BP-related
referrals [85]. Approximately 75% of adolescents receiving antihypertensive medications achieved BP targets at
both the start and end of study period. When using theolder guidelines, the proportions of adolescents who
received antihypertensive medications or who achieved
BP targets were substantially higher (varying between
8.0%-28.3% and 79.9%-100.0%, respectively). Meanwhile,
diabetes treatment rates ranged from 26.5% to 72.1%,
with no significant difference found during the study
period. There was an upward trend in glycemic control
rates (from 11.8% to 62.7%), although the difference was
not statistically significant. A consistent screening, treatment, and monitoring program for adolescents is curial
to ensure that they are receiving the best care available.

were underestimated due to a lack of data on home/yardtasks for adolescents aged 12–15 years and a lack of time
data on muscle-strengthening activities across the age
spectrum. Additionally, because data on weekly exercise
time for participants aged 12–17 years were not available during 2017-March 2020, estimates only represented
those aged 18–19 years throughout this period, potentially underestimating the prevalence of inactive physical inactivity. Fifth, the response rates for the NHANES
have declined over time. Sixth, although the combinationof two continuous NHANES cycles improved the reliability of prevalence estimates, the study may not have
had sufficient statistical power to detect small changes in
population subgroups with limited sample sizes. Finally,a proportion of nonpregnant participants (n = 368) were
excluded because of insufficient clinical information. As
the sample differed slightly from the included population
and between survey years in terms of baseline characteristics, this study may not have been completely free of
selection bias (Additional file 1: eTables 7–8). However,the proportion of missing data was low (≈ 2.4%); therefore, exclusion of the sample were not expected to significantly affect the results.

Limitations
This study has several limitations. First, misclassificationof elevated BP/hypertension, prediabetes/diabetes, andhyperlipidemia may have existed due to the use of selfreported diagnoses and dependence on single-occasion
physical examination or laboratory testing, possibly leading to an overestimation of CVRF prevalence among
adolescents. Second, recommendations for the definitionof hypertension and target BP levels have changed overthe entire study period, resulting in a higher prevalenceof hypertension and lower treatment and control rates.
Third, we assessed risk factor treatment and control relying only on medication use, without considering lifestylemodifications such as salt-reduced diets and aerobic exercise, which are usually taken prior to pharmacologic therapy. Fourth, during 2001–2006, physical activity levels

Abbreviations
BMI	Body mass index
BP	Blood pressure
CDC	Centers for Disease Control and Prevention
CI	Confidence interval
CVD	Cardiovascular disease
CVRF	Cardiovascular risk factor
FPG	Fasting plasma glucose
HbA1c	Hemoglobin ­A1c
HDL-C	High-density lipoprotein cholesterol
HEI-2015	Healthy Eating Index-2015
IPR	Income to poverty ratio
LDL-C	Low-density lipoprotein cholesterol
NCHS	National Center for Health Statistics
NHANES	National Health and Nutrition Examination Survey
NIH/NHLBI	National Institutes of Health’s National Heart, Lung, and Blood
Institutenon-HDL-C	Non-high-density lipoprotein cholesterol
STROBE	
Strengthening the Reporting of Observational Studies in
Epidemiology
TC	Total cholesterol

Conclusions
Over the past 2 decades, despite an increase in prediabetes and overweight/obesity, hypertension, hyperlipidemia, cigarette use, and inactive physical activity
decreased among US adolescents aged 12 to 19 years,
while elevated BP, diabetes, and poor diet quality
remained unchanged. There were disparities in the prevalence of and trends in CVRFs across sociodemographic
subpopulations. While treatment rates for hypertension and diabetes did not improve over time, BP control remained relatively stable, and there were numericalimprovements in glycemic control.

Qu et al. BMC Medicine(2024) 22:245

Page 14 of 16

Supplementary Information

Laboratory for Innovation and Transformation of Luobing Theory, General
Program of National Natural Science Foundation of China (81970339 and82370389), and National Key R&D Program of China (2017YFC1700505). XL
and HZ reported serving as Associate Fellows at the Collaborative Innovation
Center for Cardiovascular Disease Translational Medicine. IC reported serving
as Post-doctorate Follow at Nanjing Medical University.

The online version contains supplementary material available at https://​doi.​
org/​10.​1186/​s12916-​024-​03453-5.
Additional file 1: eMethod 1. Therapeutic Drug Classes Used to Define
Any Use of Antihypertensive and Antidiabetic Medications. eFigure 1.
Inclusion Diagram for US Adolescents Aged 12 to 19 Years, 2001 to March
2020. eFigure 2. Age-Adjusted Trends in Mean BP, Hemoglobin ­A1c, FPG,
TC, HDL-C, Non-HDL-C, LDL-C, Triglycerides Levels, Body Mass Index,
Weekly Exercise Time, and HEI-2015 for US Adolescents Aged 12 to 19
Years, 2001 to March 2020. eFigure 3. Age-Adjusted Trends in Mean BP,
Hemoglobin ­A1c, FPG, TC, HDL-C, Non-HDL-C, LDL-C, Triglycerides Levels,
Body Mass Index, Weekly Exercise Time, and HEI-2015 for US Adolescents
Aged 12 to 19 Years by Sex, 2001 to March 2020. eFigure 4. Age-Adjusted
Trends in Mean BP, Hemoglobin A­ 1c, FPG, TC, HDL-C, Non-HDL-C, LDL-C,
Triglycerides Levels, Body Mass Index, Weekly Exercise Time, and HEI-2015
for US Adolescents Aged 12 to 19 Years by Race/Ethnicity, 2001 to March
2020. eFigure 5. Age-Adjusted Trends in the Prevalence of Hypertension,
Elevated BP, Diabetes, Prediabetes, Hyperlipidemia, Obesity, Overweight,
Cigarette Use, Inactive Physical Activity, and Poor Diet Quality Among US
Adolescents Aged 12 to 19 Years by Sex, 2001 to March 2020. eFigure 6.
Age-Adjusted Trends in the Prevalence of Hypertension, Elevated BP,
Diabetes, Prediabetes, Hyperlipidemia, Obesity, Overweight, Cigarette Use,
Inactive Physical Activity, and Poor Diet Quality Among US Adolescents
Aged 12 to 19 Years by Race/Ethnicity, 2001 to March 2020. Years by Race/
Ethnicity, 2001 to March 2020. eFigure 7. Age-Adjusted Trends in thePrevalence of High BP and Hypertension Treatment and Control Rates
Among US Adolescents Aged 12 to 19 Years According to the 2003 NIH/
NHLBI and 2004 NIH/NHLBI Guidelines, 2001 to March 2020. eTable 1.
Unweighted Response Rates for the NHANES In-Home Interviews andMobile Examinations Among US Adolescents Aged 12 to 19 Years by Ageand Sex Groups, 2001 to March 2020. eTable 2. Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) Reporting Guideline for Reporting Cross-sectional Studies Checklist. eTable 3. Classificationof BP by the 2003 NIH/NHLBI, 2004 NIH/NHLBI, 2017 AAP, and 2017 ACC/AHA Guidelines. eTable 4. Trends in Age-Adjusted Means or % (95% CIs) of
Cardiovascular Parameters, Cardiovascular Risk Factors, and Hypertension
and Diabetes Treatment and Control Among US Adolescents Aged 12 to
19 Years by Age and Racial/Ethnic Groups, 2001 to March 2020. eTable 5.
Age-Adjusted Rates of Hypertension and Diabetes Treatment and Control
by Subgroups Among US Adolescents Aged 12 to 19 Years, 2001 to March
2020. eTable 6. Adjusted ORs for Hypertension and Diabetes Treatment
and Control by Subgroups Among US Adolescents Aged 12 to 19 Years,
2001 to March 2020. eTable 7. Comparison of Baseline Characteristics
Between the Included and Excluded Study Population. eTable 8. Baseline
Characteristics of the Excluded Study Population, 2001 to March 2020.
Acknowledgements
We sincerely appreciate all the staff and participants in the NHANES project for
their outstanding contributions.
Authors’ contributions
Concept and design: QQ, QG, JS1, SL, WY, and XL. Acquisition, analysis, orinterpretation of data: QQ, QG, JS1, ZC, JS2, IC, RG, YZ, HZ, SL, WY, and XL.
Drafting of the manuscript: QQ, IC, and XL. Critical revision of the manuscriptfor important intellectual content: QQ, QG, JS1, ZC, JS2, IC, RG, YZ, HZ, SL, WY,
and XL. Statistical analysis: QQ and QG. Obtained funding: RG, HZ, and XL.
Administrative, technical, or material support: YZ, HZ, and XL. Supervision: SL,
WY, and XL. Discussion with the lead author (and study team) on what shouldbe the ultimate focus of the article and conclusions: XL. All authors read andapproved the final manuscript.
Funding
RG was supported by the General Program of National Natural Science
Foundation of China (82200425), Excellent Young Scientists Fund of Jiangsu(BK20231538) and Qing Lan Project of Jiangsu. HZ was supported by theGeneral Program of National Natural Science Foundation of China (82270394),
Project from Gusu School (GSRCKY20210204), and Gusu Health Personnel Training Project (GSWS2021042). XL was supported by the State Key

Availability of data and materials
All raw data included in this study are publicly available at https://​wwwn.​cdc.​
gov/​nchs/​nhanes/.

Declarations
Ethics approval and consent to participate
The NCHS Ethics Review Board approved the study protocol, and written
informed consent was acquired from all individuals.
Consent for publication
Not applicable.
Competing interests
The authors declare that they have no competing interests.
Author details
1
Present Address: State Key Laboratory for Innovation and Transformationof Luobing Theory, Department of Cardiology, The First Affiliated Hospitalof Nanjing Medical University, 300 Guangzhou Road, Nanjing 210029, China.
2
Department of Cardiology, Gusu School, The Affiliated Suzhou Hospitalof Nanjing Medical University, Suzhou Municipal Hospital, Nanjing Medical
University, 26 Daoqian Street, Suzhou 215002, China. 3 Department of Cardiology, Jiangsu Province Hospital, 300 Guangzhou Road, Nanjing 210029, China.
Received: 2 January 2024 Accepted: 28 May 2024

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