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Association of cardiovascular
health with COPD (NHANES
2007-2020): mediating potential
of lean body mass
Ruoyu Gou
1
†
, Xiaoyu Chang
1
†
, Danni Dou
2
†
, Xin Meng
1
,
Ling Hou
2
, Lingqin Zhu
1
, Wei Tuo
3
*and Guanghua Li
1,2
*
1
School of Public Health, Ningxia Medical University, Yinchuan, China,
2
School of Basic Medical
Sciences, Ningxia Medical University, Yinchuan, China,
3
People’s Hospital of Ningxia Hui Autonomous
Region, Yinchuan, Ningxia, China
Background: Chronic Obstructive Pulmonary Disease (COPD) is a major global
health concern, with lifestyle factors playing a crucial role in its prevention. This
study aims to explore the relationship between Life’s Crucial 9 (lc9) scores and
COPD odds, and to assess the mediating potential of lean body mass (LBM) in
this association.
Methods: This study used cross-sectional study to assess the association
between lc9 score and COPD using data from the National Health and
Nutrition Examination Survey (NHANES) from 2007 to 2020. Weighted
multivariate regression analyses were performed to examine lc9 score on the
odds of COPD after adjusting for confounders. The models were adjusted for
age, gender, race/ethnicity, Marital status, education level, Family income-to-
poverty ratio, LBM and Alcohol consumption status. The discrimination ability of
lc9 on COPD odds was evaluated using (ROC) curve. Mediation analysis was used
to investigate the mediating potential of LBM between lc9 and COPD odds.
Subgroup analyses and interaction assessments were also performed.
Results: In Model 2, the results showed that for every 10-point change in the lc9
score, the odds of developing COPD decreased. The OR (95% CI) in the Moderate
and High groups were OR = 0.37; 95% CI: 0.23, 0.59 and OR = 0.16; 95% CI: 0.09,
0.27 (P for trend < 0.001), respectively. In addition, the results for quartile
subgroups were Q3, OR = 0.58; 95% CI: 0.42, 0.81), Q4, OR = 0.24; 95% CI:
0.16, 0.36) and P for trend < 0.001. This relationship was consistent across the
total population, subgroup analyses, and sensitivity analyses. There was a
nonlinear relationship between lc9 score and odds of COPD (P for Nonlinear =
0.022). The lc9 reduced the odds of COPD by increasing LBM. The lc9 is an
suggestive predictor of COPD odds association.
Conclusions: Higher LC9 scores, particularly when accompanied by increased
LBM levels, showed significant associations with reduced COPD risk in cross-
sectional analyses.
KEYWORDS
COPD, life’s crucial 9 (LC9), lean body mass (LBM), NHANES, mediating potential
Frontiers in Endocrinology frontiersin.org01
OPEN ACCESS
EDITED BY
Akinkunmi Paul Okekunle,
University of Ibadan, Nigeria
REVIEWED BY
Samuel Huang,
Virginia Commonwealth University,
United States
Oladotun Victor Olalusi,
University of Miami, United States
*CORRESPONDENCE
Wei Tuo
tuotuohappy8348@163.com
Guanghua Li
ghlee0404@163.com
†
These authors have contributed equally to
this work
RECEIVED 04 December 2024
ACCEPTED 13 March 2025
PUBLISHED 04 April 2025
CITATION
Gou R, Chang X, Dou D, Meng X,
Hou L, Zhu L, Tuo W and Li G (2025)
Association of cardiovascular health with
COPD (NHANES 2007-2020): mediating
potential of lean body mass.
Front. Endocrinol. 16:1539550.
doi: 10.3389/fendo.2025.1539550
COPYRIGHT
© 2025 Gou, Chang, Dou, Meng, Hou, Zhu,
Tuo and Li. This is an open-access article
distributed under the terms of the Creative
Commons Attribution License (CC BY). The
use, distribution or reproduction in other
forums is permitted, provided the original
author(s) and the copyright owner(s) are
credited and that the original publication in
this journal is cited, in accordance with
accepted academic practice. No use,
distribution or reproduction is permitted
which does not comply with these terms.
TYPE Original Research
PUBLISHED 04 April 2025
DOI 10.3389/fendo.2025.1539550
1 Introduction
Chronic obstructive pulmonary disease (COPD) is a
heterogeneous lung disease characterized by persistent respiratory
symptoms and airflow obstruction caused by abnormalities in the
airways or alveoli (1). With high morbidity and disability rates,
COPD has become the third leading cause of death globally (2), In
2019, there were 212 million cases of COPD worldwide, posing a
serious burden on patients’quality of life and public health (3,4).
Studies have shown that the etiology of COPD is complex, and in
addition to genetic factors, environmental factors, especially lifestyle
factors, play a crucial role in its development and progression (5,6).
Traditionally, smoking has been recognized as a major odds factor for
COPD (7), but increasing evidence suggests that other lifestyle
factors, such as an unhealthy diet (8,9), physical inactivity (10),
and obesity (11), may also be closely associated with the development
of COPD. While COPD is treatable, it is not completely curable.
The American Heart Association introduced “Life’s Simple 7”
and expanded it to “Life’s Essential 8”(LE8) and “Life’s Crucial 9”
(lc9) to promote Cardiovascular health (12–14). The “lc9”builds on
the American Heart Association’s“le8”by adding psychological
health as an extra component (12). The nine elements of lc9 are Eat
Better, Be More Active, Manage Weight, Manage Blood Sugar,
Control Cholesterol, Manage Blood Pressure, Quit Tobacco, Get
Healthy Sleep, and Address Psychological Health (15). These
lifestyle indicators’protective benefits for cardiovascular health
are widely acknowledged in academia, and they have been
demonstrated to lessen the odds of a variety of chronic illnesses.
While the associations between LC9 scores and cardiometabolic
conditions are well documented (16), their linkages to respiratory
system disorders—especially COPD remain systematically
underexplored in population-based studies.
LBM (LBM) is a reliable body measurement tool that
incorporates data such as height, age, weight, and waist
circumference (17). In recent years, it has gradually attracted the
attention of researchers. LBM is not only associated with metabolic
health, but it also plays a significant role in the prevention and
management of chronic diseases (18). According to studies, those
with more LBM typically have superior lung function, and they may
also be at a lower odds of developing respiratory disorders (19,20),
particularly COPD. LBM has thus been identified as a possible
protective factor against developing and progression of COPD.
However, the potential mediating of LBM between lc9 and COPD
has not been thoroughly investigated in the literature to date.
The aim of this study was to explore the relationship between
lc9 and COPD using the National Health and Nutrition
Examination Survey (NHANES) database, as well as to analyze
the potential mediating of LBM in it. We hypothesize that high
levels of lc9 score are associated with a lower odds of COPD, while
LBM may play an suggestive mediating role in this relationship.
This study analyzes the link between lc9, LBM, and COPD,
providing a scientific basis for prevention and management. It
also contributes to a better understanding of the role of LBM and
lifestyle factors in chronic disease prevention and control, and
serves as a reference for public health intervention strategies.
2 Methods
2.1 Study design and population
In this study, data from the NHANES were utilized to collect
information from a nationally representative sample of the United
States, employing a stratified, multi-stage probability sampling
method. Basic information about participants was initially
gathered through home interviews, followed by invitations to a
Mobile Examination Center (MEC) for a comprehensive
examination that included a physical assessment, specialized
measurements, and laboratory tests (see http://www.cdc.gov/nchs/
nhanes). A nationally representative, non-institutionalized sample
of U.S. adults was selected biennially, starting from the 1999-2000
cycle. This study included non-institutionalized U.S. adult
participants from seven two-year cycles between 2007 and 2020.
The survey protocol was approved by the Ethics Review Board of
the National Center for Health Statistics, and all participants gave
informed consent, and all participants provided informed consent,
agreeing to the use of their data for health statistics research
(https://www.cdc.gov/nchs/nhanes/irba98.htm). From the initial
sample of 75,402 cases from the NHANES 2007-2020 cycle,
31,400 cases aged <20 years were excluded, and 44,002 adult data
were retained as shown in Figure 1. By excluding key variables,
14,818 participants were finally included. Weighted analysis showed
no significant selection bias was introduced and as shown
in Table 1).
2.2 Measurement of cardiovascular health
score
The ls7 is calculated based on the AHA guidelines for blood
pressure, total cholesterol, glycosylated hemoglobin (HbA1c),
smoking, BMI, physical activity, and diet (Healthy Eating Index,
HEI) (21). The sum of all seven scores is the final ls7 score. Each
cardiovascular health factor is categorized into three groups (ideal,
moderate, and poor.) A total ls7 score of 0 to 4 is considered poor, 5
to 9 is moderate, and 10 to 14 is ideal (22). The le8 is calculated
based on the AHA Guidelines for four health behaviors (diet
(Healthy Eating Index, HEI), physical activity, smoking), and
sleep and four health factors (BMI, non-high-density lipoprotein
cholesterol (non-HDL-C), blood glucose, and blood pressure) (14).
Detailed calculations for each indicator have been documented in
previous studies (14). Dietary intake was assessed using the Healthy
Eating Index (HEI-2015), which is based on data from two 24-hour
dietary recalls and food pattern scores provided by the United States
Department of Agriculture (USDA). Information on physical
activity, medication use, smoking, history of diabetes, and sleep
duration was collected using a self-report questionnaire. During the
physical examination, weight, blood pressure, and height were
measured, and blood pressure was reported as the mean of three
measurements. Body mass index was calculated as weight
divided by the square of height. Blood samples were analyzed in a
central laboratory to assess non-high-density lipoprotein
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FIGURE 1
Flow chart of the screening process for the selection of the study population.
TABLE 1 Baseline characteristics of participants according to Non-COPD/ COPD from the U.S. National Health and Nutrition Examination Survey.
Parameter
No. of Participants (Weighted %); (mean (SE))
All Participants (N = 14, 818) Non-COPD (N = 14, 189) COPD (N = 629) P-value
a
Life's Simple 7 (LS7) 9.00 (0.04) 9.06 (0.04) 7.67 (0.10) < 0.001
Life’s Essential 8 (LE8) 72.36 (0.25) 72.66 (0.25) 65.37 (0.72) < 0.001
Life’s Crucial 9 (LC9) 74.64 (0.23) 74.92 (0.23) 68.07 (0.69) < 0.001
LC9per-10 7.46 (0.02) 7.49 (0.02) 6.81 (0.07) < 0.001
Lean Body Mass (LBM) 52.20 (0.15) 52.21 (0.16) 51.84 (0.75) 0.64
Age
20-44 6851 (47.52) 6776 (49.03) 75 (11.85)
< 0.00145-64 5160 (37.21) 4853 (36.41) 307 (56.02)
≥65 2807 (15.27) 2560 (14.56) 247 (32.13)
Sex
female 7084 (48.60) 6830 (48.71) 254 (46.10)
0.44
male 7734 (51.40) 7359 (51.29) 375 (53.90)
Ethnic/race
white people 6948 (72.08) 6504 (71.50) 444 (85.77)
< 0.001
black people 2850 (9.17) 2768 (9.35) 82 (5.09)
Mexican people 1995 (7.19) 1968 (7.44) 27 (1.28)
other people 3025 (11.56) 2949 (11.71) 76 (7.86)
(Continued)
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TABLE 1 Continued
Parameter
No. of Participants (Weighted %); (mean (SE))
All Participants (N = 14, 818) Non-COPD (N = 14, 189) COPD (N = 629) P-value
a
Marital status
Married 9021 (65.19) 8640 (65.03) 381 (68.88)
< 0.001Separated 2936 (18.75) 2889 (19.31) 47 (5.36)
Never married 2861 (16.07) 2660 (15.66) 201 (25.76)
Ratio of family income to poverty levels
<1.3 4173 (18.09) 3960 (17.98) 213 (20.73)
0.26
1.3-3 4523 (27.12) 4334 (27.06) 189 (28.63)
3-5 3079 (25.03) 2976 (25.20) 103 (20.99)
≥5 3043 (29.75) 2919 (29.76) 124 (29.65)
Education levels
No formal education 2677 (11.43) 2526 (11.24) 151 (16.08)
0.02Primary school 7435 (56.27) 7144 (56.45) 291 (52.14)
High school or above 4706 (32.30) 4519 (32.32) 187 (31.77)
Alcohol consumption status
former 2000 (11.08) 1848 (10.75) 152 (18.89)
< 0.001
heavy 3174 (21.93) 3070 (22.18) 104 (16.06)
mild 5403 (39.11) 5163 (39.00) 240 (41.62)
moderate 2531 (18.92) 2433 (18.94) 98 (18.41)
never 1710 (8.96) 1675 (9.13) 35 (5.02)
LS7
Poor 4164 (24.30) 3864 (23.44) 300 (44.48)
< 0.001Intermediate, 7252 (50.46) 6957 (50.46) 295 (50.34)
Ideal 3402 (25.24) 3368 (26.09) 34 (5.18)
LE8
Low 839 (4.31) 751 (4.01) 88 (11.42)
< 0.001Moderate 10029 (65.48) 9544 (64.92) 485 (78.63)
High 3950 (30.21) 3894 (31.07) 56 (9.95)
LC9
Low 481 (2.25) 419 (2.04) 62 (7.21)
< 0.001Moderate 9685 (62.50) 9198 (61.85) 487 (77.76)
High 4652 (35.25) 4572 (36.11) 80 (15.03)
LC9
Q1 3713 (21.30) 3449 (20.67) 264 (36.13)
< 0.001
Q2 3910 (25.84) 3717 (25.53) 193 (33.13)
Q3 3456 (24.02) 3337 (24.13) 119 (21.46)
Q4 3739 (28.83) 3686 (29.67) 53 (9.28)
(Continued)
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TABLE 1 Continued
Parameter
No. of Participants (Weighted %); (mean (SE))
All Participants (N = 14, 818) Non-COPD (N = 14, 189) COPD (N = 629) P-value
a
LE8/LC9
HEI 41.10 (0.59) 41.09 (0.59) 41.42 (1.84) 0.85
physical activity 93.70 (0.18) 93.72 (0.18) 93.30 (0.77) 0.59
Smoking 72.52 (0.60) 73.45 (0.60) 50.48 (2.01) < 0.001
Sleep 84.52 (0.32) 84.73 (0.32) 79.49 (1.52) < 0.001
BMI 62.43 (0.51) 62.53 (0.52) 60.03 (1.84) 0.20
non-HDL-C 65.38 (0.42) 65.71 (0.41) 57.62 (1.56) < 0.001
blood glucose 87.88 (0.29) 88.27 (0.30) 78.74 (1.46) < 0.001
blood pressure 71.70 (0.44) 72.11 (0.45) 62.21 (1.38) < 0.001
Depression 92.53 (0.22) 92.67 (0.21) 89.35 (1.04) 0.002
LS7
Blood pressure
0 2432 (13.61) 2287 (13.41) 145 (18.41)
< 0.0011 6595 (44.10) 6234 (43.47) 361 (58.83)
2 5791 (42.29) 5668 (43.12) 123 (22.77)
Total cholesterol
0 1821 (12.77) 1722 (12.64) 99 (15.90)
< 0.0011 6154 (41.12) 5818 (40.41) 336 (57.70)
2 6843 (46.11) 6649 (46.95) 194 (26.40)
HbA1c
0 1118 (5.27) 1044 (5.15) 74 (8.13)
< 0.0011 4051 (22.32) 3806 (21.66) 245 (37.83)
2 9649 (72.41) 9339 (73.19) 310 (54.05)
Smoking
0 2920 (18.22) 2693 (17.51) 227 (35.10)
< 0.0011 3608 (25.25) 3332 (24.45) 276 (43.99)
2 8290 (56.53) 8164 (58.04) 126 (20.91)
BMI
0 5355 (34.80) 5130 (34.74) 225 (36.31)
0.161 5007 (34.09) 4778 (33.95) 229 (37.52)
2 4456 (31.11) 4281 (31.32) 175 (26.17)
Physical activity
1 2962 (18.77) 2817 (18.69) 145 (20.59)
0.36
2 11856 (81.23) 11372 (81.31) 484 (79.41)
HEI
0 7176 (48.33) 6838 (48.18) 338 (51.98) 0.45
(Continued)
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cholesterol (non-HDL-C), glycosylated hemoglobin (HbA1c), and
blood glucose. Briefly, each of the 8 CVH indicators was scored on a
scale of 0 to 100. The overall le8 score was calculated as the
unweighted average of the 8 measures. participants with le8
scores of 80-100 were considered to have high CVH; 50-79,
moderate CVH; and 0-49, low CVH. It is reported that
consideration of mental health factors is fundamental to
achieving optimal and equitable CVH (15). An area of high
interest in mental health factors is depression. Depression is an
independent non-traditional odds factor for cardiovascular disease
(CVD) (23). Depression scores are calculated based on the Patient
Health Questionnaire 9 (PHQ-9) score, which is a validated
structured questionnaire for depression screening. Higher PHQ-9
scores indicate higher levels of currently present depressive
symptoms. Depression scores are designated as 100, 75, 50, 25,
and 0, which correspond to 0 to 4, 5 to 9, 10 to 14, 15 to 19, and 20
to 27 in the PHQ-9 score, respectively.19 The lc9 score is calculated
as the average of the le8 score and the eight indicators in the
depression score (24). The American Cardiovascular Society
emphasized the importance of mental health in the prevention of
CVD and introduced factors such as depression into a new metric
called Life’s Crucial 9 (lc9). Prior to this, only the concept of
constructing an lc9 scoring system was proposed, but the
American Cardiovascular Society did not formally publish the
composition and calculation of the lc9 index (15). The most
recent study proposed the process of constructing and calculating
the lc9 scoring system (12) and verified that it has a better ability to
predict cardiovascular health (12). Specifically, Ge et al. validated
the association of lc9 with cardiovascular mortality and all-cause
mortality, which improved the cardiovascular health odds scoring
system and provided direction for subsequent studies (12). In
summary, the addition of depression to the “Life Essential 8 (le8)
scale proposed by the American Heart Association to construct the
lc9 scoring system to measure CVH has been generally recognized.
There is currently no recognized threshold for the lc9 score. Two
tests were performed in this study: quartile grouping (Q1, Q2, Q3,
Q4) and grouping based on the le8 threshold (Low (0-49), Moderate
(50-79), High (80-100)). The scoring method for the common
indicators of le8 and lc9 is consistent.
2.3 Assessment of LBM
In this study, the assessment of LBM was based on a predictive
equation developed by Lee et al. (25), which utilized participant data
from the NHANES survey for model construction. A total of 10,518
male and 10,987 female participants in the study underwent dual-
energy X-ray bone density (DXA) scans. Multivariate linear regression
was used to estimate LBM, with LBM as the dependent variable and
predictor variables including age, gender, height (cm), weight (kg), and
waist circumference (cm). The linear regression model performed best
in terms of consistency [LBM (female: R² = 0.85; male: R² = 0.91)]. The
specificLBMformulawas:maleLBM=19.363+0.001*age(years)+
0.064 * height (cm) + 0.756 * weight (kg) - 0.366 * waist circumference
(cm) - 1.007; female LBM =- 10.683 - 0.039 * Age (years) + 0.186 *
Height (cm) + 0.383 * Weight (kg) - 0.043 * Waist (cm) - 0.340 (17).
2.4 Definition of COPD
The definition of COPD in this study was based on participants’
self-reported physician diagnosis (26,27), and COPD was
determined by three self-reported questionnaire items: “Has your
doctor ever told you that you have chronic bronchitis?”,“Has your
doctor ever diagnosed you with emphysema?”,“Is FEV1/FVC <0.7
after inhalation of bronchodilators?”,“Has a doctor or other health
professional ever told you that you have COPD?”,“Are you using
COPD medications (leukotriene modulators, inhaled corticosteroids,
selective phosphodiesterase-4 inhibitors, mast cell stabilizers)?”,
Participants who answered “yes”to any of these questions were
categorized into the COPD group, while participants who answered
“no”to all questions were categorized into the non-COPD group.
TABLE 1 Continued
Parameter
No. of Participants (Weighted %); (mean (SE))
All Participants (N = 14, 818) Non-COPD (N = 14, 189) COPD (N = 629) P-value
a
HEI
1 7125 (47.96) 6851 (48.10) 274 (44.65)
2 517 (3.71) 500 (3.72) 17 (3.37)
Percentages were adjusted for NHANES survey weights.
a
The P-value was calculated using a chi-square test and Students T test after considering the sampling weights. P-value <0.05.
Data are Mean (standard error) or No. of Participants (Weighted %).
The scoring method for the common indicators of LE8 and LC9 is consistent.
HEI, The dietary practices were assessed at the mobile examination center based on 24-hours dietary recall. For measurement of overall dietary quality used the Healthy Eating Index (HEI)
developed by the US Department of Agriculture in 1995, which included the following components: grain, vegetables, fruits, milk, meat, total fat, saturated fat, cholesterol, sodium and
food variety.
An overall LS7 score of 0 to 4 was considered poor, 5 to 9 was intermediate, and 10 to 14 was ideal. In brief, the 7 cardiovascular health factors include blood pressure, total cholesterol,
glycosylated hemoglobin (HbA1c), smoking, BMI, physical activity, and diet (HEI).
Participants with a LE8 score of 80–100 were considered high CVH; 50–79, moderate CVH; and 0–49 points, low CVH. LE8 scoring algorithm consists of 4 health behaviors (diet, physical
activity, nicotine exposure (smoking), and sleep) and 4 health factors (body mass index (BMI), non-high-density-lipoprotein cholesterol (non-HDL-C), blood glucose, and blood pressure).
LC9 scoring algorithm consists of 4 health behaviors (diet (Healthy Eating Index, HEI), physical activity, nicotine exposure (smoking), and sleep duration), 4 health factors (body mass index
[(BMI], ), non-high-density-lipoprotein cholesterol (non-HDL-C), blood glucose, and blood pressure) and Mental health (Depression).
At present, there is no recognized and applicable threshold limit for LC9 scores. Therefore, this study presents LC9 levels from multiple dimensions. For example, the following four dimensions:
quartile grouping (Q1, Q2, Q3, Q4), grouping based on the LE8 threshold (Low (0–49), Moderate (50–79), High (80–100)), LC9-per10 (continuous variable), LC9 (continuous variable).
Gou et al. 10.3389/fendo.2025.1539550
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2.5 Definition of covariates
The Centers for Disease Control and Prevention (CDC) collected
demographic characteristics, lifestyle, self-reported health status,
physical measurements, and biochemical data on participants
through a computer-assisted personal interview system. In this study,
demographic information was collected through a questionnaire, which
included age (20-44, 45-64, 65 and older, years), gender, Ethnic/race:
white people (non-Hispanic white, Hispanics white, Europeans
Americans), black people (non-Hispanic black, Indigenous Africans,
African Americans), Mexican people, other people, marital status
(married, separated, and never married), and household income-to-
poverty ratio (less than 1.30, 1.30 to <3.00, 3.00 to <5.00, 5.00 and above,
indicates the ratio of family income to the federal poverty level, adjusted
for family size, with higher ratios indicating higher income levels), and
education level (did not complete highschool(lessthan11thgrade),
high school graduation/general education, part of college or more
(college graduation and above)). Drinking status was categorized as:
current heavy drinkers (≥3drinksperdayforwomenor≥4drinksper
day for men or binge drinking on 5 or more days per month); current
moderate drinkers (≥2drinksperdayforwomenor≥3drinksperday
formen,orbingedrinkingon2ormoredayspermonth);currentlight/
moderate drinkers (not falling into the first two categories); ex-drinkers
who used to drink but do not now; and no Drinkers.
2.6 Statistical analysis
TheanalyseddatawereweightedaccordingtoNCHS
requirements. Participants were divided into two groups based on
whether they had COPD or not. Statistical tests for weight adjustment
were fully considered. The chi-square test and t-test were applied to
examine the demographic characteristics in relation to participants’
COPD status. The association between CVH (ls7, le8, lc9) and COPD
was estimated by weighted multivariate logistic regression modeling.
The association between the components of CVH (ls7, le8, lc9) and
COPD was evaluated using the weighted univariate logistic regression
model. P-values, odds ratios (ORs), and 95% confidence intervals
(CIs) between CVH and odds of COPD were reported. Three models
were developed:(1) Crude model (unadjusted); (2) Model 1, adjusted
for age, gender, and race/ethnicity;(3) Model 2, adjusted for age,
gender, race/ethnicity, Marital status, education level, Family income-
to-poverty ratio, lean body mass and Alcohol consumption status.
RCS analyses were used to show linear trends in ls7, le8 and lc9
(entered as a continuous variable into the RCS model) with COPD.
The RCS model adjusted for Sex, Age, Ethnic/race, Marital status,
Family income-to-poverty ratio, Education levels, lean body mass
and Alcohol consumption status. Stratified analyses were conducted
for Sex, Age, Ethnic/race, Marital status, Family income-to-poverty
ratio, Education levels, lean body mass and Alcohol consumption
status using weighted multivariate logistic regression. Additionally,
the interaction of lc9 with potential confounders was considered.
ROC curve analysis was conducted to assess the predictive ability of
ls7, le8 and lc9 for COPD. Results are presented as the area under the
ROC curve (AUC) along with the corresponding 95% confidence
interval (CI), as well as sensitivity and specificity metrics. The
potential mediating of LBM on the relationship between lc9 and
COPD odds was estimated using a parallel cross-sectional mediation
model implemented. Due tothe concurrent measurement of LC9 and
LBM, our analysis aimed to explore their statistical relationships
rather than establish causal pathways. We further conducted
sensitivity analyses using multiple model specifications to assess the
robustness of the LC9-COPD association, with covariate adjustments
mirroring the primary analytical approach The following sensitivity
analyses were conducted: 1. Imputation of variables with missing
values in the dataset was performed to test whether the correlation
between lc9 and COPD was tested in the complete dataset. 2. After
excluding depression and sleep indicators, the correlation between ls7
and COPD was tested. 3. After excluding depression indicators, the
correlation between le8 and COPD was tested. All statistical analyses
were conducted using R software (version 4.2.2, https://cran.r-
project.org/bin/windows/base/old/4.2.2/). Two-sided statistical tests
were used, with a significance level set at a P-value < 0.05.
3 Results
3.1 Baseline characteristics
A total of 14, 818 participants were included, and after applying
weights, the sample is representative of 99, 106, 357 individuals in the
U.S. general population. Among them, 629 were COPD patients, and
14, 189 were non-COPD patients. The proportion of COPD cases
(85.77%) among white people is the highest. The COPD group
exhibited significantly lower scores in ls7, le8, lc9-per10 and lc9,
compared to the non-COPD group (P-value < 0.05). Compared with
non-COPD participants, significant statistical differences (P-value <
0.05) were observed in Age, Ethnic/race, Marital status, Education
levels, Alcohol consumption status, and CVH Categorical Variables
(ls7, le8, lc9). In comparison with the non-COPD group, the scores of
ls7 components (blood glucose, total cholesterol, HbA1c, Smoking),
le8 components (Smoking, Sleep, non-HDL-C, blood pressure, blood
glucose) and lc9 components (Smoking, Sleep, non-HDL-C, blood
glucose, blood pressure, Depression) in the COPD group were lower
and as shown in Table 1.
3.2 Association of LC9 scores with copd
odds
After full adjustment for potential confounders, lc9 remained
significantly associated with COPD. lc9 scores showed a significant
inverse relationship with COPD odds. In Model 2, compared to Q1,
the OR (95% CI) and Pfortrendwere reported: Q3 (OR = 0.58; 95%
CI: 0.42-0.81), Q4 (OR = 0.24; 95% CI: 0.16-0.36)) and Pfortrend<
0.001. Additionally, In Model 2, compared to Low, Moderate (OR =
0.37; 95% CI: 0.23-0.59), High (OR = 0.16; 95% CI: 0.09-0.27) and Pfor
trend < 0.001. Each 10-point increase in the lc9 score was associated
with a reduced odds of COPD (OR = 0.66; 95% CI: 0.59-0.73) and as
shown in Table 2. In Model 2, the results of the association between the
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ls7 component and COPD were as follows: HbA1c (OR = 0.805; 95%
CI: 0.681-0.951), Smoking (OR = 0.387; 95% CI: 0.331, 0.452), HEI
(OR = 0.774; 95% CI: 0.630-0.951 and as shown in Supplementary
Table 4. In Model 2, the results of the association between the le8/lc9
component and COPD were as follows: Smoking (OR = 0.984; 95% CI:
0.981-0.987), Sleep (OR = 0.387; 95% CI: 0.331- 0.452), blood glucose
(OR = 0.993; 95% CI: 0.988-0.998), Depression (OR = 0.992; 95% CI:
0.987-0.997) and as shown in Supplementary Table 5.
3.3 RCS analysis
The RCS analysis revealed that lc9 had nonlinear relationship with
COPD (P for nonlinear = 0.022). To validate the association of lc9 with
COPD,wecarriedouttwotests:1.le8(excludingthedepression
indicator) was related to COPD, but no significant non-linear
relationship was identified. 2. ls7 (excluding the depression and sleep
indicators) was related to COPD and a non-linear relationship. The
RCS analysis revealed that ls7 had nonlinear relationship with COPD
(P for nonlinear =0.033)andasshowninFigure 2.
3.4 LC9 with COPD odds subgroup
analysis
Overall trend: The lc9 scores of quartile grouping and threshold
grouping based on le8 were correlated with a decreased odds of
COPD. The stratified analysis demonstrated that in all subgroups, a
higher lc9 score was significantly and negatively associated with the
reduced odds of COPD. The main findings encompassed: gender,
age, ethnicity, marital status, and educational level. The lc9 high and
Q4 groups consistently showed the strongest associations. A
significant interaction was observed between lc9 and ethnicity/
race (P for interaction = 0.02) and as shown in Table 3.
FIGURE 2
Nonlinear associations between the LC9 LE8 and LS7 scoring systems and the risk of COPD in the NHANES. The dashed line represents the
threshold where the OR=1. The shaded area represents the 95% confidence interval. The histogram illustrates the population distribution of "LC9"
"LE8" and "LS7" scores. LC9, Life's Crucial 9; LE8, Life's Essential 8; LS7, Life’s Simple 7; OR, Odds Ratio.
TABLE 2 Association of LC9 Scores with COPD Risk.
Parameter
Crude model Model 1 Model 2
P for trend
OR (95%CI) P-value OR (95%CI) P-value OR (95%CI) P-value
Per 10-score increase 0.63 (0.58, 0.68) < 0.001 0.65 (0.60, 0.71) < 0.001 0.66 (0.59, 0.73) < 0.001 –
Low ref ref ref ref ref ref
< 0.001Moderate 0.35 (0.24, 0.53) < 0.001 0.32 (0.21, 0.50) < 0.001 0.37 (0.23, 0.59) < 0.001
High 0.12 (0.07, 0.19) < 0.001 0.13 (0.08, 0.22) < 0.001 0.16 (0.09, 0.27) < 0.001
Q1 ref ref ref ref ref ref
< 0.001
Q2 0.74 (0.56, 0.98) 0.03 0.75 (0.56, 1.00) 0.05 0.80 (0.59, 1.07) 0.13
Q3 0.51 (0.38, 0.69) < 0.001 0.55 (0.41, 0.75) <0.001 0.58 (0.42, 0.81) 0.002
Q4 0.18 (0.12, 0.27) < 0.001 0.22 (0.15, 0.33) < 0.001 0.24 (0.16, 0.36) < 0.001
OR odds ratio, CI confdence interval.
Crude model, No adjustment for any potential influence factors.
Model 1, Adjusted for Sex, Age and Ethnic/race.
Model 2, Adjusted for Sex, Age, Ethnic/race, Marital status, Family income-to-poverty ratio, Education levels, lean body mass and Alcohol consumption status.
LC9 scoring algorithm consists of 4 health behaviors (diet (HEI), physical activity, nicotine exposure (smoking), and sleep) and 4 health factors (body mass index (BMI), non-high-density-
lipoprotein cholesterol (Non-HDL-c), blood glucose, and blood pressure) and Depression. At present, there is no recognized and applicable threshold limit for LC9 scores. Therefore, this study
presents LC9 levels from multiple dimensions. For example, the following four dimensions: quartile grouping (Q1, Q2, Q3, Q4), grouping based on the LE8 threshold (Low (0–49), Moderate (50–
79), High (80–100)), LC9-per10 (continuous variable), LC9 (continuous variable).
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TABLE 3 Association of LC9 Scores with COPD Risk: Subgroup Analysis.
Grouping based on the LE8 threshold (Low (0–49), Moderate (50–79), High
(80–100)) Quartile grouping (Q1, Q2, Q3, Q4)
Parameter
Low Moderate High P
for
trend
P
for
interaction
Q1 Q2 Q3 Q4 P
for
trend
P
for
interaction
OR
(95%CI)
P-
value
OR
(95%CI)
P-
value
OR
(95%CI)
P-
value
OR
(95%CI)
P-
value
OR
(95%CI)
P-
value
Sex
Female ref
0.34
(0.19,
0.63)
< 0.001
0.11
(0.05,
0.23)
< 0.001 < 0.001
0.41
ref
0.86
(0.61,
1.21)
0.38
0.76
(0.54,
1.07)
0.12
0.20
(0.11,
0.36)
< 0.001 < 0.001
0.12
Male ref
0.40
(0.20,
0.84)
0.02
0.20
(0.09,
0.49)
< 0.001 < 0.001 ref 0.73
(0.46,1.14) 0.17 0.41
(0.24,0.71) 0.002 0.24
(0.14,0.44) < 0.001 < 0.001
Age
20-44 ref 0.71
(0.23,2.13) 0.53 0.19
(0.04,0.88) 0.03 0.001
0.11
ref 0.48
(0.23,1.03) 0.06 0.43
(0.17,1.07) 0.07 0.08
(0.02,0.32) < 0.001 < 0.001
0.0645-64 ref 0.35
(0.20,0.60) < 0.001 0.13
(0.05,0.29) < 0.001 < 0.001 ref 0.90
(0.61,1.34) 0.61 0.60
(0.39,0.94) 0.03 0.20
(0.11,0.35) < 0.001 < 0.001
≥65 ref 0.38
(0.16,0.93) 0.03 0.26
(0.10,0.67) 0.01 0.02 ref 0.79
(0.51,1.22) 0.28 0.63
(0.38,1.03) 0.07 0.50
(0.27,0.91) 0.02 0.02
Ethnic/race
white people ref 0.38
(0.21,0.69) 0.002 0.18
(0.09,0.34) < 0.001 < 0.001
0.02
ref 0.76
(0.54,1.06) 0.11 0.64
(0.45,0.92) 0.02 0.26
(0.17,0.41) < 0.001 < 0.001
0.02
black people ref
0.34
(0.15,
0.76)
0.01
0.18
(0.06,
0.52)
0.002 0.01 ref
0.82
(0.49,
1.39)
0.46
0.32
(0.16,
0.67)
0.003
0.41
(0.17,
0.98)
0.05 0.001
Mexican
people ref
0.66
(0.09,
4.73)
0.68
0.19
(0.02,
2.04)
0.17 0.03 ref
0.51
(0.18,
1.45)
0.20
0.25
(0.08,
0.81)
0.02
0.09
(0.01,
1.05)
0.051 0.01
other people ref
0.25
(0.10,
0.67)
0.01
0.02
(0.01,
0.08)
< 0.001 < 0.001 ref
1.42
(0.51,
3.95)
0.50
0.25
(0.07,
0.94)
0.04
0.07
(0.02,
0.28)
< 0.001 < 0.001
Marital status
Married ref
0.54
(0.26,
1.09)
0.08
0.25
(0.11,
0.54)
< 0.001 < 0.001 0.07 ref
0.91
(0.62,
1.33)
0.61
0.69
(0.48,
0.99)
0.04
0.28
(0.18,
0.45)
< 0.001 < 0.001 0.18
(Continued)
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TABLE 3 Continued
Grouping based on the LE8 threshold (Low (0–49), Moderate (50–79), High
(80–100)) Quartile grouping (Q1, Q2, Q3, Q4)
Parameter
Low Moderate High P
for
trend
P
for
interaction
Q1 Q2 Q3 Q4 P
for
trend
P
for
interaction
OR
(95%CI)
P-
value
OR
(95%CI)
P-
value
OR
(95%CI)
P-
value
OR
(95%CI)
P-
value
OR
(95%CI)
P-
value
Marital status
Separated ref
0.34
(0.11,
1.07)
0.06
0.05
(0.01,
0.30)
0.002 < 0.001 ref
1.13
(0.43,
2.99)
0.80
0.50
(0.10,
2.52)
0.40
0.16
(0.03,
0.96)
0.05 0.04
Never married ref 0.26
(0.13,0.53) < 0.001 0.09
(0.03,0.23) < 0.001 < 0.001 ref 0.56
(0.34,0.92) 0.02 0.37
(0.18,0.74) 0.01 0.15
(0.07,0.33) < 0.001 < 0.001
Education levels
No
formal
education
ref 0.24
(0.11,0.54) < 0.001 0.06
(0.02,0.18) < 0.001 < 0.001
0.58
ref 0.60
(0.36,0.98) 0.04 0.43
(0.21,0.89) 0.02 0.11
(0.02,0.52) 0.01 < 0.001
0.12
Primary
school ref
0.44
(0.20,
0.95)
0.04
0.22
(0.09,
0.52)
< 0.001 < 0.001 ref
1.13
(0.72,
1.77)
0.58
0.91
(0.52,
1.58)
0.74
0.32
(0.18,
0.58)
< 0.001 < 0.001
High school
or above ref
0.41
(0.21,
0.80)
0.01
0.15
(0.06,
0.36)
< 0.001 < 0.001 ref
0.57
(0.35,
0.95)
0.03
0.36
(0.20,
0.66)
0.001
0.24
(0.11,
0.49)
< 0.001 < 0.001
Ratio of family income to poverty levels
< 1.3 ref 0.39
(0.21,0.72) 0.003 0.11
(0.03,0.37) < 0.001 < 0.001
0.39
ref 0.51
(0.35,0.74) < 0.001 0.32
(0.16,0.63) 0.001 0.19
(0.06,0.63) 0.01 < 0.001
0.06
1.3-3 ref
0.28
(0.12,
0.61)
0.002
0.13
(0.05,
0.38)
< 0.001 0.003 ref
0.77
(0.44,
1.36)
0.37
0.63
(0.33,
1.23)
0.17
0.36
(0.16,
0.85)
0.02 0.02
3-5 ref
1.36
(0.34,
5.43)
0.66
0.41
(0.10,
1.75)
0.23 < 0.001 ref
1.32
(0.69,
2.52)
0.39
0.47
(0.20,
1.11)
0.08
0.15
(0.06,
0.38)
< 0.001 < 0.001
≥5 ref
0.19
(0.03,
1.18)
0.07
0.11
(0.02,
0.71)
0.02 0.01 ref
0.82
(0.43,
1.59)
0.56
0.91
(0.46,
1.80)
0.78
0.33
(0.16,
0.67)
0.003 0.003
(Continued)
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TABLE 3 Continued
Grouping based on the LE8 threshold (Low (0–49), Moderate (50–79), High
(80–100)) Quartile grouping (Q1, Q2, Q3, Q4)
Parameter
Low Moderate High P
for
trend
P
for
interaction
Q1 Q2 Q3 Q4 P
for
trend
P
for
interaction
OR
(95%CI)
P-
value
OR
(95%CI)
P-
value
OR
(95%CI)
P-
value
OR
(95%CI)
P-
value
OR
(95%CI)
P-
value
Alcohol consumption status
former ref
0.73
(0.35,
1.51)
0.39
0.38
(0.12,
1.18)
0.09 0.06
0.37
ref
0.80
(0.42,
1.53)
0.49
0.65
(0.29,
1.45)
0.29
0.26
(0.08,
0.80)
0.02 0.02
0.09
heavy ref
0.18
(0.03,
1.14)
0.07
0.04
(0.01,
0.29)
0.002 < 0.001 ref
0.69
(0.24,
1.94)
0.47
0.04
(0.01,
0.24)
< 0.001
0.11
(0.02,
0.53)
0.01 < 0.001
mild ref
0.28
(0.12,
0.65)
0.004
0.09
(0.04,
0.23)
< 0.001 < 0.001 ref
0.85
(0.56,
1.28)
0.42
0.57
(0.37,
0.88)
0.01
0.21
(0.12,
0.38)
< 0.001 < 0.001
moderate ref
0.50
(0.17,
1.49)
0.21
0.38
(0.09,
1.69)
0.20 0.38 ref
1.18
(0.55,
2.51)
0.67
1.29
(0.63,
2.62)
0.48
0.67
(0.28,
1.59)
0.35 0.45
never ref
0.29
(0.13,
0.63)
0.002
0.11
(0.03,
0.39)
< 0.001 0.002 ref 0.50
(0.29,0.85) 0.01 0.43
(0.21,0.86) 0.02 0.11
(0.03,0.38) < 0.001 < 0.001
Model adjusted for Sex, Age, Ethnic/race, Marital status, Family income-to-poverty ratio, Education levels, lean body mass and Alcohol consumption status.
OR odds ratio, CI confdence interval.
LC9 scoring algorithm consists of 4 health behaviors (diet (HEI), physical activity, nicotine exposure (smoking), and sleep) and 4 health factors (body mass index (BMI), non-high-density-lipoprotein cholesterol (Non-HDL-c), blood glucose, and blood pressure) and
Depression. At present, there is no recognized and applicable threshold limit for LC9 scores. Therefore, this study presents LC9 levels from multiple dimensions. For example, quartile grouping (Q1, Q2, Q3, Q4) and grouping based on the LE8 threshold (Low (0–49),
Moderate (50–79), High (80–100)).
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3.5 ROC analysis of LC9 in predicting
COPD
ROC curves were analyzed for the efficacy of ls7, le8 and lc9 in
predicting COPD odds. To validate the association of lc9 predicts the
odds of COPD, we carried out two tests: 1. le8 (excluding the
depression indicator) predicts the odds of COPD. 2. ls7 (excluding
the depression and sleep indicators) predicts the odds of COPD. The
AUC for lc9 score is 0.656 (0.636-0.677), with an optimal threshold of
74.17, sensitivity of 72.66%, and specificity of 49.50%. The AUC for le8
score is 0.655 (0.634-0.675), with an optimal threshold of 70.31,
sensitivity of 68.36%, and specificity of 53.70%. The AUC for ls7
score is 0.675 (0.656-0.694), with an optimal threshold of 8.5, sensitivity
of 69.48%, and specificity of 56.72% and as shown in Figure 3.
3.6 Cross-sectional mediation model
The proportion of mediation was OR = -0.2168; 95% CI:
-0.6844–0.07; P-value = 0.006). These findings suggest that LBM
plays a suggestive mediating potential in the odds of COPD by lc9
score and as shown in Figure 4.
3.7 Sensitivity analysis
To verify the stability of the association between lc9 and COPD,
the following three tests were carried out: 1. Data imputation
methods were employed to impute the variables with missing
values. The association between lc9 and COPD was tested in the
complete dataset. 2. The association between le8 (excluding the
depression indicator) and COPD was examined. 3. The association
between ls7 (excluding the depression and sleep indicators) and
COPD was investigated. The results demonstrated that all CVH
indicators (ls7, le8, lc9) were correlated with COPD. The results of
the study are reported in Supplementary Tables 1–3.
4 Discussion
This study is a continuous cross-sectional study based on 7 cycles
of NHANES data 2007-2020. There were 3 important findings. First,
the lc9 score was negatively associated with the odds of COPD, and
LBM had a suggestive mediating potential. Second, compared to the
non-COPD group, the COPD group had lower levels of variables
including health behaviors healthy (smoking, Sleep), health factors
(non-HDL-C, blood glucose, blood pressure) and mental health
(Depression). Additionally, White people had the highest
percentage in the COPD group. Additionally, lc9 had interaction
with race and was more protective for White people.
We observed that the levels of Smoking, Sleep, non-HDL-C,
blood glucose, blood pressure, and Depression in the COPD group at
baseline were lower than those in the normal group. Furthermore,
white people accounted for the highest proportion within the COPD
group. Prior studies have examined the correlation between le8 and
COPD, noting that compared with the non-COPD group, the levels
of physical activity, smoking, sleep health, blood lipids, blood glucose,
and blood pressure in the COPD group were significantly lower (28).
Moreover, the COPD group with higher levels of smoking and
depression exhibited a higher odds of mortality (29). Additionally,
a 3-month cohort study discovered that among COPD patients, the
majority were male (94%) and white (91%), with a relatively higher
proportion of white COPD patients (30,31). These findings are
analogous to ours. lc9 is a measure of CVH (12). The association
between COPD and CVH has been well documented (26,28). The
possible reasons for the high proportion of white participants in the
NHANES study are shown below. NHANES utilizes a complex
stratified multistage probability cluster sampling design, which,
although designed to ensure representativeness, may result in a
higher proportion of white participants if certain minority
neighborhoods are underrepresented in selected clusters or strata
(32). Second, it is more difficult for low-income groups to coordinate
time to participate in on-site inspections. Further, such as language
barriers, cultural differences, and distrust of medical research.
FIGURE 3
ROC curves for "LC9", "LE8" and "LS7" scoring systems. The figure displays AUC with 95% confidence intervals, sensitivity, specificity, and optimal
threshold values. The diagonal line in both panels represents the line of no-discrimination (AUC = 0.5). Error bars indicate the confidence intervals at
various points along the curve.
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These factors may contribute to lower participation rates among
minorities. A similar imbalance in racial distribution was found (33).
Our results that lc9 was negatively associated with the odds of
COPD, and this association was consistent across the overall
sample, subgroup analyses, and sensitivity analyses. From the
perspective of epidemiologic studies, Liu et al. concluded that
CVH is negatively associated with the odds of developing COPD
(28), and that maintaining an optimal CVH level is beneficial in
stopping the development of COPD (28). In a population ≥40 years
old, the higher the le8 score the lower the odds of COPD (26).
Additionally, ls7 scores have been associated with lung function as
well as the prevalence of COPD (34).The lc9 score consists of four
health behaviors (diet, physical activity, nicotine exposure, and
sleep), four health factors (BMI, non-HDL-C, blood glucose and
blood pressure), and mental health (depression) (12).The lc9
considers the roles of sleep and mental health in COPD and may
capture overlapping odds factors for COPD development.
Specifically, the lc9 component has been associated with a reduced
incidence of COPD. In terms of diet, higher diet quality (e.g., adherence
to a Mediterranean dietary pattern) reduces COPD odds. However,
adherence to Western dietary patterns, e.g., high amounts of meat or
processed meats, saturated fatty acids, increases odds (35). Genetic
evidence points that exercise promotes the differentiation of lung tissue
stem cells, remodeling blood vessel formation and enhancing lung
ventilation (36). Additionally, smoking is the most important
environmental odds factor for COPD. Chronic inflammation
induced by smoking will directly contribute to COPD by reducing
insulin action and elevating blood glucose levels, leading to decreased
lung function (37). Additionally, sleep deprivation is associated with
mildly reduced FVC (-5%) and FEV 1 (-6%) (38). It has been noted
that poor sleep quality are significantly associated with the severe
COPD (39). Low LBM is associated with accelerated lung function
decline in COPD patients, while the opposite is true for high LBM. (40).
Abdominal obesity accumulates large amounts of visceral fat and
increases the odds of COPD (41). The excess visceral fat is an
excellent pro-inflammatory mediator that attracts inflammatory cells
and amplifies the inflammatory process, leading to alterations in the
structure of the small airways (42). Mechanistic studies have shown
that lipid molecules and their metabolic processes may contribute to
COPD development by increasing inflammatory substances (43).
Additionally, oxidative stress in which hyperlipidemia induces
mitochondrial damage produces excess reactive oxygen species that
impair lung function (44). Patients with type 2 diabetes have been
reported to be more likely to develop COPD (45). A retrospective study
showed that dyslipidemia, fasting hyperglycemia, abdominal obesity,
and hypertension, were independently associated with impaired lung
function (46). Epidemiologic and genetic evidence agree that
depression may play an important role in the prevalence of COPD,
clearly indicating that depression may be an etiologic factor in COPD
(47). In addition, pro-inflammatory cytokines (e.g., IL-6 and c-reactive
protein) may play a role in the relationship between depressive
symptoms and lung function in older adults, causing endothelial
dysfunction and reduced alveolar function (48), promoting the
development of COPD. In summary, the association of lc9 with
COPD may reflecttheroleofsystemicinflammation a common
pathway linking cardiovascular and pulmonary pathologies. The
elevated levels of C-reactive protein and IL-6 (both of which are
associated with poor CVH indices) may promote alveolar
destruction and airway remodeling through activation of matrix
metalloproteinases (49). Our results extend the predictive value of lc9
from cardiovascular outcomes to the domain of COPD odds,
suggestingthatlc9mayplayadualroleinCOPDprevention.The
ability of lc9 to reduce the odds of COPD was stronger than that of the
lc9 component alone.
Our findings suggest that lc9 may alleviate COPD odds by
improving LBM. cross-sectional pathway analysis indicated a
potential mediating. LBM is a key indicator of muscle mass and
metabolic health (50). LBM is associated with CVH, lung function
FIGURE 4
The mediation pathway analysis of the association between LC9 score and the risk of COPD through LBM. The arrows indicate the direction of
relationships between the variables. DE, Direct Effect (the direct relationship between LC9 and COPD); IE, Indirect Effect (the influence on COPD
mediated through LBM); Proportion of mediation = IE / (DE + IE); OR, Odds Ratio.
Gou et al. 10.3389/fendo.2025.1539550
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and respiratory health (51,52). Improving lifestyle and increasing
LBM (53) can reduce COPD odds. Furthermore, epidemiologic and
genetic evidence agree on a negative association between LBM and
COPD and also support a unidirectional causal relationship (54).
This finding suggests that increasing LBM may provide additional
benefits for the prevention and management of COPD beyond
traditional dietary and exercise interventions.
We found a nonlinear relationship between lc9 score and
COPD odds. In other words, the OR of the lc9 score associated
with COPD was significantly lower in the lower range of the
corresponding score and subsequently stabilized at higher values.
Although previous studies have shown a positive linear relationship
between LS 7 scores and lung function (55), LE 8 showed a
nonlinear negative correlation with spirometry or COPD (28).
Furthermore, a NHANES study noted a linear negative
association between LE 8 and odds of COPD (26). The
inconsistent results may be due to the different age composition
of the populations studied. When the study population was at ≥40
years old, CVH was linearly associated with the possibility of
COPD. The reason is that inflammatory response is more
pronounced in middle-aged and older groups, which may lead to
a more direct and linear relationship between CVH indicators and
disease odds (56). ROC results show low specificity and may be
hindered in predicting disease. The findings suggest that LBM is an
important node in the path between lc9 and COPD odds. Low-
specificity models may produce a higher proportion offalse-positive
predictions in populations with lower disease prevalence. This may
result in a discrepancy between the actual prevalence rate and the
false positives that are misclassified by the model. This may be
linked to inadequate subgroup sample sizes and limited inclusion
of covariates.
The findings suggest a significant interaction of lc9 with race, with
stronger protective associations for whites. Earlier uptake of smoking
cessation interventions may result in greater gains for whites. Because
higher smoking rates in other racial groups amplify the association of
“nicotine exposure”indicators (e.g., smoking cessation) with protection
in lc9. (57). African-American populations live in areas with higher
mean annual PM2.5 concentrations than white people (57,58), and
PM2.5 is able to penetrate deep lung tissues, triggering oxidative stress
and inflammatory responses that lead to mitochondrial dysfunction
andlunginjury(59), contributing to acute exacerbations of
COPD (60). The anti-inflammatory capacity of lc9 metabolic
indicators (e.g., BMI, glycemic control) may be weakened.
Additionally, the assessment of depression in the lc9 (PHQ-9 scale)
may underestimate the mental health burden of minorities, who are
more likely to attribute psychological problems to physiological or
social factors than to direct mood disorders (61). Hispanic immigrants
(especially Mexicans), who are overrepresented in NHANES, may have
systematically higher lc9 scores due to pre-immigrant health behavioral
strengths (e.g., low processed food intake, high physical activity). This
“initial health advantage”may mask the ability of lc9 scores to reduce
the odds of COPD (62). This possible explanation helps to explain
our findings.
This study has several strengths. First, it is a study based on
coverage of different age, race, gender, and socioeconomic groups in
the U.S. through stratified multistage probability sampling, and the
results are generalizable to the entire country. Second, all laboratory
tests were performed through a standardized process certified by the
CDC, ensuring comparable and reliable data. Moreover, the study
extends the predictive value of lc9 from cardiovascular outcomes to
the COPD odds domain, bringing together standardized health
indicators (lc9) that may play a dual role in COPD prevention.
However, some limitations should not be ignored. First, this was a
cross-sectional study and no causal association between lc9 and
COPD could be inferred, and reverse causality cannot be ruled out
in observational studies. Second, this study was based on a
questionnaire, which may be subject to recall bias or social
desirability bias. Some biomarkers (e.g., glucose, lipids) were based
on single measurements only, which may not reflect long-term
exposure levels. Additionally, the small sample sizes of
some subgroups resulted in wide confidence intervals for the
estimates, and the results need to be interpreted with caution.
Further, NHANES excluded hospitalized patients and specific
institutionalized populations, which may be subject to Newman’s
bias and may lead to overrepresentation of healthy populations and
underestimation of COPD odds. Moreover, this study relied on
questionnaires and lacked sufficient follow-up data, thus limiting
in-depth exploration of the association between lc9 and COPD.
Importantly, despite adjusting for demographic and socioeconomic
variables, unmeasured confounders (use of biomass fuels, air
pollution, occupational history, genetic susceptibility, household
pollution, environmental factors such as place of residence/zip
code/geographic area, etc.) may influence the association between
lc9 and COPD. Moreover, we did not consider associations with
healthy migration in the NHANES study. Then, the specificity of the
model in this study (49.50%, 53.70%, 56.72%) suggests that its ability
to distinguish true-negative cases is limited, which may lead to an
elevated false-positive rate. Finally, as the mediating potential
accounted for a relatively small proportion, there may have been
more important mediating potential factors explored. Notably, the
results are difficult to generalize to other states and countries. The
cross-sectional design precludes establishing temporal prioritization
between LC9 and LBM. Although our findings are consistent with the
hypothesis of experimental studies suggesting independent roles for
LC9 and LBM in lung function, future longitudinal or interventional
studies are needed to unravel their causal interactions.
In conclusion, our results suggest that the higher the lc9 score,
the lower the odds of COPD, and that LBM plays an important
mediating potential. Optimizing lifestyle factors, particularly
enhancing LBM, may contribute to mitigating COPD odds. Based
on cross-sectional study, the lc9 scoring system could serve as a tool
to identify odds associations.
Data availability statement
The datasets presented in this study can be found in online
repositories. The names of the repository/repositories and accession
number(s) can be found below: http://www.cdc.gov/nchs/
nhanes.htm.
Gou et al. 10.3389/fendo.2025.1539550
Frontiers in Endocrinology frontiersin.org14
Ethics statement
The studies involving humans were approved by Ethics Review
Board of the National Center for Health Statistics. The studies were
conducted in accordance with the local legislation and institutional
requirements. The participants provided their written informed
consent to participate in this study. Written informed consent was
obtained from the individual(s) for the publication of any
potentially identifiable images or data included in this article.
Author contributions
RG: Conceptualization, Data curation, Formal Analysis,
Investigation, Methodology, Software, Writing –review & editing.
XC: Investigation, Methodology, Project administration, Resources,
Supervision, Writing –original draft, Writing –review & editing.
DD: Data curation, Formal Analysis, Investigation, Writing –
review & editing. XM: Data curation, Formal Analysis, Writing –
review & editing. LH: Data curation, Formal Analysis, Investigation,
Writing –review & editing. LZ: Conceptualization, Data curation,
Formal Analysis, Supervision, Writing –review & editing. WT:
Formal Analysis, Supervision, Writing –review & editing. GL:
Conceptualization, Funding acquisition, Supervision, Validation,
Writing –review & editing.
Funding
The author(s) declare that financial support was received for the
research and/or publication of this article. This work was supported
by the Natural Science Foundation of Ningxia (grant no.
2022AAC03374, 2024A03225) and the National Natural Science
Foundation of China (NO.82060050). of China (NO.82060050).
Conflict of interest
The authors declare that the research was conducted in the
absence of any commercial or financial relationships that could be
construed as a potential conflict of interest.
Generative AI statement
The author(s) declare that no Generative AI was used in the
creation of this manuscript.
Publisher’s note
All claims expressed in this article are solely those of the authors
and do not necessarily represent those of their affiliated
organizations, or those of the publisher, the editors and the
reviewers. Any product that may be evaluated in this article, or
claim that may be made by its manufacturer, is not guaranteed or
endorsed by the publisher.
Supplementary material
The Supplementary Material for this article can be found online
at: https://www.frontiersin.org/articles/10.3389/fendo.2025.1539550/
full#supplementary-material
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