Association of Life's Essential 8 with osteoarthritis in United States adults: mediating effects of dietary intake of live microbes

Abstract

Objective
Osteoarthritis (OA) is associated with cardiovascular disease and represents a persistent economic and physical burden on patients in the United States. This study evaluated the mediating effect of dietary live microbe intake on the association between cardiovascular health [based on Life's Essential 8 (LE8) scores] and osteoarthritis (OA) in adults.

Methods
This cross-sectional study included data from the National Health and Nutrition Examination Survey, 1999–2019 (from patients aged ≥20 years). LE8 scores (0–100) were measured according to the American Heart Association definition and categorized as low (0–49), moderate (50–79), or high (80–100). OA disease status was assessed using self-reported data from patients. The relationships were evaluated using multivariate logistic and restricted cubic spline models. Mediation analysis was used to evaluate the mediating effect of dietary live microbe intake on the association between LE8 and OA risk.

Results
The study included 23,213 participants aged ≥20 years. After adjusting for latent confounders, higher LE8 scores were found to be associated with a lower incidence of OA. The odds ratios (with 95% confidence intervals) for low, moderate, and high OA risk were 0.81 (0.69, 0.96) and 0.55 (0.44, 0.69), respectively; a non-linear dose-response relationship was observed (P-nonlinear = 0.012). Health behavior and health factor scores showed a similar pattern of correlation with OA risk. Low live microbe intake mediated the association between LE8, health behavior, and health factor scores with OA risk and did not appear to reduce OA risk.

Conclusion
Our findings suggest that although higher LE8 scores reduce the risk of developing OA, low live microbe intake may reduce the protective effect of higher scores. It is, therefore, essential to emphasize adherence to a lifestyle that confers high LE8 scores. Individuals should also be advised to reduce the intake of foods with low live microbe content.

Full text

TYPE Original Research
PUBLISHED 21 December 2023
DOI 10.3389/fmed.2023.1297482
OPEN ACCESS
EDITED BY
Cong-Qiu Chu,
Oregon Health and Science University,
United States
REVIEWED BY
Ibsen Bellini Coimbra,
State University of Campinas, Brazil
S. Anand Narayanan,
Florida State University, United States
*CORRESPONDENCE
Ying Pan
18629494329@163.com
Guanghua Li
ghlee0404@163.com
RECEIVED 21 September 2023
ACCEPTED 27 November 2023
PUBLISHED 21 December 2023
CITATION
Gou R, Chang X, Li Z, Pan Y and Li G (2023)
Association of Life’s Essential 8 with
osteoarthritis in United States adults: mediating
eects of dietary intake of live microbes.
Front. Med. 10:1297482.
doi: 10.3389/fmed.2023.1297482
COPYRIGHT
©2023 Gou, Chang, Li, Pan 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.
Association of Life’s Essential 8
with osteoarthritis in
United States adults: mediating
eects of dietary intake of live
microbes
Ruoyu Gou1, Xiaoyu Chang1, Zeyuan Li1, Ying Pan2*and
Guanghua Li1*
1School of Public Health, Ningxia Medical University, Yinchuan, Ningxia, China, 2Department of Joint
Surgery, HongHui Hospital, Xi’an Jiaotong University, Xi’an, Shannxi Province, China
Objective: Osteoarthritis (OA) is associated with cardiovascular disease and
represents a persistent economic and physical burden on patients in the
United States. This study evaluated the mediating eect of dietary live microbe
intake on the association between cardiovascular health [based on Life’s Essential
8 (LE8) scores] and osteoarthritis (OA) in adults.
Methods: This cross-sectional study included data from the National Health
and Nutrition Examination Survey, 1999–2019 (from patients aged ≥20 years).
LE8 scores (0–100) were measured according to the American Heart Association
definition and categorized as low (0–49), moderate (50–79), or high (80–100).
OA disease status was assessed using self-reported data from patients. The
relationships were evaluated using multivariate logistic and restricted cubic spline
models. Mediation analysis was used to evaluate the mediating eect of dietary
live microbe intake on the association between LE8 and OA risk.
Results: The study included 23,213 participants aged ≥20 years. After adjusting
for latent confounders, higher LE8 scores were found to be associated with
a lower incidence of OA. The odds ratios (with 95% confidence intervals) for
low, moderate, and high OA risk were 0.81 (0.69, 0.96) and 0.55 (0.44, 0.69),
respectively; a non-linear dose-response relationship was observed (P-nonlinear
=0.012). Health behavior and health factor scores showed a similar pattern
of correlation with OA risk. Low live microbe intake mediated the association
between LE8, health behavior, and health factor scores with OA risk and did not
appear to reduce OA risk.
Conclusion: Our findings suggest that although higher LE8 scores reduce the risk
of developing OA, low live microbe intake may reduce the protective eect of
higher scores. It is, therefore, essential to emphasize adherence to a lifestyle that
confers high LE8 scores. Individuals should also be advised to reduce the intake of
foods with low live microbe content.
KEYWORDS
osteoarthritis, Life’s Essential 8, mediation analyses, live microbes, NHANES
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Gou et al. 10.3389/fmed.2023.1297482
1 Introduction
Osteoarthritis (OA) is the most common form of arthritis
and is caused by an imbalance between repair and destruction of
joint tissue. It entails structural changes in the joints, including
cartilage degeneration, synovial inflammation, and inflammation
of the capsular ligaments. The United States has the highest
age-standardized prevalence rate of OA (1), and the number
of affected individuals is expected to increase to 67 million
by 2030 (2). Notably, OA is associated with an increased
risk of premature death from cardiovascular disease (CVD).
Therefore, healthcare professionals need to take particular note
of modifiable cardiovascular risk factors (including hypertension,
diabetes mellitus, hyperlipidemia, smoking, and physical inactivity)
in this population (3). Certain lifestyle-related risk factors have
been demonstrated to have a definite biological effect on the
development of OA and these include age, gender, smoking, diet,
hypertension, sedentary lifestyle, body mass index (BMI), low-
density lipoprotein levels, genetics, metformin use, bone mineral
density, joint shape abnormalities, joint malalignment, decreased
muscle strength/mass, injuries, and joint loading abnormalities (4–
6). Although the causes of OA have not been fully elucidated,
there is a growing agreement that indispensable environmental
factors (health behaviors and diet) are important contributors
to this disease (6,7). In 2010, the American Heart Association
recommended Life’s Simple 7 as a measure of cardiovascular health
(CVH), with the aim of improving the health of the general
population (8). Owing to the limitations of the LS7 CVH score,
the American Heart Association recently updated the evaluation
tool to Life’s Essential 8 (LE8) (9). The LE8 scoring system is more
sensitive to differences between individuals and emphasizes the role
of maintaining or improving CVH. The components of LE8 include
diet (updated), physical activity, nicotine exposure (updated),
sleep health (new), body mass index, blood lipids (updated),
blood glucose (updated), and blood pressure. In this context,
epidemiologic studies have shown that conventional risk factors
for CVD such as age, hypertension, diabetes mellitus, obesity,
and low physical activity are associated with the development and
progression of OA (10). However, no studies have evaluated the
association between CVH (LE8 scores) and OA risk.
Notably, previous studies have shown the existence of mutual
protective factors between OA and CVD (11). In their study,
Sanders et al. assessed the number of live microbes consumed in
the diet and accordingly categorized foods into low [Lo; <104
colony forming units (CFU)/g], medium (Med; 104–107 CFU/g),
and high (Hi: >107 CFU/g) groups based on the number of live
microbes per gram. In this context, certain probiotics (including
Bifidobacterium bifidum and Lactobacillus acidophilus) may lower
elevated cholesterol levels and aid the prevention and treatment of
some CVDs (12,13). Beneficial symbiotic microbes have also been
found to be capable of exerting cholesterol-lowering effects (14).
In addition, studies have reported that gut dysbiosis exacerbates
OA; this may be explained by disruption of the host–gut microbial
balance, which, in turn, triggers the host immune response and
activates the “gut–joint axis” (15). Research suggests that LE8 scores
and live microbe intake may reduce the risk of OA by reducing
oxidative stress, inflammation, and obesity (16). However, the
relevance of these factors is limited by the lack of animal and
human studies on this association. This cross-sectional study, using
data from the National Health and Nutrition Examination Survey
(NHANES) 2005–2019 cohort, was therefore performed to evaluate
the association between LE8 scores and OA risk. The live microbe
content of 9,388 foods listed in the NHANES database was also
evaluated; the foods were grouped according to microbial content,
and their mediating effects were assessed.
2 Materials and methods
2.1 Database and study subjects
The NHANES utilizes stratified multistage probability sampling
methods to select a series of nationally representative samples
of non-institutionalized United States adults in 2-year cycles,
which started from 1999 to 2000 (http://www.cdc.go/nchs/nhanes.
htm). The NHANES program was approved by the Ethics Review
Board of the National Center for Health Statistics. All participants
provided written informed consent to participate in the survey and
agreed to the use of their data in health-related statistical research.
The study followed the Strengthening Reporting of Observational
Studies in Epidemiology reporting guidelines (17). As shown in
Figure 1, data from 23,213 participants (aged 20 years and older
with complete data from eight survey cycles spanning from 2005–
2006 to 2017–2019) were included in the study.
2.2 Measurement of LE8 scores
The LE8-scored sub-questionnaire assesses four health
behaviors (diet, physical activity, nicotine exposure, and sleep
duration) and four health factors (BMI, non-high-density
lipoprotein cholesterol, blood glucose, and blood pressure) (9).
The scores for the eight CVH metrics range from 0 to 100 (9). The
overall LE8 score is calculated as the arithmetic mean of the eight
metrics. Participants with LE8 scores of 80–100, 50–79, and 0–49
are considered to have high, moderate, and low CVH, respectively.
In this study, dietary indicators were evaluated using the Healthy
Eating Index 2015 scores; dietary intakes of participants (obtained
from two 24-h dietary recalls) were combined with United States
Department of Agriculture Food Pattern Equivalent data to
calculate the scores. Self-reported questionnaires were used to
obtain information regarding the frequency and duration of
vigorous or moderate physical activity over the past 30 days.
Smoking habits, sleep duration, history of diabetes, and medication
history were also assessed using self-reported questionnaires.
Blood pressure, height, and weight were measured during physical
examination. The blood pressure was determined by averaging
three consecutive measurements, and the BMI was calculated
by dividing the weight in kilograms by the square of the height
in meters. Blood samples were collected and sent to a central
laboratory for analysis of lipid, blood glucose, and glycosylated
hemoglobin levels (9).
2.3 OA assessment
A study has shown 81% consistency between self-reported
and clinically confirmed diagnoses (18). In the NHANES, OA
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FIGURE 1
Flowchart of the screening process for the selection of the study population. NHANES, National Health and Nutrition Examination Survey; FBG,
fasting blood glucose; HEI score, Healthy Eating Index; Non-HDL, Non-High-Density Lipoprotein Cholesterol; BP, Blood Pressure; Experts assigned
foods an estimated level of live microbes per gram [low (Lo), <104 CFU/g; medium (Med), 104–107 CFU/g; or high (Hi), >107 CFU/g]; CVH,
cardiovascular health; Life’s Essential 8, The components of Life’s Essential 8 include diet (updated), physical activity, nicotine exposure (updated),
sleep health (new), body mass index, blood lipids (updated), blood glucose (updated), and blood pressure. Each metric has a new scoring algorithm
ranging from 0 to 100 points, allowing the generation of a new composite cardiovascular health score (the unweighted average of all components)
that also varies from 0 to 100 points. The LE8 scoring algorithm consists of 4 health behaviors (diet, physical activity, nicotine exposure, and sleep
duration) and 4 health factors [body mass index (BMI), non-high-density-lipoprotein cholesterol, blood glucose, and blood pressure]. The overall LE8
score, health behavior score, and health factor score were calculated as the unweighted average of the eight metrics. Participants with a LE8 score of
80–100 were considered high CVH; 50–79, moderate CVH; and 0–49 points, low CVH.
was diagnosed by a professional, and information was obtained
using a questionnaire. All participants (aged ≥20 years) were
asked questions related to arthritis. They were asked the following
question: “Has a doctor or other health professional ever told you
that you have arthritis?” Participants were included in the study if
their responses indicated that they were diagnosed with OA (19).
2.4 Definition of live microbes in food
A classification system has been established for defining and
estimating the dietary intake of live microbes among United States
adults. The NHANES uses food codes for the assigned categories.
Among the 9,388 food codes in the NHANES database, 8,954
contain small amounts of live microbes (<104 CFU/g) (20).
Processed foods that usually undergo heat treatment (such as milk;
prepared meats including pork, poultry, and seafood dishes; and
sauces and gravies) are considered to be very low in microbes
and are therefore classified as Lo foods. Raw meats, pork, poultry,
and seafood are also classified in the Lo category, based on the
presumption that they are cooked prior to consumption (with
the exception of a few of these foods that are specified as being
eaten raw). Fresh vegetables and fruits are the top two food
items allocated to the Med category (accounting for 41 and
39%, respectively). Fresh fruit juices, such as fruit smoothies, are
allocated to the Med category; beverages, condiments, and sauces
comprise more than 10% of the food allocated to this category.
Some fermented foods (e.g., miso and sauerkraut) are also assigned
to the Med category. Notably, fermented dairy products comprise
the majority of foods assigned to the Hi category. Yogurt and other
fermented milks are assigned to this category unless they constitute
a component of another food. Codes that contain a significant
amount of fermented foods, such as yogurt or sour cream, are
assigned to the Hi category. However, foods that contain cheese
as a minor component are assigned to the Lo or Med categories,
depending on their relative amount in the food (20).
2.5 Defining covariates
Demographic information was obtained using a questionnaire.
The attributes included age (20–44, 45–64, and ≥65 years), gender,
race (non-Hispanic white, non-Hispanic Black, Hispanic, and
others), marital status (married, separated, and never married), and
family income to poverty ratio (<1.30, 1.30–<3.00, 3.00–<5.00,
and ≥5.00). This ratio represents the proportion of the family
income in relation to the federal poverty threshold after adjusting
for household size; a higher ratio indicates a higher level of income.
Data were also obtained on the level of education [less than
high school (grade 11), high school graduate/general education,
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TABLE 1 Participant demographic characteristics (NHANES, 2005–2019 years cycle).
Parameter No. of participants (weighted %)
Total Non-osteoarthritis Osteoarthritis P-value
(N=23,213) (N=16,709) (N=6,504)
CVH (Life’s Essential 8) 68.40 (0.24) 70.39 (0.25) 62.82 (0.31) <0.001
Health behavior score 66.59 (0.32) 67.65 (0.31) 63.64 (0.49) <0.001
Health factor score 70.20 (0.24) 73.14 (0.26) 62.01 (0.30) <0.001
Score HEI 39.27 (0.49) 38.71 (0.51) 40.82 (0.68) 0.001
Score PA 71.96 (0.48) 75.16 (0.50) 63.01 (0.75) <0.001
Score smoke 71.55 (0.50) 72.24 (0.52) 69.61 (0.76) <0.001
Score sleep 83.59 (0.28) 84.48 (0.28) 81.11 (0.51) <0.001
Score BMI 60.53 (0.42) 63.59 (0.48) 51.98 (0.54) <0.001
Score non-HDL 64.30 (0.34) 65.95 (0.38) 59.69 (0.55) <0.001
Score glucose 86.29 (0.24) 89.06 (0.23) 78.55 (0.49) <0.001
Score BP 69.70 (0.35) 73.95 (0.39) 57.82 (0.56) <0.001
Grams Lo 3,447.93 (20.63) 3,486.76 (23.07) 3,339.56 (26.76) <0.001
Grams Med 108.52 (2.30) 108.04 (2.39) 109.86 (3.55) 0.590
Grams Hi 23.10 (0.66) 22.76 (0.67) 24.03 (1.30) 0.340
Age, years old
20–44 9,645 (41.55) 8,831 (55.00) 814 (14.31) <0.001
45–64 8,103 (34.91) 5,314 (33.83) 2,789 (47.97)
≥65 5,465 (23.54) 2,564 (11.18) 2,901 (37.72)
Sex
Female 11,781 (50.75) 7,962 (48.14) 3,819 (60.37) <0.001
Male 11,432 (49.25) 8,747 (51.86) 2,685 (39.63)
Ethnicity/ race
White people 10,915 (47.02) 7,239 (68.69) 3,676 (79.64) <0.001
Black people 4,663 (20.09) 3,327 (10.06) 1,336 (8.95)
Mexican people 3,392 (14.61) 2,754 (8.80) 638 (3.66)
Other 4,243 (18.28) 3,389 (12.44) 854 (7.74)
Marital
Married 14,114 (60.8) 10,343 (65.19) 3,771 (64.88) <0.001
Separated 5,040 (21.71) 2,852 (14.51) 2,188 (27.53)
Never married 4,059 (17.49) 3,514 (20.29) 545 (7.59)
Ratio of family income to poverty levels
<1.3 6,771 (29.17) 4,747 (18.74) 2,024 (19.68) 0.100
1.3–3 7,323 (31.55) 5,208 (27.86) 2,115 (29.71)
3–5 4,688 (20.2) 3,491 (25.31) 1,197 (23.52)
≥5 4,431 (19.09) 3,263 (28.10) 1,168 (27.09)
Education levels
Less than 11th grade 5,009 (21.58) 3,395 (12.86) 1,614 (16.12) <0.001
High school graduate 11,123 (47.92) 8,238 (55.75) 2,885 (50.47)
College graduate or above 7,081 (30.5) 5,076 (31.40) 2,005 (33.41)
(Continued)
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TABLE 1 (Continued)
Parameter No. of participants (weighted %)
Total Non-osteoarthritis Osteoarthritis P-value
(N=23,213) (N=16,709) (N=6,504)
Alcohol consumption status
Never 3,836 (16.53) 2,296 (11.25) 1,540 (19.64) <0.001
Former 4,575 (19.71) 3,779 (23.28) 796 (13.08)
Mild 8,080 (34.81) 5,665 (36.49) 2,415 (41.66)
Moderate 3,703 (15.95) 2,813 (18.89) 890 (15.50)
Heavy 3,019 (13.01) 2,156 (10.09) 863 (10.12)
CVH (Life’s Essential 8)
Low 2,990 (12.88) 1,595 (7.69) 1,395 (17.55) <0.001
Moderate 15,696 (67.62) 11,170 (65.03) 4,526 (70.56)
High 4,527 (19.5) 3,944 (27.28) 583 (11.89)
Health behavior score
Low 5,022 (21.63) 3,265 (17.31) 1,757 (23.32) <0.001
Moderate 11,688 (50.35) 8,452 (50.06) 3,236 (49.99)
High 6,503 (28.01) 4,992 (32.63) 1,511 (26.69)
Health factor score
Low 4,024 (17.34) 2,175 (10.95) 1,849 (24.63) <0.001
Moderate 11,908 (51.3) 8,252 (48.00) 3,656 (56.67)
High 7,281 (31.37) 6,282 (41.05) 999 (18.70)
NHANES, National Health and Nutrition Examination Survey; FBG, fasting blood glucose; HEI score, Healthy Eating Index; Non-HDL, non-high-density lipoprotein cholesterol; BP, blood
pressure; Experts assigned foods an estimated level of live microbes per gram [low (Lo), <104 CFU/g; medium (Med), 104–107 CFU/g; or high (Hi), >107 CFU/g]; CVH, cardiovascular health.
Life’s Essential 8, The components of Life’s Essential 8 include diet (updated), physical activity, nicotine exposure (updated), sleep health (new), body mass index, blood lipids (updated), blood
glucose (updated), and blood pressure. Each metric has a new scoring algorithm ranging from 0 to 100 points, allowing the generation of a new composite cardiovascular health score (the
unweighted average of all components) that also varies from 0 to 100 points. The LE8 scoring algorithm consists of 4 health behaviors (diet, physical activity, nicotine exposure, and sleep
duration) and 4 health factors [body mass index (BMI), non-high-density-lipoprotein cholesterol, blood glucose, and blood pressure]. The overall LE8 score and health factor score were
calculated as the unweighted average of the eight metrics. Participants with a LE8 score of 80–100 were considered high CVH; 50–79, moderate CVH; and 0–49 points, low CVH.
Data are mean (standard error) or No. of participants (weighted %).
Percentages were adjusted for NHANES survey weights. The P-value was calculated using a chi-square test and Student’s t-test after considering the sampling weights. P-value <0.05 for each
indicator, except for Grams Med, Grams Hi, Ratio of family income to poverty levels (P-value >0.05).
HEI score, In the continuous US NHANES, healthy diet denoted the top two-fifths of the Healthy Eating Index-2015 score. Ethnicity/race: non-Hispanic white people, non-Hispanic Black
people, Hispanic people, and others; Marital (married, separated, and never married).
college, and above college level] and the alcohol consumption status
[current heavy drinker (women: ≥three drinks per day, men: ≥four
drinks per day, or binge drinking for 5 days or more per month),
current moderate drinker (women: ≥two drinks per day, men:
≥three drinks per day, or binge drinking ≥2 days per month),
current light/mild alcohol drinkers (not fulfilling the criteria for
the previous two categories) (6), former alcohol consumption
(previous history of drinking but not a current drinker), and no
history of alcohol consumption].
2.6 Statistical analysis
To estimate the statistical data representative of United States
adults, oversampling, stratification, and clustering were performed
in accordance with the NHANES guidelines; particular emphasis
was placed on weight-adjusted statistical tests. Chi-square and t-
tests were used to evaluate demographic characteristics, pertaining
to OA status. The association between LE8 scores and the risk
of OA was evaluated using a multivariate logistic regression
model; the results were presented as the odds ratio (OR) with
95% confidence intervals (95% CIs). Stratified analyses were
performed by gender, age, and ethnicity to evaluate the association
between LE8 scores and OA risk in different groups. The
group with low LE8, health behavior, and health factor scores
was considered as the reference group. Restricted cubic spline
plots were used to evaluate trends among variables that were
found to demonstrate the significance of logistic regression. They
were also used to determine the presence of any non-linear
association between exposure factors and OA risk. The potential
mediating role of live microbes (in Lo, Med, and Hi foods) in the
association between LE8 scores and OA risk was evaluated using
a parallel mediation model. Mediation analysis was performed
using the mediation package of R; a quasi-Bayesian Monte Carlo
method with 1,000 simulations was used based on the normal
approximation. The direct and indirect effects represented the
effect of LE8 scores on OA risk without mediators and via
the mediator, respectively. The proportion of mediation was
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TABLE 2 Univariate logistic regression analysis.
Parameter OR (95% CI) P-value
CVH (Life’s Essential 8) 0.97 (0.97, 0.97) <0.001
Health behavior score 0.99 (0.98, 0.99) <0.001
Health factor score 0.98 (0.98, 0.98) <0.001
Score HEI 0.99 (0.99, 0.99) <0.001
Score PA 0.99 (0.99, 0.99) <0.001
Score smoke 0.99 (0.99, 0.99) <0.001
Score sleep 0.99 (0.99, 0.99) <0.001
Score BMI 0.99 (0.99, 0.99) <0.001
Score non-HDL 0.99 (0.99, 0.99) 0.010
Score glucose 0.99 (0.99, 0.99) <0.001
Score BP 1.00 (1.00, 1.00) 0.090
Grams Lo 1.00 (1.00, 1.00) 0.690
Grams Med 0.99 (0.99, 0.99) <0.001
Grams Hi 0.99 (0.99, 1.00) <0.001
CVH (Life’s Essential 8)
Low Ref Ref
Moderate 0.58 (0.51, 0.66) <0.001
High 0.29 (0.25, 0.34) <0.001
Health behavior score
Low Ref Ref
Moderate 0.75 (0.67, 0.84) <0.001
High 0.55 (0.48, 0.62) <0.001
Health factor score
Low Ref Ref
Moderate 0.66 (0.58, 0.75) <0.001
High 0.39 (0.34, 0.45) <0.001
NHANES, National Health and Nutrition Examination Survey; FBG, fasting blood glucose;
HEI score, Healthy Eating Index; Non-HDL, non-high-density lipoprotein cholesterol; BP,
blood pressure; Experts assigned foods an estimated level of live microbes per gram [low
(Lo), <104 CFU/g; medium (Med), 104–107 CFU/g; or high (Hi), >107 CFU/g]; CVH,
cardiovascular health; OR odds ratio, CI, confidence interval.
Life’s Essential 8, The components of Life’sEssential 8 include diet (updated), physical activity,
nicotine exposure (updated), sleep health (new), body mass index, blood lipids (updated),
blood glucose (updated), and blood pressure. Each metric has a new scoring algorithm
ranging from 0 to 100 points, allowing the generation of a new composite cardiovascular
health score (the unweighted average of all components) that also varies from 0 to 100 points.
The LE8 scoring algorithm consists of 4 health behaviors (diet, physical activity, nicotine
exposure, and sleep duration) and 4 health factors [body mass index (BMI), non-high-density-
lipoprotein cholesterol, blood glucose, and blood pressure]. The overall LE8 score and health
factor score were calculated as the unweighted average of the eight metrics. Participants with
a LE8 score of 80–100 were considered high CVH; 50–79, moderate CVH; and 0–49 points,
low CVH.
Adjusted for sex, age, ethnicity/race, marital, family income-to-poverty ratio, education levels,
and alcohol consumption status.
calculated as the quotient of the indirect effect divided by the
total effect.
All remaining statistical analyses were performed using R
software (version 4.2.2, https://cran.r-project.org/bin/windows/
base/old/4.2.2/); the following packages were used: nhanesR
(version 0.9.2.8), survey, CompareGroups, dplyr, tidyverse, do,
MASS, finalfit, Hmisc, lattice, Formula, rms, and foreign. The
statistical tests were two-sided, and the results were considered
statistically significant when the P-value was <0.05.
3 Results
3.1 Baseline characteristics
The data from 23,213 participants (aged ≥20 years) were
included. The baseline characteristics of the study population
(according to OA risk) are presented in Table 1. The weighted
percentages of participants aged 20–44, 45–64, and ≥65 years
were 41.55%, 47.97%, and 37.72%, respectively. The weighted
percentages of the 11,781 female and 11, 432 male individuals
were 50.75% and 49.25%, respectively. The mean LE8 score for
the total population was 68.40, and the low, moderate, and high
scores (weighted %) were 5,022 (21.63), 1,688 (50.35), and 6,503
(28.01), respectively. The scores for those with OA (62.82) was
lower than that of participants without OA (70.39). In contrast
to those without OA, those with the condition were older, more
likely to be women, and more likely to be of non-Hispanic white
ethnicity; they also had lower LE8 (and its component metrics)
scores, consumed more foods with lower live microbe content, and
were mostly married. However, as shown in Table 1, live microbe
intake (Med and Hi foods) and the ratio of family income to poverty
levels did not differ significantly between the two groups.
3.2 Univariate logistic regression analysis
for the association between LE8 scores and
OA risk
Participants with low CVH (LE8 scores) demonstrated
moderate [OR: 0.58; 95% CI: (0.51, 0.66)] and high [OR: 0.29; 95%
CI: (0.25,0.34)] propensity for developing OA. Individuals with low
health behavior scores also demonstrated moderate [OR: 0.75; 95%
CI: (0.67, 0.84)] and high [OR: 0.55; 95% CI: (0.48, 0.62)] OA risk.
Additionally, those with low health factor scores showed moderate
[OR: 0.66; 95% CI: (0.58, 0.75)] and high [OR: 0.39; 95% CI: (0.34,
0.45)] likelihood of developing OA. The LE8, health behavior, and
health factor scores remained significantly associated with OA risk
on being used as continuous variables, and this was suggestive
of their possible protective role against OA. Live microbe intake
was associated with OA risk; in particular, the intake of Med [OR:
0.99; 95% CI: (0.99, 0.99)] and Hi [OR: 0.99; 95% CI: (0.99, 1.00)]
category foods appeared to be protective against OA (Table 2).
3.3 Multivariate logistic regression analysis
for the association between LE8 scores and
OA
After adjusting for multiple latent variables, LE8, health
behavior, and health factor scores continued to demonstrate a
significant association with OA risk. In model 2, the groups with
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TABLE 3 Multiple logistic regression models of Life’s Essential 8 with osteoarthritis for participants.
Parameter Crude model Model 1 Model 2
OR (95%CI) P-value OR (95%CI) P-value OR (95%CI) P-value
Life’s Essential 8
Low ref ref ref
Moderate 0.75 (0.65, 0.87) <0.001 0.79 (0.67, 0.94) 0.010 0.81 (0.69, 0.96) 0.020
High 0.49 (0.40, 0.61) <0.001 0.53 (0.42, 0.66) <0.001 0.55 (0.44, 0.69) <0.001
P-trend <0.001
Health behavior score
Low ref ref ref
Moderate 0.87 (0.78, 0.97) 0.010 0.79 (0.70, 0.90) <0.001 0.83 (0.73, 0.95) 0.010
High 0.93 (0.81, 1.07) 0.310 0.69 (0.59, 0.80) <0.001 0.74 (0.63, 0.87) <0.001
P-trend <0.001
Health factor score
Low ref ref ref
Moderate 0.60 (0.53, 0.68) <0.001 0.73 (0.64, 0.84) <0.001 0.73 (0.64, 0.84) <0.001
High 0.28 (0.25, 0.32) <0.001 0.52 (0.44, 0.60) <0.001 0.53 (0.45, 0.61) <0.001
P-trend <0.001
Life’s Essential 8, The components of Life’s Essential 8 include diet (updated), physical activity, nicotine exposure (updated), sleep health (new), body mass index, blood lipids (updated), blood
glucose (updated), and blood pressure. Each metric has a new scoring algorithm ranging from 0 to 100 points, allowing the generation of a new composite cardiovascular health score (the
unweighted average of all components) that also varies from 0 to 100 points. The LE8 scoring algorithm consists of 4 health behaviors (diet, physical activity, nicotine exposure, and sleep
duration) and 4 health factors (body mass index [BMI], non-high-density-lipoprotein cholesterol, blood glucose, and blood pressure). The overall LE8 score and the health factor score were
calculated as the unweighted average of the eight metrics. Participants with scores (LE8 score, health behavior score, and health factor score) of 80–100 were considered high CVH; 50–79,
moderate CVH; and 0–49 points, low CVH.
OR odds ratio, CI confidence interval.
Crude model, No adjustment for any potential influence factors.
Model 1, Adjusted for sex, age, and ethnicity/race.
Model 2, Adjusted for sex, age, ethnicity/race, marital, family income-to-poverty ratio, education levels, and alcohol consumption status.
moderate and high LE8 scores (representing CVH) demonstrated
an OR of 0.81 (95% CI: 0.69, 0.96) for developing OA; in
comparison, participants with low LE8 scores demonstrated an
OR of 0.55 (95% CI: 0.44, 0.69). The groups with moderate and
high health behavior scores demonstrated an OR of 0.83 (95%
CI: 0.73, 0.95) for OA risk; in the group with low scores, the
OR was 0.74 (95% CI: 0.63, 0.87). The groups with moderate and
high health factor scores demonstrated an OR of 0.73 (95% CI:
0.64, 0.84), while the group with low scores showed an OR of
0.53 (95% CI: 0.45, 0.61; Table 3). As shown in Figure 2, a non-
linear relationship was observed between OA risk and LE8 scores
[P-nonlinear <0.001; minimum threshold value for beneficial
association: 66.52 (estimated OR =1)], health behavior scores
[P-nonlinear <0.001; minimum threshold value for beneficial
association: 67.34 (estimated OR =1)], and health factor scores
[P-nonlinear <0.001; minimum threshold value for beneficial
association: 68.36 (estimated OR =1)].
3.4 Subgroup analysis for the association
between LE8 scores and OA risk
After adjusting for multiple latent variables, the LE8, health
behavior, and health factor scores were all found to be significantly
correlated with OA risk across the different groups (irrespective
of sex, ethnicity/race, and age); therefore, these may serve as
protective factors against OA. On comparing the group with low
LE8 scores with the group having moderate and high scores, the
ORs (with 95% CIs) for each subgroup were as follows: male:
0.55 (0.46, 0.66), 0.30 (0.23,0.38); female: 0.48 (0.40, 0.56), 0.16
(0.14,0.20); white: 0.54 (0.46, 0.64), 0.22 (0.18, 0.27); Black: 0.47
(0.40, 0.55), 0.17 (0.12, 0.24); Mexican: 0.21 (0.14, 0.32), 0.46 (0.34,
0.62); 20–44 years old: 0.37 (0.29, 0.48), 0.20 (0.14, 0.27); 45–64
years old: 0.37 (0.29, 0.48), 0.20 (0.14, 0.27), 0.20 (0.14, 0.27); 45–
64 years old: 0.63 (0.53, 0.74), 0.25 (0.20, 0.31); and ≥65 years
old: 0.75 (0.60, 0.93), 0.56 (0.41, 0.77). On comparing participants
with low health behavior scores with those having moderate and
high scores, the ORs (with 95% CIs) for each subgroup were
as follows: male: 0.81 (0.69, 0.95), 0.70 (0.57, 0.86); female: 0.73
(0.63, 0.84), 0.55 (0.47, 0.64); white: 0.82 (0.71, 0.94), 0.63 (0.54,
0.74); Black: 0.74 (0.62, 0.90), 0.58 (0.45, 0.74); Mexican: 0.51
(0.40, 0.63), 0.49 (0.37, 0.65); 20–44 years old: 0.56 (0.46, 0.69),
0.41 (0.31, 0.55); 45–64 years old: 0.78 (0.67, 0.90), 0.51 (0.43,
0.61); and ≥65 years old: 1.01 (0.82, 1.23), 0.86 (0.69, 1.08). On
comparing participants with low health factor scores with those
having moderate and high scores, the ORs (with 95% CIs) for
each subgroup were as follows: male: 0.60 (0.50, 0.71), 0.32 (0.26,
0.39); female: 0.52 (0.44, 0.62), 0.19 (0.16, 0.22); white: 0.57 (0.48,
0.68), 0.25 (0.22, 0.30); Black: 0.50 (0.43, 0.58), 0.19 (0.15, 0.24);
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FIGURE 2
Dose–response relationships between Life’s Essential 8 scores (A), health behavior score (B), health factor score (C), Grams Lo (D), Grams Med (E),
Grams Hi (F), and Osteoarthritis (OA). OR (95% CI; shaded areas) were adjusted for sex, age, ethnicity/race, marital, family income-to-poverty ratio,
education levels, and alcohol consumption status. Vertical red solid lines indicate the minimal threshold for the beneficial association with estimated
OR =1. OR, odds ratio.
Mexican: 0.49 (0.38, 0.64), 0.19 (0.14, 0.27); 20–44 years old: 0.46
(0.36, 0.59), 0.24 (0.19, 0.31); 45–64 years old: 0.69 (0.58, 0.81), 0.41
(0.35, 0.49); and ≥65 years old: 0.72 (0.60, 0.88), 0.57 (0.46, 0.72)
(Table 4).
3.5 Mediation analysis
Parallel mediation analysis was performed to evaluate the
potential mediating role of dietary active microorganisms in the
association between the LE8, health behavior, and health factor
scores and OA risk. Notably, Lo category foods demonstrated
a mediating effect on the association between the LE8, health
behavior, and health factor scores and OA risk. The mediation
proportions were: 0.01%, P≤0.001; 0.01%, P=0.018; 0.01%; and
P≤0.001, respectively. A non-linear relationship was observed
between Lo foods and OA risk (P-nonlinear =0.050; Figure 2).
As shown in Figure 3, the maximum threshold for the beneficial
association was 2,972.81 CFU/g (estimated OR =1).
4 Discussion
The present study provides two new major findings regarding
the general population of the United States (based on the NHANES
2005–2019 cohort). First, LE8 scores consistently showed protective
effects against OA. Second, Lo foods appeared to increase the risk of
developing OA; in particular, they demonstrated a mediating effect
in the protection offered by LE8 scores against OA.
Our findings suggest that higher LE8 scores reduce the risk of
OA. In addition, the number of healthy LE8 indicators is directly
proportional to the reduction in OA risk. In this context, direct
research evidence on the association between LE8 scores and the
risk of developing OA is currently lacking. Several studies have
estimated the association between modifiable lifestyle-related risk
factors and the risk of developing OA. Examples of such factors
include smoking habits, dietary patterns, hypertension, sedentary
lifestyle, BMI, low-density lipoprotein levels, exercise, and sleep
(4–6,21). CVH and OA are the most common causes of joint
pain in adults with shared risk factors. As OA and CVD coexist,
individuals who are at risk for developing one condition should
be advised to undergo testing for the other (22). A study that
evaluated four lifestyle factors (blood pressure, cholesterol levels,
smoking, and diabetes) associated with CVD-related deaths before
the age of 80 years, found that individuals with the best risk factor
characteristics demonstrated a significantly lower risk than those
with at least two major risk factors (23). Targeting these factors with
low-intensity lifestyle interventions may therefore improve joint
pain (24). Epidemiological studies have recognized healthy physical
activity to be a key factor in the prevention and management of
CVD and OA. A study also reported that 20–30 min of exercise
performed once a week had a preventive effect on OA, especially
in young patients with knee OA (25). Notably, insomnia and
shorter sleep duration have an adverse impact on the risk of OA
(21). Additionally, smoking, total counts, and triglyceride and low-
density lipoprotein levels are risk factors for OA; an elevation in the
levels of these laboratory parameters has been found to increase the
risk of OA (26,27). Emerging evidence suggests that hyperglycemia
has a detrimental effect on the knee joint (28). The findings from
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TABLE 4 Results of multiple logistic regression of participant scores using Life’s Essential 8 with Osteoarthritis subgroup analysis.
Parameter Life’s Essential 8 Health behavior score Health Factor Score
Low Moderate High P-
trend
Low Moderate High P-
trend
Low Moderate High P-
trend
Ref OR
(95%CI)
P-value OR
(95%CI)
P-value Ref OR
(95%CI)
P-value OR
(95%CI)
P-value Ref OR
(95%CI)
P-value OR
(95%CI)
P-value
Sex
Male Ref 0.55 (0.46,
0.66)
<0.001 0.30 (0.23,
0.38)
<0.001 <0.001 Ref 0.81 (0.69,
0.95)
0.010 0.70 (0.57,
0.86)
<0.001 <0.001 Ref 0.60 (0.50,
0.71)
<0.001 0.32 (0.26,
0.39)
<0.001 <0.001
Female Ref 0.48 (0.40,
0.56)
<0.001 0.16 (0.14,
0.20)
<0.001 <0.001 Ref 0.73 (0.63,
0.84)
<0.001 0.55 (0.47,
0.64)
<0.001 <0.001 Ref 0.52 (0.44,
0.62)
<0.001 0.19 (0.16,
0.22)
<0.001 <0.001
Ethnicity/race
White
people
Ref 0.54 (0.46,
0.64)
<0.001 0.22 (0.18,
0.27)
<0.001 <0.001 Ref 0.82 (0.71,
0.94)
0.004 0.63 (0.54,
0.74)
<0.001 <0.001 Ref 0.57 (0.48,
0.68)
<0.001 0.25 (0.22,
0.30)
<0.001 <0.001
Black
people
Ref 0.47 (0.40,
0.55)
<0.001 0.17 (0.12,
0.24)
<0.001 <0.001 Ref 0.74 (0.62,
0.90)
0.002 0.58 (0.45,
0.74)
<0.001 <0.001 Ref 0.50 (0.43,
0.58)
<0.001 0.19 (0.15,
0.24)
<0.001 <0.001
Mexican
people
Ref 0.46 (0.34,
0.62)
<0.001 0.21 (0.14,
0.32)
<0.001 <0.001 Ref 0.51 (0.40,
0.63)
<0.001 0.49 (0.37,
0.65)
<0.001 <0.001 Ref 0.49 (0.38,
0.64)
<0.001 0.19 (0.14,
0.27)
<0.001 <0.001
other Ref 0.39 (0.29,
0.52)
<0.001 0.15 (0.09,
0.26)
<0.001 <0.001 Ref 0.62 (0.46,
0.85)
0.004 0.51 (0.35,
0.73)
<0.001 0.001 Ref 0.56 (0.41,
0.76)
<0.001 0.16 (0.11,
0.25)
<0.001 <0.001
Age, years
20-44 Ref 0.37 (0.29,
0.48)
<0.001 0.20 (0.14,
0.27)
<0.001 <0.001 Ref 0.56 (0.46,
0.69)
<0.001 0.41 (0.31,
0.55)
<0.001 <0.001 Ref 0.46 (0.36,
0.59)
<0.001 0.24 (0.19,
0.31)
<0.001 <0.001
45-64 Ref 0.63 (0.53,
0.74)
<0.001 0.25 (0.20,
0.31)
<0.001 <0.001 Ref 0.78 (0.67,
0.90)
<0.001 0.51 (0.43,
0.61)
<0.001 <0.001 Ref 0.69 (0.58,
0.81)
<0.001 0.41 (0.35,
0.49)
<0.001 <0.001
≥65 Ref 0.75 (0.60,
0.93)
0.010 0.56 (0.41,
0.77)
<0.001 <0.001 Ref 1.01 (0.82,
1.23)
0.940 0.86 (0.69,
1.08)
0.200 0.130 Ref 0.72 (0.60,
0.88)
0.001 0.57 (0.46,
0.72)
<0.001 <0.001
OR, odds ratio; CI, confidence interval.
Life’s Essential 8, The components of Life’s Essential 8 include diet (updated), physical activity, nicotine exposure (updated), sleep health (new), body mass index, blood lipids (updated), blood glucose (updated), and blood pressure. Each metric has a new scoring
algorithm ranging from 0 to 100 points, allowing the generation of a new composite cardiovascular health score (the unweighted average of all components) that also varies from 0 to 100 points. The LE8 scoring algorithm consists of 4 health behaviors (diet, physical
activity, nicotine exposure, and sleep duration) and 4 health factors [body mass index (BMI), non-high-density-lipoprotein cholesterol, blood glucose, and blood pressure]. The overall LE8 score, health behavior score, and health factor score were calculated as the
unweighted average of the 8 metrics. Participants with scores (LE8 score, health behavior score, and health factor score) of 80–100 were considered high CVH; 50–79, moderate CVH; and 0–49 points, low CVH.
Adjusted for sex, age, ethnicity/race, marital, family income-to-poverty ratio, education levels, and alcohol consumption status.
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Gou et al. 10.3389/fmed.2023.1297482
FIGURE 3
The estimated proportions of the associations between CVH (Life’s Essential 8), health behavior score, health factor score, and OA mediated eect by
the dose of dietary intake of live microbes. Model adjusted for sex, age, ethnicity/race, marital, family income-to-poverty ratio, education levels, and
alcohol consumption status. IE, the estimate of the indirect eect; DE, the estimate of the direct eect; Proportion of mediation =IE/DE +IE, OR,
odds ratio. (A, D, G) Shows the relationship between Life’s Essential 8 and Osteoarthritis: the mediating eect of dietary live microbe (Grams Lo, Med,
Hi) intake. (B, E, H) Shows the relationship between Life’s Essential 8 and Osteoarthritis: the mediating eect of dietary live microbe (Grams Lo, Med,
Hi) intake. (C, F, I) Shows the relationship between Life’s Essential 8 and Osteoarthritis: the mediating eect of dietary live microbe (Grams Lo, Med,
Hi) intake.
studies have suggested that inflammation (29), lack of exercise (25),
and medications (4) may contribute to this interrelationship. In
this context, inflammatory mediators (including chemokines and
cytokines) (30) play a key role in the pathogenesis of OA. In our
study, higher LE8 scores (indicative of better CVH) consistently
showed a protective effect against OA. Higher health behavior and
factor scores were also found to be protective. This implies that a
change in lifestyle habits may effectively reduce the risk of OA, and
that CVH (as indicated by LE8 scores) has a non-negligible role in
the prevention of OA.
Further mediation analyses were performed based on these
findings. Foods in the Lo category demonstrated a significant
mediating effect on the association between LE8 scores and OA
risk; the mediation ratio was found to be significant at 0.01%.
Based on our findings, the intake of Lo category foods with
microbe numbers of <2,972.81 CFU/g is likely to be associated
with an increased risk of OA. Previous studies have reported
that active probiotics can alleviate the symptoms of OA (31).
In this context, interleukin-1βand tumor necrosis factor-αare
secreted by synovial fibroblasts and chondrocytes in individuals
with OA. These promote the synthesis of protein hydrolases, which
degrade the joint extracellular matrix; this, in turn, drives disease
progression, worsens disease-related synovial inflammation, and
induces cartilage degeneration and the formation of subchondral
bone lesions (32–34). Dietary probiotic supplementation promotes
balance in the intestinal flora and reduces the inflammatory
response, thereby reducing the risk of OA. Probiotics are effective
in combating inflammation caused by interleukin-1βand tumor
necrosis factor-α. Therefore, these cytokines represent important
targets for probiotics (35–37) in the treatment of arthritis. The
probiotics inhibit or reduce pro-inflammatory expression, thereby
inhibiting joint degeneration (38,39). In this context, Clostridium
butyricum (GKB7 strain) has been found to produce butyrate,
which can specifically increase mucin production and prevent
microbes and their toxins from entering the circulation, and this
reduces systemic inflammation (16). Probiotics may also reduce
intestinal damage and inflammation associated with the OA disease
process and have been found to reduce pain levels and cartilage
destruction in animal models of OA (35,40,41). The GKB7 strain
of C. butyricum, which is usually found in the environment, has
also been found to ameliorate knee OA in rats (16). These findings
suggest that dietary active microorganisms play an important role
in preventing the development of OA. As foods in the Lo category
contain fewer active microorganisms, diets incorporating a high
proportion of Lo category foods may increase the risk of OA and
weaken the protective effect of higher LE8 scores on OA.
The present study has several strengths. First, it assessed the
relationship between LE8 scores and OA risk in a relatively large
population. Second, it measured the dietary intake of live microbes
and found macroscopic dietary live microbes to have a protective
effect against OA and a mediating role in the association between
LE8 scores and OA risk. This study also has certain limitations.
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Gou et al. 10.3389/fmed.2023.1297482
First, self-reported OA diagnoses may reduce the validity of the
results. Second, LE8 scores only represented important factors
that influence the development of CVD; potential influences were
not considered in this study. Third, estimates of live microbe
intake were not measured for every sample, and this may have
introduced bias. Fourth, residual and unmeasured confounding
and measurement errors may have led to bias in our analysis. Fifth,
although we adjusted for the survey period, the time span of our
analysis was considerably long, and this may have led to bias. Sixth,
we did not consider the influence of genetic factors. In this context,
Zhang et al. proposed several central genes as possible biomarkers
for OA diagnosis and these include the POSTN,MMP 2,CTSG,
ELANE,COL3A1,MPO,COL1A1, and COL1A2 (42) genes. Finally,
we performed mediation analyses in a cross-sectional study, and
this hindered the inference of causality. Given the limitations of
the current study, the results need to be interpreted with caution.
Further research is needed to support our findings.
5 Conclusion
In conclusion, LE8 scores play an important role in OA risk
reduction. In our study, LE8 scores were found to be associated
with live microbe intake, which reduced the risk of developing
OA. Adherence to the LE8 recommendations and a reduction
in Lo category food intake may be favorable for OA control.
Our findings have helped identify protective factors against OA
and the potentially detrimental effects of foods with low live
microbe content.
Data availability statement
Publicly available datasets were analyzed in this study. All data
entered into the analysis were from NHANES, which is publicly
accessible to all.
Ethics statement
The surveys were approved by the NCHS Research Ethics
Review Board (Protocol #2011-17). All methods were performed
in accordance with the relevant guidelines and regulations
(Declaration of Helsinki). Informed consent was obtained from
all subjects and/or their legal guardian(s) (https://www.cdc.
gov/nchs/nhanes/irba98.htm). The studies were conducted
in accordance with the local legislation and institutional
requirements. Written informed consent for participation
was not required from the participants or the participants’ legal
guardians/next of kin in accordance with the national legislation
and institutional requirements.
Author contributions
RG: Conceptualization, Data curation, Formal analysis,
Methodology, Software, Supervision, Writing—original draft,
Writing—review & editing. XC: Supervision, Writing—review
& editing. ZL: Supervision, Writing—review & editing. YP:
Supervision, Writing—review & editing. GL: Funding acquisition,
Supervision, Writing—review & editing.
Funding
The author(s) declare financial support was received for
the research, authorship, and/or publication of this article. This
study was supported by the Ningxia Natural Science Foundation
Key Project (Grant No. 2022AAC03190) and the Department of
Physiology, Basic Medical School (Grant No. 2021AAC02015).
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.
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.
References
1. Chen X, Tang H, Lin J, Zeng R. Temporal trends in the disease burden of
osteoarthritis from 1990 to 2019, and projections until 2030. PLoS ONE. (2023)
18:e288561. doi: 10.1371/journal.pone.0288561
2. Hootman JM, Helmick CG. Projections of us prevalence of arthritis and associated
activity limitations. Arthritis Rheum. (2006) 54:226–9. doi: 10.1002/art.21562
3. Sheng X, Murphy MJ, Macdonald TM, Wei L. Effectiveness of statins on
total cholesterol and cardiovascular disease and all-cause mortality in osteoarthritis
and rheumatoid arthritis. J Rheumatol. (2012) 39:32–40. doi: 10.3899/jrheum.
110318
4. Allen KD, Thoma LM, Golightly YM. Epidemiology of osteoarthritis.
Osteoarthritis Cartilage. (2022) 30:184–95. doi: 10.1016/j.joca.2021.04.020
5. Ho J, Mak C, Sharma V, To K, Khan W. Mendelian randomization studies of
lifestyle-related risk factors for osteoarthritis: a prisma review and meta-analysis. Int
J Mol Sci. (2022) 23:11906. doi: 10.3390/ijms231911906
6. Palazzo C, Nguyen C, Lefevre-Colau MM, Rannou F, Poiraudeau S. Risk
factors and burden of osteoarthritis. Ann Phys Rehabil Med. (2016) 59:134–
8. doi: 10.1016/j.rehab.2016.01.006
7. Nguyen HD, Oh H, Kim MS. An increased intake of nutrients, fruits, and green
vegetables was negatively related to the risk of arthritis and osteoarthritis development
in the aging population. Nutr Res. (2022) 99:51–65. doi: 10.1016/j.nutres.2021.11.005
8. Lloyd-Jones DM, Hong Y, Labarthe D, Mozaffarian D, Appel LJ, Van Horn L, et al.
Defining and setting national goals for cardiovascular health promotion and disease
Frontiers in Medicine 11 frontiersin.org

Gou et al. 10.3389/fmed.2023.1297482
reduction: the american heart association’s strategic impact goal through 2020 and
beyond. Circulation. (2010) 121:586. doi: 10.1161/CIRCULATIONAHA.109.192703
9. Lloyd-Jones DM, Allen NB, Anderson CAM, Black T, Brewer LC, Foraker RE, et al.
(2022). Life’s essential 8: updating and enhancing the American Heart Association’s
construct of cardiovascular health: a presidential advisory from the american heart
association. Circulation 146:e18–43. doi: 10.1161/CIR.0000000000001078
10. Erb N, Pace AV, Douglas KM, Banks MJ, Kitas GD. Risk assessment for coronary
heart disease in rheumatoid arthritis and osteoarthritis. Scand J Rheumatol. (2004)
33:293–9. doi: 10.1080/03009740410006899
11. Yin M, Xu W, Pang J, Xie S, Xiang M, Shi B, et al. Causal relationship between
osteoarthritis with atrial fibrillation and coronary atherosclerosis: a bidirectional
Mendelian randomization study of european ancestry. Front Cardiovasc Med. (2023)
10:1213672. doi: 10.3389/fcvm.2023.1213672
12. Gadelha C, Bezerra AN. Effects of probiotics on the lipid profile: systematic
review. J Vasc Bras. (2019) 18:e20180124. doi: 10.1590/1677-5449.180124
13. Niamah AK, Sahi AA, Al-Sharifi A. Effect of feeding soy milk fermented
by probiotic bacteria on some blood criteria and weight of experimental animals.
Probiotics Antimicrob Proteins. (2017) 9:284–91. doi: 10.1007/s12602-017-9265-y
14. Jia B, Zou Y, Han X, Bae JW, Jeon CO. Gut microbiome-mediated mechanisms
for reducing cholesterol levels: implications for ameliorating cardiovascular disease.
Trends Microbiol. (2023) 31:76–91. doi: 10.1016/j.tim.2022.08.003
15. Sun C, Zhou X, Guo T, Meng J. The immune role of the intestinal microbiome in
knee osteoarthritis: a review of the possible mechanisms and therapies. Front Immunol.
(2023) 14:1168818. doi: 10.3389/fimmu.2023.1168818
16. Stoeva MK, Garcia-So J, Justice N, Myers J, Tyagi S, Nemchek M, et al. Butyrate-
producing human gut symbiont, Clostridium butyricum, and its role in health and
disease. Gut Microbes. (2021) 13:1–28. doi: 10.1080/19490976.2021.1907272
17. von Elm E, Altman DG, Egger M, Pocock SJ, Gotzsche PC, Vandenbroucke
JP. The strengthening the reporting of observational studies in epidemiology (strobe)
statement: guidelines for reporting observational studies. Int J Surg. (2014) 12:1495–
9. doi: 10.1016/j.ijsu.2014.07.013
18. March LM, Schwarz JM, Carfrae BH, Bagge E. Clinical validation of self-reported
osteoarthritis. Osteoarthritis Cartilage. (1998) 6:87–93. doi: 10.1053/joca.1997.0098
19. Chen L, Zhao Y, Liu F, Chen H, Tan T, Yao P, et al. Biological aging mediates the
associations between urinary metals and osteoarthritis among U.S. adults. BMC Med.
(2022) 20:207. doi: 10.1186/s12916-022-02403-3
20. Marco ML, Hutkins R, Hill C, Fulgoni VL, Cifelli CJ, Gahche J, et al. A
classification system for defining and estimating dietary intake of live microbes in us
adults and children. J Nutr. (2022) 152:1729–36. doi: 10.1093/jn/nxac074
21. Ni J, Zhou W, Cen H, Chen G, Huang J, Yin K, et al. Evidence for
causal effects of sleep disturbances on risk for osteoarthritis: a univariable and
multivariable Mendelian randomization study. Osteoarthritis Cartilage. (2022) 30:443–
50. doi: 10.1016/j.joca.2021.11.021
22. Sewell J, Hussain SM, Wang Y, Wluka AE, Lim YZ, Carrington MJ, et al.
Association between arthritis and cardiovascular risk factors in community-based
adults: an opportunity to target cardiovascular risk. BMC Cardiovasc Disord. (2022)
22:232. doi: 10.1186/s12872-022-02674-x
23. McEvoy JW. Lifetime risks of cardiovascular disease. N Engl J Med 366. (2012)
1642:1642–3. doi: 10.1056/NEJMc1202276
24. Wang Y, Lombard C, Hussain SM, Harrison C, Kozica S, Brady S, et al. Effect of
a low-intensity, self-management lifestyle intervention on knee pain in community-
based young to middle-aged rural women: a cluster randomised controlled trial.
Arthritis Res Ther. (2018) 20:74. doi: 10.1186/s13075-018-1572-5
25. Park D, Park YM, Ko SH, Choi YH, Min DU, Ahn JH, et al. Association between
knee osteoarthritis and the risk of cardiovascular disease and the synergistic adverse
effects of lack of exercise. Sci Rep. (2023) 13:2777. doi: 10.1038/s41598-023-29581-1
26. Ni J, Wang P, Yin KJ, Huang JX, Tian T, Cen H, et al. Does smoking protect
against developing osteoarthritis? Evidence from a genetically informed perspective.
Semin Arthritis Rheum. (2022) 55:152013. doi: 10.1016/j.semarthrit.2022.152013
27. Wen MT, Liang XZ, Luo D, Li JC, Yan BZ, Lu BW, et al. Plasma lipids, alcohol
intake frequency and risk of osteoarthritis: a Mendelian randomization study. BMC
Public Health. (2023) 23:1327. doi: 10.1186/s12889-023-16250-1
28. Zheng J, Huang X, Huang J, Meng B, Li F, Liu H, et al. Association of diabetes
mellitus status and hyperglycemia with symptomatic knee osteoarthritis. Arthritis Care
Res. (2023) 75:509–18. doi: 10.1002/acr.24872
29. Roman MJ, Devereux RB, Schwartz JE, Lockshin MD, Paget SA, Davis A,
et al. Arterial stiffness in chronic inflammatory diseases. Hypertension. (2005) 46:194–
9. doi: 10.1161/01.HYP.0000168055.89955.db
30. De Miguel C, Rudemiller NP, Abais JM, Mattson DL. Inflammation and
hypertension: new understandings and potential therapeutic targets. Curr Hypertens
Rep. (2015) 17:507. doi: 10.1007/s11906-014-0507-z
31. Korotkyi O, Dvorshchenko K, Falalyeyeva T, Sulaieva O, Kobyliak N, Abenavoli
L, et al. Combined effects of probiotic and chondroprotector during osteoarthritis in
rats. Panminerva Med. (2020) 62:93–101. doi: 10.23736/S0031-0808.20.03841-0
32. Bin ARH, Chew D, Kazezian Z, Bull A. Autologous protein
solution: a promising solution for osteoarthritis? Efort Open Rev. (2021)
6:716–26. doi: 10.1302/2058-5241.6.200040
33. Molnar V, Matisic V, Kodvanj I, Bjelica R, Jelec Z, Hudetz D, et al. Cytokines
and chemokines involved in osteoarthritis pathogenesis. Int J Mol Sci. (2021)
22:9208. doi: 10.3390/ijms22179208
34. Musumeci G, Aiello FC, Szychlinska MA, Di Rosa M, Castrogiovanni P,
Mobasheri A. Osteoarthritis in the xxist century: risk factors and behaviours
that influence disease onset and progression. Int J Mol Sci. (2015) 16:6093–
112. doi: 10.3390/ijms16036093
35. Jhun J, Cho KH, Lee DH, Kwon JY, Woo JS, Kim J, et al. Oral administration
of lactobacillus rhamnosus ameliorates the progression of osteoarthritis by inhibiting
joint pain and inflammation. Cells. (2021) 10:1057. doi: 10.21203/rs.3.rs-220741/v1
36. Su CH, Lin CY, Tsai CH, Lee HP, Lo LC, Huang WC, et al.
Betulin suppresses TNF-αand il-1βproduction in osteoarthritis synovial
fibroblasts by inhibiting the MEK/ERK/NF-κb pathway. J Funct Foods. (2021)
86:104729. doi: 10.1016/j.jff.2021.104729
37. Warman DJ, Jia H, Kato H. The potential roles of probiotics, resistant starch,
and resistant proteins in ameliorating inflammation during aging (inflammaging).
Nutrients. (2022) 14:747. doi: 10.3390/nu14040747
38. Jotanovic Z, Mihelic R, Sestan B, Dembic Z. Role of interleukin-1
inhibitors in osteoarthritis: an evidence-based review. Drugs Aging. (2012)
29:343–58. doi: 10.2165/11599350-000000000-00000
39. Li H, Xie S, Qi Y, Li H, Zhang R, Lian Y. TNF-alpha increases the
expression of inflammatory factors in synovial fibroblasts by inhibiting
the pi3k/akt pathway in a rat model of monosodium iodoacetate-induced
osteoarthritis. Exp Ther Med. (2018) 16:4737–44. doi: 10.3892/etm.2018.
6770
40. Korotkyi O, Kyriachenko Y, Kobyliak N, Falalyeyeva T, Ostapchenko L. Crosstalk
between gut microbiota and osteoarthritis: a critical view. J Funct Foods. (2020)
68:103904. doi: 10.1016/j.jff.2020.103904
41. Lin YY, Chen NF, Yang SN, Jean YH, Kuo HM, Chen PC, et al. Effects
of streptococcus thermophilus on anterior cruciate ligament transection-induced
early osteoarthritis in rats. Exp Ther Med. (2021) 21:222. doi: 10.3892/etm.2021.
9653
42. Zhang Y, Yang Y, Wang C, Wan S, Yao Z, Zhang Y, et al. Identification of
diagnostic biomarkers of osteoarthritis based on multi-chip integrated analysis and
machine learning. DNA Cell Biol. (2020) 39:2245–56. doi: 10.1089/dna.2020.5552
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