Background: Cohesion is an important factor affecting sports performance. This study constructed a mediating model to explore the mechanism of cohesion toward psychological collectivism, mental toughness, and athlete engagement of Chinese team sports athletes, and to investigate the mediating effect of psychological collectivism and mental toughness on cohesion and athlete engagement. Methods: A total of 326 active Chinese athletes  aged 14 to 26 years  from eight sports were investigated by questionnaire. Results: The athlete engagement can be predicted significantly and positively by cohesion and its dimensions, and ATG-T is more important in advantage analysis. Direct and indirect paths indicate that cohesion affects athlete engagement, through the mediating effects of psychological collectivism, the mediating effects of mental toughness, the serial multiple mediating of psychological collectivism and mental toughness. The mediating effect model had a satisfactory goodness of fit and explained 50.5% of the variance in athlete engagement, and the SEM revealed the mechanism of cohesion in Chinese athlete engagement to a certain extent. Conclusion: Psychological collectivism is the embodiment of high-quality cohesion in Chinese team sports. The increase in cohesion and psychological collectivism can improve Chinese athletes’ ability to cope with stressful situations in sports, which may allow them to achieve a better performance through athlete engagement. For many sporting experiences, athletes are members of groups or teams. These groups have a strong effect on the members of the group. Cohesion is one of the most important small group variables that is derived from the evolution of group culture . AlthougCohesion is one of the most important small group variables, which reflects the integration and coordination level of the group and is an important internal factor affecting the realization of the goal . CohesioThe relevant results of team cohesion have provided a good theoretical basis for studying sports team cohesion. The analysis of the relationship between sports team cohesion and sports performance is based on the basic analysis framework of team cohesion . After CThe relationship between cohesion and sports performance has always been the focus of attention ,4,5. HowRecent studies have identified the positive effects of cohesion on engagement in many fields and environments, such as employment , classroAt present, the research on the influencing factors and mechanism of athlete engagement is limited. By looking at the variables for this study, cohesion is considered within the motivation atmosphere and social support. One study found that team cohesion was positively related to organizational identity and work engagement . As a spRecent studies have found young athletes who are closely related to their own sports teams can perceive more cohesion , which sPsychological collectivism refers to individuals who regard themselves as members of one or more groups, who are mainly inspired by the norms of members of the group, give priority to the goals and well-being of members of the group, and emphasize their connection with members of other groups . From thCompetitive sports events are full of pressure and challenges. The athlete engagement promoted by cohesion and psychological collectivism aims not to eliminate the existence of negative mentality in competitive sports events, but to ensure the targeted engagement of athletes by improving individual toughness. Mental toughness is the quality that reflects the athletes’ self-confidence, focus, and motivation in stressful situations; it refers to a psychological advantage inherited or developed after birth , and selIn terms of the literature review, we found that the development of mental toughness involves several unique mechanisms that operate together over a long period of time and through unique developmental stages . In addiBased on the above discussion, cohesion, psychological collectivism, mental toughness, and athlete engagement indicate that many different constructs and relationships warrant further examination. First, athlete engagement can be used as an alternative index of sports performance to reflect the psychological mechanism of cohesion affecting sports performance. Second, many studies cited collectivism as an important feature of a cohesive group. However, there is a lack of empirical research. This study attempts to demonstrate the relationship between sports group cohesion and psychological collectivism, and the impact of psychological collectivism on sports performance/athlete engagement. Third, there are mediating factors in the influence of cohesion and psychological collectivism on athlete engagement, which need the help of stress resistance ability. Based on the above, these research hypotheses are proposed:Hypothesis 1 (H1).Cohesion and its constructs are positively related to athlete engagement.Hypothesis 2 (H2).Psychological collectivism mediates the relationship between cohesion and athlete engagement.Hypothesis 3 (H3).Mental toughness mediates the relationship between cohesion and athlete engagement.Hypothesis 4 (H4).Psychological collectivism and mental toughness sequentially mediate the relationship between cohesion and athlete engagement.n = 200 is the minimum sample size for SEM [The participants were professional athletes and high-level college athletes selected from the national training team and Zhejiang, Heilongjiang, Liaoning, and other provinces and cities in China. All of them are team sports athletes having at least three years of sports training experience. Considering that  for SEM , a totalThe questionnaire comprised three parts. First, we stated that this survey was being conducted voluntarily and anonymously. The answers to the questionnaire were only available for the researchers and not for commercial or any other use. Second, collection of the athletes’ basic information. Third, the scale of the questionnaire used in this study. All questionnaire responses involved in this study were scored in five-point Likert scales, from strongly disagree (1) to strongly agree (5); the higher the score, the higher the recognition and acceptance of the item. The details are as follows:The GEQ compiled by Carron (2010) and translated by Ma Hongyu  was adopThe PCQ compiled by Jackson (2006) and translated by Zhang Lan  was adopThe SMTQ was compiled by Sheard et al. (2009) and translated by Wang Bin et al. There are 12 items in total, including three dimensions of self-confidence, constancy, and control. In this study, the total amount and each dimension’s Cronbach’s α is 0.71~0.82.The AEQ was compiled by Lonsdal et al. (2007) and revised by Ye Lv’s translation ; there aPaper-and-pencil self-administered questionnaires were distributed to athletes after the end of training. To ensure the quality of responses, the research assistants read the instructions and explained the purposes and requirements of the questionnaire at the beginning. Completion of the survey took an average of seven minutes.The reliability and validity of the measurement scale were subsequently evaluated. Confirmatory factor analysis (CFA) was used to establish the internal validity of each construct. CFA showed that the modified model fit the data well: CMIN/DF = 3.68, RMSEA = 0.06, GFI = 0.91, NFI = 0.92, CFI = 0.94. Then, the internal consistency reliability and composite reliability (CR) were evaluated shown in . The Cro2 = 180.51, χ2/df = 3.68, RMSEA = 0.06, CFI = 0.94, GFI = 0.91, NFI = 0.92) is significantly better than the single-factor model .The data for all constructs were collected simultaneously through a self-reporting questionnaire; thus, the common method bias (CMB) was a potential problem. To reduce the interference of common method bias on validity, this study applied a balanced item order, anonymous questionnaire measurement, and standardized measurement in the process of the questionnaire. CMB can be tested by Harman’s one-factor test and the CFA marker variable approach . ResultsSPSS 22.0  was used to input the questionnaire data for descriptive analysis, reliability analysis, and hierarchical regression analysis; AMOS 21.0  was used for a common method deviation test, confirmatory factor analysis, and mediation effect model analysis.A total of 445 questionnaires were distributed from 1 June to 1 October 2020. After deleting questionnaires with an overly short response time (less than three min) and those with answers that tended to be consistent, 326 were effectively recovered, with an overall effective rate of 73.3%. Among them, 175 were male (53.7%) and 151 were female (46.3%); there were 72 state second-class athletes (22.1%), 127 state first-class athletes (40.0%), 76 state elite athletes (23.3%), and 52 people who lacked sports class information. The average age of athletes was 19.63 years (SD = 6.51), and the average training period was 6.78 years (SD = 3.37). Sports comprised basketball (74), volleyball (36), football (63), cricket (47), ice hockey (20), curling (11), group aerobics (47), and others (28).β = 0.62, t = 7.08, p < 0.001). However, the potency of various dimensions of cohesion on athlete engagement is unclear. To clarify the interpretation effect of relevant dimensions, the advantage analysis method with model independence was used to calculate the change value of R2 after each explanatory variable was added to the sub-model without the variable itself, to explain the relative contribution of each dimension of cohesion to the effect of athlete engagement. Results show that  and psychological collectivism significantly predicted athlete engagement (AE) , indicating that an indirect effect exists on the path of psychological collectivism in cohesion and athlete engagement, and the effect value is 0.76 × 0.69 = 0.52; thus, H2 is verified.In the mediating effect model , cohesioβ = 0.45, p < 0.01) and mental toughness significantly predicted athlete engagement , indicating that there is an indirect effect on the path of mental toughness in cohesion and athlete engagement, and the effect value is 0.45 × 0.39 = 0.18; thus, H3 is verified.In the mediating effect model , cohesioβ = 0.69, p < 0.01), mental toughness , and athlete engagement ; psychological collectivism significantly predicted mental toughness  and athlete engagement ; mental toughness significantly predicted athlete engagement , indicating that an indirect effect exists on the path of psychological collectivism in cohesion and athlete engagement, an indirect effect exists on the path of mental toughness in cohesion and athlete engagement, and an indirect effect exists on the path of psychological collectivism and mental toughness in cohesion and athlete engagement, and its effect value is 0.69 × 0.73 × 0.70 = 0.35; thus, H4 is verified. Among them, the total indirect effect of psychological collectivism and mental toughness accounted for 85.5%, which shows that the intermediary effect is greater than the direct effect in the effect of cohesion on athlete engagement. Through the model fit’s R2 calculation, cohesion, psychological collectivism, and mental toughness explained 50.5% of the variation in athlete engagement.In the sequential mediating effect model , cohesioThis study found that there was a significant positive correlation between cohesion and athlete engagement, and cohesion can also independently and significantly predict athlete engagement in regression model. This result shows that, in the field of competitive sports, cohesion as an important external resource that can significantly improve the athletes’ engagement and promote them to devote themselves to sports with full enthusiasm, vitality, and great self-confidence. In sports, cohesion is an important source of social support for athletes . When atCarron 2010) indicated that the development of sports cohesion includes three conditions: first, the group goal is clear and is recognized by members. Second, the needs, motives, and emotions among members are fully understood and supported. Third, those with prestige form the backbone, which plays the role of regulating and communicating interpersonal relations, decision making, organization, and leadership. When these conditions are established, the team can produce the energy amplification effect of mutual encouragement and improving behavior efficiency, while achieving the self-defense effect of mutual protection of external pressure 0 indicat,4,5. TheIn previous studies, inconsistent research results have been observed regarding the relationship between cohesion and sports performance. Ma Hongyu (2002) and others believe that this is related to the individual/collective sports of cohesion and the goal setting of the group . ConsideIn addition to the direct effect, cohesion can also affect athlete engagement through three indirect paths:Cohesion has a positive effect on athlete engagement through psychological collectivism. This result shows that psychological collectivism is not only an important embodiment of a cohesive group, but also a psychological process in which cohesion promotes the performance of team sports. Strong and stable cohesion can not only enhance group control and urge members to exert every effort to achieve their goals, but also help to strengthen the belief of group members to win and enhance cooperation . AccordiPetrovsky, a former Soviet social psychologist, paid attention to the phenomenon of collectivism in sports. He believed that the sports team is a cooperative group. The relationship between individuals depends on the success or failure of common activities. This group activity itself is the projection of social goals, tasks, and principles , reflectThus, psychological collectivism is the embodiment of high-quality cohesion. The psychological collectivism in team sports is not only affected by the social and cultural background on personality, but also includes the community phenomenon of emotional psychology in small group problems. When the desire to belong to the group occupies a greater weight in sports decision making, psychological collectivism will support individuals to produce emotion and engagement.Cohesion has a positive effect on athlete engagement through mental toughness. Under the influence of primary groups and someone prestigious, the tolerance of a single person to the current situation can be increased from 21% to 57% . Zhang . Under this tendency , athletes will improve their mental ability  and then affect their sports performance (athlete engagement). The process can be explained as follows: in a stable sports team structure, the sports team may have the perception of similarity and closeness around group tasks and social interaction. This process is a team goal, personal role, and good interpersonal relationship formed through task and social integration. On this basis, group norms, social identity, and cultural activation may affect the emotion and behavior of sports members who tend to collectivize further. When the desire of belonging to the team occupies a greater weight in sports decision making, individual members will define their preference, reliance, concern, acceptance, and goal priority in competitive sports according to their team, which is to form psychological a collectivism identity for inner groups. This identification makes members have a stronger self-confidence and sense of responsibility in the face of pressure and challenges. At the same time, relying on the social support and response resources established by the group environment, it can also effectively resolve all types of physical and psychological discomfort in the process of sports, realize the balance between the athletes and the environment, and obtain high-quality athlete engagement.The four hypotheses proposed in this study were supported. The total score of cohesion and all dimensions can significantly and positively predict athlete engagement, among which ATG-T plays an important role in predicting athlete engagement. Cohesion can affect athlete engagement through a direct and indirect path. The indirect path comprises the intermediary role of psychological collectivism, the intermediary role of mental toughness, and the sequential intermediary role of psychological collectivism and mental toughness. The intermediary effect model constructed in this study has a good fit, which explains 50.5% of the overall variation of athlete engagement, and reveals the mechanism of cohesion on athlete engagement in Chinese team sports to a certain extent.First, athlete engagement can be used as an alternative index of sports performance to appropriately reflect the mechanism of cohesion on individual sports performance from a positive psychology perspective. Compared with sensory indicators, athlete engagement can intuitively reflect the positive experience of individual cognition and behavior. Second, this study contributes to the existing literature by examining the relationship between cohesion and psychological collectivism in more depth, in which psychological collectivism is an important feature of a cohesive sports group. Third, the influence of cohesion on athlete engagement needs to be mediated by some type of stress-resistance ability, in which mental toughness, as an inherited or developed advantage, plays an important role.In Chinese team sports, psychological collectivism is the embodiment of high-quality cohesion. The increase in cohesion and psychological collectivism can improve Chinese athletes’ ability to cope with sports stress situations, which may allow them to achieve a better performance through athlete engagement.Although cross-sectional studies can provide valuable information, such studies cannot determine causality. Longitudinal tracking and experimental design may be used to test the findings of this study in the future.The priming effect of psychological collectivism in team cohesion is not clear. The theories of implicit cultural and social identity are not enough to show that psychological collectivism is a clear turning point in the process of team building. Most studies believe that coaches and athlete leaders are often viewed as responsible for initiating certain strategies targeted toward improving the group environment. Future research can focus on the coach–athlete relationship and the role of leadership in the formation of psychological collectivism in sports groups.This study only examined the mediating role of psychological collectivism and mental toughness between cohesion and athlete engagement, and failed to involve other influencing factors, such as team size, formal/informal groups, leadership and member roles, team performance, and reference groups. These factors should be further explored in the future.This study adopted the project packaging strategy, regarded the athlete engagement as an integral variable, and failed to investigate whether the intermediary model has the same effect on all of the dimensions of athlete engagement. This can be further refined in the future, and the project background cam be taken into account to provide targeted help for sports practice.The findings were generated based on a sample of Chinese athletes, wherein caution needs to be taken when generalizing the results to other populations. Future studies can utilize a random sampling strategy and collect data from various culture backgrounds.


We used Andersen's Behavioral Model in a cross-sectional study to determine the factors associated with utilization of the emergency department (ED), controlling for whether an adolescent has a developmental disability (DD) and one or more oral complications .Data from the 2016–2019 National Survey of Children's Health (NSCH) was used for this secondary data analysis study. We used frequencies and percentages to describe the sample characteristics. Chi-square tests were used for bivariate analyses. Multivariable logistic regression modeling was conducted to predict ED visits by adolescents aged 10–17 controlling for predisposing, enabling, and need variables.p < 0.0001), and those with DDs but no oral complications  were at higher odds of having an ED visit compared to those with neither a DD nor an oral complication. Not having a Medical Home increased the likelihood of ED visits by 14% (p = 0.02). Those with private insurance  and those from a family where the highest level of education was some college and above  were less likely than their counterparts to have had an ED visit.The sample consisted of 68,942 adolescents who were primarily male, non-Hispanic White, and born in the U.S. Parents reported that 69% of the adolescents had neither a DD nor an oral complication; 10% had no DD but experienced one or more oral complication; 16% had a DD but no oral complication; and 5% had both DDs and one or more oral complication. Adolescents with both a DD and an oral complication reported the highest level of ED visits at 33%, compared to 14% of adolescents with neither DD nor oral complication. Regression analysis showed that adolescents with a DD and oral complication (OR: 2.0, 95% CI: 1.64–2.54, Adolescents with DDs and oral complications utilize ED visits more frequently than those with neither DDs nor oral complications. Integrating the dental and medical health systems and incorporating concepts of a Patient-Centered Medical Home could improve overall health care and reduce ED visits for adolescents. Emergency Department (ED) use for non-traumatic dental care problems has been reported as a common and growing problem in the U.S. , 2. BetwCSHCN are defined as children with “chronic physical, developmental, behavioral, or emotional conditions who require health and related services of a type or amount beyond that required by children generally” . While CCaregivers of children with functional limitations report a variety of challenges to parenting, including having to stop or reduce their working hours, spend numerous hours providing or coordinating care for their CSHCN, experiencing financial difficulties associated with the child's condition, and are less likely to get the children to preventive dental care , 10. CarThe Patient-Centered Medical Home  is considered the standard for health care delivery among CSHCNs and their families . MedicalDevelopmental disabilities (DDs), a category within CSHCN, represent a group of conditions that result from an “impairment in physical, learning, language, or behavior areas” that begin during the developmental period and may lead to a variety of functional impacts . AccordiAdolescence is a critical time in which children develop positive and negative behaviors that will continue throughout the course of ones' life. Positive behaviors include making healthy food choices and amounts and good oral hygiene, and they can set the adolescent on a pathway for good health behaviors. Negative behaviors, such as smoking, vaping, alcohol use, or early sexual activity, lead to future health problems, including sexually transmitted diseases and chronic diseases . The AmeIn this cross-sectional analysis, the factors under consideration linked the predisposing, enabling, and need variables associated with the Andersen's Behavioral Model of Health Services  use withn = 68,942) for these analyses.We used the data from the National Survey of Children's Health (NSCH) waves 2016, 2017, 2018, and 2019, sponsored by the Maternal and Child Health Bureau of the Health Resources and Services Administration. Briefly, the data were collected by the United States Census Bureau using a two-stage paper survey instrument in conjunction with a single-stage web-based survey instrument. During data collection, a screener questionnaire was used to identify households with non-institutionalized children ages 0–17 years and included a battery of questions to identify CSHCN. One child was randomly selected from each eligible household, and that child was the subject of a more detailed topical questionnaire completed by the parent or caregiver. It covers the 50 states and the District of Columbia. There were 131,774 children and adolescents ages 0–17 years in 2016 through 2019 NSCH. We analyzed only the subpopulation of adolescents aged 10 to 17 , ADHD (attention deficit hyperactivity disorder), blindness, cerebral palsy, hearing loss/deafness, learning disability, intellectual disability, seizures in the past 12 months, stuttering or stammering in the past 12 months, or any other developmental delay . It shouPredisposing variables are the individual's social and demographic characteristics that may influence a person's decision to use a health service such as a dental office or an emergency department . The preEnabling variables are those conditions that describe the family's resources available to access services utilization . There aNeed variables can include both individual-level factors and health-related conditions of the environment, for example, the community spending on services and a shortage of professionals available to provide care . Need fap < 0.05. Analyses were conducted using Stata statistical software .Analyses were conducted only in the subpopulation of interest, adolescents aged 10–17 years. To account for the complex survey design of NSCH, we used survey weights to calculate all estimates. The appropriate primary sampling unit and strata variables were also included to correct the variance estimates for the complex survey design. All data were weighted to be representative of the U.S. population of non-institutionalized adolescents aged 10–17 years. Weighted percentages were derived to understand the demographic characteristics, the prevalence of DDs and oral complications, and other predisposing, enabling, and need variables related to the study sample. The sample for any analysis may be less than the total sample due to listwise deletion of missing data. Chi-square tests were conducted to determine the association between DD and oral complication status and utilization of ED visits in the past 12 months. Additionally, Chi-square tests determined the association between individual Medical Home components and ED visits. A logistic regression model for utilization of ED visits among adolescents in the past 12 months (One or more ED visits vs. no ED visit) was run, controlling for predisposing, enabling, and need variables. The statistical significance was set at The sample were primarily male (51%), non-Hispanic White (51%), from an English-speaking household (86%), and were born in the U.S. (95%) . Parentsp < 0.0001) (p < 0.0001). Families with Public only insurance (24.1%), Private and Public insurance (24.9%), and those with no specified insurance type (25.5%) reported higher ED visits than those with Private only or Not insured. Families who reported lower levels of income  and were twice as common if the adolescent had both a DD and one or more oral complication . Having only an oral complication did not significantly increase the odds of an ED visit.A logistic regression model was fit to understand the association between the primary independent variable  and ED visits in the past 12 months, controlling for other predisposing, enabling, and need factors . Comparep < 0.0001) and being not insured  were associated with lower odds of having an ED visit. Neither the combination of private and public nor having an unspecified insurance type was significantly associated with ED visits. Having some college or technical school and above college  was also protective. Experiencing frustration in getting care for their adolescent almost doubled the chance of an ED visit . Likewise, not having a PCMH increased the likelihood of ED visits by 14% . There was no significant association between race/ethnicity, not having a dental visit in the past 12 months, insurance coverage when needed, or the parental FPL designation and ED visits.Compared to having public only insurance, having private only insurance .Considering the potential importance of receiving good health care of having a PCMH, we compared the characteristics of the adolescents whom parents reported to have and did not have a Medical Home. Emergency department visits associated with a dental condition almost doubled between 2000 and 2010  the Autism Secondary Data Analysis Research (SDAR) Program.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.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.The information, content, and/or conclusions are those of the authors and should not be construed as the official position or policy of, nor should any endorsements be inferred by HRSA, HHS, or the U.S. Government.


Dietary vitamin A concentrations correlate with depression. Zinc has been reported to be associated with lower depression. In addition, zinc is an important cofactor in the activation of vitamin A. However, there are few studies investigating relationships between of dietary zinc intake, dietary vitamin A intake and depression.The data for this study came from the National Health and Nutrition Examination Survey (NHANES) from 2005 to 2018 and involved 70,190 participants. We stratified participants by recommended dietary zinc intake . We further assessed the association between vitamin A and depression in participants with low and high zinc intake (interaction test) using univariate logistic regression of intake participants.p = 0.011). In the male population we grouped the population by the recommended dietary zinc intake of 11(mg/day). Again, the population was divided into two groups with low and high zinc intake, however we did not find significant results for the interaction (p = 0.743 for the interaction likelihood ratio test).In the female population we grouped the population into low and high zinc intake groups using the recommended dietary zinc intake of 8 (mg/day), with an increase in total vitamin A, the risk of depression was significantly lower in the low zinc intake group (OR: 0.85 95 CI: 0.76–0.96), while the risk of depression was increased in the high zinc intake group (OR: 1.05 95 CI: 0.95 to 1.17). Thus, in the female population, there was a significant interaction between insufficient vitamin an intake and depression (interaction likelihood ratio test of Our findings suggest that zinc intake may influence the relationship between dietary vitamin A and depression. Of course, our findings require further randomized controlled trials to enhance the credibility. In recent years, the increasing prevalence of depression has become a serious public health problem , which nA possible link between vitamin A and depression has been identified in several recent research , 8. For Zinc is an important regulator of the mammalian nervous and immune systems, a neurotransmitter of excitatory synapses in humans, and has an important role in stress responses and in the activation of zinc-dependent enzymes that maintain compensatory brain function . PreviouData from the National Health and Nutrition Examination Survey (NHANES) conducted consecutively from 2005 to 2018 were used in this study. In our study, participants aged 47 years or older who underwent an interview and examination at a Mobile Examination Center (MEC) were included. Participants without relevant covariates, depression were excluded, and those without a complete 24-h dietary recall were not included. In the National Health and Nutrition Examination Survey, noninstitutionalized citizens of the United States are assessed in terms of their health and dietary habits. In order to select a representative sample of survey participants, a multistage stratified probability design was used . At the https://www.cdc.gov/nchs/nhanes/irba98.htm). Our research is based on publicly available data from NHANES, all details are from the official website (https://www.cdc.gov/nchs/nhanes/about_nhanes.htm).The National Ethical Review Board for Health Statistics Research approved the study. The original study protocol , duly approved by the Ethics Review Board, is available on the NHANES Ethics Review Board website was used to collect information on zinc intake and vitamin A intake in the past 24 h. The dietary interview component is called What We Eat in America (WWEIA), and What We Eat in America data were collected using USDA's dietary data collection instrument, the Automated Multiple Pass Method (AMPM), available at: n intake , 16, wasQuick List-Participant recalls all foods and beverages consumed the day before the interview (midnight to midnight).Forgotten Foods - Participant is asked about consumption of foods commonly forgotten during the Quick List step.Time and Occasion - Time and eating occasion are collected for each food.Detail Cycle - For each food, a detailed description, amount eaten, and additions to the food are collected. Eating occasions and times between eating occasions are reviewed to elicit forgotten foods.Final Probe - Additional foods not remembered earlier are collected.All NHANES participants are eligible for two 24-h dietary recall interviews. The first dietary recall interview is collected in-person in the Mobile Examination Center (MEC) and the second interview is collected by telephone 3 to 10 days later. However, in order to ensure the accuracy of the data, the first dietary recall interview was chosen by our reach.We stratified participants by recommended dietary zinc intake . Dietary vitamin A is a continuous variable, and its subgroups are grouped according to the median value. The decision to continue using this method in NHANES was based on consensus reached by a panel of experts at a regular workshop to evaluate NHANES data collection methods .https://wwwn.cdc.gov/Nchs/Nhanes/2005-2006/DR1IFF_D.htm).Zinc intake and vitamin A intake acquisition and measurement can be found in the NHANES database . Marital status was categorized as married or unmarried, where separated, divorced, cohabiting, married, and widowed were defined as married. Not having completed high school, having completed high school, and having completed college and above were the three levels of education. Current smokers, former smokers, and non-smokers were the three smoking categories. Current smokers were those who had smoked more than 100 cigarettes in the past and reported smoking for a few days or daily at the time of the interview. Ex-smokers were defined as people who had smoked more than 100 cigarettes in their lives but were no longer smokers. Nonsmokers were defined as those who had never smoked more than 100 cigarettes in their lives. According to the standardized protocol, BMI was calculated based on weight and height. Physical activity was classified into three levels according to the intensity of activity: non-work activity, moderate work activity and vigorous work activity. BMI was calculated based on measured height and weight. BMI was computed using height and weight measurements. On a digital scale, weight was measured in pounds and then converted to kilograms. Height was measured to the nearest millimeter using an electronic rangefinder.Age, gender, race/ethnicity, marriage, household income, body mass index (BMI), education level, smoking status, physical activity, work activity, alcohol consumption status, diabetes, and hypertension were considered as potential confounders in this study. Participants were self-classified on their race/ethnicity. Poverty Income Ratio (PIR) means a radio of family income to poverty, which can be found on the official website of NHANES  was used to conduct all of the analyses. For the stratified sampling data extracted from the nhanes database we used the statistical methods of multiple logistic regression analysis, stratified analysis, and sensitivity analysis . A multivariate linear regression approach was used to investigate the link between vitamin A and depression. Depression values were assessed at various levels of zinc intake. Interactions between subgroups were examined by likelihood ratio tests. Ninety-five percentage confidence intervals (Cls) were calculated. The statistical significance level was set at 0.05. Continuous variables were expressed as mean and standard deviation (SD) or median and interquartile range (IQR) in descriptive analysis, and categorical variables as weighted percentages (%). To examine continuous and categorical variables, the chi-square test , t-test , and Kruskal-Wallis (skewed distribution) tests were used.The statistical software R  were excluded. After excluding participants with missing covariate data, a total of 31,839 participants were included in our analysis (P >0.05).Seven cycles of NHANES 2005–2006, 2007–2008, 2009–2010, 2011–2012, 2013–2014, 2015–2016, and 2017–2018 were used in this study. There were 70,190 participants in this study, of whom 31,839 adults (≥18 years old) completed the interview, and our study also included the MEC examination. Participants with unknown classification regarding depression . This phenomenon was only observed in females.Among women, vitamin A was negatively associated with depression in the low-zinc group. However, in the high-zinc group, vitamin A was not associated with depression after adjusting for confounders (p = 0.011). In the male population, we grouped the population with the recommended dietary zinc intake of 11(mg/day), and similarly, divided the population into two groups with low and high zinc intake, however, we did not find a significant interaction (p=0.743 for the interaction likelihood ratio test). According to p-value of β remained stable, while in the high Zinc intake group, most of the P-values of β are not statistically significant in the low Zinc intake group.In the female population we grouped the population into low and high zinc intake groups using the recommended dietary zinc intake 8 (mg/day). The risk of depression was significantly reduced with increasing total vitamin A intake in the low zinc intake group (OR: 0.85 95 CI%: 0.76–0.96), while the risk of depression in the high zinc intake group (OR: 1.05 95 CI%:0.95–1.17), thus, there was a significant interaction between insufficient vitamin A intake and depression in the female population  So our findings can be applied to all populations.In our study, we found that vitamin A and low dietary zinc intake were significantly associated with a reduced prevalence of depression in a female population. This association remained significant after adjusting for confounding. In NHANES, the study sample size is quite large, and the quality is authoritative and strictly quality controlled. One-day of dietary intake data has been shown to be adequate for estimating and comparing mean intakes of population groups.  concentrations may change depression symptoms in individuals . In the According to our results, the relationship between zinc intake and depression is valid only in the female population and does not hold for men, which is in line with other reports , 26. Of However, a cross-sectional study showed that vitamin A can be an effective prevention , but notIn addition, one study found that venlafaxine treatment may reduce retinol binding protein 4 (RBP-4) levels. And the level of RBP4 in patients with major depressive depression (MDD) was lower than normal people . TherefoOur study still has some limitations. First, because of the cross-sectional design, we were unable to prove causality or directionality. Even after multiple adjustment, the results may be confounded by some other variables that were not measured. Nevertheless, some potential confounders were adjusted for in the logistic regression model, including some dietary factors. Second, there is no easy and exact way to measure total body zinc status. We obtained the zinc intake of participants by a dietary interview/24-h recall. Since dietary data were obtained from self-reported 24-h dietary recall, recall bias is difficult to avoid. Third, although a large sample was included in this study, the study population included only US residents. Therefore, practical considerations need to be taken into account when extrapolating to other populations. Therefore, well-designed multicenter controlled trials are needed to validate our findings.Conclusively, results of this study suggest that zinc intake may have an impact on the relationship between dietary vitamin A and depression. Despite providing clinical insight by this trial, more randomized controlled studies are required to provide more data.https://www.cdc.gov/nchs/nhanes/index.htm. Users can download relevant data for free for research and publish relevant articles. Our study is based on open source data, so there are no ethical issues and other conflicts of interest.NHANES has developed a public use dataset, available at: BH and Z-yL: conception and design and provision of study materials or patients. MY and D-lS: administrative support. Y-wG and R-pZ: collection and assembly of data. All authors: data analysis and interpretation, manuscript writing, and final approval of manuscript.The present study was supported by grant from National Natural Science Foundation of China (Grant No. 82000343) and grant from Natural Science Foundation of Guangdong Province (Grant No. 2019A1515110749).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.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.


Previous studies have suggested that cognitive impairment is associated with falls in older adults. However, the consistency of results among different subgroups defined by multiple characteristics of the elderly has not yet been tested. Additionally, results are inconsistent regarding the effects of different cognitive domains on falls. Therefore, this study sought to use representative data from a nationwide study to better understand the longitudinal association between cognitive impairment and subsequent falls in older adults.The current study was conducted based on the China Health and Retirement Longitudinal Study (CHARLS) data of respondents aged ≥60 years in 2015 and the fall data in 2018. The respondents were divided into subgroups according to different demographic characteristics. Multiple logistic regression analysis was conducted to adjust for various confounding factors and evaluate the association between cognitive impairment and falls.P = 0.001) after adjusting for all of the covariates related to falls. Analysis of different cognitive domains showed that orientation  and memory  were significantly associated with falls. In subgroup analysis, the ORs of people aged 60–74 years, male, with lower education level were 0.97 , 0.96 , and 0.97 , respectively, suggesting that the associations were also statistically significant in these subgroups. There was also a significant association both in urban  and in rural residents .Of the 5,110 respondents included in this study, 1,093 (21.39%) had falls within the last 2 years. A significant association was found between cognitive impairment and subsequent falls (OR = 0.97, 95% CI 0.95–0.99, Our results suggest that the associations between cognition and falls vary depending on the different demographic characteristics of older adults. These findings may be useful for designing more accurate identification and intervention for the fall risk for specific high-risk groups. Falls are a serious public health issue for the aging population. According to the World Health Organization (WHO) estimates, one-third of community-dwelling people ≥65 years of age experience falls every year, and nearly half of them sustain repeated falls , 2. FallFalls are caused by the interaction of internal and external risk factors , 6, whicMany studies have revealed that cognitive impairment is a risk factor for falls among older adults , 11. HowIn addition, cognitive function encompasses different domains such as memory, visuospatial ability, orientation, calculation, execution, and comprehension. Cognitive impairment refers to the decline of one or more than one of the above cognitive functions. Dementia is diagnosed when the cognitive function decline involves two or more of the above cognitive domains and affects one's daily or social abilities , 15. PreTherefore, this study aimed to use representative data from a nationwide study to better understand the association between cognitive impairment and subsequent falls in older adults after adjusting for various factors, adding to evidence on impairment of the main cognitive domains as a predictor of falls. Subgroup analyses were conducted to testify whether the significance of this association still exists in subgroups defined by different demographic characteristics of the elderly, so as to design targeted prevention and intervention measures for selected high-risk populations.The China Health and Retirement Longitudinal Study (CHARLS) is a biennial survey that collects a set of high-quality longitudinal data representing individuals and families of people aged ≥45 years in China. The baseline data were launched in 2011 and tracked every 2–3 years. The questionnaire of CHARLS covers many aspects of health-related information, including cognition and self-reported falls. The CHARLS data are available publicly on the China Health and Retirement Longitudinal Study website under the auspices of the National Development Research Institute of Peking University.In this study, data on demographic characteristics, lifestyle behaviors, health status, and cognition were acquired from the 2015 database of CHARLS as the baseline data. Data on falls were acquired from the tracking data in 2018, and were used as the follow-up data.Participants aged ≥60 years were selected for inclusion in the study. The exclusion criteria were as follows: (1) missing data on demographic characteristics, lifestyle behaviors, and health status in 2015; (2) age <60 years; (3) missing data on falls in 2018; (4) missing data on cognition in 2015. According to these exclusion criteria, we discarded 15,985 individuals from the overall sample in the 2015 database. Ultimately, a total of 5,110 individuals were included in this study. The detailed exclusion process is shown in Fall was an important outcome index of this study. In the CHARLS survey, participants were asked to answer “yes” or “no” to the question “Have you fallen in the last 2 years?”The assessment of cognitive function in the CHARLS questionnaire includes telephone interviews for cognitive status-10 (TICS-10), visuospatial ability test, and episodic memory capacity. TICS-10 mainly measures the abilities of orientation and calculation, with a total score of 10 points. Respondents were required to answer the year, season, date, day of the week, and month in which the survey was conducted; one point was given for each correct answer. Next, the respondents needed to perform five serial subtractions of 7 without reminding, one point will be given for each correct calculation. And if there were two consecutive calculation errors, no points will be given for subsequent answers. Regarding the visuospatial ability, the respondents were asked to draw pictures displayed by the interviewer, and the correctly drawn picture was worth 1 point. Episodic memory capacity was tested by phrase recall; specifically, the respondents were asked to recall 10 phrases provided by the interviewer, with a total score of 10. The total score of the above tests was regarded as the overall cognitive function score, with a maximum score of 21. Older adults with higher scores were considered to have a better cognitive function , 21.2; normal weight, 18.5 kg/m2 ≤BMI <24 kg/m2; overweight, 24 kg/m2 ≤BMI <28 kg/m2; and obese, ≥28 kg/m2). Moreover, the CHARLS questionnaire investigates 14 chronic diseases, including hypertension, dyslipidemia, diabetes, chronic lung diseases, liver disease, heart attack, stroke, kidney disease, digestive disease, emotional, nervous, or psychiatric problems, memory-related disease, arthritis, and asthma. The participants were asked to answer “yes” or “no” to the question “Have you been diagnosed with conditions listed below by a doctor?” Disability variables included physical disabilities, brain damage/intellectual disability, vision problems, hearing problems, and a speech impediment. History of falls was obtained by asking “Have you fallen since your last visit?” (4) Physical examination and functional test: Depressive symptoms were measured by the CES-D scale, with a maximum total score of 30. The critical value of ≥10 was used to determine the presence of significant depressive symptoms in this study  demographic characteristics: age, gender, address, marital status, and education level obtained through face-to-face interviews; (2) lifestyle and behavior: smoking, drinking, and sleep status; (3) health status: Body mass index (BMI) was obtained by calculating the height and weight data measured by CHARLS physical examination  of the total. Continuous variables were tested for normal distribution by the Kolmogorov-Smirnov test. Non-normally distributed variables were expressed as the median (IQR) and normally distributed continuous variables were expressed as mean ± SD. The Student's P-value < 0.05 was considered statistically significant in the current study.The association between cognitive function (the score of cognition as a continuous variable) and falls was evaluated by binary logistic regression models, controlled for the confounding factors. The unadjusted model was a crude model without adjustment for any of the covariates. Model 1 was adjusted for covariates including age and gender. Model 2 was adjusted for covariates including age, gender, marital status, smoking, physical disabilities, intellectual disability, night sleep duration, depression, chronic diseases, and history of falls. In subgroup analysis, the participants were divided into subgroups based on demographic characteristics including age, gender, location, and education level. Binary logistic regression was conducted in each of the subgroups after adjusting for all of the covariates listed above (model 3). The odds ratios (ORs) and 95% confidence intervals (CIs) were calculated. A two-sided 2 = 53.39, P < 0.001). Increased age was significantly related to a higher possibility of falls . Moreover, falls tended to occur in older adults with lower educational level  and unfavorable marital status .2 = 12.12, P < 0.001), physical disabilities , intellectual disability , vision problems , hearing problems , night sleep duration , nap duration , balance , chronic diseases , depression , and history of falls  between fallers and non-fallers. However, there were no significant differences in drinking behavior, speech impediment, and BMI between fallers and non-fallers (P > 0.05). In addition, there was significant association between cognition and falls .As shown in t = 5.13, P < 0.001), memory , visuospatial ability , and calculation  were significantly associated with the incidence of falls in older adults . Further analysis of the cognitive domains showed that orientation (r adults .P < 0.001) in the unadjusted model. For each additional point in cognitive function, the risk of falls can be reduced by 10%. The association remained significant after adjusting for all of the covariates, including age, gender, marital status, smoking, physical disabilities, intellectual disability, night sleep duration, depression, chronic diseases, and history of falls in model 2 .As shown in P < 0.001), 0.72 , 0.72 , and 0.25 , respectively, in the unadjusted model. After adjusting for age and gender (model 1), these associations remained significant. Model 2 showed that orientation  and memory  were two independent predictors of falls after adjusting for all of the covariates listed above.P = 0.008). In the male subgroup, the adjusted OR value was 0.96 . In the subgroup of people with a lower education level (primary school or below), the adjusted OR value was 0.97 . As for the subgroups divided according to location, the OR values showed statistically significant associations in both urban  and rural residents .In subgroup analysis, we investigated the associations between cognition and falls in the subgroups divided based on different demographic characteristics  after adjusting for all of the covariates listed above. The associations between cognition and falls in the subgroups of people aged 60–74 years, male, or with a lower education level were statistically significant, while significance was not found in the subgroups of people aged ≥75 years, female, or with a higher education level. As shown in To the best of our knowledge, this was the first study to investigate the longitudinal association between cognitive impairment and subsequent falls in older adults by conducting a subgroup analysis based on a large Chinese population. Subgroup analysis was also performed to compare the associations between the main cognitive domains and falls. The major findings of this study were as follows: first, we confirmed the significant association between cognition and subsequent falls among the Chinese elderly individuals; second, orientation and memory were two cognitive domains that were significantly associated with falls after adjusting for various confounding factors; third, the associations between cognition and falls varied depending on different characteristics of older adults.The incidence of falls is high among the Chinese elderly. According to a systematic review, the annual fall rate in Chinese older adults ranged from 14.7 to 34%, with a median of 18% . In anotPrevious studies have documented that there was a strong association between cognitive impairment and falls. The prevalence of falls is twice as high among older adults with cognitive impairment than in controls, along with an increased risk of fall-related injury as well as a worse prognosis , 26, 27.Dementia is the most severe manifestation of cognitive impairment associated with a higher risk of falls . AccordiGiven that the preclinical stage of Alzheimer's disease can be predicted by evaluating the main cognitive domains , many stOur study also demonstrated that the associations between cognition and falls varied among different subgroups defined by multiple demographic characteristics of older adults. Thus, appropriate predictors should be considered when assessing the risk of falls among the elderly with different basic characteristics and health statuses. Jae-Hyun Kim  pointed There were also some limitations in this study. First, only the Chinese elderly population was investigated in this study, which may limit the generalizability of the study. Second, the evaluation of cognitive impairment in this study was based on the questionnaire indicators in CHARLS, which may have led to evaluation bias in cognitive impairment since no other cognitive function evaluation scales were adopted. Finally, the collection of the study information was mainly based on the respondents' self-reports, and the validity of self-reported fall results was not tested. Accordingly, the possibility of recall bias cannot be excluded in this study.The results of the present study demonstrated a significant association between cognition and subsequent falls among Chinese older adults. Furthermore, assessment of orientation and memory might be an effective approach for early identification of older adults at risk of falls. Finally, attention should be paid to the specificity of subgroups with different demographic characteristics in fall risk assessment to design more accurate prevention and intervention measures for specific high-risk populations.http://charls.pku.edu.cn/en.The datasets for this study can be found in the China Health and Retirement Longitudinal Study (CHARLS). The current study is a secondary analysis of public data of CHARLS. The original datasets of CHARLS is accessible on The studies involving human participants were reviewed and approved by Biomedical Ethics Review Committee of Peking University. The patients/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.RZ contributed to conceptualization and writing the original draft preparation. JL conducted data analysis. RZ, JL, and MC revised the manuscript. All authors contributed to the article and approved the submitted version.This research was supported by Zhejiang Provincial Natural Science Foundation of China under Grant No. LGF19H260006. All sponsors had no role in the study design, data collection, and analysis, decision to publish, or preparation of the manuscript.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.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.


Frailty has been related with poor prognosis of various diseases, including ovarian cancer. We performed a systematic review and meta-analysis to evaluate the association between frailty and long-term survival of patients with ovarian cancer.Relevant cohort studies were retrieved by search of PubMed, Embase, Cochrane’s Library, and Web of Science electronic databases. Two authors independently performed literature search, data collection, and statistical analyses. A random-effect model incorporating the possible influence of heterogeneity was used to pool the results.2 = 0%) and progression-free survival . Subgroup analyses according to study design, cancer stage, age of patients, scales for frailty evaluation, follow-up duration, and quality score of the included study showed consistent association between high frailty and poor overall survival in women with ovarian cancer . After considering GRADE criteria for strength of the evidence, it was rated low for both the two outcomes.Nine cohort studies including 2497 women with confirmed diagnosis of ovarian cancer contributed to the meta-analysis, and 536 (21.5%) of them were with high frailty. The median follow-up durations varied between 24 and 69 months. Compared to patients with low or non-frailty, OC patients with high frailty were associated with poor overall survival (risk ratio [RR]: 1.61, 95% confidence interval [CI]: 1.41 to 1.85, p < 0.001; IHigh frailty may be an independent risk factor of poor survival in women with ovarian cancer. Evaluating frailty may be important for predicting the prognosis and determining the optimal anticancer treatments in women with ovarian cancer.https://inplasy.com/, identifier INPLASY202290028. Currently, ovarian cancer (OC) ranks the fifth leading cause of cancer-related mortality of the global population , 2. The Recent advances in oncogeriatrics suggested the important role of geriatric evaluation for frailty in the risk stratification and optimized management of patients with cancer . By defihttps://inplasy.com/) with the registration number of INPLASY202290028).The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement , 31 was Studies were retrieved by search of PubMed, Embase, Cochrane’s Library and Web of Science electronic databases from the inception to April 2, 2022. A combined search term was used, including (1) “frailty” OR “frail”; (2) “ovarian” OR “ovary”; and (3) “cancer” OR “carcinoma” OR “malignancy” OR “tumor” OR “neoplasm”. The search was limited to human studies published in full-length articles. No restriction was applied regarding the language of publication. As a supplementation, we manually checked the citations of the relevant original and review articles for possible studies of interest.The PICOS criteria were used for study inclusion.P (patients): Adult patients with histologically confirmed diagnosis of OC, regardless of the cancer stage or treatments.I (exposure): Patients with high frailty at admission. Methods and criteria for defining patients with frailty were consistent with the modalities used in the original studies.C (control): Patients with low or non-frailty at admission. The evaluating tools and criteria for the frailty were consistent with those applied among the included studies.O (outcomes): the primary outcome was overall survival (OS), and the secondary outcomes were progression-free survival (PFS), compared between OC patients with high versus low or non-frailty. Generally, OS was defined as the time elapsed from treatment and to the date of death from any cause, while PFS was defined as the interval between initiation of the treatment and the first recurrence or progression event.S (study design): cohort studies, including prospective and retrospective cohorts;Reviews, preclinical studies, studies including non-OC patients, studies that did not evaluate frailty, or studies that did not report the survival outcomes were excluded. In addition, studies with follow-up duration within months were also excluded because we did not aim to evaluate the immediate influence of frailty on mortality of patients with OC.via the Newcastle–Ottawa Scale , patient demographic factors, cancer stage, main treatments, scales for the evaluation of frailty, follow-up durations, outcomes reported, and variables adjusted in the regression model for the analysis of the association between frailty and survival outcomes were collected. Quality of the included studies was evaluated wa Scale  with sco2 statistic (2 < 25%), moderate (I2 25%~75%), and high (I2 >75%) according to the Cochrane’s Handbook (https://gdt.gradepro.org/app/#projects). The RevMan  and Stata  software were applied for these analyses, and a p < 0.05 suggests statistical significance.The main objective was to determine the relative risks of OS and PFS of OC patients with high versus low or non-frailty, which were presented as risk ratios (RRs) and the confidence intervals (CIs). Using the 95% CIs or p values, data of RRs and the standard errors (SEs) could be calculated, and a subsequent logarithmical transformation was conducted to keep stabilized variance and normalized distribution. Between study heterogeneity was estimated with the Cochrane’s Q test and the Itatistic . The betHandbook . A randoHandbook . InfluenHandbook . SubgrouHandbook . The GraAs presented in 2 = 0%). Influencing analyses by omitting one study at a time showed consistent results  and studies compared to patients with non-frailty . Similarly, influencing analyses by omitting one study at a time showed consistent results (RR: 1.45 to 1.55, p all < 0.01; Pooled results with nine studies –29 showeThis systematical review and meta-analysis integrated the findings of nine cohort studies, and the pooled results showed that OC patients with high frailty were associated with poor long-term survival. The results were consistent for outcomes of OS and FPS. Moreover, subsequent sensitivity analyses by omitting one study at a time showed consistent results. In addition, for the meta-analysis of the outcome of OS, subgroup analyses showed that the association between frailty and poor OS in patients with OC was robust and not significantly affected by study characteristics such as study design, cancer stage, patient age, frailty evaluation scale, level of frailty of the controls, follow-up durations, analytic models, and study quality scores. Taken together, these results indicated that frailty may be an important risk factor of the poor long-term survival in patients with OC.To the best of our knowledge, this is the first systematical review and meta-analysis which evaluated the association between frailty and long-term survival outcomes in patients with OC. The strengths of the meta-analysis included extensive literature searching to incorporate the up-to-date literature evidence, including cohort studies to indicate a longitudinal relationship between frailty and poor survival of patients with OC, and performing multiple sensitivity and subgroup analyses to confirm the robustness of the findings. The findings of the meta-analysis indicated a potential association between high frailty and poor survival of patients with OC. The mechanisms underlying the association may be multifactorial. Biologically, frailty has been associated with chronic inflammation and immunosenescence , which hResults of the meta-analysis also highlighted the significance of geriatric evaluation for frailty in patients with cancer, including those with OC. Besides the importance in prognostic prediction, identification of frailty patients with cancer may be important for the determination of appropriate anticancer modalities in these patients, considering these patients are less tolerable for surgeries and adjuvant therapies and more likely to develop complications and toxicity events. Moreover, geriatric co-management in cancer patients with frailty is also clinical significance, which may increase their tolerability to anticancer treatments and finally improve the clinical outcomes. For example, a previous study showed that in older women with advanced OC and frailty, preoperative/postoperative geriatric and surgical co-management may improve their tolerance to cytoreductive surgery and subsequent postoperative outcomes . SimilarOur study also has limitations. Firstly, as mentioned above, different tools for frailty assessment were used among the included studies, which may contribute to the clinical heterogeneity of the meta-analysis. Besides, the definition of frailty was different even among studies using the same methods. For example, for the three studies using mFI as the evaluating tool for frailty, a high frailty was defined as mFI ≥ 4 , a modifTo sum up, results of this meta-analysis indicate that high frailty may be a risk factor of poor survival in patients with OC. These results highlight the importance of geriatric evaluation for frailty in patients with OC, which may be important for prognostic prediction and determination of the appropriate anticancer treatments for these patients.The original contributions presented in the study are included in the article/Supplementary Material. Further inquiries can be directed to the corresponding author.KL and ZL designed the study. KL and RY performed database search, literature review, study quality evaluation, and data collection. KL and ZL performed statistics and interpreted the results. KL drafted the manuscript. ZL critically revised the manuscript. All authors approved the submission of the manuscript.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.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.


The risk of KL-grade progression was significantly lower in participants with iron intakes of <16.5 mg/day (per mg/day: adjusted hazard ratio (HR), 0.75; 95% CI , 0.64–0.89), and it was higher in those with iron intakes ≥16.5 mg/day . Consistently, when the iron intake was assessed as deciles, compared to those in Deciles 3–5 (10.9–23.3 mg/day), the risk of KL-grade progression was higher for Deciles 1–2  and for Deciles 6–10 . Similar U-shaped relations were found for iron intake with the risk of JSN-score progression (p for nonlinearity = 0.035). Conclusions: There was a U-shaped association between the iron intake and the progression of knee OA, with an inflection point at about 16.5 mg/day, and minimal risk from 10.9 to 23.3 mg/day of iron intake. An appropriate iron intake was advisable for knee OA, whereas excessive or deficient iron intake increased the risk of knee OA progression.Background: Iron overload is drawing attention in the development of knee osteoarthritis (OA). To identify the modifiable risk factors for iron-related pathological conditions, we examined the association between iron intake and the risk of knee OA progression. Methods: A total of 1912 participants in the Osteoarthritis Initiative (OAI), aged 45–79 years and with at least one knee radiographic OA at baseline, were identified and were followed up to 6 years. The iron and other nutrient intake was measured by the validated Block Brief 2000 Food Frequency Questionnaire. The outcome measures were by radiographic progression on the basis of the Kellgren–Lawrence (KL) grade and the joint-space-narrowing (JSN) score. The association between the iron intake and the knee OA progression was examined by Cox proportional hazards models and restricted cubic spline (RCS) regression. Results: Among the study participants, 409 participants experienced KL-grade progression, and 684 participants experienced JSN-score progression within 6 years. Overall, the association between iron intake and the risk of KL-grade progression followed a U shape ( Osteoarthritis (OA) is a chronic disabling disease that is characterized by progressive articular cartilage damage coupled with deterioration in subchondral bone and osteophyte formation . The paiIn contrast to these macronutrient compositions, the risks of micronutrients are not well understood, and especially minerals that may affect knee OA through metabolic processes and immune functions . Of thesFor the prevention of iron-related pathological conditions, it is, therefore, important to identify the modifiable risk factors for iron deficiency or overload, especially since, unlike other minerals, the iron levels in the body are controlled only by absorption . The mecWe performed a cohort study by using prospectively collected data from the Osteoarthritis Initiative (OAI) database. The OAI is a multicenter longitudinal study of 4796 men (41.5%) and women aged 45–79 years, with or at risk of knee OA, who were recruited from 2004 to 2006. The participants were followed annually by centrally trained rheumatologists or radiologists at each clinical center, about 97% of them had ≥1 follow-up visit, and the visit adherence was not influenced by the baseline characteristics. The study was fully compliant with the NIH guidelines, and all the participants provided informed consent. The detailed OAI protocol is described elsewhere .To assess the knee OA progression, we included participants with at least one knee with radiographic OA at baseline, which is defined by a Kellgren–Lawrence (KL) grade ≥ 2, on the basis of the central reading of a standardized fixed-flexion radiographic. The index knee from each participant was used, and if the participant had two eligible knees, the one with a KL grade = 3 was selected as the index knee; if both knees had the same KL grade, the knee with the higher Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) knee-pain score was selected as the index knee; if both knees had the same WOMAC pain score, we randomly selected one as the index knee by using a random number generator. Participants without radiographic OA (KL grade = 0 or 1 at baseline) were excluded, as were those with severe radiographic OA (KL grade = 4), as they were less likely to experience further radiographic progression. We also excluded participants with missing or implausible total daily calorie intakes  . FinallyThe dietary intake of minerals and other nutrients was estimated at baseline by using the validated dietary assessment tool: the Block Brief 2000 Food Frequency Questionnaire (FFQ), of seventy items . In addiOn the basis of the previous evidence of the potential determinants of the structural OA progression, the following baseline characteristics were extracted: age; sex; race; education; family income; the Physical Activity Scale for the Elderly (PASE) score (2–445); the body mass index (BMI); the nonsteroidal anti-inflammatory drug (NSAID) use; the baseline KL grade; and the baseline joint-space-narrowing (JSN) score.The knee OA progression was assessed by using two endpoints: (1) The KL-grade progression; and (2) The JSN-score progression. Knee radiographs were taken at baseline and follow-up visits by using similar acquisition and reading protocols. The KL grade and JSN scores were assessed by the same readers. The KL-grade progression was defined as an increase in the KL grade ≥ 1 at a follow-up visit, compared with that at baseline. The JSN scores were evaluated separately in the medial and lateral tibiofemoral compartments, and the JSN-score progression was defined as an increase in the JSN score ≥ 1 in either the medial or lateral tibiofemoral as the JSN progression. If a knee underwent a knee replacement, as in other studies, we assigned it a KL grade of 4 and a JSN score of 3 at the first visit after the replacement because the radiographs that showed the knee replacement did not allow for an assessment of the KL grade or JSN score ,23. The 2 statistics, and ANOVA tests were used to evaluate the continuous variables. The incidence rates of the OA progression, expressed as per 1000 person-years, were calculated as the number of OA-progression cases divided by the person-years of follow-up. The relation of the iron intake to the OA progression was estimated by using Cox proportional hazards models (hazard ratio (HR) and a 95% CI), with and without adjustments for the age, sex, BMI, PASE score, NSAID use, baseline KL grade, baseline JSN score, mineral intake , as well as the intake of other main nutrients . A threshold analysis of the relationship between iron intake and OA progression was performed by using a 2-piecewise Cox regression model with a smoothing function. The inflection point was determined by the R package “segmented”, using a likelihood-ratio test and the bootstrap resampling method. Restricted cubic spline (RCS) Cox regression was conducted, with 4 knots , to test for linearity and to explore the shape of the dose–response relation of the iron intake and the OA progression. Additionally, to test the robustness of the primary findings, we conducted sensitivity analyses for each outcome by the baseline knee KL grade and the baseline score. R software, version 4.1.2 , was used for all data analyses.The participants were classified into ten groups, according to the deciles of the iron intake. Comparisons between categorical variables by iron groups were evaluated by using χA total of 1912 participants with 1912 knees from the OAI were included in the present study. p for nonlinearity < 0.001). In the threshold effect analysis, the risk of KL-grade progression was significantly lower with the increments of iron intake  in the participants with iron intakes < 16.5 mg/day, and was higher with the increments of iron intake  in the participants with iron intakes ≥ 16.5 mg/day . In the threshold effect analysis, the risk of JSN-score progression was significantly lower with the increments of iron intake  in the participants with iron intakes < 16.0 mg/day, and it was higher with the increments of iron intake  in the participants with iron intakes ≥ 16.0 mg/day .We additionally investigated sensitivity analyses for each outcome by the KL grade and JSN score at baseline. The results were not significant (In this large prospective longitudinal study with a 6-year follow-up period, we found that there was a U-shaped association between the total iron intake and the knee OA progression, with an inflection point at about 16.5 mg/day, and minimal risk from 10.9 to 23.3 mg/day of total iron intake. These findings were independent of the effect of the major confounders and they remained stable in the sensitivity analyses.Our findings are in line with other studies that have found associations between dietary patterns, and that have provided some new insights into this field. First, among the participants with iron intakes < 16.5 mg/day, one plausible mechanism for the risk of the OA progression being significantly lower with the increments of iron intake may be that iron is an essential element of various metabolic processes in chondrocytes, including DNA synthesis, the secretion of the extracellular matrix, and oxygen transport . The lacThe potential adverse health effects of a high-iron status have been reported in recent decades. In a 10-year longitudinal study, a high-iron status was associated with an increased risk of death , and, inAlthough an investigation or detailed discussion of the specific foods that are associated with iron intake is beyond the scope of this research, it is noteworthy that iron-rich foods include red meat, whole-grain cereals, and dark-green vegetables. In addition, recent experimental results suggest that the antioxidants, and, in particular, vitamins C and E, may counteract some of the deleterious effects of dietary iron on diabetes and breast cancer by preventing lipid peroxidation ,31,32. HThis study has several limitations that should be addressed. First, because of the multidimensionality of diets, other nutrients that coexist in foods that are rich in iron are examined. To reduce this possibility, we controlled for several dietary and nondietary covariates, and particularly for the BMI and other mineral intake, in order to reduce the confounding effects. Secondly, the iron intake was assessed by self-reported dietary intakes, which may be slightly less reliable than more objective methods, although a validated FFQ was used. However, this should lead to decreased statistical power, which makes these findings all the more notable. Finally, the dietary information was only obtained at baseline, and the change in the participants’ dietary habits during the follow-up years could not be updated. However, the FFQ measures long-term diet, which is less likely to change significantly within 6 years, on the basis of previous studies ,6, and sIn summary, there was a U-shaped association between iron intake and the progression of knee OA, with an inflection point at about 16.5 mg/day, and a minimal risk at 10.9 to 23.3 mg/day of iron intake. An appropriate iron intake was advisable for knee OA, whereas excessive or deficient iron intake increased the risk of knee OA progression.


Multivariable regression analysis including body mass index (BMI), age, sex, race, education level, hypertension, diabetes, asthma and cardiovascular disease showed that smoking was an association for OA. This large national study highlights a positive association between smoking and OA prevalence in the general US population. It is necessary to further study the relationship between smoking and OA in order to determine the specific mechanism of smoking on OA.Little is currently known about the effect of smoking on osteoarthritis (OA). This study aimed to investigate the relationship between smoking and OA in the United States (US) general population. Cross-sectional study. Level of evidence, 3. 40,201 eligible participants from the National Health and Nutrition Examination Survey 1999–2018 were included and divided into OA and non-arthritis groups. Participants demographics and characteristics were compared between the two groups. Then the participants were divided into non-smokers, former smokers, and current smokers based on their smoking status, also demographics and characteristics among the three groups were compared. Multivariable logistic regression was used to determine the relationship between smoking and OA. The current and former smoking rate in the OA group (53.0%) was significantly higher than that in the non-arthritis group (42.5%;  OA is estimated to affect more than 240 million people worldwide, with more than 32 million estimated in the United States, resulting in a significant public health burden2. Based on data from the National Health Interview Survey, recently in the United States, there are approximately 14 million people who have symptomatic knee osteoarthritis of which approximately 3 million ethnic/racial minorities3. Thus, identifying potentially modifiable protective or risk factors may lead to the development of available strategies to delay the progression of OA and reduce the public health burden.Osteoarthritis is a common disease in the elderly, characterized by progressive degeneration of articular cartilage, subchondral bone changes, and osteophyte formation4. Many clinical studies in different regions and populations have shown that smoking is negatively associated with the incidence of OA7. There is some evidence suggesting that the BMI of non-smokers is usually higher than that of smokers, leading to a high OA prevalence9. However, some scholars have raised doubts on the protective effect of smoking on OA observed in some epidemiological studies due to selection bias, especially hospital selection bias10. In a cross-sectional research of nationally representative data from South Korea, no association was found between direct smoking and previous smoking with the prevalence of OA11. A cross-sectional study from Denmark also found no association between current smoking and knee OA prevalence12. Given that the relationship between smoking and OA remains unclear, the results vary widely among different populations and regions. There are still contradictions and inconsistencies in the relationship between smoking and OA risk. In addition, few studies have examined in detail the relationship between current and former smokers and OA.It is well-established that smoking is linked to chronic diseases such as diabetes, cardiovascular disease, and cancer. It is also a recognized risk factor for many chronic musculoskeletal diseases, including rheumatoid arthritis, degenerative disc disease, and low back pain. However, smoking is reportedly negatively associated with ulcerative colitis and Parkinson's disease. The relationship between smoking and OA has not been establishedTherefore, this study aimed to explore the relationship between smoking and previous smoking on OA among US adults using national cross-sectional data. It also offered a valuable opportunity for assessing the potential of pooling OA preventive health information.http://www.cdc.gov/nchs/nhanes.htm) is a national cross-sectional health survey in the US that collects laboratory, imaging, and radiological data in addition to health interviews and examination data13. Importantly, the Center for Disease Control (CDC) and Prevention uses a complex multi-stage probabilistic sampling design to examine a nationally representative sample across the country every 2 years. The NHANES study protocol has been approved by the Ethics Review Board of the National Center for Health Statistics (NCHS) Research, and all adult participants provided written informed consent. All studies were carried out in accordance with the Declaration of Helsinki. Details on Institutional Review Boards of the CDC and NCHS are available at (http://www.cdc.gov/nchs/nhanes/irba98.htm). Given the thoroughness of its methodology, NHANES data have been widely used to assess risk factors and prevalence of many diseases14. In this study, 116,876 participants older than or equal to 20 were selected from 1999 to 2018 NHANES  of the continuous National Health and Nutrition Examination Survey were used to examine the association of OA with smoking and BMI in US adults. NHANES  that (you/s/he) . . .had arthritis?" If the answer to that question is "yes" the next question is "What type of arthritis was it?". Based on the different responses to these two questions, participants were divided into an OA group (solely had OA), other arthritis participants  and no-arthritis group. Self-reported doctor-diagnosed OA is the most predominantly used case definition in epidemiological studies2) was recorded for all examinees by a trained examiner in the mobile examination center. According to the National Institutes of Health (NIH) guidelines16, BMI was divided into four grades: underweight (< 20.0 kg/m2), normal weight (20.0–24.9 kg/m2), overweight (25.0–29.9 kg/m2), and obese (≥ 30.0 kg/m2). Cardiovascular disease (CVD) included coronary heart disease, congestive heart failure, heart attack, stroke, and angina. Other covariates included discrete variables , asthma and marital status) and continuous variables .Variables associated with clinical characteristics were collected through physical examinations and self-reported questionnaires. Participants were divided into three categories based on their smoking status: never smokers (smoked less than 100 cigarettes in life), past smokers , and current smokers (smoked moth than 100 cigarettes in life and smoke some days or every day). The BMI  was statistically significant.Data were processed by SPSS version 22.0 and R version 4.1.3. The Chi-square test was used to compare the constituent ratios of each group. Continuous variables were compared using Student's t-test or one-way analysis of variance (ANOVA), followed by multiple post-hoc comparisons using least significant difference (LSD), selected based on Levene statistics in the homogeneity of variance test. Variables with a Participants younger than 20 years of age were excluded based on the epidemiological characteristics of OA. The prevalence of OA was 12.49% . The demographics and characteristics of participants in this study are shown in (Table p < 0.001), and the proportion of females with OA was higher than males (63.74% vs. 36.26%). The proportion of obese patients in the OA group was significantly higher than in the non-arthritis group (46.85% vs. 33.83%), and the proportion of non-Hispanic White also increased significantly (64.26% vs. 41.15%). At the same time, OA was associated with a higher level of education and annual household income. Besides diabetes, the prevalence of other comorbidities in the OA group was significantly higher than in the non-arthritis group (p < 0.001). Most importantly, the proportions of non-smokers and smokers in the OA group were lower than in the non-arthritis group, but the proportion of former smokers was significantly higher in the OA group .We found that OA was more common in older women (age ≥ 50), the average age of the OA group was significantly higher than the non-arthritis group . Never-smokers were predominantly women (57.59%). Compared with never-smokers, former smokers had a higher BMI, and current smokers had a lower BMI . This finding suggests that smoking may lead to a decrease in BMI, and quitting smoking increases BMI. Current smokers were associated with significantly lower levels of education, family income, and single status . In addition to asthma (smoking is considered an important risk factor) and CVD, other underlying diseases: hypertension, diabetes, smokers had significantly lower rates than non-smokers and quit smoking people (p < 0.001). Quit smoking people also had a higher prevalence of comorbidities than current smokers, which may be attributed to the fact that past smokers quit smoking after being diagnosed with comorbidities. The incidence of OA in the smoking group was 2.089 times higher than in the non-smoking group.The 40,201 participants were divided into never-smokers , former smokers (n = 9419) and current smokers (n = 8198). The characteristics of 40,201 participants are shown in (Table p < 0.001). However, there was no significant difference between the smoker and non-smoker groups . After adjusting for age, gender, BMI, race, education, PIR, marital state, hypertension, diabetes, CVD, asthma, both current smoker and former smoker groups were associated with increased risk of OA compared to the non-smoker group .Figure 18, and some studies did not focus on the relationship between smoking and OA21. The strength of this research is the numerous OA patients identified from a US population-based cross-sectional cohort. Another advantage is that, since they are acquired in the community, a lack of selection bias in exposure is assumed. Accordingly, we sought to explore the association between smoking and quitting smoking and OA. In the crude model, quitting smoking has a positive correlation with OA, while smoking has no correlation with OA. However, in our multivariable analysis model, which included variables measured in NHANES such as age, sex, BMI, and underlying disease to minimize the possibility of spurious association, we substantiated that current and past smokers had a higher risk of OA. The increased risk of OA attributed to smoking was not decreased significantly after quitting smoking , continuing to smoke was associated with a greater risk of OA than in former smokers. Our study points out that high BMI and advanced age are significant risk factors for OA, and that women are at higher risk than men, which is consistent with previous reports23. In addition, our study shows that education level and underlying diseases such as hypertension and diabetes are also related to OA.Herein, we conducted a comprehensive study of OA using a large multi-year sample from NHANES 1999–2018. Indeed, previous knee OA studies on the NHANES database have been published in the literature, but one study was too old (being published in the 1970s), with a small number of participants and only cross-sectional associations between knee OA and various putative risk factors were explored8. Consistently, cohort studies in the Korean population have shown that a decrease in BMI caused by smoking may account for the protective effect of smoking against OA1. In accordance with previous studies, in this study, we found that an increase in BMI in former smokers and smoking led to a decrease in BMI. Among smokers, the proportion of overweight and obese people is 63.6%, which is the lowest among the three groups; among ex-smokers, the proportion of overweight and obese people is 75.9%, which is the highest among the three groups; among non-smokers, the proportion of overweight and obese people is 69.2%, the difference was statistically significant. However, after adjusting for BMI, a confounding factor, we still found a positive association between smoking and OA.Large cohort studies based on the general population of the United Kingdom have shown that a weight gain of 4–5 kg after 12 months of smoking cessation may lead to increased knee degenerative disease5. A cohort study on a Rotterdam population further showed that smoking was a risk factor for OA24. The reliability of many studies that concluded that smoking is a protective factor for OA was questioned due to study design bias. Indeed, hospital-based studies represent a significant source of bias. Smoking is more likely to cause health problems, which led to more non-osteoarthritis patients who smoke being included in the study10. It is widely established that the U.S. population is dominated by non-Hispanic whites. During multivariable regression analysis, the risk of OA in non-Hispanic whites was 2.39 times higher than in Mexican Americans. We suspect that the effects of smoking on different ethnic groups may lead to regional variations in studies. A multicenter trial in 2022 found an increased risk of gastrointestinal metaplasia (GIM) among Hispanics born outside the United States, but not among Hispanics born in the United States25. This finding suggests that members of the same ethnic group, born and living in different areas, may have different susceptibility to the same risk factors. Accordingly, the effect of smoking on OA varies across countries and regions.Smoking has been documented to exert heterogeneous effects on OA in different countries and regions. For example, smoking was found to be a protective factor against joint replacement with severe knee OA in a Singaporean Chinese cohortAt present, the exact mechanism between smoking and OA is poorly understood. Interestingly, studies have shown that nicotine alters 19 proteins, including several proteases and cytokines, in models of joint inflammation, including increased nicotine secretion of matrix metalloproteinase 1 and increased secretion of two proposed markers of OA, chitinase 3-like protein 1 fibronectin. Indeed, it is essential to increase awareness of the risk of OA, prevent its occurrence and improve the quality of life of this patient population. Current evidence suggests that age is the most significant risk factor for OA after age 30. In addition, the risk of OA was also significantly correlated with BMI, gender and race. Our findings suggest that people should reduce their smoking to reduce their risk of OA. In addition smoking causes a variety of harmful effects of carcinogens and chemicals.The strengths of this study included its large-scale nature, the use of national cohort data, and the absence of selection bias. However, although NHANES is a representative sample of the general population in the United States, this is a cross-sectional study, which requires further validation of the effect of smoking on OA in prospective studies. In addition, a questionnaire was used to determine whether the participants were OA patients. Although the response rate was 81%, the gold standard for determining OA was imaging, and no physical examination was performed to determine whether participants had OA. And the absence of other covariates, such as job strain, physical activity, vitamin C, D, and K levels, and subsequent multivariate analysis in NHANES was also a limitation. Besides, we did not quantify smoking to explore the influence of the amount of smoking on the incidence of OA. However, we demonstrated that smoking increases the risk of OA in current smokers and past smokers based on data from a large population.This large national cross-sectional study showed that smoking is positively associated with the prevalence of OA in the US population. It is necessary to further study the relationship between smoking and OA to determine the specific mechanism of smoking on OA.