Hemoglobin mediates the link between 'weekend warrior' activity pattern and diabetic retinopathy

Hemoglobin mediates the link between 'weekend warrior' activity pattern and diabetic retinopathy | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Hemoglobin mediates the link between 'weekend warrior' activity pattern and diabetic retinopathy Baohua Li, Bobiao Ning, Xinyue Hou, Yipeng Shi, Zefeng Kang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4866922/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 08 Feb, 2025 Read the published version in Scientific Reports → Version 1 posted 13 You are reading this latest preprint version Abstract Diabetic retinopathy (DR), the leading cause of vision loss in the elderly, coupled with limited treatment options, has prompted efforts to identify modifiable risk factors associated with DR. The purpose of this study was to explore the association between WW physical activity patterns and DR risk in US adults and to examine how Hb levels mediate this relationship. Cross-sectional study data were obtained from nationally representative NHANES data from 2007-2018. PA patterns were categorized according to inactive, insufficiently active, WW, and regularly active (RA). Multivariate logistic regression models adjusting for demographics, behavioral factors, and health conditions were used to explore the association between PA patterns and DR. Finally, mediation analyses verified whether Hb mediated the relationship between PA and DR. The study ultimately included 5092 U.S. adults, including 857 participants with DR and 4235 participants with DM without DR. Multivariate logistic regression modelling indicated that both WW (OR=0.601, 95% CI=0.452-0.798, P <0.001) and RA (OR=0.728, 95% CI=0.554-0.956, P =0.023) were significant protective factors for DR when compared to inactive adults, and the association between RA insufficiently active, WW did not show a significant association with DR. Mediation analysis showed a significant mediation effect of Hb on the association between PA patterns and DR risk, with a mediation ratio of 5.23%. Our study reveals that WW and RA activity patterns are protective factors for DR and that Hb levels mediate this association. This suggests that WW activity patterns are more cost-effective for the prevention of DR. Health sciences/Health care/Geriatrics Health sciences/Endocrinology/Endocrine system and metabolic diseases Introduction Diabetic retinopathy (DR), a common microvascular complication of diabetes mellitus (DM), is the leading cause of vision loss in the elderly 1 , and approximately 30–40% of patients with DM are complicated by DR 2 . Lifestyle changes, increased human longevity, and global population aging have led to a dramatic increase in the prevalence of DM around the world, with a concomitant increase in the prevalence of DR 3 . Epidemiological studies have shown that the global prevalence of DR is projected to increase from 103 million in 2020 to 130 million in 2030 and to 161 million in 2045 3,4 . This will put pressure on healthcare systems and resources globally. DR increases the risk of visual impairment and blindness in patients with DM, and even predicts an increased risk of all-cause and cardiovascular disease mortality 5 – 7 . Therefore, early prevention is the key to avoiding the progression of DR, and a comprehensive understanding of the potential risk factors associated with the progression of DR is essential to establish effective therapeutic strategies to prevent and control the onset and progression of DR. Physical activity (PA), as a cost-effective, feasible and accessible lifestyle intervention, is an important protective factor in delaying the onset and progression of many diseases, including cancer, hypertension, and DM 8 – 10 . The positive impact of exercise on eye health in patients with DR has been recognized in a growing number of studies and has been widely confirmed 11 . Most cross-sectional studies have demonstrated that a higher frequency of PA is independently associated with a lower incidence of DR 11 – 13 . However, these studies are limited by small sample sizes or insufficiently systematic evaluation of PA. In order to better help patients to increase physical activity and improve their health status, the 2nd edition of the Physical Activity Guidelines for Americans gives more reasonable criteria for evaluating PA 14 . And the World Health Organization recommends that people aged 18 to 64 years perform at least 150 minutes of moderate-intensity aerobic exercise, or 75 minutes of vigorous-intensity aerobic exercise, or an equivalent combination, each week 14 . However, as the pace of society is increasing and adherence to physical activity may be a burden for people of working age, the "weekend warrior" (WW) model of exercise has been proposed. WW implies that an individual chooses to complete at least 150 minutes of moderate-intensity physical activity, or 75 minutes of vigorous-intensity physical activity, in one to two training sessions per week 15 . There have been many studies demonstrating the health benefits of WW exercise patterns 16 , 17 , but there is a lack of research with DR. Hemoglobin (Hb), an iron-containing protein present in red blood cells, transports oxygen from the lungs through the bloodstream to tissues throughout the body, providing energy for aerobic respiratory metabolic processes 18 . Investigations regarding PA and Hb are rather sparse, with only one study finding a positive correlation between sedentary time and Hb 19 . Two studies found that lower Hb levels were associated with the development of DR and retinal ischemia 20 , 21 . These lines of thought provoked us to imagine that PA might have an effect on retinal ischemia and hypoxia in DM patients by altering Hb levels and thereby. Our study focuses on analyzing the association between several patterns of PA and DR using publicly available data from the National Health and Nutrition Survey (NHANES) and whether Hb levels mediate this association. Methods Data sources and population The data for our study were obtained from NHANES 2007–2018.NHANES is a continuous cross-sectional survey conducted by the National Center for Health Statistics that implements a complex stratified, multistage probability cluster sampling methodology and contains demographic, socioeconomic, health, and nutritional information 22 . A detailed description of the NHANES database can be found at ( https://www.cdc.gov/nchs/nhanes/index.htm ). NHANES 2007–2018 had a total of 59,842 participants. First, 53,145 participants who did not have DM or could not specify a DM diagnosis were excluded, followed by 114 participants who were not explicitly told if they had DR. Also excluded were 39 individuals for whom no information on physical activity was captured, and 399 individuals with no Hb test results. Finally, 1053 participants with incomplete covariate data were excluded. The study ultimately included 5092 people for the correlation between WWs and DR, including 857 participants with DR and 4235 participants with diabetes without DR. The participant screening flowchart is shown in Fig. 1. Ethics Approval All participants in NHANES provided informed consent. NHANES was also approved by the United States National Centre for Health Statistics (NCHS) Ethics Review Board (ERB) ( https://www.cdc.gov/nchs/nhanes/irba98.htm#print ). The study is in line with the principles of the Declaration of Helsinki. Classification of physical activity The results of the Physical Activity questionnaire are given in NHANES. The questionnaire included whether or not PA was strenuous or moderate in intensity, the number of days in a week that it was done, and how much time was spent doing it each day (which was required to be for a period of time). And how much time is spent each day doing such activity (required to last at least 10 minutes)? PA is not limited to any form and can be running, brisk walking, biking, swimming, basketball, football, or other. PA that causes a large increase in respiration or heart rate is considered vigorous PA, and PA that causes a small increase in respiration or heart rate is considered moderate-intensity PA. The Physical Activity Guidelines for Americans defines 1 minute of vigorous-intensity activity as the equivalent of 2 minutes of moderate-intensity activity 14 . Total PA is obtained from 2*vigorous PA + moderate PA. PA patterns were categorized into four types: inactive, insufficiently active, WW, and regularly active (RA). Inactive means no intense or moderate PA; insufficiently active means less than 150 min of total PA per week; WW is for total PA greater than 150 min 1–2 times per week; RA refers to total PA greater than 150 min more than 2 times per week. 16 Hemoglobin level The Beckman Coulter MAXM unit at the NHANES Mobile Examination Center (MEC) performed complete blood cell counts on participants' blood specimens and provided blood cell distributions for all participants. A detailed description of the laboratory methods used can be found on the NHANES website. Diagnosis of DR and DM DR was the primary outcome of our study. Fundus examination was available in NHANES only from 2005–2008, whereas the diagnosis of DR after 2009 was obtained only from the questionnaire, and detailed calculations combining PA were available only from 2007 onwards. Thus the diagnosis of DR was made in 2007–2008 by combining the questionnaire and fundus examination of DR, and in 2009–2018 only based on the questionnaire of DR.DM was defined as (1) fasting blood glucose ≥ 7.0 mmol/L, (2) 2-hour blood glucose ≥ 11.1 mmol/L, (3) HbA1c ≥ 6.5%, (4) "Has a doctor or health professional ever told you that you have diabetes or sugar?" The answer to this question was "yes." Covariates A wide range of information on demographics, behavioral factors and health conditions such as age, gender, ethnicity, education level, poverty income ratio (PIR), marital status, alcohol consumption, smoking, body mass index (BMI), hypertension, and hyperlipidemia were assessed through face-to-face interviews and related examinations in NHANES, and these were considered as potential confounders and adjusted for. These factors were considered as potential confounders and were adjusted for in this study. BMI was calculated as weight (kg) divided by height squared (m2). Smoking status was categorized into three main groups according to never, former, and current smoking. Marital status was categorized into three main groups according to married/cohabiting, widowed/divorced/separated, or never married. Drinking status was categorized into excessive drinking, moderate drinking, light drinking and never drinking. Three drinks per day for women and four drinks per day for men were considered excessive drinking. Moderate drinking was defined as two drinks per day for women and three drinks per day for men. Other alcohol consumption was considered light. Hyperlipidemia was defined as having a total cholesterol level of at least 6.216 mmol/L or having the use of prescription medication to lower cholesterol levels. Hypertension was defined as when the mean systolic blood pressure was ≥ 140 mmHg or the mean diastolic blood pressure was ≥ 90 mmHg, or if you were taking prescription medication to lower your blood pressure, or if the questionnaire stated, "You have been told by a doctor or other health professional that you have high blood pressure." Systemic hypertension will be identified if one of the four conditions is met. Statistical Analysis Given the NHANES complex sampling design, weighted post hoc statistical analyses were used for comparisons of participants' basic characteristics. Continuous variables were described as weighted MEAN ± SE and compared between groups using weighted linear regression; categorical variables were expressed as weighted percentages (95% confidence interval), and chi-square tests were used for between-group comparisons. We constructed a binomial logistic regression model with the occurrence of DR as the outcome indicator to assess whether PA pattern and Hb level were risk factors for the occurrence of DR. A linear regression model was also constructed using Hb level as the outcome indicator to assess whether PA pattern was a risk factor for DR. Three models were constructed for all regression analyses to assess whether models with different covariate corrections were meaningful. Model 1 is the unadjusted model. Model 2 corrects for age, race, and gender variables. Model 3 builds on Model 2 by further correcting education, marital status, household poverty-to-income ratio, body mass index, smoking, alcohol consumption, hyperlipidemia, and hypertension. In order to be more explicit about the mediating effect of Hb levels on the association between PA patterns and DR risk, we also performed mediation analyses and calculated the mediation ratio. All statistical analyses were undertaken by R software ( http://www.R-project.org , The R Foundation, Austria), Empowerstats ( http://www.empowerstats.com , X&Y Solutions, Inc, CA, USA), and STATA 16.0 (StataCorp, College Station, TX, USA). The statistical significance level was set at P < 0.05. Results Characteristics of Participants Strictly following the screening criteria a total of 4235 DM participants without DR and 857 DR participants were included in this study. The age of the DR-free group was 59.07 ± 13.78 years, and the age of the DR group was 60.17 ± 12.81 years. The mean Hb level was 14.13 ± 1.57 g/dL in the no DR group and 13.72 ± 1.63 g/dL in the DR group. it was clearly seen that the inactive percentage was higher in the DR group than in the no DR group. Compared to the no DR group, DR participants exhibited lower education levels, lower household income, higher alcohol consumption, and higher prevalence of hypertension and hyperlipidemia. All baseline profile data are presented in Table 1 . Table 1 Basic participant characteristics. Variables DM without DR DR P -value No. of subjects 4235 857 Age, years 59.07 ± 13.78 60.17 ± 12.81 0.042 Sex, % 0.112 Male 52.26 (50.12, 54.39) 55.41 (50.46, 60.25) Female 47.74 (45.61, 49.88) 44.59 (39.75, 49.54) Race, % 0.102 Mexican American 9.59 (8.79, 10.45) 8.24 (6.72, 10.07) Other Hispanic 5.34 (4.78, 5.97) 5.70 (4.52, 7.17) Non-Hispanic White 63.96 (62.16, 65.73) 61.29 (56.97, 65.45) Non-Hispanic Black 12.82 (11.92, 13.77) 16.24 (13.91, 18.88) Other Race 8.29 (7.32, 9.37) 8.52 (6.39, 11.28) Education Level, % < 0.001 Less than high school 20.89 (19.51, 22.35) 27.06 (23.33, 31.15) High school or equivalent 24.99 (23.16, 26.91) 27.51 (23.14, 32.35) More than high school 54.12 (52.01, 56.21) 45.43 (40.55, 50.40) Marital status, % 0.219 Married/cohabiting 64.76 (62.76, 66.71) 63.17 (58.54, 67.57) Widowed/divorced/separated 26.04 (24.31, 27.84) 28.80 (24.84, 33.10) Never married 9.20 (8.06, 10.47) 8.03 (5.90, 10.85) Drinking status, % < 0.001 Never 13.42 (12.21, 14.73) 17.94 (14.22, 22.38) Light alcohol consumption 37.18 (35.10, 39.30) 32.89 (28.43, 37.68) Moderate alcohol consumption 9.80 (8.49, 11.28) 7.37 (4.86, 11.03) Excessive alcohol consumption 39.61 (37.53, 41.72) 41.79 (37.14, 46.60) Smoking, % 0.493 Never 50.11 (47.97, 52.25) 49.68 (44.77, 54.59) Former 33.93 (31.90, 36.01) 35.74 (31.11, 40.67) Current 15.96 (14.48, 17.57) 14.58 (11.78, 17.90) Hypertension, % < 0.001 No 31.33 (29.37, 33.36) 22.38 (18.57, 26.71) Yes 68.67 (66.64, 70.63) 77.62 (73.29, 81.43) Hyperlipemia, % 0.023 No 44.56 (42.45, 46.69) 40.10 (35.34, 45.05) Yes 55.44 (53.31, 57.55) 59.90 (54.95, 64.66) Family PIR 2.82 ± 1.62 2.61 ± 1.63 < 0.001 BMI, kg/m 2 33.17 ± 7.60 33.02 ± 7.69 0.605 Hemoglobin, g/dL 14.13 ± 1.57 13.72 ± 1.63 < 0.001 PA, % < 0.001 Inactive 60.58 (58.43, 62.69) 70.22 (65.22, 74.79) Insufficiently active 15.53 (14.01, 17.19) 13.04 (9.89, 17.01) WW 12.58 (11.14, 14.19) 9.03 (6.03, 13.31) RA 11.30 (9.98, 12.78) 7.71 (5.62, 10.48) Values are weighted mean ± SE or weighted % (95% confidence interval). P -values are weighted. Bold P -value indicates a statistically significant difference. PIR, poverty income ratio; BMI, body mass index; PA, physical activity; RA, regularly active; WW, weekend warrior. Association between PA and DR risk Logistic regression analyses of PA and DR were performed using inactive as a reference. After correcting for all covariates, no significant association was demonstrated between insufficiently active and DR (Model 3: OR = 0.810, 95% CI = 0.644–1.018, P = 0.071); and no significant association was found between WW (Model 3: OR = 0.601, 95% CI = 0.452–0.798, P < 0.001) and RA (Model 3: OR = 0.728, 95% CI = 0.554–0.956, P = 0.023) were both significant protective factors for DR (Table 2 ). Table 2 Association between PA and DR. PA Model 1 OR (95% CI) P -value Model 2 OR (95% CI) P -value Model 3 OR (95% CI) P -value First analysis Inactive Reference Reference Reference Insufficiently active 0.784 (0.627, 0.980) 0.033 0.793 (0.633, 0.994) 0.044 0.810 (0.644, 1.018) 0.071 WW 0.580 (0.440, 0.766)<0.001 0.580 (0.439, 0.766)<0.001 0.601 (0.452, 0.798)<0.001 RA 0.708 (0.544, 0.921) 0.010 0.695 (0.532, 0.909) 0.008 0.728 (0.554, 0.956) 0.023 Second analysis RA Reference Reference Reference Inactive 1.413 (1.085, 1.840) 0.010 1.438 (1.100, 1.879) 0.008 1.373 (1.046, 1.803) 0.023 Insufficiently active 1.108 (0.802, 1.530) 0.534 1.141 (0.824, 1.580) 0.428 1.112 (0.801, 1.543) 0.526 WW 0.820 (0.571, 1.178) 0.283 0.834 (0.580, 1.199) 0.326 0.825 (0.573, 1.189) 0.302 Bold P values indicate statistically significant differences. PA, physical activity; WW, weekend warrior; RA, regularly active. Model 1: no adjustment for covariates. Model 2: adjusted for age, race, gender. Model 3: further adjusted for education, marital status, household poverty-to-income ratio, body mass index, smoking, alcohol consumption, hyperlipidemia, and hypertension. Logistic regression analyses of PA and DR were performed using RA as a reference. After correcting for all covariates, insufficiently active, WW and DR did not show a significant association (insufficiently active Model 3: OR = 1.112, 95% CI = 0.801–1.543, P = 0.526; WW Model 3: OR = 0.825, 95% CI = 0.573–1.189, P = 0.302); inactive (Model 3: OR = 1.373, 95% CI = 1.046–1.803, P = 0.023) was a significant risk factor for DR (Table 2 ). Association between hemoglobin level and DR risk For total Hb level, all models showed lower Hb level as a significant risk factor for DR (Model 1: OR = 0.862, OR = 0.824–0.902, P < 0.001; Model 2: OR = 0.824, 95% CI = 0.783–0.868, P < 0.001; Model 3: OR = 0.832, 95% CI = 0.790–0.877, P < 0.001). Hb levels were divided into quartiles to investigate the association between different grades of Hb levels and DR. In Model 3 corrected for all covariates, there was a negative association between Hb levels and DR prevalence in all Q2, Q3, and Q4 quartiles compared to the Q1 quartile of Hb (Q2 Model 3: OR = 0.713, 95% CI = 0.582–0.875, P = 0.001; Q3 Model 3: OR = 0.579, 95% CI = 0.465–0.722, P < 0.001; Q4 Model 3: OR = 0.483, 95% CI = 0.380–0.614, P < 0.001) (Table 3 ). Table 3 Association between hemoglobin level and DR. Variables Model 1 OR (95% CI) P -value Model 2 OR (95% CI) P -value Model 3 OR (95% CI) P -value Hemoglobin, g/dL 0.862 (0.824, 0.902) < 0.001 0.824 (0.783, 0.868) < 0.001 0.832 (0.790, 0.877) < 0.001 Hemoglobin quartiles, g/dL 7.2–12.7 Reference Reference Reference 12.8–13.8 0.721 (0.591, 0.880) 0.001 0.694 (0.567, 0.849) < 0.001 0.713 (0.582, 0.875) 0.001 13.9–14.8 0.622 (0.507, 0.765) < 0.001 0.555 (0.446, 0.689) < 0.001 0.579 (0.465, 0.722) < 0.001 14.9–19.5 0.562 (0.458, 0.689) < 0.001 0.461 (0.365, 0.584) < 0.001 0.483 (0.380, 0.614) < 0.001 P for trend < 0.001 < 0.001 < 0.001 Bold P values indicate statistically significant differences. Model 1: no adjustment for covariates. Model 2: adjusted for age, race, gender. Model 3: further adjusted for education, marital status, household poverty-to-income ratio, body mass index, smoking, alcohol consumption, hyperlipidemia, and hypertension. Association between PA and hemoglobin level Linear regression analyses of PA and Hb levels were performed using inactive as a reference. After correcting for all covariates, no significant association was demonstrated between insufficiently active, RA and Hb levels using inactive as a reference (Insufficiently active Model 3: OR = 0.099, 95% CI=-0.015-0.213, P = 0.090; RA Model 3: OR = 0.072, 95% CI=-0.060-0.204, P = 0.285). The significant positive correlation presented between WW and Hb levels after correction for all covariates, using inactive as a reference (Model 1: OR = 0.314, OR = 0.167–0.461, P < 0.001; Model 2: OR = 0.170, 95% CI = 0.042–0.298, P = 0.009; Model 3: OR = 0.170, 95% CI = 0.042–0.299, P = 0.009) (Table 4 ). Table 4 Association between PA and hemoglobin level. Variables Model 1 β (95% CI) P -value Model 2 β (95% CI) P -value Model 3 β (95% CI) P -value PA Inactive Reference Reference Reference Insufficiently active 0.056 (-0.075, 0.187) 0.401 0.092 (-0.023, 0.206) 0.116 0.099 (-0.015, 0.213) 0.090 WW 0.314 (0.167, 0.461) < 0.001 0.170 (0.042, 0.298) 0.009 0.170 (0.042, 0.299) 0.009 RA 0.386 (0.237, 0.535) < 0.001 0.081 (-0.050, 0.213) 0.226 0.072 (-0.060, 0.204) 0.285 Bold P values indicate statistically significant differences. PA, physical activity; WW, weekend warrior. Model 1: no adjustment for covariates. Model 2: adjusted for age, race, gender. Model 3: further adjusted for education, marital status, household poverty-to-income ratio, body mass index, smoking, alcohol consumption, hyperlipidemia, and hypertension. In addition, we performed mediation analysis to assess the potential mediating effect of Hb level on the association between PA and DR risk. It was found that Hb level had a significant indirect effect (mediation effect) on the association between PA and DR, with a mediation ratio of 5.23% ( P -value = 0.038). See Table 5 . Table 5 hemoglobin level as a mediator in the associations between PA and DR Variables Estimate 95% CI lower 95% CI upper P -value Total effect -0.0208 -0.0322 -0.0098 < 0.001 Mediation effect -0.0011 -0.0022 -0.0001 0.038 Direct effect -0.0197 -0.0310 -0.0087 < 0.001 Proportion mediated 0.0523 0.0033 0.1376 0.038 Model was adjusted for age, race, gender, education, marital status, household poverty-to-income ratio, body mass index, cotinine, alcohol consumption, diabetes, hyperlipidemia, and hypertension. Discussion In this study we used WW exercise patterns to correlate PA with DR. The results found that WW exercise patterns contributed to the reduction of DR in US adults compared to INACTIVE. In particular, there was no difference in the protective effect of WW and RA modes against DR, and it can be assumed that WW mode has the same anti-DR risk effect as RA. In addition, mediation analyses revealed a significant mediation effect of Hb level on the association between PA and DR. This is great news for those who cannot adhere to daily exercise, and adherence to WW activity is equally effective in reducing the incidence of DR and improving health. A large body of evidence demonstrates that lack of physical activity or sedentary behavior is a risk factor for DM 8 , 23 , 24 . DR, the most common microvascular complication of DM, has similarly been supported by evidence that exercise is a protective factor for DR 8 . A comprehensive ophthalmological evaluation of 1000 adults with DM in a low-income community in Mexico culminated in the finding that physical inactivity was a risk factor for DR 25 . Increased physical activity was found to prevent DR in 1253 type 2 diabetic patients aged ≥ 18 years recruited in Bangladesh 26 . A Meta-analysis integrating 22 studies demonstrated that PA was associated with a lower risk of DR 27 . A careful search revealed that our study was consistent with the available evidence. What is novel is that our study is the first analysis of the impact of PA patterns on DR classified according to WW, in particular analysing the preventive effect of WW compared with RA patterns on DR. Using inactive as a reference, we finally found that both WW and RA were significant protective factors for DR. Using RA as a reference, no significant difference was observed between insufficiently active, WW in the risk of DR in adults. Our study reinforces the role of active exercise in the prevention and treatment of DR, contrary to some studies that suggest that only regular, sustained, and high-intensity exercise can be beneficial for DR 11 , 28 . Our results suggest that focused participation in WW mode over fewer days is the most cost-effective mode of exercise for DR, an important finding that liberates those who do not have the time to consistently exercise every day. Hb acts as an oxygen carrier in the blood, and our study found similar results to previous studies that high levels of Hb are associated with a reduced risk of DR. Significant inverse associations between Hb levels and DR risk have been found, both based on the data found in the 2005–2008 NHANES study 29 and the results of a study based on a cross-section of the Korean population 30 . There is limited evidence of a positive association between Hb and exercise, such as anaemic female university students in Dubai exercising less than those without anaemia 31 . However, there are contrary studies that found a positive correlation between sedentary time and Hb 19 . The updated finding of our study is the significant positive correlation presented between WW mode and Hb level compared to inactive, however no effect of RA mode on Hb level was found and Hb level mediated the potential effect of PA in association with DR risk with a mediation ratio of 5.23%. In the present study, it can be ventured that moderate exercise affects Hb levels in vivo, and Hb levels further affect arterial oxygen concentration, which in turn affects retinal tissue oxygenation function and modulates the occurrence of DR. The main strengths of this study are the use of a large and representative 6-period NHANES dataset, and the fact that we tried to take into account as much as possible a wide range of information on demographic factors and health-related behaviors as covariates, which improves the robustness of the results. The classification of PA into different patterns according to WW provided a more rational assessment of the relationship between PA and DR in adults. However, several limitations of our study must be noted. First, for the diagnosis of DR, the diagnosis of outcome could not be further graded in terms of the severity of DR, except for 2007–2008, when it was determined by fundus examination, which was based on the self-reported DIQ questionnaire. Second, the presence of reverse causality could not be inferred in this study. In addition, PA was also collected through questionnaires, which may be biased by individual cognitive judgements. In conclusion, the study needs to have more rigorously designed intervention trials or prospective cohort studies to consolidate the current findings. Conclusions Compared with inactive adults, WW adults had a lower risk of DR. Furthermore, no significant difference in DR risk was found between WW and regularly active adults, suggesting that moderate amounts of planned PA are more important for DR prevention. A deeper look at the intrinsic link between the two may be due to the fact that WW patterns increase Hb levels in the body, thereby preventing and controlling the risk of DR. Therefore, the WW exercise pattern provides a useful strategy for the prevention and control of DR. Declarations Author Contributions Conceptualization, ZFK and BHL; collected, analyzed data, writing-original manuscript preparation, BHL and BBN; reviewing, editing, and revising the paper, XYH and YPS. All authors approved the final manuscript as submitted and agreed to be accountable for all aspects of the work. All authors have read and approved the final manuscript. Funding Chinese medicine inheritance and innovation "Millions upon millions" talent project (Qihuang project) Qihuang scholars (No. 284 of the National Chinese Medicine Education Development [2018]). Conflicts of Interest The authors declare no conflict of interest. Acknowledgments Thanks to all the participants and staff of the NHANES for their tremendous contributions in data collection, management, and publication. Data availability Publicly available datasets were analyzed in this study. This data can be found here: https://www.cdc.gov/nchs/nhanes/. References Lin KY, Hsih WH, Lin YB, Wen CY, Chang TJ. Update in the epidemiology, risk factors, screening, and treatment of diabetic retinopathy. J Diabetes Investig. 2021;12(8):1322-1325. doi:10.1111/jdi.13480. Ruta LM, Magliano DJ, Lemesurier R, Taylor HR, Zimmet PZ, Shaw JE. Prevalence of diabetic retinopathy in Type 2 diabetes in developing and developed countries. Diabet Med. 2013; 30(4):387-398. doi:10.1111/dme.12119. Tan TE, Wong TY. Diabetic retinopathy: Looking forward to 2030. Front Endocrinol (Lausanne). 2022; 13:1077669. doi:10.3389/fendo.2022.1077669. Teo ZL, Tham YC, Yu M, Chee ML, Rim TH, Cheung N, et al. 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AlQabandi Y, Nandula SA, Boddepalli CS, Gutlapalli SD, Lavu VK, Abdelwahab Mohamed Abdelwahab R, et al. Physical Activity Status and Diabetic Retinopathy: A Review. Cureus 2022; 14(8):e28238. doi:10.7759/cureus.28238. Di Raimondo D, Buscemi S, Musiari G, Rizzo G, Pirera E, Corleo D, et al. Ketogenic Diet, Physical Activity, and Hypertension-A Narrative Review. Nutrients. 2021; 13(8). doi:10.3390/nu13082567. Kanaley JA, Colberg SR, Corcoran MH, Malin SK, Rodriguez NR, Crespo CJ, et al. Exercise/Physical Activity in Individuals with Type 2 Diabetes: A Consensus Statement from the American College of Sports Medicine. Med Sci Sports Exerc. 2022; 54(2):353-368. doi:10.1249/MSS.0000000000002800. Yan X, Han X, Wu C, Shang X, Zhang L, He M. Effect of physical activity on reducing the risk of diabetic retinopathy progression: 10-year prospective findings from the 45 and Up Study. PLoS One. 2021; 16(1):e0239214. doi:10.1371/journal.pone.0239214. Kuwata H, Okamura S, Hayashino Y, Tsujii S, Ishii H, Diabetes D, et al.Higher levels of physical activity are independently associated with a lower incidence of diabetic retinopathy in Japanese patients with type 2 diabetes: A prospective cohort study, Diabetes Distress and Care Registry at Tenri (DDCRT15). PLoS One. 2017; 12(3):e0172890. doi:10.1371/journal.pone.0172890. Dharmastuti DP, Agni AN, Widyaputri F, Pawiroranu S, Sofro ZM, Wardhana FS, et al. Associations of Physical Activity and Sedentary Behaviour with Vision-Threatening Diabetic Retinopathy in Indonesian Population with Type 2 Diabetes Mellitus: Jogjakarta Eye Diabetic Study in the Community (JOGED.COM). Ophthalmic Epidemiol. 2018; 25(2):113-119. doi:10.1080/09286586.2017.1367410. Piercy KL, Troiano RP, Ballard RM, Carlson SA, Fulton JE, Galuska DA, et al. The Physical Activity Guidelines for Americans. JAMA. 2018; 320(19):2020-2028. doi:10.1001/jama.2018.14854. O'Donovan G, Sarmiento OL, Hamer M. The Rise of the "Weekend Warrior". J Orthop Sports Phys Ther. 2018; 48(8):604-606. doi:10.2519/jospt.2018.0611. Liang JH, Huang S, Pu YQ, Zhao Y, Chen YC, Jiang N, et al. Whether weekend warrior activity and other leisure-time physical activity pattern reduce the risk of depression symptom in the representative adults? A population-based analysis of NHANES 2007-2020. J Affect Disord. 2023; 340:329-339.doi:10.1016/j.jad.2023.07.113. Wu J, Qiu P, Liu M, Yu W, Li M, Li Y. Physical activity patterns and cognitive function in elderly women: a cross-sectional study from NHANES 2011-2014. Front Aging Neurosci. 2024; 16:1407423. doi:10.3389/fnagi.2024.1407423. Gell DA. Structure and function of haemoglobins. Blood Cells Mol Dis. 2018; 70:13-42. doi:10.1016/j.bcmd.2017.10.006. Koivula T, Lempiainen S, Laine S, Sjoros T, Vaha-Ypya H, Garthwaite T, et al. Cross-Sectional Associations of Body Adiposity, Sedentary Behavior, and Physical Activity with Hemoglobin and White Blood Cell Count. Int J Environ Res Public Health. 2022; 19(21). doi:10.3390/ijerph192114347. Bi S, Tu Z, Chen D, Zhang S. Histone modifications in embryo implantation and placentation: insights from mouse models. Front Endocrinol (Lausanne). 2023; 14:1229862. doi:10.3389/fendo.2023.1229862. Traveset A, Rubinat E, Ortega E, Alcubierre N, Vazquez B, Hernandez M, et al. Lower Hemoglobin Concentration Is Associated with Retinal Ischemia and the Severity of Diabetic Retinopathy in Type 2 Diabetes. J Diabetes Res. 2016; 2016:3674946. doi:10.1155/2016/3674946. Tang H, Luo N, Zhang X, Huang J, Yang Q, Lin H, Zhang X. Association between biological aging and diabetic retinopathy. Sci Rep. 2024; 14(1):10123. doi:10.1038/s41598-024-60913-x. Scarborough P, Bhatnagar P, Wickramasinghe KK, Allender S, Foster C, Rayner M. The economic burden of ill health due to diet, physical inactivity, smoking, alcohol and obesity in the UK: an update to 2006-07 NHS costs. J Public Health (Oxf). 2011; 33(4):527-535. doi:10.1093/pubmed/fdr033. Cavero-Redondo I, Peleteiro B, Alvarez-Bueno C, Artero EG, Garrido-Miguel M, Martinez-Vizcaino V. The Effect of Physical Activity Interventions on Glycosylated Haemoglobin (HbA(1c)) in Non-diabetic Populations: A Systematic Review and Meta-analysis. Sports Med. 2018; 48(5):1151-1164. doi:10.1007/s40279-018-0861-0. Mendoza-Herrera K, Quezada AD, Pedroza-Tobias A, Hernandez-Alcaraz C, Fromow-Guerra J, Barquera S. A Diabetic Retinopathy Screening Tool for Low-Income Adults in Mexico. Prev Chronic Dis. 2017; 14:E95. doi:10.5888/pcd14.170157. Afroz A, Zhang W, Wei Loh AJ, Jie Lee DX, Billah B. Macro- and micro-vascular complications and their determinants among people with type 2 diabetes in Bangladesh. Diabetes Metab Syndr. 2019; 13(5):2939-2946. doi:10.1016/j.dsx.2019.07.046. Ren C, Liu W, Li J, Cao Y, Xu J, Lu P. Physical activity and risk of diabetic retinopathy: a systematic review and meta-analysis. Acta Diabetol. 2019; 56(8):823-837. doi:10.1007/s00592-019-01319-4. Praidou A, Harris M, Niakas D, Labiris G. Physical activity and its correlation to diabetic retinopathy. J Diabetes Complications. 2017; 31(2):456-461. doi:10.1016/j.jdiacomp.2016.06.027. Li X, Chen M. Correlation of hemoglobin levels with diabetic retinopathy in US adults aged >/=40 years: the NHANES 2005-2008. Front Endocrinol (Lausanne). 2023; 14:1195647. doi:10.3389/fendo.2023.1195647. Lee MK, Han KD, Lee JH, Sohn SY, Jeong JS, Kim MK, et al.High hemoglobin levels are associated with decreased risk of diabetic retinopathy in Korean type 2 diabetes. Sci Rep. 2018; 8(1):5538. doi:10.1038/s41598-018-23905-2. Al Sabbah H. Prevalence of overweight/obesity, anaemia and their associations among female university students in Dubai, United Arab Emirates: a cross-sectional study. J Nutr Sci. 2020; 9:e26. doi:10.1017/jns.2020.23. Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4866922","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":358841750,"identity":"3e3ae991-2aed-48fb-aa8f-aad943ad7918","order_by":0,"name":"Baohua Li","email":"","orcid":"","institution":"China Academy of Chinese Medical Sciences Eye Hospital","correspondingAuthor":false,"prefix":"","firstName":"Baohua","middleName":"","lastName":"Li","suffix":""},{"id":358841751,"identity":"e69a2754-0dea-40fb-befd-3efbfe134aee","order_by":1,"name":"Bobiao Ning","email":"","orcid":"","institution":"Guang’anmen Hospital","correspondingAuthor":false,"prefix":"","firstName":"Bobiao","middleName":"","lastName":"Ning","suffix":""},{"id":358841752,"identity":"990d749d-6672-462e-b554-d1758bd964ba","order_by":2,"name":"Xinyue Hou","email":"","orcid":"","institution":"China Academy of Chinese Medical Sciences Eye Hospital","correspondingAuthor":false,"prefix":"","firstName":"Xinyue","middleName":"","lastName":"Hou","suffix":""},{"id":358841753,"identity":"2a828365-8938-4ba4-9589-c6997bf6b986","order_by":3,"name":"Yipeng Shi","email":"","orcid":"","institution":"China Academy of Chinese Medical Sciences Eye Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yipeng","middleName":"","lastName":"Shi","suffix":""},{"id":358841754,"identity":"af3d4693-f3c3-4291-a604-53b9d3f672d6","order_by":4,"name":"Zefeng Kang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5klEQVRIie3PsYrCQBCA4QkLY7Mh3bFyRV5hl4XlijzM2qQK1namEDutI4LPYHnlBNscPoCNYCVYRGxSHGiEK2w0Wx64fzEwMF8xAD7fP0zSfSbX8TQ+ENUjB6Ly+0xZMId0UBaVA9HwR1aQ6U04cSCm97M5NhYZQlVTmEMcfdBrkvBh+iUsRwxma+p/g1osbQeBzEjVCI4sXJOqwMpdBzHRyUhrpUDkexpMHYgWmd5Ta5BzoNKFqOJkgtySRYGyzCvR/YvcZvrctCQu2OHyO0ri6LODtKF4WMTTs8dY7XTm8/l879sN5+9F3+xlw7YAAAAASUVORK5CYII=","orcid":"","institution":"China Academy of Chinese Medical Sciences Eye Hospital","correspondingAuthor":true,"prefix":"","firstName":"Zefeng","middleName":"","lastName":"Kang","suffix":""}],"badges":[],"createdAt":"2024-08-06 08:36:49","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4866922/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4866922/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-024-84736-y","type":"published","date":"2025-02-08T15:57:41+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":75930427,"identity":"55856e32-7902-4ad1-8059-a20b28bdd5e4","added_by":"auto","created_at":"2025-02-10 16:11:32","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1105005,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4866922/v1/6015546c-de21-4931-8e83-d862712e23e9.pdf"},{"id":66865685,"identity":"96e39307-2a9a-4db4-9e59-2bd2f4b859e5","added_by":"auto","created_at":"2024-10-17 09:05:56","extension":"doc","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":178688,"visible":true,"origin":"","legend":"","description":"","filename":"Statisticaldata.doc","url":"https://assets-eu.researchsquare.com/files/rs-4866922/v1/3810192c33a9886aa9da925d.doc"},{"id":66864977,"identity":"b365cb49-fc13-41f3-ba2c-569aebfd4135","added_by":"auto","created_at":"2024-10-17 08:57:56","extension":"csv","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":480339,"visible":true,"origin":"","legend":"","description":"","filename":"rawdata.csv","url":"https://assets-eu.researchsquare.com/files/rs-4866922/v1/14b71eeb3fdedffa7314eb5a.csv"}],"financialInterests":"No competing interests reported.","formattedTitle":"Hemoglobin mediates the link between 'weekend warrior' activity pattern and diabetic retinopathy","fulltext":[{"header":"Introduction","content":"\u003cp\u003eDiabetic retinopathy (DR), a common microvascular complication of diabetes mellitus (DM), is the leading cause of vision loss in the elderly \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e, and approximately 30\u0026ndash;40% of patients with DM are complicated by DR \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. Lifestyle changes, increased human longevity, and global population aging have led to a dramatic increase in the prevalence of DM around the world, with a concomitant increase in the prevalence of DR \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Epidemiological studies have shown that the global prevalence of DR is projected to increase from 103\u0026nbsp;million in 2020 to 130\u0026nbsp;million in 2030 and to 161\u0026nbsp;million in 2045 \u003csup\u003e3,4\u003c/sup\u003e. This will put pressure on healthcare systems and resources globally. DR increases the risk of visual impairment and blindness in patients with DM, and even predicts an increased risk of all-cause and cardiovascular disease mortality \u003csup\u003e\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. Therefore, early prevention is the key to avoiding the progression of DR, and a comprehensive understanding of the potential risk factors associated with the progression of DR is essential to establish effective therapeutic strategies to prevent and control the onset and progression of DR.\u003c/p\u003e \u003cp\u003ePhysical activity (PA), as a cost-effective, feasible and accessible lifestyle intervention, is an important protective factor in delaying the onset and progression of many diseases, including cancer, hypertension, and DM \u003csup\u003e\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. The positive impact of exercise on eye health in patients with DR has been recognized in a growing number of studies and has been widely confirmed \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. Most cross-sectional studies have demonstrated that a higher frequency of PA is independently associated with a lower incidence of DR \u003csup\u003e\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. However, these studies are limited by small sample sizes or insufficiently systematic evaluation of PA. In order to better help patients to increase physical activity and improve their health status, the 2nd edition of the Physical Activity Guidelines for Americans gives more reasonable criteria for evaluating PA \u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. And the World Health Organization recommends that people aged 18 to 64 years perform at least 150 minutes of moderate-intensity aerobic exercise, or 75 minutes of vigorous-intensity aerobic exercise, or an equivalent combination, each week \u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. However, as the pace of society is increasing and adherence to physical activity may be a burden for people of working age, the \"weekend warrior\" (WW) model of exercise has been proposed. WW implies that an individual chooses to complete at least 150 minutes of moderate-intensity physical activity, or 75 minutes of vigorous-intensity physical activity, in one to two training sessions per week \u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. There have been many studies demonstrating the health benefits of WW exercise patterns \u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e, but there is a lack of research with DR.\u003c/p\u003e \u003cp\u003eHemoglobin (Hb), an iron-containing protein present in red blood cells, transports oxygen from the lungs through the bloodstream to tissues throughout the body, providing energy for aerobic respiratory metabolic processes \u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. Investigations regarding PA and Hb are rather sparse, with only one study finding a positive correlation between sedentary time and Hb \u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. Two studies found that lower Hb levels were associated with the development of DR and retinal ischemia \u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e,\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. These lines of thought provoked us to imagine that PA might have an effect on retinal ischemia and hypoxia in DM patients by altering Hb levels and thereby.\u003c/p\u003e \u003cp\u003eOur study focuses on analyzing the association between several patterns of PA and DR using publicly available data from the National Health and Nutrition Survey (NHANES) and whether Hb levels mediate this association.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData sources and population\u003c/h2\u003e \u003cp\u003eThe data for our study were obtained from NHANES 2007\u0026ndash;2018.NHANES is a continuous cross-sectional survey conducted by the National Center for Health Statistics that implements a complex stratified, multistage probability cluster sampling methodology and contains demographic, socioeconomic, health, and nutritional information \u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. A detailed description of the NHANES database can be found at (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cdc.gov/nchs/nhanes/index.htm\u003c/span\u003e\u003cspan address=\"https://www.cdc.gov/nchs/nhanes/index.htm\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). NHANES 2007\u0026ndash;2018 had a total of 59,842 participants. First, 53,145 participants who did not have DM or could not specify a DM diagnosis were excluded, followed by 114 participants who were not explicitly told if they had DR. Also excluded were 39 individuals for whom no information on physical activity was captured, and 399 individuals with no Hb test results. Finally, 1053 participants with incomplete covariate data were excluded. The study ultimately included 5092 people for the correlation between WWs and DR, including 857 participants with DR and 4235 participants with diabetes without DR. The participant screening flowchart is shown in Fig.\u0026nbsp;1.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eEthics Approval\u003c/h2\u003e \u003cp\u003e All participants in NHANES provided informed consent. NHANES was also approved by the United States National Centre for Health Statistics (NCHS) Ethics Review Board (ERB) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cdc.gov/nchs/nhanes/irba98.htm#print\u003c/span\u003e\u003cspan address=\"https://www.cdc.gov/nchs/nhanes/irba98.htm#print\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The study is in line with the principles of the Declaration of Helsinki.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eClassification of physical activity\u003c/h2\u003e \u003cp\u003eThe results of the Physical Activity questionnaire are given in NHANES. The questionnaire included whether or not PA was strenuous or moderate in intensity, the number of days in a week that it was done, and how much time was spent doing it each day (which was required to be for a period of time). And how much time is spent each day doing such activity (required to last at least 10 minutes)? PA is not limited to any form and can be running, brisk walking, biking, swimming, basketball, football, or other. PA that causes a large increase in respiration or heart rate is considered vigorous PA, and PA that causes a small increase in respiration or heart rate is considered moderate-intensity PA. The Physical Activity Guidelines for Americans defines 1 minute of vigorous-intensity activity as the equivalent of 2 minutes of moderate-intensity activity\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. Total PA is obtained from 2*vigorous PA\u0026thinsp;+\u0026thinsp;moderate PA. PA patterns were categorized into four types: inactive, insufficiently active, WW, and regularly active (RA). Inactive means no intense or moderate PA; insufficiently active means less than 150 min of total PA per week; WW is for total PA greater than 150 min 1\u0026ndash;2 times per week; RA refers to total PA greater than 150 min more than 2 times per week.\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eHemoglobin level\u003c/h2\u003e \u003cp\u003eThe Beckman Coulter MAXM unit at the NHANES Mobile Examination Center (MEC) performed complete blood cell counts on participants' blood specimens and provided blood cell distributions for all participants. A detailed description of the laboratory methods used can be found on the NHANES website.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eDiagnosis of DR and DM\u003c/h2\u003e \u003cp\u003eDR was the primary outcome of our study. Fundus examination was available in NHANES only from 2005\u0026ndash;2008, whereas the diagnosis of DR after 2009 was obtained only from the questionnaire, and detailed calculations combining PA were available only from 2007 onwards. Thus the diagnosis of DR was made in 2007\u0026ndash;2008 by combining the questionnaire and fundus examination of DR, and in 2009\u0026ndash;2018 only based on the questionnaire of DR.DM was defined as (1) fasting blood glucose\u0026thinsp;\u0026ge;\u0026thinsp;7.0 mmol/L, (2) 2-hour blood glucose\u0026thinsp;\u0026ge;\u0026thinsp;11.1 mmol/L, (3) HbA1c\u0026thinsp;\u0026ge;\u0026thinsp;6.5%, (4) \"Has a doctor or health professional ever told you that you have diabetes or sugar?\" The answer to this question was \"yes.\"\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eCovariates\u003c/h2\u003e \u003cp\u003eA wide range of information on demographics, behavioral factors and health conditions such as age, gender, ethnicity, education level, poverty income ratio (PIR), marital status, alcohol consumption, smoking, body mass index (BMI), hypertension, and hyperlipidemia were assessed through face-to-face interviews and related examinations in NHANES, and these were considered as potential confounders and adjusted for. These factors were considered as potential confounders and were adjusted for in this study. BMI was calculated as weight (kg) divided by height squared (m2). Smoking status was categorized into three main groups according to never, former, and current smoking. Marital status was categorized into three main groups according to married/cohabiting, widowed/divorced/separated, or never married. Drinking status was categorized into excessive drinking, moderate drinking, light drinking and never drinking. Three drinks per day for women and four drinks per day for men were considered excessive drinking. Moderate drinking was defined as two drinks per day for women and three drinks per day for men. Other alcohol consumption was considered light. Hyperlipidemia was defined as having a total cholesterol level of at least 6.216 mmol/L or having the use of prescription medication to lower cholesterol levels. Hypertension was defined as when the mean systolic blood pressure was \u0026ge;\u0026thinsp;140 mmHg or the mean diastolic blood pressure was \u0026ge;\u0026thinsp;90 mmHg, or if you were taking prescription medication to lower your blood pressure, or if the questionnaire stated, \"You have been told by a doctor or other health professional that you have high blood pressure.\" Systemic hypertension will be identified if one of the four conditions is met.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eGiven the NHANES complex sampling design, weighted post hoc statistical analyses were used for comparisons of participants' basic characteristics. Continuous variables were described as weighted MEAN\u0026thinsp;\u0026plusmn;\u0026thinsp;SE and compared between groups using weighted linear regression; categorical variables were expressed as weighted percentages (95% confidence interval), and chi-square tests were used for between-group comparisons. We constructed a binomial logistic regression model with the occurrence of DR as the outcome indicator to assess whether PA pattern and Hb level were risk factors for the occurrence of DR. A linear regression model was also constructed using Hb level as the outcome indicator to assess whether PA pattern was a risk factor for DR. Three models were constructed for all regression analyses to assess whether models with different covariate corrections were meaningful. Model 1 is the unadjusted model. Model 2 corrects for age, race, and gender variables. Model 3 builds on Model 2 by further correcting education, marital status, household poverty-to-income ratio, body mass index, smoking, alcohol consumption, hyperlipidemia, and hypertension. In order to be more explicit about the mediating effect of Hb levels on the association between PA patterns and DR risk, we also performed mediation analyses and calculated the mediation ratio.\u003c/p\u003e \u003cp\u003eAll statistical analyses were undertaken by R software (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.R-project.org\u003c/span\u003e\u003cspan address=\"http://www.R-project.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, The R Foundation, Austria), Empowerstats (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.empowerstats.com\u003c/span\u003e\u003cspan address=\"http://www.empowerstats.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, X\u0026amp;Y Solutions, Inc, CA, USA), and STATA 16.0 (StataCorp, College Station, TX, USA). The statistical significance level was set at \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eCharacteristics of Participants\u003c/h2\u003e \u003cp\u003eStrictly following the screening criteria a total of 4235 DM participants without DR and 857 DR participants were included in this study. The age of the DR-free group was 59.07\u0026thinsp;\u0026plusmn;\u0026thinsp;13.78 years, and the age of the DR group was 60.17\u0026thinsp;\u0026plusmn;\u0026thinsp;12.81 years. The mean Hb level was 14.13\u0026thinsp;\u0026plusmn;\u0026thinsp;1.57 g/dL in the no DR group and 13.72\u0026thinsp;\u0026plusmn;\u0026thinsp;1.63 g/dL in the DR group. it was clearly seen that the inactive percentage was higher in the DR group than in the no DR group. Compared to the no DR group, DR participants exhibited lower education levels, lower household income, higher alcohol consumption, and higher prevalence of hypertension and hyperlipidemia. All baseline profile data are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBasic participant characteristics.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDM without DR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo. of subjects\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4235\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e857\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e59.07\u0026thinsp;\u0026plusmn;\u0026thinsp;13.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60.17\u0026thinsp;\u0026plusmn;\u0026thinsp;12.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.042\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.112\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e52.26 (50.12, 54.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e55.41 (50.46, 60.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e47.74 (45.61, 49.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44.59 (39.75, 49.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRace, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.102\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMexican American\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.59 (8.79, 10.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.24 (6.72, 10.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther Hispanic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.34 (4.78, 5.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.70 (4.52, 7.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Hispanic White\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e63.96 (62.16, 65.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e61.29 (56.97, 65.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Hispanic Black\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12.82 (11.92, 13.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.24 (13.91, 18.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther Race\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.29 (7.32, 9.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.52 (6.39, 11.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation Level, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLess than high school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20.89 (19.51, 22.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27.06 (23.33, 31.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh school or equivalent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24.99 (23.16, 26.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27.51 (23.14, 32.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMore than high school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e54.12 (52.01, 56.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45.43 (40.55, 50.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarital status, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.219\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried/cohabiting\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e64.76 (62.76, 66.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e63.17 (58.54, 67.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWidowed/divorced/separated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26.04 (24.31, 27.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28.80 (24.84, 33.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever married\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.20 (8.06, 10.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.03 (5.90, 10.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDrinking status, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13.42 (12.21, 14.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.94 (14.22, 22.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLight alcohol consumption\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37.18 (35.10, 39.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32.89 (28.43, 37.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate alcohol consumption\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.80 (8.49, 11.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.37 (4.86, 11.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExcessive alcohol consumption\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39.61 (37.53, 41.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41.79 (37.14, 46.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.493\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50.11 (47.97, 52.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e49.68 (44.77, 54.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFormer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33.93 (31.90, 36.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35.74 (31.11, 40.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15.96 (14.48, 17.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.58 (11.78, 17.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31.33 (29.37, 33.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22.38 (18.57, 26.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e68.67 (66.64, 70.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e77.62 (73.29, 81.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHyperlipemia, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.023\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44.56 (42.45, 46.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40.10 (35.34, 45.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e55.44 (53.31, 57.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e59.90 (54.95, 64.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFamily PIR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.82\u0026thinsp;\u0026plusmn;\u0026thinsp;1.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.61\u0026thinsp;\u0026plusmn;\u0026thinsp;1.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI, kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33.17\u0026thinsp;\u0026plusmn;\u0026thinsp;7.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33.02\u0026thinsp;\u0026plusmn;\u0026thinsp;7.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.605\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHemoglobin, g/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14.13\u0026thinsp;\u0026plusmn;\u0026thinsp;1.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.72\u0026thinsp;\u0026plusmn;\u0026thinsp;1.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePA, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInactive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e60.58 (58.43, 62.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e70.22 (65.22, 74.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInsufficiently active\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15.53 (14.01, 17.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.04 (9.89, 17.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12.58 (11.14, 14.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.03 (6.03, 13.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.30 (9.98, 12.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.71 (5.62, 10.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eValues are weighted mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SE or weighted % (95% confidence interval). \u003cem\u003eP\u003c/em\u003e-values are weighted. Bold \u003cem\u003eP\u003c/em\u003e-value indicates a statistically significant difference. PIR, poverty income ratio; BMI, body mass index; PA, physical activity; RA, regularly active; WW, weekend warrior.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eAssociation between PA and DR risk\u003c/h2\u003e \u003cp\u003eLogistic regression analyses of PA and DR were performed using inactive as a reference. After correcting for all covariates, no significant association was demonstrated between insufficiently active and DR (Model 3: OR\u0026thinsp;=\u0026thinsp;0.810, 95% CI\u0026thinsp;=\u0026thinsp;0.644\u0026ndash;1.018, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.071); and no significant association was found between WW (Model 3: OR\u0026thinsp;=\u0026thinsp;0.601, 95% CI\u0026thinsp;=\u0026thinsp;0.452\u0026ndash;0.798, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and RA (Model 3: OR\u0026thinsp;=\u0026thinsp;0.728, 95% CI\u0026thinsp;=\u0026thinsp;0.554\u0026ndash;0.956, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.023) were both significant protective factors for DR (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociation between PA and DR.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003cp\u003eOR (95% CI)\u0026nbsp;\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003cp\u003eOR (95% CI)\u0026nbsp;\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003cp\u003eOR (95% CI) \u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFirst analysis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInactive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInsufficiently active\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.784 (0.627, 0.980) 0.033\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.793 (0.633, 0.994) 0.044\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.810 (0.644, 1.018) 0.071\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.580 (0.440, 0.766)\u0026lt;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.580 (0.439, 0.766)\u0026lt;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.601 (0.452, 0.798)\u0026lt;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.708 (0.544, 0.921) 0.010\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.695 (0.532, 0.909) 0.008\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.728 (0.554, 0.956) 0.023\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecond analysis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInactive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1.413 (1.085, 1.840) 0.010\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1.438 (1.100, 1.879) 0.008\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1.373 (1.046, 1.803) 0.023\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInsufficiently active\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.108 (0.802, 1.530) 0.534\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.141 (0.824, 1.580) 0.428\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.112 (0.801, 1.543) 0.526\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.820 (0.571, 1.178) 0.283\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.834 (0.580, 1.199) 0.326\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.825 (0.573, 1.189) 0.302\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eBold \u003cem\u003eP\u003c/em\u003e values indicate statistically significant differences. PA, physical activity; WW, weekend warrior; RA, regularly active.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eModel 1: no adjustment for covariates.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eModel 2: adjusted for age, race, gender.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eModel 3: further adjusted for education, marital status, household poverty-to-income ratio, body mass index, smoking, alcohol consumption, hyperlipidemia, and hypertension.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eLogistic regression analyses of PA and DR were performed using RA as a reference. After correcting for all covariates, insufficiently active, WW and DR did not show a significant association (insufficiently active Model 3: OR\u0026thinsp;=\u0026thinsp;1.112, 95% CI\u0026thinsp;=\u0026thinsp;0.801\u0026ndash;1.543, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.526; WW Model 3: OR\u0026thinsp;=\u0026thinsp;0.825, 95% CI\u0026thinsp;=\u0026thinsp;0.573\u0026ndash;1.189, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.302); inactive (Model 3: OR\u0026thinsp;=\u0026thinsp;1.373, 95% CI\u0026thinsp;=\u0026thinsp;1.046\u0026ndash;1.803, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.023) was a significant risk factor for DR (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eAssociation between hemoglobin level and DR risk\u003c/h2\u003e \u003cp\u003eFor total Hb level, all models showed lower Hb level as a significant risk factor for DR (Model 1: OR\u0026thinsp;=\u0026thinsp;0.862, OR\u0026thinsp;=\u0026thinsp;0.824\u0026ndash;0.902, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Model 2: OR\u0026thinsp;=\u0026thinsp;0.824, 95% CI\u0026thinsp;=\u0026thinsp;0.783\u0026ndash;0.868, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Model 3: OR\u0026thinsp;=\u0026thinsp;0.832, 95% CI\u0026thinsp;=\u0026thinsp;0.790\u0026ndash;0.877, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Hb levels were divided into quartiles to investigate the association between different grades of Hb levels and DR. In Model 3 corrected for all covariates, there was a negative association between Hb levels and DR prevalence in all Q2, Q3, and Q4 quartiles compared to the Q1 quartile of Hb (Q2 Model 3: OR\u0026thinsp;=\u0026thinsp;0.713, 95% CI\u0026thinsp;=\u0026thinsp;0.582\u0026ndash;0.875, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001; Q3 Model 3: OR\u0026thinsp;=\u0026thinsp;0.579, 95% CI\u0026thinsp;=\u0026thinsp;0.465\u0026ndash;0.722, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Q4 Model 3: OR\u0026thinsp;=\u0026thinsp;0.483, 95% CI\u0026thinsp;=\u0026thinsp;0.380\u0026ndash;0.614, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociation between hemoglobin level and DR.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003cp\u003eOR (95% CI)\u0026nbsp;\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003cp\u003eOR (95% CI)\u0026nbsp;\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003cp\u003eOR (95% CI) \u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHemoglobin, g/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.862 (0.824, 0.902)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.824 (0.783, 0.868)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.832 (0.790, 0.877)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eHemoglobin quartiles, g/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7.2\u0026ndash;12.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12.8\u0026ndash;13.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.721 (0.591, 0.880) 0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.694 (0.567, 0.849)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.713 (0.582, 0.875) 0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e13.9\u0026ndash;14.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.622 (0.507, 0.765)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.555 (0.446, 0.689)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.579 (0.465, 0.722)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e14.9\u0026ndash;19.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.562 (0.458, 0.689)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.461 (0.365, 0.584)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.483 (0.380, 0.614)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e for trend\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eBold \u003cem\u003eP\u003c/em\u003e values indicate statistically significant differences.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eModel 1: no adjustment for covariates.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eModel 2: adjusted for age, race, gender.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eModel 3: further adjusted for education, marital status, household poverty-to-income ratio, body mass index, smoking, alcohol consumption, hyperlipidemia, and hypertension.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eAssociation between PA and hemoglobin level\u003c/h2\u003e \u003cp\u003eLinear regression analyses of PA and Hb levels were performed using inactive as a reference. After correcting for all covariates, no significant association was demonstrated between insufficiently active, RA and Hb levels using inactive as a reference (Insufficiently active Model 3: OR\u0026thinsp;=\u0026thinsp;0.099, 95% CI=-0.015-0.213, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.090; RA Model 3: OR\u0026thinsp;=\u0026thinsp;0.072, 95% CI=-0.060-0.204, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.285). The significant positive correlation presented between WW and Hb levels after correction for all covariates, using inactive as a reference (Model 1: OR\u0026thinsp;=\u0026thinsp;0.314, OR\u0026thinsp;=\u0026thinsp;0.167\u0026ndash;0.461, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Model 2: OR\u0026thinsp;=\u0026thinsp;0.170, 95% CI\u0026thinsp;=\u0026thinsp;0.042\u0026ndash;0.298, P\u0026thinsp;=\u0026thinsp;0.009; Model 3: OR\u0026thinsp;=\u0026thinsp;0.170, 95% CI\u0026thinsp;=\u0026thinsp;0.042\u0026ndash;0.299, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.009) (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociation between PA and hemoglobin level.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003cp\u003eβ (95% CI)\u0026nbsp;\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003cp\u003eβ (95% CI)\u0026nbsp;\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003cp\u003eβ (95% CI) \u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInactive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInsufficiently active\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.056 (-0.075, 0.187) 0.401\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.092 (-0.023, 0.206) 0.116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.099 (-0.015, 0.213) 0.090\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.314 (0.167, 0.461)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.170 (0.042, 0.298) 0.009\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.170 (0.042, 0.299) 0.009\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.386 (0.237, 0.535)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.081 (-0.050, 0.213) 0.226\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.072 (-0.060, 0.204) 0.285\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eBold \u003cem\u003eP\u003c/em\u003e values indicate statistically significant differences. PA, physical activity; WW, weekend warrior.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eModel 1: no adjustment for covariates.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eModel 2: adjusted for age, race, gender.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eModel 3: further adjusted for education, marital status, household poverty-to-income ratio, body mass index, smoking, alcohol consumption, hyperlipidemia, and hypertension.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn addition, we performed mediation analysis to assess the potential mediating effect of Hb level on the association between PA and DR risk. It was found that Hb level had a significant indirect effect (mediation effect) on the association between PA and DR, with a mediation ratio of 5.23% (\u003cem\u003eP\u003c/em\u003e-value\u0026thinsp;=\u0026thinsp;0.038). See Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ehemoglobin level as a mediator in the associations between PA and DR\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEstimate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95% CI lower\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95% CI upper\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal effect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.0208\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.0322\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.0098\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMediation effect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.0011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.0022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.038\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDirect effect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.0197\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.0310\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.0087\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProportion mediated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.0523\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.1376\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.038\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eModel was adjusted for age, race, gender, education, marital status, household poverty-to-income ratio, body mass index, cotinine, alcohol consumption, diabetes, hyperlipidemia, and hypertension.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study we used WW exercise patterns to correlate PA with DR. The results found that WW exercise patterns contributed to the reduction of DR in US adults compared to INACTIVE. In particular, there was no difference in the protective effect of WW and RA modes against DR, and it can be assumed that WW mode has the same anti-DR risk effect as RA. In addition, mediation analyses revealed a significant mediation effect of Hb level on the association between PA and DR. This is great news for those who cannot adhere to daily exercise, and adherence to WW activity is equally effective in reducing the incidence of DR and improving health.\u003c/p\u003e \u003cp\u003eA large body of evidence demonstrates that lack of physical activity or sedentary behavior is a risk factor for DM \u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e,\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. DR, the most common microvascular complication of DM, has similarly been supported by evidence that exercise is a protective factor for DR \u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. A comprehensive ophthalmological evaluation of 1000 adults with DM in a low-income community in Mexico culminated in the finding that physical inactivity was a risk factor for DR \u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. Increased physical activity was found to prevent DR in 1253 type 2 diabetic patients aged\u0026thinsp;\u0026ge;\u0026thinsp;18 years recruited in Bangladesh \u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. A Meta-analysis integrating 22 studies demonstrated that PA was associated with a lower risk of DR \u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. A careful search revealed that our study was consistent with the available evidence. What is novel is that our study is the first analysis of the impact of PA patterns on DR classified according to WW, in particular analysing the preventive effect of WW compared with RA patterns on DR.\u003c/p\u003e \u003cp\u003eUsing inactive as a reference, we finally found that both WW and RA were significant protective factors for DR. Using RA as a reference, no significant difference was observed between insufficiently active, WW in the risk of DR in adults. Our study reinforces the role of active exercise in the prevention and treatment of DR, contrary to some studies that suggest that only regular, sustained, and high-intensity exercise can be beneficial for DR \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. Our results suggest that focused participation in WW mode over fewer days is the most cost-effective mode of exercise for DR, an important finding that liberates those who do not have the time to consistently exercise every day.\u003c/p\u003e \u003cp\u003eHb acts as an oxygen carrier in the blood, and our study found similar results to previous studies that high levels of Hb are associated with a reduced risk of DR. Significant inverse associations between Hb levels and DR risk have been found, both based on the data found in the 2005\u0026ndash;2008 NHANES study \u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e and the results of a study based on a cross-section of the Korean population \u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. There is limited evidence of a positive association between Hb and exercise, such as anaemic female university students in Dubai exercising less than those without anaemia \u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. However, there are contrary studies that found a positive correlation between sedentary time and Hb \u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. The updated finding of our study is the significant positive correlation presented between WW mode and Hb level compared to inactive, however no effect of RA mode on Hb level was found and Hb level mediated the potential effect of PA in association with DR risk with a mediation ratio of 5.23%. In the present study, it can be ventured that moderate exercise affects Hb levels in vivo, and Hb levels further affect arterial oxygen concentration, which in turn affects retinal tissue oxygenation function and modulates the occurrence of DR.\u003c/p\u003e \u003cp\u003eThe main strengths of this study are the use of a large and representative 6-period NHANES dataset, and the fact that we tried to take into account as much as possible a wide range of information on demographic factors and health-related behaviors as covariates, which improves the robustness of the results. The classification of PA into different patterns according to WW provided a more rational assessment of the relationship between PA and DR in adults. However, several limitations of our study must be noted. First, for the diagnosis of DR, the diagnosis of outcome could not be further graded in terms of the severity of DR, except for 2007\u0026ndash;2008, when it was determined by fundus examination, which was based on the self-reported DIQ questionnaire. Second, the presence of reverse causality could not be inferred in this study. In addition, PA was also collected through questionnaires, which may be biased by individual cognitive judgements. In conclusion, the study needs to have more rigorously designed intervention trials or prospective cohort studies to consolidate the current findings.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eCompared with inactive adults, WW adults had a lower risk of DR. Furthermore, no significant difference in DR risk was found between WW and regularly active adults, suggesting that moderate amounts of planned PA are more important for DR prevention. A deeper look at the intrinsic link between the two may be due to the fact that WW patterns increase Hb levels in the body, thereby preventing and controlling the risk of DR. Therefore, the WW exercise pattern provides a useful strategy for the prevention and control of DR.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eAuthor Contributions\u003c/p\u003e\n\u003cp\u003eConceptualization, ZFK and BHL; collected, analyzed data, writing-original manuscript preparation, BHL and BBN; reviewing, editing, and revising the paper, XYH and YPS. All authors approved the final manuscript as submitted and agreed to be accountable for all aspects of the work. All authors have read and approved the final manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eChinese medicine inheritance and innovation \u0026quot;Millions upon millions\u0026quot; talent project (Qihuang project) Qihuang scholars (No. 284 of the National Chinese Medicine Education Development [2018]).\u003c/p\u003e\n\u003cp\u003eConflicts of Interest\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflict of interest.\u003c/p\u003e\n\u003cp\u003eAcknowledgments\u003c/p\u003e\n\u003cp\u003eThanks to all the participants and staff of the NHANES for their tremendous contributions in data collection, management, and publication.\u003c/p\u003e\n\u003cp\u003eData availability\u003c/p\u003e\n\u003cp\u003ePublicly available datasets were analyzed in this study. This data can be found here: https://www.cdc.gov/nchs/nhanes/.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eLin KY, Hsih WH, Lin YB, Wen CY, Chang TJ. 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The role of inflammation in immune system of diabetic retinopathy: Molecular mechanisms, pathogenetic role and therapeutic implications. Front Immunol. 2022; 13:1055087. doi:10.3389/fimmu.2022.1055087.\u003c/li\u003e\n\u003cli\u003eSabanayagam C, Chee ML, Banu R, Cheng CY, Lim SC, Tai ES,\u003cem\u003e et al.\u003c/em\u003e Association of Diabetic Retinopathy and Diabetic Kidney Disease With All-Cause and Cardiovascular Mortality in a Multiethnic Asian Population. JAMA Netw Open. 2019; 2(3):e191540. doi:10.1001/jamanetworkopen.2019.1540.\u003c/li\u003e\n\u003cli\u003eCao K, Wang B, Friedman DS, Hao J, Zhang Y, Hu A,\u003cem\u003e et al.\u003c/em\u003e Diabetic Retinopathy, Visual Impairment, and the Risk of Six-Year Death: A Cohort Study of a Rural Population in China. 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Diabetes Metab Syndr. 2019; 13(5):2939-2946. doi:10.1016/j.dsx.2019.07.046.\u003c/li\u003e\n\u003cli\u003eRen C, Liu W, Li J, Cao Y, Xu J, Lu P. Physical activity and risk of diabetic retinopathy: a systematic review and meta-analysis. Acta Diabetol. 2019; 56(8):823-837. doi:10.1007/s00592-019-01319-4.\u003c/li\u003e\n\u003cli\u003ePraidou A, Harris M, Niakas D, Labiris G. Physical activity and its correlation to diabetic retinopathy. J Diabetes Complications. 2017; 31(2):456-461. doi:10.1016/j.jdiacomp.2016.06.027.\u003c/li\u003e\n\u003cli\u003eLi X, Chen M. Correlation of hemoglobin levels with diabetic retinopathy in US adults aged \u0026gt;/=40 years: the NHANES 2005-2008. Front Endocrinol (Lausanne). 2023; 14:1195647. doi:10.3389/fendo.2023.1195647.\u003c/li\u003e\n\u003cli\u003eLee MK, Han KD, Lee JH, Sohn SY, Jeong JS, Kim MK, et al.High hemoglobin levels are associated with decreased risk of diabetic retinopathy in Korean type 2 diabetes. Sci Rep. 2018; 8(1):5538. doi:10.1038/s41598-018-23905-2.\u003c/li\u003e\n\u003cli\u003eAl Sabbah H. Prevalence of overweight/obesity, anaemia and their associations among female university students in Dubai, United Arab Emirates: a cross-sectional study. J Nutr Sci. 2020; 9:e26. doi:10.1017/jns.2020.23.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-4866922/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4866922/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDiabetic retinopathy (DR), the leading cause of vision loss in the elderly, coupled with limited treatment options, has prompted efforts to identify modifiable risk factors associated with DR. The purpose of this study was to explore the association between WW physical activity patterns and DR risk in US adults and to examine how Hb levels mediate this relationship. Cross-sectional study data were obtained from nationally representative NHANES data from 2007-2018. PA patterns were categorized according to inactive, insufficiently active, WW, and regularly active (RA). Multivariate logistic regression models adjusting for demographics, behavioral factors, and health conditions were used to explore the association between PA patterns and DR. Finally, mediation analyses verified whether Hb mediated the relationship between PA and DR. The study ultimately included 5092 U.S. adults, including 857 participants with DR and 4235 participants with DM without DR. Multivariate logistic regression modelling indicated that both WW (OR=0.601, 95% CI=0.452-0.798, \u003cem\u003eP\u003c/em\u003e\u0026lt;0.001) and RA (OR=0.728, 95% CI=0.554-0.956, \u003cem\u003eP\u003c/em\u003e=0.023) were significant protective factors for DR when compared to inactive adults, and the association between RA insufficiently active, WW did not show a significant association with DR. Mediation analysis showed a significant mediation effect of Hb on the association between PA patterns and DR risk, with a mediation ratio of 5.23%. Our study reveals that WW and RA activity patterns are protective factors for DR and that Hb levels mediate this association. This suggests that WW activity patterns are more cost-effective for the prevention of DR.\u003c/p\u003e","manuscriptTitle":"Hemoglobin mediates the link between 'weekend warrior' activity pattern and diabetic retinopathy","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-10-17 08:57:51","doi":"10.21203/rs.3.rs-4866922/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-12-02T09:31:52+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-11-30T16:58:57+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-11-25T22:07:09+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"179540686514479913200314055323018472704","date":"2024-11-23T19:57:27+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"201477209585740635889854653665362736726","date":"2024-11-22T10:50:55+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"313694418526576663354294366457516455234","date":"2024-11-21T10:19:00+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-11-04T06:33:13+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"179637674825914982343028766037623560583","date":"2024-10-21T01:12:27+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-10-17T17:00:30+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-10-17T16:45:11+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-08-29T06:35:24+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-08-27T12:56:10+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2024-08-06T08:35:23+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"94284a59-42e5-4a91-a2cc-b5a0dea15b33","owner":[],"postedDate":"October 17th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":38178447,"name":"Health sciences/Health care/Geriatrics"},{"id":38178448,"name":"Health sciences/Endocrinology/Endocrine system and metabolic diseases"}],"tags":[],"updatedAt":"2025-02-10T16:02:39+00:00","versionOfRecord":{"articleIdentity":"rs-4866922","link":"https://doi.org/10.1038/s41598-024-84736-y","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-02-08 15:57:41","publishedOnDateReadable":"February 8th, 2025"},"versionCreatedAt":"2024-10-17 08:57:51","video":"","vorDoi":"10.1038/s41598-024-84736-y","vorDoiUrl":"https://doi.org/10.1038/s41598-024-84736-y","workflowStages":[]},"version":"v1","identity":"rs-4866922","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4866922","identity":"rs-4866922","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}