Association of Dietary and Lifestyle Inflammation Score with sleep quality and mental health in hemodialysis patients: A multicenter cross-sectional study

Association of Dietary and Lifestyle Inflammation Score with sleep quality and mental health in hemodialysis patients: A multicenter cross-sectional study | 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 Research Article Association of Dietary and Lifestyle Inflammation Score with sleep quality and mental health in hemodialysis patients: A multicenter cross-sectional study Mohadeseh Soleimani Damaneh, Hossein Bavi Behbahani, Meysam Alipour, and 9 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4734732/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 01 Jul, 2025 Read the published version in Nutrition & Metabolism → Version 1 posted 14 You are reading this latest preprint version Abstract Background Poor sleep quality and mental disorders are common issues among patients undergoing dialysis. Diet and lifestyle may be associated with sleep hygiene and mental health. The current study aimed to evaluate the association between the Dietary and Lifestyle Inflammation Score (DLIS) and mental health, and sleep quality among Iranian hemodialysis patients. Methods This multicenter cross-sectional study was conducted on 423 patients undergoing hemodialysis in eight centers in three cities. The DLIS was calculated using information from a validated 168-item semi-quantitative food frequency questionnaire. Mental health was evaluated using the 21-item depression, anxiety, and stress scale (DASS-21) and the Pittsburgh Sleep Quality Index (PSQI) was used to assess sleep quality. Other assessments included physical activity levels, biochemical parameters, and dialysis data of patients. Statistical analyses using SPSS software were conducted to identify associations. Results The mean ± standard deviation of the age and BMI were 52.84 ± 14.63 years and 24.8 ± 5.11 kg/m 2 , respectively. 58.9% of participants were men. After controlling for potential confounders, participants in the top quartile of DLIS had greater odds of having poor sleep quality (OR: 3.18; 95% CI: 1.71–5.90), depression (OR: 1.94; 95% CI: 1.06–3.54), anxiety (OR: 2.82; 95% CI: 1.51–5.27), and stress (OR: 2.15; 95% CI: 1.14–4.03) compared with those in the bottom quartile. Conclusion Our findings showed that higher dietary and lifestyle inflammatory potential, characterized by higher DLIS, was positively associated with psychological disorders and poor sleep quality. hemodialysis sleep quality Inflammation Dietary pattern Lifestyle Mental health Figures Figure 1 Introduction End-Stage Renal Disease (ESRD) is a leading global cause of morbidity and mortality ( 1 ). For individuals with ESRD, hemodialysis (HD) is the principal and primary treatment ( 2 ). The prevalence of sleep disorders and mental illnesses such as depression, anxiety, and stress-related disorders in HD patients is significantly higher than that in the general population ( 2 – 5 ). It has been observed that sleep disruptions affect over 80% of dialysis patients ( 2 ). Additionally, the prevalence of depression in patients receiving dialysis is almost 15% higher than in those with a kidney transplant ( 6 ). Sleep and psychiatric disorders raise the risk of cardiovascular disease, malnutrition, and inflammation affect the risk of morbidity and mortality in hemodialysis patients ( 7 ). While several variables can impact sleep hygiene and mental health in patients on maintenance HD, including age ( 8 ), gender ( 9 ), uremic pruritus ( 10 ), and length of dialysis ( 11 , 12 ), low-grade systemic inflammation has been identified as an essential predictor by previous studies ( 14 , 15 ). The primary risk factors for systemic inflammation are thought to be obesity, age, tobacco use, physical inactivity, and unhealthy diets ( 13 ). It has been reported that a more inflammatory diet, as estimated by the dietary inflammatory index (DII), is significantly associated with symptoms of depression, anxiety, distress, and poor sleep quality ( 14 , 15 ). However, DII mostly focuses on specific nutrients without considering the dietary intake of food groups and the interaction among nutrients in the body or their synergistic effects. Novel inflammatory indices, including dietary inflammation scores (DIS) and lifestyle inflammation scores (LIS) in the Byrd et al. study, were introduced to capture the combined effect of diet and lifestyle characteristics, such as body mass index (BMI), physical activity, alcohol consumption, and smoking, on chronic inflammation ( 16 ). Some studies have reported that higher score of LIS and DIS is associated with increased risk of chronic diseases and their mortality such as cancers, and diabetes ( 17 – 19 ), to the best of our knowledge, no study has yet investigated the relationship between these indices and mental health disorders and sleep quality in maintenance HD patients. The current study examined the relationships between mental health conditions, sleep quality, and dietary and lifestyle inflammation score (DLIS) in HD patients. Method Study population This multi-center cross-sectional study was performed on 423 HD patients in five government and three private dialysis centers in Ahvaz, Shiraz, and Shushtar cities, Iran. Inclusion criteria included adult patients (aged ≥ 18 years), and receiving HD for at least 6 months. We excluded participants with enteral or parenteral feeding, active neoplastic disease, major amputation (lower/upper extremities), diagnosis of cancer, acute or chronic pancreatitis, irritable bowel syndrome, prolonged gastrointestinal symptoms, acute or chronic pancreatitis, hepatic insufficiency, and energy intake less than 800 kcal/d or above 4,200 kcal/d. Data collection Sociodemographic and clinical data including gender, age, marital status, time of dialysis start, frequency and duration of dialysis, fluid intake, urine volume, Intradialytic weight gain, prescription medicines, and comorbidities (diabetes mellitus and hypertension) were extracted from the dialysis unit's database. Diabetes was defined as a history of diabetes mellitus or the use of anti-diabetic drugs. Hypertension was defined as the recording of hypertension in medical records or the use of antihypertensive drugs. Other factors, including BMI, job, education level, monthly income, physical activity, and smoking status, were gathered by researchers according to standard operating procedures or through a questionnaire administered via interviews. Digital scales were used to measure the participants’ body weight to the nearest 100 gr with light clothes and without shoes at the end of the dialysis session. The BMI was calculated using participants’ weight and height, measured at the end of their dialysis session. A stadiometer was used to measure height to the nearest 0.5 cm standing while the individuals were barefoot. BMI was calculated as weight (kg) divided by height (m 2 ). Physical activity was measured using the International Physical Activity Questionnaire (IPAQ), which contains seven questions, the validity of which has been confirmed in HD patients and the results were expressed as metabolic equivalent hours per week (METs hr/wk) ( 20 ). Biochemical indicators, including serum albumin, calcium, phosphate, creatinine, potassium, serum total iron binding capacity, and urea nitrogen, were determined from the dialysis unit's database for the same month. Dialysis adequacy was calculated, based on the Kt/V index, using dialysis length, post-dialysis weight, ultrafiltration volume, and pre-and post-dialysis serum urea concentration ( 21 ). Dietary assessment Trained dietitians obtained information on the usual food intake of all participants using a valid and reliable 168-item semi-quantitative Food Frequency Questionnaire (FFQ) with standardized servings ( 22 ). The participants were requested to provide information on the frequency of their consumption of each food item, indicating whether it was consumed daily, weekly, or monthly, over the preceding year. These reported frequencies were then converted into gram-weight equivalents. To determine the total energy and nutrient intake, Nutritionist IV software (the Hearst Corporation) was utilized, with adjustments made to accommodate Iranian food items. Calculating the Dietary and Lifestyle Inflammation Score The inflammatory scores of participants were determined using dietary data derived from FFQ. The DIS ( 23 ) is the sum of 19 weighted components, including 18 whole food and beverage groups and 1 micronutrient supplement score. DIS encompasses originally included leafy greens and cruciferous vegetables, tomatoes, apples and berries, deep yellow or orange vegetables and fruit, other fruits, and natural fruit juices, other vegetables, legumes, fish, poultry, red and organ meats, processed meats, added sugars, high-fat dairy, low-fat dairy and tea, nuts, other fats, refined grains, starchy vegetables, and supplements intake. Mixed dishes were disaggregated to food groups. All of these components were used other than supplement score due to the lack of information regarding supplement use in the study participants. To compute the DIS score, each food item was multiplied by its specific weight (explained in Byrd et al. study) to determine the weighted values of each item. The weighted values were then standardized using the Z-score (to a mean of zero and SD of 1). Finally, all the items’ standardized weighted values were summed to calculate the DIS score for participants. The LIS components include smoking status (“former/never” or “current”), physical activity (“high or moderate” and “low or no physical activity”), BMI (kg/m 2 ) (“overweight (25–29.9)” and “obese (≥ 30)”), and alcohol intake. Alcohol intake was not included in the score because of the lack of information regarding the intake of alcohol in Iranian culture. Finally, all the weighted values were summed to calculate the LIS score. In this study, the DLIS for each subject was calculated by summing the DIS and LIS. Higher DLIS (more positive) presents a more pro-inflammatory diet and lifestyle. Assessment of sleep quality and mental health A Persianvalidated version of the Pittsburgh Sleep Quality Index (PSQI) was used to collect data on sleep hygiene ( 24 ). The PSQI is a common tool for assessing sleep problems associated with anxiety, stress, depression, and schizophrenia. The selfreported questionnaire contains 19 questions divided into seven category scores including subjective sleep quality, sleep latency, sleep duration, habitual sleep efficiency, sleep disorders, the use of sleep medications, and daytime dysfunction caused by sleep disorders. The score of each part is 0–3, and the highest total score is 21. The global PSQI score over 5 is considered as poor quality of sleep. Validity and reliability of this questionnaire have been investigated in Iran ( 25 ). Depression, Anxiety and Stress Scale (DASS-21) was used to screen depression, anxiety and stress. DASS-21 is a valid and reliable questionnaire to measure status of negative well beings for general population. The questionnaire contains 21 items with 3 subscales. Each subscale includes 7 questions in which each item rates on a 4-point Likert scale 0–3 measuring severity of depression, anxiety and stress. The participants achieve scores of ≥ 10, ≥8, and ≥ 15, they were considered to have depression, anxiety, and stress respectively( 26 ). Statistical analysis The statistical analysis was conducted using IBM SPSS Statistics software (Version 24) from IBM SPSS Statistics, Armonk, USA. The normality of variables was assessed using the Kolmogorov-Smirnov test. Baseline characteristics of the individuals are expressed as the mean ± SD for continuous variables and percentages for categorical variables. Participants were also categorized according to quartiles of DLIS cutoff points. Differences in variables across DLIS quartiles were examined using one-way analysis of variance (ANOVA) and chi-square test was used to compare the categorical variables across quartiles. Energy and nutrient intakes by DLIS quartiles were evaluated using ANOVA. Odds ratios (ORs) and 95% confidence intervals (CIs) were calculated to assess the risk of psychological disorders (depression, anxiety, stress) and poor sleep quality across quartiles DLIS applying multiple logistic regression in crude and multivariable-adjusted models. In the first adjusted model, the confounding effects of center type, city, age, sex, diabetes, hypertension, job, marital status, education, income status, inter-dialysis weight gain, dialysis vintage, dialysis time, frequency of hemodialysis sessions, fluid intake, and urine volume were controlled. Model 2 was additionally controlled for to energy intake, and medication prescriptions. Statistical significant level was considered as p-values less than 0.05. Results As shown in Fig. 1, 324 of the 755 patients that were assessed across 8 hemodialysis centers were not included in the study for a variety of reasons. Thus, 431 patients in total consented to take part in the study. Due to dietary misreporting, eight patients were excluded from the final study. Therefore, 423 patients were included in the final analysis. The mean ± SD age of 423 HD patients who contributed to the current study was 52.84 ± 14.63 years. Most patients were men (58.9%), married (73.8%), and either housekeeper (34.3%) or retired (23.2%). 42.6% of study participants had diabetes (n = 180), and 74.7% had hypertension (n = 316). The mean ± SD dialysis vintage was 49.41 ± 61.48 months. Moreover, the mean ± SD BMI was 24.8 ± 5.11 kg/m 2 . According to nutritional status, 8.4% of patients were underweight, 44.4% had normal weight, 33.3% were overweight, and 13.4% were obese. 10.2% of the patients in this study were smokers (n = 43). The prevalence of poor sleep quality in our study population was 60.5% (n = 256). Overall, 53.7% of study participants were depressed (n = 227), 53.0% were anxious (n = 224) and 47.3% stress (n = 200). Table 1 shows the general characteristics of study population across quartiles of DLIS. The proportion of current smokers increased in the top quartile of the DLIS compared with the lowest quartile. Compared with those in the lowest quartiles of DLIS, significant differences were observed for the income status of participants (P = 0.03). Participants in the highest quartile of the DLIS had significantly higher scores of all three mental health disorders (P < .001). Compared to participants in the first quartile of DLIS, the number of participants with depression (P = 0.001), anxiety (P = 0.001), and stress (P < .001), and poor sleep quality (P = 0.01) was higher in the highest quartile. Table 1 Characteristics of patients undergoing hemodialysis across quartiles of Dietary and Lifestyle Inflammation Score(DLIS) . Characteristics DLIS quartiles P value Q1 (N = 108) Q2 (N = 102) Q3 (N = 103) Q4 (N = 110) Score range Age, y 50.78 ± 14.76 54.68 ± 13.91 54.04 ± 15.19 52.05 ± 14.49 0.19 Male(%) 61(56.48) 56(54.9) 62(60.19) 69(62.73) 0.63 Job, N(%) 0.07 Unemployed 17(15.74) 11(10.78) 36(34.95) 23(20.91) Housekeeper 36(33.33) 40(39.22) 26(25.24) 33( 30 ) Retired 23(21.3) 28(27.45) 2(1.94) 21(19.09) Employee 12(11.11) 2(1.96) 15(14.56) 12(10.91) Self-employment 18(16.67) 16(15.69) 7(6.8) 14(12.73) Others 2(1.85) 5(4.9) 36(34.95) 7(6.36) City < .001 Ahvaz 44(40.74) 38(37.25) 42(40.78) 60(54.55) Shushtar 7(6.48) 15(14.71) 26(25.24) 21(19.09) Shiraz 57(52.78) 49(48.04) 35(33.98) 29(26.36) Marital status, N(%) 0.20 Married 71(65.74) 75(73.53) 76(73.79) 90(81.82) Single 29(26.85) 18(17.65) 19(18.45) 16(14.55) Divorced 6(5.56) 5(4.9) 3(2.91) 1(0.91) Dead spouse 2(1.85) 4(3.92) 5(4.85) 3(2.73) Education, N(%) 0.09 < 12 years 80(74.07) 78(76.47) 90(87.38) 86(78.18) ≥ 12 years 28(25.93) 24(23.53) 13(12.62) 24(21.82) Income status, N(%) 0.03 20 million Rials 12(11.11) 4(3.92) 6(5.83) 4(3.64) Physical activity(MET/min/week) 864.13 ± 3388.29 417.87 ± 1189.7 183.47 ± 356.81 520.55 ± 2677.44 0.18 Sleep Sleep duration 6.63 ± 2.07 6.38 ± 2.94 6.33 ± 1.97 6.41 ± 3.03 0.83 Sleep quality PSQI Score 6.12 ± 3.93 6.61 ± 3.7 7.25 ± 3.56 7.18 ± 3.42 0.08 Sleep quality category good, N (%) 58(53.7) 43(42.16) 32(31.07) 34(30.91) 0.01 poor, N (%) 50(46.3) 59(57.84) 32(31.07) 76(69.09) Depression Depression Score 11.00 ± 11.07 10.39 ± 11.77 17.59 ± 14.19 14.56 ± 12.54 < .001 Depression category No, N (%) 57(52.8) 60(58.8) 35(34.0) 44(40.0) 0.001 Yes, N (%) 51(47.2) 42(41.2) 68(66.0) 66(60.0) Anxiety Anxiety Score 9.09 ± 9.11 9.51 ± 9.22 14.54 ± 14.22 13.98 ± 11.74 < .001 Anxiety category No, N (%) 64(59.3) 56(54.9) 38(36.9) 41(37.3) 0.001 Yes, N (%) 44(40.7) 46(45.1) 65(63.1) 69(62.7) Stress Stress Score 14.19 ± 12 13.35 ± 11.68 21.05 ± 12.42 17.75 ± 12.82 < .001 Stress category No, N (%) 64(59.3) 69(67.6) 39(37.9) 51(46.4) < .001 Yes, N (%) 44(40.7) 33(32.4) 64(62.1) 59(53.6) Note: Data for quantitative variables are presented as means ± SD, obtained from ANOVA. Data for qualitative variables are presented as frequencies (percentages) and analyzed using chi-square tests. Abbreviations: DLIS, dietary and lifestyle inflammation score; PSQI, Petersburg Sleep Quality Questionnaire. Dialysis data of HD patients across quartiles of DLIS has illustrated in Table 2 . Participants in the highest quartiles of DLIS had higher frequency of dialysis per week and fluid intake. Other variables did not significantly differ across DLIS quartiles. Table 2 Dialysis data of patients undergoing hemodialysis across quartiles of DLIS. Variables DLIS quartiles P value Q1 (N = 108) Q2 (N = 102) Q3 (N = 103) Q4 (N = 110) Dialysis vintage, month 51.72 ± 56.5 52.02 ± 57.94 49.45 ± 81.72 44.72 ± 45.92 0.80 Dialysis time, hours 2.83 ± 0.49 2.88 ± 0.53 2.96 ± 0.6 2.85 ± 0.47 0.29 Frequency dialysis per week, Time/week 3.85 ± 0.48 3.74 ± 0.48 3.62 ± 0.57 3.57 ± 0.43 < .001 Fluid intake, ml 1370.64 ± 1457.42 1275.49 ± 996.86 976.7 ± 682.42 1251.45 ± 723.26 0.03 Urine volume, ml 429.91 ± 614.05 350.91 ± 523.02 299.26 ± 460.31 442.94 ± 556.55 0.18 Intradialytic weight gain ,kg 2.05 ± 1.14 2.04 ± 1.19 1.93 ± 1.25 2.02 ± 1.17 0.90 Comorbidities Diabetes(n, %) 40(37.04) 42(41.18) 49(47.57) 49(44.55) 0.44 Hypertension(n, %) 83(76.85) 76(74.51) 74(71.84) 83(75.45) 0.86 Laboratory parameters Creatinine, mg/dL 8.31 ± 3.13 7.78 ± 4.49 7.54 ± 2.84 8.54 ± 4.34 0.19 Sodium, mmol/L 139.82 ± 3.81 138.83 ± 5.06 138.91 ± 4.27 140.12 ± 6.24 0.14 Potassium, mmol/L 5.18 ± 0.87 5.04 ± 0.86 4.99 ± 0.84 5 ± 0.74 0.33 Calcium, mg/dL 8.38 ± 0.96 8.33 ± 1 8.55 ± 0.72 8.34 ± 0.89 0.65 Phosphate, mg/dL 5.27 ± 1.51 5.07 ± 1.38 4.97 ± 1.22 5.35 ± 1.34 0.15 Kt/V 1.28 ± 0.52 1.25 ± 0.63 1.21 ± 0.41 1.17 ± 0.46 0.37 Albumin, g/ dL 5.92 ± 19.15 4.08 ± 0.57 4.1 ± 0.76 4.17 ± 0.6 0.42 Serum total iron binding capacity, µg/dL 283.51 ± 90.26 294.45 ± 104.36 305.63 ± 85.59 315.64 ± 96.55 0.07 Medication prescriptions Calcium carbonate 500 mg, time/day 1.59 ± 1.93 1.18 ± 1.71 1.13 ± 1.81 1.34 ± 1.8 0.24 Sevelamer hydrochloride 800 mg, time/day 0.81 ± 1.47 1.1 ± 1.67 0.73 ± 1.27 0.83 ± 1.5 0.29 Calcitriol 0.25 mcg, time/day 0.92 ± 1.38 0.52 ± 0.89 0.7 ± 1.17 0.58 ± 1.14 0.06 furosemide time/day 0.38 ± 0.86 0.51 ± 1.13 0.43 ± 0.95 0.32 ± 0.8 0.47 Corticosteroids, N(%) 8(7.41) 2(1.96) 3(2.91) 3(2.73) 0.14 Lipid-lowering drugs, N(%) 21(19.44) 16(15.69) 18(17.48) 13(11.82) 0.46 Note: The data are presented as "mean ± SD". The significant difference based on One-way ANOVA (P < .05) Dietary intakes and DIS and LIS components of participants according to quartiles of DLIS are presented in Table 3 . Participants in the highest quartiles of DLIS had significantly higher intakes of energy and protein (P = 0.002), while the intake of carbohydrate (P < 0.001) and fat (P = 0.006) were lower, compared to participants in the lowest quartile. Additionally, BMI was increased significantly across DLIS quartiles (P < 0.001). Table 3 Dietary intakes, DIS and LIS components of patients undergoing hemodialysis according to quartiles of the DLIS. Nutrient Intake Variables DLIS quartiles P value Q1 (N = 108) Q2 (N = 102) Q3 (N = 103) Q4 (N = 110) Energy(Kcal/d) 2391.66 ± 1002.83 2112.34 ± 724.5 2002.61 ± 662.67 2422.35 ± 896.42 < .001 Carbohydrate(g/d) 89.48 ± 43.28 77.84 ± 32.12 71.82 ± 26.99 80.77 ± 31.3 0.002 Protein(g/d) 349.4 ± 149.92 309.61 ± 107.22 302.59 ± 114.47 375.79 ± 153.98 < .001 Total fat(g/d) 75.34 ± 44.09 64.87 ± 31.79 58.09 ± 25.94 68.25 ± 38.02 0.006 DIS component Leafy greens and cruciferous vegetables(g/d) 35.93 ± 58.28 29.43 ± 45.01 25.22 ± 19.87 34.97 ± 30.83 0.19 Tomatoes(g/d) 28.89 ± 38.67 15.6 ± 22.28 16.87 ± 20.49 20.53 ± 27.17 0.003 Apples and berries(g/d) 46.42 ± 42.02 42.34 ± 38.92 49.75 ± 93.88 36.71 ± 32.24 0.37 Deep yellow or orange vegetables and fruit(g/d) 47.4 ± 61.01 42.16 ± 66.95 51.74 ± 75.09 77.09 ± 114.09 0.01 Other fruits and real fruit juices(g/d) 163.9 ± 161.83 175.78 ± 157.92 174.52 ± 138.04 189.34 ± 212.22 0.74 Other vegetables(g/d) 80.68 ± 88.9 64.5 ± 55.92 65.57 ± 55.51 68.7 ± 52.6 0.24 Legumes(g/d) 56.84 ± 43.15 56.51 ± 38.86 54.33 ± 42.74 59.51 ± 48.48 0.85 Fish(g/d) 13.68 ± 17.46 12.76 ± 15.29 14.32 ± 21.74 22.91 ± 31.75 0.003 Poultry(g/d) 49.53 ± 65.36 42.55 ± 45.7 49.95 ± 70.95 55.49 ± 61.9 0.50 Red and organ meats(g/d) 18.48 ± 23.18 15.25 ± 13.77 13.87 ± 12.08 20.26 ± 26.98 0.08 Processed meats(g/d) 4.94 ± 10.75 4.1 ± 6.92 3.94 ± 8.09 4.97 ± 8.81 0.75 Added sugars(g/d) 71.6 ± 125.83 61.68 ± 77.52 86.83 ± 110.33 76.19 ± 83.4 0.35 High-fat dairy(g/d) 61.59 ± 75.69 59.01 ± 87.59 64.79 ± 84.71 67.61 ± 86.81 0.88 Low-fat dairy(g/d) 107.35 ± 169.36 89.57 ± 126.87 100 ± 110.5 95.38 ± 150.23 0.82 Coffee and tea(g/d) 346.32 ± 304.14 344 ± 312.76 338.14 ± 346.14 354.29 ± 301.24 0.98 Nuts(g/d) 6.58 ± 7.55 6.85 ± 8.23 8.17 ± 13.8 12.98 ± 31.76 0.03 Other fats(g/d) 29.28 ± 22.15 23.4 ± 17.44 26.44 ± 18.69 29.86 ± 23.83 0.09 Refined grains and starchy vegetables(g/d) 457.31 ± 212.62 464.27 ± 267.68 448.04 ± 291.83 478.6 ± 267.69 0.85 LIS component BMI(kg/m 2 ) 24.34 ± 4.35 23.21 ± 3.95 24.86 ± 4.58 26.82 ± 6.47 < .001 Underweight 8(7.41) 16(15.69) 7(6.8) 6(5.45) < .001 Normal weight 60(55.56) 56(54.9) 49(47.57) 23(20.91) Overweight 29(26.85) 29(28.43) 32(31.07) 51(46.36) Obese 11(10.19) 1(0.98) 15(14.56) 30(27.27) Physical activity;(MET-h/d) 0.68 low 91(84.26) 81(79.41) 86(83.5) 92(83.64) moderate 17(15.74) 19(18.63) 14(13.59) 16(14.55) Sever 91(84.26) 2(1.96) 3(2.91) 2(1.82) Current smoker; n(%) 8(7.41) 6(5.88) 91(88.35) 17(15.45) 0.08 Note: Data for quantitative variables are presented as means ± SD, obtained from ANOVA. Data for qualitative variables are presented as frequencies (percentages) and analyzed using chi-square tests. Abbreviations: DIS, dietary inflammation score; DLIS, dietary and lifestyle inflammation score; LIS, lifestyle inflammation score; BMI, body mass index. The association of DLIS with the risk of poor sleep quality, depression, anxiety, and stress are shown in three models in Table 4 . In the first model (univariate model), the odds of poor sleep quality were higher in individuals in the highest quartiles of the DLIS (OR: 2.59; 95% CI: 1.49, 4.51; P value < 0.001). This significant positive association between DLIS and poor sleep quality persisted in the fully adjusted model that controlled for city, age, sex, diabetes, hypertension, job, marital status, education, income status, inter-dialysis weight gain, dialysis vintage, dialysis time, frequency of hemodialysis sessions, fluid intake, and urine volume, energy intake, and medication prescriptions (OR: 3.18, 95% CI: 1.71, 5.9, P value < 0.001). In addition, in crude model, compared to those in the bottom quartile, individuals in the top quartile of DLIS had significantly higher risk of depression (OR: 1.68; 95% CI: 0.98, 2.87; P value = 0.004), anxiety (OR: 2.45; 95% CI: 1.42, 4.22; P value < 0.001), and stress (OR: 1.68; 95% CI: 0.98, 2.88; P-trend = 0.002). A similar pattern was shown in the third model (full model multivariate analysis) for depression, anxiety, stress. There was a significant positive association between the DLIS and increased likelihood of depression (OR: 1.94; 95% CI: 1.06–3.54), anxiety (OR: 2.82; 95% CI: 1.51–5.27), and psychological distress (OR: 2.15; 95% CI: 1.14–4.03) Table 4 Crude and multivariable-adjusted odds ratio (95% CI) of the associations between DLIS and sleep quality, stress, anxiety and depression Sleep quality DLIS quartiles Q1(N = 108) Q2(N = 102) Q3(N = 103) Q4(N = 110) P-value Model 0 a 1(ref) 1.59(0.92, 2.75) 2.57(1.47, 4.52) 2.59(1.49, 4.51) < .001 Model 1 b 1(ref) 1.52(0.85, 2.7) 2.62(1.42, 4.82) 2.97(1.62, 5.43) < .001 Model 2 c 1(ref) 1.64(0.9, 3) 2.79(1.49, 5.25) 3.18(1.71, 5.90) < .001 Depression Model 0 a 1(ref) 0.78(0.45, 1.35) 2.17(1.25, 3.79) 1.68(0.98, 2.87) 0.004 Model 1 b 1(ref) 0.68(0.38, 1.22) 2.03(1.11, 3.73) 1.92(1.07, 3.47) 0.002 Model 2 c 1(ref) 0.74(0.4, 1.35) 2.06(1.11, 3.83) 1.94(1.06, 3.54) 0.003 Anxiety Model 0 a 1(ref) 1.19(0.69, 2.07) 2.49(1.43, 4.33) 2.45(1.42, 4.22) < .001 Model 1 b 1(ref) 1.12(0.62, 2.03) 2.47(1.34, 4.55) 2.74(1.49, 5.03) < .001 Model 2 c 1(ref) 1.15(0.62, 2.13) 2.45(1.32, 4.57) 2.82(1.51, 5.27) < .001 Stress Model 0 a 1(ref) 0.70(0.40, 1.22) 2.39(1.37, 4.15) 1.68(0.98, 2.88) 0.002 Model 1 b 1(ref) 0.63(0.34, 1.18) 2.59(1.39, 4.83) 2.06(1.12, 3.79) 0.001 Model 2 c 1(ref) 0.68(0.36, 1.29) 2.54(1.33, 4.84) 2.15(1.14, 4.03) 0.001 DLIS: dietary and lifestyle inflammation score P < .05 statistically significant by multivariable logistic regression. Model 0. binary logistic regression analysis without adjustment. Model 1. binary logistic regression analysis with center type, city, age, sex, diabetes, hypertension, job, marital status, education, income status, inter-dialysis weight gain, dialysis vintage, dialysis time, frequency of hemodialysis sessions, fluid intake, and urine volume. Model 2. binary logistic regression analysis with adjustment for model 2 in addition to energy intake, and medication prescriptions (corticosteroids, lipid-lowering drugs, calcium carbonate, calcitriol, sevelamer hydrochloride, frusemide). Discussion In the present study, the relation between adherence to a pro-inflammatory diet and lifestyle, reflected by DLIS, and mental health and sleep quality were investigated in a multicenter cross-sectional study of Iranian maintenance HD patients. The results revealed that there was a significant association between high DLIS and having poor sleep quality and mental health disorders including stress, anxiety, and depression among HD patients. Sleep disturbances are frequent consequences of ESRD and are a major cause of death and deterioration in these patients' quality of life ( 2 ). Numerous factors can impact sleep hygiene, including age, gender, comorbidities, anemia, uremic pruritus, and the length of dialysis treatment ( 27 ). However, previous research has indicated that diet and lifestyle are also important sleep hygiene predictors. It has been found that older adults who have a Mediterranean diet, which is regarded as a healthy eating pattern, have better sleep hygiene ( 28 ). A cross-sectional study consisting of 741 maintenance HD patients also indicated that a higher intake of dietary fiber in vegetables could enhance the quality of sleep ( 29 ). Jansen et al. in a cohort study with 4467 Mexican women, showed that participants in the highest quartiles of the modern Mexican pattern (tortillas and soda, along with low fiber and dairy products) had a 23% higher likelihood of having poor sleep quality compared to those in the lowest quartile ( 30 ). Due to concerns about phosphorus and potassium, HD patients are required to undergo significant lifestyle changes, including adhering to a typical renal diet limited to vegetables, fruits, nuts, legumes, dairy, and whole grains ( 31 ). Therefore, unhealthy eating habits may be more common in HD patients, and these habits can be related to poor sleep hygiene. In the current study, more than 60.5% of hemodialysis patients had poor sleep quality. This study showed that after controlling for potential confounders, HD patients with a pro-inflammatory diet and lifestyle were more likely to experience poor sleep quality. Our findings are consistent with other research that has demonstrated positive correlations between a pro-inflammatory diet indicated by higher scores of the dietary inflammatory index (DII), and short sleep duration, sleep disturbances, poor sleep quality, higher wake-after-sleep onset, and dysfunction during the day ( 32 – 35 ). Notably, the DIS is a novel dietary inflammatory index that may offer benefits over the DII; the DII mostly comprises certain anti/pro-inflammatory nutrients, and may not take into account other dietary components in foods, that can cause inflammation. Also, the DII mostly does not consider the impacts of nutrient interactions. Furthermore, in three populations, the DIS was found to have a stronger direct correlation with the circulating levels of inflammatory markers than the DII and Empirical Dietary Inflammatory Pattern (EDIP) ( 36 ). Moreover, previous studies have indicated that the lifestyle-related components of LIS, such as smoking, BMI, and physical activity, may also have a significant impact on metabolic homeostasis and inflammatory state. According to a population-based cohort study, a higher inflammatory potential of lifestyle, measured by the higher score LIS, was associated with an increased incidence of CKD in Iranian adults ( 37 ). Since the LIS only contains lifestyle and non-dietary components and the DIS only focuses on diet, we employed the combined DLIS index ( 16 ), which is a more comprehensive index that includes both dietary and non-dietary elements. Recent studies have demonstrated a clear correlation between the DLIS and the risk of developing chronic illnesses associated with systemic low grade inflammation such as metabolic syndrome and insulin resistance ( 38 , 39 ). Currently, there is growing evidence that inflammatory factors and sleep quality are significantly associated. The biological mechanisms underlying the relationship between dietary and lifestyle factors and the quality of sleep may include the regulation of sleep by inflammatory peptides ( 40 ), neuroendocrine and autonomic pathways connecting sleep to the immune system, and cytokine responses ( 41 ). It has been previously documented that inflammatory status and metabolic homeostasis may be significantly impacted by lifestyle factors such as physical activity, BMI, and smoking. Increased adipose tissue and a raised BMI are significantly correlated with pro-inflammatory biomarkers (adipokines, TNF-α, CRP, and interleukin-6) and systemic chronic inflammation ( 42 ), which in turn can cause poor sleep quality. It is reported that higher BMI is associated with a poor quality of sleep in young adults. However, some cross-sectional studies have not discovered any correlation between obesity as an inflammatory condition and diet-induced inflammation as evaluated by DII or EDII ( 43 , 44 ). The differences between our results and the results showing no association could be attributed to variations in the population, the study design, the assessed food items, the dietary indices (like the DII, which emphasizes nutrients rather than food groups), the tools used to assess dietary intake, and the influence of unmeasurable confounding factors. Another key lifestyle component that has demonstrated promise in terms of bettering overall sleep quality, reducing sleep latency, and improving sleep quality is physical activity ( 45 ). Nonetheless, studies have revealed that HD patients, for various reasons, are less physically active than healthy age-matched controls. Through a comprehensive analysis of 23 articles, a systematic review study discovered a favorable correlation between physical exercise and sleep quality across various demographic categories ( 46 ). Several processes can explain the relationship between physical activity and better sleep, such as the release of endorphins, which can reduce stress and anxiety and improve relaxation and sleep quality, circadian rhythm regulation, and increase anti-inflammatory cytokines production ( 47 ). Furthermore, a population-based study including 26,282 Chinese people revealed that smokers experienced far worse sleep quality and disruptions from their sleep compared to nonsmokers ( 48 ). Smoking negatively affects metabolism, β-cell dysfunction, and IR, which are mostly caused by an increase in inflammatory biomarkers and cytokines such as CRP ( 49 ). Our research also suggested that a lifestyle and dietary pattern with more pro-inflammatory qualities may be associated with an increased risk of mental health issues. Our results indicate that in our HD population, the prevalence of depression, anxiety, and stress was 53.7%, 53.0% and 47.3%, respectively. A study conducted by Palmer et al. involving an observational sample of 55,982 individuals with CKD revealed that approximately 25% of these patients experienced depression, and those with HD were at an increased risk ( 50 ). The association between single components of the DLIS and mental health disorders has been also investigated in previous research. A large body of research supports the link between mental problems and diet quality. The risk of depression and anxiety in Iranian adults was found to be negatively correlated with their adherence to the healthy eating index ( 51 ). A 2021 meta-analysis looked at the relationship between dietary potential for inflammation and mental health among 92,242 men and women from Asia, Europe, America, and Australia. The findings indicated that there is a substantial correlation between symptoms of depression, anxiety, and distress and a more inflammatory diet, as measured by the DII ( 52 ). However, in a cross-sectional study involving 400 health professionals conducted by Rostami et al., no significant associations were found between adherence to the Mediterranean-DASH Intervention for Neurodegenerative Delay (MIND) diet as an anti-inflammatory dietary pattern and odds of stress, anxiety, and depression either in the crude or multivariable-adjusted models ( 53 ). The study's conclusions may apply to only a small group of populations, male health professions, and might not be generalizable to females or other populations. Furthermore, the study's results were evaluated by self-reported questionnaires, which raises the possibility of recall bias and misreporting. Nevertheless, it is still unclear what precise biological process leads to depression or other mental illnesses. A growing body of evidence suggests that systemic inflammation, as indicated by high levels of CRP, may be a significant contributor to mental health problems like depression and anxiety ( 54 ). There is two-way comorbidity between inflammation and mental illness, meaning that increased inflammation of the body is associated with an increased risk of mental problems (such as depression), and depression itself is associated with increased behaviors that trigger inflammation, such as unhealthy eating habits ( 55 ). A comprehensive meta-analysis examining the contributions of main modifiable lifestyle factors to the prevention and treatment of mental disorders revealed that smoking is a causal factor of both common and severe mental illness, while physical activity protects against some mental disorders ( 56 ). Additionally, using data from the population-wide Austrian registry, obesity was found to be a significant risk factor for obtaining additional mental health diagnoses at every decade of adulthood, highlighting the significance of obesity as a pleiotropic promotor of health problems ( 57 ). The strength of the current study is that it is the first to examine the relationship between DLIS and sleep quality and mental health conditions in HD patients. Furthermore, our study includes a relatively large sample size from eight different hemodialysis centers in three cities with a variety of dietary and lifestyle habits. In-person interviews with participants were conducted using valid questionnaires to gather information on their dietary intakes and level of physical exercise. However, the current study had some limitations. The weights assigned to the DIS and LIS have been verified for the US population; they might not be suitable for other populations. Secondly, the reliance on self-reported measures for assessing sleep quality and mental health conditions may introduce response and recall bias. Some items were excluded in the calculation of DIS and LIS. The final DIS was calculated using 18 instead of 19 items and 3 instead of 4 for LIS. Conclusion The present findings suggest that higher adherence to a pro-inflammatory lifestyle and dietary habits, determined by the higher score of DLIS, is significantly associated with poor sleep quality and mental health disorders including depression, anxiety, and stress. To confirm these findings, further investigations in Iranian and other populations are recommended. Declarations Ethics approval and consent to participate This study was conducted according to the guidelines laid down in the Declaration of Helsinki and all procedures involving human patients were approved by the Ethics Committee of Shoushtar Faculty of Medical Sciences in Shoushtar, Iran (Registration no: IR.SHOUSHTAR.REC. 1403.20). Written informed consent was obtained from all subjects. Consent for publication Not applicable Acknowledgements: Authors wish to thank all patients who participated in this research project. Financial Disclosure : The authors declare that they received no grants, funding, or other forms of support from any organization for the purpose of conducting this work. Availability of data and materials The datasets used and/or analyzed during the current study available from the corresponding author on reasonable request. Author contribution Conceptualization: M.S.D., H.B.B., M.A.Methodology: M.S.D., H.B.B., M.A., A.Z.J., S.B.E., S.S.Resources: M.S.D., H.B.B., M.A., A.Z.J., S.B.E., S.S., S.S., S.K., S.K., P.T., F.F., H.S.D., R.G., E.G.Writing - Original Draft: M.S.D., H.B.B., M.A.Writing - Review & Editing: A.Z.J., S.B.E., S.S., H.B.B., M.A., M.S.D.Supervision: M.S.D., H.B.B., M.A., A.Z.J., S.B.E., S.S.Project administration: M.S.D., H.B.B., M.A.Investigation: M.S.D., H.B.B., M.A., S.S., S.K., S.K., P.T., F.F., H.S.D., R.G., E.G.Formal analysis: H.B.B., M.A.Visualization: H.B.B., M.A. All authors read and approved the final version of the manuscript. 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Behbahani","email":"data:image/png;base64,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","orcid":"","institution":"Shoushtar Faculty of Medical Sciences, Shoushtar, Iran","correspondingAuthor":true,"prefix":"","firstName":"Hossein","middleName":"Bavi","lastName":"Behbahani","suffix":""},{"id":340901231,"identity":"28801c43-952c-4e53-bad3-c188a9c33025","order_by":2,"name":"Meysam Alipour","email":"","orcid":"","institution":"Shoushtar Faculty of Medical Sciences, Shoushtar, 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Sciences","correspondingAuthor":false,"prefix":"","firstName":"Shiva","middleName":"","lastName":"Shokri","suffix":""},{"id":340901236,"identity":"e0de7218-7d36-460d-9352-19e46dc2f84f","order_by":6,"name":"Pardis Tofighzadeh","email":"","orcid":"","institution":"Ahvaz Jundishapur University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Pardis","middleName":"","lastName":"Tofighzadeh","suffix":""},{"id":340901237,"identity":"fb7fe9e7-ade2-4abf-a589-d2d824b66bd3","order_by":7,"name":"Fatemeh Fayazfar","email":"","orcid":"","institution":"Shiraz University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Fatemeh","middleName":"","lastName":"Fayazfar","suffix":""},{"id":340901240,"identity":"712e9392-813e-4603-aa8b-32e884bd91b0","order_by":8,"name":"Haleh Soltaniyan Dehkordi","email":"","orcid":"","institution":"Shoushtar Faculty of Medical Sciences, Shoushtar, Iran","correspondingAuthor":false,"prefix":"","firstName":"Haleh","middleName":"Soltaniyan","lastName":"Dehkordi","suffix":""},{"id":340901242,"identity":"71b20755-d319-4fe3-95af-881403b145fa","order_by":9,"name":"Elahe Ghadimi","email":"","orcid":"","institution":"Ahvaz Jundishapur University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Elahe","middleName":"","lastName":"Ghadimi","suffix":""},{"id":340901243,"identity":"99c41d2a-d948-4e4e-bfd3-e4544402500d","order_by":10,"name":"Siavash Babajafari Esfandabad","email":"","orcid":"","institution":"Shiraz University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Siavash","middleName":"Babajafari","lastName":"Esfandabad","suffix":""},{"id":340901244,"identity":"135f62ad-b521-4555-992b-f36528ded24f","order_by":11,"name":"Shokouh Shayanpour","email":"","orcid":"","institution":"Ahvaz Jundishapur University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Shokouh","middleName":"","lastName":"Shayanpour","suffix":""}],"badges":[],"createdAt":"2024-07-13 10:16:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4734732/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4734732/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12986-025-00958-5","type":"published","date":"2025-07-01T15:58:35+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":63272556,"identity":"bd7ac548-5017-4971-989d-a10b7ae7bfde","added_by":"auto","created_at":"2024-08-26 11:32:17","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":47927,"visible":true,"origin":"","legend":"\u003cp\u003eParticipant flow chart.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4734732/v1/3323c65e5dee7da2e13be3d8.png"},{"id":86179202,"identity":"9ee467f8-2e57-4fc4-a6c4-8b91c85e10d0","added_by":"auto","created_at":"2025-07-07 16:17:11","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1585221,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4734732/v1/f02b86e5-615b-46e2-8e44-4bed01f864cc.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association of Dietary and Lifestyle Inflammation Score with sleep quality and mental health in hemodialysis patients: A multicenter cross-sectional study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eEnd-Stage Renal Disease (ESRD) is a leading global cause of morbidity and mortality (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). For individuals with ESRD, hemodialysis (HD) is the principal and primary treatment (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). The prevalence of sleep disorders and mental illnesses such as depression, anxiety, and stress-related disorders in HD patients is significantly higher than that in the general population (\u003cspan additionalcitationids=\"CR3 CR4\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). It has been observed that sleep disruptions affect over 80% of dialysis patients (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Additionally, the prevalence of depression in patients receiving dialysis is almost 15% higher than in those with a kidney transplant (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Sleep and psychiatric disorders raise the risk of cardiovascular disease, malnutrition, and inflammation affect the risk of morbidity and mortality in hemodialysis patients (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). While several variables can impact sleep hygiene and mental health in patients on maintenance HD, including age (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e), gender (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e), uremic pruritus (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e), and length of dialysis (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e), low-grade systemic inflammation has been identified as an essential predictor by previous studies (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). The primary risk factors for systemic inflammation are thought to be obesity, age, tobacco use, physical inactivity, and unhealthy diets (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIt has been reported that a more inflammatory diet, as estimated by the dietary inflammatory index (DII), is significantly associated with symptoms of depression, anxiety, distress, and poor sleep quality (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). However, DII mostly focuses on specific nutrients without considering the dietary intake of food groups and the interaction among nutrients in the body or their synergistic effects.\u003c/p\u003e \u003cp\u003eNovel inflammatory indices, including dietary inflammation scores (DIS) and lifestyle inflammation scores (LIS) in the Byrd et al. study, were introduced to capture the combined effect of diet and lifestyle characteristics, such as body mass index (BMI), physical activity, alcohol consumption, and smoking, on chronic inflammation (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). Some studies have reported that higher score of LIS and DIS is associated with increased risk of chronic diseases and their mortality such as cancers, and diabetes (\u003cspan additionalcitationids=\"CR18\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e), to the best of our knowledge, no study has yet investigated the relationship between these indices and mental health disorders and sleep quality in maintenance HD patients. The current study examined the relationships between mental health conditions, sleep quality, and dietary and lifestyle inflammation score (DLIS) in HD patients.\u003c/p\u003e"},{"header":"Method","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy population\u003c/h2\u003e \u003cp\u003eThis multi-center cross-sectional study was performed on 423 HD patients in five government and three private dialysis centers in Ahvaz, Shiraz, and Shushtar cities, Iran. Inclusion criteria included adult patients (aged\u0026thinsp;\u0026ge;\u0026thinsp;18 years), and receiving HD for at least 6 months. We excluded participants with enteral or parenteral feeding, active neoplastic disease, major amputation (lower/upper extremities), diagnosis of cancer, acute or chronic pancreatitis, irritable bowel syndrome, prolonged gastrointestinal symptoms, acute or chronic pancreatitis, hepatic insufficiency, and energy intake less than 800 kcal/d or above 4,200 kcal/d.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eData collection\u003c/h2\u003e \u003cp\u003eSociodemographic and clinical data including gender, age, marital status, time of dialysis start, frequency and duration of dialysis, fluid intake, urine volume, Intradialytic weight gain, prescription medicines, and comorbidities (diabetes mellitus and hypertension) were extracted from the dialysis unit's database. Diabetes was defined as a history of diabetes mellitus or the use of anti-diabetic drugs. Hypertension was defined as the recording of hypertension in medical records or the use of antihypertensive drugs. Other factors, including BMI, job, education level, monthly income, physical activity, and smoking status, were gathered by researchers according to standard operating procedures or through a questionnaire administered via interviews. Digital scales were used to measure the participants\u0026rsquo; body weight to the nearest 100 gr with light clothes and without shoes at the end of the dialysis session. The BMI was calculated using participants\u0026rsquo; weight and height, measured at the end of their dialysis session.\u003c/p\u003e \u003cp\u003eA stadiometer was used to measure height to the nearest 0.5 cm standing while the individuals were barefoot. BMI was calculated as weight (kg) divided by height (m\u003csup\u003e2\u003c/sup\u003e). Physical activity was measured using the International Physical Activity Questionnaire (IPAQ), which contains seven questions, the validity of which has been confirmed in HD patients and the results were expressed as metabolic equivalent hours per week (METs hr/wk) (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). Biochemical indicators, including serum albumin, calcium, phosphate, creatinine, potassium, serum total iron binding capacity, and urea nitrogen, were determined from the dialysis unit's database for the same month. Dialysis adequacy was calculated, based on the Kt/V index, using dialysis length, post-dialysis weight, ultrafiltration volume, and pre-and post-dialysis serum urea concentration (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eDietary assessment\u003c/h2\u003e \u003cp\u003eTrained dietitians obtained information on the usual food intake of all participants using a valid and reliable 168-item semi-quantitative Food Frequency Questionnaire (FFQ) with standardized servings (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). The participants were requested to provide information on the frequency of their consumption of each food item, indicating whether it was consumed daily, weekly, or monthly, over the preceding year. These reported frequencies were then converted into gram-weight equivalents. To determine the total energy and nutrient intake, Nutritionist IV software (the Hearst Corporation) was utilized, with adjustments made to accommodate Iranian food items.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eCalculating the Dietary and Lifestyle Inflammation Score\u003c/h2\u003e \u003cp\u003e The inflammatory scores of participants were determined using dietary data derived from FFQ. The DIS (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e) is the sum of 19 weighted components, including 18 whole food and beverage groups and 1 micronutrient supplement score. DIS encompasses originally included leafy greens and cruciferous vegetables, tomatoes, apples and berries, deep yellow or orange vegetables and fruit, other fruits, and natural fruit juices, other vegetables, legumes, fish, poultry, red and organ meats, processed meats, added sugars, high-fat dairy, low-fat dairy and tea, nuts, other fats, refined grains, starchy vegetables, and supplements intake. Mixed dishes were disaggregated to food groups. All of these components were used other than supplement score due to the lack of information regarding supplement use in the study participants. To compute the DIS score, each food item was multiplied by its specific weight (explained in Byrd et al. study) to determine the weighted values of each item. The weighted values were then standardized using the Z-score (to a mean of zero and SD of 1). Finally, all the items\u0026rsquo; standardized weighted values were summed to calculate the DIS score for participants.\u003c/p\u003e \u003cp\u003eThe LIS components include smoking status (\u0026ldquo;former/never\u0026rdquo; or \u0026ldquo;current\u0026rdquo;), physical activity (\u0026ldquo;high or moderate\u0026rdquo; and \u0026ldquo;low or no physical activity\u0026rdquo;), BMI (kg/m\u003csup\u003e2\u003c/sup\u003e) (\u0026ldquo;overweight (25\u0026ndash;29.9)\u0026rdquo; and \u0026ldquo;obese (\u0026ge;\u0026thinsp;30)\u0026rdquo;), and alcohol intake. Alcohol intake was not included in the score because of the lack of information regarding the intake of alcohol in Iranian culture. Finally, all the weighted values were summed to calculate the LIS score. In this study, the DLIS for each subject was calculated by summing the DIS and LIS. Higher DLIS (more positive) presents a more pro-inflammatory diet and lifestyle.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eAssessment of sleep quality and mental health\u003c/h2\u003e \u003cp\u003eA Persianvalidated version of the Pittsburgh Sleep Quality Index (PSQI) was used to collect data on sleep hygiene (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). The PSQI is a common tool for assessing sleep problems associated with anxiety, stress, depression, and schizophrenia. The selfreported questionnaire contains 19 questions divided into seven category scores including subjective sleep quality, sleep latency, sleep duration, habitual sleep efficiency, sleep disorders, the use of sleep medications, and daytime dysfunction caused by sleep disorders. The score of each part is 0\u0026ndash;3, and the highest total score is 21. The global PSQI score over 5 is considered as poor quality of sleep. Validity and reliability of this questionnaire have been investigated in Iran (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDepression, Anxiety and Stress Scale (DASS-21) was used to screen depression, anxiety and stress. DASS-21 is a valid and reliable questionnaire to measure status of negative well beings for general population. The questionnaire contains 21 items with 3 subscales. Each subscale includes 7 questions in which each item rates on a 4-point Likert scale 0\u0026ndash;3 measuring severity of depression, anxiety and stress. The participants achieve scores of \u0026ge;\u0026thinsp;10, \u0026ge;8, and \u0026ge;\u0026thinsp;15, they were considered to have depression, anxiety, and stress respectively(\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eThe statistical analysis was conducted using IBM SPSS Statistics software (Version 24) from IBM SPSS Statistics, Armonk, USA. The normality of variables was assessed using the Kolmogorov-Smirnov test. Baseline characteristics of the individuals are expressed as the mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD for continuous variables and percentages for categorical variables. Participants were also categorized according to quartiles of DLIS cutoff points. Differences in variables across DLIS quartiles were examined using one-way analysis of variance (ANOVA) and chi-square test was used to compare the categorical variables across quartiles. Energy and nutrient intakes by DLIS quartiles were evaluated using ANOVA.\u003c/p\u003e \u003cp\u003eOdds ratios (ORs) and 95% confidence intervals (CIs) were calculated to assess the risk of psychological disorders (depression, anxiety, stress) and poor sleep quality across quartiles DLIS applying multiple logistic regression in crude and multivariable-adjusted models. In the first adjusted model, the confounding effects of center type, city, age, sex, diabetes, hypertension, job, marital status, education, income status, inter-dialysis weight gain, dialysis vintage, dialysis time, frequency of hemodialysis sessions, fluid intake, and urine volume were controlled. Model 2 was additionally controlled for to energy intake, and medication prescriptions. Statistical significant level was considered as p-values less than 0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eAs shown in \u003cstrong\u003eFig.\u0026nbsp;1, 324\u003c/strong\u003e of the 755 patients that were assessed across 8 hemodialysis centers were not included in the study for a variety of reasons. Thus, 431 patients in total consented to take part in the study. Due to dietary misreporting, eight patients were excluded from the final study. Therefore, 423 patients were included in the final analysis. The mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD age of 423 HD patients who contributed to the current study was 52.84\u0026thinsp;\u0026plusmn;\u0026thinsp;14.63 years. Most patients were men (58.9%), married (73.8%), and either housekeeper (34.3%) or retired (23.2%). 42.6% of study participants had diabetes (n\u0026thinsp;=\u0026thinsp;180), and 74.7% had hypertension (n\u0026thinsp;=\u0026thinsp;316). The mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD dialysis vintage was 49.41\u0026thinsp;\u0026plusmn;\u0026thinsp;61.48 months. Moreover, the mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD BMI was 24.8\u0026thinsp;\u0026plusmn;\u0026thinsp;5.11 kg/m\u003csup\u003e2\u003c/sup\u003e. According to nutritional status, 8.4% of patients were underweight, 44.4% had normal weight, 33.3% were overweight, and 13.4% were obese. 10.2% of the patients in this study were smokers (n\u0026thinsp;=\u0026thinsp;43). The prevalence of poor sleep quality in our study population was 60.5% (n\u0026thinsp;=\u0026thinsp;256). Overall, 53.7% of study participants were depressed (n\u0026thinsp;=\u0026thinsp;227), 53.0% were anxious (n\u0026thinsp;=\u0026thinsp;224) and 47.3% stress (n\u0026thinsp;=\u0026thinsp;200).\u003c/p\u003e\n\u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e shows the general characteristics of study population across quartiles of DLIS. The proportion of current smokers increased in the top quartile of the DLIS compared with the lowest quartile. Compared with those in the lowest quartiles of DLIS, significant differences were observed for the income status of participants (P\u0026thinsp;=\u0026thinsp;0.03). Participants in the highest quartile of the DLIS had significantly higher scores of all three mental health disorders (P\u0026thinsp;\u0026lt;\u0026thinsp;.001). Compared to participants in the first quartile of DLIS, the number of participants with depression (P\u0026thinsp;=\u0026thinsp;0.001), anxiety (P\u0026thinsp;=\u0026thinsp;0.001), and stress (P\u0026thinsp;\u0026lt;\u0026thinsp;.001), and poor sleep quality (P\u0026thinsp;=\u0026thinsp;0.01) was higher in the highest quartile.\u003c/p\u003e\n\u003cp\u003e\u003c/p\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eCharacteristics of patients undergoing hemodialysis across quartiles of Dietary and Lifestyle Inflammation Score(DLIS) .\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eCharacteristics\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eDLIS quartiles\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eQ1\u003c/p\u003e\n \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;108)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eQ2\u003c/p\u003e\n \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;102)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eQ3\u003c/p\u003e\n \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;103)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eQ4\u003c/p\u003e\n \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;110)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eScore range\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt; \u0026ndash; 0.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.51- 0.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.65\u0026ndash;1.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.75\u0026gt;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge, y\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e50.78\u0026thinsp;\u0026plusmn;\u0026thinsp;14.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e54.68\u0026thinsp;\u0026plusmn;\u0026thinsp;13.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e54.04\u0026thinsp;\u0026plusmn;\u0026thinsp;15.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e52.05\u0026thinsp;\u0026plusmn;\u0026thinsp;14.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e61(56.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e56(54.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e62(60.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e69(62.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.63\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"5\"\u003e\n \u003cp\u003e\u003cstrong\u003eJob, N(%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"7\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnemployed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17(15.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11(10.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36(34.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23(20.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHousekeeper\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e36(33.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40(39.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26(25.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33(\u003cspan class=\"CitationRef\"\u003e30\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRetired\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23(21.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28(27.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2(1.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21(19.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEmployee\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12(11.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2(1.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15(14.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12(10.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSelf-employment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e18(16.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16(15.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7(6.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14(12.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOthers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2(1.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5(4.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36(34.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7(6.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAhvaz\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e44(40.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38(37.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e42(40.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60(54.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eShushtar\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7(6.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15(14.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26(25.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21(19.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eShiraz\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e57(52.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e49(48.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35(33.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29(26.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"5\"\u003e\n \u003cp\u003e\u003cstrong\u003eMarital status, N(%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"5\"\u003e\n \u003cp\u003e0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e71(65.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e75(73.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e76(73.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e90(81.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSingle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e29(26.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18(17.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19(18.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16(14.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDivorced\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6(5.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5(4.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3(2.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(0.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDead spouse\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2(1.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4(3.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5(4.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3(2.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"5\"\u003e\n \u003cp\u003e\u003cstrong\u003eEducation, N(%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"3\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;12 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e80(74.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e78(76.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e90(87.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e86(78.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ge;\u0026thinsp;12 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e28(25.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24(23.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13(12.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24(21.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"5\"\u003e\n \u003cp\u003e\u003cstrong\u003eIncome status, N(%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"5\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;5\u0026nbsp;million Rials\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e28(25.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32(31.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e43(41.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31(28.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u0026ndash;10 million Rials\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e42(38.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47(46.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37(35.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e42(38.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10\u0026ndash;20 million Rials\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e26(24.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19(18.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17(16.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33(\u003cspan class=\"CitationRef\"\u003e30\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;20 million Rials\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12(11.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4(3.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6(5.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4(3.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePhysical activity(MET/min/week)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e864.13\u0026thinsp;\u0026plusmn;\u0026thinsp;3388.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e417.87\u0026thinsp;\u0026plusmn;\u0026thinsp;1189.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e183.47\u0026thinsp;\u0026plusmn;\u0026thinsp;356.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e520.55\u0026thinsp;\u0026plusmn;\u0026thinsp;2677.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSleep\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSleep duration\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.63\u0026thinsp;\u0026plusmn;\u0026thinsp;2.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.38\u0026thinsp;\u0026plusmn;\u0026thinsp;2.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.33\u0026thinsp;\u0026plusmn;\u0026thinsp;1.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.41\u0026thinsp;\u0026plusmn;\u0026thinsp;3.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSleep quality\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePSQI Score\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.12\u0026thinsp;\u0026plusmn;\u0026thinsp;3.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.61\u0026thinsp;\u0026plusmn;\u0026thinsp;3.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.25\u0026thinsp;\u0026plusmn;\u0026thinsp;3.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.18\u0026thinsp;\u0026plusmn;\u0026thinsp;3.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSleep quality category\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003egood, N (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e58(53.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e43(42.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32(31.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34(30.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003epoor, N (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e50(46.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e59(57.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32(31.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e76(69.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eDepression\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eDepression Score\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.00\u0026thinsp;\u0026plusmn;\u0026thinsp;11.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.39\u0026thinsp;\u0026plusmn;\u0026thinsp;11.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17.59\u0026thinsp;\u0026plusmn;\u0026thinsp;14.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.56\u0026thinsp;\u0026plusmn;\u0026thinsp;12.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eDepression category\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo, N (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e57(52.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60(58.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35(34.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44(40.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes, N (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e51(47.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e42(41.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e68(66.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e66(60.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAnxiety\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAnxiety Score\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.09\u0026thinsp;\u0026plusmn;\u0026thinsp;9.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.51\u0026thinsp;\u0026plusmn;\u0026thinsp;9.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.54\u0026thinsp;\u0026plusmn;\u0026thinsp;14.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.98\u0026thinsp;\u0026plusmn;\u0026thinsp;11.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAnxiety category\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo, N (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e64(59.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e56(54.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38(36.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41(37.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes, N (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e44(40.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46(45.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e65(63.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e69(62.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eStress\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eStress Score\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14.19\u0026thinsp;\u0026plusmn;\u0026thinsp;12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.35\u0026thinsp;\u0026plusmn;\u0026thinsp;11.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21.05\u0026thinsp;\u0026plusmn;\u0026thinsp;12.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17.75\u0026thinsp;\u0026plusmn;\u0026thinsp;12.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eStress category\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo, N (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e64(59.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e69(67.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39(37.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e51(46.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes, N (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e44(40.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33(32.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e64(62.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e59(53.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\"\u003e\n \u003cp\u003eNote: Data for quantitative variables are presented as means \u0026plusmn; SD, obtained from ANOVA. Data for qualitative variables are presented as frequencies (percentages) and analyzed using chi-square tests.\u003c/p\u003e\n \u003cp\u003eAbbreviations: DLIS, dietary and lifestyle inflammation score; PSQI, Petersburg Sleep Quality Questionnaire.\u003c/p\u003e\u003cbr\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003eDialysis data of HD patients across quartiles of DLIS has illustrated in Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e. Participants in the highest quartiles of DLIS had higher frequency of dialysis per week and fluid intake. Other variables did not significantly differ across DLIS quartiles.\u003c/p\u003e\n\u003cp\u003e\u003c/p\u003e\u0026nbsp;\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDialysis data of patients undergoing hemodialysis across quartiles of DLIS.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eDLIS quartiles\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eQ1\u003c/p\u003e\n \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;108)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eQ2\u003c/p\u003e\n \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;102)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eQ3\u003c/p\u003e\n \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;103)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eQ4\u003c/p\u003e\n \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;110)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDialysis vintage, month\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e51.72\u0026thinsp;\u0026plusmn;\u0026thinsp;56.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e52.02\u0026thinsp;\u0026plusmn;\u0026thinsp;57.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e49.45\u0026thinsp;\u0026plusmn;\u0026thinsp;81.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44.72\u0026thinsp;\u0026plusmn;\u0026thinsp;45.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.80\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDialysis time, hours\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.83\u0026thinsp;\u0026plusmn;\u0026thinsp;0.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.88\u0026thinsp;\u0026plusmn;\u0026thinsp;0.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.96\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.85\u0026thinsp;\u0026plusmn;\u0026thinsp;0.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFrequency dialysis per week, Time/week\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.85\u0026thinsp;\u0026plusmn;\u0026thinsp;0.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.74\u0026thinsp;\u0026plusmn;\u0026thinsp;0.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.62\u0026thinsp;\u0026plusmn;\u0026thinsp;0.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.57\u0026thinsp;\u0026plusmn;\u0026thinsp;0.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFluid intake, ml\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1370.64\u0026thinsp;\u0026plusmn;\u0026thinsp;1457.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1275.49\u0026thinsp;\u0026plusmn;\u0026thinsp;996.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e976.7\u0026thinsp;\u0026plusmn;\u0026thinsp;682.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1251.45\u0026thinsp;\u0026plusmn;\u0026thinsp;723.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUrine volume, ml\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e429.91\u0026thinsp;\u0026plusmn;\u0026thinsp;614.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e350.91\u0026thinsp;\u0026plusmn;\u0026thinsp;523.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e299.26\u0026thinsp;\u0026plusmn;\u0026thinsp;460.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e442.94\u0026thinsp;\u0026plusmn;\u0026thinsp;556.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIntradialytic weight gain ,kg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.05\u0026thinsp;\u0026plusmn;\u0026thinsp;1.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.04\u0026thinsp;\u0026plusmn;\u0026thinsp;1.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.93\u0026thinsp;\u0026plusmn;\u0026thinsp;1.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.02\u0026thinsp;\u0026plusmn;\u0026thinsp;1.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.90\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eComorbidities\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiabetes(n, %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40(37.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e42(41.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e49(47.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e49(44.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.44\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHypertension(n, %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e83(76.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e76(74.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e74(71.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e83(75.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.86\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eLaboratory parameters\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCreatinine, mg/dL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.31\u0026thinsp;\u0026plusmn;\u0026thinsp;3.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.78\u0026thinsp;\u0026plusmn;\u0026thinsp;4.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.54\u0026thinsp;\u0026plusmn;\u0026thinsp;2.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.54\u0026thinsp;\u0026plusmn;\u0026thinsp;4.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSodium, mmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e139.82\u0026thinsp;\u0026plusmn;\u0026thinsp;3.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e138.83\u0026thinsp;\u0026plusmn;\u0026thinsp;5.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e138.91\u0026thinsp;\u0026plusmn;\u0026thinsp;4.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e140.12\u0026thinsp;\u0026plusmn;\u0026thinsp;6.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePotassium, mmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.18\u0026thinsp;\u0026plusmn;\u0026thinsp;0.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.04\u0026thinsp;\u0026plusmn;\u0026thinsp;0.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.99\u0026thinsp;\u0026plusmn;\u0026thinsp;0.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCalcium, mg/dL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.38\u0026thinsp;\u0026plusmn;\u0026thinsp;0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.33\u0026thinsp;\u0026plusmn;\u0026thinsp;1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.55\u0026thinsp;\u0026plusmn;\u0026thinsp;0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.34\u0026thinsp;\u0026plusmn;\u0026thinsp;0.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.65\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePhosphate, mg/dL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.27\u0026thinsp;\u0026plusmn;\u0026thinsp;1.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.07\u0026thinsp;\u0026plusmn;\u0026thinsp;1.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.97\u0026thinsp;\u0026plusmn;\u0026thinsp;1.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.35\u0026thinsp;\u0026plusmn;\u0026thinsp;1.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKt/V\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.28\u0026thinsp;\u0026plusmn;\u0026thinsp;0.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.25\u0026thinsp;\u0026plusmn;\u0026thinsp;0.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.21\u0026thinsp;\u0026plusmn;\u0026thinsp;0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.17\u0026thinsp;\u0026plusmn;\u0026thinsp;0.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAlbumin, g/ dL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.92\u0026thinsp;\u0026plusmn;\u0026thinsp;19.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.08\u0026thinsp;\u0026plusmn;\u0026thinsp;0.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.17\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSerum total iron binding capacity, \u0026micro;g/dL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e283.51\u0026thinsp;\u0026plusmn;\u0026thinsp;90.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e294.45\u0026thinsp;\u0026plusmn;\u0026thinsp;104.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e305.63\u0026thinsp;\u0026plusmn;\u0026thinsp;85.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e315.64\u0026thinsp;\u0026plusmn;\u0026thinsp;96.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003e\u003cstrong\u003eMedication prescriptions\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCalcium carbonate 500 mg, time/day\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.59\u0026thinsp;\u0026plusmn;\u0026thinsp;1.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.18\u0026thinsp;\u0026plusmn;\u0026thinsp;1.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.13\u0026thinsp;\u0026plusmn;\u0026thinsp;1.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.34\u0026thinsp;\u0026plusmn;\u0026thinsp;1.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSevelamer\u0026nbsp;hydrochloride 800 mg, time/day\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.81\u0026thinsp;\u0026plusmn;\u0026thinsp;1.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.1\u0026thinsp;\u0026plusmn;\u0026thinsp;1.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.73\u0026thinsp;\u0026plusmn;\u0026thinsp;1.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.83\u0026thinsp;\u0026plusmn;\u0026thinsp;1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCalcitriol 0.25 mcg, time/day\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.92\u0026thinsp;\u0026plusmn;\u0026thinsp;1.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.52\u0026thinsp;\u0026plusmn;\u0026thinsp;0.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.7\u0026thinsp;\u0026plusmn;\u0026thinsp;1.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.58\u0026thinsp;\u0026plusmn;\u0026thinsp;1.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003efurosemide time/day\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.38\u0026thinsp;\u0026plusmn;\u0026thinsp;0.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.51\u0026thinsp;\u0026plusmn;\u0026thinsp;1.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.43\u0026thinsp;\u0026plusmn;\u0026thinsp;0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.32\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.47\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCorticosteroids, N(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8(7.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2(1.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3(2.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3(2.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLipid-lowering drugs, N(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21(19.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16(15.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18(17.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13(11.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.46\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\"\u003eNote: The data are presented as \u0026quot;mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u0026quot;.\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\"\u003eThe significant difference based on One-way ANOVA (P\u0026thinsp;\u0026lt;\u0026thinsp;.05)\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003eDietary intakes and DIS and LIS components of participants according to quartiles of DLIS are presented in Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e. Participants in the highest quartiles of DLIS had significantly higher intakes of energy and protein (P\u0026thinsp;=\u0026thinsp;0.002), while the intake of carbohydrate (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and fat (P\u0026thinsp;=\u0026thinsp;0.006) were lower, compared to participants in the lowest quartile. Additionally, BMI was increased significantly across DLIS quartiles (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\n\u003cp\u003e\u003c/p\u003e\u0026nbsp;\u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDietary intakes, DIS and LIS components of patients undergoing hemodialysis according to quartiles of the DLIS.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003eNutrient Intake\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eDLIS quartiles\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eQ1\u003c/p\u003e\n \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;108)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eQ2\u003c/p\u003e\n \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;102)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eQ3\u003c/p\u003e\n \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;103)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eQ4\u003c/p\u003e\n \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;110)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEnergy(Kcal/d)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2391.66\u0026thinsp;\u0026plusmn;\u0026thinsp;1002.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2112.34\u0026thinsp;\u0026plusmn;\u0026thinsp;724.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2002.61\u0026thinsp;\u0026plusmn;\u0026thinsp;662.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2422.35\u0026thinsp;\u0026plusmn;\u0026thinsp;896.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCarbohydrate(g/d)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e89.48\u0026thinsp;\u0026plusmn;\u0026thinsp;43.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e77.84\u0026thinsp;\u0026plusmn;\u0026thinsp;32.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e71.82\u0026thinsp;\u0026plusmn;\u0026thinsp;26.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e80.77\u0026thinsp;\u0026plusmn;\u0026thinsp;31.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProtein(g/d)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e349.4\u0026thinsp;\u0026plusmn;\u0026thinsp;149.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e309.61\u0026thinsp;\u0026plusmn;\u0026thinsp;107.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e302.59\u0026thinsp;\u0026plusmn;\u0026thinsp;114.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e375.79\u0026thinsp;\u0026plusmn;\u0026thinsp;153.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal fat(g/d)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e75.34\u0026thinsp;\u0026plusmn;\u0026thinsp;44.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e64.87\u0026thinsp;\u0026plusmn;\u0026thinsp;31.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e58.09\u0026thinsp;\u0026plusmn;\u0026thinsp;25.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e68.25\u0026thinsp;\u0026plusmn;\u0026thinsp;38.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003e\u003cstrong\u003eDIS component\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLeafy greens and cruciferous vegetables(g/d)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35.93\u0026thinsp;\u0026plusmn;\u0026thinsp;58.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29.43\u0026thinsp;\u0026plusmn;\u0026thinsp;45.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25.22\u0026thinsp;\u0026plusmn;\u0026thinsp;19.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34.97\u0026thinsp;\u0026plusmn;\u0026thinsp;30.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTomatoes(g/d)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28.89\u0026thinsp;\u0026plusmn;\u0026thinsp;38.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.6\u0026thinsp;\u0026plusmn;\u0026thinsp;22.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16.87\u0026thinsp;\u0026plusmn;\u0026thinsp;20.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20.53\u0026thinsp;\u0026plusmn;\u0026thinsp;27.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eApples and berries(g/d)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46.42\u0026thinsp;\u0026plusmn;\u0026thinsp;42.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e42.34\u0026thinsp;\u0026plusmn;\u0026thinsp;38.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e49.75\u0026thinsp;\u0026plusmn;\u0026thinsp;93.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36.71\u0026thinsp;\u0026plusmn;\u0026thinsp;32.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDeep yellow or orange vegetables and fruit(g/d)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47.4\u0026thinsp;\u0026plusmn;\u0026thinsp;61.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e42.16\u0026thinsp;\u0026plusmn;\u0026thinsp;66.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e51.74\u0026thinsp;\u0026plusmn;\u0026thinsp;75.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e77.09\u0026thinsp;\u0026plusmn;\u0026thinsp;114.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOther fruits and real fruit juices(g/d)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e163.9\u0026thinsp;\u0026plusmn;\u0026thinsp;161.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e175.78\u0026thinsp;\u0026plusmn;\u0026thinsp;157.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e174.52\u0026thinsp;\u0026plusmn;\u0026thinsp;138.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e189.34\u0026thinsp;\u0026plusmn;\u0026thinsp;212.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.74\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOther vegetables(g/d)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e80.68\u0026thinsp;\u0026plusmn;\u0026thinsp;88.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e64.5\u0026thinsp;\u0026plusmn;\u0026thinsp;55.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e65.57\u0026thinsp;\u0026plusmn;\u0026thinsp;55.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e68.7\u0026thinsp;\u0026plusmn;\u0026thinsp;52.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLegumes(g/d)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e56.84\u0026thinsp;\u0026plusmn;\u0026thinsp;43.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e56.51\u0026thinsp;\u0026plusmn;\u0026thinsp;38.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e54.33\u0026thinsp;\u0026plusmn;\u0026thinsp;42.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e59.51\u0026thinsp;\u0026plusmn;\u0026thinsp;48.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.85\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFish(g/d)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.68\u0026thinsp;\u0026plusmn;\u0026thinsp;17.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.76\u0026thinsp;\u0026plusmn;\u0026thinsp;15.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.32\u0026thinsp;\u0026plusmn;\u0026thinsp;21.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22.91\u0026thinsp;\u0026plusmn;\u0026thinsp;31.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePoultry(g/d)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e49.53\u0026thinsp;\u0026plusmn;\u0026thinsp;65.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e42.55\u0026thinsp;\u0026plusmn;\u0026thinsp;45.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e49.95\u0026thinsp;\u0026plusmn;\u0026thinsp;70.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e55.49\u0026thinsp;\u0026plusmn;\u0026thinsp;61.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRed and organ meats(g/d)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.48\u0026thinsp;\u0026plusmn;\u0026thinsp;23.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.25\u0026thinsp;\u0026plusmn;\u0026thinsp;13.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.87\u0026thinsp;\u0026plusmn;\u0026thinsp;12.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20.26\u0026thinsp;\u0026plusmn;\u0026thinsp;26.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProcessed meats(g/d)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.94\u0026thinsp;\u0026plusmn;\u0026thinsp;10.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.1\u0026thinsp;\u0026plusmn;\u0026thinsp;6.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.94\u0026thinsp;\u0026plusmn;\u0026thinsp;8.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.97\u0026thinsp;\u0026plusmn;\u0026thinsp;8.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAdded sugars(g/d)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e71.6\u0026thinsp;\u0026plusmn;\u0026thinsp;125.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e61.68\u0026thinsp;\u0026plusmn;\u0026thinsp;77.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e86.83\u0026thinsp;\u0026plusmn;\u0026thinsp;110.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e76.19\u0026thinsp;\u0026plusmn;\u0026thinsp;83.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh-fat dairy(g/d)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e61.59\u0026thinsp;\u0026plusmn;\u0026thinsp;75.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e59.01\u0026thinsp;\u0026plusmn;\u0026thinsp;87.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e64.79\u0026thinsp;\u0026plusmn;\u0026thinsp;84.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e67.61\u0026thinsp;\u0026plusmn;\u0026thinsp;86.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.88\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLow-fat dairy(g/d)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e107.35\u0026thinsp;\u0026plusmn;\u0026thinsp;169.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e89.57\u0026thinsp;\u0026plusmn;\u0026thinsp;126.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100\u0026thinsp;\u0026plusmn;\u0026thinsp;110.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e95.38\u0026thinsp;\u0026plusmn;\u0026thinsp;150.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCoffee and tea(g/d)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e346.32\u0026thinsp;\u0026plusmn;\u0026thinsp;304.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e344\u0026thinsp;\u0026plusmn;\u0026thinsp;312.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e338.14\u0026thinsp;\u0026plusmn;\u0026thinsp;346.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e354.29\u0026thinsp;\u0026plusmn;\u0026thinsp;301.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNuts(g/d)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.58\u0026thinsp;\u0026plusmn;\u0026thinsp;7.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.85\u0026thinsp;\u0026plusmn;\u0026thinsp;8.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.17\u0026thinsp;\u0026plusmn;\u0026thinsp;13.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.98\u0026thinsp;\u0026plusmn;\u0026thinsp;31.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOther fats(g/d)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29.28\u0026thinsp;\u0026plusmn;\u0026thinsp;22.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23.4\u0026thinsp;\u0026plusmn;\u0026thinsp;17.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26.44\u0026thinsp;\u0026plusmn;\u0026thinsp;18.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29.86\u0026thinsp;\u0026plusmn;\u0026thinsp;23.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRefined grains and starchy vegetables(g/d)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e457.31\u0026thinsp;\u0026plusmn;\u0026thinsp;212.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e464.27\u0026thinsp;\u0026plusmn;\u0026thinsp;267.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e448.04\u0026thinsp;\u0026plusmn;\u0026thinsp;291.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e478.6\u0026thinsp;\u0026plusmn;\u0026thinsp;267.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.85\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003e\u003cstrong\u003eLIS component\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBMI(kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24.34\u0026thinsp;\u0026plusmn;\u0026thinsp;4.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23.21\u0026thinsp;\u0026plusmn;\u0026thinsp;3.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24.86\u0026thinsp;\u0026plusmn;\u0026thinsp;4.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26.82\u0026thinsp;\u0026plusmn;\u0026thinsp;6.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnderweight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8(7.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16(15.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7(6.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6(5.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNormal weight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60(55.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e56(54.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e49(47.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23(20.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOverweight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29(26.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29(28.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32(31.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e51(46.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eObese\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11(10.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(0.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15(14.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30(27.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePhysical activity;(MET-h/d)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003e0.68\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003elow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e91(84.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e81(79.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e86(83.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e92(83.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emoderate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17(15.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19(18.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14(13.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16(14.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSever\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e91(84.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2(1.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3(2.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2(1.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCurrent smoker; n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8(7.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6(5.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e91(88.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17(15.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\"\u003eNote: Data for quantitative variables are presented as means\u0026thinsp;\u0026plusmn;\u0026thinsp;SD, obtained from ANOVA. Data for qualitative variables are presented as frequencies (percentages) and analyzed using chi-square tests.\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\"\u003eAbbreviations: DIS, dietary inflammation score; DLIS, dietary and lifestyle inflammation score; LIS, lifestyle inflammation score; BMI, body mass index.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003eThe association of DLIS with the risk of poor sleep quality, depression, anxiety, and stress are shown in three models in Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e. In the first model (univariate model), the odds of poor sleep quality were higher in individuals in the highest quartiles of the DLIS (OR: 2.59; 95% CI: 1.49, 4.51; P value\u0026thinsp;\u0026lt;\u0026thinsp;0.001). This significant positive association between DLIS and poor sleep quality persisted in the fully adjusted model that controlled for city, age, sex, diabetes, hypertension, job, marital status, education, income status, inter-dialysis weight gain, dialysis vintage, dialysis time, frequency of hemodialysis sessions, fluid intake, and urine volume, energy intake, and medication prescriptions (OR: 3.18, 95% CI: 1.71, 5.9, P value\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In addition, in crude model, compared to those in the bottom quartile, individuals in the top quartile of DLIS had significantly higher risk of depression (OR: 1.68; 95% CI: 0.98, 2.87; P value\u0026thinsp;=\u0026thinsp;0.004), anxiety (OR: 2.45; 95% CI: 1.42, 4.22; P value\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and stress (OR: 1.68; 95% CI: 0.98, 2.88; P-trend\u0026thinsp;=\u0026thinsp;0.002). A similar pattern was shown in the third model (full model multivariate analysis) for depression, anxiety, stress. There was a significant positive association between the DLIS and increased likelihood of depression (OR: 1.94; 95% CI: 1.06\u0026ndash;3.54), anxiety (OR: 2.82; 95% CI: 1.51\u0026ndash;5.27), and psychological distress (OR: 2.15; 95% CI: 1.14\u0026ndash;4.03)\u003c/p\u003e\n\u003cp\u003e\u003c/p\u003e\u0026nbsp;\u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eCrude and multivariable-adjusted odds ratio (95% CI) of the associations between DLIS and sleep quality, stress, anxiety and depression\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003eSleep quality\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"5\"\u003e\n \u003cp\u003eDLIS quartiles\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eQ1(N\u0026thinsp;=\u0026thinsp;108)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eQ2(N\u0026thinsp;=\u0026thinsp;102)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eQ3(N\u0026thinsp;=\u0026thinsp;103)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eQ4(N\u0026thinsp;=\u0026thinsp;110)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModel 0\u003csup\u003e\u003cem\u003ea\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.59(0.92, 2.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.57(1.47, 4.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.59(1.49, 4.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModel 1\u003csup\u003e\u003cem\u003eb\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.52(0.85, 2.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.62(1.42, 4.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.97(1.62, 5.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModel 2\u003csup\u003e\u003cem\u003ec\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.64(0.9, 3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.79(1.49, 5.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.18(1.71, 5.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003e\u003cstrong\u003eDepression\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModel 0\u003csup\u003e\u003cem\u003ea\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.78(0.45, 1.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.17(1.25, 3.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.68(0.98, 2.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModel 1\u003csup\u003e\u003cem\u003eb\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.68(0.38, 1.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.03(1.11, 3.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.92(1.07, 3.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModel 2\u003csup\u003e\u003cem\u003ec\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.74(0.4, 1.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.06(1.11, 3.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.94(1.06, 3.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003e\u003cstrong\u003eAnxiety\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModel 0\u003csup\u003e\u003cem\u003ea\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.19(0.69, 2.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.49(1.43, 4.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.45(1.42, 4.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModel 1\u003csup\u003e\u003cem\u003eb\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.12(0.62, 2.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.47(1.34, 4.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.74(1.49, 5.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModel 2\u003csup\u003e\u003cem\u003ec\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.15(0.62, 2.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.45(1.32, 4.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.82(1.51, 5.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003e\u003cstrong\u003eStress\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModel 0\u003csup\u003e\u003cem\u003ea\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.70(0.40, 1.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.39(1.37, 4.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.68(0.98, 2.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModel 1\u003csup\u003e\u003cem\u003eb\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.63(0.34, 1.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.59(1.39, 4.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.06(1.12, 3.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModel 2\u003csup\u003e\u003cem\u003ec\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.68(0.36, 1.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.54(1.33, 4.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.15(1.14, 4.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\"\u003eDLIS: dietary and lifestyle inflammation score\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\"\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;.05 statistically significant by multivariable logistic regression.\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\"\u003eModel 0. binary logistic regression analysis without adjustment.\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\"\u003eModel 1. binary logistic regression analysis with center type, city, age, sex, diabetes, hypertension, job, marital status, education, income status, inter-dialysis weight gain, dialysis vintage, dialysis time, frequency of hemodialysis sessions, fluid intake, and urine volume.\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\"\u003eModel 2. binary logistic regression analysis with adjustment for model 2 in addition to energy intake, and medication prescriptions (corticosteroids, lipid-lowering drugs, calcium carbonate, calcitriol, sevelamer hydrochloride, frusemide).\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn the present study, the relation between adherence to a pro-inflammatory diet and lifestyle, reflected by DLIS, and mental health and sleep quality were investigated in a multicenter cross-sectional study of Iranian maintenance HD patients. The results revealed that there was a significant association between high DLIS and having poor sleep quality and mental health disorders including stress, anxiety, and depression among HD patients.\u003c/p\u003e \u003cp\u003eSleep disturbances are frequent consequences of ESRD and are a major cause of death and deterioration in these patients' quality of life (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Numerous factors can impact sleep hygiene, including age, gender, comorbidities, anemia, uremic pruritus, and the length of dialysis treatment (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). However, previous research has indicated that diet and lifestyle are also important sleep hygiene predictors. It has been found that older adults who have a Mediterranean diet, which is regarded as a healthy eating pattern, have better sleep hygiene (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). A cross-sectional study consisting of 741 maintenance HD patients also indicated that a higher intake of dietary fiber in vegetables could enhance the quality of sleep (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). Jansen et al. in a cohort study with 4467 Mexican women, showed that participants in the highest quartiles of the modern Mexican pattern (tortillas and soda, along with low fiber and dairy products) had a 23% higher likelihood of having poor sleep quality compared to those in the lowest quartile (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). Due to concerns about phosphorus and potassium, HD patients are required to undergo significant lifestyle changes, including adhering to a typical renal diet limited to vegetables, fruits, nuts, legumes, dairy, and whole grains (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). Therefore, unhealthy eating habits may be more common in HD patients, and these habits can be related to poor sleep hygiene.\u003c/p\u003e \u003cp\u003eIn the current study, more than 60.5% of hemodialysis patients had poor sleep quality. This study showed that after controlling for potential confounders, HD patients with a pro-inflammatory diet and lifestyle were more likely to experience poor sleep quality. Our findings are consistent with other research that has demonstrated positive correlations between a pro-inflammatory diet indicated by higher scores of the dietary inflammatory index (DII), and short sleep duration, sleep disturbances, poor sleep quality, higher wake-after-sleep onset, and dysfunction during the day (\u003cspan additionalcitationids=\"CR33 CR34\" citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e). Notably, the DIS is a novel dietary inflammatory index that may offer benefits over the DII; the DII mostly comprises certain anti/pro-inflammatory nutrients, and may not take into account other dietary components in foods, that can cause inflammation. Also, the DII mostly does not consider the impacts of nutrient interactions. Furthermore, in three populations, the DIS was found to have a stronger direct correlation with the circulating levels of inflammatory markers than the DII and Empirical Dietary Inflammatory Pattern (EDIP) (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). Moreover, previous studies have indicated that the lifestyle-related components of LIS, such as smoking, BMI, and physical activity, may also have a significant impact on metabolic homeostasis and inflammatory state. According to a population-based cohort study, a higher inflammatory potential of lifestyle, measured by the higher score LIS, was associated with an increased incidence of CKD in Iranian adults (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e). Since the LIS only contains lifestyle and non-dietary components and the DIS only focuses on diet, we employed the combined DLIS index (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e), which is a more comprehensive index that includes both dietary and non-dietary elements. Recent studies have demonstrated a clear correlation between the DLIS and the risk of developing chronic illnesses associated with systemic low grade inflammation such as metabolic syndrome and insulin resistance (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e). Currently, there is growing evidence that inflammatory factors and sleep quality are significantly associated.\u003c/p\u003e \u003cp\u003eThe biological mechanisms underlying the relationship between dietary and lifestyle factors and the quality of sleep may include the regulation of sleep by inflammatory peptides (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e), neuroendocrine and autonomic pathways connecting sleep to the immune system, and cytokine responses (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e). It has been previously documented that inflammatory status and metabolic homeostasis may be significantly impacted by lifestyle factors such as physical activity, BMI, and smoking. Increased adipose tissue and a raised BMI are significantly correlated with pro-inflammatory biomarkers (adipokines, TNF-α, CRP, and interleukin-6) and systemic chronic inflammation (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e), which in turn can cause poor sleep quality. It is reported that higher BMI is associated with a poor quality of sleep in young adults. However, some cross-sectional studies have not discovered any correlation between obesity as an inflammatory condition and diet-induced inflammation as evaluated by DII or EDII (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e). The differences between our results and the results showing no association could be attributed to variations in the population, the study design, the assessed food items, the dietary indices (like the DII, which emphasizes nutrients rather than food groups), the tools used to assess dietary intake, and the influence of unmeasurable confounding factors.\u003c/p\u003e \u003cp\u003eAnother key lifestyle component that has demonstrated promise in terms of bettering overall sleep quality, reducing sleep latency, and improving sleep quality is physical activity (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e). Nonetheless, studies have revealed that HD patients, for various reasons, are less physically active than healthy age-matched controls. Through a comprehensive analysis of 23 articles, a systematic review study discovered a favorable correlation between physical exercise and sleep quality across various demographic categories (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e). Several processes can explain the relationship between physical activity and better sleep, such as the release of endorphins, which can reduce stress and anxiety and improve relaxation and sleep quality, circadian rhythm regulation, and increase anti-inflammatory cytokines production (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e). Furthermore, a population-based study including 26,282 Chinese people revealed that smokers experienced far worse sleep quality and disruptions from their sleep compared to nonsmokers (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e). Smoking negatively affects metabolism, β-cell dysfunction, and IR, which are mostly caused by an increase in inflammatory biomarkers and cytokines such as CRP (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOur research also suggested that a lifestyle and dietary pattern with more pro-inflammatory qualities may be associated with an increased risk of mental health issues. Our results indicate that in our HD population, the prevalence of depression, anxiety, and stress was 53.7%, 53.0% and 47.3%, respectively. A study conducted by Palmer et al. involving an observational sample of 55,982 individuals with CKD revealed that approximately 25% of these patients experienced depression, and those with HD were at an increased risk (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e). The association between single components of the DLIS and mental health disorders has been also investigated in previous research. A large body of research supports the link between mental problems and diet quality. The risk of depression and anxiety in Iranian adults was found to be negatively correlated with their adherence to the healthy eating index (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e). A 2021 meta-analysis looked at the relationship between dietary potential for inflammation and mental health among 92,242 men and women from Asia, Europe, America, and Australia. The findings indicated that there is a substantial correlation between symptoms of depression, anxiety, and distress and a more inflammatory diet, as measured by the DII (\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e). However, in a cross-sectional study involving 400 health professionals conducted by Rostami et al., no significant associations were found between adherence to the Mediterranean-DASH Intervention for Neurodegenerative Delay (MIND) diet as an anti-inflammatory dietary pattern and odds of stress, anxiety, and depression either in the crude or multivariable-adjusted models (\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e). The study's conclusions may apply to only a small group of populations, male health professions, and might not be generalizable to females or other populations. Furthermore, the study's results were evaluated by self-reported questionnaires, which raises the possibility of recall bias and misreporting.\u003c/p\u003e \u003cp\u003eNevertheless, it is still unclear what precise biological process leads to depression or other mental illnesses. A growing body of evidence suggests that systemic inflammation, as indicated by high levels of CRP, may be a significant contributor to mental health problems like depression and anxiety (\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e). There is two-way comorbidity between inflammation and mental illness, meaning that increased inflammation of the body is associated with an increased risk of mental problems (such as depression), and depression itself is associated with increased behaviors that trigger inflammation, such as unhealthy eating habits (\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e). A comprehensive meta-analysis examining the contributions of main modifiable lifestyle factors to the prevention and treatment of mental disorders revealed that smoking is a causal factor of both common and severe mental illness, while physical activity protects against some mental disorders (\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e). Additionally, using data from the population-wide Austrian registry, obesity was found to be a significant risk factor for obtaining additional mental health diagnoses at every decade of adulthood, highlighting the significance of obesity as a pleiotropic promotor of health problems (\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe strength of the current study is that it is the first to examine the relationship between DLIS and sleep quality and mental health conditions in HD patients. Furthermore, our study includes a relatively large sample size from eight different hemodialysis centers in three cities with a variety of dietary and lifestyle habits. In-person interviews with participants were conducted using valid questionnaires to gather information on their dietary intakes and level of physical exercise. However, the current study had some limitations. The weights assigned to the DIS and LIS have been verified for the US population; they might not be suitable for other populations. Secondly, the reliance on self-reported measures for assessing sleep quality and mental health conditions may introduce response and recall bias. Some items were excluded in the calculation of DIS and LIS. The final DIS was calculated using 18 instead of 19 items and 3 instead of 4 for LIS.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe present findings suggest that higher adherence to a pro-inflammatory lifestyle and dietary habits, determined by the higher score of DLIS, is significantly associated with poor sleep quality and mental health disorders including depression, anxiety, and stress. To confirm these findings, further investigations in Iranian and other populations are recommended.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted according to the guidelines laid down in the Declaration of Helsinki and all procedures involving human patients were approved by the Ethics Committee of Shoushtar Faculty of Medical Sciences in Shoushtar, Iran (Registration no: IR.SHOUSHTAR.REC. 1403.20). Written informed consent was obtained from all subjects.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u0026nbsp;\u003c/strong\u003eAuthors wish to thank all patients who participated in this research project.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFinancial Disclosure\u003c/strong\u003e\u003cstrong\u003e:\u0026nbsp;\u003c/strong\u003eThe authors declare that they received no grants, funding, or other forms of support from any organization for the purpose of conducting this work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analyzed during the current study available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization: M.S.D., H.B.B., M.A.Methodology: M.S.D., H.B.B., M.A., A.Z.J., S.B.E., S.S.Resources: M.S.D., H.B.B., M.A., A.Z.J., S.B.E., S.S., S.S., S.K., S.K., P.T., F.F., H.S.D., R.G., E.G.Writing - Original Draft: M.S.D., H.B.B., M.A.Writing - Review \u0026amp; Editing: A.Z.J., S.B.E., S.S., H.B.B., M.A., M.S.D.Supervision: M.S.D., H.B.B., M.A., A.Z.J., S.B.E., S.S.Project administration: M.S.D., H.B.B., M.A.Investigation: M.S.D., H.B.B., M.A., S.S., S.K., S.K., P.T., F.F., H.S.D., R.G., E.G.Formal analysis: H.B.B., M.A.Visualization: H.B.B., M.A.\u003c/p\u003e\n\u003cp\u003eAll authors read and approved the final version of the manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of competing interest\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no personal or financial conflict of interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;Funding Declaration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was not supported by any sponsor or funder\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eThurlow JS, Joshi M, Yan G, Norris KC, Agodoa LY, Yuan CM, Nee R. 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Translational Psychiatry. 2023;13(1):175.\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":"nutrition-and-metabolism","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nuam","sideBox":"Learn more about [Nutrition \u0026 Metabolism](http://nutritionandmetabolism.biomedcentral.com/)","snPcode":"12986","submissionUrl":"https://submission.nature.com/new-submission/12986/3","title":"Nutrition \u0026 Metabolism","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"hemodialysis, sleep quality, Inflammation, Dietary pattern, Lifestyle, Mental health","lastPublishedDoi":"10.21203/rs.3.rs-4734732/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4734732/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003ePoor sleep quality and mental disorders are common issues among patients undergoing dialysis. Diet and lifestyle may be associated with sleep hygiene and mental health. The current study aimed to evaluate the association between the Dietary and Lifestyle Inflammation Score (DLIS) and mental health, and sleep quality among Iranian hemodialysis patients.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis multicenter cross-sectional study was conducted on 423 patients undergoing hemodialysis in eight centers in three cities. The DLIS was calculated using information from a validated 168-item semi-quantitative food frequency questionnaire. Mental health was evaluated using the 21-item depression, anxiety, and stress scale (DASS-21) and the Pittsburgh Sleep Quality Index (PSQI) was used to assess sleep quality. Other assessments included physical activity levels, biochemical parameters, and dialysis data of patients. Statistical analyses using SPSS software were conducted to identify associations.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation of the age and BMI were 52.84\u0026thinsp;\u0026plusmn;\u0026thinsp;14.63 years and 24.8\u0026thinsp;\u0026plusmn;\u0026thinsp;5.11 kg/m\u003csup\u003e2\u003c/sup\u003e, respectively. 58.9% of participants were men. After controlling for potential confounders, participants in the top quartile of DLIS had greater odds of having poor sleep quality (OR: 3.18; 95% CI: 1.71\u0026ndash;5.90), depression (OR: 1.94; 95% CI: 1.06\u0026ndash;3.54), anxiety (OR: 2.82; 95% CI: 1.51\u0026ndash;5.27), and stress (OR: 2.15; 95% CI: 1.14\u0026ndash;4.03) compared with those in the bottom quartile.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eOur findings showed that higher dietary and lifestyle inflammatory potential, characterized by higher DLIS, was positively associated with psychological disorders and poor sleep quality.\u003c/p\u003e","manuscriptTitle":"Association of Dietary and Lifestyle Inflammation Score with sleep quality and mental health in hemodialysis patients: A multicenter cross-sectional study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-26 11:32:12","doi":"10.21203/rs.3.rs-4734732/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-02-19T14:30:30+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-02-17T09:51:24+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"332631285076487960797371919381301912076","date":"2025-02-09T19:14:05+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-02-09T17:16:41+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"316063080222191521266310113205495437643","date":"2025-02-09T15:34:57+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-02-05T13:37:22+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"269573452693488140487382069016718514761","date":"2025-02-05T13:30:08+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"49593430381762731707082385750730117615","date":"2025-01-10T13:18:22+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"88155717259157549971389321553914654628","date":"2024-08-11T06:35:04+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"258646364555751256849320871135727544279","date":"2024-08-05T15:13:20+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-08-05T15:07:31+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-07-23T07:17:38+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-07-23T07:17:25+00:00","index":"","fulltext":""},{"type":"submitted","content":"Nutrition \u0026 Metabolism","date":"2024-07-13T10:14:44+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"nutrition-and-metabolism","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nuam","sideBox":"Learn more about [Nutrition \u0026 Metabolism](http://nutritionandmetabolism.biomedcentral.com/)","snPcode":"12986","submissionUrl":"https://submission.nature.com/new-submission/12986/3","title":"Nutrition \u0026 Metabolism","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ccf2d441-e372-4898-906a-1234459fe764","owner":[],"postedDate":"August 26th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-07-07T16:07:54+00:00","versionOfRecord":{"articleIdentity":"rs-4734732","link":"https://doi.org/10.1186/s12986-025-00958-5","journal":{"identity":"nutrition-and-metabolism","isVorOnly":false,"title":"Nutrition \u0026 Metabolism"},"publishedOn":"2025-07-01 15:58:35","publishedOnDateReadable":"July 1st, 2025"},"versionCreatedAt":"2024-08-26 11:32:12","video":"","vorDoi":"10.1186/s12986-025-00958-5","vorDoiUrl":"https://doi.org/10.1186/s12986-025-00958-5","workflowStages":[]},"version":"v1","identity":"rs-4734732","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4734732","identity":"rs-4734732","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}