A National Study Exploring the Association Between Fasting Duration and Mortality Among the Elderly

A National Study Exploring the Association Between Fasting Duration and Mortality Among the Elderly | 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 A National Study Exploring the Association Between Fasting Duration and Mortality Among the Elderly Zhixuan Zhang, Hang Zhao, Meng Jiang, Jun Pu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4174533/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Benefits from weight management have been widely accepted, and prolonged fasting duration has become a common method for weight control. The suitability of prolonged fasting duration for elderly individuals remains controversial. This study aims to examine the correlation between fasting duration and mortality within a nationally representative cohort of elderly individuals in the United States. Methods Data were extracted from a prospective cohort study conducted within the National Health and Nutrition Examination Survey (NHANES) from 2005 to 2018. Individuals over 60 with complete data on dietary intake and mortality follow-up information were included. Fasting duration was assessed using two 24-hour dietary recalls. All participants were categorized into fasting duration quantiles. Mortality outcomes were ascertained through the National Death Index. Cox proportional-hazards regression models were utilized to analyze the association between fasting duration and mortality. Results The final analysis included a total of 9,826 elderly participants (mean age 70.03, 49.33% male), with 2408 deaths observed during a median follow-up of 6.82 years. Following adjustments for covariates, the longest fasting duration (> 12.5 hours) exhibited heightened cardiovascular disease (CVD) mortality (Hazard Ratios [HR], 1.30; 95% CI, 1.01–1.66) and mortality from other causes (HR, 1.52, 95% CI, 1.07–2.16) compared to those with the shortest fasting duration (< 10 hours). Notably, the CVD mortality was significantly increased in males and in individuals aged 60–69 with a fasting duration exceeding 12.5 hours (HR, 1.49 and 2.87; 95% CI, 1.00-2.20 and 1.32–6.23, respectively). A non-linear relationship was observed between fasting duration and all-cause mortality ( P = 0.03), with a fasting duration of 11.89 hours linked to the lowest mortality. Conclusions Prolonged fasting periods are associated with increased cardiovascular mortality and mortality from other causes. Fasting duration of 11.89 hours is associated with the lowest mortality rate. Caution should be exercised by clinicians when recommending time-restricted feeding for the elderly. Further research through randomized controlled trials should be conducted to comprehensively investigate the impact of TRF on mortality. Cardiac & Cardiovascular Systems Time restricted feeding Fasting duration Mortality Elderly population NHANES Figures Figure 1 Figure 2 Figure 3 Introduction Obesity has evolved into a global epidemic over the past decade, impacting approximately 75% of individuals in the United States( 1 ). Current evidence suggests that obesity is intricately linked to the incidence of cardiovascular diseases( 2 ), diabetes( 3 ), and other metabolic disorders( 4 ). In recent years. the benefits of weight management have been increasingly recognized( 5 ). It is now understood that even a modest reduction of 5% in initial body weight could reduce, eliminate, or prevent the metabolic comorbidities in overweight or obese individuals( 6 ). Among the various strategies for weight management, ‘Intermittent Fasting (IF)’ refers to a dietary approach that involves restricting the eating window to specific periods each day or week, resulting in extended fasting intervals( 7 ). One of IF approaches was Time-restricted feeding (TRF), which refers to eating within a predetermined time window each day( 8 ) and may be classified into 16/8, 18/6, and 20/4 eating schedules( 9 ). The “16/8” regime is the most popular TRF, requiring a fasting duration of at least 16 hours per day, with the remaining 8 hours for unrestricted eating( 10 ). The potential health benefits of TRF have been explored in several previous studies, including body weight reduction, blood pressure normalization, enhanced blood glucose and insulin sensitivity, mitigation of inflammation, and even potential inhibition of cancer cell growth( 11 – 15 ). However, whether TRF is suitable for elderly people remains controversial since this population may be more vulnerable to the adverse effects of TRF. For example, prolonged fasting may increase the risk of hypoglycemia (especially in patients with Type 2 Diabetes)( 16 ). Moreover, TRF usually involves lower calorie intake, a monotonous diet, as well as inadequate consumption of protein or vegetables, which may cause malnutrition, decreased bone density, loss of lean mass, and constipation in the elderly population( 17 – 19 ). To date, the impact of prolonged fasting duration on survival has not been assessed among the elderly population. The present study aimed to examine the association between fasting duration and mortality in a nationally representative cohort of elderly people in the United States. Using dietary data from the National Health and Nutrition Examination Survey (NHANES) from 2005 to 2018, we analyzed the association between the timing of overnight fasting and all-cause and different cause-specific mortality in participants over 60 years old. We conducted subgroup analyses to ascertain whether the influence of fasting duration on mortality might differ based on factors such as gender, age, body weight status, lifestyle variables, or prevalent chronic diseases. A comprehensive understanding of the link between fasting duration and mortality will offer insights into the safety and feasibility of TRF in the elderly population. Materials and Methods Study Population NHANES is a large-scale, multistage, ongoing, nationally representative health survey of the civilian noninstitutionalized population in the United States( 20 ). Participants are invited to complete an interviewer-based questionnaire followed by a physical examination and laboratory measurements at a mobile examination center. NHANES was approved by the National Center for Health Statistics(NCHS)'s Ethics Review Board. Written informed consent was obtained from all participants. In this study, we extracted data from 2005–2018, covering 7 survey cycles and involving a total of 70,191 individuals. In accordance with the Center for Disease Control and Prevention's definition of elderly individuals( 21 ), all participants aged 60 were initially chosen. Those with missing dietary intake or mortality data were excluded. We also excluded participants with extremely short fasting periods (less than 4.25 hours) or extremely long fasting periods (more than 18.3 hours), as these values were considered outliers (falling below − 3 Standard Deviations [SDs] or above + 3 SDs)( 22 ). Moreover, we implemented additional exclusion criteria, consistent with previous research( 23 , 24 ), which included: ( 1 ) uncontrolled hypertension, characterized by systolic blood pressure ≥ 180 mmHg or diastolic blood pressure ≥ 100 mmHg; ( 2 ) recent diagnoses of unstable angina, heart attack, or stroke within the past year; ( 3 ) diagnosed Parkinson's disease; ( 4 ) history of epilepsy; and ( 5 ) diagnosed severe chronic kidney disease (CKD) with an estimated Glomerular Filtration Rate of < 15 (ml/min/m 2 ). All diagnoses were defined based on earlier self-reported information from the Database Questionnaire. The estimated Glomerular Filtration Rate was calculated using laboratory data, following established clinical practice guidelines( 25 ). To ensure the reliability of our findings, we compared the characteristics of the subset of participants with complete fasting duration and mortality data to verify that this selected subgroup accurately represented the entire population. Exposure Assessment To determine micro and macronutrient intake, NHANES employed the US Department of Agriculture 's Food and Nutrient Database for Dietary Studies to process dietary information. Participants underwent two 24-hour dietary recalls. The first recall consisted of an in-person interview, while the second recall was conducted over the phone, with a time gap of 3 to 10 days after the initial dietary interview, though not consistently on the same day of each week. In the current study, we included those participants who had successfully completed the interviews of both days, utilizing the average fasting duration of these two days. During each recall session, participants were instructed to report all meals consumed from midnight to 11:59 p.m. The length of the overnight fasting period was calculated by subtracting the time between the first and last consumption of calorie-containing (or 5 kcal) food or beverage during each 24-hour dietary recall day from 24. The calculation equation used was: 24 - time of last calorie intake + time of first calorie intake( 26 ). To determine fasting periods, the duration of all food consumption was converted into hours over a 24-hour period. For instance, food consumed at 8:30 a.m. and 9:15 a.m. were assigned time values of 8.5 and 9.25 hours, respectively. For an individual who had the first meal at 8:30 a.m. (8.5 hours) and the last meal at 10 p.m. (22 hours), the fasting period would be calculated as 24 − 22 + 8.5 = 10.5 hours. Outcome Ascertainment The primary outcome of our study was mortality from various causes, including all-cause mortality, cancer (codes C00-C97), cardiovascular diseases (CVD, classified by codes I00-I09, I11, I13, I20-I25, I26-I51 and I60-I69); and other causes in compliance with ICD-10 (10th revision of the international statistical classification of diseases). Mortality status and cause of death were ascertained by NHANES linked National Death Index public-access files ( https://www.cdc.gov/nchs/data-linkage/mortality-public.htm ) through December 31, 2019.The duration of follow-up was calculated from the date of examination at the mobile examination center to the occurrence of the recorded death. Covariate Assessment Demographic variates included age, gender, educational attainment, ethnicity, marital status, family poverty income ratio, and. Body Mass Index (BMI) was calculated to evaluate the weight status of the participants. A value of BMI ≥ 25 indicated "Overweight," while a BMI value of ≥ 30 indicated “Obesity”( 27 ). Other relevant variables encompassed diagnosis of chronic diseases (including diabetes, hypertension, cardiovascular disease( 28 ), depression and CKD), dietary inflammatory index, timing of first meal, smoking, and alcohol consumption. A detailed description for the covariate assessment is presented in the eAppendix in Supplement 1 . Statistical Analysis Based on the NCHS's recommendation, the dietary sampling weights were used in all analyses to interpret the complex NHANES survey design. Continuous variables were represented by mean (standard error), and categorical variables were expressed as the weighted percentage. Demographic and clinical characteristics were compared among the groups with different categories of fasting duration using the Student’s t-test, analysis of variance with post-hoc Bonferroni correction for continuous variables, the Mann-Whitney U test and Kruskal-Wallis test, or the chi-squared test for categorical variables, as appropriate. The associations between fasting duration and all-cause and cause-specific mortality were investigated by using multivariate Cox proportional-hazards regression models. In selecting covariates, we employed the bidirectional stepwise regression method to determine the most fitting adjusted variables for each model. Hazard ratios (HRs) and their corresponding 95% confidence intervals (CIs) were used to present the outcomes of the regression models. Restricted cubic spline analyses were conducted to explore the potential non-linear associations between fasting duration and all-cause and cause-specific mortality. Additionally, sensitivity analyses were performed within gender and age subgroups. Furthermore, adjusted models were constructed to assess the link between CVD mortality and fasting duration, stratified by diabetes, cancer, CKD, BMI status, alcohol use, smoking habits, and timing of the first meal. The results of these stratified analyses were visualized in forest plots. Statistical analyses were carried out using R software ( http://www.R-project.org , The R Foundation). A two-sided P -value less than 0.05 was statistically significant. Results Description of Study Participants From 2005 to 2018, there were 13,481 consecutive participants aged over 60 from NHANES datasets, with 9,826 individuals included in our final analysis (Fig. 1 ). No significant difference in fasting duration and demographic characteristics was observed between the analyzed population and the total population with complete fasting duration or mortality data (Table S1). Table 1 summarized the characteristics of the study cohort, stratified according to fasting duration quantiles. Compared to individuals with the shortest nighttime fasting duration (≤ 10h, Quantile 1), those with the longest nighttime fasting duration (> 12.5h, Quantile 4) tended to be older, predominantly female, non-Hispanic black, widowed, possessed a lower income-to-poverty level ratio, and had lower educational attainment. Individuals with lengthier fasting periods were also less prone to smoking or alcohol consumption and tended to start their first meal at a later time. The subgroup with the longest fasting duration was also correlated with a higher prevalence of diabetes, CKD, depression, CVD, hypertension, and higher dietary inflammation index values. Table 1 Characteristics of study participants according to fasting duration categories. Characteristics Quantiles of fasting duration Quantile 1 (≤ 10h) n = 2,067 Quantile 2 (> 10h, ≤ 11.25h) n = 1,807 Quantile 3 (> 11.25h,≤12.5h) n = 1,752 Quantile 4 (> 12.5h) n = 1,792 P value Age, mean (SE), years 68.45 (0.19) 69.17 (0.18) 70.34 (0.23) 71.27 (0.20) < 0.01 Age (Categorial, %) < 0.01 60–64 34.19 32.29 26.03 22.43 65–69 27.85 23.66 21.64 21.54 70–74 16.81 18.66 21.10 19.12 ≥ 75 21.15 25.39 31.23 36.91 Gender (%) 0.03 Male 48.15 46.41 45.55 41.29 Female 51.85 53.59 54.45 58.71 Ethnicity (%) < 0.01 Mexican American 2.39 3.95 4.43 6.62 Other Hispanic 2.63 3.72 3.77 3.64 Non-Hispanic white 84.21 80.74 78.30 71.76 Non-Hispanic black 6.12 6.34 8.71 13.72 Other Race(s) 4.65 5.25 4.79 4.27 Educational level (%) < 0.01 Less than 9th grade 4.35 6.19 8.16 12.43 9-11th grade 7.78 8.89 10.63 14.02 High school graduate 23.70 23.64 26.55 27.09 Some college or AA 29.83 27.98 28.65 25.17 College or above 34.34 33.31 26.01 21.29 Marital status (%) < 0.01 Married 62.83 65.90 65.88 56.44 Widowed 16.66 17.46 17.40 23.24 Divorced 14.24 11.28 10.20 11.54 Separated 1.52 0.93 1.05 1.54 Never married 3.05 2.46 3.65 4.62 Living with a partner 1.69 1.98 1.82 2.61 RIP (Categorial, %) 1 87.08 86.25 82.29 78.08 Missing 6.68 6.69 8.80 8.74 Smoking (%) 0.01 Never 48.34 47.60 48.31 52.19 Former 39.23 40.86 43.78 37.50 Now 12.42 11.53 7.91 10.31 Alcohol user (%) < 0.01 No 29.91 29.17 33.86 38.19 Yes 64.14 62.98 58.60 53.38 Missing 5.95 7.85 7.53 8.43 First meal (%) < 0.01 Before 8 a.m. 89.45 64.84 48.42 25.76 After 8 a.m. 10.55 35.16 51.58 74.24 BMI, (Categorial, %), kg/m 2 0.98 Normal (< 25) 24.34 24.70 24.09 24.28 Overweight (≥ 25, < 30) 36.52 34.79 36.60 36.04 Obese (≥ 30) 39.13 40.51 39.32 39.69 Diabetes (%) < 0.01 No 65.51 62.81 60.12 56.68 Yes 34.49 37.19 39.88 43.32 CKD (%) < 0.01 No 69.90 67.36 64.66 56.29 Yes 26.55 29.22 30.92 38.45 Missing 3.55 3.42 4.42 5.26 Depression (%) < 0.01 No 77.03 78.96 79.30 72.16 Yes 19.85 17.28 16.69 22.32 Missing 3.12 3.76 4.01 5.52 CVD (%) 0.04 No 83.94 83.34 79.76 81.20 Yes 16.06 16.66 20.24 18.80 Hypertension (%) < 0.01 No 35.18 34.79 29.95 27.02 Yes 64.82 65.21 70.05 72.98 Cancer (%) 0.71 No 74.80 76.50 74.39 75.21 Yes 25.20 23.50 25.61 24.79 Fasting duration, mean (SE), hour 8.66(0.04) 10.74(0.01) 11.95(0.01) 13.86(0.03) < 0.01 DII, mean (SE) 1.16(0.06) 1.33(0.06) 1.57(0.06) 1.94(0.07) < 0.01 Living status (%) < 0.01 Alive 81.68 81.72 78.51 72.67 Deceased 18.32 18.28 21.49 27.33 Note: Weighted percentage was used to represent categorical variables, while mean (standard error) was used to represent continuous variables. ANOVA for continuous variables and Chi-Square for categorical variables were used to calculate P value. Abbreviations: BMI, Body Mass Index; CVD, Cardiovascular Disease; CKD, Chronic Kidney Disease; DII, Dietary Inflammation Index; RIP, Ratio of Income to Poverty; SE, Standard Error; NHANES, National Health, and Nutrition Examination Survey. Relationship between Fasting Duration and Mortality We found that 2,408 participants in the cohort passed away over a median follow-up period of 6.82 years (standard deviation: 3.90 person-years), with a mean lifespan of 5.70 person-years. An upward pattern in unadjusted all-cause mortality rates was observed across the four fasting duration quantiles (21.97%, 21.67%, 24.77%, and 29.47%, respectively). Notably, the group with the longest fasting period (Quantile 4) exhibited the highest mortality rate. The comprehensive analysis of causes of death across each quantile is provided in Table S2. Non-linear association between fasting duration and all-cause mortality is depicted in Fig. 2 . A statistically significant U-shaped relationship between fasting duration and all-cause mortality was observed (non-linear P value = 0.03). A fasting duration of 11.89 hours was associated with the lowest mortality. However, there was no association between fasting duration and cause-specific mortality rates (non-linear P value 0.37–0.98, Figure S1). Additionally, we performed the multivariable Cox regression models in the younger population. After excluding participants with missing data of fasting duration (n = 16,677), mortality (n = 54) and those of age under 18 (n = 22,137) and over 60 (n = 10,561), there were 20,761 participants aged 18–59 from NHANES 2005–2018. We found that there was no significant association between overnight fasting time and adjusted all-cause mortality in the younger participants (non-linear P value = 0.22, Figure S2). Table 2 presented the correlations between fasting duration quantiles and all-cause, as well as cause-specific mortality. After adjustment for age, gender, educational level, marital status, income-to-poverty ratio, ethnicity, diabetes, CKD, BMI, depression, smoking, alcohol use, dietary inflammation index, hypertension, and CVD, Quantile 4 displayed significantly elevated CVD mortality (HR: 1.30, 95% CI: 1.01–1.66) and mortality from other causes (HR: 1.52, 95% CI: 1.07–2.16) compared to Quantile 1. Examining fasting duration as a continuous variable revealed that each additional hour of fasting duration correlated with a 7% increase in mortality from other causes (HR: 1.07, 95% CI: 1.01–1.12). Table 2 Association between fasting duration, all-cause mortality, and cause-specific mortality according to fasting duration categories. Quantiles of fasting duration a Quantile 1 Quantile 2 Quantile 3 Quantile 4 Per 1 h increment in fasting duration a All-cause mortality c 1.00 (Ref) 0.85 (0.72,0.99) 0.90 (0.74,1.10) 0.99 (0.84,1.16) 1.01 (0.98,1.04) CVD mortality c 1.00 (Ref) 1.20 (0.90,1.61) 0.87 (0.66,1.14) 1.30 (1.01,1.66) 1.04 (0.99,1.08) Cancer mortality d 1.00 (Ref) 0.76 (0.58,1.02) 0.89 (0.68,1.15) 0.76 (0.64,1.06) 0.95 (0.90,1.00) Other mortality b 1.00 (Ref) 1.13 (0.80,1.63) 1.22 (0.90,1.67) 1.52 (1.07,2.16) 1.07 (1.01,1.12) Note: Results were presented as hazard ratios (95% confidence intervals). Significant values in bold ( P < 0.05). Other mortality was adjusted for age (Categorial), gender, educational level, marital status, ratio of income to poverty (Categorial), ethnicity, diabetes, chronic kidney diseases, body mass index, depression, smoking, alcohol user, and dietary inflammatory index. CVD mortality and all-cause mortality were further adjusted (from other mortality) for hypertension and CVD. Cancer mortality was further adjusted (from other mortality) for cancer. Quantile ranges: Quantile 1:≤10h; Quantile 2:>10h, ≤ 11.25h; Quantile 3:>11.25h,≤12.5h; Quantile 4:>12.5h. a Hazard ratios for each hour increment in fasting duration. Abbreviation: CVD, Cardiovascular Disease; Ref, reference Relationship between Fasting Duration and Mortality in Gender and Age Subgroups To increase the robustness of our findings, we further explored the link between fasting duration and mortality risk among gender and age subgroups (Table S3). HR values and 95% CIs for participants within the longest fasting duration quantile (Quantile 4) in comparison to those within the shortest fasting duration quantile (Quantile 1) are presented in Fig. 3 A. We found that CVD mortality was substantially increased among male participants (HR: 1.49, 95% CI: 1.00–2.20) and individuals aged 60–69 (HR: 2.87, 95% CI: 1.32–6.23) within Quantile 4 compared to Quantile 1. Similarly, mortality from other causes (HR: 1.67, 95% CI: 1.13–2.46) increased among those aged over 70 within Quantile 4. In contrast, a reduction in cancer mortality was noted in females (HR: 0.49, 95% CI: 0.29–0.84) and those aged 60–69 (HR: 0.29, 95% CI: 0.13–0.65) within Quantile 4. Analyzing fasting time as a continuous variable, each additional hour of fasting duration correlated with a 6% increase in CVD mortality risk in males (95% CI: 1.00–1.13) and a 13% increase in CVD mortality risk among those aged 60–69 (95% CI: 1.00–1.29, Table S3). Relationship between Fasting Duration and CVD Mortality Stratified by Diseases and Lifestyles To elucidate the influence of disease status or lifestyle on CVD mortality between the longest and shortest fasting duration quantiles, Cox proportional regression models were conducted in Quantile 4 (with Quantile 1 as the reference) across various subgroups. Model I was adjusted for covariates including age, gender, and ethnicity, while Model II was the fully-adjusted model (Fig. 3 B). In terms of interaction effects, the impact of extended fasting duration on CVD mortality was unaffected by BMI status, diabetes, CKD, cancer, timing of first meal, or alcohol consumption after full covariate adjustment ( P interaction ranging from 0.06 to 0.93). However, a significant interaction effect was observed between smoking status and fasting duration ( P interaction < 0.05). In the fully-adjusted Model II, non-smokers demonstrated a heightened CVD mortality risk (HR: 1.67, 95% CI: 1.09–2.56). Discussion Utilizing nationally prospective cohort data, our study revealed a significant association between prolonged night fasting duration and heightened CVD mortality, along with mortality from other causes in the elderly population. Specifically, CVD mortality risk was substantially increased in males and individuals aged 60–69 with a fasting duration exceeding 12.5 hours. Furthermore, we found a fasting duration of 11.89 hours was linked to the lowest overall mortality. To our knowledge, this is the first prospective analysis to explore the correlation between fasting duration and mortality among elderly adults, drawing from nationally representative U.S. population data. Our findings underscore the potential risks associated with TRF (Time Restricted Feeding) for the elderly and imply that clinicians should exercise caution when advising TRF for those above 60 years old. To date, limited research has been undertaken on the effects of TRF or IF (Intermittent Fasting) within the elderly population. A preliminary study involving ten overweight adults aged over 65 demonstrated a mean weight loss of 2.6 kg and improvements in walking speed and quality of life following four weeks of TRF( 29 ). Another study showed that the application of IF resulted in body fat mass reduction in 45 women participants aged over 60( 30 ). Our study is the first to reveal that extended fasting periods are associated with elevated CVD mortality and mortality from other causes in the elderly population. Several factors may contribute to these findings. First, prolonged fasting periods often involve reduced calorie intake and potential inadequacy in protein, fiber, and essential nutrients. In elderly adults, these conditions can give rise to various health concerns, such as compromised immune function( 31 ), muscle weakness( 18 ), decreased bone mass( 17 ), as well as an increased risk of death( 32 ). Additionally, prolonged fasting could reflect unhealthy dietary or lifestyle habits, like breakfast skipping or irregular meal patterns( 33 ), which was also associated with higher mortality( 33 – 35 ). Moreover, extended fasting may trigger stress-induced overactivity and heightened serum cortisol levels( 36 ), further linked to increased cardiovascular risk( 37 , 38 ). Upon stratified analysis, the correlation between prolonged fasting and heightened CVD mortality remained significant irrespective of factors such as weight, meal timing, alcohol consumption, and chronic diseases. The only significant interaction factor was smoking, impacting the association between fasting duration and CVD mortality. Nonetheless, within each stratum, no significant variance in mortality risk emerged between groups with the longest and shortest fasting periods. It should be noted that the sample size of current smoker is relatively small (n = 305) in current study. Further studies are warranted to explore the effect of TRF on mortality among individuals with diverse habits and lifestyles. Various TRF regimes, such as 16/8, 18/6, and 20/4 methods, entail daily fasting durations ranging from 12 to 21 hours( 9 ). Previous research has shown that different fasting durations may yield varying effects on weight loss and metabolic parameters( 13 , 39 ). For example, a 6-hour TRF (18-hour fasting duration) was associated with reduced blood pressure and fasting insulin but increased serum triglycerides( 39 ), while an 8-hour TRF (16-hour fasting duration) led to significant triglyceride reduction( 13 ). The optimal fasting duration for cardiometabolic benefits remains inconclusive. Our study revealed that a fasting duration of 11.89 hours corresponded to the lowest all-cause mortality rate among the elderly, which may provide implications for future fasting recommendations. Strengths and Limitations Our findings indicated that the elderly adults might be more susceptible to the adverse effect of TRF. Comprehensive health assessment, individualized dietary plans and close monitoring is necessary for achieving and maintaining a healthy weight in the elderly people. However, several limitations need to be considered. Firstly, similar to prior studies( 34 , 35 ), NHANES data analyzed in our study was derived from 24-hour food recall questionnaires, which could introduce potential biases, including recall bias or social-desirability bias( 40 ), ultimately leading to underreporting or over-reporting of caloric intake. Secondly, our analysis did not encompass comprehensive dietary information (such as diet components, quality, or eating schedules) during non-fasting periods, which might exert additional influence on participant mortality. Furthermore, residual confounding factors may persist beyond demographic, disease-related, or dietary influences. Further randomized controlled trials are warranted to gain deeper insights into the impact of TRF on mortality. Conclusions Prolonged fasting periods are associated with an increased risk of cardiovascular mortality and mortality from other causes in the elderly population. A fasting duration of 11.89 hours is associated with the lowest mortality rate. These findings may have implications for future fasting guidelines. Currently, caution should be exercised by clinicians when recommending time-restricted feeding for the elderly. Further research through randomized controlled trials should be conducted to comprehensively investigate the impact of TRF on mortality. Declarations Acknowledgements We acknowledge NHANES database for providing their platforms and contributors for uploading their meaningful datasets. And we thank all participants included in our present study. Funding This study received funding support from National Natural Science Foundation of China (U21A20341, 81971570, 82202159, 31900821), Science and Technology Commission of Shanghai Municipality (21XD1432100, 22JC1402100, 22DZ2292400, 20Y11910500, 2022ZZ01008, 201409005200), Shanghai Hospital Development Center (SHDC2020CR2025B, SHDC12022102), Shanghai Municipal Health Commission (2022JC013, SHSLCZDZK06204), Shanghai Pudong New Area Health Commission (PW2019D-11), Shanghai Jiao Tong University (YG2019ZDA13), University of Shanghai for Science and Technology (10-20-302-425), Shanghai Clinical Research Center for Aging and Medicine (19MC1910500), Shanghai Cancer Institute (ZZ-20-22SYL). Data Availability Publicly available datasets were analyzed in this study. All the raw data used in this study are derived from the public NHANES data portal (https://wwwn.cdc. gov/nchs/nhanes/analyticguidelines.aspx) Ethics Declarations Competing interests The authors declare no competing interests. Ethical approval and Consent to Participate The NCHS Ethics Review Board protects the rights and welfare of NHANES participants. The NHANES protocol complies with the U.S. Department of Health and Human Services Policy for the Protection of Human Research Subjects. Ethical review and approval were waived for this study as it solely used publicly available data for research and publication. Informed consent was obtained from all subjects involved in the NHANES. Consent for publication Not applicable. Author’s contributions Concept and design: Zhixuan Zhang, Hang Zhao, Zhengyu Tao and Meng Jiang. Acquisition, analysis, or interpretation of data: All authors. Drafting of the manuscript: Zhixuan Zhang, Hang Zhao, Meng Jiang, and Jun Pu. Critical revision of the manuscript for important intellectual content: All authors. Statistical analysis: Zhixuan Zhang. Administrative, technical, or material support: Meng Jiang and Jun Pu. Supervision: Meng Jiang and Jun Pu. 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Anton S, Ezzati A, Witt D, McLaren C, Vial P. The effects of intermittent fasting regimens in middle-age and older adults: Current state of evidence. Exp Gerontol. 2021;156:111617. Anton SD, Moehl K, Donahoo WT, Marosi K, Lee SA, Mainous AG, 3rd, et al. Flipping the Metabolic Switch: Understanding and Applying the Health Benefits of Fasting. Obesity (Silver Spring). 2018;26(2):254-68. Conley M, Le Fevre L, Haywood C, Proietto J. Is two days of intermittent energy restriction per week a feasible weight loss approach in obese males? A randomised pilot study. Nutr Diet. 2018;75(1):65-72. CDC. Nhanes - about the National Health and Nutrition Examination Survey [cited 2023 1 January 2023]. Available from: https://www.cdc.gov/nchs/nhanes/about_nhanes.htm. Control CfD, Health PJAUDo, Services H. Identifying vulnerable older adults and legal options for increasing their protection during all-hazards emergencies: A cross-sector guide for states and communities. 2012;15. Rousseeuw PJ, Hubert M. Anomaly detection by robust statistics. WIREs Data Mining and Knowledge Discovery. 2017;8(2). Prasad M, Fine K, Gee A, Nair N, Popp CJ, Cheng B, et al. A Smartphone Intervention to Promote Time Restricted Eating Reduces Body Weight and Blood Pressure in Adults with Overweight and Obesity: A Pilot Study. Nutrients. 2021;13(7). Grajower MM, Horne BD. Clinical Management of Intermittent Fasting in Patients with Diabetes Mellitus. Nutrients. 2019;11(4). international JK. KDIGO 2021 Clinical Practice Guideline for the Management of Glomerular Diseases2021. S1-S276 p. Wirth MD, Zhao L, Turner-McGrievy GM, Ortaglia A. Associations between Fasting Duration, Timing of First and Last Meal, and Cardiometabolic Endpoints in the National Health and Nutrition Examination Survey. Nutrients. 2021;13(8). Jan A, Weir CBJSTI, FL, USA. BMI Classification Percentile and Cut Off Points. 2021:1-4. Group WCRCW. World Health Organization cardiovascular disease risk charts: revised models to estimate risk in 21 global regions. Lancet Glob Health. 2019;7(10):e1332-e45. Anton SD, Lee SA, Donahoo WT, McLaren C, Manini T, Leeuwenburgh C, et al. The Effects of Time Restricted Feeding on Overweight, Older Adults: A Pilot Study. 2019;11(7):1500. P D, M K, P P, D B, research S-KEJIjoe, health p. Effect of a Six-Week Intermittent Fasting Intervention Program on the Composition of the Human Body in Women over 60 Years of Age. 2020;17(11). Janssen H, Kahles F, Liu D, Downey J, Koekkoek LL, Roudko V, et al. Monocytes re-enter the bone marrow during fasting and alter the host response to infection. Immunity. 2023. Z Y, D K, J P, Z W, Chen Y %J Nutrition m, NMCD cd. Association of malnutrition with all-cause mortality in the elderly population: A 6-year cohort study. 2021;31(1):52-9. S P, SJ R, Nutrition LJJ, Australia dtjotDAo. Associations between dietary behaviours and perceived physical and mental health status among Korean adolescents. 2018;75(5):488-93. Rong S, Snetselaar LG, Xu G, Sun Y, Liu B, Wallace RB, et al. Association of Skipping Breakfast With Cardiovascular and All-Cause Mortality. J Am Coll Cardiol. 2019;73(16):2025-32. Sun Y, Rong S, Liu B, Du Y, Wu Y, Chen L, et al. Meal Skipping and Shorter Meal Intervals Are Associated with Increased Risk of All-Cause and Cardiovascular Disease Mortality among US Adults. J Acad Nutr Diet. 2023;123(3):417-26 e3. Y N, BR W, Stress ITJ. Systematic review and meta-analysis reveals acutely elevated plasma cortisol following fasting but not less severe calorie restriction. 2016;19(2):151-7. E P, M W, Endocrinology SMJ. Adverse cardiovascular outcomes of corticosteroid excess. 2012;153(11):5137-42. AA C, S S, C K, L M, M E, S E, et al. Morning plasma cortisol as a cardiovascular risk factor: findings from prospective cohort and Mendelian randomization studies. 2019;181(4):429-38. EF S, R B, KS E, WT C, E R, metabolism PCJC. Early Time-Restricted Feeding Improves Insulin Sensitivity, Blood Pressure, and Oxidative Stress Even without Weight Loss in Men with Prediabetes. 2018;27(6):1212-21.e3. Krok-Schoen JL, Archdeacon Price A, Luo M, Kelly OJ, Taylor CA. Low Dietary Protein Intakes and Associated Dietary Patterns and Functional Limitations in an Aging Population: A NHANES analysis. J Nutr Health Aging. 2019;23(4):338-47. Supplementary Information Supplement 1, Supplementary Figures and Supplementary Tables are not available with this version. Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4174533","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":284418835,"identity":"d485c0d9-5315-4b55-aee9-32664ed745f2","order_by":0,"name":"Zhixuan Zhang","email":"","orcid":"https://orcid.org/0009-0008-7117-2121","institution":"Shanghai Jiaotong University School of Medicine, Renji Hospital","correspondingAuthor":false,"prefix":"","firstName":"Zhixuan","middleName":"","lastName":"Zhang","suffix":""},{"id":284419569,"identity":"3ef826c2-fdcf-4584-a0dd-0e5f21cebd70","order_by":1,"name":"Hang Zhao","email":"","orcid":"","institution":"Shanghai Jiaotong University School of Medicine, Renji Hospital","correspondingAuthor":false,"prefix":"","firstName":"Hang","middleName":"","lastName":"Zhao","suffix":""},{"id":284419570,"identity":"8d9df5db-bb1b-47a2-a1bf-7f6c386b8d27","order_by":2,"name":"Meng Jiang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+ElEQVRIiWNgGAWjYBACxmYYi5n5wIEPFRJy8sRrYW9LPDjjjIWxYQPR9vGcMT7M21aRyHCAgELmduZnD7/m2OTJR+QYHOadJ5HA2MD88NENvA5jMzeW3ZZWbHgjreDg3G0SeewMbMbGOfj9YiYtue1w4sYZyRsOvN0mUczYwMMmjV8L+zeglv9ALQkGB3jnSCQ2HCCohcdM8uO2A4nzeY4YHORtIE5LmTTjtuTEDextCQdnHJMwNmwm4BfD/uPbJH9us0uc38x8+MOHmjo5efbmh4/xamkABjQPkGFwACbEjEc5CICSB+MPEKOBgMpRMApGwSgYuQAAKm9SS7cyaj4AAAAASUVORK5CYII=","orcid":"","institution":"Shanghai Jiaotong University School of Medicine, Renji Hospital","correspondingAuthor":true,"prefix":"","firstName":"Meng","middleName":"","lastName":"Jiang","suffix":""},{"id":284419571,"identity":"4439b167-cc69-4ec3-a3fc-f41f860c4433","order_by":3,"name":"Jun Pu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2klEQVRIiWNgGAWjYHCCBAaGHxI8/BJgjoQMcVoYeyzkJGcwMDYAtfAQaRFbhbHBDbAWBsJazCUSnkkX8Egkbr7dfPzRjRoLHgb2w0c34NNiOSMhTXqGhUTitjvHEptzjgEdxpOWdgOfFoMbQC08QFu23cgxbM5hA2qR4DEjQgsb0GEzQFr+kaDF2EACqCW3jQgtlj0Pkq15eyTkJG6kJc7O7ZPgYSPkF3P2nMTbPD/qePhnJB/4nPOtTo6f/fAx/A5j4ElAFWHDpxyihf0AITWjYBSMglEw0gEA/ftDKFn2vjYAAAAASUVORK5CYII=","orcid":"","institution":"Shanghai Jiaotong University School of Medicine, Renji Hospital","correspondingAuthor":true,"prefix":"","firstName":"Jun","middleName":"","lastName":"Pu","suffix":""}],"badges":[],"createdAt":"2024-03-27 08:25:41","currentVersionCode":1,"declarations":{"humanSubjects":true,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":true,"humanSubjectConsent":true,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-4174533/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4174533/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":53748507,"identity":"0de43b67-4dc0-4d92-8b7b-8c247b11f86f","added_by":"auto","created_at":"2024-03-29 18:24:26","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":111385,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFlow chart depicting the inclusion of participants in the present study.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAbbreviations: CKD, Chronic Kidney Diseases; DBP, Diastolic Blood Pressure; eGFR, estimated Glomerular Filtration Rate; NHANES, National Health, and Nutrition Examination Survey; SBP, Systolic Blood Pressure.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-4174533/v1/15610915c15cf8b5799bff5e.png"},{"id":53748508,"identity":"0d26f9c5-22f1-47e4-9f80-eff967c91b31","added_by":"auto","created_at":"2024-03-29 18:24:26","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":31870,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eNon-linear association between fasting duration and all-cause mortality examined by multivariable Cox regression models.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNote: Solid red lines representhazard ratios adjusted for age (Categorial), gender, educational level, marital status, ratio of income to poverty (Categorial), ethnicity, diabetes, chronic kidney disease, body mass index, depression, smoking, use of alcohol, dietary inflammatory index, hypertension, and cardiovascular disease, with light-red areas showing the 95% confidence intervals derived from restricted cubic spline regressions with four knots.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-4174533/v1/6183367a461f755315b10928.png"},{"id":53748509,"identity":"bce80b9f-4950-4f3a-bd59-f623e74d9117","added_by":"auto","created_at":"2024-03-29 18:24:26","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":686824,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eStratified analysis for the relationship between fasting duration and mortality among the elderly population\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(A)\u003c/strong\u003e \u003cstrong\u003eRelationship between fasting duration and mortality in gender and age subgroups.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNote: Values are n or hazard ratios (95% confidence intervals) of Quantile 4 (fasting duration \u0026gt; 12.5h) with Quantile 1 (fasting duration ≤ 10h) as the reference.\u003c/p\u003e\n\u003cp\u003eOther mortality was adjusted for age (Categorial), gender, educational level, marital status, ratio of income to poverty (Categorial), ethnicity, diabetes, chronic kidney diseases, body mass index, depression, smoking, use of alcohol, and dietary inflammatory index. CVD mortality and all-cause mortality were further adjusted (from other mortality) for hypertension and CVD. Cancer mortality was further adjusted (from other mortality) for cancer.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(B) Relationship between fasting duration and CVD mortality stratified by diseases and lifestyles.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNote: Values are n or hazard ratios (95% confidence intervals) of Quantile 4 (fasting duration \u0026gt; 12.5h) with Quantile 1 (fasting duration ≤ 10h) as the reference.\u003c/p\u003e\n\u003cp\u003eModel I: adjusted for age (Categorial), gender, and ethnicity.\u003c/p\u003e\n\u003cp\u003eModel II: Model I + educational level, marital status, ratio of income to poverty (Categorial), smoking, alcohol user, dietary inflammatory index, hypertension, CVD, body mass index, depression, chronic kidney diseases, and diabetes.\u003c/p\u003e\n\u003cp\u003eAbbreviation: CVD, Cardiovascular Diseases.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-4174533/v1/da6ae5f02b4df988691457fd.png"},{"id":53749364,"identity":"9589141f-9bc5-47d6-8010-5159ddaa990a","added_by":"auto","created_at":"2024-03-29 18:32:27","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1374619,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4174533/v1/80d68c98-d14e-461c-bc61-737e056e76d1.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eA National Study Exploring the Association Between Fasting Duration and Mortality Among the Elderly\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eObesity has evolved into a global epidemic over the past decade, impacting approximately 75% of individuals in the United States(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Current evidence suggests that obesity is intricately linked to the incidence of cardiovascular diseases(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e), diabetes(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e), and other metabolic disorders(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). In recent years. the benefits of weight management have been increasingly recognized(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). It is now understood that even a modest reduction of 5% in initial body weight could reduce, eliminate, or prevent the metabolic comorbidities in overweight or obese individuals(\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAmong the various strategies for weight management, \u0026lsquo;Intermittent Fasting (IF)\u0026rsquo; refers to a dietary approach that involves restricting the eating window to specific periods each day or week, resulting in extended fasting intervals(\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). One of IF approaches was Time-restricted feeding (TRF), which refers to eating within a predetermined time window each day(\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e) and may be classified into 16/8, 18/6, and 20/4 eating schedules(\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). The \u0026ldquo;16/8\u0026rdquo; regime is the most popular TRF, requiring a fasting duration of at least 16 hours per day, with the remaining 8 hours for unrestricted eating(\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). The potential health benefits of TRF have been explored in several previous studies, including body weight reduction, blood pressure normalization, enhanced blood glucose and insulin sensitivity, mitigation of inflammation, and even potential inhibition of cancer cell growth(\u003cspan additionalcitationids=\"CR12 CR13 CR14\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHowever, whether TRF is suitable for elderly people remains controversial since this population may be more vulnerable to the adverse effects of TRF. For example, prolonged fasting may increase the risk of hypoglycemia (especially in patients with Type 2 Diabetes)(\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). Moreover, TRF usually involves lower calorie intake, a monotonous diet, as well as inadequate consumption of protein or vegetables, which may cause malnutrition, decreased bone density, loss of lean mass, and constipation in the elderly population(\u003cspan additionalcitationids=\"CR18\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). To date, the impact of prolonged fasting duration on survival has not been assessed among the elderly population.\u003c/p\u003e \u003cp\u003eThe present study aimed to examine the association between fasting duration and mortality in a nationally representative cohort of elderly people in the United States. Using dietary data from the National Health and Nutrition Examination Survey (NHANES) from 2005 to 2018, we analyzed the association between the timing of overnight fasting and all-cause and different cause-specific mortality in participants over 60 years old. We conducted subgroup analyses to ascertain whether the influence of fasting duration on mortality might differ based on factors such as gender, age, body weight status, lifestyle variables, or prevalent chronic diseases. A comprehensive understanding of the link between fasting duration and mortality will offer insights into the safety and feasibility of TRF in the elderly population.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Population\u003c/h2\u003e \u003cp\u003eNHANES is a large-scale, multistage, ongoing, nationally representative health survey of the civilian noninstitutionalized population in the United States(\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). Participants are invited to complete an interviewer-based questionnaire followed by a physical examination and laboratory measurements at a mobile examination center. NHANES was approved by the National Center for Health Statistics(NCHS)'s Ethics Review Board. Written informed consent was obtained from all participants.\u003c/p\u003e \u003cp\u003eIn this study, we extracted data from 2005\u0026ndash;2018, covering 7 survey cycles and involving a total of 70,191 individuals. In accordance with the Center for Disease Control and Prevention's definition of elderly individuals(\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e), all participants aged 60 were initially chosen. Those with missing dietary intake or mortality data were excluded. We also excluded participants with extremely short fasting periods (less than 4.25 hours) or extremely long fasting periods (more than 18.3 hours), as these values were considered outliers (falling below \u0026minus;\u0026thinsp;3 Standard Deviations [SDs] or above +\u0026thinsp;3 SDs)(\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). Moreover, we implemented additional exclusion criteria, consistent with previous research(\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e), which included: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) uncontrolled hypertension, characterized by systolic blood pressure\u0026thinsp;\u0026ge;\u0026thinsp;180 mmHg or diastolic blood pressure\u0026thinsp;\u0026ge;\u0026thinsp;100 mmHg; (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) recent diagnoses of unstable angina, heart attack, or stroke within the past year; (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) diagnosed Parkinson's disease; (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) history of epilepsy; and (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) diagnosed severe chronic kidney disease (CKD) with an estimated Glomerular Filtration Rate of \u0026lt;\u0026thinsp;15 (ml/min/m\u003csup\u003e2\u003c/sup\u003e). All diagnoses were defined based on earlier self-reported information from the Database Questionnaire. The estimated Glomerular Filtration Rate was calculated using laboratory data, following established clinical practice guidelines(\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). To ensure the reliability of our findings, we compared the characteristics of the subset of participants with complete fasting duration and mortality data to verify that this selected subgroup accurately represented the entire population.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eExposure Assessment\u003c/h2\u003e \u003cp\u003eTo determine micro and macronutrient intake, NHANES employed the US Department of Agriculture 's Food and Nutrient Database for Dietary Studies to process dietary information. Participants underwent two 24-hour dietary recalls. The first recall consisted of an in-person interview, while the second recall was conducted over the phone, with a time gap of 3 to 10 days after the initial dietary interview, though not consistently on the same day of each week. In the current study, we included those participants who had successfully completed the interviews of both days, utilizing the average fasting duration of these two days. During each recall session, participants were instructed to report all meals consumed from midnight to 11:59 p.m. The length of the overnight fasting period was calculated by subtracting the time between the first and last consumption of calorie-containing (or 5 kcal) food or beverage during each 24-hour dietary recall day from 24. The calculation equation used was: 24 - time of last calorie intake\u0026thinsp;+\u0026thinsp;time of first calorie intake(\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). To determine fasting periods, the duration of all food consumption was converted into hours over a 24-hour period. For instance, food consumed at 8:30 a.m. and 9:15 a.m. were assigned time values of 8.5 and 9.25 hours, respectively. For an individual who had the first meal at 8:30 a.m. (8.5 hours) and the last meal at 10 p.m. (22 hours), the fasting period would be calculated as 24\u0026thinsp;\u0026minus;\u0026thinsp;22\u0026thinsp;+\u0026thinsp;8.5\u0026thinsp;=\u0026thinsp;10.5 hours.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eOutcome Ascertainment\u003c/h2\u003e \u003cp\u003e The primary outcome of our study was mortality from various causes, including all-cause mortality, cancer (codes C00-C97), cardiovascular diseases (CVD, classified by codes I00-I09, I11, I13, I20-I25, I26-I51 and I60-I69); and other causes in compliance with ICD-10 (10th revision of the international statistical classification of diseases). Mortality status and cause of death were ascertained by NHANES linked National Death Index public-access files (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cdc.gov/nchs/data-linkage/mortality-public.htm\u003c/span\u003e\u003cspan address=\"https://www.cdc.gov/nchs/data-linkage/mortality-public.htm\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) through December 31, 2019.The duration of follow-up was calculated from the date of examination at the mobile examination center to the occurrence of the recorded death.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eCovariate Assessment\u003c/h2\u003e \u003cp\u003eDemographic variates included age, gender, educational attainment, ethnicity, marital status, family poverty income ratio, and. Body Mass Index (BMI) was calculated to evaluate the weight status of the participants. A value of BMI\u0026thinsp;\u0026ge;\u0026thinsp;25 indicated \"Overweight,\" while a BMI value of \u0026ge;\u0026thinsp;30 indicated \u0026ldquo;Obesity\u0026rdquo;(\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). Other relevant variables encompassed diagnosis of chronic diseases (including diabetes, hypertension, cardiovascular disease(\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e), depression and CKD), dietary inflammatory index, timing of first meal, smoking, and alcohol consumption. A detailed description for the covariate assessment is presented in the eAppendix in \u003cem\u003eSupplement 1\u003c/em\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eBased on the NCHS's recommendation, the dietary sampling weights were used in all analyses to interpret the complex NHANES survey design. Continuous variables were represented by mean (standard error), and categorical variables were expressed as the weighted percentage. Demographic and clinical characteristics were compared among the groups with different categories of fasting duration using the Student\u0026rsquo;s t-test, analysis of variance with post-hoc Bonferroni correction for continuous variables, the Mann-Whitney U test and Kruskal-Wallis test, or the chi-squared test for categorical variables, as appropriate.\u003c/p\u003e \u003cp\u003eThe associations between fasting duration and all-cause and cause-specific mortality were investigated by using multivariate Cox proportional-hazards regression models. In selecting covariates, we employed the bidirectional stepwise regression method to determine the most fitting adjusted variables for each model. Hazard ratios (HRs) and their corresponding 95% confidence intervals (CIs) were used to present the outcomes of the regression models. Restricted cubic spline analyses were conducted to explore the potential non-linear associations between fasting duration and all-cause and cause-specific mortality. Additionally, sensitivity analyses were performed within gender and age subgroups. Furthermore, adjusted models were constructed to assess the link between CVD mortality and fasting duration, stratified by diabetes, cancer, CKD, BMI status, alcohol use, smoking habits, and timing of the first meal. The results of these stratified analyses were visualized in forest plots.\u003c/p\u003e \u003cp\u003eStatistical analyses were carried out using R software (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.R-project.org\u003c/span\u003e\u003cspan address=\"http://www.R-project.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, The R Foundation). A two-sided \u003cem\u003eP\u003c/em\u003e-value less than 0.05 was statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eDescription of Study Participants\u003c/h2\u003e \u003cp\u003eFrom 2005 to 2018, there were 13,481 consecutive participants aged over 60 from NHANES datasets, with 9,826 individuals included in our final analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). No significant difference in fasting duration and demographic characteristics was observed between the analyzed population and the total population with complete fasting duration or mortality data (Table S1).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e summarized the characteristics of the study cohort, stratified according to fasting duration quantiles. Compared to individuals with the shortest nighttime fasting duration (\u0026le;\u0026thinsp;10h, Quantile 1), those with the longest nighttime fasting duration (\u0026gt;\u0026thinsp;12.5h, Quantile 4) tended to be older, predominantly female, non-Hispanic black, widowed, possessed a lower income-to-poverty level ratio, and had lower educational attainment. Individuals with lengthier fasting periods were also less prone to smoking or alcohol consumption and tended to start their first meal at a later time. The subgroup with the longest fasting duration was also correlated with a higher prevalence of diabetes, CKD, depression, CVD, hypertension, and higher dietary inflammation index values.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCharacteristics of study participants according to fasting duration categories.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e \u003cp\u003eQuantiles of fasting duration\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQuantile 1 (\u0026le;\u0026thinsp;10h)\u003c/p\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;2,067\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQuantile 2 (\u0026gt;\u0026thinsp;10h, \u0026le;\u0026thinsp;11.25h) n\u0026thinsp;=\u0026thinsp;1,807\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eQuantile 3 (\u0026gt;\u0026thinsp;11.25h,\u0026le;12.5h) n\u0026thinsp;=\u0026thinsp;1,752\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eQuantile 4 (\u0026gt;\u0026thinsp;12.5h)\u003c/p\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;1,792\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge, mean (SE), years\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e68.45 (0.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e69.17 (0.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e70.34 (0.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e71.27 (0.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge (Categorial, %)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e60\u0026ndash;64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e65\u0026ndash;69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e21.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e70\u0026ndash;74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e36.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGender (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.03\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e48.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e46.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e45.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e41.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e51.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e53.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e54.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e58.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEthnicity (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMexican American\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther Hispanic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Hispanic white\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e84.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e80.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e78.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e71.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Hispanic black\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther Race(s)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducational level (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLess than 9th grade\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9-11th grade\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh school graduate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e27.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSome college or AA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCollege or above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e21.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMarital status (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e62.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e65.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e65.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e56.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWidowed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDivorced\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeparated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever married\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiving with a partner\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRIP (Categorial, %)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e87.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e86.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e82.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e78.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMissing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSmoking (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e48.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e48.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e52.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFormer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e43.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e37.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAlcohol user (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e38.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e64.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e62.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e58.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e53.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMissing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFirst meal (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBefore 8 a.m.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e89.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e64.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e48.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAfter 8 a.m.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e51.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e74.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBMI, (Categorial, %), kg/m\u003c/b\u003e\u003csup\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNormal (\u0026lt;\u0026thinsp;25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e24.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverweight (\u0026ge;\u0026thinsp;25, \u0026lt;\u0026thinsp;30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e36.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e36.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObese (\u0026ge;\u0026thinsp;30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e39.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDiabetes (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e65.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e62.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e60.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e56.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e43.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCKD (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e69.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e67.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e64.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e56.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e38.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMissing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDepression (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e77.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e78.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e79.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e72.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMissing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCVD (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.04\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e83.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e83.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e79.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e81.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHypertension (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e27.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e64.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e65.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e70.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e72.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCancer (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e74.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e76.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e74.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e75.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e24.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFasting duration, mean (SE), hour\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.66(0.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.74(0.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.95(0.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13.86(0.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDII, mean (SE)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.16(0.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.33(0.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.57(0.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.94(0.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLiving status (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e81.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e81.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e78.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e72.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDeceased\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e27.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eNote: Weighted percentage was used to represent categorical variables, while mean (standard error) was used to represent continuous variables. ANOVA for continuous variables and Chi-Square for categorical variables were used to calculate \u003cem\u003eP\u003c/em\u003e value.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eAbbreviations: BMI, Body Mass Index; CVD, Cardiovascular Disease; CKD, Chronic Kidney Disease; DII, Dietary Inflammation Index; RIP, Ratio of Income to Poverty; SE, Standard Error; NHANES, National Health, and Nutrition Examination Survey.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eRelationship between Fasting Duration and Mortality\u003c/h2\u003e \u003cp\u003eWe found that 2,408 participants in the cohort passed away over a median follow-up period of 6.82 years (standard deviation: 3.90 person-years), with a mean lifespan of 5.70 person-years. An upward pattern in unadjusted all-cause mortality rates was observed across the four fasting duration quantiles (21.97%, 21.67%, 24.77%, and 29.47%, respectively). Notably, the group with the longest fasting period (Quantile 4) exhibited the highest mortality rate. The comprehensive analysis of causes of death across each quantile is provided in Table S2.\u003c/p\u003e \u003cp\u003eNon-linear association between fasting duration and all-cause mortality is depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. A statistically significant U-shaped relationship between fasting duration and all-cause mortality was observed (non-linear \u003cem\u003eP\u003c/em\u003e value\u0026thinsp;=\u0026thinsp;0.03). A fasting duration of 11.89 hours was associated with the lowest mortality. However, there was no association between fasting duration and cause-specific mortality rates (non-linear \u003cem\u003eP\u003c/em\u003e value 0.37\u0026ndash;0.98, Figure S1). Additionally, we performed the multivariable Cox regression models in the younger population. After excluding participants with missing data of fasting duration (n\u0026thinsp;=\u0026thinsp;16,677), mortality (n\u0026thinsp;=\u0026thinsp;54) and those of age under 18 (n\u0026thinsp;=\u0026thinsp;22,137) and over 60 (n\u0026thinsp;=\u0026thinsp;10,561), there were 20,761 participants aged 18\u0026ndash;59 from NHANES 2005\u0026ndash;2018. We found that there was no significant association between overnight fasting time and adjusted all-cause mortality in the younger participants (non-linear \u003cem\u003eP\u003c/em\u003e value\u0026thinsp;=\u0026thinsp;0.22, Figure S2).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presented the correlations between fasting duration quantiles and all-cause, as well as cause-specific mortality. After adjustment for age, gender, educational level, marital status, income-to-poverty ratio, ethnicity, diabetes, CKD, BMI, depression, smoking, alcohol use, dietary inflammation index, hypertension, and CVD, Quantile 4 displayed significantly elevated CVD mortality (HR: 1.30, 95% CI: 1.01\u0026ndash;1.66) and mortality from other causes (HR: 1.52, 95% CI: 1.07\u0026ndash;2.16) compared to Quantile 1. Examining fasting duration as a continuous variable revealed that each additional hour of fasting duration correlated with a 7% increase in mortality from other causes (HR: 1.07, 95% CI: 1.01\u0026ndash;1.12).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociation between fasting duration, all-cause mortality, and cause-specific mortality according to fasting duration categories.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e \u003cp\u003eQuantiles of fasting duration \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eQuantile 1\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eQuantile 2\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eQuantile 3\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eQuantile 4\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003ePer 1 h increment in fasting duration\u003c/b\u003e \u003csup\u003e\u003cb\u003ea\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAll-cause mortality \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00 (Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.85 (0.72,0.99)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.90 (0.74,1.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.99 (0.84,1.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.01 (0.98,1.04)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCVD mortality \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00 (Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.20 (0.90,1.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.87 (0.66,1.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e1.30 (1.01,1.66)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.04 (0.99,1.08)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCancer mortality \u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00 (Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.76 (0.58,1.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.89 (0.68,1.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.76 (0.64,1.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.95 (0.90,1.00)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther mortality \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00 (Ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.13 (0.80,1.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.22 (0.90,1.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e1.52 (1.07,2.16)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e1.07 (1.01,1.12)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eNote: Results were presented as hazard ratios (95% confidence intervals). Significant values in bold (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eOther mortality was adjusted for age (Categorial), gender, educational level, marital status, ratio of income to poverty (Categorial), ethnicity, diabetes, chronic kidney diseases, body mass index, depression, smoking, alcohol user, and dietary inflammatory index.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eCVD mortality and all-cause mortality were further adjusted (from other mortality) for hypertension and CVD.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eCancer mortality was further adjusted (from other mortality) for cancer.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eQuantile ranges: Quantile 1:\u0026le;10h; Quantile 2:\u0026gt;10h, \u0026le;\u0026thinsp;11.25h; Quantile 3:\u0026gt;11.25h,\u0026le;12.5h; Quantile 4:\u0026gt;12.5h.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003csup\u003ea\u003c/sup\u003eHazard ratios for each hour increment in fasting duration.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eAbbreviation: CVD, Cardiovascular Disease; Ref, reference\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eRelationship between Fasting Duration and Mortality in Gender and Age Subgroups\u003c/h2\u003e \u003cp\u003eTo increase the robustness of our findings, we further explored the link between fasting duration and mortality risk among gender and age subgroups (Table S3). HR values and 95% CIs for participants within the longest fasting duration quantile (Quantile 4) in comparison to those within the shortest fasting duration quantile (Quantile 1) are presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA. We found that CVD mortality was substantially increased among male participants (HR: 1.49, 95% CI: 1.00\u0026ndash;2.20) and individuals aged 60\u0026ndash;69 (HR: 2.87, 95% CI: 1.32\u0026ndash;6.23) within Quantile 4 compared to Quantile 1. Similarly, mortality from other causes (HR: 1.67, 95% CI: 1.13\u0026ndash;2.46) increased among those aged over 70 within Quantile 4. In contrast, a reduction in cancer mortality was noted in females (HR: 0.49, 95% CI: 0.29\u0026ndash;0.84) and those aged 60\u0026ndash;69 (HR: 0.29, 95% CI: 0.13\u0026ndash;0.65) within Quantile 4. Analyzing fasting time as a continuous variable, each additional hour of fasting duration correlated with a 6% increase in CVD mortality risk in males (95% CI: 1.00\u0026ndash;1.13) and a 13% increase in CVD mortality risk among those aged 60\u0026ndash;69 (95% CI: 1.00\u0026ndash;1.29, Table S3).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eRelationship between Fasting Duration and CVD Mortality Stratified by Diseases and Lifestyles\u003c/h2\u003e \u003cp\u003eTo elucidate the influence of disease status or lifestyle on CVD mortality between the longest and shortest fasting duration quantiles, Cox proportional regression models were conducted in Quantile 4 (with Quantile 1 as the reference) across various subgroups. Model I was adjusted for covariates including age, gender, and ethnicity, while Model II was the fully-adjusted model (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003eIn terms of interaction effects, the impact of extended fasting duration on CVD mortality was unaffected by BMI status, diabetes, CKD, cancer, timing of first meal, or alcohol consumption after full covariate adjustment (\u003cem\u003eP\u003c/em\u003e \u003csub\u003einteraction\u003c/sub\u003e ranging from 0.06 to 0.93). However, a significant interaction effect was observed between smoking status and fasting duration (\u003cem\u003eP\u003c/em\u003e \u003csub\u003einteraction\u003c/sub\u003e \u0026lt; 0.05). In the fully-adjusted Model II, non-smokers demonstrated a heightened CVD mortality risk (HR: 1.67, 95% CI: 1.09\u0026ndash;2.56).\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eUtilizing nationally prospective cohort data, our study revealed a significant association between prolonged night fasting duration and heightened CVD mortality, along with mortality from other causes in the elderly population. Specifically, CVD mortality risk was substantially increased in males and individuals aged 60\u0026ndash;69 with a fasting duration exceeding 12.5 hours. Furthermore, we found a fasting duration of 11.89 hours was linked to the lowest overall mortality. To our knowledge, this is the first prospective analysis to explore the correlation between fasting duration and mortality among elderly adults, drawing from nationally representative U.S. population data. Our findings underscore the potential risks associated with TRF (Time Restricted Feeding) for the elderly and imply that clinicians should exercise caution when advising TRF for those above 60 years old.\u003c/p\u003e \u003cp\u003eTo date, limited research has been undertaken on the effects of TRF or IF (Intermittent Fasting) within the elderly population. A preliminary study involving ten overweight adults aged over 65 demonstrated a mean weight loss of 2.6 kg and improvements in walking speed and quality of life following four weeks of TRF(\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). Another study showed that the application of IF resulted in body fat mass reduction in 45 women participants aged over 60(\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). Our study is the first to reveal that extended fasting periods are associated with elevated CVD mortality and mortality from other causes in the elderly population. Several factors may contribute to these findings. First, prolonged fasting periods often involve reduced calorie intake and potential inadequacy in protein, fiber, and essential nutrients. In elderly adults, these conditions can give rise to various health concerns, such as compromised immune function(\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e), muscle weakness(\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e), decreased bone mass(\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e), as well as an increased risk of death(\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). Additionally, prolonged fasting could reflect unhealthy dietary or lifestyle habits, like breakfast skipping or irregular meal patterns(\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e), which was also associated with higher mortality(\u003cspan additionalcitationids=\"CR34\" citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e). Moreover, extended fasting may trigger stress-induced overactivity and heightened serum cortisol levels(\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e), further linked to increased cardiovascular risk(\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eUpon stratified analysis, the correlation between prolonged fasting and heightened CVD mortality remained significant irrespective of factors such as weight, meal timing, alcohol consumption, and chronic diseases. The only significant interaction factor was smoking, impacting the association between fasting duration and CVD mortality. Nonetheless, within each stratum, no significant variance in mortality risk emerged between groups with the longest and shortest fasting periods. It should be noted that the sample size of current smoker is relatively small (n\u0026thinsp;=\u0026thinsp;305) in current study. Further studies are warranted to explore the effect of TRF on mortality among individuals with diverse habits and lifestyles.\u003c/p\u003e \u003cp\u003eVarious TRF regimes, such as 16/8, 18/6, and 20/4 methods, entail daily fasting durations ranging from 12 to 21 hours(\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). Previous research has shown that different fasting durations may yield varying effects on weight loss and metabolic parameters(\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e). For example, a 6-hour TRF (18-hour fasting duration) was associated with reduced blood pressure and fasting insulin but increased serum triglycerides(\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e), while an 8-hour TRF (16-hour fasting duration) led to significant triglyceride reduction(\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). The optimal fasting duration for cardiometabolic benefits remains inconclusive. Our study revealed that a fasting duration of 11.89 hours corresponded to the lowest all-cause mortality rate among the elderly, which may provide implications for future fasting recommendations.\u003c/p\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eStrengths and Limitations\u003c/h2\u003e \u003cp\u003eOur findings indicated that the elderly adults might be more susceptible to the adverse effect of TRF. Comprehensive health assessment, individualized dietary plans and close monitoring is necessary for achieving and maintaining a healthy weight in the elderly people. However, several limitations need to be considered. Firstly, similar to prior studies(\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e), NHANES data analyzed in our study was derived from 24-hour food recall questionnaires, which could introduce potential biases, including recall bias or social-desirability bias(\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e), ultimately leading to underreporting or over-reporting of caloric intake. Secondly, our analysis did not encompass comprehensive dietary information (such as diet components, quality, or eating schedules) during non-fasting periods, which might exert additional influence on participant mortality. Furthermore, residual confounding factors may persist beyond demographic, disease-related, or dietary influences. Further randomized controlled trials are warranted to gain deeper insights into the impact of TRF on mortality.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eProlonged fasting periods are associated with an increased risk of cardiovascular mortality and mortality from other causes in the elderly population. A fasting duration of 11.89 hours is associated with the lowest mortality rate. These findings may have implications for future fasting guidelines. Currently, caution should be exercised by clinicians when recommending time-restricted feeding for the elderly. Further research through randomized controlled trials should be conducted to comprehensively investigate the impact of TRF on mortality.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe acknowledge NHANES database for providing their platforms and contributors for uploading their meaningful datasets. And we thank all participants included in our present study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study received funding support from National Natural Science Foundation of China (U21A20341, 81971570, 82202159, 31900821), Science and Technology Commission of Shanghai Municipality (21XD1432100, 22JC1402100, 22DZ2292400, 20Y11910500, 2022ZZ01008, 201409005200), Shanghai Hospital Development Center (SHDC2020CR2025B, SHDC12022102), Shanghai Municipal Health Commission (2022JC013, SHSLCZDZK06204), Shanghai Pudong New Area Health Commission (PW2019D-11), Shanghai Jiao Tong University (YG2019ZDA13), University of Shanghai for Science and Technology (10-20-302-425), Shanghai Clinical Research Center for Aging and Medicine (19MC1910500), Shanghai Cancer Institute (ZZ-20-22SYL).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePublicly available datasets were analyzed in this study. All the raw data used in this study are derived from the public NHANES data portal (https://wwwn.cdc. gov/nchs/nhanes/analyticguidelines.aspx)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval and Consent to Participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe NCHS Ethics Review Board protects the rights and welfare of NHANES participants. The NHANES protocol complies with the U.S. Department of Health and Human Services Policy for the Protection of Human Research Subjects. Ethical review and approval were waived for this study as it solely used publicly available data for research and publication. Informed consent was obtained from all subjects involved in the NHANES.\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\u003eAuthor\u0026rsquo;s contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConcept and design: Zhixuan Zhang, Hang Zhao, Zhengyu Tao and Meng Jiang.\u003c/p\u003e\n\u003cp\u003eAcquisition, analysis, or interpretation of data: All authors.\u003c/p\u003e\n\u003cp\u003eDrafting of the manuscript: Zhixuan Zhang, Hang Zhao, Meng Jiang, and Jun Pu.\u003c/p\u003e\n\u003cp\u003eCritical revision of the manuscript for important intellectual content: All authors.\u003c/p\u003e\n\u003cp\u003eStatistical analysis: Zhixuan Zhang.\u003c/p\u003e\n\u003cp\u003eAdministrative, technical, or material support: Meng Jiang and Jun Pu.\u003c/p\u003e\n\u003cp\u003eSupervision: Meng Jiang and Jun Pu.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eLavie CJ, Laddu D, Arena R, Ortega FB, Alpert MA, Kushner RF. 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Systematic review and meta-analysis reveals acutely elevated plasma cortisol following fasting but not less severe calorie restriction. 2016;19(2):151-7.\u003c/li\u003e\n\u003cli\u003eE P, M W, Endocrinology SMJ. Adverse cardiovascular outcomes of corticosteroid excess. 2012;153(11):5137-42.\u003c/li\u003e\n\u003cli\u003eAA C, S S, C K, L M, M E, S E, et al. Morning plasma cortisol as a cardiovascular risk factor: findings from prospective cohort and Mendelian randomization studies. 2019;181(4):429-38.\u003c/li\u003e\n\u003cli\u003eEF S, R B, KS E, WT C, E R, metabolism PCJC. Early Time-Restricted Feeding Improves Insulin Sensitivity, Blood Pressure, and Oxidative Stress Even without Weight Loss in Men with Prediabetes. 2018;27(6):1212-21.e3.\u003c/li\u003e\n\u003cli\u003eKrok-Schoen JL, Archdeacon Price A, Luo M, Kelly OJ, Taylor CA. Low Dietary Protein Intakes and Associated Dietary Patterns and Functional Limitations in an Aging Population: A NHANES analysis. J Nutr Health Aging. 2019;23(4):338-47.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Supplementary Information","content":"\u003cp\u003eSupplement 1, Supplementary Figures and Supplementary Tables are not available with this version.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Shanghai Jiaotong University School of Medicine, Renji Hospital","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Time restricted feeding, Fasting duration, Mortality, Elderly population, NHANES","lastPublishedDoi":"10.21203/rs.3.rs-4174533/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4174533/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eBenefits from weight management have been widely accepted, and prolonged fasting duration has become a common method for weight control. The suitability of prolonged fasting duration for elderly individuals remains controversial. This study aims to examine the correlation between fasting duration and mortality within a nationally representative cohort of elderly individuals in the United States.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eData were extracted from a prospective cohort study conducted within the National Health and Nutrition Examination Survey (NHANES) from 2005 to 2018. Individuals over 60 with complete data on dietary intake and mortality follow-up information were included. Fasting duration was assessed using two 24-hour dietary recalls. All participants were categorized into fasting duration quantiles. Mortality outcomes were ascertained through the National Death Index. Cox proportional-hazards regression models were utilized to analyze the association between fasting duration and mortality.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe final analysis included a total of 9,826 elderly participants (mean age 70.03, 49.33% male), with 2408 deaths observed during a median follow-up of 6.82 years. Following adjustments for covariates, the longest fasting duration (\u0026gt;\u0026thinsp;12.5 hours) exhibited heightened cardiovascular disease (CVD) mortality (Hazard Ratios [HR], 1.30; 95% CI, 1.01\u0026ndash;1.66) and mortality from other causes (HR, 1.52, 95% CI, 1.07\u0026ndash;2.16) compared to those with the shortest fasting duration (\u0026lt;\u0026thinsp;10 hours). Notably, the CVD mortality was significantly increased in males and in individuals aged 60\u0026ndash;69 with a fasting duration exceeding 12.5 hours (HR, 1.49 and 2.87; 95% CI, 1.00-2.20 and 1.32\u0026ndash;6.23, respectively). A non-linear relationship was observed between fasting duration and all-cause mortality (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.03), with a fasting duration of 11.89 hours linked to the lowest mortality.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eProlonged fasting periods are associated with increased cardiovascular mortality and mortality from other causes. Fasting duration of 11.89 hours is associated with the lowest mortality rate. Caution should be exercised by clinicians when recommending time-restricted feeding for the elderly. Further research through randomized controlled trials should be conducted to comprehensively investigate the impact of TRF on mortality.\u003c/p\u003e","manuscriptTitle":"A National Study Exploring the Association Between Fasting Duration and Mortality Among the Elderly","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-29 18:24:21","doi":"10.21203/rs.3.rs-4174533/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"db376939-dcc1-4e1f-88dd-40d06e948efc","owner":[],"postedDate":"March 29th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":29941028,"name":"Cardiac \u0026 Cardiovascular Systems"}],"tags":[],"updatedAt":"2024-03-29T18:24:22+00:00","versionOfRecord":[],"versionCreatedAt":"2024-03-29 18:24:21","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4174533","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4174533","identity":"rs-4174533","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}