Dietary flavonoid intake is negatively associated with accelerating aging: an American population-based cross-sectional study

Dietary flavonoid intake is negatively associated with accelerating aging: an American population-based cross-sectional study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Dietary flavonoid intake is negatively associated with accelerating aging: an American population-based cross-sectional study Jintao Zhong, Jiamin Fang, Yixuan Wang, Pinli Lin, Biyu Wan, Mengya Wang, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4790160/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 20 Dec, 2024 Read the published version in Nutrition Journal → Version 1 posted 8 You are reading this latest preprint version Abstract Background Flavonoids are believed to have potential anti-aging effects due to their anti-inflammatory and antioxidant properties. However, the effectiveness of dietary flavonoids and their subclasses in delaying aging has yet to be confirmed. Our study intends to examine relationship between them. Methods Data from three survey cycles (2007–2008, 2009–2010, and 2017–2018) of the National Health and Nutrition Examination Survey (NHANES) was used to investigate the relationship between PhenoAgeAccel and dietary flavonoid intake. Weighted linear regression was conducted to evaluate the relationship between dietary flavonoid intake and PhenoAgeAccel, and the dose-response relationship was investigated by limited cubic spline (RCS) analysis. Mixed effects were explored using weighted quantile sum (WQS) regression. Further, the subgroup analyses were also conducted. Results A total of 5391 participants were included, after multivariable adjustments, a negative association was found with total dietary flavonoid, flavan-3-ols, flavanone, flavones and flavonols with a β (95% CI) of -0.87 ( -1.61, -0.13), -0.83 (-1.95, -0.08), -1.18 (-1.98, -0.39), -1.64 (-2.52, -0.77) and − 1.18 (-1.98, -0.39) for the higher quintile compared to the lowest quintile. The RCS analysis show a non-linear relationship between flavan-3-ols ( P for nonlinear = 0.024), flavanones ( P for nonlinear = 0.005), flavones ( P for nonlinear < 0.001), flavonols ( P for nonlinear < 0.001) and total flavonoid intake ( P for nonlinear < 0.001) and PhenoAgeAccel. WQS regression indicated that flavones had the primary effect on the mixture exposures (52.72%). Finally, the subgroup analysis indicated that participants without chronic kidney disease are more likely to benefit from dietary flavanone and flavone intake in mitigating aging, while the benefits of flavanone intake are more significant in participants with a lower body mass index. Conclusion Our study suggested that dietary flavonoid intake is negatively associated with accelerating aging in adults of American, and the most influential ones are flavones, flavanones, flavan-3-ols and flavonols. Flavonoid Aging Flavones NHANES Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Aging has emerged as a critical global issue. By 2050, the population aged 60 and over is projected to reach 2 billion, accounting for 22% of the global population[ 1 ]. According to the 2017 Global Burden of Disease (GBD) study, 92 diseases have been identified as age-related, accounting for 51.3% of the total disease burden among adults worldwide[ 2 ]. This poses substantial challenges to families, healthcare systems, and social security frameworks. Consequently, the identification and deceleration of the aging process are imperative for promoting healthy aging. Due to the inherent inter-individual variability in the aging process, discernible differences in the aging process among individuals are observed[ 3 , 4 ]. Therefore, efforts have been initiated to develop measures that capture the concept of biological age (BA), which integrates composite clinical biomarkers with chronological age[ 5 , 6 ]. These aging biomarkers serve to elucidate the underlying biological heterogeneity in aging and uncover factors that influence the rate of aging, playing a critical role in human research aimed at decelerating the aging process[ 7 , 8 ]. Phenotypic age, developed by Levine et al, is a validated biological age predictor[ 3 ], which has been shown to effectively predict morbidity and mortality risk[ 9 ]. Diet and aging are intrinsically linked, with a balanced diet being the safest and most effective method to delay the aging process and extend healthy lifespan[ 10 , 11 ]. Flavonoids, a group of natural polyphenols, which found in vegetables, fruits, cocoa, oilseeds and tea, consists of six subclasses: isoflavones, anthocyanins, flavan-3-ols, flavanones, flavones and flavonols[ 12 ]. Flavonoids exhibit potent anti-inflammatory and antioxidant properties, offering significant potential to attenuate the aging process. Consequently, they have been recognized as promising candidate compounds for retarding aging[ 13 , 14 ]. As polyphenolic compounds, the hydroxyl groups in the structure of flavonoids are capable of scavenging various types of reactive oxygen species (ROS)[ 15 ]. Additionally, flavonoids could maintain and activate SIRT1, and consequently inhibit NF-κB, which could prevent oxidative stress and neuroinflammation to delay brain aging[ 16 ]. Research shows that oral treatment with naringenin in young rats could prevent alterations in the brain antioxidant defense system, thereby ameliorating cognitive decline[ 17 ], hesperetin could enhance antioxidant cellular defenses through the ERK/Nrf2 signaling pathway[ 18 ]. Further, flavonoids have been shown to extend the lifespan of worms, flies, and mice[ 19 ]. It is worth noting that fisetin and quercetin, as flavonols, are currently widely studied senolytic agents and are being used in multiple clinical trials[ 20 , 21 ]. However, the relationship between dietary flavonoid intake and aging process has not been fully studied, especially regarding the mixed effects of different flavonoid subclasses. Therefore, this study intends to use data from the National Health and Nutrition Examination Survey (NHANES) to examine the aging process within the American population by calculating phenotypic age and to explore the association between dietary flavonoid intake and aging. Materials and methods Study population In our study, participants are based on the National Health and Nutrition Examination Survey (NHANES), which aims to assess the health and nutritional status of adults and children in the United States using a complex, multistage probability sampling design[ 22 ]. Since the availability of flavonoid databases, we only included data from three survey cycles (2007–2008, 2009–2010, and 2017–2018), and retrieved subject information during these cycles. The flowchart illustrating the selection procedure for all participants involved in the study is exhibited in Fig. 1 . Initially, 29940 participants were included. Subsequently, we excluded participants under the age of 20 (N = 12,218), with incomplete information to Phenotypic Age (N = 4,673), without flavonoid information and dietary recall status below the minimum criteria (N = 2,168) and missing demographic characteristics (N = 5,490). Finally, a total of 5391 qualified participants was in the final analysis. The final sample represented a weighted population of 83.3 million non-institutionalized residents in United States. Prior to the survey, all participants provided written informed consent. Furthermore, the National Center for Health Statistics (NCHS) Ethics Review Board granted approval for the NHANES protocol. Assessment of phenotypic age acceleration Phenotypic age is calculated by a combination of chronological age and nine biomarkers: albumin, creatinine, glucose, C-reactive protein (log-transformed), lymphocyte percent, mean cell volume, red blood cell distribution width, alkaline phosphatase, and white blood cell count. The formula for the determination of phenotypic age is as follows[ 4 ]: PhenoAgeAccel is defined as the residual resulting from a linear model when regressing phenotypic age on chronological age. The positive value represents a person appearing older than expected while the negative value represents the opposite. Assessment of dietary flavonoid intake Data on dietary flavonoid intake were obtained from the USDA Food and Nutrient Database for Dietary Studies (FNDDS), which includes calculated data from two 24-hour dietary recall interviews conducted as part of NHANES[ 23 ]. As of now, data on dietary flavonoid intake for the years 2007–2008, 2009–2010, and 2017–2018 have been published. The FNDDS database categorizes dietary flavonoid into six subclasses: isoflavones, anthocyanins, flavan-3-ols, flavanones, flavones and flavonols. Total dietary flavonoid intake is defined as the sum of these six subclasses. The mean value of dietary flavonoid intake from the two 24-hour dietary recalls was defined as the final dietary flavonoid intakes. Covariates In our analysis, covariates encompassed three categories. Sociodemographic characteristics included gender, age, race, poverty income ratio (PIR), education level and marital status. Behavioral characteristics encompassed drinking, smoking, body mass index (BMI), physical activity. Health characteristics encompassed the history of diabetes, chronic kidney disease (CKD) and hypertension. The family poverty income ratio (PIR) was computed by dividing the household income by the poverty threshold corresponding to the household size, PIR values < 1 indicate poverty, while higher values indicate higher socioeconomic status[ 24 ]; Drinking was defined as participants who consumed alcohol drink more than 12 times per year; smoking was defined as participants who smoked at least 100 cigarettes in their lifetime; BMI was stratified into three levels: 30 kg m − 2 ; physical activity was categorized into four groups: inactive (participants lacking regular physical exercise), insufficient ( 1000 MET per week)[ 25 ]; participants with diabetes were defined by fasting plasma glucose ≥ 7.0 mmol L − 1 , HbA1c ≥ 6.5% or self-reported diabetes status; CKD was defined by urinary albumin-to-creatinine ratio over 30 mg g − 1 , the estimated glomerular filtration rate (eGFR) lower than 60 ml/min/1.73 m 2 or self-reported CKD status; Hypertension was defined by the average of three systolic pressure of ≥ 130 mmHg or diastolic pressure of ≥ 80 mmHg or self-reported hypertension status. Statistical analysis According to NHANES analytic guidelines, our analyses were incorporated appropriate sample weights to account for the complex sampling design. As dietary flavonoid intakes, participants were divided into five quintiles [Q1 (quintile 1), Q2 (quintile 2), Q3 (quintile 3), Q4 (quintile 4) and Q5 (quintile 5)]. PhenoAgeAccel was described as a categorical variable (participants with PhenoAgeAccel or participants without PhenoAgeAccel) and continuous variable. Since all the variables were categorical, the baseline data were described as frequency with weighted percentages. They were then compared using the Scott-Rao chi-square test. Furthermore, three weighted linear regression models were used to investigate the relationship between six subclasses of flavonoid and PhenoAgeAccel. In Model 1, no adjustments were implemented; Model 2 was adjusted for gender, age, race, PIR, education level and marital status; Model 3 was adjusted for gender, age, race, PIR, education level, marital status, drinking, smoking, BMI, physical activity, diabetes, CKD and hypertension. We used dietary flavonoid intake (quintile-categorical) as a continuous variable in all models to conduct trend tests ( P for trend). Subsequently, the restricted cubic splines (RCS) analyses were applied to examine the dose-response relationships between four flavonoid subclasses and PhenoAgeAccel with four knots (5th, 35th, 65th and 95th percentiles), the RCS plots have been adjusted for all covariates. Consequently, the weighted quantile sum (WQS) regression model was used to explore the overall effect of flavonoid subclasses on PhenoAgeAccel and the WQS index calculation was utilized for the determination of advantage type[ 26 ]. For further investigate the association between flavonoid subclasses and PhenoAgeAccel in different population, the subgroup analysis and interaction test was conducted. All statistical analyses were performed using R software (version 4.3.2). P < 0.05 indicated statistical significance (two-sided). Results Baseline characteristics A total of 5391 participants were enrolled for the associated analysis of dietary flavonoid intakes and PhenoAgeAccel. Table 1 presents the sociodemographic, behavioral health and dietary flavonoid intake characteristics of participants with or without PhenoAgeAccel. Significant differences were observed in age, race, PIR, education level, marital status, drinking, smoking, BMI, physical activity, diabetes, chronic kidney disease, and hypertension between the two groups. Furthermore, participants with higher intake of isoflavones, anthocyanins, flavan-3-ols, flavanones, flavones, flavonols and total flavonoid, were less likely to experience PhenoAgeAccel. Table 1 Baseline characteristics of the study population Variables Total (n = 5391) Without PhenoAgeAccel (n = 3581) With PhenoAgeAccel (n = 1810) P-value Sociodemographic characteristics Gender, n (%) 0.109 Male 2590 (48.47) 1642 (47.39) 948 (51.04) Female 2801 (51.53) 1939 (52.61) 862 (48.96) Age, n (%) 65 1381 (18.64) 809 (15.88) 572 (25.21) Race, n (%) < 0.001 Non-Hispanic White 2609 (69.87) 1792 (72.56) 817 (63.43) Mexican American 863 (8.28) 608 (8.06) 255 (8.85) Other Hispanic 503 (4.87) 343 (4.59) 160 (5.54) Non-Hispanic Black 1008 (10.02) 551 (7.72) 457 (15.51) Other Race 408 (6.96) 287 (7.07) 121 (6.68) PIR, n (%) < 0.001 3 1997(49.69) 1469 (54.23) 528 (38.86) Education level, n (%) < 0.001 Below high school 1286 (15.15) 794 (13.33) 492 (19.51) High school graduate or GED 1276 (24.84) 792 (22.55) 484 (30.29) Some colleges or above 2829 (60.01) 1995 (64.12) 834 (50.19) Marital status, n (%) 0.012 Married/cohabiting 3280 (62.74) 2235 (63.79) 1045 (60.24) Widowed/divorced/separated 1253 (19.23) 748 (17.34) 505 (23.74) Never married 858 (18.03) 598 (18.87) 260 (16.01) Behavioral characteristics Drink, n (%) < 0.001 No 3272 (60.87) 2327 (65.52) 945 (49.78) Yes 2119 (39.13) 1254 (34.48) 865 (50.22) Smoke, n (%) < 0.001 No 2929 (54.96) 2095 (58.47) 834 (46.57) Yes 2462 (45.04) 1486 (41.53) 976 (53.43) BMI, n (%) < 0.001 30 2098(37.86) 1072 (28.77) 1026 (59.57) Physical activity, n (%) < 0.001 Inactive 2251 (35.68) 1318 (30.68) 933 (47.61) Variables Total (n = 5391) Without PhenoAgeAccel (n = 3581) With PhenoAgeAccel (n = 1810) P-value Insufficient 889 (16.74) 606 (17.70) 283 (14.45) Moderate 701 (14.19) 502 (14.82) 199 (12.69) High 1550 (33.39) 1155 (36.79) 395 (25.25) Health characteristics Diabetes, n (%) < 0.001 No 4523 (88.89) 3309 (94.97) 1214 (74.35) Yes 868 (11.11) 272 (5.03) 596 (25.65) Chronic Kidney Disease, n (%) < 0.001 No 4730 (91.18) 3316 (94.58) 1414 (83.04) Yes 661 (8.82) 265 (5.42) 396 (16.96) Hypertension, n (%) < 0.001 No 3358 (67.59) 2454 (72.84) 904 (55.04) Yes 2033 (32.41) 1127 (27.16) 906 (44.96) Dietary flavonoid intake (mg per day) Isoflavones, n (%) 0.017 Q1 (0–0) 2039 (36.60) 1292 (34.49) 747 (41.63) Q2 (0-0.005) 354 (7.19) 228 (6.97) 126 (7.71) Q3 (0.005–0.02) 788 (15.88) 520 (16.20) 268 (15.11) Q4 (0.02–0.145) 1106 (17.98) 773 (18.49) 333 (16.74) Q5 (0.145–390.6) 1104 (22.36) 768 (23.84) 336 (18.81) Anthocyanidins, n (%) < 0.001 Q1 (0-0.020) 1102 (21.84) 651 (19.88) 451 (26.52) Q2 (0.02–1.005) 1055 (18.54) 658 (17.33) 397 (21.43) Q3 (1.005–4.010) 1079 (18.21) 729 (18.99) 350 (16.35) Q4 (4.010-16.815) 1078 (19.78) 758 (20.79) 320 (17.36) Q5 (16.815–543.83) 1077 (21.63) 785 (23.01) 292 (18.34) Flavan-3-ols, n (%) 0.007 Q1 (0-3.560) 1079 (18.50) 620 (16.31) 459 (23.73) Q2 (3.560-10.055) 1080 (18.14) 729 (17.96) 351 (18.56) Q3 (10.055–24.890) 1076 (19.08) 757 (19.95) 319 (16.98) Q4 (24.890-229.820) 1078 (20.97) 735 (21.79) 343 (18.99) Q5 (229.820-4939.790) 1078 (23.32) 740 (23.99) 338 (21.74) Flavanones, n (%) < 0.001 Q1 (0-0.005) 1108 (21.17) 634 (18.99) 474 (26.37) Q2 (0.005–0.250) 1056 (20.96) 682 (20.52) 374 (22.02) Q3 (0.250–2.495) 1073 (21.79) 745 (22.66) 328 (19.70) Q4 (2.495–25.400) 1076 (20.00) 774 (21.56) 302 (16.28) Q5 (25.400-345.685) 1078 (16.08) 746 (16.26) 332 (15.63) Variables Total (n = 5391) Without PhenoAgeAccel (n = 3581) With PhenoAgeAccel (n = 1810) P-value Flavones, n (%) < 0.001 Q1 (0-0.135) 1094 (19.41) 619 (16.33) 475 (26.76) Q2 (0.135–0.365) 1073 (18.64) 668 (17.63) 405 (21.05) Q3 (0.365–0.705) 1078 (19.07) 750 (20.11) 328 (16.59) Q4 (0.705–1.315) 1072 (20.55) 760 (21.94) 312 (17.23) Q5 (1.315–87.245) 1074 (22.33) 784 (23.99) 290 (18.38) Flavonols, n (%) 0.006 Q1 (0-5.880) 1079 (17.46) 633 (15.68) 446 (21.71) Q2 (5.880–10.250) 1080 (18.09) 708 (17.47) 372 (19.57) Q3 (10.250–15.900) 1077 (19.97) 730 (20.14) 347 (19.58) Q4 (15.900-25.390) 1078 (20.74) 756 (21.91) 322 (17.94) Q5 (25.390-262.435) 1077 (23.74) 754 (24.81) 323 (21.20) Total flavonoids, n (%) 0.005 Q1 (0-19.350) 1079 (19.65) 622 (17.26) 457 (25.35) Q2 (19.350-44.555) 1078 (17.64) 728 (18.13) 350 (16.46) Q3 (44.555–97.660) 1078 (18.66) 738 (19.05) 340 (17.72) Q4 (97.660-283.950) 1078 (20.57) 758 (21.29) 320 (18.85) Q5 (283.950-5177.470) 1078 (23.48) 735 (24.26) 343 (21.61) Abbreviations: BMI, body mass index; PIR, poverty income ratio; Q1, quintile 1; Q2, quintile 2; Q3, quintile 3; Q4, quintile 4; Q5, quintile 5. Association between total flavonoid, flavonoid subclasses and PhenoAgeAccel The results of three weighted linear regression models investigating the association between six flavonoids subclasses and PhenoAgeAccel are presented in Table 2. In model 3, after adjusting all covariates, compared to the lowest quintile, the second (β: -0.95, 95% CI: -1.61, -0.30), third (β: -1.05, 95% CI: -1.79, -0.31) and highest (β: -0.83, 95% CI: -1.95, -0.08) quintiles of flavan-3-ols ( P for trend =0.473); the third (β: -1.14, 95% CI: -1.90, -0.38) and fourth (β: -1.18, 95% CI: -1.98, -0.39) quintiles of flavanone ( P for trend =0.016); the third (β: -1.75, 95% CI: -2.56, -0.95), fourth (β: -1.83, 95% CI: -2.83, -0.84) and highest (β: -1.64, 95% CI: -2.52, -0.77) quintiles of flavones ( P for trend <0.001); the third (β: -1.14, 95% CI: -1.90, -0.38) and fourth (β: -1.18, 95% CI: -1.98, -0.39) quintiles of flavonols ( P for trend =0.032); the third (β: -0.87, 95% CI: -1.61, -0.13) quintile of total dietary flavonoid intake exhibited a significant associated with the decreased of PhenoAgeAccel. However, the result indicated no significant association between isoflavones, anthocyanidins and PhenoAgeAccel. Consequently, they were excluded from subsequent analyses. Restricted cubic splines (RCS) analyses were employed to assess the dose-response relationship between four flavonoids, total flavonoid intake and PhenoAgeAccel, the result is presented in Fig. 2 and Fig. 3. After adjusting for all covariates, we found there is a U-shaped association between flavan-3-ols ( P for nonlinear = 0.024), flavanones ( P for nonlinear = 0.005), flavonols ( P for nonlinear < 0.001), and total flavonoid intake ( P for nonlinear < 0.001) suggesting that intermediate intakes of these subclasses of flavonoids and total flavonoid intake may contribute to the reduction of PhenoAgeAccel. Meanwhile, an L-shaped association was observed between flavones and PhenoAgeAccel ( P for nonlinear < 0.001), suggesting that higher intake of flavones could potentially delay senescence. To analyze the mixed effects of four flavonoid subclasses on PhenoAgeAccel, weighted quantile sum (WQS) regression was conducted. The result indicates that the WQS index had statistically significant effect in reducing PhenoAgeAccel (OR = 0.9, 95% CI: 0.83, 0.98). The estimated weights of the WQS index are exhibited in Fig. 4. The largest weight in the reduction of PhenoAgeAccel is attributed to flavones (52.72%), and the following by flavanones (22.44%), flavan-3-ols (19.43%), with flavonols (5.4%) having the smallest weight. Table 2 Association between total flavonoid, six flavonoid subclasses and PhenoAgeAccel Variable Model 1 Model 2 Model 3 β (95% CI) β (95% CI) β (95% CI) Isoflavones Q1 Reference Reference Reference Q2 -0.26 (-1.20, 0.68) 0.32 (-0.59, 1.23) -0.27 (-1.34, 0.79) Q3 -1.00 (-1.89, -0.11) -0.54 (-1.35, 0.26) -0.49 (-1.19, 0.21) Q4 -0.87 (-1.84, 0.11) -0.52 (-1.42, 0.38) -0.73 (-1.49, 0.03) Q5 -1.55 (-2.46, -0.63) -0.9 (-1.80, -0.00) -0.51 (-1.17, 0.15) P for trend < 0.001 0.020 0.049 Anthocyanidins Q1 Reference Reference Reference Q2 -0.37 (-1.22, 0.48) -0.33 (-1.16, 0.51) -0.27 (-1.34, 0.79) Q3 -1.42 (-2.44, -0.40) -1.58 (-2.56, -0.61) -0.49 (-1.19, 0.21) Q4 -1.82 (-2.64, -1.01) -1.74 (-2.42, -1.06) -0.73 (-1.49, 0.03) Q5 -2.00 (-3.00, -1.00) -1.52 (-2.42, -0.61) -0.51 (-1.17, 0.15) P for trend < 0.001 < 0.001 0.049 Flavan-3-ols Q1 Reference Reference Reference Q2 -1.57 (-2.30, -0.83) -1.47 (-2.25, -0.69) -0.95 (-1.61, -0.30) Q3 -2.06 (-2.95, -1.18) -1.90 (-2.73, -1.08) -1.05 (-1.79, -0.31) Q4 -1.62 (-2.57, -0.68) -1.23 (-2.14, -0.32) 0.08 (-0.78, 0.94) Q5 -1.90 (-2.68, -1.12) -1.34 (-2.06, -0.63) -0.83 (-1.59, -0.08) P for trend < 0.001 0.007 0.473 Variable Model 1 Model 2 Model 3 β (95% CI) β (95% CI) β (95% CI) Flavanones Q1 Reference Reference Reference Q2 -0.97 (-1.88, -0.06) -0.44 (-1.41, 0.52) -0.05 (-0.85, 0.75) Q3 -2.17 (-3.02, -1.31) -1.58 (-2.46, -0.70) -1.14 (-1.90, -0.38) Q4 -2.71 (-3.65, -1.77) -2.32 (-3.29, -1.34) -1.18 (-1.98, -0.39) Q5 -1.55 (-2.59, -0.51) -1.66 (-2.68, -0.63) -0.57 (-1.43, 0.28) P for trend < 0.001 < 0.001 0.016 Flavones Q1 Reference Reference Reference Q2 -0.66 (-1.61, 0.30) -0.61 (-1.52, 0.31) -0.73 (-1.56, 0.10) Q3 -2.36 (-3.10, -1.62) -2.08 (-2.81, -1.35) -1.75 (-2.56, -0.95) Q4 -2.89 (-3.97, -1.80) -2.5 (-3.59, -1.41) -1.83 (-2.83, -0.84) Q5 -2.69 (-3.63, -1.74) -2.27 (-3.22, -1.31) -1.64 (-2.52, -0.77) P for trend < 0.001 < 0.001 < 0.001 Flavonols Q1 Reference Reference Reference Q2 -0.94 (-1.91, 0.03) -0.87 (-1.81, 0.07) -0.49 (-1.40, 0.43) Q3 -1.65 (-2.63, -0.67) -1.47 (-2.35, -0.59) -0.86 (-1.72, -0.01) Q4 -2.33 (-3.20, -1.47) -2.05 (-2.82, -1.28) -1.20 (-2.01, -0.39) Q5 -1.92 (-2.76, -1.09) -1.46 (-2.25, -0.68) -0.90 (-1.84, 0.04) P for trend < 0.001 < 0.001 0.032 Total flavonoids Q1 Reference Reference Reference Q2 -1.61 (-2.49, -0.73) -1.51 (-2.33, -0.68) -0.78 (-1.57, 0.01) Q3 -1.76 (-2.58, -0.94) -1.89 (-2.68, -1.09) -0.87 (-1.61, -0.13) Q4 -1.68 (-2.66, -0.69) -1.36 (-2.29, -0.42) -0.07 (-0.92, 0.77) Q5 -1.81 (-2.63, -0.99) -1.34 (-2.16, -0.52) -0.74 (-1.64, 0.15) P for trend < 0.001 0.008 0.413 Abbreviations: CI, confidence interval; Q1, quintile 1; Q2, quintile 2; Q3, quintile 3; Q4, quintile 4; Q5, quintile 5. Model 1: No adjustments were implemented; Model 2: Adjusted for gender, age, race, poverty income ratio, education level and marital status; Model 3: Adjusted for gender, age, race, PIR, education level, marital status, drinking, smoking, body mass index, physical activity, diabetes, chronic kidney disease and hypertension. Data in bold type indicate the P values below 0.05. Subgroup analysis For the purpose of investigating the association between total flavonoid, flavonoid subclasses intake and PhenoAgeAccel, subgroup analysis was conducted. We stratified by behavioral (drinking, smoking, BMI, physical activity) and health characteristics (participants with diabetes, chronic kidney disease and hypertension). The results are presented in supplementary Table 1-5. The Fig. 5A and Fig. 5B indicates a negative association between flavanones ( P for interaction = 0.047), flavones ( P for interaction = 0.012) intake and PhenoAgeAccel in participants without chronic kidney disease. In Fig. 6, the interaction was also observed in the participants with a BMI of 25-30 ( P for interaction = 0.044). However, no statistically significant results were found in the other flavonoid subclasses and subgroups. or without chronic kidney disease. Discussion In this cross-sectional American population-based study, we investigated the association between dietary flavonoid intake and aging process. The results indicated that the total dietary flavonoid intake has a negative association with PhenoAgeAccel, and the similar association were observed in the four flavonoid subclasses (flavan-3-ols, flavanones, flavones and flavonols). Moreover, a nonlinear dose-response relationship was identified between flavonoid intake and PhenoAgeAccel. The mixed effect analyses indicated that flavones were the primary contributors to the deceleration of the aging process. Previous studies have indicated a beneficial association between flavonoid intake and delayed aging, which is consistent with our findings. A study from TwinsUK cohort suggests that increasing the intake of foods rich in flavonoids may potentially attenuate cognitive ageing[27]. Meanwhile, another cohort study indicates that women who have a higher intake of flavonoids during their middle age are more likely to experience better health and well-being in their later years[28]. Similarly, a study conducted among American adults found that flavonoid intake positively contributes to delaying the biological aging process[29]. Furthermore, additional research indicates that higher consumption of flavonoid-rich foods and beverages is associated with a lower all-cause mortality rate[30, 31]. In our study, we found that moderate dietary total flavonoid intake was associated with a younger phenotypic age. To date, the specific mechanisms by which flavonoids influence aging remain unclear, but they may involve the following mechanisms: Firstly, flavonoids could inhibit the formation of the senescence-associated secretory phenotype (SASP) and selectively eliminate senescent cells, thereby mitigating the aging process[32-34]. Secondly, due to the structural basis of flavonoids, they possess direct antioxidant activity which could directly scavenge ROS and upregulate antioxidant responses through the transcription factor NRF2 (Nuclear factor erythroid 2-related factor 2), which enhances proteasome activity and maintaining proteostasis to delay the aging process[35, 36]. Thirdly, flavonoids could inhibit the release of pro-inflammatory cytokines and reduce inflammation by modulating the MAPK and NF-κB signaling pathways[37, 38]. Fourthly, flavonoids could further induce autophagy by modulating autophagy-related signaling pathways, including PI3K/Akt/mTOR and AMPK/mTOR. This regulation promotes the clearance of abnormal protein aggregates within cells, maintains cellular homeostasis, and prevents the deterioration of cellular function[39-41]. Furthermore, we investigated the association between flavonoid subclasses and PhenoAgeAccel by examining both single and mixed effects. In the single effect analysis, we found that moderate intake of flavanones and flavonols, as well as moderate to high intake of flavan-3-ols and flavones, is associated with a younger phenotypic age. Meanwhile, all of these subclasses exhibit a non-linear association with PhenoAgeAccel. Several studies supported our results, derivatives of flavan-3-ols, such as catechins, have been demonstrated to enhance the overall health and survival rate of aged mice fed a standard diet[42]. Additionally, epicatechin has been shown to ameliorate age-related degenerative changes in the neuromuscular system of mice[43]. Naringenin, a flavanone, has been shown to extend lifespan and slow down aging through the IIS and MAPK pathways in Caenorhabditis elegans[44]. Apigenin is a kind of flavones with great anti-aging capability, a vivo research shows that apigenin could prevents signs of skin aging[45]. Moreover, recent studies have demonstrated that apigenin could reduce the SASP in a human fibroblast strain induced to senescence by bleomycin[46]. As for flavonols, their subclass quercetin, which is abundant in many fruits, vegetables, leafy greens, seeds, and grains, has been widely applied as a senolytics, demonstrating potent efficacy in the treatment of various age-related diseases and in anti-aging[47-49]. In the mix effect analysis, the results of WQS regression model indicated that flavones had the primary effect on the mixture exposures, followed by flavanones, flavan-3-ols and flavonols, which consistent with the results of the single effect analysis. Interestingly, flavonols were the least significant contributors to the mixture exposures, although quercetin having demonstrated a potent effect in delaying senescence. This may be related to the low bioavailability and actual dietary intake concentration[50]. The subgroup analysis indicated that participants without CKD are more likely to benefit from dietary flavanones and flavones intake in mitigating aging. This may be attributed to the fact that individuals without CKD generally exhibit better health, allowing their bodies to utilize the health benefits of flavonoids more efficiently. In contrast, the protective effects of flavonoids in individuals with CKD may be diminished due to the impact of disease[51, 52]. Meanwhile, the benefits of flavanone intake are more significant in participants with a lower BMI. Research shows that the metabolic status of high BMI population is different, which may affect the absorption and metabolic efficiency of flavonoids[53]. There are several significant strengths exhibited in our study. To the best of our knowledge, this research is the first to examine the association between PhenoAgeAccel and dietary flavonoid intake. Further, our study included a substantial sample size of 5,391 participants and employed complex weight sampling, ensuring the representativeness of our findings for the overall adult population in the United States. Finally, we employed linear regression, RCS analyses, WQS regression and subgroup analysis to improve the reliability and robustness of our results. However, there are several limitations in our study. Firstly, a causal relationship between dietary flavonoid intake and aging cannot be determined because of the cross-sectional design. Secondly, dietary flavonoid intake data was collected by a two-day 24-hour dietary recall survey, which may lead to recall bias and might not reflect the long-term dietary intake habits of participants accurately. Thirdly, phenotypic age is calculated using multiple biomarkers. Although it has been shown to predict age-related diseases in different populations, it may differ from actual aging. Finally, our study population was derived from the NHANES database, so the generalizability of our findings to populations in other regions may be limited. Conclusion To summarize, our study suggests that dietary total flavonoid intake and the four flavonoid subclasses (including flavan-3-ols, flavanones, flavones and flavonols) is associated with a younger phenotypic age. Mixed effects analyses indicated that the anti-aging effects may primarily stem from flavones. Furthermore, participants without CKD are more likely to benefit from dietary flavanone and flavone intake in mitigating aging, while the benefits of flavanone intake are more significant in participants with a lower BMI. Our findings suggested that flavonoid-rich diets may be beneficial for delaying aging. However, due to the limitations of our study, further prospective studies are needed to validate the causal relationship of dietary flavonoid intake and aging. Declarations Author contributions Conceptualization: Jintao Zhong, Yixuan Wang; Data Curation: Biyu Wan, Mengya Wang; Formal analysis: Jiamin Fang, Pinli Lin; Writing – original draft: Jintao Zhong; Writing – review and editing: Jintao Zhong, Xiaona Tang. Funding acquisition: Lili Deng; Supervision: Xiaona Tang. All authors have read and agreed to the published version of the manuscript. Funding statement This work was supported by Sanming Project of Medicine in Shenzhen (No.SZZYSM202206014) and Guangdong province Graduate Education Innovation Program (2024XSLT_019). Data availability This study was conducted using publicly available data, which can be accessed at: https://www.cdc.gov/nchs/nhanes/. 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Baky MH, Elshahed M, Wessjohann L, Farag MA: Interactions between dietary flavonoids and the gut microbiome: a comprehensive review. The British journal of nutrition 2022, 128 (4):577-591. Additional Declarations No competing interests reported. Supplementary Files SupplementaryTable.docx Cite Share Download PDF Status: Published Journal Publication published 20 Dec, 2024 Read the published version in Nutrition Journal → Version 1 posted Editorial decision: Revision requested 13 Nov, 2024 Reviews received at journal 13 Nov, 2024 Reviewers agreed at journal 13 Nov, 2024 Reviewers agreed at journal 07 Aug, 2024 Reviewers invited by journal 31 Jul, 2024 Editor assigned by journal 29 Jul, 2024 Submission checks completed at journal 25 Jul, 2024 First submitted to journal 23 Jul, 2024 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. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4790160","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":342890730,"identity":"aa7fb02a-d538-49c2-af8c-dc6ef54b3baf","order_by":0,"name":"Jintao Zhong","email":"","orcid":"","institution":"The Second Clinical College of Guangzhou University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Jintao","middleName":"","lastName":"Zhong","suffix":""},{"id":342890731,"identity":"62de0384-9da7-4271-b84f-19fee1ec35b2","order_by":1,"name":"Jiamin Fang","email":"","orcid":"","institution":"The Second Clinical College of Guangzhou University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Jiamin","middleName":"","lastName":"Fang","suffix":""},{"id":342890732,"identity":"a6d5ddf7-8c37-4981-944c-90383a6c9132","order_by":2,"name":"Yixuan Wang","email":"","orcid":"","institution":"Chinese Medical College of Tianjin University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Yixuan","middleName":"","lastName":"Wang","suffix":""},{"id":342890733,"identity":"da85fc3d-5c89-4ef7-94ea-4a420bc52f1d","order_by":3,"name":"Pinli Lin","email":"","orcid":"","institution":"Guangdong Pharmaceutical University","correspondingAuthor":false,"prefix":"","firstName":"Pinli","middleName":"","lastName":"Lin","suffix":""},{"id":342890734,"identity":"8d4b32f3-ef61-40e7-b37d-5cf96a14b730","order_by":4,"name":"Biyu Wan","email":"","orcid":"","institution":"The Third Affiliated Hospital of Guangzhou University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Biyu","middleName":"","lastName":"Wan","suffix":""},{"id":342890735,"identity":"165f812e-3882-4d95-b3d9-6ba61ce49cea","order_by":5,"name":"Mengya Wang","email":"","orcid":"","institution":"Hunan University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Mengya","middleName":"","lastName":"Wang","suffix":""},{"id":342890736,"identity":"b5800f02-074c-4583-9607-fcba957ab240","order_by":6,"name":"Lili Deng","email":"","orcid":"","institution":"Guangzhou University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Lili","middleName":"","lastName":"Deng","suffix":""},{"id":342890737,"identity":"0682b64b-38b8-4afa-a2be-e6f39fab028a","order_by":7,"name":"Xiaona Tang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABFklEQVRIiWNgGAWjYNACAxDBfODAB5gADx7FPAgtbIkHZxCvBcI0PsyDRRgD2LOfPfyap8BOzpz9WMJhm1918gbXDjA+eNvGIG+OyxaevDTLGQbJxpY9yQcO5/axGW64ncBsOLeNwXBnAy6H5ZgZfDBgTtxwIC3hcG4PT4LB7QQ2ad42hgSDAzi08L8xM0gwqE/ccP6NwWHLHgmQFvbfeLVI5Bg/+GBwOHHDjRyDwww/DMC2MOPVcuONGeMMg+PGBjeeJRzsbUgwnHk7sVlyzjkJww04tLD35xh/5vlTLWdwPvnwhx9/6uT5bicf/PCmzEYely1AwCYBZzK2gckGICGBQzUYMH9AsP/gUzgKRsEoGAUjFQAAE/te4f1CXt8AAAAASUVORK5CYII=","orcid":"","institution":"Shenzhen Bao'an Traditional Chinese Medicine Hospital Group","correspondingAuthor":true,"prefix":"","firstName":"Xiaona","middleName":"","lastName":"Tang","suffix":""}],"badges":[],"createdAt":"2024-07-23 16:06:00","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4790160/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4790160/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12937-024-01052-x","type":"published","date":"2024-12-20T15:57:52+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":63369179,"identity":"af5cf352-9cee-403e-a7f6-20c698cfb8f1","added_by":"auto","created_at":"2024-08-27 11:42:47","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":438658,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of the participants\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-4790160/v1/2deed760605e1db7be2caa19.png"},{"id":63369175,"identity":"0eff478b-d1b5-4847-8cf6-afa019dee9c0","added_by":"auto","created_at":"2024-08-27 11:42:47","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":485066,"visible":true,"origin":"","legend":"\u003cp\u003eDose-response relationship between four flavonoid subclasses, total flavonoid intake and PhenoAgeAccel: (A) flavan-3-ols (B) flavanones (C) flavones (D) flavonols. Covariates included gender, age, race, poverty income ratio, education level, marital status, drinking, smoking, BMI, physical activity, diabetes, chronic kidney disease and hypertension.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-4790160/v1/8f328d13d9e26da91255d065.png"},{"id":63370397,"identity":"f77fb56c-f737-49b6-910d-2b46be80e61f","added_by":"auto","created_at":"2024-08-27 11:50:47","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":316157,"visible":true,"origin":"","legend":"\u003cp\u003eDose-response relationship between total flavonoid intake and PhenoAgeAccel Covariates included gender, age, race, poverty income ratio, education level, marital status, drinking, smoking, BMI, physical activity, diabetes, chronic kidney disease and hypertension.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-4790160/v1/f8c4ad05280caac1d5b865b5.png"},{"id":63370398,"identity":"b4e894d4-a282-4945-8cb3-2d4ad8654a44","added_by":"auto","created_at":"2024-08-27 11:50:47","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":42213,"visible":true,"origin":"","legend":"\u003cp\u003eWQS model regression index weights for dietary flavonoid intake and PhenoAgeAccel. The model was adjusted for gender, age, race, poverty income ratio, education level, marital status, drinking, smoking, body mass index physical activity, diabetes, chronic kidney disease and hypertension.\u003c/p\u003e","description":"","filename":"Onlinefloatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-4790160/v1/33545b321f686a1b444af979.png"},{"id":63370396,"identity":"83720869-db98-41bf-92c5-f226dda9219e","added_by":"auto","created_at":"2024-08-27 11:50:47","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":540545,"visible":true,"origin":"","legend":"\u003cp\u003eSubgroup analysis for the association between flavanones, flavones intake and PhenoAgeAccel.\u003c/p\u003e\n\u003cp\u003e(A) Association between flavanones intake and PhenoAgeAccel in different CKD status.\u003c/p\u003e\n\u003cp\u003e(B) Association between flavones intake and PhenoAgeAccel in different CKD status.\u003c/p\u003e\n\u003cp\u003eAbbreviations: CI, confidence interval; CKD, chronic kidney disease.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-4790160/v1/302c0a4dfeb7273f3f22c8bc.png"},{"id":63369174,"identity":"58f2e6a7-f4a2-4e2f-a67b-e231fd6feedd","added_by":"auto","created_at":"2024-08-27 11:42:47","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":208560,"visible":true,"origin":"","legend":"\u003cp\u003eSubgroup analysis for the association between flavanones intake and PhenoAgeAccel in different BMI groups.\u003c/p\u003e\n\u003cp\u003eAbbreviations: CI, confidence interval; BMI, body mass index.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-4790160/v1/9a471081ccad2d36acdbe73c.png"},{"id":72202104,"identity":"a062d203-42e5-4793-a215-0ea9f4c12267","added_by":"auto","created_at":"2024-12-23 16:14:27","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4640755,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4790160/v1/25242902-583c-4a46-bb14-ad618d5d1394.pdf"},{"id":63369177,"identity":"5dcd4758-0722-489c-b250-a14c8c24619d","added_by":"auto","created_at":"2024-08-27 11:42:47","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":68355,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable.docx","url":"https://assets-eu.researchsquare.com/files/rs-4790160/v1/26ccfa914a63ffb50da0d2d8.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Dietary flavonoid intake is negatively associated with accelerating aging: an American population-based cross-sectional study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAging has emerged as a critical global issue. By 2050, the population aged 60 and over is projected to reach 2\u0026nbsp;billion, accounting for 22% of the global population[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. According to the 2017 Global Burden of Disease (GBD) study, 92 diseases have been identified as age-related, accounting for 51.3% of the total disease burden among adults worldwide[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. This poses substantial challenges to families, healthcare systems, and social security frameworks. Consequently, the identification and deceleration of the aging process are imperative for promoting healthy aging.\u003c/p\u003e \u003cp\u003eDue to the inherent inter-individual variability in the aging process, discernible differences in the aging process among individuals are observed[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Therefore, efforts have been initiated to develop measures that capture the concept of biological age (BA), which integrates composite clinical biomarkers with chronological age[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. These aging biomarkers serve to elucidate the underlying biological heterogeneity in aging and uncover factors that influence the rate of aging, playing a critical role in human research aimed at decelerating the aging process[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Phenotypic age, developed by Levine et al, is a validated biological age predictor[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], which has been shown to effectively predict morbidity and mortality risk[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDiet and aging are intrinsically linked, with a balanced diet being the safest and most effective method to delay the aging process and extend healthy lifespan[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Flavonoids, a group of natural polyphenols, which found in vegetables, fruits, cocoa, oilseeds and tea, consists of six subclasses: isoflavones, anthocyanins, flavan-3-ols, flavanones, flavones and flavonols[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Flavonoids exhibit potent anti-inflammatory and antioxidant properties, offering significant potential to attenuate the aging process. Consequently, they have been recognized as promising candidate compounds for retarding aging[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. As polyphenolic compounds, the hydroxyl groups in the structure of flavonoids are capable of scavenging various types of reactive oxygen species (ROS)[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Additionally, flavonoids could maintain and activate SIRT1, and consequently inhibit NF-κB, which could prevent oxidative stress and neuroinflammation to delay brain aging[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Research shows that oral treatment with naringenin in young rats could prevent alterations in the brain antioxidant defense system, thereby ameliorating cognitive decline[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], hesperetin could enhance antioxidant cellular defenses through the ERK/Nrf2 signaling pathway[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Further, flavonoids have been shown to extend the lifespan of worms, flies, and mice[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. It is worth noting that fisetin and quercetin, as flavonols, are currently widely studied senolytic agents and are being used in multiple clinical trials[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. However, the relationship between dietary flavonoid intake and aging process has not been fully studied, especially regarding the mixed effects of different flavonoid subclasses. Therefore, this study intends to use data from the National Health and Nutrition Examination Survey (NHANES) to examine the aging process within the American population by calculating phenotypic age and to explore the association between dietary flavonoid intake and aging.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003eStudy population\u003c/h2\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003eIn our study, participants are based on the National Health and Nutrition Examination Survey (NHANES), which aims to assess the health and nutritional status of adults and children in the United States using a complex, multistage probability sampling design[\u003cspan class=\"CitationRef\"\u003e22\u003c/span\u003e]. Since the availability of flavonoid databases, we only included data from three survey cycles (2007\u0026ndash;2008, 2009\u0026ndash;2010, and 2017\u0026ndash;2018), and retrieved subject information during these cycles. The flowchart illustrating the selection procedure for all participants involved in the study is exhibited in Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. Initially, 29940 participants were included. Subsequently, we excluded participants under the age of 20 (N\u0026thinsp;=\u0026thinsp;12,218), with incomplete information to Phenotypic Age (N\u0026thinsp;=\u0026thinsp;4,673), without flavonoid information and dietary recall status below the minimum criteria (N\u0026thinsp;=\u0026thinsp;2,168) and missing demographic characteristics (N\u0026thinsp;=\u0026thinsp;5,490). Finally, a total of 5391 qualified participants was in the final analysis. The final sample represented a weighted population of 83.3\u0026nbsp;million non-institutionalized residents in United States. Prior to the survey, all participants provided written informed consent. Furthermore, the National Center for Health Statistics (NCHS) Ethics Review Board granted approval for the NHANES protocol.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003eAssessment of phenotypic age acceleration\u003c/h2\u003e\n \u003cp\u003ePhenotypic age is calculated by a combination of chronological age and nine biomarkers: albumin, creatinine, glucose, C-reactive protein (log-transformed), lymphocyte percent, mean cell volume, red blood cell distribution width, alkaline phosphatase, and white blood cell count. The formula for the determination of phenotypic age is as follows[\u003cspan class=\"CitationRef\"\u003e4\u003c/span\u003e]:\u003c/p\u003e\n \u003cdiv id=\"Equa\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\u003cimg src=\"https://myfiles.space/user_files/122228_c8a1650c59388082/122228_custom_files/img1724231860.png\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003ePhenoAgeAccel is defined as the residual resulting from a linear model when regressing phenotypic age on chronological age. The positive value represents a person appearing older than expected while the negative value represents the opposite.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003eAssessment of dietary flavonoid intake\u003c/h2\u003e\n \u003cp\u003eData on dietary flavonoid intake were obtained from the USDA Food and Nutrient Database for Dietary Studies (FNDDS), which includes calculated data from two 24-hour dietary recall interviews conducted as part of NHANES[\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e]. As of now, data on dietary flavonoid intake for the years 2007\u0026ndash;2008, 2009\u0026ndash;2010, and 2017\u0026ndash;2018 have been published. The FNDDS database categorizes dietary flavonoid into six subclasses: isoflavones, anthocyanins, flavan-3-ols, flavanones, flavones and flavonols. Total dietary flavonoid intake is defined as the sum of these six subclasses. The mean value of dietary flavonoid intake from the two 24-hour dietary recalls was defined as the final dietary flavonoid intakes.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n \u003ch2\u003eCovariates\u003c/h2\u003e\n \u003cp\u003eIn our analysis, covariates encompassed three categories. Sociodemographic characteristics included gender, age, race, poverty income ratio (PIR), education level and marital status. Behavioral characteristics encompassed drinking, smoking, body mass index (BMI), physical activity. Health characteristics encompassed the history of diabetes, chronic kidney disease (CKD) and hypertension.\u003c/p\u003e\n \u003cp\u003eThe family poverty income ratio (PIR) was computed by dividing the household income by the poverty threshold corresponding to the household size, PIR values\u0026thinsp;\u0026lt;\u0026thinsp;1 indicate poverty, while higher values indicate higher socioeconomic status[\u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e]; Drinking was defined as participants who consumed alcohol drink more than 12 times per year; smoking was defined as participants who smoked at least 100 cigarettes in their lifetime; BMI was stratified into three levels: \u0026lt; 25, 25\u0026ndash;30, and \u0026gt;\u0026thinsp;30 kg m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e; physical activity was categorized into four groups: inactive (participants lacking regular physical exercise), insufficient (\u0026lt;\u0026thinsp;500 MET per week), moderate (500\u0026ndash;1000 MET per week), and high (\u0026gt;\u0026thinsp;1000 MET per week)[\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e]; participants with diabetes were defined by fasting plasma glucose\u0026thinsp;\u0026ge;\u0026thinsp;7.0 mmol L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, HbA1c\u0026thinsp;\u0026ge;\u0026thinsp;6.5% or self-reported diabetes status; CKD was defined by urinary albumin-to-creatinine ratio over 30 mg g\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, the estimated glomerular filtration rate (eGFR) lower than 60 ml/min/1.73 m\u003csup\u003e2\u003c/sup\u003e or self-reported CKD status; Hypertension was defined by the average of three systolic pressure of \u0026ge;\u0026thinsp;130 mmHg or diastolic pressure of \u0026ge;\u0026thinsp;80 mmHg or self-reported hypertension status.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n \u003ch2\u003eStatistical analysis\u003c/h2\u003e\n \u003cp\u003eAccording to NHANES analytic guidelines, our analyses were incorporated appropriate sample weights to account for the complex sampling design. As dietary flavonoid intakes, participants were divided into five quintiles [Q1 (quintile 1), Q2 (quintile 2), Q3 (quintile 3), Q4 (quintile 4) and Q5 (quintile 5)]. PhenoAgeAccel was described as a categorical variable (participants with PhenoAgeAccel or participants without PhenoAgeAccel) and continuous variable. Since all the variables were categorical, the baseline data were described as frequency with weighted percentages. They were then compared using the Scott-Rao chi-square test. Furthermore, three weighted linear regression models were used to investigate the relationship between six subclasses of flavonoid and PhenoAgeAccel. In Model 1, no adjustments were implemented; Model 2 was adjusted for gender, age, race, PIR, education level and marital status; Model 3 was adjusted for gender, age, race, PIR, education level, marital status, drinking, smoking, BMI, physical activity, diabetes, CKD and hypertension. We used dietary flavonoid intake (quintile-categorical) as a continuous variable in all models to conduct trend tests (\u003cem\u003eP\u003c/em\u003e for trend). Subsequently, the restricted cubic splines (RCS) analyses were applied to examine the dose-response relationships between four flavonoid subclasses and PhenoAgeAccel with four knots (5th, 35th, 65th and 95th percentiles), the RCS plots have been adjusted for all covariates.\u003c/p\u003e\n \u003cp\u003eConsequently, the weighted quantile sum (WQS) regression model was used to explore the overall effect of flavonoid subclasses on PhenoAgeAccel and the WQS index calculation was utilized for the determination of advantage type[\u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e]. For further investigate the association between flavonoid subclasses and PhenoAgeAccel in different population, the subgroup analysis and interaction test was conducted.\u003c/p\u003e\n \u003cp\u003eAll statistical analyses were performed using R software (version 4.3.2). \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 indicated statistical significance (two-sided).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003eBaseline characteristics\u003c/h2\u003e\n \u003cp\u003eA total of 5391 participants were enrolled for the associated analysis of dietary flavonoid intakes and PhenoAgeAccel. Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e presents the sociodemographic, behavioral health and dietary flavonoid intake characteristics of participants with or without PhenoAgeAccel. Significant differences were observed in age, race, PIR, education level, marital status, drinking, smoking, BMI, physical activity, diabetes, chronic kidney disease, and hypertension between the two groups. Furthermore, participants with higher intake of isoflavones, anthocyanins, flavan-3-ols, flavanones, flavones, flavonols and total flavonoid, were less likely to experience PhenoAgeAccel. \u0026nbsp;\u003c/p\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eBaseline characteristics of the study population\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;5391)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eWithout PhenoAgeAccel (n\u0026thinsp;=\u0026thinsp;3581)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eWith PhenoAgeAccel (n\u0026thinsp;=\u0026thinsp;1810)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eSociodemographic characteristics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eGender, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.109\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e2590 (48.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e1642 (47.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e948 (51.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e2801 (51.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e1939 (52.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e862 (48.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eAge, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e20\u0026ndash;50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e2527 (54.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e1805 (57.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e722 (46.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e50\u0026ndash;65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e1483 (26.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e967 (26.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e516 (28.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e1381 (18.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e809 (15.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e572 (25.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eRace, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eNon-Hispanic White\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e2609 (69.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e1792 (72.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e817 (63.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eMexican American\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e863 (8.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e608 (8.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e255 (8.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eOther Hispanic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e503 (4.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e343 (4.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e160 (5.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eNon-Hispanic Black\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e1008 (10.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e551 (7.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e457 (15.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eOther Race\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e408 (6.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e287 (7.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e121 (6.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003ePIR, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e1005 (13.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e607 (11.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e398 (17.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e1\u0026ndash;3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e2389 (36.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e1505 (33.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e884 (43.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e1997(49.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e1469 (54.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e528 (38.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eEducation level, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eBelow high school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e1286 (15.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e794 (13.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e492 (19.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eHigh school graduate or GED\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e1276 (24.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e792 (22.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e484 (30.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eSome colleges or above\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e2829 (60.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e1995 (64.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e834 (50.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eMarital status, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.012\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eMarried/cohabiting\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e3280 (62.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e2235 (63.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1045 (60.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eWidowed/divorced/separated\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e1253 (19.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e748 (17.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e505 (23.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eNever married\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e858 (18.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e598 (18.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e260 (16.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eBehavioral characteristics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eDrink, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e3272 (60.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e2327 (65.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e945 (49.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e2119 (39.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e1254 (34.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e865 (50.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eSmoke, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e2929 (54.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e2095 (58.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e834 (46.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e2462 (45.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e1486 (41.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e976 (53.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eBMI, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e1484(29.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e1179 (35.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e305 (15.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e25\u0026ndash;30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e1809(32.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e1330 (35.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e479 (24.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e2098(37.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e1072 (28.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1026 (59.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003ePhysical activity, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eInactive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e2251 (35.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e1318 (30.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e933 (47.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;5391)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eWithout PhenoAgeAccel (n\u0026thinsp;=\u0026thinsp;3581)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWith PhenoAgeAccel (n\u0026thinsp;=\u0026thinsp;1810)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eInsufficient\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e889 (16.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e606 (17.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e283 (14.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e701 (14.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e502 (14.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e199 (12.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e1550 (33.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e1155 (36.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e395 (25.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eHealth characteristics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eDiabetes, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e4523 (88.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e3309 (94.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1214 (74.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e868 (11.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e272 (5.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e596 (25.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eChronic Kidney Disease, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e4730 (91.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e3316 (94.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1414 (83.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e661 (8.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e265 (5.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e396 (16.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eHypertension, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e3358 (67.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e2454 (72.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e904 (55.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e2033 (32.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e1127 (27.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e906 (44.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eDietary flavonoid intake (mg per day)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eIsoflavones, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.017\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eQ1 (0\u0026ndash;0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e2039 (36.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e1292 (34.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e747 (41.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eQ2 (0-0.005)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e354 (7.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e228 (6.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e126 (7.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eQ3 (0.005\u0026ndash;0.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e788 (15.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e520 (16.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e268 (15.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eQ4 (0.02\u0026ndash;0.145)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e1106 (17.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e773 (18.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e333 (16.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eQ5 (0.145\u0026ndash;390.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e1104 (22.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e768 (23.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e336 (18.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eAnthocyanidins, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eQ1 (0-0.020)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e1102 (21.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e651 (19.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e451 (26.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eQ2 (0.02\u0026ndash;1.005)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e1055 (18.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e658 (17.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e397 (21.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eQ3 (1.005\u0026ndash;4.010)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e1079 (18.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e729 (18.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e350 (16.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eQ4 (4.010-16.815)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e1078 (19.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e758 (20.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e320 (17.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eQ5 (16.815\u0026ndash;543.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e1077 (21.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e785 (23.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e292 (18.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eFlavan-3-ols, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.007\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eQ1 (0-3.560)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e1079 (18.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e620 (16.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e459 (23.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eQ2 (3.560-10.055)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e1080 (18.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e729 (17.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e351 (18.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eQ3 (10.055\u0026ndash;24.890)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e1076 (19.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e757 (19.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e319 (16.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eQ4 (24.890-229.820)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e1078 (20.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e735 (21.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e343 (18.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eQ5 (229.820-4939.790)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e1078 (23.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e740 (23.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e338 (21.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eFlavanones, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eQ1 (0-0.005)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e1108 (21.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e634 (18.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e474 (26.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eQ2 (0.005\u0026ndash;0.250)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e1056 (20.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e682 (20.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e374 (22.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eQ3 (0.250\u0026ndash;2.495)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e1073 (21.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e745 (22.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e328 (19.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eQ4 (2.495\u0026ndash;25.400)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e1076 (20.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e774 (21.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e302 (16.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eQ5 (25.400-345.685)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e1078 (16.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e746 (16.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e332 (15.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;5391)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eWithout PhenoAgeAccel (n\u0026thinsp;=\u0026thinsp;3581)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eWith PhenoAgeAccel (n\u0026thinsp;=\u0026thinsp;1810)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eFlavones, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eQ1 (0-0.135)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e1094 (19.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e619 (16.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e475 (26.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eQ2 (0.135\u0026ndash;0.365)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e1073 (18.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e668 (17.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e405 (21.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eQ3 (0.365\u0026ndash;0.705)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e1078 (19.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e750 (20.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e328 (16.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eQ4 (0.705\u0026ndash;1.315)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e1072 (20.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e760 (21.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e312 (17.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eQ5 (1.315\u0026ndash;87.245)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e1074 (22.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e784 (23.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e290 (18.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eFlavonols, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.006\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eQ1 (0-5.880)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e1079 (17.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e633 (15.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e446 (21.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eQ2 (5.880\u0026ndash;10.250)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e1080 (18.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e708 (17.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e372 (19.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eQ3 (10.250\u0026ndash;15.900)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e1077 (19.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e730 (20.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e347 (19.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eQ4 (15.900-25.390)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e1078 (20.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e756 (21.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e322 (17.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eQ5 (25.390-262.435)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e1077 (23.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e754 (24.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e323 (21.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eTotal flavonoids, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.005\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eQ1 (0-19.350)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e1079 (19.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e622 (17.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e457 (25.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eQ2 (19.350-44.555)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e1078 (17.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e728 (18.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e350 (16.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eQ3 (44.555\u0026ndash;97.660)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e1078 (18.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e738 (19.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e340 (17.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eQ4 (97.660-283.950)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e1078 (20.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e758 (21.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e320 (18.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eQ5 (283.950-5177.470)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e1078 (23.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e735 (24.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e343 (21.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"10\"\u003eAbbreviations: BMI, body mass index; PIR, poverty income ratio; Q1, quintile 1; Q2, quintile 2; Q3, quintile 3; Q4, quintile 4; Q5, quintile 5.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eAssociation between total flavonoid, flavonoid subclasses and PhenoAgeAccel\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eThe results of three weighted linear regression models investigating the association between six flavonoids subclasses and PhenoAgeAccel are presented in Table 2. In model 3, after adjusting all covariates, compared to the lowest quintile, the second (\u0026beta;: -0.95, 95% CI: -1.61, -0.30), third (\u0026beta;: -1.05, 95% CI: -1.79, -0.31) and highest (\u0026beta;: -0.83, 95% CI: -1.95, -0.08) quintiles of flavan-3-ols (\u003cem\u003eP\u003c/em\u003e for trend =0.473); the third (\u0026beta;: -1.14, 95% CI: -1.90, -0.38) and fourth (\u0026beta;: -1.18, 95% CI: -1.98, -0.39) quintiles of flavanone (\u003cem\u003eP\u003c/em\u003e for trend =0.016); the third (\u0026beta;: -1.75, 95% CI: -2.56, -0.95), fourth (\u0026beta;: -1.83, 95% CI: -2.83, -0.84) and highest (\u0026beta;: -1.64, 95% CI: -2.52, -0.77) quintiles of flavones (\u003cem\u003eP\u003c/em\u003e for trend \u0026lt;0.001); the third (\u0026beta;: -1.14, 95% CI: -1.90, -0.38) and fourth (\u0026beta;: -1.18, 95% CI: -1.98, -0.39) quintiles of flavonols (\u003cem\u003eP\u003c/em\u003e for trend =0.032); the third (\u0026beta;: -0.87, 95% CI: -1.61, -0.13) quintile of total dietary flavonoid intake exhibited a significant associated with the decreased of PhenoAgeAccel. However, the result indicated no significant association between isoflavones, anthocyanidins and PhenoAgeAccel. Consequently, they were excluded from subsequent analyses.\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eRestricted cubic splines (RCS) analyses were employed to assess the dose-response relationship between four flavonoids, total flavonoid intake and PhenoAgeAccel, the result is presented in Fig. 2 and Fig. 3. After adjusting for all covariates, we found there is a U-shaped association between flavan-3-ols (\u003cem\u003eP\u003c/em\u003e for nonlinear = 0.024), flavanones (\u003cem\u003eP\u003c/em\u003e for nonlinear = 0.005), flavonols (\u003cem\u003eP\u003c/em\u003e for nonlinear \u0026lt; 0.001), and total flavonoid intake (\u003cem\u003eP\u003c/em\u003e for nonlinear \u0026lt; 0.001) suggesting that intermediate intakes of these subclasses of flavonoids and total flavonoid intake may contribute to the reduction of PhenoAgeAccel. Meanwhile, an L-shaped association was observed between flavones and PhenoAgeAccel (\u003cem\u003eP\u003c/em\u003e for nonlinear \u0026lt; 0.001), suggesting that higher intake of flavones could potentially delay senescence.\u003c/p\u003e\n \u003cp\u003eTo analyze the mixed effects of four flavonoid subclasses on PhenoAgeAccel, weighted quantile sum (WQS) regression was conducted. The result indicates that the WQS index had statistically significant effect in reducing PhenoAgeAccel (OR = 0.9, 95% CI: 0.83, 0.98). The estimated weights of the WQS index are exhibited in Fig. 4. The largest weight in the reduction of PhenoAgeAccel is attributed to flavones (52.72%), and the following by flavanones (22.44%), flavan-3-ols (19.43%), with flavonols (5.4%) having the smallest weight.\u0026nbsp;\u003c/p\u003e\u0026nbsp;\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eAssociation between total flavonoid, six flavonoid subclasses and PhenoAgeAccel\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eModel 1\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eModel 2\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eModel 3\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026beta; (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026beta; (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026beta; (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eIsoflavones\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.26 (-1.20, 0.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.32 (-0.59, 1.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.27 (-1.34, 0.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.00 (-1.89, -0.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.54 (-1.35, 0.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.49 (-1.19, 0.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.87 (-1.84, 0.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.52 (-1.42, 0.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.73 (-1.49, 0.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.55 (-2.46, -0.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.9 (-1.80, -0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.51 (-1.17, 0.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e for trend\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.020\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.049\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAnthocyanidins\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.37 (-1.22, 0.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.33 (-1.16, 0.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.27 (-1.34, 0.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.42 (-2.44, -0.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.58 (-2.56, -0.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.49 (-1.19, 0.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.82 (-2.64, -1.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.74 (-2.42, -1.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.73 (-1.49, 0.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.00 (-3.00, -1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.52 (-2.42, -0.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.51 (-1.17, 0.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e for trend\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.049\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eFlavan-3-ols\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e-1.57 (-2.30, -0.83)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e-1.47 (-2.25, -0.69)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.95 (-1.61, -0.30)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e-2.06 (-2.95, -1.18)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e-1.90 (-2.73, -1.08)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e-1.05 (-1.79, -0.31)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e-1.62 (-2.57, -0.68)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e-1.23 (-2.14, -0.32)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.08 (-0.78, 0.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e-1.90 (-2.68, -1.12)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e-1.34 (-2.06, -0.63)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.83 (-1.59, -0.08)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e for trend\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.007\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.473\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModel 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModel 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModel 3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026beta; (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026beta; (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026beta; (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eFlavanones\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.97 (-1.88, -0.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.44 (-1.41, 0.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.05 (-0.85, 0.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e-2.17 (-3.02, -1.31)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e-1.58 (-2.46, -0.70)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e-1.14 (-1.90, -0.38)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e-2.71 (-3.65, -1.77)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e-2.32 (-3.29, -1.34)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e-1.18 (-1.98, -0.39)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e-1.55 (-2.59, -0.51)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e-1.66 (-2.68, -0.63)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.57 (-1.43, 0.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e for trend\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.016\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eFlavones\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.66 (-1.61, 0.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.61 (-1.52, 0.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.73 (-1.56, 0.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e-2.36 (-3.10, -1.62)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e-2.08 (-2.81, -1.35)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e-1.75 (-2.56, -0.95)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e-2.89 (-3.97, -1.80)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e-2.5 (-3.59, -1.41)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e-1.83 (-2.83, -0.84)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e-2.69 (-3.63, -1.74)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e-2.27 (-3.22, -1.31)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e-1.64 (-2.52, -0.77)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e for trend\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eFlavonols\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.94 (-1.91, 0.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.87 (-1.81, 0.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.49 (-1.40, 0.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e-1.65 (-2.63, -0.67)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e-1.47 (-2.35, -0.59)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.86 (-1.72, -0.01)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e-2.33 (-3.20, -1.47)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e-2.05 (-2.82, -1.28)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e-1.20 (-2.01, -0.39)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e-1.92 (-2.76, -1.09)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e-1.46 (-2.25, -0.68)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.90 (-1.84, 0.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e for trend\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.032\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal flavonoids\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e-1.61 (-2.49, -0.73)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e-1.51 (-2.33, -0.68)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.78 (-1.57, 0.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e-1.76 (-2.58, -0.94)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e-1.89 (-2.68, -1.09)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.87 (-1.61, -0.13)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e-1.68 (-2.66, -0.69)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e-1.36 (-2.29, -0.42)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.07 (-0.92, 0.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e-1.81 (-2.63, -0.99)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e-1.34 (-2.16, -0.52)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.74 (-1.64, 0.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e for trend\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.008\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.413\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\"\u003eAbbreviations: CI, confidence interval; Q1, quintile 1; Q2, quintile 2; Q3, quintile 3; Q4, quintile 4; Q5, quintile 5.\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\"\u003eModel 1: No adjustments were implemented; Model 2: Adjusted for gender, age, race, poverty income ratio, education level and marital status; Model 3: Adjusted for gender, age, race, PIR, education level, marital status, drinking, smoking, body mass index, physical activity, diabetes, chronic kidney disease and hypertension.\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\"\u003eData in bold type indicate the \u003cem\u003eP\u003c/em\u003e values below 0.05.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eSubgroup analysis\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eFor the purpose of investigating the association between total flavonoid, flavonoid subclasses intake and PhenoAgeAccel, subgroup analysis was conducted. We stratified by behavioral (drinking, smoking, BMI, physical activity) and health characteristics (participants with diabetes, chronic kidney disease and hypertension). The results are presented in supplementary Table 1-5. The Fig. 5A and Fig. 5B indicates a negative association between flavanones (\u003cem\u003eP\u003c/em\u003e for interaction = 0.047), flavones (\u003cem\u003eP\u003c/em\u003e for interaction = 0.012) intake and PhenoAgeAccel in participants without chronic kidney disease. In Fig. 6, the interaction was also observed in the participants with a BMI of 25-30 (\u003cem\u003eP\u003c/em\u003e for interaction = 0.044). However, no statistically significant results were found in the other flavonoid subclasses and subgroups. or without chronic kidney disease.\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this cross-sectional American population-based study, we investigated the association between dietary flavonoid intake and aging process. The results indicated that the total dietary flavonoid intake has a negative association with PhenoAgeAccel, and the similar association were observed in the four flavonoid subclasses (flavan-3-ols, flavanones, flavones and flavonols). Moreover, a nonlinear dose-response relationship was identified between flavonoid intake and PhenoAgeAccel. The mixed effect analyses indicated that flavones were the primary contributors to the deceleration of the aging process.\u003c/p\u003e\n\u003cp\u003ePrevious studies have indicated a beneficial association between flavonoid intake and delayed aging, which is consistent with our findings. A study from TwinsUK cohort suggests that increasing the intake of foods rich in flavonoids may potentially attenuate cognitive ageing[27]. Meanwhile, another cohort study indicates that women who have a higher intake of flavonoids during their middle age are more likely to experience better health and well-being in their later years[28]. Similarly, a study conducted among American adults found that flavonoid intake positively contributes to delaying the biological aging process[29]. Furthermore, additional research indicates that higher consumption of flavonoid-rich foods and beverages is associated with a lower all-cause mortality rate[30, 31]. In our study, we found that moderate dietary total flavonoid intake was associated with a younger phenotypic age. To date, the specific mechanisms by which flavonoids influence aging remain unclear, but they may involve the following mechanisms: Firstly, flavonoids could inhibit the formation of the senescence-associated secretory phenotype (SASP) and selectively eliminate senescent cells, thereby mitigating the aging process[32-34]. Secondly, due to the structural basis of flavonoids, they possess direct antioxidant activity which could directly scavenge ROS and upregulate antioxidant responses through the transcription factor NRF2 (Nuclear factor erythroid 2-related factor 2), which enhances proteasome activity and maintaining proteostasis to delay the aging process[35, 36]. Thirdly, flavonoids could inhibit the release of pro-inflammatory cytokines and reduce inflammation by modulating the MAPK and NF-\u0026kappa;B signaling pathways[37, 38]. Fourthly, flavonoids could further induce autophagy by modulating autophagy-related signaling pathways, including PI3K/Akt/mTOR and AMPK/mTOR. This regulation promotes the clearance of abnormal protein aggregates within cells, maintains cellular homeostasis, and prevents the deterioration of cellular function[39-41].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFurthermore, we investigated the association between flavonoid subclasses and PhenoAgeAccel by examining both single and mixed effects. In the single effect analysis, we found that moderate intake of flavanones and flavonols, as well as moderate to high intake of flavan-3-ols and flavones, is associated with a younger phenotypic age. Meanwhile, all of these subclasses exhibit a non-linear association with PhenoAgeAccel. Several studies supported our results, derivatives of flavan-3-ols, such as catechins, have been demonstrated to enhance the overall health and survival rate of aged mice fed a standard diet[42]. Additionally, epicatechin has been shown to ameliorate age-related degenerative changes in the neuromuscular system of mice[43].\u0026nbsp;Naringenin, a flavanone, has been shown to extend lifespan and slow down aging through the IIS and MAPK pathways in Caenorhabditis elegans[44].\u0026nbsp;Apigenin is a kind of flavones with great anti-aging capability, a vivo research shows that apigenin could prevents signs of skin aging[45]. Moreover, recent studies have demonstrated that apigenin could reduce the SASP in a human fibroblast strain induced to senescence by bleomycin[46]. As for flavonols, their subclass quercetin, which is abundant in many fruits, vegetables, leafy greens, seeds, and grains, has been widely applied as a senolytics, demonstrating potent efficacy in the treatment of various age-related diseases and in anti-aging[47-49]. In the mix effect analysis, the results of WQS regression model indicated that flavones had the primary effect on the mixture exposures, followed by flavanones, flavan-3-ols and flavonols, which consistent with the results of the single effect analysis. Interestingly, flavonols were the least significant contributors to the mixture exposures, although quercetin having demonstrated a potent effect in delaying senescence. This may be related to the low bioavailability and actual dietary intake concentration[50].\u003c/p\u003e\n\u003cp\u003eThe subgroup analysis indicated that participants without CKD are more likely to benefit from dietary flavanones and flavones intake in mitigating aging. This may be attributed to the fact that individuals without CKD generally exhibit better health, allowing their bodies to utilize the health benefits of flavonoids more efficiently. In contrast, the protective effects of flavonoids in individuals with CKD may be diminished due to the impact of disease[51, 52]. Meanwhile, the benefits of flavanone intake are more significant in participants with a lower BMI. Research shows that the metabolic status of high BMI population is different, which may affect the absorption and metabolic efficiency of flavonoids[53].\u003c/p\u003e\n\u003cp\u003eThere are several significant strengths exhibited in our study. To the best of our knowledge, this research is the first to examine the association between PhenoAgeAccel and dietary flavonoid intake. Further, our study included a substantial sample size of 5,391 participants and employed complex weight sampling, ensuring the representativeness of our findings for the overall adult population in the United States. Finally, we employed linear regression, RCS analyses, WQS regression and subgroup analysis to improve the reliability and robustness of our results. However, there are several limitations in our study. Firstly, a causal relationship between dietary flavonoid intake and aging cannot be determined because of the cross-sectional design. Secondly, dietary flavonoid intake data was collected by a two-day 24-hour dietary recall survey, which may lead to recall bias and might not reflect the long-term dietary intake habits of participants accurately. Thirdly, phenotypic age is calculated using multiple biomarkers. Although it has been shown to predict age-related diseases in different populations, it may differ from actual aging. Finally, our study population was derived from the NHANES database, so the generalizability of our findings to populations in other regions may be limited.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eTo summarize, our study suggests that dietary total flavonoid intake and the four flavonoid subclasses (including flavan-3-ols, flavanones, flavones and flavonols) is associated with a younger phenotypic age. Mixed effects analyses indicated that the anti-aging effects may primarily stem from flavones. Furthermore, participants without CKD are more likely to benefit from dietary flavanone and flavone intake in mitigating aging, while the benefits of flavanone intake are more significant in participants with a lower BMI. Our findings suggested that flavonoid-rich diets may be beneficial for delaying aging. However, due to the limitations of our study, further prospective studies are needed to validate the causal relationship of dietary flavonoid intake and aging.\u003c/p\u003e\n"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization: Jintao Zhong, Yixuan Wang; Data Curation: Biyu Wan, Mengya Wang; Formal analysis: Jiamin Fang, Pinli Lin; Writing \u0026ndash; original draft: Jintao Zhong; Writing \u0026ndash; review and editing: Jintao Zhong, Xiaona Tang. Funding acquisition: Lili Deng; Supervision: Xiaona Tang. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by Sanming Project of Medicine in Shenzhen (No.SZZYSM202206014) and Guangdong province Graduate Education Innovation Program\u0026nbsp;(2024XSLT_019).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted using publicly available data, which can be accessed at: https://www.cdc.gov/nchs/nhanes/.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe NHANES was approved by National center for Health Statistics Research Ethics Review Board, all participants provided informed consent.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThere are no conflicts to declare.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBellantuono I: \u003cstrong\u003eFind drugs that delay many diseases of old age.\u003c/strong\u003e \u003cem\u003eNATURE\u003c/em\u003e 2018, \u003cstrong\u003e554\u003c/strong\u003e(7692):293-295.\u003c/li\u003e\n\u003cli\u003eChang AY, Skirbekk VF, Tyrovolas S, Kassebaum NJ, Dieleman JL: \u003cstrong\u003eMeasuring population ageing: an analysis of the Global Burden of Disease Study 2017\u003c/strong\u003e. \u003cem\u003eThe Lancet Public Health\u003c/em\u003e 2019, \u003cstrong\u003e4\u003c/strong\u003e(3):e159-e167.\u003c/li\u003e\n\u003cli\u003eLevine ME, Lu AT, Quach A, Chen BH, Assimes TL, Bandinelli S, Hou L, Baccarelli AA, Stewart JD, Li Y\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eAn epigenetic biomarker of aging for lifespan and healthspan.\u003c/strong\u003e \u003cem\u003eAging\u003c/em\u003e 2018, \u003cstrong\u003e10\u003c/strong\u003e(4):573-591.\u003c/li\u003e\n\u003cli\u003eLiu Z, Kuo P, Horvath S, Crimmins E, Ferrucci L, Levine M: \u003cstrong\u003eA new aging measure captures morbidity and mortality risk across diverse subpopulations from NHANES IV: A cohort study.\u003c/strong\u003e \u003cem\u003ePLOS MED\u003c/em\u003e 2018, \u003cstrong\u003e15\u003c/strong\u003e(12):e1002718.\u003c/li\u003e\n\u003cli\u003eKennedy BK, Berger SL, Brunet A, Campisi J, Cuervo AM, Epel ES, Franceschi C, Lithgow GJ, Morimoto RI, Pessin JE\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eGeroscience: linking aging to chronic disease.\u003c/strong\u003e \u003cem\u003eCELL\u003c/em\u003e 2014, \u003cstrong\u003e159\u003c/strong\u003e(4):709-713.\u003c/li\u003e\n\u003cli\u003eFerrucci L, Gonzalez-Freire M, Fabbri E, Simonsick E, Tanaka T, Moore Z, Salimi S, Sierra F, de Cabo R: \u003cstrong\u003eMeasuring biological aging in humans: A quest.\u003c/strong\u003e \u003cem\u003eAGING CELL\u003c/em\u003e 2020, \u003cstrong\u003e19\u003c/strong\u003e(2):e13080.\u003c/li\u003e\n\u003cli\u003eKuo C, Pilling LC, Liu Z, Atkins JL, Levine ME: \u003cstrong\u003eGenetic associations for two biological age measures point to distinct aging phenotypes.\u003c/strong\u003e \u003cem\u003eAGING CELL\u003c/em\u003e 2021, \u003cstrong\u003e20\u003c/strong\u003e(6):e13376.\u003c/li\u003e\n\u003cli\u003eLevine ME: \u003cstrong\u003eModeling the rate of senescence: can estimated biological age predict mortality more accurately than chronological age?\u003c/strong\u003e \u003cem\u003eThe journals of gerontology. 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\u003cstrong\u003e128\u003c/strong\u003e(4):577-591.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nutrition-journal","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nutj","sideBox":"Learn more about [Nutrition Journal](http://nutritionj.biomedcentral.com/)","snPcode":"12937","submissionUrl":"https://submission.nature.com/new-submission/12937/3","title":"Nutrition Journal","twitterHandle":"@NutrJournal","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Flavonoid, Aging, Flavones, NHANES","lastPublishedDoi":"10.21203/rs.3.rs-4790160/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4790160/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eFlavonoids are believed to have potential anti-aging effects due to their anti-inflammatory and antioxidant properties. However, the effectiveness of dietary flavonoids and their subclasses in delaying aging has yet to be confirmed. Our study intends to examine relationship between them.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eData from three survey cycles (2007\u0026ndash;2008, 2009\u0026ndash;2010, and 2017\u0026ndash;2018) of the National Health and Nutrition Examination Survey (NHANES) was used to investigate the relationship between PhenoAgeAccel and dietary flavonoid intake. Weighted linear regression was conducted to evaluate the relationship between dietary flavonoid intake and PhenoAgeAccel, and the dose-response relationship was investigated by limited cubic spline (RCS) analysis. Mixed effects were explored using weighted quantile sum (WQS) regression. Further, the subgroup analyses were also conducted.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA total of 5391 participants were included, after multivariable adjustments, a negative association was found with total dietary flavonoid, flavan-3-ols, flavanone, flavones and flavonols with a β (95% CI) of -0.87 ( -1.61, -0.13), -0.83 (-1.95, -0.08), -1.18 (-1.98, -0.39), -1.64 (-2.52, -0.77) and \u0026minus;\u0026thinsp;1.18 (-1.98, -0.39) for the higher quintile compared to the lowest quintile. The RCS analysis show a non-linear relationship between flavan-3-ols (\u003cem\u003eP\u003c/em\u003e for nonlinear\u0026thinsp;=\u0026thinsp;0.024), flavanones (\u003cem\u003eP\u003c/em\u003e for nonlinear\u0026thinsp;=\u0026thinsp;0.005), flavones (\u003cem\u003eP\u003c/em\u003e for nonlinear\u0026thinsp;\u0026lt;\u0026thinsp;0.001), flavonols (\u003cem\u003eP\u003c/em\u003e for nonlinear\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and total flavonoid intake (\u003cem\u003eP\u003c/em\u003e for nonlinear\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and PhenoAgeAccel. WQS regression indicated that flavones had the primary effect on the mixture exposures (52.72%). Finally, the subgroup analysis indicated that participants without chronic kidney disease are more likely to benefit from dietary flavanone and flavone intake in mitigating aging, while the benefits of flavanone intake are more significant in participants with a lower body mass index.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eOur study suggested that dietary flavonoid intake is negatively associated with accelerating aging in adults of American, and the most influential ones are flavones, flavanones, flavan-3-ols and flavonols.\u003c/p\u003e","manuscriptTitle":"Dietary flavonoid intake is negatively associated with accelerating aging: an American population-based cross-sectional study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-27 11:42:42","doi":"10.21203/rs.3.rs-4790160/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-11-13T08:18:11+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-11-13T08:16:22+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"148322752658596487507992304949133146426","date":"2024-11-13T08:12:31+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"86810133760329972406577371929149414098","date":"2024-08-08T02:09:23+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-08-01T02:29:09+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-07-30T00:09:31+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-07-25T09:03:25+00:00","index":"","fulltext":""},{"type":"submitted","content":"Nutrition Journal","date":"2024-07-23T16:03:29+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"nutrition-journal","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nutj","sideBox":"Learn more about [Nutrition Journal](http://nutritionj.biomedcentral.com/)","snPcode":"12937","submissionUrl":"https://submission.nature.com/new-submission/12937/3","title":"Nutrition Journal","twitterHandle":"@NutrJournal","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ecd888d3-37b1-4602-a65d-934643710b48","owner":[],"postedDate":"August 27th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-12-23T16:07:46+00:00","versionOfRecord":{"articleIdentity":"rs-4790160","link":"https://doi.org/10.1186/s12937-024-01052-x","journal":{"identity":"nutrition-journal","isVorOnly":false,"title":"Nutrition Journal"},"publishedOn":"2024-12-20 15:57:52","publishedOnDateReadable":"December 20th, 2024"},"versionCreatedAt":"2024-08-27 11:42:42","video":"","vorDoi":"10.1186/s12937-024-01052-x","vorDoiUrl":"https://doi.org/10.1186/s12937-024-01052-x","workflowStages":[]},"version":"v1","identity":"rs-4790160","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4790160","identity":"rs-4790160","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}