Abstract
Background: Dietary flavonoids have been related to lower risks of various chronic diseases, but it is unclear whether flavonoid intake in midlife helps to maintain good health and wellbeing in aging.
Objective: We examined the relation of flavonoid intake in midlife with the prevalence of healthy aging.
Design: We included 13,818 women from the Nurses’ Health Study with dietary data and no major chronic diseases in 1984–1986 when they were aged in their late 50s (median age: 59 y); all women provided information on multiple aspects of aging an average of 15 y later. Intakes of 6 major flavonoid subclasses in midlife were ascertained on the basis of averaged intakes of flavonoid-rich foods from 2 food-frequency questionnaires (1984–1986). We defined healthy compared with usual aging as of age 70 y; healthy aging was based on survival to ≥70 y with maintenance of 4 health domains (no major chronic diseases or major impairments in cognitive or physical function or mental health).
Results: Of women who survived until ≥70 y of age, 1517 women (11.0%) met our criteria for healthy aging. Compared with women in the lowest quintile of intake, women in the highest quintile of intake of several flavonoid subclasses at midlife had greater odds of healthy aging. After multivariable adjustment, ORs were as follows: flavones, 1.32 (95% CI: 1.10, 1.58); flavanone, 1.28 (95% CI: 1.08, 1.53); anthocyanin, 1.25 (95% CI: 1.04, 1.50); and flavonol, 1.18 (95% CI: 0.98, 1.42) (all P-trend ≤ 0.02). Consistently, greater intakes of major sources of these flavonoids (i.e., oranges, berries, onions, and apples) were associated with increased odds of healthy aging. We showed no association with flavan-3-ol monomers (P-trend = 0.80) or polymers (P-trend = 0.63).
Conclusion: Higher intake of flavonoids at midlife, specifically flavones, flavanones, anthocyanins, and flavonols, is associated with greater likelihood of health and wellbeing in individuals surviving to older ages.
Keywords: cohort studies, flavonoid, healthy aging, risk factors in epidemiology, public health
INTRODUCTION
Finding strategies to maintain health and wellbeing in aging populations represents a major challenge (1). Because the development of many chronic diseases and conditions associated with aging involve oxidative pathways, approaches to reducing oxidative stress, e.g., via greater consumption of dietary antioxidants (2), could be important to promoting healthy aging. Specifically, greater intakes of various subclasses of flavonoids, which are bioactive phenolic compounds provided by plant foods, have benefits on endothelial function and blood pressure (3–5), and these effects might be attributed to their antioxidant role and anti-inflammatory properties (6). More-recent experimental studies have also established that some dietary polyphenols may modulate memory and learning by increasing concentrations of brain-derived neurotrophic factor in the hippocampus (7) and lower anxiety, depression, and age-related cognitive deficits (8, 9).
In epidemiologic studies, flavonoids and flavonoid-containing foods have been associated with lower risks of fatal or nonfatal cardiovascular disease (10–13), hypertension (4, 14), stroke (15–17), cancer (18), diabetes (19, 20) and neurodegenerative diseases (21–23). Hence, overall, there is a strong biological and epidemiologic rationale for a role of flavonoids in healthy aging, despite the limited research in this area.
Therefore, we investigated the relation of the intake of major subclasses of flavonoids at midlife (because midlife factors may be critical to preventing chronic conditions of aging which generally evolve over decades) to healthy aging in women from the Nurses’ Health Study. As in previous publications (24–26), we defined healthy aging as surviving to older ages free of major chronic diseases and maintaining good cognitive, physical, and mental health.
SUBJECTS AND METHODS
Nurses’ Health Study
The Nurses’ Health Study began in 1976 when 121,700 female registered nurses aged 30–55 y, who were residing in 11 US states, completed a mailed questionnaire about their medical history, health, and lifestyle. Follow-up questionnaires are sent every 2 y; follow-up of the cohort remains >90% complete with 99% follow-up for mortality. In 1980, participants completed a 61-item semiquantitative food-frequency questionnaire (FFQ)4 (27). In 1984, the FFQ was expanded to 116 items, and similar FFQs were sent in 1986 and every 4 y thereafter. In 1992, 1996, and 2000, the Medical Outcomes Study Short-Form-36 (SF-36) was administered, which is a 36 item-questionnaire that evaluates 8 health concepts including mental health and physical functioning (28). From 1995 to 2001, a cognitive study was initiated in 21,202 participants who were ≥70 y old at that time and free of stroke. The study was approved by the Institutional Review Board of Brigham and Women's hospital (Boston, MA).
Ascertainment of flavonoid intake
We used 1984 and 1986 FFQs to ascertain intake of flavonoid-rich foods at midlife (except onion intake, which was determined by using the 1990 FFQ, which was the first questionnaire to include a specific question on onions). We computed midlife intakes of flavonoid subclasses by averaging intakes from1984 and 1986 dietary assessments (or 1990 when relevant).
On the FFQs, participants were asked how often, on average, they consumed a standard portion size of each food. The reliability and validity of the FFQ has been described elsewhere in detail (29).
Intakes of flavonoid-rich foods were converted into different flavonoid subclasses by multiplying the consumption of each food by its flavonoid content. In this study, we focused on the following 6 main subclasses commonly consumed in the US diet: flavonols (quercetin, kaempferol, myricetin, and isohamnetin), flavones (luteolin and apigenin), flavanones (eriodictyol, hesperetin, and naringenin), flavan-3-ol monomers (catechins and epicatechins), flavan-3-ol polymers (including proanthocyanidins, theflavins, and thearubigins), and anthocyanins (cyanidin, delphinidin, malvidin, pelargonin, petunidin, and peonidin). Flavonoid contents of foods were obtained from a specific database constructed previously by using, as a primary source, the updated and expanded USDA flavonoid contents of foods and proanthocyanidin databases (30, 31). Moreover, for foods in the FFQ for which there were no values available in the USDA database, we searched a European database (EuroFIR eBASIS; http://www.eurofir.org) and other sources to ensure all available high-quality data on flavonoid values were included in the database. Thus, we were able to quantify a broad range of flavonoid subclass intakes more robustly than in previous flavonoid-based analyses (32).
Ascertainment of covariates
Sociodemographic, lifestyle, and health-related covariates (e.g., age, nurse's education, husband's education, marital status, father's and mother's occupations when the nurse was 16 y old, family histories of diabetes, cancer, and myocardial infarction, physical activity, smoking, multivitamin and aspirin use, BMI (in kg/m2), history of high blood pressure, and history of hypercholesterolemia) were obtained from biennial questionnaires. Because the covariates of husband's education and father's and mother's occupations did not influence primary relations of diet to healthy aging in multivariable models, these covariates were not controlled for in the results presented in this article. Median annual household income and home value were estimated from the census tract of each participant's residence geocoded to the 2000 US Census. We averaged energy intakes across 1984 and 1986 questionnaires to represent the mean exposure at baseline. In addition, the overall diet quality was evaluated by using the Alternate Healthy Eating Index-2010, which reflects Dietary Guidelines for Americans and incorporates recent knowledge on nutrients and chronic disease risk (33); we averaged scores between 1984 and 1986 FFQs to represent he midlife diet quality. Physical activity and histories of smoking, hypercholesterolemia, and hypertension were ascertained in 1986. All other covariates were determined at the time of first dietary assessment (i.e., in 1984), when these data were available. Thus, we had information on demographic, lifestyle, health, socioeconomic, and dietary variables to carefully control for potential confounding.
Ascertainment of healthy aging
We separated healthy aging from usual aging on the basis of 4 health domains as of 2000 when the simultaneous assessment of mental, physical, and cognitive function was completed for participants aged ≥70 y. Of subjects who survived to age ≥70 y as of 2000, we considered individuals to be healthy agers if they were free of 11 major chronic diseases with no impairment in cognitive function, no physical disabilities, and intact mental health (24–26); all remaining women were considered usual agers.
Assessment of chronic diseases
The prevalence of the 11 chronic diseases of interest as of 2000 (i.e., cancer other than nonmelanoma skin cancer, type 2 diabetes, myocardial infarction, coronary artery bypass surgery, or percutaneous transluminal coronary angioplasty, congestive heart failure, stroke, kidney failure, chronic obstructive pulmonary disease, Parkinson's disease, multiple sclerosis, and amyotrophic lateral sclerosis) was determined from biennial questionnaires. In this cohort of nurses, we have shown a high validity of self-reported information on health (34–37).
Assessment of cognitive function
Cognitive testing was performed by trained interviewers by using validated telephone interviews in all women aged ≥70 y as of 2000 who were free of stroke. For these analyses, we focused on scores from the Telephone Interview of Cognitive Status to evaluate global cognitive performance in our participants. The Telephone Interview of Cognitive Status (38) (range: 0–41 points) is a telephone adaptation of the Mini-Mental State Examination (39). To construct the healthy aging outcome, we considered a score >31 as the absence of cognitive impairment according to standard criteria (40).
Assessment of physical function and mental health
We identified the impairment of physical function as the presence of any of the following limitations: 1) limited at least a little on moderate activities as assessed by the SF-36 (such as moving a table, bowling, or pushing a vacuum cleaner, climbing one flight of stairs, walking >1 mile or walking several blocks, and bathing or dressing); or 2) limited a lot on more-difficult items (such as running, lifting heavy objects, lifting or carrying groceries, climbing several flights of stairs; bending, kneeling, or stooping).
In addition, on the basis of answers to the SF-36 Mental Health Index (MHI) (41), we constructed a mental health score (range: 0–100 points) (24). Good mental health was defined as an MHI score >84 (the median value in the analytic cohort).
Population for analysis
Of 19,415 nurses who participated in the cognitive function substudy, we excluded 2671 women with a history of the 11chronic diseases previously listed as of 1986 and 2596 women with no dietary data in 1984 and 1986. We further excluded 43 nurses who did not complete the SF-36 and 287 women who skipped >2 items on the MHI or >5 items on the physical function scale, which left 13,818 participants available for analyses.
Statistical analyses
We assessed flavonoid intake when women were aged in their late 50s and very early 60s (median age: 59 y; upper and lower quartiles: 57 and 61 y, respectively) for several reasons. First, most chronic diseases and health conditions develop over many years, and thus, midlife risk factors are likely a key determinant of health in older ages. Second, imposing a long lag period between the ascertainment of flavonoid intakes and determination of healthy aging (average follow-up: 15.2 y) minimizes the possibility of reverse causation (i.e., an effect of disease or its treatment on dietary choices).
With the use of logistic regression, we estimated the odds of healthy aging compared with usual aging according to quintiles of mean intake of each flavonoid subclass across 1984–1986. Very few data were missing; for nurses’ education and physical activity, data were missing for >4% of the sample (4.6% and 7.9%, respectively), thus, specific missing categories were created. We examined linear trends across quintiles of flavonoid intake by using a continuous variable in which participants in a given category were assigned the median value. All P values were 2 sided. Data were analyzed with the SAS software package version 9.1 (SAS Institute).
We conducted a series of secondary analyses. First, to identify whether associations with dietary flavonoids might be attributable to specific healthy aging domains, we modeled separately relations between flavonoids and each of the 4 healthy aging components. We also secondarily examined top dietary sources of flavonoid subclasses in relation to healthy aging (i.e., citrus fruit, onions, apples, tea, and berries, which accounted for >80% of the between-person variation in total flavonoid consumption in this cohort). In another set of secondary analyses, we examined the robustness of our definition of healthy aging; because there is no standard definition, we investigated alternative classifications. Specifically, we tried by using a more stringent definition for mental health limitations such that impairment in mental health was defined as an MHI score ≤60 [a standard cutoff for major depression in older populations (42)].
RESULTS
Of 13,818 participants in our study, 12,403 subjects (89.8%) had no cognitive impairment, 9565 subjects (69.2%) had none of the 11 chronic diseases in our definition of healthy aging, 5947 subjects (43.0%) had no mental health limitations, and 3750 subjects (27.1%) had no impairment of physical function. Overall, 1517 subjects (11.0%) were considered healthy agers, whereas the remaining 12,301 participants (89.0%) were considered usual agers (Table 1).
TABLE 1.
Age-standardized baseline characteristics (in 1984–1986) of healthy agers and usual agers in the Nurses’ Health Study1
| Healthy agers (n = 1517) | Usual agers (n = 12,301) | |
| Age at baseline,2 y | 58.6 ± 2.53 | 59.1 ± 2.5 |
| Education, % | ||
| Associate's degree | 73 | 78 |
| Bachelor's degree | 18 | 15 |
| Graduate degree | 9 | 6 |
| Husband's education, % | ||
| High school degree or less | 47 | 52 |
| College degree | 29 | 28 |
| Graduate school | 24 | 20 |
| Marital status, % | ||
| Married | 93 | 93 |
| Widowed | 5 | 5 |
| Separated/divorced | 2 | 3 |
| BMI, % | ||
| <22 kg/m2 | 34 | 22 |
| 22–24.9 kg/m2 | 38 | 33 |
| 25–29.9 kg/m2 | 24 | 32 |
| ≥30 kg/m2 | 3 | 13 |
| Smoking, % | ||
| Never | 54 | 47 |
| Former | 35 | 36 |
| Current | 11 | 17 |
| Physical activity, metabolic equivalent task-hours/wk | 19.3 ± 22.2 | 14.1 ± 19.8 |
| Energy intake, kcal/d | 1701 ± 489 | 1736 ± 484 |
| Alcohol intake, g/d | 7.2 ± 10.6 | 6.7 ± 10.5 |
| Regular aspirin use, % | ||
| <1 tablet/wk | 37 | 31 |
| 1–2 tablets/wk | 38 | 32 |
| >2 tablets/wk | 25 | 37 |
| Multivitamin use, % | 57 | 61 |
| History of high blood pressure, % | 21 | 32 |
| History of hypercholesterolemia, % | 12 | 17 |
| Family history of diabetes, % | 26 | 29 |
| Family history of cancer, % | 16 | 18 |
| Family history of myocardial infarction, % | 16 | 18 |
Values were standardized to the age distribution of the study population. Percentages are of nonmissing values.
Value was not age standardized.
Mean ± SD (all such values).
Of usual agers, multiple health domains were generally impaired. For example, 31.5% of usual agers had both chronic diseases and limitations in cognitive, physical, or mental health; 65.4% of usual agers had limitations in cognitive, physical, or mental health only, whereas 3.1% of usual agers had one or more chronic diseases only (data not shown in tables). After standardization for age, compared with usual agers, healthy agers had a lower prevalence of obesity and smoking, exercised more, and reported higher alcohol intake at midlife. Healthy agers also had a lower prevalence of hypertension and hypercholesterolemia than usual agers (Table 1).
When we considered flavonoid intake at midlife, healthy agers had higher baseline intakes of all subclasses of flavonoids (e.g., age-standardized average flavonol and anthocyanin intakes were 20.1 and 14.3 mg/d, respectively) compared with those of usual agers (19.1 and 12.2 mg/d, respectively) (Table 2). The overall diet, as reflected by the Alternate Healthy Eating Index-2010 score, was also of higher quality in healthy agers.
TABLE 2.
Mean (±SD) age-standardized baseline flavonoid intake and overall diet (in 1984–1986) of healthy agers and usual agers in the Nurses’ Health Study1
| Intake | Healthy agers (n = 1517) | Usual agers (n = 12,301) |
| Flavonols,2 mg/d | 20.1 ± 12.8 | 19.1 ± 12.6 |
| Flavones, mg/d | 2.4 ± 1.3 | 2.2 ± 1.2 |
| Flavanones, mg/d | 52.2 ± 36.9 | 47.0 ± 33.3 |
| Flavan-3-ol monomers, mg/d | 54.0 ± 64.6 | 51.9 ± 62.2 |
| Flavan-3-ol polymers, mg/d | 233 ± 219 | 219 ± 211 |
| Anthocyanins, mg/d | 14.3 ± 16.9 | 12.2 ± 12.1 |
| Total flavonoids,2 mg/d | 376 ± 298 | 352 ± 282 |
| Alternative Healthy Eating Index-2010 score | 53.0 ± 10.4 | 50.6 ± 10.1 |
All values are average of intakes from 1984 and 1986 dietary assessments, except for flavonols, of which intakes were ascertained in 1990.
Values are given for the subgroup of women with flavonols ascertained in 1990 (i.e., 11,040 usual agers and 1401 healthy agers).
Flavonoid intake at midlife and odds of healthy aging
In age-adjusted analyses of flavonoid intake at midlife and healthy aging, greater intakes of most flavonoid subclasses (e.g., flavonols, flavones, flavanones, flavan-3-ol polymers, and anthocyanins), as well as total flavonoids, were related to better odds of healthy aging (all P-trend across quintiles < 0.05; Table 3). For example, the odds of healthy compared with usual aging was 1.47 (95% CI: 1.22, 1.76) for women in the upper compared with lower quintiles of total flavonoid intake. We showed no significant association with flavan-3-ol monomers (P-trend = 0.33).
TABLE 3.
ORs (95% CIs) of healthy aging, according to quintiles of flavonoid intakes at midlife1
| Flavonoid intake |
||||||
| Quintile 1 | Quintile 2 | Quintile 3 | Quintile 4 | Quintile 5 | P-trend | |
| Flavonols, mg/d | 7.7 (6.2–8.8)2 | 11.6 (10.7–12.5) | 15.4 (14.4–16.7) | 21.7 (19.7–24.1) | 35.9 (31.2–43.2) | — |
| Healthy ager, n | 243 | 272 | 267 | 308 | 311 | — |
| Age adjusted | Reference | 1.13 (0.95, 1.36) | 1.11 (0.93, 1.34) | 1.32 (1.10, 1.57) | 1.35 (1.13, 1.61) | <0.001 |
| Multivariable adjusted3 | Reference | 1.01 (0.84, 1.22) | 0.95 (0.78, 1.15) | 1.13 (0.94, 1.36) | 1.18 (0.98, 1.42) | 0.02 |
| Flavones, mg/d | 0.9 (0.6–1.0) | 1.5 (1.4–1.7) | 2.1 (2.0–2.3) | 2.7 (2.5–2.9) | 3.7 (3.4–4.4) | — |
| Healthy ager, n | 242 | 287 | 300 | 325 | 363 | — |
| Age adjusted | Reference | 1.22 (1.02, 1.46) | 1.30 (1.09, 1.55) | 1.41 (1.18, 1.68) | 1.61 (1.36, 1.91) | <0.001 |
| Multivariable adjusted3 | Reference | 1.11 (0.92, 1.33) | 1.15 (0.95, 1.38) | 1.19 (0.99, 1.44) | 1.32 (1.10, 1.58) | 0.002 |
| Flavanones, mg/d | 9.0 (5.0–13.4) | 26.8 (22.5–31.0) | 43.1 (39.2–46.9) | 60.0 (55.4–64.9) | 89.8 (78.3–108.2) | — |
| Healthy ager, n | 260 | 276 | 310 | 298 | 373 | — |
| Age adjusted | Reference | 1.07 (0.90, 1.28) | 1.22 (1.03, 1.46) | 1.18 (0.99, 1.41) | 1.53 (1.29, 1.81) | <0.001 |
| Multivariable adjusted3 | Reference | 1.00 (0.83, 1.20) | 1.11 (0.93, 1.33) | 1.05 (0.87, 1.26) | 1.28 (1.08, 1.53) | 0.003 |
| Flavan-3-ol monomers, mg/d | 9.2 (6.9–11.3) | 16.6 (14.7–18.7) | 27.7 (24.0–32.8) | 54.5 (46.0–64.3) | 128.2 (96.6–178.0) | — |
| Healthy ager, n | 255 | 326 | 319 | 305 | 312 | — |
| Age adjusted | Reference | 1.32 (1.11, 1.57) | 1.30 (1.09, 1.55) | 1.23 (1.03, 1.47) | 1.26 (1.06, 1.50) | 0.33 |
| Multivariable adjusted3 | Reference | 1.11 (0.92, 1.33) | 1.07 (0.89, 1.28) | 1.03 (0.86, 1.24) | 1.04 (0.87, 1.25) | 0.80 |
| Flavan-3-ol polymers, mg/d | 55 (41–67) | 100 (89–112) | 152 (138–169) | 238 (211–271) | 478 (377–641) | — |
| Healthy ager, n | 251 | 307 | 319 | 315 | 325 | — |
| Age adjusted | Reference | 1.27 (1.06, 1.51) | 1.32 (1.11, 1.57) | 1.30 (1.09, 1.55) | 1.35 (1.13, 1.60) | 0.02 |
| Multivariable adjusted3 | Reference | 1.11 (0.92, 1.33) | 1.11 (0.92, 1.33) | 1.05 (0.88, 1.27) | 1.10 (0.92, 1.32) | 0.63 |
| Anthocyanins, mg/d | 2.8 (1.8–3.6) | 5.6 (4.9–6.4) | 9.4 (8.3–10.6) | 14.5 (13.1–16.0) | 24.2 (20.4–32.1) | — |
| Healthy ager, n | 242 | 262 | 289 | 342 | 382 | — |
| Age adjusted | Reference | 1.10 (0.91, 1.32) | 1.23 (1.02, 1.47) | 1.47 (1.24, 1.75) | 1.69 (1.43, 2.01) | <0.001 |
| Multivariable adjusted3 | Reference | 0.97 (0.80, 1.18) | 1.04 (0.86, 1.26) | 1.18 (0.98, 1.42) | 1.25 (1.04, 1.50) | 0.002 |
| Total flavonoids, mg/d | 124 (99–144) | 194 (178–211) | 269 (248–291) | 383 (348–428) | 705 (568–918) | — |
| Healthy ager, n | 221 | 281 | 291 | 299 | 309 | — |
| Age adjusted | Reference | 1.32 (1.09, 1.59) | 1.38 (1.15, 1.66) | 1.42 (1.18, 1.71) | 1.47 (1.22, 1.76) | 0.001 |
| Multivariable adjusted3 | Reference | 1.14 (0.94, 1.38) | 1.12 (0.92, 1.35) | 1.12 (0.92, 1.36) | 1.18 (0.97, 1.42) | 0.22 |
ORs >1 denote greater odds of healthy aging. These analyses included 1517 healthy agers compared with 12,301 usual agers for all flavonoid subclasses except flavonols and total flavonoids, for which analyses included 1401 healthy agers and 11,036 usual agers (e.g., women with flavonols ascertained in 1990).
Median; IQR in parentheses (all such values).
Logistic regression models were adjusted for age (y); education (associate's degree, bachelor's degree, graduate degree); marriage status (married, widowed, or separated/divorced); median income (quintiles); median house value (quintiles); family histories of diabetes, cancer, and myocardial infarction (yes or no); physical activity (quintiles of metabolic equivalent task-hours); energy intake (quintiles of kcal); smoking (never smoker, past smoker of 1–14, 15–24, or ≥25 cigarettes/d, or current smoker of 1–14, 15–24, or ≥25 cigarettes/d); multivitamin use (yes or no); aspirin use (<1, 1–2, or >2 tablets/wk); BMI (in kg/m2; <22, 23–24, 25–29, or ≥30); history of high blood pressure (yes or no); history of hypercholesterolemia (yes or no); and Alternative Healthy Eating Index-2010 score (quintiles).
In multivariable analyses, greater intakes of the majority of flavonoid classes (flavonols, flavones, flavanones, and anthocyanins) were related to better odds of healthy aging, with trends of increasingly better odds of healthy aging with increasing quintile of intake (all P-trend ≤ 0.02; Table 3). However, associations for specific quintile comparisons were generally significant in only the highest compared with lowest quintiles of intake. For example, compared with women in the lowest quintile of intake, women in the highest quintiles of flavone (32%; 95% CI: 10%, 58%), flavanone (28%; 95% CI: 8%, 53%), anthocyanin (25%; 95% CI: 4%, 50%), and flavonol (18%; 95% CI: −2%, 42%) intakes at midlife had greater odds of healthy aging after multivariable adjustment.
In secondary analyses of each component of healthy aging, flavones and flavanones were significantly associated with 2 of 4 domains in our definition of healthy aging (i.e., absence mental health limitations and impairment of physical function; all P-trend < 0.005; Table 4), although associations were generally weaker than for overall healthy aging. For example, compared with women in the lowest quintile, women in the highest quintiles of flavone and flavanone intakes had 32% (95% CI: 18%, 47%) and 24% (95% CI: 11%, 38%), respectively, better odds of no mental health limitations and 23% (95% CI: 8%, 40%) and 15% (95% CI: 1%, 31%), respectively, better odds of no physical function limitations. Flavonol and anthocyanin intakes at midlife were marginally associated with an absence of mental health limitations and of impairment of physical function, respectively. For example, compared with the lowest quintile, women in the highest quintile of flavonol intake had 9% (95% CI: −2%, 23%) better odds of no limitation in mental health, and women with highest anthocyanin intake had 14% (95% CI: 0%, 30%) better odds of no physical function limitations. Greater flavonoid intake in midlife was not significantly related to odds of no chronic diseases or cognitive impairment (all P-trend ≥ 0.17; Table 4). In secondary analyses that examined a more stringent definition of mental health limitations (i.e., with impaired mental health defined as an MHI score ≤60), results were generally similar (data not shown in tables), which suggested that findings were robust to variations in this cutoff.
TABLE 4.
ORs (95% CIs) of each component of healthy aging according to quintiles of flavonoid intakes at midlife1
| Quintile 1 | Quintile 2 | Quintile 3 | Quintile 4 | Quintile 5 | P-trend | |
| Flavonols | ||||||
| Healthy aging component: | ||||||
| No chronic disease | Reference | 1.05 (0.93, 1.19) | 1.12 (0.99, 1.27) | 1.11 (0.98, 1.26) | 1.00 (0.89, 1.13) | 0.75 |
| No cognitive impairment (TICS score ≥31) | Reference | 0.85 (0.70, 1.02) | 0.90 (0.74, 1.09) | 0.88 (0.72, 1.06) | 0.92 (0.76, 1.11) | 0.82 |
| No impairment of physical function | Reference | 1.03 (0.90, 1.18) | 0.95 (0.83, 1.09) | 1.19 (1.04, 1.36) | 1.07 (0.94, 1.23) | 0.10 |
| No limitation of mental health (MHI score <84) | Reference | 1.00 (0.90, 1.13) | 0.97 (0.87, 1.09) | 1.06 (0.95, 1.19) | 1.09 (0.98, 1.23) | 0.04 |
| Flavones | ||||||
| No chronic disease | Reference | 1.02 (0.90, 1.14) | 1.06 (0.94, 1.19) | 1.02 (0.91, 1.15) | 1.09 (0.97, 1.23) | 0.17 |
| No cognitive impairment (TICS score ≥31) | Reference | 1.00 (0.84, 1.20) | 1.04 (0.87, 1.24) | 1.15 (0.96, 1.38) | 1.00 (0.84, 1.20) | 0.63 |
| No impairment of physical function | Reference | 1.09 (0.95, 1.24) | 1.12 (0.98, 1.27) | 1.09 (0.96, 1.25) | 1.23 (1.08, 1.40) | 0.004 |
| No limitation of mental health (MHI score <84) | Reference | 1.08 (0.96, 1.20) | 1.18 (1.05, 1.31) | 1.21 (1.08, 1.35) | 1.32 (1.18, 1.47) | <0.001 |
| Flavanones | ||||||
| No chronic disease | Reference | 1.07 (0.96, 1.21) | 1.14 (1.01, 1.28) | 1.07 (0.95, 1.21) | 1.10 (0.97, 1.24) | 0.20 |
| No cognitive impairment (TICS score ≥31) | Reference | 1.24 (1.04, 1.48) | 1.14 (0.96, 1.36) | 1.28 (1.07, 1.53) | 1.06 (0.90, 1.26) | 0.61 |
| No impairment of physical function | Reference | 0.89 (0.78, 1.01) | 1.03 (0.91, 1.17) | 0.97 (0.86, 1.11) | 1.15 (1.01, 1.31) | 0.005 |
| No limitation of mental health (MHI score <84) | Reference | 1.09 (0.98, 1.22) | 1.15 (1.03, 1.28) | 1.15 (1.03, 1.28) | 1.24 (1.11, 1.38) | <0.001 |
| Anthocyanins | ||||||
| No chronic disease | Reference | 0.99 (0.88, 1.11) | 0.93 (0.83, 1.05) | 1.06 (0.94, 1.20) | 1.02 (0.90, 1.16) | 0.36 |
| No cognitive impairment (TICS score ≥31) | Reference | 1.02 (0.85, 1.21) | 1.12 (0.93, 1.34) | 1.07 (0.89, 1.28) | 1.04 (0.86, 1.25) | 0.74 |
| No impairment of physical function | Reference | 0.96 (0.84, 1.09) | 0.96 (0.84, 1.09) | 1.01 (0.89, 1.15) | 1.14 (1.00, 1.30) | 0.009 |
| No limitation of mental health (MHI score <84) | Reference | 1.03 (0.92, 1.14) | 1.08 (0.96, 1.20) | 1.08 (0.97, 1.21) | 1.09 (0.97, 1.23) | 0.12 |
ORs >1 denote greater odds of the healthy aging component. For flavones, flavanones, and anthocyanins (all ascertained in 1984–1986), analyses included comparison of 9565 women with no chronic disease and 4253 women with at least one chronic disease; 12,403 women with an absence of cognitive impairment and 1415 women with presence of cognitive impairment; 3750 women with unimpaired physical function and 10,068 women with impaired physical function; and 5947 women with no limitation and 7871 women with a limitation of mental health. For flavonols (ascertained in 1990), analyses included comparison of 8634 women with no chronic disease and 3807 women with at least one chronic disease; 11,195 women with an absence of cognitive impairment and 1246 women with a presence of cognitive impairment; 3418 women with unimpaired physical function and 9023 women with impaired physical function; and 5403 women with no limitation and 7038 women with a limitation of mental health. Logistic regression models were adjusted for age (y); education (associate's degree, bachelor's degree, graduate degree); marriage status (married, widowed, or separated/divorced); median income (quintiles); median house value (quintiles); family histories of diabetes, cancer, and myocardial infarction (yes or no); physical activity (quintiles of metabolic equivalent task-hours); energy intake (quintiles of kcal); smoking (never smoker, past smoker of 1–14, 15–24, or ≥25 cigarettes/d, or current smoker of 1–14, 15–24, or ≥25 cigarettes/d); multivitamin use (yes or no); aspirin use (<1, 1–2, or >2 tablets/wk); BMI (in kg/m2; <22, 23–24, 25–29, or ≥30); history of high blood pressure (yes or no); history of hypercholesterolemia (yes or no); and Alternative Healthy Eating Index-2010 score (quintiles). MHI, Mental Health Index; TICS, Telephone Interview for Cognitive Status.
Intake of dietary sources of flavonoids at midlife and odds of healthy aging
We further examined major dietary sources of flavones, flavanones, anthocyanins, and flavonols (i.e., the flavonoid subclasses significantly associated with healthy aging in the main analysis). Citrus fruit were main food sources of flavones and flavanones in our cohort (23), and when we examined the relation of citrus intake (fresh fruit and juices) at midlife to healthy aging, we showed that higher intake of oranges was related to modestly better odds of healthy aging after multivariable adjustment [OR: 1.30 (95% CI: 1.01, 1.67) for ≥5 servings/wk compared with <1 serving/mo; P-trend = 0.01; Table 5]. Intake of grapefruit [less-important contributors of flavonols and flavones than oranges were in the cohort (23)] at midlife was not associated with odds of healthy aging (P-trend = 0.27). Likewise, onions, which were the main source of flavonols, were related to modestly better odds of healthy aging [OR (95% CI: 1.10, 1.64) for ≥5 servings onions/wk compared with <1 serving onions/mo = 1.34; P-trend = 0.003]. There was a trend of increasingly better odds of healthy aging with increasing intake of apples, which are another source of flavonols (P-trend = 0.008), although the increased odds of healthy aging in women with ≥5 servings/wk compared with <1 serving/mo was quite modest and did not reach significance (OR: 1.14; 95% CI: 0.89, 1.46). Moreover, tea, which is a source of flavonols (and the main source of flavan-3-ol and polymers) was not related to healthy aging (P-trend = 0.89). Finally, berries (strawberries and blueberries) were the top contributors of anthocyanins, and higher berry intake at midlife was significantly related to better odds of healthy aging (P-trend = 0.04). For example, compared with women with <1 serving berries/wk, women with ≥2 servings berries/wk had a 24% (95% CI: 0%, 54%) greater odds of healthy aging in multivariate models.
TABLE 5.
ORs (95% CIs) of healthy aging according to categories of intake of major food sources of flavones, flavanones, anthocyanins, and flavonols at midlife1
| Intake category |
||||||
| <1 serving/mo | 1–3 servings/mo | 1 serving/wk | 2–4 servings/wk | ≥5 servings/wk | P-trend | |
| Oranges2 | Reference | 1.29 (0.95, 1.74) | 1.06 (0.81, 1.37) | 1.20 (0.92, 1.55) | 1.30 (1.01, 1.67) | 0.01 |
| Grapefruit2 | Reference | 1.15 (0.98, 1.35) | 1.14 (0.97, 1.33) | 1.22 (1.02, 1.45) | 1.12 (0.90, 1.40) | 0.27 |
| Onions | Reference | 1.09 (0.86, 1.37) | 1.17 (0.96, 1.43) | 1.23 (1.00, 1.51) | 1.34 (1.10, 1.64) | 0.003 |
| Apples | Reference | 0.92 (0.72, 1.16) | 0.87 (0.70, 1.08) | 1.05 (0.83, 1.31) | 1.14 (0.89, 1.46) | 0.008 |
| Tea | Reference | 0.89 (0.70, 1.12) | 0.86 (0.60, 1.26) | 0.87 (0.69, 1.10) | 0.97 (0.80, 1.18) | 0.89 |
| Berries3 | Reference4 | 1.14 (1.02, 1.29)5 | 1.24 (1.00, 1.54)6 | — | — | 0.04 |
ORs >1 denote greater odds of healthy aging. These analyses included 1517 healthy agers versus 12,301 usual agers for all foods except onions, for which analyses included 1401 healthy agers and 11,036 usual agers (e.g., women with onion intake assessed in 1990). Logistic regression models were adjusted for age (y); education (associate's degree, bachelor's degree, graduate degree); marriage status (married, widowed, or separated/divorced); median income (quintiles), median house value (quintiles); family histories of diabetes, cancer, and myocardial infarction (yes or no); physical activity (quintiles of metabolic equivalent task-hours); energy intake (quintiles of kcal); smoking (never smoker, past smoker of 1–14, 15–24, or ≥25 cigarettes/d, or current smoker of 1–14, 15–24, or ≥25 cigarettes/d); multivitamin use (yes or no); aspirin use (<1, 1–2, or >2 tablets/wk); BMI (in kg/m2; <22, 23–24, 25–29, or ≥30); history of high blood pressure (yes or no); history of hypercholesterolemia (yes or no); and Alternative Healthy Eating Index-2010 score (quintiles). Foods were introduced in separate models.
Included intakes of fresh fruit and juices.
Strawberries and blueberries.
<1 serving/wk.
1 serving/wk.
2 servings/wk.
DISCUSSION
In this large prospective study, higher intake of several flavonoid subclasses (i.e., flavones, flavanones, anthocyanins, and flavonols) was associated with better odds of healthy aging. Relations were supported by consistent associations with the main food sources of these flavonoids (i.e., oranges, berries, onions, apples). Together, our findings suggest that flavonoids may be important to promoting broad health and wellbeing in aging.
Our study had important strengths, including the large sample size, high follow-up, and a comprehensive, multidomain evaluation of healthy aging with validated instruments. Moreover, we evaluated dietary habits by using repeated, validated FFQs in midlife, which is likely the most-relevant period of exposure for preventing chronic conditions of aging that develop over many years. We also used an updated flavonoid database containing a large number of foods that are relevant in an American population for the assessment of 6 flavonoid subclasses. We minimized possible bias because of reverse causation (i.e., that poor health leads to poor diet rather than the reverse) by excluding participants with chronic diseases at baseline and imposing a long lag between the dietary assessment and healthy aging. Potential limitations of our study should also be considered. Our sample included female, mostly white health care professionals with generally high levels of education. This inclusion was useful to decrease extraneous variability and enhance the validity of health information, but results may not be generalizable to populations with different demographic features. Moreover, there might also have been measurement error in the assessment of dietary exposure, especially for total flavonoid intakes that combined subclasses derived from different FFQs (i.e., 1984–1986 and 1990). However, such measurement error was not likely to have affected differently cases and controls and, thus, would most likely have led to underestimations of the magnitude of the associations between flavonoids and healthy aging. Because we examined several flavonoid subclasses and flavonoid-rich foods, multiple tests were conducted (e.g., the main analysis on flavonoid subclasses was based on 7 different statistical tests), and there was some possibility that multiple comparisons may have been an issue. Furthermore, in studies of older populations, there is risk of survival bias. However, death rates before age 70 y were quite low in our cohort; in addition, by imposing similar requirements for survival on both healthy and usual agers, our methods limited opportunities for such bias. Finally, in an observational study, there is always concern for confounding. We adjusted for many potential confounders, including lifestyle, socioeconomic, and health factors; however, we could not exclude the possibility of unmeasured confounding, and results should be interpreted with caution.
In previous research, a number of diseases and conditions considered in our healthy aging outcome have been individually associated with flavonoid intake. For example, the 3 most-common chronic diseases in our population were myocardial infarction and coronary artery bypass surgery (12% of usual agers), diabetes (8%), and breast cancer (6%), and consistent associations between greater flavonoid intake and lower risk of these diseases have been reported. In a pooled analyses of cohort studies that involved ∼200,000 US participants, anthocyanins were related to lower risk of type 2 diabetes (19), and meta-analyses have established associations between flavonols and flavones and lower risks of coronary heart diseases (43, 44) and breast cancer (especially in postmenopausal women) (45). Associations with other types of cancers (e.g., lung, colorectal, or ovarian cancers) have been less consistent in previous studies (46–49), although these cancers contributed minimally to our healthy aging outcome (e.g., <1.5% of usual agers).
Flavonoid intake has been related to better cognitive health, although data has been more limited. In several small randomized controlled trials, flavonoid supplementation improved cognitive function (50, 51), and accordingly, greater intakes of total flavonoids and of anthocyanins (and berries, which are their main food source) were related to a lower rate of cognitive decline in 2 large populations of older persons (22, 23). Therefore, the overall, previous literature supports a role of flavonoids on a wide range of age-related diseases and brain health in aging as we showed in our large epidemiologic study.
Flavonoids may contribute to reducing chronic diseases and maintaining physical, cognitive, and mental health with aging via their potential to decrease oxidative stress and inflammation, which are 2 general pathways underlying many age-related chronic diseases and health conditions. Indeed, aging has been associated with chronic, low-grade oxidative stress and inflammation, both in the periphery and central nervous system, which may trigger the development of chronic diseases and cognitive and behavioral changes (52–54). Antioxidant and anti-inflammatory properties of flavonoids have been shown in vitro (6), and a few observational studies have related specific flavonoids (e.g., flavones and flavanones) to lower blood concentrations of oxidative and inflammatory markers (55, 56), suggesting a potential ability to improve systemic inflammatory status in humans. Moreover, animal studies have shown that flavonoids, especially anthocyanins, improve glucose metabolism and insulin resistance by modulating the expression of glucose transporter proteins (57, 58). Also, substantial experimental data have suggested that flavonoids may decrease neuroinflammation via their potential to modulate signaling pathways controlling the activation of glial cells and those determining neuronal apoptosis and neurogenesis in the hippocampus (a key structure for memory and mood), thereby lowering age-related cognitive impairment, depression, and anxiety in rodents (8, 59).
In conclusion, our findings suggest that intake of dietary flavonoids at midlife may be related to improved odds of overall health and wellbeing in aging. Because the avoidance of the spectrum of health conditions in aging may be of more importance to individuals than avoiding any single chronic disease, these findings could help the adherence to public health recommendations regarding diet quality.
Acknowledgments
The authors’ responsibilities were as follows—EBR and FG: designed and conducted research; CS, QS, and MKT: conducted research; CS: analyzed data and wrote the manuscript; QS, MKT, EBR, and FG: edited the manuscript; and CS: had primary responsibility for the final content of the manuscript. Although unrelated to the funding and completion of this manuscript, EBR as an investigator, receives research funds from the US Blueberry Highbush Council. CS, QS, MKT, and FG had no conflicts of interest. Sponsors had no role in the design and conduct of the study; collection, management, analysis, and interpretation of data; preparation, review, or approval of the manuscript; or decision to submit the manuscript for publication.
Footnotes
Abbreviations used: FFQ, food-frequency questionnaire; MHI, Mental Health Index; SF-36, Medical Outcomes Study Short-Form-36.
REFERENCES
- 1.Christensen K, Doblhammer G, Rau R, Vaupel JW. Ageing populations: the challenges ahead. Lancet 2009;374:1196–208. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Halliwell B. Antioxidants in human health and disease. Annu Rev Nutr 1996;16:33–50. [DOI] [PubMed] [Google Scholar]
- 3.Rodriguez-Mateos A, Rendeiro C, Bergillos-Meca T, Tabatabaee S, George TW, Heiss C, Spencer JP. Intake and time dependence of blueberry flavonoid-induced improvements in vascular function: a randomized, controlled, double-blind, crossover intervention study with mechanistic insights into biological activity. Am J Clin Nutr 2013;98:1179–91. [DOI] [PubMed] [Google Scholar]
- 4.Hooper L, Kroon PA, Rimm EB, Cohn JS, Harvey I, Le Cornu KA, Ryder JJ, Hall WL, Cassidy A. Flavonoids, flavonoid-rich foods, and cardiovascular risk: a meta-analysis of randomized controlled trials. Am J Clin Nutr 2008;88:38–50. [DOI] [PubMed] [Google Scholar]
- 5.de Pascual-Teresa S, Moreno DA, Garcia-Viguera C. Flavanols and anthocyanins in cardiovascular health: a review of current evidence. Int J Mol Sci 2010;11:1679–703. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Scalbert A, Manach C, Morand C, Remesy C, Jimenez L. Dietary polyphenols and the prevention of diseases. Crit Rev Food Sci Nutr 2005;45:287–306. [DOI] [PubMed] [Google Scholar]
- 7.Rendeiro C, Vauzour D, Rattray M, Waffo-Teguo P, Merillon JM, Butler LT, Williams CM, Spencer JP. Dietary levels of pure flavonoids improve spatial memory performance and increase hippocampal brain-derived neurotrophic factor. PLoS ONE 2013;8:e63535. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Dias GP, Cavegn N, Nix A, do Nascimento Bevilaqua MC, Stangl D, Zainuddin MS, Nardi AE, Gardino PF, Thuret S. The role of dietary polyphenols on adult hippocampal neurogenesis: molecular mechanisms and behavioural effects on depression and anxiety. Oxid Med Cell Longev 2012;2012:541971. [DOI] [PMC free article] [PubMed]
- 9.Rendeiro C, Guerreiro JD, Williams CM, Spencer JP. Flavonoids as modulators of memory and learning: molecular interactions resulting in behavioural effects. Proc Nutr Soc 2012;71:246–62. [DOI] [PubMed] [Google Scholar]
- 10.Mink PJ, Scrafford CG, Barraj LM, Harnack L, Hong CP, Nettleton JA, Jacobs DR., Jr Flavonoid intake and cardiovascular disease mortality: a prospective study in postmenopausal women. Am J Clin Nutr 2007;85:895–909. [DOI] [PubMed] [Google Scholar]
- 11.McCullough ML, Peterson JJ, Patel R, Jacques PF, Shah R, Dwyer JT. Flavonoid intake and cardiovascular disease mortality in a prospective cohort of US adults. Am J Clin Nutr 2012;95:454–64. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Cassidy A, Mukamal KJ, Liu L, Franz M, Eliassen AH, Rimm EB. High anthocyanin intake is associated with a reduced risk of myocardial infarction in young and middle-aged women. Circulation 2013;127:188–96. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Bhupathiraju SN, Wedick NM, Pan A, Manson JE, Rexrode KM, Willett WC, Rimm EB, Hu FB. Quantity and variety in fruit and vegetable intake and risk of coronary heart disease. Am J Clin Nutr 2013;98:1514–23. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Cassidy A, O'Reilly EJ, Kay C, Sampson L, Franz M, Forman JP, Curhan G, Rimm EB. Habitual intake of flavonoid subclasses and incident hypertension in adults. Am J Clin Nutr 2011;93:338–47. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Joshipura KJ, Ascherio A, Manson JE, Stampfer MJ, Rimm EB, Speizer FE, Hennekens CH, Spiegelman D, Willett WC. Fruit and vegetable intake in relation to risk of ischemic stroke. JAMA 1999;282:1233–9. [DOI] [PubMed] [Google Scholar]
- 16.Cassidy A, Rimm EB, O'Reilly EJ, Logroscino G, Kay C, Chiuve SE, Rexrode KM. Dietary flavonoids and risk of stroke in women. Stroke 2012;43:946–51. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Larsson SC, Virtamo J, Wolk A. Total and specific fruit and vegetable consumption and risk of stroke: a prospective study. Atherosclerosis 2013;227:147–52. [DOI] [PubMed] [Google Scholar]
- 18.Cutler GJ, Nettleton JA, Ross JA, Harnack LJ, Jacobs DR, Jr, Scrafford CG, Barraj LM, Mink PJ, Robien K. Dietary flavonoid intake and risk of cancer in postmenopausal women: the Iowa Women's Health Study. Int J Cancer 2008;123:664–71. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Wedick NM, Pan A, Cassidy A, Rimm EB, Sampson L, Rosner B, Willett W, Hu FB, Sun Q, van Dam RM. Dietary flavonoid intakes and risk of type 2 diabetes in US men and women. Am J Clin Nutr 2012;95:925–33. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Muraki I, Imamura F, Manson JE, Hu FB, Willett WC, van Dam RM, Sun Q. Fruit consumption and risk of type 2 diabetes: results from three prospective longitudinal cohort studies. BMJ 2013;347:f5001. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Gao X, Cassidy A, Schwarzschild MA, Rimm EB, Ascherio A. Habitual intake of dietary flavonoids and risk of Parkinson disease. Neurology 2012;78:1138–45. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Letenneur L, Proust-Lima C, Le Gouge A, Dartigues JF, Barberger-Gateau P. Flavonoid intake and cognitive decline over a 10-year period. Am J Epidemiol 2007;165:1364–71. [DOI] [PubMed] [Google Scholar]
- 23.Devore EE, Kang JH, Breteler MM, Grodstein F. Dietary intakes of berries and flavonoids in relation to cognitive decline. Ann Neurol 2012;72:135–43. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.Sun Q, Townsend MK, Okereke OI, Franco OH, Hu FB, Grodstein F. Adiposity and weight change in mid-life in relation to healthy survival after age 70 in women: prospective cohort study. BMJ 2009;339:b3796. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25.Sun Q, Townsend MK, Okereke OI, Franco OH, Hu FB, Grodstein F. Physical activity at midlife in relation to successful survival in women at age 70 years or older. Arch Intern Med 2010;170:194–201. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.Sun Q, Townsend MK, Okereke OI, Rimm EB, Hu FB, Stampfer MJ, Grodstein F. Alcohol consumption at midlife and successful ageing in women: a prospective cohort analysis in the nurses’ health study. PLoS Med 2011;8:e1001090. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Willett WC, Sampson L, Stampfer MJ, Rosner B, Bain C, Witschi J, Hennekens CH, Speizer FE. Reproducibility and validity of a semiquantitative food frequency questionnaire. Am J Epidemiol 1985;122:51–65. [DOI] [PubMed] [Google Scholar]
- 28.Ware JE. SF-36 health survey: manual and interpretation guide. Boston, MA: The Health Institute, New England Medical Center; 1993. [Google Scholar]
- 29.Willett W. Nutritional epidemiology. 2nd ed. New York, NY: Oxford University Press; 1998. [Google Scholar]
- 30.USDA. USDA database for the flavonoid content of selected foods. Release 2.1. Washington, DC: USDA; 2007. [Google Scholar]
- 31. USDA. USDA database for the proanthocyanidin content of selected foods. Washington, DC: USDA; 2004.
- 32.Erdman JW, Jr, Balentine D, Arab L, Beecher G, Dwyer JT, Folts J, Harnly J, Hollman P, Keen CL, Mazza G, et al. Flavonoids and heart health: proceedings of the ILSI North America Flavonoids Workshop, May 31-June 1, 2005, Washington, DC. J Nutr 2007;137(3 Suppl 1):718S–37S. [DOI] [PubMed] [Google Scholar]
- 33.Chiuve SE, Fung TT, Rimm EB, Hu FB, McCullough ML, Wang M, Stampfer MJ, Willett WC. Alternative dietary indices both strongly predict risk of chronic disease. J Nutr 2012;142:1009–18. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 34.Colditz GA, Willett WC, Rotnitzky A, Manson JE. Weight gain as a risk factor for clinical diabetes mellitus in women. Ann Intern Med 1995;122:481–6. [DOI] [PubMed] [Google Scholar]
- 35.Barr RG, Herbstman J, Speizer FE, Camargo CA., Jr Validation of self-reported chronic obstructive pulmonary disease in a cohort study of nurses. Am J Epidemiol 2002;155:965–71. [DOI] [PubMed] [Google Scholar]
- 36.Alonso A, Hernan MA, Ascherio A. Allergy, family history of autoimmune diseases, and the risk of multiple sclerosis. Acta Neurol Scand 2008;117:15–20. [DOI] [PubMed] [Google Scholar]
- 37.Sun Q, Ma J, Campos H, Hankinson SE, Manson JE, Stampfer MJ, Rexrode KM, Willett WC, Hu FB. A prospective study of trans fatty acids in erythrocytes and risk of coronary heart disease. Circulation 2007;115:1858–65. [DOI] [PubMed] [Google Scholar]
- 38.Brandt J, Spencer M, Folstein M. The telephone interview for cognitive status. Neuropsychiatry Neuropsychol Behav Neurol 1988;1:111–7. [Google Scholar]
- 39.Folstein MF, Folstein SE, McHugh PR. “Mini-mental state”. A practical method for grading the cognitive state of patients for the clinician. J Psychiatr Res 1975;12:189–98. [DOI] [PubMed] [Google Scholar]
- 40.Stampfer MJ, Kang JH, Chen J, Cherry R, Grodstein F. Effects of moderate alcohol consumption on cognitive function in women. N Engl J Med 2005;352:245–53. [DOI] [PubMed] [Google Scholar]
- 41.Berwick DM, Murphy JM, Goldman PA, Ware JE, Jr, Barsky AJ, Weinstein MC. Performance of a five-item mental health screening test. Med Care 1991;29:169–76. [DOI] [PubMed] [Google Scholar]
- 42.Friedman B, Heisel M, Delavan R. Validity of the SF-36 five-item Mental Health Index for major depression in functionally impaired, community-dwelling elderly patients. J Am Geriatr Soc 2005;53:1978–85. [DOI] [PubMed] [Google Scholar]
- 43.Huxley RR, Neil HA. The relation between dietary flavonol intake and coronary heart disease mortality: a meta-analysis of prospective cohort studies. Eur J Clin Nutr 2003;57:904–8. [DOI] [PubMed] [Google Scholar]
- 44.Peterson JJ, Dwyer JT, Jacques PF, McCullough ML. Associations between flavonoids and cardiovascular disease incidence or mortality in European and US populations. Nutr Rev 2012;70:491–508. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 45.Hui C, Qi X, Qianyong Z, Xiaoli P, Jundong Z, Mantian M. Flavonoids, flavonoid subclasses and breast cancer risk: a meta-analysis of epidemiologic studies. PLoS ONE 2013;8:e54318. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 46.Tang NP, Zhou B, Wang B, Yu RB, Ma J. Flavonoids intake and risk of lung cancer: a meta-analysis. Jpn J Clin Oncol 2009;39:352–9. [DOI] [PubMed] [Google Scholar]
- 47.Jin H, Leng Q, Li C. Dietary flavonoid for preventing colorectal neoplasms. Cochrane Database Syst Rev 2012;8:CD009350. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 48. Gates MA, Tworoger SS, Hecht JL, De Vivo I, Rosner B, Hankinson SE. A prospective study of dietary flavonoid intake and incidence of epithelial ovarian cancer. International journal of cancer. Int J Cancer 2007;121:2225–32. [DOI] [PubMed]
- 49. Gates MA, Vitonis AF, Tworoger SS, Rosner B, Titus-Ernstoff L, Hankinson SE, Cramer DW. Flavonoid intake and ovarian cancer risk in a population-based case-control study. Int J Cancer 2009;124:1918–25. [DOI] [PMC free article] [PubMed]
- 50.Macready AL, Kennedy OB, Ellis JA, Williams CM, Spencer JP, Butler LT. Flavonoids and cognitive function: a review of human randomized controlled trial studies and recommendations for future studies. Genes Nutr 2009;4:227–42. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 51.Desideri G, Kwik-Uribe C, Grassi D, Necozione S, Ghiadoni L, Mastroiacovo D, Raffaele A, Ferri L, Bocale R, Lechiara MC, et al. Benefits in cognitive function, blood pressure, and insulin resistance through cocoa flavanol consumption in elderly subjects with mild cognitive impairment: the Cocoa, Cognition, and Aging (CoCoA) study. Hypertension 2012;60:794–801. [DOI] [PubMed] [Google Scholar]
- 52.Cavanagh MM, Weyand CM, Goronzy JJ. Chronic inflammation and aging: DNA damage tips the balance. Curr Opin Immunol 2012;24:488–93. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 53.Prasad S, Sung B, Aggarwal BB. Age-associated chronic diseases require age-old medicine: role of chronic inflammation. Prev Med 2012;54(Suppl):S29–37. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 54.Sparkman NL, Johnson RW. Neuroinflammation associated with aging sensitizes the brain to the effects of infection or stress. Neuroimmunomodulation 2008;15:323–30. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 55.Chun OK, Chung SJ, Claycombe KJ, Song WO. Serum C-reactive protein concentrations are inversely associated with dietary flavonoid intake in U.S. adults. J Nutr 2008;138:753–60. [DOI] [PubMed] [Google Scholar]
- 56.Landberg R, Sun Q, Rimm EB, Cassidy A, Scalbert A, Mantzoros CS, Hu FB, van Dam RM. Selected dietary flavonoids are associated with markers of inflammation and endothelial dysfunction in U.S. women. J Nutr 2011;141:618–25. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 57.Prior RL, Wu X. Anthocyanins: structural characteristics that result in unique metabolic patterns and biological activities. Free Radic Res 2006;40:1014–28. [DOI] [PubMed] [Google Scholar]
- 58.Nizamutdinova IT, Jin YC, Chung JI, Shin SC, Lee SJ, Seo HG, Lee JH, Chang KC, Kim HJ. The anti-diabetic effect of anthocyanins in streptozotocin-induced diabetic rats through glucose transporter 4 regulation and prevention of insulin resistance and pancreatic apoptosis. Mol Nutr Food Res 2009;53:1419–29. [DOI] [PubMed] [Google Scholar]
- 59.Spencer JP, Vafeiadou K, Williams RJ, Vauzour D. Neuroinflammation: modulation by flavonoids and mechanisms of action. Mol Aspects Med 2012;33:83–97. [DOI] [PubMed] [Google Scholar]