Abstract
Background: Although studies to date have confirmed the association between nutrition and frailty, the impact of dietary intake and dietary patterns on survivorship in those with frailty is yet to be examined in a well-powered cohort with validated frailty status. Moreover, previous studies were limited by measurement error from dietary self-reports.
Objective: We derived biomarker-calibrated dietary energy and protein intakes to address dietary self-report error. Using these data, we then evaluated the association of mortality in older women with frailty and dietary intake and healthy diet indexes, such as the alternate Mediterranean Diet (aMED), the Dietary Approaches to Stop Hypertension (DASH) score, and the Dietary Inflammatory Index (DII).
Design: The analytic sample included 10,034 women aged 65–84 y with frailty and complete dietary data from the Women’s Health Initiative Observational Study. Frailty was assessed with modified Fried’s criteria. Dietary data were collected by food-frequency questionnaire.
Results: Over a mean follow-up period of 12.4 y, 3259 (31%) deaths occurred. The HRs showed progressively decreased rates of mortality in women with higher calibrated dietary energy intakes (P-trend = 0.003), higher calibrated dietary protein intakes (P-trend = 0.03), higher aMED scores (P-trend = 0.006), and higher DASH scores (P-trend = 0.02). Although the adjusted point estimates of HRs (95% CIs) for frail women scoring in the second, third, and fourth quartiles on DII measures were 1.15 (1.03, 1.27), 1.28 (1.15, 1.42), and 1.24 (1.12, 1.38), respectively, compared with women in the first quartile, no overall effect was observed across quartiles (P-trend = 0.35). Subgroup analyses by chronic morbidity or smoking status or by excluding women with early death did not substantially change these findings.
Conclusions: The current study highlights the importance of nutrition in older, frail women. Diet quality and quantity should be considered in managing persons with frailty.
Keywords: aging, biomarker, frailty, inflammation, mortality
INTRODUCTION
Current demographic trends indicate that the percentage of persons aged ≥65 y will increase by almost 20% by 2030 and that women will continue to account for the majority of this older population (1). Frailty, which is commonly associated with aging, is both a serious and potentially modifiable geriatric syndrome (2). Persons with frailty are at increased risk of adverse health outcomes (3, 4). The prevalence of frailty increases with age, affecting more than one-third of nonagenarians (2). A need to address the unfolding frailty epidemic led to a concerted effort to understand modifiable factors that prevent frailty and to improve outcomes in those who transitioned to frailty. Nutritional deficits are especially pertinent in that regard because they are modifiable and have been implicated in observational studies in a transition to frailty and in its progression (5, 6). Likewise, clinical trials also corroborated observational results and confirmed that nutritional interventions improve function in persons with frailty, but these were limited by small sample sizes, ambiguous frailty criteria, and short follow-up periods (7, 8). Although this evidence suggests that diet is instrumental in the development of and progression of frailty, the role of dietary intake on outcomes such as mortality has yet to be examined in a well-powered cohort of persons with a validated definition of frailty. Other reasons for this study include emerging evidence that factors affecting propensity to frailty might have different effects on survivorship in those with the condition (9) and potential self-report measurement bias that limited previous reports. Evidence exists that, in overweight persons and among racial and ethnic minorities, misreport might bias recall of diet (10, 11). Because overweight and minority race and ethnicity are risk factors for frailty (4), measurement error from self-report may also impede the ability to investigate these factors with regard to health consequences in persons with frailty.
Along with overall nutrient intake, dietary indexes are also pertinent to nutrition and frailty research (12, 13). Conceptually, dietary indexes represent an integrated picture of food and nutrient consumption and may be complementary to the investigation of individual food groups to assess health risks. For this study, we selected dietary patterns that were shown to relate to cardiovascular health and inflammation because of heightened vascular morbidity and proinflammatory state in persons with frailty (14). Thus, the goals of our analyses were 2-fold: 1) to evaluate the association of self-report corrected energy and protein intakes with mortality in older women with frailty and 2) to examine the association of dietary indexes such as the alternate Mediterranean diet (aMED)12, the Dietary Approaches to Stop Hypertension (DASH) score, and the Dietary Inflammatory Index (DII) with mortality. We hypothesized that total energy and protein intakes and aMED and DASH scores would be inversely associated with overall mortality in women with frailty. Because for the DII a higher score (described later) indicates less benefit and a higher proinflammatory state, we hypothesized that DII scores would be directly associated with mortality.
METHODS
Analytic sample
The Women’s Health Initiative (WHI) Observational Study (OS) enrolled 93,676 women 50–79 y of age at baseline between 1993 and 1998 at 40 US clinical centers. At baseline and then at the first follow-up clinic visit, which occurred 3 y after baseline (1996–2001), WHI OS participants completed questionnaires on medical and psychosocial characteristics and provided weight and height measures. Because the sample used here was limited to those characterized as frail, the first follow-up visit was required to define the weight-loss component of the frailty phenotype. Thus, the eligible sample comprised participants ≥65 y old with a year 3 clinic visit (n = 55,654). Participants with missing data on ≥1 of the frailty criteria (n = 12,464) were further excluded, as reported elsewhere (9). The analytic sample was limited to 11,070 participants who screened positively for ≥3 frailty criteria (thoroughly defined below). A total of 623 women with extreme energy intakes (i.e., <600 or >5000 kcal/d) were excluded because these reports were judged to be implausible; 16 women with an end-of-follow-up date that preceded frailty assessment also were excluded. The final analytic sample included data from 10,431 older, frail WHI OS participants (see Supplemental Figure 1).
Calibration sample
Two ancillary studies within the WHI measured recovery biomarkers with the aim of facilitating the diet-disease association assessment: the WHI Nutrition Biomarker Study (NBS) (10) and the WHI Nutrition and Physical Activity Assessment Study (NPAAS) (15). These ancillary studies compared self-reported intakes of energy and protein, assessed by semiquantitative food-frequency questionnaire (FFQ) completed during the NBS and NPAAS, with recovery biomarkers of energy (doubly labeled water as deuterium and oxygen-18) and protein (total urinary nitrogen excretion). Both studies excluded women who had any medical conditions precluding participation, weight instability, diabetes, or travel plans during the study period. The NPAAS and NBS enrolled 450 and 544 postmenopausal women from the WHI OS and WHI Dietary Modification trial, respectively. For the current study, we excluded women who participated in the Dietary Modification intervention arm because of a possible intervention effect. Furthermore, because the calibration sample was used to approximate dietary intake in older women with frailty, we further excluded those who were <65 y of age, reported above-median physical activity, or had implausible values from the dietary assessment instruments. The final calibration sample included 261 older women.
Frailty criteria
Frailty was operationally defined congruent with the definition by Fried et al. (3) as the presence of ≥3 of the following 5 criteria: muscle weakness, slow walking speed, exhaustion, low physical activity, and unintentional weight loss (shrinking). This approach was adapted and validated in the WHI and has been used extensively in the WHI OS cohort (4, 5). For each criterion, 1 or 2 points were assigned if the participant’s assessment was below a criterion-specific cutoff, as described in detail elsewhere (9). Points were summed to provide a score ranging from 0 to 5. A score of ≥3 was used to characterize a woman as frail. Criteria-specific cutoffs were based on the WHI frailty index and are described in Supplemental Table 1.
Biomarker-calibrated dietary energy and protein intakes
Diet was measured at enrollment and at the year 3 visit among OS participants by using a self-administered semiquantitative FFQ developed for and validated specifically in the WHI (16). The 3 sections of the WHI-FFQ included 122 composite and single-food line items asking about the frequency of consumption and portion size, 19 adjustment questions related to type of fat intake, and 4 summary questions asking about the usual intakes of fruit and vegetables and added fats for comparison with information gathered from the line items.
Calibrated energy and protein intake estimates were calculated by using WHI NBS and NPAAS calibration guidelines (17). These equations were developed for the current study by using linear regression models that predicted log-transformed calibrated intakes of energy and protein on the basis of the log-transformed self-reported intakes along with covariates related to characteristics of the study participants: BMI, age, race-ethnicity, income, education, smoking, and physical activity as metabolic equivalents per week.
Assessment of dietary scores or indexes
On the basis of nutrient and food item intakes estimated from the WHI FFQ, dietary indexes (aMED, DASH, and DII) were used to assess the extent of adherence to various healthy dietary patterns. Food items were transformed into standardized quantities with the help of the MyPyramid Equivalents Database (18).
The aMED index was designed to assess adherence to a Mediterranean dietary pattern. aMED scoring (19) ranges from 0 (nonadherence) to 9 (perfect adherence). It includes the following components: 1) fruit, 2) vegetables, 3) nuts, 4) legumes, 5) whole grains, 6) fish, 7) ratio of monounsaturated to saturated fat, 8) red and processed meats, and 9) alcohol.
The DASH index (20) considers intakes of 1) fruit, 2) vegetables, 3) nuts and legumes, 4) low-fat dairy, 5) whole grains, 6) sodium, 7) sweetened beverages, and 8) red and processed meats. The score is based on quintile ranking within the specific population under investigation.
The DII was created by scientists in the University of South Carolina’s Cancer Prevention and Control Program (21). DII scores were calculated on the basis of algorithms derived from an extensive review of the diet-inflammation literature and development of a world database from which comparative scores could be derived. Although in the WHI FFQ only 32 of the 45 original DII components were available for inclusion in the overall DII measure, the range of scores in the WHI data were consistent with the study that used 44 of 45 components, as described elsewhere (22). Energy-adjusted DII scores were calculated per 1000 kcal consumed to accommodate variations in energy consumption. DII scores characterize individuals’ diets on a continuum from maximally anti-inflammatory to maximally proinflammatory, with a higher score indicating a more proinflammatory diet. In other words, for the DII score, a higher value indicates less benefit. The DII has previously been validated in the WHI (22) and shown to be associated with mortality in other countries (23).
The calibrated estimates and DII scores were divided into quartiles for analysis on the basis of their observed distribution. The cutoffs for aMED and DASH scores were consistent with categories used in another study (24).
All-cause mortality
Medical updates were collected annually by mail. Participants’ deaths were adjudicated by study physicians with the use of hospital records, autopsy or coroner reports, and/or death certificates. Periodic checks of the National Death Index for all participants, including those lost to follow-up, were performed. At the time of this analysis, the latest WHI data on mortality were available through December 2015.
Covariates
Baseline data on demographic and health variables, including age, race/ethnicity, family income, highest level of education completed, smoking status (never, past, or current), and history of common diseases related to body weight and mortality, such as cancer (excluding nonmelanoma skin cancer), diabetes, emphysema, and cardiovascular disease (CVD), were obtained by self-report. Previous studies have indicated that smoking and the above-mentioned pre-existing conditions confound the relation between obesity and mortality (25) and were found to be associated with prevalent and incident frailty (4). At the year 3 follow-up visit, trained staff performed anthropometric measurements in the clinic. Weight to the nearest 0.1 kg and height to the nearest 0.1 cm were measured and used to compute BMI (kg/m2). Physical activity was assessed at the first follow-up clinical visit by self-report of frequency and duration of walking and mild, moderate, and strenuous activities, with total expenditure of energy calculated in metabolic equivalents (26).
Statistical approach
Out-of-sample predictions of diet measures accounted for self-report bias. More specifically, the above-mentioned calibration sample of participants with both biomarker and self-report data were used to derive calibration equations and the formulas were then used in a larger sample to calculate biomarker-calibrated dietary energy and protein intakes.
Frequencies and means and SDs were estimated for categorical and continuous variables, respectively, and descriptive statistics were used to compare distributions across calibrated energy and protein intakes and dietary score or index categories. Crude rates of mortality per 1000 person-years were calculated to express the number of deaths for each dietary measure separately. Cox proportional hazards models were used to estimate the association of calibrated dietary energy and protein and dietary pattern categories with all-cause mortality rate. By using a staged approach, dietary energy intake models were adjusted for age and then for race/ethnicity, education, income, smoking, number of frailty criteria, physical activity, and BMI. Dietary protein models were adjusted for baseline age and calibrated energy intake and then for race-ethnicity, education, income, smoking, number of frailty criteria, physical activity, and BMI. Dietary pattern models included adjustment for baseline age and calibrated energy intake and then for calibrated protein intake, race/ethnicity, education, income, smoking, number of frailty criteria, physical activity, and BMI. The time-to-event was defined as number of years from the WHI year 3 follow-up visit to death from any cause, with censoring at the last known contact or the date of last available National Death Index search, whichever occurred later. Because the relation of diet and mortality might be distorted by reverse causality of worse nutritional status due to prodromal disease symptoms and inadequate adjustment for the effects of smoking and chronic morbidity (27), regression models that stratified by the presence or absence of chronic conditions that included cancer, emphysema, CVD, and diabetes and by smoking status were also performed. A sensitivity analysis that excluded women who died within 3 y after the year 3 follow-up period and analyses without the inclusion of BMI in the mortality models also were completed. Finally, the interaction terms between nutritional measures and smoking and between nutritional measures and chronic morbidity were calculated as well.
For the SEs and significance level estimates, where sampling variation in the calibration coefficient estimates needs to be taken into account, a bootstrap procedure (1000 bootstrap samples) was applied. Bootstrapping allows computation of estimated SEs, CIs, and hypothesis testing under circumstances of estimated uncertainty, as with calibration equation coefficient estimates. The 95% CIs for HRs were calculated as the exponential of log-estimated HR ± 1.96 SE estimate. Schoenfeld residuals were used to test proportionality. Statistical analyses were completed by using R version 3.3.1.
RESULTS
Baseline characteristics of the study participants by lowest and highest quartiles of the various dietary measures are reported in Table 1. Women scoring in the highest quartile on calibrated dietary energy and protein intakes were more likely to be younger and white and have a higher BMI; they also were more likely to have diabetes and less likely to have unintentional weight loss. In contrast, women scoring in the highest quartiles of adherence to the aMED and DASH healthy dietary patterns were more likely to be older and white and to have a lower BMI; they also were less likely to smoke and less likely to have a low physical activity level. Women with higher anti-inflammatory diet scores were more likely to have a higher income, be college-educated, have a lower BMI, less likely to smoke, and have a higher physical activity level than those with a more proinflammatory diet.
TABLE 1.
Baseline and year 3 characteristics by lowest and highest quartiles of biomarker-calibrated nutrient intake and healthy diet index scores of the frail and older WHI OS participants1
| Total energy intake2 |
Total protein intake3 |
aMED |
DASH |
DII |
||||||
| Characteristics | Q1 | Q4 | Q1 | Q4 | Q1 | Q4 | Q1 | Q4 | Q1 | Q4 |
| Participants, n | 2364 | 2363 | 2148 | 2199 | 2643 | 1926 | 3174 | 2114 | 2594 | 2615 |
| Age, y | 76.4 ± 3.674 | 69.1 ± 3.14 | 76.19 ± 3.81 | 69.02 ± 3.27 | 72.28 ± 4.56 | 72.88 ± 4.59 | 71.97 ± 4.54 | 73.05 ± 4.57 | 72.68 ± 4.51 | 72.09 ± 4.56 |
| Race/ethnicity, n (%) | ||||||||||
| White | 1948 (82.40) | 2116 (89.55) | 1704 (79.33) | 2004 (91.13) | 2274 (86.30) | 1678 (87.53) | 2618 (82.80) | 1915 (90.76) | 2337 (90.37) | 2100 (80.52) |
| Black | 162 (6.85) | 181 (7.66) | 335 (15.60) | 47 (2.14) | 219 (8.31) | 119 (6.21) | 361 (11.42) | 81 (3.84) | 109 (4.22) | 323 (12.38) |
| Hispanic | 52 (2.20) | 36 (1.52) | 42 (1.96) | 37 (1.68) | 69 (2.62) | 26 (1.36) | 74 (2.34) | 41 (1.94) | 29 (1.12) | 95 (3.64) |
| Other | 202 (8.54) | 30 (1.27) | 67 (3.12) | 111 (5.05) | 73 (2.77) | 94 (4.90) | 109 (3.45) | 73 (3.46) | 111 (4.29) | 90 (3.45) |
| Income, n (%) | ||||||||||
| <$20,000 | 941 (39.81) | 389 (16.46) | 706 (32.87) | 498 (22.65) | 703 (29.21) | 355 (19.73) | 856 (29.42) | 412 (20.68) | 465 (19.26) | 797 (33.22) |
| $20,000–$34,999 | 573 (24.24) | 803 (33.98) | 530 (24.67) | 747 (33.97) | 776 (32.24) | 500 (27.79) | 880 (30.24) | 603 (30.27) | 698 (28.92) | 747 (31.14) |
| $35,000–$49,999 | 211 (9.93) | 708 (29.96) | 417 (19.42) | 426 (19.37) | 439 (18.25) | 361 (20.07) | 566 (19.46) | 396 (19.88) | 502 (20.80) | 409 (17.06) |
| $50,000–$75,000 | 357 (15.10) | 288 (12.19) | 237 (11.03) | 372 (16.92) | 324 (13.46) | 315 (17.51) | 365 (12.54) | 328 (16.47) | 416 (17.23) | 268 (11.17) |
| >$75,000 | 281 (11.89) | 175 (7.41) | 258 (12.01) | 156 (7.09) | 164 (6.81) | 268 (14.90) | 243 (8.35) | 254 (12.75) | 332 (13.75) | 178 (7.42) |
| Education, n (%) | ||||||||||
| High school or lower | 197 (8.33) | 157 (6.64) | 210 (9.78) | 123 (5.59) | 242 (9.21) | 88 (4.60) | 318 (10.08) | 97 (4.63) | 129 (5.01) | 285 (10.98) |
| College or higher | 1332 (56.35) | 1447 (61.24) | 1151 (53.58) | 1403 (63.80) | 1344 (51.12) | 1373 (71.81) | 1599 (50.70) | 1472 (70.23) | 1744 (67.70) | 1307 (50.35) |
| BMI, kg/m2 | 24.00 (3.50) | 36.52 (6.32) | 25.38 (4.43) | 35.04 (7.16) | 29.73 (6.43) | 28.61 (6.32) | 30.34 (6.69) | 28.11 (6.02) | 28.28 (5.91) | 30.59 (6.97) |
| Smoker, n (%) | 92 (3.89) | 130 (5.50) | 164 (7.64) | 66 (3.00) | 200 (7.61) | 49 (2.56) | 242 (7.70) | 33 (1.57) | 75 (2.91) | 208 (8.02) |
| CVD, n (%) | 808 (34.83) | 665 (28.78) | 722 (34.38) | 649 (30.20) | 794 (30.75) | 641 (34.01) | 957 (30.92) | 720 (34.80) | 806 (31.79) | 772 (30.30) |
| Diabetes, n (%) | 124 (5.25) | 242 (10.25) | 136 (6.34) | 226 (10.29) | 166 (6.29) | 132 (6.86) | 225 (7.09) | 143 (6.77) | 145 (5.59) | 239 (9.15) |
| Cancer, n (%) | 457 (19.47) | 364 (15.48) | 412 (19.30) | 358 (16.39) | 474 (18.05) | 333 (17.45) | 555 (17.67) | 328 (15.64) | 421 (16.33) | 450 (17.36) |
| Emphysema, n (%) | 197 (8.44) | 163 (7.05) | 180 (8.53) | 167 (7.77) | 225 (8.69) | 132 (6.97) | 292 (9.42) | 120 (5.78) | 159 (6.23) | 226 (8.84) |
| Physical activity, METs/wk | 5.07 ± 7.80 | 6.36 ± 10.48 | 3.96 ± 6.21 | 7.33 ± 11.23 | 4.09 ± 7.86 | 7.44 ± 9.53 | 4.14 ± 7.51 | 7.52 ± 9.70 | 7.61 ± 9.86 | 4.11 ± 7.58 |
| Frailty criteria,5 n (%) | ||||||||||
| Low physical activity6 | 1373 (58.08) | 1391 (58.87) | 1370 (63.78) | 1179 (53.62) | 1786 (67.57) | 882 (45.79) | 2112 (66.54) | 969 (45.84) | 1190 (45.88) | 1743 (66.65) |
| Weight loss7 | 606 (25.63) | 187 (7.91) | 501 (23.32) | 185 (8.41) | 436 (16.50) | 244 (12.67) | 513 (16.16) | 288 (13.62) | 364 (14.03) | 413 (15.79) |
| Low physical function8 | 2329 (98.52) | 2347 (99.32) | 2116 (98.51) | 2187 (9.45) | 2620 (99.13) | 1910 (99.17) | 3141 (98.96) | 2096 (99.15) | 2571 (99.11) | 2591 (99.08) |
| Fatigue9 | 1735 (73.39) | 1849 (78.25) | 1528 (71.14) | 1774 (80.67) | 1922 (72.72) | 1516 (78.71) | 2314 (72.90) | 1659 (78.48) | 2040 (78.64) | 1942 (74.26) |
| Number of frailty criteria, n (%) | ||||||||||
| 3 | 1298 (54.91) | 1393 (58.95) | 1145 (53.31) | 1331 (60.53) | 1353 (51.19) | 1289 (66.93) | 1670 (52.61) | 1403 (66.37) | 1718 (66.23) | 1330 (50.86) |
| 4 | 852 (36.04) | 908 (38.43) | 819 (38.13) | 821 (37.34) | 1125 (42.57) | 590 (30.63) | 1309 (41.24) | 656 (31.03) | 798 (30.76) | 1135 (43.40) |
| 5 | 214 (9.05) | 62 (2.62) | 184 (8.57) | 47 (2.14) | 165 (6.24) | 47 (2.44) | 195 (6.14) | 55 (2.60) | 78 (3.01) | 150 (5.74) |
| Nutritional measures | ||||||||||
| Total energy intake, kcal/d | 1768.8 ± 60.6 | 2200.1 ± 126.1 | 1798.4 ± 93.6 | 2171.0 ± 156.6 | 1961.9 ± 172.5 | 1970.3 ± 173.4 | 1991.5 ± 180.4 | 1946.7 ± 163.2 | 1943.0 ± 158.3 | 2000.0 ± 185.2 |
| Total protein intake, g/d | 58.00 ± 5.66 | 79.86 ± 8.82 | 55.49 ± 3.57 | 82.59 ± 6.81 | 65.96 ± 9.84 | 70.58 ± 10.38 | 68.33 ± 10.61 | 68.46 ± 9.95 | 66.81 ± 10.04 | 69.39 ± 11.46 |
| aMED score | 3.77 ± 1.78 | 3.83 ± 1.74 | 3.41 ± 1.69 | 4.14 ± 1.75 | 1.54 ± 0.62 | 6.47 ± 0.66 | 2.60 ± 1.41 | 5.37 ± 1.45 | 4.82 ± 1.65 | 2.93 ± 1.57 |
| DASH score | 22.36 ± 4.29 | 21.28 ± 4.28 | 21.70 ± 4.01 | 21.81 ± 4.38 | 18.61 ± 3.30 | 25.73 ± 3.53 | 16.91 ± 1.95 | 27.93 ± 1.94 | 24.61 ± 3.79 | 19.09 ± 3.61 |
| DII score | −0.79 ± 2.64 | −0.91 ± 2.56 | −0.22 ± 2.72 | −1.30 ± 2.43 | 0.26 ± 2.62 | −2.30 ± 2.14 | 0.12 ± 2.62 | −2.12 ± 2.20 | −4.78 ± 0.47 | 0.06 ± 1.14 |
n = 10,431. aMED, alternate Mediterranean diet; CVD, cardiovascular disease; DASH, Dietary Approaches to Stop Hypertension; DII, Dietary Inflammatory Index; FFQ, food-frequency questionnaire; MET, metabolic equivalent task; Q, quartile; WHI OS, Women’s Health Initiative Observational Study.
Calibrated log-energy (expressed in kcal/d) was calculated as 7.65 + 0.04 (log FFQ energy − 7.22) + 0.01 (BMI − 30.16) − 0.01 (age − 68.63) − 0.02 (black ethnicity) − 0.02 (Hispanic ethnicity) − 0.05 (other minority ethnicity) + 0.01 (high school or less educational attainment) + 0.01 (some college and more educational attainment) − 0.06 (annual household income <$20,000) − 0.02 (income of $20,000–$34,999) − 0.04 (income of $50,000–$75,000) − 0.03 (income >$75,000) − 0.03 (current smoking) + 0.002 (physical activity expressed in METs/wk − 5.94).
Calibrated log-protein (expressed in g/d) was calculated as 4.26 + 0.19 (log FFQ protein − 4.04) + 0.01 (BMI − 30.16) − 0.02 (age − 68.63) − 0.14 (black ethnicity) − 0.02 (Hispanic ethnicity) + 0.05 (other minority ethnicity) − 0.003 (high school or less educational attainment) + 0.02 (college or more educational attainment) + 0.01 (annual household income <$20,000) + 0.03 (income of $20,000–$34,999) + 0.02 (income of $50,000–$75,000) − 0.03 (income >$75,000) + 0.03 (current smoking) + 0.001 (physical activity expressed in METs/wk − 5.94).
Mean ± SD (all such values).
Frailty was operationally defined as the presence of ≥3 of the following 5 criteria: muscle weakness, slow walking speed, exhaustion, low physical activity, and unintentional weight loss. To align the scoring with conventional frailty measures, poor physical function was scored as 2 points, because the scale indicated both muscle strength and walking ability components.
The lowest quartile on the Women’s Health Initiative physical activity questionnaire.
Weight loss of >5% between year 3 and baseline and “yes” to the question “In the past 2 y, did you lose 5 or more pounds not on purpose at any time?”
RAND-36 Physical Function scale scores <75.
RAND-36 Vitality scale scores <55; in the aMED and DASH, higher scores indicate a better diet; in the DII, a higher score indicates a more proinflammatory diet
Over a mean follow-up of 12.4 y (range: 3–21.0 y), there were 3259 (31%) deaths from all causes. The mean ± SD age at death was 81.8 ± 6.2 y. In general, crude death rates, expressed in number of deaths per 1000 person-years, showed a decrease in mortality with higher calibrated energy and protein intakes and better dietary scores (Table 2). The adjusted HRs also showed progressively decreased rates of mortality in older and frail women with higher calibrated dietary energy intakes (P-trend = 0.003), higher calibrated dietary protein intakes (P-trend = 0.03), higher aMED scores (P-trend = 0.006), and higher DASH scores (P-trend = 0.02). Although the adjusted point estimates of HRs (95% CIs) for frail women scoring in the second, third, and fourth quartiles on DII measures were 1.15 (1.03, 1.27), 1.28 (1.15, 1.42), and 1.24 (1.12, 1.38), respectively, compared with women in the first quartile, no overall effect was observed across quartiles (P-trend = 0.35).
TABLE 2.
HRs (95% CIs) for all-cause mortality according to year 3 biomarker-calibrated nutrient intakes and healthy diet index scores in the frail and older WHI OS participants1
| Model 12 |
Model 23 |
||||
| Variable | Crude rate per 1000 person-years | HR (95% CI) | P-trend | HR (95% CI) | P-trend |
| Calibrated total energy intake, kcal/d | |||||
| Q1: <1850 | 34.9 | Ref | 0.07 | Ref4 | 0.003 |
| Q2: 1850–1949 | 26.8 | 0.87 (0.75, 1.01) | 0.84 (0.71, 1.00) | ||
| Q3: 1950–2100 | 21.7 | 0.82 (0.70, 0.97) | 0.78 (0.63, 0.96) | ||
| Q4: >2100 | 19.3 | 0.85 (0.69, 1.04) | 0.73 (0.53, 1.01) | ||
| Calibrated total protein intake, g/d | |||||
| Q1: <60 | 35.2 | Ref | 0.15 | Ref5 | 0.03 |
| Q2: 60–66 | 27.1 | 0.85 (0.70, 1.03) | 0.84 (0.71, 1.00) | ||
| Q3: 67–75 | 21.9 | 0.78 (0.62, 1.00) | 0.77 (0.62, 0.95) | ||
| Q4: >75 | 19.2 | 0.80 (0.56, 1.14) | 0.74 (0.54, 1.01) | ||
| aMED score | |||||
| Q1: <2 | 26.2 | Ref | <0.001 | Ref | 0.006 |
| Q2: 2–3 | 25.9 | 0.96 (0.87, 1.05) | 0.98 (0.89, 1.08) | ||
| Q3: 4–5 | 24.8 | 0.87 (0.78, 0.98) | 0.91 (0.81, 1.03) | ||
| Q4: >5 | 23.1 | 0.79 (0.71, 0.89) | 0.86 (0.76, 0.97) | ||
| DASH score | |||||
| Q1: <19 | 26.0 | Ref | <0.001 | Ref | 0.02 |
| Q2: 19–21 | 25.4 | 0.94 (0.86, 1.04) | 0.97 (0.88, 1.07) | ||
| Q3: 22–25 | 25.2 | 0.90 (0.81, 0.99) | 0.95 (0.86, 1.05) | ||
| Q4: >25 | 24.0 | 0.80 (0.72, 0.89) | 0.88 (0.79, 0.98) | ||
| DII score | |||||
| Q1: <−4.2 | 22.6 | Ref | 0.04 | Ref | 0.35 |
| Q2: −4.2 to –3.1 | 25.6 | 1.17 (1.05, 1.29) | 1.15 (1.03, 1.27) | ||
| Q3: −3.0 to –1.5 | 27.6 | 1.35 (1.22, 1.49) | 1.28 (1.15, 1.42) | ||
| Q4: >−1.5 | 25.3 | 1.24 (1.12, 1.38) | 1.24 (1.12, 1.38) | ||
n = 10,431. Results were derived by using a Cox proportional hazard model. aMED, alternate Mediterranean diet; DASH, Dietary Approaches to Stop Hypertension; DII, Dietary Inflammatory Index; Q, quartile; Ref, reference; WHI OS, Women’s Health Initiative Observational Study.
Adjusted for age and calibrated dietary energy intake.
Adjusted as for model 1 plus calibrated protein intake, race/ethnicity, income, education, BMI, physical activity, smoking, and number of frailty criteria.
Not adjusted for calibrated dietary energy intake.
Not adjusted for calibrated dietary protein intake; 95% CIs for calibrated HRs are based on log-estimated HRs ± 1.96 bootstrap SEs (1000 bootstrapped samples); in the aMED and DASH, higher scores indicate a better diet; in the DII, a higher score indicates a more proinflammatory diet.
Indirect dose-response associations between higher dietary energy intake categories and mortality rates were sustained in a subgroup of women with no early death (defined as death within 3 y after the year 3 follow-up visit; Table 3). Similar, indirect associations between higher calibrated protein intakes and mortality were observed in specific subgroups: women with and without chronic morbidity, in ever smokers, and in women without impending death. With regard to dietary patterns, the sensitivity analysis also indicated a persistent association between higher aMED scores and a lower hazard of death in all except for never-smoker subgroups (P-interaction = 0.04). The association between higher DASH scores and mortality was observed in the subgroup of those without early mortality. Finally, direct associations between higher (i.e., more proinflammatory) DII scores and mortality rates were shown in the no-early-mortality and chronic-morbidity subgroups. Repeated analyses without the inclusion of BMI in the mortality model were comparable to fully adjusted models (data not shown).
TABLE 3.
Subgroup analyses for HRs (95% CIs) for all-cause mortality according to year 3 biomarker-calibrated nutrient intakes and healthy diet index scores in the frail and older WHI OS participants1
| Variable | No chronic conditions2 (n = 5057) | Chronic conditions2 (n = 4993) | No early mortality2,3 (n = 9626) | Never smoker4 (n = 5306) | Ever smoker4 (n = 4985) |
| Calibrated total energy intake, kcal/d5 | |||||
| Q1: <1850 | Ref | Ref | Ref* | Ref | Ref |
| Q2: 1850–1949 | 0.84 (0.66, 1.08) | 0.85 (0.68, 1.05) | 0.85 (0.71, 1.02) | 0.92 (0.72,1.18) | 0.84 (0.64, 1.11) |
| Q3: 1950–2100 | 0.77 (0.56, 1.08) | 0.79 (0.61, 1.03) | 0.77 (0.62, 0.97) | 0.89 (0.64, 1.12) | 0.80 (0.55, 1.15) |
| Q4: >2100 | 0.68 (0.42, 1.12) | 0.75 (0.51, 1.11) | 0.67 (0.49, 0.95) | 1.01 (0.64, 1.59) | 0.70 (0.39, 1.27) |
| Calibrated total protein intake,6 g/d | |||||
| Q1: <60 | Ref* | Ref* | Ref** | Ref | Ref*** |
| Q2: 60–66 | 0.88 (0.74, 1.05) | 0.81 (0.69, 0.95) | 0.84 (0.74, 0.96) | 0.89 (0.76, 1.06) | 0.74 (0.62, 0.88) |
| Q3: 67–75 | 0.73 (0.57, 0.93) | 0.76 (0.61, 0.94) | 0.76 (0.64, 0.90) | 0.85 (0.68, 1.06) | 0.61 (0.47, 0.78) |
| Q4: >75 | 0.73 (0.50, 1.05) | 0.71 (0.52, 0.97) | 0.72 (0.57, 0.93) | 0.99 (0.71, 1.39) | 0.46 (0.31, 0.69) |
| aMED score7 | |||||
| Q1: <2 | Ref* | Ref* | Ref** | Ref | Ref** |
| Q2: 2–3 | 0.96 (0.82, 1.11) | 0.99 (0.88, 1.13) | 0.99 (0.90, 1.10) | 0.99 (0.86, 1.14) | 0.96 (0.84, 1.09) |
| Q3: 4–5 | 0.92 (0.77, 1.11) | 0.89 (0.75, 1.04) | 0.91 (0.79, 1.03) | 0.85 (0.71, 1.02) | 0.97 (0.82, 1.14) |
| Q4: >5 | 0.77 (0.63, 0.94) | 0.87 (0.74, 1.03) | 0.85 (0.74, 0.97) | 0.95 (0.79, 1.13) | 0.76 (0.64, 0.91) |
| DASH score8 | |||||
| Q1: <19 | Ref | Ref | Ref* | Ref | Ref |
| Q2: 19–21 | 1.03 (0.88, 1.20) | 0.91 (0.81, 1.04) | 0.96 (0.86, 1.07) | 1.01 (0.88, 1.18) | 0.97 (0.85, 1.11) |
| Q3: 22–25 | 0.94 (0.80, 1.11) | 0.98 (0.86, 1.12) | 0.94 (0.84, 1.05) | 1.00 (0.86, 1.16) | 0.95 (0.82, 1.09) |
| Q4: >25 | 0.87 (0.73, 1.03) | 0.89 (0.77, 1.03) | 0.87 (0.78, 0.99) | 0.92 (0.77, 1.08) | 0.86 (0.74, 1.00) |
| DII score9 | |||||
| Q1: <−4.2 | Ref | Ref* | Ref* | Ref | Ref |
| Q2: −4.2 to –3.1 | 1.12 (0.95, 1.32) | 1.16 (1.01, 1.33) | 1.12 (1.00, 1.25) | 1.20 (1.03, 1.40) | 1.13 (0.98, 1.31) |
| Q3: −3.0 to –1.5 | 1.29 (1.09, 1.52) | 1.26 (1.09, 1.45) | 1.27 (1.14, 1.43) | 1.37 (1.18, 1.61) | 1.20 (1.03, 1.39) |
| Q4: >−1.5 | 1.06 (0.89, 1.27) | 1.18 (1.02, 1.37) | 1.12 (1.00, 1.27) | 1.11 (0.94, 1.32) | 1.16 (0.98, 1.36) |
n = 10,431. Results were derived by using a Cox proportional hazard model. “Chronic conditions” included cancer, emphysema, cardiovascular disease, and diabetes. *P -trend < 0.05; **P-trend < 0.01; ***P-trend < 0.001. aMED, alternate Mediterranean diet; DASH, Dietary Approaches to Stop Hypertension; DII, Dietary Inflammatory Index; Q, quartile; Ref, reference; WHI OS, Women’s Health Initiative Observational Study.
Adjusted for age, calibrated dietary energy intake, calibrated protein intake, race/ethnicity, income, education, BMI, physical activity, smoking, and number of frailty criteria.
Early mortality was defined as death within 3 y of the study follow-up.
Adjusted for age, calibrated energy intake, calibrated protein intake, race/ethnicity, income, education, BMI, physical activity, and number of frailty criteria.
Not adjusted for calibrated energy intake. P-interaction with chronic morbidity = 0.04 and with smoking = 0.87.
Not adjusted for calibrated protein intake. P-interaction with chronic morbidity = 0.007 and with smoking = 0.67.
P-interaction with chronic morbidity = 0.23 and with smoking = 0.04.
P-interaction with chronic morbidity = 0.89 and with smoking = 0.11.
P-interaction with chronic morbidity = 0.52 and with smoking = 0.82. The 95% CIs for calibrated HRs are based on log-estimated HRs ± 1.96 bootstrap SEs (1000 bootstrapped samples); in the aMED and DASH, higher scores indicate a better diet; in the DII, a higher score indicates a more proinflammatory diet.
DISCUSSION
We derived biomarker-calibrated energy and protein intakes to address dietary self-report error in frail, older women. We used these data to evaluate the associations of mortality in older women with frailty and nutrient intakes and healthy eating indexes that included aMED, DASH, and DII. Over an average of 12.4 y of study follow-up, frail, older women with quantitatively and qualitatively better nutrition in terms of higher dietary energy and protein intakes and better adherence to healthful diets such as the Mediterranean diet and the DASH diet had lower rates of all-cause mortality than did frail women with lower nutrient intake and worse dietary scores. A higher biomarker-calibrated dietary energy and protein intake was inversely associated with mortality, with a stronger association among women with chronic morbidity, in ever smokers, and in those without impending death. The associations of diet scores and mortality were controlled for biomarker-calibrated energy and protein intakes, implying that despite the importance of increased nutrient intake in older populations observed in other studies (5, 28), diet quality may independently relate to mortality rates in persons with frailty. Stratifying by chronic morbidity, smoking status, or by excluding women with early death did not substantially change our results, further reinforcing the validity of the findings. A more proinflammatory diet was associated with a higher hazard of death only in subgroup analyses.
Our study highlights the importance of dietary energy and protein intakes in mortality risk in frail, older women. A nutritional intervention study supported our findings and showed that daily calorie and protein supplementation over 3 mo led to improvement in physical function in community-dwelling older and frail individuals with suboptimal nutritional status (7). Because functional limitations lead to impairment, disability, and increased mortality, these findings are conceptually aligned with the results presented here. Prospective studies that focused on dietary factors also partially corroborated our findings and showed that higher nutritional intakes and in particular a higher protein intake are negatively associated with incident frailty (5). Protein findings are consistent with evidence that women aged >65 y may have an increased requirement for dietary protein that might be even higher than the current recommendation of 0.8 g · kg−1 · d−1 (29). This recommendation might be pertinent to the general older population and even to a greater extent in those with frailty because frailty exacerbates age-related changes in protein metabolism and increases muscle protein catabolism along with decreasing muscle mass (30). In our analyses, higher calibrated quartile estimates for protein intake per kilogram of body weight ranged from an average of 0.9 to almost 1.0 g · kg−1 · d−1. Plausible biological mechanisms for the association between higher protein intakes and reduced mortality include the modulation of lean mass and bone density (31). Furthermore, older people need to compensate for age-related changes in protein metabolism, such as high splanchnic extraction and declining anabolic responses to ingested protein (32). Their protein requirement also may be greater to offset inflammatory and catabolic conditions associated with chronic and acute diseases that occur commonly with aging (33). In support of the latter assertion, in our study, the inverse association between protein intake and mortality appeared to be stronger among frail women with chronic morbidity or in ever smokers.
To our knowledge, this is the first study to evaluate the association of adherence to healthy diets and all-cause mortality in a sample of frail, older women. Another WHI study that included postmenopausal and older women reported that having a better diet quality (as assessed by aMED and DASH scores) was associated with an 18–26% lower all-cause and CVD mortality risk (34). Other large cohort studies also corroborate our findings, although in a mixed population of older and frail individuals. In a cohort of older European men and women [EPIC (European Prospective Investigation into Cancer and Nutrition)-Elderly cohort], a greater adherence to a Mediterranean diet (35) was associated with lower overall mortality. The Mediterranean diet pattern has been repeatedly shown to relate to reduced chronic disease morbidity and mortality in other studies (12, 13, 24). The DASH diet, originally shown to be effective for blood pressure control, mostly overlaps with the Mediterranean diet but also includes low-sodium and low-fat-dairy components (20), and thus has been shown to confer similar health benefits (34). The DII is another diet quality index that was developed through an extensive literature review to approximate the inflammatory propensity of the diet. Studies have shown construct validity of the DII in relation to inflammatory plasma biomarkers (22) and its association with inflammation-related conditions that increased the risk of death (36, 37). Pending further investigation, one cautious interpretation is that because frailty and cardiovascular health are interrelated (14), and because in older women with frailty CVD was the most common cause of death (38), buffering CVD factors through diet might have positive health effects in vulnerable older adults. Likewise, in this population, reducing proinflammatory food intake might also have health benefits, especially in those with comorbidities.
Reverse causation and confounding by chronic morbidity might provide possible methodologic explanations for our findings. An unintentional change in dietary intake may be due to a reduced intake of healthy foods because of low appetite and disease symptoms. To address this potential concern, we repeated the analysis excluding frail women who died within 3 y of follow-up and limited the analyses to those without chronic morbidity, and the results did not substantially change. Thus, reverse causation and confounding by chronic morbidity appear unlikely to explain our findings.
The strengths of this study include the large sample size of frail, older women, the use of well-validated frailty criteria, the use of biomarker-calibrated nutrient intakes for energy and protein, and a long study follow-up with adjudicated mortality protocols. The long follow-up is important because the effect of diet on health may not become significant for many years, and studies with a short follow-up may not sufficiently show an association between diet and mortality. Finally, the most salient strength of our study may come from minimizing the common misreporting of energy and protein consumption from a self-reported FFQ, a major obstacle in the field of nutritional epidemiology, by using the calibrated measures of total energy and protein intakes (10).
Our study has limitations. The lack of objective physical performance measures in the operationalization of frailty might be a weakness of the WHI frailty phenotype. However, our recent analyses indicated that the WHI phenotype measure provides an operational definition of frailty whose predictive ability for adverse health outcomes is at least as good as that of the conventional phenotype (39). As with any observational study, there exists the possibility of unmeasured and residual confounding, including the possibility that as individuals became frail they might also change their food intake. However, rather than comparing frail and nonfrail individuals, our analyses aimed at evaluating diet-mortality associations in those with frailty. Differences in BMI by quartiles of calibrated nutrient intake might raise concern that the results are mainly driven by body size rather than nutrition; however, we controlled for BMI and also conducted sensitivity analyses by excluding BMI from the list of adjustment variables. Healthy eating indexes relied on FFQ data without correction for measurement error; however, our regression models accounted for calibrated dietary energy and protein intakes and provided adjusted diet and health analysis estimates. Given a preponderance of white female participants in our study sample, our results may not be widely generalizable. Furthermore, change in diet over time rather than single measures might provide more nuanced information on whether the risk of developing adverse outcomes is greater or less for frail persons with distinct dynamics in nutrient intake and dietary pattern measures.
In conclusion, the current study underscores public health messages about the importance of adequate nutritional intake and a healthy diet in older and frail women. We also provide important data that both diet quality and quantity should be considered in managing older, frail persons.
Acknowledgments
The authors’ responsibilities were as follows—OZ and YB: had full access to all of the data used in the study, took responsibility for the integrity of the data and the accuracy of data analysis, and were responsible for data analysis and visualization; OZ, SZ-S, and LFT: developed the study concept and design, interpretated the data, and prepared the manuscript; OZ, SZ-S, JRH, SES, NS, FKT, MDW, JMS, TO, RBW, LS, and LFT: prepared the manuscript; and all authors: read and approved the final manuscript. JRH owns a controlling interest in Connecting Health Innovations LLC (CHI), a company planning to license the right to his invention of the Dietary Inflammatory Index (DII) from the University of South Carolina to develop computer and smartphone applications for patient counseling and dietary intervention in clinical settings. MDW and NS are employees of CHI. The other authors did not have any conflicts of interest related to the study.
Footnotes
Abbreviations used: aMED, alternate Mediterranean diet; CVD, cardiovascular disease; DASH, Dietary Approaches to Stop Hypertension; DII, Dietary Inflammatory Index; FFQ, food-frequency questionnaire; NBS, Nutrition Biomarker Study; NPAAS, Nutrition and Physical Activity Assessment Study; OS, Observational Study; WHI, Women’s Health Initiative.
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