Skip to content

Advertisement

  • Research article
  • Open Access
  • Open Peer Review

Frailty, nutrition-related parameters, and mortality across the adult age spectrum

  • 1, 2,
  • 2, 3,
  • 4,
  • 2, 5 and
  • 3, 6Email author
BMC Medicine201816:188

https://doi.org/10.1186/s12916-018-1176-6

  • Received: 9 July 2018
  • Accepted: 19 September 2018
  • Published:

The Correction to this article has been published in BMC Medicine 2018 16:235

Open Peer Review reports

Abstract

Background

Nutritional status and individual nutrients have been associated with frailty in older adults. The extent to which these associations hold in younger people, by type of malnutrition or grades of frailty, is unclear. Our objectives were to (1) evaluate the relationship between individual nutrition-related parameters and frailty, (2) investigate the association between individual nutrition-related parameters and mortality across frailty levels, and (3) examine whether combining nutrition-related parameters in an index predicts mortality risk across frailty levels.

Methods

This observational study assembled 9030 participants aged ≥ 20 years from the 2003–2006 cohorts of the National Health and Nutrition Examination Survey who had complete frailty data. A 36-item frailty index (FI) was constructed excluding items related to nutritional status. We examined 62 nutrition-related parameters with established cut points: 34 nutrient intake items, 5 anthropometric measurements, and 23 relevant blood tests. The 41 nutrition-related parameters which were associated with frailty were combined into a nutrition index (NI). All-cause mortality data until 2011 were identified from death certificates.

Results

All 5 anthropometric measurements, 21/23 blood tests, and 19/34 nutrient intake items were significantly related to frailty. Although most nutrition-related parameters were directly related to frailty, high alcohol consumption and high levels of serum alpha-carotene, beta-carotene, beta-cryptoxanthin, total cholesterol, and LDL-c were associated with lower frailty scores. Only low vitamin D was associated with increased mortality risk across all frailty levels. Seventeen nutrition-related parameters were associated with mortality in the 0.1–0.2 FI group, 11 in the 0.2–0.3 group, and 16 in the > 0.3 group. Overall, 393 (5.8%) of the participants had an NI score less than 0.1 (abnormality in ≤ 4 of the 41 parameters examined). Higher levels of NI were associated with higher mortality risk after adjusting for frailty and other covariates (HR per 0.1: 1.19 [95%CI 1.133–1.257]).

Conclusions

Most nutrition-related parameters were correlated to frailty, but only low vitamin D was associated with higher risk for mortality across levels of frailty. As has been observed with other age-related phenomena, even though many nutrition-related parameters were not significantly associated with mortality individually, when combined in an index, they strongly predicted mortality risk.

Keywords

  • Nutrition
  • Dietary intake
  • Frailty
  • Frailty index
  • Mortality
  • NHANES

Background

Reflecting the increasing life expectancy of the global population [1], the number of adults aged 65 years or older is predicted to double by 2050 [2]. In parallel, the prevalence of age-related health deficits including cardiovascular, metabolic, cognitive, and musculoskeletal diseases is growing [36]. Frailty is a multiply determined, age-related state of vulnerability to adverse health outcomes compared with others of the same age [7, 8]. It is associated with a range of adverse outcomes, including morbidity, mortality, and increased healthcare costs [9, 10]. Frailty can be observed at all adult ages and is closely tied to ageing, suggesting that the prevalence of frailty is likely to increase as populations age [11]. Even so, two European cohorts have observed only very modest increases with age in the mean frailty, despite varying estimates in the extent of its lethality, especially in people with milder degrees of frailty [12, 13].

Against this background, two considerations motivate a more comprehensive understanding of the relationship between nutrition and frailty. First, the two are linked. The prevalence of malnourished individuals can be high in ageing populations, especially in rehabilitation, hospital, and nursing home settings [14, 15]. Malnutrition, which is affected by inadequate, excessive, or imbalance of energy or nutrient consumption, is associated with physical and cognitive impairment, poor quality of life, morbidity, and mortality in older individuals [1620]. Malnutrition is also associated with higher levels of frailty [8, 21].

Second, optimal nutrition management can improve frailty [22, 23] and some nutrient intakes or supplements, for example, fish oil and antioxidants, are associated with reduced frailty levels [2427]. Nutrition management therefore appears to make poor nutrition a modifiable risk factor in relation to frailty. Importantly too, nutrition management appears to work well, in both hospital and community settings, as part of multidimensional interventions that also include exercise, pharmacological treatment, and social support [2831].

Despite these promising insights, the evidence about the relationship of nutrition-related parameters with frailty, and whether these associations hold in younger people and by type of malnutrition, is limited and inconsistent [3235]. Further, the multiplicity of claims about which nutritional factors might be most important is a pragmatic obstacle to uptake [8, 3638]. This obscures how the relationship might arise, and where new interventions might best be targeted. In other contexts in which the impact of age-related adverse outcomes varies by which items are studied, it has been useful to study deficits in the aggregate [39], something which has been variably applied in nutrition studies [40]. To help improve the understanding of the relationship between frailty and nutrition, this study aims (1) to evaluate the relationship between individual nutrition-related parameters and frailty, (2) to investigate the effect of these parameters on mortality risk across levels of frailty, and (3) to examine whether combining nutrition-related parameters in an index predicts mortality risk across frailty levels.

Methods

Study population and design

This observational study used data from 10,020 individuals aged 20 years or more from the 2003–2004 and 2005–2006 cohorts of the National Health and Nutrition Examination Survey (NHANES). NHANES is a series of publicly available, cross-sectional surveys focusing on the health and nutrition of non-institutionalized US residents [41, 42]. For the purpose of this study, 990 individuals with missing FI scores were excluded. The final sample included 9030 participants. Mortality status was identified from the death certificate records from the National Death Index in December 31, 2011, and survival time was counted from the date of the clinical examination to the death event.

Each participant signed written informed consent provided to participate. The NHANES protocol was approved by the institutional review board of the Centers for Disease Control and Prevention (CDC). As a matter of policy, our local Research Ethics Committee does not review secondary analyses of duly approved, publicly available data.

Nutrition-related data

Of 84 nutrition-related parameters included in NHANES, 62 items had established cut points. Among them, 34 energy and nutrient intake items were estimated from dietary information recalled during the 24-h period prior to the interview. Five anthropometric measurements and 23 blood tests related to nutrition were collected with standard techniques. The normal range of each parameter is shown in Table 5 in Appendix. These cut points were taken from a standard textbook, the Dietary Reference Intake (DRIs), published guidelines, and previous studies [11, 4355].

Frailty index

The FI used in this study included 36 items and was modified from a previously validated FI in NHANES [11, 56] (Table 6 in Appendix). We excluded from the FI all items related to dietary intake or nutritional status (i.e. difficulty using fork and knife, difficulty preparing meals, glycohaemoglobin, triglyceride, creatinine, haemoglobin, mean corpuscular volume, total cholesterol, glucose, and sodium). The FI score, the number of deficits present divided by the total deficits considered, ranges between 0 and 1, and a higher score is associated with higher frailty. For stratification purposes, we grouped participants into 4 FI groups: FI ≤ 0.1 (fit), 0.1 < FI ≤ 0.2 (vulnerable), 0.2 < FI ≤ 0.3 (mildly frail), and FI > 0.3 (moderately/severely frail) [56].

Nutrition index

A nutrition index (NI) was constructed following the deficit accumulation approach [57] by combining the 41 nutrition-related parameters that were related with higher frailty: counting the number of nutritional deficits in an individual and dividing by the total deficits considered. Low-density lipoprotein cholesterol (LDL-c) and subscapular skinfold were excluded from the NI due to high number of missing data: 53.9% for LDL-c and 23.8% for subscapular skinfold. Each nutritional parameter was scored “1” if the value fell outside the normal range and “0” otherwise. Abnormal values that were found to be protective for frailty (associated with lower levels of frailty) were also scored as 0 (Table 5 in Appendix). An NI score was only calculated for individuals with > 80% of the variables complete. The NI score ranges between 0 and 1; an NI score of 0 represents full nutritional health, while a score of 1 represents complete nutritional deficits. In the analysis, we used both the continuous NI score and a categorical variable: NI ≤ 0.2, 0.2 < NI ≤ 0.3, 0.3 < NI ≤ 0.4, 0.4 < NI ≤ 0.5, and NI > 0.5.

Statistical analysis

Demographic characteristics of the subjects are presented as mean ± standard deviation (SD) for continuous variables and as frequency (%) for binary or categorical variables. All percentages and mean values were weighted using the sampling weights provided by NHANES. Multiple linear regression analysis was used to assess the associations between each nutrition-related parameter, NI and FI scores and is presented by β-coefficient with 95% confidence interval (CI). The mortality risk from each parameter across the FI group was analysed using Cox regression models, and the odds of mortality risk was presented using the hazard ratios and the associated 95%CI. All regression models were adjusted for potential covariates including age, sex, race, energy intake, educational level, marital status, employment status, smoking, and study cohort. Models which included energy, energy per weight, dietary fiber per energy intakes, and NI as predictors were not adjusted for energy intake. Annual household income was not included as covariate due to missing data. Statistical significance was considered as a p value < 0.05, and all reported probability tests were two-sided. The statistical analysis was conducted using IBM SPSS Statistics for Windows, Version 24.0. Armonk, NY: IBM Corp.

Results

Of the 9030 included participants, 48% were male; their weighted mean age was 46.6 ± 16.9 years. When we stratified the sample by frailty, 5119 (56.7%), 2009 (22.2%), 1014 (11.2%), and 888 (9.8%) had an FI score < 0.1, 0.1–0.2, 0.2–0.3, and > 0.3, respectively. The weighted mortality rate was 6.5% (940/9030). The demographic characteristics of the sample by frailty categories are presented in Table 1. In the frailer groups, the mean age and number of people with female gender, lower education, non-full-time work, and low income were significantly higher (p < 0.001) (Table 1).
Table 1

Demographic characteristics of participants by frailty level

Characteristics

Frailty index score

≤ 0.1

N = 5119

> 0.1 to 0.2

N = 2009

> 0.2 to 0.3

N = 1014

> 0.3

N = 888

Age (year), mean ± SD

39.7 ± 13.2

54.8 ± 15.8

62.8 ± 14.5

65.3 ± 14.4

Sex, female, N (%)

2540 (48.3)

1114 (58.7)

529 (56.2)

504 (60.9)

Race, N (%)

 Non-Hispanic White

2478 (70.4)

1112 (75.6)

611 (79.9)

493 (73.1)

 Non-Hispanic Black

1057 (10.6)

409 (10.8)

196 (10.7)

212 (15.1)

 Hispanic

1356 (13.5)

416 (8.8)

179 (5.5)

144 (5.8)

 Other

228 (5.5)

72 (4.7)

28 (4.0)

39 (5.9)

Education, N (%)

 Less than high school

1193 (14.3)

614 (19.5)

384 (27.6)

386 (33.1)

 High school

1195 (24.4)

513 (27.4)

277 (30.3)

211 (29.3)

 Some college/associated education

1560 (32.7)

528 (31.1)

226 (26.4)

204 (27.6)

 College graduate or more

1167 (28.6)

352 (22.0)

127 (15.7)

80 (10.0)

Annual household Income (USD), N (%)

 0–19,999

802 (11.1)

478 (18.2)

335 (27.3)

385 (39.2)

 20,000–44,999

1533 (27.0)

686 (33.0)

354 (38.3)

266 (34.6)

 45,000–74,999

1149 (26.2)

391 (25.6)

143 (21.2)

120 (18.4)

 ≥ 75,000

1336 (35.7)

335 (23.3)

107 (13.2)

55 (7.8)

Marital status, N (%)

 Married

3376 (67.8)

1245 (65.4)

569 (59.9)

402 (50.0)

 Widowed

129 (1.9)

280 (10.7)

225 (16.8)

260 (24.2)

 Divorced or separated

500 (10.2)

294 (14.8)

154 (16.7)

164 (18.7)

 Never married

1110 (20.2)

190 (9.1)

65 (6.6)

61 (7.2)

Full-time working, N (%)

3819 (80.7)

882 (53.4)

214 (28.1)

72 (11.7)

Smoking status, N (%)

 Never

2864 (53.5)

988 (47.4)

411 (40.1)

377 (41.2)

 Former

1021 (20.5)

600 (29.7)

414 (38.1)

346 (37.7)

 Current

1234 (26.0)

421 (22.9)

189 (21.8)

165 (21.1)

The percentages and mean values are weighted

USD United States Dollar

Regarding objective 1 (to evaluate the relationship between individual nutrition-related parameters and frailty), many but not all nutrition-related parameters—especially those related to self-reported intake—varied in relation to the degree of frailty. The proportion of individuals who had abnormal dietary intakes differed significantly between FI groups in almost all variables, except high intake of saturated fat (%), vitamin A, iron, zinc, copper, selenium, and caffeine, and low intake of vitamin A and vitamin C (Table 2). Related to anthropometric measurement, only the percentage of individuals who were underweight and had low subscapular skinfold thickness did not significantly differ between FI groups (Table 3). Similarly, the proportion of individuals who had abnormal blood tests differed significantly between FI groups in almost all variables, except low MCV, low levels of folate in red blood cell and plasma glucose, and high levels of haemoglobin, serum beta-carotene, serum lutein/zeaxanthin, and serum iron (Table 4).
Table 2

Number of participants with abnormal range of daily nutrient intakes by frailty level

Nutrients, N (%)*

Frailty index score

≤ 0.1

N = 5119

> 0.1 to 0.2

N = 2009

> 0.2 to 0.3

N = 1014

> 0.3

N = 888

Energy (N = 8614)

Low

2218 (44.4)

1157 (55.3)

297 (63.8)

203 (71.7)

Energy per weight (N = 8510)

Low

1950 (39.8)

1051 (54.1)

605 (60.9)

566 (69.7)

High

1479 (30.8)

307 (17.4)

108 (13.9)

64 (7.9)

Protein (N = 8614)

Low

821 (15.6)

450 (20.9)

297 (27.5)

303 (33.5)

Protein per weight (N = 8510)

Low

1524 (29.0)

955 (46.8)

563 (55.0)

524 (63.6)

Carbohydrate (N = 8614)

Low

1068 (22.8)

608 (31.1)

357 (35.5)

360 (41.2)

Simple sugar (N = 8614)

High

4633 (94.6)

1778 (92.9)

896 (93.1)

758 (91.7)

Dietary fiber per energy (N = 8613)

Low

4590 (94.6)

1713 (91.0)

870 (91.9)

755 (92.8)

Percentage of fat (N = 8614)

Low

119 (2.0)

83 (3.6)

41 (4.2)

46 (4.6)

High

4413 (91.1)

1650 (88.1)

799 (85.0)

670 (82.7)

Percentage of saturated fat (N = 8613)

High

2827 (59.6)

1078 (59.0)

554 (57.4)

479 (60.8)

Cholesterol (N = 8614)

High

1924 (39.2)

652 (33.4)

312 (30.9)

255 (28.5)

Vitamin A, RAE (N = 8614)

Low

3725 (75.0)

1502 (76.8)

745 (76.1)

647 (76.7)

High

31 (0.7)

5 (0.1)

11 (1.0)

4 (0.5)

Vitamin C (N = 8614)

Low

2903 (62.2)

1165 (61.8)

598 (63.2)

516 (65.1)

High

0 (0.0)

0 (0.0)

0 (0.0)

1 (0.1)

Vitamin E (N = 8614)

Low

4548 (92.4)

1814 (93.2)

931 (94.9)

802 (95.9)

Vitamin K (N = 8614)

Low

3754 (74.4)

1503 (76.0)

776 (78.0)

679 (80.6)

Thiamin (N = 8614)

Low

1411 (27.3)

700 (34.3)

362 (35.2)

375 (42.6)

Riboflavin (N = 8614)

Low

831 (14.5)

359 (15.7)

189 (17.4)

212 (23.6)

Niacin (N = 8614)

Low

981 (18.0)

544 (25.3)

301 (26.2)

332 (37.1)

High

1020 (23.0)

223 (13.1)

95 (13.0)

65 (8.7)

Pyridoxine (N = 8614)

Low

1596 (32.2)

898 (43.7)

507 (47.9)

470 (54.0)

Folate (N = 8614)

Low

2751 (54.8)

1236 (63.3)

658 (64.6)

606 (71.3)

High

138 (3.2)

38 (2.1)

19 (2.7)

10 (1.3)

Cobalamin (N = 8614)

Low

1252 (24.5)

593 (28.5)

307 (30.5)

287 (32.8)

Calcium (N = 8614)

Low

3150 (63.7)

1457 (73.4)

787 (78.4)

698 (81.0)

High

125 (2.8)

30 (1.8)

9 (1.2)

4 (0.8)

Phosphorous (N = 8614)

Low

551 (10.1)

322 (14.7)

187 (18.5)

217 (24.7)

High

29 (0.5)

8 (0.5)

1 (0.3)

0 (0.0)

Magnesium (N = 8614)

Low

3656 (74.2)

1526 (76.9)

828 (82.7)

731 (87.1)

Iron (N = 8614)

Low

1750 (34.7)

579 (30.7)

223 (23.3)

228 (29.0)

High

65 (1.4)

21 (1.1)

7 (1.0)

5 (0.7)

Zinc (N = 8614)

Low

1863 (36.3)

898 (42.8)

531 (49.7)

468 (52.5)

High

56 (1.2)

14 (0.8)

8 (1.0)

3 (0.3)

Copper (N = 8614)

Low

1322 (25.5)

663 (31.9)

369 (34.8)

379 (44.4)

High

10 (0.3)

1 (0.0)

2 (0.1)

1 (0.1)

Sodium (N = 8614)

Low

359 (6.2)

183 (8.0)

81 (7.5)

117 (12.4)

High

3742 (79.2)

1219 (65.8)

599 (64.5)

435 (54.2)

Potassium (N = 8614)

Low

4484 (91.4)

1799 (92.4)

935 (95.6)

810 (96.7)

Selenium (N = 8614)

Low

571 (10.8)

344 (16.9)

203 (20.4)

228 (26.4)

High

15 (0.3)

8 (0.5)

1 (0.1)

0 (0.0)

Caffeine (N = 8614)

High

489 (14.2)

191 (13.5)

82 (12.3)

80 (11.4)

Alcohol (N = 8614)

High

885 (21.7)

270 (16.8)

111 (12.9)

59 (8.8)

Linoleic acid (N = 8614)

Low

2414 (47.9)

1030 (51.3)

562 (54.7)

531 (62.1)

α-Linolenic acid (N = 8614)

Low

2491 (49.8)

1100 (53.8)

603 (58.4)

552 (63.9)

Fish oil (N = 8614)

Low

4343 (88.7)

1700 (88.5)

872 (90.6)

764 (91.1)

RAE retinol activity equivalents

*The percentages are weighted

Table 3

Number of participants with abnormal range of anthropometric measurement by frailty level

Anthropometric measurements, N (%)*

Frailty index score

≤ 0.1

N = 5119

> 0.1 to 0.2

N = 2009

> 0.2 to 0.3

N = 1014

> 0.3

N = 888

Body mass index (N = 8873)

Underweight

91 (1.9)

22 (1.3)

17 (1.8)

10 (1.2)

Overweight

1816 (34.5)

702 (33.8)

341 (31.5)

244 (29.3)

Obese

1519 (28.6)

735 (38.9)

408 (44.1)

359 (44.2)

Body weight change in past 1 year (N = 8852)

Loss > 10%

381 (6.8)

194 (9.7)

122 (10.9)

151 (15.6)

Gain > 10%

872 (13.7)

252 (12.1)

115 (13.3)

104 (14.0)

Waist circumference (N = 8644)

High

3444 (67.2)

1603 (82.2)

815 (85.9)

643 (86.1)

Triceps skinfold (N = 7885)

Low

538 (11.3)

147 (8.1)

84 (8.6)

76 (10.3)

High

415 (9.3)

184 (12.3)

108 (15.9)

93 (13.5)

Subscapular skinfold (N = 6884)

Low

428 (11.1)

143 (9.3)

66 (8.4)

62 (11.2)

High

281 (7.2)

140 (9.0)

62 (10.0)

45 (6.8)

*The percentages and mean values are weighted

Table 4

Number of participants with abnormal range of blood levels by frailty level

Blood tests, N (%)*

Frailty index score

≤ 0.1

N = 5119

> 0.1 to 0.2

N = 2009

> 0.2 to 0.3

N = 1014

> 0.3

N = 888

Total lymphocyte count (N = 8965)

Low

862 (17.8)

451 (20.9)

272 (24.2)

304 (34.6)

Haemoglobin (N = 9017)

Low

304 (3.4)

224 (7.4)

175 (12.6)

216 (20.9)

High

40 (1.0)

25 (1.4)

13 (2.1)

9 (0.8)

Mean corpuscular volume (N = 9017)

Low

170 (2.4)

130 (5.3)

30 (2.3)

43 (4.2)

High

43 (0.9)

74 (3.7)

56 (5.8)

56 (6.6)

Albumin (N = 8916)

Low

308 (1.8)

84 (1.8)

28 (2.2)

68 (7.2)

Vitamin A (N = 8889)

Low

1 (0.0)

2 (0.1)

3 (0.1)

5 (0.6)

High

168 (4.4)

148 (8.9)

128 (13.7)

159 (19.0)

Vitamin C (N = 8886)

Low

264 (6.6)

147 (7.4)

78 (8.3)

82 (8.0)

High

77 (1.8)

66 (3.4)

44 (4.6)

36 (4.4)

Vitamin D (N = 8976)

Low

1906 (29.4)

740 (30.5)

422 (35.6)

438 (44.6)

High

59 (1.5)

9 (0.6)

2 (0.2)

2 (0.3)

Pyridoxine (N = 8926)

Low

869 (15.0)

380 (16.5)

206 (19.4)

231 (25.6)

Folate, RBC (N = 8959)

Low

249 (4.1)

73 (2.7)

40 (3.1)

31 (3.5)

Cobalamin (N = 8865)

Low

112 (2.0)

50 (2.4)

43 (4.4)

39 (5.3)

α-carotene (N = 8885)

Low

1045 (21.4)

396 (20.4)

220 (22.5)

241 (30.3)

High

562 (11.4)

223 (11.1)

72 (6.1)

54 (6.6)

β-carotene (N = 8501)

Low

908 (19.5)

345 (20.0)

197 (21.9)

189 (24.7)

High

565 (11.9)

277 (13.4)

131 (11.8)

101 (11.0)

β-cryptoxanthin (N = 8865)

Low

619 (15.5)

368 (21.4)

247 (28.8)

257 (35.6)

High

876 (12.3)

294 (12.1)

122 (8.7)

76 (7.0)

Lutein/Zeaxanthin (N = 8889)

Low

1131 (26.5)

531 (32.0)

307 (34.8)

346 (46.5)

High

229 (3.8)

109 (4.8)

46 (4.5)

34 (3.5)

Lycopene (N = 8889)

Low

584 (10.7)

401 (16.3)

317 (29.2)

369 (40.3)

High

666 (14.0)

163 (10.0)

55 (6.3)

34 (5.4)

Iron, serum (N = 8910)

Low

669 (11.6)

309 (13.8)

145 (15.3)

180 (20.8)

High

84 (1.8)

22 (1.1)

10 (1.0)

7 (1.0)

Creatinine (N = 8916)

Low

337 (3.4)

103 (3.7)

40 (3.3)

30 (4.1)

High

68 (1.2)

145 (6.0)

166 (13.9)

232 (24.7)

Total cholesterol (N = 8950)

High

2380 (46.1)

1053 (52.6)

445 (44.2)

367 (43.7)

Triglyceride (N = 8911)

High

1574 (29.1)

734 (39.2)

402 (42.2)

370 (44.1)

HDL-c (N = 8949)

Low

1453 (30.1)

576 (30.9)

290 (29.8)

312 (37.9)

LDL-c (N = 4161)

High

789 (32.7)

318 (32.5)

119 (24.0)

115 (29.0)

Glucose (N = 8916)

Low

141 (2.0)

25 (1.0)

16 (1.4)

20 (2.6)

High

814 (15.3)

666 (31.5)

439 (39.7)

423 (46.5)

Homocysteine (N = 8979)

High

21 (0.5)

25 (1.1)

26 (2.1)

46 (5.0)

HDL-c high-density lipoprotein cholesterol, LDL-c low-density lipoprotein cholesterol, RBC red blood cell

*The percentages are weighted

Linear regression models, adjusted for the potential covariates, revealed statistically significant associations between frailty and the inappropriate intake of many nutrients (Table 7 in Appendix), the abnormal range of many anthropometric measures (Table 8 in Appendix), and the abnormality of many nutrition-related blood tests (Table 9 in Appendix). To summarize, frailty was associated with 19 nutrient intakes (Fig. 1a). Low energy intake per weight showed the highest positive correlation with frailty (β-coefficient 0.018, 95%CI 0.014–0.021) followed by low protein per weight intake (0.016, 0.011–0.020), whereas high consumption of energy per weight, sodium, and alcohol were significantly associated with lower FI score. With regard to anthropometric measurements, only being overweight was significantly associated with lower frailty. Obesity, high waist circumference, triceps and subscapular skinfold thickness, and body weight change (loss and gain more than 10%) were significantly associated with higher FI score (Fig. 1b). Almost all blood tests (21/23) were significantly correlated with frailty. The highest association was found in low serum vitamin A (β-coefficient 0.085, 95%CI 0.030–0.139). High serum levels of alpha-carotene, beta-carotene, beta-cryptoxanthin, lutein/zeaxanthin, lycopene, total cholesterol, and LDL-c were inversely associated with FI score (Fig. 1c).
Fig. 1
Fig. 1

Association between abnormal nutritional-related parameters and frailty. a Nutrient intakes. b Anthropometric measurements. c Blood tests. HDL-c, high-density lipoprotein cholesterol; LDL-c, low-density lipoprotein cholesterol; MCV, mean corpuscular volume. All analyses were adjusted for age, sex, race, energy intake, educational level, marital status, employment status, smoking, and study cohort except for energy, energy per weight, and dietary fiber per energy which were not adjusted for energy intake

Results related to the relationship of the nutrition-related parameters with mortality risk (objective 2) are presented in Fig. 2 and Tables 10, 11, and 12 in Appendix. To summarize, only one abnormal blood test (low vitamin D which was associated with mortality risk at all grades of frailty) showed a relationship with mortality in people with FI ≤ 0.1; four nutrient intakes, three anthropometric measurements, and ten blood tests in people with 0.1–0.2 FI; one nutrient intake, four anthropometric measurements, and six blood tests in people with 0.2–0.3 FI; and three nutrient intakes, three anthropometric measurements, and ten blood tests in people with FI > 0.3. Participants with FI > 0.1 who reported that they lost more than 10% of their weight in the past year had higher mortality risk. Being underweight and low serum creatinine levels were associated with higher mortality risk in individuals with FI > 0.2. Being overweight, having high waist circumference, and caffeine consumption were significantly associated with lower mortality risk in individuals with FI > 0.3.
Fig. 2
Fig. 2

Association between abnormal nutritional-related parameters and mortality across levels of frailty. a Nutrient intakes. N/A, results are not available due to low sample sizes and mortality rate. b Anthropometric measurements. c Blood tests. FI, frailty index. All analyses were adjusted for age, sex, race, energy intake, educational level, marital status, employment status, smoking, and study cohort except for energy and energy per weight which were not adjusted for energy intake. *p value < 0.05

Regarding objective 3 (to examine whether combining nutrition-related parameters in an index predicts mortality risk across frailty levels), we could not calculate the NI score for 500 individuals due to missing > 20% of the nutritional parameters included in the index (total included n = 8530). Overall, 393 (5.8%) of the participants had an NI score less than 0.1 (abnormality in ≤ 4 of the 41 parameters examined). This proportion decreased with higher frailty, from 7.4% among those with FI < 0.1 to 0.7% among those with FI > 0.3 (Fig. 3 and Table 13 in Appendix). The weighted mean NI score was 0.29 ± 0.13 (range 0.00–0.79) and was significantly higher for those people with higher frailty levels: 0.26 ± 0.12 for FI ≤ 1, 0.31 ± 0.13 for 0.1–0.2 FI, 0.35 ± 0.13 for 0.2–0.3 FI, and 0.40 ± 0.14 for FI > 0.3. Higher NI score was significantly associated with higher frailty (β-coefficient 1.46, 95%CI 1.459–1.461) and higher mortality risk (HR per 0.1 NI score 1.30, 95%CI 1.23–1.36) after adjusting the models for potential covariates. After adjusting the survival analysis additionally for the FI, the HR per 0.1 NI score was 1.19 (95%CI 1.13–1.26). When analysis was stratified by frailty level, higher NI scores were significantly correlated with higher mortality in individual with FI > 0.1; HR per 0.1 NI score was 1.17 (1.06–1.30) for those with 0.1–0.2 FI, 1.20 (1.08–1.32) for those with 0.2–0.3 FI, and 1.27 (1.16–1.38) for those with FI > 0.3 (Fig. 4 and Table 14 in Appendix). When we examined the joint effect of nutrition and frailty status on mortality, we found a dose-response relationship (Fig. 5 and Table 15 in Appendix). People with FI > 0.3 had a higher mortality risk regardless of nutrition status, whereas having an FI ≤ 0.1 was not associated with frailty even for those with NI > 0.5. People with FI > 0.3 and NI > 0.5 had the highest mortality risk (HR 8.17, 95%CI 5.16–12.94).
Fig. 3
Fig. 3

Percentage of participants in each level of nutritional index score by frailty level. The percentages are weighted

Fig. 4
Fig. 4

Association between nutritional index and mortality across levels of frailty. FI, frailty index; NI, nutritional index. All analyses were adjusted for age, sex, race, educational level, marital status, employment status, smoking, and study cohort except for energy and energy per weight which were not adjusted for energy intake. *p value < 0.05

Fig. 5
Fig. 5

Combined effect of frailty and nutrition on mortality. FI, frailty index; NI, nutritional index. All analyses were adjusted for age, sex, race, educational level, marital status, employment status, smoking, and study cohort

Discussion

This observational study aimed to improve our understanding of the relationship between frailty and nutrition. As expected, we found that the two are related. When we looked at one nutritional parameter at a time (objective 1), the details are complicated: most but not all of the abnormal nutrition-related parameters included in NHANES were related to frailty (19/34 of nutrient intakes, all 5 anthropometric measurements and 21/23 of blood tests). Nevertheless, fewer than half were individually associated with higher mortality risk across frailty levels and their impact differed across levels of frailty (objective 2). A relationship with all-cause mortality was found with one parameter in the FI ≤ 0.1 group, 17 parameters in the 0.1–0.2 FI group, 11 parameters in the 0.2–0.3 FI group, and 16 parameters in the > 0.3 FI group. Only low serum vitamin D significantly increased the mortality risk across all levels of frailty. Even so, when we combined the nutrition-related parameters, including those not significantly associated with mortality, the resulting NI strongly predicted mortality risk, especially among those with higher FI scores (objective 3). In short, overall, the results show that frailty and nutrition are related, and for the most part, unless people are in good health, poor nutritional status increases mortality in a dose-dependent fashion, independent of age, sex, marital status, and education.

Several features of these results require additional comment. Regarding the individual items, vitamin D plays an important role in both bone metabolism and non-bony tissue function including skeletal muscles which relate with function in elderly people [58]. Previous observational studies [59, 60] including one using the NHANES III data [61] showed that serum vitamin D levels were correlated with frailty and all-cause mortality in older adults. Moreover, a meta-analysis of RCTs [62] reported the benefit of daily vitamin D supplementation on muscle strength and balance in older people. Concerning cognitive function, severe vitamin D deficiency was also correlated with visual memory decline [63]. The current study confirmed the association between low serum vitamin D levels and both frailty levels and mortality risk across levels of frailty, not only in older people but also in younger people.

According to World Health Organization (WHO), the normal range of weight in healthy adults is defined by body mass index (BMI) or Quetelet index between 18.5 and 24.9 kg/m2 [64]. Even so, human physiology and mortality risk factors change with ageing. A previous meta-analysis [65] showed that a BMI < 23 kg/m2 was associated with higher mortality risk in older people. BMI alone may not be a good indicator of adiposity in this population and this has been widely demonstrated based on the obesity paradox seen in the older people [66, 67]. The present study showed that obesity was associated with higher frailty but had no relationship with mortality. In contrast, being underweight increased mortality risk in individuals with FI > 0.2 and the mortality risk was lower in people with FI > 0.3 who were overweight. It is possible that body composition and weight change may be better predictors in older people than BMI. This study revealed that excessive fat accumulation, high triceps and subscapular skinfold thickness, waist circumference, and change of body weight (loss and gain) more than 10% in the past year were correlated with higher frailty. Moreover, low triceps skinfold in people with 0.1–0.3 FI and weight loss more than 10% in the past year in people with FI > 0.1 were associated with higher mortality risk.

On the subject of phytochemicals, previous studies [68, 69] showed that low serum carotenoids levels were associated with higher frailty. This study also confirmed that low serum alpha-carotene, beta-carotene, beta-cryptoxanthin, lutein/zeaxanthin, and lycopene levels increased the risks of frailty and mortality; high serum levels of these carotenoids were associated with lower frailty levels. The relationship between the amount of dietary carotenoid intakes and their serum levels in older adults should be explored further. Recommending carotenoids-rich fruits and vegetables consumption could be the focus of dietary interventions to improve frailty status.

This study illustrates the virtue of considering deficit accumulation as a means of providing context in age-related disorders. As put pithily in a 2014 Nature commentary, “the problems of old age come as a package” [70]. Deficit accumulation indices can quantify those packages of age-associated problems [71] and have been used by our group and others in a variety of contexts to quantify the cumulative impact of brain MRI changes [72], social vulnerability measures [73], laboratory measures [74], and ageing biomarkers [75]. An NI, constructed using the deficit accumulation approach, was a stronger prediction of frailty and mortality risk than were single nutritional parameters. This study, similarly to previous studies [76, 77], highlights that the accumulation of small deficits, even those that may not result in clinically detectable problems, corresponds to the ability of the organism to respond and recover from stressors [78]. A recent report noted the benefit to considering 11 nutrition-related parameters in mortality prediction, but did not evaluate frailty [40]. The findings from that work do not contradict our key clinical message: patient management should reflect not just nutritional parameters that cross an illness threshold, but the overall nutritional status.

In addition, there appears to be some merit in broader modeling of the nutrition risk as part of age-related deficit accumulation [79]. For example, the doubling time of biomarker deficits appears to be longer than laboratory ones, which in turn are longer than clinical deficits [74, 75, 80], something which appears to reflect their relative connectivity as nodes in a network. How the various types of nutritional deficits fit in this spectrum is of interest, with an initial hypothesis that their variable relationships with mortality might reflect their connectivity (or other network properties). Recent work suggests that information theory might help better analyse factors that influence the health trajectories of individuals [79], offering pragmatic new approaches to studying age-related disease [81].

Here, participants with low energy consumption for their body weight were more likely to be frail. Lower than recommended calorie intake can cause malnutrition; high levels of frailty are common among malnourished people [8]. We also showed a strong association between frailty and body weight changes of more than 10%, both losing and gaining weight in 1 year. Weight loss is a major sign of malnutrition, is included in most of the nutritional screening tools, and is one of the five criteria used in defining the “frailty phenotype” [82]. Weight loss can be caused not only by loss of fat but also by loss of muscle and bony mass [83]. On the other hand, weight gain leads to more fat mass than muscle mass in sedentary young individuals. The fat accumulation itself is associated with many health deficits, especially the metabolic syndrome and metabolic-related diseases. Even so, how the metabolic syndrome and frailty interact in relation to mortality appears to change across the life course [84].

The causes of frailty may be different at each age group. For example, younger people may accumulate deficits due to a chronic condition whereas older people may accumulate deficits even when few comorbidities are present [85]. Similarly, nutritional problems are altered across the lifespan. For example, older people may require more protein and calcium intake than do younger people [45, 86] whereas the requirement for iron typically declines after the menopause [52]. Here, we recognized this by using cutoff points of normal intake according to the recommendation for each age and gender group. Even so, the effect of abnormal nutrition on frailty can be different in each age group and future interventional studies need to investigate this.

We used publicly available data from NHANES, a large population-based study with a well-controlled and rigorous protocol. We analysed a huge number of nutrition-related parameters. Mortality was extracted from death certificate data and was examined 5–8 years after testing. However, our data must be interpreted with caution: (a) Due to the cross-sectional design, the causal relationship between frailty and nutrition cannot be examined and the duration of exposure to each parameter cannot be explored. For example, here, daily alcohol consumption of more than 2 standard drinks (28 g) in men and 1 standard drink in women (14 g) was associated with lower frailty but was not related with mortality risk. Nevertheless, alcohol consumption more than 3 standard drinks (42 g) per day was not associated with frailty (data not shown). (b) Since dietary data (including alcohol use) were recorded by 24-h recall, day-to-day variation could not be counted, and food intake could be altered along the study period. (c) People who have chronic abnormal serum levels of some nutrients may have experienced temporally normal levels during testing.

The absence of longitudinal data also makes it difficult to discern age from period and cohort effects. Our data do however demonstrate that both frailty and nutritional deficiencies can be detected at all adult ages. Nutritional deficiencies, at least in the aggregate, can also be seen more commonly at higher ages and with frailty, and increase the lethality of frailty. Here, for similar levels of deficit accumulation, at all ages, impaired nutrition reduced survival in people whose FI score were higher than 0.1.

Conclusions

This study revealed that most nutritional parameters were related with frailty, but the impact of individual parameters on mortality differed across levels of frailty. Only low vitamin D was associated with higher levels of frailty and higher risk for mortality across all levels of frailty. Weight loss more than 10% in the past year also increased mortality risk, except in very fit people. Nevertheless, mortality risk was decreased by being overweight, having high waist circumference and subscapular skinfold and consuming more than 400 mg of caffeine daily in people FI > 0.3. Even though many nutrition-related parameters were not significantly associated with mortality, we found that in people with FI > 0.1, they strongly predicted mortality risk when combined in an index. The combined effect of frailty and nutrition deficits had the most impact on mortality risk. Balanced nutritional interventions appear to be reasonable approaches to remediating frailty. Further studies are needed to examine the impact of nutritional interventional studies on frailty levels and to evaluate whether the number of nutritional deficits relates to other health outcomes such as hospitalization, institutionalization, and quality of life.

Notes

Abbreviations

BMI: 

Body mass index

CI: 

Confidential interval

FI: 

Frailty index

LDL-c: 

Low-density lipoprotein cholesterol

NHANES: 

National Health and Nutrition Examination Survey

NI: 

Nutrition index

Declarations

Acknowledgements

We are grateful to the Faculty of Medicine Ramathibodi Hospital, Mahidol University, for supporting KJ with a research fellowship to conduct this research; our colleagues in Geriatric Medicine Research, at Dalhousie University & Nova Scotia Health Authority for their support; all NHANES participants; and the NHANES researchers for making this data publicly available.

Funding

This study was not funded entirely or partially by an outside source.

Availability of data and materials

The National Health and Nutrition Examination Survey (NHANES) data are publically available at https://www.cdc.gov/nchs/nhanes/index.htm. The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

Authors’ contributions

KJ, OT, and KR conceived and designed the study, interpreted the data, and co-drafted the manuscript. JB assisted with data analysis and revised the manuscript. LC designed the study and revised the manuscript. All authors reviewed and approved the final manuscript before submission.

Ethics approval and consent to participate

The protocols of NHANES were approved by the institutional review board of the National Center for Health Statistics, Centers for Disease Control and Prevention (CDC). Written informed consent was obtained from each participant before participation in this study.

Consent for publication

Not applicable.

Competing interests

All authors declare that they have no competing interests.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

(1)
Chakri Naruebodindra Medical Institute, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, Thailand
(2)
Department of Medicine, Dalhousie University, Halifax, Nova Scotia, Canada
(3)
Centre for Health Care of the Elderly, QEII Health Sciences Centre, Nova Scotia Health Authority, Halifax, Nova Scotia, Canada
(4)
MRC Unit for Lifelong Health and Ageing, UCL, London, UK
(5)
Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
(6)
Division of Geriatric Medicine, Department of Medicine, Dalhousie University, Camp Hill Veterans’ Memorial Bldg., 5955 Veterans’ Memorial Lane, Halifax, Nova Scotia, B3H 2E1, Canada

References

  1. Lutz W, Sanderson W, Scherbov S. The coming acceleration of global population ageing. Nature. 2008;451(7179):716–9.PubMedGoogle Scholar
  2. Ferrucci L, Giallauria F, Guralnik JM. Epidemiology of aging. Radiol Clin N Am. 2008;46(4):643–52 v.PubMedGoogle Scholar
  3. Yazdanyar A, Newman AB. The burden of cardiovascular disease in the elderly: morbidity, mortality, and costs. Clin Geriatr Med. 2009;25(4):563–77 vii.PubMedPubMed CentralGoogle Scholar
  4. Saad MA, Cardoso GP, Martins Wde A, Velarde LG, Cruz Filho RA. Prevalence of metabolic syndrome in elderly and agreement among four diagnostic criteria. Arq Bras Cardiol. 2014;102(3):263–9.PubMedPubMed CentralGoogle Scholar
  5. Plassman BL, Langa KM, Fisher GG, Heeringa SG, Weir DR, Ofstedal MB, et al. Prevalence of dementia in the United States: the aging, demographics, and memory study. Neuroepidemiology. 2007;29(1–2):125–32.PubMedPubMed CentralGoogle Scholar
  6. Gheno R, Cepparo JM, Rosca CE, Cotten A. Musculoskeletal disorders in the elderly. J Clin Imaging Sci. 2012;2:39.PubMedPubMed CentralGoogle Scholar
  7. Hubbard RE, Theou O. Frailty: enhancing the known knowns. Age Ageing. 2012;41(5):574–5.PubMedGoogle Scholar
  8. Lorenzo-Lopez L, Maseda A, de Labra C, Regueiro-Folgueira L, Rodriguez-Villamil JL, Millan-Calenti JC. Nutritional determinants of frailty in older adults: a systematic review. BMC Geriatr. 2017;17(1):108.PubMedPubMed CentralGoogle Scholar
  9. Muscedere J, Waters B, Varambally A, Bagshaw SM, Boyd JG, Maslove D, et al. The impact of frailty on intensive care unit outcomes: a systematic review and meta-analysis. Intensive Care Med. 2017;43(8):1105–22.PubMedPubMed CentralGoogle Scholar
  10. Walters K, Frost R, Kharicha K, Avgerinou C, Gardner B, Ricciardi F, et al. Home-based health promotion for older people with mild frailty: the HomeHealth intervention development and feasibility RCT. Health Technol Assess. 2017;21(73):1–128.PubMedPubMed CentralGoogle Scholar
  11. Blodgett JM, Theou O, Howlett SE, Rockwood K. A frailty index from common clinical and laboratory tests predicts increased risk of death across the life course. GeroScience. 2017. https://doi.org/10.1007/s11357-017-9993-7.
  12. Backman K, Joas E, Falk H, Mitnitski A, Rockwood K, Skoog I. Changes in the lethality of frailty over 30 years: evidence from two cohorts of 70-year-olds in Gothenburg Sweden. J Gerontol A Biol Sci Med Sci. 2017;72(7):945–50.PubMedGoogle Scholar
  13. Mousa A, Savva GM, Mitnitski A, Rockwood K, Jagger C, Brayne C, et al. Is frailty a stable predictor of mortality across time? Evidence from the Cognitive Function and Ageing Studies. Age Ageing. 2018;47(5):721-7.Google Scholar
  14. Constans T, Bacq Y, Brechot JF, Guilmot JL, Choutet P, Lamisse F. Protein-energy malnutrition in elderly medical patients. J Am Geriatr Soc. 1992;40(3):263–8.PubMedGoogle Scholar
  15. Kaiser MJ, Bauer JM, Ramsch C, Uter W, Guigoz Y, Cederholm T, et al. Frequency of malnutrition in older adults: a multinational perspective using the mini nutritional assessment. J Am Geriatr Soc. 2010;58(9):1734–8.PubMedGoogle Scholar
  16. Kiesswetter E, Pohlhausen S, Uhlig K, Diekmann R, Lesser S, Heseker H, et al. Malnutrition is related to functional impairment in older adults receiving home care. J Nutr Health Aging. 2013;17(4):345–50.PubMedGoogle Scholar
  17. Rasheed S, Woods RT. Malnutrition and quality of life in older people: a systematic review and meta-analysis. Ageing Res Rev. 2013;12(2):561–6.PubMedGoogle Scholar
  18. Correia MI, Waitzberg DL. The impact of malnutrition on morbidity, mortality, length of hospital stay and costs evaluated through a multivariate model analysis. Clin Nutr. 2003;22(3):235–9.PubMedGoogle Scholar
  19. Ruengurairoek T, Vathesatogkit P, Boonhat H, Warodomwichit D, Thongmuang N, Matchariyakul D, et al. The association between processed meat intake and the prevalence of type 2 diabetes in Thais: a cross-sectional study from the Electricity Generating Authority of Thailand. Ramathibodi Med J. 2017;40(3):1–10.Google Scholar
  20. Ribeiro RV, Hirani V, Senior AM, Gosby AK, Cumming RG, Blyth FM, et al. Diet quality and its implications on the cardio-metabolic, physical and general health of older men: the Concord Health and Ageing in Men Project (CHAMP). Br J Nutr. 2017;118(2):130–43.PubMedGoogle Scholar
  21. Sao Romao Preto L, Dias Conceicao MDC, Figueiredo TM, Pereira Mata MA, Barreira Preto PM, Mateo Aguilar E. Frailty, body composition and nutritional status in non-institutionalised elderly. Enferm Clin. 2017;27(6):339–45.PubMedGoogle Scholar
  22. Shlisky J, Bloom DE, Beaudreault AR, Tucker KL, Keller HH, Freund-Levi Y, et al. Nutritional considerations for healthy aging and reduction in age-related chronic disease. Adv Nutr. 2017;8(1):17–26.PubMedPubMed CentralGoogle Scholar
  23. Theou O, Chapman I, Wijeyaratne L, Piantadosi C, Lange K, Naganathan V, et al. Can an intervention with testosterone and nutritional supplement improve the frailty level of under-nourished older people? J Frailty Aging. 2016;5(4):247–52.PubMedGoogle Scholar
  24. Strike SC, Carlisle A, Gibson EL, Dyall SC. A high omega-3 fatty acid multinutrient supplement benefits cognition and mobility in older women: a randomized, double-blind, placebo-controlled pilot study. J Gerontol A Biol Sci Med Sci. 2016;71(2):236–42.PubMedGoogle Scholar
  25. Hutchins-Wiese HL, Kleppinger A, Annis K, Liva E, Lammi-Keefe CJ, Durham HA, et al. The impact of supplemental n-3 long chain polyunsaturated fatty acids and dietary antioxidants on physical performance in postmenopausal women. J Nutr Health Aging. 2013;17(1):76–80.PubMedGoogle Scholar
  26. van Dijk M, Dijk FJ, Hartog A, van Norren K, Verlaan S, van Helvoort A, et al. Reduced dietary intake of micronutrients with antioxidant properties negatively impacts muscle health in aged mice. J Cachexia Sarcopenia Muscle. 2018;9(1):146–59.PubMedGoogle Scholar
  27. Bonnefoy M, Berrut G, Lesourd B, Ferry M, Gilbert T, Guerin O, et al. Frailty and nutrition: searching for evidence. J Nutr Health Aging. 2015;19(3):250–7.PubMedGoogle Scholar
  28. Cesari M, Theou O. Frailty: The Broad View. In: Fillit HM, Rockwood K, Young JB. editor. Brocklehurst’s Textbook of Geriatric Medicine and Gerontology. 8th ed. Philadelphia: Elsevier, Inc.; 2017. p. 84–7.Google Scholar
  29. Aguirre LE, Villareal DT. Physical exercise as therapy for frailty, Nestle Nutrition Institute workshop series, vol. 83; 2015. p. 83–92.Google Scholar
  30. Kelaiditi E, van Kan GA, Cesari M. Frailty: role of nutrition and exercise. Curr Opin Clin Nutr Metab Care. 2014;17(1):32–9.PubMedGoogle Scholar
  31. Theou O, Stathokostas L, Roland KP, Jakobi JM, Patterson C, Vandervoort AA, et al. The effectiveness of exercise interventions for the management of frailty: a systematic review. J Aging Res. 2011;2011:569194.PubMedPubMed CentralGoogle Scholar
  32. Soysal P, Isik AT, Carvalho AF, Fernandes BS, Solmi M, Schofield P, et al. Oxidative stress and frailty: a systematic review and synthesis of the best evidence. Maturitas. 2017;99:66–72.PubMedGoogle Scholar
  33. Ble A, Cherubini A, Volpato S, Bartali B, Walston JD, Windham BG, et al. Lower plasma vitamin E levels are associated with the frailty syndrome: the InCHIANTI study. J Gerontol A Biol Sci Med Sci. 2006;61(3):278–83.PubMedGoogle Scholar
  34. Bollwein J, Volkert D, Diekmann R, Kaiser MJ, Uter W, Vidal K, et al. Nutritional status according to the mini nutritional assessment (MNA(R)) and frailty in community dwelling older persons: a close relationship. J Nutr Health Aging. 2013;17(4):351–6.PubMedGoogle Scholar
  35. Shikany JM, Barrett-Connor E, Ensrud KE, Cawthon PM, Lewis CE, Dam TT, et al. Macronutrients, diet quality, and frailty in older men. J Gerontol A Biol Sci Med Sci. 2014;69(6):695–701.PubMedGoogle Scholar
  36. Beasley JM, LaCroix AZ, Neuhouser ML, Huang Y, Tinker L, Woods N, et al. Protein intake and incident frailty in the Women’s Health Initiative observational study. J Am Geriatr Soc. 2010;58(6):1063–71.PubMedPubMed CentralGoogle Scholar
  37. Rahi B, Colombet Z, Gonzalez-Colaço Harmand M, Dartigues J-F, Boirie Y, Letenneur L, et al. Higher protein but not energy intake is associated with a lower prevalence of frailty among community-dwelling older adults in the French three-city cohort. J Am Med Dir Assoc. 2016;17(7):672.e7–11.Google Scholar
  38. Cruz-Jentoft AJ, Kiesswetter E, Drey M, Sieber CC. Nutrition, frailty, and sarcopenia. Aging Clin Exp Res. 2017;29(1):43–8.PubMedGoogle Scholar
  39. Theou O, Rockwood K. China's oldest-old-prospects for good health in late life. Lancet. 2017;389(10079):1584–6.PubMedGoogle Scholar
  40. Huang YC, Wahlqvist ML, Lo YC, Lin C, Chang HY, Lee MS. A non-invasive modifiable Healthy Ageing Nutrition Index (HANI) predicts longevity in free-living older Taiwanese. Sci Rep. 2018;8(1):7113.PubMedPubMed CentralGoogle Scholar
  41. National Center for Health Statistics (NCHS) of the Centers for Disease Control and Prevention (CDC). https://www.cdc.gov/nchs/nhanes/. Cited 28 Dec 2017.
  42. Zipf G, Chiappa M, Porter KS, Ostchega Y, Lewis BG, Dostal J. National health and nutrition examination survey: plan and operations, 1999-2010. Vital and health statistics Ser 1, Programs and collection procedures. 2013(56):1–37.Google Scholar
  43. Aparicio-Ugarriza R, Palacios G, Alder M, Gonzalez-Gross M. A review of the cut-off points for the diagnosis of vitamin B12 deficiency in the general population. Clin Chem Lab Med. 2015;53(8):1149–59.PubMedGoogle Scholar
  44. Ross C, Caballero B, Tucker KL, Cousins RJ. Modern nutrition in health and disease. 11th ed. Baltimore: Lippincott Williams & Wilkins; 2014.Google Scholar
  45. Institute of Medicine. Dietary Reference Intakes for Calcium, Phosphorus, Magnesium, Vitamin D, and Fluoride. Washington, DC: The National Academies Press; 1997. https://doi.org/10.17226/5776.
  46. Institute of Medicine. Dietary Reference Intakes for Thiamin, Riboflavin, Niacin, Vitamin B6, Folate, Vitamin B12, Pantothenic Acid, Biotin, and Choline. Washington, DC: The National Academies Press; 1998. https://doi.org/10.17226/6015.
  47. Kromhout D, Giltay EJ, Geleijnse JM, Alpha Omega Trial G. n-3 fatty acids and cardiovascular events after myocardial infarction. N Engl J Med. 2010;363(21):2015–26.PubMedGoogle Scholar
  48. Genuth S, Alberti KG, Bennett P, Buse J, Defronzo R, Kahn R, et al. Follow-up report on the diagnosis of diabetes mellitus. Diabetes Care. 2003;26(11):3160–7.PubMedGoogle Scholar
  49. National Cholesterol Education Program Expert Panel on Detection E, Treatment of High Blood Cholesterol in A. Third Report of the National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III) final report. Circulation. 2002;106(25):3143–421.Google Scholar
  50. Fryar CD, Gu Q, Ogden CL, Flegal KM. Anthropometric Reference Data for Children and Adults: United States, 2011–2014, Vital and health statistics series 3, Analytical Studies, no. 39; 2016. p. 1–46.Google Scholar
  51. Alberti KG, Zimmet P, Shaw J, Group IDFETFC. The metabolic syndrome--a new worldwide definition. Lancet. 2005;366(9491):1059–62.Google Scholar
  52. Trumbo P, Yates AA, Schlicker S, Poos M. Dietary reference intakes: vitamin A, vitamin K, arsenic, boron, chromium, copper, iodine, iron, manganese, molybdenum, nickel, silicon, vanadium, and zinc. J Am Diet Assoc. 2001;101(3):294–301.PubMedGoogle Scholar
  53. U.S. Department of Health and Human Services, U.S. Department of Agriculture. 2015 – 2020 Dietary guidelines for Americans. 8th ed; 2015.Google Scholar
  54. Prevention and management of osteoporosis. World Health Organ Tech Rep Ser. 2003;921:1-164, back cover.Google Scholar
  55. Hosten AO. BUN and Creatinine. In: Walker HK, Hall WD, Hurst JW, editors. Clinical Methods: The History, Physical, and Laboratory Examinations. Boston: Butterworths; 1990. p. 874–8.Google Scholar
  56. Theou O, Blodgett JM, Godin J, Rockwood K. Association between sedentary time and mortality across levels of frailty. CMAJ. 2017;189(33):E1056–E64.PubMedPubMed CentralGoogle Scholar
  57. Mitnitski AB, Mogilner AJ, Rockwood K. Accumulation of deficits as a proxy measure of aging. TheScientificWorldJOURNAL. 2001;1:323–36.PubMedPubMed CentralGoogle Scholar
  58. Halfon M, Phan O, Teta D. Vitamin D: a review on its effects on muscle strength, the risk of fall, and frailty. Biomed Res Int. 2015;2015:953241.PubMedPubMed CentralGoogle Scholar
  59. Vogt S, Decke S, de Las Heras Gala T, Linkohr B, Koenig W, Ladwig KH, et al. Prospective association of vitamin D with frailty status and all-cause mortality in older adults: results from the KORA-Age Study. Prev Med. 2015;73:40–6.PubMedGoogle Scholar
  60. Vaes AMM, Brouwer-Brolsma EM, Toussaint N, de Regt M, Tieland M, van Loon LJC, et al. The association between 25-hydroxyvitamin D concentration, physical performance and frailty status in older adults. Eur J Nutr. 2018.Google Scholar
  61. Wilhelm-Leen ER, Hall YN, Deboer IH, Chertow GM. Vitamin D deficiency and frailty in older Americans. J Intern Med. 2010;268(2):171–80.PubMedPubMed CentralGoogle Scholar
  62. Muir SW, Montero-Odasso M. Effect of vitamin D supplementation on muscle strength, gait and balance in older adults: a systematic review and meta-analysis. J Am Geriatr Soc. 2011;59(12):2291–300.PubMedGoogle Scholar
  63. Kuzma E, Soni M, Littlejohns TJ, Ranson JM, van Schoor NM, Deeg DJ, et al. Vitamin D and memory decline: two population-based prospective studies. J Alzheimers Dis. 2016;50(4):1099–108.PubMedPubMed CentralGoogle Scholar
  64. World Health Organization: Body mass index – BMI. http://www.euro.who.int/en/health-topics/disease-prevention/nutrition/a-healthy-lifestyle/body-mass-index-bmi. Cited 27 Apr 2018.
  65. Winter JE, MacInnis RJ, Wattanapenpaiboon N, Nowson CA. BMI and all-cause mortality in older adults: a meta-analysis. Am J Clin Nutr. 2014;99(4):875–90.PubMedGoogle Scholar
  66. Chapman IM. Obesity paradox during aging. Interdiscip Top Gerontol. 2010;37:20–36.PubMedGoogle Scholar
  67. Chang SH, Beason TS, Hunleth JM, Colditz GA. A systematic review of body fat distribution and mortality in older people. Maturitas. 2012;72(3):175–91.PubMedPubMed CentralGoogle Scholar
  68. Rietman ML, Spijkerman AMW, Wong A, van Steeg H, Burkle A, Moreno-Villanueva M, et al. Antioxidants linked with physical, cognitive and psychological frailty: analysis of candidate biomarkers and markers derived from the MARK-AGE study. Mech Ageing Dev. 2018.Google Scholar
  69. Semba RD, Bartali B, Zhou J, Blaum C, Ko CW, Fried LP. Low serum micronutrient concentrations predict frailty among older women living in the community. J Gerontol A Biol Sci Med Sci. 2006;61(6):594–9.PubMedGoogle Scholar
  70. Fontana L, Kennedy BK, Longo VD, Seals D, Melov S. Medical research: treat ageing. Nature. 2014;511(7510):405–7.PubMedGoogle Scholar
  71. Howlett SE, Rockwood K. Ageing: develop models of frailty. Nature. 2014;512(7514):253.PubMedGoogle Scholar
  72. Guo H, Siu W, D'Arcy RC, Black SE, Grajauskas LA, Singh S, et al. MRI assessment of whole-brain structural changes in aging. Clin Interv Aging. 2017;12:1251–70.PubMedPubMed CentralGoogle Scholar
  73. Armstrong JJ, Andrew MK, Mitnitski A, Launer LJ, White LR, Rockwood K. Social vulnerability and survival across levels of frailty in the Honolulu-Asia Aging Study. Age Ageing. 2015;44(4):709–12.PubMedPubMed CentralGoogle Scholar
  74. Howlett SE, Rockwood MR, Mitnitski A, Rockwood K. Standard laboratory tests to identify older adults at increased risk of death. BMC Med. 2014;12:171.PubMedPubMed CentralGoogle Scholar
  75. Mitnitski A, Collerton J, Martin-Ruiz C, Jagger C, von Zglinicki T, Rockwood K, et al. Age-related frailty and its association with biological markers of ageing. BMC Med. 2015;13:161.PubMedPubMed CentralGoogle Scholar
  76. Wallace LM, Theou O, Kirkland SA, Rockwood MR, Davidson KW, Shimbo D, et al. Accumulation of non-traditional risk factors for coronary heart disease is associated with incident coronary heart disease hospitalization and death. PLoS One. 2014;9(3):e90475.PubMedPubMed CentralGoogle Scholar
  77. Brothers TD, Kirkland S, Theou O, Zona S, Malagoli A, Wallace LMK, et al. Predictors of transitions in frailty severity and mortality among people aging with HIV. PLoS One. 2017;12(10):e0185352.PubMedPubMed CentralGoogle Scholar
  78. Howlett SE, Rockwood K. New horizons in frailty: ageing and the deficit-scaling problem. Age Ageing. 2013;42(4):416–23.PubMedGoogle Scholar
  79. Rutenberg AD, Mitnitski AB, Farrell SG, Rockwood K. Unifying aging and frailty through complex dynamical networks. Exp Gerontol. 2018;107:126–9.PubMedGoogle Scholar
  80. Mitnitski A, Rockwood K. The rate of aging: the rate of deficit accumulation does not change over the adult life span. Biogerontology. 2016;17(1):199–204.PubMedGoogle Scholar
  81. Pincus Z. Ageing: A stretch in time. Nature. 2016;530(7588):37–8.PubMedGoogle Scholar
  82. Fried LP, Tangen CM, Walston J, Newman AB, Hirsch C, Gottdiener J, et al. Frailty in older adults: evidence for a phenotype. J Gerontol A Biol Sci Med Sci. 2001;56(3):M146–56.Google Scholar
  83. Huo YR, Suriyaarachchi P, Gomez F, Curcio CL, Boersma D, Gunawardene P, et al. Comprehensive nutritional status in sarco-osteoporotic older fallers. J Nutr Health Aging. 2015;19(4):474–80.PubMedGoogle Scholar
  84. Kane AE, Gregson E, Theou O, Rockwood K, Howlett SE. The association between frailty, the metabolic syndrome, and mortality over the lifespan. Geroscience. 2017;39(2):221–9.PubMedPubMed CentralGoogle Scholar
  85. Theou O, Rockwood MR, Mitnitski A, Rockwood K. Disability and co-morbidity in relation to frailty: how much do they overlap? Arch Gerontol Geriatr. 2012;55(2):e1–8.PubMedGoogle Scholar
  86. Deutz NE, Bauer JM, Barazzoni R, Biolo G, Boirie Y, Bosy-Westphal A, et al. Protein intake and exercise for optimal muscle function with aging: recommendations from the ESPEN Expert Group. Clin Nutr. 2014;33(6):929–36.PubMedPubMed CentralGoogle Scholar

Copyright

© The Author(s). 2018
corrected publication December/2018

Advertisement