Association of Proinflammatory Diet With Frailty Onset Among Adults With and Without Depressive Symptoms: Results From the Framingham Offspring Study

Courtney L Millar; Alyssa B Dufour; James R Hebert; Nitin Shivappa; Olivia I Okereke; Douglas P Kiel; Marian T Hannan; Shivani Sahni

doi:10.1093/gerona/glac140

. 2022 Jul 13;78(2):250–257. doi: 10.1093/gerona/glac140

Association of Proinflammatory Diet With Frailty Onset Among Adults With and Without Depressive Symptoms: Results From the Framingham Offspring Study

Courtney L Millar ^1,², Alyssa B Dufour ^3,⁴, James R Hebert ^5,⁶, Nitin Shivappa ^7,⁸, Olivia I Okereke ^9,¹⁰, Douglas P Kiel ^11,¹², Marian T Hannan ^13,¹⁴, Shivani Sahni ^15,^16,^✉

Editor: Roger Fielding

¹ Hinda and Arthur Marcus Institute for Aging Research, Hebrew SeniorLife, Boston, Massachusetts, USA

² Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, USA

³ Hinda and Arthur Marcus Institute for Aging Research, Hebrew SeniorLife, Boston, Massachusetts, USA

⁴ Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, USA

⁵ Department of Epidemiology and Biostatistics and the Cancer Prevention and Control Program, Arnold School of Public Health, University of South Carolina, Columbia, South Carolina, USA

⁶ Department of Nutrition, Connecting Health Innovations LLC, Columbia, South Carolina, USA

⁷ Department of Epidemiology and Biostatistics and the Cancer Prevention and Control Program, Arnold School of Public Health, University of South Carolina, Columbia, South Carolina, USA

⁸ Department of Nutrition, Connecting Health Innovations LLC, Columbia, South Carolina, USA

⁹ Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA

¹⁰ Department of Epidemiology, Harvard TH Chan School of Public Health, Boston, Massachusetts, USA

¹¹ Hinda and Arthur Marcus Institute for Aging Research, Hebrew SeniorLife, Boston, Massachusetts, USA

¹² Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, USA

¹³ Hinda and Arthur Marcus Institute for Aging Research, Hebrew SeniorLife, Boston, Massachusetts, USA

¹⁴ Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, USA

¹⁵ Hinda and Arthur Marcus Institute for Aging Research, Hebrew SeniorLife, Boston, Massachusetts, USA

¹⁶ Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, USA

^✉

Address correspondence to: Shivani Sahni, PhD, Marcus Institute for Aging Research, Hebrew SeniorLife, 1200 Centre Street, Boston, MA 02131, USA. E-mail: ShivaniSahni@hsl.harvard.edu

Roles

Roger Fielding: PhD, FGSA, Decision Editor

PMCID: PMC9951064 PMID: 35830506

Abstract

Background

Dietary inflammation is associated with increased risk of frailty. Those with depressive symptoms may be at higher risk of frailty onset because they typically have higher levels of inflammation. The study objective was to determine the association between a proinflammatory diet and frailty onset in those with and without clinically relevant depressive symptoms.

Methods

This prospective study included 1 701 nonfrail individuals with self-reported baseline (1998–2001) data available for the evaluation of energy-adjusted dietary inflammatory index (E-DII^TM; calculated from food frequency questionnaires), depressive symptoms (from the Center for Epidemiologic Studies Depression; CES-D), and follow-up frailty measurements (2011–2014). Frailty was defined as fulfilling ≥3 Fried frailty criteria (i.e., slow gait, weak grip strength, unintentional weightloss, low physical activity, and self-reported exhaustion). Results are presented by baseline CES-D scores <16 or ≥16 points, which denotes the absence or presence of clinically relevant depressive symptoms, respectively. Logistic regression estimated odds ratios (OR) and 95% confidence intervals (95% CI) between E-DII and frailty onset, adjusting for confounders.

Results

In all study participants, mean (SD) age was 58(8) years and E-DII was −1.95 (2.20; range: −6.71 to +5.40, higher scores denote a more proinflammatory diet), and 45% were male. In those without clinically relevant depressive symptoms, 1-unit higher E-DII score was associated with 14% increased odds (95% CI: 1.05–1.24) of frailty. In those with depressive symptoms, 1-unit higher E-DII score was associated with 55% increased odds of frailty (95% CI: 1.13–2.13).

Conclusions

The association between inflammatory diet and increased odds of frailty appeared somewhat stronger among those with depressive symptoms. This preliminary finding warrants further investigation.

Keywords: Aging, Inflammation, Nutrition, Prospective Cohort, Well-being

An estimated 10%–15% of community-dwelling older adults have frailty (1,2), which is a geriatric condition of increased vulnerability. Frailty increases the risk of falls, hospitalizations, and mortality (3,4). Similarly, 8%–16% of community-dwelling older adults have clinically relevant depressive symptoms (5), which are associated with disability (6) and mortality (7). Older adults often have multiple chronic conditions such as frailty and depression, resulting in compounded negative health consequences (8). Frail adults with symptoms of depression have a lower probability of reversal of frailty (9) and have a heightened risk of mortality (10). Therefore, both these interrelated conditions are urgent public health concerns that need to be addressed. Of the many risk factors in developing frailty and depression, inflammation has been identified as a key contributor to both conditions.

Inflammation is theorized to be the central pathway related to frailty development (11). Studies have reported that inflammatory cytokines promote muscle degradation (12) and reduce muscle mass and motor function (13–16), ultimately leading to frailty (17). Inflammation is also implicated in the development of depression (18). Depressed individuals typically have higher levels of inflammation compared with their nondepressed counterparts (19). It is hypothesized that chronic inflammation can perturb neurotransmitter metabolism and neuroendocrine functioning, resulting in downstream impairments in neurosignaling pathways, ultimately leading to the development of depression (20).

Diet is a well-established modulator of systemic inflammation (21) and a key risk factor for frailty development (22). Results from a multidomain intervention conducted in frail adults suggest that multimineral and multivitamin supplements given over 6 months not only reversed frailty (23), but also reduced depressive symptoms (24); this implies a potential relationship between diet, depression, and frailty. Consistent evidence from the Framingham Heart Study and other studies has shown that a proinflammatory diet (as reflected by the dietary inflammatory index; DII) is associated with frailty (25–30). Similarly, dietary inflammation has also been linked with depression (31). Yet, the inter-relatedness between dietary inflammation, frailty, and depression is unclear. Therefore, the objective of this study was to determine whether the association between a proinflammatory diet (as reflected by a higher energy-adjusted DII) and odds of frailty onset differs in subgroups of individuals with and without clinically relevant depressive symptoms. Given that individuals with depressive symptoms typically have higher levels of inflammation (19), they may be predisposed to frailty development when consuming a proinflammatory diet. Thus, we measured the association between E-DII and odds of frailty onset separately among individuals with clinically relevant depressive symptoms and those without them. Additionally, we performed a secondary analysis on the onset of both clinically relevant depressive symptoms and frailty to better understand the relationship between dietary inflammation, depressive symptoms, and frailty.

Method

Parent Study

This study utilizes data from participants in the Framingham Heart Offspring Study (n = 5 124), in which offspring of the Original Framingham Heart Study participants were enrolled between the years 1971–1975 to investigate cardiovascular-related risk factors at roughly 4-year intervals (32). The Framingham Heart Study was reviewed by the Boston University Medical Center Institutional Review Board and participants signed informed consent.

Study Design and Participants

This longitudinal study included data from 2 651 participants who had complete information on diet and frailty at the baseline examination (1998–2001). After excluding those with frailty at baseline (n = 68), those missing baseline covariates (n = 163), and those missing follow-up examination in 2011–2014 (n = 719), 1 701 individuals remained in the primary analysis (Figure 1). There were no exclusions related to extreme energy intakes (eg, <600 kcal/d, >4000 kcal/d for females, or <600 kcal/d, >4200 kcal/d for males). Those taking antidepressants were included in our primary analysis because we are interested in how the severity of depressive symptoms may affect the association between diet and frailty, regardless of antidepressant treatment. Some individuals (particularly middle-aged and older adults) are not responsive to antidepressive treatment (33). Thus, taking antidepressants may not appreciably change the severity of depressive symptoms. However, a sensitivity analysis was conducted after excluding those taking antidepressants (n = 111), yielding a total of 1 590 individuals with evaluable data.

Figure 1. — Flow chart of participants from the Framingham Heart Study Offspring Cohort that were included in the study.

Dietary Inflammatory Index

In 1998–2001, participants were asked to complete a validated Harvard 126-item semiquantitative food frequency questionnaire (FFQ). Participants reported the frequency of consumption for the food items and supplements during the year prior (34). The nutrient and food intake variables from the FFQ were used to calculate DII (35) and E-DII (36) scores for each individual, which reflect the inflammatory potential of the diet. The DII was originally developed by Cavicchia et al. in 2009 (37), modified to produce the current version (35), and further modified to include the use of energy-adjusted nutrient database, E-DII (36). The DII was designed to be universally applicable across populations and using different dietary assessment methods. In addition to using energy-adjusted nutrient database, the E-DII scores were calculated per 1 000 kcal. Thus, the E-DII accounts for energy intake, which is an important determinant of the proinflammatory impact of the diet (36). Moreover, energy intake is an important confounder for diet and frailty. Thus, for the present study, our primary exposure is E-DII.

Briefly, to develop the DII researchers reviewed the literature and identified 45 food measures (eg, macronutrient, vitamin, mineral intake, and specific food items) that appeared to have an effect on select inflammatory biomarkers (various interleukins, tumor-necrosis factor-alpha, and C-reactive protein). Using a novel scoring algorithm, a weighted, congregate inflammatory effect score for each food parameter, based on the inflammatory effects reviewed in the literature, was calculated. For each individual, z-scores and proportions (values from 0 to 1) of the nutrient intakes of the identified food parameters were calculated. Each proportion of the food measure was centered on zero by doubling and subtracting 1 and then multiplied by the “overall food parameter-specific inflammatory effect score” previously described (35). Then, all the scores were summed to provide the overall DII or E-DII score for each individual. These scores have a potential range from approximately −8 to +8, that is, from minimally to maximally proinflammatory, respectively.

The DII and E-DII are scored similarly and scaled identically; so, the scores are comparable across studies (36). For this study, the following 33 of the 45 food parameters were used to calculate an individual’s overall DII or E-DII: alcohol, beta-carotene, carbohydrates, cholesterol, calories, total fat, fiber, folate, iron, magnesium, monounsaturated fatty acids, niacin, omega-3 fatty acids, omega-6 fatty acids, protein, polyunsaturated fatty acids, riboflavin, saturated fat, selenium, thiamin, trans fat, vitamin A, vitamin B6, vitamin B12, vitamin C, vitamin D, vitamin E, zinc, flavan-3-ols, flavones, flavonols, flavonones, and anthocyanidins. The food parameters that were not incorporated in our DII/E-DII calculation include spices (eg, pepper), foods (eg, garlic), and other nutrients (eg, caffeine).

Depressive Symptoms

Information on depressive symptoms was evaluated by the 20-item Center for Epidemiologic Studies Depression (CES-D) questionnaire (range: 0–60 points), which was administered by a trained evaluator at the baseline examination in 1998–2001. The CES-D is a validated tool for screening for depression, including among older adults (38), where higher scores indicate more severe depressive symptoms. For this study, a cut-point of CES-D ≥ 16 was used, which has good sensitivity and specificity for identifying individuals with clinically relevant symptoms of depression, including older adults (39).

Frailty Phenotype

Frailty was evaluated at baseline (1998–2001) and follow-up (2011–2014) using a modified version of the Fried Frailty Phenotype (4), which evaluates frailty on the 5 following criteria: self-reported exhaustion, weakness, slowness, unintentional weight loss, and low physical activity. Exhaustion was evaluated using 2 of the items from the CES-D scale (40). Weakness defined using the hand grip strength (kg) cutoffs outlined in Fried et al., and grip strength was measured using a Jamar dynamometer (Lafayette Instrument Co, Lafayette, IN) (4). Slowness was defined using the gait speed criteria per Fried et al. (4) and the faster of the 2 walk trials (m/s) of 4 m at usual pace was used in this analysis. The definitions for physical activity and unintentional weight loss in the original Fried phenotype were modified to examine frailty over time. Physical activity was evaluated using the Framingham Physical Activity Index (PAI; range: 24–120) (41). Briefly, the number of self-reported hours spent sleeping, sitting, engaging in light activity, moderate activity, and heavy activity is then scaled by an established factor based on the oxygen consumption required to perform the type of activity, and totaled to get a PAI. Sex-specific cutoffs were calculated for the lowest quintile of the PAI at the baseline examination. The sex-specific cutoffs of the PAI defined at baseline were also applied at the follow-up examination. Participants at or below the sex-specific cutoffs of PAI were considered to meet the criteria for low physical activity. Unintentional weight loss was defined by the following criteria (a) self-reported unintentional weight loss of more than 10 pounds (ie, 4.5 kg) in the last year, or (b) body mass index (BMI) less than 18.5 kg/m², or (c) annualized weight loss of more than 10 (ie, 4.5 kg) pounds between 2 examinations 4–8 years apart. Individuals meeting at least one of the criteria were considered to meet the definition for unintentional weight loss.

Missing Data on Frailty Criteria

If participants were missing any of the 5 criteria for the frailty definition and we could not mathematically determine their frailty status, they were excluded (n = 222). For example, if someone was missing 1 of the 5 criteria and met 1 of the remaining criteria, then they were included and considered nonfrail since mathematically, even if they met the definition for the 1 missing criteria, they would not be frail. However, if someone were missing 1 of the 5 criteria and met 2 of the remaining criteria, then they were excluded because the person could theoretically be frail or not frail. Additional examples are provided in Supplementary Table 1. Additionally, participants who were missing gait (slow) or grip (weak) tests due to a “physical limitation” were assumed to be slow (n = 14) or weak (n = 16), respectively.

Covariates

We utilized the covariate information from the baseline examination (1998–2001), which included age (years), sex, energy intake (kcal/d), current smoking (yes/no), BMI (kg/m²), and PAI. Information on energy intake was taken from the FFQ. The smoking status of the participants was assessed via the self-report (yes/no) question, “Have you smoked cigarettes regularly in the last year?” Current smoking status was defined as reporting yes to the question on smoking. Height without shoes (inches) was measured to the nearest quarter inch with a stadiometer. Weight, in light clothing, (pounds) was measured with a standardized balance-beam scale. Body mass index (BMI) was calculated as weight in kilograms divided by the square of height in meters (kg/m²). The Framingham PAI (range 24–120) was used as an indicator of physical activity (41).

Descriptive Variables

Persons with a history of diabetes mellitus, who used oral hypoglycemic medications or insulin, or who had a baseline fasting plasma glucose level ≥126 mg/dL (>7.0 mmol/L) or a baseline postoral glucose tolerance test plasma glucose level >200 mg/dL (>11.1 mmol/L) were categorized as having diabetes (42). Prevalent cardiovascular disease was defined as coronary heart disease (coronary death, myocardial infarction, coronary insufficiency, and angina), cerebrovascular events (including ischemic stroke, hemorrhagic stroke, and transient ischemic attack) stroke, peripheral artery disease (intermittent claudication), and heart failure. Cancer (excluding all skin cancers except for melanoma) was evaluated by valid tumor pathology examination. Use of antidepressant medication (yes/no) was assessed via self-reported questionnaire.

Statistical Analysis

For the primary analysis, we evaluated the association between E-DII score and frailty onset within strata of CES-D scores. Frailty onset (yes/no) was evaluated using the Fried Frailty Phenotype, as described above. If individuals met the definition for 3 or more of the 5 criteria, then they were considered to have frailty. All other individuals were classified as nonfrail. Baseline characteristics are presented for the entire cohort and by CES-D strata (CES-D < 16 vs ≥ 16 points). Continuous variables are presented as mean ± SD, whereas categorical variables are presented as percentages. The baseline characteristics of individuals who were missing frailty assessments at follow-up examination (n = 719) were compared with those participants who were included in the primary analysis using Student’s T-test for continuous variables and χ ² test for categorical variables.

To determine the association between dietary inflammation and frailty among those with and without baseline clinically relevant depressive symptoms, we conducted stratified (strata: CES-D < 16 and ≥ 16 points) logistic regression to calculate odds ratios (OR) and 95% confidence intervals (95% CI) for frailty incidence, adjusting for select covariables. Separate models were examined using the DII and the E-DII as the exposure. The main results presented utilize the E-DII, and the results with the DII are included as supplementary to facilitate comparison with previous studies that only use DII. We modeled the E-DII score as a continuous variable. Initial models were adjusted for baseline age and sex (Model 1) and Model 2 further adjusted for current smoking status and energy intake. Model 3 additionally adjusted for baseline BMI (kg/m²) and PAI. The same methods were used for the sensitivity analysis, which excluded those reporting antidepressants.

We performed a secondary analysis to better understand the association of dietary inflammation with new onset of frailty (yes/no) and clinically relevant depressive symptoms (yes/no) over time in a sample of individuals without frailty or clinically relevant depressive symptoms at baseline. In this secondary analysis, we excluded those with clinically relevant depressive symptoms (n = 94) and those reporting use of antidepressants at baseline (n = 94) from the primary analysis, yielding a total of 1 513 nonfrail individuals without clinically relevant depressive symptoms. There were 4 potential outcomes at follow-up: (a) no frailty, no clinically relevant depressive symptoms; (b) clinically relevant depressive symptoms onset only; (c) frailty onset only, and (d) both frailty and clinically relevant depressive symptoms onset. As in the primary analysis, if individuals met the definition for 3 or more of the 5 criteria at follow-up, then they were considered to have frailty. All other individuals were classified as nonfrail. Individuals meeting the cut-point of CES-D ≥ 16 point, were considered to have clinically relevant depressive symptoms. Baseline characteristics are presented by each of the 4 potential outcomes. Continuous variables are presented as mean ± SD, whereas categorical variables are presented as percentages.

A multinomial logistic regression was used to calculate odds ratios (OR) and 95% confidence intervals (95% CI) for the 4 potential outcomes, adjusting for select covariables. The outcome of no frailty and no clinically relevant depressive symptoms was defined as the referent group. The secondary analyses used the continuous E-DII variable as the exposure. Model 3 was used for the secondary analysis, which adjusted for baseline age, sex, smoking status, energy intake, BMI, and PAI.

A nominal 2-sided p-value of .05 was considered statistically significant for all analyses unless otherwise noted. All analyses were performed in SAS statistical software version 9.4 (SAS Institute Inc., Cary, NC).

Results

Participant Characteristics

The baseline characteristics of the study participants are presented in Table 1. The sample consisted of 55.4% female participants with a mean age of 58 years (SD: 8), a mean E-DII of −1.95 ± 2.20, and 6.5% reported the use of antidepressants. After 12.4 (SD: 0.8) years, 224 individuals developed frailty over the follow-up. Of those without clinically relevant depressive symptoms (CES-D < 16 points), 54.5% were female and had an E-DII of −1.97 ± 2.18. Of those with clinically relevant depressive symptoms (CES-D ≥ 16 points), 71.3% were female and had a mean E-DII of −1.68 ± 2.48.

Table 1.

Study Population Characteristics of 1 701 Men and Women From the Framingham Heart Study Offspring Cohort

Descriptive Variables^*	All (n = 1 701)	CES-D < 16 (n = 1 607)	CES-D ≥ 16 (n = 94)
Age (range: 33–81, y)	58 ± 8	58 ± 8	57 ± 8
Female, n (%)	943 (55.4)	876 (54.5)	67 (71.3)
Calorie intake (kcal /d)	1 861 ± 600	1 856 ± 600	1 918 ± 604
Current smoking, n (%)	170 (10.0)	158 (9.8)	12 (12.8)
CVD, n (%)	137 (8.1)	133 (8.3)	4 (4.3)
Nonskin cancer, n (%)	77 (4.5)	72 (4.5)	5 (5.3)
Diabetes, n (%)	110 (6.5)	106 (6.6)	4 (4.3)
BMI (kg/m²)	27.8 ± 5.1	27.8 ± 5.1	28.0 ± 5.5
Grip strength (kg)	34.7 ± 12.9	35.0 ± 12.9	30.0 ± 12.0
Gait speed (m/s)	1.23 ± 0.25	1.22 ± 0.25	1.27 ± 0.28
PAI (range: 24–120)	38.2 ± 5.9	38.2 ± 5.9	37.6 ± 5.7
E-DII score (range: −6.71 to 5.40)	−1.95 ± 2.20	−1.97 ± 2.18	−1.68 ± 2.48
DII score (range: −7.70 to 5.44)	−1.66 ± 2.52	−1.66 ± 2.52	−1.72 ± 2.49
CES-D score	4.6 ± 5.6	3.6 ± 3.7	21.7 ± 6.0
Antidepressant use, n (%)	111 (6.5)	93 (5.8)	18 (19.2)

Open in a new tab

Notes: BMI = body mass index; CES-D = Center for Epidemiological Studies Depression; CVD = cardiovascular disease; DII = dietary inflammatory index; E-DII = energy-adjusted dietary inflammatory index; PAI = physical activity index.

*Values are n (%) or mean ± SD.

Individuals excluded due to missing frailty assessments at follow-up (n = 719) had a mean E-DII score of −1.92 (SD: 2.16), a mean CES-D score of 4.7 points (SD: 5.8) and roughly 7.5% reported antidepressant use at baseline. Furthermore, they were older (mean age 66 years [SD: 9]), had higher percentages of current smoking (15.4%), and comorbidities (cardiovascular disease, 19.1%; non-melanoma cancer, 9.6%; diabetes 17.5%) compared with those who were included in the primary analysis (n = 1 701).

Those With CES-D < 16 Points

Of the 1 607 individuals without clinically relevant depressive symptoms (ie, CES-D < 16 points), 12.6% (n = 202) were considered frail at follow-up. In the age- and sex-adjusted model, a 1-unit higher E-DII score (ie, a more proinflammatory diet) was associated with 17% increased odds of frailty (OR: 1.17, 95% CI = 1.09–1.27; Table 2) in those without clinically relevant depressive symptoms. Further adjustment did not appreciably change the association (Table 2). When the DII was used as an exposure, the magnitude of the associations was lower but showed the same significant trends (Supplementary Table 2).

Table 2.

Association of E-DII Score and Odds of Frailty Onset in 1 701 Men and Women From the Framingham Heart Study Offspring Cohort Stratified by CES-D Scores

	CES-D < 16 (n = 1 607)			CES-D ≥ 16 (n = 94)
	Odds Ratio	95% CI	p-Value	Odds Ratio	95% CI	p-Value
Model 1	1.17	1.09–1.27	<.01	1.47	1.11–1.94	<.01
Model 2	1.16	1.07–1.26	<.01	1.48	1.11–1.98	<.01
Model 3	1.14	1.05–1.24	<.01	1.55	1.13–2.13	<.01

Open in a new tab

Notes: CES-D = Center for Epidemiological Studies Depression; E-DII = energy-adjusted dietary inflammatory index. A p-value of <.05 was considered statistically significant. Model 1: Adjusted for age and sex. Model 2: Adjusted for age, sex, energy intake, and smoking. Model 3: Adjusted for age, sex, energy intake, smoking, body mass index, and physical activity.

Those With CES-D ≥ 16 Points

Of the 94 individuals with clinically relevant depressive symptoms (ie, CES-D ≥ 16 points), 23.4% (n = 22) were considered frail at follow-up. In the age- and sex-adjusted model, a one-unit higher E-DII score (ie, a more proinflammatory diet) was associated with a 47% increased odds of frailty (OR: 1.47, 95% CI = 1.11–1.94; Table 2) in those with clinically relevant depressive symptoms. Further adjustment for energy intake and smoking in model 2 did not appreciably change these associations (Table 2). However, additionally adjusting for BMI and physical activity increased the magnitude of the association (OR: 1.55 95% CI = 1.13–2.13; Table 2). When the DII was used as an exposure, the magnitude of the associations was lower but showed the same significant trends (Supplementary Table 2).

Sensitivity Analyses

After excluding those taking antidepressants, our results did not change compared with the primary results (Supplementary Table 3 and 4).

Secondary Analyses

In a secondary analysis, we evaluated the association between the E-DII and 4 potential outcomes in 1 513 individuals: (a) no frailty, no clinically relevant depressive symptoms; (b) clinically relevant depressive symptoms onset only; (c) frailty onset only, and (d) both frailty and clinically relevant depressive symptoms onset. Baseline characteristics by the 4 potential outcomes are shown in Supplementary Table 4. Those who remained nonfrail and did not develop clinically relevant depressive symptoms (n = 1 272) had a mean age of 57 years (SD: 8) and a mean E-DII of −1.98 (SD: 2.19). Those who only developed clinically relevant depressive symptoms (n = 54) had a mean age of 57 years (SD: 8) and a mean E-DII of −2.10 (SD: 2.60), whereas those who only developed frailty (n = 159) had a mean age of 66 years (SD: 8) and a mean E-DII of −1.96 (SD: 2.12). In the final group who developed both frailty and clinically relevant depressive symptoms (n = 28), mean age was 63 years (SD: 9) and mean E-DII was −0.95 (SD: 2.10).

Results from the secondary analysis are shown in Figure 2. After adjusting for relevant confounders, 1-unit higher E-DII was borderline significantly (p = .07) associated with 9% increased odds of only frailty onset (OR: 1.09, 95% CI = 0.99–1.20) given that an individual could have also developed both frailty and clinically relevant depressive symptoms or clinically relevant depressive symptoms alone. Similarly, 1-unit higher E-DII was associated with 32% increased odds of both frailty and clinically relevant depressive symptoms onset (OR: 1.32, 95% CI = 1.10–1.59) given that an individual could also have developed frailty alone or clinically relevant depressive alone; however, there was no association with only clinically relevant depressive symptoms onset alone (OR: 1.01, 95% CI = 0.88–1.16) given that an individual could have developed both frailty and clinically relevant depressive symptoms or frailty alone.

Discussion

In this prospective cohort study, a proinflammatory diet as reflected by a higher E-DII score was associated with 14% increased odds (95% CI: 1.05–1.24) of incident frailty over a 12-year follow-up in adults without clinically relevant depressive symptoms (CES-D < 16 points) at baseline; a proinflammatory diet was associated with 55% increased odds (95% CI: 1.13–2.13) of frailty onset among those with clinically relevant depressive symptoms (CES-D ≥ 16 points) at baseline. Findings were similar in sensitivity analyses excluding those with self-reported antidepressant use at baseline. In a secondary analysis in nonfrail individuals without clinically relevant depressive symptoms at baseline, a proinflammatory diet was associated with onset of frailty, as well as with onset of both frailty and depressive symptoms, after 12 years.

Several epidemiological studies report that a proinflammatory diet (as reflected by either the DII or the E-DII) is associated with frailty (25–29) and with depression/depressive symptoms (31). Despite evidence that frailty and depressive symptoms are often related/coexisting syndromes (9,43–45), there is a paucity of research evaluating the inter-relatedness between dietary inflammation, frailty, and depression. Previous studies solely reporting the association between a proinflammatory diet and frailty have all reported similar directions of the association, but the magnitudes have differed (25–29). Although differences in magnitude of the association between a proinflammatory diet and frailty (regardless of depressive symptoms) is likely attributed to differences in study design, it is also possible that baseline depressive symptoms may have confounded the associations. Yet, to the best of our knowledge, studies have not reported on the prospective association between proinflammatory diet and frailty among those with and without depressive symptoms at baseline.

It is well established that consumption of a proinflammatory diet (as reflected by the DII or E-DII) is associated with increased serum inflammatory cytokines (35,36,46–48). Coincidently, individuals with depressive symptoms often have higher levels of inflammatory cytokines compared with nondepressed counterparts (19). If depressed individuals (who already have high levels of inflammation) consume a proinflammatory diet, their level of inflammation may be much higher compared with a nondepressed individual. Thus, it is possible that depressed individuals have exacerbated inflammation, which may accelerate their development of frailty onset. We observed that the point estimate of the association between a proinflammatory diet and odds of frailty onset was higher in those with clinically relevant depressive symptoms compared with those without. Alternatively, depressed individuals are less likely to consume fruits and vegetables, which are rich in anti-inflammatory compounds. Thus, depressed individuals with higher levels of inflammation may inadvertently consume a proinflammatory diet, which could accelerate onset of frailty. Regardless, it is important to note that the confidence intervals for the subgroups overlapped. Thus, these results should be interpreted with caution and as preliminary evidence.

Additionally, in secondary analyses among participants who were nonfrail and without clinically relevant depressive symptoms at baseline, a higher E-DII score was borderline significantly (p = .07) associated with 9% increased odds (95% CI = 0.99–1.20) of frailty onset alone and significantly associated with a 32% increased odds (95% CI = 1.10–1.59) of both frailty and clinically relevant depressive symptoms onset. This finding warrants further investigation, especially since those that developed both frailty and clinically relevant depressive symptoms tended to consume a more proinflammatory diet compared with those who developed only frailty.

Unexpectedly, there was no association between proinflammatory diet and new onset of only depressive symptoms in our secondary analysis, a finding that should be considered preliminary since it represents a subgroup analysis of only 54 individuals. A meta-analysis of 11 studies found that a proinflammatry diet (as evaluated by DII/E-DII) was associated with depression/depressive symptoms onset (31). When the meta-analysis was stratified by age, the association between inflammatory diet and depression measures was stronger in adults <50 years (n = 5 studies; OR 1.53, 95% CI = 1.20–1.94), compared with adults older than 50 years (n = 6 studies; OR 1.30, 95% CI = 1.16–1.44) (31), suggesting that a proinflammatoy diet may be less strongly associated with depressive symptoms in older adults compared with younger adults. Of the 6 studies conducted in adults older than 50 years included in the meta-analysis (31), only one of the studies also report no association between a proinflammatory diet and new onset of depressive symptoms over 9 years; that is, in a cohort of Italian older adults (49). However, it is important to note that in our study, those who only developed clinically relevant depressive symptoms, tended to consume a more anti-inflammatory diet compared with those who developed both conditions, and this may have prevented those individuals from developing frailty. Future work to better address the relationship between age, inflammatory diet, frailty, and depressive symptoms is needed.

This study has several strengths, including the use of a prospective design, inclusion of both men and women, the use of a validated FFQ for the dietary assessment, and detailed assessment of covariates. Importantly, this is one of the first studies to evaluate the association of dietary inflammation with frailty in subgroups of individuals with and without clinically relevant depressive symptoms. Larger studies are warranted to provide additional evidence on the relationship between proinflammatory diet, frailty, and depressive symptoms. Furthermore, we present our results for both the E-DII and the DII. Overall, our results were similar, suggesting that that energy intake neither modified nor confounded the effect of the inflammation associations.

This study also has limitations. First, the number of individuals that met the criteria CES-D ≥ 16 points was relatively low (n = 94), especially in the sensitivity analysis (n = 76). In our study, the prevalence of clinically relevant depressive symptoms was 5.5%, whereas other studies report higher prevalence estimates (range 8%–16% (5)). Despite lower prevalence estimates in our sample, the prevalence ratio of clinically relevant depressive symptoms in women compared with men is ~2:1, which is commonly observed in the literature (50). Nevertheless, our results should be viewed as hypothesis generating and must be interpreted with caution because our study was likely underpowered to detect the true association between dietary inflammation and frailty development in individuals with depressive symptoms. Future work should confirm these findings in studies with larger numbers of individuals with depressive symptoms. Second, the Harvard FFQ captures only 33 of the 45 food parameters of the DII. However, not including the food parameters (eg, saffron, turmeric, or eugenol) does not impede the ability of the DII/E-DII to predict the association with inflammatory markers (48). Third, both frailty and depression are dynamic conditions. An individual may move between frailty or depressive statuses. This issue poses additional challenges in studies of frailty and depression over time. However, our previous work found that the estimated associations between E-DII and short-term frailty (accounting for changes in frailty status) were not appreciably different from the associations between E-DII and long-term frailty (30). Given that the present study utilized data from the same cohort as our previous work, we do not anticipate changing of frailty status largely affected our results.

Fourth, there is likely residual confounding as that this is an observational study, and we could not account for other socioeconomic factors that may affect our results. Furthermore, our assessment of frailty includes exhaustion as one of the criteria, which is defined by the use of 2 CES-D questions, which may confound our analyses. However, the 2 CES-D questions that are used to define exhaustion is operationalized differently compared with the full CES-D assessment score, and since there are many other items in the CES-D measure, the impact of retaining these 2 questions in both the “exposure” and the “outcome” is not expected to introduce bias. Regardless, future studies should consider alternative assessments of depressive symptoms and frailty to avoid this issue. Finally, given that there was 30% loss to follow-up and those lost to follow-up were older and had more comorbidities, thus were more likely to be frail suggests that the study findings could be affected by presence of healthy survival bias. Our results are generalizable to relatively healthier older adults of European ancestry; further study in other races and ethnicities is essential.

The point estimates of the association between a proinflammatory diet and frailty were higher in individuals with clinically relevant depressive symptoms compared with those without clinically relevant depressive symptoms; while preliminary, this finding suggests that the association between dietary inflammation and frailty onset may be influenced by the level of depressive symptoms. However, the confidence intervals overlapped, and results must be interpreted with caution. Other large-scale, prospective studies are required to corroborate our findings. Future work should also aim to investigate how diet, frailty status, and depressive symptoms change over time, and how associations may vary by age and biological sex.

Supplementary Material

glac140_suppl_Supplementary_Material

Click here for additional data file.^{(17.4KB, docx)}

Contributor Information

Courtney L Millar, Hinda and Arthur Marcus Institute for Aging Research, Hebrew SeniorLife, Boston, Massachusetts, USA; Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, USA.

Alyssa B Dufour, Hinda and Arthur Marcus Institute for Aging Research, Hebrew SeniorLife, Boston, Massachusetts, USA; Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, USA.

James R Hebert, Department of Epidemiology and Biostatistics and the Cancer Prevention and Control Program, Arnold School of Public Health, University of South Carolina, Columbia, South Carolina, USA; Department of Nutrition, Connecting Health Innovations LLC, Columbia, South Carolina, USA.

Nitin Shivappa, Department of Epidemiology and Biostatistics and the Cancer Prevention and Control Program, Arnold School of Public Health, University of South Carolina, Columbia, South Carolina, USA; Department of Nutrition, Connecting Health Innovations LLC, Columbia, South Carolina, USA.

Olivia I Okereke, Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA; Department of Epidemiology, Harvard TH Chan School of Public Health, Boston, Massachusetts, USA.

Douglas P Kiel, Hinda and Arthur Marcus Institute for Aging Research, Hebrew SeniorLife, Boston, Massachusetts, USA; Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, USA.

Marian T Hannan, Hinda and Arthur Marcus Institute for Aging Research, Hebrew SeniorLife, Boston, Massachusetts, USA; Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, USA.

Shivani Sahni, Hinda and Arthur Marcus Institute for Aging Research, Hebrew SeniorLife, Boston, Massachusetts, USA; Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, USA.

Funding

This work was supported by the National Institute of Aging (NIA) AG051728 and the Heart, Lung and Blood Institute’s Framingham Heart Study (NHLBI) contract numbers HHSN268201500001I and N01-HC 25195. C.L.M. was supported by the NIA (grant no. T32-AG023480) and the Beth and Richard Applebaum Research Fund. S.S. was supported by R01 AG051728; the NIA’s support of the Boston Claude D. Peppercenter OAIC (OAIC; 1P30AG031679), and Peter and Barbara Sidel Fund. D.P.K. was funded by R01 AR041398 and R01 AR061445. Any opinions, findings, conclusions, or recommendations expressed in this publication are those of the authors and do not necessarily reflect the official views of the National Institutes of Health or the USDA.

Conflict of Interest

J.R.H. owns controlling interest in Connecting Health Innovations LLC (CHI), a company that has licensed the right to his invention of the DII from the University of South Carolina to develop computer and smart phone applications for patient counseling and dietary intervention in clinical settings. N.S. is an employee of CHI. The subject matter of this article will not have any direct bearing on that work nor has that activity exerted any influence on this project. O.I.O. reports royalties from Springer Publishing for a book on late-life depression prevention. D.P.K. has received grant funding from Radius Health and Amgen and serves as a consultant for Solarea Bio Inc. He receives royalties for publications in UpToDate from Wolters Kluwer. D.P.K. also serves on the Data Safety Monitoring Board for a clinical trial conducted by Agnovos, Inc. None of these activities are related to the subject matter of this project. M.T.H. has received grant funding from Amgen for an unrelated study. S.S. reports institutional grants from Dairy Management Inc. and Solarea Bio Inc., has reviewed grants for the American Egg Board’s Egg Nutrition Center, and serves on a scientific advisory board for the Protein Committee, Institute for the Advancement of Food and Nutrition Sciences. The other authors declare no conflict.

Author Contributions

The authors’ responsibilities were as follows: C.L.M., D.P.K., M.T.H., and S.S. designed the research; C.L.M. analyzed data with critical input from all authors; A.B.D. provided statistical expertise; N.S. and J.R.H. calculated the DII/E-DII variables and helped in the interpretation of these variables. O.I.O. provided expertise in depression and related variables. C.L.M. drafted the manuscript and all authors provided critical revisions to the manuscript for important intellectual content; S.S. has primary responsibility for the final content of this paper. All authors reviewed and approved the final manuscript.

References

1. Bandeen-Roche K, Seplaki CL, Huang J, et al. Frailty in older adults: a nationally representative profile in the United States. J Gerontol A Biol Sci Med Sci. 2015;70(11):1427–1434. doi: 10.1093/gerona/glv133 [DOI] [PMC free article] [PubMed] [Google Scholar]
2. Xue QL. The frailty syndrome: definition and natural history. Clin Geriatr Med. 2011;27(1):1–15. doi: 10.1016/j.cger.2010.08.009 [DOI] [PMC free article] [PubMed] [Google Scholar]
3. Ensrud KE, Ewing SK, Taylor BC, et al. Frailty and risk of falls, fracture, and mortality in older women: the study of osteoporotic fractures. J Gerontol A Biol Sci Med Sci. 2007;62(7):744–751. doi: 10.1093/gerona/62.7.744 [DOI] [PubMed] [Google Scholar]
4. Fried LP, Tangen CM, Walston J, et al. Frailty in older adults: evidence for a phenotype. J Gerontol A Biol Sci Med Sci. 2001;56(3):M146–M156. doi: 10.1093/gerona/56.3.m146 [DOI] [PubMed] [Google Scholar]
5. Blazer DG. Depression in late life: review and commentary. J Gerontol A Biol Sci Med Sci. 2003;58(3):M249–M265. doi: 10.1093/gerona/58.3.M249 [DOI] [PubMed] [Google Scholar]
6. Cronin-Stubbs D, de Leon CFM, Beckett LA, Field TS, Glynn RJ, Evans DA. Six-year effect of depressive symptoms on the course of physical disability in community-living older adults. Arch Intern Med. 2000;160(20):3074–3080. doi: 10.1001/archinte.160.20.3074 [DOI] [PubMed] [Google Scholar]
7. Unützer J, Patrick DL, Marmon T, Simon GE, Katon WJ. Depressive symptoms and mortality in a prospective study of 2,558 older adults. Am J Geriatr Psychiatry. 2002;10(5):521–530. doi: 10.1097/00019442-200209000-00005 [DOI] [PubMed] [Google Scholar]
8. Hajat C, Stein E. The global burden of multiple chronic conditions: a narrative review. Prev Med Rep. 2018;12:284–293. doi: 10.1016/j.pmedr.2018.10.008 [DOI] [PMC free article] [PubMed] [Google Scholar]
9. Chang HY, Fang HL, Ting TT, et al. The co-occurrence of frailty (accumulation of functional deficits) and depressive symptoms, and its effect on mortality in older adults: a longitudinal study. Clin Interv Aging. 2019;14:1671–1680. doi: 10.2147/CIA.S210072 [DOI] [PMC free article] [PubMed] [Google Scholar]
10. Arts MHL, van den Berg KS, Marijnissen RM, et al. Frailty as a predictor of mortality in late-life depression: a prospective clinical cohort study. J Clin Psychiatry. 2021;82(3):1. doi: 10.4088/JCP.20m13277 [DOI] [PubMed] [Google Scholar]
11. Vatic M, von Haehling S, Ebner N. Inflammatory biomarkers of frailty. Exp Gerontol. 2020;133:110858. doi: 10.1016/j.exger.2020.110858 [DOI] [PubMed] [Google Scholar]
12. Wang J, Leung K-S, Chow SK-H, Cheung W-H. Inflammation and age-associated skeletal muscle deterioration (sarcopaenia). J Orthop Trans. 2017;10:94–101. doi: 10.1016/j.jot.2017.05.006 [DOI] [PMC free article] [PubMed] [Google Scholar]
13. Li H, Manwani B, Leng SX. Frailty, inflammation, and immunity. Aging Dis. 2011;2(6):466–473. [PMC free article] [PubMed] [Google Scholar]
14. Ferrucci L, Penninx BW, Volpato S, et al. Change in muscle strength explains accelerated decline of physical function in older women with high interleukin-6 serum levels. J Am Geriatr Soc. 2002;50(12):1947–1954. doi: 10.1046/j.1532-5415.2002.50605.x [DOI] [PubMed] [Google Scholar]
15. Miller RR, Shardell MD, Hicks GE, et al. Association between interleukin-6 and lower extremity function after hip fracture--the role of muscle mass and strength. J Am Geriatr Soc. 2008;56(6):1050–1056. doi: 10.1111/j.1532-5415.2008.01708.x [DOI] [PMC free article] [PubMed] [Google Scholar]
16. Santos ML, Gomes WF, Pereira DS, et al. Muscle strength, muscle balance, physical function and plasma interleukin-6 (IL-6) levels in elderly women with knee osteoarthritis (OA). Arch Gerontol Geriatr. 2011;52(3):322–326. doi: 10.1016/j.archger.2010.05.009 [DOI] [PubMed] [Google Scholar]
17. Blodgett J, Theou O, Kirkland S, Andreou P, Rockwood K. The association between sedentary behaviour, moderate–vigorous physical activity and frailty in NHANES cohorts. Maturitas. 2015;80(2):187–191. doi: 10.1016/j.maturitas.2014.11.010 [DOI] [PubMed] [Google Scholar]
18. Felger JC, Lotrich FE. Inflammatory cytokines in depression: neurobiological mechanisms and therapeutic implications. Neuroscience. 2013;246:199–229. doi: 10.1016/j.neuroscience.2013.04.060 [DOI] [PMC free article] [PubMed] [Google Scholar]
19. Raison CL, Capuron L, Miller AH. Cytokines sing the blues: inflammation and the pathogenesis of depression. Trends Immunol. 2006;27(1):24–31. doi: 10.1016/j.it.2005.11.006 [DOI] [PMC free article] [PubMed] [Google Scholar]
20. Vancassel S, Capuron L, Castanon N. Brain kynurenine and BH4 pathways: relevance to the pathophysiology and treatment of inflammation-driven depressive symptoms. Front Neurosci. 2018;12(499):1. doi: 10.3389/fnins.2018.00499 [DOI] [PMC free article] [PubMed] [Google Scholar]
21. Galland L. Diet and inflammation. Nutr Clin Pract. 2010;25(6):634–640. doi: 10.1177/0884533610385703 [DOI] [PubMed] [Google Scholar]
22. 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. doi: 10.1186/s12877-017-0496-2 [DOI] [PMC free article] [PubMed] [Google Scholar]
23. Ng TP, Feng L, Nyunt MS, et al. Nutritional, physical, cognitive, and combination interventions and frailty reversal among older adults: a randomized controlled trial. Am J Med. 2015;128(11):1225–1236.e1. doi: 10.1016/j.amjmed.2015.06.017 [DOI] [PubMed] [Google Scholar]
24. Ng TP, Nyunt MSZ, Feng L, et al. Multi-domains lifestyle interventions reduces depressive symptoms among frail and pre-frail older persons: randomized controlled trial. J Nutr Health Aging. 2017;21(8):918–926. doi: 10.1007/s12603-016-0867-y [DOI] [PubMed] [Google Scholar]
25. Kim D, Park Y. Association between the dietary inflammatory index and risk of frailty in older individuals with poor nutritional status. Nutrients. 2018;10(10):13631. doi: 10.3390/nu10101363 [DOI] [PMC free article] [PubMed] [Google Scholar]
26. Laclaustra M, Rodriguez-Artalejo F, Guallar-Castillon P, et al. The inflammatory potential of diet is related to incident frailty and slow walking in older adults. Clin Nutr. 2020;39(1):185–191. doi: 10.1016/j.clnu.2019.01.013 [DOI] [PubMed] [Google Scholar]
27. Lohman MC, Resciniti NV, Wirth MD, Shivappa N, Hébert JR. Obesity, dietary inflammation, and frailty among older adults: evidence from the National Health and Nutrition Examination Survey. J Nutr Gerontol Geriatr. 2019;38(1):18–32. doi: 10.1080/21551197.2018.1552226 [DOI] [PubMed] [Google Scholar]
28. Shivappa N, Stubbs B, Hebert JR, et al. The relationship between the dietary inflammatory index and incident frailty: a longitudinal cohort study. J Am Med Dir Assoc. 2018;19(1):77–82. doi: 10.1016/j.jamda.2017.08.006 [DOI] [PMC free article] [PubMed] [Google Scholar]
29. Resciniti NV, Lohman MC, Wirth MD, Shivappa N, Hebert JR. Dietary inflammatory index, pre-frailty and frailty among older US adults: evidence from the National Health and Nutrition Examination Survey, 2007–2014. J Nutr Health Aging. 2019;23(4):323–329. doi: 10.1007/s12603-019-1164-3 [DOI] [PubMed] [Google Scholar]
30. Millar CL, Dufour AB, Shivappa N, et al. A pro-inflammatory diet is associated with increased odds of frailty after 12-year follow-up in a cohort of adults. Am J Clin Nutr. 2022;115(2):334–343. doi: 10.1093/ajcn/nqab317 [DOI] [PMC free article] [PubMed] [Google Scholar]
31. Tolkien K, Bradburn S, Murgatroyd C. An anti-inflammatory diet as a potential intervention for depressive disorders: a systematic review and meta-analysis. Clin Nutr. 2019;38(5):2045–2052. doi: 10.1016/j.clnu.2018.11.007 [DOI] [PubMed] [Google Scholar]
32. Feinleib M, Kannel WB, Garrison RJ, McNamara PM, Castelli WP. The Framingham Offspring Study. Design and preliminary data. Prev Med. 1975;4(4):518–525. doi: 10.1016/0091-7435(75)90037-7 [DOI] [PubMed] [Google Scholar]
33. Dodd S, Bauer M, Carvalho AF, et al. A clinical approach to treatment resistance in depressed patients: What to do when the usual treatments don’t work well enough? World J Biol Psychiatry. 2020;1:20. doi: 10.1080/15622975.2020.1851052 [DOI] [PubMed] [Google Scholar]
34. Rimm EB, Giovannucci EL, Stampfer MJ, Colditz GA, Litin LB, Willett WC. Reproducibility and validity of an expanded self-administered semiquantitative food frequency questionnaire among male health professionals. Am J Epidemiol. 1992;135(10):1114–1126; discussion 1127; discussion 1127–1136 doi: 10.1093/oxfordjournals.aje.a116211 [DOI] [PubMed] [Google Scholar]
35. Shivappa N, Steck SE, Hurley TG, Hussey JR, Hebert JR. Designing and developing a literature-derived, population-based dietary inflammatory index. Public Health Nutr. 2014;17(8):1689–1696. doi: 10.1017/S1368980013002115 [DOI] [PMC free article] [PubMed] [Google Scholar]
36. Hébert JR, Shivappa N, Wirth MD, Hussey JR, Hurley TG. Perspective: The Dietary Inflammatory Index (DII)—lessons learned, improvements made, and future directions. Adv Nutr. 2019;10(2):185–195. doi: 10.1093/advances/nmy071 [DOI] [PMC free article] [PubMed] [Google Scholar]
37. Cavicchia PP, Steck SE, Hurley TG, et al. A new dietary inflammatory index predicts interval changes in serum high-sensitivity C-reactive protein. J Nutr. 2009;139(12):2365–2372. doi: 10.3945/jn.109.114025 [DOI] [PMC free article] [PubMed] [Google Scholar]
38. Lewinsohn PM, Seeley JR, Roberts RE, Allen NB. Center for Epidemiologic Studies Depression Scale (CES-D) as a screening instrument for depression among community-residing older adults. Psychol Aging. 1997;12(2):277–287. doi: 10.1037//0882-7974.12.2.277 [DOI] [PubMed] [Google Scholar]
39. Vilagut G, Forero CG, Barbaglia G, Alonso J. Screening for depression in the general population with the Center for Epidemiologic Studies Depression (CES-D): a systematic review with meta-analysis. PLoS One. 2016;11(5):e0155431. doi: 10.1371/journal.pone.0155431 [DOI] [PMC free article] [PubMed] [Google Scholar]
40. Radloff LS. The CES-D scale: a self-report depression scale for research in the general population. Appl Psychol Meas. 1977;1(3):385–401. doi: 10.1177/014662167700100306 [DOI] [Google Scholar]
41. Kannel WB, Sorlie P. Some health benefits of physical activity. The Framingham Study. Arch Intern Med. 1979;139(8):857–861. doi: 10.1001/archinte.1979.03630450011006 [DOI] [PubMed] [Google Scholar]
42. Fox CS, Coady S, Sorlie PD, et al. Increasing cardiovascular disease burden due to diabetes mellitus: the Framingham Heart Study. Circulation. 2007;115(12):1544–1550. doi: 10.1161/CIRCULATIONAHA.106.658948 [DOI] [PubMed] [Google Scholar]
43. Brown PJ, Roose SP, O’Boyle KR, et al. Frailty and its correlates in adults with late life depression. Am J Geriatr Psychiatry. 2020;28(2):145–154. doi: 10.1016/j.jagp.2019.10.005 [DOI] [PMC free article] [PubMed] [Google Scholar]
44. Brown PJ, Rutherford BR, Yaffe K, et al. The depressed frail phenotype: the clinical manifestation of increased biological aging. Am J Geriatr Psychiatry. 2016;24(11):1084–1094. doi: 10.1016/j.jagp.2016.06.005 [DOI] [PMC free article] [PubMed] [Google Scholar]
45. Chu W, Chang SF, Ho HY, Lin HC. The relationship between depression and frailty in community-dwelling older people: a systematic review and meta-analysis of 84,351 older adults. J Nurs Scholarsh. 2019;51(5):547–559. doi: 10.1111/jnu.12501 [DOI] [PubMed] [Google Scholar]
46. Shivappa N, Hebert JR, Marcos A, et al. Association between dietary inflammatory index and inflammatory markers in the HELENA study. Mol Nutr Food Res. 2017;61(6):16007071. doi: 10.1002/mnfr.201600707 [DOI] [PMC free article] [PubMed] [Google Scholar]
47. Shivappa N, Hebert JR, Rietzschel ER, et al. Associations between dietary inflammatory index and inflammatory markers in the Asklepios Study. Br J Nutr. 2015;113(4):665–671. doi: 10.1017/S000711451400395X [DOI] [PMC free article] [PubMed] [Google Scholar]
48. Shivappa N, Steck SE, Hurley TG, et al. A population-based dietary inflammatory index predicts levels of C-reactive protein in the Seasonal Variation of Blood Cholesterol Study (SEASONS). Public Health Nutr. 2014;17(8):1825–1833. doi: 10.1017/S1368980013002565 [DOI] [PMC free article] [PubMed] [Google Scholar]
49. Vermeulen E, Brouwer IA, Stronks K, et al. Inflammatory dietary patterns and depressive symptoms in Italian older adults. Brain Behav Immun. 2018;67:290–298. doi: 10.1016/j.bbi.2017.09.005 [DOI] [PMC free article] [PubMed] [Google Scholar]
50. Harkness KL, Alavi N, Monroe SM, Slavich GM, Gotlib IH, Bagby RM. Gender differences in life events prior to onset of major depressive disorder: the moderating effect of age. J Abnorm Psychol. 2010;119(4):791–803. doi: 10.1037/a0020629 [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

glac140_suppl_Supplementary_Material

Click here for additional data file.^{(17.4KB, docx)}

[CIT0001] 1. Bandeen-Roche K, Seplaki CL, Huang J, et al. Frailty in older adults: a nationally representative profile in the United States. J Gerontol A Biol Sci Med Sci. 2015;70(11):1427–1434. doi: 10.1093/gerona/glv133 [DOI] [PMC free article] [PubMed] [Google Scholar]

[CIT0002] 2. Xue QL. The frailty syndrome: definition and natural history. Clin Geriatr Med. 2011;27(1):1–15. doi: 10.1016/j.cger.2010.08.009 [DOI] [PMC free article] [PubMed] [Google Scholar]

[CIT0003] 3. Ensrud KE, Ewing SK, Taylor BC, et al. Frailty and risk of falls, fracture, and mortality in older women: the study of osteoporotic fractures. J Gerontol A Biol Sci Med Sci. 2007;62(7):744–751. doi: 10.1093/gerona/62.7.744 [DOI] [PubMed] [Google Scholar]

[CIT0004] 4. Fried LP, Tangen CM, Walston J, et al. Frailty in older adults: evidence for a phenotype. J Gerontol A Biol Sci Med Sci. 2001;56(3):M146–M156. doi: 10.1093/gerona/56.3.m146 [DOI] [PubMed] [Google Scholar]

[CIT0005] 5. Blazer DG. Depression in late life: review and commentary. J Gerontol A Biol Sci Med Sci. 2003;58(3):M249–M265. doi: 10.1093/gerona/58.3.M249 [DOI] [PubMed] [Google Scholar]

[CIT0006] 6. Cronin-Stubbs D, de Leon CFM, Beckett LA, Field TS, Glynn RJ, Evans DA. Six-year effect of depressive symptoms on the course of physical disability in community-living older adults. Arch Intern Med. 2000;160(20):3074–3080. doi: 10.1001/archinte.160.20.3074 [DOI] [PubMed] [Google Scholar]

[CIT0007] 7. Unützer J, Patrick DL, Marmon T, Simon GE, Katon WJ. Depressive symptoms and mortality in a prospective study of 2,558 older adults. Am J Geriatr Psychiatry. 2002;10(5):521–530. doi: 10.1097/00019442-200209000-00005 [DOI] [PubMed] [Google Scholar]

[CIT0008] 8. Hajat C, Stein E. The global burden of multiple chronic conditions: a narrative review. Prev Med Rep. 2018;12:284–293. doi: 10.1016/j.pmedr.2018.10.008 [DOI] [PMC free article] [PubMed] [Google Scholar]

[CIT0009] 9. Chang HY, Fang HL, Ting TT, et al. The co-occurrence of frailty (accumulation of functional deficits) and depressive symptoms, and its effect on mortality in older adults: a longitudinal study. Clin Interv Aging. 2019;14:1671–1680. doi: 10.2147/CIA.S210072 [DOI] [PMC free article] [PubMed] [Google Scholar]

[CIT0010] 10. Arts MHL, van den Berg KS, Marijnissen RM, et al. Frailty as a predictor of mortality in late-life depression: a prospective clinical cohort study. J Clin Psychiatry. 2021;82(3):1. doi: 10.4088/JCP.20m13277 [DOI] [PubMed] [Google Scholar]

[CIT0011] 11. Vatic M, von Haehling S, Ebner N. Inflammatory biomarkers of frailty. Exp Gerontol. 2020;133:110858. doi: 10.1016/j.exger.2020.110858 [DOI] [PubMed] [Google Scholar]

[CIT0012] 12. Wang J, Leung K-S, Chow SK-H, Cheung W-H. Inflammation and age-associated skeletal muscle deterioration (sarcopaenia). J Orthop Trans. 2017;10:94–101. doi: 10.1016/j.jot.2017.05.006 [DOI] [PMC free article] [PubMed] [Google Scholar]

[CIT0013] 13. Li H, Manwani B, Leng SX. Frailty, inflammation, and immunity. Aging Dis. 2011;2(6):466–473. [PMC free article] [PubMed] [Google Scholar]

[CIT0014] 14. Ferrucci L, Penninx BW, Volpato S, et al. Change in muscle strength explains accelerated decline of physical function in older women with high interleukin-6 serum levels. J Am Geriatr Soc. 2002;50(12):1947–1954. doi: 10.1046/j.1532-5415.2002.50605.x [DOI] [PubMed] [Google Scholar]

[CIT0015] 15. Miller RR, Shardell MD, Hicks GE, et al. Association between interleukin-6 and lower extremity function after hip fracture--the role of muscle mass and strength. J Am Geriatr Soc. 2008;56(6):1050–1056. doi: 10.1111/j.1532-5415.2008.01708.x [DOI] [PMC free article] [PubMed] [Google Scholar]

[CIT0016] 16. Santos ML, Gomes WF, Pereira DS, et al. Muscle strength, muscle balance, physical function and plasma interleukin-6 (IL-6) levels in elderly women with knee osteoarthritis (OA). Arch Gerontol Geriatr. 2011;52(3):322–326. doi: 10.1016/j.archger.2010.05.009 [DOI] [PubMed] [Google Scholar]

[CIT0017] 17. Blodgett J, Theou O, Kirkland S, Andreou P, Rockwood K. The association between sedentary behaviour, moderate–vigorous physical activity and frailty in NHANES cohorts. Maturitas. 2015;80(2):187–191. doi: 10.1016/j.maturitas.2014.11.010 [DOI] [PubMed] [Google Scholar]

[CIT0018] 18. Felger JC, Lotrich FE. Inflammatory cytokines in depression: neurobiological mechanisms and therapeutic implications. Neuroscience. 2013;246:199–229. doi: 10.1016/j.neuroscience.2013.04.060 [DOI] [PMC free article] [PubMed] [Google Scholar]

[CIT0019] 19. Raison CL, Capuron L, Miller AH. Cytokines sing the blues: inflammation and the pathogenesis of depression. Trends Immunol. 2006;27(1):24–31. doi: 10.1016/j.it.2005.11.006 [DOI] [PMC free article] [PubMed] [Google Scholar]

[CIT0020] 20. Vancassel S, Capuron L, Castanon N. Brain kynurenine and BH4 pathways: relevance to the pathophysiology and treatment of inflammation-driven depressive symptoms. Front Neurosci. 2018;12(499):1. doi: 10.3389/fnins.2018.00499 [DOI] [PMC free article] [PubMed] [Google Scholar]

[CIT0021] 21. Galland L. Diet and inflammation. Nutr Clin Pract. 2010;25(6):634–640. doi: 10.1177/0884533610385703 [DOI] [PubMed] [Google Scholar]

[CIT0022] 22. 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. doi: 10.1186/s12877-017-0496-2 [DOI] [PMC free article] [PubMed] [Google Scholar]

[CIT0023] 23. Ng TP, Feng L, Nyunt MS, et al. Nutritional, physical, cognitive, and combination interventions and frailty reversal among older adults: a randomized controlled trial. Am J Med. 2015;128(11):1225–1236.e1. doi: 10.1016/j.amjmed.2015.06.017 [DOI] [PubMed] [Google Scholar]

[CIT0024] 24. Ng TP, Nyunt MSZ, Feng L, et al. Multi-domains lifestyle interventions reduces depressive symptoms among frail and pre-frail older persons: randomized controlled trial. J Nutr Health Aging. 2017;21(8):918–926. doi: 10.1007/s12603-016-0867-y [DOI] [PubMed] [Google Scholar]

[CIT0025] 25. Kim D, Park Y. Association between the dietary inflammatory index and risk of frailty in older individuals with poor nutritional status. Nutrients. 2018;10(10):13631. doi: 10.3390/nu10101363 [DOI] [PMC free article] [PubMed] [Google Scholar]

[CIT0026] 26. Laclaustra M, Rodriguez-Artalejo F, Guallar-Castillon P, et al. The inflammatory potential of diet is related to incident frailty and slow walking in older adults. Clin Nutr. 2020;39(1):185–191. doi: 10.1016/j.clnu.2019.01.013 [DOI] [PubMed] [Google Scholar]

[CIT0027] 27. Lohman MC, Resciniti NV, Wirth MD, Shivappa N, Hébert JR. Obesity, dietary inflammation, and frailty among older adults: evidence from the National Health and Nutrition Examination Survey. J Nutr Gerontol Geriatr. 2019;38(1):18–32. doi: 10.1080/21551197.2018.1552226 [DOI] [PubMed] [Google Scholar]

[CIT0028] 28. Shivappa N, Stubbs B, Hebert JR, et al. The relationship between the dietary inflammatory index and incident frailty: a longitudinal cohort study. J Am Med Dir Assoc. 2018;19(1):77–82. doi: 10.1016/j.jamda.2017.08.006 [DOI] [PMC free article] [PubMed] [Google Scholar]

[CIT0029] 29. Resciniti NV, Lohman MC, Wirth MD, Shivappa N, Hebert JR. Dietary inflammatory index, pre-frailty and frailty among older US adults: evidence from the National Health and Nutrition Examination Survey, 2007–2014. J Nutr Health Aging. 2019;23(4):323–329. doi: 10.1007/s12603-019-1164-3 [DOI] [PubMed] [Google Scholar]

[CIT0030] 30. Millar CL, Dufour AB, Shivappa N, et al. A pro-inflammatory diet is associated with increased odds of frailty after 12-year follow-up in a cohort of adults. Am J Clin Nutr. 2022;115(2):334–343. doi: 10.1093/ajcn/nqab317 [DOI] [PMC free article] [PubMed] [Google Scholar]

[CIT0031] 31. Tolkien K, Bradburn S, Murgatroyd C. An anti-inflammatory diet as a potential intervention for depressive disorders: a systematic review and meta-analysis. Clin Nutr. 2019;38(5):2045–2052. doi: 10.1016/j.clnu.2018.11.007 [DOI] [PubMed] [Google Scholar]

[CIT0032] 32. Feinleib M, Kannel WB, Garrison RJ, McNamara PM, Castelli WP. The Framingham Offspring Study. Design and preliminary data. Prev Med. 1975;4(4):518–525. doi: 10.1016/0091-7435(75)90037-7 [DOI] [PubMed] [Google Scholar]

[CIT0033] 33. Dodd S, Bauer M, Carvalho AF, et al. A clinical approach to treatment resistance in depressed patients: What to do when the usual treatments don’t work well enough? World J Biol Psychiatry. 2020;1:20. doi: 10.1080/15622975.2020.1851052 [DOI] [PubMed] [Google Scholar]

[CIT0034] 34. Rimm EB, Giovannucci EL, Stampfer MJ, Colditz GA, Litin LB, Willett WC. Reproducibility and validity of an expanded self-administered semiquantitative food frequency questionnaire among male health professionals. Am J Epidemiol. 1992;135(10):1114–1126; discussion 1127; discussion 1127–1136 doi: 10.1093/oxfordjournals.aje.a116211 [DOI] [PubMed] [Google Scholar]

[CIT0035] 35. Shivappa N, Steck SE, Hurley TG, Hussey JR, Hebert JR. Designing and developing a literature-derived, population-based dietary inflammatory index. Public Health Nutr. 2014;17(8):1689–1696. doi: 10.1017/S1368980013002115 [DOI] [PMC free article] [PubMed] [Google Scholar]

[CIT0036] 36. Hébert JR, Shivappa N, Wirth MD, Hussey JR, Hurley TG. Perspective: The Dietary Inflammatory Index (DII)—lessons learned, improvements made, and future directions. Adv Nutr. 2019;10(2):185–195. doi: 10.1093/advances/nmy071 [DOI] [PMC free article] [PubMed] [Google Scholar]

[CIT0037] 37. Cavicchia PP, Steck SE, Hurley TG, et al. A new dietary inflammatory index predicts interval changes in serum high-sensitivity C-reactive protein. J Nutr. 2009;139(12):2365–2372. doi: 10.3945/jn.109.114025 [DOI] [PMC free article] [PubMed] [Google Scholar]

[CIT0038] 38. Lewinsohn PM, Seeley JR, Roberts RE, Allen NB. Center for Epidemiologic Studies Depression Scale (CES-D) as a screening instrument for depression among community-residing older adults. Psychol Aging. 1997;12(2):277–287. doi: 10.1037//0882-7974.12.2.277 [DOI] [PubMed] [Google Scholar]

[CIT0039] 39. Vilagut G, Forero CG, Barbaglia G, Alonso J. Screening for depression in the general population with the Center for Epidemiologic Studies Depression (CES-D): a systematic review with meta-analysis. PLoS One. 2016;11(5):e0155431. doi: 10.1371/journal.pone.0155431 [DOI] [PMC free article] [PubMed] [Google Scholar]

[CIT0040] 40. Radloff LS. The CES-D scale: a self-report depression scale for research in the general population. Appl Psychol Meas. 1977;1(3):385–401. doi: 10.1177/014662167700100306 [DOI] [Google Scholar]

[CIT0041] 41. Kannel WB, Sorlie P. Some health benefits of physical activity. The Framingham Study. Arch Intern Med. 1979;139(8):857–861. doi: 10.1001/archinte.1979.03630450011006 [DOI] [PubMed] [Google Scholar]

[CIT0042] 42. Fox CS, Coady S, Sorlie PD, et al. Increasing cardiovascular disease burden due to diabetes mellitus: the Framingham Heart Study. Circulation. 2007;115(12):1544–1550. doi: 10.1161/CIRCULATIONAHA.106.658948 [DOI] [PubMed] [Google Scholar]

[CIT0043] 43. Brown PJ, Roose SP, O’Boyle KR, et al. Frailty and its correlates in adults with late life depression. Am J Geriatr Psychiatry. 2020;28(2):145–154. doi: 10.1016/j.jagp.2019.10.005 [DOI] [PMC free article] [PubMed] [Google Scholar]

[CIT0044] 44. Brown PJ, Rutherford BR, Yaffe K, et al. The depressed frail phenotype: the clinical manifestation of increased biological aging. Am J Geriatr Psychiatry. 2016;24(11):1084–1094. doi: 10.1016/j.jagp.2016.06.005 [DOI] [PMC free article] [PubMed] [Google Scholar]

[CIT0045] 45. Chu W, Chang SF, Ho HY, Lin HC. The relationship between depression and frailty in community-dwelling older people: a systematic review and meta-analysis of 84,351 older adults. J Nurs Scholarsh. 2019;51(5):547–559. doi: 10.1111/jnu.12501 [DOI] [PubMed] [Google Scholar]

[CIT0046] 46. Shivappa N, Hebert JR, Marcos A, et al. Association between dietary inflammatory index and inflammatory markers in the HELENA study. Mol Nutr Food Res. 2017;61(6):16007071. doi: 10.1002/mnfr.201600707 [DOI] [PMC free article] [PubMed] [Google Scholar]

[CIT0047] 47. Shivappa N, Hebert JR, Rietzschel ER, et al. Associations between dietary inflammatory index and inflammatory markers in the Asklepios Study. Br J Nutr. 2015;113(4):665–671. doi: 10.1017/S000711451400395X [DOI] [PMC free article] [PubMed] [Google Scholar]

[CIT0048] 48. Shivappa N, Steck SE, Hurley TG, et al. A population-based dietary inflammatory index predicts levels of C-reactive protein in the Seasonal Variation of Blood Cholesterol Study (SEASONS). Public Health Nutr. 2014;17(8):1825–1833. doi: 10.1017/S1368980013002565 [DOI] [PMC free article] [PubMed] [Google Scholar]

[CIT0049] 49. Vermeulen E, Brouwer IA, Stronks K, et al. Inflammatory dietary patterns and depressive symptoms in Italian older adults. Brain Behav Immun. 2018;67:290–298. doi: 10.1016/j.bbi.2017.09.005 [DOI] [PMC free article] [PubMed] [Google Scholar]

[CIT0050] 50. Harkness KL, Alavi N, Monroe SM, Slavich GM, Gotlib IH, Bagby RM. Gender differences in life events prior to onset of major depressive disorder: the moderating effect of age. J Abnorm Psychol. 2010;119(4):791–803. doi: 10.1037/a0020629 [DOI] [PMC free article] [PubMed] [Google Scholar]

PERMALINK

Association of Proinflammatory Diet With Frailty Onset Among Adults With and Without Depressive Symptoms: Results From the Framingham Offspring Study

Courtney L Millar, PhD

Alyssa B Dufour, PhD

James R Hebert, DSc

Nitin Shivappa, PhD

Olivia I Okereke, MD, MS

Douglas P Kiel, MD, MPH

Marian T Hannan, DSc, MPH

Shivani Sahni, PhD

Roles

Abstract

Background

Methods

Results

Conclusions

Method

Parent Study

Study Design and Participants

Figure 1.

Dietary Inflammatory Index

Depressive Symptoms

Frailty Phenotype

Missing Data on Frailty Criteria

Covariates

Descriptive Variables

Statistical Analysis

Results

Participant Characteristics

Table 1.

Those With CES-D < 16 Points

Table 2.

Those With CES-D ≥ 16 Points

Sensitivity Analyses

Secondary Analyses

Figure 2.

Discussion

Supplementary Material

Contributor Information

Funding

Conflict of Interest

Author Contributions

References

Associated Data

Supplementary Materials

ACTIONS

PERMALINK

RESOURCES

Similar articles

Cited by other articles

Links to NCBI Databases