# Associations Between Nutritional Deficits and Physical Performance in Community-Dwelling Older Adults

Wan-Hsuan Lu<sup>1,2*</sup>, Kelly Virecoulon Giudici<sup>1</sup>, Yves Rolland<sup>1,2</sup>, Sophie Guyonnet<sup>1,2</sup>, Jean-François Mangin<sup>3,4</sup>, Bruno Vellas<sup>1,2</sup> and Philipe de Souto Barreto<sup>1,2</sup> for the MAPT/DSA Group

OPEN ACCESS

**Edited by:**

Harriët Jager-Wittenaar,
Hanze University of Applied
Sciences, Netherlands

**Reviewed by:**

Denise Philomene Joseph Van
Aanholt,
University of São Paulo, Brazil
Mar Ruperto,
Universidad San Pablo CEU, Spain

**\*Correspondence:**

Wan-Hsuan Lu
wan-hsuan.lu1@univ-tlse3.fr

**Specialty section:**

This article was submitted to
Clinical Nutrition,
a section of the journal
Frontiers in Nutrition

**Received:** 06 September 2021

**Accepted:** 08 October 2021

**Published:** 11 November 2021

**Citation:**

Lu W-H, Giudici KV, Rolland Y, Guyonnet S, Mangin J-F, Vellas B and de Souto Barreto P (2021) *Associations Between Nutritional Deficits and Physical Performance in Community-Dwelling Older Adults*. Front. Nutr. 8:771470
doi: 10.3389/fnut.2021.771470

**Background:** Whether multiple nutritional deficiencies have a synergic effect on mobility loss remains unknown. This study aims to evaluate associations between multi-nutritional deficits and physical performance evolution among community-dwelling older adults.

**Methods:** We included 386 participants from the Multidomain Alzheimer Preventive Trial (MAPT) (75.6 ± 4.5 years) not receiving omega-3 polyunsaturated fatty acid (PUFA) supplementation and who had available data on nutritional deficits. Baseline nutritional deficits were defined as plasma 25 hydroxyvitamin D < 20 ng/ml, plasma homocysteine >14 µmol/L, or erythrocyte omega-3 PUFA index ≤4.87% (lower quartile). The Short Physical Performance Battery (SPPB), gait speed, and chair rise time were used to assess physical performance at baseline and after 6, 12, 24, 36, 48, and 60 months. We explored if nutrition-physical performance associations varied according to the presence of low-grade inflammation (LGI) and brain imaging indicators.

**Results:** Within-group comparisons showed that physical function (decreased SPPB and gait speed, increased chair rise time) worsened over time, particularly in participants with ≥2 nutritional deficits; however, no between-group differences were observed when individuals without deficit and those with either 1 or ≥2 deficits were compared. Our exploratory analysis on nutritional deficit-LGI interactions showed that, among people with ≥2 deficits, chair rise time was increased over time in participants with LGI (adjusted mean difference: 3.47; 95% CI: 1.03, 5.91; *p* = 0.017), compared with individuals with no LGI.

**Conclusions:** Accumulated deficits on vitamin D, homocysteine, and omega-3 PUFA were not associated with physical performance evolution in older adults, but they determined declined chair rise performance in subjects with low-grade inflammation.

**Clinical Trial Registration:** [https://clinicaltrials.gov/ct2/show/NCT00672685], identifier [NCT00672685].

**Keywords:** vitamin D, homocysteine, omega-3 fatty acids, physical performance, inflammation

<footnote n="1"><sup>1</sup>Gerontopole of Toulouse, Institute of Ageing, Toulouse University Hospital (CHU Toulouse), Toulouse, France, <sup>2</sup>Maintain Aging Research Team, CERPOP, INSERM, Université Paul Sabatier, Toulouse, France, <sup>3</sup>CATI Multicenter Neuroimaging Platform, Neurospin, CEA, Gif-sur-Yvette, France, <sup>4</sup>Université Paris-Saclay, CEA, CNRS, Neurospin, Baobab, Gif-sur-Yvette, France</footnote>
# INTRODUCTION

Decline in physical performance, as measured by lower extremity function, often marks the early stage of disability in older age (1, 2). It is crucial to identify modifiable factors, such as nutritional risk factors, and their underlying biological mechanisms leading to impaired mobility in older individuals. Indeed, several blood-based nutritional markers, such as homocysteine, vitamin D, and omega-3 polyunsaturated fatty acids (PUFAs), have gradually become the focus of research and clinical interventions (3, 4). Hyperhomocysteinemia (HHcy) has been associated with faster physical impairment, such as in walking test and chair rise test, in several longitudinal studies (5-7). Vitamin D deficiency has been cross-sectionally associated with poor physical performance (8-10); however, similar associations were not discovered in longitudinal studies (11, 12). Although the literature on omega-3 PUFAs is mixed, some studies have found increased omega-3 PUFAs was associated with low risk of mobility disability (13), poor Short Physical Performance Battery (SPPB) score, and slower gait speed over time (14).

Considering that the accumulation of deficits can be related with the ability of an individual to respond to stressors (15), it is possible that combined deficiencies in homocysteine, vitamin D, and omega-3 PUFAs would work synergistically to determine physical performance over time. This concept had been supported by the findings of a previous study, which have indicated that an increasing number of nutritional deficits were associated with faster cognitive decline (16). In another study, the nutritional index, which was constructed with 41 nutrition-related parameters from anthropometric, plasma, and nutrient intake measurements, had shown a stronger prediction of frailty and mortality risk compared with single nutritional parameters separately (15). Furthermore, there is a lack of studies investigating underlying mechanisms behind multi-nutritional deficits and physical impairment. Indeed, several physiological deteriorations that drive age-related mobility loss (17), including changes in the central nervous system (CNS) and chronic inflammation, have shown intimate associations with these nutritional markers (18, 19). For instance, HHcy can promote inflammation (20). Higher circulating levels of omega-3 were associated with larger hippocampal volume (21); on the other hand, smaller brain volumes were observed in people with low vitamin D status (22). Therefore, exploring the interactions between nutritional deficits and physiological alterations (i.e., chronic inflammation and CNS changes) might allow us to understand better their shared biological pathways leading to mobility decline.

The main objective of this study was to investigate the associations between multi-nutritional deficits (i.e., vitamin D deficiency, HHcy, and low omega-3 PUFA index) and physical performance in community-dwelling older adults over 5 years. In addition, we explored if the nutrition-physical performance associations varied according to the presence of low-grade inflammation (LGI) and brain imaging indicators.

# METHODS

## Design and Ethical Statement

This observational study used data from Multidomain Alzheimer Preventive Trial (MAPT), whose details have been described in previous publications (23, 24). Briefly, MAPT was a multicenter, 3-year randomized controlled trial that aimed to evaluate the protective effect of omega-3 PUFA supplementation and multidomain lifestyle interventions (exercise advice, cognitive training, and nutritional counseling), combined or alone, on cognitive decline in community-dwelling older adults (23). No significant effect of the interventions on cognitive function (24) or muscle strength (25) was found over 3 years. In this secondary analysis, 5-year follow-up data (3-year intervention plus an additional 2-year observation period after the end of interventions) were retrieved. The MAPT study was registered at ClinicalTrials.gov (no: NCT00672685), was approved by the French Ethical Committee located in Toulouse (CPP SOOM II), and was authorized by the French Health Authority. All the participants had signed informed consent.

## Study Population

The MAPT study enrolled 1680 dementia-free adults aged ≥70 years, recruited from 13 memory centers in France and Monaco between 2008 to 2011, presenting at least one of the following conditions: spontaneous memory complaint, limitations in one instrumental Activities of daily living (such as disability in using telephone and transportation.), or slow gait speed (≤0.8 m/s). In this study, 840 subjects who received the intervention of omega-3 supplementation and 454 subjects without nutritional markers measurement at baseline were not included; we finally considered data from 386 subjects into this study.

## Measures

### Definition of Nutritional Deficits

Three nutritional markers were used to define nutritional deficits: plasma 25-hydroxyvitamin D [25(OH)D], plasma homocysteine, and erythrocyte membrane omega-3 PUFA concentration. Details of nutritional marker assessment are described in **Supplementary Materials**. Nutritional deficits were determined at baseline according to the clinical cutoffs below: (1) vitamin D deficiency, if 25(OH)D <20 ng/ml (26); (2) HHcy, if homocysteine >14 µmol/L (27); (3) low omega-3 PUFA index, defined as omega-3 index (28) [% docosahexaenoic acid (DHA) + % eicosapentaenoic acid (EPA)] below the lower quartile of study population (≤4.87%) (16). The participants were then classified into three groups based on the counting of nutritional deficits: no deficit, 1 deficit, and ≥2 deficits.

### Physical Performance

Three outcomes of physical performance were evaluated in this study: 4-m usual pace gait speed (in m/s), 5-repetition maximal chair rise time (in s), and Short Physical Performance Battery (SPPB) (29) score. The SPPB consisted of a walk test, a chair rise test, and a standing balance test with three challenging positions; each component was scored ranging from 0 (inability to complete
the test) to 4 (best performance). The overall SPPB score was calculated by summing the three component results (ranging from 0 to 12, higher score indicates better performance) (29). All the measurements were assessed at baseline, and after 6, 12, 24, 36, 48, and 60 months of follow-up.

## Low-Grade Inflammation (LGI)

In this study, 293 of the 386 participants had C-reactive protein (CRP) measured at baseline, 6- and 12-month visits, using immunoturbidity according to standard protocols. LGI (dichotomous variable) was defined as having at least two consecutively high CRP values (3–10 mg/L) between baseline and the 12-month visit, according to previous studies (30–32). Participants we could not categorize as LGI or non-LGI (e.g., people with CRP value >10 mg/L in the intermediate 6-month measurement) were excluded. Finally, we included 267 participants in the exploratory analysis.

## Magnetic Resonance Imaging (MRI) Variables

Several MRI variables that had been reported to be associated with impaired mobility (18, 33, 34) were retrieved: total gray matter volume (cm<fnref n="3" />), hippocampal volume (mm<fnref n="3" />) and white matter hyperintensity (WMH) volume (cm<fnref n="3" />). Total intracranial volume (TICV) was also collected for model adjustment. The acquisition protocol for brain MRI has been detailed elsewhere (23) and in **Supplementary Materials**.

## Confounders

Several confounding variables were selected: age, sex, MAPT intervention groups (i.e., multidomain intervention alone or placebo), level of education (ordinal), and body mass index (BMI; kg/m<fnref n="2" />). We also controlled the baseline physical activity status using a dichotomous variable (active or inactive) based on low physical activity component in the Fried's frailty criteria (35). In the analysis for MRI variables, adjusted models included the confounders mentioned above as well as TICV.

<table><caption>TABLE 1 | Baseline characteristics of the study population<fnref n="1" />.</caption><thead><tr><th rowspan="2"></th><th rowspan="2">Total population<br>(N = 386)</th><th colspan="4">Number of nutritional deficits<sup>2</sup></th></tr><tr><th>No deficit<br>(N = 84)</th><th>1 deficit<br>(N = 153)</th><th>≥2 deficits<br>(N = 149)</th><th>p-value<sup>3</sup></th></tr></thead><tbody><tr><td>Age (years)</td><td>75.6 (4.5)</td><td>74.8 (4.1)<sup>a</sup></td><td>75.0 (4.3)<sup>b</sup></td><td>76.7 (4.8)<sup>a,b</sup></td><td>0.001</td></tr><tr><td>Sex (female)</td><td>263 (68.1%)</td><td>66 (78.6%)</td><td>111 (72.6%)</td><td>86 (57.7%)</td><td>0.002</td></tr><tr><td><strong>MAPT groups</strong></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Multidomain intervention</td><td>191 (49.5%)</td><td>40 (47.6%)</td><td>77 (50.3%)</td><td>74 (49.7%)</td><td>0.922</td></tr><tr><td>Placebo</td><td>195 (50.5%)</td><td>44 (52.4%)</td><td>76 (49.7%)</td><td>75 (50.3%)</td><td></td></tr><tr><td><strong>Education (n = 376)</strong></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>No diploma or primary school certificate</td><td>80 (21.3%)</td><td>13 (15.9%)</td><td>30 (19.9%)</td><td>37 (25.9%)</td><td>0.407</td></tr><tr><td>Secondary education</td><td>122 (32.4%)</td><td>28 (34.1%)</td><td>47 (31.1%)</td><td>47 (32.9%)</td><td></td></tr><tr><td>High school diploma</td><td>58 (15.4%)</td><td>12 (14.6%)</td><td>29 (19.2%)</td><td>17 (11.9%)</td><td></td></tr><tr><td>University level</td><td>116 (30.9%)</td><td>29 (35.4%)</td><td>45 (29.8%)</td><td>42 (29.3%)</td><td></td></tr><tr><td>Body mass index (kg/m<sup>2</sup>) (n = 385)</td><td>26.1 (3.9)</td><td>25.1 (4.0)<sup>a</sup></td><td>25.9 (3.9)</td><td>26.8 (3.8)<sup>a</sup></td><td>0.003</td></tr><tr><td>CDR status: 0.5 (mild cognitive impairment)</td><td>155 (40.2%)</td><td>27 (32.1%)</td><td>64 (41.8%)</td><td>64 (43.0%)</td><td>0.234</td></tr><tr><td>Physical status: inactive (n = 384)</td><td>53 (13.8%)</td><td>5 (6.0%)</td><td>22 (14.5%)</td><td>26 (17.5%)</td><td>0.051</td></tr><tr><td><strong>Nutritional risk factors</strong></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Vitamin D (ng/ml)</td><td>23.2 (12.5)</td><td>30.8 (12.2)<sup>a,b</sup></td><td>25.2 (12.7)<sup>a,c</sup></td><td>16.9 (8.8)<sup>b,c</sup></td><td>&lt;0.001</td></tr><tr><td>Deficiency (&lt;20 ng/ml)</td><td>163 (42.2%)</td><td>0 (0%)</td><td>57 (37.3%)</td><td>106 (71.1%)</td><td>&lt;0.001</td></tr><tr><td>Homocysteine (µmol/L)</td><td>15.64 (5.27)</td><td>11.32 (1.41)<sup>a,b</sup></td><td>15.31 (4.88)<sup>a,c</sup></td><td>18.41 (5.32)<sup>b,c</sup></td><td>&lt;0.001</td></tr><tr><td>Hyperhomocysteinemia (&gt;14 µmol/L)</td><td>220 (57.0%)</td><td>0 (0%)</td><td>85 (55.6%)</td><td>135 (90.6%)</td><td>&lt;0.001</td></tr><tr><td>Omega-3 index (%)</td><td>5.86 (1.44)</td><td>6.61 (1.13)<sup>a</sup></td><td>6.21 (1.25)<sup>b</sup></td><td>5.08 (1.41)<sup>a,b</sup></td><td>&lt;0.001</td></tr><tr><td>≤4.87 (lower quartile)</td><td>98 (25.4%)</td><td>0 (0%)</td><td>11 (7.2%)</td><td>87 (58.4%)</td><td>&lt;0.001</td></tr><tr><td><strong>Physical performance</strong></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>SPPB score, 0–12 (n = 378)</td><td>10.7 (1.7)</td><td>10.9 (1.5)</td><td>10.7 (1.7)</td><td>10.5 (1.8)</td><td>0.156</td></tr><tr><td>Gait speed (m/s) (n = 383)</td><td>1.08 (0.25)</td><td>1.10 (0.25)</td><td>1.08 (0.25)</td><td>1.06 (0.25)</td><td>0.590</td></tr><tr><td>Chair rise time (s) (n = 367)</td><td>11.6 (3.9)</td><td>10.4 (2.4)<sup>a</sup></td><td>11.6 (3.9)</td><td>12.3 (4.5)<sup>a</sup></td><td>0.004</td></tr></tbody></table>

<footnote n="1">¹Values presented in number (%) for categorical variables or mean (standard deviation) for continuous variables; CDR, clinical dementia rating scale; MAPT, Multidomain Alzheimer Preventive Trial; SPPB, Short Physical Performance Battery.</footnote>

<footnote n="2">²Cutoff of nutritional deficits: Vitamin D < 20 ng/ml, homocysteine > 14 µmol/L, omega-3 index ≤ lower quartile (≤4.87%).</footnote>

<footnote n="3">³P-value based on ANOVA or Chi-square test across groups; a,b,c same letters indicate significant differences between groups (p < 0.05).</footnote>
<table><caption>**TABLE 2** | Linear mixed-effect regressions examining cross-sectional associations between nutritional deficitsª and physical performance at baseline.</caption><thead><tr><th rowspan="2"></th><th colspan="3">Unadjusted model</th><th colspan="3">Adjusted model<sup>b</sup></th></tr><tr><th>β</th><th>95% CI</th><th>p-value</th><th>β</th><th>95% CI</th><th>p-value</th></tr></thead><tbody><tr><td colspan="7"><strong>Outcome: SPPB score (0–12)</strong></td></tr><tr><td colspan="7">Nutritional deficits</td></tr><tr><td>No deficit</td><td>Ref.</td><td>–</td><td>–</td><td>Ref.</td><td>–</td><td>–</td></tr><tr><td>1 deficit</td><td>–0.20</td><td>–0.60, 0.19</td><td>0.318</td><td>–0.16</td><td>–0.53, 0.21</td><td>0.391</td></tr><tr><td>≥2 deficits</td><td>–0.59</td><td>–0.99, –0.20</td><td>0.004</td><td>–0.33</td><td>–0.71, 0.05</td><td>0.089</td></tr><tr><td colspan="7"><strong>Outcome: gait speed (m/s)</strong></td></tr><tr><td colspan="7">Nutritional deficits</td></tr><tr><td>No deficit</td><td>Ref.</td><td>–</td><td>–</td><td>Ref.</td><td>–</td><td>–</td></tr><tr><td>1 deficit</td><td>–0.01</td><td>–0.07, 0.05</td><td>0.703</td><td>–0.01</td><td>–0.06, 0.05</td><td>0.906</td></tr><tr><td>≥2 deficits</td><td>–0.06</td><td>–0.12, –0.01</td><td>0.041</td><td>–0.02</td><td>–0.08, 0.04</td><td>0.525</td></tr><tr><td colspan="7"><strong>Outcome: chair rise time (s)</strong></td></tr><tr><td colspan="7">Nutritional deficits</td></tr><tr><td>No deficit</td><td>Ref.</td><td>–</td><td>–</td><td>Ref.</td><td>–</td><td>–</td></tr><tr><td>1 deficit</td><td>0.70</td><td>–0.22, 1.61</td><td>0.137</td><td>0.60</td><td>–0.28, 1.49</td><td>0.182</td></tr><tr><td>≥2 deficits</td><td>1.58</td><td>0.66, 2.50</td><td>0.001</td><td>0.99</td><td>0.07, 1.90</td><td>0.036</td></tr></tbody></table>

CI, confidence interval; Ref, reference group; SPPB, Short Physical Performance Battery.

## Statistical Analysis

Baseline characteristic comparisons across the nutritional deficit groups were performed by Chi-square test for categorical variables and ANOVA for continuous variables. Linear mixed-effect regressions, including a random effect at participant level and a random slope on time, were conducted to evaluate the cross-sectional and longitudinal associations between nutritional deficits and physical performance outcomes.

A series of exploratory analyses were conducted to explore the roles of LGI (among 267 subjects with available CRP data) and imaging markers (among 164 subjects with MRI measures) in the association between nutritional deficits and physical performance. We first performed logistic regressions to examine the association between nutritional deficits and LGI. Then, an interaction term by LGI and nutritional deficits was introduced into the same mixed-effect models for main analysis; only assessments of the outcomes performed at 12 months and after were considered for this analysis. For imaging markers, cross-sectional associations with nutritional deficits were tested by linear mixed-effect regressions (with random intercept for the center effect). Longitudinal analysis considering the interaction effect (MRI variable × nutritional deficits) on physical performance was examined by linear mixed-effect regressions (three-level nested model, with the participants nested into the center); for participants who received MRI scans at 6-month visit and 12-month visit, measurements of physical performances before MRI scans were excluded from the analysis. Statistical significance was defined as *p* < 0.05; the *p*-values of between-group mean differences are presented after false discovery rate correction (36). All the statistical analyses were

performed with Statistical Analysis Software (SAS) version 9.4 (Cary, NC, USA).

## RESULTS

Among the overall 386 participants, 21.8% (n = 84) had no nutritional deficit at baseline, 39.6% (n = 153) presented 1 deficit, and 38.6% (n = 149) presented ≥2 deficits. Participants with more nutritional deficits tended to be older and male, and to present with higher BMI (Table 1). At baseline, compared with those without any deficit, having ≥2 deficits was associated with longer chair rise time, i.e., poor chair rise performance (Table 2). No cross-sectional associations were found with SPPB score or gait speed.

After 5 years of follow-up, decreased SPPB score and gait speed, and increased chair rise time were observed among the participants with ≥ 2 nutritional deficits (Table 3). *P*-values for linear trend for within-group change in physical performance outcomes were all significant (*p* for trend <0.001). However, no significant between-group differences were discovered for the changes in SPPB, gait speed, and chair rise time when individuals without deficit and those with either 1 or ≥2 deficits were compared (Table 3).

In the logistic regression for LGI and nutritional deficits, people with ≥2 deficits had higher likelihood of having LGI (adjusted OR = 2.53; 95% CI: 1.01 to 6.33; *p* = 0.006: **Supplementary Table 1**), compared with those without deficits. Significant interaction effects by LGI and nutritional deficits on chair rise time were observed in the linear mixed-effect regression. Among people with ≥2 deficits, the adjusted mean difference in chair rise time over 5 years

<footnote>ªCutoff of nutritional deficits: vitamin D < 20 ng/ml, homocysteine > 14 µmol/L, omega-3 index ≤ lower quartile (4.87%).</footnote>

<footnote>ᵇAdjustments for age, sex, Multidomain Alzheimer Preventive Trial (MAPT) groups, education, body mass index, and physical activity status.</footnote>
<table><caption>**TABLE 3** | Linear mixed-effect regressions examining variation in physical performance over 5 years according to nutritional deficits<sup>a</sup>.</caption><thead><tr><th rowspan="2"></th><th rowspan="2">Within-group 5-year change<br>from baseline<br>β (95% CI); p-value</th><th rowspan="2">P for trend</th><th colspan="2">Between-group difference</th></tr><tr><th>Unadjusted model<br>β (95% CI); p-value<sup>c</sup></th><th>Adjusted model<sup>b</sup><br>β (95% CI); p-value<sup>c</sup></th></tr></thead><tbody><tr><td colspan="5"><strong>Outcome: SPPB score (0–12)</strong></td></tr><tr><td>Nutritional deficits</td><td></td><td>&lt;0.001</td><td></td><td></td></tr><tr><td>No deficit,</td><td>-0.43 (-0.86, 0.01); 0.057</td><td></td><td>Ref.</td><td>Ref.</td></tr><tr><td>1 deficit,</td><td>-0.47 (-0.84, -0.11); 0.011</td><td></td><td>-0.05 (-0.62, 0.52); 0.871</td><td>-0.03 (-0.59, 0.53); 0.921</td></tr><tr><td>≥2 deficits)</td><td>-0.80 (-1.17, -0.42); &lt;0.001</td><td></td><td>-0.37 (-0.95, 0.20); 0.411</td><td>-0.23 (-0.80, 0.33); 0.843</td></tr><tr><td colspan="5"><strong>Outcome: gait speed (m/s)</strong></td></tr><tr><td>Nutritional deficits</td><td></td><td>&lt;0.001</td><td></td><td></td></tr><tr><td>No deficit,</td><td>-0.09 (-0.16, -0.02); 0.010</td><td></td><td>Ref.</td><td>Ref.</td></tr><tr><td>1 deficit,</td><td>-0.05 (-0.11, 0.01); 0.052</td><td></td><td>0.03 (-0.05, 0.12); 0.879</td><td>0.03 (-0.06, 0.11); 0.813</td></tr><tr><td>≥2 deficits)</td><td>-0.08 (-0.14, -0.03); 0.005</td><td></td><td>0.01 (-0.08, 0.09); 0.879</td><td>-0.01 (-0.10, 0.08); 0.813</td></tr><tr><td colspan="5"><strong>Outcome: chair rise time (s)</strong></td></tr><tr><td>Nutritional deficits</td><td></td><td>&lt;0.001</td><td></td><td></td></tr><tr><td>No deficit,</td><td>0.77 (-0.08, 1.62); 0.075</td><td></td><td>Ref.</td><td>Ref.</td></tr><tr><td>1 deficit,</td><td>0.46 (-0.24, 1.17); 0.198</td><td></td><td>-0.31 (-1.41, 0.80); 0.585</td><td>-0.18 (-1.26, 0.90); 0.739</td></tr><tr><td>≥2 deficits)</td><td>1.13 (0.39, 1.86); 0.003</td><td></td><td>0.36 (-0.77, 1.48); 0.585</td><td>0.40 (-0.71, 1.50); 0.739</td></tr></tbody></table>

CI, confidence interval; Ref, reference group; SPPB, Short Physical Performance Battery.

between those with and without LGI (reference group) was 3.47 s (95% CI: 1.03, 5.91; *p* = 0.017), indicating that LGI reinforced the impact of ≥2 deficits on worsening chair rise performance (Supplementary Table 2). On the other hand, no association between imaging markers and nutritional deficits was found (Supplementary Table 3). There was no evidence of any significant interaction between each imaging marker and nutritional deficits on physical performances in the linear mixed-effect models (Supplementary Table 4).

DISCUSSION

To our knowledge, this is the first study to investigate the associations between accumulated nutritional deficits and physical performance in community-dwelling older adults. We discovered that presenting two or more nutritional deficits (i.e., vitamin D deficiency, HHcy, and low omega-3 PUFA index) was cross-sectionally associated with poor chair rise performance at baseline. We did not observe associations of combined nutritional deficits with mobility decline over 5 years; however, our exploratory analysis found that the association of nutritional deficits with chair rise performance could vary according to LGI status, with a more pronounced increase in chair rise time (worse performance; 0.69 s more per year) among older adults with ≥2 deficits and LGI compared with their non-LGI counterparts.

The relationship between the nutritional markers investigated in our study and physical performance has mixed findings in

the literature (8–13). In this study, although the between-group differences did not reach significance, within-group changes for all the three physical performance outcomes showed higher overtime declines as the number of deficits increased (*p* for trend <0.001). Noteworthy, after the 5-year follow-up, more than half of the participants with ≥2 nutritional deficits became octogenarians whose mobility tends to decline faster than in younger people (17). On the other hand, our exploratory analysis found that LGI, an important mechanism implicated in both aging (37) and mobility disability (17, 38), contributed to this accelerated decline of physical performance in older individuals with combined nutritional deficits. This finding suggests that both the presence of nutritional deficits and chronic inflammation contribute to physical impairment. Indeed, omega-3 PUFAs and HHcy had been proposed to affect mobility outcomes through inflammatory pathway. Omega-3 PUFAs can suppress chronic inflammation, further inhibiting muscle catabolism (39); HHcy can lead to inflammation by causing reactive oxygen species accumulation and pro-inflammatory cytokine secretion (4, 20). Although vitamin D is well-known for its metabolic roles in muscle synthesis and bone formation (3), the recent evidence had suggested it has immunomodulatory effects by regulating both innate and adaptive immunity (40). On the other hand, it is plausible that LGI status is independent of the presence of nutritional deficits, but that their joint effect enhances the detrimental impact on physical function. For example, accelerated muscle catabolism caused by inflammation (41), combined with muscle weakness caused by vitamin D

<footnote><sup>a</sup>Cutoff of nutritional deficits: vitamin D < 20 ng/ml, homocysteine >14 µmol/L, omega-3 index ≤ lower quartile (≤4.87%).</footnote>

<footnote><sup>b</sup>Adjustments for age, sex, Multidomain Alzheimer Preventive Trial (MAPT) groups, education, body mass index, physical activity status, and time interactions.</footnote>

<footnote><sup>c</sup>P-value adjusted for multiple comparisons using the Benjamini-Hochberg procedure.</footnote>
deficiency (10), can lead to faster decline in overall muscle function and physical performance.

Our cross-sectional and exploratory analyses only observed significant associations between nutritional deficits and chair rise performance, suggesting nutritional deficits would affect physical performance through a muscle quality-related mechanism rather than changes in the central nervous system. This is also supported by our findings related to MRI indicators, which showed no significant interaction between brain volumes and nutritional deficits on changes in physical performance over time. Compared with gait speed, a functional vital sign (42) relying on complex movement controls with executive function involved (43), chair rise test is a more specific measure of muscle function (44), strongly determined by muscle mass and power in older adults (45, 46). Another possible explanation for the limited findings on change in SPPB is that only a few participants of this study had mobility limitation at baseline, with 6% having SPPB ≤7 (1) and about 20% having SPPB ≤9 (47). Although it is possible that people who started having mobility limitations would decline faster in mobility (48), the associations of nutritional deficits with mobility limitation in mobility-impaired individuals require further investigations.

A number of strengths should be mentioned in our study. We evaluated multiple nutritional deficits, assessed by three blood-based biomarkers, and several measures of physical performance in older adults over 5 years. In addition, we explored the potential role of inflammatory and neuroimaging markers in nutrition-physical performance associations. However, some limitations are worth mentioning. First, this is an observational study with data retrieved from a randomized controlled trial. Even though MAPT multidomain intervention did not affect physical performance (25), our results need to be interpreted cautiously, since the exercise advice and nutritional counseling part of the multidomain intervention could have modified the nutritional markers overtime. In order to minimize this bias, MAPT group allocation was added as a confounder in the models. Residual confounding may not be excluded, since some other potential confounders, such as nutritional supplementation (except for omega-3 PUFAs), inadequate dietary protein intake, and smoking and drinking habits (12, 49), were not available. Finally, the MAPT study enrolled a sample of community-dwelling older adults at risk of cognitive decline, which might affect the generalizability of our findings to other populations.

To conclude, this study did not observe prospective associations between combined nutritional deficits (vitamin D deficiency, HHcy, and low omega-3 index) and overtime mobility decline in community-dwelling older adults. However, different trajectories of chair rise performance were observed among people with two or more deficits, once the presence of chronic, low-grade inflammation was considered. Future studies that will investigate nutritional deficits and physical impairment focused on older adults with different conditions characterized by LGI, including subjects with mobility limitation, could shed light on this topic.

# DATA AVAILABILITY STATEMENT

Data described in the article, code book, and analytic code will be made available upon request pending application and approval. Requests to access these datasets should be directed to Wan-Hsuan Lu, wan-hsuan.lu1@univ-tlse3.fr.

# ETHICS STATEMENT

The studies involving human participants were reviewed and approved by French Ethical Committee located in Toulouse (CPP SOOM II). The patients/participants provided their written informed consent to participate in this study.

# MAPT/DSA GROUP

MAPT study group: principal investigator: Bruno Vellas (Toulouse); coordination: Sophie Guyonnet; project leader: Isabelle Carrié; CRA: Lauréane Brigitte; investigators: Catherine Faisant, Françoise Lala, Julien Delrieu, and Hélène Villars; psychologists: Emeline Combrouze, Carole Badufle, and Audrey Zueras; methodology, statistical analysis, and data management: Sandrine Andrieu, Christelle Cantet, and Christophe Morin; multidomain group: Gabor Abellan Van Kan, Charlotte Dupuy, and Yves Rolland (physical and nutritional components), Céline Caillaud, Pierre-Jean Ousset (cognitive component), and Françoise Lala (preventive consultation) (Toulouse). The cognitive component was designed in collaboration with Sherry Willis from the University of Seattle, and Sylvie Belleville, Brigitte Gilbert, and Francine Fontaine from the University of Montreal. Co-investigators in associated centers: Jean-François Dartigues, Isabelle Marcet, Fleur Delva, Alexandra Foubert, and Sandrine Cerda (Bordeaux); Marie-Noëlle-Cuffi, Corinne Costes (Castres); Olivier Rouaud, Patrick Manckoundia, Valérie Quipourt, Sophie Marilier, and Evelyne Franon (Dijon); Lawrence Bories, Marie-Laure Pader, Marie-France Basset, Bruno Lapojade, Valérie Faure, Michael Li Yung Tong, Christine Malick-Loiseau, and Evelyne Cazaban-Campistron (Foix); Françoise Desclaux and Colette Blatge (Lavaur); Thierry Dantoine, Cécile Laubarie-Mouret, Isabelle Saulnier, Jean-Pierre Clément, Marie-Agnès Picat, Laurence Bernard-Bourzeix, Stéphanie Willebois, Iléana Désormais, and Noëlle Cardinaud (Limoges); Marc Bonnefoy, Pierre Livet, Pascale Rebaudet, Claire Gédéon, Catherine Burdet, and Flavien Terracol (Lyon), Alain Pesce, Stéphanie Roth, Sylvie Chaillou, and Sandrine Louchart (Monaco); Kristel Sudres, Nicolas Lebrun, and Nadège Barro-Belaygues (Montauban); Jacques Touchon, Karim Bennys, Audrey Gabelle, Aurélia Romano, Lynda Touati, Cécilia Marelli, and Cécile Pays (Montpellier); Philippe Robert, Franck Le Duff, Claire Gervais, and Sébastien Gonfrier (Nice); Yannick Gasnier, Serge Bordes, Danièle Begorre, Christian Carpuat, Khaled Khales, Jean-François Lefebvre, Samira Misbah El Idrissi, Pierre Skolil, and Jean-Pierre Salles (Tarbes). MRI group: Carole Dufoul (Bordeaux); Stéphane Lehéricy, Marie Chupin, Jean-François Mangin, and Ali Bouhayia (Paris); Michèle Allard (Bordeaux);
Frédéric Ricolfi (Dijon); Dominique Dubois (Foix); Marie Paule Bonceour Martel (Limoges); François Cotton (Lyon); Alain Bonafé (Montpellier); Stéphane Chanalet (Nice); Françoise Hugon (Tarbes); Fabrice Bonneville, Christophe Cognard, and François Chollet (Toulouse). PET scans group: Pierre Payoux, Thierry Voisin, Julien Delrieu, Sophie Peiffer, and Anne Hitzel, (Toulouse); Michèle Allard (Bordeaux); Michel Zanca (Montpellier); Jacques Monteil (Limoges); Jacques Darcourt (Nice). Medico-economics group: Laurent Molinier, Hélène Derumeaux, and Nadège Costa (Toulouse). Biological sample collection: Bertrand Perret, Claire Vinel, and Sylvie Caspar-Bauguil (Toulouse). Safety management: Pascale Olivier-Abbal.

DSA group: Sandrine Andrieu, Christelle Cantet, and Nicola Coley.

## AUTHOR CONTRIBUTIONS

BV conceived the MAPT study. W-HL and PSB designed current research. J-FM managed image data. W-HL performed statistical analysis, drafted the manuscript, and had a primary responsibility for the final content. All authors contributed to the article and approved the submitted version.

## REFERENCES

1. Guralnik JM, Ferrucci L, Simonsick EM, Salive ME, Wallace RB. Lower-extremity function in persons over the age of 70 years as a predictor of subsequent disability. *N Engl J Med.* (1995) 332:556-62. doi: 10.1056/NEJIM199503023320902

2. Minneci C, Mello AM, Mossello E, Baldasseroni S, Macchi L, Cipolletti S, et al. Comparative study of four physical performance measures as predictors of death, incident disability, and falls in unselected older persons: the Insufficienza Cardiaca negli Anziani residenti a Dicomano study. *J Am Geriatr Soc.* (2015) 63:136-41. doi: 10.1111/jgs.13195

3. Tessier AJ, Chevalier S. An update on protein, leucine, omega-3 fatty acids, and vitamin D in the prevention and treatment of sarcopenia and functional decline. *Nutrients.* (2018) 10:1-17. doi: 10.3390/nu10081099

4. Vidoni ML, Pettee Gabriel K, Luo ST, Simonsick EM, Day RS. Relationship between homocysteine and muscle strength decline: the baltimore longitudinal study of aging. *J Gerontol Ser A Biol Sci Med Sci.* (2018) 73:546-51. doi: 10.1093/gerona/glx161

5. Kado DM, Bucur A, Selhub J, Rowe JW, Seeman T. Homocysteine levels and decline in physical function: MacArthur studies of successful aging. *Am J Med.* (2002) 113:537-42. doi: 10.1016/S0002-9343(02)01269-X

6. Rolita L, Holtzer R, Wang C, Lipton RB, Derby CA, Verghese J. Homocysteine and mobility in older adults. *J Am Geriatr Soc.* (2010) 58:545-50. doi: 10.1111/j.1532-5415.2010.02718.x

7. Van Schoor NM, Swart KMA, Pluijm SMF, Visser M, Simsek S, Mulders Y, et al. Cross-sectional and longitudinal association between homocysteine, vitamin B12 and physical performance in older persons. *Eur J Clin Nutr.* (2012) 66:174-81. doi: 10.1038/ejcn.2011.151

8. Houston DK, Cesari M, Ferrucci L, Cherubini A, Maggio D, Bartali B, et al. Association between vitamin D status and physical performance: the inCHIANTI study. *J Gerontol Ser A Biol Sci Med Sci.* (2007) 62:440-6. doi: 10.1093/gerona/62.4.440

9. Mendes J, Santos A, Borges N, Afonso C, Moreira P, Padrão P, et al. Vitamin D status and functional parameters: a cross-sectional study in an older population. *PLoS ONE.* (2018) 13:e0201840. doi: 10.1371/journal.pone.0201840

10. Aspell N, Laird E, Healy M, Lawlor B, O'sullivan M. Vitamin D deficiency is associated with impaired muscle strength and physical

## FUNDING

This study was performed in the context of the Inspire Program, a research platform supported by grants from the Region Occitanie/Pyrénées-Méditerranée (Reference number: 1901175) and the European Regional Development Fund (ERDF) (Project number: MP0022856). The MAPT study was supported by grants from the Gérontopôle of Toulouse, the French Ministry of Health (PHRC 2008, 2009), Pierre Fabre Research Institute (manufacturer of the omega-3 supplement), ExonHit Therapeutics SA, and Avid Radiopharmaceuticals Inc. The promotion of this study was supported by the University Hospital Center of Toulouse. The data sharing activity was supported by the Association Monegasque pour la Recherche sur la maladie'Alzheimer's (AMPA) and the INSERM-University of Toulouse III UMR 1295 Unit.

## SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fnut.2021.771470/full#supplementary-material

performance in community-dwelling older adults: findings from the english longitudinal study of ageing. *Clin Interv Aging.* (2019) 14:1751-61. doi: 10.2147/CIA.S222143

11. Houston DK, Tooze JA, Hausman DB, Johnson MA, Nicklas BJ, Miller ME, et al. Change in 25-hydroxyvitamin D and physical performance in older adults. *J Gerontol Ser A Biol Sci Med Sci.* (2011) 66:430-6. doi: 10.1093/gerona/glq235

12. Houston DK, Tooze JA, Neiberg RH, Hausman DB, Johnson MA, Cauley JA, et al. 25-hydroxyvitamin D status and change in physical performance and strength in older adults. *Am J Epidemiol.* (2012) 176:1025-34. doi: 10.1093/aje/kws147

13. Reinders I, Murphy RA, Song X, Visser M, Cotch MF, Lang TF, et al. Polyunsaturated fatty acids in relation to incident mobility disability and decline in gait speed; The Age, Gene/Environment Susceptibility-Reykjavik Study. *Eur J Clin Nutr.* (2015) 69:489-93. doi: 10.1038/ejcn.2014.277

14. Abbatecola AM, Cherubini A, Guralnik JM, Lacueva CA, Ruggiero C, Maggio M, et al. Plasma polyunsaturated fatty acids and age-related physical performance decline. *Rejuvenation Res.* (2009) 12:25-32. doi: 10.1089/rej.2008.0799

15. Jayanama K, Theou O, Blodgett JM, Cahill L, Rockwood K. Frailty, nutrition-related parameters, and mortality across the adult age spectrum. *BMC Med.* (2018) 16:235. doi: 10.1186/s12916-018-1227-z

16. Bowman GL, Dodge HH, Guyonnet S, Zhou N, Donohue J, Bichsel A, et al. A blood-based nutritional risk index explains cognitive enhancement and decline in the multidomain Alzheimer prevention trial. *Alzheimer's Dement Transl Res Clin Interv.* (2019) 5:953-63. doi: 10.1016/j.trci.2019.11.004

17. Ferrucci L, Cooper R, Shardell M, Simonsick EM, Schrack JA, Kuh D. Age-related change in mobility: perspectives from life course epidemiology and geroscience. *J Gerontol Ser A Biol Sci Med Sci.* (2016) 71:1184-94. doi: 10.1093/gerona/glw043

18. DiSalvio NL, Rosano C, Aizenstein HJ, Redfern MS, Furman JM, Jennings JR, et al. Gray matter regions associated with functional mobility in community-dwelling older adults. *J Am Geriatr Soc.* (2020) 68:1023-8. doi: 10.1111/jgs.16309

19. Cesari M, Penninx BWJH, Pahor M, Lauretani F, Corsi AM, Williams GR, et al. Inflammatory markers and physical performance in older persons: the InCHIANTI study. *J Gerontol Ser A Biol Sci Med Sci.* (2004) 59:242-8. doi: 10.1093/gerona/59.3.M242
20. Veeranki S, Tyagi SC. Defective homocysteine metabolism: potential implications for skeletal muscle malfunction. *Int J Mol Sci.* (2013) 14:15074-91. doi: 10.3390/ijms140715074

21. Macaron T, Giudici KV, Bowman GL, Sinclair A, Stephan E, Vellas B, et al. Associations of Omega-3 fatty acids with brain morphology and volume in cognitively healthy older adults: a narrative review. *Ageing Res Rev.* (2021) 67:101300. doi: 10.1016/j.arr.2021.101300

22. Croll PH, Boelens M, Vernooij MW, van de Rest O, Zillikens MC, Ikram MA, et al. Associations of vitamin D deficiency with MRI markers of brain health in a community sample. *Clin Nutr.* (2021) 40:72-8. doi: 10.1016/j.clnu.2020.04.027

23. Vellas B, Carrie I, Gillette-Guyonnet S, Touchon J, Dantoine T, Dartigues JF, et al. Mapt study: a multidomain approach for preventing Alzheimer's disease: design and baseline data. *J Prev Alzheimer's Dis.* (2014) 1:13-22. doi: 10.14283/jpad.2014.34

24. Andrieu S, Guyonnet S, Coley N, Cantet C, Bonnefoy M, Bordes S, et al. Effect of long-term omega 3 polyunsaturated fatty acid supplementation with or without multidomain intervention on cognitive function in elderly adults with memory complaints (MAPT): a randomised, placebo-controlled trial. *Lancet Neurol.* (2017) 16:377-89. doi: 10.1016/S1474-4422(17)30040-6

25. Rolland Y, Barreto P de S, Maltais M, Guyonnet S, Cantet C, Andrieu S, et al. Effect of long-term omega 3 polyunsaturated fatty acid supplementation with or without multidomain lifestyle intervention on muscle strength in older adults: secondary analysis of the multidomain alzheimer preventive trial (MAPT). *Nutrients.* (2019) 11:1931. doi: 10.3390/nu11081931

26. Holick MF, Binkley NC, Bischoff-Ferrari HA, Gordon CM, Hanley DA, Heaney RP, et al. Evaluation, treatment, and prevention of vitamin D deficiency: an endocrine society clinical practice guideline. *J Clin Endocrinol Metab.* (2011) 96:1911-30. doi: 10.1210/jc.2011-0385

27. Seshadri S, Beiser A, Selhub J, Jacques PF, Rosenberg IH, D'Agostino RB, et al. Plasma homocysteine as a risk factor for dementia and Alzheimer's disease. *N Engl J Med.* (2002) 346:476-83. doi: 10.1056/NEJMoa011613

28. Harris WS, Von Schacky C. The omega-3 index: a new risk factor for death from coronary heart disease? *Prev Med.* (2004) 39:212-20. doi: 10.1016/j.ypmed.2004.02.030

29. Guralnik JM, Simonsick EM, Ferrucci L, Glynn RJ, Berkman LF, Blazer DG, et al. A short physical performance battery assessing lower extremity function: association with self-reported disability and prediction of mortality and nursing home admission. *J Gerontol.* (1994) 49:M85-94. doi: 10.1093/geronj/49.2.M85

30. Ridker PM. Clinical application of C-reactive protein for cardiovascular disease detection and prevention. *Circulation.* (2003) 107:363-9. doi: 10.1161/01.CIR.0000053730.47739.3C

31. Hooper C, De Souto Barreto P, Cantet C, Cesari M, Payoux P, Salabert AS, et al. Chronically raised C-reactive protein is inversely associated with cortical β-amyloid in older adults with subjective memory complaints. *Exp Gerontol.* (2018) 108:226-30. doi: 10.1016/j.exger.2018.04.014

32. Giudici KV, de Souto Barreto P, Guerville F, Beard J, Araujo de Carvalho I, Andrieu S, et al. Associations of C-reactive protein and homocysteine concentrations with the impairment of intrinsic capacity domains over a 5-year follow-up among community-dwelling older adults at risk of cognitive decline (MAPT Study). *Exp Gerontol.* (2019) 127:110716. doi: 10.1016/j.exger.2019.110716

33. Ezzati A, Katz MJ, Lipton ML, Lipton RB, Verghese J. The association of brain structure with gait velocity in older adults: a quantitative volumetric analysis of brain MRI. *Neuroradiology.* (2015) 57:851-61. doi: 10.1007/s00234-015-1536-2

34. Moon SY, de Souto Barreto P, Rolland Y, Chupin M, Bouyahia A, Fillon L, et al. Prospective associations between white matter hyperintensities and lower extremity function. *Neurology.* (2018) 90:e1291-7. doi: 10.1212/WNL.0000000000005289

35. Fried LP, Tangen CM, Walston J, Newman AB, Hirsch C, Gottdiener J, et al. Frailty in older adults: evidence for a phenotype. *J Gerontol Ser A Biol Sci Med Sci.* (2001) 56:M146-57. doi: 10.1093/gerona/56.3.M146

36. Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. *J R Stat Soc Ser B.* (1995) 57:289-300. doi: 10.1111/j.2517-6161.1995.tb02031.x

37. Bektas A, Schurman SH, Sen R, Ferrucci L. Aging, inflammation and the environment. *Exp Gerontol.* (2018) 105:10-8. doi: 10.1016/j.exger.2017.12.015

38. Osawa Y, Semba RD, Fantoni G, Candia J, Biancotto A, Tanaka T, et al. Plasma proteomic signature of the risk of developing mobility disability: a 9-year follow-up. *Aging Cell.* (2020) 19:e13132. doi: 10.1111/acel.13132

39. Dupont J, Dedeyne L, Dalle S, Koppo K, Gielen E. The role of omega-3 in the prevention and treatment of sarcopenia. *Aging Clin Exp Res.* (2019) 31:825-36. doi: 10.1007/s40520-019-01146-1

40. Wei R, Christakos S. Mechanisms underlying the regulation of innate and adaptive immunity by vitamin D. *Nutrients.* (2015) 7:8251-60. doi: 10.3390/nu7105392

41. Ferrucci L, Penninx BWJH, Volpato S, Harris TB, Bandeen-Roche K, Balfour J, 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) 50:1947-54. doi: 10.1046/j.1532-5415.2002.50605.x

42. Middleton A, Fritz SL, Lusardi M. Walking speed: the functional vital sign. *J Aging Phys Activity.* (2015) 23:314-22. doi: 10.1123/japa.2013-0236

43. Hausdorff JM, Yogev G, Springer S, Simon ES, Giladi N. Walking is more like catching than tapping: gait in the elderly as a complex cognitive task. *Exp Brain Res.* (2005) 164:541-8. doi: 10.1007/s00221-005-2280-3

44. de Souto Barreto P, Cesari M, Rolland Y, Salabert AS, Payoux P, Andrieu S, et al. Cross-Sectional and prospective associations between β-amyloid in the brain and chair rise performance in nondementia older adults with spontaneous memory complaints. *J Gerontol A Biol Sci Med Sci.* (2017) 72:278-83. doi: 10.1093/gerona/glw195

45. Whitney SL, Wrisley DM, Marchetti GF, Gee MA, Redfern MS, Furman JM. Clinical measurement of sit-to-stand performance in people with balance disorders: validity of data for the Five-Times-Sit-to-Stand Test. *Phys Ther.* (2005) 85:1034-45. doi: 10.1093/ptj/85.10.1034

46. Sekhon H, Launay CP, Chabot J, Allali G, Beauchet O. Motoric cognitive risk syndrome: could it be defined through increased five-times-sit-to-stand test time, rather than slow walking speed? *Front Aging Neurosci.* (2019) 11:434. doi: 10.3389/fnagi.2018.00434

47. von Berens Å, Cederholm T, Fielding RA, Gustafsson T, Kirn D, Laussen J, et al. Physical performance and serum 25(OH)vitamin D status in community dwelling old mobility limited adults: a cross-sectional study. *J Nutr Heal Aging.* (2018) 22:1-7. doi: 10.1007/s12603-016-0849-0

48. Tiainen K, Raitanen J, Vaara E, Hervonen A, Jylhä M. Longitudinal changes in mobility among nonagenarians: the Vitality 90+ Study. *BMC Geriatr.* (2015) 15:1-8. doi: 10.1186/s12877-015-0116-y

49. Dam TTL, Von Mühlen D, Barrett-Connor EL. Sex-specific association of serum vitamin D levels with physical function in older adults. *Osteoporos Int.* (2009) 20:751-60. doi: 10.1007/s00198-008-0749-1

**Conflict of Interest:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

**Publisher's Note:** All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Copyright © 2021 Lu, Giudici, Rolland, Guyonnet, Mangin, Vellas and de Souto Barreto. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.