Body composition, physical function, and dietary patterns in people from 20 to over 80 years old: the INSPIRE-T cohort

doi:10.21203/rs.3.rs-2830859/v1

Objectives: Nutrition is a modifiable factor that may influence biochemical pathways associated with age-related body composition and physical function changes. This study aimed to describe dietary patterns and their relationships with body composition, physical performance, and grip strength according to age and sex.

Design: Cross-sectional study.

Setting: The data were collected in either the Clinical Research Center (CRC) of the Gérontopôle of the Centre Hospitalier Universitaire (CHU) of Toulouse or in participant’s home for those who were unable to come to the research facilities.

Participants: 470 (63% female) participants of the INSPIRE-T cohort, from 20 to 93 years old.

Measurements: The “Mediterranean-like” (i.e., plant-based foods, dairy), “Animal products” (i.e., meat, processed meat, butter, refined starch), and “Sugar and fast food” (i.e., ultra-processed and sugary foods) dietary patterns were extracted by principal component analysis. Total and trunk fat mass indexes (FMI, kg/m²), and total and appendicular lean mass indexes (LMI, kg/m²) were assessed by DXA. Physical performance comprised gait speed (m/sec), chair rise (sec), the Short Physical Performance Battery test (/12 points), and handgrip strength (kg) was assessed. The associations were explored through multivariate linear regressions by sex and age groups: ≥20 to <50, ≥50 to <65, and ≥65 years.

Results: In middle-aged women, greater adherence to the “Animal products” diet was associated with higher total and appendicular LMI, whether higher intake of “Sugar and fast food” resulted in worse physical performance. In middle-aged men, the “Mediterranean-like” diet was associated with lower total and trunk FMI, and total and appendicular LMI. Greater compliance with the “Animal products” diet was related to lower strength. Higher “Sugar and fast food” diet intake was associated with greater total and trunk FMI, and slower gait speed. Body composition did not mediate these relationships.

Conclusion: Men were more prone to have less healthy food intake in all age groups. Diets composed of sugary foods, fast foods, and processed meat were associated with higher fat mass and poorer physical performance and strength.

body composition

dietary patterns

physical performance

grip strength

Changes in body composition happen with aging. Muscle mass tends to decrease [1], resulting in a reduced basal metabolic rate that favors an increase in body weight and fat mass [2]. Furthermore, there is a fat redistribution in favor of higher deposition in the abdominal region. Greater trunk adiposity is associated with oxidative stress and inflammation which increases the risk for comorbidities, such as diabetes mellitus [3], and negatively impacts the quality and quantity of the lean mass [4]. Finally, changes in body composition can result in geriatric conditions that compromise physical function, particularly sarcopenia [1] and frailty [5].

The pattern of changes in body composition, however, is suggested to differ according to sex [6–8]. In a large cohort of USA citizens while the fat mass increased monotonically up until age 85 in men, in women the peak occurred at age 65 [6]. Furthermore, when evaluating cohorts of different generations, it has been shown that the more recent the cohort birth, the more prevalent overweight and obesity [9], highlighting the influence of the current dietary patterns on the changes in body composition. Also, it is suggested that diet components could influence biochemical pathways associated with the overlap between biological aging and the biology of age-related diseases and conditions (i.e., hallmarks of aging) [10]. Thus, by reducing damaging processes such as inflammation and oxidative stress, or by enhancing cellular capacities for damage repair [11], nutrients and bioactive compounds could affect the changes in body composition and the loss of physical function that occur with biological aging. In addition, evaluating dietary patterns allows to assess the overall diet’s role, considering all the complexity of dietary intake (i.e., nutrient interactions and cultural aspects).

Epidemiological studies have been associating different dietary patterns with improvements in common relevant clinical outcomes during aging [12]. Diets rich in fruits, vegetables, wholegrain cereals, and low-fat milk have been associated with lower both body fat percentage and the Body Mass Index (BMI), the contrary is observed among people in higher adherence to dietary patterns mainly composed by meat [13]. Regarding muscle function, greater adherence to the “Mediterranean-Dietary Approaches to Stop Hypertension (DASH) Intervention for Neurodegenerative Delay (MIND)” demonstrated to improve physical function, slower functional decline, and maintain muscle strength over six years of follow-up [14].

Although still scarce, current evidence thus suggests that dietary habits may play a role in body composition and physical function changes during aging. However, to date the studies usually focused in only one age group (e.g., older adults), in one of the sexes or in one single component of body composition or physical function. The objective of the current study is to investigate the cross-sectional associations of dietary patterns with body composition, physical performance, and strength in a wide age range of adults (from 20 to 80+), considering the differences between the sexes. Additionally, the potential mediating role of body composition in the diet-physical function associations is explored.

Participants

The present study comprised participants from the INStitute for Prevention healthy agIng and REjuvenative medicine Human Translational Cohort (INSPIRE-T cohort). The INSPIRE-T cohort is an ongoing cohort that includes persons 20 years or older, with no upper limit of age. It consists of a 10-year observational study with follow-up visits regularly scheduled to assess biological, clinical, digital, and imaging data [15].

People were excluded from the INSPIRE-T cohort if they were not affiliated with social security, had a disease compromising life expectancy at 5 years (or 1 year for those in nursing homes), and if they were under administrative or juridical protection, guardianship, or supervision. For the present study we further excluded those with missing valid information on dietary intake, or the outcomes (i.e., body composition, physical performance, and strength), those on hemodialysis, with moderate or severe hepatopathy, bad oral health, food allergy, and present or past anorexia or cancer (Fig. 1). The cohort was divided into three groups to explore the differences in the outcomes of interest according to the chronological age: ≥20 to < 50 years; ≥50 to < 65 years; and ≥ 65 years.

Study design and procedures

This cross-sectional study was performed with baseline data from the first wave of the INSPIRE-T cohort, which started on October 2019. All the participants gave their written consent, and the study protocol was approved by the French Ethical Committee in Rennes (CPP Ouest V). The study protocol is available at “clinicaltrials.gov” under the registration code NCT04224038.

Data collection

Data collection was performed by trained clinical research members in either the Clinical Research Center (CRC) of the Gérontopôle of the Centre Hospitalier Universitaire (CHU) of Toulouse or in participant’s home for those who were unable to come to the research facilities.

Outcomes

Anthropometric and body composition measurements

The following measures were assessed: weight (kg) and height (m) with the following calculation of the body mass index (BMI; kg/m²). Total body and trunk fat mass (kg), and total and appendicular lean mass (i.e., the sum of the lean mass in the arms and legs; kg) were assessed by Dual Energy X-Ray absorptiometry (DXA; GE Healthcare Lunar iDXA). To avoid the influence of body size, the body composition was adjusted for height squared (m²), obtaining the total (FMI; kg/m²) and the trunk fat mass indexes (Trunk FMI; kg/m²), and the total (LMI; kg/m²) and the appendicular lean mass indexes (Appendicular LMI; kg/m²) [1].

Physical function measurements

The assessment comprised: the gait speed on a 4-meter distance (m/sec); the time to rise five times from a chair (sec); the Short Physical Performance Battery (SPPB) test (i.e., it includes the two previous tests with the addition of a balance test; scores range from 0 to 12, higher is better); and muscle strength (maximum grip strength value on a hydraulic dynamometer Jamar® after three attempts with both hands) [16].

Dietary data

The habitual food intake was collected with a non-quantitative Food Frequency Questionnaire (FFQ). The frequencies of consumption (never or rarely; less than once a week; once a week; two or three times a week; four to six times a week; once or more a day) were converted to a daily frequency (0; 0.071; 0.143; 0.358; 0.714; 1 or more times per day) to standardize the variables for the extraction of the dietary patterns.

The 34 food items of the FFQ were reduced into 18 food groups (Table S1), followed by a data-driven principal component analysis (PCA). The PCA calculates the inter-correlation between the food groups to combine those mostly related to generate the dietary patterns (factors). Three main dietary patterns were retained based on the eigenvalue greater than 1.30 [17] and the examination of the breaking point in the scree plot [18]. The adequacy was confirmed by the significant Bartlett test of sphericity (p < 0.001) and by a Kaiser-Meier-Olkin (KMO) test equal to 0.573 (0.5 is the minimal acceptable) [19].

After being rotated by the orthogonal transformation VARIMAX (maximize the differences), the food groups with an absolute factor loading > 0.30 were retained for the interpretation of the dietary patterns [20]. The label of each pattern was based on the foods most strongly correlated to it. The level of compliance of the participants to the dietary patterns is represented by the factor score (the sum of the consumption frequency of all food groups weighted by the food group factor loading), with a higher factor score meaning better compliance to the dietary pattern. The food groups with a negative factor loading were kept presenting the complexity of eating habits.

Three dietary patterns were extracted: 1)“Mediterranean-like”, characterized by positive factor loadings for legumes, dairy products, wholegrain cereals, fruits, vegetables, and vegetal oil; 2) “Animal products”, consisting of positive factor loadings for meat and poultry, charcuterie, refined cereals, and butter or margarine, and a negative factor loading for breakfast cereals; and 3)” Sugar and fast food”, in which there were positive loadings for salted biscuits, pre-prepared and fast foods, soft drinks, desserts, sweets and pastries, and a negative loading for fish (Tables S1 and S2).

Covariates

The following confounding factors were considered: visit date (to control potential seasonal bias on dietary intake), the number of medications, educational level (i.e., higher or lower than a college degree), revenues (< 1500; 1500–2100; 2100–2800; 2800–4200; >4200 euros), living status (i.e., living alone or living not alone), and physical activity during leisure time (min/week) assessed by the International Physical Activity Questionnaire (IPAQ) [21].

Extremes values in the leisure time physical activity were identified using the Tukey’s method, with values above three interquartile range from the third quartile value excluded from the analysis to avoid any impact on the linear regressions [22].

Statistical analysis

The distribution of the continuous variables was checked through the Shapiro-Wilk test. As all the continuous variables did not follow a Gaussian distribution they were expressed as median (interquartile range). The binary and categorical variables were indicated as absolute (n) and relative (%) numbers.

The characteristics of the participants were compared across the age groups (≥ 20 to < 50 years; ≥50 to < 65 years; and ≥ 65 years) by Chi-square for categorical data and by Kruskal-Wallis for continuous data. The adherence to each dietary pattern was explored according to the age groups through the Kruskal-Wallis test and the sex using the Wilcoxon Rank Sum test. The associations of body composition and physical function with each dietary pattern were assessed by multiple linear regression models stratified by sex and age group and adjusted for the covariates previously described. The robust Maximum Likelihood method was used when the distribution of the residuals was not Gaussian.

A secondary analysis was performed to verify if the associations between the dietary patterns, physical performance, and muscle strength were mediated by body composition (FMI and appendicular LMI). The mediation analysis was built using the structural equation modeling framework to draw the direct pathway (links the dietary patterns and the physical function while adjusts for body composition) and the indirect pathway (links the dietary pattern to physical function through the body composition) [23]. The models included the same covariates of the linear regression, except visit date, which is only related to the diet but not to the other outcomes of the model (Figure S1). The model goodness of fit was demonstrated by a Standardized Root Mean Square Residual (SRMR) score lower than 0.08. Other indexes exist but SRMR was the only one that could be executed since the robust Maximum Likelihood method was applied to the model.

In the case where grip strength was associated to a dietary pattern, exploratory analysis on muscle quality (i.e., the ratio of muscle strength to muscle mass in the arms) could be performed to deepen the results and the information on the relationships between muscle and dietary pattern.

All the statistical analyses were undertaken using the Stata Software package (StataCorp LLC, College Station, Texas, USA) version 17, and the level of statistical significance was reached for p-values < 0.05.

The present study encompassed 470 participants (63% female), median age 56 (IQR: 40–71) years. All the socioeconomic and lifestyle variables significantly differed among the age groups, except the leisure time spent in physical activities, which was in average 180 (50–360) minutes per week (Table 1). Regarding body composition, while the indicators of fat mass increased with age, there was no difference in terms of lean mass. A significant lower physical performance and grip strength was observed in the oldest group (Table 1). Regarding the dietary patterns, we observed greater adherence to the “Sugar and fast food” dietary pattern in the youngest group for both women and men (≥ 20 and < 50 years). After this age range, the diet profile differed between the sexes. Women showed better adherence to the “Mediterranean-like” diet in the middle and older age groups, while men maintained greater adherence to the “Sugar and fast food” dietary pattern until the age 65 and then switched to the “Animal products” dietary pattern (Fig. 2 and Table S3).

Table 1

Characteristics of the included participants according to the age group.
Characteristics	All participants (n = 470)	≥ 20 – <50 years (n = 185)	≥ 50 – <65 years (n = 127)	≥ 65 years (n = 158)	p-value*
Sex, n (%)
Female	296 (63.0)	124 (67.0)	81 (63.8)	91 (57.6)	0.19
Male	174 (37.0)	61 (33.0)	46 (36.2)	67 (42.4)
Age	56.3 (39.5–70.9)	34.7 (27.9–43.9)	57.7 (53.2–61.8)	75.0 (70.7–81.4)	0.0001
Academic level, n (%)
Lower than college	92 (19.6)	7 (3.8)	30 (23.6)	55 (34.8)	< 0.001
College and higher	378 (80.4)	178 (96.2)	97 (76.4)	103 (65.2)
Revenues, n (%)
< 1500 €	47 (10.0)	26 (14.1)	7 (5.5)	14 (8.9)	0.004
1500–2100 €	67 (14.3)	29 (15.7)	11 (8.7)	27 (17.1)
2100–2800 €	82 (17.4)	31 (16.8)	17 (13.4)	34 (21.5)
2800–4200 €	139 (29.6)	49 (26.5)	41 (32.3)	49 (31.0)
> 4200 €	135 (28.7)	50 (27.0)	51 (40.2)	34 (21.5)
Way of living, n (%)
Living alone	129 (27.4)	37 (20.0)	33 (26.0)	59 (37.3)	0.002
Living not alone^£	341 (72.6)	148 (80.0)	94 (74.0)	99 (62.6)
Number of medications	1 (0–2)	0 (0–1)	1 (0–2)	2 (1–5)	0.0001
Smoking status, n (%) ^$	(n = 396)	(n = 171)	(n = 107)	(n = 118)
Never	242 (61.1)	114 (66.7)	62 (57.9)	66 (55.9)	< 0.001
Past smoker	120 (30.3)	37 (21.6)	36 (33.6)	47 (39.8)
Current smoker	34 (8.6)	20 (11.7)	9 (8.4)	5 (4.2)
Physical activity (leisure time, min/week)	180.0 (50.0–360.0)	180.0 (45.0–300.0)	180.0 (30.0–395.0)	180.0 (60.0–420.0)	0.41
Physical components
Weight (kg)	67.0 (58.0–77.0)	66.0 (58.0–76.0)	69.0 (60.0–79.0)	67.2 (58.0–77.0)	0.20
Body Mass Index (kg/m²)	24.2 (21.7–26.8)	22.7 (21.3 (25.9)	24.7 (22.2–26.6)	25.1 (22.8–28.0)	0.0001
Total FMI (kg/m²)	7.5 (5.7–9.9)	6.5 (4.7–8.9)	7.6 (6.0–9.9)	8.6 (6.5–10.2)	0.0001
Trunk FMI (kg/m²)	3.7 (2.5–5.1)	2.8 (1.9–4.4)	3.9 (2.6–5.0)	4.4 (3.2–5.7)	0.0001
Total LMI (kg/m²)	15.6 (14.4–17.2)	15.3 (14.2–16.9)	15.7 (14.5–17.3)	16.0 (14.3–17.4)	0.25
Appendicular LMI (kg/m²)	7.3 (6.5–8.2)	7.2 (6.5–8.1)	7.3 (6.5–8.3)	7.3 (6.4–8.1)	0.61
Gait speed (m/s)	1.3 (1.1–1.4)	1.3 (1.2–1.4)	1.3 (1.2–1.5)	1.2 (1.0–1.3)	0.0001
Chair rise test (s)^µ	8.3 (6.9–9.8)	7.4 (6.0–8.9)	8.1 (6.8–9.3)	9.6 (8.0–11.0)	0.0001
SPPB (/12 points)^µ	12.0 (12.0–12.0)	12.0 (12.0–12.0)	12.0 (12.0–12.0)	12.0 (11.0–12.0)	0.0001
Grip strength (kg)	34.0 (27.0–42.0)	36.0 (32.0–46.0)	34.0 (28.0–43.0)	28.0 (22.0–37.0)	0.0001
FMI: fat mass index; LMI: lean mass index. *Comparison between groups. ^£Living with partner, family, friends, or in a residence/community. ^$This variable showed data for 396 participants. ^µ Chair rise test was available for 90 women and 66 men in the oldest group, and the total SPPB score was available in 80 middle-aged and in 89 old women, and in 66 older men.

Dietary patterns, body composition, physical performance, and muscle strength

The associations between the dietary patterns and body composition (Table 2) and physical performance and grip strength (Table 3) were explored in each age group according to the sex.

Table 2

Adjusted multiple linear regressions between the dietary pattern adherence and the body composition variables.
Female (n = 296)
Dietary Patterns	BMI (kg/m²) β (95% CI)	Total FMI (kg/m²) β (95% CI)	Trunk FMI (kg/m²) β (95% CI)	Total LMI (kg/m²) β (95% CI)	Appendicular LMI (kg/m²) ^µ β (95% CI)
Med-like ≥ 20-<50 years ≥ 50-<65 years ≥ 65 years	-0.23 (-0.73;0.27) 0.32 (-0.31;0.95) 0.10 (-0.69;0.88)	-0.22 (-0.64;0.20) 0.19 (-0.33;0.71) 0.06 (-0.51;0.63)	-0.11 (-0.33;0.12) 0.10 (-0.22;0.41) -0.20 (-0.55;0.16)	-0.01 (-0.17;0.16) 0.14 (-0.06;0.34) 0.06 (-0.16;0.28)	-0.04 (-0.13;0.05) 0.09 (-0.02;0.20) 0.01 (-0.13;0.14)
Animal products ≥ 20-<50 years ≥ 50-<65 years ≥ 65 years	-0.10 (-0.75;0.55) 0.55 (-0.17;1.28) 0.78 (-0.33;1.89)	-0.05 (-0.57;0.47) 0.27 (-0.32;0.87) 0.50 (-0.31;1.31)	0.50 (-0.31;1.31) 0.19 (-0.16;0.55) 0.24 (-0.27;0.74)	-0.03 (-0.026;0.19) 0.26 (0.06;0.47)* 0.28 (-0.11;0.68)	0.01 (-0.13;0.15) 0.14 (0.01;0.28)* 0.13 (-0.05;0.31)
Sugar and fast food ≥ 20-<50 years ≥ 50-<65 years ≥ 65 years	-0.12 (-0.83;0.58) 0.19 (-0.54;0.91) -0.24 (-1.59;1.10)	-0.11 (-0.63;0.41) 0.13 (-0.44;0.70) -0.36 (-1.23;0.51)	-0.004 (-0.31;0.31) 0.18 (-0.18;0.53) -0.21 (-0.73;0.31)	-0.02 (-0.22;0.19) 0.01 (-0.20;0.22) 0.15 (-0.37;0.67)	-0.02 (-0.14;0.09) 0.03 (-0.12;0.17) 0.08 (-0.16;0.32)
Male (n = 174)
Dietary Patterns	BMI (kg/m²) β (95% CI)	Total FMI (kg/m²) β (95% CI)	Trunk FMI (kg/m²) β (95% CI)	Total LMI (kg/m²) β (95% CI)	Appendicular LMI (kg/m²) β (95% CI)
Medi-like ≥ 20-<50 years ≥ 50-<65 years ≥ 65 years	-0.30 (-1.42;0.83) -0.95 (-1.55;-0.35)* -0.16 (-0.95;0.63)	-0.31 (-1.10;0.47) -0.61 (-1.07;-0.16)* -0.34 (-0.83;0.15)	-0.18 (-0.72;0.36) -0.41 (-0.69;0.12)* 0.03 (-0.31;0.37)	-0.07 (-0.58;0.44) -0.28 (-0.48;-0.09)* 0.23 (-0.06;0.53)	-0.05 (-0.32;0.22) -0.20 (-0.30;-0.10)* 0.10 (-0.07;0.28)
Animal products ≥ 20-<50 years ≥ 50-<65 years ≥ 65 years	0.51 (-0.09;1.12) -0.26 (-1.13;0.61) 0.02 (-0.64;0.68)	0.33 (-0.08;0.75) -0.21 (-0.78;0.37) 0.02 (-0.41;0.45)	0.24 (-0.02;0.50) -0.11 (-0.52;0.30) 0.02 (-0.33;0.37)	0.16 (-0.19;0.51) -0.07 (-0.44;0.29) 0.001 (-0.23;0.23)	0.12 (-0.08;0.32) -0.03 (-0.32;0.26) 0.03 (-0.10;0.16)
Sugar and fast food ≥ 20-<50 years ≥ 50-<65 years ≥ 65 years	0.73 (-0.96;2.41) 0.60 (0.03;1.16)* -0.60 (-1.28;0.07)	0.65 (-0.09;1.39) 0.53 (0.04;1.02)* -0.42 (-0.88;0.05)	0.37 (-0.27;1.01) 0.37 (0.07;0.67)* -0.33 (-0.65;0.01)*	0.14 (-0.69;0.96) 0.03 (-0.21;0.28) -0.14 (-0.41;0.13)	0.10 (-0.30;0.50) 0.10 (-0.04;0.23) -0.06 (-0.21;0.09)
*Results being significant with a p-value < 0.05. Models adjusted for visit date, number of treatments, educational level, revenues, way of living, and physical activity level. ^µ Appendicular LMI was assessed in 80 middle-aged women instead of 81. Med: Mediterranean

Table 3

Adjusted multiple linear regressions between the dietary pattern adherence and physical performance and grip strength.
Female (n = 296)
Dietary Patterns	Gait speed (m/s) β (95% CI)	Chair rise test (s)^µ β (95% CI)	SPPB (/12 points) ^µ β (95% CI)	Grip strength (kg) β (95% CI)
Mediterranean-like ≥ 20-<50 years ≥ 50-<65 years ≥ 65 years	0.02 (-0.01;0.04) 0.0004 (-0.03;0.03) 0.03 (-0.01;0.07)	-0.12 (-0.36;0.12) 0.43 (-0.004;0.85) -0.15 (-0.65;0.34)	0.01 (-0.01;0.04) -0.05 (-0.21;0.11) 0.13 (-0.04;0.30)	-0.23 (-0.95;0.50) 0.46 (-0.59;1.50) -0.42 (-1.11;0.26)
Animal products ≥ 20-<50 years ≥ 50-<65 years ≥ 65 years	0.01 (-0.02;0.04) 0.01 (-0.03;0.05) -0.05 (-01.0;0.003)	0.05 (-0.22;0.31) -0.26 (-0.71;0.19) 0.38 (-0.39;1.15)	0.01 (-0.02;0.04) 0.09 (-0.04;0.22) -0.23 (-0.54;0.07)	0.49 (-0.31;1.29) 0.31 (-0.78;1.41) -0.73 (-1.86;0.41)
Sugar and fast food ≥ 20-<50 years ≥ 50-<65 years ≥ 65 years	-0.001 (-0.03;0.02) 0.01 (-0.04;0.05) -0.04 (-0.10;0.03)	0.04 (-0.21;0.30) 0.35 (0.06;0.64) * -0.04 (-0.88;0.81)	-0.02 (-0.05;0.02) 0.01 (-0.02;0.05) -0.14 (-0.57;0.28)	0.31 (-0.54;1.16) 0.55 (-0.33;1.44) -0.17 (-1.67;1.32)
Male (n = 174)
Dietary Patterns	Gait speed (m/s) β (95% CI)	Chair rise test (s)^µ β (95% CI)	SPPB (/12 points) ^µ β (95% CI)	Grip strength (kg) β (95% CI)
Mediterranean-like ≥ 20-<50 years ≥ 50-<65 years ≥ 65 years	-0.03 (-0.07;0.01) 0.03 (-0.004;0.06) -0.0003 (-0.05;0.05)	-0.05 (-0.46;0.37) -0.01 (-0.23;0.21) -0.15 (-0.60;0.30)	--- 0.04 (-0.10;0.18) 0.001 (-0.19;0.19)	-0.14 (-2.09;1.82) -0.81 (-2.38;0.75) 0.73 (-0.63;2.09)
Animal products ≥ 20-<50 years ≥ 50-<65 years ≥ 65 years	-0.02 (-0.06;0.01) -0.0001 (-0.05;0.05) 0.01 (-0.03;0.05)	0.03 (-0.30;0.36) -0.10 (-0.46;0.26) 0.04 (-0.32;0.40)	--- 0.08 (-0.05;0.20) -0.06 (-0.25;0.13)	0.31 (-0.78;1.41) -2.65 (-4.53;-0.78) * 1.20 (0.32;2.08)*
Sugar and fast food ≥ 20-<50 years ≥ 50-<65 years ≥ 65 years	-0.003 (-0.04;0.03) -0.05 (-0.09;-0.003) * -0.03 (-0.08;0.03)	-0.10 (-0.52;0.32) 0.32 (-0.01;0.64) -0.38 (-0.99;0.24)	--- -0.18 (-0.44;0.09) 0.09 (-0.17;0.34)	-0.43 (-2.77;1.91) -1.04 (-2.87;0.79) 0.37 (-1.23;1.97)
*Results being significant with a p-value < 0.05. Models adjusted for visit date, number of treatments, educational level, revenues, way of living, and physical activity level. ^µ The chair rise test was assessed in 90 women in the oldest group instead of 91, and in 66 old men instead of 67, and the SPPB was complete in 80 instead of 81 middle-aged women (balance test not performed), in 89 instead of 91 women in the oldest group (one chair rise test and one balance test missing), and in 66 instead of 67 old men (chair rise test missing).

Young group (≥ 20 to < 50 years old)

In young women, there was no significant association between the dietary patterns and any body composition measure. In men, the “Animal products” dietary pattern was not associated with physical performance or grip strength in this age range.

Middle-aged group (≥ 50 to < 65 years old)

In middle-aged women, a greater consumption of the “Animal products” diet resulted in significantly higher total and appendicular lean mass. On the other hand, greater adherence to the "Sugar and fast food" dietary pattern was significantly associated with worst performance on the chair rise test. Among middle-aged men, greater adherence to the “Mediterranean-like” diet was associated with lower BMI, total and trunk FMI, and total and appendicular LMI; furthermore, a greater consumption of the “Sugar and fast food” diet resulted in significantly higher BMI, total and trunk FMI as well as slower gait speed. Finally, greater consumption of the "Animal products" dietary pattern was associated with lower grip strength.

Older group (≥ 65 years old)

For women, none of the dietary patterns were significantly associated with body composition or physical performance and grip strength in this age range. The older men with greater adherence to the “Sugar and fast food” diet showed lower trunk fat, while higher intake of the “Animal products” diet was associated with higher grip strength.

Secondary analysis

The previous significant results between the dietary patterns and the physical function components were further analyzed in a mediation model to check whether they were mediated by body composition (Figure S1 and Table S4). The Structural Equation Modeling (SEM) indicated that the observed relationships between the dietary patterns and the physical function components were not mediated by fat mass nor appendicular lean mass. That is, the associations between gait speed (β: -0.05, 95%CI: -0.10 to -0.01) and chair rise test (β: 0.36, 95%CI: 0.08 to 0.63) with the “Sugar and fast food” diet in middle-aged men and women, respectively, and the associations of grip strength with the “Animal products” diet in middle-aged (β: -2.53, 95%CI: -4.09 to -0.97) and older men (β: 1.15, 95%CI: 0.35 to 1.94) were significant through the direct pathway, but not through indirect pathways.

As the results between the “Animal products” diet and grip strength were significant, the impact of this dietary pattern was further explored on muscle quality (Table S5). A greater adherence to the “Animal products” diet was significantly associated with lower muscle quality in middle-aged men (β: -0.85, 95%CI: -1.39 to -0.31), while in older men the association was not significant (β: 0.32, 95%CI: -0.02 to 0.67).

The pattern of food intake differed across the age-ranges and according to the sex. Although both men and women showed high adherence to the “Sugar and fast food” pattern of food intake in the youngest age range, after this age, while women adhered more to a “Mediterranean-like” diet, men kept in “Sugar and fast food” diet, changing to greater adherence to the “Animal products” dietary pattern after the age of 65. The dietary patterns had significant associations with body composition and physical function, with more significant results in the middle-aged groups. Among middle-aged women, whether higher intake of “Animal products” was associated with higher lean mass, greater adherence to the “Sugar and fast food” diet was related to worse chair rise performance. Among middle-aged men, the more adherence to a “Mediterranean-like” diet, the lower body fat and lean mass. On the contrary, higher intake of a “Sugar and fast food” diet was associated with higher fat mass and poorer performance on gait speed, and higher intake “Animal products” was associated with lower grip strength. The associations between diet and physical function were independent of the body composition of the participants.

The preference for a diet rich in ultra-processed foods and lacking fruits and vegetables, as the “Sugar and fast food” dietary pattern, is often related to the low cost of these foods, lack of time, skills, and facilities to cook, their widespread presence, preferred taste, and emotional responses [24]. As the food components of this dietary pattern are energy-dense [25], it is not a surprise that greater intake has been associated with higher fat mass, as observed in the middle-aged men. These findings are in accordance with pooled data from cross-sectional studies that indicated a 39% higher risk of overweight/obesity with the highest consumption of ultra-processed foods [26]. In addition, in a crossover randomized controlled trial the ad libitum consumption for two weeks of an ultra-processed diet resulted in an energy intake 500 kcal higher than the alternate diet. As a result, participants had their body weight increased by ~ 1kg and body fat by ~ 0.5kg [27].

It is important to mention that among men over 65 years, greater adherence to the “Sugar and fast food” dietary pattern was significantly associated with reduced body fat, and tended to be inversely related to all anthropometric components, suggesting a risk of malnutrition [28]. Ultra-processed foods are poor in proteins, fibers, micronutrients, and bioactive compounds [25]. Thus, the “Sugar and fast food” dietary pattern may not provide enough essential nutrients for this stage of life [25]. In older adults, the consumption of ultra-processed foods was already reported to increase the risk of frailty [29] and grip strength decline [30]. In our study, higher adherence to the “Sugar and fast food” diet was associated with poorer performance on chair rise test in middle-aged women and in gait speed test in middle-aged men, independently of body composition.

The food components of the “Sugar and fast food” dietary pattern were already associated with some markers of biological aging. The intake of ultra-processed foods [31] and specifically sweetened carbonated beverages [32] have been associated with shorter telomeres. Furthermore, additives, added sugars, and trans fatty acids commonly presented in the ready-to-eat foods, could activate inflammatory patterns [33]. Longer time to complete the chair rise test is associated with shorter telomere length [34] and both chair raise and gait speed tests were already associated with inflammatory markers [35]. Thus, we can speculate that the pathways by which higher consumption of the “Sugar and fast food” diet could negatively impact physical performance involve telomere attrition and inflammation.

Higher intake of the “Animal products” diet was associated with higher lean mass in middle-aged women, but with a reduced grip strength in middle-aged men, which was unexpected. Meat is an important food source of high-quality protein and B vitamins essential to skeletal muscle; however, it also contains high amounts of saturated (SFA) and trans fatty acids (FA), especially when red-processed (which was the main type of meat in this dietary pattern). SFA and trans FA increase the circulating levels of pro-inflammatory markers [36]. In addition, we demonstrated a significant association between greater intake of “Animal products” and lower muscle quality in upper body by using the ratio of muscle strength to muscle mass in the arms as a proxy. Myosteatosis and the local inflammation as consequence of the SFA and trans fatty acids intake can lead to mitochondrial dysfunction, compromising the synthesis of ATP and increasing the levels of reactive oxygen species (ROS) [37]. This scenario favors the development of insulin resistance (IR) and impairs the fiber contractility, essential to muscle strength [38]. It has been demonstrated that the aging-associated immune-inflammatory changes occur first in men than in women, which may explain the negative association at an early age for men [39]. Interestingly, although not statically significant, greater adherence to this dietary pattern also tended to be associated with lower grip strength in women (non-significant association), but only when over 65 years.

The intake of processed meat (e.g. ham, bacon, sausages) have been associated with lower grip strength in the literature [40]. Processed meat has been additionally related to increased risk of frailty [41] and to impaired agility and lower-extremity physical function [42]. It is noteworthy that among the older men, the direction of the association between the “Animal products” dietary pattern and grip strength changed, a surprising result that needs further investigation. A potential hypothesis emerges from the concept of the “muscle clock”, in which the time of day when the intake of proteins occurs influences muscle response: the higher the intake at breakfast, the higher the grip strength compared to when protein is eaten primarily at dinner [43]. It has been observed that the percentage of protein intake at breakfast tends to be significantly higher in older adults compared to middle-aged counterparts [44].

On the contrary, the substitution of processed meat with fish, legumes, dairy, or nuts has been associated with a reduced risk of functional impairment [41, 42]. Since the foods contained in the “Mediterranean-like” diet are rich in antioxidants and nutrients with anti-inflammatory properties, this could mitigate the increased inflammatory environment associated with aging. However, in our population, the adherence to this diet was not associated with better physical function. We only observed significant association with body composition. In middle-aged men higher adherence to the “Mediterranean-like” dietary pattern was associated with lower body fat but also lower lean mass. This finding suggests that for men, perhaps the amount of protein achieved with the “Mediterranean-like” dietary pattern was not enough, requiring either increasing the protein sources presented in this diet (e.g., legumes and dairy) or including other foods rich in protein but low in fat, such as lean meat and eggs.

The limitations of this study include the cross-sectional design, which does not allow us to trace the trajectory of the changes in dietary pattern adherence, body composition, and physical performance and grip strength. Applying a retrospective food questionnaire has intrinsic bias related to memory; in addition, the use of a non-quantitative FFQ makes it unfeasible to estimate the nutritional intake of the participants. The results of this study should be interpreted with caution, as it is based on a population with higher economic and educational levels and with a good global lifestyle. Despite these limitations, this study provides concise information on the role of eating habits on body composition, physical performance, and muscle strength in a wide age range of men and women, corroborating in the understanding of the changes observed with aging.

Different profiles of food intake among the sexes and age groups were observed, with men being more prone to have a less healthy food intake in all age groups. The extracted dietary patterns were significantly associated with body composition and physical function mainly in middle-aged adults. Diets composed of sugary foods, fast foods, and processed meat were associated with higher fat mass and poorer physical performance and grip strength. This work highlights different physiologic processes with age and reinforce the need for preventive strategies to reduce the intake of ultra-processed foods and high-fat meat to minimize the impact of aging on body composition and physical function. Furthermore, these preventive strategies should be adapted according to the sex since their differences in dietary habits and food preferences.

Ethics approval and consent to participate: All the participants gave their written consent, and the study protocol was approved by the French Ethical Committee in Rennes (CPP Ouest V). The study protocol is available at “clinicaltrials.gov” under the registration code NCT04224038.

Availability of data and materials: The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

Competing interests: The authors declare that they have no competing interests.

Funding : The present work 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).

Authors' contributions: LL, NAGF, and PSB designed research. LL and NAGF conducted research. LL analyzed data. LL and NAGF wrote the paper. YR, SG, PSB, and BV critically revised the paper. LL and NAGF had primary responsibility for final content. All authors read and approved the final manuscript.

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Body composition, physical function, and dietary patterns in people from 20 to over 80 years old: the INSPIRE-T cohort

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