Vol.:(0123456789) 
European Journal of Nutrition (2023) 62:2303–2315 
https://doi.org/10.1007/s00394-023-03141-9
ORIGINAL CONTRIBUTION
Association of meal timing with body composition 
and cardiometabolic risk factors in young adults
Manuel Dote‑Montero1  · Francisco M. Acosta1,2,3,4 · Guillermo Sanchez‑Delgado1,5,6,10 · Elisa Merchan‑Ramirez1 · 
Francisco J. Amaro‑Gahete1,6,10 · Idoia Labayen6,7,8,9 · Jonatan R. Ruiz1,6,10
Received: 15 November 2022 / Accepted: 24 March 2023 / Published online: 26 April 2023 
© The Author(s) 2023, corrected publication 2023
Abstract
Purpose To investigate the association of meal timing with body composition and cardiometabolic risk factors in young 
adults.
Methods In this cross-sectional study participated 118 young adults (82 women; 22 ± 2 years old; BMI: 25.1 ± 4.6 kg/m2). 
Meal timing was determined via three non-consecutive 24-h dietary recalls. Sleep outcomes were objectively assessed using 
accelerometry. The eating window (time between first and last caloric intake), caloric midpoint (local time at which ≥ 50% 
of daily calories are consumed), eating jetlag (variability of the eating midpoint between non-working and working days), 
time from the midsleep point to first food intake, and time from last food intake to midsleep point were calculated. Body 
composition was determined by DXA. Blood pressure and fasting cardiometabolic risk factors (i.e., triglycerides, total cho-
lesterol, high-density lipoprotein-cholesterol, low-density lipoprotein-cholesterol, and insulin resistance) were measured.
Results Meal timing was not associated with body composition (p > 0.05). The eating window was negatively related to 
HOMA-IR and cardiometabolic risk score in men (R2 = 0.348, β = − 0.605; R2 = 0.234, β = − 0.508; all p ≤ 0.003). The 
time from midsleep point to first food intake was positively related to HOMA-IR and cardiometabolic risk score in men 
(R2 = 0.212, β = 0.485; R2 = 0.228, β = 0.502; all p = 0.003). These associations remained after adjusting for confounders and 
multiplicity (all p ≤ 0.011).
Conclusions Meal timing seems unrelated to body composition in young adults. However, a longer daily eating window and 
a shorter time from midsleep point to first food intake (i.e., earlier first food intake in a 24 h cycle) are associated with better 
cardiometabolic health in young men.
Clinical trial registration NCT02365129 (https:// www. clini caltr ials. gov/ ct2/ show/ NCT02 365129? term= ACTIB ATE& draw= 
2& rank=1).
Keywords Chrononutrition · Circadian rhythms · Timing of food intake · Intermittent fasting · Fat mass · Insulin resistance
Introduction
The obesity epidemic is one of the leading contributors to 
the global burden of cardiometabolic diseases and disability, 
resulting in a significant economic impact on health care 
systems [1, 2]. However, despite the known consequences of 
excess body weight, the prevalence of obesity continues to 
rise, and currently there are more than 1.9 billion adults with 
overweight or obesity worldwide [1]. Body weight regula-
tion and obesity are highly influenced by several factors such 
as genetics, physiology, and socioeconomic factors [3]. For 
instance, in recent years, emerging evidence has shown that 
the timing of food intake may be a relevant risk factor for 
obesity and cardiometabolic diseases [4, 5].
The importance of when we eat is tied to our circadian 
system, which temporally orchestrates numerous metabolic 
processes across the body over a 24 h period [6, 7]. The 
circadian system is constituted by a central clock located 
in the suprachiasmatic nuclei of the hypothalamus and a 
series of peripheral clocks placed in virtually all cells and 
tissues of the body [6, 7]. The central clock is primarily con-
trolled by external light via direct connection to the retina, 
 * Manuel Dote-Montero 
 manueldote@ugr.es
 * Jonatan R. Ruiz 
 ruizj@ugr.es
Extended author information available on the last page of the article
2304 European Journal of Nutrition (2023) 62:2303–2315
1 3
whereas peripheral clocks are regulated by the central clock 
and external time cues, such as eating, physical activity and 
sleep among other behavioural tasks [6, 7]. Since different 
stimuli impact the central and peripheral clocks, the two 
clock systems become misaligned whenever their respective 
time cues are out of synchrony [6, 7]. The modern lifestyle 
promotes this circadian misalignment by allowing exposure 
to artificial light at night and by allowing access to food all 
day long [6–8].
Epidemiological data have shown that eating late in the 
day is associated with greater energy intake, adiposity and 
worst cardiometabolic health [9–16]. Furthermore, consider-
able variability in meal timing between non-working days 
and working days, coined eating jet lag, has been linked 
to higher body mass index (BMI) [17]. Clinical trials have 
also revealed that reducing the daily eating window (i.e., the 
period of time between the first and the last caloric intake) 
and/or shifting dietary energy intake earlier in the day results 
in body weight loss or optimized cardiometabolic health in 
adults with overweight or obesity [9, 18–22]. Nonetheless, 
one of the main limitations of meal timing studies is the use 
of clock time to illustrate the timing of food intake, which 
fails to correctly characterize meal timing in relation to the 
internal circadian time [10, 23]. Because obtaining measures 
of dim-light melatonin is not practical in large clinical trials, 
it has been proposed that the timing of food intake should 
be assessed in relation to the sleep/wake cycle as a proxy 
of circadian time [10, 23, 24]. Only a few studies have fol-
lowed this methodology, indicating that a longer time period 
from dinner to midsleep point or bedtime is associated with 
lower adiposity and BMI, respectively [10, 23]. However, 
these studies also present important limitations, such as the 
self-reported assessment of sleep timing, solely including 
anthropometric variables or fat mass measured by non-state-
of-the-art methods and the absence of various cardiometa-
bolic risk. Moreover, although sex is known to influence sev-
eral aspects of biology, physiology and psychology previous 
studies have not investigated if there are sex differences in 
the relationship between meal timing with body composition 
and cardiometabolic risk factors.
Our study was aimed at elucidating the association of 
meal timing [i.e., eating window, caloric midpoint (the time 
at which ≥ 50% of daily calories are consumed), eating jet 
lag, time from midsleep point to first food intake, and time 
from last food intake to midsleep point] with anthropom-
etry [i.e., body weight, BMI and waist circumference], body 
composition [i.e., fat mass, lean mass, and visceral adipose 
tissue (VAT) mass] and cardiometabolic risk factors [i.e., 
blood pressure, triglycerides, total cholesterol, high-density 
lipoprotein-cholesterol (HDL-C), low-density lipoprotein-
cholesterol (LDL-C), insulin resistance] in young adults. 
Special attention was paid to the influence of sex on these 
associations. We hypothesized that a shorter daily eating 
window, earlier food intake schedule and less variability in 
meal timing between non-working days and working days 
would be associated with healthier body composition and 
cardiometabolic risk factors.
Materials/subjects and methods
Participants
A total of 118 young adults (n = 82 women; see Flow Chart, 
Figure S1) took part in this cross-sectional study which 
includes the baseline measurements of the ACTIBATE study 
(ClinicalTrials.gov NCT02365129) [25] and follows the 
STROBE-nut guidelines (Table S1) [26]. All assessments 
were performed in Granada (Spain) during September-
December in 2015 and 2016. The inclusion/exclusion crite-
ria included the following: (i) being 18–25 years, (ii) having 
a BMI ranging from 18 to 35 kg/m2, (iii) being non-smok-
ers, (iv) not being enrolled in a weight loss program, (iii) 
having a stable body weight (body weight changes < 3 kg 
over 3 months), (iv) not being physically active (< 20 min 
on < 3 days/week), (v) not taking any medication or drugs, 
and (vi) not suffering from any acute or chronic illness.
Outcome measurements
Anthropometry and body composition
Body weight and height were determined with participants 
wearing light clothing and without shoes using a SECA scale 
and stadiometer (model 799; Electronic Column Scale, Seca 
GmbH, Hamburg, Germany). The BMI was calculated as 
weight (kilograms) divided by height squared (square 
meters). Waist circumference (centimetres) was assessed 
midway between the lowest rib and the top of the iliac crest. 
Alternatively, when participants showed abdominal obesity, 
waist circumference was determined in a horizontal plane 
above the umbilicus after exhalation.
Body composition was measured by dual-energy X-ray 
absorptiometry (DiscoveryWi; Hologic, Inc., Marlborough, 
MA, USA) strictly following the manufacturer’s instructions, 
obtaining fat mass, lean mass, and VAT mass. The lean mass 
index was calculated as lean mass (kilograms) divided by 
height squared (square meters).
Cardiometabolic profile
The systolic and diastolic blood pressure were meas-
ured on three different days with an automatic monitor 
(Omron Healthcare Europe B.V. Hoofddorp, The Neth-
erlands), and the average was used in later analyses. We 
calculated the mean blood pressure as diastolic blood 
2305European Journal of Nutrition (2023) 62:2303–2315 
1 3
pressure + 1/3(systolic blood pressure − diastolic blood 
pressure). Blood samples were collected from the antecu-
bital area in the morning after fasting for > 10 h. All sam-
ples were centrifuged, and aliquots of serum were stored 
at − 80 °C until analysis. All participants were requested 
to abstain from drugs and caffeine and to avoid moderate-
intensity physical activity and vigorous-intensity activity 
for 24 and 48 h before testing, respectively. Serum glucose, 
total cholesterol, HDL-C and triglycerides were assessed fol-
lowing standard methods using an AU5832 automated ana-
lyzer (Beckman Coulter Inc., Brea CA, USA). LDL-C was 
then estimated [27]. Serum insulin was measured using the 
Access Ultrasensitive Insulin Chemiluminescent Immunoas-
say Kit (Beckman Coulter Inc., Brea, CA, USA). The home-
ostasis model assessment of insulin resistance (HOMA-IR) 
was calculated.
Cardiometabolic risk scores were calculated for each sex 
based on variables included in the diagnosis of Metabolic 
Syndrome [28]: waist circumference, blood pressure, plasma 
glucose, HDL-C, and triglyceride concentrations. A Z-score 
was calculated for each variable. The HDL-C standardized 
values were multiplied by − 1 to be directly proportional to 
the cardiometabolic risk. The final score was determined 
as the average of the 5 Z-scores. Thus, the cardiometabolic 
risk score is a continuous variable with a mean of 0 and a 
standard deviation of 1; higher scores indicating higher risk.
Sleep parameters, sedentary time and physical activity 
assessment
Participants were instructed to wear a triaxial accelerometer 
(ActiGraph GT3X+, Pensacola, FL, USA) on the non-dom-
inant wrist 24 h a day for seven consecutive days, removing 
them only when swimming or bathing, and to record infor-
mation on sleep onset and wakeup times each day in a sleep 
diary. The midsleep point and other sleep-related outcomes 
were objectively assessed using an algorithm guided by the 
participants' reported times. The raw data were processed 
in R [version 3.1.2, https:// www.R- proje ct. org/] using the 
GGIR package [version 1.5-12, https:// cran.r- proje ct. org/ 
web/ packa ges/ GGIR/]. Actigraphy recordings were used to 
determine: (i) sleep onset (time at which the subject fell 
asleep); (ii) sleep offset (time at which the subject woke 
up); and (iii) sleep duration (time between falling asleep and 
waking up). Daytime naps were not considered. Those par-
ticipants registering less than 16 h/day of wear time for more 
than 4 days and/or not having data from at least one weekend 
day were excluded from the final analysis. We used these 
sleep variables to calculate the midsleep point (the middle 
time point between sleep onset and sleep offset) as follows: 
midsleep point (local time) = (sleep duration/2) + sleep 
onset [24]. We determined the midsleep point for non-
working days and working days separately. Subsequently, 
the midsleep point for the week was calculated as a weighted 
mean, which was used in later analyses. Last, we computed 
social jetlag (a proxy of the discrepancy between social and 
biological time) by subtracting each participant’s midsleep 
point for working days from non-working days [24].
We estimated the time spent in sedentary behavior and 
different physical activity intensities (i.e., light, moderate, 
vigorous, and moderate to vigorous) using age-specific cut 
points [29].
Dietary assessment and meal timing
Dietary intake and meal timing were recorded using three 
non-consecutive 24 h dietary recalls (one of them for a non-
working day) distributed over three weeks with the partici-
pants unaware of when their diets were going to be recorded. 
In face-to-face interviews performed by trained dietitians, 
participants were asked to recall all food consumed on the 
previous day, using photographs of portion sizes [30], as 
well as the time of the meal event. Data recorded in the 
interviews were independently introduced by two dieticians 
in the  EvalFINUT® software When the coefficient of vari-
ance between the two datasets was > 5%, a third dietician 
reintroduced the data obtained from the 24-h recalls in the 
 EvalFINUT® software and the mean of the two datasets with 
the best agreement (i.e., lower coefficient of variance) was 
used. From these data, we calculated the following meal 
timing variables:
The eating window (i.e., the period of time between the 
first and the last caloric intake) was calculated for each 
day [31]. The caloric midpoint is defined as the time at 
which ≥ 50% of daily calories are consumed and is expressed 
in local time [12]. We determined the eating window and the 
caloric midpoint for non-working days and working days. 
Subsequently, the value for the week was calculated as a 
weighted mean, which was used in later analyses. Eating 
jetlag (i.e., the variability of the eating midpoint between 
non-working days and working days) was determined as 
[24]: eating midpoint for non-working days − eating mid-
point for working days. The time from the midsleep point 
to first food intake and the time from last food intake to the 
midsleep point were also calculated.
Dietary patterns and quality were determined by analys-
ing the participants' data from the 24 h recalls and a vali-
dated food frequency questionnaire [32]. Three The follow-
ing Mediterranean dietary patterns [33–35] were computed: 
the a priori Mediterranean dietary pattern [33], the Mediter-
ranean diet score, and the dietary quality index for a Mediter-
ranean diet [34, 35]. Adherence to the Dietary Approaches to 
Stop Hypertension guidelines was also calculated. The diet 
quality index [36] and the dietary inflammatory index [37], 
were also determined. Breakfast consumers were defined as 
consuming breakfast in all of the three non-consecutive 24 h 
2306 European Journal of Nutrition (2023) 62:2303–2315
1 3
dietary recalls, whereas breakfast skippers were defined as 
not consuming breakfast on at least one of the three non-
consecutive 24 h dietary recalls.
Statistical analyses
The distribution of the variables was verified using the Sha-
piro–Wilk test, skewness and kurtosis values, visual check-
ing of histograms, and Q–Q and box plots. The descriptive 
parameters are reported as mean and standard deviation 
when normally distributed, or medians (interquartile range) 
when a Gaussian distribution was not found. Triglycer-
ides, total cholesterol, HDL-C, LDL-C, and HOMA-IR 
were log10-transformed to bring their distributions closer 
to normal and used in subsequent analyses. A one-way 
analysis of variance was used to compare baseline charac-
teristics between men and women. We conducted simple 
linear regression models to examine the association of meal 
timing (i.e., eating window, caloric midpoint, eating jetlag, 
time from midsleep point to first food intake, and time from 
last food intake to midsleep point) with anthropometry (i.e., 
weight, BMI and waist circumference), body composition 
(i.e., fat mass percentage, lean mass index, and VAT mass) 
and cardiometabolic risk factors (i.e., mean blood pressure, 
triglycerides, total cholesterol, HDL-C, LDL-C, HOMA-IR, 
and cardiometabolic risk score). We also conducted multi-
ple linear regression models to examine these associations 
adjusting for potential confounders (i.e., sex, a priori Medi-
terranean diet pattern, light physical activity, midsleep point 
or sleep duration, and BMI). Differences between breakfast 
skippers and consumers in anthropometry, body composi-
tion, cardiometabolic risk factors and potential confounders 
were determined by the Welch's t test. Some statistical dif-
ferences were observed; therefore, additional analyses were 
performed. Concretely, the simple linear regression models 
were repeated to examine the association of meal timing 
with anthropometry, body composition and cardiometabolic 
risk factors only in breakfast consumers.
The main analyses were corrected for multiple compari-
son errors (familywise error rate [Hochberg procedure]) 
[38]. The Statistical Package for the Social Sciences (SPSS) 
v.25.0 (IBM Corporation, Chicago, IL, USA) was used for 
all analyses. GraphPad Prism 8 (GraphPad Software, San 
Diego, CA, USA) was used for plots. Significance was set 
at p < 0.05.
Results
Table  1 shows the main characteristics of the study 
participants.
Meal timing and anthropometry/body composition
The caloric midpoint was negatively associated with fat 
mass percentage in men (R2 = 0.089, β = − 0.342, p = 0.048; 
Table 2), but became non-significant after adjusting for 
multiplicity (p > 0.05). No further associations were found 
between meal timing and anthropometry/body composition 
(all p ≥ 0.068; Table 2).
Meal timing and cardiometabolic risk factors
The eating window was negatively related to HOMA-IR 
in all participants and in men (R2 = 0.100, β = − 0.328; 
R2 = 0.348, β = − 0.605, respectively; all p ≤ 0.001; Table 3, 
Fig. 1 and S2). The eating window was also negatively 
associated with cardiometabolic risk score in all par-
ticipants and in men (R2 = 0.079, β = − 0.296; R2 = 0.234, 
β = − 0.508, respectively; all p ≤ 0.003; Table 3, Fig. 1 and 
S2). A negative association between the eating window and 
triglycerides was observed in all participants (R2 = 0.044, 
β = − 0.229, p = 0.013; Table 3 and figure S2). The eating 
window was positively related to HDL-C in all participants 
and in men (R2 = 0.048, β = 0.238; R2 = 0.162, β = 0.431, 
respectively; all p ≤ 0.010; Table  3 and figure S2). No 
associations were found between caloric midpoint and eat-
ing jetlag with cardiometabolic risk factors (all p ≥ 0.150; 
Table 3). The time from midsleep point to first food intake 
was positively related to HOMA-IR in all participants and 
in men (R2 = 0.078, β = 0.294; R2 = 0.212, β = 0.485, respec-
tively; all p ≤ 0.003; Table 3, Fig. 1 and S2). The time from 
midsleep point to first food intake was also positively asso-
ciated with cardiometabolic risk score in all participants 
and in men (R2 = 0.081, β = 0.300; R2 = 0.228, β = 0.502, 
respectively; all p ≤ 0.003; Table 3, Fig. 1 and S2). A posi-
tive association between the time from midsleep point to 
first food intake and mean blood pressure was found in all 
participants (R2 = 0.030, β = 0.197, p = 0.037; Table 3 and 
figure S2). The time from midsleep point to first food intake 
was negatively associated with HDL-C in all participants 
and in men (R2 = 0.045, β = − 0.231; R2 = 0.137, β = − 0.403, 
respectively; all p ≤ 0.016; Table 3 and figure S2). A sig-
nificant positive association between the time from last 
food intake to midsleep point and triglycerides was found 
in women (R2 = 0.087, β = 0.314, p = 0.005; Table 3). Sev-
eral variables of meal timing were significantly associated 
with potential confounders such as a priori Mediterranean 
diet pattern, light physical activity and midsleep point (all 
p ≤ 0.05; figure S3). Of note is that only the associations of 
eating window and time from midsleep point to first food 
intake with HOMA-IR and cardiometabolic risk score—
in all participants and men—remained after adjusting for 
potential confounders and false discovery rate (all p ≤ 0.011; 
Table 2, 3 and S2–S3).
2307European Journal of Nutrition (2023) 62:2303–2315 
1 3
Differences between breakfast skipper 
and consumer
Significant differences between breakfast skipper and 
consumer were found for fasting blood glucose, midsleep 
point, dietary patterns and quality, eating window, caloric 
midpoint, eating jetlag, and time from midsleep point 
to first food intake (all p ≤ 0.045; table S4). Breakfast 
consumption influenced the relationship between meal 
timing and body composition in women observing that 
the eating window was positively associated with BMI and 
VAT mass in women breakfast consumers (all p ≤ 0.036; 
figure S4). Breakfast consumption did not modify the rela-
tionship shown by meal timing with cardiometabolic risk 
factors (figure S5).
Table 1  Descriptive characteristics of participants
Data are presented as sample size, mean (standard deviation) when normally distributed, or medians (interquartile range) when not
HOMA-IR homeostasis model assessment of insulin resistance
*p ≤ 0.05 for sex comparisons by a one-way analysis of variance (all cardiometabolic risk factors except for blood pressure variables were log10-
transformed to bring their distributions closer to normal)
a Time shown in local time
b Time shown in decimal format
All Men Women
Age (years) 118 22.2 (2.2) 36 22.5 (2.3) 82 22.1 (2.1)
Height (cm) 116 168.0 (8.5)* 34 176.3 (6.4) 82 164.5 (6.7)
Weight (kg) 116 71.2 (16.8)* 34 85.5 (18.5) 82 65.2 (11.7)
Body composition
 Body mass index (kg/m2) 116 25.1 (4.6)* 34 27.4 (5.4) 82 24.1 (3.9)
 Fat mass (kg) 116 25.6 (9.0) 34 27.0 (11.6) 82 25.0 (7.6)
 Fat mass (%) 116 36.4 (7.3)* 34 31.1 (7.7) 82 38.6 (5.9)
 Lean mass (kg) 116 41.6 (9.8)* 34 53.7 (7.5) 82 36.6 (5.0)
 Lean mass index (kg/m2) 116 14.6 (2.4)* 34 17.3 (2.2) 82 13.5 (1.4)
 Visceral adipose tissue mass (g) 116 346.0 (187.3)* 34 446.7 (188.7) 82 304.3 (171.2)
 Waist circumference (cm) 114 80.8 (14.5)* 34 91.4 (16.4) 80 76.3 (10.9)
Cardiometabolic risk factors
Systolic blood pressure (mmHg) 116 116.2 (12.4)* 36 126.0 (12.5) 80 111.8 (9.6)
Diastolic blood pressure (mmHg) 116 70.7 (7.9)* 36 73.0 (10.1) 80 69.7 (6.6)
Mean blood pressure (mmHg) 116 85.9 (8.1)* 36 90.7 (8.8) 80 83.7 (6.7)
Triglycerides (mg/dl) 117 70.0 (52.5, 97.5) 36 74.0 (57.8, 105.8) 81 68.0 (52.0, 89.5)
Total cholesterol (mg/dl) 117 158.0 (143.0, 179.0) 36 153.0 (140.0, 174.8) 81 164.0 (143.0, 183.5)
High-density lipoprotein cholesterol (mg/dl) 117 51.0 (45.5, 58.0)* 36 46.5 (39.3, 51.0) 81 53.0 (47.5, 63.0)
Low-density lipoprotein cholesterol (mg/dl) 117 92.0 (78.5, 109.0) 36 91.0 (81.3, 108.8) 81 92.0 (75.5, 109.0)
Glucose (mg/dl) 117 87.0 (83.0, 92.0)* 36 89.5 (82.3, 97.0) 81 86.0 (83.0, 90.0)
Insulin (μIU/ml) 117 7.2 (5.5, 10.1) 36 7.3 (5.2, 11.4) 81 7.1 (5.5, 9.8)
HOMA-IR 117 1.5 (1.1, 2.2)* 36 1.6 (1.2, 2.5) 81 1.5 (1.1, 2.1)
Chronobiology
Sleep onset (hh:mm)a 113 01:13 (01:13) 34 01:15 (01:20) 79 01:12 (01:10)
Sleep offset (hh:mm)a 113 08:52 (01:02) 34 09:00 (01:12) 79 08:48 (00:57)
Sleep duration (h)b 113 7.85 (1.2) 34 7.98 (1.25) 79 7.8 (1.17))
Midsleep point (hh:mm)a 113 05:04 (01:05) 34 05:10 (01:15) 79 05:02 (01:00)
Social jetlag (h)b 113 1.42 (1.23) 34 1.40 (1.10) 79 1.42 (1.30)
Meal timing
Eating window (h)b 117 12.10 (1.5) 36 12.00 (1.70) 81 12.20 (1.40)
Caloric midpoint (hh:mm)a 117 15:54 (01:36) 36 15:48 (01:36) 81 15:54 (01:42)
Eating jetlag (h)b 117 1.20 (1.10) 36 1.20 (1.33) 81 1.20 (1.00)
Time from midsleep point to first food intake (h)b 113 4.87 (1.40) 34 4.87 (1.72) 79 4.87 (1.23)
Time from last food intake to midsleep point (h)b 113 7.05 (1.08) 34 7.22 (1.23) 79 6.97 (1.02)
2308 European Journal of Nutrition (2023) 62:2303–2315
1 3
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17
Ti
m
e f
ro
m
 m
id
sle
ep
 po
in
t t
o fi
rst
 fo
od
 in
tak
e (
h)
11
3
− 
0.0
01
0.0
91
0.3
39
34
0.0
30
0.2
43
0.1
66
79
− 
0.0
12
− 
0.0
24
0.8
33
Ti
m
e f
ro
m
 la
st 
fo
od
 in
tak
e t
o m
id
sle
ep
 po
in
t (
h)
11
3
− 
0.0
08
− 
0.0
32
0.7
37
34
− 
0.0
31
− 
0.0
10
0.9
56
79
− 
0.0
01
− 
0.1
10
0.3
34
Fa
t m
as
s (
%
)
Ea
tin
g w
in
do
w 
(h
)
11
6
− 
0.0
09
0.0
03
0.9
79
34
− 
0.0
15
− 
0.1
25
0.4
82
82
− 
0.0
12
0.0
16
0.8
86
Ca
lo
ric
 m
id
po
in
t (
h)
11
6
− 
0.0
07
− 
0.0
37
0.6
91
34
0.0
89
− 
0.3
42
0.0
48
82
− 
0.0
06
0.0
83
0.4
60
Ea
tin
g j
etl
ag
 (h
)
11
6
− 
0.0
06
− 
0.0
54
0.5
63
34
− 
0.0
31
− 
0.0
13
0.9
43
82
− 
0.0
09
− 
0.0
55
0.6
25
Ti
m
e f
ro
m
 m
id
sle
ep
 po
in
t t
o fi
rst
 fo
od
 in
tak
e (
h)
11
3
− 
0.0
06
0.0
53
0.5
78
34
− 
0.0
13
0.1
31
0.4
59
79
− 
0.0
13
0.0
05
0.9
68
Ti
m
e f
ro
m
 la
st 
fo
od
 in
tak
e t
o m
id
sle
ep
 po
in
t (
h)
11
3
− 
0.0
02
− 
0.0
82
0.3
90
34
− 
0.0
31
− 
0.0
14
0.9
38
79
− 
0.0
10
− 
0.0
54
0.6
34
Le
an
 m
as
s i
nd
ex
 (k
g/
m
2 )
Ea
tin
g w
in
do
w 
(h
)
11
6
0.0
01
− 
0.0
99
0.2
89
34
0.0
72
− 
0.3
16
0.0
68
82
0.0
04
0.1
28
0.2
50
Ca
lo
ric
 m
id
po
in
t (
h)
11
6
− 
0.0
07
0.0
41
0.6
59
34
− 
0.0
31
0.0
22
0.9
01
82
0.0
06
0.1
35
0.2
28
Ea
tin
g j
etl
ag
 (h
)
11
6
− 
0.0
08
0.0
27
0.7
76
34
− 
0.0
23
− 
0.0
87
0.6
25
82
− 
0.0
10
0.0
52
0.6
42
Ti
m
e f
ro
m
 m
id
sle
ep
 po
in
t t
o fi
rst
 fo
od
 in
tak
e (
h)
11
3
− 
0.0
03
0.0
74
0.4
34
34
0.0
54
0.2
87
0.1
00
79
− 
0.0
10
− 
0.0
52
0.6
51
Ti
m
e f
ro
m
 la
st 
fo
od
 in
tak
e t
o m
id
sle
ep
 po
in
t (
h)
11
3
− 
0.0
08
0.0
34
0.7
24
34
− 
0.0
30
0.0
29
0.8
73
79
0.0
02
− 
0.1
20
0.2
92
Vi
sc
er
al
 a
di
po
se
 ti
ss
ue
 m
as
s (
g)
Ea
tin
g w
in
do
w 
(h
)
11
6
0.0
04
− 
0.1
11
0.2
36
34
0.0
37
− 
0.2
57
0.1
42
82
− 
0.0
12
0.0
02
0.9
89
Ca
lo
ric
 m
id
po
in
t (
h)
11
6
− 
0.0
08
0.0
27
0.7
71
34
− 
0.0
14
− 
0.1
30
0.4
64
82
0.0
00
0.1
12
0.3
15
Ea
tin
g j
etl
ag
 (h
)
11
6
− 
0.0
06
− 
0.0
49
0.6
00
34
− 
0.0
30
− 
0.0
34
0.8
48
82
− 
0.0
05
− 
0.0
87
0.4
40
Ti
m
e f
ro
m
 m
id
sle
ep
 po
in
t t
o fi
rst
 fo
od
 in
tak
e (
h)
11
3
0.0
02
0.1
07
0.2
61
34
0.0
51
0.2
82
0.1
06
79
− 
0.0
13
0.0
06
0.9
60
Ti
m
e f
ro
m
 la
st 
fo
od
 in
tak
e t
o m
id
sle
ep
 po
in
t (
h)
11
3
− 
0.0
09
0.0
02
0.9
82
34
− 
0.0
29
− 
0.0
46
0.7
98
79
− 
0.0
12
− 
0.0
28
0.8
09
W
ai
st 
ci
rc
um
fe
re
nc
e 
(c
m
)
Ea
tin
g w
in
do
w 
(h
)
11
4
− 
0.0
01
− 
0.0
88
0.3
52
34
0.0
49
− 
0.2
79
0.1
11
80
− 
0.0
01
0.1
09
0.3
37
Ca
lo
ric
 m
id
po
in
t (
h)
11
4
− 
0.0
08
− 
0.0
28
0.7
70
34
0.0
20
− 
0.2
24
0.2
03
80
− 
0.0
03
0.1
01
0.3
75
Ea
tin
g j
etl
ag
 (h
)
11
4
− 
0.0
05
− 
0.0
65
0.4
91
34
− 
0.0
22
− 
0.0
95
0.5
94
80
− 
0.0
02
− 
0.1
05
0.3
52
Ti
m
e f
ro
m
 m
id
sle
ep
 po
in
t t
o fi
rst
 fo
od
 in
tak
e (
h)
11
1
− 
0.0
04
0.0
72
0.4
50
34
0.0
07
0.1
92
0.2
77
77
− 
0.0
13
− 
0.0
18
0.8
76
Ti
m
e f
ro
m
 la
st 
fo
od
 in
tak
e t
o m
id
sle
ep
 po
in
t (
h)
11
1
− 
0.0
09
0.0
16
0.8
66
34
− 
0.0
19
0.1
10
0.5
36
77
0.0
08
− 
0.1
47
0.2
03
2309European Journal of Nutrition (2023) 62:2303–2315 
1 3
Ta
bl
e 
3 
 A
ss
oc
iat
io
n o
f m
ea
l t
im
in
g w
ith
 ca
rd
io
m
eta
bo
lic
 ri
sk
 fa
cto
rs 
in
 yo
un
g a
du
lts
Ca
rd
io
m
eta
bo
lic
 ri
sk
 fa
cto
rs
Al
l
M
en
W
om
en
N
R2
β
P 
va
lu
e
N
R2
β
P 
va
lu
e
N
R2
β
P 
va
lu
e
M
ea
n 
bl
oo
d 
pr
es
su
re
 (m
m
H
g)
Ea
tin
g w
in
do
w 
(h
)
11
6
0.0
15
− 
0.1
53
0.1
00
36
0.0
51
− 
0.2
80
0.0
99
80
− 
0.0
11
− 
0.0
36
0.7
49
Ca
lo
ric
 m
id
po
in
t (
h)
11
6
− 
0.0
06
0.0
54
0.5
62
36
− 
0.0
07
0.1
49
0.3
87
80
− 
0.0
12
0.0
33
0.7
73
Ea
tin
g j
etl
ag
 (h
)
11
6
− 
0.0
09
− 
0.0
15
0.8
71
36
− 
0.0
29
− 
0.0
13
0.9
40
80
− 
0.0
10
− 
0.0
56
0.6
24
Ti
m
e f
ro
m
 m
id
sle
ep
 po
in
t t
o fi
rst
 fo
od
 in
tak
e (
h)
11
2
0.0
30
0.1
97
0.0
37
35
0.0
31
0.2
45
0.1
56
77
0.0
21
0.1
85
0.1
07
Ti
m
e f
ro
m
 la
st 
fo
od
 in
tak
e t
o m
id
sle
ep
 po
in
t (
h)
11
2
− 
0.0
06
− 
0.0
52
0.5
85
35
− 
0.0
30
0.0
06
0.9
73
77
0.0
17
− 
0.1
74
0.1
29
Tr
ig
ly
ce
ri
de
s
Ea
tin
g w
in
do
w 
(h
)
11
7
0.0
44
− 
0.2
29
0.0
13
36
0.0
40
− 
0.2
59
0.1
27
81
0.0
30
− 
0.2
04
0.0
68
Ca
lo
ric
 m
id
po
in
t (
h)
11
7
− 
0.0
08
0.0
32
0.7
30
36
− 
0.0
05
0.1
54
0.3
71
81
− 
0.0
12
− 
0.0
22
0.8
44
Ea
tin
g j
etl
ag
 (h
)
11
7
− 
0.0
05
0.0
63
0.5
02
36
0.0
32
0.2
45
0.1
50
81
− 
0.0
10
− 
0.0
51
0.6
50
Ti
m
e f
ro
m
 m
id
sle
ep
 po
in
t t
o fi
rst
 fo
od
 in
tak
e (
h)
11
3
0.0
05
0.1
19
0.2
09
35
0.0
72
0.3
15
0.0
66
78
− 
0.0
12
− 
0.0
26
0.8
23
Ti
m
e f
ro
m
 la
st 
fo
od
 in
tak
e t
o m
id
sle
ep
 po
in
t (
h)
11
3
0.0
15
0.1
54
0.1
03
35
− 
0.0
17
− 
0.1
13
0.5
16
78
0.0
87
0.3
14
0.0
05
To
ta
l c
ho
le
ste
ro
l
Ea
tin
g w
in
do
w 
(h
)
11
7
− 
0.0
06
− 
0.0
52
0.5
77
36
− 
0.0
12
− 
0.1
30
0.4
51
81
− 
0.0
12
− 
0.0
17
0.8
78
Ca
lo
ric
 m
id
po
in
t (
h)
11
7
− 
0.0
09
− 
0.0
13
0.8
92
36
− 
0.0
20
0.0
96
0.5
79
81
− 
0.0
07
− 
0.0
73
0.5
16
Ea
tin
g j
etl
ag
 (h
)
11
7
− 
0.0
01
0.0
88
0.3
46
36
− 
0.0
09
0.1
42
0.4
09
81
− 
0.0
09
0.0
58
0.6
04
Ti
m
e f
ro
m
 m
id
sle
ep
 po
in
t t
o fi
rst
 fo
od
 in
tak
e (
h)
11
3
− 
0.0
05
0.0
63
0.5
05
35
0.0
57
0.2
91
0.0
90
78
− 
0.0
03
− 
0.1
01
0.3
80
Ti
m
e f
ro
m
 la
st 
fo
od
 in
tak
e t
o m
id
sle
ep
 po
in
t (
h)
11
3
− 
0.0
09
0.0
08
0.9
36
35
− 
0.0
03
− 
0.1
64
0.3
47
78
0.0
07
0.1
41
0.2
18
H
ig
h-
de
ns
ity
 li
po
pr
ot
ei
n 
ch
ol
es
te
ro
l
Ea
tin
g w
in
do
w 
(h
)
11
7
0.0
48
0.2
38
0.0
10
36
0.1
62
0.4
31
0.0
09
81
0.0
08
0.1
41
0.2
08
Ca
lo
ric
 m
id
po
in
t (
h)
11
7
− 
0.0
06
0.0
54
0.5
60
36
0.0
13
0.2
02
0.2
37
81
− 
0.0
12
− 
0.0
21
0.8
55
Ea
tin
g j
etl
ag
 (h
)
11
7
− 
0.0
09
0.0
04
0.9
64
36
0.0
04
− 
0.1
79
0.2
96
81
− 
0.0
03
0.0
99
0.3
79
Ti
m
e f
ro
m
 m
id
sle
ep
 po
in
t t
o fi
rst
 fo
od
 in
tak
e (
h)
11
3
0.0
45
− 
0.2
31
0.0
14
35
0.1
37
− 
0.4
03
0.0
16
78
0.0
21
− 
0.1
84
0.1
06
Ti
m
e f
ro
m
 la
st 
fo
od
 in
tak
e t
o m
id
sle
ep
 po
in
t (
h)
11
3
− 
0.0
08
− 
0.0
27
0.7
74
35
− 
0.0
30
− 
0.0
07
0.9
66
78
− 
0.0
11
0.0
41
0.7
21
Lo
w
-d
en
si
ty
 li
po
pr
ot
ei
n 
ch
ol
es
te
ro
l
Ea
tin
g w
in
do
w 
(h
)
11
7
− 
0.0
01
− 
0.0
88
0.3
45
36
− 
0.0
01
− 
0.1
67
0.3
29
81
− 
0.0
11
− 
0.0
39
0.7
30
Ca
lo
ric
 m
id
po
in
t (
h)
11
7
− 
0.0
08
− 
0.0
20
0.8
30
36
− 
0.0
29
− 
0.0
08
0.9
63
81
− 
0.0
12
− 
0.0
27
0.8
12
Ea
tin
g j
etl
ag
 (h
)
11
7
0.0
00
0.0
94
0.3
13
36
− 
0.0
03
0.1
61
0.3
49
81
− 
0.0
10
0.0
53
0.6
37
Ti
m
e f
ro
m
 m
id
sle
ep
 po
in
t t
o fi
rst
 fo
od
 in
tak
e (
h)
11
3
0.0
11
0.1
40
0.1
39
35
0.0
77
0.3
23
0.0
58
78
− 
0.0
13
0.0
07
0.9
53
Ti
m
e f
ro
m
 la
st 
fo
od
 in
tak
e t
o m
id
sle
ep
 po
in
t (
h)
11
3
− 
0.0
07
− 
0.0
41
0.6
65
35
− 
0.0
08
− 
0.1
46
0.4
01
78
− 
0.0
12
0.0
32
0.7
84
H
O
M
A-
IR
Ea
tin
g w
in
do
w 
(h
)
11
7
0.1
00
− 
0.3
28
< 
0.0
01
*
36
0.3
48
− 
0.6
05
< 
0.0
01
*
81
− 
0.0
02
− 
0.1
01
0.3
69
Ca
lo
ric
 m
id
po
in
t (
h)
11
7
− 
0.0
02
0.0
79
0.3
99
36
− 
0.0
29
0.0
15
0.9
35
81
0.0
05
0.1
31
0.2
45
Ea
tin
g j
etl
ag
 (h
)
11
7
− 
0.0
02
0.0
80
0.3
93
36
− 
0.0
22
0.0
85
0.6
23
81
− 
0.0
07
0.0
76
0.4
99
2310 European Journal of Nutrition (2023) 62:2303–2315
1 3
Discussion
Our findings suggest that meal timing is not related to 
anthropometry or body composition parameters in young 
adults. Similarly, caloric midpoint, eating jetlag and the 
time from last food intake to midsleep point are not associ-
ated with cardiometabolic risk factors. On the other hand, 
our results show that a longer daily eating window and a 
shorter time from midsleep point to first food intake (i.e., 
earlier first food intake in a 24 h cycle) are associated with 
a healthier cardiometabolic profile (i.e., lower HOMA-
IR and cardiometabolic risk score) in young men. These 
results refute our previous hypothesis, by which a shorter 
eating window would be associated with a healthier status. 
However, these findings confirm previous evidence that 
eating early in alignment with circadian rhythms may play 
an important role in cardiometabolic health.
The habitual daily eating window in adults in modern 
societies is ≥ 12 h which is abnormal from an evolutionary 
perspective [4, 31]. The eating pattern of our hunter-gath-
erer ancestors was characterized by eating sporadically 
with inter-meal intervals that depend upon the availability 
of food sources which included extended fasting period 
[4]. Moreover, clinical trials have also revealed that reduc-
ing the daily eating window results in body weight loss 
and improvement in cardiometabolic health in adults with 
overweight or obesity [20–22]. Therefore, we hypothe-
sized that a shorter daily eating window would be associ-
ated with better body composition and cardiometabolic 
health in our cohort of young adults. However, interpreting 
the cross-sectional relationship between meal timing and 
health status is a complex task, as it can be influenced 
by several factors. Indeed, contradictory findings have 
been reported [9–14, 17, 23, 31, 39, 40]. For instance, we 
found that the daily eating window is not related to body 
composition, which could be partly explained by the low 
variability (standard deviation of 1.5 h) of the daily eat-
ing window in our sample. Our results concur with other 
studies in young adults [12], middle-aged adults [31] and 
adults with prediabetes [39]. In contrast, others reported 
that a longer daily eating window is related to a lower 
BMI [40] and lower fat mass percentage in adults [11]. A 
confounding factor may be the consumption of breakfast; 
epidemiological data have constantly shown that breakfast 
consumption is associated with lower BMI and adiposity 
[41–43]. Greater energy intake, lower diet quality, lower 
physical activity levels and misalignment of circadian 
rhythms are thought to be the physiological mechanisms 
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adults, we observed that adherence to the Mediterranean 
diet and Mediterranean diet quality was lower in break-
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2311European Journal of Nutrition (2023) 62:2303–2315 
1 3
A B
C D
E
Fig. 1  Scatterplots of the associations of meal timing with cardio-
metabolic risk score (calculated for each sex based on waist circum-
ference, blood pressure, plasma glucose, high-density lipoprotein 
cholesterol, and triglyceride concentrations, see methods for further 
details) in young adults. Adjusted R2, β standardized regression coef-
ficients and p values are obtained from single linear regressions
2312 European Journal of Nutrition (2023) 62:2303–2315
1 3
breakfast consumption influences the relationship between 
meal timing and body composition in women. Specifically, 
a longer daily eating window was associated with worst 
body composition (i.e., higher BMI and VAT mass) in 
women breakfast consumers which is in line with our pre-
vious hypothesis and with other study findings in middle-
aged women [16] and in older adults [14].
The daily eating window does not take into account 
the time distribution of food intake, which may be a more 
important factor than its duration. Indeed, Xiao et al. [14] 
observed that when a short daily eating window occurs 
early in the day is associated with a reduced likelihood of 
being overweight or obese in older adults; while, when a 
short daily eating window occurs late in the day is related 
to a higher likelihood of being overweight or obese. For 
this reason, other meal timing variables have been pro-
posed. One of them is the caloric midpoint, that links 
meal timing to clock time. A study conducted in Spanish 
middle-aged adults found that late eaters (i.e., caloric mid-
point after 3 pm) had higher BMI, fat mass percentage and 
waist circumference than early eaters [9], findings that par-
tially concur with those observed in Brazilian young adults 
[13]. In our study, the caloric midpoint was negatively 
associated with fat mass percentage in men but became 
non-significant after adjusting for multiplicity. Another 
meal-timing variable is eating jetlag, which indicates the 
variability of the eating midpoint between working and 
non-working days, being informative about the circadian 
misalignment. For example, Zerón-Rugerio et  al. [17] 
showed that a greater eating jet lag is related to higher 
BMI in Spanish and Mexican young adults. Conversely, 
we observed no association between eating jetlag and body 
composition in our cohort of Spanish young adults.
Nonetheless, the caloric midpoint and eating jetlag use 
clock time to illustrate the timing of food intake, which 
fails to correctly characterize meal timing in relation to 
the internal circadian time [10, 23]. In this sense, McHill 
et al. [12] found that a long time from caloric midpoint 
to melatonin onset is associated with lower BMI and fat 
mass percentage. However, obtaining measures of dim-
light melatonin is not practical in large clinical trials; thus, 
it has been proposed to measure the timing of food intake 
relative to the sleep/wake cycle as a proxy for circadian 
time [10, 23, 24]. Using this methodology, two studies 
observed that a longer time period from dinner to mid-
sleep point or bedtime is associated with lower adiposity 
and BMI, respectively [10, 23]. In contrast, we found no 
relationship between the time from the midsleep point to 
first food intake or time from last food intake to midsleep 
point with anthropometry or body composition parameters, 
which concurs with another study [14]. These discrepan-
cies could be partially explained by the sample size and 
the assessment of meal and sleep timing.
Regarding cardiometabolic health, we found that a 
longer daily eating window and a shorter time from mid-
sleep point to first food intake are associated with healthier 
cardiometabolic profile in men, which agree with previous 
studies. Concretely, a longer daily eating window has been 
related to decreased insulin, total cholesterol, LDL-C and 
increased HDL-C in middle-aged adults [44]. In addition, 
in concordance with our results, previous studies have 
found that late eating is related to worst cardiometabolic 
health [9–16]. Circadian misalignment between the central 
clock (controlled by external light) and peripheral clocks 
(regulated by eating, physical activity and sleep among 
other factors) is one of the mechanisms that may explain 
these results [6, 7]. In addition, glucose tolerance is higher 
in the biological morning, which appear to be driven by 
diurnal variations in β-cell responsiveness, peripheral 
insulin sensitivity, insulin clearance, and glucose effec-
tiveness [6, 7]. Skeletal muscle fatty acid oxidation and 
the thermic effect of food are also higher in the biological 
morning or around noon, which implicates earlier in the 
daytime is optimal for eating whereas night time is better 
for fasting and sleep [6, 7]. Our findings could also be 
partly explained by the consumption of breakfast; epide-
miological data have systematically reported that breakfast 
skipping (i.e., shorter and later daily eating window) is 
associated with worst cardiometabolic health [41, 42]. As 
mentioned above, this association could be driven in part 
by lower diet quality and lower physical activity levels 
[41–43]. Indeed, in our cohort of young men, we observed 
that breakfast skippers had a more inflammatory diet and 
tended to be less physically active. Lastly, others [16] and 
we did not observe a relationship between meal timing and 
cardiometabolic risk factors in women, which is intriguing 
and requires further investigation.
Our findings should be interpreted with caution because 
the current study suffers from several limitations. Firstly, 
the cross-sectional design prevents establishing causal 
relationships. Secondly, we measured the clock timing of 
food intake via three non-consecutive 24 h recalls. Thirdly, 
the study population comprised young and healthy adults, 
limiting the generalizability of the results to older or meta-
bolically compromised individuals. Lastly, the statistical 
power of the study may have been insufficient to com-
prehensively investigate potential sex differences in the 
relationship between meal timing with body composition 
and cardiometabolic risk factors. Despite these limitations, 
this study is one of the pioneers in investigating the rela-
tionship between meal timing (characterized relative to 
the sleep/wake cycle) and cardiometabolic risk factors. 
Further well-designed long-term prospective studies and 
randomized controlled trials are needed to elucidate the 
effects of meal timing on body composition and cardio-
metabolic health.
2313European Journal of Nutrition (2023) 62:2303–2315 
1 3
Conclusion
Meal timing is not related to either anthropometry or body 
composition parameters in young adults. Similarly, caloric 
midpoint, eating jetlag and the time from last food intake 
to midsleep point are not associated with cardiometabolic 
risk factors. Nonetheless, a longer daily eating window and 
a shorter time from midsleep point to first food intake are 
associated with better cardiometabolic health in men. These 
results confirm previous evidence that eating early in align-
ment with circadian rhythms may improve cardiometabolic 
health. Nutrition strategies aimed to improve cardiometa-
bolic health may contemplate advancing the timing of food 
intake. Further well-designed studies are needed to confirm 
these findings and unravel potential mechanisms.
Supplementary Information The online version contains supplemen-
tary material available at https:// doi. org/ 10. 1007/ s00394- 023- 03141-9.
Acknowledgements This study is part of a Ph.D. Thesis conducted in 
the Biomedicine Doctoral Studies of the University of Granada, Spain.
Author contributions MD-M: Formal analysis, Writing—Original 
Draft. FMA: Conceptualization, Methodology, Investigation, Writ-
ing—Review & Editing. GS-D: Conceptualization, Methodology, 
Investigation, Project administration, Writing—Review & Editing. 
EM-R: Investigation, Writing—Review & Editing. FJA-G: Investiga-
tion, Writing—Review & Editing. IL: Writing—Review & Editing. 
JRR: Conceptualization, Methodology, Supervision, Funding acquisi-
tion, Writing—Review & Editing.
Funding Funding for open access publishing: Universidad de Granada/
CBUA. This study was funded by the Spanish Ministry of Economy 
and Competitiveness via the Fondo de Investigación Sanitaria del Insti-
tuto de Salud Carlos III (PI13/01393) and PTA 12264-I, by Retos de 
la Sociedad (DEP2016-79512-R), European Regional Development 
Funds (ERDF), Spanish Ministry of Universities (FPU 13/03410, 
FPU14/04172 and FPU18/03357), the Fundación Iberoamericana 
de Nutrición (FINUT), the Redes Temáticas de Investigación Coop-
erativa RETIC network (Red SAMID RD16/0022), the AstraZeneca 
HealthCare Foundation, Junta de Andalucía, Consejería de Transfor-
mación económica, Industria, Conocimiento y Universidades (A-CTS-
516-UGR20), the University of Granada's Plan Propio de Investigación 
2016—Excellence actions: Unit of Excellence on Exercise and Health 
(UCEES), the Junta de Andalucía, Consejería de Conocimiento, Inves-
tigación y Universidades (ERDF, SOMM17/6107/UGR), and a post-
doctoral grant from the Fundación Alfonso Martín Escudero.
Data availability The deidentified participant data that support the find-
ings of this study are available from the corresponding author upon 
reasonable request.
Declarations 
Conflict of interest The authors declared no conflict of interest.
Ethical standards The study protocol and written informed consent 
procedures contemplated the latest revised Declaration of Helsinki 
(2013), and they were approved by the Human Research Ethics Com-
mittee of the University of Granada (Nº 924) and the Servicio Andaluz 
de Salud (CEI-Granada, nº 0838-N-2017). All participants provided 
written informed consent in order to be included in the study.
Open Access This article is licensed under a Creative Commons Attri-
bution 4.0 International License, which permits use, sharing, adapta-
tion, distribution and reproduction in any medium or format, as long 
as you give appropriate credit to the original author(s) and the source, 
provide a link to the Creative Commons licence, and indicate if changes 
were made. The images or other third party material in this article are 
included in the article's Creative Commons licence, unless indicated 
otherwise in a credit line to the material. If material is not included in 
the article's Creative Commons licence and your intended use is not 
permitted by statutory regulation or exceeds the permitted use, you will 
need to obtain permission directly from the copyright holder. To view a 
copy of this licence, visit http:// creat iveco mmons. org/ licen ses/ by/4. 0/.
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Authors and Affiliations
Manuel Dote‑Montero1  · Francisco M. Acosta1,2,3,4 · Guillermo Sanchez‑Delgado1,5,6,10 · Elisa Merchan‑Ramirez1 · 
Francisco J. Amaro‑Gahete1,6,10 · Idoia Labayen6,7,8,9 · Jonatan R. Ruiz1,6,10
1 Department of Physical Education and Sports, Faculty 
of Sports Science, Sport and Health University Research 
Institute (iMUDS), University of Granada, Carretera de 
Alfacar s/n, 18071 Granada, Spain
2 Turku PET Centre, University of Turku, Turku, Finland
3 Turku PET Centre, Turku University Hospital, Turku, 
Finland
4 InFLAMES Research Flagship Center, University of Turku, 
Turku, Finland
5 Division of Endocrinology, Department of Medicine, 
Centre de Recherche du Centre Hospitalier Universitaire 
de Sherbrooke, Université de Sherbrooke, Sherbrooke, QC, 
Canada
6 CIBER de Fisiopatología de la Obesidad y Nutrición 
(CIBEROBN), Instituto de Salud Carlos III, Granada, Spain
7 Institute for Sustainability and Food Chain Innovation 
(ISFOOD), University of Navarra, Pamplona, Spain
8 Navarra Institute for Health Research, IdiSNA, Pamplona, 
Spain
9 Department of Health Sciences, Public University 
of Navarra, Campus de Arrosadia, Pamplona, Spain
10 Instituto de Investigación Biosanitaria, Ibs.Granada, Granada, 
Spain