Eating Chocolate at the Right Time Benefits the Circadiam Sytem and Metabolic Efficiency.

Not Applicable

Completed

Conditions: Menopause

Interventions: Behavioral: Evening Chocolate Timing
Behavioral: No Chocolate Timing
Behavioral: Morning Chocolate Timing

Registration Number: NCT03949803

Lead Sponsor: Universidad de Murcia

Brief Summary: The purpose of this investigation is to test the hypothesis that in humans, eating a relatively big amount of chocolate at the wrong time (bedtime) may disrupt our circadian system (change the circadian phase), while taking this same amount of chocolate in the morning (wake up condition) may synchronize it. Other related factors may be also affected such as total body weight and body fat, dietary habits (total energy intake and macronutrient distribution), the timing of food intake and of sleep, daily rhythms of TAP, microflora composition and postprandial glycemia.

Detailed Description: Recent studies suggest that not only "what" the people eat, but also "when" the people eat may have a significant role in obesity treatment and in the regulation of the circadian system. Thus, the hypothesis of this study is eating a relatively big amount of chocolate at the wrong time, bedtime may affect:

1. Metabolism: resting energy expenditure, corrected resting energy respiratory quotient (RQ)

2. Glucose metabolism

3. Total weight loss

4. Food intake, total energy intake, and type of food

5. Microflora (feces)

6. Mood

7. Disrupt our circadian system 7a) Changes in Temperature, Actimetry, and Position 7b) Electrocardiogram (ECG) 7c) Melatonin (two points) cortisol rhythm (three points) While having this same amount of chocolate in the morning (wake up condition) may synchronize it.

Other related factors may be also affected such as total body weight and body fat, dietary habits (total energy intake and macronutrient distribution), the timing of food intake and of sleep, daily rhythms of TAP, microflora composition and postprandial glycemia.

19 women (postmenopausal) following the habitual dietary habits of participants (ad libitum) will have 30% of the habitual total daily calories in chocolate of participants (Nestle, "chocolate with milk") during two consecutive weeks each under three conditions: eating chocolate within 1 hour of habitual wake-time, eating chocolate within 1 hours of habitual bedtime, or eating no chocolate. No other chocolate (i.e., none at all in control and in the washout weeks).

The protocol will be a randomized, cross-over design, with a 1-week washout between each condition.

During the 14 days in each condition, the participants will record sleep and activity schedules by dairy, food intake and food timing by phone application, daily rhythms of wrist temperature, activity and position (TAP).

Recruitment & Eligibility

Status: COMPLETED

Sex: Female

Target Recruitment: 19

Inclusion Criteria

Healthy women
Age: between 45 and 65 year of age
Caucasian
Menopause

Exclusion Criteria

Pre-menopause women
Endocrine (Diabetes mellitus or others), renal, hepatic, cancer or psychiatric disorders
Receiving any pharmacologic treatment other than oral contraceptives
Bulimia diagnosis, prone to binge eating
Pregnancy

Study & Design

Study Type: INTERVENTIONAL

Study Design: CROSSOVER

Arm && Interventions

Group	Intervention	Description
Evening Chocolate	Evening Chocolate Timing	Test the if Chocolate Timing before bedtime changes the metabolism
No Chocolate (Control)	No Chocolate Timing	Test the if no Chocolate Timing may affect the metabolism
Morning Chocolate	Morning Chocolate Timing	Test the if Chocolate Timing in the morning changes the metabolism

Primary Outcome Measures

Name	Time	Method
Total Body Weight	Baseline and after 2 weeks in each condition	The investigators will measure total body weight after each condition (no chocolate, Morning Chocolate, Night Chocolate)
Resting Energy Expenditure (Indirect Calorimetry)	Baseline and after 2 weeks in each condition	The investigators will measure by indirect calorimetry the resting energy expenditure after each condition (no chocolate, Morning Chocolate, Night Chocolate) Changes in Resting Energy Expenditure (Indirect Calorimetry) were determined by changes in resting energy expenditure occurred in the participants between baseline and after 2 weeks in each of the three different experimental conditions. Oxygen (O2) consumption (mL/min) and carbon dioxide (CO2) production (mL/min) were measured. The Respiratory Quotient (RQ) was calculated out of O2 consumption (mL/min) and CO2 production (mL/min). Energy expenditure values (kcal/day) were calculated according to the Weir equation: Metabolic rate (kcal per day) = 1440 (3.9 VO2 + 1.1 VCO2)\*

Secondary Outcome Measures

Name	Time	Method
Fragmentation of Wrist Temperature (WT) Daily Rhythm	Baseline and after 2 weeks in each condition	The investigators will measure the fragmentation parameter derived from wrist temperature after each condition (no chocolate, Morning Chocolate, Night Chocolate). Intradaily variability (IV) characterizes the rhythm fragmentation. Changes in daily rhythm of wrist temperature fragmentation were measured by changes in the fragmentation parameter derived from wrist temperature along the two weeks of intervention in each of the three conditions. The participants wore a wristwatch during the last 7 days of each condition on the non-dominant wrist to record body temperature and activity. Wrist temperature parameters were measured to represent the wrist body temperature pattern, which provides an objectively measure of sleep and siesta (together with activity rhythm). A low fragmentation involves a low intraday rhythmicity of the circadian rhythm. Its values oscillated between 0, when the wave was perfectly sinusoidal, and 2, when the wave was as Gaussian noise.
Regularity of Wrist Temperature (WT) Daily Rhythm	Baseline and after 2 weeks in each condition	The investigators will measure the regularity parameter derived from wrist temperature after each condition (no chocolate, Morning Chocolate, Night Chocolate). RegularIty is measured as interdaily stability (IS), that means rhythm stability over different days; it varied between 0 for Gaussian noise to 1 for perfect stability, where the rhythm repeated itself exactly day after day. Changes in daily rhythm of wrist temperature regularity were measured by changes in the regularity parameter derived from wrist temperature along the two weeks of intervention in each of the three conditions. A regular rhythm means recurring at uniform intervals. The participants wore a wristwatch during the last 7 days of each condition on the non-dominant wrist to record body temperature and activity. Wrist temperature parameters were measured to represent the wrist body temperature pattern, which provides an objectively measure of sleep and siesta (together with activity rhythm).
Amplitude of Wrist Temperature (WT) Daily Rhythm	Baseline and after 2 weeks in each condition	The investigators will measure the amplitude parameter derived from wrist temperature after each condition (no chocolate, Morning Chocolate, Night Chocolate). In the cosinor analysis to characterize the WT rhythm, we calculate amplitude (difference between the maximum \[or minimum\] value of the cosine function and mesor) Changes in daily rhythm of wrist temperature amplitude were measured by changes in the amplitude parameter derived from wrist temperature along the two weeks of intervention in each of the three conditions. The participants wore a wristwatch during the last 7 days of each condition on the non-dominant wrist to record body temperature and activity. Wrist temperature parameters were measured to represent the wrist body temperature pattern, which provides an objectively measure of sleep and siesta (together with activity rhythm). A healthy circadian rhythm is considered as the one with high amplitude.
Changes in Microbiota Diversity (Inverse Simpson Index)	Baseline and after 2 weeks in each condition	Microbiota diversity changes were measured using Inverse Simpson Index, accounting for both species richness (number of species) and evenness (distribution of species). A higher Inverse Simpson Index indicates greater diversity because it reduces the impact of dominant species and gives more weight to rarer species. Unlike the regular Simpson Index (which decreases as diversity increases), the inverse form is more intuitive: higher values mean a more diverse microbiota. This index is widely used in microbiome studies to compare microbial diversity across different samples, such as in health versus disease states.
Concentration of Total Short-chain Fatty Acids (SCFAs)	After 2 weeks in each condition	Short-chain fatty acids (SCFAs) in fecal samples were analyzed using gas-liquid chromatography (GLC) with a GC-FID system. Samples were mixed with NaOH, lyophilized, and homogenized with formic acid, methanol, and an internal standard (2-ethyl butyric acid). After ultrasonic treatment and centrifugation, the supernatant was filtered and injected into an Agilent 7890A GC system equipped with a Nukol capillary column. Helium was used as the carrier gas, and the FID temperature was set at 220°C. SCFAs were identified by comparing retention times with authentic standards, and quantification was performed using calibration curves with a high correlation (R² = 0.99). Total average values of SCFA (acetate, propionate, other minorities) in each condition were calculated.
Energy Intake	Baseline and after 2 weeks in each condition	The investigators will measure changes in total energy intake measured as kilocalories between baseline and after 2 weeks in each condition (no chocolate, Morning Chocolate, Night Chocolate) At baseline and during the 2 weeks of each condition, time-stamped photographs were captured with a cell phone app. Total energy intake during the 14 days of each condition was analyzed with the nutritional evaluation software (Grunumur 2.0 8).
Macronutrient Composition	Baseline and after 2 weeks in each condition	The investigators will measure macronutrient composition measured as and grams per day between baseline and after 2 weeks in each condition (no chocolate, Morning Chocolate, Night Chocolate). At baseline and during the 2 weeks of each condition, time-stamped photographs were captured with a cell phone app. Macronutrient composition during the 14 days of each condition was analyzed with the nutritional evaluation software (Grunumur 2.0 8).