Within-subject rhythmicity and stability of hunger, satiety, and physiological markers: insights from a five-day laboratory study in time-isolation conditions

Within-subject rhythmicity and stability of hunger, satiety, and physiological markers: insights from a five-day laboratory study in time-isolation conditions | bioRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-M677548'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search New Results Within-subject rhythmicity and stability of hunger, satiety, and physiological markers: insights from a five-day laboratory study in time-isolation conditions View ORCID Profile Xi Wang , Abhishek S. Prayag , Ni Tang , View ORCID Profile Yanlong Hou , View ORCID Profile Claude Gronfier , Tao Jiang doi: https://doi.org/10.1101/2025.10.10.681393 Xi Wang a Lyon Neuroscience Research Center (CRNL), ENES team, Inserm UMRS 1028, CNRS UMR 5292, Université Claude Bernard Lyon 1, Université de Lyon , 69000, Lyon, France b Lyon Neuroscience Research Center (CRNL), Waking team, Inserm UMRS 1028, CNRS UMR 5292, Université Claude Bernard Lyon 1, Université de Lyon , 69000, Lyon, France Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Xi Wang Abhishek S. Prayag b Lyon Neuroscience Research Center (CRNL), Waking team, Inserm UMRS 1028, CNRS UMR 5292, Université Claude Bernard Lyon 1, Université de Lyon , 69000, Lyon, France Find this author on Google Scholar Find this author on PubMed Search for this author on this site Ni Tang b Lyon Neuroscience Research Center (CRNL), Waking team, Inserm UMRS 1028, CNRS UMR 5292, Université Claude Bernard Lyon 1, Université de Lyon , 69000, Lyon, France Find this author on Google Scholar Find this author on PubMed Search for this author on this site Yanlong Hou b Lyon Neuroscience Research Center (CRNL), Waking team, Inserm UMRS 1028, CNRS UMR 5292, Université Claude Bernard Lyon 1, Université de Lyon , 69000, Lyon, France Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Yanlong Hou Claude Gronfier b Lyon Neuroscience Research Center (CRNL), Waking team, Inserm UMRS 1028, CNRS UMR 5292, Université Claude Bernard Lyon 1, Université de Lyon , 69000, Lyon, France Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Claude Gronfier For correspondence: tao.jiang{at}univ-lyon1.fr claude.gronfier{at}inserm.fr Tao Jiang a Lyon Neuroscience Research Center (CRNL), ENES team, Inserm UMRS 1028, CNRS UMR 5292, Université Claude Bernard Lyon 1, Université de Lyon , 69000, Lyon, France Find this author on Google Scholar Find this author on PubMed Search for this author on this site For correspondence: tao.jiang{at}univ-lyon1.fr claude.gronfier{at}inserm.fr Abstract Full Text Info/History Metrics Supplementary material Preview PDF Abstract Hunger and satiety are interoceptions involved in the control of food intake. How they change across the day and from one day to the next, as well as their relationships with physiological parameters, remain poorly understood. In a five-day laboratory study, 20 healthy male participants (24.2 ± 3.3 years) were given 3 meals per day (breakfast, lunch, dinner), at times based on each individual’s internal time. Subjects had a 8-h sleep opportunity at night recorded by polysomnography, during which time they were exposed to 4 different light intensities (0, 3, 8, or 20 lux). Hunger and satiety were assessed before and after each meal. Heart rate was measured continuously and glucose levels were sampled every 15 minutes. We found that: 1) hunger and satiety varied dynamically with the lowest levels at breakfast and the highest at lunch and dinner ( p < 0.001); 2) Hunger and satiety were subject-dependent, and keep stable across the five experimental days; 3) heart rate was higher after than before meals ( p < 0.0001), and higher around breakfast than dinner; 4) Glycemia before and after meals was respectively stable throughout the day and over the five-day study; 5) interoception was correlated with fluctuations in heart rate, glucose levels and sleep duration. Overall, our results reveal that hunger and satiety perception are individual characteristics. To better understand the role of interoception in food intake control, both hunger and satiety should be measured, but separately in their respective states. 1. Introduction Food intake is a critical behavior for living organisms because it satisfies energy needs and maintains internal homeostasis. Interoception, the process by which internal physiological states (e.g., the body’s energy status) are sensed and interpreted, plays a central role in regulating appetite status and controlling the behaviors necessary for establishing and maintaining internal balance. The two main interoceptions related to food intake are hunger and satiety. They have long been considered as the most important biological drivers of eating behavior. Hunger is the body’s natural response to energy deficiency, and is characterized by sensations such as a pit in the stomach, gurgling, or cramping. These sensations signal to the brain that the body is running low on energy and requires calories. In contrast, satiety reflects a state of fullness, that develops gradually during the course eating and peaks 20-30 minutes afterward ( Blundell et al., 2010 ; Green et al., 1997 ; Holt et al., 1995; Mela, 2006 ). As satiety progresses, the motivation to eat decreases until food intake ceases, reflecting energy repletion ( Bellisle, 2005 ; Woods et al., 1998 ). The alternation between hunger and satiety is influenced by both physiological processes as well as external factors, such as food products, cognition, socioculture, and the environment, which determine the choice, amount, timing, frequency and rhythm of food intake ( Bellisle, 2005 ; Berthoud et al., 2020 ; Broussard et al., 2016 ; Kabir et al., 2018 ; Marcelino et al., 2001 ; McNeil et al., 2017 ; Paoli et al., 2019 ). However, the temporal variation of hunger and satiety over the course of a day, as well as over several consecutive days, remains underexplored. Furthermore, the interactions between these sensations and physiological markers, such as heart rate and glucose levels, are unclear. The fluctuation of hunger and satiety around a meal is well documented. Hunger peaks before a meal and then decreases and disappears afterward, while satiety does the opposite. Several chronobiological studies have examined these fluctuations over the course of a day and have demonstrated that they are rhythmic. Hunger peaks in the evening ( Sargent et al., 2016 ) or in the morning ( Qian et al., 2019 ; Scheer et al., 2013 ), depending on the study design. One study found that satiety peaks at the beginning of the day ( Sargent et al., 2016 ). While the rhythmicity of hunger and satiety has been documented, few studies have examined these sensations in the same individuals over several days, at multiple times (meals) throughout the day, and under an ecological protocol. In addition, the question of whether an individual’s interoception varies from one day to the next remains unanswered. This knowledge gap is particularly relevant in an obesogenic environment where constant food availability increasingly emphasizes exteroception (external sensory cues) over interoception. As exteroception plays a more dominant role in controlling food intake in an obesogenic environment, the precise contribution of interoception to controlling food intake becomes even less clear, and thus warrants further investigation. At the level of conscious perception, hunger and satiety may be subjective. Some people base their perceptions on overall discomfort, while others base them on stomach fullness. More objective physiological measures may be useful for understanding the nature of these perceptions. Heart rate (HR) reflects autonomic nervous system activity ( Nederkoorn et al., 2000 ), however, the relationship between HR and the timing of hunger and satiety measures is rarely explored. Glucose levels are an indicator of energy availability and should be closely related to hunger and satiety. Decreases in blood glucose levels before a meal are often associated with the onset of hunger, while increases in glucose levels after a meal contribute to the onset of satiety ( Campfield et al., 1985 ; Campfield & Smith, 1990a ; Louis-Sylvestre, 1976 ). Understanding the interplay between glucose dynamics and hunger or satiety sensations for different meals (e.g., breakfast, lunch and dinner) could provide valuable insights into the interaction between metabolic signals and interoception. For instance, some studies suggest that glucose responses are more pronounced after breakfast than after other meals,, possibly due to circadian influences on glucose metabolism ( Van Cauter et al., 1997 ). However, the relationship between glucose patterns and hunger or satiety at different times of the day remains unclear, especially with repeated daily assessments. Hunger and satiety are also both closely linked to sleep. Disruptions in sleep quality or duration can impair the perception of hunger and satiety. This can lead to increased resting energy expenditure ( Markwald et al., 2013 ), an altered food preference for energy-dense foods ( McNeil et al., 2017 ), and greater overall food consumption ( Brondel et al., 2010 ). Additionally, light as the primary cue that synchronizes the circadian clock and the sleep-wake cycle, affects many physiological functions ( Prayag et al., 2019 ). Nevertheless, the potential influence of nocturnal light exposure on food intake through interoception (hunger and satiety) remains unexplored. The current study aimed to investigate: 1) characteristics of interoception (hunger and satiety) related to food intake and how they evolve over one day and several days, and 2) physiological patterns, such as heart rate and glucose levels, in relation to the hunger and satiety sensations. 3) the effect of low-intensity light exposure at night on interoception the next day and whether this effect is secondary to light-induced sleep duration. 2. Material and Methods 2.1 Participants Twenty healthy male participants (age 24.2 ± 3.3 years) were recruited for a five-day inpatient laboratory study. Each participant completed several questionnaires: general health, sleep quality (Pittsburgh Sleep Quality Questionnaire, PSQI, Buysse et al., 1989 ); chronotype typology ( Horne & Ostberg, 1976 ), and Munich Chronotype Questionnaire, MCTQ, Roenneberg et al., 2003 ), depression (Beck Depression Inventory, BDI, Beck et al., 1961 )) and food intake behavior (Eating Attitude Test, Garner & Garfinkel, 1979 ). The inclusion criteria for participants were: PSQI score ≤5, Horne & Ostberg score between 31 and 73, BDI score ≤10, and Eating Attitude Test score ≤ 20. Participants were excluded if they had performed shift work or traveled across time zones in the previous two months. They were instructed to maintain a consistent sleep/wake schedule for an average of two weeks prior to the laboratory session, with a deviation of less than 30 minutes from the target bed and wake times. This was verified using both a daily sleep diary and an actimeter (ActTrust, Condor Instruments, São Paulo, Brazil) worn on the non-dominant wrist. Participants also completed light and food exposure diaries. 2.2. Study design Participants were admitted to the laboratory on Monday and were discharged on Friday evening (see Figure 1a ). Download figure Open in new tab Figure 1. Illustration of the 5-day experimental protocol in the laboratory. a) Subjects entered the laboratory on Monday, two hours after their habitual waketime, and were discharged on Friday at their habitual bedtime. Three meals were provided each day (except Monday because subjects arrived in the laboratory after their breakfast at home): breakfast (B), lunch (L), and dinner (D). Sleep times were scheduled at each subject’s habitual sleep times (here, bedtime and wake time are set at 2330 and 0730, respectively). b) The pre-sensory tests were conducted 15 minutes before breakfast and lasted 10 minutes and 25 minutes before lunch and dinner, and lasted 20 minutes. Participants started with a 1-minute electrocardiogram (ECG1) measurement, followed by an assessment of hunger (H1) and satiety (S1). Next, participants underwent olfactory and visual tests. At the end of the test, a new self-evaluation of hunger (H2) and satiety (S2) was performed, and a final one-minute ECG-2 was taken. The post-meal test was performed 20 minutes after the end of meal and followed the same procedure as the pre-meal measurements. Experimental and light conditions During the study, participants were in a time-isolation conditions (deprived of all time cues, such as natural light, watches, clocks, TV, computers, smartphones, and radios), and were not allowed to lie down in bed or take a nap during the day. Physical activity was limited to walking around the room and stretching, in order to avoid significant increases in body temperature (more than 0.5°C). Light intensity was controlled both during the day and at night. During the day, from awakening until bedtime, the light intensity was maintained at 90 lux (average angle of gaze). At night, from bedtime to wake time (eight hours), low artificial light at night was set at one of four light conditions: 0, 3, 8, or 20 lux. Each participant was exposed to all four light conditions (one light condition per night). The order of light presentation was counterbalanced according to a 4x4 Latin square design. Individual scheduling Sleep, mealtimes and all other measures and recordings were calculated and scheduled individually, based on each participant’s habitual sleep-wake schedule at home. Habitual sleep times were calculated for each subject using the following procedure: First, we averaged the bedtimes and wake-up times of the last seven sleep episodes documented by each subject in their sleep diary and verified with actimetry. Second, we found the mid-sleep time between the average bedtime and wake time. Third, we subtracted four hours from the mid-sleep time to obtain the average habitual bedtime, and added four hours to the mid-sleep time to obtain the average habitual wake time ( Daguet et al., 2022 ). Mealtimes were set at the habitual wake time (HWT) plus 50 minutes for breakfast, plus 320 minutes for lunch, and plus 720 minutes for dinner (corresponding to 7:50 a.m., 12:20 p.m., and 7:00 p.m., respectively, for who habitually wakes up at 7:00 a.m.). Meals For breakfast (B), participants selected their first laboratory breakfast from a predefined list and were required to maintain this, choice for consecutive mornings. Available options s included dairy products (milk, yogurt, and cheese), fruit juices (apple and orange), fresh fruit (apples and oranges), bread, crispbread, butter, jam, cereal, and caffeine-free coffee. Lunch (L) and dinner (D) were provided by the hospital restaurant which had rotating menus on a quarterly basis with quasi equal calories. A typical meal included a starter of either a green salad with dressing or a mixed vegetable salad (tomato, potato etc., with dressing); a main course of meat, fish, or eggs with vegetables; a side dish (e.g., rice or noodles with bread); a dessert (e.g., an orange, a banana, an apple, a fruit salad, a chocolate mousse, a compote), and a dairy product (e.g., cheese, yogurt, or pudding). The meals were tailored to the participants’ age group and nutritional needs. They were designed to provide an average daily caloric intake of ∼2430 kcal per day, distributed as follows: ∼55% carbohydrates, ∼15% protein, and ∼30% fat. The estimated average calorie intake per meal was ∼600 kcal for breakfast, 915 kcal for lunch and 915 kcal at for dinner. Breakfast energy distribution was as follows: ∼90 kcal from fruit juices, ∼170 kcal from dairy products, ∼190 kcal from cereals, bread or crispbread, ∼35 kcal from fresh fruits, ∼85 kcal from butter, and ∼30 kcal from other food items. Lunch and dinner followed a similar structure, with an additional dairy product served at dinner. The calorie contributions were approximately ∼40 kcal for a mixed salad and vinaigrette, ∼540 kcal for the main course, ∼200 kcal for the side dish, ∼65 kcal for the dairy product, and ∼70 kcal for dessert. Participants were informed that they could stop eating when they felt full. Each plate was weighed before and after each meal. Participants finished almost all of their food at each meal. 2.3 Measurements of hunger and satiety and sensory tests During the day, the olfactory tests consisted of rating the three items (liking, wanting, and disgust) on a visual analog scale (VAS) for each of the 14 food odors. The visual tests consisted of rating the same three scales after viewing a picture corresponding to the food odors presented in the olfactory test. Both tests were administered six times per day, before and after each meal. For the sessions before meals, the sensory tests started 15 minutes before breakfast (B) and lasted 10 minutes, and started 25 minutes before lunch (L) or dinner (D) and lasted 20 minutes. The same sensory tests were performed 25 minutes after the end of each of the three daily meals (see Figure 1b ). This daily measurement pattern was repeated during the five-day study. Participants sat 60 cm in front of the computer screen and communicated with the experimenter via interphone. All sensory tests and instructions were provided via Superlab software (Cedrus, San Pedro, CA, version 5.0). The pre- and post-meal tests began with a one-minute baseline electrocardiogram (ECG) recording. During this time, participants were asked to remain calm and motionless while their heart rate (HR) was recorded. Self-assessments of hunger and satiety were conducted just before and just after each sensory test, using a computerized VAS ( Figure 1b ). 2.4 Measures 2.4.1 Hunger and satiety Participants rated their hunger and satiety levels using a Visual Analog Scale (VAS). A 30-cm-long line was displayed on the screen with the left end labeled with “not hungry at all”, or “not satiated at all”, and the right end labeled with “extremely hungry” or “extremely satiated.” Participants were asked to click on the line that corresponded to their level of hunger or satiety. The click points on the VAS were converted to numerical values ranging from 0 to 15, with 0 representing the leftmost point and 15 representing the rightmost point. 2.4.2 Heart rate Heart rate (HR) was recorded at a frequency of 256 Hz using a polysomnography (PSG) monitoring system (Vitaport-4 digital recorder, TEMEC Instruments, Kerkrade, Netherlands). Two electrodes were placed: one at the right infraclavicular fossa, and one at the V6 position. HR was analyzed from one minute before the first hunger assessment (H1) to one minute after the second satiety assessment (S2), and before (pre) and after (post) each meal (see Figure 1b ). The mean HR was calculated as the average HR before (ECG1) and after (ECG2) each sensory test. 2.4.3 Glucose Glucose concentrations were recorded using a continuous glucose monitoring (CGM) system (FreeStyle Libre 2). The CGM sensor was placed on the back of the upper arm on day one and glucose levels were obtained every 15 minutes throughout the five--days study. 2.4.4 Sleep Sleep polysomnography was recorded every night from habitual bedtime to habitual wake time using a Vitaport-4 digital recorder (TEMEC Instruments, Kerkrade, Netherlands). Four EEG (electroencephalogram) electrodes were placed on the scalp at the C3, C4, O1, and O2 sites, according to the 10-20 international system. They were referenced to the contralateral mastoids (A1 and A2). Two electro-oculogram) electrodes were placed at 1 cm from the external canthi of the eyes and cross-referenced. Three electromyogram electrodes were placed on the chin muscle (upper right, upper left, and lower center) and referenced (left-center and right-center). All signals were filtered using a high- and low-pass filter at 0.3 and 45 Hz, respectively, as well as a notch filter at 50 Hz. The sampling rate was 256 Hz. 2.5 Statistical analysis Statistical analyses were conducted using RStudio (version 2023.06.2, PBC, Boston, MA, USA). All analyses (hunger, satiety, heart rate, blood glucose, total sleep time, and sleep latency (SL) were based on raw data. To eliminate the “first day effect,” analyses were performed from Tuesday (Day 2) to Friday (Day 5), with data collected before and after each of the three daily meals. To ensure the reliability of the analyses, outliers were systematically identified and removed using a robust statistical approach. Specifically, outliers were detected using the interquartile range (IQR) method, in which outliers were defined as data points falling outside the acceptable range: below the first quartile (Q1 - 1.5 IQR ) or above the third quartile (Q3 + 1.5 IQR). Pre- and post-hunger/ satiety were defined as the mean of the two measures around a sensory test. For example, pre-hunger is the mean of H1 and H2 around the pre-meal sensory test (see Figure 1b ). To examine how meal type (B, L, D) or day (D2 to D5), affected interoception, we applied a linear mixed-effects model that considered meal type, and the interaction between day and meal type. When the interaction was significant, we examined the simple effect (the effect of meal type on a particular day or the effect of day on a particular meal type). When there was no interaction, we considered a one-factor model. To investigate whether different meals (breakfast, lunch, and dinner) influence interoception (hunger and satiety) during a day, we analyzed hunger and satiety separately under pre- and post-meal conditions. When then applied a linear mixed-effects model to the data. The model specification for pre-meal hunger, for example, was as follows: PreHunger <-lme (Hunger ∼ Meal type, random = ∼1 | participants) . This model analyzed how pre-meal hunger perception varied as a function of meal type. The random factor, participants, was included to account for individual variability due to uncontrolled factors ( Cheon & Mattes, 2024 ; Ruddick-Collins et al., 2019 ; Stevenson et al., 2015 ). Post hoc analysis with Bonferroni correction was used to adjust for multiple comparisons. The same analyses were performed for post-meal hunger, and pre- and post-meal satiety. To examine how interoception varied from one day to day, we compared the variances (F-test) of within-subject variability for a particular meal type over the course of a week and of between-subject variability for the same meal averaged over several days. This analysis quantifies how much of the total variation in interoception is attributable to intra-individual versus between-individual differences, or both. We applied the same approach to post-meal hunger and pre- and post-meal satiety. To investigate whether physiological markers such as heart rate (HR) and glucose levels, were influenced by meal type (B, L, D) or the day (D2 to D5), we applied the following model: For pre-meal HR, for example: PreHR <- lme (HR∼ Meal type + Day + Meal type x Day, random = 1 | Participants) . When no significant interaction effect was found, we focused on the main effect (meal type or day). When an interaction was statistically significant, simple effects were tested. Post hoc analysis with Bonferroni correction was used to adjust for multiple comparisons. We applied similar models to post-meal HR, and pre- and post-meal glucose levels. We applied a linear mixed model to assess the effect of low artificial light at night on sleep characterization indicators such as total sleep time (TST) and sleep latency (SL). The example model as follows: TST <-lme (Total sleep time ∼ Light, random= ∼1 | Participant) . We used a similar approach to analyze SL. To investigate whether low artificial light at night influences the following day’s measured interoception, we applied a linear mixed model respectively for each meal (B, L, D) and each interoception condition (pre- or post-meal). PreHungerBreakfast <- lme (Hunger ∼ Light, random= ∼1 | Participant) . Similar models were applied to post-hunger, and pre- and post-satiety conditions. To better understand the relationship between interoception (e.g., hunger, satiety) and physiological parameters (e.g., HR, glucose), we applied the Pearson coefficient ( r ) to quantify the strength and direction of the linear association between these variables within the same internal state. For example, we examined the correlations between pre-meal hunger and pre-meal HR, post-meal satiety and post- meal HR, as well as between pre-meal hunger and pre-meal glucose levels, and post-meal satiety and post-meal glucose levels. Similarly, Pearson correlation analyses were performed to explore the relationship between sleep indicators such as total sleep time (TST) and sleep latency (SL), and the following day’s interoception measures. All findings were considered statistically significant when p <= 0.05. Data are presented as the mean ± standard error of the mean (SEM) in the graphs. 3. Results 3.1 Fluctuation in interoception during the day In this study, although both hunger and satiety were measured before and after each meal. In real-life conditions, these two indicators of interoception occur only under different metabolic states. Hunger occurs typically before meals, while satiety after meals. Therefore, the following results will only consider hunger before meals and satiety after meals. Since preliminary analysis showed no interaction between days and meal types, the meal type effect reported here is the mean of all days. The linear mixed-effects model revealed a significant main effect of meal type on hunger levels ( F (2, 206) = 8.43, p < 0.0001). Post hoc comparisons showed that hunger levels before meals were significantly higher at L (11.25 ± 0.4, p < 0.001) and D (11.19 ± 0.4, p < 0.001) compared to B (10.37 ± 0.4), with no significant difference between L and D (see Figure 2 ). Download figure Open in new tab Figure 2. Dynamic hunger before meal and satiety after meal throughout the day, with measurements taken at breakfast (B), lunch (L), and dinner (D). Data are presented as means ± SEMs. The same model was applied to satiety (after meals), and showed a significant main effect of meal type throughout the day ( F (2, 208) = 7.56, p < 0.0001). Post hoc comparisons revealed that satiety was significantly higher at L (13.4 ± 0.34, p < 0.001) and D (13.5 ± 0.34, p < 0.001) than at B (12.93 ± 0.34), with no significant difference between L and D (see Figure 2 , and Supplementary Figure 1). 3.2 Stability of interoception throughout the week To examine how individual hunger and satiety changed from day to day, we compared within-individual variability (over 4 days) with between-individual variability (cumulated over the four days). We found that between-individual variability was significantly greater than within-individual variability for both hunger ( F (76, 60) = 10.5, p < 0.0001) ( Figure 3a ) and satiety ( F (76, 60) = 9.37, p < 0.0001) ( Figure 3b ). Regarding hunger levels, the average value remained stable from D2 to D5 (D2: 10.6 ± 0.41; D3: 10.9 ± 0.41; D4: 11.1 ± 0.41; D5: 11.2 ± 0.41). A similar pattern was observed for satiety levels (D2: 13.1 ± 0.35; D3: 13 ± 0.35; D4: 13.4 ± 0.35; D5: 13.3 ± 0.35). This stability across days was also evident in the three meals analyzed separately (see Supplementary Figure 2). Download figure Open in new tab Figure 3. Stability of individual hunger (a) and satiety (b) from D2 (Day 2) to D5 (Day 5). Each point represents the average values of three meals (breakfast, lunch and dinner), and each colored line corresponds to one participant’s raw data. 3.3 Physiological parameters 3.3.1 Heart rate Pre-meal There were significant main effects for meal type ( F (2, 208) = 18.86, p < 0.0001) and day ( F (3, 208) = 3.03, p = 0.03) with no significant interaction between meal type and day ( F (6, 208) = 1.3, p = 0.26). Specifically, HR was significantly higher at B (66.4 ± 1.86 bpm) than at L (63.3 ± 1.86 bpm, p < 0.0001) and D (62.9 ± 1.86 bpm, p < 0.0001). There was no significant difference between L and D ( p = 0.99) (see Figure 4a ). Regarding the effect of day, pre-meal HR on D2 (63.2 ± 1.88 bpm) was significantly lower than on D5 (65.1 ± 1.88 bpm, p = 0.04), no other significant differences were observed between the other days (see Figure 4b ). Download figure Open in new tab Figure 4. HR before and after meals. HR was examined before (a, b) and after (c, d, e, f) breakfast (B), lunch (L) and dinner (D), and before (c) and after (d) meals (average across B, L, D) from day 2 (D2) to day 5 (D5). Data are presented as means ± SEMs. Post-meal HR showed significant main effects for meal type ( F (2, 208) = 27.55, p < 0.0001) and day ( F (3, 208) = 13.45, p < 0.0001), as well as a significant meal type x day interaction ( F (6, 208) = 2.29, p = 0.037). Post hoc pairwise comparisons revealed that for all days, no difference was found in HR between B (73.15 ± 2.05) and L (72.8 ± 2.05) while there was a significantly higher HR at L than at D (69.58 ± 2.05). The interaction between the meal types and days comes from the fact that the heart rate is significantly higher at B (73.2 ± 2.05) than at D (68.63 ± 2.05) only for D2, D3 and D4 ( p < 0.05), while for the D5 no this difference was observed (72.4 ± 2.05, p = 0.5) (see Figures 4c-f ). Effect of meal Looking at the changes between pre- and post-meal, a significant increase in HR was observed from pre-to post-meal, for each type of meal: B (from 66.20 to 73.17, t = 4.96, p < 0.0001), L (from 63.32 to 72.82, t = 6.34, p < 0.0001), and D (from 62.89 to 69.57, t = 4.96, p < 0.0001). 3.3.2 Glucose For pre-meal blood sugar level, a significant main effect of day was observed ( F (3, 205) = 15.39, p < 0.0001). There was no significant interaction between meal type and day ( F (6, 205) = 1.59, p = 0.15), and sugar levels remained stable across the three meals ( F (2, 205) = 2.21, p = 0.11); see Figure 5a ). Download figure Open in new tab Figure 5. Glucose levels before and after meals, and across experimental days. Glucose is shown pre-meal (a) and post-meal (c), at B (breakfast), L (lunch), and D (dinner). Glucose is shown across the experimental days, from Day 2 (D2) to Day 5 (D5), both pre-meal (b) and post-meal (d). Data are presented as means ± SEMs. Specifically, glucose levels on D2 (97.9 ± 2.0 mg/dL) were significantly higher than on D3 (92.2 ± 2.0 mg/dL), D4 (90.8 ± 2.0 mg/dL), and D5 (90.4 ± 2.0 mg/dL) (all p < 0.0001; see Figure 5b ). For post-meal blood sugar levels, there was no significant interaction between meal type and day ( F (6, 203) = 0.34, p = 0.92). Additionally, there were no significant main effects of meal type ( F (2, 203) = 1.3, p = 0.27; see Figure 5c ) or day ( F (3, 203) = 2.51, p = 0.06; see Figure 5d ) 3.4 Correlations between hunger, satiety and physiological parameters 3.4.1 Between hunger and satiety Hunger and satiety were negatively correlated respectively at pre- ( r (226) = -0.68, p < 0.0001) and post-meal( r (219) = -0.72, p < 0.0001) states. In addition, a significant positive correlation was found between pre-meal hunger and post-meal satiety ( r (216) = 0.3, p < 0.0001). 3.4.2 Between interoception and heart rate (HR) A significant negative correlation was observed between hunger and HR on pre- and post-meal data pooled, for all three meals combined ( r (474) = -0.42, p < 0.0001), as well as for all meals analyzed separately: B ( r (157) = -0.42, p < 0.0001), L ( r (156) = -0.47, p < 0.0001), and D ( r (157) = -0.39, p < 0.0001) (see Figure 6a ). We also found a positive correlation between satiety and HR when the three meals were analyzed together ( r (474) = 0.40, p < 0.0001) or separately: B ( r (157) = 0.42, p < 0.0001), L ( r (156) = 0.45, p < 0.0001), and D ( r (157) = 0.35, p < 0.0001) (see Figure 6b ). Download figure Open in new tab Figure 6. Correlation between interoception and HR. Interoception is shown hunger (a) and satiety (b), at breakfast, lunch and dinner When pre- and post-meal data were analyzed separately, a significant positive correlation was only found between post-meal satiety and HR ( r (227) = 0.25, p = 0.0001). 3.4.3 Interoception and Glucose levels A significant negative correlation was observed between hunger and glucose when pre- and post-meal data were pooled, either when all three meals were combined ( r (473) = -0.61, p < 0.001) or when analyzed separately: B ( r (154) = -0.62, p < 0.0001), L ( r (158) = -0.64, p < 0.0001), and D ( r (157) = - 0.58, p < 0.001) (see Figure 7a ). On the opposite, a significant positive correlation was observed between satiety and glucose levels, both when the three meals were combined ( r (473) = 0.62, p < 0.001) and when analyzed separately for B ( r (154) = 0.65, p < 0.0001), L ( r (158) = 0.62, p < 0.0001), and D ( r (157) = 0.60, p < 0.0001) (see Figure 7b ). Download figure Open in new tab Figure 7. Correlation between interoception and glucose. Interoception is shown hunger (a) and satiety (b), at breakfast, lunch and dinner. Data are presented as raw data from each individual. However, when we analyzed pre- and post-meal separately, we found no significant correlation was found between hunger and glucose levels in the pre-meal condition ( r (222) = -0.02, p = 0.80), or between satiety and glucose levels in the post-meal condition ( r (222) = -0.08, p = 0.23). 3.5 Effects of low light intensity at night on interoception and sleep No significant effect of light on pre-meal hunger was found when all three meals were combined ( F (3, 205) = 0.80, p = 0.50) or when the meals were analyzed separately: B ( F (3, 53) = 0.47, p = 0.70), L ( F (3, 54) = 1.36, p = 0.26), and D ( F (3, 54) = 1.36, p = 0.26) the following day. Similarly, no significant effect of light on post-meal satiety was found when all three meals were combined ( F (3, 207) = 0.20, p = 0.89) or when meals were analyzed separately: at B ( F (3, 52) = 1.06, p = 0.37), L ( F (3, 55) = 0.94, p = 0.43) and D ( F (3, 54) = 0.29, p = 0.83). To investigate a potential indirect link between light exposure at night and the following day’s interoception, we analyzed the relationship between light exposure at night and sleep duration (total sleep time [TST]). No effect of light at night on total sleep time was found ( F (3, 52) = 1.31, p = 0.28). We also examined at a potential link between sleep duration and interoception of the following day. We found that total sleep time (TST) was significantly, but weakly correlated with pre-meal hunger when all three meals were combined ( r (216) = 0.24, p = 0.0004). When meals were analyzed separately, this correlation was significant for B ( r (70) = 0.24, p = 0.04) and L ( r (71) = 0.33, p = 0.004), but not for D ( r (71) = 0.13, p = 0.25). Furthermore, TST was not correlated with post-meal satiety, either when meals were combined ( r (216) = 0.06, p = 0.38) or when examined separately: B ( r (69) = 0.01, p = 0.90), L ( r (72) = 0.02, p = 0.89) and D ( r (71) = 0.18, p = 0.16). 4. Discussion The current study examined the characteristics of food-related interoception using a highly controlled, five-day laboratory protocol. Hunger and satiety were measured before and after each of 14 consecutive meals in 20 male subjects. Our results showed that interoception (hunger and satiety): 1) fluctuates throughout the day (daily variation); 2) remains stable across days; 3) is regulated by distinct mechanisms; 4) correlates with physiological parameters, such as heart rate and glycemia, and is partially correlated with prior sleep duration (pre-meal hunger). Dynamic interoception during the day Hunger and satiety are momentary sensations that reflect respectively the body’s different metabolic and energy states ( Schwartz et al., 2000 ; Woods et al., 1998 ), especially the emptiness or fullness of the stomach. Hunger typically increases before a meal and decreases and disappears with food intake, while satiety shows the opposite pattern. In this study, we found that these sensations vary depending on the meal type during a day. Specifically, pre-meal hunger and post-meal satiety are lower at breakfast than at lunch or dinner. These results suggest that hunger and satiety signals fluctuate throughout the day, with weaker perceptions in the morning (around breakfast) and stronger perceptions at midday (around lunch) and in the evening (around dinner). Several studies have investigated interoception throughout the day, assessing hunger levels multiple times. These studies have suggested that hunger may fluctuate on a daily basis. A weak sense of hunger is typically observed at the start of the day and becomes progressively stronger hunger later in the day ( Cugini et al., 1998 ; Fatati et al., 2001 ; Qian et al., 2019 ; Sargent et al., 2016 ; Scheer et al., 2013 ). However, fewer studies have examined variation in satiety throughout the day. One exception is the study by Sargent et al. (2016) , which measured hunger and satiety during an eight-day, 28-hour forced desynchrony protocol. While our results regarding dynamic changes in hunger throughout the day are similar to that of Sargent and colleagues, we find the opposite result for satiety. Indeed, contrary to us, Sargent et al. (2016) found the lowest satiety levels in the evening (5:00 p.m. to 9:00 p.m.) and the highest levels at the end of the night (or beginning of the day, 1:00 a.m. to 5:00 a.m.). One possible explanation for this difference is that the authors did not specify when the satiety measurements were taken relative to meal (i.e., before or after). If satiety and hunger were assessed simultaneously, the expected negative correlation between the two could result in an inverse relationship, whereby lower hunger would correspond to higher satiety. Another reason is that Sargent and his colleagues used a forced-desynchrony protocol, which may have resulted in progressive sleep debt accumulation throughout the study, and influenced the participants’ interoceptive dynamics. The sustained higher pre-meal hunger and post-meal satiety experienced at lunch and dinner may have evolutionary significance. A strong hunger signal at the end of the day could increase motivation to eat. Concurrently, the ability to achieve greater satiety levels could extend the time period during which food is consumed. Together, these two mechanisms ensure adequate energy availability throughout the day and in anticipation of sleep-related fasting. However, in today’s obesogenic environment, resulting from food availability and attractivity, and influenced by psychological factors (e.g. expectation and motivation) ( de-Arruda et al., 2024 ; Mathew et al., 2022 ), such a dual mechanism may lead to overeating at lunch and dinner. The increasing tendency to skip breakfast and curtail our sleep (i.e., carry a sleep debt) in our modern societies may constitute an additional vulnerability leading to an increased food intake before bedtime, consequently contributing to an increased risk of cardiometabolic disorders ( Anses, 2024 ). Stability of interoception from one day to another Although interoception fluctuates throughout the day, our findings indicate that it remained stable over the course of the four-days study. Each participant exhibited consistent hunger and satiety levels respectively before or after for each of the three meal types (B, L, D). Intra-individual variability over the 4 days was significantly lower than inter-individual variability. In other words, differences in interoception between individuals were far greater than the day-to-day fluctuations observed within the same individual. While several studies have measured hunger or fullness (satiety) over multiple days or weeks ( Cheon & Mattes, 2024 ; Ruddick-Collins et al., 2019 ; Stevenson et al., 2015 ), few have specifically examined longitudinal changes in interoception over time. Only the study by Cheon and Mattes (2024) found that hunger and satiety remained relatively stable when comparing measures taken from the same subjects three times, with eight weeks apart (weeks 1, 9, and 17). They also reported considerable interindividual variability, with which our findings are consistent. The stability of individual interoception observed in our study is unlikely to be due to the habitual use of the line scale or repeated measures of interoception. Participants reported lower levels of hunger and satiety in the morning, with higher ratings at lunch and dinner. Since hunger and satiety are regulated by stomach fullness and hormones such as ghrelin and leptin ( Cummings et al., 2001 ; Klok et al., 2007 ), the consistency of interoception across days may rather reflect stable individual hormone secretion patterns ( Scheer et al., 2009 ) or metabolic states at specific times. This stability may also be linked to a stable sleep-wake cycle and circadian rhythmicity, which are inherent to our highly controlled pre-laboratory and laboratory conditions. These findings suggest that the daily fluctuations in hunger and satiety, as well as stability over times, represent characteristic individual traits. The level of an individual’s interoception may be used to differentiate subjects. This individual trait, the perception of hunger and satiety, may result from long- term physiological adaptations that may contribute to the maintaining stable food intake and body weight over time. Hunger and satiety are closely related but two distinct parameters Hunger and satiety are generally considered opposite interoceptive states that reflect energy deficits and surplus, respectively. Consequently, most studies usually assess only one or the other. When both are measured simultaneously, however, they are typically negatively correlated ( Scheer et al., 2013 ). But is it necessary and pertinent to measure the both simultaneously? Although hunger and satiety are statistically significantly correlated when measured simultaneously ( r = -0.68 in our study), less than 50% of the variation of hunger can be explained by satiety variation., Thus, the two are not perfectly opposite interoceptions. In fact, hunger and satiety operate under different physiological conditions and rely on different neural circuits and neurotransmitters to control different steps of food intake ( Amin & Mercer, 2016 ; Campfield & Smith, 1990b , 1990a; Cummings, 2006 ; Cummings et al., 2001 ; Schwartz et al., 2000 ). Although both hunger and satiety are physiological and psychological interoceptions influenced by internal and external factors, they reflect different internal states: if hunger is perceived, satiety is usually absent, and vice versa. Therefore, it is pertinent to measure hunger before food intake and satiety after. When we correlated the pre-meal hunger with post-meal satiety, the correlation was statistically significant, with a R 2 at 0.09. This very small coefficient of determination favors our hypothesis that these two interoceptions function independently of each other, controlling different aspects of food intake (e.g., the motivation to initiate food intake and tolerance of ingestion quantity). This hypothesis is also supported by two studies. One study found that physical exercise can modify post-meal satiety perception but not pre-meal appetite indicators ( Pélissier et al., 2024 ). Another study found that the synchronized release of monoamines between the medial and lateral hypothalamus occurs only during the satiety period ( Fetissov et al., 2000 ). Therefore, measuring these two types of interoception, at the right time, is both pertinent and necessary for thoroughly investigating their role in controlling food intake ( Amin & Mercer, 2016 ; Nakamura & Nakamura, 2018 ). Interoception and physiological parameters The relationship between interoception and physiological parameters (heart rate, glycemia) has rarely been studied together. Yet, these parameters reflect different aspects of the internal states ( Harthoorn & Dransfield, 2008 ). Heart rate and hunger/satiety One finding of the present study is that heart rate is higher in a satiety state than in a hunger state for all meal types. At first glance, this finding seemed contrary to the classical assumption. It is well known that the sympathetic system increases heart rate, while parasympathetic activity decreases it. Since hunger is usually considered an internal signal that drive exploration and intake behavior, and satiety occurs after intake and during digestion, and since the sympathetic system activates ‘fight or flight’ responses and the parasympathetic system predominates during “rest and digest”, one might expect the satiety state, where digestion and nutrient absorption occur, to be linked to parasympathetic activity and thus associated with a reduction in heart rate. Several studies have examined the relationship between metabolic state and cardiac activity under different nutritional conditions or at a particular metabolic state. However, none of these studies have provided evidence to support the hypothesis that heart rate shifts from hunger to satiety ( Flasbeck et al., 2021 ; Herbert et al., 2012 ; Peschel et al., 2018 ; Polito et al., 2022 ). However, our counterintuitive finding is supported by one study in which heart rate was recorded during 4.75 hours at a sampling frequency of four times per hour, covering a lunch period states ( Harthoorn & Dransfield, 2008 ). This study showed that heart rate increases after lunch more than before lunch. Although they did not measure heart rate increases around all meal types or repeat their measurements on another day, our 14 consecutive measurements confirmed that at heart rate increases at satiety for all meal types, and these results is reproducible from one day to the next. In addition, either our study or that of Harthoorn and Dransfield (2008) have shown that this increased heart rate after a meal may last at least one hour. Although few studies have directly measured the effects of meal type on heart rate, several indirect studies support these results, as there is higher sympathetic tone associated with satiety ( Harthoorn and Dransfield, 2008 ). The mechanisms involved in this higher sympathetic activity after meals may be linked to the physiological regulation of blood flow redistribution in the body. Although the detailed mechanisms have not been directly investigated, we found a positive correlation between post-meal satiety and heart rate. Therefore, this raises the question of whether people who regularly eat until full satiety are at a higher risk for cardio-metabolic diseases due to their consistently higher HR after eating. Glycemia and hunger/satiety Our results showed a strong positive correlation between interoception and glycemia, with higher glucose levels being associated with increased satiety. This finding is consistent with previous studies ( Campfield et al., 1996 ; Melanson et al., 1999 ). However, the decrease in hunger associated with falling glucose levels may suggest a causal relationship. Recent evidence suggests that other mechanisms, such as hormonal signaling (ghrelin) ( Cummings & Overduin, 2007 ), sensory reward systems ( Monteleone et al., 2012 ), and cognitive and behavioral factors ( Berthoud, 2011 ), may play a more dominant role in hunger and satiety perception. Therefore, the expression of interoception may not be exclusively driven by acute fluctuations in blood glucose. We did not find a strong overall relationship between interoception and sleep, but only a positive correlation between pre-meal hunger and sleep duration. This may be due to the fact that in the present study, light intensity was maintained at very low levels (0, 3, 8, and 20 lux) at night. These levels were selected to be compatible with real-life nighttime lighting conditions observed at home, contrasting with the higher intensities typically used in other studies ( Chang et al., 2012 ; Gronfier et al., 2004 , 2007; Rahman et al., 2019 ; Zeitzer et al., 2000 ). As these low light intensities did not significantly influence total sleep time or sleep latency, they may not have been sufficient to disrupt the relationship between interoception and sleep in a detectable way. It is plausible that the homeostatic regulations of both sleep and energy balance were robust enough to maintain stability despite these minor and unimpactful light intensities. The positive correlation we found between total sleep time (TST) and pre-meal hunger at breakfast and lunch suggests that, under good sleep conditions, as opposed to sleep deprivation conditions often used in others studies, adequate sleep might play a role in optimizing energy balance and promoting metabolic health. Several limitations should be noted. First, our study was conducted under highly controlled laboratory conditions. Although appetite and hunger in real-life settings may be modulated by other environmental factors such as exercise and light exposure, our results indicating systematic diurnal variation and stability over several days suggest that the existence of internal regulatory mechanisms that may persist beyond the laboratory environment. Secondly, the meals provided to our subjects for breakfast, lunch and dinner were not identical. This may have contributed to some of the observed diurnal variability. However, our results are consistent with previous studies suggesting a possible circadian rhythm in hunger perception ( Scheer et al., 2013 ), supporting the notion of a dynamic and endogenous rhythm in interoception. Finally, since the study was conducted exclusively with male participants, and there may be gender difference in interoception ( Monrroy et al., 2019 ) further research with a more diverse population is needed to ensure the generalizability of our findings. 5. Conclusion From the data presented, we conclude that: 1) hunger and satiety are the two interoceptive processes that may act in concert but are distinct; 2) perceived levels of hunger and satiety fluctuate during the day, with lower intensities at the beginning of the day and higher intensities around noon and dinner; 3) each individual has unique perceived levels of hunger and satiety that remain stable over relatively long periods of time; 4) both pre-meal hunger and post-meal satiety measures are pertinent and necessary to explaining food intake strategies; 5) heart rate increases in a satiety state. The relationship between these interoceptive processes and physiological parameters is complex. An increase in post-meal heart rate may be a useful indicator for predicting certain food-intake-related illnesses, but further investigation is required. Credit authorship contribution statement Xi Wang: Investigation, Data curation, Formal analysis, Visualization, Validation, Writing – original draft, Writing – review and editing. Abhishek S. Prayag: Project administration, Investigation, Data curation, Visualization, Validation, Writing – review and editing. Ni Tang: Investigation, Data curation, Formal analysis, Visualization, Validation, Writing – review and editing. Yanlong Hou: Investigation, Data curation, Formal analysis, Visualization, Validation, Writing – review and editing. Claude Gronfier: Conceptualization, Methodology, Funding acquisition, Project administration, Investigation, Supervision, Data curation, Visualization, Validation, Writing – original draft, Writing – review and editing. Tao Jiang: Conceptualization, Methodology, Funding acquisition, Project administration, Investigation, Supervision, Data curation, Visualization, Validation, Writing – original draft, Writing – review and editing. Ethics statement All experimental procedures adhered to the Declaration of Helsinki, and the protocol was approved by the Institutional Review Board (Comité de Protection des Personnes Ouest VI, Brest, France, N°ID- RCB: 2023-A01561-44). Informed consent was obtained from all participants. Funding source This work was supported by grants from the ANR (Idex Breakthough ALAN, 16 IDEX‐0005) and the University of Lyon (Rectolux) to CG, as well as by funds from Inserm and CNRS to CG and TJ. Declaration of competing interest CG and TJ were paid experts for the French Agency for Food, Environmental and Occupational Health & Safety (Anses, Chrononutrition task force and report), and CG was also paid expert for the LED2 task force and report (Anses). The other authors report no conflicts of interest. Acknowledgments The authors would like to thank Anissa Dahmani and Lydie Merle for their contributions to this study. They also would like to thank Marc Thevenet for his technical assistance, code writing, and help with data analysis. Finally, the authors thank all the participants who were interested in or contributed to this study. Funder Information Declared ANR , Idex Breakthough ALAN, 16 IDEX‐0005 References ↵ Anses . 2024 . Avis de l’Anses relatif à l’actualisation des repères du PNNS: répartition temporelle des prises alimentaires . ( Maison-Alfort : Anses ). https://www.anses.fr/fr/system/files/NUT2019SA0001Ra.pdf , p236 p. ↵ Amin , T. , & Mercer , J. G. ( 2016 ). Hunger and Satiety Mechanisms and Their Potential Exploitation in the Regulation of Food Intake . Current Obesity Reports , 5 , 106 – 112 . doi: 10.1007/s13679-015-0184-5 OpenUrl CrossRef PubMed ↵ Beck , A. T. , Ward , C. H. , Mendelson , M. , Mock , J. , & Erbaugh , J. ( 1961 ). An inventory for measuring depression . Archives of General Psychiatry , 4 , 561 – 571 . doi: 10.1001/archpsyc.1961.01710120031004 OpenUrl CrossRef PubMed Web of Science ↵ Bellisle , F. ( 2005 ). Faim et satiété, contrôle de la prise alimentaire . EMC - Endocrinologie , 2 ( 4 ), 179 – 197 . doi: 10.1016/j.emcend.2005.08.003 OpenUrl CrossRef ↵ Berthoud , H.-R. ( 2011 ). Metabolic and hedonic drives in the neural control of appetite: Who’s the boss? Current Opinion in Neurobiology , 21 ( 6 ), 888 – 896 . doi: 10.1016/j.conb.2011.09.004 OpenUrl CrossRef PubMed ↵ Berthoud , H.-R. , Morrison , C. D. , & Münzberg , H. ( 2020 ). The obesity epidemic in the face of homeostatic body weight regulation: What went wrong and how can it be fixed? Physiology & Behavior , 222 , 112959 . doi: 10.1016/j.physbeh.2020.112959 OpenUrl CrossRef PubMed ↵ Blundell , J. , de Graaf , C. , Hulshof , T. , Jebb , S. , Livingstone , B. , Lluch , A. , Mela , D. , Salah , S. , Schuring , E. , van der Knaap , H. , & Westerterp , M. ( 2010 ). APPETITE CONTROL: METHODOLOGICAL ASPECTS OF THE EVALUATION OF FOODS . Obesity Reviews: An Official Journal of the International Association for the Study of Obesity , 11 ( 3 ), 251 – 270 . doi: 10.1111/j.1467-789X.2010.00714.x OpenUrl CrossRef PubMed Web of Science ↵ Brondel , L. , Romer , M. A. , Nougues , P. M. , Touyarou , P. , & Davenne , D. ( 2010 ). Acute partial sleep deprivation increases food intake in healthy men123 . The American Journal of Clinical Nutrition , 91 ( 6 ), 1550 – 1559 . doi: 10.3945/ajcn.2009.28523 OpenUrl Abstract / FREE Full Text ↵ Broussard , J. L. , Kilkus , J. M. , Delebecque , F. , Abraham , V. , Day , A. , Whitmore , H. R. , & Tasali , E. ( 2016 ). Elevated ghrelin predicts food intake during experimental sleep restriction . Obesity (Silver Spring, Md .), 24 ( 1 ), 132 – 138 . doi: 10.1002/oby.21321 OpenUrl CrossRef PubMed ↵ Buysse , D. J. , Reynolds , C. F. , Monk , T. H. , Berman , S. R. , & Kupfer , D. J. ( 1989 ). The Pittsburgh sleep quality index: A new instrument for psychiatric practice and research . Psychiatry Research , 28 ( 2 ), 193 – 213 . doi: 10.1016/0165-1781(89)90047-4 OpenUrl CrossRef PubMed Web of Science ↵ Campfield , L. A. , Brandon , P. , & Smith , F. J. ( 1985 ). On-line continuous measurement of blood glucose and meal pattern in free-feeding rats: The role of glucose in meal initiation . Brain Research Bulletin , 14 ( 6 ), 605 – 616 . doi: 10.1016/0361-9230(85)90110-8 OpenUrl CrossRef PubMed Web of Science ↵ Campfield , L. A. , & Smith , F. J. ( 1990a ). Systemic factors in the control of food intake: Evidence for patterns as signals . In Neurobiology of food and fluid intake (pp. 183 – 206 ). Plenum Press . doi: 10.1007/978-1-4613-0577-4_8 OpenUrl CrossRef ↵ Campfield , L. A. , & Smith , F. J. ( 1990b ). Transient declines in blood glucose signal meal initiation . International Journal of Obesity , 14 Suppl 3 , 15 – 31 ; discussion 31-4. OpenUrl PubMed ↵ Campfield , L. A. , Smith , F. J. , Rosenbaum , M. , & Hirsch , J. ( 1996 ). Human eating: Evidence for a physiological basis using a modified paradigm . Neuroscience and Biobehavioral Reviews , 20 ( 1 ), 133 – 137 . doi: 10.1016/0149-7634(95)00043-e OpenUrl CrossRef PubMed Web of Science ↵ Chang , A.-M. , Santhi , N. , St Hilaire , M. , Gronfier , C. , Bradstreet , D. S. , Duffy , J. F. , Lockley , S. W. , Kronauer , R. E. , & Czeisler , C. A. ( 2012 ). Human responses to bright light of different durations . The Journal of Physiology , 590 ( 13 ), 3103 – 3112 . doi: 10.1113/jphysiol.2011.226555 OpenUrl CrossRef PubMed ↵ Cheon , E. , & Mattes , R. D. ( 2024 ). Interindividual variability in appetitive sensations and relationships between appetitive sensations and energy intake . International Journal of Obesity (2005) , 48 ( 4 ), 477 – 485 . doi: 10.1038/s41366-023-01436-9 OpenUrl CrossRef ↵ Cugini , P. , Ventura , M. , Ceccotti , P. , Cilli , M. , Marcianò , F. , Salandri , A. , Di Marzo , A. , Fontana , S. , Pellegrino , A. M. , Vacca , K. , & Di Siena , G. ( 1998 ). Hunger sensation: A chronobiometric approach to its within-day and intra-day recursivity in anorexia nervosa restricting type . Eating and Weight Disorders: EWD , 3 ( 3 ), 115 – 123 . doi: 10.1007/BF03339998 OpenUrl CrossRef ↵ Cummings , D. E. ( 2006 ). Ghrelin and the short- and long-term regulation of appetite and body weight . Physiology & Behavior , 89 ( 1 ), 71 – 84 . doi: 10.1016/j.physbeh.2006.05.022 OpenUrl CrossRef PubMed Web of Science ↵ Cummings , D. E. , & Overduin , J. ( 2007 ). Gastrointestinal regulation of food intake . Journal of Clinical Investigation , 117 ( 1 ), 13 – 23 . doi: 10.1172/JCI30227 OpenUrl CrossRef PubMed Web of Science ↵ Cummings , D. E. , Purnell , J. Q. , Frayo , R. S. , Schmidova , K. , Wisse , B. E. , & Weigle , D. S. ( 2001 ). A preprandial rise in plasma ghrelin levels suggests a role in meal initiation in humans . Diabetes , 50 ( 8 ), 1714 – 1719 . doi: 10.2337/diabetes.50.8.1714 OpenUrl Abstract / FREE Full Text ↵ Daguet , I. , Raverot , V. , Bouhassira , D. , & Gronfier , C. ( 2022 ). Circadian rhythmicity of pain sensitivity in humans . Brain: A Journal of Neurology , 145 ( 9 ), 3225 – 3235 . doi: 10.1093/brain/awac147 OpenUrl CrossRef PubMed ↵ de-Arruda , J. P. , de-Souza , A. P. A. , Pereira , L. P. , Fonseca , L. B. , Nogueira , P. S. , Rodrigues , P. R. M. , Muraro , A. P. , & Ferreira , M. G. ( 2024 ). Short Sleep Duration and Skipping Main Meals among University Students . Sleep Science (Sao Paulo, Brazil) , 17 ( 4 ), e414 – e421 . doi: 10.1055/s-0044-1782178 OpenUrl CrossRef ↵ Fatati , G. , Vendetli , A. L. , Puxeddu , A. , De Francesco , G. P. , Coda , S. , De Rosa , R. , De Marco , E. , De Laurentis , T. , Fontana , S. , & Cugini , P. ( 2001 ). Circadian rhythm of hunger sensation in obese patients: Effects of a short-term, moderately hypocaloric diet with a substitutive meal . Eating and Weight Disorders: EWD , 6 ( 4 ), 214 – 219 . doi: 10.1007/BF03339745 OpenUrl CrossRef ↵ Fetissov , S. O. , Meguid , M. M. , Chen , C. , & Miyata , G. ( 2000 ). Synchronized release of dopamine and serotonin in the medial and lateral hypothalamus of rats . Neuroscience , 101 ( 3 ), 657 – 663 . doi: 10.1016/S0306-4522(00)00374-2 OpenUrl CrossRef PubMed Web of Science ↵ Flasbeck , V. , Bamberg , C. , & Brüne , M. ( 2021 ). Short-Term Fasting and Ingestion of Caloric Drinks Affect Heartbeat-Evoked Potentials and Autonomic Nervous System Activity in Males . Frontiers in Neuroscience , 15 , 622428 . doi: 10.3389/fnins.2021.622428 OpenUrl CrossRef PubMed ↵ Garner , D. M. , & Garfinkel , P. E. ( 1979 ). The Eating Attitudes Test: An index of the symptoms of anorexia nervosa . Psychological Medicine , 9 ( 2 ), 273 – 279 . doi: 10.1017/s0033291700030762 OpenUrl CrossRef PubMed Web of Science ↵ Green , S. M. , Delargy , H. J. , Joanes , D. , & Blundell , J. E. ( 1997 ). A satiety quotient: A formulation to assess the satiating effect of food . Appetite , 29 ( 3 ), 291 – 304 . doi: 10.1006/appe.1997.0096 OpenUrl CrossRef PubMed Web of Science Gronfier , C. , Wright , K. P. , Kronauer , R. E. , & Czeisler , C. A. ( 2007 ). Entrainment of the human circadian pacemaker to longer-than-24-h days . Proceedings of the National Academy of Sciences , 104 ( 21 ), 9081 – 9086 . doi: 10.1073/pnas.0702835104 OpenUrl Abstract / FREE Full Text ↵ Gronfier , C. , Wright , K. P. , Kronauer , R. E. , Jewett , M. E. , & Czeisler , C. A. ( 2004 ). Efficacy of a single sequence of intermittent bright light pulses for delaying circadian phase in humans . American Journal of Physiology - Endocrinology and Metabolism , 287 ( 1 ), E174 – E181 . doi: 10.1152/ajpendo.00385.2003 OpenUrl CrossRef PubMed Web of Science ↵ Harthoorn , L. F. , & Dransfield , E. ( 2008 ). Periprandial changes of the sympathetic–parasympathetic balance related to perceived satiety in humans . European Journal of Applied Physiology , 102 ( 5 ), 601 – 608 . doi: 10.1007/s00421-007-0622-5 OpenUrl CrossRef PubMed ↵ Herbert , B. M. , Muth , E. R. , Pollatos , O. , & Herbert , C. ( 2012 ). Interoception across modalities: On the relationship between cardiac awareness and the sensitivity for gastric functions . PloS One , 7 ( 5 ), e36646 . doi: 10.1371/journal.pone.0036646 OpenUrl CrossRef PubMed ↵ Horne , J. A. , & Ostberg , O. ( 1976 ). A self-assessment questionnaire to determine morningness-eveningness in human circadian rhythms . International Journal of Chronobiology , 4 ( 2 ), 97 – 110 . OpenUrl CrossRef PubMed ↵ Kabir , A. , Miah , S. , & Islam , A. ( 2018 ). Factors influencing eating behavior and dietary intake among resident students in a public university in Bangladesh: A qualitative study . PLoS ONE , 13 ( 6 ), e0198801 . doi: 10.1371/journal.pone.0198801 OpenUrl CrossRef PubMed ↵ Klok , M. D. , Jakobsdottir , S. , & Drent , M. L. ( 2007 ). The role of leptin and ghrelin in the regulation of food intake and body weight in humans: A review . Obesity Reviews: An Official Journal of the International Association for the Study of Obesity , 8 ( 1 ), 21 – 34 . doi: 10.1111/j.1467-789X.2006.00270.x OpenUrl CrossRef PubMed Web of Science ↵ Louis-Sylvestre , J. ( 1976 ). Preabsorptive insulin release and hypoglycemia in rats . The American Journal of Physiology , 230 ( 1 ), 56 – 60 . doi: 10.1152/ajplegacy.1976.230.1.56 OpenUrl CrossRef PubMed Web of Science ↵ Marcelino , A. S. , Adam , A. S. , Couronne , T. , Köster , E. P. , & Sieffermann , J. M. ( 2001 ). Internal and external determinants of eating initiation in humans . Appetite , 36 ( 1 ), 9 – 14 . doi: 10.1006/appe.2000.0375 OpenUrl CrossRef PubMed Web of Science ↵ Markwald , R. R. , Melanson , E. L. , Smith , M. R. , Higgins , J. , Perreault , L. , Eckel , R. H. , & Wright , K. P. ( 2013 ). Impact of insufficient sleep on total daily energy expenditure, food intake, and weight gain . Proceedings of the National Academy of Sciences , 110 ( 14 ), 5695 – 5700 . doi: 10.1073/pnas.1216951110 OpenUrl Abstract / FREE Full Text ↵ Mathew , G. M. , Reichenberger , D. A. , Master , L. , Buxton , O. M. , Hale , L. , & Chang , A.-M. ( 2022 ). Worse sleep health predicts less frequent breakfast consumption among adolescents in a micro-longitudinal analysis . The International Journal of Behavioral Nutrition and Physical Activity , 19 ( 1 ), 70 . doi: 10.1186/s12966-022-01265-5 OpenUrl CrossRef ↵ McNeil , J. , Forest , G. , Hintze , L. J. , Brunet , J.-F. , Finlayson , G. , Blundell , J. E. , & Doucet , É . ( 2017 ). The effects of partial sleep restriction and altered sleep timing on appetite and food reward . Appetite , 109 , 48 – 56 . doi: 10.1016/j.appet.2016.11.020 OpenUrl CrossRef PubMed ↵ Mela , D. J. ( 2006 ). Eating for pleasure or just wanting to eat? Reconsidering sensory hedonic responses as a driver of obesity . Appetite , 47 ( 1 ), 10 – 17 . doi: 10.1016/j.appet.2006.02.006 OpenUrl CrossRef PubMed Web of Science ↵ Melanson , K. J. , Westerterp-Plantenga , M. S. , Campfield , L. A. , & Saris , W. H. ( 1999 ). Blood glucose and meal patterns in time-blinded males, after aspartame, carbohydrate, and fat consumption, in relation to sweetness perception . The British Journal of Nutrition , 82 ( 6 ), 437 – 446 . OpenUrl PubMed Web of Science ↵ Monrroy , H. , Borghi , G. , Pribic , T. , Galan , C. , Nieto , A. , Amigo , N. , Accarino , A. , Correig , X. , & Azpiroz , F. ( 2019 ). Biological Response to Meal Ingestion: Gender Differences . Nutrients , 11 ( 3 ), 702 . doi: 10.3390/nu11030702 OpenUrl CrossRef PubMed ↵ Monteleone , P. , Piscitelli , F. , Scognamiglio , P. , Monteleone , A. M. , Canestrelli , B. , Di Marzo , V. , & Maj , M. ( 2012 ). Hedonic eating is associated with increased peripheral levels of ghrelin and the endocannabinoid 2-arachidonoyl-glycerol in healthy humans: A pilot study . The Journal of Clinical Endocrinology and Metabolism , 97 ( 6 ), E917 – 924 . doi: 10.1210/jc.2011-3018 OpenUrl CrossRef PubMed ↵ Nakamura , K. , & Nakamura , Y. ( 2018 ). Hunger and Satiety Signaling: Modeling Two Hypothalamomedullary Pathways for Energy Homeostasis . BioEssays: News and Reviews in Molecular, Cellular and Developmental Biology , 40 ( 8 ), e1700252 . doi: 10.1002/bies.201700252 OpenUrl CrossRef ↵ Nederkoorn , C. , Smulders , F. T. , & Jansen , A. ( 2000 ). Cephalic phase responses, craving and food intake in normal subjects . Appetite , 35 ( 1 ), 45 – 55 . doi: 10.1006/appe.2000.0328 OpenUrl CrossRef PubMed Web of Science ↵ Paoli , A. , Tinsley , G. , Bianco , A. , & Moro , T. ( 2019 ). The Influence of Meal Frequency and Timing on Health in Humans: The Role of Fasting . Nutrients , 11 ( 4 ), 719 . doi: 10.3390/nu11040719 OpenUrl CrossRef PubMed ↵ Pélissier , L. , Lambert , C. , Stensel , D. J. , Beraud , D. , Finlayson , G. , Pereira , B. , Boirie , Y. , Duclos , M. , Isacco , L. , & Thivel , D. ( 2024 ). Individual variability and consistency of post-exercise energy and macronutrient intake, appetite sensations, and food reward in healthy adults . Appetite , 200 , 107568 . doi: 10.1016/j.appet.2024.107568 OpenUrl CrossRef PubMed ↵ Peschel , S. K. V. , Tylka , T. L. , Williams , D. P. , Kaess , M. , Thayer , J. F. , & Koenig , J. ( 2018 ). Is intuitive eating related to resting state vagal activity? Autonomic Neuroscience: Basic & Clinical , 210 , 72 – 75 . doi: 10.1016/j.autneu.2017.11.005 OpenUrl CrossRef PubMed ↵ Polito , R. , Valenzano , A. , Monda , V. , Cibelli , G. , Monda , M. , Messina , G. , Villano , I. , & Messina , A. ( 2022 ). Heart Rate Variability and Sympathetic Activity Is Modulated by Very Low-Calorie Ketogenic Diet . International Journal of Environmental Research and Public Health , 19 ( 4 ), 2253 . doi: 10.3390/ijerph19042253 OpenUrl CrossRef ↵ Prayag , A. S. , Münch , M. , Aeschbach , D. , Chellappa , S. L. , & Gronfier , C. ( 2019 ). Light Modulation of Human Clocks, Wake, and Sleep . Clocks & Sleep , 1 ( 1 ), 193 – 208 . doi: 10.3390/clockssleep1010017 OpenUrl CrossRef PubMed ↵ Qian , J. , Morris , C. J. , Caputo , R. , Garaulet , M. , & Scheer , F. A. ( 2019 ). Ghrelin is Impacted by the Endogenous Circadian System and by Circadian Misalignment in Humans . International Journal of Obesity (2005) , 43 ( 8 ), 1644 – 1649 . doi: 10.1038/s41366-018-0208-9 OpenUrl CrossRef ↵ Rahman , S. A. , Wright , K. P. , Lockley , S. W. , Czeisler , C. A. , & Gronfier , C. ( 2019 ). Characterizing the temporal Dynamics of Melatonin and Cortisol Changes in Response to Nocturnal Light Exposure . Scientific Reports , 9 , 19720 . doi: 10.1038/s41598-019-54806-7 OpenUrl CrossRef PubMed ↵ Roenneberg , T. , Wirz-Justice , A. , & Merrow , M. ( 2003 ). Life between clocks: Daily temporal patterns of human chronotypes . Journal of Biological Rhythms , 18 ( 1 ), 80 – 90 . doi: 10.1177/0748730402239679 OpenUrl CrossRef PubMed Web of Science ↵ Ruddick-Collins , L. C. , Byrne , N. M. , & King , N. A. ( 2019 ). Assessing the influence of fasted and postprandial states on day-to-day variability of appetite and food preferences . Physiology & Behavior , 199 , 219 – 228 . doi: 10.1016/j.physbeh.2018.11.015 OpenUrl CrossRef PubMed ↵ Sargent , C. , Zhou , X. , Matthews , R. W. , Darwent , D. , & Roach , G. D. ( 2016 ). Daily Rhythms of Hunger and Satiety in Healthy Men during One Week of Sleep Restriction and Circadian Misalignment . International Journal of Environmental Research and Public Health , 13 ( 2 ). doi: 10.3390/ijerph13020170 OpenUrl CrossRef ↵ Scheer , F. A. J. L. , Hilton , M. F. , Mantzoros , C. S. , & Shea , S. A. ( 2009 ). Adverse metabolic and cardiovascular consequences of circadian misalignment . Proceedings of the National Academy of Sciences of the United States of America , 106 ( 11 ), 4453 – 4458 . doi: 10.1073/pnas.0808180106 OpenUrl Abstract / FREE Full Text ↵ Scheer , F. A. J. L. , Morris , C. J. , & Shea , S. A. ( 2013 ). The Internal Circadian Clock Increases Hunger and Appetite in the Evening Independent of Food Intake and Other Behaviors . Obesity (Silver Spring, Md .), 21 ( 3 ), 421 – 423 . doi: 10.1002/oby.20351 OpenUrl CrossRef PubMed ↵ Schwartz , M. W. , Woods , S. C. , Porte , D. , Seeley , R. J. , & Baskin , D. G. ( 2000 ). Central nervous system control of food intake . Nature , 404 ( 6778 ), 661 – 671 . doi: 10.1038/35007534 OpenUrl CrossRef PubMed Web of Science ↵ Stevenson , R. J. , Mahmut , M. , & Rooney , K. ( 2015 ). Individual differences in the interoceptive states of hunger, fullness and thirst . Appetite , 95 , 44 – 57 . doi: 10.1016/j.appet.2015.06.008 OpenUrl CrossRef PubMed ↵ Van Cauter , E. , Polonsky , K. S. , & Scheen , A. J. ( 1997 ). Roles of Circadian Rhythmicity and Sleep in Human Glucose Regulation* . Endocrine Reviews , 18 ( 5 ), 716 – 738 . doi: 10.1210/edrv.18.5.0317 OpenUrl CrossRef PubMed Web of Science ↵ Woods , S. C. , Seeley , R. J. , Porte , D. , & Schwartz , M. W. ( 1998 ). Signals that regulate food intake and energy homeostasis . Science (New York, N.Y .), 280 ( 5368 ), 1378 – 1383 . doi: 10.1126/science.280.5368.1378 OpenUrl Abstract / FREE Full Text ↵ Zeitzer , J. M. , Dijk , D. J. , Kronauer , R. , Brown , E. , & Czeisler , C. ( 2000 ). Sensitivity of the human circadian pacemaker to nocturnal light: Melatonin phase resetting and suppression . The Journal of Physiology, 526 Pt 3 ( Pt 3 ), 695 – 702 . doi: 10.1111/j.1469-7793.2000.00695.x OpenUrl CrossRef View the discussion thread. Back to top Previous Next Posted October 11, 2025. Download PDF Supplementary Material Email Thank you for your interest in spreading the word about bioRxiv. NOTE: Your email address is requested solely to identify you as the sender of this article. Your Email * Your Name * Send To * Enter multiple addresses on separate lines or separate them with commas. You are going to email the following Within-subject rhythmicity and stability of hunger, satiety, and physiological markers: insights from a five-day laboratory study in time-isolation conditions Message Subject (Your Name) has forwarded a page to you from bioRxiv Message Body (Your Name) thought you would like to see this page from the bioRxiv website. Your Personal Message CAPTCHA This question is for testing whether or not you are a human visitor and to prevent automated spam submissions. Share Within-subject rhythmicity and stability of hunger, satiety, and physiological markers: insights from a five-day laboratory study in time-isolation conditions Xi Wang , Abhishek S. Prayag , Ni Tang , Yanlong Hou , Claude Gronfier , Tao Jiang bioRxiv 2025.10.10.681393; doi: https://doi.org/10.1101/2025.10.10.681393 Share This Article: Copy Citation Tools Within-subject rhythmicity and stability of hunger, satiety, and physiological markers: insights from a five-day laboratory study in time-isolation conditions Xi Wang , Abhishek S. Prayag , Ni Tang , Yanlong Hou , Claude Gronfier , Tao Jiang bioRxiv 2025.10.10.681393; doi: https://doi.org/10.1101/2025.10.10.681393 Citation Manager Formats BibTeX Bookends EasyBib EndNote (tagged) EndNote 8 (xml) Medlars Mendeley Papers RefWorks Tagged Ref Manager RIS Zotero Tweet Widget Facebook Like Google Plus One Subject Area Physiology Subject Areas All Articles Animal Behavior and Cognition (7635) Biochemistry (17691) Bioengineering (13892) Bioinformatics (41937) Biophysics (21452) Cancer Biology (18588) Cell Biology (25504) Clinical Trials (138) Developmental Biology (13378) Ecology (19899) Epidemiology (2067) Evolutionary Biology (24320) Genetics (15609) Genomics (22506) Immunology (17736) Microbiology (40394) Molecular Biology (17181) Neuroscience (88605) Paleontology (666) Pathology (2832) Pharmacology and Toxicology (4824) Physiology (7641) Plant Biology (15156) Scientific Communication and Education (2045) Synthetic Biology (4294) Systems Biology (9825) Zoology (2271)