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Physiological Regulation of Appetite
1Department of Neuropharmacology, The Scripps Research Institute, La Jolla, California; 2Department of Neuropsychiatry, Osaka City University Medical School, Osaka, Japan; 3Institute of Physiology, Pecs University Medical School, Pecs, Hungary; and 4Laboratoire EA 3666 Homéostasie-Allostasie-Pathologie, Université de Bordeaux 2, Bordeaux, France
Submitted 17 March 2004 ; accepted in final form 3 January 2005
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ABSTRACT |
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 TOP ABSTRACT MATERIALS AND METHODSRESULTSDISCUSSIONGRANTSREFERENCES |
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feeding or drinking; food-associated drinking; meal size or duration; eating rate; intermeal interval; behavioral satiety sequence; bout microstructure analysis; meal pattern analysis; satiation
Although meal patterns have often been analyzed, there has been little uniformity in how meals have been defined (e.g., Refs. 7, 10, 19, 42, 58, 59). Typically, a meal has been conceptualized with two-process models, whereby a cluster of feeding events (i.e., a "meal") is separated from other clusters by a nonfeeding interval (i.e., "intermeal interval") that is long compared with the intervals between feeding events within clusters (i.e., "intrameal intervals"). Drinking has generally not been considered in rodent meal definitions (i.e., drinking-naive meal definitions).
Most species, however, show a close relationship between eating and drinking (17, 46, 52, 56). In the rat, 70–85% of spontaneous daily water intake is temporally associated with feeding (21, 32). In intact rats, the relation of feeding and drinking is reflected in alternating bursts of feeding and drinking, hereafter referred to as "bouts." Several findings indicate that this temporal contiguity is partly regulatory and is not just a by-product of coincident behavioral activity. First, between individuals, rats maintain a consistent food-to-water ratio across feeding episodes (9). Second, within individual rats, the correlation between the amount of food and water consumed in the 40 min surrounding feeding episodes is extremely high (average r = 0.76, calculated from Ref. 21). Third, rats gradually increase their "mealwise" water-to-food ratios to accommodate changes in diet composition that impose greater fluid intake requirements for homeostasis (21, 50). Fourth, restricting food access to the diurnal cycle not only increases diurnal drinking in a stable mealwise fashion but also markedly decreases the amount drunk during the nocturnal cycle, during which rats remain active (21). Fifth, conditions that increase "dry mouth" (e.g., desalivation, decreased food hydration) markedly reduce food intake when water is not available and strongly motivate presumed compensatory increases in prandial drinking when water becomes available (32). Most (
73%) daily water intake in rats occurs in drinking bouts shortly (10 min) before or after feeding bouts, but a significant amount (8–10%) occurs in intervals between closely spaced feeding bouts (11, 21, 48). That drinking precedes, follows, and, especially, links bouts of feeding raises the fundamental question of whether individual feeding bouts as opposed to conjoined clusters of feeding and drinking bouts are better regarded as meals.
Supporting the latter possibility, three-process models, which include a process thought to reflect intrameal (i.e., interbout) drinking, provide better fits for interfeeding intervals than do two-process models (4, 46, 65). Moreover, meals, replete with drinking interruptions, but not the individual feeding bouts that constitute meals, conform to predictions of satiety in the cow (63), observed as an initially low likelihood of resuming feeding immediately after completing a meal, which monotonically increases with time. Although the ingestive physiology of ruminants differs in important respects from that of single-stomached animals, we hypothesize that prandial drinking is a shared, integral component of meal taking for many species.
The method to determine the precise threshold meal interval, or breakpoint, that best distinguishes intrameal intervals from intermeal intervals also has been discussed (see Ref. 19 for a review). Many studies have used arbitrary breakpoints (from <1 to as many as 40 min). More recently, investigators have used several forms of empirical analysis to estimate the threshold meal interval (19, 58, 59, 65). Existing methods focus on the constituent interfeeding intervals, rather than on the resulting estimated meal patterns. Therefore, investigators may not adequately weigh the relative costs of misassigning between-meal intervals as opposed to within-meal intervals.
One mathematical approach that frequently is used to derive breakpoints for meal and lick pattern studies (log-survivorship analysis) has also recently been challenged (61, 62). A core assumption of breakpoint methods based on negative exponentials, like log-survivorship analysis, is that the probability of an event occurring (e.g., starting a meal) does not change as a function of time elapsed from the previous event (i.e., feeding) (20). This premise directly contradicts the concept of satiety, which suggests that the likelihood to initiate feeding would be very low immediately after meal completion and grow with the passage of time. Evidence supporting the predictions of satiety for postmeal intervals has been observed in feeding records of farm animals (47, 65) and indirectly in lick patterns of rodents (31). However, a recent rebuttal concluded that visual inspection of interfeeding intervals in rodents provided no strong evidence that precludes the appropriateness of log-survivorship analysis for breakpoint analysis (10). Resolving this issue is critical. If the instantaneous likelihood of initiating a meal in fact grows with time since an animal has last eaten, then the log-survivorship method is not valid for the study of ingestion because it would inherently split meals.
In light of the preceding discussion, the purpose of the present study was to estimate the threshold meal interval in rats through direct analyses of estimated meal size and of estimated meal duration. The procedure was to determine the interevent interval(s) between feeding- and drinking-directed behavior (i.e., a drinking-explicit meal definition) that provided the most stable joint estimates of meal size and meal duration. Subsequent analyses validated the resultant meal structure by determining 1) the existence of stable individual differences, 2) the latent structure of prandial food and water intake, 3) whether the subsequent likelihood of initiating a new meal over time conformed to predictions of satiety, 4) whether how much a rat ate during a meal was related to how long it previously had not eaten or would not subsequently eat, and 5) the emission of the behavioral satiety sequence in relation to estimated meal termination. The validities of alternate meal breakpoints derived from log-survivorship or frequency histogram analyses of interfeeding intervals were compared (i.e., drinking-naive meal definitions). Because of the possible difficulty of accurately measuring drinking behavior and to allow comparisons to studies that have used breakpoints based only on interfeeding intervals, threshold interfeeding intervals that sought to account for drinking indirectly were also evaluated as potential surrogate breakpoints ("drinking-implicit meals").
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MATERIALS AND METHODS |
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TOPABSTRACTMATERIALS AND METHODSRESULTSDISCUSSIONGRANTSREFERENCES |
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Mature (401–522 g, 13–16 wk of age at the time of testing), male Wistar rats (n = 52; Charles River, Hollister, CA) were used. On arrival, animals were group housed in a 12:12-h reverse-lit, humidity- (60%) and temperature-controlled (22°C) vivarium. Outside of nosepoke testing, standard rodent chow (LM-485 diet 7012, Harlan Teklad, Madison, WI) and water were available ad libitum unless otherwise stated. Animals were acclimated to the vivarium for at least 1 wk before the start of experiments. Surgical and experimental procedures adhered to the National Institutes of Health Guide for the Care and Use of Laboratory Animals (publication no. 85-23, revised 1996) and were approved by the Institutional Animal Care and Use Committee of The Scripps Research Institute.
Microstructural Analyses of Ingestion
For microstructural analyses of ingestion, rats were tested individually in Plexiglas test cages (22 x 22 x 35 cm). Cages had a wire-mesh floor and were located in ventilated, sound-attenuating enclosures equipped with a 1.1-W miniature bulb synchronized to the vivarium's light-dark cycle. Animals were allowed to obtain palatable chow pellets (45-mg precision food pellets, Formula A/I: 60.0% carbohydrate, 3.7% fat, 24.1% protein, 7.0% ash, 5.2% moisture, 370 cal/100 g; P.J. Noyes, Lancaster, NH) from a trough replenished by an automated pellet dispenser (Med Associates, St. Albans, VT). The acquisition of individual pellets was detected by photobeams that were broken when the rat displaced a freely swinging door to access the pellet in the trough. An additional pellet was not delivered until the door returned to a neutral position, thereby allowing resolution of food-directed behavior at the unit of an individual pellet, similar to a classical "eatometer" (33) or "panel-push" system (2). From a hole on the opposite wall of the test cage, rats could make nosepoke responses, detected by photobeams, to obtain delivery of 100-µl aliquots of water governed by a solenoid water valve (W.W. Grainger, Lincolnshire, IL) into an adjacent reservoir. Responses, defined as photobeam breaks of at least 0.5-s duration, were recorded automatically by an IBM PC-compatible microcomputer with 10-ms resolution.
Videotape analyses (n = 12 rats for 2 h each) revealed that almost all (97.2%) nosepoke responses for food were followed by immediate consumption of a pellet. The vast majority (96.2%) of "false-positive" (not immediate) trials occurred when the rat responded twice in rapid succession after the feeder failed to deliver a pellet, suggesting that the false-positive response was food directed. Similarly, 96.8% of nosepokes for water were followed by drinking within 5 s. All false-positive (>5 s latency) water responses observed were followed by drinking within 1 min. Spillage of food pellets in this system was low (mean ± SE: 1.2 ± 0.2% of total responses; n = 105 sessions). The results suggest that the present procedures allow highly reliable study of spontaneous feeding and drinking with excellent quantitative and temporal resolutions (see also Ref. 2).
Behavioral Testing
Experiment 1. The purpose of experiment 1 was to establish the threshold interval between inter-ingestive events (i.e., feeding or drinking) that defined the intermeal interval and to apply this interval to determine the topography, consistency, individual differences, and factor structure in spontaneous prandial intake. Rats in experiment 1 received daily 15-h sessions spanning their active cycle (–1 h lights off through +2 h lights on) until responses had stabilized (±20% responding for initial 12-h food intake for 3 consecutive days). To determine the threshold meal interval, data from four subsequent treatment-free sessions of naive rats (n = 25) were analyzed. Sessions began at 1700 [–1 h lights off, the time at which anticipatory nocturnal feeding often begins (19, 21)], and consecutive test days were 5 ± 0.5 (SE) days apart. Thus stable individual differences were assessed across 3 wk.
Experiment 2. Experiment 2 had five objectives. The first objective was to derive alternative meal breakpoint estimates using prevailing approaches for defining meals. Drinking-naive breakpoints were derived from calculated (20) and subjective (10) applications of log-survivorship analyses of interfeeding intervals and from subjective analyses of the frequency histograms of interfeeding intervals under the assumption of a two-process model (i.e., within-meal vs. between-meal interfeeding intervals) (45). Drinking-implicit breakpoints were also identified as the threshold interfeeding breakpoint that would classify between- and within-meal intervals most similarly to the proposed three-process drinking-explicit definition (i.e., within-meal intervals without drinking vs. within-meal intervals with drinking vs. between-meal intervals). A drinking-implicit interfeeding breakpoint, if valid, could be used in the absence of fluid intake data to perform meal pattern analyses that would indirectly account for prandial drinking.
The second objective was to describe the frequency and duration of prandial drinking pauses as well as the volume of and latency to begin water intake within each pause. For testing, rats in experiment 2 (n = 6) lived in the test cages continuously (24 h/day) except for brief periods (30–60 min) immediately before the onset of their dark cycle, during which they were removed to permit maintenance and cleaning. Once stable responding was attained, records of nocturnal interfeeding intervals from two consecutive sessions were used to estimate alternative meal breakpoints.
The third objective was to test the hypothesis that the meal breakpoints led to a unimodal distribution of estimated postmeal intervals. A bimodal or otherwise heterogeneous distribution of postmeal intervals would suggest that a breakpoint was inappropriately splitting or merging meals.
The fourth objective was to test the hypothesis that the time since a rat had last eaten was associated with its moment-to-moment likelihood of initiating a meal. Specifically, it was determined whether meal definition(s) produced nocturnal postmeal intervals that conformed to predictions of satiety (i.e., an initially very low probability of initiating a meal after completion of a meal that monotonically increases thereafter) or, in contrast, whether nocturnal meal onsets had a constant starting probability across time, a core assumption of log-survivorship analysis.
The fifth objective was to test the hypotheses that the time since a rat had last eaten was associated with the size of its next meal (i.e., the "preprandial correlation") and also that the size of a rat's meal was associated with the duration for which it subsequently would not eat again (i.e., the "postprandial correlation"). These correlations are postulated to reveal how rats flexibly regulate their intake from meal to meal. A significant preprandial correlation is hypothesized to reflect the influence of a positive drive state of short-term insufficiency (colloquially "hunger") on subsequent meal size. A significant postprandial correlation is hypothesized to reflect the influence of the amount consumed within a meal on the subsequent persistence of the state of not eating (colloquially "satiety"). Since the initial studies of Le Magnen and Tallon (43, 44), the magnitude of these correlations have been examined frequently. The prevailing dogma is that rats do not exhibit a preprandial correlation but that they perhaps exhibit a postprandial correlation (15, 16, 18, 19, 43, 57, 60). Some (but not all) investigators have reported postprandial correlations, but such correlations have been suggested to be artifacts of excessively long meal breakpoints or of the inappropriate grouping of data.
For objectives 3–5, records from four sessions within a 7-day period were used to analyze the distribution of postmeal intervals. The first meal from each session was excluded because of the potentially confounding influence of recent experimenter intervention, the uncertainty of the premeal interval, and the lack of access to food during cage cleaning. The final nocturnal postmeal interval was excluded because of the confounding influence of the switch to the diurnal cycle.
Experiment 3. Experiment 3 determined which criteria defined meal terminations that were reliably followed by emission of a behavioral satiety sequence (1, 24), in which sated rats transitioned from termination of feeding to increased behavioral activity (e.g., grooming, sniffing) and finally to rest. The behavioral satiety sequence has most often been studied in food-deprived rats that are provided renewed access to food during their light cycle. However, postprandial resting also occurs after meal offset during the dark cycle in free-feeding rats (10, 15, 48). Nocturnal postprandial resting reportedly occurs in direct proportion to meal size even in the absence of separate sleeping niches (5).
For testing, rats (n = 10) in experiment 3 resided in test cages as did rats in experiment 2 (23.5 h/day). After attainment of stable intake, rats were individually videotaped for 2 h from the onset of their daily nosepoke session concurrent with automated recording of responses for food and water. To compare the construct validity of the definitions, meals were defined using the three-process drinking-explicit [i.e., interresponse interval of 300 s (IRI-300) food or water] or drinking-implicit criteria (i.e., IRI-840 or IRI-1065 food) or using the two-process, drinking-naive criteria (i.e., IRI-19 or IRI-120 food). To assess the behavioral satiety sequence, a reliable, hypothesis-naive rater (É. M. Fekete) coded each rat's behavior from videotape at 5-s intervals as resting (defined to include standing inactivity, but more often observed as the rat lying on the wire-mesh floor), active, eating, or drinking.
Experiment 4. The purpose of experiment 4 was to compare the effects of 22-h food deprivation and chronic food restriction on the estimated meal structure that resulted from the competing meal definitions. If the definitions led to different estimates of the microstructural effects of food restriction, then it would demonstrate the practical relevance of defining meals as outlined here. Rats (n = 11) were provided daily 6-h nocturnal nosepoke sessions, outside of which food and water were available ad libitum, until stable responding for food was achieved. Thereafter, rats were limited to 2 h of daily nosepoke access (day 0) with ad libitum water, but not food, access outside of nosepoke testing. The microstructure of 2-h intake under ad libitum conditions (day 0) was compared with the microstructure of intake after a single 22-h food deprivation (day 1) and also after chronic, scheduled food restriction (day 7) using the proposed drinking-explicit meal definition (IRI-300 food or water) and alternative two-process, drinking-naive (IRI-19 or IRI-120 food) and three-process, drinking-implicit (IRI-840 or IRI-1065 food) meal definitions.
Statistical Analyses
Logarithmic scales were used for graphical presentation of frequencies and rates of change involving time, as has been recommended to resolve meal structure (62).
Experiment 1.
ESTIMATION OF DRINKING-EXPLICIT THRESHOLD MEAL INTERVAL.
The drinking-explicit threshold meal interval was estimated by determining the interevent interval(s) between feeding- and drinking-directed nosepokes that provided the most stable, joint estimates of meal size for food and total meal duration, thereby minimizing the negative consequences of misassigned events and time. This method is related to previous approaches in which transitions or stabilities in the slope of a function were identified through first-derivative analyses (14). The present approach relies on the assumption that, if the distributions of within- and between-meal intervals overlap, they do so on descending and ascending portions of their distributions, respectively, as shown previously (10, 65). Intervals close to the true meal threshold criterion misassign relatively few events and, therefore, are associated with a local stability in the rate of change in both measures of estimated meal structure. Intervals further from the "true" threshold interval will misassign varying quantities of events determined by the underlying distributions of within- and between-meal intervals. Consequently, for first-derivative functions of estimated meal size and duration, the threshold interval is marked not only as a stability but also as a local minimum inflection point (or range of points).
A meal for rats was defined as any burst of responses for food or water that contained at least five food-directed responses, or 0.225 g, a value more than twofold lower than empirically estimated lower bounds for meal size (0.457–0.617 g) (19). Average meal characteristics (i.e., meal size for food and total duration) were estimated by using a series of maximum IRIs ranging from 30 s to 30 min, where 30 min was used as the upper limit because it was the first interval at which all nocturnal ingestion for any subject was characterized as a single meal, thereby creating an artificial floor effect for longer intervals. Having determined the zero-order functions for the estimated meal characteristics, we calculated local rates of change in the slope as the difference in the value of the zero-order function for consecutive intervals per standardized unit of time (30 s).
Three approaches were used to define the minimum inflection point(s) that marked the threshold meal interval in the "first-order" function. First, the absolute minimum was identified visually. Second, linear regression was performed on mean values spanning the candidate minimum to determine whether the average first-order function was fit significantly better in this range by one line as opposed to two or more lines intersecting at the minimum (i.e., segmented, multi-phase, or "hockey stick" regression) (25). The latter solution would suggest a threshold meal interval at the minimum(s) that provided the best joint fit. Finally, multivariate adaptive regression splines (MARS), a brute force segmented regression procedure (22), was applied to the aggregate, first-order individual data. MARS, an objective, exhaustive, stepwise regression method, conjointly seeks to maximize the variance explained and minimize model overfitting by identifying incrementally significant breakpoints in the slope of the predictor space. The specific question of interest was whether MARS identified the candidate minimum as a statistically relevant inflection point. Analyses were limited to the nocturnal hours of observation because the microstructures of diurnal and nocturnal intake differ (47). Day was the unit of analysis.
MEAL STRUCTURE PARAMETERS.
The estimated threshold meal interval was used to calculate descriptive statistics of average nocturnal meal structure. Parameters included the number of meals; the average size, duration, and response rate of meals; the average intermeal interval; the food-to-water ratio; and the satiety ratio. Meal duration was calculated as the total time from the first to last response of a meal, and duration of eating and drinking within the meal was calculated as the duration of consecutive responses for food or water, respectively. Thus transitions between eating and drinking were included in total meal duration but not in the specific durations of eating or drinking. Meal sizes for eating and drinking were calculated separately as the average number of food or water-directed responses during meals. Rates of eating and drinking were calculated by dividing each meal size with its respective duration. The intermeal interval was defined as the interval from the last feeding response of a meal to the first feeding response of the next meal. The food-to-water ratio, an index of the balance between food and fluid intake, was defined as the ratio between the quantities of food and water consumed per meal. Finally, satiety ratio, an index of the noneating (i.e., satiety) time produced by each gram of food consumed, was calculated as the average intermeal interval divided by the average meal size for food.
INDIVIDUAL DIFFERENCES.
To determine whether stable, individual differences existed in the microstructure of ingestion and to determine whether group means were consistent over time (23), a mixed-model ANOVA was performed with subject as a grouping factor and day of testing as a within-subject factor. The magnitude of individual differences, indicated by significant effects of subject, were quantified with two-way random effect intraclass correlations (ICC; 57a), indicating the reliability of absolute differences between single observations [ICC(2,1)] or the means of the subjects' four observations [ICC(2,4)]. To allow comparison to individual differences, significant day effects, indicating varying group means, were calculated as an 
measure of effect size from the following simplified formula (Eq. 1)
 | (1) |
(from David C. Howell, University of Vermont, personal communication).
FACTOR STRUCTURE OF THE MICROSTRUCTURE OF INGESTION.
To identify constructs underlying differences in the microstructure of daily, nocturnal ingestion, meal parameters were subjected to a principal component factor analysis with varimax rotation. Five orthogonal factors with eigenvalues >1 were retained (30), a threshold that corresponded to visual identification of the scree plot (8). Factor loadings were computed and interpreted.
Experiment 2.
OBJECTIVE 1: ALTERNATIVE DRINKING-NAïVE AND DRINKING-IMPLICIT BREAKPOINTS.
Log-survivorship analysis was performed as described previously (20). Briefly, the attrition of interfeeding intervals (log y-axis) was plotted as a function of increasing duration (x-axis). The following double negative exponential (Eq. 2) was fit to the resulting semi-logarithmic scatterplot
 | (2) |
where y(x) represents the proportion of interfeeding intervals longer than duration x, p represents the proportion of interfeeding intervals that were alleged between meal intervals, 1 – p represents the proportion of interfeeding intervals that were alleged within-meal intervals, and w and b represent the initiation rates of within- and between-meal responses, respectively. The associated threshold meal criterion (T), reflecting the intersection of the more vertical, fast process (intrameal intervals) with the more horizontal, slow process (intermeal intervals), was calculated as follows (Eq. 3)
 | (3) |
To identify a two-process, drinking-naive, frequency histogram-based breakpoint, the frequency histogram of interfeeding intervals was constructed using equal-sized logarithmic time bins, and the trough between the alleged distributions of within- and between-meal intervals was estimated.
To identify a three-process, drinking-implicit breakpoint, separate probability functions were fit to the frequency histograms of 1) intrameal feeding intervals that did not contain drinking, 2) intrameal feeding intervals that contained drinking, and 3) intermeal feeding intervals, as determined from the proposed drinking-explicit definition. For each distribution, the peak distribution function with the greatest r2 was accepted (TableCurve 2D 5.01, Systat Software, Point Richmond, CA). The possibility of non-Gaussian functions (i.e., log-normal, Pearson family, or Weibull) was included to permit the possibility of finite/semi-infinite (as opposed to infinite) as well as time-dependent, asymmetric distributions, both of which would be consistent with the constructs of within-meal satiation and between-meal waning of satiety. We assessed the fit of the combined mixed distribution model with PeakFit 4.12 (Systat Software).
OBJECTIVE 2: DESCRIPTION OF PRANDIAL DRINKING PAUSES.
Descriptive statistics were calculated for the frequency and duration of prandial drinking pauses as estimated under the drinking-explicit definition and for the quantity of and latency to initiate water intake within each pause. The frequency distribution of the latency to initiate drinking within each pause was graphed as raw data and as a logarithmic function of time to determine whether a single distribution described the initiation of drinking between presumed bouts of feeding. The relation of the duration of drinking pauses to the amount drunk during the pause was calculated as a Pearson's correlation.
OBJECTIVE 3: DISTRIBUTION OF POSTMEAL INTERVALS.
The frequency histograms of estimated postmeal intervals under each breakpoint were graphed as raw data and as a logarithmic function of time to visualize the underlying distribution(s).
OBJECTIVE 4: PROBABILITY OF MEAL INITIATION.
To compare the estimated likelihood of initiating a meal across time under each meal definition, the "instantaneous" probability of meal initiation was calculated as 100 x (the incremental number of rats that initiated their second meal within the time bin of interest/the number of rats that had not yet initiated a second meal at the onset of the time bin of interest). Meal initiation probabilities were examined on short (0–16 min; starting within 4 min) and long (0–4.5 h; starting within 15 min) postmeal time scales through an instantaneous starting probability of 100%. For comparison, the random slow process that purportedly governs meal initiation under the assumptions of log-survivorship analysis was depicted as a function of the initiation rate b, as calculated in objective 1.
OBJECTIVE 5: PREPRANDIAL AND POSTPRANDIAL CORRELATIONS.
To determine the magnitude of the pre- and postprandial correlations, separate Pearson's correlations were performed for each rat to determine the relation between the sizes of all the nocturnal meals consumed during the test sessions and the log-transformed duration of the rat's contiguous pre- or postmeal intervals, respectively. To determine the average pre- and postprandial correlations across rats, individual Pearson's correlations were combined across animals using df-weighted fixed effect meta-analysis, as described elsewhere (66). This procedure eliminates the artifacts introduced from performing correlations on raw intermeal intervals, which have a highly positively skewed distribution (see RESULTS), from averaging each animal's data before the correlation was performed, or from performing the correlation on all meals across circadian phases or across all subjects [i.e., "ecological fallacies" (53)]. Averaging and pooling have distorted estimates of the true pre- and postprandial correlations in previous studies (see also Refs. 7, 15, 49, 60). A modification of the Stouffer's method was used to calculate associated P values (54). The relative magnitudes of the pre- and postprandial correlations were then compared by use of df-weighted fixed effect meta-analysis. The software packages used were Excel 2003 (Microsoft, Redmond, WA) and Comprehensive Meta-Analysis 1.0 (Biosoft, Englewood, NJ).
Experiment 3.
Behaviors were analyzed for 15 min beginning from the first full minute after the last food response of the estimated meal. A behavioral satiety sequence was judged to be present if the rat initially exhibited primarily active (i.e., grooming, rearing, sniffing, locomotion) or drinking behaviors that were subsequently replaced by a predominance of resting behavior in the absence of feeding. To determine whether the meal definitions influenced the frequency with which a behavioral satiety sequence was observed shortly after estimated meal completion, a 
2 analysis was performed. In addition, we compared the average frequency of each behavior category using separate 5 (meal definition) x 15 (time: 1-min bins) repeated-measure ANOVAs.
Experiment 4. To determine whether the meal definitions gave rise to different interpretations of the effects of acute or chronic food restriction on the microstructure of feeding, 2-h meal pattern measures were subjected to a 5 (meal definition) x 3 (restriction: ad libitum vs. 1 or 7 days of restricted feeding) repeated-measures ANOVA, and post hoc within-subject Newman-Keul's tests were used to interpret significant effects. The focus of the present analyses was on the effects of meal definition; detailed analyses and interpretations of the microstructural effects of food restriction are discussed elsewhere (26).
Additional software packages used were Systat 10.0 (SPSS, Chicago, IL), SPSS 10.0 (SPSS), InStat 3.0 (GraphPad, San Diego, CA), DataFit 8.0 (Oakdale Engineering, Oakdale, PA), and MARS 2.0 (Salford Systems, San Diego, CA).
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RESULTS |
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TOPABSTRACTMATERIALS AND METHODSRESULTSDISCUSSIONGRANTSREFERENCES |
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Estimation of drinking-explicit threshold meal interval. Figure 1 shows the zero- and first-order functions of estimated average meal duration for rats as a function of the maximum IRI between food and water responses defined to continue a meal (i.e., drinking-explicit meal definition). A large range (4–62 min) was evident in the estimated average meal duration depending on the maximum IRI used (Fig. 1, top). Linear regression revealed that the resulting first-order scatterplot (Fig. 1, middle) was fit better by a two-line solution than by a single regression line (P < 0.0001). IRI values in the range of 210–720 showed a relative stability in the rate of change, with an absolute minimum at 300 s. Linear regression on mean values revealed that two lines intersecting at this minimum provided the best joint fit of any two-line solutions. Values to the left of the minimum indicated a decreasing rate of change (y = 120 – 0.324x, r2 = 0.91), consistent with the predicted decrease in misassigned time. Values to the right of the minimum indicated an increasing rate of change (y = 16.6 + 0.35x, r2 = 0.77), consistent with the predicted increase in misassigned time. MARS analysis of the aggregate individual data revealed breakpoints in the slope of the first-order function at the following four intervals: IRI 90, 210, 300, and 1,200 s [Fig. 1, bottom; basis functions (BF): BF1 = max(0,IRI-300), BF2 = max(0,IRI-300), BF3 = max(0,IRI-90), BF4 = max(0,IRI-210), BF5 = max(0,IRI-1200); y = 240.82 + 0.78BF1 – 0.68BF2 – 1.15BF3 + 0.39BF4 + 0.06BF5]. Of these, the IRI-300 knot was the only minimum inflection point. Similar results were obtained from inspection of the first-order function of estimated meal size (data not shown). Accordingly, the maximum IRI for rats was defined as 300 s between feeding or drinking events.
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50–60% at 35–65 min after the last meal.
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Factor structure of nocturnal prandial intake. Table 2 shows the factor structure of the measures of nocturnal prandial intake as revealed by principal components factor analysis. Five interpretable, orthogonal factors, each accounting for 13.5–26.2% of the total variance, were retained. Factor 1, a "meal patterning" factor, described whether subjects had few, but large, meals or many, but small, meals. Factor 2, which included the food-to-water ratio measure and was termed "prandial thirst," supported the hypothesis that meal-related drinking is partly determined by individual-specific, regulatory needs related to feeding. Prandial thirst was dissociable from food intake per se, however, as evidenced by the absence of large loadings for the feeding measures. Factor 3, a satiety factor, was the only factor predictive of the rats' total nocturnal food intake and associated strongly with the aptly named satiety ratio. Factors 4 and 5 reflected the rats' drinking and eating rates, respectively. In summary, constructs underlying "how" subjects ate and drank (factors 1, 4, and 5) were largely dissociable from "how much" (factors 2 and 3). The only "how" measures that specifically loaded on the factors describing "how much" was consumed were the food-to-water ratio for fluid and the satiety ratio for food.
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Objective 1: estimation of alternative drinking-naive and drinking-implicit threshold meal intervals. To compare the validity of other methods used to estimate the meal threshold criterion, drinking-naive meal breakpoints were identified using prevailing approaches: log-survivorship and frequency histogram analysis. Figure 3 depicts the aggregate frequency histogram of all interfeeding intervals (n = 6,361) from 12 nocturnal feeding sessions of six rats (bottom) and the corresponding log-survivorship breakpoint analysis (top). An inverse y-weighted double exponential function fit the interval attrition scatterplot (y = 0.9693e–0.2295x + 0.0307 e–0.0003977x; r2 = 0.96) and indicated an intermeal breakpoint of 18.6 s between feeding events (see Fig. 3, top). However, the aggregate frequency histogram (Fig. 3, bottom) clearly showed that many intervals beyond the purported meal threshold (e.g., 18–47 s) were continuous with the large distribution of presumed intrameal intervals and discontinuous from the distributions of much less frequent, longer intervals. The results suggested that the calculated breakpoint misclassified intervals at the vertex of the log-survivor function and thereby split meals near the shoulder of the fast (i.e., intrameal) distribution. Therefore, we also identified an intermeal breakpoint of 120 s based on the subjective convention of defining the threshold just to the right of the rapid acceleration in slope, an approach still in practice and that gave a value consistent with rat feeding studies that used log-survivorship analysis in this manner (10, 23).
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Objective 2: description of prandial drinking pauses.
Under the drinking-explicit definition, each of the six rats exhibited prandial drinking pauses during their nocturnal meals (mean + SE: 4.7 + 1.0 pauses/night). Individual pauses averaged slightly longer than 5 min (5.7 + 0.6; range: 0.4–12.7 min) and contained 2 ml of water intake (1.9 + 0.3; range: 0.1–6.2 ml), with a strong correlation present between the duration of the pause and the quantity drunk [r(28) = 0.64, P < 0.0001]. Latencies to initiate drinking within the pause were distributed unimodally as a log-normal function of time (not shown), with a mean transformed interval of 45 s (range: 7–269 s) from the prior feeding response.
Objective 3: effect of meal definition on the estimated postmeal interval. Figure 5 shows the effects of the meal definition used on the estimated distribution of postmeal intervals. Figure 5, left, depicts postmeal intervals on a linear time scale as has been conventionally done. All breakpoints suggested a highly positively skewed, non-Gaussian distribution for postmeal intervals. Logarithmic transformation of time (Fig. 5, right) revealed a homogenous, unimodal distribution of postmeal intervals for the drinking-explicit breakpoint (IRI-300 food or water), indicating that postmeal intervals are approximately log-normally distributed. Thus, under the drinking-explicit breakpoint, meals were estimated to be spontaneously initiated as an exponential function of time since the prior meal, entirely consistent with the predictions of satiety.
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The drinking-implicit breakpoints (IRI-840 or IRI-1065 food) yielded unimodal log-normal distributions. However, the drinking-implicit log-transformed distributions were slightly truncated at the left tail relative to the drinking-explicit definition (see Fig. 5).
Objective 4: effect of meal definition on the estimated instantaneous probability of initiating a meal. Figure 6 shows the effects of the meal definition used on the estimated instantaneous probability of initiating a meal as a function of the time since the rat had last eaten. Starting probabilities are shown on short (Fig. 6, left, 0–16 min) and long (Fig. 6, right, 0–4.5 h) postmeal time scales. As shown in Fig. 6, none of the observed meal initiation functions fits the basic assumption of log-survivorship analysis (Fig. 6, top, dashed lines), which is that the meal initiation rate (b) was constant regardless of the time since prior meal completion.
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The drinking-naive log-survivorship and frequency histogram definitions (IRI-19 or IRI-120 food, Fig. 6, middle) did not suggest a monotonically increasing likelihood of initiating a meal after meal completion. Rather, at brief postmeal intervals (<12 min), instantaneous probabilities of starting a meal within 4 min were as high as 30% and then decreased substantially to levels that resembled the drinking-explicit definition (Fig. 6, left). The relative excess of starting probabilities at brief postmeal intervals is reflected in the bimodal distribution of postmeal intervals in Fig. 5 and supports the hypothesis that the definitions systematically (but inadvertently) split meals.
On long time scales (Fig. 6, right), the three-process drinking-implicit definitions (IRI-840 or IRI-1065 food, bottom) suggested monotonically increasing starting probabilities, which closely resembled those that resulted from the drinking-explicit definition. However, at short postmeal intervals (Fig. 6, left), the drinking-implicit definitions yielded smaller starting probabilities than the drinking-explicit definition. The hypothesized underestimation of early starting probabilities is reflected in the truncated distribution of postmeal intervals in Fig. 5 and is hypothesized to reflect that the drinking-implicit definitions systematically overlooked the briefest postmeal intervals and thereby merged some meals.
Objective 5: effect of meal definition on the estimated preprandial and postprandial correlations. Table 3 summarizes the observed relation of meal size to the contiguous preprandial and postprandial intervals. The drinking-explicit meal definition yielded small to moderate, but significant, average preprandial and postprandial correlations that were of comparable magnitude (see Table 3). Within every individual subject, the correlations were positive. Thus, as shown in Fig. 7, subjects reliably ate larger meals when they had not eaten for longer periods of time (a preprandial correlation) and went longer without eating after larger meals (a postprandial correlation). No other meal definition led to the identical pattern of findings. Only one of the drinking-implicit definitions (IRI-1065 food) also led to the conclusion that the magnitude of the preprandial and postprandial correlations did not differ reliably, but it suggested correlations that were less robust than those obtained from the drinking-explicit definition (see Table 3).
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Experiment 3
Effect of meal definition on estimated emission of the behavioral satiety sequence.
Meal definition also significantly influenced the frequency with which a behavioral satiety sequence was observed shortly after estimated meal termination [2(4) = 35.4, P < 0.0001]. A behavioral satiety sequence was evident within the 15-min analysis period in 9 of 10 rats under each of the drinking-inclusive definitions, with the IRI-300 food or water and IRI-840 food definitions yielding identical results. Under each of the drinking-inclusive definitions, the lone rat not to exhibit a behavioral satiety sequence did not resume feeding within the observation window, but simply remained active. In contrast, a subsequent behavioral satiety sequence was very rare under the drinking-naive, two-process meal definitions (0/10 and 1/10 for the IRI-19 and IRI-120 food definitions, respectively). Under the drinking-naive definitions, 60% of rats ate during more than one-third of the rating samples within 6 min of meal completion.
Figure 8 shows the average time-sampled behavior of rats after estimated meal termination as judged by each definition. Meal definition significantly influenced the frequency with which eating [definition: F(4,36) = 17.55; definition x time: F(56,504) = 1.85, P < 0.001], active behaviors [F(4,36) = 5.78; F(56,504) = 3.13, P < 0.001], drinking [F(56,504) = 2.26, P < 0.001], and rest [F(4,36) = 30.43; F(56,504) = 5.56, P < 0.001] were observed after estimated meal termination. Under the drinking-inclusive definitions, primarily active and drinking behaviors initially followed meal completion but subsequently diminished until resting predominated after 6 min. Under the drinking-naive definitions, activity and drinking also initially predominated, but they were rapidly supplanted by resumption of feeding after 3–5 min. At no time under the drinking-naive definitions did resting comprise more than one-third of behavioral samples.
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Effect of meal definition on estimated effect of food restriction on meal patterning. Meal definition also significantly influenced estimates of meal frequency, meal size, duration of feeding within meals, and eating rate in rats under ad libitum or time-restricted feeding schedules. This was reflected in highly significant meal definition and, more importantly, meal definition x restriction effects on meal frequency [F(4,40) = 159.21, P < 0.0001; F(8,80) = 54.84, P < 0.0001], meal size [F(4,40) = 55.94, P < 0.0001; F(8,80) = 12.72, P < 0.0001], and meal duration [F(4,40) = 33.62, P < 0.0001; F(8,80) = 5.43, P < 0.0001]. Meal definition also affected the estimated rate of eating within meals [F(4,40) = 164.82, P < 0.0001]. As shown in Fig. 9, the drinking-explicit meal definition (IRI-300 food or water) led to the perception that food restriction selectively increased meal size and duration (Fig. 9, B and C) and tended to slow the average sustained rate of eating (Fig. 9D) without altering meal frequency (Fig. 9A).
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In contrast to all of the drinking-inclusive definitions, the drinking-naive definitions (IRI-19 and IRI-120 food) led to the perception that food restriction selectively increased meal frequency (see Fig. 9A) without altering the size or duration of meals (Fig. 9, B and C). Across feeding conditions, the drinking-naive definitions also tended to overestimate meal frequency and underestimate meal size and duration.
To illustrate the bases for these differences, Fig. 10 depicts the event records of feeding and drinking for two representative rats under ad libitum and food-restricted conditions. As can be seen, the log-survivorship and frequency histogram-based drinking-naive definitions (IRI-19 and IRI-120 food) did not consider meals to continue across bouts of drinking that were interposed between feeding bouts. In contrast, both the drinking-explicit (IRI-300 food or water) and drinking-implicit definitions (IRI-840 and IRI-1065 food) characterized meals as continuous periods of ingestion, with alternating bouts of feeding and drinking. These differences underlie the contrasting effects of meal definition on meal frequency (Fig. 9A) and meal size (Fig. 9B).
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The event records also illustrate that the quantity of prandial drinking (i.e., beginning or terminating water intake within 5 min of feeding) increased markedly in association with the restriction-induced hyperphagia, despite the fact that water was available ad libitum throughout food deprivation. From baseline levels (6.7 ± 1.7 ml), prandial drinking during the 2-h period doubled (13.1 ± 1.2 ml) and more than tripled (22.8 ± 2.0 ml) after acute and chronic food restriction, respectively (P < 0.00005). The finding further supports the hypothesis that food-associated drinking is regulatory and not coincidental.
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DISCUSSION |
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TOPABSTRACTMATERIALS AND METHODSRESULTSDISCUSSIONGRANTSREFERENCES |
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The inclusion of drinking had dramatic effects on the interpretation of changes in feeding patterns. Under the drinking-explicit definition, food restriction was observed to increase meal size and duration selectively. In contrast, the drinking-naive meal definitions did not consistently link feeding bouts across interposed prandial drinking bouts. Consequently, drinking-naive definitions reported that food restriction selectively increased meal frequency. The findings support the hypothesis that food-associated drinking is a behaviorally integrated component of meals that should be allowed for in meal definitions. Furthermore, the findings demonstrate the utility of identifying transitions in the slope of a function through first-derivative analysis, a method that can be applied to understanding better many behaviors regulated by multiple underlying processes.
Relevance for Log-Survivorship Analysis
As discussed earlier, prior rodent meal pattern studies have used arbitrary or, less often, mathematically derived meal definitions. The most prevalent mathematical method has been log-survivorship analysis (20, 59). As discussed, however, survivorship analysis assumes that the probability of initiating a meal is independent of the time from the last feeding event. The present data forcefully show that meal initiation does not follow the assumed time course in the rat. None of the observed meal initiation functions (not even those derived from log-survivorship analysis itself) suggested a constant probability of meal initiation. Rather, the drinking-inclusive definitions indicated that the likelihood of initiating a meal increased monotonically as a function of time since the prior meal. Relative to the calculated log-survivorship null hypothesis, this reflected a lower probability of resuming feeding following brief postmeal intervals and a higher probability of resuming feeding following longer postmeal intervals. Together with similar observations in pigs and cows (47, 65), the results support the hypothesis that a construct like satiety regulates meal initiation across diverse species. As important, the present results indicate that log-survivorship analysis is not valid for identifying breakpoints in meal or lick pattern analysis. Because the instantaneous likelihood of initiating a meal in fact grows with time since an animal has last eaten, the log-survivorship method will inherently split meals at brief postmeal intervals.
Relevance for Drinking-Naive Meal Definitions
Previous studies that derived intermeal breakpoints in the rat using log-survivorship or frequency histogram analysis have relied on interfeeding intervals without regard to drinking behavior. With the present data set, a similar two-process (drinking-naive) analysis of frequency histograms would have suggested a meal breakpoint of 1–5 min between feeding responses, thresholds that systematically split clustered bouts of feeding (i.e., meals) at brief prandial drinking pauses. This was evident in the present study as 1) the nonunimodal distribution of postmeal intervals and meal starting probabilities, 2) the relative lack of a behavioral satiety sequence following estimated meal termination, and 3) the failure to consider meals to continue across interposed drinking bouts under ad libitum and restricted feeding conditions. Inspection of frequency histograms under the drinking-explicit definition indicated a distribution of intermediate duration drinking pauses that extended beyond the drinking-naive breakpoint of interfeeding intervals. During pauses to drink, draughts were initiated rapidly (45 s latency) as a log-normal function of time and averaged 1.9 ml. The substantially greater influence of more frequent intrabout intervals (in the y-dimension)
