A few years ago, we published a fantastic article about chrononutrition by Danny Lennon of Sigma Nutrition. The article was called, “Chrononutrition: Why Meal Timing, Calorie Distribution & Feeding Windows Really Do Matter.” I suspect that more than a few people were surprised by this article. Anyone familiar with Stronger By Science and Sigma Nutrition content knows we’re perpetually reluctant to create content that promotes “majoring in the minors,” sweating the small details, or creating theoretical mountains out of mechanistic molehills.
For the last decade or two, a particular subsect of the evidence-based nutrition world has been engaged in a mad dash to simplify every exercise and nutrition topic to the most basic elements and heuristics possible, such that every possible question entered into the theoretical flowchart ends at one of three recommendations: eat the right number of calories, eat enough protein, and lift weights sometimes. Without question, it is very useful to take a hierarchical approach to categorizing training and nutrition variables based on their importance and impact. One might even arrange these variables in a pyramid-type structure, with more impactful variables serving as the “base,” and relatively less impactful variables filling in the upper levels of the structure, effectively serving as “icing on the cake.” I don’t support allocating excessive stress and focus toward low-impact training and nutrition variables, nor do I promote the stressful practice of majoring in the minors. However, my general preference is to have a relatively large menu of safe, effective, and evidence-based strategies at my disposal. It seems counterproductive to be so eager to dunk on biohackers that we abandon pragmatism entirely in the process. Why should we insist upon restricting ourselves to the most minimalistic toolkit possible, when there is empirical evidence supporting feasible “tools” with no cost and excellent safety profiles? You can build a house without much more than your hands, but a full toolbox can be awfully convenient.
Being a Research Spotlight, this isn’t really the time or place for a comprehensive overview of the mechanisms by which timing and distribution of energy intake, relative to one’s circadian rhythm, might influence metabolic outcomes. The short version of the concept is that circadian rhythms exist, and are important in the fields of biology and physiology. We have multiple “body clocks” that are simultaneously running, and they coordinate a wide range of hormone cascades and physiological processes. The simplest and most obvious example is our sleep/wake cycle, which corresponds to environmental light/dark cycles. We’re exposed to morning sunlight, and we experience physiological processes that wake us up; we’re exposed to dim light (leading to darkness) in the evening, and we experience physiological processes that ease us into sleep. Several hormonal and metabolic adjustments happen along the way, which help nudge us toward wakefulness and nudge us toward sleepiness. Circadian rhythms are implicated in the development of key metabolic diseases (such as obesity, diabetes, and cardiovascular disease), and research indicates that nutrient ingestion can impact our body clocks. As such, there are mechanistic links between timing of nutrient ingestion and physiological responses to meals, which may have implications for things like energy expenditure, appetite regulation, body composition, glycemic control, and other cardiometabolic risk factors. For a deeper dive into the mechanistic basis of “chrononutrition,” be sure to check out the Stronger By Science article by Danny Lennon, or a recent review paper by Charlot et al (2).
Now, moving on to the presently reviewed study (1). This is actually a secondary analysis of previously collected data, which were obtained during the Comprehensive Assessment of Long-term Effects of Reducing Intake of Energy (CALERIE) study. In short, this was a multi-center trial that recruited healthy young participants (aged 21-50) with BMI values between 22-27.9. Participants were either randomly assigned to the control group (keep doing your thing), or an experimental group that underwent an intensive behavioral intervention. This intervention involved striving to maintain a 25% energy deficit over the two-year study period; dietary targets were provided by the research team, but participants were instructed to consume a self-selected diet while monitoring their calorie intake on a daily basis. The purpose of the trial was to explore the potential cardiometabolic benefits of calorie restriction in healthy adults whose cardiometabolic risk factors were already within “normal” ranges at baseline.
The primary focus of this secondary analysis was to explore how calorie distribution (i.e., timing of energy intake throughout the day) impacted participants’ ability to successfully restrict their calorie intake or achieve weight loss during the trial. For the purpose of this Research Spotlight, I want to focus on what is, in my opinion, the most straightforward analysis performed. For each participant, they calculated their typical “time of 50% of calories,” which serves as a rough indicator of calorie distribution. If you tend to shift your calories earlier in the day, you’ll reach your 50% point (that is, the time at which you’ve consumed half of your total calories for that day) earlier, and this value would be lower. Conversely, someone who shifts their calories later in the day will find that they don’t reach the 50% mark of their daily caloric intake until later in the day, and their value for this variable would be higher. Other relevant variables include calorie restriction (as a percentage, with positive values representing successful restriction, or a drop from baseline calorie intake) and weight change (as a percentage, with negative values representing weight loss).
The researchers constructed three regression models to determine how this variable (time of 50% of calories) impacted participants’ ability to restrict calories and lose weight. In model 1, the researchers assessed how well a model including calorie restriction (%), time of 50% of calories, and the interaction term between the two variables (calorie restriction × time of 50% of calories) predicted weight change (%) during the trial. In model 2, the researchers assessed how well a model including only one variable (time of 50% of calories) predicted weight change (%) during the trial. Finally, in model 3, the researchers assessed how well a model including this same predictor variable (time of 50% of calories) predicted calorie restriction (%) during the trial.
The results from all three regression models are presented in Table 1. In short, calorie restriction was highly predictive of weight change, and explained nearly 40% of the variance in this outcome. The key variable of interest (time of 50% of calories) was a statistically significant predictor of weight change and calorie restriction, which suggested that late-shifted eating patterns were associated with slightly less weight loss and slightly less energy restriction. However, this variable only explained about 2% of the variance in each outcome. In other words, the impact of shifting calories earlier or later (based on this particular metric) was different from zero (statistically speaking), but the magnitude of this effect was so close to zero that one could very justifiably consider it to be functionally irrelevant. The researchers did a bunch of other analyses with different metrics reflecting the timing of meals and energy intake, but this particular analysis provides a pretty suitable snapshot of the overall findings.
This study certainly isn’t the first to hint at some modest physiological benefits from earlier feeding windows. In fact, quite a few studies have reported beneficial effects on outcomes such as energy expenditure, weight loss, and glycemic control by implementing “early time-restricted feeding,” which emphasizes a relatively short daily feeding window that is biased toward the earlier hours of the day. With this in mind, early time-restricted might sound like a slam dunk, but it’s not quite that simple. It’s not hard to find individual examples of randomized controlled trials reporting favorable cardiometabolic outcomes in response to time-restricted feeding, it’s important to survey the collective literature rather than focusing on isolated findings.
A 2021 meta-analysis by de Oliveira Maranhão Pureza sought to quantify the effects of early time-restricted feeding on outcomes including resting metabolic rate, triglycerides, total cholesterol, HDL-cholesterol, LDL-cholesterol, fasting blood glucose, insulin levels, insulin sensitivity, C-reactive protein, interleukin-6, cortisol, leptin, ghrelin, peptide YY, glucagon-like peptide, hemodynamic parameters, and appetite (3). They identified 9 randomized controlled trials that fit their inclusion criteria, but only found statistically significant benefits for fasting blood glucose and HOMA-IR, which is an index used to assess insulin sensitivity. Furthermore, the funnel plot for fasting blood glucose response reflected a catastrophic degree of publication bias, and the quality of the evidence (assessed outcome-by-outcome) was typically “very low-quality,” with only one outcome reaching the threshold to be considered “low quality.” It’s also important to note that the included studies tended to be very short in duration (often a week or less), and early time-restricted feeding interventions were often compared to control groups with longer feeding windows that terminated later in the evening (i.e., it’s hard to say if benefits should be attributed to a shorter feeding window or an earlier feeding window, or a combination of both). In summary, this body of literature is starting to grow pretty rapidly, and generally hints at cardiometabolic effects that are either neutral or slightly positive, but findings are quite preliminary in nature.
At this point, you might be wondering, “Are early feeding windows optimal or not?” I’d like to counter with a question that is, in my opinion, even more important: what exactly does “optimal” mean, practically speaking?
The evidence-based fitness world has a tendency to get fixated on the concept of optimality. We constantly observe discussions about the optimal protein intake, the optimal training frequency, the optimal set volume, the optimal rate of weight loss, and so on. There’s nothing inherently wrong with this quest for optimality, but the term “optimal” gets used so frequently that I wonder if people take time to carefully consider what it means. From my perspective, optimality simultaneously exists across several domains. Something can be physiologically optimal, psychologically optimal, or behaviorally optimal. A strategy might be optimal for your specific skill set or experience level, or optimal for your unique collection of preferences or lifestyle constraints. When we try to identify a single strategy that is simultaneously optimal (i.e., as good or better than all possible alternatives) across every possible domain of optimality, we often put ourselves in a very difficult position.
In contrast, it’s more feasible and productive to look at your training and nutrition decisions in the context of a “composite optimality score.” You don’t have to literally quantify it and tabulate a numerical score; a thorough qualitative review process should be more than sufficient. When considering two (or more) potential strategies, carefully consider the pros and cons of each strategy across each individual domain of optimality. The goal isn’t to find one that happens to beat out all others, every single time. The goal is to find the strategy or approach with the most overall upside and the least overall downside. In this context, the strategy that’s optimal for you might not necessarily be the strategy that is consistently optimal in every possible context for every possible individual. You might even select a strategy that is not the most physiologically optimal option (gasp), simply because it’s more suitable for you based on your experience, lifestyle, skill set, preferences, psychological considerations, or behavioral considerations.
In the past, I’ve hinted at the idea that early feeding windows might have a small, slight, very tiny physiological advantage when compared to later feeding windows. The presently reviewed study supports this concept, but also gives us an idea of how tiny that advantage might be. I personally find myself in a position to declare that early feeding windows might be slightly more advantageous than later feeding windows, yet I virtually always opt for later feeding windows in my own life, and I never nudge reluctant clients toward an obligation to select earlier feeding windows.
For most people, a 1-2% advantage (like that observed in the present study) is so small as to be functionally zero; perhaps a useful tiebreaker when considering two very equivalent approaches, but certainly not the determining factor between walking through door number one and door number two. So, we keep that knowledge in our back pocket, but we assess other domains of optimality. Personally, I like to wake up, pour a cup of coffee, and get straight to work. I usually don’t even think about food until the afternoon, and I’ve already gotten a few very solid hours of work under my belt. In addition, I hate “running out of calories” early in the day. When I consume my final calorie of the day at 4 or 5pm, I often find myself wrestling with a mild concern… Am I really full enough to carry me through the rest of the evening? Psychologically, I find it very reassuring to keep a few extra calories in my back pocket. I’m not so bold as to suggest that this perspective generalizes to everyone, but I’m quite certain that I’m not alone.
After years of dieting with just about every strategy under the sun, I observed that it was very uncommon for me to exceed my daily calorie target, but there was one single factor that led me down that road more reliably than all others: an early feeding window. For me, the juice (a very slight physiological advantage based on very preliminary evidence that may or may not pan out in the long run) is consistently insufficient to justify the squeeze (a feeding window that contradicts my preferences and psychological responses to dieting, while often predisposing me to poorer dietary adherence). Of course, everyone is different, so my set of constraints could be very different from yours. You might prefer to wake up, have a big, energizing meal, and get your calories “out of the way” earlier in the day so you can get food off of your mind and enjoy an uninterrupted evening.
So, let’s wrap up with some tangible takeaways from this study (and the emerging research on early feeding windows). You can make a sound, evidence-based argument that there might be small physiological advantages associated with an early, time-capped feeding window. I say “time-capped” because “restricted” provides the wrong connotation – we aren’t talking about creating the narrowest feeding window possible, but rather establishing a discrete feeding window that ends in the late afternoon or early evening (usually between 2pm and 6pm, give or take). I say “sound” rather than “rock-solid” because this evidence is a bit preliminary – randomized controlled trials are starting to trickle out with increasing frequency, so we should soon develop a better understanding of who benefits, how much they benefit, and how specific populations can most effectively time their feeding window to maximize said benefits (assuming this follow-up research continues to find small benefits, that is).
If you wish to implement early time-restricted feeding, it’s a low-risk, no-cost strategy that warrants consideration or experimentation among those who are interested. It’s also very easy to implement without deviating from the basic, more important guidelines dictating “best practices” for protein intake. All you’d need to do is identify a feeding window (I’d aim for around 8 hours, at minimum), which terminates some time in the late afternoon or early evening (between 2 and 6pm). You’d set a daily protein target (around 1.6-2.2g/kg of total body mass, or 2-2.75g/kg of fat-free mass), and split that among three meals. The first meal would occur at the beginning of the feeding window, the second would occur in the middle, and the third would occur at the end. Ideally, your daily workout would occur somewhere within the feeding window, or immediately preceding it if necessary. Some might find that this strategy offers the appetite-blunting benefits of time-restricted feeding, with some very modest additional benefits coming from better alignment between nutrient intake and cardiadian clocks. For others, the juice won’t be worth the squeeze, or it simply might not suit their lifestyle or preferences, and that’s totally fine.
Note: This article was published in partnership with MASS Research Review. Full versions of Research Spotlight breakdowns are originally published in MASS Research Review. Subscribe to MASS to get a monthly publication with breakdowns of recent exercise and nutrition studies.
References
- Fleischer JG, Das SK, Bhapkar M, Manoogian ENC, Panda S. Associations Between The Timing Of Eating And Weight-Loss In Calorically Restricted Healthy Adults: Findings From The CALERIE Study. Exp Gerontol. 2022 Aug 1;165:111837.
- Charlot A, Hutt F, Sabatier E, Zoll J. Beneficial Effects of Early Time-Restricted Feeding on Metabolic Diseases: Importance of Aligning Food Habits with the Circadian Clock. Nutrients. 2021 Apr 22;13(5):1405.
- Pureza IR de OM, Macena M de L, da Silva Junior AE, Praxedes DRS, Vasconcelos LGL, Bueno NB. Effect Of Early Time-Restricted Feeding On The Metabolic Profile Of Adults With Excess Weight: A Systematic Review With Meta-Analysis. Clin Nutr. 2021 Apr;40(4):1788–99.
