A cross-sectional analysis published in Frontiers evaluated how self-reported chronotype relates to dietary intake, meal timing, body composition, and metabolic biomarkers. The analysis used data from 287 women enrolled in the PROMISE (PRedictors linking Obesity and the gut MIcrobiome) study. Participants were aged 18–45 and had body mass index (BMI) values either in the healthy range or in the obesity range. The investigators excluded individuals who were pregnant or taking medications known to affect appetite.
Data sources included fasting blood samples, five days of estimated food records, chronotype and sleep questionnaires, work-history information, body-composition measures, and accelerometer-derived activity and sleep data. The authors used these measures to explore associations between sleep–wake preference and dietary, anthropometric, and metabolic outcomes.
Participants were categorized into three chronotypes: evening types (commonly termed “night owls”), morning types (“larks”), and intermediate types. Slightly more than half of participants were intermediate, and the investigators ultimately combined the morning and intermediate groups for several comparisons. Evening-type participants were more likely to be of Pacific descent and scored higher on a deprivation index compared with the combined morning/intermediate group.
Compared with morning/intermediate participants, evening types demonstrated higher BMI, larger hip and waist circumferences, greater visceral fat percentage, and increased android fat (upper-body fat). These anthropometric differences formed a central part of the observed associations between chronotype and metabolic risk.
Daily nutrient analyses found that evening-type participants reported a slightly larger total energy intake and consumed a higher proportion of carbohydrates overall. When researchers examined meal timing windows, patterns diverged by chronotype. Evening types had greater energy intake and higher carbohydrate, fat, and protein intake during the late evening and early night periods. By contrast, morning/intermediate participants showed higher intake measures in the late-night and early-morning window.
After statistical adjustment, higher fat and protein intake in the late evening/early night period remained apparent for evening-type participants. The morning/intermediate group reported higher median intakes of certain vitamins but also higher alcohol and caffeine consumption.
Evening chronotype status was associated with less favorable metabolic markers in this cohort. The evening group tended to show higher blood-sugar parameters and elevated triglycerides. In one set of comparisons, the combined morning/intermediate group had higher levels of “bad” cholesterol.
Later sleep timing correlated with higher insulin and triglyceride concentrations. The investigators also linked higher evening energy intake to elevated hemoglobin A1c (HbA1c), a biomarker that indicates increased risk for prediabetes or type 2 diabetes.
The researchers examined whether body fat percentage modified associations between chronotype and meal timing. Among participants with higher body fat percentage who were also evening types, investigators observed lower energy, carbohydrate, protein, and fat intake during the late-night/early-morning window, but higher overall energy intake and greater carbohydrate and fat consumption in the late evening/early night window. In the authors’ words, evening type was associated with lower intake in the late-night/early-morning window for the high body-fat group, which reversed in the late evening/early night and included higher fat intake.
These interaction findings indicate that both chronotype and body composition influence when and what people eat, and that eating timing patterns differ across subgroups defined by body fat.
The study authors and independent clinicians highlighted the potential clinical relevance of meal timing and chronotype. Sura Alqaisi, MD, noted that meal timing in addition to total caloric intake may affect body-fat distribution and metabolic health and pointed to the cluster of higher triglycerides, insulin, HbA1c, and leptin with lower HDL cholesterol observed in evening types as supporting this association.
Alex Dimitriu, MD, commented that the findings are consistent with prior observations that late-evening types tend to choose less healthy foods after sunset, have shorter overnight fasting periods, and present with higher BMI and body-fat percentages.
Mir Ali, MD, suggested there may be benefit for some patients in adjusting eating times—specifically reducing carbohydrate and sugar intake at night and emphasizing protein and non-starchy vegetables—to help reduce body fat. Alqaisi added that chronotype could be considered within nutritional assessment, particularly for patients with obesity, prediabetes, type 2 diabetes, or metabolic syndrome, supporting a more individualized approach that considers both what and when patients eat.
The investigators and article authors outlined several limitations that constrain interpretation. The sample included only women from specific ethnic backgrounds (European and Pacific New Zealand), so results may not generalize to men or other populations. Evening-type participants were disproportionately of Pacific ethnicity and scored higher on deprivation indices, raising the possibility that socioeconomic factors and access to healthy foods contributed to observed differences.
Much of the dietary data were self-reported, introducing risk of reporting error; the authors specifically note that dietary inaccuracies may be more likely among evening types. The study’s cross-sectional, observational design cannot establish causation between chronotype and metabolic outcomes. The authors call for larger, longer-term, and more diverse studies to verify these associations and to test whether chronotype-informed interventions (for example, modifying meal timing) produce clinical benefit.
From the available data, clinicians may consider discussing both meal composition and meal timing with patients who identify as evening types, particularly when obesity, prediabetes, or metabolic syndrome is present. The study suggests evening eating patterns—higher late-evening energy intake and macronutrient intake—coincide with less favorable body composition and metabolic markers in this female cohort. Any clinical application should account for individual work schedules, cultural factors, and socioeconomic context, and should recognize that current evidence is associative rather than causal.
Overall, this analysis supports further exploration of chronotype as a factor in personalized nutrition and metabolic-risk management, while underscoring the need for prospective intervention trials and broader, more representative samples.