We identified dietary biomarkers by using metabolomics in an epidemiologic data set. Given the strength of the associations observed, we expect that some of these metabolites will be validated in future studies and later used as biomarkers in large cohorts to study diet-disease associations. The PLCO trial was registered at clinicaltrials.gov as NCT00002540.

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Background Timing of dietary intake may play a role in obesity. However, previous studies produced mixed findings possibly due to inconsistent approaches to characterize meal timing and not taking into account chronotype and macronutrients. To address the aforementioned limitations, we have defined meal timing relative to sleep/wake timing, investigated the relationship between meal timing and BMI dependent on chronotype, and examined the associations between obesity and the timing of individual macronutrient intakes. Methods BMI, chronotype, and dietary intakes were measured in 872 middle-to-older aged adults by six 24-hour dietary recalls in one year. We defined four time windows of intake relative to sleep timing: morning (within two hours after getting out of bed), night (within two hours before bedtime), and two midday periods in between (split by the midpoint of the waking period). Results A higher percent of total daily energy intake consumed during the morning window was associated with lower odds of being overweight or obese (odds ratio (95% confidence intervals), 0.53 (0.31, 0.89)). This association was stronger in people with an earlier chronotype (0.32 (0.16, 0.66)). A higher percent of total daily energy intake consumed during the night window was associated with higher odds of being overweight or obese (1.82 (1.07, 3.08)), particularly in people with a later chronotype (4.94 (1.61, 15.14)). These associations were stronger for the intakes of carbohydrates and protein than for fat intake. Conclusion Our study suggests that higher dietary consumption after waking up and lower consumption close to bedtime associate with lower BMI, but the relationship differs by chronotype. Furthermore, the data demonstrate a clear relationship between the timing of carbohydrate and protein intake and obesity. Our findings highlight the importance of considering timing of intake relative to sleep timing when studying the associations of meal timing with obesity and metabolic health.

Background A high body mass index (BMI) is a major risk factor for several chronic diseases, but the biology underlying these associations is not well-understood. Dyslipidemia, inflammation, and elevated levels of growth factors and sex steroid hormones explain some of the increased disease risk, but other metabolic factors not yet identified may also play a role. Design In order to discover novel metabolic biomarkers of BMI, we used non-targeted metabolomics to assay 317 metabolites in blood samples from 947 participants and examined the cross-sectional associations between metabolite levels and BMI. Participants were from three studies in the United States and China. Height, weight, and potential confounders were ascertained by questionnaire (US studies) or direct measurement (Chinese study). Metabolite levels were measured using liquid-phase chromatography and gas chromatography coupled with mass spectrometry. We evaluated study-specific associations using linear regression, adjusted for age, gender, and smoking, and we estimated combined associations using random effects meta-analysis. Results The meta-analysis revealed 37 metabolites significantly associated with BMI, including 19 lipids, 12 amino acids, and 6 others, at the Bonferroni significance threshold (p<0.00016). Eighteen of these associations had not been previously reported, including histidine, an amino acid neurotransmitter, and butyrylcarnitine, a lipid marker of whole-body fatty acid oxidation. Heterogeneity by study was minimal (all Pheterogeneity >0.05). In total, 110 metabolites were associated with BMI at the p<0.05 level. Conclusion These findings establish a baseline for the BMI metabolome, and suggest new targets for researchers attempting to clarify mechanistic links between high BMIs and disease risk.