Reliability estimates for assessing meal timing derived from longitudinal repeated 24-hour dietary recalls.

BACKGROUND: Regulating meal timing may have efficacy for improving metabolic health for preventing or managing chronic disease. However, the reliability of measuring meal timing with commonly used dietary assessment tools needs characterization prior to investigating meal timing and health outcomes in epidemiologic studies. OBJECTIVES: To evaluate the reliability of estimating meal timing parameters, including overnight fasting duration, the midpoint of overnight fasting time, the number of daily eating episodes, the period with the largest percentage of daily caloric intake, and late last eating episode (> 09:00 pm) from repeated 24-h dietary recalls (24HRs). METHODS: Intraclass correlation coefficients (ICC), Light's Kappa estimates, and 95% CIs were calculated from repeated 24HR administered in 3 epidemiologic studies: The United States-based Interactive Diet and Activity Tracking in AARP (IDATA) study (n = 996, 6 24HR collected over 12-mo), German EPIC-Potsdam Validation Study (European Prospective Investigation into Cancer and Nutrition Potsdam Germany cohort) (n = 134, 12 24HR collected over 12-mo) and EPIC-Potsdam BMBF-II Study (Federal Ministry of Education and Research, "Bundesministerium für Bildung und Forschung") (n = 725, 4 24HR collected over 36 mo). RESULTS: Measurement reliability of overnight fasting duration based on a single 24HR was "poor" in all studies [ICC range: 0.27; 95% CI: 0.23, 0.32 - 0.46; 95% CI: 0.43, 0.50]. Reliability was "moderate" with 3 24HR (ICC range: 0.53; 95% CI: 0.47, 0.58 in IDATA, 0.62; 95% CI: 0.52, 0.69 in the EPIC-Potsdam Validation Study, and 0.72; 95% CI: 0.70-0.75 in the EPIC-Potsdam BMBF-II Study). Results were similar for the midpoint of overnight fasting time and the number of eating episodes. Reliability of measuring late eating was "fair" in IDATA (Light's Kappa: 0.30; 95% CI: 0.21, 0.39) and "slight" in the EPIC-Potsdam Validation study and the EPIC-Potsdam BMBF-II study (Light's Kappa: 0.19; 95% CI: 0.15, 0.25 and 0.09; 95% CI: 0.06, 0.12, respectively). Reliability estimates differed by sex, BMI, weekday, and season of 24HR administration in some studies. CONCLUSIONS: Our results show that ≥ 3 24HR over a 1-3-y period are required for reliable estimates of meal timing variables.

Metabolic dysfunction and obesity-related cancer: Beyond obesity and metabolic syndrome.

OBJECTIVES: The metabolic dysfunction driven by obesity, including hyperglycemia and dyslipidemia, increases risk for developing at least 13 cancer types. The concept of "metabolic dysfunction" is often defined by meeting various combinations of criteria for metabolic syndrome. However, the lack of a unified definition of metabolic dysfunction makes it difficult to compare findings across studies. This review summarizes 129 studies that evaluated variable definitions of metabolic dysfunction in relation to obesity-related cancer risk and mortality after a cancer diagnosis. Strategies for metabolic dysfunction management are also discussed. METHODS: A comprehensive search of relevant publications in MEDLINE (PubMed) and Google Scholar with review of references was conducted. RESULTS: Metabolic dysfunction, defined as metabolic syndrome diagnosis or any number of metabolic syndrome criteria out of clinical range, inflammatory biomarkers, or markers of metabolic organ function, has been associated with risk for, and mortality from, colorectal, pancreatic, postmenopausal breast, and bladder cancers. Metabolic dysfunction associations with breast and colorectal cancer risk have been observed independently of BMI, with increased risk in individuals with metabolically unhealthy normal weight or overweight/obesity compared with metabolically healthy normal weight. CONCLUSION: Metabolic dysfunction is a key risk factor for obesity-related cancer, regardless of obesity status. Nonetheless, a harmonized definition of metabolic dysfunction will further clarify the magnitude of the relationship across cancer types, enable better comparisons across studies, and further guide criteria for obesity-related cancer risk stratification.