This observational pilot study examined the association between diet, meal pattern and glucose over a 2-week period under free-living conditions in 26 adults with dysglycemia (D-GLYC) and 14 with normoglycemia (N-GLYC). We hypothesized that a prolonged eating window and late eating occasions (EOs), along with a higher dietary carbohydrate intake, would result in higher glucose levels and glucose variability (GV). General linear models were run with meal timing with time-stamped photographs in real time, and diet composition by dietary recalls, and their variability (SD), as predictors and glucose variables (mean glucose, mean amplitude of glucose excursions [MAGE], largest amplitude of glucose excursions [LAGE] and GV) as dependent variables. After adjusting for calories and nutrients, a later eating midpoint predicted a lower GV (β = −2.3, SE = 1.0, p = 0.03) in D-GLYC, while a later last EO predicted a higher GV (β = 1.5, SE = 0.6, p = 0.04) in N-GLYC. A higher carbohydrate intake predicted a higher MAGE (β = 0.9, SE = 0.4, p = 0.02) and GV (β = 0.4, SE = 0.2, p = 0.04) in N-GLYC, but not D-GLYC. In summary, our data suggest that meal patterns interact with dietary composition and should be evaluated as potential modifiable determinants of glucose in adults with and without dysglycemia. Future research should evaluate causality with controlled diets.

Background/Objectives: Nocturnal glucose regulation is a critical but underexplored determinant of next-day fasting glucose (FG), particularly in individuals with dysglycemia. This study examined the role of glucose levels after the last eating occasion (LEO) and during the overnight fast in predicting FG, considering the potential influence of carbohydrate content in LEO and insulin sensitivity. Methods: In a controlled 24 h protocol, 33 adults (50–75 years) with prediabetes or diet-controlled type 2 diabetes followed a standardized feeding schedule with meals at fixed times, including a LEO at 10:00 p.m. Continuous glucose monitoring was used to assess glucose during the 3 h postprandial period (LEO-PPGR) and two fasting intervals: chronological overnight fast (COF) and biological overnight fast (BOF). Associations with FG were tested using general linear models, adjusting for carbohydrate intake and insulin sensitivity (Matsuda index). Results: Glucose responses during LEO-PPGR—assessed by mean glucose, peak, and AUC—were strongly correlated with FG the next morning (r = 0.704, 0.535, and 0.708, p < 0.001). Similarly, glucose levels during COF and BOF were also correlated with FG (r = 0.878, p < 0.001 for both), but these associations weakened after adjustment for LEO carbohydrate content. The Matsuda index was positively correlated with glucose in all three periods (p < 0.05), yet its inclusion in the model attenuated all previously significant associations with FG. Conclusions: These findings suggest that the glycemic response to the last meal and subsequent overnight glucose levels contribute to next-day FG, but their impact is modulated by carbohydrate content and individual insulin sensitivity. Understanding nocturnal glycemic dynamics may inform strategies for improving metabolic outcomes in dysglycemia.

Self-report methods are widely used to assess energy intake but are prone to measurement errors. We aimed to identify under-reported, over-reported, and plausible self-reported energy intake by dietary recalls (rEI) using a standard method (Method 1) that calculates the rEI ratio against measured energy expenditure (mEE) by doubly-labeled water (DLW), and compare it to a novel method (Method 2), which calculates the rEI ratio against measured energy intake (mEI) by the principle of energy balance (EB = mEE + changes in energy stores). The rEI:mEE and rEI:mEI ratios were assessed for each subject. Group cut-offs were calculated for both methods, using the coefficient of variations of rEI, mEE, and mEI. Entries within ± 1SD of the cutoffs were categorized as plausible, < 1SD as under-reported, and > 1SD as over-reported. Kappa statistics was calculated to assess the agreement between both methods. Percentage bias (bβ) was estimated by linear regression. Remaining bias (dβ) was calculated after applying each method cutoffs. The percentage of under-reporting was 50% using both methods. Using Method 1, 40.3% of recalls were categorized as plausible, and 10.2% as over-reported. With Method 2, 26.3% and 23.7% recalls were plausible and over-reported, respectively. There was a significant positive relationship between mEI with weight (ß = 21.7, p < 0.01) and BMI (ß = 48.8, p = 0.04), but not between rEI with weight (ß = 13.1, p = 0.06) and BMI (ß = 41.8, p = 0.11). The rEI relationships were significant when only plausible entries were included using Method 1 (weight: ß = 17.4, p < 0.01, remaining bias = 49.5%; BMI: ß = 44.6, p = 0.01, remaining bias = 60.2%) and Method 2 (weight: ß = 19.5, p < 0.01, remaining bias = 24.9%; BMI: ß = 44.8, p = 0.03, remaining bias = 56.9%). The choice of method significantly impacts plausible and over-reported classification, with the novel method identifying more over-reported entries. While rEI showed no relationships with anthropometric measurements, applying both methods reduced bias. The novel method showed greater bias reduction, suggesting that it may have superior performance when identifying plausible rEI. NCT04465721.

BACKGROUND: LCAT (lecithin cholesterol acyl transferase) catalyzes the conversion of unesterified, or free cholesterol, to cholesteryl ester, which moves from the surface of HDL (high-density lipoprotein) into the neutral lipid core. As this iterative process continues, nascent lipid-poor HDL is converted to a series of larger, spherical cholesteryl ester–enriched HDL particles that can be cleared by the liver in a process that has been termed reverse cholesterol transport. METHODS: We conducted a randomized, placebocontrolled, crossover study in 5 volunteers with atherosclerotic cardiovascular disease, to examine the effects of an acute increase of recombinant human (rh) LCAT via intravenous administration (300-mg loading dose followed by 150 mg at 48 hours) on the in vivo metabolism of HDL APO (apolipoprotein)A1 and APOA2, and the APOB100-lipoproteins, very low density, intermediate density, and low-density lipoproteins. RESULTS: As expected, recombinant human LCAT treatment significantly increased HDL-cholesterol (34.9 mg/dL; P ≤0.001), and this was mostly due to the increase in cholesteryl ester content (33.0 mg/dL; P =0.014). This change did not affect the fractional clearance or production rates of HDL-APOA1 and HDL-APOA2. There were also no significant changes in the metabolism of APOB100-lipoproteins. CONCLUSIONS: Our results suggest that an acute increase in LCAT activity drives greater flux of cholesteryl ester through the reverse cholesterol transport pathway without significantly altering the clearance and production of the main HDL proteins and without affecting the metabolism of APOB100-lipoproteins. Long-term elevations of LCAT might, therefore, have beneficial effects on total body cholesterol balance and atherogenesis.

Introduction: Meal timing can influence metabolism and health. Correct meal timing data is crucial to accurately assess eating patterns and develop effective interventions. However, commonly used methods like dietary recalls are often inaccurate for caloric and nutrient intake. In this pilot study, we aimed to assess the mealtime agreement between dietary recalls and documentation in real-time. Methods: This is an observational 2-week study in older adults with prediabetes and obesity. Participants reported their diet with (1) 2-6 non-consecutive dietary recalls (Automated Self-Administered 24-h (ASA24 ^® ) Dietary Assessment Tool), and (2) time-stamped photographs of all caloric eating and drinking events via the myCircadianClock (mCC) mobile application. Data analysis was performed on days when both methods were used to assess the first eating occasion (FEO) and the last eating occasion (LEO) of the day. Significance set at P < .05. Results: Adults (n = 43, 14 men, age 60 ± 7 years, body mass index (BMI) 33.0 ± 6.2 kg/m ² ) documented 4 ± 1 ASA24 recalls over 2 weeks. Agreement between tools ranged between 62% to 73% for the FEO and 49% to 61% for the LEO. The FEO and LEO agreement were positively correlated (β = .4, P = .01). Differences between tools were higher with fewer recalls (β = −.3, P = .04) for the LEO, but were not associated with age, sex, or body composition for neither the FEO or the LEO. The FEO agreement between tools was higher when the FEO was consumed earlier, when agreement was defined by ±45 min (estimate = −0.94, P = .001) and ±60 min (estimate = −1.11, P < .001). Conclusions: Our results suggest that mealtime agreement between dietary recalls and a time-stamped tool is consistently below 75%, and lowers for meals recorded later in the day. The inter-tool agreement is higher with increased overlap tolerance and greater number of dietary recalls. Clinical trials registration number: NCT04465721 https://clinicaltrials.gov/study/NCT04465721