Glucose (A), insulin (B), FFA (C) and TG (D) levels for 24 h and the AUCs of the LFr (dense black circle) and HFr (open gray circle) diet.

<p>*P<0.05 LFr vs. HFr diet. P-values were derived by analysis of mixed models for the 24h profiles and by a paired t-test for the AUCs. ^aValues are expressed as mean±SEM.</p

Hunger (A), and satiety (B) levels for 24 h and the AUCs of the LFr (dense black circle ) and HFr (open gray circle ) diet.

<p>*P<0.05 LFr vs. HFr diet. P-values were derived by analysis of mixed models for the 24 h profiles and by a paired t-test for the AUCs. ^aValues are expressed as mean±SEM.</p

CGMS glucose levels for 24 h in the LFr and HFr diet.

<p>^aValues are expressed as mean.</p

Subject characteristics at baseline.

<p>Values are expressed as mean±SEM.</p

GLP-1 active (A), ghrelin-active (B) and adiponectin (C) levels for 24 h and the AUCs of the LFr (dense black circle ) and HFr (open gray circle ) diet.

<p>*P<0.05 LFr vs. HFr diet. P-values were derived by analysis of mixed models for the 24 h profiles and by a paired t-test for the AUCs. ^aValues are expressed as mean±SEM.</p

Image_1_Fast and Accurate Approaches for Large-Scale, Automated Mapping of Food Diaries on Food Composition Tables.PDF

<p>Aim of Study: The use of weighed food diaries in nutritional studies provides a powerful method to quantify food and nutrient intakes. Yet, mapping these records onto food composition tables (FCTs) is a challenging, time-consuming and error-prone process. Experts make this effort manually and no automation has been previously proposed. Our study aimed to assess automated approaches to map food items onto FCTs.</p><p>Methods: We used food diaries (~170,000 records pertaining to 4,200 unique food items) from the DiOGenes randomized clinical trial. We attempted to map these items onto six FCTs available from the EuroFIR resource. Two approaches were tested: the first was based solely on food name similarity (fuzzy matching). The second used a machine learning approach (C5.0 classifier) combining both fuzzy matching and food energy. We tested mapping food items using their original names and also an English-translation. Top matching pairs were reviewed manually to derive performance metrics: precision (the percentage of correctly mapped items) and recall (percentage of mapped items).</p><p>Results: The simpler approach: fuzzy matching, provided very good performance. Under a relaxed threshold (score > 50%), this approach enabled to remap 99.49% of the items with a precision of 88.75%. With a slightly more stringent threshold (score > 63%), the precision could be significantly improved to 96.81% while keeping a recall rate > 95% (i.e., only 5% of the queried items would not be mapped). The machine learning approach did not lead to any improvements compared to the fuzzy matching. However, it could increase substantially the recall rate for food items without any clear equivalent in the FCTs (+7 and +20% when mapping items using their original or English-translated names). Our approaches have been implemented as R packages and are freely available from GitHub.</p><p>Conclusion: This study is the first to provide automated approaches for large-scale food item mapping onto FCTs. We demonstrate that both high precision and recall can be achieved. Our solutions can be used with any FCT and do not require any programming background. These methodologies and findings are useful to any small or large nutritional study (observational as well as interventional).</p

Regression of increases in total daily energy expenditure (MJ/day) and state 4 respiration (pmol O₂/(s·mg muscle·CS activity)) (p<0.02, R² = 0.50).

<p>Regression of increases in total daily energy expenditure (MJ/day) and state 4 respiration (pmol O₂/(s·mg muscle·CS activity)) (p<0.02, R² = 0.50).</p

Baseline and mild cold values of a) total daily energy expenditure, b) activity, c) state 4 respiration, and d) state 3 respiration.

<p>The figures show mean (±SEM) in both situations. * p<0.05, ** p<0.01</p

Flowchart for individuals' selection from the DiOGenes cohort.

<p>Participants entering subsequent phases of the study as well as dropouts are indicated in total. AT, adipose tissue; CID, clinical investigation day; HGI, high glycemic index; HOMA-IR, Individual Homeostasis Model Assessment of Insulin Resistance; HP, high protein; LCD, low calorie diet; LGI, low glycemic index; LP, low protein; WMD, weight maintenance diet.</p

Clustering of insulin resistance profiles in obese individuals during the dietary intervention (n = 216).

<p>Individual Homeostasis Model Assessment of Insulin Resistance (HOMA-IR) changes during an 8-week calorie restriction (LCD) and a 6-month weight maintenance diet (WMD) were clustered, resulting in 3 groups. The panel shows mean HOMA-IR changes expressed as change after LCD and at the end of the 6-montho weight maintenance (WMD) <i>vs.</i> baseline (BAS) values according to groups. The black line shows the HOMA-IR profile for group A (n = 94). The dashed line shows the HOMA-IR profile for group B (n = 48). The dotted line shows the HOMA-IR profile for group C (n = 74). The A, B, and C are the mean of HOMA-IR changes in each clustered group. <b>◊</b>: p<0.05, data different in group A. Δ: p<0.05, data different in group B. #: p<0.05, data different in group C. §: p<0.05, data difference between groups. Values are means ± SEM.</p