Plasma Amino Acids During 8 Weeks of Overfeeding: Relation to Diet Body Composition and Fat Cell Size in the PROOF Study

Effect of Overeating Dietary Protein at Different Levels on Circulating Lipids and Liver Lipid: The PROOF Study

Background: During overeating, a low protein diet slowed the rate of weight gain and increased the energy cost of the added weight, suggesting that low protein diets reduced energy efficiency. The Protein Overfeeding (PROOF) study explored the metabolic changes to low and high protein diets, and this sub-study examined the changes in body composition and blood lipids when eating high and low protein diets during overeating. Methods: Twenty-three healthy volunteers (M = 14; F = 9) participated in an 8-week, parallel arm study where they were overfed by ~40% with diets containing 5% (LPD = low protein diet), 15% (NPD = normal protein diet), or 25% (HPD = high protein diet) protein. Dual energy X-ray absorptiometry (DXA) and computer tomography (CT) were used to quantify whole body and abdominal fat and intrahepatic lipid, respectively. Metabolites were measured by standard methods. Results: Protein intake and fat intake were inversely related since carbohydrate intake was fixed. Although overeating the LPD diet was associated with a significant increase in high density lipoprotein (HDL)-cholesterol (p < 0.001) and free fatty acids (p = 0.034), and a significant decrease in fat free mass (p < 0.0001) and liver density (p = 0.038), statistical models showed that dietary protein was the main contributor to changes in fat free mass (p = 0.0040), whereas dietary fat was the major predictor of changes in HDL-cholesterol (p = 0.014), free fatty acids (p = 0.0016), and liver fat (p = 0.0007). Conclusions: During 8 weeks of overeating, the level of dietary protein intake was positively related to the change in fat free mass, but not to the change in HDL-cholesterol, free fatty acids, and liver fat which were, in contrast, related to the intake of dietary fat.

Metabolite Profiling of Osteoporosis and Atherosclerosis in Postmenopausal Women: A Cross-Sectional Study

Purpose

Atherosclerosis (AS) and osteoporosis (OP) are common causes of morbidity and mortality in postmenopausal women and are connected via an unknown mechanistic link. Metabolite profiling of blood samples may allow the identification of new biomarkers and pathways for this enigmatic association.

Patients and Methods

We studied the difference in 148 metabolite levels from serum samples in postmenopausal women with AS and OP compared with those in healthy participants in this cross-sectional study. Quantitative AS was assessed by carotid artery intima-media thickness (cIMT) and carotid artery calcifications (CACs) by ultrasound, as well as OP by femoral neck (FN) bone mineral density (BMD) and 148 metabolic measures with high-throughput proton (¹H) nuclear magnetic resonance (NMR) in serum samples from 280 postmenopausal (PM) women. Subjects were a randomly selected subsample from the population-based Kuopio Osteoporosis Risk Factor and Prevention (OSTPRE) study. The final study population included the following groups: OP with CAC (n=16, group I), non-OP with no CAC (n=59, group II), high cIMT tertile with OP (n=11, group III) and low cIMT tertile without OP (n=48, group IV).

Results

There were differences in several metabolite levels between groups I and II. The acetate level was lower in group I compared to that in group II (group I mean ± SD: 0.033 ± 0.0070; group II: 0.041 ± 0.014, CI95%: 0.018‒0.15, p=0.014). The result was similar with diacylglycerol (p=0.002), leucine (p=0.031), valine (p=0.022) and several very low-density lipoprotein (VLDL) metabolite levels, which were lower in group I compared to those in group II. However, no associations were found in adjusted analyses with total body (TB) fat mass (FM), age and statin use (p>0.05).

Conclusion

Our novel study found differences in the metabolite profiling of altered amino acid and lipoprotein metabolism in participants with OP and AS compared with those in healthy women. The causative mechanisms remain unknown and further studies are needed.

The biology of human overfeeding: A systematic review

This systematic review has examined more than 300 original papers dealing with the biology of overfeeding. Studies have varied from 1 day to 6 months. Overfeeding produced weight gain in adolescents, adult men and women and in older men. In longer term studies, there was a clear and highly significant relationship between energy ingested and weight gain and fat storage with limited individual differences. There is some evidence for a contribution of a genetic component to this response variability. The response to overfeeding was affected by the baseline state of the groups being compared: those with insulin resistance versus insulin sensitivity; those prone to obesity versus those resistant to obesity; and those with metabolically abnormal obesity versus those with metabolically normal obesity. Dietary components, such as total fat, polyunsaturated fat and carbohydrate influenced the patterns of adipose tissue distribution as did the history of low or normal birth weight. Overfeeding affected the endocrine system with increased circulating concentrations of insulin and triiodothyronine frequently present. Growth hormone, in contrast, was rapidly suppressed. Changes in plasma lipids were influenced by diet, exercise and the magnitude of weight gain. Adipose tissue and skeletal muscle morphology and metabolism are substantially altered by chronic overfeeding.

In the Footsteps of Wilbur Olin Atwater: The Atwater Lecture for 2019

A central theme of Atwater's research was the development and application of methods to understand how human beings and animals adapt to the nutrients they ingest. The research described in this article also deals with adaptation to nutrition focusing on adaptation to overnutrition, adaptation to undernutrition, adaptation to dietary fat, adaptation to dietary protein, adaptation to micronutrients, and adaptation to sugar and high-fructose corn syrup (HFCS). Studies using overfeeding have shown several things. First, overfeeding did not change the thermic response to ingestion of food nor the coupling of oxidative phosphorylation in muscle to energy expended by muscles during work on a bicycle ergometer between 25 and 100 watts. Second, the response to overfeeding was significantly influenced by the quantity of protein in the diet. During carefully controlled studies of underfeeding of people with obesity, the macronutrient composition of the diet did not affect the magnitude of weight loss. However, baseline genetic and metabolic information could provide guidance for selecting among the lower or higher protein diets, and lower or higher fat diets. Adaptation to an increase in dietary fat from 35% to 50% is slow and variable in healthy sedentary men. Adaptation is more rapid and complete when these same men were physically active. This effect of muscular exercise was traced to changes in the metabolism of glucose in muscles where pathways inhibiting glucose metabolism were activated by exercise. Dietary patterns that increased the intake of calcium, magnesium, and potassium effectively lower blood pressure in individuals with high normal blood pressure. Finally, the intake of sugary beverages was related to the onset of the current epidemic of obesity.

Generating the Blood Exposome Database Using a Comprehensive Text Mining and Database Fusion Approach

BACKGROUND

Blood chemicals are routinely measured in clinical or preclinical research studies to diagnose diseases, assess risks in epidemiological research, or use metabolomic phenotyping in response to treatments. A vast volume of blood-related literature is available via the PubMed database for data mining.

OBJECTIVES

We aimed to generate a comprehensive blood exposome database of endogenous and exogenous chemicals associated with the mammalian circulating system through text mining and database fusion.

METHODS

Using NCBI resources, we retrieved PubMed abstracts, PubChem chemical synonyms, and PMC supplementary tables. We then employed text mining and PubChem crowdsourcing to associate phrases relating to blood with PubChem chemicals. False positives were removed by a phrase pattern and a compound exclusion list.

RESULTS

A query to identify blood-related publications in the PubMed database yielded 1.1 million papers. Matching a total of 15 million synonyms from 6.5 million relevant PubChem chemicals against all blood-related publications yielded 37,514 chemicals and 851,999 publications records. Mapping PubChem compound identifiers to the PubMed database yielded 49,940 unique chemicals linked to 676,643 papers. Analysis of open-access metabolomics papers related to blood phrases in the PMC database yielded 4,039 unique compounds and 204 papers. Consolidating these three approaches summed up to a total of 41,474 achiral structures that were linked to 65,957 PubChem CIDs and to over 878,966 PubMed articles. We mapped these compounds to 50 databases such as those covering metabolites and pathways, governmental and toxicological databases, pharmacology resources, and bioassay repositories. In comparison, HMDB, the Human Metabolome Database, links 1,075 compounds to blood-related primary publications.

CONCLUSION

This new Blood Exposome Database can be used for prioritizing chemicals for systematic reviews, developing target assays in exposome research, identifying compounds in untargeted mass spectrometry, and biological interpretation in metabolomics data. The database is available at http://bloodexposome.org. https://doi.org/10.1289/EHP4713.

Plasma fatty acyl-carnitines during 8 weeks of overfeeding: relation to diet energy expenditure and body composition: the PROOF study

OBJECTIVE

Overfeeding is a strategy for evaluating the effects of excess energy intake. In this secondary analysis we tested the possibility that different levels of dietary protein might differentially modify the response of fatty acyl-carnitines to overfeeding.

METHODS

Twenty-three healthy adult men and women were overfed by 40% for 8 weeks while in-patients with diets containing 5% (LPD), 15% (NPD) or 25% (HPD) protein. Plasma fatty acyl-carnitines were measured by gas chromatography/mass spectrometry (GC/MS) at baseline and after 8 weeks of overfeeding. Measurements included: body composition by DXA, energy expenditure by ventilated hood and doubly-labeled water, fat cell size from subcutaneous fat biopsies, and fat distribution by CT scan.

RESULTS

Analysis was done on 5 groups of fatty acyl-carnitines identified by principal components analysis and 6 individual short-chain fatty acyl carnitines. Higher protein intake was associated with significantly lower 8 week levels of medium chain fatty acids and C2, C4-OH and C 6:1, but higher values of C3 and C5:1 acyl-carnitines derived from essential amino acids. In contrast energy and fat intake were only weakly related to changes in fatty acyl-carnitines. A decease or smaller rise in 8 week medium chain acyl-carnitines was associated with an increase in sleeping energy expenditure (P = 0.0004), and fat free mass (P < 0.0001) and a decrease in free fatty acid concentrations (FFA) (P = 0.0067). In contrast changes in short-chain fatty acyl-carnitines were related to changes in resting energy expenditure (P = 0.0026), and fat free mass (P = 0.0007), and C4-OH was positively related to FFA (P = 0006).

CONCLUSION

Protein intake was the major factor influencing changes in fatty acyl carnitines during overfeeding with higher values of most acyl-fatty acids on the low protein diet. The association of dietary protein and fat intake may explain the changes in energy expenditure and metabolic variables resulting in the observed patterns of fatty acyl carnitines.