Lorraine Brennan

Gut microbiota composition correlates with diet and health in the elderly

Alterations in intestinal microbiota composition are associated with several chronic conditions, including obesity and inflammatory diseases. The microbiota of older people displays greater inter-individual variation than that of younger adults. Here we show that the faecal microbiota composition from 178 elderly subjects formed groups, correlating with residence location in the community, day-hospital, rehabilitation or in long-term residential care. However, clustering of subjects by diet separated them by the same residence location and microbiota groupings. The separation of microbiota composition significantly correlated with measures of frailty, co-morbidity, nutritional status, markers of inflammation and with metabolites in faecal water. The individual microbiota of people in long-stay care was significantly less diverse than that of community dwellers. Loss of community-associated microbiota correlated with increased frailty. Collectively, the data support a relationship between diet, microbiota and health status, and indicate a role for diet-driven microbiota alterations in varying rates of health decline upon ageing.

Mass-spectrometry-based metabolomics: limitations and recommendations for future progress with particular focus on nutrition research

Mass spectrometry (MS) techniques, because of their sensitivity and selectivity, have become methods of choice to characterize the human metabolome and MS-based metabolomics is increasingly used to characterize the complex metabolic effects of nutrients or foods. However progress is still hampered by many unsolved problems and most notably the lack of well established and standardized methods or procedures, and the difficulties still met in the identification of the metabolites influenced by a given nutritional intervention. The purpose of this paper is to review the main obstacles limiting progress and to make recommendations to overcome them. Propositions are made to improve the mode of collection and preparation of biological samples, the coverage and quality of mass spectrometry analyses, the extraction and exploitation of the raw data, the identification of the metabolites and the biological interpretation of the results.

Metabolomics in human nutrition: opportunities and challenges

Metabolomics has been widely adopted in pharmacology and toxicology but is relatively new in human nutrition. The ultimate goal, to understand the effects of exogenous compounds on human metabolic regulation, is similar in all 3 fields. However, the application of metabolomics to nutritional research will be met with unique challenges. Little is known of the extent to which changes in the nutrient content of the human diet elicit changes in metabolic profiles. Moreover, the metabolomic signal from nutrients absorbed from the diet must compete with the myriad of nonnutrient signals that are absorbed, metabolized, and secreted in both urine and saliva. The large-bowel microflora also produces significant metabolic signals that can contribute to and alter the metabolome of biofluids in human nutrition. Notwithstanding these possible confounding effects, every reason exists to be optimistic about the potential of metabolomics for the assessment of various biofluids in nutrition research. This potential lies both in metabolic profiling through the use of pattern-recognition statistics on assigned and unassigned metabolite signals and in the collection of comprehensive data sets of identified metabolites; both objectives have the potential to distinguish between different dietary treatments, which would not have been targeted with conventional techniques. The latter objective sets out a well-recognized challenge to modern biology: the development of libraries of small molecules to aid in metabolite identification. The purpose of the present review was to highlight some early challenges that need to be addressed if metabolomics is to realize its great potential in human nutrition.

The food metabolome: a window over dietary exposure

The food metabolome is defined as the part of the human metabolome directly derived from the digestion and biotransformation of foods and their constituents. With >25,000 compounds known in various foods, the food metabolome is extremely complex, with a composition varying widely according to the diet. By its very nature it represents a considerable and still largely unexploited source of novel dietary biomarkers that could be used to measure dietary exposures with a high level of detail and precision. Most dietary biomarkers currently have been identified on the basis of our knowledge of food compositions by using hypothesis-driven approaches. However, the rapid development of metabolomics resulting from the development of highly sensitive modern analytic instruments, the availability of metabolite databases, and progress in (bio)informatics has made agnostic approaches more attractive as shown by the recent identification of novel biomarkers of intakes for fruit, vegetables, beverages, meats, or complex diets. Moreover, examples also show how the scrutiny of the food metabolome can lead to the discovery of bioactive molecules and dietary factors associated with diseases. However, researchers still face hurdles, which slow progress and need to be resolved to bring this emerging field of research to maturity. These limits were discussed during the First International Workshop on the Food Metabolome held in Glasgow. Key recommendations made during the workshop included more coordination of efforts; development of new databases, software tools, and chemical libraries for the food metabolome; and shared repositories of metabolomic data. Once achieved, major progress can be expected toward a better understanding of the complex interactions between diet and human health.

Dietary intake patterns are reflected in metabolomic profiles: potential role in dietary assessment studies

BACKGROUND It has been suggested that metabolomics could play a role in dietary assessment and identification of novel biomarkers of dietary intake. OBJECTIVE This study examined the link between habitual dietary patterns and metabolomic profiles. DESIGN A total of 160 volunteers participated in a double-blind, randomized, placebo-controlled dietary intervention. We collected biofluids and recorded 3-d food diaries. Food data were reduced to 33 food groups, and a k-means cluster analysis was performed to identify dietary patterns. (1)H Nuclear magnetic resonance (NMR) spectra were acquired for plasma and urine samples, and gas chromatography was used for plasma fatty acid profiling. RESULTS Cluster analysis identified 3 distinct dietary patterns on the basis of the energy contribution of different food groups. Dietary clusters were reflected in plasma fatty acid profiles and in metabolomic data. (1)H NMR spectra of urine allowed the identification of metabolites associated with different dietary patterns. Several of the metabolites identified were linked to the intake of specific food groups; in particular, there was a positive association between O-acetylcarnitine and phenylacetylglutamine and red-meat and vegetable intakes, respectively. CONCLUSIONS Habitual dietary patterns are shown in metabolomic data. This approach successfully identified potential biomarkers of red-meat and vegetable intakes.

New insights into amino acid metabolism, β-cell function and diabetes

Specific amino acids are now known to acutely and chronically regulate insulin secretion from pancreatic beta-cells in vivo and in vitro. Understanding the molecular mechanisms by which amino acids regulate insulin secretion may identify novel targets for future diabetes therapies. Mitochondrial metabolism is crucial for the coupling of amino acid and glucose recognition to the exocytosis of the insulin granules. This is illustrated by in vitro and in vivo observations discussed in the present review. Mitochondria generate ATP, which is the main coupling factor in insulin secretion; however, the subsequent Ca2+ signal in the cytosol is necessary, but not sufficient, for full development of sustained insulin secretion. Hence mitochondria generate ATP and other coupling factors serving as fuel sensors for the control of the exocytotic process. Numerous studies have sought to identify the factors that mediate the amplifying pathway over the Ca2+ signal in nutrient-stimulated insulin secretion. Predominantly, these factors are nucleotides (GTP, ATP, cAMP and NADPH), although metabolites have also been proposed, such as long-chain acyl-CoA derivatives and the key amino acid glutamate. This scenario highlights further the importance of the key enzymes or transporters, glutamate dehydrogenase, the aspartate and alanine aminotransferases and the malate/aspartate shuttle, in the control of insulin secretion. Therefore amino acids may play a direct or indirect (via generation of putative messengers of mitochondrial origin) role in insulin secretion.

Standardizing the experimental conditions for using urine in NMR-based metabolomic studies with a particular focus on diagnostic studies: a review

AbstractThe metabolic composition of human biofluids can provide important diagnostic and prognostic information. Among the biofluids most commonly analyzed in metabolomic studies, urine appears to be particularly useful. It is abundant, readily available, easily stored and can be collected by simple, noninvasive techniques. Moreover, given its chemical complexity, urine is particularly rich in potential disease biomarkers. This makes it an ideal biofluid for detecting or monitoring disease processes. Among the metabolomic tools available for urine analysis, NMR spectroscopy has proven to be particularly well-suited, because the technique is highly reproducible and requires minimal sample handling. As it permits the identification and quantification of a wide range of compounds, independent of their chemical properties, NMR spectroscopy has been frequently used to detect or discover disease fingerprints and biomarkers in urine. Although protocols for NMR data acquisition and processing have been standardized, no consensus on protocols for urine sample selection, collection, storage and preparation in NMR-based metabolomic studies have been developed. This lack of consensus may be leading to spurious biomarkers being reported and may account for a general lack of reproducibility between laboratories. Here, we review a large number of published studies on NMR-based urine metabolic profiling with the aim of identifying key variables that may affect the results of metabolomics studies. From this survey, we identify a number of issues that require either standardization or careful accounting in experimental design and provide some recommendations for urine collection, sample preparation and data acquisition.

Metabolite concentrations in follicular fluid may explain differences in fertility between heifers and lactating cows.

There has been a marked decline in the fertility of dairy cows over the past decades, and metabolomic analysis offers a potential to investigate the underlying causes. Metabolite composition of the follicular fluid, which presents the intrafollicular environment, may be an important factor affecting oocyte maturation and subsequent early embryo development. The aim of the present study was to investigate the metabolic differences between follicular fluid from the dominant follicle of lactating cows and heifers using gas chromatography mass spectrometry (GC-MS)-based metabolomics. Follicular fluid and serum were collected from cows and heifers over three phases of follicle development: newly selected dominant follicles, preovulatory follicles prior to oestrus and post-LH surge follicles. Analysis of the fatty acids revealed that there were 24 fatty acids and 9 aqueous metabolites significantly different between cows and heifers. Of particular interest were the higher concentrations of saturated fatty acids (palmitic acid, P=0.001; stearic acid, P=0.005) in follicular fluid from cows and higher docosahexaenoic acid levels (P=0.022) in follicular fluid from heifers. Analysis of the metabolite composition of serum revealed that follicular fluid had a unique lipid composition. The higher concentrations of detrimental saturated fatty in cows will have a negative impact on oocyte maturation and early embryo development. Overall, the results suggest that the follicle microenvironment in cows potentially places their oocytes at a developmental disadvantage compared with heifers, and that this may contribute to well-characterised differences in fertility.

Online Dietary Intake Estimation: Reproducibility and Validity of the Food4Me Food Frequency Questionnaire Against a 4-Day Weighed Food Record

Background Advances in nutritional assessment are continuing to embrace developments in computer technology. The online Food4Me food frequency questionnaire (FFQ) was created as an electronic system for the collection of nutrient intake data. To ensure its accuracy in assessing both nutrient and food group intake, further validation against data obtained using a reliable, but independent, instrument and assessment of its reproducibility are required. Objective The aim was to assess the reproducibility and validity of the Food4Me FFQ against a 4-day weighed food record (WFR). Methods Reproducibility of the Food4Me FFQ was assessed using test-retest methodology by asking participants to complete the FFQ on 2 occasions 4 weeks apart. To assess the validity of the Food4Me FFQ against the 4-day WFR, half the participants were also asked to complete a 4-day WFR 1 week after the first administration of the Food4Me FFQ. Level of agreement between nutrient and food group intakes estimated by the repeated Food4Me FFQ and the Food4Me FFQ and 4-day WFR were evaluated using Bland-Altman methodology and classification into quartiles of daily intake. Crude unadjusted correlation coefficients were also calculated for nutrient and food group intakes. Results In total, 100 people participated in the assessment of reproducibility (mean age 32, SD 12 years), and 49 of these (mean age 27, SD 8 years) also took part in the assessment of validity. Crude unadjusted correlations for repeated Food4Me FFQ ranged from .65 (vitamin D) to .90 (alcohol). The mean cross-classification into “exact agreement plus adjacent” was 92% for both nutrient and food group intakes, and Bland-Altman plots showed good agreement for energy-adjusted macronutrient intakes. Agreement between the Food4Me FFQ and 4-day WFR varied, with crude unadjusted correlations ranging from .23 (vitamin D) to .65 (protein, % total energy) for nutrient intakes and .11 (soups, sauces and miscellaneous foods) to .73 (yogurts) for food group intake. The mean cross-classification into “exact agreement plus adjacent” was 80% and 78% for nutrient and food group intake, respectively. There were no significant differences between energy intakes estimated using the Food4Me FFQ and 4-day WFR, and Bland-Altman plots showed good agreement for both energy and energy-controlled nutrient intakes. Conclusions The results demonstrate that the online Food4Me FFQ is reproducible for assessing nutrient and food group intake and has moderate agreement with the 4-day WFR for assessing energy and energy-adjusted nutrient intakes. The Food4Me FFQ is a suitable online tool for assessing dietary intake in healthy adults.

Enhancing cognitive functioning in the elderly: multicomponent vs resistance training

Purpose The primary purpose of this study was to compare the effects of two different exercise training programs on executive cognitive functions and functional mobility in older adults. A secondary purpose was to explore the potential mediators of training effects on executive function and functional mobility with particular reference to physical fitness gains. Methods A sample of 42 healthy community dwelling adults aged 65 to 75 years participated twice weekly for 3 months in either: (1) multicomponent training, prioritizing neuromuscular coordination, balance, agility, and cognitive executive control; or (2) progressive resistance training for strength conditioning. Participants were tested at baseline (T1), following a 4-week control period (T2), and finally at postintervention (T3) for executive function (inhibition and cognitive flexibility) and functional mobility (maximal walking speed with and without additional task requirements). Cardiorespiratory and muscular fitness were also assessed as potential mediators. Results Indices of inhibition, the functions involved in the deliberate withholding of prepotent or automatic responses, and measures of functional mobility improved after the intervention, independent of training type. Mediation analysis suggested that different mechanisms underlie the effects of multicomponent and progressive resistance training. While multicomponent training seemed to directly affect inhibitory capacity, resistance training seemed to affect it indirectly through gains in muscular strength. Physical fitness and executive function variables did not mediate functional mobility changes. Conclusion These results confirm that physical training benefits executive function and suggest that different training types might lead to such benefits through different pathways. Both types of training also promoted functional mobility in older adulthood; however, neither inhibitory capacity, nor muscular strength gains seemed to explain functional mobility outcomes.