Metabolic phenotypes are the products of interactions among a variety of factors-dietary, other lifestyle/environmental, gut microbial and genetic. We use a large-scale exploratory analytical approach to investigate metabolic phenotype variation across and within four human populations, based on 1H NMR spectroscopy. Metabolites discriminating across populations are then linked to data for individuals on blood pressure, a major risk factor for coronary heart disease and stroke (leading causes of mortality worldwide). We analyse spectra from two 24-hour urine specimens for each of 4,630 participants from the INTERMAP epidemiological study, involving 17 population samples aged 40-59 in China, Japan, UK and USA. We show that urinary metabolite excretion patterns for East Asian and western population samples, with contrasting diets, diet-related major risk factors, and coronary heart disease/stroke rates, are significantly differentiated (P < 10(-16)), as are Chinese/Japanese metabolic phenotypes, and subgroups with differences in dietary vegetable/animal protein and blood pressure. Among discriminatory metabolites, we quantify four and show association (P < 0.05 to P < 0.0001) of mean 24-hour urinary formate excretion with blood pressure in multiple regression analyses for individuals. Mean 24-hour urinary excretion of alanine (direct) and hippurate (inverse), reflecting diet and gut microbial activities, are also associated with blood pressure of individuals. Metabolic phenotyping applied to high-quality epidemiological data offers the potential to develop an area of aetiopathogenetic knowledge involving discovery of novel biomarkers related to cardiovascular disease risk.

Rapid evaporative ionization mass spectrometry (REIMS) is an emerging technique that allows near-real-time characterization of human tissue in vivo by analysis of the aerosol ("smoke") released during electrosurgical dissection. The coupling of REIMS technology with electrosurgery for tissue diagnostics is known as the intelligent knife (iKnife). This study aimed to validate the technique by applying it to the analysis of fresh human tissue samples ex vivo and to demonstrate the translation to real-time use in vivo in a surgical environment. A variety of tissue samples from 302 patients were analyzed in the laboratory, resulting in 1624 cancerous and 1309 noncancerous database entries. The technology was then transferred to the operating theater, where the device was coupled to existing electrosurgical equipment to collect data during a total of 81 resections. Mass spectrometric data were analyzed using multivariate statistical methods, including principal components analysis (PCA) and linear discriminant analysis (LDA), and a spectral identification algorithm using a similar approach was implemented. The REIMS approach differentiated accurately between distinct histological and histopathological tissue types, with malignant tissues yielding chemical characteristics specific to their histopathological subtypes. Tissue identification via intraoperative REIMS matched the postoperative histological diagnosis in 100% (all 81) of the cases studied. The mass spectra reflected lipidomic profiles that varied between distinct histological tumor types and also between primary and metastatic tumors. Thus, in addition to real-time diagnostic information, the spectra provided additional information on divergent tumor biochemistry that may have mechanistic importance in cancer.

Chemical shift variation in small-molecule (1)H NMR signals of biofluids complicates biomarker information recovery in metabonomic studies when using multivariate statistical and pattern recognition tools. Current peak realignment methods are generally time-consuming or align major peaks at the expense of minor peak shift accuracy. We present a novel recursive segment-wise peak alignment (RSPA) method to reduce variability in peak positions across the multiple (1)H NMR spectra used in metabonomic studies. The method refines a segmentation of reference and test spectra in a top-down fashion, sequentially subdividing the initial larger segments, as required, to improve the local spectral alignment. We also describe a general procedure that allows robust comparison of realignment quality of various available methods for a range of peak intensities. The RSPA method is illustrated with respect to 140 (1)H NMR rat urine spectra from a caloric restriction study and is compared with several other widely used peak alignment methods. We demonstrate the superior performance of the RSPA alignment over a wide range of peaks and its capacity to enhance interpretability and robustness of multivariate statistical tools. The approach is widely applicable for NMR-based metabolic studies and is potentially suitable for many other types of data sets such as chromatographic profiles and MS data.

Autism is an early-onset developmental disorder with a severe life-long impact on behavior and social functioning that has associated metabolic abnormalities. The urinary metabolic phenotypes of individuals (age range=3-9 years old) diagnosed with autism using the DSM-IV-TR criteria (n=39; male=35; female=4), together with their non-autistic siblings (n=28; male=14; female=14) and age-matched healthy volunteers (n=34, male=17; female=17) have been characterized for the first time using Additionally, metabolic phenotype (metabotype) differences were observed between autistic and control children, which were associated with perturbations in the relative patterns of urinary mammalian-microbial co-metabolites including dimethylamine, hippurate and phenyacetylglutamine. These biochemical changes are consistent with the known abnormalities of gut microbiota found in autistic individuals and the associated gastrointestinal dysfunction and may be of value in monitoring the success of therapeutic interventions.3