Prediction of future visceral adiposity and application to cancer research: The Multiethnic Cohort Study.

BACKGROUND: We previously developed a prediction score for MRI-quantified abdominal visceral adipose tissue (VAT) based on concurrent measurements of height, body mass index (BMI), and nine blood biomarkers, for optimal performance in five racial/ethnic groups. Here we evaluated the VAT score for prediction of future VAT and examined if enhancement with additional biomarkers, lifestyle behavior information, and medical history improves the prediction. METHODS: We examined 500 participants from the Multiethnic Cohort (MEC) with detailed data (age 50-66) collected 10 years prior to their MRI assessment of VAT. We generated three forecasted VAT prediction models: first by applying the original VAT equation to the past data on the predictors ("original"), second by refitting the past data on anthropometry and biomarkers ("refit"), and third by building a new prediction model based on the past data enhanced with lifestyle and medical history ("enhanced"). We compared the forecasted prediction scores to future VAT using the coefficient of determination (R2). In independent nested case-control data in MEC, we applied the concurrent and forecasted VAT models to assess association of the scores with subsequent incident breast cancer (950 pairs) and colorectal cancer (831 pairs). RESULTS: Compared to the VAT prediction by the concurrent VAT score (R2 = 0.70 in men, 0.68 in women), the forecasted original VAT score (R2 = 0.54, 0.48) performed better than past anthropometry alone (R2 = 0.47, 0.40) or two published scores (VAI, METS-VF). The forecasted refit (R2 = 0.61, 0.51) and enhanced (R2 = 0.62, 0.55) VAT scores each showed slight improvements. Similar to the concurrent VAT score, the forecasted VAT scores were associated with breast cancer, but not colorectal cancer. Both the refit score (adjusted OR for tertile 3 vs. 1 = 1.27; 95% CI: 1.00-1.62) and enhanced score (1.27; 0.99-1.62) were associated with breast cancer independently of BMI. CONCLUSIONS: Predicted VAT from midlife data can be used as a surrogate to assess the effect of VAT on incident diseases associated with obesity, as illustrated for postmenopausal breast cancer.

Risk Factors for Type 2 Diabetes in the Multiethnic Cohort.

OBJECTIVES: In this report, we investigated the association between established risk factors and type 2 diabetes (T2D) across 5 distinct ethnic groups and explored differences according to T2D definition within the Multiethnic Cohort (MEC) Study. METHODS: Using the full MEC, with participants in Hawaii and Los Angeles (N=172,230), we applied Cox regression to estimate hazard ratios (HRs) and 95% confidence intervals (CIs). All participants completed questionnaires asking about demographics, anthropometrics, lifestyle factors, and regular diet. T2D status was determined from self-reported diagnosis/medication and Medicare claims. We assessed the associations between well-established risk factors and T2D in the full cohort, after stratification by ethnic group, according to the T2D definition, and in a biorepository subset. Effect modification by ethnicity was evaluated using Wald's tests. RESULTS: Overall, 46,500 (27%) participants had an incident T2D diagnosis after a mean follow-up of 17.1±6.9 years. All predictors were significantly associated with T2D: overweight (HR=1.74), obesity (HR=2.90), red meat intake (HR=1.15), short (HR=1.04) and long (HR=1.08) sleep duration, and smoking (HR=1.26) predicted a significantly higher T2D incidence, whereas coffee (HR=0.90) and alcohol (HR=0.78) consumption, physical activity (HR=0.89), and diet quality (HR=0.96) were associated with lower T2D incidence. The strength of these associations was similar across ethnic groups with noteworthy disparities for overweight/obesity, physical activity, alcohol intake, coffee consumption, and diet quality. CONCLUSIONS: These findings confirm the importance of known risk factors for T2D across ethnic groups, but small differences were detected that may contribute to disparate incidence rates in some ethnic groups, especially for obesity and physical activity.

Associations of the gut microbiome with hepatic adiposity in the Multiethnic Cohort Adiposity Phenotype Study.

Nonalcoholic fatty liver disease (NAFLD) is a risk factor for liver cancer and prevalence varies by ethnicity. Along with genetic and lifestyle factors, the gut microbiome (GM) may contribute to NAFLD and its progression to advanced liver disease. Our cross-sectional analysis assessed the association of the GM with hepatic adiposity among African American, Japanese American, White, Latino, and Native Hawaiian participants in the Multiethnic Cohort. We used MRI to measure liver fat and determine nonalcoholic fatty liver disease (NAFLD) status (n = 511 cases) in 1,544 participants, aged 60-77 years, with 12-53% overall adiposity (BMI of 17.8-46.2 kg/m2). The GM was measured by 16S rRNA gene sequencing and, on a subset, by metagenomic sequencing. Alpha diversity was lower overall with NAFLD and in certain ethnicities (African Americans, Whites, and Latinos). In models regressing genus on NAFLD status, 62 of 149 genera (40%) exhibited a significant interaction between NAFLD and ethnicity stratified analysis found 69 genera significantly associated with NAFLD in at least one ethnic group. No single genus was significantly associated with NAFLD across all ethnicities. In contrast, the same bacterial metabolic pathways were over-represented in participants with NAFLD regardless of ethnicity. Imputed secondary bile acid and carbohydrate pathways were associated with NAFLD, the latter of which was corroborated by metagenomics, although different genera in different ethnicities were associated with these pathways. Overall, we found that NAFLD was associated with altered bacterial composition and metabolism, and that bacterial endotoxin, assessed by plasma lipopolysaccharide binding protein (LBP), may mediate liver fat-associated systemic inflammation in a manner that seems to vary by ethnicity.

Circulating Biomarker Score for Visceral Fat and Risks of Incident Colorectal and Postmenopausal Breast Cancer: The Multiethnic Cohort Adiposity Phenotype Study.

BACKGROUND: Visceral adipose tissue (VAT) may play a greater role than subcutaneous fat in increasing cancer risk but is poorly estimated in epidemiologic studies. METHODS: We developed a VAT prediction score by regression equations averaged across 100 least absolute shrinkage and selection operator models in a cross-sectional study of 1,801 older adults in the Multiethnic Cohort (MEC). The score was then used as proxy for VAT in case-control studies of postmenopausal breast (950 case-control pairs) and colorectal (831 case-control pairs) cancer in an independent sample in MEC. Abdominal MRI-derived VAT; circulating biomarkers of metabolic, hormonal, and inflammation dysfunctions; and ORs for incident cancer adjusted for BMI and other risk factors were assessed. RESULTS: The final score, composed of nine biomarkers, BMI, and height, explained 11% and 15% more of the variance in VAT than BMI alone in men and women, respectively. The area under the receiver operator curve for VAT >150 cm2 was 0.90 in men and 0.86 in women. The VAT score was associated with risk of breast cancer [OR (95% confidence interval [CI]) by increasing tertiles: 1.00, 1.09 (0.86-1.39), 1.48 (1.16-1.89); P trend = 0.002] but not with colorectal cancer (P = 0.84), although an association [1.00, 0.98 (0.68-1.39), 1.24 (0.88-1.76); P trend = 0.08] was suggested for this cancer after excluding cases that occurred within 7 years of blood draw (P heterogeneity = 0.06). CONCLUSIONS: The VAT score predicted risks of postmenopausal breast cancer and can be used for risk assessment in diverse populations. IMPACT: These findings provide specific evidence for a role of VAT in breast cancer.

The Consortium of Metabolomics Studies (COMETS): Metabolomics in 47 Prospective Cohort Studies.

The Consortium of Metabolomics Studies (COMETS) was established in 2014 to facilitate large-scale collaborative research on the human metabolome and its relationship with disease etiology, diagnosis, and prognosis. COMETS comprises 47 cohorts from Asia, Europe, North America, and South America that together include more than 136,000 participants with blood metabolomics data on samples collected from 1985 to 2017. Metabolomics data were provided by 17 different platforms, with the most frequently used labs being Metabolon, Inc. (14 cohorts), the Broad Institute (15 cohorts), and Nightingale Health (11 cohorts). Participants have been followed for a median of 23 years for health outcomes including death, cancer, cardiovascular disease, diabetes, and others; many of the studies are ongoing. Available exposure-related data include common clinical measurements and behavioral factors, as well as genome-wide genotype data. Two feasibility studies were conducted to evaluate the comparability of metabolomics platforms used by COMETS cohorts. The first study showed that the overlap between any 2 different laboratories ranged from 6 to 121 metabolites at 5 leading laboratories. The second study showed that the median Spearman correlation comparing 111 overlapping metabolites captured by Metabolon and the Broad Institute was 0.79 (interquartile range, 0.56-0.89).

Surgical injury induces local and distant adipose tissue browning.

The adipose organ, which comprises brown, white and beige adipocytes, possesses remarkable plasticity in response to feeding and cold exposure. The development of beige adipocytes in white adipose tissue (WAT), a process called browning, represents a promising route to treat metabolic disorders. While surgical procedures constantly traumatize adipose tissue, its impact on adipocyte phenotype remains to be established. Herein, we studied the effect of trauma on adipocyte phenotype one day after sham, incision control, or surgical injury to the left inguinal adipose compartment. Caloric restriction was used to control for surgery-associated body temperature changes and weight loss. We characterized the trauma-induced cellular and molecular changes in subcutaneous, visceral, interscapular, and perivascular adipose tissue using histology, immunohistochemistry, gene expression, and flow cytometry analysis. After one day, surgical trauma stimulated adipose tissue browning at the site of injury and, importantly, in the contralateral inguinal depot. Browning was not present after incision only, and was largely independent of surgery-associated body temperature and weight loss. Adipose trauma rapidly recruited monocytes to the injured site and promoted alternatively activated macrophages. Conversely, PDGF receptor-positive beige progenitors were reduced. In this study, we identify adipose trauma as an unexpected driver of selected local and remote adipose tissue browning, holding important implications for the biologic response to surgical injury.

Preoperative dietary restriction reduces intimal hyperplasia and protects from ischemia-reperfusion injury.

OBJECTIVE: Whereas chronic overnutrition is a risk factor for surgical complications, long-term dietary restriction (reduced food intake without malnutrition) protects in preclinical models of surgical stress. Building on the emerging concept that acute preoperative dietary perturbations can affect the body's response to surgical stress, we hypothesized that short-term high-fat diet (HFD) feeding before surgery is detrimental, whereas short-term nutrient/energy restriction before surgery can reverse negative outcomes. We tested this hypothesis in two distinct murine models of vascular surgical injury, ischemia-reperfusion (IR) and intimal hyperplasia (IH). METHODS: Short-term overnutrition was achieved by feeding mice a HFD consisting of 60% calories from fat for 2 weeks. Short-term dietary restriction consisted of either 1 week of restricted access to a protein-free diet (protein/energy restriction) or 3 days of water-only fasting immediately before surgery; after surgery, all mice were given ad libitum access to a complete diet. To assess the impact of preoperative nutrition on surgical outcome, mice were challenged in one of two fundamentally distinct surgical injury models: IR injury to either kidney or liver, or a carotid focal stenosis model of IH. RESULTS: Three days of fasting or 1 week of preoperative protein/energy restriction attenuated IH development measured 28 days after focal carotid stenosis. One week of preoperative protein/energy restriction also reduced plasma urea, creatinine, and damage to the corticomedullary junction after renal IR and decreased aspartate transaminase, alanine transaminase, and hemorrhagic necrosis after hepatic IR. However, exposure to a HFD for 2 weeks before surgery had no significant impact on kidney or hepatic function after IR or IH after focal carotid stenosis. CONCLUSIONS: Short-term dietary restriction immediately before surgery significantly attenuated the vascular wall hyperplastic response and improved IR outcome. The findings suggest plasticity in the body's response to these vascular surgical injuries that can be manipulated by novel yet practical preoperative dietary interventions.

Adipose phenotype predicts early human autogenous arteriovenous hemodialysis remodeling.

OBJECTIVE: Substantial proportions of autogenous arteriovenous fistulas (AVFs) for hemodialysis access fail to mature for unclear reasons. AVFs develop in a large mass of surrounding adipose tissue that is increasingly recognized as an active participant in the vascular response to injury via paracrine and endocrine mechanisms. We thus hypothesized that baseline phenotypic characteristics of the adipose tissue juxtaposed to the developing AVF associate with subsequent inward or outward vein wall remodeling. METHODS: Clinical data and subcutaneous adipose tissue were collected from 22 consented patients undergoing AVF creation. Tissue was assayed (protein levels) for interleukin (IL)-6, IL-8, leptin, tumor necrosis factor-α, monocyte chemoattractant protein-1 (MCP-1), resistin, and adiponectin. Vein dimensions were acquired by duplex ultrasound imaging, preoperatively and at 4 to 6 weeks postoperatively, 1 cm cephalad to the arteriovenous anastomosis, which is the most common location of AVF stenosis). RESULTS: The vein at the assayed location outwardly remodeled 55.7% on average (median before, 3.7 mm; median after, 4.7 mm; P = .005). The preoperative vein diameter failed to correlate with postoperative size at the point of assay (R = 0.31; P = .155) unless two outliers were excluded (R = 0.64; P = .002). After removal of the same outliers, the correlation coefficient between venous diameter change (preoperative vs postoperative) and IL-8, tumor necrosis factor-α, MCP-1, resistin, and adiponectin was -0.49, -0.79, -0.66, -0.64, and -0.69, respectively (P < .05). Postoperative AVF flow volume correlated with MCP-1 (R = -0.53; P < .05) and adiponectin (R = -0.47; P < .05). CONCLUSIONS: These data reveal a novel relationship between local adipose phenotype and the eventual venous wall response to hemodynamic perturbation in humans. The predictive value of these mediators generally equaled or exceeded that of preoperative vein size. Beyond providing mechanistic insights into vascular wall adaptations due to flow perturbations, this discovery suggests that strategies focused on altering adipose tissue biology may improve AVF maturation.

Identification of metabolites from liquid chromatography-coulometric array detection profiling: gas chromatography-mass spectrometry and refractionation provide essential information orthogonal to LC-MS/microNMR.

Liquid chromatography-coulometric array detection (LC-EC) is a sensitive, quantitative, and robust metabolomics profiling tool that complements the commonly used mass spectrometry (MS) and nuclear magnetic resonance (NMR)-based approaches. However, LC-EC provides little structural information. We recently demonstrated a workflow for the structural characterization of metabolites detected by LC-EC profiling combined with LC-electrospray ionization (ESI)-MS and microNMR. This methodology is now extended to include (i) gas chromatography (GC)-electron ionization (EI)-MS analysis to fill structural gaps left by LC-ESI-MS and NMR and (ii) secondary fractionation of LC-collected fractions containing multiple coeluting analytes. GC-EI-MS spectra have more informative fragment ions that are reproducible for database searches. Secondary fractionation provides enhanced metabolite characterization by reducing spectral overlap in NMR and ion suppression in LC-ESI-MS. The need for these additional methods in the analysis of the broad chemical classes and concentration ranges found in plasma is illustrated with discussion of four specific examples: (i) characterization of compounds for which one or more of the detectors is insensitive (e.g., positional isomers in LC-MS, the direct detection of carboxylic groups and sulfonic groups in (1)H NMR, or nonvolatile species in GC-MS), (ii) detection of labile compounds, (iii) resolution of closely eluting and/or coeluting compounds, and (iv) the capability to harness structural similarities common in many biologically related, LC-EC-detectable compounds.

The use of underloaded C(18) solid-phase extraction plates increases reproducibility of analysis of tryptic peptides from unfractionated human plasma.

Bottom-up proteomics requires the digestion of proteins into peptides by processes that use salts for denaturing and buffering purposes. These salts need to be removed prior to mass spectrometry analysis to reduce ion suppression; solid-phase extraction (SPE) is a commonly used strategy. There are many commercially available SPE sorbent types and sizes, which are generally provided with manufacturer recommendations for use, including protein loading capacity. We found that these general suggestions were often not ideal, and our data suggest that context-specific evaluation of sorbent type and amount can improve reproducibility. Specifically, the universal Oasis HLB sorbent provided better retention of the more hydrophilic peptides than the traditional C(18) reversed-phase SPE, but it did so at the expense of an increased loss of the more hydrophobic peptides. We found that increasing the amount of the C(18) sorbent beyond the manufacturer's guidelines decreased breakthrough (i.e., increased retention) of 12 hydrophilic, identifiable peptides without loss of hydrophobic peptides. This procedure was robust in a 96-well plate format.

Isolation of functional mitochondria from rat kidney and skeletal muscle without manual homogenization.

Isolation of functional and intact mitochondria from solid tissue is crucial for studies that focus on the elucidation of normal mitochondrial physiology and/or mitochondrial dysfunction in conditions such as aging, diabetes, and cancer. There is growing recognition of the importance of mitochondria both as targets for drug development and as off-target mediators of drug side effects. Unfortunately, mitochondrial isolation from tissue is generally carried out using homogenizer-based methods that require extensive operator experience to obtain reproducible high-quality preparations. These methods limit dissemination, impede scale-up, and contribute to difficulties in reproducing experimental results over time and across laboratories. Here we describe semiautomated methods to disrupt tissue using kidney and muscle mitochondria preparations as exemplars. These methods use the Barocycler, the PCT Shredder, or both. The PCT Shredder is a mechanical grinder that quickly breaks up tissue without significant risk of overhomogenization. Mitochondria isolated using the PCT Shredder are shown to be comparable to controls. The Barocycler generates controlled pressure pulses that can be adjusted to lyse cells and release organelles. The mitochondria subjected to pressure cycling-mediated tissue disruption are shown to retain functionality, enabling combinations of the PCT Shredder and the Barocycler to be used to purify mitochondrial preparations.

LigSeeSVM: ligand-based virtual screening using support vector machines and data fusion.

Ligand-based in silico drug screening is useful for lead discovery, in particular for those targets without structures. Here, we have developed LigSeeSVM, a ligand-based screening tool using data fusion and Support Vector Machines (SVMs). We used Atom Pair (AP) structure descriptors and Physicochemical (PC) descriptors of compounds to generate SVM-AP and SVM-PC models. Sequentially, the two models were combined using rank-based data fusion to create LigSeeSVM model. LigSeeSVM was evaluated on five data sets. Experimental results show that the performance of LigSeeSVM is better than other ligand-based virtual screening approaches. We believe that LigSeeSVM is useful for lead compounds.

Biological variability dominates and influences analytical variance in HPLC-ECD studies of the human plasma metabolome.

BACKGROUND: Biomarker-based assessments of biological samples are widespread in clinical, pre-clinical, and epidemiological investigations. We previously developed serum metabolomic profiles assessed by HPLC-separations coupled with coulometric array detection that can accurately identify ad libitum fed and caloric-restricted rats. These profiles are being adapted for human epidemiology studies, given the importance of energy balance in human disease. METHODS: Human plasma samples were biochemically analyzed using HPLC separations coupled with coulometric electrode array detection. RESULTS: We identified these markers/metabolites in human plasma, and then used them to determine which human samples represent blinded duplicates with 100% accuracy (N = 30 of 30). At least 47 of 61 metabolites tested were sufficiently stable for use even after 48 hours of exposure to shipping conditions. Stability of some metabolites differed between individuals (N = 10 at 0, 24, and 48 hours), suggesting the influence of some biological factors on parameters normally considered as analytical. CONCLUSION: Overall analytical precision (mean median CV, ~9%) and total between-person variation (median CV, ~50-70%) appear well suited to enable use of metabolomics markers in human clinical trials and epidemiological studies, including studies of the effect of caloric intake and balance on long-term cancer risk.

Consensus scoring criteria for improving enrichment in virtual screening.

MOTIVATION: Virtual screening of molecular compound libraries is a potentially powerful and inexpensive method for the discovery of novel lead compounds for drug development. The major weakness of virtual screening-the inability to consistently identify true positives (leads)-is likely due to our incomplete understanding of the chemistry involved in ligand binding and the subsequently imprecise scoring algorithms. It has been demonstrated that combining multiple scoring functions (consensus scoring) improves the enrichment of true positives. Previous efforts at consensus scoring have largely focused on empirical results, but they have yet to provide a theoretical analysis that gives insight into real features of combinations and data fusion for virtual screening. RESULTS: We demonstrate that combining multiple scoring functions improves the enrichment of true positives only if (a) each of the individual scoring functions has relatively high performance and (b) the individual scoring functions are distinctive. Notably, these two prediction variables are previously established criteria for the performance of data fusion approaches using either rank or score combinations. This work, thus, establishes a potential theoretical basis for the probable success of data fusion approaches to improve yields in in silico screening experiments. Furthermore, it is similarly established that the second criterion (b) can, in at least some cases, be functionally defined as the area between the rank versus score plots generated by the two (or more) algorithms. Because rank-score plots are independent of the performance of the individual scoring function, this establishes a second theoretically defined approach to determining the likely success of combining data from different predictive algorithms. This approach is, thus, useful in practical settings in the virtual screening process when the performance of at least two individual scoring functions (such as in criterion a) can be estimated as having a high likelihood of having high performance, even if no training sets are available. We provide initial validation of this theoretical approach using data from five scoring systems with two evolutionary docking algorithms on four targets, thymidine kinase, human dihydrofolate reductase, and estrogen receptors of antagonists and agonists. Our procedure is computationally efficient, able to adapt to different situations, and scalable to a large number of compounds as well as to a greater number of combinations. Results of the experiment show a fairly significant improvement (vs single algorithms) in several measures of scoring quality, specifically "goodness-of-hit" scores, false positive rates, and "enrichment". This approach (available online at http://gemdock.life. nctu.edu.tw/dock/download.php) has practical utility for cases where the basic tools are known or believed to be generally applicable, but where specific training sets are absent.

Chemotherapy for the brain: the antitumor antibiotic mithramycin prolongs survival in a mouse model of Huntington's disease.

Huntington's disease (HD) is a fully penetrant autosomal-dominant inherited neurological disorder caused by expanded CAG repeats in the Huntingtin gene. Transcriptional dysfunction, excitotoxicity, and oxidative stress have all been proposed to play important roles in the pathogenesis of HD. This study was designed to explore the therapeutic potential of mithramycin, a clinically approved guanosine-cytosine-rich DNA binding antitumor antibiotic. Pharmacological treatment of a transgenic mouse model of HD (R6/2) with mithramycin extended survival by 29.1%, greater than any single agent reported to date. Increased survival was accompanied by improved motor performance and markedly delayed neuropathological sequelae. To identify the functional mechanism for the salubrious effects of mithramycin, we examined transcriptional dysfunction in R6/2 mice. Consistent with transcriptional repression playing a role in the pathogenesis of HD, we found increased methylation of lysine 9 in histone H3, a well established mechanism of gene silencing. Mithramycin treatment prevented the increase in H3 methylation observed in R6/2 mice, suggesting that the enhanced survival and neuroprotection might be attributable to the alleviation of repressed gene expression vital to neuronal function and survival. Because it is Food and Drug Administration-approved, mithramycin is a promising drug for the treatment of HD.

Nutrition and physical activity and chronic disease prevention: research strategies and recommendations.

A shortage of credible information exists on practical dietary and physical activity patterns that have potential to reverse the national obesity epidemic and reduce the risk of major cancers and other chronic diseases. Securing such information is a challenging task, and there is considerable diversity of opinion concerning related research designs and priorities. Here, we put forward some perspectives on useful methodology and infrastructure developments for progress in this important area, and we list high-priority research topics in the areas of 1) assessment of nutrient intake and energy expenditure; 2) development of intermediate outcome biomarkers; 3) enhancement of cohort and cross-cultural studies; and 4) criteria for and development of full-scale nutrition and physical activity intervention trials.

Prophylactic creatine administration mediates neuroprotection in cerebral ischemia in mice.

Creatine mediates remarkable neuroprotection in experimental models of amyotrophic lateral sclerosis, Huntington's disease, Parkinson's disease, and traumatic brain injury. Because caspase-mediated pathways are shared functional mechanistic components in these diseases, as well as in ischemia, we evaluated the effect of creatine supplementation on an experimental stroke model. Oral creatine administration resulted in a remarkable reduction in ischemic brain infarction and neuroprotection after cerebral ischemia in mice. Postischemic caspase-3 activation and cytochrome c release were significantly reduced in creatine-treated mice. Creatine administration buffered ischemia-mediated cerebral ATP depletion. These data provide the first direct correlation between the preservation of bioenergetic cellular status and the inhibition of activation of caspase cell-death pathways in vivo. An alternative explanation to our findings is that creatine is neuroprotective through other mechanisms that are independent of mitochondrial cell-death pathways, and therefore postischemic ATP preservation is the result of tissue sparing. Given its safety record, creatine might be considered as a novel therapeutic agent for inhibition of ischemic brain injury in humans. Prophylactic creatine supplementation, similar to what is recommended for an agent such as aspirin, may be considered for patients in high stroke-risk categories.

Neuroprotective mechanisms of creatine occur in the absence of mitochondrial creatine kinase.

There is substantial evidence that creatine administration exerts neuroprotective effects both in vitro and in vivo. The precise mechanisms for these neuroprotective effects however are as yet unclear. We investigated whether creatine administration could exert neuroprotective effects in mice deficient in ubiquitous mitochondrial creatine kinase (UbMi-CK). UbMi-CK-deficient mice showed increased sensitivity to 1-methyl-1, 2, 3, 6-tetrahydropyridine (MPTP)-induced dopamine depletion and loss of tyrosine hydroxylase (TH) stained neurons. Isolated mitochondria from these mice showed no alterations in calcium retention, oxygen utilization, membrane potential, or swelling in response to a calcium challenge. Creatine administration significantly increased brain concentrations of both creatine and PCr in the UbMi-CK knockout mice. Creatine administration to the UbMi-CK-deficient mice exerted significant neuroprotective effects against MPTP toxicity that were comparable in magnitude to those seen in wild-type mice. These results suggest that the neuroprotective effects of creatine are not mediated by an effect on UbMi-CK to inhibit the mitochondrial permeability transition, and are more likely to be mediated by maintenance of appropriate ATP/ADP and PCr/Cr levels.

Development of biomarkers based on diet-dependent metabolic serotypes: practical issues in development of expert system-based classification models in metabolomic studies.

Dietary restriction (DR)-induced changes in the serum metabolome may be biomarkers for physiological status (e.g., relative risk of developing age-related diseases such as cancer). Megavariate analysis (unsupervised hierarchical cluster analysis [HCA]; principal components analysis [PCA]) of serum metabolites reproducibly distinguish DR from ad libitum fed rats. Component-based approaches (i.e., PCA) consistently perform as well as or better than distance-based metrics (i.e., HCA). We therefore tested the following: (A) Do identified subsets of serum metabolites contain sufficient information to construct mathematical models of class membership (i.e., expert systems)? (B) Do component-based metrics out-perform distance-based metrics? Testing was conducted using KNN (k-nearest neighbors, supervised HCA) and SIMCA (soft independent modeling of class analogy, supervised PCA). Models were built with single cohorts, combined cohorts or mixed samples from previously studied cohorts as training sets. Both algorithms over-fit models based on single cohort training sets. KNN models had >85% accuracy within training/test sets, but were unstable (i.e., values of k could not be accurately set in advance). SIMCA models had 100% accuracy within all training sets, 89 % accuracy in test sets, did not appear to over-fit mixed cohort training sets, and did not require post-hoc modeling adjustments. These data indicate that (i) previously defined metabolites are robust enough to construct classification models (expert systems) with SIMCA that can predict unknowns by dietary category; (ii) component-based analyses outperformed distance-based metrics; (iii) use of over-fitting controls is essential; and (iv) subtle inter-cohort variability may be a critical issue for high data density biomarker studies that lack state markers.

Development of biomarkers based on diet-dependent metabolic serotypes: concerns and approaches for cohort and gender issues in serum metabolome studies.

Mathematical models that reflect the effects of dietary restriction (DR) on the sera metabolome may have utility in understanding the mechanisms of DR and in applying this knowledge to human epidemiological studies. Previous studies demonstrated both the feasibility of identifying biomarkers through metabolome analysis and the validity of our approach in independent cohorts of 6-month-old male and female ad libitum fed or DR rats. Cross-cohort studies showed that cohort-specific effects distorted the dataset. The present study extends these observations across the entire sample set, thereby validating our markers independently of specific cohorts. Metabolites originally identified in males were examined in females and vice-versa. DR's effect on the metabolome is partially gender-specific and is modulated by environmental factors. DR reduces inter-gender differences in the metabolome. Univariate statistical methods showed that 56/93 metabolites in the female samples and 39/93 metabolites in the male samples were significantly altered (using our previous cut-off criteria of p < or = 0.2) by DR. The metabolites modulated by DR present a wide spectrum of concentration, redox reactivity and hydrophilicity, suggesting that our serotype is broadly representative of the metabolome and that DR has broad effects on the metabolome. These studies, coupled with those in the preceding and following reports, also highlight the utility for consideration of the metabolome as a network of metabolites using appropriate data analysis approaches. The inter-cohort and inter-gender differences addressed herein suggest potential cautions, and potential approaches, for identification of multivariate biomarker profiles that reflect changes in physiological status, such as a metabolism that predisposes to increased risk of neoplasia.