Work Ability in the Year after Rehabilitation-Results from the RehabNytte Cohort.

BACKGROUND: There is limited knowledge regarding the impact of rehabilitation on work ability. The aim of this study was to explore factors associated with work ability 12 months following a multidisciplinary rehabilitation program in a cohort with different diagnoses. METHODS: Of 9108 potentially eligible participants for the RehabNytte research project, 3731 were eligible for the present study, and 2649 participants (mean age 48.6 years, 71% female) consented to contribute with work-related data, and were included. Self-perceived work ability was assessed by the Work Ability Score (WAS) (0-10, 10 = best), during the follow-up period using paired t-tests and logistic regression to examine associations between demographic and disease-related factors and work ability at 12-month follow-up. RESULTS: The mean baseline WAS for the total cohort was 3.53 (SD 2.97), and increased significantly to 4.59 (SD 3.31) at 12-month follow-up. High work ability (WAS ≥ 8) at 12 months was associated with high self-perceived health at the baseline (OR 3.83, 95% CI 2.45, 5.96), while low work ability was associated with a higher number of comorbidities (OR 0.26, 95% CI 0.11, 0.61), medium pain intensity (OR 0.56, 95% CI 0.38, 0.83) and being married or cohabiting (OR 0.61, 95% CI 0.43, 0.88). There were no significant differences in work ability between participants receiving occupational and standard rehabilitation. CONCLUSIONS: Work ability increased significantly over the follow-up period. High work ability at 12-month follow-up was associated with high self-perceived health at baseline, while being married or cohabiting, having higher number of comorbidities, and experiencing medium baseline pain intensity was associated with lower work ability. Rehabilitation interventions targeting these factors may potentially enhance work ability, leading to a positive impact on work participation among people in need of rehabilitation.

Identifying possible biomarkers of lower urinary tract symptoms using metabolomics and partial least square regression.

INTRODUCTION: The objective of this study was to explore potential novel biomarkers for moderate to severe lower urinary tract symptoms (LUTS) using a metabolomics-based approach, and statistical methods with significant different features than previous reported. MATERIALS AND METHODS: The patients and the controls were selected to participate in the study according to inclusion/exclusion criteria (n = 82). We recorded the following variables: International prostatic symptom score (IPSS), prostate volume, comorbidities, PSA, height, weight, triglycerides, glycemia, HDL cholesterol, and blood pressure. The study of 41 plasma metabolites was done using the nuclear magnetic resonance spectroscopy technique. First, the correlations between the metabolites and the IPSS were done using Pearson. Second, significant biomarkers of LUTS from metabolites were further analysed using a multiple linear regression model. Finally, we validated the findings using partial least square regression (PLS). RESULTS: Small to moderate correlations were found between IPSS and methionine (-0.301), threonine (-0.320), lactic acid (0.294), pyruvic acid (0.207) and 2-aminobutyric-acid (0.229). The multiple linear regression model revealed that only threonine (p = 0.022) was significantly associated with IPSS, whereas methionine (p = 0.103), lactic acid (p = 0.093), pyruvic acid (p = 0.847) and 2-aminobutyric-acid (p = 0.244) lost their significance. However, all metabolites lost their significance in the PLS model. CONCLUSION: When using the robust PLS-regression method, none of the metabolites in our analysis had a significant association with lower urinary tract symptoms. This highlights the importance of using appropriate statistical methods when exploring new biomarkers in urology.

An approach to assess and adjust for the influence of multicollinear covariates on metabolomics association patterns-applied to a study of the associations between a comprehensive lipoprotein profile and the homeostatic model assessment of insulin resistance.

INTRODUCTION: Comprehensive lipoprotein profiling using proton nuclear magnetic resonance (NMR) spectroscopy of serum represents an alternative to the homeostatic model assessment of insulin resistance (HOMA-IR). Both adiposity and physical (in)activity associate to insulin resistance, but quantification of the influence of these two lifestyle related factors on the association pattern of HOMA-IR to lipoproteins suffers from lack of appropriate methods to handle multicollinear covariates. OBJECTIVES: We aimed at (i) developing an approach for assessment and adjustment of the influence of multicollinear and even linear dependent covariates on regression models, and (ii) to use this approach to examine the influence of adiposity and physical activity on the association pattern between HOMA-IR and the lipoprotein profile. METHODS: For 841 children, lipoprotein profiles were obtained from serum proton NMR and physical activity (PA) intensity profiles from accelerometry. Adiposity was measured as body mass index, the ratio of waist circumference to height, and skinfold thickness. Target projections were used to assess and isolate the influence of adiposity and PA on the association pattern of HOMA-IR to the lipoproteins. RESULTS: Adiposity explained just over 50% of the association pattern of HOMA-IR to the lipoproteins with strongest influence on high-density lipoprotein features. The influence of PA was mainly attributed to a strong inverse association between adiposity and moderate and high-intensity physical activity. CONCLUSION: The presented covariate projection approach to obtain net association patterns, made it possible to quantify and interpret the influence of adiposity and physical (in)activity on the association pattern of HOMA-IR to the lipoprotein features.

Associations of lipoprotein particle profile and objectively measured physical activity and sedentary time in schoolchildren: a prospective cohort study.

BACKGROUND: Our understanding of the mechanisms through which physical activity might benefit lipoprotein metabolism is inadequate. Here we characterise the continuous associations between physical activity of different intensities, sedentary time, and a comprehensive lipoprotein particle profile. METHODS: Our cohort included 762 fifth grade (mean [SD] age = 10.0 [0.3] y) Norwegian schoolchildren (49.6% girls) measured on two separate occasions across one school year. We used targeted proton nuclear magnetic resonance (1H NMR) spectroscopy to produce 57 lipoprotein measures from fasted blood serum samples. The children wore accelerometers for seven consecutive days to record time spent in light-, moderate-, and vigorous-intensity physical activity, and sedentary time. We used separate multivariable linear regression models to analyse associations between the device-measured activity variables-modelled both prospectively (baseline value) and as change scores (follow-up minus baseline value)-and each lipoprotein measure at follow-up. RESULTS: Higher baseline levels of moderate-intensity and vigorous-intensity physical activity were associated with a favourable lipoprotein particle profile at follow-up. The strongest associations were with the larger subclasses of triglyceride-rich lipoproteins. Sedentary time was associated with an unfavourable lipoprotein particle profile, the pattern of associations being the inverse of those in the moderate-intensity and vigorous-intensity physical activity analyses. The associations with light-intensity physical activity were more modest; those of the change models were weak. CONCLUSION: We provide evidence of a prospective association between time spent active or sedentary and lipoprotein metabolism in schoolchildren. Change in activity levels across the school year is of limited influence in our young, healthy cohort. TRIAL REGISTRATION: ClinicalTrials.gov , # NCT02132494 . Registered 7th April 2014.

Influence of adiposity and physical activity on the cardiometabolic association pattern of lipoprotein subclasses to aerobic fitness in prepubertal children.

Aerobic fitness (AF) and lipoprotein subclasses associate to each other and to cardiovascular health. Adiposity and physical activity (PA) influence the association pattern of AF to lipoproteins almost inversely making it difficult to assess their independent and joint influence on the association pattern. This study, including 841 children (50% boys) 10.2 ± 0.3 years old with BMI 18.0 ± 3.0 kg/m2 from rural Western Norway, aimed at examining the association pattern of AF to the lipoprotein subclasses and to estimate the independent and joint influence of PA and adiposity on this pattern. We used multivariate analysis to determine the association pattern of a profile of 26 lipoprotein features to AF with and without adjustment for three measures of adiposity and a high-resolution PA descriptor of 23 intensity intervals derived from accelerometry. For data not adjusted for adiposity or PA, we observed a cardioprotective lipoprotein pattern associating to AF. This pattern withstood adjustment for PA, but the strength of association to AF was reduced by 58%, while adjustment for adiposity weakened the association of AF to the lipoproteins by 85% and with strongest changes in the associations to a cardioprotective high-density lipoprotein subclass pattern. When adjusted for both adiposity and PA, the cardioprotective lipoprotein pattern still associated to AF, but the strength of association was reduced by 90%. Our results imply that the (negative) influence of adiposity on the cardioprotective association pattern of lipoproteins to AF is considerably stronger than the (positive) contribution of PA to this pattern. However, our analysis shows that PA contributes also indirectly through a strong inverse association to adiposity. The trial was registered 7 May, 2014 in clinicaltrials.gov with trial reg. no.: NCT02132494 and the URL is https://clinicaltrials.gov/ct2/results?term=NCT02132494&cntry=NO.

Cardiometabolic Associations between Physical Activity, Adiposity, and Lipoprotein Subclasses in Prepubertal Norwegian Children.

Lipoprotein subclasses possess crucial cardiometabolic information. Due to strong multicollinearity among variables, little is known about the strength of influence of physical activity (PA) and adiposity upon this cardiometabolic pattern. Using a novel approach to adjust for covariates, we aimed at determining the "net" patterns and strength for PA and adiposity to the lipoprotein profile. Principal component and multivariate pattern analysis were used for the analysis of 841 prepubertal children characterized by 26 lipoprotein features determined by proton nuclear magnetic resonance spectroscopy, a high-resolution PA descriptor derived from accelerometry, and three adiposity measures: body mass index, waist circumference to height, and skinfold thickness. Our approach focuses on revealing and validating the underlying predictive association patterns in the metabolic, anthropologic, and PA data to acknowledge the inherent multicollinear nature of such data. PA associates to a favorable cardiometabolic pattern of increased high-density lipoproteins (HDL), very large and large HDL particles, and large size of HDL particles, and decreasedtriglyceride, chylomicrons, very low-density lipoproteins (VLDL), and their subclasses, and to low size of VLDL particles. Although weakened in strength, this pattern resists adjustment for adiposity. Adiposity is inversely associated to this pattern and exhibits unfavorable associations to low-density lipoprotein (LDL) features, including atherogenic small and very small LDL particles. The observed associations are still strong after adjustment for PA. Thus, lipoproteins explain 26.0% in adiposity after adjustment for PA compared to 2.3% in PA after adjustment for adiposity.

Cross-sectional and prospective associations between aerobic fitness and lipoprotein particle profile in a cohort of Norwegian schoolchildren.

BACKGROUND AND AIMS: The associations between aerobic fitness and traditional measures of lipid metabolism in children are uncertain. We investigated whether higher levels of aerobic fitness benefit lipoprotein metabolism by exploring associations with a comprehensive lipoprotein particle profile. METHODS: In our prospective cohort study, we used targeted proton nuclear magnetic resonance (1H NMR) spectroscopy to profile 57 measures of lipoprotein metabolism from fasting serum samples of 858 fifth-grade Norwegian schoolchildren (49.0% girls; mean age 10.0 years). Aerobic fitness was measured using an intermittent shuttle run aerobic fitness test. We used multiple linear regression adjusted for potential confounders to examine cross-sectional and prospective associations between aerobic fitness and lipoprotein particle profile. RESULTS: Higher levels of aerobic fitness were associated with a favourable lipoprotein particle profile in the cross-sectional analysis, which included inverse associations with all measures of very low-density lipoprotein (VLDL) particles (e.g., -0.06 mmol·L-1 or -0.23 SD units; 95% CI = -0.31, -0.16 for VLDL cholesterol concentration). In the prospective analysis, the favourable pattern of associations persisted, though the individual associations tended to be more consistent with those of the cross-sectional analysis for the VLDL subclass measures compared to the low-density lipoproteins and high-density lipoproteins. Adjustment for adiposity attenuated the associations in both cross-sectional and prospective models. Nevertheless, an independent effect of aerobic fitness remained for some measures. CONCLUSIONS: Improving children's aerobic fitness levels should benefit lipoprotein metabolism, though a concomitant reduction in adiposity would likely potentiate this effect.

Associations of physical activity and sedentary time with lipoprotein subclasses in Norwegian schoolchildren: The Active Smarter Kids (ASK) study.

BACKGROUND AND AIMS: Physical activity is favourably associated with certain markers of lipid metabolism. The relationship of physical activity with lipoprotein particle profiles in children is not known. Here we examine cross-sectional associations between objectively measured physical activity and sedentary time with serum markers of lipoprotein metabolism. METHODS: Our cohort included 880 children (49.0% girls, mean age 10.2 years). Physical activity intensity and time spent sedentary were measured objectively using accelerometers. 30 measures of lipoprotein metabolism were quantified using nuclear magnetic resonance spectroscopy. Multiple linear regression models adjusted for age, sex, sexual maturity and socioeconomic status were used to determine associations of physical activity and sedentary time with lipoprotein measures. Additional models were adjusted for adiposity. Isotemporal substitution models quantified theoretical associations of replacing 30 min of sedentary time with 30 min of moderate- to vigorous-intensity physical activity (MVPA). RESULTS: Time spent in MVPA was associated with a favourable lipoprotein profile independent of sedentary time. There were inverse associations with a number of lipoprotein measures, including most apolipoprotein B-containing lipoprotein subclasses and triglyceride measures, the ratio of total to high-density lipoprotein (HDL) cholesterol, and non-HDL cholesterol concentration. There were positive associations with larger HDL subclasses, HDL cholesterol concentration and particle size. Reallocating 30 min of sedentary time to MVPA had broadly similar associations. Sedentary time was only partly and weakly associated with an unfavourable lipoprotein profile. CONCLUSIONS: Physical activity of at least moderate-intensity is associated with a favourable lipoprotein profile in schoolchildren, independent of time spent sedentary, adiposity and other confounders.

Aerobic fitness and metabolic health in children: A clinical validation of directly measured maximal oxygen consumption versus performance measures as markers of health.

High aerobic fitness is consistently associated with a favorable metabolic health profile in children. However, measurement of oxygen uptake, regarded as the gold standard for evaluating aerobic fitness, is often not feasible. Thus, the aim of the present study was to perform a clinical validation of three measures of aerobic fitness (peak oxygen consumption [VO2peak] and time to exhaustion [TTE] determined from a graded treadmill protocol to exhaustion, and the Andersen intermittent running test) with clustered metabolic health in 10-year-old children. We included 93 children (55 boys and 38 girls) from Norway during 2012-2013 in the study. Associations between aerobic fitness and three different composite metabolic health scores (including lipoprotein subgroup particle concentrations, triglyceride, glucose, systolic blood pressure, and waist-to-height ratio) were determined by regression analyses adjusting for sex. The relationships among the measures of aerobic fitness were r = 0.78 for VO2peak vs. TTE, r = 0.63 for VO2peak vs. the Andersen test, and r = 0.67 for TTE vs. the Andersen test. The Andersen test showed the strongest associations across all markers of metabolic health (r = - 0.45 to - 0.31, p < 0.002), followed by VO2peak (r = - 0.35 to - 0.12, p < 0.256), and TTE (r = - 0.28 to - 0.10, p < 0.334). Our findings indicate that indirect measures of aerobic fitness do not stand back as markers of metabolic health status in children, compared to VO2peak. This is of great importance as good field tests provide opportunities for measuring aerobic fitness in many settings where measuring VO2peak are impossible.

Serum fatty acid and lipoprotein subclass concentrations and their associations in prepubertal healthy Norwegian children.

INTRODUCTION: The lipid metabolism is one of the most important and complex processes in the body. Serum concentrations of 18 fatty acids (FAs) and 24 lipoprotein features, i.e. concentrations of lipoprotein main and subclasses and average particle size in main classes, in 195 ethnic Norwegian children from the rural Fjord region were quantified by chromatography. OBJECTIVES: To assess gender differences in prepubertal children and reveal predictive FA patterns for lipoprotein features. METHODS: Lipoprotein features were modelled from FA profiles using multivariate regression. RESULTS: Contrary to observations for adults from the same region, gender differences in prepubertal children were generally small. However, higher concentrations of C16-C18 FAs for girls compared to boys correlated to higher concentrations of triglycerides (TG) and very low density lipoprotein (VLDL) particles and larger average size of VLDL particles. Concentrations of high density lipoprotein (HDL) and its subclass of medium particle size were higher in boys than in girls. These findings are opposite to observations in adults from the same region, but reflect that prepubertal boys are more physically active than girls. Furthermore, children possessed only half the serum levels of eicosapentaenoic acid and docosahexaenoic acid measured in adults. Since sampling was done after 12 h of fasting, these differences may reflect higher rate of utilization of these crucial FAs in children. CONCLUSION: Good predictive models were obtained for TGs, VLDL and chylomicrons with C14-C18 FAs as major contributors. Weak predictive associations were observed for HDL and Apolipoprotein A1 (ApoA1) with C20-C24 FAs as contributors.

Changes in serum fatty acid and lipoprotein subclass concentrations from prepuberty to adulthood and during aging.

Concentrations in serum were determined for 18 fatty acids (FAs) and 21 lipoprotein main and subclasses by chromatographic analyses and the average size was calculated for very low density (VLDL), low density (LDL) and high density (HDL) particles. 283 ethnic Norwegian children and adults from the rural Fjord region of Western Norway were compared with the objectives to reveal patterns and gender differences during the development from prepuberty to adulthood and during aging in adults. Both genders showed a large increase in eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) from child to adult. Males, but not females, show a significant increase in most C16-C18 FAs from prepuberty to adulthood. These changes in males correlate to a pattern of increased concentrations of triglycerides, VLDL and LDL particles, especially the atherogenic subclasses of small and very small LDL particles. Furthermore, concentrations of medium, large and very large HDL particles decrease, while concentration of very small HDL particles increase leading to reduced average size of HDL particles. Females only showed significant increase in concentrations of small and very small LDL particles, very small HDL particles and apolipoprotein B. While EPA and DHA continued to increase during aging in women, no validated model for connecting age to FA profile was obtained for men. Women showed significant increase in concentrations of all subclasses of LDL particles during aging, while men exhibited a more complex pattern with increase also in apolipoprotein A1 and HDL particles.

Predictive associations between serum fatty acids and lipoproteins in healthy non-obese Norwegians: implications for cardiovascular health.

A battery of methods for multivariate data analysis has been used to assess the associations between concentrations of fatty acids (FAs) and lipoprotein subclasses and particle size in serum for a normolipidemic population of ethnic Norwegians living in the rural Fjord region. Significant gender differences were found in the lipoprotein and FA patterns. Predictive FA patterns were revealed for lipoprotein features of importance for cardiovascular (CV) health. Thus, the subclasses of atherogenic small and very small low density lipoprotein (LDL) particles and the same subclasses of high density lipoprotein (HDL) particles were associated with a pattern of saturated FAs and mono-unsaturated C16-C18 FAs. Eicosapentaenoic acid (EPA) and the ratio of EPA to arachidonic acid (AA) had strongest associations to features that promotes CV health: (i) large average size of HDL and LDL particles, and, (ii) small average size of very low density lipoprotein (VLDL) particles. Total concentration of HDL in both genders correlated to EPA, but docosahexaenoic acid (DHA) correlated just as strongly for women. For men, docosapentaenoic acid (DPA) showed stronger association to HDL concentration than EPA. For both genders, concentration of large LDL particles showed associations to levels of EPA, but stronger to DHA and DPA. High values of EPA/AA seem to be the strongest single biomarker for good CV health in both men and women.

Multivariate data analysis in pharmaceutics: a tutorial review.

We provide an overview of latent variable methods used in pharmaceutics and integrated with advanced characterization techniques such as vibrational spectroscopy. The basics of the most common latent variable methods, principal component analysis (PCA), principal component regression (PCR) and partial least-squares (PLS) regression, are presented. Multiple linear regression (MLR) and methods for improved interpretation, variable selection, classification and validation are also briefly discussed. Extensive use of the methods is demonstrated by compilation of the recent literature.

A multivariate approach to reveal biomarker signatures for disease classification: application to mass spectral profiles of cerebrospinal fluid from patients with multiple sclerosis.

Mass spectral profiles from cerebrospinal fluid (CSF) are used as input to a novel multivariate approach to select features responsible for the separation of patients with multiple sclerosis (MS) from control groups. Our targeted statistical approach makes it possible to systematically remove features in the spectral fingerprints masking the components expressing the disease pattern. The low molecular weight CSF proteome from 54 patients with MS and a range of other neurological diseases (OND), as well as neurological healthy controls (NHC), is analyzed in replicates using mass spectral profiling. Statistically validated partial least-squares discriminant analysis (PLS-DA) models are created as a first step to separate the groups. Using the group membership as a target, the most discriminatory projection in the multivariate space spanned by the spectral profiles is revealed. From the resulting target-projected component, the spectral regions most significantly contributing to group separation are identified using the nonparametric discriminating variable (DIVA) test together with the so-called selectivity ratio (SR) plot. Our approach is general and can be applied for other diseases and instrumental techniques as well.

Discriminating variable test and selectivity ratio plot: quantitative tools for interpretation and variable (biomarker) selection in complex spectral or chromatographic profiles.

The discriminating variable (DIVA) test and the selectivity ratio (SR) plot are developed as quantitative tools for revealing the variables in spectral or chromatographic profiles discriminating best between two groups of samples. The SR plot is visually similar to a spectrum or a chromatogram, but with the most intense regions corresponding to the most discriminating variables. Thus, the variables with highest SR represent the variables most important for interpretation of differences between groups. Regions with variables that are positively or negatively correlated to each other are displayed as corresponding negative and positive regions in the SR plot. The nonparametric DIVA test is designed for connecting SR to discriminatory ability of a variable quantified as probability for correct classification. A mean probability for a certain SR range is calculated as the mean correct classification rate (MCCR) for all variables in the same SR interval. The MCCR is thus similar to a mean sensitivity in each SR interval. In addition to the ranking of all variables according to their discriminatory ability provided by the SR plot, the DIVA test connects a probability measure to each SR interval. Thus, the DIVA test makes it possible to objectively define thresholds corresponding to mean probability levels in the SR plot and provides a quantitative means to select discriminating variables. In order to validate the approach, samples of untreated cerebrospinal fluid (CSF) and samples spiked with a multicomponent peptide standard were analyzed by matrix-assisted laser desorption ionization (MALDI) mass spectrometry. The differences in the multivariate spectral profiles of the two groups were revealed using partial least-squares discriminant analysis (PLS-DA) followed by target projection (TP). The most discriminating mass-to-charge (m/z) regions were revealed by calculating the ratio of explained to unexplained variance for each m/z number on the target-projected component and displaying this measure in SR plots with quantitative boundaries determined from the DIVA test. The results are compared to some established methods for variable selection.

Pretreatment of mass spectral profiles: application to proteomic data.

Mass spectral profiles are influenced by several factors that have no relation to compositional differences between samples: baseline effects, shifts in mass-to-charge ratio (m/z) (synchronization/alignment problem), structured noise (heteroscedasticity), and, differences in signal intensities (normalization problem). Different procedures for pretreatment of whole mass spectral profiles described by almost 50,000 m/z values are investigated in order to find optimal approaches with respect to revealing the information content in the data. In order to quantitatively assess the impact of different procedures for pretreatment of mass spectral profiles, we use factorial designs with the ratio between intergroup and intragroup (replicate) variance as response. We have examined the influence of smoothing, binning, alignment/synchronization, noise pattern, and normalization on data interpretation. Our analysis shows that the spectral profiles have to be corrected for heteroscedastic noise prior to normalization. An nth root transform, where n is a small, positive integer, is used to create a homoscedastic noise structure without destroying the linear correlation structures describing individual components when using whole mass spectral profiles. The choice of n is decided by a simple graphic procedure using replicate information. Log transform is shown to change the heteroscedastic noise structure from being dominant in high-intensity regions, to produce the largest noise in the low-intensity regions. In addition, log transform has a negative effect on the collinearity in the profiles. Factorial designs reveal strong interactions between several of the pretreatment steps, e.g., noise structure and normalization. This underlines the limited usability of looking at the different pretreatment steps in isolation. Binning turns out to be able to substitute smoothing of spectra by, for example, moving average or Savitsky-Golay, while, at the same time, reducing the data point description of the profiles by 1 order of magnitude. Thus, if the sampling density is high, binning seems to be an attractive option for data reduction without the risk of losing information accompanying the integration of profiles into peaks. In the absence of smoothing, binning should be executed prior to alignment. If binning is not performed, the order of pretreatment should be smoothing, alignment, nth root transform, and normalization.

Proteins in different Synechocystis compartments have distinguishing N-terminal features: a combined proteomics and multivariate sequence analysis.

Cyanobacteria have a cell envelope consisting of a plasma membrane, a periplasmic space with a peptidoglycan layer, and an outer membrane. A third, separate membrane system, the intracellular thylakoid membranes, is the site for both photosynthesis and respiration. All membranes and luminal spaces have unique protein compositions, which impose an intriguing mechanism for protein sorting of extracytoplasmic proteins due to single sets of translocation protein genes. It is shown here by multivariate sequence analyses of many experimentally identified proteins in Synechocystis, that proteins routed for the different extracytosolic compartments have correspondingly different physicochemical properties in their signal peptide and mature N-terminal segments. The full-length mature sequences contain less significant information. From these multivariate, N-terminal property-profile models for proteins with single experimental localization, proteins with ambiguous localization could, to a large extent, be predicted to a defined compartment. The sequence properties involve amino acids varying especially in volume and polarizability and at certain positions in the sequence segments, in a manner typical for the various compartment classes. Potential means of the cell to recognize the property features are discussed, involving the translocation channels and two Type I signal peptidases with different cellular localization, and charge features at their membrane interfaces.

Pre-analytical influence on the low molecular weight cerebrospinal fluid proteome.

Cerebrospinal fluid (CSF) is a perfect source to search for new biomarkers to improve early diagnosis of neurological diseases. Standardization of pre-analytical handling of the sample is, however, important to obtain acceptable analytical quality. In the present study, MALDI-TOF MS was used to examine the influence of pre-analytical sample procedures on the low molecular weight (MW) CSF proteome. Different storage conditions like temperature and duration or the addition of as little as 0.2 µL blood/mL neat CSF caused significant changes in the mass spectra. The performance of different types of MW cut-off spin cartridges from different suppliers used to enrich the low MW CSF proteome showed great variance in cut-off accuracy, stability and reproducibility. The described analytical method achieved a polypeptide discriminating limit of approximately 800 pM, two to three orders of magnitude lower than reported for plasma. Based on this study, we recommend that CSF is centrifuged immediately after sampling, prior to storage at -80ºC without addition of protease inhibitors. Guanidinium hydrochloride is preferred to break protein-protein interactions. A spin cartridge with cut-off limit above the intended analytical mass range is recommended. Our study contributes to the important task of developing standardized pre-analytical protocols for the proteomic study of CSF.