Respiratory function in 105 monotonic dystrophy type 1 (DM1) patients, normal weight (body mass index [BMI] < 25 kg/m²) and overweight (BMI ≥ 25 kg/m²).

<p>Respiratory function in 105 monotonic dystrophy type 1 (DM1) patients, normal weight (body mass index [BMI] < 25 kg/m²) and overweight (BMI ≥ 25 kg/m²).</p

Pearson’s correlation coefficients for the relation between total lung capacity (TLC) and parameters of body composition and inspiratory muscle strength.

<p>Pearson’s correlation coefficients for the relation between total lung capacity (TLC) and parameters of body composition and inspiratory muscle strength.</p

Stacked-bar histogram of total lung capacity (TLC) (% of predicted) in patients with normal weight (body mass index [BMI] < 25 kg/m², n = 43) and overweight (BMI ≥ 25 kg/m², n = 62), compared with their predicted values.

<p>The black section of the stacked-bar indicates the residual volume (RV), the gray section the expiratory reserve volume (ERV) and the white section the inspiratory capacity (IC). The RV and ERV combined (black plus grey) is the functional reserve capacity (FRC). A restrictive pattern of pulmonary function is shown for both groups, and the TLC is further decreased in overweight compared with normal-weight patients, mainly due to the decreased ERV.</p

Scatter plot of fat-free mass index (FFMI) and body mass index (BMI) in DM1, n = 71.

<p>The x-axis denotes the FFMI, expressed as percentage of gender-specific cut-off points, where 100% indicates FFMI of 16 kg/m² for men and 15 kg/m² for women. The y-axis denotes the BMI, with horizontal lines at 21 and 25 kg/m². The different body compositions are defined as cachexia (BMI < 21 kg/m² and FFMI < 100%), normal weight with muscle atrophy (21 kg/m² ≤ BMI < 25 kg/m² and FFMI < 100%), normal weight with muscle atrophy (BMI ≥ 25 kg/m² and FFMI < 100%), no impairment (21 kg/m² ≤ BMI < 25 kg/m² and FFMI ≥ 100%) and overweight (BMI ≥ 25 kg/m² and FFMI ≥ 100%).</p

Overweight Is an Independent Risk Factor for Reduced Lung Volumes in Myotonic Dystrophy Type 1

<div><p>Background</p><p>In this large observational study population of 105 myotonic dystrophy type 1 (DM1) patients, we investigate whether bodyweight is a contributor of total lung capacity (TLC) independent of the impaired inspiratory muscle strength.</p><p>Methods</p><p>Body composition was assessed using the combination of body mass index (BMI) and fat-free mass index. Pulmonary function tests and respiratory muscle strength measurements were performed on the same day. Patients were stratified into normal (BMI < 25 kg/m²) and overweight (BMI ≥ 25 kg/m²) groups. Multiple linear regression was used to find significant contributors for TLC.</p><p>Results</p><p>Overweight was present in 59% of patients, and body composition was abnormal in almost all patients. In overweight patients, TLC was significantly (<i>p</i> = 2.40×10⁻³) decreased, compared with normal-weight patients, while inspiratory muscle strength was similar in both groups. The decrease in TLC in overweight patients was mainly due to a decrease in expiratory reserve volume (ERV) further illustrated by a highly significant (<i>p</i> = 1.33×10⁻¹⁰) correlation between BMI and ERV. Multiple linear regression showed that TLC can be predicted using only BMI and the forced inspiratory volume in 1 second, as these were the only significant contributors.</p><p>Conclusions</p><p>This study shows that, in DM1 patients, overweight further reduces lung volumes, as does impaired inspiratory muscle strength. Additionally, body composition is abnormal in almost all DM1 patients.</p></div

Multiple linear regression model for predicting total lung capacity (TLC) with the significant contributors forced inspiratory volume in 1 second (FIV1) and body mass index (BMI).

<p>Patients in the model and validation set are represented by gray circles and black crosses, respectively. The x-axis denotes the TLC expressed as percentage of the predicted value for each individual and the y-axis denotes the calculated predicted TLC (% pred.), based on FIV1 and BMI.</p

Optimized Phenotypic Biomarker Discovery and Confounder Elimination via Covariate-Adjusted Projection to Latent Structures from Metabolic Spectroscopy Data

Metabolism is altered by genetics, diet, disease status, environment, and many other factors. Modeling either one of these is often done without considering the effects of the other covariates. Attributing differences in metabolic profile to one of these factors needs to be done while controlling for the metabolic influence of the rest. We describe here a data analysis framework and novel confounder-adjustment algorithm for multivariate analysis of metabolic profiling data. Using simulated data, we show that similar numbers of true associations and significantly less false positives are found compared to other commonly used methods. Covariate-adjusted projections to latent structures (CA-PLS) are exemplified here using a large-scale metabolic phenotyping study of two Chinese populations at different risks for cardiovascular disease. Using CA-PLS, we find that some previously reported differences are actually associated with external factors and discover a number of previously unreported biomarkers linked to different metabolic pathways. CA-PLS can be applied to any multivariate data where confounding may be an issue and the confounder-adjustment procedure is translatable to other multivariate regression techniques

Nya extra-posten. (N:r 39)

Parasitic infections such as leishmaniasis induce a cascade of host physiological responses, including metabolic and immunological changes. Infection with <i>Leishmania major</i> parasites causes cutaneous leishmaniasis in humans, a neglected tropical disease that is difficult to manage. To understand the determinants of pathology, we studied <i>L. major</i> infection in two mouse models: the self-healing C57BL/6 strain and the nonhealing BALB/c strain. Metabolic profiling of urine, plasma, and feces via proton NMR spectroscopy was performed to discover parasite-specific imprints on global host metabolism. Plasma cytokine status and fecal microbiome were also characterized as additional metrics of the host response to infection. Results demonstrated differences in glucose and lipid metabolism, distinctive immunological phenotypes, and shifts in microbial composition between the two models. We present a novel approach to integrate such metrics using correlation network analyses, whereby self-healing mice demonstrated an orchestrated interaction between the biological measures shortly after infection. In contrast, the response observed in nonhealing mice was delayed and fragmented. Our study suggests that trans-system communication across host metabolism, the innate immune system, and gut microbiome is key for a successful host response to <i>L. major</i> and provides a new concept, potentially translatable to other diseases

Urinary Metabolic Phenotyping the slc26a6 (Chloride–Oxalate Exchanger) Null Mouse Model

The prevalence of renal stone disease is increasing, although it remains higher in men than in women when matched for age. While still somewhat controversial, several studies have reported an association between renal stone disease and hypertension, but this may be confounded by a shared link with obesity. However, independent of obesity, hyperoxaluria has been shown to be associated with hypertension in stone-formers, and the most common type of renal stone is composed of calcium oxalate. The chloride–oxalate exchanger slc26a6 (also known as CFEX or PAT-1), located in the renal proximal tubule, was originally thought to have an important role in sodium homeostasis and thereby blood pressure control, but it has recently been shown to have a key function in oxalate balance by mediating oxalate secretion in the gut. We have applied two orthogonal analytical platforms (NMR spectroscopy and capillary electrophoresis with UV detection) in parallel to characterize the urinary metabolic signatures related to the loss of the renal chloride–oxalate exchanger in slc26a6 null mice. Clear metabolic differentiation between the urinary profiles of the slc26a6 null and the wild type mice were observed using both methods, with the combination of NMR and CE-UV providing extensive coverage of the urinary metabolome. Key discriminating metabolites included oxalate, <i>m</i>-hydroxyphenylpropionylsulfate (<i>m</i>-HPPS), trimethylamine-<i>N</i>-oxide, glycolate and <i>scyllo</i>-inositol (higher in slc26a6 null mice) and hippurate, taurine, trimethylamine, and citrate (lower in slc26a6 null mice). In addition to the reduced efficiency of anion transport, several of these metabolites (hippurate, <i>m</i>-HPPS, methylamines) reflect alteration in gut microbial cometabolic activities. Gender-related metabotypes were also observed in both wild type and slc26a6 null groups. Urinary metabolites that showed a sex-specific pattern included trimethylamine, trimethylamine-<i>N</i>-oxide, citrate, spermidine, guanidinoacetate, and 2-oxoisocaproate. The gender-dependent metabolic expression of the consequences of slc26a6 deletion might have relevance to the difference in prevalence of renal stone formation in men and women. The different composition of microbial metabolites in the slc26a6 null mice is consistent with the fact that the slc26a6 transporter is found in a range of tissues, including the kidney and intestine, and provides further evidence for the “long reach” of the microbiota in physiological and pathological processes

<i>J</i>‑Resolved ¹H NMR 1D-Projections for Large-Scale Metabolic Phenotyping Studies: Application to Blood Plasma Analysis

¹H nuclear magnetic resonance (NMR) spectroscopy-based metabolic phenotyping is now widely used for large-scale epidemiological applications. To minimize signal overlap present in 1D ¹H NMR spectra, we have investigated the use of 2D <i>J</i>-resolved (JRES) ¹H NMR spectroscopy for large-scale phenotyping studies. In particular, we have evaluated the use of the 1D projections of the 2D JRES spectra (pJRES), which provide single peaks for each of the <i>J</i>-coupled multiplets, using 705 human plasma samples from the FGENTCARD cohort. On the basis of the assessment of several objective analytical criteria (spectral dispersion, attenuation of macromolecular signals, cross-spectral correlation with GC-MS metabolites, analytical reproducibility and biomarker discovery potential), we concluded that the pJRES approach exhibits suitable properties for implementation in large-scale molecular epidemiology workflows