Exploring the Interactions between Obesity and Diabetes: Implications for Understanding Metabolic Dysregulation in a Saudi Arabian Adult Population

The rising prevalence of obesity in Saudi Arabia is a major contributor to the nation's high levels of cardiometabolic diseases such as type 2 diabetes. To assess the impact of obesity on the diabetic metabolic phenotype presented in young Saudi Arabian adults, participants (n = 289, aged 18-40 years) were recruited and stratified into four groups: healthy weight (BMI 18.5-24.99 kg/m2) with (n = 57) and without diabetes (n = 58) or overweight/obese (BMI > 24.99 kg/m2) with (n = 102) and without diabetes (n = 72). Distinct plasma metabolic phenotypes associated with high BMI and diabetes were identified using nuclear magnetic resonance spectroscopy and ultraperformance liquid chromatography mass spectrometry. Increased plasma glucose and dysregulated lipoproteins were characteristics of obesity in individuals with and without diabetes, but the obesity-associated lipoprotein phenotype was partially masked in individuals with diabetes. Although there was little difference between diabetics and nondiabetics in the global plasma LDL cholesterol and phospholipid concentration, the distribution of lipoprotein particles was altered in diabetics with a shift toward denser and more atherogenic LDL5 and LDL6 particles, which was amplified in the presence of obesity. Further investigation is warranted in larger Middle Eastern populations to explore the dysregulation of metabolism driven by interactions between obesity and diabetes in young adults.

Finding Correspondence between Metabolomic Features in Untargeted Liquid Chromatography-Mass Spectrometry Metabolomics Datasets

Integration of multiple datasets can greatly enhance bioanalytical studies, for example, by increasing power to discover and validate biomarkers. In liquid chromatography–mass spectrometry (LC–MS) metabolomics, it is especially hard to combine untargeted datasets since the majority of metabolomic features are not annotated and thus cannot be matched by chemical identity. Typically, the information available for each feature is retention time (RT), mass-to-charge ratio (m/z), and feature intensity (FI). Pairs of features from the same metabolite in separate datasets can exhibit small but significant differences, making matching very challenging. Current methods to address this issue are too simple or rely on assumptions that cannot be met in all cases. We present a method to find feature correspondence between two similar LC–MS metabolomics experiments or batches using only the features’ RT, m/z, and FI. We demonstrate the method on both real and synthetic datasets, using six orthogonal validation strategies to gauge the matching quality. In our main example, 4953 features were uniquely matched, of which 585 (96.8%) of 604 manually annotated features were correct. In a second example, 2324 features could be uniquely matched, with 79 (90.8%) out of 87 annotated features correctly matched. Most of the missed annotated matches are between features that behave very differently from modeled inter-dataset shifts of RT, MZ, and FI. In a third example with simulated data with 4755 features per dataset, 99.6% of the matches were correct. Finally, the results of matching three other dataset pairs using our method are compared with a published alternative method, metabCombiner, showing the advantages of our approach. The method can be applied using M2S (Match 2 Sets), a free, open-source MATLAB toolbox, available at https://github.com/rjdossan/M2S.

Abstract MP09: Untargeted 1 H Nmr Metabolomics Metabolomic Analysis Reveals Pathways Of Protection Between Mediterranean-style Diet And Incident Cardiovascular Disease In The Multi-ethnic Study Of Atherosclerosis

Background: The metabolites associated with a Mediterranean-style (AMED) diet may give insight into why AMED is robustly associated with protection against CVD. Previous investigations seeking to identify these have been limited by the use of modest sample sizes and targeted assays.

Methods: Using samples from the Multi-Ethnic Study of Atherosclerosis (MESA), we conducted untargeted 1 H NMR spectroscopy (600 MHz) with internal and external annotation on the sera of ~3,698 participants, ages 45-84, who were free from overt CVD. We included data on baseline dietary intake (self-reported), and all incident CVD events (excluding stroke) over a 10-year period. From >100,000 spectral features, 845 significant associations (P<2.2*10 -6 ) were identified via linear regression, and reduced to a putative list of 46 via elastic net regularized models. Hierarchical clustering identified 11 feature groups, from which cluster scores were constructed. The ‘mediate’ package in R used bootstrapping (1000 simulations) to partition the association between AMED and incident CVD into the effects of dietary intake on CVD which are mediated by the metabolomic cluster scores (the “indirect” / mediated effects) and those effects which are not (“direct effects”). All association analyses controlled for age, sex, ethnicity, data collection site and daily caloric intake.

Results: AMED score was associated with reductions in the incidence of CVD (HR=0.95; 95% CI: 0.91 - 0.99; P=0.02). All metabolomic cluster scores were associated with AMED (all P<2*10 -06 ; Table 1 ), with 6 significantly associated with incident CVD (all P<2.0*10 -4 ) in the expected direction given their associations with AMED score. Four of these six significantly mediated the association of AMED score with incident CVD (P<0.05; Table 1 ).

Conclusions: These preliminary data suggest that specific molecules, if replicated in other studies, hold promise to identify the underlying pathways by which an AMED diet offers protection against CVD.

Association of Untargeted Urinary Metabolomics and Lung Cancer Risk Among Never-Smoking Women in China

IMPORTANCE

Chinese women have the highest rate of lung cancer among female never-smokers in the world, and the etiology is poorly understood.

OBJECTIVE

To assess the association between metabolomics and lung cancer risk among never-smoking women.

DESIGN, SETTING, AND PARTICIPANTS

This nested case-control study included 275 never-smoking female patients with lung cancer and 289 never-smoking cancer-free control participants from the prospective Shanghai Women's Health Study recruited from December 28, 1996, to May 23, 2000. Validated food frequency questionnaires were used for the collection of dietary information. Metabolomic analysis was conducted from November 13, 2015, to January 6, 2016. Data analysis was conducted from January 6, 2016, to November 29, 2018.

EXPOSURES

Untargeted ultra-high-performance liquid chromatography-tandem mass spectrometry and nuclear magnetic resonance metabolomic profiles were characterized using prediagnosis urine samples. A total of 39 416 metabolites were measured.

MAIN OUTCOMES AND MEASURES

Incident lung cancer.

RESULTS

Among the 564 women, those who developed lung cancer (275 participants; median [interquartile range] age, 61.0 [52-65] years) and those who did not develop lung cancer (289 participants; median [interquartile range] age, 62.0 [53-66] years) at follow-up (median [interquartile range] follow-up, 10.9 [9.0-11.7] years) were similar in terms of their secondhand smoke exposure, history of respiratory diseases, and body mass index. A peak metabolite, identified as 5-methyl-2-furoic acid, was significantly associated with lower lung cancer risk (odds ratio, 0.57 [95% CI, 0.46-0.72]; P < .001; false discovery rate = 0.039). Furthermore, this peak was weakly correlated with self-reported dietary soy intake (ρ = 0.21; P < .001). Increasing tertiles of this metabolite were associated with lower lung cancer risk (in comparison with first tertile, odds ratio for second tertile, 0.52 [95% CI, 0.34-0.80]; and odds ratio for third tertile, 0.46 [95% CI, 0.30-0.70]), and the association was consistent across different histological subtypes and follow-up times. Additionally, metabolic pathway analysis found several systemic biological alterations that were associated with lung cancer risk, including 1-carbon metabolism, nucleotide metabolism, oxidative stress, and inflammation.

CONCLUSIONS AND RELEVANCE

This prospective study of the untargeted urinary metabolome and lung cancer among never-smoking women in China provides support for the hypothesis that soy-based metabolites are associated with lower lung cancer risk in never-smoking women and suggests that biological processes linked to air pollution may be associated with higher lung cancer risk in this population.

Abstract 4974: Prospective study of untargeted urinary metabolomics and risk of lung cancer among female never-smokers in Shanghai, China

The lung cancer rate among never-smokers is the highest among Asian women, however its etiology and any relevant non-smoking related biomarkers are still unclear. Pre-diagnostic lung cancer-related metabolic biomarkers may provide novel insights into lung cancer mechanisms, and may contribute to the discovery of etiologic factors for the high lung cancer prevalence among Asian women. We evaluated the role of the urinary metabolome in lung cancer development among female never-smokers in China by conducting a nested case-control study of 275 lung cancer cases and 289 healthy controls from the Shanghai Women's Health Study, a prospective cohort comprised of 73,363 Chinese female never-smokers. Metabolic profiling of urinary chemical features was conducted using ultrahigh-performance liquid chromatography - tandem mass spectrometry (UPLC-MS) (39,409 spectral features) and 600 MHz 1H nuclear magnetic resonance (NMR) spectroscopy (386 features). Unconditional logistic regression models were used to estimate the odds ratios (ORs) and 95% confidence intervals (CIs) for the association between each log-transformed metabolite level and lung cancer risk, adjusting for potential confounders such as age, body mass index, history of respiratory disease and passive smoking. Spearman correlation and linear regression were used to estimate associations between the most significant metabolites and pre-diagnosis dietary factors. Three detected UPLC-MS urinary metabolites were negatively associated with lung cancer risk with a false discovery rate of less than 10%: pos_2.61_127.0382m/z (OR = 0.57, 95% CI = 0.46-0.72, P = 1.98 x 10-6), neg_2.60_369.0408m/z (OR = 0.97, 95% CI = 0.96-0.98, P = 1.36 x 10-6), and pos_2.61_184.0325n (OR = 0.55, 95% CI = 0.43-0.71, P = 4.91 x 10-6). These were strongly correlated with each other (rho &gt; 0.65, p &lt; 0.0001). The significant metabolite (pos_2.61_127.0382m/z) was identified as 5-methyl-2-furoic acid and was moderately correlated with self-reported dietary intake of soy (rho = 0.21, p &lt; 0.001). In conclusion, we identified a metabolite in urine (5-methyl-2-furoic acid) that provides support for the protective association of soy-based foods on lung cancer risk that was previously observed in this population of never-smoking women. Further studies are warranted to replicate these findings.

Citation Format: Wei Jie Seow, Xiao-Ou Shu, Jeremy Nicholson, Elaine Holmes, Wei Hu, Qiuyin Cai, Yu-Tang Gao, Yong-Bing Xiang, Steve Moore, Bryan A. Bassig, Jason Yy Wong, Jinming Zhang, Bu-Tian Ji, Claire Boulange, Manuja Kaluarachchi, Kyrillos F. Adesina-Georgiadis, Anisha Wijeyesekera, Wei Zheng, Paul Elliot, Nathaniel Rothman, Qing Lan. Prospective study of untargeted urinary metabolomics and risk of lung cancer among female never-smokers in Shanghai, China [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 4974.

A comparison of human serum and plasma metabolites using untargeted 1H NMR spectroscopy and UPLC-MS

INTRODUCTION

Differences in the metabolite profiles between serum and plasma are incompletely understood.

OBJECTIVES

To evaluate metabolic profile differences between serum and plasma and among plasma sample subtypes.

METHODS

We analyzed serum, platelet rich plasma (PRP), platelet poor plasma (PPP), and platelet free plasma (PFP), collected from 8 non-fasting apparently healthy women, using untargeted standard 1D and CPMG ¹H NMR and reverse phase and hydrophilic (HILIC) UPLC-MS. Differences between metabolic profiles were evaluated using validated principal component and orthogonal partial least squares discriminant analysis.

RESULTS

Explorative analysis showed the main source of variation among samples was due to inter-individual differences with no grouping by sample type. After correcting for inter-individual differences, lipoproteins, lipids in VLDL/LDL, lactate, glutamine, and glucose were found to discriminate serum from plasma in NMR analyses. In UPLC-MS analyses, lysophosphatidylethanolamine (lysoPE)(18:0) and lysophosphatidic acid(20:0) were higher in serum, and phosphatidylcholines (PC)(16:1/18:2, 20:3/18:0, O-20:0/22:4), lysoPC(16:0), PE(O-18:2/20:4), sphingomyelin(18:0/22:0), and linoleic acid were lower. In plasma subtype analyses, isoleucine, leucine, valine, phenylalanine, glutamate, and pyruvate were higher among PRP samples compared with PPP and PFP by NMR while lipids in VLDL/LDL, citrate, and glutamine were lower. By UPLC-MS, PE(18:0/18:2) and PC(P-16:0/20:4) were higher in PRP compared with PFP samples.

CONCLUSIONS

Correction for inter-individual variation was required to detect metabolite differences between serum and plasma. Our results suggest the potential importance of inter-individual effects and sample type on the results from serum and plasma metabolic phenotyping studies.

Development of a Pipeline for Exploratory Metabolic Profiling of Infant Urine

Numerous metabolic profiling pipelines have been developed to characterize the composition of human biofluids and tissues, the vast majority of these being for studies in adults. To accommodate limited sample volume and to take into account the compositional differences between adult and infant biofluids, we developed and optimized sample handling and analytical procedures for studying urine from newborns. A robust pipeline for metabolic profiling using NMR spectroscopy was established, encompassing sample collection, preparation, spectroscopic measurement, and computational analysis. Longitudinal samples were collected from five infants from birth until 14 months of age. Methods of extraction and effects of freezing and sample dilution were assessed, and urinary contaminants from breakdown of polymers in a range of diapers and cotton wool balls were identified and compared, including propylene glycol, acrylic acid, and tert-butanol. Finally, assessment of urinary profiles obtained over the first few weeks of life revealed a dramatic change in composition, with concentrations of phenols, amino acids, and betaine altering systematically over the first few months of life. Therefore, neonatal samples require more stringent standardization of experimental design, sample handling, and analysis compared to that of adult samples to accommodate the variability and limited sample volume.

Muscular dystrophies due to defective glycosylation of dystroglycan

Muscular dystrophies are a clinically and genetically heterogeneous group of disorders. Until recently most of the proteins associated with muscular dystrophies were believed to be proteins of the sarcolemma associated with reinforcing the plasma membrane or in facilitating its re-sealing following injury. In the last few years a novel and frequent pathogenic mechanism has been identified that involves the abnormal glycosylation of alpha-dystroglycan (ADG). This peripheral membrane protein undergoes complex and crucial glycosylation steps that enable it to interact with LG domain containing extracellular matrix proteins such as laminins, agrin and perlecan. Mutations in six genes (POMT1, POMT2, POMGnT1, fukutin, FKRP and LARGE) have been identified in patients with reduced glycosylation of ADG. While initially a clear correlation between gene defect and phenotype was observed for each of these 6 genes (for example, Walker Warburg syndrome was associated with mutations in POMT1 and POMT2, Fukuyama congenital muscular dystrophy associated with fukutin mutations, and Muscle Eye Brain disease associated with POMGnT1 mutations), we have recently demonstrated that allelic mutations in each of these 6 genes can result in a much wider spectrum of clinical conditions. Thus, the crucial aspect in determining the phenotypic severity is not which gene is primarily mutated, but how severely the mutation affects the glycosylation of ADG. Systematic mutation analysis of these 6 glycosyltransferases in patients with a dystroglycan glycosylation disorder identifies mutations in approximately 65% suggesting that more genes have yet to be identified.

Lamins A and C are differentially dysfunctional in autosomal dominant Emery-Dreifuss muscular dystrophy

Mutations in the LMNA gene, which encodes nuclear lamins A and C by alternative splicing, can give rise to Emery-Dreifuss muscular dystrophy. The mechanism by which lamins A and C separately contribute to this molecular phenotype is unknown. To address this question we examined ten LMNA mutations exogenously expressed as lamins A and C in COS-7 cells. Eight of the mutations when expressed in lamin A, exhibited a range of nuclear mislocalisation patterns. However, two mutations (T150P and delQ355) almost completely relocated exogenous lamin A from the nuclear envelope to the cytoplasm, disrupted nuclear envelope reassembly following cell division and altered the protein composition of the mid-body. In contrast, exogenously expressed DsRed2-tagged mutant lamin C constructs were only inserted into the nuclear lamina if co-expressed with any EGFP-tagged lamin A construct, except with one carrying the T150P mutation. The T150P, R527P and L530P mutations reduced the ability of lamin A, but not lamin C from binding to emerin. These data identify specific functional roles for the emerin-lamin C- and emerin-lamin A- containing protein complexes and is the first report to suggest that the A-type lamin mutations may be differentially dysfunctional for the same LMNA mutation.